CN117813402A - Methods of classifying and treating patients - Google Patents

Abstract

Provided herein are systems and methods for developing classifiers that can be used to predict responses to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating a subject having an autoimmune disorder, the method comprising: anti-TNF therapy is administered to subjects that have been determined to be responsive by a classifier established to distinguish between responsive prior subjects that have received anti-TNF therapy and non-responsive prior subjects in a cohort. For example, in some embodiments, the present disclosure provides a method of treating a subject having an autoimmune disorder during therapeutic therapy, the method comprising: the prior subjects who responded and prior subjects who did not respond were identified for a period of time from the start of administration of the anti-TNF therapy.

Description

Approach to triaging and treating patients

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 63/163,414, filed on March 19, 2021, and U.S. Provisional Application No. 63/306,054, filed on February 2, 2022, the entire contents of which are incorporated herein by reference.

Background Art

Autoimmune diseases such as rheumatoid arthritis (RA) affect millions of patients and their treatment costs account for a significant portion of overall healthcare expenditures. Autoimmune diseases can be divided into two categories, namely organ-specific and systemic autoimmunity. Rheumatoid diseases, including RA, are systemic autoimmune diseases that primarily manifest in the synovial joints, ultimately leading to irreversible destruction of tendons, cartilage, and bones. Although there is currently no cure for RA, significant progress has been made in the therapeutic management of these patients, primarily through the development of anti-TNF (tumor necrosis factor) drugs that neutralize the pro-inflammatory signaling of this cytokine. Such biologic therapies (e.g., and ) significantly improved treatment outcomes for some RA patients.

Approximately 34% of RA patients (a low percentage) show a clinical response to anti-TNF therapy, achieving low disease activity (LDA) and sometimes even remission. In these so-called "responder" patients, disease progression may be the result of an inappropriate TNF-driven proinflammatory response. For patients who do not respond to anti-TNF, approved alternative therapies such as anti-CD20, co-stimulatory blockers, JAK and anti-IL6 therapies are available. However, patients may switch to such alternative therapies after the first cycle of a different anti-TNF, which may take more than a year, while symptoms persist and the disease progresses further, making it more difficult to achieve treatment goals. In addition to the problem of delayed treatment, the risk of serious infections and malignancies associated with anti-TNF therapy is so great that the product may need to include a so-called "black box warning" on the label when it is approved. Other potential side effects of this therapy include, for example, congestive heart failure, demyelinating disease and other systemic side effects.

Summary of the invention

An important issue with anti-TNF therapy is the inconsistent response rates. Regardless of the method used to define response, a subset of RA patients may have an adequate response to TNFi therapy, with 50–70% achieving ACR20, 30–40% achieving ACR50, 15–25% achieving ACR70 responses, and 10–25% achieving remission. Many studies have attempted to identify biomarkers and develop models to predict response to TNFi therapy prior to treatment initiation. Failure to validate and reproduce the performance of these predictive biomarkers in new patient populations and clinical trials is a typical outcome. Different characteristics between patient populations, laboratory methods and procedures for generating molecular data, and other biases inherent in single-cohort retrospective blood studies have hampered advances in precision medicine not only in rheumatology but also in other medical specialties.

In some aspects, the methods and compositions described herein allow care providers to distinguish between different categories of subjects, e.g., subjects who may benefit from a particular treatment (e.g., anti-TNF therapy) versus subjects who do not, subjects who are more likely to achieve or suffer a particular outcome or side effect, etc. In some embodiments, such technologies are provided thereby reducing risk to patients, increasing the timing and quality of care for non-responder patient populations, increasing the efficiency of drug development, or avoiding the costs of administering ineffective therapies to non-responder patients or treating side effects that occur in these patients while receiving the relevant therapy (e.g., anti-TNF therapy).

In some aspects, the present disclosure provides a method for treating an object with a specific therapy (e.g., an anti-TNF therapy), in some embodiments, the method comprising: administering therapy to an object that has been determined to be responsive by a classifier, the classifier being established to distinguish between an object that is expected to respond to the therapy and an object that is not responsive. In some embodiments, the classifier identifies 60% or more non-responders in a treatment-naive cohort. In some embodiments, the classifier identifies 60% or more non-responders in a treatment-naive cohort with at least 350 objects.

The classifier can be a molecular signature response classifier derived from known gene expression differences between responders and non-responders within the cohort. In some embodiments, one or more genes with statistically significant differences in expression between responders and non-responders are included as part of the molecular signature response classifier. In some embodiments, proteins associated with genes with statistically significant differences in expression between responders and non-responders are mapped to the human interactome to validate the relationship between the selected genes and disease biology.

The provided classifiers also incorporate other elements, such as clinical features or single nucleotide polymorphisms that can be used to classify response or non-response in a given patient.

In some embodiments, the present disclosure provides a method for treating a subject with an autoimmune disorder, in some embodiments, the method comprising: administering an anti-TNF therapy to a subject who has been determined to be a responder by a classifier, wherein the classifier is established to distinguish between responding prior subjects and non-responding prior subjects in a cohort who have received the anti-TNF therapy; wherein the classifier is developed by evaluating: one or more genes whose expression levels are significantly correlated with clinical responsiveness or non-responsiveness (e.g., in a linear or non-linear manner); at least one of the following: the presence of one or more single nucleotide polymorphisms (SNPs) in the expressed sequences of the one or more genes; or at least one clinical feature of the responding prior subjects and the non-responding prior subjects; and wherein the classifier is validated by an independent cohort different from the cohort that has received the anti-TNF therapy.

In some embodiments, the subject has previously been administered anti-TNF therapy. In some embodiments, the subject has been administered anti-TNF therapy for at least one month, at least two months, at least three months, at least four months, at least five months, or at least six months prior to the administration.

In some embodiments, the classifier identifies 60% or more non-responders in a treatment naive cohort. In some embodiments, the classifier identifies 60% or more non-responders in a treatment naive cohort of at least 350 subjects.

In some embodiments, one or more genes are characterized by their topological properties when mapped onto a human interactome map. In some embodiments, SNPs are identified with reference to the human genome. In some embodiments, a classifier is developed by evaluating each of: one or more genes whose expression levels are significantly correlated (e.g., in a linear or nonlinear manner) with clinical responsiveness or non-responsiveness; the presence of one or more SNPs; and at least one clinical feature.

In some embodiments, the one or more genes include ALPL, ATRAID, BCL6, CDK11A, CFLAR, COMMD5, GOLGA1, IL1B, IMPDH2, JAK3, KLHDC3, LIMK2, NOD2, NOTCH1, SPINT2, SPON2, STOML2, TRIM25, or ZFP36.

In some embodiments, the one or more genes include ALPL, BCL6, CDK11A, CFLAR, IL1B, JAK3, LIMK2, NOD2, NOTCH1, TRIM25, or ZFP36.

In some embodiments, at least one clinical characteristic is selected from the group consisting of body mass index (BMI), sex, age, race, prior therapy treatment, disease duration, C-reactive protein level, presence of anti-cyclic citrullinated peptide, presence of rheumatoid factor, patient global assessment, treatment response rate (e.g., ACR20, ACR50, ACR70), and combinations thereof.

In some embodiments, anti-TNF therapy comprises administration of infliximab, adalimumab, etanercept, cirtolizumab pegol, golilumab or a biosimilar thereof. In some embodiments, the disease, disorder or condition is selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn's disease, ulcerative colitis, chronic psoriasis, hidradenitis suppurativa, multiple sclerosis and juvenile idiopathic arthritis. In some embodiments, a classifier is established using microarray analysis of previous subjects derived from responsiveness and previous subjects without responsiveness. In some embodiments, RNAseq data derived from an independent cohort is used to validate the classifier. In some embodiments, the SNP is selected from Table 3.

In some embodiments, the present disclosure provides a system for classifying a subject with an autoimmune disease as being likely to respond or likely to not respond to anti-TNF therapy, before any administration of the anti-TNF therapy to the subject, the system comprising: a processor; and a memory having instructions thereon, which, when executed by the processor, cause the processor to: a) receive a set of data comprising the expression level of each of one or more genes of the subject, the genes comprising ALPL, ATRAID, BCL6, CDK11A, CFLAR, COMMD5, GOLGA1, IL1B, IMPDH2, JAK3, KLHDC3, LIMK2, NOD2, NOTCH1, SPINT2, SPON2, STOML2, TRIM25, or ZFP36.

Other aspects and advantages of the present disclosure will become apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be appreciated, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the following drawings and descriptions are to be regarded as illustrative in nature, rather than restrictive.

Incorporation by Reference

All publications, patents and patent applications mentioned in this specification are incorporated into this specification by reference to the same extent as if each publication, patent or patent application was specifically and individually incorporated into this specification by reference. If the publications and patents or patent applications incorporated by reference conflict with the disclosure contained in this specification, this specification shall supersede or take precedence over any such conflicting material.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is an exemplary embodiment of mapping proteins encoded by transcripts of predicted responses to the human interactome. Proteins are represented by circles and paired physical protein-protein interactions are represented by lines. The RA disease module consists of seed genes (red) and DIAMOnD genes (brown). Proteins encoded by 11 transcript features (squares) are significantly associated with the RA disease module (p value < 0.05).

