WO2022011429A1 - Procédés permettant d'augmenter la réponse à la modulation de points de contrôle immunitaires - Google Patents

Procédés permettant d'augmenter la réponse à la modulation de points de contrôle immunitaires Download PDF

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WO2022011429A1
WO2022011429A1 PCT/AU2021/050764 AU2021050764W WO2022011429A1 WO 2022011429 A1 WO2022011429 A1 WO 2022011429A1 AU 2021050764 W AU2021050764 W AU 2021050764W WO 2022011429 A1 WO2022011429 A1 WO 2022011429A1
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cancer
immune checkpoint
inhibitor
tumours
irnb
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Willem Joost LESTERHUIS
Timo Lassmann
Rachael Zemek
Wee Loong CHIN
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University of Western Australia
Telethon Kids Institute TVW
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University of Western Australia
Telethon Kids Institute TVW
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P35/00—Antineoplastic agents
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K16/00—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies
    • C07K16/18—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans
    • C07K16/24—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against cytokines, lymphokines or interferons
    • C07K16/249—Interferons
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K16/00—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies
    • C07K16/18—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans
    • C07K16/28—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
    • C07K16/2803—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily
    • C07K16/2818—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily against CD28 or CD152
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K16/00—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies
    • C07K16/18—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans
    • C07K16/28—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
    • C07K16/2803—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily
    • C07K16/2827—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against the immunoglobulin superfamily against B7 molecules, e.g. CD80, CD86
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K16/00—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies
    • C07K16/18—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans
    • C07K16/28—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
    • C07K16/2866—Immunoglobulins [IG], e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants against receptors for cytokines, lymphokines, interferons
    • 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
    • A61K2039/505—Medicinal preparations containing antigens or antibodies comprising antibodies
    • 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
    • A61K2039/505—Medicinal preparations containing antigens or antibodies comprising antibodies
    • A61K2039/507—Comprising a combination of two or more separate antibodies
    • 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
    • A61K2039/545—Medicinal preparations containing antigens or antibodies characterised by the dose, timing or administration schedule
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2310/00—Structure or type of the nucleic acid
    • C12N2310/10—Type of nucleic acid
    • C12N2310/17—Immunomodulatory nucleic acids

Definitions

  • the present invention relates to methods for treating cancer, particularly increasing the response to immune checkpoint modulation in individuals with cancer.
  • immune checkpoint modulation i.e. blockade (ICB) of suppressive immune checkpoints or activation of stimulatory immune checkpoints
  • ICB blockade
  • the response to immune checkpoint modulation, i.e. blockade (ICB) of suppressive immune checkpoints or activation of stimulatory immune checkpoints, in cancer is highly variable, with a majority of patients experiencing disease progression.
  • targets of ICB antibodies are known, the downstream therapeutic effector mechanisms are incompletely understood.
  • the development of novel combination therapies is therefore mainly empiric. Specific aspects of the pre-treatment tumour microenvironment, such as PD-L1 expression, immune cell infiltration or tumour mutation burden have been shown to correlate with response, but none of these biomarkers are sufficiently robust to guide clinical decisions regarding treatment across cancers. When perturbing complex systems, some important effects may only become apparent over time.
  • the present invention provides a method of increasing the response in a subject to an immune checkpoint modulator comprising: administering an immune checkpoint modulator; subsequently to administering the immune checkpoint modulator, administering an interferon b (IRNb) inhibitor, thereby increasing the response in the subject to an immune checkpoint modulator.
  • IRNb interferon b
  • the present invention provides a method of treating, preventing or minimising progression of cancer in a subject comprising: administering an immune checkpoint modulator; subsequently to administering the immune checkpoint modulator, administering an interferon b (IRNb) inhibitor, thereby treating, preventing or minimising progression of cancer in the subject.
  • the present invention provides a method of treating, preventing or minimising progression of cancer in a subject who has received, or who is receiving, an immune checkpoint modulator, the method comprising: administering an interferon b (IRNb) inhibitor, thereby treating, preventing or minimising progression of cancer in the subject.
  • the subject who has received, or who is receiving, an immune checkpoint modulator may have exhibited, or be exhibiting, a clinically relevant response to that immune checkpoint modulator.
  • an immune checkpoint modulator may be resistant or not exhibiting a clinically relevant response to that immune checkpoint modulator.
  • the present invention provides a method of treating, preventing or minimising progression of cancer in a subject comprising the steps of:
  • the present invention provides a method of treating, preventing or minimising progression of cancer in a subject comprising the steps of:
  • the present invention provides a method of treating, preventing or minimising progression of cancer in a subject comprising the steps of:
  • the IRNb inhibitor may be administered in a composition.
  • the composition does not include an inhibitor of IFNa, an inhibitor of a type II IFN, or an inhibitor of any other IFN.
  • the only IFN inhibitor included in the composition is an IRNb inhibitor.
  • the only active ingredient in the composition is the IRNb inhibitor.
  • the immune checkpoint modulator may be an immune checkpoint inhibitor, i.e. an inhibitor of a suppressive (or negative) immune checkpoint, or an immune checkpoint stimulator or activator, i.e. a stimulator or activator of a stimulatory (or positive) immune checkpoint.
  • an immune checkpoint modulator herein may therefore also refer to an immune checkpoint inhibitor and/or an immune checkpoint stimulator or activator.
  • the immune checkpoint modulator may be administered in a composition.
  • the composition further comprises a pharmaceutically acceptable carrier, diluent or excipient.
  • the composition may be formulated for intravenous administration to the subject. In other words, the composition is suitable for administration intravenously.
  • the IRNb inhibitor is administered at least about 1 day after an immune checkpoint modulator.
  • the IRNb inhibitor is administered not more than about 2 or about 3 months after administration of an immune checkpoint modulator.
  • the IRNb inhibitor is administered at a time at least about 1 day after an immune checkpoint modulator but before about 2 or about 3 months after administration of an immune checkpoint modulator.
  • the present invention further provides a composition
  • a composition comprising, consisting essentially of or consisting of an IRNb inhibitor and an immune checkpoint modulator, and a pharmaceutically acceptable carrier, diluent or excipient.
  • the composition is constructed such that in use, or when used, the IRNb inhibitor is administered after the immune checkpoint modulator.
  • the present invention further provides a method of increasing survival of a subject having cancer comprising administering a therapeutically effective amount of an IRNb inhibitor and an immune checkpoint modulator to a subject, thereby increasing survival of the subject having cancer, wherein the IRNb inhibitor is administered after the immune checkpoint modulator.
  • the present invention further provides a method of minimising, reducing or preventing growth of a tumour in a subject having cancer comprising administering a therapeutically effective amount of an IRNb inhibitor and an immune checkpoint modulator to a subject, thereby minimising, reducing or preventing growth of a tumour in the subject having cancer, wherein the IRNb inhibitor is administered after the immune checkpoint modulator.
  • the present invention further provides a method of minimising, reducing or preventing metastasis in a subject having cancer comprising administering of a therapeutically effective amount of an IRNb inhibitor and an immune checkpoint modulator to a subject, thereby minimising, reducing or preventing metastasis in the subject having cancer, wherein the IRNb inhibitor is administered after the immune checkpoint modulator.
  • the invention further provides a method of minimising, reducing or preventing cancer in a subject comprising:
  • the invention further provides a method of minimising, reducing or preventing metastasis in a subject having cancer comprising: - identifying a subject having a primary tumour at risk of, or capable, of metastasising;
  • the present invention further provides a method of minimising, reducing or preventing growth of a tumour in at least one site distant from the site of the primary tumour in a subject comprising administering a therapeutically effective amount of an IRNb inhibitor and an immune checkpoint modulator to a subject, thereby minimising, reducing or preventing minimising, reducing or preventing growth of a tumour in at least one site distant from the site of the primary tumour in the subject.
  • the methods described herein further comprise identifying a subject having cancer.
  • the cancer may be pre- cancerous or non-metastatic.
  • the cancer may be malignant or metastatic.
  • the present invention further provides use of a compound comprising, consisting or consisting essentially of an IRNb inhibitor and an immune checkpoint modulator in the preparation of a medicament for treating, preventing or minimising progression of cancer in a subject.
  • a compound comprising, consisting or consisting essentially of an IRNb inhibitor and an immune checkpoint modulator in the preparation of a medicament for treating, preventing or minimising progression of cancer in a subject.
  • the medicament constructed such that in use, or when used, the IRNb inhibitor is administered after the immune checkpoint modulator.
  • the present invention further provides use of a compound comprising, consisting or consisting essentially of an IRNb inhibitor in the manufacture of a first medicament, and an immune checkpoint modulator in the preparation of a second medicament, wherein the first and second medicaments are for: treating, preventing or minimising progression of cancer in a subject, minimising, reducing or preventing growth of a tumour in a subject,
  • the first and second medicaments are for any other method or use of the invention as described herein.
  • the present invention further provides use of an IRNb inhibitor and an immune checkpoint modulator for treating, preventing, or preventing progression of cancer in a subject.
  • the present invention further provides an IRNb inhibitor and an immune checkpoint modulator for use in treating, preventing, or preventing progression of cancer in a subject.
  • the IRNb inhibitor and the immune checkpoint modulator is for use in any other method or use of the invention as described herein.
  • the present invention further provides the use of an IRNb inhibitor in the manufacture of a medicament for:
  • the present invention further provides an IRNb inhibitor for use in treating, preventing or minimising progression of cancer in a subject who has received or who is receiving an immune checkpoint modulator.
  • the compound is for use in any other method or use of the invention as described herein.
  • the immune checkpoint inhibitor may be a PD-1, PD-L1 or a CTLA-4 checkpoint inhibitor, or any other inhibitor described herein.
  • the checkpoint inhibitor is an antibody.
  • the immune checkpoint stimulator or activator is any one described herein.
  • the method or use further provides administering an IRNb agonist prior to administering the immune checkpoint modulator.
  • the method or use does not include administering an inhibitor of IFNa, an inhibitor of a type II IFN, or an inhibitor of any other IFN.
  • the only IFN inhibitor administered is an IRNb inhibitor.
  • the only inhibitor administered after an immune checkpoint modulator is an IRNb inhibitor.
  • the IRNb inhibitor may be any as described or defined herein including inhibitors that bind to IRNb or IFNAR, preferably IFNAR1, and inhibiting the interaction between IRNb and IFNAR, preferably IFNAR1.
  • the IRNb inhibitor and/or the immune checkpoint modulator may be administered by any known administration routes in the art including intraperitoneally, intratumorally, topically, orally, intravenously, subcutaneously or intramuscularly.
  • an IRNb inhibitor and/ or immune checkpoint modulator are administered intravenously.
  • the cancer is selected from the group consisting of breast cancer, colorectal cancer, adenocarcinomas, mesothelioma, bladder cancer, prostate cancer, germ cell cancer, hepatoma/cholongio carcinoma, neuroendocrine cancer, pituitary neoplasm, small round cell tumour, squamous cell cancer, melanoma, atypical fibroxanthoma, seminomas, nonseminomas, stromal leydig cell tumours, Sertoli cell tumours, skin tumours, kidney tumours, testicular tumours, brain tumours, ovarian tumours, stomach tumours, oral tumours, bladder tumours, bone tumours, cervical tumours, esophageal tumours, laryngeal tumours, liver tumours, lung tumours, vaginal tumours, Wilm's tumours, pancreatic tumours, sarcomas, lymphomas or leukaemias..
  • the cancer or tumour may be any one described herein.
  • the cancer is mesothelioma or renal cell cancer.
  • the term “comprise” and variations of the term, such as “comprising”, “comprises” and “comprised”, are not intended to exclude further additives, components, integers or steps.
  • Figure 2 Network construction approach from bulk RNA-seq data in AB1 and Renca.
  • the inventors constructed two “direct-interaction networks”, one for AB1 and one for Renca using all responder samples for each strain across four experimental timepoints.
  • the output from GENIE3 is a weighted, complete graph composed of all genes in the input count matrix, so we pruned this graph to isolate biologically relevant edges.
  • the inventors integrated information from differential expression analysis and transcription factor binding site prediction results from JASPAR.
  • the inventors identified and retained “direct connections”, defined as connections between a transcription factor (TF) to differentially expressed genes only if the transcription factor binding site (TFBS) for the TF was situated in a genomic window 400 base pairs upstream or 300 basepairs downstream of a DE gene’s transcription start site (TSS).
  • TSS transcription start site
  • the bottom left diagram describes the pruning schema for these networks - as illustrated, the inventors eliminate the GENIE3 edge from TF2 to differentially expressed gene 3 because gene 3 does not possess the transcription factor binding site for TF2.
  • the right diagram demonstrates threshold selection after considering the union set of all GENIE3 weights from Renca and AB1 direct networks (red line - 0.0003125).
  • FIG. 3 An IFN module displaying on/fast-off kinetics is associated with response to ICB.
  • a GENIE3 subnetwork of direct interactions between TFs and their target genes in AB1 and b, in Renca, separated into responders and non-responders, depicting gene expression over time, with fast-off IFN genes highlighted within the boxes
  • c Top 10 TFs with highest GENIE3 scores between ISGs for AB1 and Renca responders (left sub-panel), with average expression for ISGs in responders across time points (right sub-panel).
