WO2025050009A2 - Identification de cibles pour une immunothérapie dans un mélanome à l'aide de néo-antigènes dérivés d'épissage - Google Patents

Identification de cibles pour une immunothérapie dans un mélanome à l'aide de néo-antigènes dérivés d'épissage Download PDF

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WO2025050009A2
WO2025050009A2 PCT/US2024/044819 US2024044819W WO2025050009A2 WO 2025050009 A2 WO2025050009 A2 WO 2025050009A2 US 2024044819 W US2024044819 W US 2024044819W WO 2025050009 A2 WO2025050009 A2 WO 2025050009A2
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cancer
neoantigen
seq
immunotherapy
nos
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WO2025050009A3 (fr
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Nathan SALOMONIS
Guangyuan Li
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Cincinnati Childrens Hospital Medical Center
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Cincinnati Childrens Hospital Medical Center
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
    • A61K39/00—Medicinal preparations containing antigens or antibodies
    • A61K39/0005—Vertebrate antigens
    • A61K39/0011—Cancer antigens
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • C07K14/435—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans
    • C07K14/46—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans from vertebrates
    • C07K14/47—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans from vertebrates from mammals
    • C07K14/4701—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from animals; from humans from vertebrates from mammals not used
    • C07K14/4748—Tumour specific antigens; Tumour rejection antigen precursors [TRAP], e.g. MAGE
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • the disclosure generally relates to methods of treating cancer, and in particular to computational methods for identifying molecular targets for use in cancer immunotherapy, as well as to the molecular targets per se and uses thereof.
  • BACKGROUND [0003] Immunotherapy has emerged as a promising strategy to combat cancer by ‘reprogramming’ a patient’s own immune system.
  • the immunogenic target includes a shared splicing amino acid-based neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-2545, or a nucleotide coding therefor.
  • the target includes a shared splicing neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-2241.
  • the target includes a shared splicing neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-4.
  • the target includes a shared splicing neoantigen present in a protein having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 2242-2545. In some embodiments, the target includes a shared splicing neoantigen present in a protein having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 2242-2245. In some embodiments, the target includes a nucleotide coding for a shared splicing neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-2545.
  • the target includes a nucleotide coding for a shared splicing neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-4 or SEQ ID NOs 2242-2245.
  • the immunogenic target includes at least 90% sequence homology to any of SEQ ID NOs: 1-2545, or a nucleotide coding therefor.
  • the immunogenic target is in vitro.
  • the target includes a shared splicing neoantigen selected from SEQ ID NOs: 1-2545.
  • the target includes a shared splicing neoantigen selected from SEQ ID NOs: 1-2241.
  • the target includes a shared splicing neoantigen selected from SEQ ID NOs: 1-4. In some embodiments, the target includes a shared splicing neoantigen selected from SEQ ID NOs: 2242-2545. [0008] In some embodiments, the target is a shared splicing neoantigen selected from SEQ ID NOs: 1-2545. In some embodiments, the target is a shared splicing neoantigen selected from SEQ ID NOs: 1-2241. In some embodiments, the target is a shared splicing neoantigen selected from SEQ ID NOs: 1-4.
  • the target is a shared splicing neoantigen selected from SEQ ID NOs: 2242-2545.
  • Further embodiments of the disclosure include cells having any of the aforementioned immunogenic targets.
  • Additional embodiments of the disclosure include immunotherapies for treating one or more types of cancer, wherein the immunotherapy is specific for, or includes, one or more of the aforementioned immunogenic targets.
  • the immunotherapy is specific for, or includes, one or more shared splicing neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-2241, or one or more nucleotide coding therefor, and the type of cancer includes skin cancer, breast cancer, head and neck cancer, ovarian cancer, and/or myelodysplastic syndrome (MDS).
  • MDS myelodysplastic syndrome
  • the immunotherapy is specific for, or includes, one or more shared splicing neoantigen selected from SEQ ID NOs: 1-2241, or one or more nucleotide coding therefor, and the type of cancer includes skin cancer, breast cancer, head and neck cancer, ovarian cancer, and/or myelodysplastic syndrome (MDS).
  • the immunotherapy is specific for, or includes, one or more shared splicing neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-4, or one or more nucleotide coding therefor, and wherein the type of cancer includes skin cancer.
  • the immunotherapy is specific for, or includes, one or more shared splicing neoantigen selected from SEQ ID NOs: 1-4, or one or more nucleotide coding therefor, and wherein the type of cancer includes skin cancer.
  • the immunotherapy is specific for, or includes, one or more shared splicing neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 2242-2545, or one or more nucleotide coding therefor, and the type of cancer includes skin cancer, breast cancer, head and neck cancer, ovarian cancer, and/or myelodysplastic syndrome (MDS).
  • MDS myelodysplastic syndrome
  • the immunotherapy is specific for, or includes, one or more shared splicing neoantigen selected from SEQ ID NOs: 2242-2545, or one or more nucleotide coding therefor, and the type of cancer includes skin cancer, breast cancer, head and neck cancer, ovarian cancer, and/or myelodysplastic syndrome (MDS).
  • the immunotherapy is specific for, or includes, one or more shared splicing neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 2242-2245, or one or more nucleotide coding therefor, and the type of cancer includes skin cancer and/or head and neck cancer.
  • the immunotherapy is specific for, or includes, one or more shared splicing neoantigen selected from SEQ ID NOs: 2242-2245, or one or more nucleotide coding therefor, and the type of cancer includes skin cancer and/or head and neck cancer.
  • the immunotherapy includes an RNA-, DNA-, or peptide-based vaccine, dendritic cell vaccine, chimeric antigen receptor T cell (CAR-T) therapy, T cell receptor-engineered T cell (TCR-T) therapy, antibody or antibody derivative, cell-based immunotherapy, immune checkpoint inhibitor, immunomodulator, topical immunotherapy, injection immunotherapy, adoptive cell transfer, oncolytic virus therapy, immunosuppressive drug, helminthic therapy, and/or other non-specific immunotherapy.
  • the immunotherapy includes an RNA-, DNA-, or peptide-based vaccine, dendritic cell vaccine, CAR-T therapy, and/or antibody or antibody derivative.
  • compositions including any of the aforementioned immunogenic targets and/or immunotherapies.
  • the compositions can be formulated as a vaccine.
  • the compositions can further include one or more cytokines, growth factors, or adjuvants.
  • Further embodiments of the disclosure include in vitro isolated dendritic cells including any of the aforementioned immunogenic targets, and methods of making the same.
  • a dendritic cell (such as a dendritic cell vaccine) can be prepared by contacting a mature dendritic cell in vitro with any of the aforementioned immunogenic targets, and optionally screening the dendritic cell for one or more cellular properties.
  • the dendritic cell includes a mature dendritic cell.
  • the cell can be a cell with an HLA type selected from HLA-A, HLA-B, or HLA-C.
  • the immunogenic target is on the surface of the dendritic cell.
  • the immunogenic target is bound to a MHC molecule on the surface of the dendritic cell.
  • the dendritic cell includes a monocyte-derived dendritic cell.
  • the dendritic cell can be derived from a CD34+ hematopoietic stem or progenitor cell.
  • the dendritic cell can be derived from a peripheral blood monocyte (PBMC).
  • PBMC peripheral blood monocyte
  • the dendritic cells or the cells in which the DCs are derived from can be isolated by leukaphereses.
  • Further embodiments of the disclosure include engineered T-cell receptors (TCRs) or chimeric antigen receptors (CARs) that specifically recognize any of the aforementioned immunogenic targets, and methods of making the same. Additional embodiments of the disclosure include cells including one or more of the aforementioned TCRs or CARs.
  • Further embodiments of the disclosure include antibodies, and antigen binding fragments thereof, that specifically recognize any of the aforementioned immunogenic targets, and methods of making the same.
  • Additional embodiments of the disclosure include methods of treating one or more type of cancer, the method including: administering, to a patient having a type of cancer, a treatment including therapeutically effective amount of a composition including any of the aforementioned immunotherapies. Further embodiments include methods of stimulating an immune response in a subject, the method including: administering, to a subject having a type of cancer, a treatment including therapeutically effective amount of a composition including one or more of the aforementioned immunotherapies. [0019] In some embodiments, the immunotherapy can be administered in combination with one or more additional therapies. In some embodiments, the immunotherapy and the one or more additional therapies can be administered together in one administration or composition.
  • the immunotherapy and the one or more additional therapies can be administered separately in more than one administration or more than one composition.
  • the one or more additional therapies include surgical intervention, chemotherapy, radiation therapy, hormone therapies, immunotherapy, and/or adjuvant systematic therapies.
  • the subject has a tumor resistant to chemotherapy and/or radiation therapy.
  • the subject has been determined to be resistant to the previous treatment.
  • the cancer includes stage I, II, III, or IV cancer.
  • the cancer includes metastatic and/or recurrent cancer.
  • the subject is enrolled in a clinical trial.
  • Further embodiments of the disclosure include methods of identifying one or more immunogenic targets for treating a type of cancer, wherein the method includes: receiving a sample dataset, wherein the sample dataset includes mRNA from a cancer cell line, from a tumor cohort, or from a tumor sample from an individual patient; analyzing the sample dataset by a machine learning model including: identifying and quantifying post transcriptional regulation to detect one or more neojunctions and/or alternative splicing and/or alternative promoter regulatory events; determining one or more candidate neoantigen targets based on the neojunctions and/or alternative splicing and/or alternative promoter regulatory events; filtering the candidate neoantigen targets by: a) setting a minimum threshold for antigen abundance in the dataset; and/or b) assessing the specificity of exon-exon or exon-intron junctions for tumor samples versus a healthy reference cell line or cohort dataset based on junction counts and isoform structure association; and identifying a validated tumor-
  • the methods further include identifying candidate neoantigen targets by one or more of the following: a. immunopeptidomics validation; b. long- read RNA isoform sequence validation; c. single-cell short-read and/or long-read RNA-Seq validation; d. identification of unique tumor-specific extracellular protein sequences; e. splicing event type filtering; f. BayesTS probability; g. survival association h. SashimiPlot visualization; i. BLAT searching; j.3D topology evaluation; and/or i. filtering the candidate neoantigen targets based on one or more of the above steps and/or one or more additional filtering step.
  • immunopeptidomics validation includes filtering candidate neoantigen targets using one or more immunopeptidomics datasets including LC/MS data, with or without matching bulk RNA-Seq data; long-read RNA isoform sequence validation includes confirming candidate neoantigen targets in long-read isoform sequencing data from the cancer cell line, tumor cohort, or tumor sample and/or identifying tumor-specific full-length transmembrane protein isoforms; single-cell short-read and/or long-read RNA-Seq validation includes identifying common candidate neoantigen targets which are detected in one or more patients in a tumor sample versus microenvironment in a matching cancer and/or identifying tumor-specific full-length transmembrane protein isoforms; identification of unique extracellular tumor-specific protein sequences includes sequence comparison and alignment with a prior database (e.g.
  • splicing event type filtering includes detecting splice- junctions not present in a reference database (e.g. Ensembl);
  • Bayes TS probability includes evaluating BayesTS for the healthy reference cell line or cohort dataset and/or the sample dataset; survival association includes using a univariate Cox regression analysis to identify candidate neoantigen targets with a parental PSI value that is positively or negatively associated with patient outcome;
  • SashimiPlot visualization includes determining the specificity of exon- exon or exon-intron junctions for the healthy reference cell line or cohort dataset vs the sample dataset via junction counts and isoform structure association relative to known isoforms in a reference database (e.g.
  • filtering the candidate neoantigen targets can include one or more of the following: a. determining if the splicing event is previously unknown; b. setting a threshold percentage of the sample dataset having the candidate neoantigen target; c. predicting 2D and/or 3D folding; d. predicting extracellular domain impact; e.
  • SashimiPlot validation f. evaluating secondary evidence from long-read sequencing; g. exclusion of protein isoform sequence data from a reference database; h. setting a threshold BayesTS probability score; i. setting a threshold average control tissue count; j. setting a threshold MHCNetPan binding; and/or k. setting a threshold DeepImmuno score > 0.5.
  • a. the candidate neoantigen target is present in greater than 5%, 10%, 15%, 20%, 25%, 30%, or higher, of the sample dataset; b. the BayesTS probability score is less than 10, less than 5, less than 4, less than 3, less than 2, less than 1, or lower; c.
  • the average control tissue count mean is less than 10, less than 5, less than 3, less than 2, less than 1, less than 0.5, or lower; and/or d.
  • the MHCNetPan binding is less than 5%, less than 4%, less than 3%, less than 2%, less than 1%, less than 0.5%, less than 0.1%, or lower.
  • a. the candidate neoantigen target is present in greater than 15% of patients in the cohort;
  • b. the BayesTS probability score is less than 3;
  • the average control tissue count mean is less than 1; and/or d.
  • the MHCNetPan binding is ⁇ 2% as a weak binder and ⁇ 0.5% as a strong binder.
  • filtering the candidate neoantigen targets includes: a. long-read single-cell genomics data; b. patient survival association data; c. immunopeptidomics data; and/or d. patient survival association with antigen presence.
  • the methods further include validating candidate neoantigen targets by LC/MS, peptide-MHC binding, proteomics, and/or immunogenicity.
  • a. validation via LC/MS includes spectral matching
  • b. validation via peptide-MHC binding includes performing an MHC stabilization assay
  • c. validation via immunogenicity includes testing pMHCs on HLA genotyped healthy blood PMBC.
  • a ranking is derived for the candidate neoantigen target.
  • the ranking further includes immunogenicity score, HLA-binding score, neoantigen tumor specificity scores relative to healthy control samples (maximum likelihood Estimation, Bayesian hierarchical model, control cohort mean counts), alternative splicing PSI value, mRNA junction occurrence in a reference transcriptome, patient cohort occurrence frequency, recurrence in independent cancers and cohorts, splice-event type, full- length peptide prediction length versus the reference, peptide sequence similarity to reference peptides, the presence of signal peptide sequence, secondary structure, 3D protein structure prediction, insertion versus deletion of amino acids, absolute junction read counts, topology predictions, and/or other programmatic outputs.
  • alternative splicing and alternative promoter regulatory events includes cassette exon, 3′/5′ splice site exon, intron retention, alternative terminal exon, trans-splicing, and/or other event which can produce unique exon-exon or exon-intron junctions for in silico translation.
  • the method identifies intron retention associated antigens, and/or predicts immunogenicity of the candidate neoantigen target.
  • the neojunctions include gene-associated exon-exon and exon-intron junctions.
  • the machine learning model includes a deep learning model, probabilistic algorithms (Maximum Likelihood Estimation (MLE), hierarchical Bayesian model), and/or long-read sequencing concordance analysis. In some embodiments, the machine learning model includes a Maximum Likelihood Estimation (MLE), hierarchical Bayesian model, and long-read sequencing concordance analysis.
  • the immunogenic target is located on a transmembrane protein. In some embodiments, the sample dataset includes mRNA from a cancer cell line. In some embodiments, the immunogenic target is shared among a patient population. In some embodiments, the sample dataset includes mRNA from a tumor sample.
  • the immunogenic target includes a patient-specific neoantigen. In some embodiments, the method further includes one or more additional reference datasets and/or algorithms. In some embodiments, the method is automated. [0031] In some embodiments, the immunogenic target includes a neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-2545, or a nucleotide coding therefor. In some embodiments, the immunogenic target includes a neoantigen present in any of SEQ ID NOs: 1-2545, or a nucleotide coding therefor.
  • the immunogenic target includes a neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-2241. In some embodiments, the immunogenic target includes a neoantigen selected from SEQ ID NOs: 1-2241. In some embodiments, the immunogenic target includes a neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 1-4. In some embodiments, the immunogenic target includes a neoantigen selected from SEQ ID NOs: 1-4. In some embodiments, the immunogenic target comprises a neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID NOs: 2242-2545.
  • the immunogenic target includes a neoantigen present in a protein selected from SEQ ID NOs: 2242-2545. In some embodiments, wherein the immunogenic target comprises a neoantigen having at least 80% sequence homology to the sequence in any of SEQ ID Nos: 2242-2245. In some embodiments, the immunogenic target includes a neoantigen present in a protein selected from SEQ ID Nos: 2242-2245. In some embodiments, the immunogenic target includes a nucleotide coding for a shared splicing neoantigen selected from SEQ ID NOs: 1-2545.
  • the immunogenic target includes a nucleotide coding for a neoantigen selected from SEQ ID NOs: 1-4 or SEQ ID NOs 2242-2245.
  • the method further includes treating the type of cancer by administering, to a subject with the type of cancer, a therapeutically effective amount of a treatment, wherein the treatment targets the tumor or cancer-specific immunogenic target.
  • the sample is a tumor sample from the subject, and wherein the treatment targets the tumor-specific immunogenic target.
  • the type of cancer includes melanoma, ovarian, head and neck, breast, skin, lung, adult and pediatric acute myeloid leukemia, myelodysplastic syndrome (MDS), bladder, cervical, colorectal, kidney, low grade glioma, glioblastoma, liver, prostate, pancreatic, esophageal, rectal, sarcomas, stomach, thymus, thyroid, testicular, and/or uterine cancer.
  • the type of cancer includes melanoma, ovarian, head and neck, breast, skin, and/or MDS.
  • the treatment includes an immunotherapy.
  • the immunotherapy includes an RNA-, DNA-, or peptide-based vaccine, dendritic cell vaccine, chimeric antigen receptor T cell (CAR-T) therapy, T cell receptor-engineered T cell (TCR-T) therapy, antibody or antibody derivative, cell-based immunotherapy, immune checkpoint inhibitor, immunomodulator, topical immunotherapy, injection immunotherapy, adoptive cell transfer, oncolytic virus therapy, immunosuppressive drug, helminthic therapy, and/or other non-specific immunotherapy.
  • the immunotherapy includes an RNA vaccine, chimeric antigen receptor T cell (CAR-T) therapy, and/or antibody or antibody derivative.
  • the immunotherapy includes any of the aforementioned immunotherapy.
  • the type of cancer is responsive to targeting of the tumor- or cancer-specific immunogenic target.
  • Further embodiments of the disclosure relate to methods of treating a type of cancer in a subject in need thereof, the methods including administering to the subject a therapeutically effective amount of immunotherapy including an immunogenic target selected from the peptides and proteins listed in SEQ ID NOs: 1-2545, or a nucleotide coding therefor.
  • Further embodiments of the disclosure include methods of treating a type of cancer in a subject in need thereof, the methods including administering to the subject a therapeutically effective amount of a compound or composition directed to an immunogenic target selected from the peptides and proteins listed in SEQ ID NOs: 1-2545, or a nucleotide coding therefor.
  • the compound binds to an immunogenic target selected from the peptides and proteins listed in SEQ ID NOs: 1-2545.
  • Additional embodiments of the disclosure include methods of activating or expanding peptide-specific T cells. For example, such methods include contacting a starting population of T cells from a mammalian subject ex vivo with any of the aforementioned immunogenic targets, thereby activating, stimulating proliferation, and/or expanding immunogenic target-specific T cells in the starting population. In some embodiments, contacting is further defined as co-culturing the starting population of T cells with antigen presenting cells (APCs), wherein the APCs can present any of the aforementioned immunogenic targets on their surface.
  • APCs antigen presenting cells
  • the APCs are dendritic cells.
  • the dendritic cells are autologous dendritic cells obtained from the mammalian subject.
  • contacting is further defined as co-culturing the starting population of T cells with artificial antigen presenting cells (aAPCs).
  • aAPCs artificial antigen presenting cells
  • the T cells are CD8+ T cells or CD4+ T cells.
  • the T cells are cytotoxic T lymphocytes (CTLs).
  • the starting population of cells includes or consists of peripheral blood mononuclear cells (PBMCs).
  • the method further includes isolating or purifying the T cells from the peripheral blood mononuclear cells (PBMCs).
  • the mammalian subject is a human.
  • the method further includes reinfusing or administering the activated or expanded peptide-specific T cells to the subject. Additional embodiments of the disclosure include peptide-specific T cell activated or expanded according to any of the aforementioned methods.
  • computer system 100 can include a bus 102 or other communication mechanism for communicating information and a processor 104 coupled with bus 102 for processing information.
  • computer system 100 can also include a memory, which can be a random-access memory (RAM) 106 or other dynamic storage device, coupled to bus 102 for determining instructions to be executed by processor 104.
  • RAM random-access memory
  • Memory can also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 104.
  • computer system 100 can further include a read only memory (ROM) 108 or other static storage device coupled to bus 102 for storing static information and instructions for processor 104.
  • ROM read only memory
  • a storage device 110 such as a magnetic disk or optical disk, can be provided and coupled to bus 102 for storing information and instructions.
  • Figure 2 Automated discovery of immunogenic and transmembrane splicing neoantigens with SNAF, with an exemplary outline of the two parallel workflows in the software SNAF to predict splicing neoantigens derived from proteolytic peptides or full-length proteins.
  • SNAF begins with the identification and quantification of alternative splice junctions (exon-exon and exon-intron) from RNA-Seq BAM files and filters these against normal tissue reference RNA-Seq profiles (BayesTS).
  • SNAF-T T-cell antigens
  • SNAF-B B-cell antigens
  • SNAF-T performs in silico translation of each junction, MHC binding affinity prediction (netMHCpan or MHCflurry) and identifies high-confidence immunogenic neoantigens through deep learning (DeepImmuno).
  • SNAF-B predicts full-length protein coding isoforms that produce cancer-specific extracellular neo-epitopes (ExNeoEpitopes), considering existing transcript annotations and full-length isoform sequencing for targeted antibodies.
  • Fig. 3A Cartoon depicting the detection of exon-exon junctions from genome/transcriptome aligned BAM files, denoting the 5′ and 3′ splice sites for single-end and paired-end RNA-Seq.
  • Fig. 3B Detection of exon-intron junction (intron retention) for single-end versus paired-end RNA-Seq.
  • Fig. 3C Cartoon illustrating an example AltAnalyze gene model denoting different transcripts for the same gene, collapsed into a single combined exon and intron model (model used for exon-intron detection in Fig. 3B).
  • FIG. 6A Validation workflow schematic for Ovarian, Melanoma immunopeptidomics with either matched or unmatched RNA-Seq. MaxQuant is applied to find Peptide-Spectrum Match (PSM), followed by quantitative and expert MS2 spectra prioritization. HPLC-MS/MS confirmation is performed on synthesized nominated neoantigens.
  • Fig. 6B Number of SNAF-T predicted neoantigens and those confirmed by immunopeptidomics. Fig.
  • FIG. 6C SashimiPlot visualization of HAAASFETL, derived from an exon-exon junction in the gene FCRLA, along with the junction/peptide sequence, binding affinity and immunogenicity prediction.
  • Fig. 6D Raw read counts of the FCRLA neojunction between normal controls and TCGA melanoma cohort.
  • Fig.6E Mirror plot of the immunopeptidomics and spike-in MS spectrum for HAAASFETL.
  • Fig. 7A NQDEDPLEV occurring from C6orf52 in ovarian cancer.
  • Fig. 7B KGPWYPLSL occurring from C20orf204 in ovarian cancer.
  • Fig. 7C VAPGEAKNL occurring from RASA3 in melanoma.
  • Fig. 7D YALANIKWI occurring from DYNLT5 in melanoma.
  • Fig. 7E KEKLDQLVY occurring from FBXO7 in melanoma.
  • Fig.7F TELQRTLSL occurring from NGLY1 in melanoma.
  • Figure 8. Splicing-neoantigen burden predicts response to therapy in melanoma.
  • Van Allen cohort patients were subject to immune checkpoint inhibitors whereas TCGA was not. The number of neojunctions or Neoantigen peptides are shown at the top of each plot.
  • Fig. 8C Volcano plots of genes differentially expressed in patients with high versus low immunogenic splicing neoantigen burden in TCGA- SKCM, with a fold>1.5 and eBayes t-test p ⁇ 0.05 (FDR corrected). Red dots indicate genes that are up-regulated in the high burden group whereas blue dots represent down-regulated genes in the high burden group.
  • Fig.8D Gene-set enrichment with GO-Elite of Gene Ontology and cell- type specific marker gene-sets (AltAnalyze) for genes down-regulated in high splicing neoantigen burden versus low patients (panel C).
  • Fig. 8E Immunogenic splicing neoantigen burden between TCGA Melanoma patients with or without mutations in CAMKK2. Mann Whitney two-sided test.
  • Fig. 8F Bubble-plot of survival associated splicing neoantigens from SNAF in TCGA-SKCM.
  • Fig. 9A Gene-set enrichment plot of high-neoantigen associated genes in TCGA-SKCM (fold>1.5 and empirical Bayes t-test p ⁇ 0.05, FDR corrected) in the software GO-Elite.
  • Fig. 9A Gene-set enrichment plot of high-neoantigen associated genes in TCGA-SKCM (fold>1.5 and empirical Bayes t-test p ⁇ 0.05, FDR corrected) in the software GO-Elite.
  • Fig. 9A Gene-set enrichment plot of high-neoantigen associated genes in TCGA-SKCM (
  • FIG. 10B The correlation of inferred RBP activity with splicing neoantigen burden for all melanoma patients.
  • Fig. 10C Comparison of RBP activity-burden correlations with RBP differential gene expression, for high versus low burden.
  • Fig. 10D Type of splicing events observed with exon/intron inclusion or exclusion comparing high versus low burden. type distribution between low and high burden groups.
  • Figure 11. Validation and benchmarking of 12 TF activity methods in HepG2 cells.
  • Fig.11A Label Ranking Average Precision (LRAP) of 12 evaluated methods for splicing factor activity inference.
  • Fig. 11B Normalized Discounted Cumulative Gain (NDCG) of the 12 evaluated methods.
  • FIG. 11C Comparison of inferred splicing factor activity in cancer implicated splicing factors for RBP knockdown and controls.
  • Figure 12. Shared splicing neoantigens are frequently detected by MS and are defined by their sequence composition.
  • Fig. 12A Identification of common (shared) and unique immunogenic splicing neoantigens in the TCGA Melanoma cohort, based on their frequency of occurrence among patients.
  • Fig. 12B Frequency of splicing-event types for shared and unique splicing neoantigen junctions.
  • Fig. 12C Gene-set enrichment with GO-Elite of the Gene Ontology and pathways of shared neoantigens (present in >15% of patients).
  • Fig. 12D MS recovery rate in an independent melanoma immunopeptidome dataset (Bassani-Sternberg et al.) between shared and unique neoantigens considered in the query database.
  • Fig. 12E Kernel density estimate plot comparing the observed occurrence in an independent immunopeptidomics MS experimental cohort, for all detected shared (>15% of patients) versus unique splicing- neoantigens.
  • Fig.12F Re-defined shared and unique neoantigens by normalizing the occurrence of their parental splicing junction, leveraging their respective observed amino acid bias (see Example 1).
  • Fig. 12D MS recovery rate in an independent melanoma immunopeptidome dataset (Bassani-Sternberg et al.) between shared and unique neoantigens considered in the query database.