Figures 2A, 2B, 2C and 2D show cross-validation of the molecular signature response classifier ("MSRC") in 245 patients from the CorronaCERTAIN study. Figure 2A shows the object working curve for patient stratification based on CDAI, DAS28-CRP, ACR70 and ACR50 clinical results. Figure 2B shows a comparison of model scores for patients with or without non-responsive molecular features. The boxes and cross lines represent the interquartile range and median, respectively. The bisecting colored lines represent changes in the mean. The percentage ratios of patients with or without non-responsive molecular features in CDAI remission, LDA, moderate or high disease activity are shown in Figures 2C (CDAI) and 2D (DAS28-CRP). The bars represent a larger proportion of patients with molecular features above 1.0 or without molecular features below 1.0. NA: Not applicable, no patients in the category.

Figure 3A, Figure 3B, Figure 3C, Figure 3D, Figure 3E and Figure 3F show the validation of MSRC to identify patients who have not received targeted therapy and are unlikely to respond to TNFi therapy. Object working curve diagram of patient stratification based on CDAI, DAS28-CRP, ACR70 and ACR50 clinical results at 3 months (Figure 3A) and 6 months (Figure 3B). Comparison of model scores of patients with or without non-responsive molecular features at 3 months (Figure 3C) and 6 months (Figure 3D). Boxes and cross lines represent interquartile ranges and medians, respectively. Bisection colored lines represent changes in mean values. The percentage ratio of patients with or without non-responsive molecular features in CDAI remission, LDA, moderate or high disease activity according to CDAI (Figure 3E) and DAS28-CRP (Figure 3F). The column represents a larger proportion of patients with molecular features above 1.0 or without molecular features below 1.0. NA: Not applicable, no patients in the category; NS: Not significant.

Figures 4A and 4B show the validation of the MSRC to identify patients exposed to TNFi who are unlikely to respond to TNFi therapy. Figure 4A shows the object working curve for stratifying patients receiving TNFi therapy based on the achievement of CDAI remission or DAS28-CRP remission 3 months after the test results. Figure 4B shows a comparison of model scores for patients with or without non-responsive molecular features. The boxes and cross lines represent the interquartile range and median, respectively. The bisection colored line represents the change in the mean.

The biology of an inadequate response to TNFi therapy is demonstrated in Figure 5. The MSRC includes transcripts encoding proteins involved in numerous aspects of RA pathophysiology: innate immune response, cytokine biosynthesis, T and B cell homeostasis, bone homeostasis, unfolded protein response, autophagy, apoptosis, and pro-inflammatory signaling.

Figure 6 is a flow chart of the study design. A subset of 345 patients from the CERTAIN study was analyzed: 100 for identification of transcript biomarkers of non-response to TNFi therapy and 245 for cross-validation. 273 were enrolled in the NETWORK-004 prospective observational study; 244 passed the initial screening for enrollment, 194 completed 3 months of follow-up, and 168 completed 6 months of follow-up. 87% (146/168) of patients who completed the study had complete molecular and clinical data required for validation analysis.

7 is a Venn diagram showing the classification of patients who provided samples at 3 months, 6 months, and both 3 and 6 months of exposure to TNF therapy.

Figures 8A, 8B, 8C, and 8D provide ROC curves showing PrismRA performance in patient samples collected 3 months and 6 months after TNF initiation. Figure 8A shows the 3 month sample using +3 month results. Figure 8B shows the 3 month sample using +6 month results. Figure 8C shows the 6 month sample using +3 month results. Figure 8D shows the 6 month sample using +6 month results.

Figures 9A and 9B provide ROC curves showing model performance in 122 patients providing 3-month and 6-month samples. Figure 9A shows 3-month samples using the +6-month endpoint. Figure 9B shows 6-month samples using the +3-month endpoint.

FIG. 10 provides an example computer system for executing methods according to some aspects or embodiments of the present disclosure.

DETAILED DESCRIPTION

An important issue with various therapies (e.g., anti-TNF therapy) is the inconsistent response rates. Indeed, recent international conferences designed to bring together leading scientists and clinicians in the fields of immunology and rheumatology to identify unmet needs in these areas almost universally recognized that uncertainty in response rates is an ongoing challenge. For example, the 19th Annual International Conference on Targeted Therapies, which held a breakout session on treatment challenges in multiple diseases, including rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, systemic lupus erythematosus, and connective tissue diseases (e.g., Sjögren's syndrome, systemic sclerosis, vasculitis, including Bechet and IgG4-related disease), identified certain issues common to all of these diseases, particularly the “need for a better understanding of the heterogeneity of each disease… so that predictive tools for treatment response can be developed.” See Winthrop, et al., "The unmet need in rheumatology: Reports from the targeted therapies meeting 2017," Clin. Immunol. pii: S1521-6616 (17) 30543-0, Aug. 12, 2017, the contents of which are incorporated herein by reference for all purposes. Similarly, a large literature on anti-tumor necrosis factor therapy for Crohn's disease has also complained about the unstable response rates and the inability to predict which patients will benefit from it. For example, see M.T.Abreu, “Anti-TNF Failures in Crohn’s Disease,” Gastroenterol Hepatol (NY), 7(1):37-39 (Jan. 2011), and also see Ding et al., “Systematic review: predicting and optimizing response to anti-TNF therapy in Crohn’s disease-algorithm for practical management,” Aliment Pharmacol. Ther., 43(1):30-51 (Jan. 2016), the contents of which are incorporated herein by reference for all purposes (reporting that “initial nonresponse to anti-TNF therapy affects 13-40% of patients”).

As a result, a large number of patients currently receiving anti-TNF therapy do not benefit from treatment and may even be harmed. The risk of serious infections and malignancies associated with anti-TNF therapy is so great that a so-called "black box warning" may need to be stated on the label when the product is approved. Other potential side effects of this type of treatment include, for example, congestive heart failure, demyelinating disease, and other systemic side effects. In addition, given that patients require weeks to months of treatment before being identified as unresponsive to anti-TNF therapy (e.g., as non-responders to anti-TNF therapy), appropriate treatment of such patients may be greatly delayed due to the current inability to identify responders and non-responders. For example, see Roda et al., “Loss of Response to Anti-TNFs: Definition, Epidemiology, and Management,” Clin. Trani. Gastroenterol., 7(1): el35 (Jan. 2016), the contents of which are incorporated herein by reference for all purposes (citing Hanauer et al., “ACCENT I Study group. Maintenance Infliximab for Crohn’s disease: the ACCENT I randomized trial,” Lancet 59: 1541-1549 (2002); Sands et al., “Infliximab maintenance therapy for fistulizing Crohn’s disease,” N. Engl. J. Med. 350: 876-885 (20004)).

Thus, in some embodiments, the present disclosure provides a method of treating a subject with an anti-TNF therapy, the method comprising: administering the anti-TNF therapy to a patient determined to be a responder by a classifier established to distinguish between responding prior subjects and non-responding prior subjects in a cohort who have received the anti-TNF therapy, wherein the classifier is developed by evaluating: one or more genes whose expression levels are significantly correlated with clinical responsiveness or non-responsiveness (e.g., in a linear or non-linear manner); and at least one of: the presence of one or more single nucleotide polymorphisms (SNPs) in the expressed sequences of the one or more genes; or at least one clinical characteristic of the responding prior subjects and the non-responding prior subjects.

Provided herein are systems and methods for automatically predicting a subject's response to anti-TNF therapy. Also provided herein are modular systems for automatically interpreting genomic or multi-omics data.

As used herein, the term "administering" generally refers to administering a composition to a subject or system, for example, to effect delivery of an agent that is or is contained in or otherwise delivered by the composition.

As used herein, the term "agent" generally refers to an entity (e.g., a lipid, a metal, a nucleic acid, a polypeptide, a polysaccharide, a small molecule, etc., or a complex, combination, mixture or system thereof [e.g., a cell, a tissue, an organism]) or a phenomenon (e.g., heat, electric current or field, magnetic force or magnetic field, etc.).