  • FIG. 5 Poly(l:C) induces IFNp production in Ly6C+ monocytes in AB1 tumours.
  • Tumours from AB1 bearing mice that were treated with poly(l:C) or untreated were harvested on day 6, dissociated, stained with antibodies and FACS sorted.
  • RNA from each population was extracted for qPCR analysis for IRNb compared to GAPDH. a, IFNB expression in untreated compared to poly(l:C) treated tumours b, IFNp expression within the CD11b + Ly6C + monocyte population in untreated compared to poly(l:C) treated tumours.
  • Figure 6 Targeting IFNp in a directionally opposite, time-dependent manner improves the response to ICB.
  • a Treatment strategy
  • c,d Deconvolution analysis on bulk RNA seq data from AB1 (c) and Renca (d), using an IFNp-stimulated T cell signature between responders (red) and non-responders (blue). Bars represent standard deviation. *p £ 0.05, **p£
  • Figure 7 Therapeutically phenocopying on/off type I IFN kinetics with poly(l:C) and anti-IFNAR improves response in AB1 tumours.
  • Mice bearing AB1 tumours were treated with poly(l:C) (day 12, 13, 14), followed by CPB (day 17), followed by an antibody against IFNAR1, IFNy, or both (day 20, 23, 26).
  • FIG. 8 Blocking type I IFN after ICB improves responses.
  • pre-treatment with poly(l:C) was omitted from the schedule a
  • AB1 tumours treated with ICB followed by anti-IFNAR or anti-IFNB showed a decrease in tumour size and delay in growth when complete response was not achieved.
  • Growth curves were analysed longitudinally using type II ANOVA and pairwise comparisons across groups with Bonferroni correction for multiple comparisons (lower table) b, Blocking anti-IFNAR, but not IFNy, after ICB significantly improved response in the AE17 tumour model c, Blocking IBNb, but not IFNa, after ICB significantly improved response in the Renca tumour model.
  • Figure 9 Time-dependent scheduling of type I IFN inhibition in the context of ICB.
  • a Schedule of pre-treatment anti-IFNAR followed by ICB.
  • b Dosing anti-IFNAR before ICB, rather than after, negates the effect of ICB on AE17.
  • c Dosing anti-IFNAR concurrently with poly(l:C) (orange line) negates the priming effect of poly(l:C); scheduling IFNAR blockade prior to ICB followed by poly (l:C) (red line) provides no improvement to ICB.
  • d Dosing qh ⁇ -IBNb concurrently with ICB, does not result in improved efficacy (orange versus blue line), while dosing 3 days after start of ICB does (green line).
  • FIG. 10 Patients treated with ICB display early enrichment of the on/fast- off IFN signature, a, Gene set enrichment analysis using our dynamic interferon gene- set in glioblastoma patients within three weeks of treatment with PD-1 blockade versus controls.
  • the dynamic interferon signal demonstrates higher normalized enrichment compared to hallmark gene sets for interferon alpha or interferon gamma gene sets b, On/fast-off IFN-related TF activation across human peripheral blood monocytes, separated by treatment timepoint and response to ICB (C1, C3, C5; cycle of ICB, EOT; end of treatment) c-d, Comparison of human peripheral blood monocytes to Ly6chi AB1 monocytes c, Distribution of predicted labels from mouse AB1 Ly6chi monocytes onto human peripheral blood single cell data, corresponding to human monocytes d, Human monocytes demonstrate differential capacity for dynamic ISG activation.
  • FIG. 11 Monocytes in breast cancer patients with expanded T cell following treatment with anti-PD1 display on/fast-off IFN transcriptional activity a, results of SCENIC network analysis showing distribution of IFN related transcription factor activation across cell subsets in the breast cancer dataset based on singleR labels b, Results of ssGSEA showing distribution of IFN-related gene set scores across cell subsets in the breast cancer dataset based on singleR labels c, Results of differential expression analysis comparing pre- versus on-treatment monocytes showing early downregulation of fast-off signal in patients displaying T-cell expansion in response to anti-PD1.
  • the terms “about” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Typical, exemplary degrees of error are within 20 percent (%), preferably within 10%, and more preferably within 5% of a given value or range of values. Alternatively, and particularly in biological systems, the terms “about” and “approximately” may mean values that are within an order of magnitude, preferably within 5-fold and more preferably within 2-fold of a given value. Numerical quantities given herein are approximate unless stated otherwise, meaning that the term “about” or “approximately” can be inferred when not expressly stated.
  • tumour cells are more or less tolerated by the patient's own immune system, as they are the patient's own cells (e.g., they are self) and are not effectively recognised by the patient’s immune system, allowing the tumour cells to grow and divide without proper regulatory control. Accordingly, the patient’s own immune system requires stimulation to attack the cancer cells.
  • Cancer immunotherapy involves the utilisation of the immune system of a cancer patient to reject the cancer by stimulating the patient's immune system. In turn, the activated immune system attacks the cancer cells, sparing the normal cells of the patient.
  • a so called immunotherapy that has been shown to be useful for the treatment of cancer is the use of checkpoint modulators.
  • PD-1 blockade alone has been shown to be ineffective in subsets of patients in some types of cancer such as melanoma and large B cell lymphoma. There is therefore a need for more reliable and efficacious immunotherapy regimes that have utility in the treatment of cancer.
  • the present inventors have unexpectedly found that when an IRNb inhibitor is administered subsequent to (i.e. after) administration of an immune checkpoint modulator, a greater positive response to the immunotherapy is observed. This response includes a greater rate of response and depth of response. Further, this advantage is observed in tumours both intrinsically responsive and non-responsive to immune checkpoint inhibition.
  • the inventors have shown this remarkable effect in three different models of cancer of differing aetiology, pathogeneses and sensitivity to immune checkpoint blockade. A skilled person would therefore understand the applicability of the invention to any of the other cancers described herein. Further, the inventors have confirmed these effects are independent of immune checkpoint target as the beneficial effect was observed with inhibitors against PD-L1 and CTLA-4.
  • immune checkpoint modulator includes inhibitors of suppressive (or negative) immune checkpoints and agonists or activators of stimulatory (or positive) immune checkpoints.
  • an “immune checkpoint inhibitor” refers to any molecule that directly or indirectly inhibits, partially or completely, a suppressive or negative immune checkpoint pathway.
  • an “immune checkpoint stimulator” or “immune checkpoint activator” refers to any molecule that directly or indirectly agonises, promotes or stimulates, partially or completely, a stimulatory or positive immune checkpoint pathway.
  • immune checkpoint pathways function to turn on or off aspects of the immune system, particularly T cells, but also for instance myeloid cells, NK cells and B cells.
  • T cells a number of inhibitory receptors can be upregulated and present on the surface of the T cell in order to suppress the immune response at the appropriate time.
  • immune checkpoint pathways include, without limitation, PD-1/PD-L1 , CTLA4/B7-1 , TIM-3, LAG3, By-He, H4, HAVCR2, ID01, CD276 and VTCN1, B7-H3, B7-H4, CD47, or KIR. Immune checkpoints and modulators thereof as well as methods of using such compounds are described in the literature.
  • immune checkpoint inhibitors or modulators include fully human monoclonal antibodies, such as BMS-936558/MDX-1106, BMS- 936559/MDX-1 105, ipilimumab/Yervoy, tremelimumab, BMS-986016, Durvalumab, MEDI4736, Urelumab, CDX-1127, and Avelumab; humanized antibodies, such as CT-011 , IV1K-3475, Hu5F9- G4, CC-90002, MBG453, TSR-022, and Atezolizumab; and fusion proteins, such as AMP-224 and TTI-621 , and others.
  • immune checkpoint modulators include antibodies directed against e.g. CD40, 0X40, GITR, CD137 (4-1 BB), CD27, ICOS, and TRAIL.
  • Non- limiting examples of agonistic immune checkpoint modulators are those that exert an agonist function in the sense that they are capable of stimulating or reinforcing stimulatory or positive immune checkpoint pathways or signals, for example those mediated by CD28, ICOS, CD137 (or 4-1 BB), 0X40, CD27, CD40 and GITR immune checkpoints.
  • an immune checkpoint modulator, stimulator or activator may also be used, except in those cases where it is apparent from the context of the wording that this is not the case.
  • checkpoint inhibitors regulate the immune system by blocking proteins that stop the immune system from attacking cancer cells. In particular, they control how detection-evading cancer cells and T-cells interact so that T-cells can recognize tumour cells and mount an appropriate immune response against them.
  • Non limiting examples of checkpoint inhibitors that may be used in accordance with the methods described herein include inhibitors that target PD-1 (programmed cell death protein 1), CTLA-4 (cytotoxic T lymphocyte associated protein 4) and PD-L1 (programmed death ligand 1).
  • CTLA-4 and PD-1 are found on T cells and that PD-L1 is expressed on cancer cells.
  • Non-limiting examples include PD-L2, TIM3, LAG3, CEACAM (e.g., CEACAM-1, CEACAM-3 and/or CEACAM-5), VISTA, BTLA, TIGIT, LAIR1, CD160, 2B4, CD80, CD86, B7-H3 (CD276), B7-H4 (VTCN1), HVEM (TNFRSF14 or CD107), KIR, A2aR, SIGLEC7, NOX2, MHC class I, MHC class II, GAL9, adenosine, and TGF-beta.
  • CEACAM e.g., CEACAM-1, CEACAM-3 and/or CEACAM-5
  • VISTA e.g., CEACAM-1, CEACAM-3 and/or CEACAM-5
  • VISTA e.g., CEACAM-1, CEACAM-3 and/or CEACAM-5
  • VISTA e.g., CEACAM-1, CEACAM-3 and/or CEACAM-5
  • immune checkpoints include Indoleamine 2,3-dioxygenase (IDO) and CSF- R1. Inhibitors of those proteins are also contemplated as immune checkpoint inhibitors for use in the invention.
  • IDO Indoleamine 2,3-dioxygenase
  • CSF- R1 CSF- R1. Inhibitors of those proteins are also contemplated as immune checkpoint inhibitors for use in the invention.
  • PD-1 Protein Determination-1
  • PD-1 Protein Deformation-1
  • PD-1 is expressed predominantly on activated T cells in vivo, and binds to two ligands, PD-L1 and PD-L2.
  • the term “PD-1” as used herein includes human PD-1 (hPD-1), variants, isoforms, and species homologs of hPD-1, and analogs having at least one common epitope with hPD-1. The complete hPD-1 sequence can be found under GenBank Accession No. U64863.
  • Programmed cell death ligand 1 Upon binding of PD-1 to programmed cell death ligand 1 (PD-L1), an immune reaction is turned off so as to prevent T-cells from damaging or killing the cell.
  • cancer cells can be covered with PD-L1 proteins to camouflage themselves as healthy cells thus avoiding an immune response.
  • Programmed Death Ligand-1 is one of two cell surface glycoprotein ligands for PD-1 (the other being PD-L2) that downregulate T cell activation and cytokine secretion upon binding to PD-1.
  • PD-L1 as used herein includes human PD-L1 (HPD-L1), variants, isoforms, and species homologs of hPD-L1, and analogs having at least one common epitope with hPD-L1.
  • HPD-L1 human PD-L1
  • variants variants
  • isoforms and species homologs of hPD-L1, and analogs having at least one common epitope with hPD-L1.
  • the complete hPD-L1 sequence can be found under GenBank Accession No. Q9NZQ7.
  • CTLA-4 Cytotoxic T-Lymphocyte Antigen-4 refers to an immunoinhibitory receptor belonging to the CD28 family. CTLA-4 is expressed exclusively on T cells in vivo, and binds to two ligands, CD80 and CD86 (also called B7-1 and B7-2, respectively).
  • CTLA-4 as used herein includes human CTLA-4 (hCTLA-4), variants, isoforms, and species homologs of hCTLA-4, and analogs having at least one common epitope with hCTLA-4.
  • the complete hCTLA-4 sequence can be found under GenBank Accession No. AAB59385.
  • the one or more immune checkpoint modulator(s) may independently be a polypeptide or a polypeptide- encoding nucleic acid molecule; said polypeptide comprising a domain capable of binding the targeted immune checkpoint and/or inhibiting the binding of a ligand to said targeted immune checkpoint so as to exert an antagonist function (i.e. being capable of antagonizing an immune checkpoint-mediated inhibitory signal) or an agonist function (i.e. being capable of boosting an immune checkpoint-mediated stimulatory signal).
  • an antagonist function i.e. being capable of antagonizing an immune checkpoint-mediated inhibitory signal
  • an agonist function i.e. being capable of boosting an immune checkpoint-mediated stimulatory signal.
  • Such one or more immune checkpoint modulator(s) can be independently selected from the group consisting of peptides (e.g.
  • the immune checkpoint modulator is an antibody.
  • the immune check modulator antibody is used in the broadest sense and encompasses e.g. naturally occurring and engineered by man as well as full length antibodies or functional fragments or analogs thereof that are capable of binding the target immune checkpoint or epitope (thus retaining the target-binding portion). It can be of any origin, e.g. human, humanized, animal (e.g. rodent or camelid antibody) or chimeric. It may be of any isotype with a specific preference for an IgGI or lgG4 isotype.