  • Fig. 12E Kernel density estimate plot comparing the observed occurrence in an independent immunopeptidomics MS experimental cohort, for all detected shared (>15% of patients) versus unique splic
  • Fig.14A-B Histograms and graph show HLA-A*02-PE staining on HLA-A*02 containing TAP deficient T2 cells without peptide (no pep), loaded with FLU and HCMV control peptides and RLLGTEFQT (RLL) and FQTTRRAMTL (FQT) peptide neoantigens.
  • MFI median fluorescence intensity.
  • PE Phycoerythrin conjugated antibodies.
  • Fig. 16A-B Venn diagrams comparing the number of parental neojunctions for TCGA SKCM splicing neoantigens unique to a single-patient (Fig. 16A) or shared in >15% of patients (Fig.16B) to the specific cell-types they derive from in independent melanoma tumor biopsies by single-cell RNA-Seq analysis.
  • Neojunctions are defined as tumor or immune if they are >2 fold enriched in either cell-population (absolute number of reads in all patients and cells for each lineage).
  • Figure 17. Comparison of melanoma specific splicing junctions to normal proliferating tissues.
  • Fig. 17A Overview of collected RNA-Seq data from five independent study on proliferating tissues.
  • Fig. 17B Decreasing number of splicing junctions specific to TCGA melanoma when normal controls were applied.
  • Fig. 17C Number of shared splicing neojunction reads in TCGA and proliferating skin cells.
  • Fig. 17D Gene pathway enrichment analysis of the parental genes of overlapping splicing junctions.
  • FIG. 18 finds full-length mRNAs and stable-proteoforms for targeted therapies.
  • Fig. 18A The SNAF-B prediction workflow.
  • Fig. 18B Comparison of a SNAF-B predicted full-length isoform in the transmembrane protein SIRPA to documented mRNA isoforms and those predicted from PacBio long-read IsoSeq of melanoma cell lines.
  • Fig. 18C SashimiPlot of alternative 3’ splice site selection in Melanoma and Brain RNA-Seq for SIRPA.
  • Fig.18D Specificity of the indicated SIRPA ExNeoEpitope to melanoma versus healthy controls tissue samples.
  • Fig. 18A The SNAF-B prediction workflow.
  • Fig. 18B Comparison of a SNAF-B predicted full-length isoform in the transmembrane protein SIRPA to documented mRNA isoforms and those predicted from PacBio long-read IsoS
  • FIG. 18E Alphafold2 3D modeling of the reference isoform and the long-read verified ExNeoEpitope.
  • FIG. 18F, Fig. 18G Confocal microscopy detection co- localization if SIRPA reference (Fig.18F) or melanoma specific splice isoform (Fig.18G) with a cell surface stain (phalloidin). Arrow indicates the vector used to quantify fluorophore spatial coincidence.
  • Figure 19 SLC45A2 encodes for a novel transmembrane isoform localized to the plasma membrane. Fig.
  • Fig.20B, 20E, 20H, 20K Tumor specificity of each junction in TCGA- SKCM versus GTEx.
  • Fig.20C, 20F, 20I, 20L Alphafold23D modeling of the reference isoform and the long-read verified ExNeoEpitope contained the indicated inserted residues in the transmembrane protein extracellular domain.
  • Figure 21 ExNeoEpitopes localize to the cell surface.
  • Fig. 21A Protter 2D structural prediction of SIRPA reference and SNAF-B identified alternative isoform.
  • Fig. 21A Protter 2D structural prediction of SIRPA reference and SNAF-B identified alternative isoform.
  • FIG. 21B Quantification of fluorophore spatial coincidence to assess co-localization of the reference or melanoma specific splice isoform with DAPI and/or phalloidin of SIRPA.
  • FIG. 21C, 21D Confocal microscopy co-localization of the MET and SEMA6A reference or melanoma-specific splice isoform with a cell surface stain (phalloidin). Fluorescent intensity along the indicated red line crossing the cell membrane is quantified in the bottom line plots for each of the three indicated channels (color corresponding to the above image) for all reference and SNAF-B isoforms.
  • Figure 22 Shared splicing antigens in MDS.
  • FIG. 22A Splicing antigens derived from intron retention, in representative donor/patient samples.
  • Fig. 22B Splicing antigens derived from alternative splicing, in representative donor/patient samples. The specificity of each splicing antigen to MDS versus 60 tissue regions (bulk RNA-Seq) is displayed in the dot plot at right in Figs.22A and 22B.
  • Figure 23 MDS-specific extracellular domain epitopes. Example result from the SNAF-B workflow of a novel alternative N-terminus in the transmembrane protein UMODL1.
  • FIG. 23A The shorter MDS-isoform (MAS-Seq) occurs due to a novel N-terminal exon not found in the UCSC genome database.
  • Fig. 23B The novel junction is only detected 0.2% of healthy tissue samples and 18% of MDS patients in this cohort.
  • Fig.23C and Fig.23 D Protter 2D structure prediction of the novel (Fig.23C) and the reference (Fig.23D) isoforms [0060]
  • Figure 24 SNAF interactive exploration of putative splicing neoantigens, probabilities, and selective amino acid preferences.
  • the SNAF virtual explorer provides advanced interactive analysis of SNAF results.
  • the SNAF-T viewer allows users to explore predicted T antigens and enriched amino acid motifs in neoantigen UMAP clusters.
  • Fig. 24B The SNAF-B viewer allows for the interactive query of B-cell antigens, side-by-side comparison with annotated transcripts and Neoantigen peptides and neojunction expression in cancerous and healthy tissues.
  • DETAILED DESCRIPTION OF THE INVENTION [0061] Unless otherwise noted, terms are to be understood according to conventional usage by those of ordinary skill in the relevant art. [0062] This specification describes various exemplary embodiments of identifying one or more antigen targets for treating a type of cancer.
  • sample encompasses a sample obtained from a subject or patient.
  • the sample can be of any biological tissue or fluid.
  • samples include, but are not limited to, sputum, saliva, buccal sample, oral sample, blood, serum, mucus, plasma, urine, blood cells (e.g., white cells), circulating cells (e.g. stem cells or endothelial cells in the blood), tissue, core or fine needle biopsy samples, cell-containing body fluids, free floating nucleic acids, urine, stool, peritoneal fluid, and pleural fluid, tear fluid, or cells therefrom. Samples can also include sections of tissues such as frozen or fixed sections taken for histological purposes or micro-dissected cells or extracellular parts thereof.
  • a sample to be analyzed can be tissue material from a tissue biopsy obtained by aspiration or punch, excision or by any other surgical method leading to biopsy or resected cellular material.
  • Such a sample can comprise cells obtained from a subject or patient.
  • the sample is a body fluid that include, for example, blood fluids, serum, mucus, plasma, lymph, ascitic fluids, gynecological fluids, or urine but not limited to these fluids.
  • the sample can be a non-invasive sample, such as, for example, a saline swish, a buccal scrape, a buccal swab, and the like.
  • blood can include, for example, plasma, serum, whole blood, blood lysates, and the like.
  • the term “assessing” includes any form of measurement, and includes determining if an element is present or not.
  • the terms “determining,” “measuring,” “evaluating,” “assessing” and “assaying” can be used interchangeably and can include quantitative and/or qualitative determinations.
  • the term “monitoring” with reference to a type of cancer refers to a method or process of determining the severity or degree of the type of cancer or stratifying the type of cancer based on risk and/or probability of mortality. In some embodiments, monitoring relates to a method or process of determining the therapeutic efficacy of a treatment being administered to a patient.
  • “outcome” can refer to an outcome studied.
  • “outcome” can refer to survival / mortality over a given time horizon. For example, “outcome” can refer to survival / mortality over 1 month, 3 months, 6 months, 1 year, 5 years, or 10 years or longer. In some embodiments, “outcome” can refer to 28-day survival / mortality. In some embodiments, an increased risk for a poor outcome indicates that a therapy has had a poor efficacy, and a reduced risk for a poor outcome indicates that a therapy has had a good efficacy. In some embodiments, “outcome” can refer to resolution of organ failure after 14 days or 28 days or limb loss.
  • predicting outcome and “outcome risk stratification” refers to a method or process of predicting and/or prognosticating a patient’s risk of a certain outcome.
  • predicting an outcome relates to monitoring the therapeutic efficacy of a treatment being administered to a patient.
  • predicting an outcome relates to determining a relative risk of an adverse outcome (e.g. complicated course) and/or mortality.
  • the predicted outcome is associated with administration of a particular treatment or treatment regimen.
  • Such adverse outcome risk and/or mortality can be high risk, moderate risk, moderate-high risk, moderate-low risk, or low risk.
  • adverse outcome risk can be described simply as high risk or low risk, corresponding to high risk of adverse outcome (e.g. complicated course) and/or mortality probability, or high likelihood of therapeutic effectiveness, respectively.
  • high risk clinical trial refers to one in which the test agent has “more than minimal risk” (as defined by the terminology used by institutional review boards, or IRBs).
  • a high risk clinical trial is a drug trial.
  • the term “low risk clinical trial” refers to one in which the test agent has “minimal risk” (as defined by the terminology used by IRBs).
  • a low risk clinical trial is one that is not a drug trial. In some embodiments, a low risk clinical trial is one that that involves the use of a monitor or clinical practice process. In some embodiments, a low risk clinical trial is an observational clinical trial.
  • the terms “modulated” or “modulation,” or “regulated” or “regulation” and “differentially regulated” can refer to both up regulation (i.e., activation or stimulation, e.g., by agonizing or potentiating) and down regulation (i.e., inhibition or suppression, e.g., by antagonizing, decreasing or inhibiting), unless otherwise specified or clear from the context of a specific usage.
  • the term “subject” refers to any member of the animal kingdom. In some embodiments, a subject is a human patient. In some embodiments, a subject is a pediatric patient. In some embodiments, a pediatric patient is a patient under 18 years of age, while an adult patient is 18 or older. [0074] As used herein, the term “treating” (and its variations, such as “treatment,” “treating,” “treat,” and the like) is, unless stated otherwise, to be considered in its broadest context and refers to obtaining a desired pharmacologic and/or physiologic effect. In particular, for example, the term “treating” may not necessarily imply or require that an animal is treated until total recovery.
  • treating includes amelioration of the symptoms, relief from the symptoms or effects associated with a condition, decrease in severity of a condition, or preventing, preventively ameliorating symptoms, or otherwise reducing the risk of developing a particular condition.
  • “treating” may not require or include prevention.
  • “treatment” can refer to obtaining a desired pharmacologic and/or physiologic effect. The effect can be prophylactic in terms of completely or partially preventing a disease or symptom thereof and/or can be therapeutic in terms of a partial or complete cure for a disease and/or adverse effect attributable to the disease.
  • Treatment covers any treatment of a disease in a subject, particularly in a human, and includes: (a) preventing the disease from occurring in a subject which may be predisposed to the disease but has not yet been diagnosed as having it; (b) inhibiting the disease, i.e., arresting its development; and (c) relieving the disease, i.e., causing regression of the disease and/or relieving one or more disease symptoms. “Treatment” can also encompass delivery of an agent or administration of a therapy in order to provide for a pharmacologic effect, even in the absence of a disease or condition. [0075] As used herein, reference to “treating” an animal includes but is not limited to prophylactic treatment and therapeutic treatment.
  • Treatment covers any treatment of a disease in a subject, preferably in a mammal (e.g., in a human), and may include one or more of: (a) preventing the disease from occurring in a subject which may be predisposed to the disease but has not yet been diagnosed as having it; (b) inhibiting the disease, i.e., arresting its development; and (c) relieving the disease, i.e., causing regression or elimination of the disease and/or relieving one or more disease symptoms.
  • Treatment can also encompass delivery of an agent or administration of a therapy in order to provide for a pharmacologic effect, even in the absence of a disease or condition. Any of the compositions (e.g., pharmaceutical compositions) described herein can be used to treat a suitable subject.
  • “Therapeutically effective amount” means an amount effective to achieve a desired and/or beneficial effect. An effective amount can be administered in one or more administrations. In the methods, a therapeutically effective amount is an amount appropriate to treat an indication. By treating an indication is meant achieving any desirable effect, such as one or more of palliate, ameliorate, stabilize, reverse, slow, or delay disease progression, increase the quality of life, or to prolong life.
  • the term “marker” or “biomarker” refers to a biological molecule, such as, for example, a nucleic acid, peptide, protein, hormone, and the like, whose presence or concentration can be detected and correlated with a known condition, such as a disease state. It can also be used to refer to a differentially expressed gene whose expression pattern can be utilized as part of a predictive, prognostic or diagnostic process in healthy conditions or a disease state, or which, alternatively, can be used in methods for identifying a useful treatment or prevention therapy.
  • an mRNA “isoform” is an alternative transcript for a specific mRNA or gene. This term includes pre-mRNA, immature mRNA, mature mRNA, cleaved or otherwise truncated, shortened, or aberrant mRNA, modified mRNA (e.g. containing any residue modifications, capping variants, polyadenylation variants, etc.), and the like.
  • “Antibody” or “antibody peptide(s)” refer to an intact antibody, or a binding fragment thereof that competes with the intact antibody for specific binding; this definition also encompasses monoclonal and polyclonal antibodies.
  • Binding fragments are produced by recombinant DNA techniques, or by enzymatic or chemical cleavage of intact antibodies. Binding fragments include Fab, Fab′, F(ab′)2, Fv, and single-chain antibodies.
  • An antibody other than a “bispecific” or “bifunctional” antibody is understood to have each of its binding sites identical.
  • An antibody for example, substantially inhibits adhesion of a receptor to a counterreceptor when an excess of antibody reduces the quantity of receptor bound to counterreceptor by at least about 20%, 40%, 60% or 80%, and more usually greater than about 85% (as measured in an in vitro competitive binding assay).
  • the term “expression levels” refers, for example, to a determined level of biomarker expression.
  • pattern of expression levels refers to a determined level of biomarker expression compared either to a reference (e.g. a housekeeping gene or inversely regulated genes, or other reference biomarker) or to a computed average expression value (e.g. in RNA sequencing analysis, or DNA-chip analyses).
  • a pattern is not limited to the comparison of two biomarkers but is more related to multiple comparisons of biomarkers to reference biomarkers or samples.
  • a certain “pattern of expression levels” can also result and be determined by comparison and measurement of several biomarkers as disclosed herein and display the relative abundance of these transcripts to each other.
  • a “reference pattern of expression levels” refers to any pattern of expression levels that can be used for the comparison to another pattern of expression levels.
  • a reference pattern of expression levels is, for example, an average pattern of expression levels observed in a group of healthy or diseased individuals, serving as a reference group.
  • a “neoantigen” is a tumor antigen, or a cancer antigen, i.e. an antigen found on or in cancer cells when certain mutations occur in tumor DNA.
  • a “splicing neoantigen” is a neoantigen that can be shared among patients with a type of cancer, or unique to a particular tumor.
  • a splicing neoantigen can be present in >15% of patients with a type of cancer. In some embodiments, a splicing neoantigen can be present in more than 8%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 50%, 60%, 70%, 80%, or 90% of patients with a type of cancer. In some embodiments, a splicing neoantigen can predict survival. In some embodiments, a splicing neoantigen can be validated by immunopeptidomics (e.g., SNAF-T) or long-read isoform sequencing (e.g., SNAF-B).
  • immunopeptidomics e.g., SNAF-T
  • long-read isoform sequencing e.g., SNAF-B
  • Splicing neoantigens can be produced through various alternative mRNA processing mechanisms, including, but not limited to, alternative splice-site selection, alternative exon inclusion, alternative intron inclusion, alternative promoter selection, alternative C-terminal exon selection, internal-hybrid splicing events, circular mRNA splicing, genomic rearrangements, alternative polyadenylation and exon bleeding.
  • embodiments of the disclosure set forth herein include methods of identifying one or more targets for treating a disease or disorder, such as a type of cancer. Still other embodiments of the disclosure include methods of treating a disease or disorder, by administering a treatment which relates to an identified target.
  • Some embodiments include methods of determining whether a patient is suitable for, or likely to respond favorably to, a particular treatment. Additional embodiments of the disclosure are also discussed herein.
  • the present disclosure relates to computational methods and molecular targets for cancer immunotherapy. These molecular targets derive, for example, from aberrant alternative splicing or alternative promoter mRNA products from tumors, that encode for neoantigens. Such neoantigens can be amino acid- or nucleotide-based and can be used in immunotherapy, including for the creation of RNA vaccines or synthetic chimeric antigen receptor T cell (CAR-T) therapy.
  • CAR-T synthetic chimeric antigen receptor T cell
  • neoantigens produced from post-transcriptional regulation can provide an untapped reservoir of common immunogenic targets for new targeted cancer therapies.
  • the identification and prioritization of shared neoantigens based on alternative splicing events unique to cancer cells within and across cancers can to lead to new targeted immunotherapies.
  • the development of existing targeted neoantigen immunotherapies is time-consuming and costly, as they must exploit specific MHC-presented mutations that are evidenced by proteomics and immunogenicity assays, in a precision manner.
  • the present disclosure relates to a computational tool to more accurately predict T cell-directed immunogenicity of putative MHC-I bound peptides for immunotherapy (PMID: 33398286).
  • algorithm MultiPath-PSI in the alternative splicing analysis software AltAnalyze (https://www.nature.com/articles/s41467-021-26623-y) and the peptide immunogenicity algorithm DeepImmuno (https://academic.oup.com/bib/article/22/6/bbab160/6261914), a new computational workflow, called SNAF (Splicing Neo Antigen Finder), was developed.
  • This tool can find high-confidence common (shared) neoantigens from large cancer patient RNA- Sequencing data cohorts (T-cell and B-cell targets).
  • the tool finds splicing-derived neoantigens that can be readily validated from existing experimental validation data (HLA targeted mass spectrometry).
  • HLA targeted mass spectrometry As such neoantigens are frequently shared among cancer patients, this prognostic has a broad and valuable use in treating cancer via immunotherapy, which is the most effective strategy for treatment (e.g., anti-PD-1, check-point inhibitor immunotherapy, anti-PD-L1, CAR- T, TCR-T therapies, neoantigen vaccines, and the like).
  • the computational systems developed as described herein are designed to systematically identify new classes of neoepitopes, e.g. splicing-neoantigens from tumor cohorts or individual tumor RNA-Sequencing (RNA-Seq) samples, and prioritize and interpret distinct classes of splicing-neoantigens.
  • Two new computational workflows focused on T-cell and B-cell based therapies were developed.
  • the workflow incorporates advanced deep-learning and probabilistic algorithms to discover immunogenic (SNAF-T workflow), and full-length protein- coding transmembrane tumor-specific isoforms (SNAF-B workflow).
  • T-cell based therapies include, but are not limited to, cancer vaccines, which require that target antigens are processed, presented by MHC and immunogenic.
  • B-cell based therapies such as monoclonal antibodies, require the identification of transmembrane protein encoding neoantigens that will enable targeted approaches to selectively recognize cancer cells.
  • SNAF was developed to recognize and prioritize both classes of neoantigens (SNAF-T and SNAF-B) in individual patient samples, while assessing the aggregate importance of each neoantigen at a population level (neoantigen burden) ( Figure 2).
  • the disclosure describes the identification of numerous novel small molecule peptide and full length protein neoantigen targets to predict response to immunotherapy in cancer treatment. Such neoantigens are produced through a common splicing mechanism. In particular, a broad and clear amino acid preference has been found to be responsible for nearly all commonly produced melanoma neoantigens; this workflow can therefore be used to prioritize additional neoantigen candidates in the future (not just for melanoma).
  • chimeric antigen receptor (CAR) T- cell therapy for the neoantigen can be introduced in a patient’s own T-cells, ex vivo.
  • splicing neoantigens can be used as shared (recurrent) targets for immunotherapy and overall splicing neoantigen burden as an independent prognostic indicator.
  • top-prioritized targets for RNA vaccine and CAR-T immunotherapy were identified from the analysis of multiple melanoma RNA-Seq cohorts and independently observed in multiple independent cancers, considering patient biopsies collected at the time of diagnosis with and without immunotherapy treatment following diagnosis. These targets are relevant in multiple cancer types, including melanoma, solid, and blood cancers, in which the underlying associated splicing junctions are recurrently detected.
  • neoantigens can be produced from diverse post-transcriptionally regulatory mechanisms.
  • Alternative splicing is one of the primary mechanisms used to achieve mRNA transcript and proteomic diversity in higher-order eukaryotes. In cancer, altered mRNA splicing can lead to aberrant protein products that promote oncogenic transformation, metastasis and confer chemotherapy resistance.
  • neoantigens can be produced from diverse post-transcriptionally regulatory mechanisms. Following their initial identification using proteogenomics approaches, splicing neoantigens have become increasingly recognized as a potent source of neo-peptides that can elicit immune response and induce cancer cell death.
  • splicing neoantigens appear to often be the dominant source of tumor-specific peptides.
  • Such splicing events include intron retention, which typically results in nonsense-mediated decay, but which produce MHC-presented neopeptides that can be detected by mass-spectrometry (MS).
  • MS mass-spectrometry
  • Such peptides require further experimental validation, as MHC presentation alone does not dictate binding to T-cell receptors (immunogenicity).
  • the prediction of such antigens remains non-trivial, as splicing neoantigens must be degraded, bound, and presented by specific cognate HLA alleles, and interact with patient-specific T-cell receptors on CD8+ T cells to induce an immune response.
  • splicing neoantigens as targets for therapy is that the occurrence of a splicing event is often non-binary (changes in percent exon/intron inclusion), relative to mutations (change in percent exon inclusion), making it difficult to know which splicing events are truly tumor-specific.
  • An alternative strategy to target tumor-specific splicing is to focus on events that specifically result in novel translated transmembrane proteins that can expose tumor-specific epitopes, bypassing the need to be presented by HLA.
  • Such peptides can be recognized by new CAR-T therapies which use B-cell receptors to bind epitopes or selective monoclonal antibodies to mediate targeted tumor cell death. While attractive, identifying such novel isoforms requires an accurate prediction of full-length novel isoforms that do not undergo nonsense-mediated decay and result in properly folded protein structures that conserve the major domains of the reference protein.
  • the disclosure relates to the development of the Splicing Neo-Antigen Finder (SNAF), an easy-to-use computational tool to identify, prioritize and interpret distinct classes of splicing-neoantigens, which can be used to perform a system analysis of splicing-neoantigens in cancer.
  • SNAF Splicing Neo-Antigen Finder
  • the workflow incorporates advanced deep-learning and probabilistic algorithms to discover immunogenic (SNAF-T workflow), and full-length protein-coding transmembrane tumor specific isoforms (SNAF-B workflow) and regulators of altered splicing (RNA-SPRINT).
  • SNAF-T workflow full-length protein-coding transmembrane tumor specific isoforms
  • RNA-SPRINT regulators of altered splicing
  • the work described herein demonstrates that splicing neoantigens in melanoma are frequently shared among patients, can predict survival and can be validated in vitro, by immunopeptidomics (SNAF-T), single-cell genomics, and/or long-read isoform sequencing (SNAF-B). These analyses show that splicing-neoantigens represent an untapped reservoir of shared targets for targeted cancer immunotherapy.
  • HLA-II and CD4 T-cells have also been reported to play an essential role in enhancing anti-tumor activities, together with other major immune cell types (i.e. neutrophils, dendritic cells).
  • other major immune cell types i.e. neutrophils, dendritic cells.
  • future methods can be designed to incorporate additional T-cell and antigen presentation mechanisms.
  • Such rich experimental data can further enable improved learning methods to identify neoantigens with the best likelihood of being either immunogenic or resulting in full-length transmembrane proteins with altered extracellular epitopes.
  • Targets identified via the methods described herein can be validated experimentally, by validating a range of predicted immunogenic and non-immunogenic peptides (MHC-presentation, immunogenicity) from mutations and splicing that are shared or unique and derived from different tools and statistical cutoffs.
  • ExNeoEpitopes can be validated by new proteogenomics approaches that leverage targeted long sequencing isoform sequencing and proteomics along with antibodies that target specific conformational epitopes.
  • Such antibodies can represent powerful new molecular reagents for CAR-T or monoclonal antibody strategies, for shared and patient-specific neoantigens.
  • RNA-Seq studies are on a limited set of adult human tissues, without considering rare cell-types or fetal developmental isoforms.
  • SNAF can be easily extended to new datasets, sequencing technologies, and neoantigen prediction libraries which can be deployed in a modular manner in custom bioinformatics pipelines.
  • SNAF can be used to identify splicing neoantigens that are unique and shared across malignancies and discover novel sequence motif preferences that expand the repertoire of targets for precision cancer therapy.
  • the present disclosure describes a reusable and comprehensive neoantigen identification and prediction workflow, to unbiasedly and confidently identify splicing neoantigens that can be targeted for treatment, including current and emerging immunotherapy strategies.
  • the disclosure encompasses shared splicing neoantigens and neoantigen-derived targets, including polypeptides and full length proteins/antibodies, as well as methods of identifying shared splicing neoantigen targets, as well as methods of treatment leveraging the identification of such shared splicing neoantigens, such as immunotherapy treatments, and the like.
  • SNAF Workflow and Secondary Target Prioritization and Validation [00108] SNAF applies unique deep-learning, Bayesian, and long-read sequencing concordance analyses to identify shared high-confidence splicing neoantigens that can be experimentally confirmed using diverse orthogonal methods. SNAF-T and SNAF-B frequently identify tens of thousands of predictions.
  • neoantigen validation using the procedures described herein, currently necessitates reduction of targets to a small number of targets (dozens) for 9-11 amino acid antigens or full-length transmembrane isoforms. While the SNAF source code represents the foundation of splicing neoantigen target selection, a series of non-public domain computational and filtering strategies developed as described herein prioritizes optimal targets for validation.
  • Splicing neoantigens predicted in the immunopeptidomics datasets are matched to common splicing neoantigens identified in one or more larger cancer RNA-Seq cohorts. These datasets are analyzed as described in the method using the SNAF-T and MaxQuant workflows. As LC/MS data is subject to false positive peptide-database matches report, to prioritize targets, a rigorous selection criterion have been developed for high-confidence matching hits.
  • Long-read sequence validation Long-read RNA isoform sequencing data are used to identify splicing neoantigens that can be confirmed in long-read isoform sequencing data that is selective to patient tumor samples or cell lines from any available malignancy.
  • Single-cell validation Single-cell short-read and/or long-read RNA-Seq is employed to select common splicing neoantigens that can be detected in one or more patients restricted to presumed tumor versus microenvironment in the matching cancer.
  • Splicing-event type filtering Splice-junctions not present in public RNA databases (Ensembl).
  • BayesTS probability BayesTS is evaluated for both the entire default healthy tissue atlas control dataset (GTEx + TCGA non-tumor control samples), and an optional tumor- specific user-provided control dataset.
  • Survival association To identify individual neojunctions or Neoantigens associated with survival, an univariate Cox Regression analysis is used to identify events/antigens with a parental PSI value that are positively or negatively associated with patient outcome. Here, the neojunction and its parental PSI value is ignored if the neoantigen was not predicted to be presented in that sample, resulting in different survival associations for different neoantigens produced from the same neojunction.