As used herein, the term "amino acid" generally refers to any compound or substance that can be incorporated into a polypeptide chain, for example, by forming one or more peptide bonds. In some embodiments, an amino acid has the general structure H ₂ N-C(H)(R)-COOH. In some embodiments, the amino acid is a naturally occurring amino acid. In some embodiments, the amino acid is a non-natural amino acid; in some embodiments, the amino acid is a D-amino acid; in some embodiments, the amino acid is an L-amino acid. As used herein, the term "standard amino acid" refers to any of the twenty L-amino acids commonly found in naturally occurring peptides. "Non-standard amino acid" refers to any amino acid other than a standard amino acid, regardless of whether it may be present in a natural source. In some embodiments, amino acids, including the carboxyl or amino terminal amino acid in a polypeptide, may contain structural modifications compared to the general structure above. For example, in some embodiments, an amino acid may be modified by methylation, amidation, acetylation, pegylation, glycosylation, phosphorylation, or substitution (e.g., amino group, carboxylic acid group, one or more protons or hydroxyl groups) compared to the general structure. In some embodiments, such modifications may, for example, change the stability or circulation half-life of a polypeptide containing a modified amino acid compared to a polypeptide containing the same unmodified amino acid. In some embodiments, such modifications do not significantly change the relevant activity of a polypeptide containing a modified amino acid compared to a polypeptide containing the same unmodified amino acid. As can be clearly seen from the context, in some embodiments, the term "amino acid" can be used to refer to a free amino acid; in some embodiments, it can be used to refer to an amino acid residue of a polypeptide, for example, an amino acid residue within a polypeptide. As used herein, the term "analog" generally refers to a substance that shares one or more specific structural features, elements, components, or parts with a reference substance. Typically, an "analog" shows significant structural similarity to a reference substance, such as a shared core or a shared structure, but also differs in some discrete ways. In some embodiments, an analog is a substance that can be generated from a reference substance, such as by chemical manipulation of a reference substance. In some embodiments, an analog is a substance that can be generated by performing a synthetic process that is substantially similar to (e.g., sharing multiple steps) the synthetic process for generating a reference substance. In some embodiments, an analog is or can be generated by performing a synthetic process that is different from the synthetic process used to generate a reference substance.

As used herein, the term "antagonist" may generally refer to an agent or condition whose presence, level, degree, type, or form is associated with a decrease in the level or activity of a target. Antagonists may include agents of any chemical class, including, for example, small molecules, polypeptides, nucleic acids, carbohydrates, lipids, metals, or any other entity that exhibits the relevant inhibitory activity. In some embodiments, an antagonist may be a "direct antagonist" because it binds directly to its target; in some embodiments, an antagonist may be an "indirect antagonist" because it exerts its influence through a mechanism other than directly binding to its target; for example, by interacting with a modulator of the target, thereby changing the level or activity of the target). In some embodiments, an "antagonist" may be referred to as an "inhibitor".

As used herein, the term "antibody" generally refers to a polypeptide comprising a classical immunoglobulin sequence element sufficient to confer specific binding to a specific target antigen. In some embodiments, a complete antibody produced in nature is a tetrameric agent of about 150 kD, consisting of two identical heavy chain polypeptides (each about 50 kD) and two identical light chain polypeptides (each about 25 kD), which are associated with each other into a substance commonly referred to as a "Y-shaped" structure. In some embodiments, each heavy chain consists of at least four domains (each about 110 amino acids long) - an amino terminal variable (VH) domain (located at the top of the Y structure), followed by three constant domains: CH1, CH2, and carboxyl terminal CH3 (located at the bottom of the stem of the Y). In some embodiments, a short region called a "switch" connects the heavy chain variable region and the constant region. A "hinge" connects the CH2 and CH3 domains to the rest of the antibody. In some embodiments, two disulfide bonds in the hinge region connect two heavy chain polypeptides to another in the complete antibody. In some embodiments, each light chain consists of two domains - an amino-terminal variable (VL) domain, followed by a carboxyl-terminal constant (CL) domain, separated from each other by another "switch". In some embodiments, a complete antibody tetramer consists of two heavy chain-light chain dimers, in which the heavy chain and the light chain are interconnected by a single disulfide bond; two additional disulfide bonds interconnect the heavy chain hinge regions, thereby connecting the dimers to each other and forming a tetramer. In some embodiments, naturally produced antibodies are also glycosylated, such as on the CH2 domain. In some embodiments, each domain in a natural antibody has a structure characterized by an "immunoglobulin fold" formed by two beta folds (e.g., 3, 4 or 5 chain folds) that are tightly packed with each other in a compressed antiparallel beta barrel. In some embodiments, each variable domain contains three hypervariable loops (CDR1, CDR2, and CDR3) called "complementarity determining regions" and four "framework" regions (FR1, FR2, FR3, and FR4) that are invariant to a certain extent. In some embodiments, when the natural antibody is folded, the FR region forms a β fold that provides a structural framework for the domain, and the CDR loop regions from the heavy chain and the light chain are brought together in three-dimensional space so that they form a single hypervariable antigen binding site located at the top of the Y structure. In some embodiments, the Fc region of a naturally occurring antibody binds to elements of the complement system and also binds to receptors on effector cells, including, for example, effector cells that mediate cytotoxicity. In some embodiments, the affinity or other binding properties of the Fc region to the Fc receptor can be regulated by glycosylation or other modifications. In some embodiments, antibodies produced or utilized according to the present disclosure include glycosylated Fc domains, including such glycosylated Fc domains with modifications or engineering. For the purposes of the present disclosure, in certain embodiments, any polypeptide or polypeptide complex including sufficient immunoglobulin domain sequences found in natural antibodies can be referred to or used as an "antibody", whether the polypeptide is naturally produced (e.g., produced by an organism in response to an antigen) or produced by recombinant engineering, chemical synthesis or other artificial systems or methods. In some embodiments, the antibody is polyclonal; in some embodiments, the antibody is monoclonal. In some embodiments, the antibodies have constant region sequences that are specific to mouse, rabbit, primate, or human antibodies. In some embodiments, the antibody sequence elements are humanized, primatized, chimeric, and the like. In addition, as used herein, the term "antibody" may refer to any construct or form for utilizing the structural and functional characteristics of antibodies in alternative presentations in appropriate embodiments (unless otherwise specified or clear from the context). For example, in some embodiments, the form of an antibody used in accordance with the present disclosure is selected from, but not limited to, a complete IgA, IgG, IgE, or IgM antibody; a bispecific or multispecific antibody (e.g., etc.); antibody fragments, such as Fab fragments, Fab' fragments, F(ab')2 fragments, Fd' fragments, Fd fragments and isolated CDRs or collections thereof; single-chain Fv; polypeptide-Fc fusions; single-domain antibodies (e.g., shark single-domain antibodies, such as IgNAR or fragments thereof); camel-like antibodies; hidden antibodies (e.g., ); Small Modular ImmunoPharmaceuticals (“SMIPs ^™ ”); Single-chain or tandem dimers VI-11-1s; Mini-antibodies; Ankyrin repeat protein or DART;TCR-like antibody; MicroProteins; as well as In some embodiments, an antibody may lack a covalent modification (e.g., an attached glycan) that it would have if it were naturally produced. In some embodiments, an antibody may comprise a covalent modification (e.g., an attached glycan, a payload [e.g., a detectable moiety, a therapeutic moiety, a catalytic moiety, etc.] or other side groups [e.g., polyethylene glycol, etc.]).

If the presence, level, degree, type or form of one event or entity is related to another, then two events or entities are generally "associated" with each other, as the term is used herein. For example, if the presence, level or form of a particular entity (e.g., a polypeptide, a genetic signature, a metabolite, a microorganism, etc.) is related to the incidence or susceptibility of a disease, disorder or condition (e.g., in a related population), the entity is considered to be associated with a particular disease, disorder or condition. In some embodiments, if two or more entities interact directly or indirectly so that they are in physical proximity to each other or remain in physical proximity to each other, they are physically "associated" with each other. In some embodiments, two or more entities that are physically associated with each other are covalently linked to each other; in some embodiments, two or more entities that are physically associated with each other are not covalently linked to each other, but are non-covalently associated, such as by means of hydrogen bonds, van der Waals interactions, hydrophobic interactions, magnetism, and combinations thereof.

As used herein, the term "biological sample" generally refers to a sample obtained or derived from a biological source of interest (e.g., tissue or organism or cell culture) as described herein. In some embodiments, the source of interest includes an organism, such as an animal or a human. In some embodiments, the biological sample is or includes a biological tissue or fluid. In some embodiments, the biological sample can be or include bone marrow; blood; blood cells; ascites; tissue or fine needle biopsy samples; body fluids containing cells; free floating nucleic acids; sputum; saliva; urine; cerebrospinal fluid, peritoneal fluid; pleural fluid; feces; lymph; gynecological fluid; skin swab; vaginal swab; oral swab; nasal swab; washing fluid or lavage fluid, such as catheter lavage fluid or bronchoalveolar lavage fluid; aspirate; scrapings; bone marrow sample; tissue biopsy sample; surgical sample; feces, other body fluids, secretions or excretions; or cells therein, etc. In some embodiments, the biological sample is or includes cells obtained from an individual. In some embodiments, the obtained cells are or include cells from the individual from which the sample was obtained. In some embodiments, the sample is a "raw sample" obtained directly from a source of interest by any appropriate means. For example, in some embodiments, the original biological sample is obtained by a method selected from the following: biopsy (e.g., fine needle aspiration or tissue biopsy), surgery, collection of body fluids (e.g., blood, lymph, feces, etc.), etc. In some embodiments, as can be clearly seen from the context, the term "sample" refers to a preparation obtained by processing the original sample (e.g., by removing one or more components thereof or by adding one or more agents thereto). For example, filtering using a semi-permeable membrane. Such a "processed sample" may include, for example, nucleic acids or proteins extracted from the sample or obtained by subjecting the original sample to techniques such as amplification or reverse transcription of mRNA, separation or purification of certain components.