  • antibody also includes bispecific or multi- specific antibodies so long as they exhibit the binding specificity described herein. Standard assays to evaluate the binding ability of the antibodies toward immune checkpoints are known in the art, including for example, ELISAs, Western blots, RIAs and flow cytometry. The binding kinetics (e.g., binding affinity) of the antibodies also can be assessed by standard assays known in the art, such as by Biacore analysis.
  • an "antibody” shall include, without limitation, a glycoprotein immunoglobulin which binds specifically to an antigen and comprises at least two heavy (H) chains and two light (L) chains interconnected by disulfide bonds, or an antigen binding portion thereof.
  • Each H chain comprises a heavy chain variable region (abbreviated herein as VH) and a heavy chain constant region.
  • the heavy chain constant region comprises three constant domains, CH1, CH2 and CH3.
  • Each light chain comprises a light chain variable region (abbreviated herein as VL) and a light chain constant region.
  • the light chain constant region is comprises one constant domain, CL.
  • VH and VL regions can be further subdivided into regions of hypervariability, termed complementarity determining regions (CDRs), interspersed with regions that are more conserved, termed framework regions (FR).
  • CDRs complementarity determining regions
  • FR framework regions
  • Each VH and VL comprises three CDRs and four FRs, arranged from amino-terminus to carboxy- terminus in the following order: FRI, CDR1, FR2, CDR2, FR3, CDR3, FR4.
  • the variable regions of the heavy and light chains contain a binding domain that interacts with an antigen.
  • the constant regions of the Abs can mediate the binding of the immunoglobulin to host tissues or factors, including various cells of the immune system (e.g., effector cells) and the first component (Clq) of the classical complement system.
  • antibody includes, by way of example monoclonal and polyclonal Abs; chimeric and humanized Abs; human or nonhuman Abs; wholly synthetic Abs; and single chain Abs.
  • a nonhuman Ab can be humanized by recombinant methods to reduce its immunogenicity in humans.
  • the term "antibody” also includes an antigen-binding fragment or an antigen-binding portion of any of the aforementioned immunoglobulins, and includes a monovalent and a divalent fragment or portion, and a single chain Ab.
  • an “isolated antibody” refers to an Ab that is substantially free of other Abs having different antigenic specificities (e.g., an isolated Ab that binds specifically to PD- 1 is substantially free of Abs that bind specifically to antigens other than PD-1).
  • An isolated Ab that binds specifically to PD-1 can, however, have cross- reactivity to other antigens, such as PD-1 molecules from different species.
  • an isolated Ab can be substantially free of other cellular material and/or chemicals.
  • the term “monoclonal antibody” (mAb) refers to a non-naturally occurring preparation of Ab molecules of single molecular composition, i.e.
  • mAb Ab molecules whose primary sequences are essentially identical, and which exhibits a single binding specificity and affinity for a particular epitope.
  • a mAb is an example of an isolated Ab.
  • mAbs can be produced by hybridoma, recombinant, transgenic or other techniques known to those skilled in the art.
  • a “human” antibody refers to an Ab having variable regions in which both the framework and CDR regions are derived from human germline immune globulin sequences. Furthermore, if the Ab contains a constant region, the constant region also is derived from human germline immunoglobulin sequences.
  • the human Abs of the invention can include amino acid residues not encoded by human germline immunoglobulin sequences (e.g., mutations introduced by random or site - specific mutagenesis in vitro or by somatic mutation in vivo).
  • the term “human antibody,” as used herein is not intended to include Abs in which CDR sequences derived from the germline of another mammalian species, such as a mouse, have been grafted onto human framework sequences.
  • a “humanized antibody” refers to an Ab in which some, most or all of the amino acids outside the CDR domains of a non-human Ab are replaced with corresponding amino acids derived from human immunoglobulins. In one embodiment of a humanized form of an Ab, some, most or all of the amino acids outside the CDR domains have been replaced with amino acids from human immunoglobulins, whereas some, most or all amino acids within one or more CDR regions are unchanged. Small additions, deletions, insertions, substitutions or modifications of amino acids are permissible as long as they do not abrogate the ability of the Ab to bind to a particular antigen.
  • a “humanized” Ab retains an antigenic specificity similar to that of the original Ab.
  • a “chimeric antibody” refers to an Ab in which the variable regions are derived from one species and the constant regions are derived from another species, such as an Ab in which the variable regions are derived from a mouse Ab and the constant regions are derived from a human Ab.
  • an “anti-antigen” Ab refers to an Ab that binds specifically to the antigen.
  • an anti-PD-1 Ab binds specifically to PD-1 and an anti-CTLA-4 Ab binds specifically to CTLA-4.
  • an “antigen-binding portion” of an Ab refers to one or more fragments of an Ab that retain the ability to bind specifically to the antigen bound by the whole Ab.
  • immune checkpoints and antibody inhibitors examples include anti-CTLA-4 (e.g., Ipilimumab, Tremelimumab, KAHR-102), anti- TIM3 (e.g., F38-2E2. ENUM005), anti-LAG3 (e.g., BMS-986016, IMP701. IMP321, C9B7W), anti-KIR (e.g., Lirilumab, IPH2101, IPH4102), anti-PD-1 (e.g., Nivolumab, Pidilizumab, Pembrolizumab, BMS-936559, atezolizumab, Lambrolizumab, MK-3475.
  • anti-CD73 e.g., AR-42 (OSU-HDAC42, HDAC-42, AR42, AR 42, OSU-HDAC 42, OSU-HDAC-42, NSC D736012, HDAC-42, HD AC 42, HDAC42, NSCD736012, NSC-D736012), MEDI- 9447), anti-B7-H3 (e.g., MGA271, DS-5573a, 8H9), anti-CD47 (e.g., CC-90002, TTI- 621, VLST-007), anti-BTLA, anti-VISTA, anti-A2aR, anti-B7-1, anti-B7-H4, anti-CD52 (such as alemtuzumab), anti-IL-10, anti-IL-35, anti-TGF-b (such as Fresolumimab), anti- C
  • Anti-PD-1 inhibitors and anti-PD-L1 inhibitors are examples of Anti-PD-1 inhibitors and anti-PD-L1 inhibitors.
  • Suitable PD-1 inhibitors include Keytruda (pembrolizumab), Opdivo (nivolumab), AGEN 2034, BGB- A317, BI-754091, CBT-501 (genolimzumab), MEDI0680, MGA012, PDR001, PF- 06801591, REGN2810 (SAR439684), and TSR-042 or those that are disclosed in US Pat. No. 8,008,449.
  • Other anti-PD-1 mAbs have been described in, for example, US Pat. Nos. 6,808,710, 7,488,802, 8,168,757 and 8,354,509, and PCT Publication No. WO 2012/145493.
  • Nivolumab (also known as “Opdivo®”; formerly designated 5C4, BMS-936558, MDX - 1106, or 0N04538) is a fully human lgG4 (S228P) PD-1 immune check point inhibitor Ab that selectively prevents interaction with PD-1 ligands (PD-L1 and PD-L2), thereby blocking the down-regulation of antitumor T-cell functions (U.S. Pat. No. 8,008,449).
  • Pembrolizumab (also known as “Keytruda®”, lambrolizumab, and MK-3475) is a humanized monoclonal lgG4 antibody directed against human cell surface receptor PD- 1 (programmed death-1 or programmed cell death-1). Pembrolizumab is described for example, in U.S. Pat. Nos. 8,354,509 and 8,900,587). Pembrolizumab has been approved by the FDA for the treatment of relapsed or refractory melanoma.
  • Suitable PD-1 inhibitors include Libtayo (cemiplimab), Blincyto (blinatumomab), Dostarlimab, Spartalizumab, Cetrelimab, Pidilizumab and BI-754091.
  • Anti-PD-1 Abs suitable for use in the disclosed methods or compositions are Abs that bind to PD-1 with high specificity and affinity, block the binding of PD-L1 and or PD- L2 , and inhibit the immunosuppressive effect of the PD-1 signalling pathway.
  • an anti-PD-1 antibody includes an antigen-binding portion or fragment that binds to the PD-1 receptor and exhibits the functional properties similar to those of whole Abs in inhibiting ligand binding and upregulating the immune system.
  • an anti-PD-1 antibody used in the methods can be replaced with another PD-1 or anti-PD-L1 antagonist.
  • an anti-PD-L1 antibody prevents interaction between PD-1 and PD-L1, thereby exerting similar effects to the signaling pathway of PD-1
  • an anti-PD-L1 antibody can replace the use of an anti-PD-1 antibody in the methods disclosed herein.
  • suitable PD-L1 inhibitors include Imfinzi (durvalumab or MEDI4736), Tecentriq (atezolizumab or MPDL3280A), Bavencio (avelumab; MSB0010718C), MS-936559 (12A4 or MDX-1105) and CX-072.
  • Anti-CTLA-4 antibodies for use in accordance with the instant invention bind to human CTLA-4 so as to disrupt the interaction of CTLA-4 with a human B7 receptor. It will be understood that because the interaction of CTLA-4 with B7 transduces a signal leading to inactivation of T cells bearing the CTLA-4 receptor, disruption of the interaction effectively induces, enhances or prolongs the activation of such T cells, thereby inducing, enhancing or prolonging an immune response.
  • Suitable CTLA-4 inhibitors that may be used in accordance with the invention include Yervoy (ipilimumab), Tremelimumab and AGEN 1884 or those disclosed in U.S. Pat. Nos. 6,984,720 and 7,605,238.
  • Ipilimumab is a fully human, lgG1 monoclonal Ab that blocks the binding of CTLA-4 to its B7 ligands, thereby stimulating T cell activation.
  • Tremelimumab is human lgG2 monoclonal anti-CTLA-4 antibody.
  • Another is Blincyto (blinatumomab) which is a Bispecific CD19-directed CD3 T cell engager.
  • an “interferon (IRN)-b inhibitor” or “interferon (IRN)-b antagonist” includes refers to any molecule that reduces or inhibits the activity of, level of or expression of IRNb, for example interferon (IRN)-b signalling and function and/or interferon IRNb production.
  • the molecule may be DNA, RNA (siRNA, antisense molecules, an sgRNA molecule for use in a CRISPR/Cas9 or related system), peptide, protein (including fusion protein), antibody or small molecules.
  • interferon (IRN)-b inhibitors examples include (i) humanized or human anti-IFN-b whole antibodies or antibody fragments (Fab or scFv), antagonizing secreted IFN-b, (ii) short interfering (si) RNA or antisense oligonucleotides inhibiting IFN-b production, (iii) qh ⁇ -IRNb-Gbobr ⁇ qG antibodies, mutant IFN ⁇ /Fc fusion proteins, IFN-b receptor fusion proteins, or small molecules interfering with IFN-b signalling.
  • IRN interferon
  • the interferon IRNb inhibitor does not significantly reduce or inhibit interferon (IFN)-a signalling and function and/or interferon (IFN)-a production.
  • the interferon (IRN)-b inhibitor does not significantly reduce or inhibit signalling, function and/or production of one, or all type II IFNs.
  • the only IFN the IRNb inhibitor inhibits or reduces the signalling, function or production of, is of IRNb.
  • the inhibitor reduces the binding of IRNb to its receptor, IFNAR.
  • the inhibitor may therefore bind to IRNb, IFNAR (particularly IFNAR1) or both IRNb and IFNAR (particularly IFNAR1).
  • IRNb inhibitors include PF-06823859 and Anifrolumab.
  • an IRNb antagonist or inhibitor may inhibit the signalling of IRNb and/or inhibit or reduce the expression or activity or level of IRNb by at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or more.
  • an "interferon (IRN)-b agonist” includes a molecule that increases the activity of, level of, or expression of IFN-b.
  • An IFN-b agonist also includes a “IFN-b receptor agonist” which binds to IFN-alpha/beta receptor (IFNAR), subunits IFNAR-1 or IFNAR-2, and which elicits a response typical of IFN-b.
  • An exemplary response includes any one or more of the functions of the IFNAR, particularly those described herein.
  • the IFN-b receptor agonist comprises, consists essentially of or consists of a polypeptide.
  • a fragment of interferon beta is preferably a fragment that binds to and activates an interferon beta receptor.
  • the fragment of interferon beta binds to and activates the same receptors as full-length interferon beta.
  • the fragment of the interferon beta binds to IFNAR present on the surface of a cell, preferably an immune cell, resulting in phosphorylation of one or more tyrosine residues on an IFNAR.
  • IFN-b agonists include, for example, MEM-288, the IFN-b polypeptide, IFN-bI a, e.g., Avonex® (Biogen, Inc.), Rebif® (Serono, SA), SNG001 (Synairgen); IFN-bI b (Betaseron®; Betaferon ®; Bayer); Extavia (Novartis) CinnoVex ® and the like (including pegylated forms of I FN-bI a, Plegridy ® and IFN- b1 ⁇ BBT-032). Mammalian IFN-b sequences such as human (Gray and Goeddel (1982).
  • IFN- b receptor agonist is an IFN-b receptor agonist antibody (eg anti-IFN anti-idotypic antibody (Osheroff et al. (1985). J Immunol, 135:306).
  • Non-limiting examples include Poly(l:C), or variations thereof, such as poly-ICLC (Hiltonol® from Oncovir), poly(A:U) (Innate Pharma), Rintatolimod (AIM ImmunoTech), STING agonists, including: ADU-S100 (Aduro), MK-1454 (Merck), MAVU-104 (Abbvie), BMS-986301 (BMS), GSK532 (GSK), RIG-I agonists, including: MK-4621 (Merck), KIN131A (Kineta), inarigivir (Spring bank).