  • SashimiPlot visualization The specificity of exon-exon or exon-intron junctions for tumor samples versus an independent (non-GTEx and non-TCGA) cohorts are assessed at the level junction counts and isoform structure association relative to known isoforms (Ensembl).
  • Non-GTEx datasets comprise both internal bulk RNA-Seq data collections and prior published (e.g., EvoDevo).
  • SNAF-T event filtering Using the above filtering methods, SNAF-T shared- splicing neoantigens are defined on the basis of: a. Optimized RNA-sequence alignment strategy to define challenging to detect exon-exon junctions; b.
  • Additional filtering steps which can be incorporated include: a. Novel-evidenced splicing event (Ensembl and GTEx); b. BayesTS score ⁇ 3 (applied to default and custom tissue databases); c. Average control tissue count mean ⁇ 1 (default and custom tissue databases); d. MHCNetPan binding ⁇ 2% of weak binder and ⁇ 0.5% as strong binder; and/or e. DeepImmuno score > 0.5. [00118] The following optional filtering steps can improve results and can be incorporated as well: a. Evidence from long-read single-cell genomics; b.
  • LC/MS validation To confirm that splicing neoantigens can be detected as cell membrane bound peptide-MHCs, high confidence peptides defined by our HC-PSMp thresholds were synthesized, and spectral matching was defined by targeted LC/MS. Synthetic MS2 spectrum that match the cancer patient spectrum are considered validated ( Figure 8).
  • the resultant MS2 spectrum of this antigen has a high-confidence match (Andromeda score: 149.06, P-value: 0.0), with a synthetic spectrum Pearsonr similarity of 0.63 (p-value: 0.05, cosine similarity: 0.87).
  • ubiquitin protein ligase complex ubiquitin protein ligase complex
  • NGLY1 asparagine amidase
  • DYNLT5 cytoskeletal motor protein
  • RASA3 negative regulator of RAS signaling
  • C20orf204, C6orf52 Current uncharacterized protein coding genes
  • Lower spectrum similarity scores are obseved in the ovarian cohort (Pearsonr 0.55) compared to the melanoma cohort (Pearsonr: 0.84), which can be attributed to differences in the MS technology applied (Ion trap MS versus Fourier transform MS) and fragmentation methods (CID versus HID).
  • Peptide-MHC binding To assess pMHC binding of splicing neoantigens, an MHC stabilization assay was performed in T2 cells, that contain the A allele 02:01 and are defective in TAP and thus cannot process endogenously derived peptides. Neoantigens were synthesized as custom 9AA peptides using a commercial solid-phase protocol (Abclonal). For these assays, these T2 cells were loaded with relevant HLA-A2 binding peptides (neoantigens), and stabilization of the pMHC and resurfaces was measured. This workflow was tested using IgG as a negative control, with flu vaccine and HCMV peptides used as positive controls.
  • Reconstituted lyophilized neopeptides were co-cultured (pulsed) with these cells for 4-6 hours, prior inducing DC maturation (IL-4, GM-CSF, IL-1B, IL-6, TNF-alpha, LPS).
  • T-cells (frozen PBMCs at day 7) were then stimulated via co-cultured with the antigen presenting DCs from the same donor co-cultured with to (IL-2, IL-6, IL-7, IL-12, IL-15), 2-to-3 times, prior to evaluation of immunogenicity in the expanded T-cells. Immunogenicity was evaluated at day 28 using flow cytometry to assess interferon gamma response in CD8+ T-cells.
  • This assay confirmed CD8+ immunogenicity of both positive controls (PMA/I, FLU and/or HMCV) relative to unstimulated DCs, as well as 5 frequently shared SNAF-T melanoma splicing neoantigens (derived from three unique neojunctions - SLC45A2, CDH19, PMEL), with a preference for HLA A*02, C*04 or C*08 ( Figure 2).
  • Long-read sequence evidence In addition to using long-read isoform sequencing data to verify full-length isoform predictions derived from the short-read dataset, bulk and single-cell long-read isoform datasets were collected and aggregated to directly evidence tumor-specific full-length transmembrane protein isoforms (ExNeoEpitopes) for validation. These dataset span diverse cancers and include primary patient specimens and cell lines, from prior published studies and in house generated datasets.
  • Isoform evidence is evaluated at the level of isoform GTF files and matched to exon-exon or exon-intron junction genomic coordinate positions for shared SNAF-B splicing neoantigens evidenced from short- read RNA-Seq cancer cohort datasets.
  • Unique extracellular protein sequences To confirm the specificity of extracellular domain encoding novel tumor-specific protein sequences in the ExNeoEpitope. A series of semi-automated analyses are performed. First, ExNeoEpitope sequence is compared to existing protein isoforms for all common gene annotated protein sequences from the Ensembl and UCSC Genome databases.
  • BayesTS probability BayesTS is evaluated for both the entire default healthy tissue atlas control dataset (GTEx + TCGA non-tumor control samples), and an optional tumor- specific user-provided control dataset.
  • Survival association To identify individual neojunctions or Neoantigens associated with survival, an univariate Cox Regression analysis is used to identify events/antigens with a parental PSI value that are positively or negatively associated with patient outcome. Here, the neojunction and its parental PSI value is ignored if the neoantigen was not predicted to be presented in that sample, resulting in different survival associations for different neoantigens produced from the same neojunction.
  • SashimiPlot visualization The specificity of exon-exon or exon-intron junctions for tumor samples versus an independent (non-GTEx and non-TCGA) cohorts are assessed at the level junction counts and isoform structure association relative to known isoforms (Ensembl).
  • Non-GTEx datasets comprise both internal bulk RNA-Seq data collections and prior published (e.g., EvoDevo).
  • Human BLAT search Full-length predicted mRNA sequences corresponding to ExNeoEpitopes are evaluated sequentially in the UCSC Genome Browser via BLAT search (HG38) to confirm exon-region isoform specificity relative to prior annotated mRNA and ESTs and evaluate transposable element associations.
  • the SNAF-B experimental workflow includes protein expression and transmembrane localization.
  • the long-read sequencing evidenced alternative isoforms and their annotated reference isoforms that are C-terminal tagged with either mNeon-Green or eGFP (VectorBuilder) are synthesized.
  • These constructs (CMV promoter) are transfected with TransIT-LT1, separately into HEK-293T cells with 1 ⁇ g of plasmid (99% pUc19 dummy plasmid+1% engineered plasmid).
  • SNAF-T and SNAF-B workflows described above have a number of points of differentiation and tremendous advantages over current and previously described process. First, they are productionized and fully automated. Further, they provide the first validated transmembrane cancer isoforms that localize to the plasma membrane. SNAF-T and SNAF-B also represent the first probabilistic model of tumor specificity using a novel hierarchical Bayesian framework (BayesTS).
  • RNA-SPRINT first splicing factor regulatory prediction
  • Peptides can also be synthesized utilizing molecular tools and a host cell. Nucleic acid sequences corresponding with antigenic peptides can be synthesized. In some embodiments, synthetic nucleic acids synthesized in in vitro synthesizers (e.g., phosphoramidite synthesizer), bacterial recombination system, or other suitable methods. Furthermore, synthesized nucleic acids can be purified and lyophilized, or kept stored in a biological system (e.g., bacteria, yeast).
  • a biological system e.g., bacteria, yeast
  • Embodiments include expression vectors and expression systems that produce antigenic peptides or proteins. These expression systems can incorporate an expression vector to express transcripts and proteins in a suitable expression system. Typical expression systems include bacterial (e.g., E. coli), insect (e.g., SF9), yeast (e.g., S. cerevisiae), animal (e.g., CHO), or human (e.g., HEK 293) cell lines.
  • E. coli E.g., E. coli
  • insect e.g., SF9
  • yeast e.g., S. cerevisiae
  • animal e.g., CHO
  • human e.g., HEK 293
  • RNA and/or protein molecules can be purified from these systems using standard biotechnology production procedures.
  • Assays to determine immunogenicity and/or TCR binding can be performed.
  • custom-made HLA-matched MHC Class I dextramer:peptide (pMHC) complexes are developed or purchased (Immudex, Copenhagen, Denmark).
  • T cells from peripheral blood mononuclear cells (PBMCs) or tumor- infiltrating lymphocytes (TILs) are incubated the pMHC complexes and stained, which are then run through a flow cytometer to determine if the peptide is capable of binding a TCR of a T cell.
  • PBMCs peripheral blood mononuclear cells
  • TILs tumor- infiltrating lymphocytes
  • T-cell receptors comprise two different polypeptide chains, termed the T-cell receptor ⁇ (TCR ⁇ ) and ⁇ (TCR ⁇ ) chains, linked by a disulfide bond. These ⁇ : ⁇ heterodimers are very similar in structure to the Fab fragment of an immunoglobulin molecule, and they account for antigen recognition by most T cells. A minority of T cells bear an alternative, but structurally similar, receptor made up of a different pair of polypeptide chains designated ⁇ and ⁇ .
  • Both types of T cell receptor differ from the membrane-bound immunoglobulin that serves as the B- cell receptor: a T cell receptor has only one antigen-binding site, whereas a B-cell receptor has two, and T-cell receptors are never secreted, whereas immunoglobulin can be secreted as antibody.
  • Both chains of the T-cell receptor have an amino-terminal variable (V) region with homology to an immunoglobulin V domain, a constant (C) region with homology to an immunoglobulin C domain, and a short hinge region containing a cysteine residue that forms the interchain disulfide bond. Each chain spans the lipid bilayer by a hydrophobic transmembrane domain, and ends in a short cytoplasmic tail.
  • the three-dimensional structure of the T-cell receptor has been determined.
  • the structure is indeed similar to that of an antibody Fab fragment, as was suspected from earlier studies on the genes that encoded it.
  • the T-cell receptor chains fold in much the same way as those of a Fab fragment, although the final structure appears a little shorter and wider.
  • the most striking difference is in the C ⁇ domain, where the fold is unlike that of any other immunoglobulin-like domain.
  • the half of the domain that is juxtaposed with the C ⁇ domain forms a ⁇ sheet similar to that found in other immunoglobulin-like domains, but the other half of the domain is formed of loosely packed strands and a short segment of ⁇ helix.
  • the intramolecular disulfide bond which in immunoglobulin-like domains normally joins two ⁇ strands, in a C ⁇ domain joins a ⁇ strand to this segment of ⁇ helix.
  • the interface between the V and C domains of both T-cell receptor chains is more extensive than in antibodies, which may make the hinge joint between the domains less flexible.
  • the interaction between the C ⁇ and C ⁇ domains is distinctive in being assisted by carbohydrate, with a sugar group from the C ⁇ domain making a number of hydrogen bonds to the C ⁇ domain.
  • the term “engineered” refers to T cell receptors that have TCR variable regions grafted onto TCR constant regions to make a chimeric polypeptide that binds to peptides and antigens of the disclosure.
  • the TCR comprises intervening sequences that are used for cloning, enhanced expression, detection, or for therapeutic control of the construct, but are not present in endogenous TCRs, such as multiple cloning sites, linker, hinge sequences, modified hinge sequences, modified transmembrane sequences, a detection polypeptide or molecule, or therapeutic controls that may allow for selection or screening of cells comprising the TCR.
  • the TCR comprises non-TCR sequences.
  • certain embodiments relate to TCRs with sequences that are not from a TCR gene.
  • the TCR is chimeric, in that it contains sequences normally found in a TCR gene, but contains sequences from at least two TCR genes that are not necessarily found together in nature.
  • Antibodies [00147] Various aspects of the disclosure include antibodies that target the peptides of the disclosure, or fragments thereof.
  • the term “antibody” refers to an intact immunoglobulin of any isotype, or a fragment thereof that can compete with the intact antibody for specific binding to the target antigen, and includes chimeric, humanized, fully human, and bispecific antibodies.
  • antibody or “immunoglobulin” are used interchangeably and refer to any of several classes of structurally related proteins that function as part of the immune response of an animal, including IgG, IgD, IgE, IgA, IgM, and related proteins, as well as polypeptides comprising antibody CDR domains that retain antigen-binding activity.
  • antigen refers to a molecule or a portion of a molecule capable of being bound by a selective binding agent, such as an antibody. An antigen may possess one or more epitopes that are capable of interacting with different antibodies.
  • epitope includes any region or portion of molecule capable eliciting an immune response by binding to an immunoglobulin or to a T-cell receptor.
  • Epitope determinants may include chemically active surface groups such as amino acids, sugar side chains, phosphoryl or sulfonyl groups, and may have specific three-dimensional structural characteristics and/or specific charge characteristics.
  • antibodies specific for a particular target antigen will preferentially recognize an epitope on the target antigen within a complex mixture.
  • epitope regions of a given polypeptide can be identified using many different epitope mapping techniques are well known in the art, including: x-ray crystallography, nuclear magnetic resonance spectroscopy, site-directed mutagenesis mapping, protein display arrays, see, e.g., Epitope Mapping Protocols, (Johan Rockberg and Johan Nilvebrant, Ed., 2018) Humana Press, New York, N.Y. Such techniques are known in the art and described in, e.g., U.S. Pat. No.4,708,871; Geysen et al. Proc. Natl. Acad. Sci. USA 81:3998-4002 (1984); Geysen et al. Proc. Natl.
  • immunogenic sequence means a molecule that includes an amino acid sequence of at least one epitope such that the molecule is capable of stimulating the production of antibodies in an appropriate host.
  • immunogenic composition means a composition that comprises at least one immunogenic molecule (e.g., an antigen or carbohydrate).
  • An intact antibody is generally composed of two full-length heavy chains and two full length light chains, but in some instances may include fewer chains, such as antibodies naturally occurring in camelids that may comprise only heavy chains.
  • Antibodies as disclosed herein may be derived solely from a single source or may be “chimeric,” that is, different portions of the antibody may be derived from two different antibodies.
  • the variable or CDR regions may be derived from a rat or murine source, while the constant region is derived from a different animal source, such as a human.
  • the antibodies or binding fragments may be produced in hybridomas, by recombinant DNA techniques, or by enzymatic or chemical cleavage of intact antibodies.
  • the term “antibody” includes derivatives, variants, fragments, and muteins thereof, examples of which are described below (Sela-Culang et al., Front Immunol.2013; 4: 302; 2013).
  • the term “light chain” includes a full-length light chain and fragments thereof having sufficient variable region sequence to confer binding specificity.
  • a full-length light chain has a molecular weight of around 25,000 Daltons and includes a variable region domain (abbreviated herein as VL), and a constant region domain (abbreviated herein as CL).
  • VL variable region domain
  • CL constant region domain
  • VL fragment means a fragment of the light chain of a monoclonal antibody that includes all or part of the light chain variable region, including CDRs.
  • a VL fragment can further include light chain constant region sequences.
  • the variable region domain of the light chain is at the amino- terminus of the polypeptide.
  • the term “heavy chain” includes a full-length heavy chain and fragments thereof having sufficient variable region sequence to confer binding specificity.
  • a full-length heavy chain has a molecular weight of around 50,000 Daltons and includes a variable region domain (abbreviated herein as VH), and three constant region domains (abbreviated herein as CH1, CH2, and CH3).
  • VH fragment means a fragment of the heavy chain of a monoclonal antibody that includes all or part of the heavy chain variable region, including CDRs.
  • a VH fragment can further include heavy chain constant region sequences. The number of heavy chain constant region domains will depend on the isotype.
  • the VH domain is at the amino-terminus of the polypeptide, and the CH domains are at the carboxy-terminus, with the CH3 being closest to the —COOH end.
  • the isotype of an antibody can be IgM, IgD, IgG, IgA, or IgE and is defined by the heavy chains present of which there are five classifications: mu ( ⁇ ), delta ( ⁇ ), gamma ( ⁇ ), alpha ( ⁇ ), or epsilon ( ⁇ ) chains, respectively.
  • IgG has several subtypes, including, but not limited to, IgG1, IgG2, IgG3, and IgG4.
  • IgM subtypes include IgM1 and IgM2.
  • IgA subtypes include IgA1 and IgA2. 1.
  • Antibodies can be whole immunoglobulins of any isotype or classification, chimeric antibodies, or hybrid antibodies with specificity to two or more antigens. They may also be fragments (e.g., F(ab ⁇ )2, Fab ⁇ , Fab, Fv, and the like), including hybrid fragments.
  • An immunoglobulin also includes natural, synthetic, or genetically engineered proteins that act like an antibody by binding to specific antigens to form a complex.
  • the term antibody includes genetically engineered or otherwise modified forms of immunoglobulins.
  • the term “monomer” means an antibody containing only one Ig unit. Monomers are the basic functional units of antibodies.
  • the term “dimer” means an antibody containing two Ig units attached to one another via constant domains of the antibody heavy chains (the Fc, or fragment crystallizable, region).
  • the complex may be stabilized by a joining (J) chain protein.
  • the term “multimer” means an antibody containing more than two Ig units attached to one another via constant domains of the antibody heavy chains (the Fc region).
  • the complex may be stabilized by a joining (J) chain protein.
  • bivalent antibody means an antibody that comprises two antigen- binding sites. The two binding sites may have the same antigen specificities or they may be bi- specific, meaning the two antigen-binding sites have different antigen specificities.
  • Bispecific antibodies are a class of antibodies that have two paratopes with different binding sites for two or more distinct epitopes.
  • bispecific antibodies can be biparatopic, wherein a bispecific antibody may specifically recognize a different epitope from the same antigen.
  • bispecific antibodies can be constructed from a pair of different single domain antibodies termed “nanobodies”. Single domain antibodies are sourced and modified from cartilaginous fish and camelids. Nanobodies can be joined together by a linker using techniques typical to a person skilled in the art; such methods for selection and joining of nanobodies are described in PCT Publication No. WO2015044386A1, No.
  • Bispecific antibodies can be constructed as: a whole IgG, Fab′2, Fab′PEG, a diabody, or alternatively as scFv. Diabodies and scFvs can be constructed without an Fc region, using only variable domains, potentially reducing the effects of anti-idiotypic reaction. Bispecific antibodies may be produced by a variety of methods including, but not limited to, fusion of hybridomas or linking of Fab′ fragments. See, e.g., Songsivilai and Lachmann, Clin. Exp.
  • the antigen-binding domain may be multispecific or heterospecific by multimerizing with VH and VL region pairs that bind a different antigen.
  • the antibody may bind to, or interact with, (a) a cell surface antigen, (b) an Fc receptor on the surface of an effector cell, or (c) at least one other component.
  • aspects may include, but are not limited to, bispecific, trispecific, tetraspecific, and other multispecific antibodies or antigen binding fragments thereof that are directed to epitopes and to other targets, such as Fc receptors on effector cells.
  • multispecific antibodies can be used and directly linked via a short flexible polypeptide chain, using routine methods known in the art.
  • diabodies that are bivalent, bispecific antibodies in which the VH and VL domains are expressed on a single polypeptide chain, and utilize a linker that is too short to allow for pairing between domains on the same chain, thereby forcing the domains to pair with complementary domains of another chain creating two antigen binding sites.
  • the linker functionality is applicable for embodiments of triabodies, tetrabodies, and higher order antibody multimers.
  • Bispecific diabodies as opposed to bispecific whole antibodies, may also be advantageous because they can be readily constructed and expressed in E. coli.
  • Diabodies (and other polypeptides such as antibody fragments) of appropriate binding specificities can be readily selected using phage display (WO94/13804) from libraries.
  • a library can be made where the other arm is varied and an antibody of appropriate specificity selected.
  • Bispecific whole antibodies may be made by alternative engineering methods as described in Ridgeway et al., (Protein Eng., 9:616-621, 1996) and Krah et al., (N Biotechnol. 39:167-173, 2017), each of which is hereby incorporated by reference in their entirety.
  • Heteroconjugate antibodies are composed of two covalently linked monoclonal antibodies with different specificities. See, e.g., U.S. Patent No. 6,010,902, incorporated herein by reference in its entirety.
  • the part of the Fv fragment of an antibody molecule that binds with high specificity to the epitope of the antigen is referred to herein as the “paratope.”
  • the paratope consists of the amino acid residues that make contact with the epitope of an antigen to facilitate antigen recognition.
  • Each of the two Fv fragments of an antibody is composed of the two variable domains, VH and VL, in dimerized configuration.
  • the primary structure of each of the variable domains includes three hypervariable loops separated by, and flanked by, Framework Regions (FR).
  • the hypervariable loops are the regions of highest primary sequences variability among the antibody molecules from any mammal.
  • hypervariable loop is sometimes used interchangeably with the term “Complementarity Determining Region (CDR).”
  • CDR Complementarity Determining Region
  • the length of the hypervariable loops (or CDRs) varies between antibody molecules.
  • the framework regions of all antibody molecules from a given mammal have high primary sequence similarity/consensus.
  • the consensus of framework regions can be used by one skilled in the art to identify both the framework regions and the hypervariable loops (or CDRs) which are interspersed among the framework regions.
  • the hypervariable loops are given identifying names which distinguish their position within the polypeptide, and on which domain they occur.
  • CDRs in the VL domain are identified as L1, L2, and L3, with L1 occurring at the most distal end and L3 occurring closest to the CL domain.
  • the CDRs may also be given the names CDR-1, CDR-2, and CDR-3.
  • the L3 (CDR-3) is generally the region of highest variability among all antibody molecules produced by a given organism.
  • the CDRs are regions of the polypeptide chain arranged linearly in the primary structure, and separated from each other by Framework Regions.
  • the amino terminal (N-terminal) end of the VL chain is named FR1.
  • the region identified as FR2 occurs between L1 and L2 hypervariable loops.
  • FR3 occurs between L2 and L3 hypervariable loops, and the FR4 region is closest to the CL domain. This structure and nomenclature is repeated for the VH chain, which includes three CDRs identified as H1, H2 and H3.
  • variable domains or Fv fragments (VH and VL)
  • Fv fragments are part of the framework regions (approximately 85%).
  • the three dimensional, or tertiary, structure of an antibody molecule is such that the framework regions are more internal to the molecule and provide the majority of the structure, with the CDRs on the external surface of the molecule.
  • Several methods have been developed and can be used by one skilled in the art to identify the exact amino acids that constitute each of these regions. This can be done using any of a number of multiple sequence alignment methods and algorithms, which identify the conserved amino acid residues that make up the framework regions, therefore identifying the CDRs that may vary in length but are located between framework regions.
  • IMGT immunoglobulin and T cell receptor variable domains and Ig superfamily V-like domains
  • These methods each include unique numbering systems for the identification of the amino acid residues that constitute the variable regions.
  • the amino acid residues that actually contact the epitope of the antigen occur in the CDRs, although in some cases, residues within the framework regions contribute to antigen binding.
  • One skilled in the art can use any of several methods to determine the paratope of an antibody.
  • affinity matured antibodies are enhanced with one or more modifications in one or more CDRs thereof that result in an improvement in the affinity of the antibody for a target antigen as compared to a parent antibody that does not possess those alteration(s).
  • affinity matured antibodies will have nanomolar or picomolar affinities for the target antigen.
  • Affinity matured antibodies are produced by procedures known in the art, e.g., Marks et al., Bio/Technology 10:779 (1992) describes affinity maturation by VH and VL domain shuffling, random mutagenesis of CDR and/or framework residues employed in phage display is described by Rajpal et al., PNAS. 24: 8466-8471 (2005) and Thie et al., Methods Mol Biol. 525:309-22 (2009) in conjugation with computation methods as demonstrated in Tiller et al., Front. Immunol.8:986 (2017).
  • Chimeric immunoglobulins are the products of fused genes derived from different species; “humanized” chimeras generally have the framework region (FR) from human immunoglobulins and one or more CDRs are from a non-human source.
  • portions of the heavy and/or light chain are identical or homologous to corresponding sequences from another particular species or belonging to a particular antibody class or subclass, while the remainder of the chain(s) is identical or homologous to corresponding sequences in antibodies derived from another species or belonging to another antibody class or subclass, as well as fragments of such antibodies, so long as they exhibit the desired biological activity.
  • minimizing the antibody polypeptide sequence from the non-human species optimizes chimeric antibody function and reduces immunogenicity.
  • Specific amino acid residues from non-antigen recognizing regions of the non-human antibody are modified to be homologous to corresponding residues in a human antibody or isotype.
  • One example is the “CDR grafted” antibody, in which an antibody comprises one or more CDRs from a particular species or belonging to a specific antibody class or subclass, while the remainder of the antibody chain(s) is identical or homologous to a corresponding sequence in antibodies derived from another species or belonging to another antibody class or subclass.
  • the V region composed of CDR1, CDR2, and partial CDR3 for both the light and heavy chain variance region from a non-human immunoglobulin are grafted with a human antibody framework region, replacing the naturally occurring antigen receptors of the human antibody with the non-human CDRs.
  • corresponding non-human residues replace framework region residues of the human immunoglobulin.
  • humanized antibodies may comprise residues that are not found in the recipient antibody or in the donor antibody to further refine performance.
  • the humanized antibody may also comprise at least a portion of an immunoglobulin constant region (Fc), typically that of a human immunoglobulin.
  • Fc immunoglobulin constant region
  • Intrabodies are intracellularly localized immunoglobulins that bind to intracellular antigens as opposed to secreted antibodies, which bind antigens in the extracellular space.
  • Polyclonal antibody preparations typically include different antibodies against different determinants (epitopes).
  • a host such as a rabbit or goat, is immunized with the antigen or antigen fragment, generally with an adjuvant and, if necessary, coupled to a carrier.
  • Antibodies to the antigen are subsequently collected from the sera of the host.
  • the polyclonal antibody can be affinity purified against the antigen rendering it monospecific.
  • Monoclonal antibodies or “mAb” refer to an antibody obtained from a population of homogeneous antibodies from an exclusive parental cell, e.g., the population is identical except for naturally occurring mutations that may be present in minor amounts. Each monoclonal antibody is directed against a single antigenic determinant.
  • Functional Antibody Fragments and Antigen-Binding Fragments Certain aspects relate to antibody fragments, such as antibody fragments that bind to a peptide of the disclosure.
  • the term functional antibody fragment includes antigen- binding fragments of an antibody that retain the ability to specifically bind to an antigen.
  • VH variable region heavy chain
  • VL variable region heavy chain
  • CH1 constant region heavy chain 1
  • CL constant region heavy chain
  • they lack the Fc region constituted of heavy chain 2 (CH2) and 3 (CH3) domains.
  • Embodiments of antigen binding fragments and the modifications thereof may include: (i) the Fab fragment type constituted with the VL, VH, CL, and CHl domains; (ii) the Fd fragment type constituted with the VH and CHl domains; (iii) the Fv fragment type constituted with the VH and VL domains; (iv) the single domain fragment type, dAb, (Ward, 1989; McCafferty et al., 1990; Holt et al., 2003) constituted with a single VH or VL domain; (v) isolated complementarity determining region (CDR) regions.
  • CDR complementarity determining region
  • Antigen-binding fragments also include fragments of an antibody that retain exactly, at least, or at most 1, 2, or 3 complementarity determining regions (CDRs) from a light chain variable region. Fusions of CDR-containing sequences to an Fc region (or a CH2 or CH3 region thereof) are included within the scope of this definition including, for example, scFv fused, directly or indirectly, to an Fc region are included herein.