As used herein, the term "combination therapy" generally refers to a clinical intervention in which an object is exposed to two or more treatment regimens (e.g., two or more therapeutic agents) at the same time. In some embodiments, two or more treatment regimens may be administered simultaneously. In some embodiments, two or more treatment regimens may be administered sequentially (e.g., the first regimen is administered before the second regimen of any dose is administered). In some embodiments, two or more treatment regimens are administered with overlapping dosing regimens. In some embodiments, the administration of combination therapy may involve administering one or more therapeutic agents or modes to an object receiving another one or more agents or modes. In some embodiments, combination therapy does not necessarily require that individual agents be administered together in a single composition (or not even necessarily at the same time). In some embodiments, two or more therapeutic agents or modes of combination therapy are administered to an object separately (e.g., in the form of a separate composition) by a separate route of administration (e.g., one agent is administered orally, and another agent is administered intravenously) or at different time points. In some embodiments, two or more therapeutic agents or modes of combination therapy may be administered together by the same route of administration or simultaneously in a combination composition or even in a combination compound (e.g., as part of a single chemical complex or covalent entity).

As used herein, the term "comparable" generally refers to two or more agents, entities, situations, condition sets, etc. that may be different from each other, but are similar enough to allow comparison with each other, so that conclusions can be reasonably drawn based on observed differences or similarities. In some embodiments, the characteristics of a comparable condition set, environment, individual or colony are a plurality of substantially identical features and one or a small amount of change features. It is understood that in the context, in any given case, two or more such agents, entities, situations, condition sets, etc. are considered to be comparable required degrees of identity. For example, when characterized by substantially the same features of sufficient quantity and type to ensure the following reasonable conclusions, the set of environments, individuals or colonies can be comparable to each other: the difference in the results or observed phenomena obtained under or using the different environments, individuals or colony sets is caused by the changes in the features that have changed or indicates the changes in the features.

As used herein, the phrase "corresponding to" generally refers to a relationship between two entities, events, or phenomena that share sufficient reasonably comparable features so that the "corresponding" attribute is obvious. For example, in some embodiments, the term may be used with reference to a compound or composition to specify the position or identity of a structural element in a compound or composition by comparison with an appropriate reference compound or composition. For example, in some embodiments, a monomer residue in a polymer (e.g., an amino acid residue in a polypeptide or a nucleic acid residue in a polynucleotide) may be identified as a residue "corresponding to" an appropriate reference polymer. For example, a person of ordinary skill will understand that, for simplicity, a canonical numbering system is generally used to specify residues in a polypeptide based on a reference to a related polypeptide, so that, for example, the amino acid "corresponding to" the residue at position 190 does not actually need to be the 190th amino acid in a particular amino acid chain, but rather corresponds to the residue at 190 in a reference polypeptide; a variety of approaches can be used to identify "corresponding" amino acids. For example, there are various sequence alignment strategies, including software programs such as BLAST, CS-BLAST, CUSASW++, DIAMOND, FASTA, GGSEARCH/GLSEARCH, Genoogle, HMMER, HHpred/HHsearch, IDF, Infernal, KLAST, USEARCH, parasail, PSI-BLAST, PSI-Search, ScalaBLAST, Sequilab, SAM, SSEARCH, SWAPHI, SWAPHI-LS, SWIMM, or SWIPE, which can be used according to the present disclosure, for example, to identify "corresponding" residues in a polypeptide or nucleic acid.

As used herein, the term "dosage regimen" generally refers to a group of unit doses (e.g., more than one) administered separately to a subject, for example, separated by time periods. In some embodiments, a given therapeutic agent has a recommended dosing regimen, which may involve one or more doses. In some embodiments, the dosing regimen includes multiple doses, each of which is separated from other doses in time. In some embodiments, single doses are separated by time periods of the same length from each other; in some embodiments, the dosing regimen includes multiple doses and at least two different time periods separating single doses. In some embodiments, all doses within the dosing regimen have the same unit dose amount. In some embodiments, different doses within the dosing regimen have different amounts. In some embodiments, the dosing regimen includes a first dose of a first dose amount, followed by one or more additional doses of a second dose amount different from the first dose amount. In some embodiments, the dosing regimen includes a first dose of a first dose amount, followed by one or more additional doses of a second dose amount identical to the first dose amount. In some embodiments, when administered in a related population, the dosing regimen is associated with a desired or beneficial outcome (e.g., a therapeutic dosing regimen).

As used herein, the terms "improve", "increase" or "decrease", or their grammatically equivalent comparative terms, generally indicate a value relative to a comparable reference measurement result. For example, in some embodiments, the evaluation value obtained using the agent of interest may be "improved" relative to the evaluation value obtained using a comparable reference agent. Alternatively or additionally, in some embodiments, the evaluation value obtained in the object or system of interest may be "improved" relative to the evaluation value obtained under different conditions (e.g., before or after an event such as administration of the agent of interest) in the same object or system, or in a different comparable object (e.g., in the presence of one or more indicators of a specific disease, disorder or condition of interest, a comparable object or system different from the object or system of interest, or previously exposed to a condition or agent, etc.).

As used herein, the term "pharmaceutical composition" generally refers to an active agent formulated with one or more pharmaceutically acceptable carriers. In some embodiments, the active agent is present in a unit dosage amount suitable for administration to a relevant subject in a treatment regimen (e.g., an amount that has been shown to show a statistically significant probability of achieving a predetermined therapeutic effect when administered), or in a different comparable subject (e.g., in the presence of one or more indicators of a particular disease, disorder, or condition of interest, or before exposure to a condition or agent, etc., a comparable subject or system different from the subject or system of interest). In some embodiments, comparative terms refer to statistically relevant differences (e.g., having a prevalence or amplitude sufficient to achieve statistical correlation).

As used herein, the phrase "pharmaceutically acceptable" generally refers to those compounds, materials, compositions or dosage forms which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other problem or complication, commensurate with a reasonable benefit/risk ratio.

As used herein, the term "reference" generally describes a standard or control with which it is compared. For example, in some embodiments, an agent, animal, individual, colony, sample, sequence or value of interest is compared to a reference or control agent, animal, individual, colony, sample, sequence or value. In some embodiments, the reference or control is tested or determined substantially simultaneously with the test or determination of interest. In some embodiments, the reference or control is a historical reference or control, optionally embodied in a tangible medium. In some embodiments, the reference or control is determined or characterized under conditions or circumstances comparable to those evaluated. It can be determined that there is enough similarity to prove the reliance or comparison on a specific possible reference or control.

As used herein, the term "therapeutically effective amount" generally refers to the amount of a substance (e.g., a therapeutic agent, composition, or formulation) that causes a desired biological response when administered as part of a treatment regimen. In some embodiments, the therapeutically effective amount of a substance is an amount sufficient to treat, diagnose, prevent, or delay the onset of a disease, disorder, or condition when administered to a subject suffering from or susceptible to a disease, disorder, or condition. As will be understood by those of ordinary skill in the art, the effective amount of a substance may vary according to factors such as the desired biological endpoint, the substance to be delivered, the target cell, or tissue. For example, the effective amount of a compound in a formulation for treating a disease, disorder, or condition is an amount that mitigates, improves, alleviates, inhibits, prevents, delays the onset of one or more symptoms or features of a disease, disorder, or condition, reduces its severity, or reduces its incidence. In some embodiments, a therapeutically effective amount is administered in a single dose; in some embodiments, multiple unit doses are required to deliver a therapeutically effective amount.