  • poly-ICLC Hiltonol® from Oncovir
  • poly(A:U) Innate Pharma
  • Rintatolimod AIM ImmunoTech
  • STING agonists including: ADU-S100 (Aduro), MK-1454 (Merck), MAVU-104 (Abbvie), BMS-986301 (BMS), GSK532 (GSK), RIG-I agonists, including: MK-46
  • therapeutically effective amounts of an immune checkpoint modulator and IRNb inhibitor are administered to the subject.
  • Administering refers to the physical introduction of a composition comprising a therapeutic agent to a subject, using any of the various methods and delivery systems known to those skilled in the art including those described herein.
  • Pharmaceutical compositions may be formulated from compounds of the invention as described herein for any appropriate route of administration.
  • a pharmaceutical composition in addition to the therapeutic agent (eg an immune checkpoint modulator and/or IRNb inhibitor, a pharmaceutical composition comprises a pharmaceutically acceptable excipient, carrier and/or diluent.
  • a pharmaceutically acceptable excipient, carrier and/or diluent examples of suitable components for inclusion in a pharmaceutical composition are described in Martindale - The Extra Pharmacopoeia (Pharmaceutical Press, London 1993) and Martin (ed.), Remington's Pharmaceutical Sciences.
  • the pharmaceutically acceptable excipient, carrier and/or diluent is non-toxic.
  • pharmaceutically acceptable carrier refers to a substance that aids the administration of an active agent to a cell, an organism, or a subject.
  • “Pharmaceutically acceptable carrier” refers to a carrier or excipient that can be included in the compositions of the invention and that causes no significant adverse toxicological effect on the subject.
  • Non-limiting examples of pharmaceutically acceptable carriers include water, NaCI, normal saline solutions, lactated Ringer's, normal sucrose, normal glucose, binders, fillers, disintegrants, lubricants, coatings, sweeteners, flavors and colors, liposomes, dispersion media, microcapsules, cationic lipid carriers, isotonic and absorption delaying agents, and the like.
  • the carrier may also be substances for providing the formulation with stability, sterility and isotonicity (e.g. antimicrobial preservatives, antioxidants, chelating agents and buffers), for preventing the action of microorganisms (e.g. antimicrobial and antifungal agents, such as parabens, chlorobutanol, sorbic acid and the like) or for providing the formulation with an edible flavor etc.
  • the carrier is an agent that facilitates the delivery of a modified cancer cell to a target cell or tissue.
  • pharmaceutical carriers are useful in the present invention.
  • Suitable routes of administration for implementing the defined methods include oral, intravenous, intramuscular, topical, subcutaneous, intraperitoneal, spinal or other parenteral routes of administration, for example by injection or infusion.
  • parenteral administration means modes of administration other than enteral and topical administration, usually by injection, and includes, without limitation, intravenous, intramuscular, intraarterial, intrathecal, intralymphatic, intralesional, intracapsular, intraorbital, intracardiac, intradermal, intraperitoneal, transtracheal, subcutaneous, subcuticular, intraarticular, subcapsular, subarachnoid, intraspinal, epidural and intrastemal injection and infusion, as well as in vivo electroporation.
  • Administering can also be performed, for example, once, a plurality of times, and/or over one or more extended periods.
  • therapeutically effective amount or ‘effective amount’ generally refers to an amount of an immune checkpoint modulator and/or IFN-b inhibitor, a pharmaceutically acceptable salt, polymorph or prodrug thereof of the present invention that (i) treats the particular disease, condition, or disorder, (ii) attenuates, ameliorates, or eliminates one or more symptoms of the particular disease, condition, or disorder, or (iii) delays the onset of one or more symptoms of the particular disease, condition, or disorder described herein.
  • Undesirable effects e.g. side effects, are sometimes manifested along with the desired therapeutic effect; hence, a practitioner balances the potential benefits against the potential risks in determining what is an appropriate "effective amount”.
  • a therapeutically effective amount of the compounds or compositions described herein can inhibit tumour growth by at least about 10%, by at least about 20%, by at least about 30%, by at least about 40%, by at least about 50%, by at least about 60%, by at least about 70%, by at least about 80%, or by at least about 90% or more, relative to untreated subjects.
  • the treatments described herein may cause complete regression of the tumour mass.
  • tumour regression can be observed and continue for a period of at least about 10 days, at least about 20 days, at least about 30 days, at least about 40 days, at least about 50 days or at least about 60 days, at least about 70 days, at least about 80 days, at least about 90 days, at least about 100 days or longer.
  • a therapeutically effective amount of a drug may also include a “preventative” or “prophylactically effective amount,” which is any amount of the an immune checkpoint modulator and/or an IRNb inhibitor administered to a subject at risk of developing a cancer (eg a subject having a pre-malignant condition) or of suffering a recurrence of cancer, that inhibits the development or recurrence of the cancer.
  • the prophylactically effective amount prevents the development or recurrence of the cancer entirely. “Inhibiting” or “preventing” the development or recurrence of a cancer means either lessening the likelihood of the cancer's development or recurrence, or preventing the development or recurrence of the cancer entirely.
  • the exact amount of the therapeutically effective amount required will vary from subject to subject, depending on the species, age and general condition of the subject, mode of administration and the like. Thus, it may not be possible to specify an exact therapeutically effective amount. However, an appropriate therapeutically effective amount in any individual case may be determined by one of ordinary skill in the art using routine experimentation.
  • the dose administered to a subject is any therapeutically effective amount that reduces symptoms associated with the cancer as a result of any one of a reduction in the number of cancer cells; a reduction in the tumour size; an inhibition (i.e., slow to some extent and preferably stop) of cancer cell infiltration into peripheral organs; an inhibition (i.e., slow to some extent and preferably stop) of tumour metastasis; an inhibition, to some extent, of tumour growth; or relieving, to some extent, of one or more of the symptoms associated with the cancer.
  • the therapeutically effective amount may lead to increased survival of the subject.
  • a therapeutically effective amount of an IRNb inhibitor for a human subject lies in the range of about 250 nmoles/kg body weight/dose to 0.005 nmoles/kg body weight/dose.
  • the range is about 250 nmoles/kg body weight/dose to 0.05 nmoles/kg body weight/dose.
  • the body weight/dose range is about 250 nmoles/kg, to 0.1 nmoles/kg, about 50 nmoles/kg to 0.1 nmoles/kg, about 5 nmoles/kg to 0.1 nmol/kg, about 2.5 nmoles/kg to 0.25 nmoles/kg, or about 0.5 nmoles/kg to 0.1 nmoles/kg body weight/dose.
  • the amount is at, or about, 250 nmoles, 50 nmoles, 5 nmoles, 2.5 nmoles, 0.5 nmoles, 0.25 nmoles, 0.1 nmoles or 0.05nmoles/kg body weight/dose of the compound. Dosage regimes are adjusted to suit the exigencies of the situation and may be adjusted to produce the optimum therapeutic dose.
  • a therapeutically effective dosage is formulated to contain a concentration (by weight) of at least about 0.1% up to about 50% or more, and all combinations and sub-combinations of ranges therein.
  • the compositions can be formulated to contain one or more compounds, or a pharmaceutically acceptable salt, polymorph or prodrug thereof in a concentration of from about 0.1 to less than about 50%, for example, about 49, 48, 47, 46, 45, 44, 43, 42, 41 or 40%, with concentrations of from greater than about 0.1%, for example, about 0.2, 0.3, 0.4 or 0.5%, to less than about 40%, for example, about 39, 38, 37, 36, 35, 34, 33, 32, 31 or 30%.
  • compositions may contain from about 0.5% to less than about 30%, for example, about 29, 28, 27, 26, 25, 25, 24, 23, 22, 21 or 20%, with concentrations of from greater than about 0.5%, for example, about 0.6, 0.7, 0.8, 0.9 or 1%, to less than about 20%, for example, about 19, 18, 17, 16, 15, 14, 13, 12, 11 or 10%.
  • the compositions can contain from greater than about 1% for example, about 2%, to less than about 10%, for example about 9 or 8%, including concentrations of greater than about 2%, for example, about 3 or 4%, to less than about 8%, for example, about 7 or 6%.
  • the active agent can, for example, be present in a concentration of about 5%.
  • the dosage can range from about 0.01 to about 20mg/kg, about 0.1 to about 10 mg/kg, about 0.1 to about 5mg/kg, about 1 to about 5mg/kg, about 2 to about 5 mg/g, about 7.5 to about 12.5 mg/kg, or about 0.1 to about 30 mg/kg of the subject's body weight.
  • dosages can be about 0.1, about 0.3, about 1, about 2, about 3, about 5 or about 10 mg/kg body weight, or, about 0.3, about 1, about 2, about 3, or about 5 mg / kg body weight.
  • the dosing schedule is typically designed to achieve exposures that result in sustained receptor occupancy (RO) based on typical pharmacokinetic properties of an Ab.
  • An exemplary treatment regime entails administration about once per week, about once every 2 weeks, about once every 3 weeks, about once every 4 weeks, about once a month, about once every 3 - 6 months or longer.
  • a immune checkpoint modulator is administered to the subject about once every 2 weeks.
  • the Ab is administered about once every 3 weeks.
  • the dosage and scheduling can change during a course of treatment.
  • a dosing schedule for anti-PD-1 therapy can comprise administering the Ab: (i) about every 2 weeks in about 6-week cycles; (ii) about every 4 weeks for about six dosages, then about every three months; (iii) about every 3 weeks; (iv) about 3-about 10 mg/kg once followed by about 1 mg/kg every about 2-3 weeks.
  • a dosage regimen for an anti-PD-1 Ab of the invention comprises about 0.3-1 about 0 mg/kg body weight, 1-5 mg/kg body weight, or about 1-about 3 mg/kg body weight via intravenous administration, with the Ab being given every about 14-21 days in up to about 6-week or about 12-week cycles until complete response or confirmed progressive disease.
  • the immune checkpoint modulator and/or IRNb inhibitor treatment disclosed herein is continued for at least about 1 month, at least about 2 months, at least about 3 months, at least about 4 months, at least about 5 months, at least about 6 months, at least about 7 months, at least about 8 months, at least about 9 months, at least about 10 months, at least about 11 months, at least about 1 year, at least about 18 months, at least about 24 months, at least about 3 years, at least about 5 years, or at least about 10 years.
  • the specific dose level for any particular patient will depend upon a variety of factors including the activity of the specific compound employed, the age, body weight, general health, sex, diet, time of administration, route of administration, and rate of excretion, drug combination i.e. other drugs being used to treat the patient), and the severity of the particular disorder undergoing therapy.
  • treatment or “treating” of a subject includes the application or administration of a compound of the invention to a subject with the purpose of delaying, slowing, stabilizing, curing, healing, alleviating, relieving, altering, remedying, less worsening, ameliorating, improving, or affecting the disease or condition, the symptom of the disease or condition, or the risk of (or susceptibility to) the disease or condition.
  • treating refers to any indication of success in the treatment or amelioration of an injury, pathology or condition, including any objective or subjective parameter such as abatement; remission; lessening of the rate of worsening; lessening severity of the disease; stabilization, diminishing of symptoms or making the injury, pathology or condition more tolerable to the subject; slowing in the rate of degeneration or decline; making the final point of degeneration less debilitating; or improving a subject's physical or mental well-being.
  • minimising or preventing the progression of cancer means treating the subject so as to prevent or delay the recurrence or metastasis of a tumour, or to prevent growth of an existing tumour.
  • Minimising or preventing the progression of cancer includes preventing or delaying the recurrence of cancer, or preventing growth of an existing tumour, following treatment of cancer.
  • the recurrence that is being prevented includes a recurrence for example, in the tumour bed, following surgical excision.
  • recurrence includes metastasis of the cancer in another part of the body.
  • the terms “preventing recurrence” and “preventing relapse” as used herein, are interchangeable.
  • the present invention also includes methods of preventing the development of cancer in an individual.
  • the individual for whom prevention of cancer is required may be considered to be at risk of developing cancer, but does not yet have detectable cancer.
  • An individual at risk of the development of cancer may be an individual with a family history of cancer, and/or an individual for whom genetic testing or other testing indicates a high risk or high likelihood of the development of cancer.
  • the individual may have cancer stem cells but does not yet have any detectable tumours. It will be understood that methods of preventing the development of cancer include methods of delaying the onset of cancer in a subject.
  • the terms “subject” and “patient” will be understood to be interchangeable. Although the invention finds application in humans, the invention is also useful for therapeutic veterinary purposes. The invention is useful for domestic or farm animals such as cattle, sheep, horses and poultry; for companion animals such as cats and dogs; and for zoo animals.
  • cancer will be understood to include benign, pre-cancerous, pre neoplastic or non-metastatic tumours or metastatic tumours.
  • the type of cancer to be treated includes those having a benign, pre-cancerous, pre-neoplastic or non-metastatic tumour.
  • a benign tumour will be understood to not be a malignant tumour and to not invade nearby tissue or spread to other parts of the body.
  • non-metastatic cancer will be understood to not invade nearby tissue or spread to other parts of the body.