  • CDRs complementarity determining regions
  • a Fab′ fragment includes the VL, VH, CL and CH1 domains and all or part of the hinge region.
  • F(ab′)2 fragment means a bivalent antigen-binding fragment of a monoclonal antibody comprising two Fab′ fragments linked by a disulfide bridge at the hinge region.
  • An F(ab′)2 fragment includes, for example, all or part of the two VH and VL domains, and can further include all or part of the two CL and CH1 domains.
  • the term Fd fragment means a fragment of the heavy chain of a monoclonal antibody, which includes all or part of the VH, including the CDRs.
  • An Fd fragment can further include CH1 region sequences.
  • Fv fragment means a monovalent antigen-binding fragment of a monoclonal antibody, including all or part of the VL and VH, and absent of the CL and CH1 domains.
  • the VL and VH include, for example, the CDRs.
  • Single-chain antibodies are Fv molecules in which the VL and VH regions have been connected by a flexible linker to form a single polypeptide chain, which forms an antigen-binding fragment. Single chain antibodies are discussed in detail in International Patent Application Publication No. WO 88/01649 and U.S. Pat. Nos. 4,946,778 and 5,260,203, the disclosures of which are herein incorporated by reference.
  • (scFv)2 means bivalent or bispecific sFv polypeptide chains that include oligomerization domains at their C-termini, separated from the sFv by a hinge region (Pack et al.1992).
  • the oligomerization domain comprises self-associating a-helices, e.g., leucine zippers, which can be further stabilized by additional disulfide bonds.
  • (scFv)2 fragments are also known as “miniantibodies” or “minibodies.”
  • a single domain antibody is an antigen-binding fragment containing only a VH or the VL domain. In some instances, two or more VH regions are covalently joined with a peptide linker to create a bivalent domain antibody.
  • the two VH regions of a bivalent domain antibody may target the same or different antigens.
  • An Fc region Frament Crystallizable Region
  • the term “Fc polypeptide” as used herein includes native and mutein forms of polypeptides derived from the Fc region of an antibody. Truncated forms of such polypeptides containing the hinge region that promotes dimerization are included.
  • Polypeptides can be combined or integrated with antibody complementarity- determining regions (CDRs) and scaffolding domains that display the CDRs.
  • CDRs complementarity- determining regions
  • Antigen-binding peptide scaffolds such as CDRs are used to generate protein-binding molecules in accordance with the embodiments.
  • CDRs Antigen-binding peptide scaffolds
  • a person skilled in the art can determine the type of protein scaffold on which to graft at least one of the CDRs. It is known that scaffolds, optimally, must meet a number of criteria such as: good phylogenetic conservation; known three-dimensional structure; small size; few or no post transcriptional modifications; and/or be easy to produce, express, and purify. Skerra, J Mol Recognit, 13:167-87 (2000).
  • the protein scaffolds can be sourced from, but not limited to: fibronectin type III FN3 domain (known as “monobodies”), fibronectin type III domain 10, lipocalin, anticalin, Z- domain of protein A of Staphylococcus aureus, thioredoxin A or proteins with a repeated motif such as the “ankyrin repeat”, the “armadillo repeat”, the “leucine-rich repeat” and the “tetratricopeptide repeat”.
  • Such proteins are described in US Patent Publication Nos. 2010/0285564, 2006/0058510, 2006/0088908, 2005/0106660, and PCT Publication No. WO2006/056464, each of which are specifically incorporated herein by reference in their entirety.
  • binding agent refers to a molecule that binds to an antigen.
  • Non limiting examples include antibodies, antigen-binding fragments, scFv, Fab, Fab′, F(ab′)2, single chain antibodies, peptides, peptide fragments and proteins.
  • binding refers to a direct association between two molecules, due to, for example, covalent, electrostatic, hydrophobic, and ionic and/or hydrogen-bond interactions, including interactions such as salt bridges and water bridges.
  • immunologically reactive means that the selective binding agent or antibody of interest will bind with antigens present in a biological sample.
  • immuno complex refers the combination formed when an antibody or selective binding agent binds to an epitope on an antigen. 4.
  • affinity refers the strength with which an antibody or selective binding agent binds an epitope.
  • affinity constant Ka or ka sometimes referred to as the association constant
  • Affinity is measured as a comparison of the binding strength of the antibody to its antigen relative to the binding strength of the antibody to an unrelated amino acid sequence. Affinity can be expressed as, for example, 20- fold greater binding ability of the antibody to its antigen then to an unrelated amino acid sequence.
  • vidity refers to the resistance of a complex of two or more agents to dissociation after dilution.
  • immunoreactive and “preferentially binds” are used interchangeably herein with respect to antibodies and/or selective binding agent.
  • KD equilibrium dissociation constant
  • koff is the rate of dissociation between the antibody and antigen per unit time, and is related to the concentration of antibody and antigen present in solution in the unbound form at equilibrium.
  • kon is the rate of antibody and antigen association per unit time, and is related to the concentration of the bound antigen-antibody complex at equilibrium.
  • the units used for measuring the KD are mol/L (molarity, or M), or concentration.
  • examples of some experimental methods that can be used to determine the KD value are: enzyme-linked immunosorbent assays (ELISA), isothermal titration calorimetry (ITC), fluorescence anisotropy, surface plasmon resonance (SPR), and affinity capillary electrophoresis (ACE).
  • Antibodies deemed useful in certain embodiments may have an affinity constant (Ka) of about, at least about, or at most about 106, 107, 108,109, or 1010 M or any range derivable therein.
  • antibodies may have a dissociation constant of about, at least about or at most about 10-6, 10-7, 10-8, 10-9, 10-10 M, or any range derivable therein. These values are reported for antibodies discussed herein and the same assay may be used to evaluate the binding properties of such antibodies.
  • An antibody of the invention is said to “specifically bind” its target antigen when the dissociation constant (KD) is ⁇ 10 ⁇ 8 M. The antibody specifically binds antigen with “high affinity” when the KD is ⁇ 5 ⁇ 10 ⁇ 9 M, and with “very high affinity” when the KD is ⁇ 5 ⁇ 10 ⁇ 10 M. 5.
  • epitope Specificity is the specific region of the antigen for which an antibody has binding affinity.
  • the epitope is the specific residues (or specified amino acids or protein segment) that the antibody binds with high affinity.
  • An antibody does not necessarily contact every residue within the protein. Nor does every single amino acid substitution or deletion within a protein necessarily affect binding affinity.
  • epitope and antigenic determinant are used interchangeably to refer to the site on an antigen to which B and/or T cells respond or recognize.
  • Polypeptide epitopes can be formed from both contiguous amino acids and noncontiguous amino acids juxtaposed by tertiary folding of a polypeptide.
  • An epitope typically includes at least 3, and typically 5-10 amino acids in a unique spatial conformation.
  • Epitope specificity of an antibody can be determined in a variety of ways. One approach, for example, involves testing a collection of overlapping peptides of about 15 amino acids spanning the full sequence of the protein and differing in increments of a small number of amino acids (e.g., 3 to 30 amino acids). The peptides are immobilized in separate wells of a microtiter dish. Immobilization can be accomplished, for example, by biotinylating one terminus of the peptides.
  • This process may affect the antibody affinity for the epitope, therefore different samples of the same peptide can be biotinylated at the N and C terminus and immobilized in separate wells for the purposes of comparison. This is useful for identifying end-specific antibodies.
  • additional peptides can be included terminating at a particular amino acid of interest. This approach is useful for identifying end-specific antibodies to internal fragments. An antibody or antigen binding fragment is screened for binding to each of the various peptides.
  • the epitope is defined as a segment of amino acids that is common to all peptides to which the antibody shows high affinity binding. 6.
  • the antibodies of the present invention may be modified, such that they are substantially identical to the antibody polypeptide sequences, or fragments thereof, and still bind the epitopes of the present invention.
  • Polypeptide sequences are “substantially identical” when optimally aligned using such programs as Clustal Omega, IGBLAST, GAP or BESTFIT using default gap weights, they share at least 80% sequence identity, at least 90% sequence identity, at least 95% sequence identity, at least 96% sequence identity, at least 97% sequence identity, at least 98% sequence identity, or at least 99% sequence identity or any range therein.
  • amino acid sequences of antibodies or antigen-binding regions thereof are contemplated as being encompassed by the present invention, providing that the variations in the amino acid sequence maintain at least 75%, more preferably at least 80%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98% and most preferably at least 99% sequence identity.
  • conservative amino acid replacements are contemplated.
  • Conservative replacements are those that take place within a family of amino acids that are related in their side chains.
  • Genetically encoded amino acids are generally divided into families based on the chemical nature of the side chain; e.g., acidic (aspartate, glutamate), basic (lysine, arginine, histidine), nonpolar (alanine, valine, leucine, isoleucine, proline, phenylalanine, methionine, tryptophan), and uncharged polar (glycine, asparagine, glutamine, cysteine, serine, threonine, tyrosine).
  • Standard ELISA, Surface Plasmon Resonance (SPR), or other antibody binding assays can be performed by one skilled in the art to make a quantitative comparison of antigen binging affinity between the unmodified antibody and any polypeptide derivatives with conservative substitutions generated through any of several methods available to one skilled in the art.
  • Fragments or analogs of antibodies or immunoglobulin molecules can be readily prepared by those skilled in the art. Preferred amino- and carboxy-termini of fragments or analogs occur near boundaries of functional domains. Structural and functional domains can be identified by comparison of the nucleotide and/or amino acid sequence data to public or proprietary sequence databases.
  • computerized comparison methods are used to identify sequence motifs or predicted protein conformation domains that occur in other proteins of known structure and/or function.
  • Standard methods to identify protein sequences that fold into a known three-dimensional structure are available to those skilled in the art; Dill and McCallum., Science 338:1042-1046 (2012).
  • Several algorithms for predicting protein structures and the gene sequences that encode these have been developed, and many of these algorithms can be found at the National Center for Biotechnology Information (on the World Wide Web at ncbi.nlm.nih.gov/guide/proteins/) and at the Bioinformatics Resource Portal (on the World Wide Web at expasy.org/proteomics).
  • the foregoing examples demonstrate that those of skill in the art can recognize sequence motifs and structural conformations that may be used to define structural and functional domains in accordance with the invention.
  • Framework modifications can be made to antibodies to decrease immunogenicity, for example, by “backmutating” one or more framework residues to a corresponding germline sequence.
  • the antigen-binding domain may be multi-specific or multivalent by multimerizing the antigen-binding domain with VH and VL region pairs that bind either the same antigen (multi-valent) or a different antigen (multi-specific).
  • Proteinaceous Compositions [00196] As used herein, a “protein” “peptide” or “polypeptide” refers to a molecule comprising at least five amino acid residues. As used herein, the term “wild-type” refers to the endogenous version of a molecule that occurs naturally in an organism. In some embodiments, wild-type versions of a protein or polypeptide are employed, however, in many embodiments of the disclosure, a modified protein or polypeptide is employed to generate an immune response. The terms described above may be used interchangeably.
  • a “modified protein” or “modified polypeptide” or a “variant” refers to a protein or polypeptide whose chemical structure, particularly its amino acid sequence, is altered with respect to the wild-type protein or polypeptide.
  • a modified/variant protein or polypeptide has at least one modified activity or function (recognizing that proteins or polypeptides may have multiple activities or functions). It is specifically contemplated that a modified/variant protein or polypeptide may be altered with respect to one activity or function yet retain a wild-type activity or function in other respects, such as immunogenicity.
  • a protein is specifically mentioned herein, it is in general a reference to a native (wild-type) or recombinant (modified) protein or, optionally, a protein in which any signal sequence has been removed.
  • the protein may be isolated directly from the organism of which it is native, produced by recombinant DNA/exogenous expression methods, or produced by solid phase peptide synthesis (SPPS) or other in vitro methods.
  • SPPS solid phase peptide synthesis
  • recombinant may be used in conjunction with a polypeptide or the name of a specific polypeptide, and this generally refers to a polypeptide produced from a nucleic acid molecule that has been manipulated in vitro or that is a replication product of such a molecule.
  • the size of a protein or polypeptide may comprise, but is not limited to, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210
  • polypeptides may be mutated by truncation, rendering them shorter than their corresponding wild-type form, also, they might be altered by fusing or conjugating a heterologous protein or polypeptide sequence with a particular function (e.g., for targeting or localization, for enhanced immunogenicity, for purification purposes, etc.).
  • polypeptides, proteins, or polynucleotides encoding such polypeptides or proteins of the disclosure may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 50 (or any derivable range therein) or more variant amino acids or nucleic acid substitutions or be at least 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% (or any derivable
  • a peptide or polypeptide described herein comprises, comprises at least, or comprises at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 substitutions (or any derivable range therein) at amino acid position 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
  • the protein or polypeptide may comprise amino acids 1 to 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, , 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,6, 107, 108, 109, 110, 111, 112, 113,
  • the protein, polypeptide, or nucleic acid may comprise, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29,0, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,6, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81,2, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,06, 107, 108, 109, 110, 111, 112,
  • the polypeptide, protein, or nucleic acid may comprise, comprise at least, comprises at most, or comprise about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107
  • nucleic acid molecule or polypeptide starting at position 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111
  • nucleotide as well as the protein, polypeptide, and peptide sequences for various genes have been previously disclosed, and may be found in the recognized computerized databases.
  • Two commonly used databases are the National Center for Biotechnology Information’s Genbank and GenPept databases (on the World Wide Web at ncbi.nlm.nih.gov/) and The Universal Protein Resource (UniProt; on the World Wide Web at uniprot.org).
  • Genbank and GenPept databases on the World Wide Web at ncbi.nlm.nih.gov/
  • the Universal Protein Resource UniProt; on the World Wide Web at uniprot.org.
  • the coding regions for these genes may be amplified and/or expressed using the techniques disclosed herein or as would be known to those of ordinary skill in the art.
  • compositions of the disclosure there is between about 0.001 mg and about 10 mg of total polypeptide, peptide, and/or protein per ml.
  • concentration of protein in a composition can be about, at least about or at most about 0.001, 0.010, 0.050, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0 mg/ml or more (or any range derivable therein).
  • amino acid subunits of a protein to create an equivalent, or even improved, second-generation variant polypeptide or peptide.
  • certain amino acids may be substituted for other amino acids in a protein or polypeptide sequence with or without appreciable loss of interactive binding capacity with structures such as, for example, antigen-binding regions of antibodies or binding sites on substrate molecules. Since it is the interactive capacity and nature of a protein that defines that protein’s functional activity, certain amino acid substitutions can be made in a protein sequence and in its corresponding DNA coding sequence, and nevertheless produce a protein with similar or desirable properties.
  • a variation in a polypeptide of the disclosure may affect 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or more non-contiguous or contiguous amino acids of the protein or polypeptide, as compared to wild-type.
  • a variant can comprise an amino acid sequence that is at least 50%, 60%, 70%, 80%, or 90%, including all values and ranges there between, identical to any sequence provided or referenced herein.
  • a variant can include 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more substitute amino acids.
  • amino acid and nucleic acid sequences may include additional residues, such as additional N- or C-terminal amino acids, or 5 ⁇ or 3 ⁇ sequences, respectively, and yet still be essentially identical as set forth in one of the sequences disclosed herein, so long as the sequence meets the criteria set forth above, including the maintenance of biological protein activity where protein expression is concerned.
  • the addition of terminal sequences particularly applies to nucleic acid sequences that may, for example, include various non-coding sequences flanking either of the 5 ⁇ or 3 ⁇ portions of the coding region.
  • Deletion variants typically lack one or more residues of the native or wild type protein. Individual residues can be deleted or a number of contiguous amino acids can be deleted.
  • a stop codon may be introduced (by substitution or insertion) into an encoding nucleic acid sequence to generate a truncated protein.
  • Insertional mutants typically involve the addition of amino acid residues at a non-terminal point in the polypeptide. This may include the insertion of one or more amino acid residues. Terminal additions may also be generated and can include fusion proteins which are multimers or concatemers of one or more peptides or polypeptides described or referenced herein.
  • Substitutional variants typically contain the exchange of one amino acid for another at one or more sites within the protein or polypeptide, and may be designed to modulate one or more properties of the polypeptide, with or without the loss of other functions or properties.
  • substitutions may be conservative, that is, one amino acid is replaced with one of similar chemical properties. “Conservative amino acid substitutions” may involve exchange of a member of one amino acid class with another member of the same class. Conservative substitutions are well known in the art and include, for example, the changes of: alanine to serine; arginine to lysine; asparagine to glutamine or histidine; aspartate to glutamate; cysteine to serine; glutamine to asparagine; glutamate to aspartate; glycine to proline; histidine to asparagine or glutamine; isoleucine to leucine or valine; leucine to valine or isoleucine; lysine to arginine; methionine to leucine or isoleucine; phenylalanine to tyrosine, leucine or methionine; serine to threonine; threonine to serine; tryptophan to tyrosine; t
  • substitutions may encompass non- naturally occurring amino acid residues, which are typically incorporated by chemical peptide synthesis rather than by synthesis in biological systems. These include peptidomimetics or other reversed or inverted forms of amino acid moieties.
  • substitutions may be “non-conservative”, such that a function or activity of the polypeptide is affected. Non-conservative changes typically involve substituting an amino acid residue with one that is chemically dissimilar, such as a polar or charged amino acid for a nonpolar or uncharged amino acid, and vice versa. Non-conservative substitutions may involve the exchange of a member of one of the amino acid classes for a member from another class.
  • One skilled in the art can determine suitable variants of polypeptides as set forth herein using well-known techniques.
  • One skilled in the art may identify suitable areas of the molecule that may be changed without destroying activity by targeting regions not believed to be important for activity.
  • the skilled artisan will also be able to identify amino acid residues and portions of the molecules that are conserved among similar proteins or polypeptides.
  • areas that may be important for biological activity or for structure may be subject to conservative amino acid substitutions without significantly altering the biological activity or without adversely affecting the protein or polypeptide structure.
  • the hydropathy index of amino acids may be considered.
  • hydropathy profile of a protein is calculated by assigning each amino acid a numerical value (“hydropathy index”) and then repetitively averaging these values along the peptide chain.
  • Each amino acid has been assigned a value based on its hydrophobicity and charge characteristics. They are: isoleucine (+4.5); valine (+4.2); leucine (+3.8); phenylalanine (+2.8); cysteine/cysteine (+2.5); methionine (+1.9); alanine (+1.8); glycine ( ⁇ 0.4); threonine ( ⁇ 0.7); serine ( ⁇ 0.8); tryptophan ( ⁇ 0.9); tyrosine ( ⁇ 1.3); proline (1.6); histidine ( ⁇ 3.2); glutamate ( ⁇ 3.5); glutamine ( ⁇ 3.5); aspartate ( ⁇ 3.5); asparagine ( ⁇ 3.5); lysine ( ⁇ 3.9); and arginine ( ⁇ 4.5).
  • hydropathy amino acid index in conferring interactive biologic function on a protein is generally understood in the art (Kyte et al., J. Mol. Biol. 157:105-131 (1982)). It is accepted that the relative hydropathic character of the amino acid contributes to the secondary structure of the resultant protein or polypeptide, which in turn defines the interaction of the protein or polypeptide with other molecules, for example, enzymes, substrates, receptors, DNA, antibodies, antigens, and others. It is also known that certain amino acids may be substituted for other amino acids having a similar hydropathy index or score, and still retain a similar biological activity.
  • the substitution of amino acids whose hydropathy indices are within ⁇ 2 is included. In some aspects of the invention, those that are within ⁇ 1 are included, and in other aspects of the invention, those within ⁇ 0.5 are included. [00217] It also is understood in the art that the substitution of like amino acids can be effectively made based on hydrophilicity.
  • U.S. Patent 4,554,101 incorporated herein by reference, states that the greatest local average hydrophilicity of a protein, as governed by the hydrophilicity of its adjacent amino acids, correlates with a biological property of the protein.
  • the greatest local average hydrophilicity of a protein correlates with its immunogenicity and antigen binding, that is, as a biological property of the protein.
  • the following hydrophilicity values have been assigned to these amino acid residues: arginine (+3.0); lysine (+3.0); aspartate (+3.0 ⁇ 1); glutamate (+3.0 ⁇ 1); serine (+0.3); asparagine (+0.2); glutamine (+0.2); glycine (0); threonine ( ⁇ 0.4); proline ( ⁇ 0.5 ⁇ 1); alanine ( ⁇ 0.5); histidine ( ⁇ 0.5); cysteine ( ⁇ 1.0); methionine ( ⁇ 1.3); valine ( ⁇ 1.5); leucine ( ⁇ 1.8); isoleucine ( ⁇ 1.8); tyrosine ( ⁇ 2.3); phenylalanine ( ⁇ 2.5); and tryptophan ( ⁇ 3.4).
  • the substitution of amino acids whose hydrophilicity values are within ⁇ 2 are included, in other embodiments, those which are within ⁇ 1 are included, and in still other embodiments, those within ⁇ 0.5 are included.
  • amino acid residues in a protein that correspond to amino acid residues important for activity or structure in similar proteins.
  • One skilled in the art may opt for chemically similar amino acid substitutions for such predicted important amino acid residues.
  • One skilled in the art can also analyze the three-dimensional structure and amino acid sequence in relation to that structure in similar proteins or polypeptides. In view of such information, one skilled in the art may predict the alignment of amino acid residues of an antibody with respect to its three-dimensional structure.
  • One skilled in the art may choose not to make changes to amino acid residues predicted to be on the surface of the protein, since such residues may be involved in important interactions with other molecules.
  • test variants containing a single amino acid substitution at each desired amino acid residue. These variants can then be screened using standard assays for binding and/or activity, thus yielding information gathered from such routine experiments, which may allow one skilled in the art to determine the amino acid positions where further substitutions should be avoided either alone or in combination with other mutations.
  • Various tools available to determine secondary structure can be found on the world wide web at expasy.org/proteomics/protein_structure.
  • amino acid substitutions are made that: (1) reduce susceptibility to proteolysis, (2) reduce susceptibility to oxidation, (3) alter binding affinity for forming protein complexes, (4) alter ligand or antigen binding affinities, and/or (5) confer or modify other physicochemical or functional properties on such polypeptides.
  • single or multiple amino acid substitutions may be made in the naturally occurring sequence.
  • Substitutions can be made in that portion of the antibody that lies outside the domain(s) forming intermolecular contacts.
  • nucleic acid sequences can exist in a variety of instances such as: isolated segments and recombinant vectors of incorporated sequences or recombinant polynucleotides encoding one or both chains of an antibody, or a fragment, derivative, mutein, or variant thereof, polynucleotides sufficient for use as hybridization probes, PCR primers or sequencing primers for identifying, analyzing, mutating or amplifying a polynucleotide encoding a polypeptide, anti-sense nucleic acids for inhibiting expression of a polynucleotide, and complementary sequences of the foregoing described herein.
  • nucleic acids that encode the epitope to which certain of the antibodies provided herein are also provided.
  • Nucleic acids encoding fusion proteins that include these peptides are also provided.
  • the nucleic acids can be single-stranded or double-stranded and can comprise RNA and/or DNA nucleotides and artificial variants thereof (e.g., peptide nucleic acids).
  • polynucleotide refers to a nucleic acid molecule that either is recombinant or has been isolated from total genomic nucleic acid.
  • polynucleotide oligonucleotides (nucleic acids 100 residues or less in length), recombinant vectors, including, for example, plasmids, cosmids, phage, viruses, and the like.
  • Polynucleotides include, in certain aspects, regulatory sequences, isolated substantially away from their naturally occurring genes or protein encoding sequences.
  • Polynucleotides may be single- stranded (coding or antisense) or double- stranded, and may be RNA, DNA (genomic, cDNA or synthetic), analogs thereof, or a combination thereof. Additional coding or non-coding sequences may, but need not, be present within a polynucleotide.
  • the term “gene,” “polynucleotide,” or “nucleic acid” is used to refer to a nucleic acid that encodes a protein, polypeptide, or peptide (including any sequences required for proper transcription, post-translational modification, or localization). As will be understood by those in the art, this term encompasses genomic sequences, expression cassettes, cDNA sequences, and smaller engineered nucleic acid segments that express, or may be adapted to express, proteins, polypeptides, domains, peptides, fusion proteins, and mutants.
  • a nucleic acid encoding all or part of a polypeptide may contain a contiguous nucleic acid sequence encoding all or a portion of such a polypeptide. It also is contemplated that a particular polypeptide may be encoded by nucleic acids containing variations having slightly different nucleic acid sequences but, nonetheless, encode the same or substantially similar protein.
  • polynucleotide variants having substantial sequence identity to the sequences disclosed herein; those comprising at least 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% or higher sequence identity, including all values and ranges there between, compared to a polynucleotide sequence provided herein using the methods described herein (e.g., BLAST analysis using standard parameters).
  • the isolated polynucleotide will comprise a nucleotide sequence encoding a polypeptide that has at least 90%, preferably 95% and above, identity to an amino acid sequence described herein, over the entire length of the sequence; or a nucleotide sequence complementary to said isolated polynucleotide.
  • the nucleic acid segments regardless of the length of the coding sequence itself, may be combined with other nucleic acid sequences, such as promoters, polyadenylation signals, additional restriction enzyme sites, multiple cloning sites, other coding segments, and the like, such that their overall length may vary considerably.
  • the nucleic acids can be any length.
  • nucleic acid fragments of almost any length may be employed, with the total length preferably being limited by the ease of preparation and use in the intended recombinant nucleic acid protocol.
  • a nucleic acid sequence may encode a polypeptide sequence with additional heterologous coding sequences, for example to allow for purification of the polypeptide, transport, secretion, post-translational modification, or for therapeutic benefits such as targeting or efficacy.
  • a tag or other heterologous polypeptide may be added to the modified polypeptide-encoding sequence, wherein “heterologous” refers to a polypeptide that is not the same as the modified polypeptide.
  • a moderately stringent hybridization condition uses a prewashing solution containing 5 ⁇ sodium chloride/sodium citrate (SSC), 0.5% SDS, 1.0 mM EDTA (pH 8.0), hybridization buffer of about 50% formamide, 6 ⁇ SSC, and a hybridization temperature of 55° C. (or other similar hybridization solutions, such as one containing about 50% formamide, with a hybridization temperature of 42° C), and washing conditions of 60° C. in 0.5 ⁇ SSC, 0.1% SDS.
  • a stringent hybridization condition hybridizes in 6 ⁇ SSC at 45° C., followed by one or more washes in 0.1 ⁇ SSC, 0.2% SDS at 68° C.
  • one of skill in the art can manipulate the hybridization and/or washing conditions to increase or decrease the stringency of hybridization such that nucleic acids comprising nucleotide sequence that are at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 96%, at least 97%, at least 98% or at least 99% identical to each other typically remain hybridized to each other.