As used herein, the term "variant" generally refers to an entity that exhibits significant structural identity with a reference entity, but is structurally different from the reference entity in the presence or level of one or more chemical moieties compared to the reference entity. In many embodiments, the variant is also functionally different from its reference entity. Generally, whether a particular entity is properly regarded as a "variant" of a reference entity depends on the degree of structural identity with the reference entity. Any biological or chemical reference entity has certain characteristic structural elements. According to the definition, a variant is a unique chemical entity that shares one or more such characteristic structural elements. To name just a few examples, a small molecule may have a characteristic core structural element (e.g., a macrocyclic core) or one or more characteristic side groups, so that the variant of a small molecule is a variant (single to double, E to Z, etc.) that shares a core structural element and a characteristic side group part but is different in the bond type present in other side groups or cores, a polypeptide may have a characteristic sequence element composed of multiple amino acids that have a specified position relative to each other in a linear or three-dimensional space or contribute to a specific biological function, and a nucleic acid may have a characteristic sequence element composed of multiple nucleotide residues that have a specified position relative to each other in a linear or three-dimensional space. For example, due to one or more differences in the amino acid sequence or one or more differences in the chemical part (e.g., carbohydrates, lipids, etc.) covalently connected to the polypeptide backbone, the variant polypeptide may be different from the reference polypeptide. In some embodiments, the variant polypeptide shows an overall sequence identity of at least 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97% or 99% with the reference polypeptide. Alternatively or additionally, in some embodiments, the variant polypeptide does not share at least one characteristic sequence element with the reference polypeptide. In some embodiments, the reference polypeptide has one or more biological activities. In some embodiments, the variant polypeptide shares one or more biological activities of the reference polypeptide. In some embodiments, the variant polypeptide lacks one or more biological activities of the reference polypeptide. In some embodiments, compared with the reference polypeptide, the variant polypeptide exhibits one or more biological activities of reduced levels. In many embodiments, if the polypeptide of interest has an amino acid sequence identical to the parent but with a small amount of sequence changes at a specific position, the polypeptide of interest is considered to be a "variant" of the parent or reference polypeptide. For example, compared with the parent, less than 20%, 15%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2% of the residues are replaced in the variant. In some embodiments, compared with the parent, the variant has 10, 9, 8, 7, 6, 5, 4, 3, 2 or 1 substituted residues. Typically, the variant has a very small amount (e.g., less than 5, 4, 3, 2 or 1) of substituted functional residues (e.g., residues involved in specific biological activity). In addition, compared with the parent, the variant can have no more than 5, 4, 3, 2 or 1 additions or deletions, and generally no additions or deletions. In addition, any addition or deletion can be less than about 25, about 20, about 19, about 18, about 17, about 16, about 15, about 14, about 13, about 10, about 9, about 8, about 7, about 6, and generally less than about 5, about 4, about 3 or about 2 residues. In some embodiments, a parent or reference polypeptide is a polypeptide found in nature.

A. Provided classifiers

The present disclosure provides a classifier and the development of such a classifier, which can identify (e.g., predict) which patients will or will not respond to a particular therapy. In some embodiments, the classifier is established to distinguish between responders and non-responders of previous subjects who have received anti-TNF therapy (e.g., a specific anti-TNF agent or regimen).

Among other things, the present disclosure encompasses the insight that expression levels of specific sets of genes, alone or in combination with each other, optionally in combination with specific clinical features or the presence or absence of specific single nucleotide polymorphisms, can be used to predict response (e.g., one or more features of response) to anti-TNF therapy.

In some embodiments, the present disclosure provides a classifier that is or includes such gene expression levels, clinical features, or SNPs, and proves to have been established to distinguish between subjects who respond to and do not respond to anti-TNF therapy. In some embodiments, the classifier provided is established to distinguish between subjects who respond or do not respond to anti-TNF therapy (e.g., anti-TNF therapy naive subjects) by retrospective analysis of a historical (e.g., previous) subject population that receives anti-TNF therapy and whose responsiveness is known (e.g., previously determined). In some embodiments, when applied to such historical (e.g., previous) populations, a classifier that identifies at least 50% of non-responders in a cohort with an accuracy of at least 70% is considered "validated". In some embodiments, when applied to such historical (e.g., previous) populations, a classifier that identifies at least 60% of non-responders in a cohort with an accuracy of at least 70% is considered "validated". In some embodiments, when applied to such historical (e.g., previous) populations, a classifier that identifies at least 70% of non-responders in a cohort with an accuracy of at least 70% is considered "validated". In some embodiments, when applied to such historical (e.g., previous) populations, a classifier that identifies at least 70% of non-responders in a cohort with an accuracy of at least 70% is considered "validated". In some embodiments, a classifier that identifies at least 80% of non-responders in a cohort with an accuracy of at least 70% when applied to such a historical (e.g., previous) population is considered "validated". In some embodiments, a classifier that identifies at least 90% of non-responders in a cohort with an accuracy of at least 70% when applied to such a historical (e.g., previous) population is considered "validated". In some embodiments, a classifier that identifies at least 99% of non-responders in a cohort with an accuracy of at least 70% when applied to such a historical (e.g., previous) population is considered "validated".

In some embodiments, a classifier that identifies at least 50% of non-responders in a cohort with an accuracy of at least 80% when applied to such a historical (e.g., previous) population is considered "validated". In some embodiments, a classifier that identifies at least 50% of non-responders in a cohort with an accuracy of at least 90% when applied to such a historical (e.g., previous) population is considered "validated". In some embodiments, a classifier that identifies at least 50% of non-responders in a cohort with an accuracy of at least 99% when applied to such a historical (e.g., previous) population is considered "validated".

In some embodiments, the present disclosure provides methods of treating a subject having a disease, disorder, or condition, comprising administering an anti-TNF therapy to one or more subjects that have been determined by application of a provided classifier to be likely to respond to such anti-TNF therapy; alternatively or additionally, in some embodiments, the present disclosure provides methods of treating a subject having a disease, disorder, or condition, comprising discontinuing anti-TNF therapy, or administering an alternative to anti-TNF therapy to one or more subjects that have been determined by application of a provided classifier to be unlikely to respond to such anti-TNF therapy.

In some embodiments, the classifier provided can be or include the gene expression information of one or more genes.Alternately or additionally, in some embodiments, the classifier provided can be or include the presence or absence of one or more single nucleotide polymorphisms (SNPs) or one or more clinical features or characteristics of related objects.

In some embodiments, a classifier is developed by evaluating: one or more genes whose expression levels are significantly correlated (e.g., in a linear or nonlinear manner) with clinical responsiveness or non-responsiveness; the presence of one or more SNPs; and at least one clinical feature.

In some embodiments, as described herein, the classifier is developed by retrospective analysis of one or more features (e.g., gene expression levels, presence or absence of one or more SNPs, etc.) of biological samples from patients (e.g., previous subjects) who have received anti-TNF therapy and have been determined to have a response (e.g., responder) or a non-responder (e.g., non-responder); alternatively or additionally, in some embodiments, the classifier is developed by retrospective analysis of one or more clinical features of such patients, which may or may not involve the evaluation of any biological samples (and can be completed, for example, by reference to medical records). In some embodiments, all such patients have received the same anti-TNF therapy (optionally for the same or different time periods); alternatively or additionally, in some embodiments, all such patients have been diagnosed with the same disease, disorder or condition. In some embodiments, the patients whose biological samples are analyzed in the retrospective analysis have received different anti-TNF therapies (e.g., using different anti-TNF agents or according to different regimens); alternatively or additionally, in some embodiments, the patients whose biological samples are analyzed in the retrospective analysis have been diagnosed with different diseases, disorders or conditions.

Many statistical classification techniques are suitable for use as methods to perform the above classification (e.g., distinguishing subjects who respond to anti-TNF therapy from subjects who do not respond). These methods include, but are not limited to, supervised learning methods.

In supervised learning methods, a set of samples from two or more groups (e.g., samples that respond and do not respond to anti-TNF therapy) are analyzed or processed using statistical classification methods. The absence/presence of genes or specific SNPs or variants described herein, or the expression levels of genes or biomarkers can be used as the basis for classifiers that distinguish between two or more groups. New samples can then be analyzed or processed so that the classifier can associate the new sample with one of the two or more groups.

Commonly used supervised classifiers include, but are not limited to, neural networks (e.g., artificial neural networks, multilayer perceptrons), support vector machines, k-nearest neighbors, Gaussian mixture models, Gaussian, naive Bayes, decision trees, and radial basis function (RBF) classifiers. Linear classification methods include Fisher linear discriminant, logistic regression, naive Bayes classifier, perceptron, and support vector machine (SVM). Other classifiers used together with the method according to the present disclosure include quadratic classifiers, k-nearest neighbors, boosting, decision trees, random forests, neural networks, pattern recognition, Bayesian networks, and hidden Markov models. Other classifiers commonly used in supervised learning include improvements or combinations thereof, and may also be applicable to the methods described herein.

Classification using supervised methods can usually be performed as follows.

1) Collect training sets. These may include, for example, expression levels of one or more genes or biomarkers described herein from samples of patients who have or have not responded to anti-TNF therapy. The training samples are used to "train" the classifier.

2) Determine the input "feature" representation of the learned function. The accuracy of the learned function depends on how the input object is represented. For example, the input object is converted into a feature vector, which contains multiple features that describe the object. These features may include a set of genes detected in a sample from a patient or subject.

3) Determine the structure of the learned function and the corresponding learning algorithm. Select a learning algorithm, such as an artificial neural network, decision tree, Bayesian classifier, or support vector machine. The learning algorithm is used to build a classifier.

4) Build a classifier (e.g., a classification model). Run a learning algorithm on the collected training set. The parameters of the learning algorithm can be adjusted by optimizing the performance on a subset of the training set (called a validation set) or by cross-validation. After parameter adjustment and learning, the performance of the algorithm can be measured on a test set of original samples independent of the training set. The model established may involve feature coefficients or importance measures assigned to each feature.