  • Pre-cancerous or pre neoplasia generally refers to a condition or a growth that typically precedes or develops into a cancer.
  • a "pre-cancerous" growth may have cells that are characterized by abnormal cell cycle regulation, proliferation, or differentiation, which can be determined by markers of cell cycle.
  • the cancer is a secondary cancer or metastases.
  • the secondary cancer may be located in any organ or tissue, and particularly those organs or tissues having relatively higher hemodynamic pressures, such as lung, liver, kidney, pancreas, bowel and brain.
  • the secondary cancer may be detected in the ascites fluid and/or lymph nodes.
  • Pre-neoplastic, neoplastic and metastatic cancers are particular examples to which the methods of the invention may be applied.
  • Broad examples include breast tumours, colorectal tumours, adenocarcinomas, mesothelioma, bladder tumours, prostate tumours, germ cell tumour, hepatoma/cholongio, carcinoma, neuroendocrine tumours, pituitary neoplasm, small round cell tumour, squamous cell cancer, melanoma, atypical fibroxanthoma, seminomas, nonseminomas, stromal leydig cell tumours, Sertoli cell tumours, skin tumours, kidney tumours, testicular tumours, brain tumours, ovarian tumours, stomach tumours, oral tumours, bladder tumours, bone tumours, cervical tumours, esophageal tumours, laryngeal tumours, liver tumours, lung tumours, vaginal tumours, Wilm's tumours, pancreatic tumours, sar
  • cancers include but are not limited to adenocarcinoma, adenoma, adenofibroma, adenolymphoma, adontoma, AIDS related cancers, acoustic neuroma, acute lymphocytic leukemia, acute myeloid leukemia, adenocystic carcinoma, adrenocortical cancer, agnogenic myeloid metaplasia, alopecia, alveolar soft-part sarcoma, ameloblastoma, angiokeratoma, angiolymphoid hyperplasia with eosinophilia, angioma sclerosing, angiomatosis, apudoma, anal cancer, angiosarcoma, aplastic anaemia, astrocytoma, ataxia-telangiectasia, basal cell carcinoma (skin), bladder cancer, bone cancers, bowel cancer, brain stem glioma, brain and
  • B-cell mixed cell, null-cell, T-cell, T-cell chronic, HTLV-llassociated, lymphangiosarcoma, lymphocytic acute, lymphocytic chronic, mast-cell and myeloid), leukosarcoma, leydig cell tumour, liposarcoma, leiomyoma, leiomyosarcoma, lymphangioma, lymphangiocytoma, lymphagioma, lymphagiomyoma, lymphangiosarcoma, male breast cancer, malignant- rhabdoid-tumour-of-kidney, medulloblastoma, melanoma, Merkel cell cancer, mesothelioma, metastatic cancer, mouth cancer, multiple endocrine neoplasia, mycosis fungoides, myelodysplastic syndromes, myeloma, myeloproliferative disorders, malignant carcinoid syndrome carcinoid heart disease, medulloblastoma
  • ocular cancers oesophageal cancer, oral cavity cancer, oropharynx cancer, osteosarcoma, ostomy ovarian cancer, pancreas cancer, paranasal cancer, parathyroid cancer, parotid gland cancer, penile cancer, peripheral- neuroectodermal-tumours, pituitary cancer, polycythemia vera, prostate cancer, osteoma, osteosarcoma, ovarian carcinoma, papilloma, paraganglioma, paraganglioma nonchromaffin, pinealoma, plasmacytoma, protooncogene, rare-cancers-and-associated- disorders, renal cell carcinoma, retinoblastoma, rhabdomyosarcoma, Rothmund-Thomson syndrome, reticuloendotheliosis, rhabdomyoma, salivary gland cancer, sarcoma, schwannoma
  • a positive response to treatment or a minimisation of progression of a cancer may be determined by any method known in the art and may include the determination of:
  • tumour metastasis an inhibition (i.e., slow to some extent and preferably stop) of tumour metastasis
  • any of the above may be considered to be a positive response to an immune checkpoint modulator and/or a IRNb inhibitor described herein.
  • the subject may have previously received treatment.
  • the previous treatment is an immune checkpoint modulator which may be in the form of an inhibitor of PD-1, PD-L1 or CTLA-4.
  • the checkpoint modulator is in the form of an antibody.
  • the subject who has received the treatment for cancer may be in partial or complete remission.
  • the subject, having received a treatment for cancer, as described above may have a 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or greater reduction in the measurable parameters of tumour growth as may be found on physical examination, radiologic study, or by biomarker levels from a blood or urine test.
  • the subject may have substantially undetectable signs of cancer.
  • a cancer that is “substantially undetectable” generally refers to a circumstance where therapy has depleted the size, volume or other physical measure of a cancer so that using relevant standard detection techniques such as in vivo imaging, the cancer, as a consequence of the therapy, is not clearly detectable.
  • the objective or outcome of treatment with an immune checkpoint modulator and/or IRNb inhibitor may be to reduce the number of cancer cells; reduce the primary tumour size; inhibit (i.e. , slow to some extent and preferably stop) cancer cell infiltration into peripheral organs; inhibit (i.e., slow to some extent and preferably stop) tumour metastasis; inhibit, to some extent, tumour growth; and/or relieve to some extent one or more of the symptoms associated with the disorder.
  • Efficacy of treatment can be measured by assessing the duration of survival, time to disease progression, the response rates (RR), duration of response, and/or quality of life.
  • the method is particularly useful for delaying cancer progression. In one embodiment, the method is particularly useful for extending survival of the subject, including overall survival as well as progression free survival. It will be understood that overall survival is the length of time from either the date of diagnosis or the start of treatment of a cancer, that patients diagnosed with the cancer are still alive. It will be understood that progression free survival is the length of time during and after the treatment of a cancer that a patient lives with the disease but it does not get worse.
  • the Kaplan-Meier method estimates the survival function from life-time data. In medical research, it can be used to measure the fraction of patients living for a certain amount of time after treatment.
  • a plot of the Kaplan-Meier method of the survival function is a series of horizontal steps of declining magnitude which, when a large enough sample is taken, approaches the true survival function for that population. The value of the survival function between successive distinct sampled observations ("clicks") is assumed to be constant.
  • Kaplan-Meier curve An important advantage of the Kaplan-Meier curve is that the method can take into account "censored" data- losses from the sample before the final outcome is observed (for instance, if a patient withdraws from a study). On the plot, small vertical tick-marks indicate losses, where patient data has been censored. When no truncation or censoring occurs, the Kaplan-Meier curve is equivalent to the empirical distribution.
  • the method is particularly useful for providing a complete response to therapy whereby all signs of cancer in response to treatment have disappeared. This does not always mean the cancer has been cured. In one embodiment, the method is particularly useful for providing a partial response to therapy whereby there has been a decrease in the size of one or more tumours or lesions, or in the extent of cancer in the body, in response to treatment.
  • kit or article of manufacture comprising an immune checkpoint modulator and/or an IRNb inhibitor as described herein, a pharmaceutically acceptable salt, diluent or excipient and/or pharmaceutical composition as described above.
  • kit may comprise instructions for use in any method or use of the invention as described herein.
  • kits for use in a therapeutic and/or prophylactic application mentioned above comprising:
  • the kit may contain one or more further active principles or ingredients for treatment of cancer.
  • the kit or “article of manufacture” may comprise a container and a label or package insert on or associated with the container.
  • Suitable containers include, for example, bottles, vials, syringes, blister pack, etc.
  • the containers may be formed from a variety of materials such as glass or plastic.
  • the container holds a therapeutic composition which is effective for treating the condition and may have a sterile access port (for example the container may be an intravenous solution bag or a vial having a stopper pierceable by a hypodermic injection needle).
  • the label or package insert indicates that the therapeutic composition is used for treating the condition of choice.
  • the label or package insert includes instructions for use and indicates that the therapeutic or prophylactic composition can be used to treat a cancer described herein.
  • the kit may comprise (a) a therapeutic or prophylactic composition; and (b) a second container with a second active principle or ingredient contained therein.
  • the kit in this embodiment of the invention may further comprise a package insert indicating the composition and other active principle can be used to treat a cancer or prevent progression of a cancer described herein.
  • the cells producing this type I IFN were tumour- infiltrating Ly6c hi inflammatory monocytes that were more abundant in responders at the start of treatment, but swiftly differentiated towards a Ly6c'° phenotype. Phenocopying of this fast-off kinetics using time-dependent sequential dosing of IFN agonists and neutralizing antibodies markedly improved efficacy, but only when IRNb or its receptor IFNAR1 were blocked, not IFNa. Together, the results suggest that fast-off dynamics of IRNb signalling underlie the therapeutic response to immune checkpoint therapy in cancer.
  • BALB/cArc, Balb/cAusB, C57BL6/J or lfnb1 tm1Lky /J mice 8-12 weeks of age were used for all experiments.
  • BALB/cArc or C57BL6/J mice were obtained from the Animal Resource Centre (Murdoch, WA), Balb/cAusB mice were obtained from the Harry Perkins Institute for Medical Research Bioresources Centre South (Murdoch, WA).
  • mice generated by knock-in of a yellow fluorescent protein (YFP) reporter cas-sette into the endogenous Ifnb locus (Scheu, Dresing and Locksley, Proc Natl Acad Sci USA, 105(51), 20416-20421 (2008)), were imported from The Jackson Laboratory (Bar Harbour, Maine) and maintained at the Harry Perkins Institute for Medical Research Bioresources Centre South (Murdoch, WA). All mice were housed at the Harry Perkins Institute of Medical Research Bioresources Facility North under specific pathogen free conditions. Mice were fed Rat and Mouse cubes (Specialty Feeds, Glen Forrest, Australia) and had access to water ad libitum.
  • YFP yellow fluorescent protein
  • Cell lines AB1 and AE17 were obtained from CellBank Australia.
  • Cell line Renca was kindly donated by Dr E. Sotomayor and Dr F. Cheng (University of South Florida, Tampa, FL).
  • Cell lines were maintained in RPMI 1640 (Invitrogen, Mulgrave, Australia) supplemented with 20 mM HEPES, 0.05 mM 2-mercaptoethanol, 100 units/ml penicillin/streptomycin (Thermo Fisher), and 10% FCS (Invitrogen).
  • Cells were grown to 70-80% before passage and passaged 3-5 times before inoculation. Cells were frequently tested for mycoplasma by PCR and remained negative.
  • tumours were resected eight (AB1) or 10 (Renca) days post tumour inoculation, when tumours were ⁇ 9 mm 2 , and mice were administered ICB 1 hour after surgery.
  • tumours were resected 2, 4 or 6 days after administration of ICB.
  • Mice were dosed with 0.1 mg/kg buprenorphine in 100 mI s.c. (30 min prior) and anesthetized using isoflurane (4% in 100% oxygen at a flow rate of 2 L/min).
  • RNAIater Whole tumours and the corresponding draining inguinal lymph node on the RHS were removed by surgical excision and immediately immersed in RNAIater (Life Technologies, Australia). The wound was closed with staples (Able Scientific, Australia). Mice were placed in a heat box for recovery. The remaining tumour was monitored for response as an indicator of response for the removed tumour. Mice were designated as responders when their tumour completely regressed and they remained tumour free for up to 4 weeks after treatment. Mice were designated as non-responders if their tumours grew to 100 mm 2 within 4 weeks after start of treatment, similar to saline-treated controls. Mice that had a delay in tumour growth or partial regression were designated as intermediate responders and excluded from the analysis. For internal consistency, the inventors only used experiments in which mice displayed a dichotomous response, i.e. in any cage there had to be at least one non-responder amongst responders or vice versa.
  • the anti-PD-L1 hybridoma (clone MIH5) and the anti-CLTA4 hybridoma (clone 9H10) were cultured in IMDM containing 1% of FCS and gentamycin at Bioceros (Utrecht, The Netherlands). Clarified supernatants were used to purify the antibody using affinity chromatography.
  • the antibodies were sterile formulated in PBS. Alternatively, antibodies from the same clones were obtained from BioXcell (New Hampshire, US).
  • Mice received an intraperitoneal (i.p.) dose of 100 pg of anti-CTLA4 and 100 pg anti-PDL1 combined in 100 pi phosphate-buffered solution (PBS). Mice received additional doses of 100 pg anti-PDL1 two and four days later. The inventors had not found any difference in effect of control IgG versus PBS, and therefore vehicle controls received PBS alone.
  • RNAIater Whole tumours and lymph nodes were surgically resected, the surrounding tissue was removed and immediately submerged in RNAIater (Life Technologies, Australia). Samples were stored at 4°C for 24 hours, after which supernatant was removed and samples transferred to -80°C. Frozen tumours were dissociated in Trizol (Life Technologies, Australia) using a TissueRuptor (QIAgen, Australia). RNA was extracted using chloroform and purified on RNeasy MinElute columns (QIAgen, Australia). RNA integrity was confirmed on the Bioanalyzer (Agilent Technologies, USA). Library preparation and sequencing (50 bp, single-end) was performed by Australian Genome Research Facility, using lllumina HiSeq standard protocols.