  • Mutations can be introduced by mutation into a nucleic acid, thereby leading to changes in the amino acid sequence of a polypeptide (e.g., an antibody or antibody derivative) that it encodes. Mutations can be introduced using any technique known in the art. In one embodiment, one or more particular amino acid residues are changed using, for example, a site- directed mutagenesis protocol. In another embodiment, one or more randomly selected residues are changed using, for example, a random mutagenesis protocol. However it is made, a mutant polypeptide can be expressed and screened for a desired property. [00229] Mutations can be introduced into a nucleic acid without significantly altering the biological activity of a polypeptide that it encodes.
  • nucleic acid molecules are suitable for use as primers or hybridization probes for the detection of nucleic acid sequences.
  • a nucleic acid molecule can comprise only a portion of a nucleic acid sequence encoding a full-length polypeptide, for example, a fragment that can be used as a probe or primer or a fragment encoding an active portion of a given polypeptide.
  • the nucleic acid molecules may be used as probes or PCR primers for specific antibody sequences.
  • a nucleic acid molecule probe may be used in diagnostic methods or a nucleic acid molecule PCR primer may be used to amplify regions of DNA that could be used, inter alia, to isolate nucleic acid sequences for use in producing variable domains of antibodies. See, eg., Gaily Kivi et al., BMC Biotechnol.
  • the nucleic acid molecules are oligonucleotides.
  • the oligonucleotides are from highly variable regions of the heavy and light chains of the antibody of interest.
  • the oligonucleotides encode all or part of one or more of the CDRs.
  • Probes based on the desired sequence of a nucleic acid can be used to detect the nucleic acid or similar nucleic acids, for example, transcripts encoding a polypeptide of interest.
  • the probe can comprise a label group, e.g., a radioisotope, a fluorescent compound, an enzyme, or an enzyme co-factor.
  • Antibody Production Methods for preparing and characterizing antibodies for use in diagnostic and detection assays, for purification, and for use as therapeutics are well known in the art as disclosed in, for example, U.S. Pat. Nos.4,011,308; 4,722,890; 4,016,043; 3,876,504; 3,770,380; and 4,372,745 (see, e.g., Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, 1988; incorporated herein by reference).
  • antibodies may be polyclonal or monoclonal antibody preparations, monospecific antisera, human antibodies, hybrid or chimeric antibodies, such as humanized antibodies, altered antibodies, F(ab′)2 fragments, Fab fragments, Fv fragments, single-domain antibodies, dimeric or trimeric antibody fragment constructs, minibodies, or functional fragments thereof which bind to the antigen in question.
  • polypeptides, peptides, and proteins and immunogenic fragments thereof for use in various embodiments can also be synthesized in solution or on a solid support in accordance with conventional techniques. See, for example, Stewart and Young, (1984); Tarn et al, (1983); Merrifield, (1986); and Barany and Merrifield (1979), each incorporated herein by reference.
  • a polyclonal antibody is prepared by immunizing an animal with an antigen or a portion thereof and collecting antisera from that immunized animal.
  • the antigen may be altered compared to an antigen sequence found in nature.
  • a variant or altered antigenic peptide or polypeptide is employed to generate antibodies.
  • Inocula are typically prepared by dispersing the antigenic composition in a physiologically tolerable diluent to form an aqueous composition.
  • Antisera is subsequently collected by methods known in the arts, and the serum may be used as-is for various applications or else the desired antibody fraction may be purified by well known methods, such as affinity chromatography (Harlow and Lane, Antibodies: A Laboratory Manual 1988).
  • Myeloma cell lines suited for use in hybridoma- producing fusion procedures preferably are non-antibody-producing and have high fusion efficiency and enzyme deficiencies that render then incapable of growing in certain selective media that support the growth of only the desired fused cells (hybridomas).
  • the fusion partner includes a property that allows selection of the resulting hybridomas using specific media.
  • fusion partners can be hypoxanthine/aminopterin/thymidine (HAT)- sensitive.
  • Methods for generating hybrids of antibody-producing spleen or lymph node cells and myeloma cells usually comprise mixing somatic cells with myeloma cells in the presence of an agent or agents (chemical or electrical) that promote the fusion of cell membranes.
  • hybridomas can be performed by culturing the cells by single-clone dilution in microtiter plates, followed by testing the individual clonal supernatants (after about two to three weeks) for the desired reactivity. Fusion procedures for making hybridomas, immunization protocols, and techniques for isolation of immunized splenocytes for fusion are known in the art.
  • Other techniques for producing monoclonal antibodies include the viral or oncogenic transformation of B-lymphocytes, a molecular cloning approach may be used to generate a nucleic acid or polypeptide, the selected lymphocyte antibody method (SLAM) (see, e.g., Babcook et al., Proc. Natl.
  • Monoclonal antibodies may be further purified using filtration, centrifugation, and various chromatographic methods such as HPLC or affinity chromatography.
  • Monoclonal antibodies may be further screened or optimized for properties relating to specificity, avidity, half-life, immunogenicity, binding association, binding disassociation, or overall functional properties relative to being a treatment for infection.
  • monoclonal antibodies may have alterations in the amino acid sequence of CDRs, including insertions, deletions, or substitutions with a conserved or non-conserved amino acid.
  • the immunogenicity of a particular immunogen composition can be enhanced by the use of non-specific stimulators of the immune response, known as adjuvants.
  • Adjuvants that may be used in accordance with embodiments include, but are not limited to, IL-1, IL-2, IL- 4, IL-7, IL 12, -interferon, GMCSP, BCG, aluminum hydroxide, MDP compounds, such as thur- MDP and nor MDP, CGP (MTP-PE), lipid A, and monophosphoryl lipid A (MPL).
  • exemplary adjuvants may include complete Freund’s adjuvant (a non-specific stimulator of the immune response containing killed Mycobacterium tuberculosis), incomplete Freund’s adjuvants, and/or aluminum hydroxide adjuvant.
  • BRM biologic response modifiers
  • Cimetidine Cimetidine
  • CYP Cyclophosphamide
  • cytokines such as ⁇ interferon, IL-2, or IL-12
  • genes encoding proteins involved in immune helper functions such as B-7.
  • a phage-display system can be used to expand antibody molecule populations in vitro. Saiki, et al., Nature 324:163 (1986); Scharf et al., Science 233:1076 (1986); U.S. Pat. Nos.
  • Fully Human Antibody Production Methods are available for making fully human antibodies. Using fully human antibodies can minimize the immunogenic and allergic responses that may be caused by administering non-human monoclonal antibodies to humans as therapeutic agents.
  • human antibodies may be produced in a non-human transgenic animal, e.g., a transgenic mouse capable of producing multiple isotypes of human antibodies to protein (e.g., IgG, IgA, and/or IgE) by undergoing V-D-J recombination and isotype switching.
  • a non-human transgenic animal e.g., a transgenic mouse capable of producing multiple isotypes of human antibodies to protein (e.g., IgG, IgA, and/or IgE) by undergoing V-D-J recombination and isotype switching.
  • this aspect applies to antibodies, antibody fragments, and pharmaceutical compositions thereof, but also non-human transgenic animals, B-cells, host cells, and hybridomas that produce monoclonal antibodies.
  • Applications of humanized antibodies include, but are not limited to, detect a cell expressing an anticipated protein, either in vivo or in vitro, pharmaceutical preparations containing the antibodies of the present invention, and methods of treating disorders by administer
  • Fully human antibodies can be produced by immunizing transgenic animals (usually mice) that are capable of producing a repertoire of human antibodies in the absence of endogenous immunoglobulin production.
  • Antigens for this purpose typically have six or more contiguous amino acids, and optionally are conjugated to a carrier, such as a hapten.
  • a carrier such as a hapten.
  • transgenic animals are produced by incapacitating the endogenous mouse immunoglobulin loci encoding the mouse heavy and light immunoglobulin chains therein, and inserting into the mouse genome large fragments of human genome DNA containing loci that encode human heavy and light chain proteins. Partially modified animals, which have less than the full complement of human immunoglobulin loci, are then crossbred to obtain an animal having all of the desired immune system modifications. When administered an immunogen, these transgenic animals produce antibodies that are immunospecific for the immunogen but have human rather than murine amino acid sequences, including the variable regions. For further details of such methods, see, for example, International Patent Application Publication Nos.
  • mice described above contain a human immunoglobulin gene minilocus that encodes unrearranged human heavy ( ⁇ and ⁇ ) and ⁇ light chain immunoglobulin sequences, together with targeted mutations that inactivate the endogenous ⁇ and ⁇ chain loci (Lonberg et al., Nature 368:856-859 (1994)). Accordingly, the mice exhibit reduced expression of mouse IgM or ⁇ chains and in response to immunization, the introduced human heavy and light chain transgenes undergo class switching and somatic mutation to generate high affinity human IgG ⁇ monoclonal antibodies (Lonberg et al., supra; Lonberg and Huszar, Intern. Ref.
  • HuMAb mice The preparation of HuMAb mice is described in detail in Taylor et al., Nucl. Acids Res. 20:6287-6295 (1992); Chen et al., Int. Immunol. 5:647- 656 (1993); Tuaillon et al., J. Immunol. 152:2912-2920 (1994); Lonberg et al., supra; Lonberg, Handbook of Exp. Pharmacol.113:49-101 (1994); Taylor et al., Int. Immunol.6:579-591 (1994); Lonberg and Huszar, Intern.
  • WO 93/1227; WO 92/22646; and WO 92/03918 the disclosures of all of which are hereby incorporated by reference in their entirety for all purposes.
  • Technologies utilized for producing human antibodies in these transgenic mice are disclosed also in WO 98/24893, and Mendez et al., Nat. Genetics 15:146-156 (1997), which are herein incorporated by reference.
  • the HCo7 and HCo12 transgenic mice strains can be used to generate human antibodies.
  • antigen-specific humanized monoclonal antibodies with the desired specificity can be produced and selected from the transgenic mice such as those described above.
  • Such antibodies may be cloned and expressed using a suitable vector and host cell, or the antibodies can be harvested from cultured hybridoma cells.
  • Fully human antibodies can also be derived from phage-display libraries (as disclosed in Hoogenboom et al., J. Mol. Biol. 227:381 (1991); and Marks et al., J. Mol. Biol. 222:581 (1991)).
  • phage-display libraries as disclosed in Hoogenboom et al., J. Mol. Biol. 227:381 (1991); and Marks et al., J. Mol. Biol. 222:581 (1991)).
  • WO 99/10494 herein incorporated by reference
  • Antibody fragments that retain the ability to recognize the antigen of interest will also find use herein.
  • a number of antibody fragments are known in the art that comprise antigen-binding sites capable of exhibiting immunological binding properties of an intact antibody molecule and can be subsequently modified by methods known in the arts.
  • Functional fragments including only the variable regions of the heavy and light chains, can also be produced using standard techniques such as recombinant production or preferential proteolytic cleavage of immunoglobulin molecules. These fragments are known as Fv. See, e.g., Inbar et al., Proc. Nat. Acad. Sci. USA 69:2659-2662 (1972); Hochman et al., Biochem.
  • Single-chain variable fragments may be prepared by fusing DNA encoding a peptide linker between DNAs encoding the two variable domain polypeptides (VL and VH).
  • scFvs can form antigen-binding monomers, or they can form multimers (e.g., dimers, trimers, or tetramers), depending on the length of a flexible linker between the two variable domains (Kortt et al., Prot. Eng.10:423 (1997); Kort et al., Biomol. Eng.18:95-108 (2001)).
  • VL- and VH-comprising polypeptides By combining different VL- and VH-comprising polypeptides, one can form multimeric scFvs that bind to different epitopes (Kriangkum et al., Biomol. Eng. 18:31-40 (2001)). Antigen-binding fragments are typically produced by recombinant DNA methods known to those skilled in the art.
  • the two domains of the Fv fragment, VL and VH are coded for by separate genes, they can be joined using recombinant methods by a synthetic linker that enables them to be made as a single chain polypeptide (known as single chain Fv (sFv or scFv); see e.g., Bird et al., Science 242:423-426 (1988); and Huston et al., Proc. Natl. Acad. Sci. USA 85:5879-5883 (1988).
  • Design criteria include determining the appropriate length to span the distance between the C-terminus of one chain and the N-terminus of the other, wherein the linker is generally formed from small hydrophilic amino acid residues that do not tend to coil or form secondary structures.
  • Suitable linkers generally comprise polypeptide chains of alternating sets of glycine and serine residues, and may include glutamic acid and lysine residues inserted to enhance solubility.
  • Antigen-binding fragments are screened for utility in the same manner as intact antibodies. Such fragments include those obtained by amino-terminal and/or carboxy-terminal deletions, where the remaining amino acid sequence is substantially identical to the corresponding positions in the naturally occurring sequence deduced, for example, from a full- length cDNA sequence.
  • Antibodies may also be generated using peptide analogs of the epitopic determinants disclosed herein, which may consist of non-peptide compounds having properties analogous to those of the template peptide.
  • peptide mimetics these types are termed “peptide mimetics” or “peptidomimetics”. Fauchere, J. Adv. Drug Res.15:29 (1986); Veber and Freidinger TINS p.392 (1985); and Evans et al., J. Med. Chem.30:1229 (1987). Liu et al. (2003) also describe “antibody like binding peptidomimetics” (ABiPs), which are peptides that act as pared-down antibodies and have certain advantages of longer serum half-life as well as less cumbersome synthesis methods. These analogs can be peptides, non-peptides or combinations of peptide and non-peptide regions. Fauchere, Adv. Drug Res.
  • peptidomimetics of the invention are proteins that are structurally similar to an antibody displaying a desired biological activity, such as the ability to bind a protein, but have one or more peptide linkages optionally replaced by a linkage selected from: —CH2NH—, —CH2S—, —CH2—CH2—, —CH ⁇ CH— (cis and trans), —COCH2—, —CH(OH)CH2—, and —CH2SO— by methods well known in the art.
  • Systematic substitution of one or more amino acids of a consensus sequence with a D-amino acid of the same type may be used in certain embodiments of the invention to generate more stable proteins.
  • constrained peptides comprising a consensus sequence or a substantially identical consensus sequence variation may be generated by methods known in the art (Rizo and Gierasch, Ann. Rev. Biochem. 61:387 (1992), incorporated herein by reference), for example, by adding internal cysteine residues capable of forming intramolecular disulfide bridges which cyclize the peptide.
  • a phage display library can be used to improve the immunological binding affinity of the Fab molecules using known techniques. See, e.g., Figini et al., J. Mol. Biol. 239:68 (1994).
  • the coding sequences for the heavy and light chain portions of the Fab molecules selected from the phage display library can be isolated or synthesized and cloned into any suitable vector or replicon for expression. Any suitable expression system can be used.
  • Polypeptide Expression there are nucleic acid molecule encoding polypeptides or peptides of the disclosure (e.g antibodies, TCR genes, and immunogenic peptides). These may be generated by methods known in the art, e.g., isolated from B cells of mice that have been immunized and isolated, phage display, expressed in any suitable recombinant expression system and allowed to assemble to form antibody molecules or by recombinant methods. 1.
  • expression vectors comprising nucleic acid molecules may encode fusion proteins, modified antibodies, antibody fragments, and probes thereof.
  • vectors and expression vectors may contain nucleic acid sequences that serve other functions as well.
  • DNAs encoding the polypeptides or peptides are inserted into expression vectors such that the gene area is operatively linked to transcriptional and translational control sequences.
  • expression vectors used in any of the host cells contain sequences for plasmid or virus maintenance and for cloning and expression of exogenous nucleotide sequences.
  • Expression Systems Numerous expression systems exist that comprise at least a part or all of the expression vectors discussed above. Prokaryote- and/or eukaryote-based systems can be employed for use with an embodiment to produce nucleic acid sequences, or their cognate polypeptides, proteins and peptides. Commercially and widely available systems include in but are not limited to bacterial, mammalian, yeast, and insect cell systems. Different host cells have characteristic and specific mechanisms for the post-translational processing and modification of proteins. Appropriate cell lines or host systems can be chosen to ensure the correct modification and processing of the foreign protein expressed.
  • Host Cells [00253] In another aspect, contemplated are the use of host cells into which a recombinant expression vector has been introduced. Antibodies can be expressed in a variety of cell types. An expression construct encoding an antibody can be transfected into cells according to a variety of methods known in the art. Vector DNA can be introduced into prokaryotic or eukaryotic cells via conventional transformation or transfection techniques.
  • Some vectors may employ control sequences that allow it to be replicated and/or expressed in both prokaryotic and eukaryotic cells.
  • the antibody expression construct can be placed under control of a promoter that is linked to T-cell activation, such as one that is controlled by NFAT-1 or NF- ⁇ , both of which are transcription factors that can be activated upon T-cell activation.
  • Control of antibody expression allows T cells, such as tumor- targeting T cells, to sense their surroundings and perform real-time modulation of cytokine signaling, both in the T cells themselves and in surrounding endogenous immune cells.
  • T cells such as tumor- targeting T cells, to sense their surroundings and perform real-time modulation of cytokine signaling, both in the T cells themselves and in surrounding endogenous immune cells.
  • nucleic acid molecule encoding either or both of the entire heavy and light chains of an antibody or the variable regions thereof may be obtained from any source that produces antibodies. Methods of isolating mRNA encoding an antibody are well known in the art. See e.g., Sambrook et al., supra. The sequences of human heavy and light chain constant region genes are also known in the art. See, e.g., Kabat et al., 1991, supra.
  • the disease or disorder is a type of cancer.
  • the type of cancer is melanoma (skin cancer), breast cancer, head and neck cancer, ovarian cancer, and/or myelodysplastic syndrome (MDS).
  • treating can include but is not limited to prophylactic treatment and therapeutic treatment.
  • a type of cancer e.g., melanoma, ovarian, head and neck, breast, skin, lung, adult and pediatric acute myeloid leukemia, myelodysplastic syndrome, bladder, cervical, colorectal, kidney, low grade glioma, glioblastoma, liver, prostate, pancreatic, esophageal, rectal, sarcomas, stomach, thymus, thyroid, testicular, uterine cancer, and combinations thereof, and the like).
  • treating can include but is not limited to prophylactic treatment and therapeutic treatment.
  • treatment can include, but is not limited to: preventing cancer (e.g., melanoma, ovarian, head and neck, breast, lung adult and pediatric acute myeloid leukemia, myelodysplastic syndrome, bladder, cervical, colorectal, kidney, low grade glioma, glioblastoma, liver, prostate, pancreatic, esophageal, rectal, sarcomas, stomach, thymus, thyroid, testicular, uterine cancer, and combinations thereof, and the like); reducing the risk of cancer (e.g., melanoma, ovarian, head and neck, breast, lung adult and pediatric acute myeloid leukemia, myelodysplastic syndrome, bladder, cervical, colorectal, kidney, low grade glioma, glioblastoma, liver, prostate, pancreatic, esophageal, rectal, sarcomas, stomach, thymus, thyroid, testicular, uterine cancer, and
  • treating does not include prophylactic treatment of cancer (e.g., preventing or ameliorating future cancer).
  • Treatment of a subject can occur using any suitable administration method (such as those disclosed herein) and using any suitable amount of a treatment.
  • methods of treatment comprise treating an animal for a type of cancer (e.g., melanoma, ovarian, head and neck, breast, skin, lung, adult and pediatric acute myeloid leukemia, myelodysplastic syndrome, bladder, cervical, colorectal, kidney, low grade glioma, glioblastoma, liver, prostate, pancreatic, esophageal, rectal, sarcomas, stomach, thymus, thyroid, testicular, and/or uterine cancer), and combinations thereof, and the like).
  • a type of cancer e.g., melanoma, ovarian, head and neck, breast, skin, lung, adult and pediatric acute myeloid leukemia, myelodysplastic syndrome, bladder, cervical, colorectal, kidney, low grade glioma, glioblastoma, liver, prostate, pancreatic, esophageal, rectal, sarcomas, stomach, thymus, thyroid, testi
  • inventions include treatment after one or more of an occurrence of chemical exposure, an exposure to ionizing radiation, or a treatment for a hematopoietic cancer (e.g., with chemotherapy, ionizing radiation, or both).
  • Some embodiments of the disclosure include a method for treating a subject (e.g., an animal such as a human or primate) with a treatment which comprises one or more administrations of one or more such compositions; the compositions may be the same or different if there is more than one administration.
  • Methods of Treatment [00259]
  • Some embodiments of the disclosure further comprise administering a treatment to a subject.
  • the treatment is a type of immunotherapy.
  • Some embodiments of the disclosure further comprise administering to the subject an immunotherapy in combination with one or more additional therapies.
  • the immunotherapy and the one or more additional therapies are administered together in one administration or composition.
  • the immunotherapy and the one or more additional therapies are administered separately in more than one administration or more than one composition.
  • methods of treatment include administering to a subject a therapeutically effective amount of an immunotherapy comprising a neoantigen target identified as described herein, such as, for example, the polypeptides and/or proteins listed in Tables 3 and 4.
  • an effective amount refers to a dosage or a series of dosages sufficient to affect treatment (e.g., to treat a type of cancer, such as melanoma, ovarian, head and neck, breast, skin, lung, adult and pediatric acute myeloid leukemia, myelodysplastic syndrome, bladder, cervical, colorectal, kidney, low grade glioma, glioblastoma, liver, prostate, pancreatic, esophageal, rectal, sarcomas, stomach, thymus, thyroid, testicular, uterine cancer, and the like).
  • an effective amount can encompass a therapeutically effective amount.
  • an effective amount can vary depending on the subject and the particular treatment being affected.
  • the exact amount that is required can, for example, vary from subject to subject, depending on the age and general condition of the subject, the particular adjuvant being used (if applicable), administration protocol, and the like.
  • the effective amount can, for example, vary based on the particular circumstances, and an appropriate effective amount can be determined in a particular case.
  • An effective amount can, for example, include any dosage or composition amount determined to be clinically relevant, as would be appreciated by those skilled in the art.
  • the treatments can also include one or more of surgical intervention, chemotherapy, radiation therapy, hormone therapies, immunotherapy, and adjuvant systematic therapies.
  • Adjuvants may include but are not limited to chemotherapy (e.g., temozolomide), radiation therapy, antiangiogenic therapy (e.g., bevacizumab), and hormone therapies, such as administration of LHRH agonists; antiestrogens, such as tamoxifen; high-dose progestogens; aromatase inhibitors; and/or adrenalectomy.
  • Chemotherapy can be used as a single- agent or as a combination with known or new therapies.
  • Adjuvant treatments include treatments by the mechanisms disclosed herein and of cancers as disclosed herein, including, but not limited to tumors.
  • Corresponding primary therapies can include, but are not limited to, surgery, chemotherapy, or radiation therapy.
  • the adjuvant treatment can be a combination of chemokine receptor antagonists with traditional chemotoxic agents or with immunotherapy that increases the specificity of treatment to the cancer and potentially limits additional systemic side effects.
  • the administration to a subject may decrease the incidence of one or more symptoms associated with a type of cancer.
  • the type of cancer comprises melanoma, ovarian, head and neck, breast, skin, lung, adult and pediatric acute myeloid leukemia, myelodysplastic syndrome, bladder, cervical, colorectal, kidney, low grade glioma, glioblastoma, liver, prostate, pancreatic, esophageal, rectal, sarcomas, stomach, thymus, thyroid, testicular, and/or uterine cancer.
  • the administration may decrease cancer-associated illness severity or mortality, or combinations thereof in said subject, as compared to a subject not receiving said composition.
  • the method may decrease a marker of viability of cancer cells in a subject.
  • the method may decrease a marker of viability of cancer cells.
  • the marker may be selected from survival over time, proliferation, growth, migration, formation of colonies, chromatic assembly, DNA binding, RNA metabolism, cell migration, cell adhesion, inflammation, or a combination thereof.
  • Immunotherapies including one or more immunogenic target.
  • the immunogenic target is identified via the methods described herein, or a nucleotide coding therefor.
  • the immunogenic target is a shared splicing neoantigen or protein including a neoantigen, wherein the neoantigen is identified via the methods described herein, or a nucleotide coding therefor.
  • the immunogenic target includes a neoantigen target identified as described herein, such as, for example, the polypeptides and/or proteins listed in Tables 3 and 4, or a nucleotide coding therefor.
  • Cancer immunotherapy is the use of the immune system to treat cancer. Immunotherapies can be categorized as active, passive or hybrid (active and passive). These approaches exploit the fact that cancer cells often have molecules on their surface that can be detected by the immune system, known as tumor-associated antigens (TAAs); they are often proteins or other macromolecules (e.g. carbohydrates). Active immunotherapy directs the immune system to attack tumor cells by targeting TAAs.
  • TAAs tumor-associated antigens
  • immunotherapies include cell-based immunotherapies, such as those involving cells which effect an immune response (such as, for example, lymphocytes, macrophages, natural killer (NK) cells, dendritic cells, cytotoxic T lymphocytes (CTL), and the like), CAR-T therapies, antibodies and antibody derivatives (such as, for example, monoclonal antibodies, conjugated monoclonal antibodies, polyclonal antibodies, antibody fragments, radiolabeled antibodies, chemolabeled antibodies, etc.), immune checkpoint inhibitors, vaccines (such as, for example, cancer vaccines (e.g.
  • tumor cell vaccines antigen vaccines, dendritic cell vaccines, vector-based vaccines, etc.
  • immunomodulators such as, for example, interleukins, cytokines, chemokines, etc.
  • topical immunotherapies such as, for example, imiquimod, and the like
  • injection immunotherapies adoptive cell transfer
  • oncolytic virus therapies such as, for example, talimogene laherparepvec (T-VEC), and the like
  • immunosuppressive drugs helminthic therapies, other non-specific immunotherapies, and the like.
  • Immune checkpoint inhibitor immunotherapies are those that target one or more specific proteins or receptors, such as PD-1, PD-L1, CTLA-4, and the like.
  • Immune checkpoint inhibitor immunotherapies include ipilimumab (Yervoy), nivolumab (Opdivo), pembrolizumab (Keytruda), and the like.
  • Non-specific immunotherpaies include cytokines, interleukins, interferons, and the like.
  • an immunotherapy assigned or administered to a subject can include an interleukin, and/or interferon (IFN), and/or one or more suitable antibody-based reagent, such as denileukin diftitox and/or administration of an antibody-based reagent selected from the group consisting of ado- trastuzumab emtansine, alemtuzumab, atezolizumab, bevacizumab, blinatumomab, brentuximab vedotin, cetuximab, catumaxomab, gemtuzumab, ibritumomab tiuxetan, ilipimumab, natalizumab, nimotuzumab, nivolumab, ofatumumab, panitumumab, pembrolizumab, rituximab, tositumomab, trastuzumab
  • IFN
  • an immunotherapy assigned or administered to a subject can include an indoleamine 2,3-dioxygenase (IDO) inhibitor, adoptive T-cell therapy, virotherapy (T-VEC), and/or any other immunotherapy whose efficacy extensively depends on anti-tumor immunity.
  • IDO indoleamine 2,3-dioxygenase
  • T-VEC virotherapy
  • any relevant immunotherapy treatment strategies, alone or in combination with one or more additional cancer therapy, can be utilized in the practice of the present disclosure. Additional detail regarding various types of immunotherapy is provided below. 1.