In some cases, individual characteristics are individual genes or the level of individual genes. In some cases, the level of a gene is a normalized value, mean value, median value, average, adjusted mean value or other adjusted level or value. Individual characteristics can include or consist of a gene set or gene panel, such as a gene set provided herein.

Once a classifier (eg, a classification model) has been determined ("trained") as described above, it can be used to classify a sample, eg, a patient sample comprising expressed genes analyzed or processed according to the methods described herein.

1. Gene expression

In some embodiments, the gene expression aspect of the classifier as described herein is determined by evaluating one or more genes whose expression levels are significantly correlated with clinical responsiveness or non-responsiveness (e.g., in a linear or non-linear manner); and at least one of the following: the presence of one or more single nucleotide polymorphisms (SNPs) in the expressed sequence of one or more genes; or at least one clinical feature of a prior subject with and without response. Gene response features may include genes whose expression levels show statistically significant differences between responder and non-responder populations.

In some embodiments, the present disclosure embodies the insight that a problem with certain previous efforts to identify or provide classifiers between responders and non-responders is that comparisons of gene expression levels in responder and non-responder populations emphasize or focus (often only on) genes that exhibit the greatest differences in expression levels between populations (e.g., greater than a 2-fold change). The present disclosure recognizes that even those genes that have relatively small differences in expression levels (e.g., less than a 2-fold change in expression) provide useful information and are valuable to include in the classifiers in the embodiments described herein.

Furthermore, in some embodiments, the present disclosure embodies the insight that analysis of interaction patterns of genes whose expression levels show statistically significant differences (optionally including small differences) between responder and non-responder groups as described herein provides new and valuable information that substantially improves the quality and predictive power of classifiers.

In some embodiments, the classifier provided is or includes a gene or gene set that can be used to determine whether a subject will respond to a particular therapy (e.g., an anti-TNF therapy) (e.g., expression levels are correlated therewith). In some embodiments, the classifier is developed by evaluating the following: one or more genes whose expression levels are significantly correlated (e.g., in a linear or nonlinear manner) with clinical responsiveness or non-responsiveness; and at least one of the following: the presence of one or more single nucleotide polymorphisms (SNPs); and at least one clinical feature of a prior subject with a response and a non-response.

In some embodiments, the one or more genes for use in a classifier or for measuring gene expression are selected from the genes in Table 1 and combinations thereof:

Table 1

In some embodiments, the genes used for the classifier or for measuring gene expression are selected from two or more genes in Table 1. In some embodiments, the genes used for the classifier or for measuring gene expression are selected from two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, or all nineteen genes in Table 1.

In some embodiments, the genes used in the classifier or for measuring gene expression are selected from one or more genes in Table 2 and combinations thereof.

Table 2

ALPL BCL6 CDK11A CFLAR IL1B JAK3 LIMK2 NOD2 NOTCH1 TRIM25 ZFP36

In some embodiments, the genes used for the classifier or for measuring gene expression are selected from two or more genes in Table 2. In some embodiments, the genes used for the classifier or for measuring gene expression are selected from two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, or all eleven genes in Table 2.

In some embodiments, the gene expression patterns in the classifier can be identified or detected using mRNA or protein expression data sets, for example, can be or have been prepared from validated biological data (e.g., biological data derived from publicly available databases such as the Gene Expression Omnibus ("GEO")). In some embodiments, the classifier can be derived by comparing the gene expression levels of previous subjects known to respond and known to not respond to a particular therapy (e.g., anti-TNF therapy). In some embodiments, certain genes (e.g., signature genes) are selected from the gene expression data cohort to be used to develop the classifier.

In some embodiments, characteristic genes or expression patterns are identified by methods similar to those reported in Santolini, "A personalized, multiomics approach identifies genes involved in cardiachypertrophy and heart failure", Systems Biology and Applications, (2018) 4: 12; doi: 10.1038/s41540-018-0046-3, which is incorporated herein by reference for all purposes. In some embodiments, characteristic genes or expression patterns are identified by comparing the gene expression levels of previous subjects known to have responses and no responses and identifying significant changes between the two groups, wherein the significant changes can be large differences in expression (e.g., changes greater than 2 times), small differences in expression (e.g., changes less than 2 times), or both. In some embodiments, genes are ranked by the significance of the expression differences. In some embodiments, significance is measured by the Pearson correlation between gene expression and response results. In some embodiments, characteristic genes are selected from the ranking by the significance of the expression differences. In some embodiments, the number of selected characteristic genes is less than the total number of genes analyzed. In some embodiments, 200 or fewer characteristic genes are selected. In some embodiments, 100 or fewer genes are selected.

In some embodiments, the characteristic gene is selected in conjunction with its position on the human interactome (HI) (a map of protein-protein interactions) or the characteristic gene is characterized by the position. Using HI in this way includes recognizing that mRNA activity is dynamic and determining the actual excessive and insufficient expression of proteins that are crucial to understanding certain diseases. In some embodiments, genes associated with responses to certain therapies (e.g., anti-TNF therapies) can be clustered in discrete modules on the HI map (e.g., forming clusters of genes). The presence of such clusters is associated with the presence of basic potential disease biology. In some embodiments, the classifier is derived from the characteristic genes selected from the gene clusters on the HI map. Therefore, in some embodiments, the classifier is derived from the gene clusters associated with the response to anti-TNF therapies on the human interactome map.

In some embodiments, genes associated with response to certain therapies exhibit certain topological properties when mapped onto the human interactome map. For example, in some embodiments, multiple genes are associated with response to anti-TNF therapy and are characterized by their location (e.g., topological properties, e.g., their proximity to each other) on the human interactome map.

In some embodiments, genes associated with responses to certain therapies (e.g., anti-TNF therapies) may exist very close to each other on the HI map. The neighboring genes do not necessarily share basic underlying disease biology. That is, in some embodiments, neighboring genes do not share significant protein interactions. Therefore, in some embodiments, classifiers are derived from genes that are adjacent on the human interaction group map. In some embodiments, classifiers are derived from certain other topological features on the human interaction group map.

In some embodiments, when used in conjunction with the HI profile, genes associated with response to certain therapies (e.g., anti-TNF therapy) can be determined by Diffusion State Distance (DSD) (see Cao et al., PLOS One, 8(10):e76339 (Oct. 23, 2013), which is incorporated herein by reference for all purposes).

In some embodiments, the signature genes are selected by (1) ranking the genes based on the significance of the gene expression difference compared to known responders and known non-responders; (2) selecting genes from the ranked genes and mapping the selected genes to the human interactome map; and (3) selecting signature genes from the genes mapped to the human interactome map. Thus, in some embodiments, the signature genes are characterized by their relative ranking of expression differences in responder and non-responder subjects or populations.

In some embodiments, the feature genes (e.g., selected from the Santolini method, or selected using various network topology properties, including but not limited to methods based on clustering, proximity and diffusion) are provided to a probabilistic neural network or other classifiers described herein, thereby providing (e.g., "training") a classifier. In some embodiments, the probabilistic neural network implements the algorithm proposed by DFSecht in "Probabilistic Neural Networks," Neural Networks, 3(1): 109-118 (1990) (which is incorporated herein by reference). In some embodiments, the probabilistic neural network is written in the R statistical language, and a set of observations described by a vector of quantitative variables is known, and the observations are classified into a given number of groups (e.g., responders and non-responders). The algorithm is trained using a data set of feature genes obtained from known responders and non-responders, and provides new observations. In some embodiments, the probabilistic neural network is a probabilistic neural network derived from The Comprehensive R Archive Network of pnn: Probabilistic neural networks v1.0.1 . In some embodiments, the feature genes are analyzed according to a random forest model to provide a classifier.

2. Single Nucleotide Polymorphism

The disclosure also includes such insights, i.e. single nucleotide polymorphisms (SNPs) can be identified by RNA sequence data. That is, by comparing RNA sequence data with reference to the human genome, for example, by mapping RNA sequence data to the GRCh38 human genome. Without wishing to be bound by theory, it is believed that the presence of the SNP related to the RNA sequence used in the classifier can contribute to identifying a subgroup of objects that respond or do not respond to certain therapies (e.g., anti-TNF therapy). That is, network medicine and pathway enrichment analysis can be used to analyze the protein product of the RNA containing distinguishing genes or SNPs. Proteins encoded by the RNA containing SNPs or the distinguishing genes included in the classifier can overlap on the spectrum of, for example, the human interactome, to help identify certain subgroups of objects by identifying certain distinguishing gene sets.

In some embodiments, the provided classifiers and methods using such classifiers incorporate assessments associated with single nucleotide polymorphisms (SNPs). In some embodiments, the present disclosure provides a method for developing a classifier for stratifying an object for one or more therapeutic attributes, comprising: analyzing sequence data of RNA expressed in an object representing at least two different categories for at least one therapeutic attribute; assessing the presence of one or more single nucleotide polymorphisms (SNPs) from the sequence data; determining that the presence of one or more SNPs is associated with at least one therapeutic attribute; and including one or more SNPs in a classifier.