  • the inventors processed 72 RNA-seq single-end read samples across four time points for each of our mouse models. There were an equal number of responder and non-responder samples, with 12 samples for Day 0 and 8 samples for each of the other time points (Day 2, Day 4 and Day 6). After reviewing quality control on all samples using FastQC software, the inventors used Kallisto (Bray, et al. Nat Biotechnol 34, 525- 527, (2016); (v0.43.0)) for transcript abundance estimation.
  • RNA-Seq data for this analysis are available in the Gene Expression Omnibus under accession number [GEO: GSE117358]
  • the inventors clustered time course RNAseq data using the fuzzy c-means (FC ) ciustering algorithm fuzz (Futschik and Carlisle, J Bioinform Comput Biol, 3(4), 965-988 (2005)) in the TCseq package (Wu and Gu, TCseq: Time course sequencing data analysis (2020)), Z-norma!ised/scaled counts were used in the algorithm and expression profiles were grouped clusters (k 6) based on their dynamic patterns.
  • the inventors constructed two networks, one for Renca responders and one for AB1 responders.
  • the inventors used the GENIE3 algorithm (Huynh-Thu ,et al. PLoS One 5, (2010)), which achieved the best performance on the DREAM5 network inference challenge.
  • the inventors used 36 responder samples across all time points as input to the GENIE3 algorithm. Since GENIE3 requires gene counts as input, the inventors summarised transcript abundances derived from Kallisto as gene counts using the Bioconductor tximport (Soneson, et al. FIOOORes 4, 1521, (2015)) package.
  • GENIE3 v1.8.0
  • the inventors filtered the network produced by GENIE3 to keep direct interactions between transcription factors and differentially expressed genes. For this, the inventors retained edges where the transcription factor binding site (TFBS) was located in a genomic window 400 base pairs upstream and 300 base pairs downstream of a gene’s transcription start site (TSS).
  • TSS transcription start site
  • the inventors used the JASPAR database to obtain genome wide TFBS predictions and only retained those with a confidence score above 500 (corresponding to p-value of ⁇ 0.05).
  • the inventors also used the UCSC genome browser 35 to programmatically obtain a bedfile of TSS sites for differentially expressed genes based on our Sleuth analysis. Subsequently, the inventors used the BEDtools (Quinlan, et al. Bioinformatics 26, 841-842, (2010)) “window” function on these two bedfiles, with parameters “-I 400 -r 300 -sw” to obtain all direct regulatory interactions. To these direct interactions, the inventors appended their corresponding GENIE3 importance scores using the R data. table package (v1.12.2).
  • the inventors used the R igraph package (v1.2.4.2) (Csardi, et al. InterJournal Complex Systems 1695 (2006)). To denote confident regulatory interactions in the network, the inventors derived a numerical threshold from the KDE plots of the union set of all GENIE3 scores from AB1 and Renca responder networks using the elbow method. For each network, the inventors calculated network statistics to obtain the top 10 genes with the highest number of outgoing edges. For visualisation, the inventors used a force-directed layout and extracted a subnetwork comprising these top 10 genes with all of their first-degree outgoing neighbours. The inventors assigned colour to genes in the network by their average gene expression across time, normalised by Z-score.
  • the inventors extracted dynamically changing genes between day 0 to day 2 for functional annotation. Specifically, the inventors extracted a gene list comprising the transcription factor and its surrounding first-order neighbours. The inventors used these genes as input to Enrichr’s web interface (Kuleshov, et al. Nucleic Acids Res 44, W90-97, (2016)) to obtain GO Biological Process annotations and annotations from the LINCS1000 ligand perturbation database. The top 5 terms in both databases, ranked by -logio (p-value) were visualised using the ggplot2 R package (v3.2.1) (Wickham, H. et al. ggplot2: Elegant Graphics for Data Analysis. (Springer- Verlag, 2016)).
  • the inventors obtained a list of known IFN-stimulated genes (ISGs) from the Broad Institute’s MSigDB database (Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1, 417-425, (2015)). The inventors used two gene sets in the hallmark gene set collection for Type I and Type II interferon. Using BiomaRt (Smedley, D. et al. Nucleic Acids Res 43, (2015)), the inventors converted HGNC symbols in these gene sets to their murine (mm10) equivalents. After conversion, the inventors retained 88/97 genes from the alpha dataset and 186/200 genes from the gamma dataset.
  • ISGs IFN-stimulated genes
  • the inventors From each direct regulatory network, the inventors extracted the edge-weights of all edges containing these ISGs as the target gene. The inventors selected the top 10 TFs common to both Renca and AB1 mouse models by ranking TFs according to the sum of their edge-weights across ISGs. The inventors visualized these important TF to ISG interactions in an edge-weight matrix using the ComplexHeatmap (Gu, Z., et al. Bioinformatics 32, 2847-2849 (2016)) package (v2.2.0). The edge-weight matrices were irregular (different number of elements in each row) since each ISG TSS could be surrounded by a variety of different TF binding sites, so any non-existent interactions in the matrix were set to zero prior to visualization.
  • Hive plots provide an alternative approach to visualise network topology eliminating visualisation bias due to network layout.
  • the inventors used the HiveR package (Hanson, B. A. GitHub repository (2017). (vO.3.42) on filtered AB1 and Renca networks to plot all direct TF to DE gene interactions, assigning genes to axes depending on (1) whether a gene was a TF, ISG or non-ISG target and (2) the importance of the TF based on sum of overall scores Single cell sample pre-processing.
  • the Seurat R package (v3.0.3) was used for clustering and visualisation. Gene counts were normalised against library size and mitochondrial content. Cells were subsequently clustered using Seurat’s graph-based clustering algorithm at default cluster resolution (Louvain - 0.8). To automatically label cells, the inventors used SingleR (v1.0.5) on Seurat clusters, assigning cluster identity based on top-level annotations using the mouse RNAseq reference. Subsequently, the inventors merged clusters with the same annotations for downstream analysis.
  • Tumour clusters were inferred in a 2-stage process.
  • candidate tumour clusters were identified using Seurat’s label transfer functions (FindlntergrationAnchors, IntegrateData) using projection from a reference dataset.
  • candidate tumour clusters were used as input to inferCNV package (Tickle, et al. GitHub repository (2019)), using copy number amplification as a confirmatory step. Only those clusters labelled as tumour clusters via projection which were also found to contain CNVs amplification were identified as tumour cells.
  • the inventors used transferred labels from a single cell mouse kidney dataset (Park, J. et al.
  • Loom files quantifying spliced vs unspliced transcripts were first generated on a per-sample basis. These separate loomfiles were then merged using Loompy (v3.0.0). For downstream analysis, the velocyto.py package (vO.17.16) package was used on these merged loomfiles (La Manno, G. et al. Nature 560, 494-498 (2016)). After normalisation and feature selection and the gamma-fit and velocity calculations. The resulting velocity predictions were projected onto Seurat’s UMAP embedding for visualisation.
  • the CIBERSORT algorithm (Newman, A. M. et al. Nat Methods 12, 453-457, (2015)) was used to estimate the relative proportions of 7 cytokine induced T cell signatures based on the transcriptomic profiles of each sample, where the induced T cell gene signature developed by Cano-Gamez et al. Nat Commun 11, 1801, (2020)). was used as a reference.
  • the inventors broadly classified the 94 samples into 7 major populations by collapsing several related sub-populations by their cytokine treatment: IFNB, Resting, Th17, Th2, Th1, ThO, iTreg to generate the reference file.
  • gene count data for both AB1 and Renca was normalized to TPM. The data was filtered to retain genes with an TPM value > 0.3 in at least 8 samples (being the smallest experimental group size).
  • CIBERSORT was run on AB1 and Renca separately, with quantile normalisation disabled as recommended for RNAseq data.
  • Anti- IFNAR1 (Bioxcell, clone MAR1-5A3, 0.5 mg i.p.), anti-IFNy (Bioxcell, clone XMG1.2, 0.5 mg i.p.), anti-IFNa (Leinco, clone TIF-3C5, 1mg i.p.), anti-l RNb (Leinco, clone HDB-4A7, 0.6 mg i.p.), or lgG2a isotype (Leinco, clone C1.18.4, 0.6 mg i.p.). Treatment began 2 days after the first day of ICB administration, on day 23, and dosed every 3 rd day until the tumour reached 100 m 2 or regressed.
  • AB1 tumours were harvested 6 days after inoculation and immediately submerged in cold PBS, cut into 1-2 mm pieces with a scalpel blade and dissociated using the GentleMACS system (Miltenyi).
  • Fc block anti-CD 16/CD32, BD
  • Fc block was used for 10 minutes on ice.
  • Cell were stained with Fixable Viability Stain 780 (BD) for 30 minutes at RT, to discriminate live cells.
  • Cells were stained using antibodies for surface markers for 30 minutes at 4°C (Table 1).
  • the inventors used the PrimeFlow Kit (Invitrogen).
  • RNA fixation buffer 1 permeabilised with RNA Permeabilization buffer with RNase inhibitors, then fixed with RNA fixation buffer 2 before using Target Probes against Irf1.
  • the signal was then amplified, followed by addition of fluorescent label probes (Alexa Fluor 647).
  • Data were acquired on a BD Fortessa flow cytometer and analysed using FlowJo software (TreeStar).
  • Cells were gated on Irf1+, followed by CD45+ to identify immune infiltrating cells, and CD45- non- immune cells (e.g. tumour cells).
  • Ly6C- Monocytes CD11b+, Ly6C-
  • Ly6C+ Monocytes CD11b+, Ly6C+, also F4/80-, CD3-, CD335-
  • Other Ly6C+ cells CD11b+/-, Ly6C+
  • remaining cells CD11b-, Ly6C-
  • YFP+ cells were stained with Fixable Viability Stain 780 (BD) for 30 minutes at RT, to discriminate live cells. Cells were stained using antibodies for surface markers for 30 minutes at 4°C (Table 1). To detect YFP+ cells, cells were fixed using a cytofix/cytoperm kit (BD), then stained using antibodies against GFP (YFP cross-reactive) in perm buffer overnight. Data were acquired on a BD Fortessa flow cytometer and analyzed using FlowJo software (TreeStar). Cells were gated for single and live cells. Cells were then gated on YFP+, which were also CD45+ indicating they are immune infiltrating cells. Immune cell populations were analyzed by their expression of CDUb and Ly6c. YFP+ CD11b+ immune cells were MHC-II-, CD11c-, Ly6G-, CD19-, CD3- and NK1.1-.
  • RNAIater Invitrogen
  • CD45- non-immune cells
  • Ly6C hi monocytes CD45+ CD11b+ Ly6C hi CD3- CD335-
  • Ly6C l0/ - monocytes CD45+ CD11b+ Ly6C l0/ - CD3- CD335-
  • the remaining immune cell CD45+CD11b-
  • RNA was extracted using the RNAqueous- Micro Kit (Life Technologies). Resulting purified RNA was reverse transcribed using a High-Capacity cDNA Reverse Transcription Kit.
  • the inventors performed RT-PCR using TaqMan Fast Advanced Master Mix (Applied Biosystems) and TaqMan Assay mouse IRNb1 (Mm00439552_s1, ThermoFisher) or mouse GAPDH (Mm99999915_g1, ThermoFisher) in triplicate for each sample in a MicroAmp optical plate (Applied Biosystems) using QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems). IRNb expression was calculated as dCT of the housekeeping gene GAPDH.
  • ACt Ct (IFI ⁇ ) - Ct (GAPDH)
  • the sample size calculation for in vivo mouse experiments was based on prior experiments in which the inventors found that the median survival time on the control treatment (ICB alone) was 35 days. Using a proportional hazards model the inventors determined that, if the true hazard ratio (relative risk) of control subjects relative to experimental subjects is 5, the inventors would need to study 10 experimental subjects and 10 control subjects to be able to reject the null hypothesis that the experimental and control survival curves are equal with probability (power) 0.8. The type I error probability associated with this test of this null hypothesis is 0.05. Differences in population frequencies in responders and non-responders using flow cytometry were determined using Mann-Whitney U testing on means.
  • Prism software (GraphPad) was used to analyse tumour growth and to determine statistical significance of differences between groups by applying a Mann-Whitney U test. P-values were adjusted for multiple comparisons using the Benjamini-Hochberg (B-H) method; those ⁇ 0.05 were considered significant. The Kaplan-Meier method was used for survival analysis, and p- values were calculated using the log-rank test (Mantel-Cox). For comparison of deconvolution estimations, the inventors used two-way ANOVA with Tukey's multiple comparisons test.
  • Raw sequencing data both for single cell RNAseq and bulk RNAseq datasets will be deposited at GEO (accession codes will be available before publication).
  • the computational workflow allows for programmatic downloads of processed sequencing data from a CloudStor remote repository prior to workflow execution - this processed sequencing data is available on request. Code availability
  • the inventors used the GENIE3 R Bioconductor package for network inference.
  • the inventors constructed two networks, one for AB1 and one for Renca using all responder samples for each strain across four experimental timepoints.
  • the count matrices used as input to GENIE3 were produced using the tximport (Soneson, et al. FIOOOResearch 4, 1521 (2015)) R package, allowing us to summarise Kallisto’s transcript-level abundances to gene- counts. For this analysis, the inventors retained all genes with non-zero counts in at least one sample.