  • PD-1 can act in the tumor microenvironment where T cells encounter an infection or tumor. Activated T cells upregulate PD-1 and continue to express it in the peripheral tissues. Cytokines such as IFN-gamma induce the expression of PDL1 on epithelial cells and tumor cells. PDL2 is expressed on macrophages and dendritic cells. The main role of PD-1 is to limit the activity of effector T cells in the periphery and prevent excessive damage to the tissues during an immune response. Inhibitors of the disclosure may block one or more functions of PD-1 and/or PDL1 activity.
  • PD-1 include CD279 and SLEB2.
  • PDL1 include B7-H1, B7-4, CD274, and B7-H.
  • Alternative names for “PDL2” include B7- DC, Btdc, and CD273.
  • PD-1, PDL1, and PDL2 are human PD-1, PDL1 and PDL2.
  • the PD-1 inhibitor is a molecule that inhibits the binding of PD-1 to its ligand binding partners.
  • the PD-1 ligand binding partners are PDL1 and/or PDL2.
  • a PDL1 inhibitor is a molecule that inhibits the binding of PDL1 to its binding partners.
  • PDL1 binding partners are PD-1 and/or B7-1.
  • the PDL2 inhibitor is a molecule that inhibits the binding of PDL2 to its binding partners.
  • a PDL2 binding partner is PD-1.
  • the inhibitor may be an antibody, an antigen binding fragment thereof, an immunoadhesin, a fusion protein, or oligopeptide. Exemplary antibodies are described in U.S. Patent Nos. 8,735,553, 8,354,509, and 8,008,449, all incorporated herein by reference.
  • Other PD-1 inhibitors for use in the methods and compositions provided herein are known in the art such as described in U.S. Patent Application Nos.
  • the PD-1 inhibitor is an anti-PD-1 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody).
  • the anti- PD-1 antibody is selected from the group consisting of nivolumab, pembrolizumab, and pidilizumab.
  • the PD-1 inhibitor is an immunoadhesin (e.g., an immunoadhesin comprising an extracellular or PD-1 binding portion of PDL1 or PDL2 fused to a constant region (e.g., an Fc region of an immunoglobulin sequence).
  • the PDL1 inhibitor comprises AMP- 224.
  • Nivolumab also known as MDX-1106-04, MDX-1106, ONO-4538, BMS-936558, and OPDIVO®, is an anti-PD-1 antibody described in WO2006/121168.
  • Pembrolizumab also known as MK-3475, Merck 3475, lambrolizumab, KEYTRUDA®, and SCH-900475, is an anti-PD-1 antibody described in WO2009/114335.
  • Pidilizumab also known as CT-011, hBAT, or hBAT-1, is an anti-PD-1 antibody described in WO2009/101611.
  • AMP-224 also known as B7-DCIg, is a PDL2-Fc fusion soluble receptor described in WO2010/027827 and WO2011/066342.
  • Additional PD-1 inhibitors include MEDI0680, also known as AMP-514, and REGN2810.
  • the immune checkpoint inhibitor is a PDL1 inhibitor such as Durvalumab, also known as MEDI4736, atezolizumab, also known as MPDL3280A, avelumab, also known as MSB00010118C, MDX-1105, BMS-936559, or combinations thereof.
  • the immune checkpoint inhibitor is a PDL2 inhibitor such as rHIgM12B7.
  • the inhibitor comprises the heavy and light chain CDRs or VRs of nivolumab, pembrolizumab, or pidilizumab.
  • the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of nivolumab, pembrolizumab, or pidilizumab, and the CDR1, CDR2 and CDR3 domains of the VL region of nivolumab, pembrolizumab, or pidilizumab.
  • the antibody competes for binding with and/or binds to the same epitope on PD-1, PDL1, or PDL2 as the above- mentioned antibodies.
  • the antibody has at least about 70, 75, 80, 85, 90, 95, 97, or 99% (or any derivable range therein) variable region amino acid sequence identity with the above-mentioned antibodies.
  • CTLA-4 cytotoxic T-lymphocyte-associated protein 4
  • CD152 cytotoxic T-lymphocyte-associated protein 4
  • the complete cDNA sequence of human CTLA-4 has the Genbank accession number L15006.
  • CTLA-4 is found on the surface of T cells and acts as an “off” switch when bound to B7-1 (CD80) or B7-2 (CD86) on the surface of antigen-presenting cells.
  • CTLA4 is a member of the immunoglobulin superfamily that is expressed on the surface of Helper T cells and transmits an inhibitory signal to T cells.
  • CTLA4 is similar to the T-cell co-stimulatory protein, CD28, and both molecules bind to B7-1 and B7-2 on antigen-presenting cells.
  • CTLA-4 transmits an inhibitory signal to T cells, whereas CD28 transmits a stimulatory signal.
  • Intracellular CTLA-4 is also found in regulatory T cells and may be important to their function. T cell activation through the T cell receptor and CD28 leads to increased expression of CTLA-4, an inhibitory receptor for B7 molecules.
  • Inhibitors of the disclosure may block one or more functions of CTLA-4, B7-1, and/or B7-2 activity.
  • the inhibitor blocks the CTLA-4 and B7-1 interaction. In some embodiments, the inhibitor blocks the CTLA-4 and B7-2 interaction.
  • the immune checkpoint inhibitor is an anti-CTLA-4 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody), an antigen binding fragment thereof, an immunoadhesin, a fusion protein, or oligopeptide.
  • Anti-human-CTLA-4 antibodies (or VH and/or VL domains derived therefrom) suitable for use in the present methods can be generated using methods well known in the art. Alternatively, art recognized anti-CTLA-4 antibodies can be used.
  • the anti- CTLA-4 antibodies disclosed in: US 8,119,129, WO 01/14424, WO 98/42752; WO 00/37504 (CP675,206, also known as tremelimumab; formerly ticilimumab), U.S. Patent No. 6,207,156; Hurwitz et al., 1998; can be used in the methods disclosed herein.
  • the teachings of each of the aforementioned publications are hereby incorporated by reference.
  • Antibodies that compete with any of these art-recognized antibodies for binding to CTLA-4 also can be used.
  • a humanized CTLA-4 antibody is described in International Patent Application No. WO2001/014424, WO2000/037504, and U.S. Patent No.
  • a further anti-CTLA-4 antibody useful as a checkpoint inhibitor in the methods and compositions of the disclosure is ipilimumab (also known as 10D1, MDX- 010, MDX- 101, and Yervoy®) or antigen binding fragments and variants thereof (see, e.g., WO0 1/14424).
  • the inhibitor comprises the heavy and light chain CDRs or VRs of tremelimumab or ipilimumab.
  • the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of tremelimumab or ipilimumab, and the CDR1, CDR2 and CDR3 domains of the VL region of tremelimumab or ipilimumab.
  • the antibody competes for binding with and/or binds to the same epitope on PD-1, B7-1, or B7-2 as the above- mentioned antibodies.
  • the antibody has at least about 70, 75, 80, 85, 90, 95, 97, or 99% (or any derivable range therein) variable region amino acid sequence identity with the above-mentioned antibodies. 2.
  • the immunotherapy comprises an inhibitor of a co- stimulatory molecule.
  • the inhibitor comprises an inhibitor of B7-1 (CD80), B7-2 (CD86), CD28, ICOS, OX40 (TNFRSF4), 4-1BB (CD137; TNFRSF9), CD40L (CD40LG), GITR (TNFRSF18), and combinations thereof.
  • Inhibitors include inhibitory antibodies, polypeptides, compounds, and nucleic acids. 3.
  • Dendritic cell therapy provokes anti-tumor responses by causing dendritic cells to present tumor antigens to lymphocytes, which activates them, priming them to kill other cells that present the antigen.
  • Dendritic cells are antigen presenting cells (APCs) in the mammalian immune system. In cancer treatment they aid cancer antigen targeting.
  • APCs antigen presenting cells
  • One example of cellular cancer therapy based on dendritic cells is sipuleucel-T.
  • One method of inducing dendritic cells to present tumor antigens is by vaccination with autologous tumor lysates or short peptides (small parts of protein that correspond to the protein antigens on cancer cells).
  • peptides are often given in combination with adjuvants (highly immunogenic substances) to increase the immune and anti- tumor responses.
  • adjuvants include proteins or other chemicals that attract and/or activate dendritic cells, such as granulocyte macrophage colony-stimulating factor (GM-CSF).
  • GM-CSF granulocyte macrophage colony-stimulating factor
  • Dendritic cells can also be activated in vivo by making tumor cells express GM-CSF. This can be achieved by either genetically engineering tumor cells to produce GM- CSF or by infecting tumor cells with an oncolytic virus that expresses GM-CSF.
  • Another strategy is to remove dendritic cells from the blood of a patient and activate them outside the body.
  • the dendritic cells are activated in the presence of tumor antigens, which may be a single tumor-specific peptide/protein or a tumor cell lysate (a solution of broken down tumor cells). These cells (with optional adjuvants) are infused and provoke an immune response.
  • tumor antigens which may be a single tumor-specific peptide/protein or a tumor cell lysate (a solution of broken down tumor cells). These cells (with optional adjuvants) are infused and provoke an immune response.
  • Dendritic cell therapies include the use of antibodies that bind to receptors on the surface of dendritic cells. Antigens can be added to the antibody and can induce the dendritic cells to mature and provide immunity to the tumor. Dendritic cell receptors such as TLR3, TLR7, TLR8 or CD40 have been used as antibody targets. 4.
  • CAR-T Cell Therapy refers to a treatment that uses such transformed cells for cancer therapy.
  • the basic principle of CAR-T cell design involves recombinant receptors that combine antigen-binding and T-cell activating functions.
  • CAR-T cells The general premise of CAR-T cells is to artificially generate T-cells targeted to markers found on cancer cells.
  • Scientists can remove T-cells from a person, genetically alter them, and put them back into the patient for them to attack the cancer cells.
  • CAR-T cells create a link between an extracellular ligand recognition domain to an intracellular signalling molecule which in turn activates T cells.
  • the extracellular ligand recognition domain is usually a single-chain variable fragment (scFv).
  • scFv single-chain variable fragment
  • CAR-T cells The specificity of CAR-T cells is determined by the choice of molecule that is targeted.
  • Exemplary CAR-T therapies include Tisagenlecleucel (Kymriah) and Axicabtagene ciloleucel (Yescarta).
  • the CAR-T therapy targets CD19. 5.
  • Cytokine Therapy [00292] Cytokines are proteins produced by many types of cells present within a tumor. They can modulate immune responses. The tumor often employs them to allow it to grow and reduce the immune response. These immune-modulating effects allow them to be used as drugs to provoke an immune response. Two commonly used cytokines are interferons and interleukins. [00293] Interferons are produced by the immune system.
  • Adoptive T-Cell Therapy is a form of passive immunization by the transfusion of T-cells (adoptive cell transfer). They are found in blood and tissue and usually activate when they find foreign pathogens. Specifically they activate when the T-cell's surface receptors encounter cells that display parts of foreign proteins on their surface antigens.
  • TILs tumor infiltrating lymphocytes
  • APCs antigen presenting cells
  • Chemotherapies / Targeted Therapies / Alternative Therapies Cancers are commonly treated with chemotherapy and/or targeted therapy and/or alternative therapy. Chemotherapies act by indiscriminately targeting rapidly dividing cells, including healthy cells as well as tumor cells, whereas targeted cancer therapies rather act by interfering with specific molecules, or molecular targets, which are involved in cancer growth and progression. Targeted therapy generally targets cancer cells exclusively, having minimal damage to normal cells. Chemotherapies and targeted therapies which are approved and/or in the clinical trial stage are known to those skilled in the art. Any such compound can be utilized in the practice of the present disclosure.
  • approved chemotherapies include abitrexate (Methotrexate Injection), abraxane (Paclitaxel Injection), adcetris (Brentuximab Vedotin Injection), adriamycin (Doxorubicin), adrucil Injection (5-FU (fluorouracil)), Armor (Everolimus), Armitor Disperz (Everolimus), alimta (PEMETREXED), alkeran Injection (Melphalan Injection), alkeran Tablets (Melphalan), aredia (Pamidronate), arimidex (Anastrozole), aromasin (Exemestane), arranon (Nelarabine), arzerra (Ofatumumab Injection), avastin (Bevacizumab), beleodaq (Belinostat Injection), bexxar (Tositumomab), BiC
  • approved targeted therapies include ado-trastuzumab emtansine (Kadcyla), afatinib (Gilotrif), aldesleukin (Proleukin), alectinib (Alecensa), alemtuzumab (Campath), axitinib (Inlyta), bosutinib (Bosulif), brentuximab vedotin (Adcetris), cabozantinib (Cabometyx [tablet], Cometriq [capsule]), canakinumab (Ilaris), carfilzomib (Kyprolis), ceritinib (Zykadia), cetuximab (Erbitux), cobimetinib (Cotellic), crizotinib (Xalkori), dabrafenib (Tafinlar), daratumumab (Darzalex), dasatinib
  • the approved chemotherapy is an anthracycline, such as Doxorubicen, Daunarubicin, Epirubicin, and/or Idarubicin.
  • additional therapy options e.g. chemotherapy, targeted therapy, alternative therapy, and the like
  • treatments that have been approved and those that in clinical trials or otherwise under development are also immunotherapies.
  • Any relevant chemotherapy, target therapy, and alternative therapy treatment strategies can be utilized, alone or in combination with one or more additional cancer therapy, in the practice of the present disclosure. thereof.
  • Other Cancer Treatments [00300] In addition to chemotherapies, targeted therapies, alternative therapies, and immunotherapies, cancer can additionally be treated by other strategies.
  • the additional therapy or prior therapy comprises radiation, such as ionizing radiation.
  • ionizing radiation means radiation comprising particles or photons that have sufficient energy or can produce sufficient energy via nuclear interactions to produce ionization (gain or loss of electrons).
  • An exemplary and preferred ionizing radiation is an x-radiation. Means for delivering x-radiation to a target tissue or cell are well known in the art.
  • the additional therapy comprises surgery.
  • Curative surgery includes resection in which all or part of cancerous tissue is physically removed, excised, and/or destroyed and may be used in conjunction with other therapies, such as the treatment of the present embodiments, chemotherapy, radiotherapy, hormonal therapy, gene therapy, immunotherapy, and/or alternative therapies.
  • Tumor resection refers to physical removal of at least part of a tumor.
  • treatment by surgery includes laser surgery, cryosurgery, electrosurgery, and microscopically-controlled surgery (Mohs’ surgery).
  • a cavity may be formed in the body.
  • Treatment may be accomplished by perfusion, direct injection, or local application of the area with an additional anti-cancer therapy. Such treatment may be repeated, for example, every 1, 2, 3, 4, 5, 6, or 7 days, or every 1, 2, 3, 4, and 5 weeks or every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months (or any range derivable therein). These treatments may be of varying dosages as well.
  • Alternative treatment strategies have also been used with various types of cancers. Such treatment can be used alone or in combination with any other treatment modality.
  • Dosage and Administration Routes [00305] Other embodiments of the disclosure can include methods of administering or treating an animal, which can involve administering an amount of at least one treatment, such as an immunotherapy, that is effective to treat the disease, condition, or disorder that the organism has, or is suspected of having, or is susceptible to, or to bring about a desired physiological effect.
  • an immunotherapy such as an immunotherapy
  • the composition or pharmaceutical composition comprises at least one treatment, such as an immunotherapy, which can be administered to an animal (e.g., mammals, primates, monkeys, or humans) in an amount of about 0.005 to about 50 mg/kg body weight, about 0.01 to about 15 mg/kg body weight, about 0.1 to about 10 mg/kg body weight, about 0.5 to about 7 mg/kg body weight, about 0.005 mg/kg, about 0.01 mg/kg, about 0.05 mg/kg, about 0.1 mg/kg, about 0.5 mg/kg, about 1 mg/kg, about 3 mg/kg, about 5 mg/kg, about 5.5 mg/kg, about 6 mg/kg, about 6.5 mg/kg, about 7 mg/kg, about 7.5 mg/kg, about 8 mg/kg, about 10 mg/kg, about 12 mg/kg, or about 15 mg/kg.
  • an immunotherapy which can be administered to an animal (e.g., mammals, primates, monkeys, or humans) in an amount of about 0.005 to about 50 mg/kg body weight, about 0.
  • the dosage can be about 0.5 mg/kg human body weight or about 6.5 mg/kg human body weight.
  • some subjects e.g., mammals, mice, rabbits, feline, porcine, or canine
  • a dose or a therapeutically effective dose of a compound disclosed herein will be that which is sufficient to achieve a plasma concentration of the compound or its active metabolite(s) within a range set forth herein, e.g., about 1-10 nM, 10-100 nM, 0.1-1 ⁇ M, 1-10 ⁇ M, 10-100 ⁇ M, 100-200 ⁇ M, 200-500 ⁇ M, or even 500-1000 ⁇ M, preferably about 1-10 nM, 10-100 nM, or 0.1-1 ⁇ M.
  • a treatment can be administered in combination with one or more other therapeutic agents for a given disease, condition, or disorder.
  • the compounds and pharmaceutical compositions are preferably prepared and administered in dose units.
  • Solid dose units are tablets, capsules and suppositories.
  • different daily doses can be used for treatment of a subject.
  • higher or lower daily doses can be appropriate.
  • the administration of the daily dose can be carried out both by single administration in the form of an individual dose unit or else several smaller dose units and also by multiple administrations of subdivided doses at specific intervals.
  • a treatment, such as an immunotherapy treatment as described herein can be administered locally or systemically in a therapeutically effective dose.
  • Amounts effective for this use will, of course, depend on the severity of the disease or disorder and the weight and general state of the subject. Typically, dosages used in vitro can provide useful guidance in the amounts useful for in situ administration of the pharmaceutical composition, and animal models can be used to determine effective dosages for treatment of particular disorders. [00310] Various considerations are described, e. g. , in Langer, 1990, Science, 249: 1527; Goodman and Gilman's (eds.), 1990, Id., each of which is herein incorporated by reference and for all purposes. Dosages for parenteral administration of active pharmaceutical agents can be converted into corresponding dosages for oral administration by multiplying parenteral dosages by appropriate conversion factors.
  • the parenteral dosage in mg/mL times 1.8 the corresponding oral dosage in milligrams (“mg”).
  • the parenteral dosage in mg/mL times 1.6 the corresponding oral dosage in mg.
  • An average adult weighs about 70 kg. See e.g., Miller-Keane, 1992, Encyclopedia & Dictionary of Medicine, Nursing & Allied Health, 5th Ed., (W. B. Saunders Co.), pp.1708 and 1651.
  • the administration can include a unit dose of one or more treatments in combination with a pharmaceutically acceptable carrier and, in addition, can include other medicinal agents, pharmaceutical agents, carriers, adjuvants, diluents, and excipients.
  • the carrier, vehicle or excipient can facilitate administration, delivery and/or improve preservation of the composition.
  • the one or more carriers include but are not limited to, saline solutions such as normal saline, Ringer's solution, PBS (phosphate-buffered saline), and generally mixtures of various salts including potassium and phosphate salts with or without sugar additives such as glucose.
  • Carriers can include aqueous and non-aqueous sterile injection solutions that can contain antioxidants, buffers, bacteriostats, bactericidal antibiotics, and solutes that render the formulation isotonic with the bodily fluids of the intended recipient; and aqueous and non-aqueous sterile suspensions, which can include suspending agents and thickening agents.
  • the one or more excipients can include, but are not limited to water, saline, dextrose, glycerol, ethanol, or the like, and combinations thereof.
  • Nontoxic auxiliary substances such as wetting agents, buffers, or emulsifiers may also be added to the composition.
  • Oral formulations can include such normally employed excipients as, for example, pharmaceutical grades of mannitol, lactose, starch, magnesium stearate, sodium saccharine, cellulose, and magnesium carbonate.
  • the quantity of active component in a unit dose preparation can be varied or adjusted from 0.1 mg to 10000 mg, more typically 1.0 mg to 1000 mg, most typically 10 mg to 500 mg, according to the particular application and the potency of the active component.
  • the composition can, if desired, also contain other compatible therapeutic agents.
  • a treatment, such as an immunotherapy can be administered to subjects by any number of suitable administration routes or formulations.
  • the treatment, such as an immunotherapy can also be used to treat subjects for a variety of diseases.
  • Subjects include but are not limited to mammals, primates, monkeys (e.g., macaque, rhesus macaque, or pig tail macaque), humans, canine, feline, bovine, porcine, avian (e.g., chicken), mice, rabbits, and rats.
  • the route of administration of the compounds of the treatments described herein can be of any suitable route. Administration routes can be, but are not limited to the oral route, the parenteral route, the cutaneous route, the nasal route, the rectal route, the vaginal route, and the ocular route.
  • administration routes can be parenteral administration, a mucosal administration, intravenous administration, subcutaneous administration, topical administration, intradermal administration, oral administration, sublingual administration, intranasal administration, or intramuscular administration.
  • the choice of administration route can depend on the compound identity (e.g., the physical and chemical properties of the compound) as well as the age and weight of the animal, the particular disease (e.g., type of cancer), and the severity of the disease (e.g., stage or severity of cancer). Of course, combinations of administration routes can be administered, as desired.
  • Some embodiments of the disclosure include a method for providing a subject with a treatment which comprises one or more administrations of one or more compositions; the compositions may be the same or different if there is more than one administration.
  • Cell Culture and Cellular Compositions [00316]
  • the cells of the disclosure may be specifically formulated and/or they may be cultured in a particular medium. The cells may be formulated in such a manner as to be suitable for delivery to a recipient without deleterious effects.
  • the medium in certain aspects can be prepared using a medium used for culturing animal cells as their basal medium, such as any of AIM V, X-VIVO-15, NeuroBasal, EGM2, TeSR, BME, BGJb, CMRL 1066, Glasgow MEM, Improved MEM Zinc Option, IMDM, Medium 199, Eagle MEM, ⁇ MEM, DMEM, Ham, RPMI-1640, and Fischer's media, as well as any combinations thereof, but the medium may not be particularly limited thereto as far as it can be used for culturing animal cells. Particularly, the medium may be xeno-free or chemically defined.
  • a medium used for culturing animal cells as their basal medium, such as any of AIM V, X-VIVO-15, NeuroBasal, EGM2, TeSR, BME, BGJb, CMRL 1066, Glasgow MEM, Improved MEM Zinc Option, IMDM, Medium 199, Eagle MEM, ⁇ MEM, DMEM, Ham
  • the medium can be a serum-containing or serum-free medium, or xeno-free medium. From the aspect of preventing contamination with heterogeneous animal-derived components, serum can be derived from the same animal as that of the stem cell(s).
  • the serum- free medium refers to medium with no unprocessed or unpurified serum and accordingly, can include medium with purified blood-derived components or animal tissue-derived components (such as growth factors).
  • the medium may contain or may not contain any alternatives to serum.
  • the alternatives to serum can include materials which appropriately contain albumin (such as lipid- rich albumin, bovine albumin, albumin substitutes such as recombinant albumin or a humanized albumin, plant starch, dextrans and protein hydrolysates), transferrin (or other iron transporters), fatty acids, insulin, collagen precursors, trace elements, 2-mercaptoethanol, 3'-thiolgiycerol, or equivalents thereto.
  • albumin such as lipid- rich albumin, bovine albumin, albumin substitutes such as recombinant albumin or a humanized albumin, plant starch, dextrans and protein hydrolysates
  • transferrin or other iron transporters
  • fatty acids insulin
  • collagen precursors such as recombinant albumin or a humanized albumin
  • trace elements such as 2-mercaptoethanol, 3'-thiolgiycerol, or equivalents thereto.
  • 2-mercaptoethanol such as 2-mercaptoethanol, 3'-thiolgiycerol
  • the medium may comprise one, two, three, four, five, six, seven, eight, nine, ten, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more of the following: Vitamins such as biotin; DL Alpha Tocopherol Acetate; DL Alpha-Tocopherol; Vitamin A (acetate); proteins such as BSA (bovine serum albumin) or human albumin, fatty acid free Fraction V; Catalase; Human Recombinant Insulin; Human Transferrin; Superoxide Dismutase; Other Components such as Corticosterone; D-Galactose; Ethanolamine HCl; Glutathione (reduced); L-Carnitine HCl; Linoleic Acid; Linolenic Acid; Progesterone; Putrescine 2HCl; Sodium Selenite; and/or
  • the medium further comprises vitamins.
  • the medium comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, or 13 of the following (and any range derivable therein): biotin, DL alpha tocopherol acetate, DL alpha-tocopherol, vitamin A, choline chloride, calcium pantothenate, pantothenic acid, folic acid nicotinamide, pyridoxine, riboflavin, thiamine, inositol, vitamin B12, or the medium includes combinations thereof or salts thereof.
  • the medium comprises or consists essentially of biotin, DL alpha tocopherol acetate, DL alpha-tocopherol, vitamin A, choline chloride, calcium pantothenate, pantothenic acid, folic acid nicotinamide, pyridoxine, riboflavin, thiamine, inositol, and vitamin B12.
  • the vitamins include or consist essentially of biotin, DL alpha tocopherol acetate, DL alpha-tocopherol, vitamin A, or combinations or salts thereof.
  • the medium further comprises proteins.
  • the medium comprises or futher comprises amino acids, monosaccharides, inorganic ions.
  • the amino acids comprise arginine, cystine, isoleucine, leucine, lysine, methionine, glutamine, phenylalanine, threonine, tryptophan, histidine, tyrosine, or valine, or combinations thereof.
  • the inorganic ions comprise sodium, potassium, calcium, magnesium, nitrogen, or phosphorus, or combinations or salts thereof.
  • the medium further comprises one or more of the following: molybdenum, vanadium, iron, zinc, selenium, copper, or manganese, or combinations thereof.
  • the medium comprises or consists essentially of one or more vitamins discussed herein and/or one or more proteins discussed herein, and/or one or more of the following: corticosterone, D-Galactose, ethanolamine, glutathione, L-carnitine, linoleic acid, linolenic acid, progesterone, putrescine, sodium selenite, or triodo-I-thyronine, a B- 27® supplement, xeno-free B-27® supplement, GS21TM supplement, an amino acid (such as arginine, cystine, isoleucine, leucine, lysine, methionine, glutamine, phenylalanine, threonine, tryptophan, histidine, tyrosine, or valine), monosaccharide, inorganic ion (such as sodium, potassium, calcium, magnesium, nitrogen, and/or phosphorus) or salts thereof, and/or molyb
  • the medium can also contain one or more externally added fatty acids or lipids, amino acids (such as non-essential amino acids), vitamin(s), growth factors, cytokines, antioxidant substances, 2-mercaptoethanol, pyruvic acid, buffering agents, and/or inorganic salts. . In specific embodiments, one or more of these may be explicitly excluded.
  • One or more of the medium components may be added at a concentration of at least, at most, or about 0.1, 0.5, 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 180, 200, 250 ng/L, ng/ml, ⁇ g/ml, mg/ml, or any range derivable therein.