In some embodiments, the present disclosure provides improvements in methods for developing a classifier for stratifying subjects for one or more therapeutic attributes by analyzing sequence data of RNA expressed in subjects representing at least two different categories for at least one therapeutic attribute, comprising: assessing the presence of one or more single nucleotide polymorphisms (SNPs) from the sequence data; and determining that the presence of the one or more SNPs is associated with at least one therapeutic attribute; and including the presence of the one or more SNPs in the classifier.

In some embodiments, the one or more SNPs are selected from Table 3.

Table 3

In some embodiments, the SNPs are selected from two or more of the SNPs in Table 3. In some embodiments, the SNPs are selected from two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, twenty-six or more, twenty-seven or more, twenty-eight or more, twenty-nine or more, thirty or more, thirty-one or more, thirty-two or more, thirty-three or more, thirty-four or more, thirty-five or more, thirty-six or more, thirty-seven or more, thirty-eight or more, or all 39 SNPs in Table 3.

3. Clinical characteristics

In some embodiments, the classifier can also be combined with additional information, such as to further improve the predictive ability of the classifier to identify between responders and non-responders. For example, in some embodiments, the classifier is developed or evaluated (for example, detected) by evaluating the following: one or more genes whose expression level is significantly related to clinical responsiveness or non-responsiveness (for example, in a linear or non-linear manner); and at least one of the following: the presence of one or more single nucleotide polymorphisms (SNPs) in the expression sequence of one or more genes; or at least one clinical feature of a previous object with response and no response. That is, in some embodiments, the presence of one or more single nucleotide polymorphisms (SNPs) in the expression sequence of one or more genes whose expression level is significantly related to clinical responsiveness or non-responsiveness (for example, in a linear or non-linear manner) and one or more genes is developed or evaluated (for example, detected) a classifier. In some embodiments, the classifier is developed or evaluated (for example, detected) by evaluating one or more genes whose expression level is significantly related to clinical responsiveness or non-responsiveness (for example, in a linear or non-linear manner) and at least one clinical feature of a previous object with response and no response.

The present disclosure also includes such insights that certain clinical features (e.g., BMI, gender, age, etc.) can be incorporated into the classifiers provided herein. In some embodiments, the classifiers provided and methods using such classifiers incorporate assessments associated with clinical features. In some embodiments, the present disclosure provides a method for developing a classifier for stratifying an object for one or more therapeutic attributes, comprising: analyzing sequence data of RNA expressed in an object representing at least two different categories for at least one therapeutic attribute; assessing the presence of one or more clinical features; determining that the expression associated with the clinical feature is associated with at least one therapeutic attribute; and including one or more clinical features in a classifier.

In some embodiments, at least one clinical characteristic is selected from the group consisting of body mass index (BMI), sex, age, race, prior therapy treatment, disease duration, C-reactive protein (CRP) level, presence of anti-cyclic citrullinated peptide, presence of rheumatoid factor, patient global assessment, treatment response rate (e.g., ACR20, ACR50, ACR70), and combinations thereof.

In some embodiments, the clinical characteristics are selected from Table 4.

Table 4

In some embodiments, the clinical features are selected from two or more clinical features in Table 4. In some embodiments, the clinical features are selected from two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, twenty or more, twenty-one or more, twenty-two or more, twenty-three or more, twenty-four or more, twenty-five or more, or all twenty-six clinical features in Table 4.

4. Validate the classifier

Alternatively or additionally, in some embodiments, a cohort of known responders and non-responders can be used, using leave-one-out crossover or k-fold cross validation, to train a classifier in a probabilistic neural network. In some embodiments, such a process leaves one sample (e.g., leave-one-out) in the analysis, and trains a classifier based on the remaining samples. In some embodiments, the probability of response of the sample left is then predicted using the upgraded classifier. In some embodiments, for example, such a process can be repeated iteratively until all samples are left once. In some embodiments, such a process randomly assigns a cohort of known responders and non-responders to k groups of equal size. Among the k groups, one group is retained as validation data, for testing the model, and the remaining groups are used as training data. Such a process can be repeated k times, wherein each of the k groups is used only once as validation data. In some embodiments, the result is a probability score for each sample in the training set. This probability score can be related to the actual response result. Recursive Operating Curves (ROC) can be used to estimate the performance of the classifier. In some embodiments, an area under the curve (AUC) of about 0.6 or higher reflects a suitable validated classifier. In some embodiments, a negative predictive value (NPV) of 0.9 reflects a suitable validated classifier. In some embodiments, the classifier can be tested in a completely independent (e.g., blind) cohort, for example, to confirm suitability (e.g., using leave-one-out or k-fold cross validation). Therefore, in some embodiments, the provided methods also include validating the classifier, for example, by assigning a response probability to a group of known responders and non-responders; and checking the classifier for blind group responders and non-responders. The output of these processes is a trained classifier that can be used to determine whether a subject will respond to a particular therapy (e.g., anti-TNF therapy).

In some embodiments, a classifier is established to distinguish between responders and non-responders who have received a type of therapy (e.g., anti-TNF therapy). The classifier can predict whether a subject will respond to a given therapy. In some embodiments, responders and non-responders have the same disease, disorder, or condition.

In some embodiments, the effectiveness of treatment is assessed by monitoring specific clinical features. For example, in some embodiments, the treatment response is verified in an object by the statistical analysis of clinical features. In certain embodiments, the development, verification or use of a related classifier may involve or have involved the assessment of one or more clinical parameters (for example, the performance or disease state of the patient). The disclosure recognizes that changes may occur in such clinical assessments, which may, for example, represent inputs external to the patient (for example, the difference in the application of assessing or explaining patient features or responses). The disclosure provides a solution to this identified problem by providing a patient self-assessment of one or more related parameters.

In some embodiments, the validation of the classifier includes a statistical analysis of clinical features to analyze the changes in clinical features of patients who have been so classified by the classifier and received anti-TNF therapy. Such validation methods recognize that, compared with the methods described herein, certain subjective measurements of clinical changes cannot be quantified and involve self-assessment. The present disclosure encompasses such insights: the patient's self-assessment is not necessarily consistent, but can provide valuable information about the therapeutic response over time. This self-assessment response can be used to confirm whether the patient is a true responder or a non-responder. For example, statistical analysis of certain clinical features of a patient cohort can verify the accuracy of the classifier. In some embodiments, the statistical analysis of clinical features analyzes the changes in one or more of ACR50, ACR70, CDAI LDA, CDAI relief, DAS28-CRP LDA and DAS28-CRP relief and combinations thereof. In some embodiments, statistical analysis is performed by Monte Carlo simulation.

In some embodiments, the classifier is validated using a cohort of subjects that have been previously treated with anti-TNF therapy but is independent of the cohort of subjects used to prepare the classifier. In some embodiments, the classifier is upgraded using gene expression data, SNP data, or clinical features. In some embodiments, a classifier is considered "validated" when 90% or more of the non-responding subjects are predicted with an accuracy of 60% or more in the validation cohort.

In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 60%, thereby predicting non-responsiveness in a population of at least 100 subjects. In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 60% in a population of at least 150 subjects. In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 60% in a population of at least 170 subjects. In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 60% in a population of at least 200 subjects or more.

In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 80% in a population of at least 100 subjects. In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 80% in a population of at least 150 subjects. In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 80% in a population of at least 170 subjects. In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 80% in a population of at least 200 or more subjects. In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 80% in a population of at least 300 or more subjects. In some embodiments, the classifier predicts non-responsiveness of a subject with an accuracy of at least 80% in a population of at least 350 or more subjects.

B. Detection of genetic characteristics or SNPs

It is possible to use a trained classifier to detect the gene signature in an object. In other words, by first defining the gene signature (from the classifier), a variety of methods can be used to determine whether an object or a group of objects expresses an established gene signature. For example, in some embodiments, a practitioner can obtain blood or tissue samples from an object before administering therapy, and extract and analyze mRNA profiles from the blood or tissue samples. The analysis of mRNA profiles can be by a variety of methods, including but not limited to gene arrays, RNA sequencing, nanostring sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead arrays or enzyme-linked immunosorbent assays (ELISA) and combinations thereof. Therefore, in some embodiments, the present disclosure provides a method for determining whether an object is classified as a responder or a non-responder, which includes measuring gene expression by at least one of microarrays, RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead arrays and ELISA and combinations thereof. In some embodiments, the present disclosure provides a method for determining whether an object is classified as a responder or a non-responder, which includes measuring gene expression of an object by RNA sequencing (e.g., RNAseq).