  • the inventors incorporated differential expression information using the R package Sleuth. For this, we used a likelihood ratio test with a design formula incorporating phenotype (ICB response), time and an interaction term to capture differentially expressed genes displaying time-dependent differences in expression between phenotypes. The inventors performed p-value aggregation of transcript-level results using Fisher’s method (Yi et al. Genome Biol 19, 53 (2018)), with transcript- to- gene mapping relying on the latest Gencode reference M25 (GRCm38.p6). This analysis yielded 4040 differentially expressed genes in AB1 and 510 differentially expressed genes in Renca at an adjusted p-value of ⁇ 0.05
  • JASPAR transcription factor binding site prediction
  • TFBS transcription factor binding site prediction
  • PFMs vertebrate position frequency matrices
  • the confidence of predictions is quantified by JAPAR relative scores.
  • the inventors considered relative scores above 500, corresponding to a p-value of 10-5.
  • the inventors identified and retained “direct connections”, defined as connections between a transcription factor (TF) to differentially expressed genes only if the TFBS for the TF was situated in a genomic window 400 base pairs upstream or 300 base pairs downstream of a DE gene’s transcription start site (TSS).
  • TSS transcription start site
  • the inventors visualised them with superimposed gene expression data.
  • the inventors ranked regulators by connectivity (total degree), taking higher connectivity as a surrogate for greater biological influence in these networks.
  • the inventors visualised the induced subgraph of the top ten nodes and their first order neighbours in both AB1 and Renca.
  • the inventors used the R igraph package to isolate these induced subgraphs and displayed them using the kamada-kawai layout.
  • the inventors visualised TF-to-ISG GENIE3 scores and expression profiles for ISGs common to both AB1 and Renca networks using the R ComplexHeatmap package. To aid visualization, the inventors ranked each TF’s influence by the sum of GENIE3 scores on these target ISGs. By these criteria, the inventors saw regulation of fast-off ISGs confined to just 5 key interferon-related transcription factors - Irf1, Statl, Stat2, Irf7 and Irf9, with minimal regulatory impact from other transcription factors in these networks (Figure 2).
  • the inventors wanted to understand how the distribution of GENIE3 scores related to the interferon signalling, since stronger GENIE3 weights can be taken to represent stronger dynamic regulatory interactions in these networks.
  • a hive plot visualisation This visualisation strategy allowed the inventors to partition the edges in these networks according to the source and target genes using a consistent layout, allowing an objective comparison between the two networks.
  • the hive plots also allowed the inventors to visualise how the magnitude GENIE3 weights were distributed amongst the edges, and hence between genes involved in the same signalling pathways.
  • Green axis - non-interferon related important TFs comprising the union set of top TFs from both networks in figure 2.
  • the inventors selected the union set of transcription factors in both networks based on the connectivity criterion described herein.
  • This node topology allowed the inventors to more easily visualise the “quadrant” of graph edges from IFN-related TFs to fast-off- 1 SGs, demonstrating that this quadrant contained edges with high value GENIE3 scores (above 0.9 quantile) denoting important dynamic regulatory links in the network.
  • the inventors processed FASTQ files from 6 AB1 and 6 Renca samples using cellranger v3.0 (10X genomics). For each sample, the inventors performed demultiplexing and read alignment using the Cellranger count function, using cellranger 1 s pre-supplied mm10 reference with an expect-cells parameter of 6000. The inventors used the filtered matrices from this processing step for downstream analysis. This step also produced aligned reads in binary (BAM) format and TSV files of valid cell identifiers - these files were used for velocity analysis, described in more detail herein.
  • BAM binary
  • the inventors used a standard processing pipeline using the Seurat (version 3.14) R package to combine samples for downstream analysis (Stuart, T. et at. Cell 177, 1888-1902 (2019)).
  • the inventors used the filtered count matrices for each sample after cellranger processing to construct individual Seurat objects, which were then subjected to basic filtering and QC. Specifically, the inventors excluded cells with less than 200 features and excluded genes detected in less than 3 cells. Normalisation was performed using Seurat’s SCtransform function, regressing against both sequencing depth and also against the percentage of mitochondrial DNA in each cell. Subsequently, for both AB1 and Renca, all samples were merged using the FindlntergrationAnchors and IntegrateData Seurat functions. The resulting Pearson residuals from these processing steps were used for downstream PCA, cluster identification and UMAP embedding and visualisation.
  • the inventors used an automated labelling strategy based on bulk RNAseq references.
  • the R package SingleR was used in “cluster mode”.
  • the inventors used the mouse RNAseq dataset from Benayoun et al. Genome Res. 29, 697-709 (2019). This dataset consisted of 358 samples, with 18 main (level 1) labels and 28 finer (level 2) labels. Labelling was performed on a per-cluster basis using clusters defined from Seurat’s FindClusters function at default (0.8) resolution. SingleR’s Level 1 annotations were used for each cluster and similarly labelled clusters were merged. The inventors confirmed that this approach was robust to cluster size by showing that labels were consistent even when cluster size was modified by changing resolution parameter in the FindClusters function. Additional annotation diagnostics are further described in herein.
  • the SingleR reference used in this labelling strategy did not contain references for tumour cells. Hence, the inventors relied on a different approach to locate tumour cells in our samples. The inventors used a strategy known as ‘label transfer’ in the Seurat package, allowing cell identities from a reference sample to be projected onto our target dataset. Tumour labelling of AB1 mesothelioma cells
  • the inventors first constructed a reference set for mesothelioma cells. In tandem with the 6 AB1 and Renca samples, the inventors also sequenced 4 samples from AB1 mesothelioma tumours in which the tumour cells were tagged with influenza hemagglutinin (Marzo, et al. Cancer Res. 59, 1071-1079 (1999)). To detect cells containing this HA-tag, the cellranger mm 10 index was rebuilt to incorporate the tag sequence. This rebuilt index was used during read alignment of these 4 reference samples. Filtered count matrices were processed similarly to our other AB1 and Renca samples as described herein. The inventors labelled any cell containing the HA-tag as a tumour cell and others as non-tumour. These labels were subsequently projected onto the combined AB1 dataset composed of 6 AB1 tumours. Clusters containing more than 10% tumour cells were deemed to be putative tumour clusters and their labels were switched from the SingleR labels to reflect these new identities
  • Renal cell carcinoma is known to transcriptionally recapitulate elements of the renal tubule, so the inventors used a reference dataset of mouse kidney single cells from Park et al. Science 360, 758-763 (2016) in which the authors identified gene markers mapping to anatomical elements of the mouse renal tubular system.
  • the inventors selected clusters which expressed the highest average expression of markers specific proximal and distal tubules (Lrp2, Slc27a2). Cells in these reference clusters - which the inventors labelled as “renal tubule elements” - had their identities projected onto the Renca dataset. Cells acquiring this label in our target dataset of 6 Renca tumour samples were labelled as putative tumour clusters.
  • tumour cell identity As a final test for tumour cell identity, the inventors inferred copy number variation in single cells. Tumour cells usually display evidence for somatic large-scale chromosomal copy number alterations, such as gains or deletions of entire chromosomes or large segments of chromosomes. For this, the inventors adopted the approach in Tirosh et al. Science 352, 189-196 (2016). The strategy used by these authors is formalised by the R inferCNV package. In short, inferCNV detects CNV changes by examining changes in levels of gene expression in specific chromosomal regions against a background signal. The inventors used inferCNV on a per-sample basis.
  • tumour clusters labeled by Seurat’s projection strategy were in good agreement with clusters of cells deemed to be tumour clusters based on the existence of copy number changes inferred by inferCNV. This served as an additional validation step indicating successful identification of these tumour cells in both types of tumours.
  • the inventors used the SCDE/pagoda 12 package, which detects statistically significant coordinated variability at single cell level. Briefly, from the original 38K cells in our AB1 samples, the inventors constructed KNN error models for the 17K surviving default SCDE’s library size filters. The inventors used raw counts and normalised variance across these cells. For speed and computational efficiency, the inventors used the multi-core implementation of the knn. error. models function which required the co-installation of the WGCNA package 13 and also supplied cell identities from SingleR (Fan, J. et al. Nat. Methods 13, 241-244 (2016)).
  • the inventors tested GO terms for interferon production and interferon response extracted from the org.Mm.eg.db Bioconductor package and also their “custom” gene set of fast-ISGs derived from bulk expression data.
  • the inventors visualised enrichment scores using python’s Seaborn scatter plot with a colour scale mapped to enrichment score intensity.
  • the inventors used the method described above for gene set enrichment analysis on AB1 cells. Due to the quality and sequencing depth of our single cell data, the inventors found that AB1 samples showed low expression of transcripts for IFN-gamma and almost no transcripts for IFN-alpha and IFN-beta, making it difficult to interpret direct visualisations of normalised counts of these transcripts. Furthermore, the inventors wanted to study coordinated upregulation of groups of genes corresponding to a biological pathway, so the inventors chose this analysis approach rather than inspecting and interpreting expression profiles of individual genes across our cell populations.
  • the inventors performed annotation diagnostics by checking cell cluster identities in our AB1 samples against the ImmGen (Heng, T. S. et al. Nat. Immunol. 9, 1091-1094 (2008)) reference, consisting of 252 samples of microarray data with an annotation focus on hematopoietic and immune cell types.
  • the inventors found both references to be in agreement with the immune- orientated Immgen reference allowing us to reclassify a proportion of monocytes as dendritic cells.
  • the inventors harmonised labels (Aran, D. et al. Nat. Immunol.
  • RNA normalised RNA counts in the Seurat object
  • assay “RNA” and slot “data”.
  • cells were partitioned by response and by cell label.
  • the non-parametric Wilcoxon was used as the statistical test for differentially expressed genes with FDR correction.
  • the inventors deemed genes to be differentially expressed at an absolute log-fold change of 0.5 and a q-value of below 0.05. Consistent with previous analyses in both bulk and single cell data, responder monocytes displayed high levels of interferon gamma and interferon beta signalling. DE genes in AB1 separated by cell type is provided in table 1.
  • the inventors also analysed 6 samples from Renca for differentially expressed genes in the same way as AB1 samples.
  • the same strategy for label harmonisation used in AB1 was performed on Renca single cell samples showing compatible cell labels using both references.
  • the inventors observed far fewer differentially expressed genes between responders and non-responders at Day 0 across all cell types. Consequently, the inventors observed no significant terms on pathway enrichment using Metascape.
  • the inventors used network inference to analyse single cell data.
  • the objective with this approach closely mirrors network analysis on bulk data - to discover co expression modules for important transcription factors governing the checkpoint blockade response, with the potential benefit of a larger number of gene expression data points in single cell data as compared to bulk samples.
  • the inventors used the SCENIC pipeline (Aibar, S. et at. Nat. Methods 14, 1083-1086 (2017)), with the costly computational step of GRN construction performed by the arboreto package.
  • This package uses a variation of the XG Boost algorithm built on python’s Dask as a resource scheduler.
  • the SCENIC pipeline involves the identification of high confidence “regulons”, comprising a transcription factor and its downstream effector genes. Identification was followed by regulon activity detection in individual cells using a gene set enrichment approach known as AUCell.
  • the inventors ran the analysis across cells in the AB1 dataset, with a presupplied list of mm10 transcription factors provided by the SCENIC package authors.
  • Candidate regulons detected from this inference step were pruned, informed by a ranking database of species-specific regulatory features and a species-specific motif annotations database which were also included with this software. These “regulons” were tested for enrichment using AUCell. Automated calculation of thresholds was performed on each regulon, allowing enrichment, and hence activity of each regulon to be binarised.
  • the inventors summarised the result of these analyses using a difference heatmap between responders and non-responders of average binarised TF activity per Seurat cluster. Specifically, the regulon binarisation scores were averaged across clusters, separated by response and the difference between responder and non responder averages were visualised using the R pheatmap package.
  • RNA velocity models the time derivative of the gene expression state, allowing us to quantify activity of expression for a gene of interest.
  • the inventors also derive a measure of the general “transcriptional activity” of a cell in high dimensional space, which can in turn be used to predict future cell state.
  • the inventors used RNA velocity analysis to analyse differences in the monocyte population between responders and non responders.
  • the inventors analysed both “general transcriptional activity”, and more specifically the gene velocities of ISGs involved in the fast phase of the ICB response.
  • the inventors processed BAM files of aligned reads produced by cellranger as described herein.
  • For quantification of spliced vs unspliced transcripts we used the veloctyo run command from the velocyto.py’s command line interface on these BAM files.
  • the inventors downloaded an expressed repeat annotation from UCSC genome browser in GTF format which the inventors also supplied as input to the run command.
  • the resulting loomfiles produced from running individual samples were combined using the python loompy package.
  • the inventors merged 6 samples from each strain into a single, combined strain-specific loomfile for downstream analysis.
  • the inventors incorporated UMAP embedding information from our Seurat analysis into the velocytoLoom object. Transition probabilities and embedding shifts were calculated with respect to Seurat-derived UMAP coordinates using the vlm.estimate_transition_prob and vlm.calculate_embedding_shift function. Visualisation of cell velocities was performed using matplotlib helper functions from the velocyto.py package.
  • the inventors compared their “transcriptional momentum”, which was defined as the squared L2 norm for each cell’s embedding vectors with respect to their Seurat UMAP (Mclnes, et al. J. Open Source Softw. 3, 861 (2016)). These 2D embedding vectors were extracted from the “delta_embedding” slot of the velocytoLoom object. The inventors compared KDE distributions for momentum in various AB1 clusters, separated by response.