  • the cells of the disclosure are specifically formulated. They may or may not be formulated as a cell suspension. In specific cases they are formulated in a single dose form. They may be formulated for systemic or local administration.
  • the cells are formulated for storage prior to use, and the cell formulation may comprise one or more cryopreservation agents, such as DMSO (for example, in 5% DMSO).
  • the cell formulation may comprise albumin, including human albumin, with a specific formulation comprising 2.5% human albumin.
  • the cells may be formulated specifically for intravenous administration; for example, they are formulated for intravenous administration over less than one hour.
  • the cells are in a formulated cell suspension that is stable at room temperature for 1, 2, 3, or 4 hours or more from time of thawing.
  • the method further comprises priming the T cells.
  • the T cells are primed with antigen presenting cells.
  • the antigen presenting cells present tumor antigens or peptides, such as those disclosed herein.
  • the cells of the disclosure comprise an exogenous TCR, which may be of a defined antigen specificity.
  • the TCR can be selected based on absent or reduced alloreactivity to the intended recipient (examples include certain virus-specific TCRs, xeno-specific TCRs, or cancer-testis antigen-specific TCRs).
  • the exogenous TCR is non-alloreactive
  • the exogenous TCR suppresses rearrangement and/or expression of endogenous TCR loci through a developmental process called allelic exclusion, resulting in T cells that express only the non- alloreactive exogenous TCR and are thus non-alloreactive.
  • the choice of exogenous TCR may not necessarily be defined based on lack of alloreactivity.
  • the endogenous TCR genes have been modified by genome editing so that they do not express a protein. Methods of gene editing such as methods using the CRISPR/Cas9 system are known in the art and described herein.
  • the cells of the disclosure further comprise one or more chimeric antigen receptors (CARs).
  • CARs chimeric antigen receptors
  • tumor cell antigens to which a CAR may be directed include at least 5T4, 8H9, ⁇ v ⁇ 6 integrin, BCMA, B7-H3, B7-H6, CAIX, CA9, CD19, CD20, CD22, CD30, CD33, CD38, CD44, CD44v6, CD44v7/8, CD70, CD123, CD138, CD171, CEA, CSPG4, EGFR, EGFR family including ErbB2 (HER2), EGFRvIII, EGP2, EGP40, ERBB3, ERBB4, ErbB3/4, EPCAM, EphA2, EpCAM, folate receptor-a, FAP, FBP, fetal AchR, FR ⁇ , GD2, G250/CAIX, GD3, Glypican-3 (GPC3), Her2, IL-13R ⁇ 2, Lambda, Lewis-Y, Kap
  • the CAR may be a first, second, third, or more generation CAR.
  • the CAR may be bispecific for any two nonidentical antigens, or it may be specific for more than two nonidentical antigens.
  • Toxicity [00328] The ratio between toxicity and therapeutic effect for a particular treatment is its therapeutic index and can be expressed as the ratio between LD50 (the amount of compound lethal in 50% of the population) and ED50 (the amount of compound effective in 50% of the population).
  • Therapeutic index data obtained from in vitro assays, cell culture assays and/or animal studies can be used in formulating a range of dosages for use in humans.
  • the dosage of such compounds preferably lies within a range of plasma concentrations that include the ED50 with little or no toxicity.
  • the dosage can vary within this range depending upon the dosage form employed and the route of administration utilized. See, e.g. Fingl et al., In: THE PHARMACOLOGICAL BASIS OF THERAPEUTICS, Ch.1, p.l, 1975.
  • the exact formulation, route of administration, and dosage can be chosen by the individual practitioner in view of the patient’s condition and the particular method in which the compound is used.
  • kits [00329] Certain aspects of the present invention also concern kits containing compositions of the disclosure or compositions to implement methods of the invention. In some embodiments, kits can be used to evaluate one or more biomarkers or HLA types.
  • kits contains, contains at least or contains at most 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 100, 500, 1,000 or more probes, primers or primer sets, synthetic molecules or inhibitors, or any value or range and combination derivable therein.
  • Kits may comprise components, which may be individually packaged or placed in a container, such as a tube, bottle, vial, syringe, or other suitable container means.
  • kits Individual components may also be provided in a kit in concentrated amounts; in some embodiments, a component is provided individually in the same concentration as it would be in a solution with other components. Concentrations of components may be provided as 1x, 2x, 5x, 10x, or 20x or more.
  • negative and/or positive control nucleic acids, probes, and inhibitors are included in some kit embodiments.
  • a kit may include a sample that is a negative or positive control for methylation of one or more biomarkers.
  • any method or composition described herein can be implemented with respect to any other method or composition described herein and that different embodiments may be combined.
  • Fig. 1 is a block diagram illustrating a computer system 100 upon which embodiments of the present teachings may be implemented.
  • computer system 100 can include a bus 102 or other communication mechanism for communicating information and a processor 104 coupled with bus 102 for processing information.
  • computer system 100 can also include a memory, which can be a random-access memory (RAM) 106 or other dynamic storage device, coupled to bus 102 for determining instructions to be executed by processor 104. Memory can also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 104.
  • computer system 100 can further include a read only memory (ROM) 108 or other static storage device coupled to bus 102 for storing static information and instructions for processor 104.
  • ROM read only memory
  • a storage device 110 such as a magnetic disk or optical disk, can be provided and coupled to bus 102 for storing information and instructions.
  • computer system 100 can be coupled via bus 102 to a display 112, such as a cathode ray tube (CRT) or liquid crystal display (LCD), for displaying information to a computer user.
  • a display 112 such as a cathode ray tube (CRT) or liquid crystal display (LCD)
  • An input device 114 can be coupled to bus 102 for communication of information and command selections to processor 104.
  • a cursor control 116 such as a mouse, a trackball or cursor direction keys for communicating direction information and command selections to processor 104 and for controlling cursor movement on display 112.
  • This input device 114 typically has two degrees of freedom in two axes, a first axis (i.e., x) and a second axis (i.e., y), that allows the device to specify positions in a plane.
  • a first axis i.e., x
  • a second axis i.e., y
  • input devices 114 allowing for 3-dimensional (x, y and z) cursor movement are also contemplated herein.
  • results can be provided by computer system 100 in response to processor 104 executing one or more sequences of one or more instructions contained in memory 106. Such instructions can be read into memory 106 from another computer-readable medium or computer-readable storage medium, such as storage device 110.
  • Computer-readable medium e.g., data store, data storage, etc.
  • computer-readable storage medium refers to any media that participates in providing instructions to processor 104 for execution. Such a medium can take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Examples of non-volatile media can include, but are not limited to, dynamic memory, such as memory 106.
  • transmission media can include, but are not limited to, coaxial cables, copper wire, and fiber optics, including the wires that comprise bus 102.
  • Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, PROM, and EPROM, a FLASH-EPROM, another memory chip or cartridge, or any other tangible medium from which a computer can read.
  • instructions or data can be provided as signals on transmission media included in a communications apparatus or system to provide sequences of one or more instructions to processor 104 of computer system 100 for execution.
  • a communication apparatus may include a transceiver having signals indicative of instructions and data.
  • the instructions and data are configured to cause one or more processors to implement the functions outlined in the disclosure herein.
  • Representative examples of data communications transmission connections can include, but are not limited to, telephone modem connections, wide area networks (WAN), local area networks (LAN), infrared data connections, NFC connections, etc.
  • the processing unit may be implemented within one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.
  • ASICs application specific integrated circuits
  • DSPs digital signal processors
  • DSPDs digital signal processing devices
  • PLDs programmable logic devices
  • FPGAs field programmable gate arrays
  • processors controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, or a combination thereof.
  • the methods of the present teachings may be implemented as firmware and/or a software program and applications written in conventional programming languages such as C, C++, Python, etc.
  • the embodiments described herein can be implemented on a non-transitory computer- readable medium in which a program is stored for causing a computer to perform the methods described above.
  • the various engines described herein can be provided on a computer system, such as computer system 100, whereby processor 104 would execute the analyses and determinations provided by these engines, subject to instructions provided by any one of, or a combination of, memory components 106/108/110 and user input provided via input device 114.
  • the methods of the present teachings can involve deep learning and/or machine learning and/or one or more neural network, such as a deep neural network, and the like.
  • a deep neural network generally, such as a convolutional neural network (CNN), generally accomplishes an advanced form of image processing and classification/detection by first looking for low level features such as, for example, edges and curves, and then advancing to more abstract (e.g., unique to the type of images being classified) concepts through a series of convolutional layers.
  • CNN convolutional neural network
  • a DNN/CNN can do this by passing an image through a series of convolutional, nonlinear, pooling (or downsampling, as will be discussed in more detail below), and fully connected layers, and get an output. Again, the output can be a single class or a probability of classes that best describes the image or detects objects on the image.
  • the first layer is generally a convolutional layer (Conv). This first layer will process the image’s representative array using a series of parameters. Rather than processing the image as a whole, a CNN will analyze a collection of image sub-sets using a filter (or neuron or kernel). The sub-sets will include a focal point in the array as well surrounding points.
  • a filter can examine a series of 5 x 5 areas (or regions) in a 32 x 32 image. These regions can be referred to as receptive fields. Since the filter must possess the same depth of the input, an image with dimensions of 32 x 32 x 3 would have a filter of the same depth (e.g., 5 x 5 x 3). The actual step of convolving, using the exemplary dimensions above, would involve sliding the filter along the input image, multiplying filter values with the original pixel values of the image to compute element wise multiplications, and summing these values to arrive at a single number for that examined portion of the image.
  • an activation map (or filter map) having dimensions of 28 x 28 x 1 will result.
  • spatial dimensions are better preserved such that using two filters will result in an activation map of 28 x 28 x 2.
  • Each filter will generally have a unique feature it represents (e.g., colors, edges, curves, etc.) that, together, represent the feature identifiers required for the final image output.
  • a filter serves as a curve detector
  • the convolving of the filter along the image input will produce an array of numbers in the activation map that correspond to high likelihood of a curve (high summed element wise multiplications), low likelihood of a curve (low summed element wise multiplications) or a zero value where the input volume at certain points provided nothing that would activate the curve detector filter.
  • the greater number of filters (also referred to as channels) in the Conv the more depth (or data) that is provided on the activation map, and therefore more information about the input that will lead to a more accurate output.
  • Balanced with accuracy of the CNN is the processing time and power needed to produce a result.
  • Conv layers can be added to analyze what outputs from the previous Conv layer (i.e., activation maps). For example, if a first Conv layers looks for a basic feature such as a curve or an edge, a second Conv layer can look for a more complex feature such as shapes, which can be a combination of individual features detected in an earlier Conv layer.
  • a CNN architecture generally consists of a group of processing blocks, including at least one processing block for convoluting an input volume (image) and at least one for deconvolution block (or transpose convolution). Additionally, the processing blocks can include at least one pooling block and unpooling block.
  • Pooling blocks can be used to scale down an image in resolution to produce an output available for Conv. This can provide computational efficiency (efficient time and power), which can in turn improve actual performance of the CNN. Those these pooling, or subsampling, blocks keep filters small and computational requirements reasonable, these blocks coarsen the output (can result in lost spatial information within a receptive field), reducing it from the size of the input by a factor equal to the pixel stride of the receptive fields of the output units. [00351] Unpooling blocks can be used to reconstruct a these coarse outputs to produce an output volume with the same dimensions as the input volume. An unpooling block can be considered a reverse operation of a convoluting block to return an activation output to the original input volume dimension.
  • the unpooling process generally just simply enlarges the coarse outputs into a sparse activation map.
  • the deconvolution block densifies this sparse activation map to produce both and enlarged and dense activation map that eventually, after any further necessary processing, a final output volume with size and density much closer to the input volume.
  • the deconvolution block associate a single activation output point with a multiple outputs to enlarge and densify the resulting activation output.
  • pooling blocks can be used to scale down an image and unpooling blocks can be used to enlarge these scaled down activation maps
  • convolution and deconvolution blocks can be structured to both convolve/deconvolve and scale down/enlarge without separate pooling and unpooling blocks.
  • the pooling and unpooling process can be limited depending on the objects of interest being detected in an image input. Since pooling generally scales down an image by looking at sub-image windows without overlap of windows, there is a clear loss in spatial info as the scaling down occurs.
  • a processing block can include other layers that are packaged with a convolutional or deconvolutional layer.
  • the CNN is then prepared for a training process to hone its accuracy in image classification/detection (of objects of interest). Using training data sets, or sample images used to train the CNN so that it updates its parameters in reaching an optimal, or threshold, accuracy, a process called backpropagation (backprop) occurs.
  • backpropagation backprop
  • Backpropagation involves a series of repeated steps (training iterations) that, depending on the parameters of the backprop, either will slowly or quickly train the CNN.
  • Backprop steps generally include forward pass, loss function, backward pass, and parameter (weight) update according to a given learning rate.
  • the forward pass involves passing a training image through the CNN.
  • the loss function is a measure of error in the output.
  • the backward pass determines the contributing factors to the loss function.
  • the weight update involves updating the parameters of the filters to move the CNN towards optimal.
  • the learning rate determines the extent of weight update per iteration to arrive at optimal. If the learning rate is too low, the training may take too long and involve too much processing capacity. If the learning rate is too fast, each weight update may be too large to allow for precise achievement of a given optimum or threshold.
  • the backprop process can cause complications in training, thus leading to the desire for lower learning rates and more specific and carefully determined initial parameters upon start of training.
  • One such complication is that, as weight updates occur at the conclusion of each iteration, the changes to the parameters of the Conv layers amplify the deeper the network goes. For example, if a CNN has a plurality of Conv layers that, as discussed above, allows for higher-level feature analysis, the parameter update to the first Conv layer is multiplied at each subsequent Conv layer. The net effect is that the smallest changes to parameters have large impact depending on the depth of a given CNN. This phenomenon is referred to as internal covariate shift.
  • EXAMPLE 1 Materials and Methods [00362] The materials and methods used in Examples 2-11 are described below.
  • SNAF Architecture Splicing Neo-Antigen Finder (SNAF) was designed as a modular python package to automate splicing neoantigen identification, using a series of embedded workflows.
  • This workflow consists of distinct steps (see below) which are divided into functionally distinct modules which can be called on a single SNAF python object or independently produced data files, which can be mixed and matched to identify T-cell or B-cell neoantigens or perform orthogonal evaluations (e.g., survival, MS proteomics, long-read analysis). Additional documentation and tutorials are provided from the GitHub repository (https ⁇ colon slash slash> github ⁇ dot> com ⁇ slash> frankligy ⁇ slash> SNAF).
  • Step 1 SNAF depends on the identification and annotation of gene-associated exon- exon and exon-intron junctions, in addition to alternative splicing quantification. As it has been extensively validated for splicing quantification (experimental, synthetic), relative to other well- described approaches, the previously described method Multipath-PSI embedded in the software suite AltAnalyze was integrated for junction and splicing analyses.
  • AltAnalyze iterates over each BAM file, to extract exon-exon junctions from alignments (STAR 2.6.1 in these analyses), based on the presence of an “N” in the CIGAR string, exported to a sample-specific exon-exon junction genome coordinate file, considering predicted strand ( Figure 3A).
  • AltAnalyze re-iterates over these BAM files to identify high-confidence intron retention events, considering reads that span the exon-intron boundary, have paired-end reads in the intron ( Figure 3B).
  • each intron Prior to exporting exon-intron spanning read counts to a separate file, each intron is required to have evidence of intron retention on both ends of the intron (5′ and 3′) with at least 5 supporting paired-end reads (exon-intron spanning and intron only pair) on each side of the intron.
  • the method also eliminates reads that meet this criterion, but have a splice-site in one of the examined paired-ends within the intron (e.g., novel exons within the intron or alternative splice-sites). Both exon-exon and exon-intron splicing events have been previously experimentally validated.
  • AltAnalyze applies a rigorous annotation model to curate detected exon-exon junctions and retention introns, based on an Ensembl gene model supplemented by transcripts downloaded from UCSC (annotated mRNAs) for matched genome and transcriptome builds (Figure 3C). This is the same gene model used by SNAF; however, the software can effectively use any Ensembl version database via AltAnalyze. Only exon-exon junctions in which one splice-site is matched to this database of known isoforms are confidently assigned to a gene model.
  • This gene model has standardized annotations for each exon, divided into blocks (E1, E2, E3, etc.), where each block represents a consensus exon from different transcript sources, and regions (E1.1, E1.2, E1.3, etc.) are defined by different annotated splice-sites.
  • Novel splice-sites from exon-exon junctions sequenced are annotated according to their proximity to known splice sites or as novel exons within an intron. Such novel splice sites are prime targets for neoantigen predictions.
  • the final junction IDs are noted based on their gene, exon block, regions and genomic coordinates.
  • junction read counts are used directly for analysis in SNAF and in conjunction with alternative splicing measurements derived from AltAnalyze automatically curated events (cassette exon, 3′/5′ splice site exon, intron retention, alternative terminal exon, trans-splicing, alternative promoter), using the MultiPath- PSI.
  • MultiPath-PSI each junction is compared to those it overlaps within the genome and that are associated with the same gene (exon-exon or exon-intron) to compute a ratio of that junction relative to all overlapping junctions (Percent Spliced-In or PSI). For each splicing event, only those with a read count > 19, considering the sum of counts for all overlapping junctions, are computed.
  • the workflow optionally provides a proliferating tissue database as an optional control dataset; however, this is not included by default as aberrant cell proliferation is indeed a hallmark of various cancer types ( Figure 17).
  • AltAnalyze v.2.1.4 is only compatible with Python 2.7, it has been integrated with SNAF via Docker containerization.
  • SNAF and other splicing neoantigen approaches uses standardized alignment of FASTQ files to the genome and transcriptome.
  • STAR version 2.4.0 with two- pass aligned to GENCODE version 36, was applied for the control database to match the detection thresholds used for the TCGA samples analyzed.
  • Step 2 Prediction of Immunogenic MHC-Bound T-cell Neoantigens (Step 2) [00371] To identify splice-junctions that are specific to tumor samples, relative to a database of healthy control tissues (GTEx, 54 body sites), SNAF computes the tumor specificity of each junction. This score is defined as the average read count for a splice junction in GTEx as , and the maximum read count for a junction across samples in the evaluated tumor cohort . By default, a splice event is considered tumor-specific (neojunction) if and only if the two criteria are met: (1) [00372] These datasets and conditions.
  • neojunction For each neojunction, the junction RNA sequence is first retrieved by concatenating the 5′ and 3′ exon/intron sequences, followed by in silico translation into possible k-mers, considering all the three possible reading frames ( Figure 2).
  • a valid short peptide should meet two criteria: (1) absence of a stop codon, and (2) at least one amino acid should span the splice-junction site, such that the junction peptide will not derive from only the 5′ donor or the 3′ acceptor exon/intron. All possible strand-specific reading frames are used to account for both potential tumor-specific alternative protein translations and to increase the sensitivity to detect novel neoantigens. [00374] SNAF next restricts the number of T cell neoantigen candidates by considering MHC binding prediction and T cell immunogenicity. Only a few MHC-bound peptides are likely to elicit T cell response (Figure 2).
  • SNAF provides interfaces to two popular MHC binding prediction models – NetMHCpan4.1 and MHCflurry2.0 to prioritize junction-peptides that are likely to bind with patient-specific HLA sequences.
  • NetMHCpan4.1 and MHCflurry2.0 have the highest reported accuracy for MHC binding based on recent benchmarking.
  • MHCflurry is directly embedded with SNAF, while NetMHCpan4.1 is available for direct integration, following authorization of the software’s license from the software’s website.
  • SNAF leverages the inventors’ recently published deep convolutional neural network approach DeepImmuno, which ranked among the best-performing models in a recent independent benchmarking study.
  • a heuristic approach is used to infer full-length isoform from all existing transcripts and local splicing events. For example, if a tumor-specific splicing event E2.1-E3.1 is observed, along with a documented transcript whose exon composition is E1.1
  • SNAF implements a parallel strategy leveraging input cancer long-read sequencing data from a gtf file. Specifically, for a specific alternative splicing event associated junction (i.e. E2.1-E3.1), SNAF identifies an associated full-length isoform by matching the exon-exon junction splice-site coordinates to those present in the long-read isoform (presumably tumor-associated) GTF file. For intron retention, SNAF determines whether both intron boundaries fall within a long-read supported single-exon region. Such matching long-read evidenced isoforms represent additional candidate ExNeoEpitopes.
  • E2.1-E3.1 alternative splicing event associated junction
  • SNAF-B reference-augmented workflow (de novo prediction): When no matching mRNA isoform contains the neojunction, the most appropriate reference isoform is first selected for a given neojunction. This analysis (short_read mode), produces a theoretical mRNA isoform based on the software selected reference isoform. To accomplish this, SNAF-B leverages a unique feature of the software AltAnalyze, which is the explicit annotation of exon and intron blocks and regions. Specifically, all AltAnalyze documented isoforms are decomposed into program annotated exon blocks and regions, where each exon block is subdivided into regions defined by alternative splice site locations, using a series of prior described rules.
  • RNA-Seq data When RNA-Seq data is analyzed, all exon-exon and exon-intron junctions are annotated according to this schema, with novel exons or splice sites assigned unique IDs relative to the block and region they are situated within.
  • the software identifies the the longest protein- coding isoform from AltAnalyze’s reference isoform collection that contains both exon regions that comprise the junction. The collection consolidates mRNA and protein isoforms from Ensembl the UCSC genome browser database (Ensembl version 91 and matched UCSC database for this paper).
  • the neojunction ENSG00000056291:E2.2-E5.1 represents an exon skipping isoform.
  • the corresponding longest protein coding isoform that contains these two exons is ENST00000395999 (ENSP00000379321), which is represented as E2.1
  • SNAF represents the ExNeoEpitope from this reference form as E2.1
  • SNAF-B Long-Read Alignment workflow As an alternative approach to directly identify novel mRNA isoforms associated with short-read bulk RNA-Seq-derived neojunctions from SNAF, the SNAF-B pipeline includes specialized methods to identify novel mRNA isoforms that can encode for full-length transmembrane proteins. This analysis (long_read mode), produces an inferred protein isoform based on a long-read sequence match.
  • This workflow leverages an externally provided mRNA isoform gene transfer format (GTF) text files, containing the genomic coordinates corresponding to the isoform exon positions for the genome database being analyzed (e.g., hg38).
  • GTF mRNA isoform gene transfer format
  • the SNAF-B predicted neojunction genomic coordinates are matched to the provided GTF to identify the longest cDNA junction match.
  • GTF mRNA isoform gene transfer format
  • ORF Open Reading Frame
  • Codon usage bias measures the species-specific tendency to prefer one codon over the other, which can be used to prioritize valid protein coding genes.
  • the codon frequency table is obtained from https ⁇ colon slash slash> www ⁇ dot> genscript ⁇ dot> com ⁇ slash> tools ⁇ slash> codon-frequency-table.
  • the cumulative frequency value across the ORF is normalized to a value between 0-1 and added to the GC content percentage as the score for ORF prioritization.
  • the predicted ORF is assessed for Nonsense Mediated Decay (NMD) potential ( Figure 2).
  • the NMD check is performed for all inferred short-read predicted inferred isoforms, as a reference isoform with an established ORF, but not for long-read isoforms, as the ORF must be inferred. Any predicted isoforms that have a 100% match with a documented membrane protein isoform in the Uniprot database are excluded, as these are presumed to be expressed in non-malignant tissues.
  • TMHMM TMHMM
  • HMM Hidden Markov Model
  • Benchmark of MultiPath-PSI on Splicing Detection was performed against several recently reported methods for local splicing variation analyses (MAJIQ (version 1.0), LeafCutter (version 0.2.1), and rMATS (version 3.0.9)). As a gold-standard to evaluate prediction accuracy and error rates, a simulated RNA-Seq dataset derived from known and novel isoform predictions was created for pluripotent stem cells (PSC) and in vitro derived day 30 cardiomyocytes (CM) (https://www.synapse.org/#!Synapse:syn12104338).
  • PSC pluripotent stem cells
  • CM cardiomyocytes
  • the resulting aligned sequencing data were evaluated with each analysis method and benchmarked against isoform-derived alternative splicing events (exon- exon and exon-intron junctions) comparing CM to PSC.
  • Supervised group comparison analyses with MultiPath-PSI were performed using an FDR corrected (Benjamini-Hocherg) empirical Bayes moderated t-test P-value ⁇ 0.05.
  • precision-recall curves were generated using Matlab for the synthetic dataset. For each method, the significant splicing events were ranked in descending order according to the empirical score or p-value reported by each algorithm.
  • SNAF can calculate two optional continuous scores (maximum likelihood estimation and Bayesian hierarchical), measuring the tumor specificity of each splice junction based on its expression across diverse tissues. These methods are designed to rank and prioritize SNAF neoantigens for experimental validation, using different normal tissue references. These scores can be optionally used to filter results by the user (not by default).
  • Bayesian Hierarchical Approach Although the MLE approach takes into account the whole expression vector across GTEx samples, the distribution of splicing expressions across tissue types was not explicitly modeled. The present study therefore implemented a hierarchical Bayesian model to consider the different distributions of the splicing expression values, which has been further developed into an unified tumor specificity score model called BayesTS for all types of molecular tumor targets including protein-coding genes. Briefly, this method assigns a higher tumor specificity score to predicted neojunctions with infrequent versus frequent expression in healthy controls (GTEx) across all tissues and donors.
  • GTEx healthy controls
  • neojunction expression represents an unacceptable risk of off-target effects.
  • a random variable has been defined, representing the number of samples with non-zero read counts for a ( ) in each tissue ( ). For example, if there are 5 out of 25 hippocampus samples that have non-zero read counts, then .
  • RNA-Seq samples were downloaded spanning 191 RBPs. These FASTQ files were aligned to human genome version hg38 using STAR version 2.6.1, followed by AltAnalyze 2.1.4.2 to obtain splicing junctions from the resultant BAM files.
  • IDR Irreproducible Discovery Rate
  • the accuracy of inferred RBP activity was evaluated in ENCODE HepG2 cell line RNA-Seq knockdown data, spanning 193 RBPs across 474 samples. Splicing events with less than 75% non-zero PSI values were filtered out and the missing PSI values were imputed by the median of present values per event. Finally, splicing events with Coefficient of Variation (CV) less than 0.1 were excluded (uninformative).
  • CV Coefficient of Variation
  • An accurate inference should reflect the reduced activity of each RBP in the knockdown samples compared to controls, based on the K562 prior network. This is analogous to the multi-label ranking problem and hence, Label Ranking Average Precision (LRAP) and Normalized Discounted Cumulative Gain (NDCG) can be used to quantify performance.
  • LRAP Label Ranking Average Precision
  • NDCG Normalized Discounted Cumulative Gain
  • S The total number of knockdown and control samples was denoted as S, the ground state truth label as a binary vector X S , and the predicted RBP activity as a vector Y S where the higher value indicates higher RBP activity.
  • the metrics are defined as below: . identify samples whose predicted RBP activity is higher than itself.