  • the inventors showed from these KDE distributions that the most highly interferon-stimulated, Ly6c Hi responder monocytes possessed more transcriptional momentum than their non-responder counterparts, indicating that differential transcriptional activity in this population was important to ICB response.
  • the momentum calculation described above is derived from genes surviving feature selection and filtering.
  • the inventors examined gene velocities for IFN-related TFs and ISGs in the fast-off component gene set which survived the above-mentioned filtering.
  • the inventors extracted the normalised velocities of 42 of these genes which survived data preprocessing, which the inventors visualised in seaborn’s Clustermap. Hierarchical clustering showed separation of responder versus non responder monocytes based on ISG velocities.
  • the inventors also compared the velocity of IFN-related TF, Irf1, across their AB1 cells.
  • Global Irf 1 gene velocities displayed across the Seurat UMAP showed that the monocytes in AB1 had the largest negative velocities. These negative velocities can be interpreted as a “deceleration”, where the cells actively downregulate expression of this transcription factor.
  • the only large population of cells demonstrating negative Irf1 is the monocyte population, showing that this population is actively downregulating Irf1 expression.
  • Cellrouter identifies trajectories using a network flow approach rather than by constructing a pseudotime manifold based on a low dimensional embedding.
  • the topGenes function was used to extract genes highly correlated with derived pseudotime, specifically those above the 0.8 quantile for positively correlated genes and those below the 0.1 quantile for negatively correlated genes.
  • the inventors split the most highly correlated genes into 5 components for enrichment analysis using Metascape.
  • diffusion analysis shows a component which was strongly enriched for interferon signalling, with reduced signalling intensity along pseudotime from Ly6c Hi to Ly6c Lo monocytes.
  • RNA velocity arrows from previous analysis indicated a trajectory from cluster 1 to cluster 2 monocytes.
  • responder and non-responder monocytes share the same pseudotime axis and share similar transcriptional features, a product of analysis in a common low-dimensional embedding.
  • Cellrouter reveals important signalling pathways correlated with this trajectory, but it does not address the potential for preferential signalling pathways along this pseudotime between responder and non-responder monocytes.
  • This Bayesian approach performs trajectory inference whilst allowing the inclusion of ICB response as an additional sample-specific covariate. In turn, it potentially reveals subtle differences between gene expression along the pseudotime trajectory which are associated with ICB response.
  • the preferred input to phenopath is a count matrix of log-normalised, variance stabilised counts of highly variable genes in the cell populations of interest.
  • HVGs highly variable genes
  • the inventors selected highly variable genes (HVGs) using the “vst” option using Seurat’s FindVariableFeatures function, selecting 3000 features for analysis. From this, 253 genes were deemed to have a significant interaction with the responder phenotype, whilst 847 genes were associated with non-responder phenotype.
  • Pathway analysis using Metascape were consistent with previous analyses.
  • the monocyte pseudo time in responders was enriched for terms associated with interferon response and cellular migration, in turn implying ingress of an interferon-activated monocyte population into the tumour microenviroment (data not shown)
  • ICB immune checkpoint blockade
  • CTLA4 and PD-L1 antibodies against CTLA4 and PD-L1 either leads to a symmetric bilateral response or a symmetric failure to respond in both tumours.
  • the inventors To map the dynamic processes underlying the response to ICB, the inventors removed responsive and non-responsive tumours at 1-hour prior and at 2, 4 and 6 days following administration of anti-CTLA4/anti-PD-L1 therapy (Fig. 1a), and examined the transcriptomes of these tumours using RNA-sequencing. To avoid bias towards one tumour type, the inventors utilised two different tumour models, AB1 mesothelioma and Renca renal cell carcinoma, and explored pathways that were consistently differentially regulated between responders and non-responders in both models.
  • the inventors first determined whether there were differences in cellular composition between responders and non-responders at each time point using CIBERSORT analysis (Chen et al., Methods Mol. Biol. 1711, 243-259 (2016)). Although some differences between responders and non-responders were observed, none of these differences were consistent between the two models, except for a significantly higher proportion of NK cells in responders prior to treatment (data not shown). Although CD8+ T cell gene signatures in baseline samples have been reported in the literature as predictors of response, the inventors observed an increase in CD8+ T cells after ICB, irrespective of response (Figure 1c).
  • Clusters 1 and 2 contained genes associated with activation of myeloid cells and T cells. The expression of these genes gradually increased over time in both responders and non-responders, albeit to a greater magnitude in responders ( Figure 1d and 1e), which was in agreement with earlier results obtained using CIBER SORT (data not shown).
  • Cluster 3 Genes associated with cancer cell signaling (cluster 3) decreased in expression over time, but again in both responders and non-responders ( Figure 1f and 1h). In contrast, cluster 4, demonstrated a kinetic profile that was strikingly different between responders and non-responders ( Figure 1g and 1i). Cluster 4 contained genes associated with IFN signaling, which showed a gradual increase in expression over time in non-responders, while in responders it was initially highly expressed, followed by a rapid decrease in both AB1 and Renca (Figure 1g).
  • the inventors constructed gene regulatory networks for responders using the GENIE3 algorithm. For each gene, the algorithm calculates an importance score reflecting the inferred effect of the gene on all other genes. A high importance score denotes a strong effect of a gene (putative regulator) on the dynamics of expression of a downstream gene (target) in the network. The inventors ranked putative regulators by the sum of their outgoing importance scores and plotted the expression of the top 100 regulators over time (Fig. 1b). This showed dynamic changes in regulators after treatment, with consistent differences between responders and non-responders in both mouse models, suggesting a common dynamic gene signature associated with response to ICB treatment.
  • TF transcription factors
  • ISGs In contrast, non responders displayed a slower and less intense activation of ISGs which remained chronically active over time.
  • the inventors confirmed that ISG expression segregated into an early and late phase, with the fast-off component containing genes stimulated by IFNg (54 of 124, 42%), IFN a/b (14 of 124, 11%) or a combination of both (58 of 125, 47%).
  • Expression of these ISGs in both AB1 and Renca was similarly regulated (Fig. 3c).
  • the most important TFs across ISGs were Irf1, Statl, Stat2, Irf7 and Irf9, indicating that regulation of ISG kinetics was confined to this small subset of TFs, independent of tumour type.
  • the inventors performed single cell transcriptome sequencing one hour prior to ICB.
  • the inventors interrogated responder and non-responder samples (Fig. 4a) and confirmed the global difference in type 1 IFN signalling in responders (Fig. 4b).
  • Gene set enrichment analysis demonstrated that responder monocytes expressed the highest level of the fast-off ISGs (Fig. 4c).
  • the inventors observed a gradient of ISG enrichment across 3 distinct monocyte sub-clusters (Fig. 4d).
  • the inventors found that the highest levels of fast-off ISG signalling occurred in cluster 1 monocytes (Fig. 4d), which displayed a Ly6c hi phenotype (Fig. 4e).
  • the inventors exploited the fact that single cell data allows capturing of cells at various stages of response within one sample, by ordering cells by their response phenotype, and explored whether this ordering reflected the gradient in IFN signalling (Fig. 3).
  • the inventors observed a differentiation trajectory from Ly6c hi to Ly6c'° monocytes that was more pronounced in responder samples consistent with our observation of a fast-off dynamic (Fig. 4h). Furthermore, this trajectory showed diminishing transcriptional activity of ISG genes, with a more pronounced activity in responders compared to non-responders.
  • velocity analysis showed that monocytes downregulated transcription of ISGs such as Irf1, and this was more pronounced in responders than non-responders.
  • Example 5 Dynamic targeting of type I IFN improves response rate to ICB in both ICB responsive and resistant tumour models
  • the inventors plotted the enrichment of the fast-off ISG gene set (Fig. 4c), genes involved in type I IFN (a/b) production (Fig. 4i), and IFNy signalling (Fig. 4j). Based on this data, the inventors were unable to resolve the IFN type driving the response. As computational analysis of gene expression data did not allow robust dissection of type I and II IFN pathways, the inventors tested this experimentally. To phenocopy the active IFN signature in vivo, prior to ICB, the inventors pre-treated mice with intratumoural injections of poly(l:C), which is known to induce both type I and II IFNs, particularly IBNb (Fig. 5).
  • IFNa and IRNb signal through IFNAR1, but IRNb can bind to IFNAR1 in an IFNAR2-independent manner.
  • the inventors therefore used single cell RNAseq data from T cells stimulated with a diverse array of cytokines, including IFNp to construct a reference matrix using CIBERSORT. Deconvolution analysis revealed that genes associated with IFNp signalling followed the on/fast-off IFN signature in responders in both AB1 and Renca tumour models, suggesting IRNb was responsible for these observed dynamics (Fig. 6c, d.).
  • Example 7 Promotion of response to ICB does not require IFNp induction
  • Example 8 Inhibiting IFNp after ICB, not before, underlies response to ICB
  • the inventors assessed the effect of blocking IFNAR1 before rather than after ICB initiation, or concomitantly with poly(l:C) prior to ICB. This treatment completely abrogated both the response to ICB and the priming effect of poly(l:C), confirming the crucial time- dependent nature of IRNb signalling underlying the therapeutic response to ICB (Fig. 9).
  • mice were inoculated with AE17, treated with poly(l:C) (blue dotted lines) followed by anti-CTLA4/anti-PD-L1 ICB (black dotted lines), with concurrent or delayed (3 days later) anti-IFI ⁇ (Figure 9d).
  • Dosing anti-IRNb concurrently with ICB does not result in improved efficacy (orange versus blue line), while dosing 3 days after start of ICB does (green line) as shown in Figure 9d.
  • the inventors confirmed statistical enrichment of the on/fast-off-IFN signature in the neoadjuvant cohort.
  • the on/fast-off signature showed the highest enrichment score, indicating that upregulation of this signature is an early important transcriptomic event in the response to ICB therapy ( Figure 10a).
  • the inventors used peripheral blood data from a clinical trial investigating anti-PD1 in combination with chemotherapy in gastrointestinal cancer patients (Griffiths et al., Proc. Natl. Acad. Sci. USA 117, 16072-16082 (2020)).
  • the inventors examined single cell data from four treatment time points and mapped the on/fast-off response signature to immune cell populations from peripheral blood (Figure 10b). Supporting observations in mice, the analysis of patient data showed that type I IFN signaling was activated in monocytes, but only in responders (Figure 10c-d). In these cells, upregulation of the fast-off-IFN signature occurred early after ICB administration which disappeared later, emphasizing the similarity of these IFN kinetics in patients and murine models.
  • the inventors used single cell RNAseq samples from breast cancer patients treated with an anti-PD1 antibody, obtained prior to treatment and 9 days after PD-1 treatment. One-third of patients displayed T cell expansion, while the rest did not.
  • the inventors show that fast-off dynamics of IFN signalling underlie the response to ICB and that this can be therapeutically exploited using antibodies against IFNp or its receptor IFNAR1, resulting in enhanced tumour clearing.
  • the inventors demonstrate in intrinsically responsive tumours that type I IFN, specifically IRNb, plays a dual role and that the response rate and depth of response can be improved by therapeutically mimicking these on/fast-off dynamics.
  • antibodies targeting the IRNb/IRNAB1 pathway have been fully developed in the context of autoimmunity, these results could be readily translated into the clinic.

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Abstract

La présente invention concerne des méthodes de traitement du cancer, augmentant en particulier la réponse au blocage de point de contrôle immunitaire chez des individus atteints d'un cancer. Dans un autre aspect, la présente invention concerne une méthode de traitement, de prévention ou de réduction de la progression d'un cancer chez un sujet, comprenant l'administration d'un modulateur de point de contrôle immunitaire ; après administration du modulateur de point de contrôle immunitaire, l'administration d'un interféron β (IFNβ), ce qui permet de traiter, de prévenir ou de réduire au minimum la progression du cancer chez le sujet.
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WO2019020593A1 (fr) * 2017-07-25 2019-01-31 INSERM (Institut National de la Santé et de la Recherche Médicale) Méthodes et compositions pharmaceutiques pour la modulation de la monocytopoïèse
WO2020097393A1 (fr) * 2018-11-07 2020-05-14 Gritstone Oncology, Inc. Vecteurs de néoantigènes d'alphavirus et inhibiteurs d'interférons

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Publication number Priority date Publication date Assignee Title
WO2019020593A1 (fr) * 2017-07-25 2019-01-31 INSERM (Institut National de la Santé et de la Recherche Médicale) Méthodes et compositions pharmaceutiques pour la modulation de la monocytopoïèse
WO2020097393A1 (fr) * 2018-11-07 2020-05-14 Gritstone Oncology, Inc. Vecteurs de néoantigènes d'alphavirus et inhibiteurs d'interférons

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GONG KE, GUO GAO, PANCHANI NISHAH, BENDER MATTHEW E., GERBER DAVID E., MINNA JOHN D., FATTAH FARJANA, GAO BONING, PEYTON MICHAEL, : "EGFR inhibition triggers an adaptive response by co-opting antiviral signaling pathways in lung cancer", NATURE CANCER, vol. 1, no. 4, 1 April 2020 (2020-04-01), pages 394 - 409, XP055899717, DOI: 10.1038/s43018-020-0048-0 *
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