  • NDCG is originally used to assess the usefulness of a web research result, by determining the fraction of top results which are relevant to the query, times a decay factor based on the rank of the results. First, all samples are ranked based on the predicted RBP activity in descending order, so the highest sample will receive the rank 1.
  • the Discounted Cumulative Gain (DCG) is computed as below: (16)
  • DCG Discounted Cumulative Gain
  • a customized MHC-bound splicing neoantigen database was built using snaf.proteomics.remove_redundant and snaf.proteomics.compare_two_fasta function.
  • the snaf.proteomics.compare_two_fasta function removes all redundant short peptides from the prediction, whereas the snaf.proteomics.remove_redundant function discards splicing neoantigens overlapping with non- diseased human proteome sequences (UniProt).
  • the custom splicing neoantigen database (FASTA files) used for Peptide Spectrum Matching (PSM) search is available in synapse (https ⁇ colon slash slash> www ⁇ dot> synapse ⁇ dot> org ⁇ slash> #!synapse:syn32057176), and the MaxQuant configuration files mqpal.xml and the code SNAF uses to automatically generate the xml files are available in GitHub (https ⁇ colon slash slash> github ⁇ dot> com ⁇ slash> frankligy ⁇ SNAF> reproduce).
  • Peptides were pooled (except for LELLVKGTV and STLEFGLRV, which did not solubilize sufficiently) at a concentration of 1 pmol/ ⁇ L and then diluted 1:10 for a 100 fmol/ ⁇ L working solution.
  • LC-MS analysis was performed on a 50 fmol injection of pooled peptides using a Ultimate 3000 nanoflow HPLC (Dionex) and Orbitrap Eclipse Tribrid mass spectrometer (ThermoFisher Scientific) as described below.
  • the peptides were eluted at 300 nL/min using the following gradient: 4% B from 0-5 min, 4 to 10% B from 5-10 min, 10-35% B from 10-60 min, 35-55% B from 60-70 min, 55-90% B from 70-71 min, 90% B from 71-73 min, 90-4%B from 73-74 min and 4% B from 74-90 min.
  • the Orbitrap Eclipse was operated in positive ion mode with 2.0 kV at the spray source, RF lens at 30% and data dependent MS/MS acquisition with XCalibur version 4.3.73.11. Positive ion Full MS scans were acquired in the Orbitrap from 375-1500 m/z with 120,000 resolution.
  • the ion intensities for MS2 b- and y- fragment ions were plotted for each synthetic peptide sequence. Peptides with coverage for at least six fragment ions were considered “observed”.
  • the Skyline software enables visualization of the observed fragment ions in the context of the entire raw MS2 spectrum via color-coding and labeling of the b- and –y ions.
  • the synthetic peptide MS2 spectra were then compared to experimental spectra which were identified by search algorithm software to have the same peptide sequence.
  • the b- and y- ions identified from both the synthetic and experimental spectrums were selected, and the quantitative correspondence were calculated based on their associated ion densities.
  • T2 cells were used without and loaded with 100ug/ml peptides and thereafter incubated overnight. All T2 cells were harvested and stained with HLA-A2-PE antibody (clone BB7.2; Biolegend) for 30 mins on ice. Cells were washed once with cell culture medium and acquired on a Fortessa II flow cytometer. Median fluorescence intensity was determined using FlowJo software. Immunogenicity Assay [00425] Immunogenicity of predicted splicing neoantigen peptides was determined as described previously.
  • HLA-typed PBMCs from leukocyte reduction system (LRS) chambers were isolated using Ficoll hypaque density gradient centrifugation, aliquoted in 20x10 6 cells per vial and frozen in liquid nitrogen until use.
  • PBMC were thawed and used to set up monocyte derived dendritic cells by plating 4x10 6 PBMC in a 24 well.
  • Cells were incubated at 37°C, 5% CO2 in DC medium (RPMI 1640 supplemented with 10% FBS, 1% L- Glutamine (200mM) + IL-4 (1000 U/ml) and GM-CSF (800U/ml).
  • DC maturation medium was added (RPMI 1640 supplemented with 10% FBS, 1% L-Glutamine, IL-4 (1000U/ml), GM-CSF (800U/ml), IL-1 ⁇ (10ng/ml), IL-6 (10ng/ml), TNF- ⁇ (10ng/ml) and LPS (30ng/ml)).
  • peptide loaded DCs were used to stimulate autologous PBMC, by adding 1x10 6 PBMC of the same donor to the DC cultures.
  • DC and PBMC co-cultures were grown in T cell medium (60% RPMI 1640, 40% Click’s medium supplemented with 10% FBS, 1% L-glutamine, IL-6 (100ng/ml), IL-7 (10ng/ml), IL-12 (10ng/ml), and IL-15 (5ng/ml). Medium was changed on day 3 and day 6 based on medium color change. On day 14 and 21 T cells were harvested and stimulated with new autologous peptide loaded DCs.
  • T cells were harvested and tested for their IFN ⁇ response to peptide loaded 721.221 (221) single HLA antigen target cells. Fist the target cells 221.A*02:01, 221.C*04:01, and 221.C*08:01 were loaded with relevant peptides at 10 ⁇ g/ml in separate wells.221 and peptides were incubated for 4h at 37°C, 5% CO2 in RPMI 1640 supplemented with 10% FBS.
  • Peptide loaded 221 target cells with and without peptides were co-cultured with primed T cells at 3:1 ratio in the presence of 50 ng/ml PMA for 6h at 37°C, 5% CO2 in RPMI 1640 supplemented with 10% FBS and monensin. PMA and ionomycin stimulation (each at 1 ⁇ g/ml) was used at positive control. Thereafter the cells were harvested and stained for extracellular CD45-BV786 (Biolegend), CD14-PerCP (Biolegend) and CD8-PE (biolegend) for 30 mins on ice.
  • HEK-293T cells reached 60% of confluency, these constructs (CMV promoter) were transfected into the cells separately with 1 ⁇ g of plasmid (99% pUC19 negative control plasmid+1% engineered plasmid) with TransIT-LT1 (Mirus) following the manufacturer's protocol.
  • the cells were fixed and permeabilized with 4% PFA 24 hours post- transfection. After two rounds of washing, the cells were treated with a warm 1x citrate buffer (diluted from 10X stock; Sigma-Aldrich) to break the protein cross-links.
  • RNA was isolated (Trizol) and analyzed on a Thermo Nanodrop UV-Vis and an Agilent Bioanalyzer to confirm the nominal concentration and ensure RNA integrity.
  • cDNA was synthesized using the Clontech SMARTr cDNA Synthesis Kit, in which a barcode was added to the oligo-dT at the 3′ end.
  • Each melanoma cell line cDNA was pooled and then converted into a SMRTbell library using the Iso-Seq Express Kit SMRT Bell Express Template prep kit 2.0 ( Pacific Biosciences). Each library was sequenced on a SMRT cell on the Sequel II system using a 30 hour movie collection time.
  • the “ccs” command from the PacBio SMRTLink suite (SMRTLink version 9) was used to convert Raw reads into Circular Consensus Sequence (CCS) reads.
  • CCS Circular Consensus Sequence
  • the resulting data was analyzed in SQUANTI to assign reads to full- length collapsed reference or novel isoforms.
  • the isoform GTF files, barcode sequences and raw data are available in Synapse (https ⁇ colon slash slash> www ⁇ dot> synapse ⁇ dot> org ⁇ slash> #!Synapse:syn32057176). Results are shown in Table 1.
  • Mutation analysis was conducted using snaf.mutation_analysis function.
  • a univariate Cox Regression analysis was used to identify events/antigens with a parental PSI value that are positively or negatively associated with patient outcome.
  • the neojunction and its parental PSI value is ignored if the neoantigen was not predicted to be presented in that sample, resulting in different survival associations for different neoantigens produced from the same neojunction.
  • RNA-Seq FASTQ files from dbGAP (phs000424.v8.p2). These FASTQ files were aligned to the reference human genome (hg38) and transcriptome (Ensembl 91) using STAR prior to analysis in SNAF.
  • Selective Amino Acid Motif Enrichment Analysis [00434] The shared and unique neoantigens were defined by accounting for the frequency of their parental splicing junction. The occurrence frequency of a neoantigen were denoted as and its parental splicing junction (neojunction) as .
  • a detailed description of the feature selection criteria and dimension reduction procedures is provided in DeepImmuno. Briefly speaking, each amino acid was associated with over 500 numerical properties.
  • a Principal Component Analysis (PCA) was performed to reduce the dimension to 12, which was further used for encoding the peptide sequence.
  • PCA Principal Component Analysis
  • the encoded neoantigens were embedded into a 2D space using Uniform Manifold Approximation and Projection (UMAP).
  • SNAF Interactive Web Application [00437] An interactive web application for both SNAF-T and SNAF-B visualization results was created using the Python Dash framework. The front-end was written in React.js and the backend in the Flask framework. Plotly-py was used for interactive visualization and the Ensembl REST API used for retrieving transcript reference sequences and Needleman-Wunsch global alignment. The peptide weblogo plot was implemented.
  • the height of the stack (i) represents the difference between the maximum entropy and the actual entropy among the observed amino acids distribution.
  • the observed frequency of amino acids at position Within each stack, the of each amino acid was determined by their relative frequencies.
  • AlphaFold23D modeling [00439] AlphaFold2 was obtained from https ⁇ colon slash slash> github ⁇ dot> com ⁇ slash> deepmind ⁇ slash> alphafold and implemented as a Singularity container from the provided Dockerfile, to be run on a Linux HPC, along with all training databases.
  • SNAF Computational Workflows – Inferring New Classes of Neoepitopes from RNA-Seq [00440] The SNAF workflow begins with user-supplied BAM files from tumor samples or cancer cell lines, followed by the identification and quantification of diverse classes of post-transcriptional regulation.
  • the workflow applies a highly accurate approach for local splicing variation (MultiPath-PSI) from the AltAnalyze framework, to detect known and novel alternative splicing (cassette exon, 3′/5′ splice site exon, intron retention, alternative terminal exon, trans-splicing) and alternative promoter regulatory events, which can produce unique exon-exon or exon-intron junctions for in silico translation (Figure 3).
  • This approach has been extensively benchmarked against diverse local- splicing variation (LSV) approaches, including rMATs, Leafcutter and MAJIQ, with specialized methods to accurately quantify retained introns (Figure 4).
  • SNAF-T consists of: 1) HLA type prediction from sample FASTQ files (user provided); 2) in-silico translation; 3) MHC-binding prediction (NetMHCpan or embedded calls to MHCflurry); and 4) HLA-allele specific immunogenicity prediction (DeepImmuno).
  • SNAF-B consists of: 1) full-length isoform prediction for each tumor-specific splice-junction by augmenting existing isoform references; 2) exclusion of isoforms predicted to induce nonsense-mediated decay (NMD); 3) transmembrane topology prediction; and 4) long-read isoform sequence validation and augmented prediction (optional).
  • NMD nonsense-mediated decay
  • SNAF-T full-length isoform prediction for each tumor-specific splice-junction by augmenting existing isoform references
  • NMD nonsense-mediated decay
  • transmembrane topology prediction e.g., a Maximum Likelihood Estimation and separate hierarchical Bayesian model (BayesTS) can optionally be applied to assess the tumor specificity of each neojunction, e.g.
  • RNA-Seq RNA-based Splicing PRotein activity INference from multivariate decision Trees
  • RNA-SPRINT RNA-based Splicing PRotein activity INference from multivariate decision Trees
  • SNAF-B provides independent evidence of tumor-specific transmembrane proteins (ExNeoEpitopes) that can uncover novel extracellular epitopes for antibody recognition.
  • SNAF predicts ExNeoEpitopes from a reference mRNA transcript database through the removal of alternative spliced-out transcript sequences in the longest reference model and through matching of tumor-specific splice-junctions to large long-read isoform sequencing experiments or reposited expressed sequence tags (ESTs).
  • ESTs reposited expressed sequence tags
  • SNAF further automates comparative proteomics analysis of produced candidate splicing neoantigen peptide sequences to produced mass- spectrum profiling datasets, such as immunopeptidomics and surface proteomics datasets, to confirm neoantigen cell-surface or MHC presentation.
  • Such predictions are used to derive a ranking of splicing neoantigen importance, in conjunction with other SNAF produced outputs, including, but not limited to, immunogenicity score, HLA-binding score, neoantigen tumor specificity scores relative to healthy control samples (maximum likelihood Estimation, Bayesian hierarchical model, control cohort mean counts), alternative splicing PSI value, mRNA junction occurrence in the reference transcriptome (Ensembl version 91), patient cohort occurrence frequency, recurrence in independent cancers and cohorts, splice-event type, full-length peptide prediction length versus the reference, peptide sequence similarity to reference peptides, the presence of signal peptide sequence, secondary structure, 3D protein structure prediction, insertion versus deletion of amino acids, absolute junction read counts, topology predictions and other programmatic outputs.
  • SNAF can identify both precision (patient specific) and shared neoantigens, which are frequently presented by MHC and are immunogenic or which produce ExNeoEpitopes.
  • the present inventors have found that shared splicing neoantigens can be distinguished from patient-specific neoantigens in their amino acid sequence and corresponding physicochemical parameters, further enabling systemic prioritization.
  • SNAF has a modular design with well-described Python classes, enabling extensive customization to incorporate additional reference datasets for verification (e.g., custom control RNA-Seq, long-read sequencing) and alternative algorithms (e.g,. MHC binding prediction).
  • a central component of the SNAF prediction workflow is the ability to more accurately predict which splicing neoantigens will selectively induce a T-cell response based on which HLA alleles are present in a given patient specimen.
  • the DeepImmuno workflow can predict immunogenic tumor neoantigens with up-to a 2-fold greater sensitivity than alternative approaches to assess antigen immunogenicity. This workflow has been independently validated as a top-performer in predicting immunogenicity versus other publicly available tools.
  • the DeepImmuno workflow can predict immunogenic tumor neoantigens with up-to a 2-fold greater sensitivity than alternative approaches.
  • SKCM skin cutaneous melanoma
  • TCGA Cancer Genome Atlas
  • Figure 6A unmatched immunopeptidome data from 24 melanoma patients
  • Lower spectrum similarity scores are observed in the Ovarian cohort (Pearsonr 0.55) compared to the Melanoma cohort (Pearsonr: 0.84), which can be attributed to differences in the MS technology applied (Ion trap MS versus Fourier transform MS) and fragmentation methods (CID versus HID).
  • EXAMPLE 4 Splicing Neoantigen Burden Predicts Overall Survival and Response to Immunotherapy [00456] To broadly assess the clinical relevance of splicing neoantigens, SNAF was applied to >500 melanoma patient biopsies with (i.e., Van Allen cohort) (33) and without immunotherapy (TCGA-SKCM). These analyses separately considered the number of predicted neojunctions, MHC-bound peptides, immunogenic neoantigens and overall neoantigen burden, considering RNA-Seq determined HLA alleles for each patient.
  • neojunctions 528 tumor-specific splicing junctions (neojunctions) were found per patient on average, ranging from 28 to 1,549 neojunctions in each patient. From these neojunctions, an average of 1,090 MHC-bound peptides were predicted, ranging from 75-2,981 peptides per patient. DeepImmuno predictions reduced the number of neoantigens to 915 on average (ranging from 74-2,486/patient), filtering out 16% of potentially non-immunogenic bound peptides, as current immunogenicity approaches still suffer from low precision, which may explain the relatively low filtering rates from netMHCpan-reported bound peptides.
  • CAMKK2 has been previously identified as a target to improve immunotherapy outcomes, as its mutation or inhibition leads to increased anti-PD1 immunotherapy efficacy. This effect is believed to occur via CAMKK2’s ability to negatively regulate ferroptosis, a mechanism of cell death that is induced by iron-dependent lipid peroxidation, via the AMPK ⁇ NRF2 pathway.
  • a splicing neoantigen in the melanin synthesis associated glucose transporter SLC45A2 was detected in 212/472 TCGA SKCM patients; this neoantigen is associated with poor overall survival in TCGA (Wald p ⁇ 0.05, z-score>2) and is associated with good overall survival in the Van Allen cohort (Wald p ⁇ 0.05, z-score ⁇ -2), indicating that it represents a relatively common target for therapy and prognosis (Figure 8F).
  • Splicing neoantigens are likely produced by modulation of such interactions, driven by direct mutations or RNA-editing that result in unique tumor-specific mRNAs.
  • the most likely mediator of differential splicing neoantigen burden is a change in the expression or activity of one or more splicing factors.
  • RNA-SPRINT to all TCGA melanoma patient PSI profiles, finds that 209 out of 221 examined RBPs have reduced versus increased activity in the high versus low splicing neoantigen group (Mann-Whitney p ⁇ 0.05) (Figure 10B-10C).
  • Figure 10D While contrary to expectations based on RBP gene expression, these data indicate that there exists a coordinated failure to properly splice transcripts, as previously described. This result is further supported by an observed increase in intron-retention in the high- burden patients (failure to excise introns) (Figure 10D).
  • EXAMPLE 7 Shared splicing neoantigens are presented by MHC and display amino acid compositional bias [00473] As an important observation from the SNAF-T analyses is the co-occurrence of splicing neoantigens among many patients, the study examined broadly shared splicing neoantigens observed in melanoma, versus those observed in individual patients, to understand such regulation.
  • each 9-mer was encoded as a numerical vector based on all amino acid physicochemical parameters in the AAIndex database (566 parameters) (see the methods described in Example 1), and each neoantigen vector was projected as a point in UMAP space ( Figure 12G). While the majority of shared and unique splicing neoantigens have similar physicochemical characteristics, a few empirically observed clusters were found to be enriched in shared versus unique neoantigens.
  • the tumor microenvironment appears to be a significant contributor to splicing neoantigen burden, but not a primary source for shared neoantigens.
  • neoantigens are not only a byproduct of a normal cellular proliferative program, thus limiting their therapeutic use.
  • Neojunctions from shared splicing neoantigens were frequently observed in only one sample from these data, specifically from 16 week fetal fibroblasts, but not from other fetal fibroblasts profiled. Furthermore, these overlapping melanoma neojunctions were only weakly detected (1-50 reads) in any cultured epidermal sample and were associated with cell cycle regulation (Figure 17D).
  • EXAMPLE 9 SNAF accurately predicts full-length mRNAs and stable proteoforms [00485]
  • An interesting set of SNAF-T splicing neoantigens were those that occur in transmembrane proteins, such as GPR143 and SLC45A2, which occurred due to novel in-frame alternative splice-sites (5′ or 3′). Such splicing events can result in novel cell-surface expressed proteins.
  • SNAF-B was applied to the same TCGA melanoma cohort.
  • This workflow can optionally exclude predicted mRNAs that are expected to result in NMD and selectively include those that have high-confidence transmembrane domains based on a prior published Hidden Markov Model based topology prediction approach (TMHMM). Putative ExNeoEpitopes that result in deleted or novel extracellular polypeptides can be determined using the SNAF-B interactive viewer. [00487] This analysis found 378 initial ExNeoEpitopes in the TCGA melanoma cohort, using prior well annotated transcripts as reference models (Ensembl, UCSC mRNAs).
  • Iso- Seq long-read RNA isoform sequencing
  • SNAF-B can match short-read identified junctions to other supplied isoform models (e.g., long-read or EST). Matching exon- exon and exon-intron junctions from the TCGA melanoma cohort to a publicly available PacBio Iso-Seq dataset of 10 commonly used cancer cell lines (Universal Human Reference) found 1207 additional full-length neo-isoforms associated with junctions not readily detected in GTEx.
  • the study identified 12 initial candidate long-read supported isoforms with high GTEx evidence of tumor-specificity (mean reads/sample ⁇ 0.1) that result in the inclusion of novel alternative first or cassette exons. These were initially identified through manual inspection of BLAT sequence matches to the human genome and mRNA transcript databases (UCSC and Ensembl) and biased to ExNeoEpitopes detected in >15% of patients.
  • Neoantigen target-containing peptides and proteins identified via SNAF-T and SNAF-B were applied to 5 cancers: 1) melanoma, 2) ovarian cancer, 3) head and neck cancer (HNC), 4) breast cancer, and 5) myelodysplastic syndrome (MDS).
  • RNA-Seq patient individual tumor biopsies NCI Genomics Data Commons or NIH Sequence Read Archive
  • immunopeptidomics mass spectrometry profiles 3) single- RNA-Seq data
  • cancer cell lines long-read mRNA sequencing isoform models cancer cell lines long-read mRNA sequencing isoform models, and/or 5) associated open-access clinical metadata.
  • MDS is a heterogeneous disorder of the hematopoietic stem cell (HSC) resulting in bone marrow failure and frequent progression to acute myeloid leukemia (AML). MDS is diagnosed in over 10,000 patients yearly (98). Survival varies greatly by the subtype of MDS, with survival ranging from few months (high risk MDS, t-MDS) to several years (low risk). Despite the introduction of new therapies, such as hypomethylating agents and immunomodulatory drugs, the only curative therapy to date remains allogeneic stem cell transplant.
  • HSC hematopoietic stem cell
  • AML acute myeloid leukemia
  • Candidate 1 [00504] Two splicing-derived neoantigens (RLLGTEFQTRRAMTLK and IPDSQGNDI), encoded by the gene SLC45A2 (ENSG00000164175:E3.2_33963931-E4.2), were identified and found to be associated with poor overall survival (Wald test p ⁇ 0.05, z-score >2) in a large diagnostic melanoma RNA-Seq cohort (TCGA) and good overall survival (Wald test p ⁇ 0.05, z-score ⁇ -2.0) in a melanoma immunotherapy RNA-Seq cohort (phs000452.v3).
  • This neoantigen is not readily detected in normal healthy tissues (GTEx, see Figure 6B) but is frequently detected in melanoma (328/472 patients) by SNAF and by immunopeptidomics (4 independent melanoma patients). [00505] Accordingly, this neoantigen is predictive of good response with standard anti-PD-1 checkpoint blockade therapy. As such, this candidate can be used: A) as a diagnostic (quantitative PCR for exon-exon junction chr5:33954504-33963931) to identify patients that are likely to benefit from standard therapy.
  • Candidate 2 [00506] A splicing-derived neoantigen (QRETDFKMKF), encoded by the gene TBC1D16 (ENSG00000167291:E38.6-E39.1, chr17:79950594-79950756), was identified and found to be associated with poor overall survival (Wald test p ⁇ 0.0005, z-score >3) in a large diagnostic melanoma RNA-Seq cohort (TCGA) and decent overall survival (Wald z-score ⁇ -1.5) in a melanoma immunotherapy RNA-Seq cohort (phs000452.v3).
  • QRETDFKMKF splicing-derived neoantigen
  • This neoantigen is not readily detected in normal healthy tissues (GTEx, see Figure 7 and Figure 8) but is frequently detected in Melanoma (158/472 patients) by SNAF. [00508] Accordingly, this neoantigen is a predictive of poor survival. As such, this candidate can be used: A) as a diagnostic (quantitative PCR for exon-exon junction chr17:79950594-79950756) to identify patients that may benefit (mixed outcome) from standard therapy, and/or B) targeted immunotherapy using this neoantigen.
  • Candidate 3 [00510] A splicing-derived neoantigen (TVTAGRQGIY), encoded by the gene GPR143 (ENSG00000101850:E8.1-E9.1_9743655, chrX:9743655-9746044), was found to be associated with poor overall survival (Wald test p ⁇ 0.05, z-score >2) in a large diagnostic melanoma RNA-Seq cohort (TCGA). This neoantigen is not readily detected in normal healthy tissues (GTEx, see Figure 6B) but is frequently detected in melanoma (192/472 patients) by SNAF.
  • GTEx normal healthy tissues
  • SNAF splicing-derived neoantigen
  • this neoantigen is a predictive of poor response with standard anti-PD-1 checkpoint blockade therapy.
  • this candidate can be used: A) as a diagnostic (quantitative PCR for exon-exon junction chrX:9743655-9746044) to identify patients that are candidates for new therapeutics, and/or B) targeted immunotherapy using this neoantigen.
  • This neoantigen is not readily detected in normal healthy tissues (GTEx) but is frequently detected in melanoma (136/472 patients) by SNAF. [00514] Accordingly, this neoantigen is a predictive of poor response with standard anti-PD-1 checkpoint blockade therapy. As such, this candidate can be used: A) as a diagnostic (quantitative PCR for exon-exon junction chr13:113044888-113045271) to identify patients that are unlikely to benefit from standard therapy but are candidates for new therapeutics, and/or B) targeted immunotherapy using this neoantigen.
  • neoantigens identified by the processes described herein include RETDFKMKF, YQDWQQNLY, FNEDGLRQV, as well as the immunogenic targets listed in SEQ ID NOs: 1-2545 submitted concurrently herewith.
  • EXAMPLE 12 Interactive Neoantigen Web Explorer [00516] To facilitate the exploration and prioritization of the predicted neoantigens from SNAF, two interactive web applications were developed to visualize both T-cell and B-cell neoantigens ( Figure 24). The SNAF-T viewer allows users to explore different global neoantigen features (i.e., amino acid length, and frequency within a cohort) in a projected 2D UMAP space.
  • each neoantigen is embedded based on its physicochemical properties. Users can manually select clusters to identify enriched web logo AA motifs and explore individual neojunctions and neoantigens.
  • the SNAF-B viewer can be used to interactively visualize GTEx and tumor counts for neojunctions, generate protein sequence alignments between a putative ExNeoEpitope and selected reference proteins, compare protein feature composition, secondary structure and solvability prediction, and perform topology modeling. Hence, these tools can be used to select optimal targets for experimental validation. [00517]
  • the various methods and techniques described above provide a number of ways to carry out the disclosure .
  • the numbers expressing quantities of ingredients, properties such as molecular weight, reaction conditions, and so forth, used to describe and claim certain embodiments of the application are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the application are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable.

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Abstract

L'invention concerne des cibles immunogènes associées à un ou plusieurs types de cancer, la cible immunogène comprenant un néo-antigène à base d'acide aminé d'épissage partagé présent dans l'une quelconque des SEQ ID NO : 1-2545, ou un codage nucléotidique associé, ainsi que leurs utilisations, et des méthodes d'identification de telles cibles immunogènes. Des modes de réalisation particuliers concernent des immunothérapies pour traiter un type de cancer, et des méthodes de traitement d'un type de cancer par administration de telles immunothérapies, l'immunothérapie comprenant une cible immunogène comprenant un néo-antigène à base d'acide aminé d'épissage partagé présent dans l'une quelconque des SEQ ID NO : 1-2545, ou un codage nucléotidique associé.
PCT/US2024/044819 2023-09-01 2024-08-30 Identification de cibles pour une immunothérapie dans un mélanome à l'aide de néo-antigènes dérivés d'épissage Pending WO2025050009A2 (fr)

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CN121789769A (zh) * 2026-03-03 2026-04-03 广州赛业百沐生物科技有限公司 一种b细胞免疫组库抗体序列特征分析及特异性筛选方法和相关设备
CN121789769B (zh) * 2026-03-03 2026-05-08 广州赛业百沐生物科技有限公司 一种b细胞免疫组库抗体序列特征分析及特异性筛选方法和相关设备

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