The disclosure also includes such insights, i.e. single nucleotide polymorphisms (SNPs) can be identified by RNA sequence data. That is, by comparing RNA sequence data with reference to the human genome, for example, by mapping RNA sequence data to the GRCh38 human genome. Without wishing to be bound by theory, it is believed that the presence of the SNP related to the RNA sequence used in the sorter can contribute to identifying a subgroup of objects that respond or do not respond to certain therapies (e.g., anti-TNF therapy). That is, network medicine and pathway enrichment analysis can be used to analyze the protein products of the RNA containing distinguishing genes and SNPs. Proteins encoded by the RNA containing SNPs and the distinguishing genes included in the sorter can overlap on the spectrum of, for example, the human interactome, to help identify certain subgroups of objects by identifying certain distinguishing gene sets.

In some embodiments, gene expression is measured by subtracting background data, correcting batch effects and dividing by the average expression of housekeeping genes. See Eisenberg & Levanon, "Human housekeeping genes, revisited," Trends in Genetics, 29 (10): 569-574 (October 2013), which is incorporated herein by reference for all purposes. In the context of microarray data analysis, background subtraction refers to the average fluorescence signal generated by the probe features that are not complementary to any mRNA sequence on the chip, such as the signal generated by non-specific binding, from the fluorescence signal intensity of each probe feature. Background subtraction can be performed using different software packages, such as Affymetrix Gene Expression Console. Housekeeping genes are involved in basic cell maintenance, and therefore, it is expected to maintain a constant expression level under all cells and conditions. The expression level of a gene of interest (such as those in the response feature) can be normalized by dividing the expression level by the average expression level of a set of selected housekeeping genes. The housekeeping gene normalization program calibrates the gene expression level for experimental variability. In addition, normalization methods such as robust multi-array average ("RMA") corrected for variability in different batches of microarrays can be obtained in the R software package recommended by Illumina or Affymetrix platforms. The normalized data were log-transformed and probes with low detection rates in the samples were removed. In addition, probes without available gene symbols or Entrez IDs were removed from the analysis.

In some embodiments, the present disclosure provides a kit comprising a classifier established to distinguish between responders and non-responders in previous subjects who have received anti-TNF therapy.

C. Using a classifier

1. Patient stratification

Among other things, the present disclosure provides techniques for predicting responsiveness to anti-TNF therapy.In some embodiments, the provided techniques demonstrate consistency or accuracy across cohorts that is superior to other methods.

Thus, the present disclosure provides techniques for patient stratification, defining or distinguishing responder and non-responder populations. For example, in some embodiments, the present disclosure provides methods for treating a subject with an anti-TNF therapy, in some embodiments, the method comprising: administering an anti-TNF therapy to a subject that has been determined to be a responder by a classifier, the classifier being established to distinguish between responders and non-responders of previous subjects who have received anti-TNF therapy.

In some embodiments, the present disclosure provides a method for developing a classifier for stratifying subjects for one or more therapeutic attributes, comprising: analyzing sequence data of RNA expressed in subjects representing at least two different categories for at least one therapeutic attribute; assessing the presence of one or more single nucleotide polymorphisms (SNPs) from the sequence data; determining that the presence of the one or more SNPs is associated with the at least one therapeutic attribute; and including the one or more SNPs in the classifier.

The classifiers described herein can be used by analyzing the gene expression of the subject. In some embodiments, the genes of the subject are measured by at least one of microarray, RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead array, ELISA and protein expression and combinations thereof.

2. Therapy monitoring

In addition, the present disclosure provides techniques for monitoring therapy for a given subject or cohort of subjects.Since the gene expression levels of a subject may change over time, it may be desirable to evaluate a subject at one or more time points (e.g., at specified and/or periodic intervals).

In some embodiments, the effectiveness of treatment is assessed by monitoring specific clinical features. For example, in some embodiments, the treatment response is verified in an object by the statistical analysis of clinical features. In certain embodiments, the development, verification or use of a related classifier may involve or have involved the assessment of one or more clinical parameters (for example, the performance or disease state of the patient). The disclosure recognizes that changes may occur in such clinical assessments, which may, for example, represent the input of the patient's external (for example, the application of the assessment or the difference in the explanation of the patient's features or responses). The disclosure provides a solution to this identified problem by providing a patient self-assessment of one or more related parameters.

In some embodiments, the validation of the classifier includes a statistical analysis of clinical features to analyze the changes in clinical features of patients who have been so classified by the classifier and received anti-TNF therapy. Such validation methods recognize that, compared with the methods described herein, certain subjective measurements of clinical changes cannot be quantified and involve self-assessment. The present disclosure encompasses such insights: the patient's self-assessment is not necessarily consistent, but can provide valuable information about the therapeutic response over time. This self-assessment response can be used to confirm whether the patient is a true responder or a non-responder. For example, statistical analysis of certain clinical features of a patient cohort can verify the accuracy of the classifier. In some embodiments, the statistical analysis of clinical features analyzes the changes in one or more of ACR50, ACR70, CDAI LDA, CDAI relief, DAS28-CRP LDA and DAS28-CRP relief and combinations thereof. In some embodiments, statistical analysis is performed by Monte Carlo simulation.

In some embodiments, repeated monitoring allows or enables detection of one or more changes in the gene expression profile or feature of the object that may affect the ongoing treatment plan at a certain time. In some embodiments, changes are detected in response to the continuation, change or suspension of the specific therapy applied to the object. In some embodiments, therapy can be, for example, changed by increasing or reducing the frequency of administration or the amount of one or more medicaments or treatments used to treat the object. Alternatively or additionally, in some embodiments, therapy can be changed by adding a therapy with one or more new medicaments or treatments. In some embodiments, therapy can be changed by pausing or stopping one or more specific medicaments or treatments.

As just one example, if a subject is initially classified as responsive (because the subject's gene expression is determined by the classifier to be associated with a disease, disorder, or condition), a given anti-TNF therapy may be administered. At given intervals (e.g., every six months, yearly, etc.), the subject may be tested again to ensure that they still meet the criteria for being "responsive" to the given anti-TNF therapy. In the event that a given subject's gene expression levels change over time, and the subject no longer expresses genes associated with a disease, disorder, or condition, or now expresses genes associated with non-responsiveness, the subject's therapy may be altered to accommodate the change in gene expression.

Thus, in some embodiments, the present disclosure provides methods of administering a therapy to a subject previously determined by a classifier to be responsive to an anti-TNF therapy.

In some embodiments, the present disclosure provides methods, further comprising, prior to administering, determining by a classifier that the subject is not a responder; and administering an alternative therapy to the anti-TNF therapy.

In some embodiments, the subject's gene is measured by at least one of microarray, RNA sequencing, real-time quantitative reverse transcription PCR (qRT-PCR), bead array, ELISA, and protein expression, and combinations thereof.

In some embodiments, the subject has a disease, disorder, or condition selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn's disease, ulcerative colitis, chronic psoriasis, hidradenitis suppurativa, multiple sclerosis, and juvenile idiopathic arthritis, and combinations thereof.

In some embodiments, the anti-TNF therapy is or includes the administration of infliximab, adalimumab, etanercept, certolizumab pegol, golilumab, or biosimilars thereof, and combinations thereof. In some embodiments, the anti-TNF therapy is or includes the administration of infliximab or adalimumab.

In some embodiments, the prior subjects who responded and those who did not respond had the same disease, disorder, or condition.

In some embodiments, the subject administered the anti-TNF therapy has the same disease, disorder, or condition as previous subjects who previously responded and did not respond.

In some embodiments, the disease, disorder, or condition is selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn's disease, ulcerative colitis, chronic psoriasis, hidradenitis suppurativa, multiple sclerosis, and juvenile idiopathic arthritis, and combinations thereof.

In some embodiments, the disease, disorder, or condition is rheumatoid arthritis.

In some embodiments, the disease, disorder, or condition is ulcerative colitis.

D. Treatment methods

In some embodiments, the subject or population for which anti-TNF therapy is administered or anti-TNF therapy is discontinued (or an alternative therapy is administered) is an object or population determined to exhibit a particular expression level of one or more genes (and in some cases, multiple genes). In some embodiments, one or more genes are determined to have an expression level below a particular threshold; alternatively or additionally, in some embodiments, one or more genes are determined to have an expression level below a particular threshold. In some embodiments, a particular set of genes is determined to have an expression pattern in which each gene is evaluated relative to a particular threshold (and, for example, determined to be above, below, or equivalent to such a threshold).

In some embodiments, the present disclosure provides a method of treating a subject having a disease, disorder, or condition comprising administering an alternative to anti-TNF therapy to a subject that has been determined to exhibit below a particular expression level of one or more genes.

In some embodiments, the present disclosure provides a method of administering an anti-TNF therapy to a subject who has been determined to be a responder by a classifier, wherein the classifier is established to distinguish between responders and non-responders of previous subjects who have received anti-TNF therapy (e.g., wherein the classifier has been established by retrospective analysis to distinguish between patients who responded to the anti-TNF therapy they received and those who did not respond); wherein the classifier is developed by evaluating: one or more genes whose expression levels are significantly correlated with clinical responsiveness or non-responsiveness (e.g., in a linear or non-linear manner); and at least one of the following: the presence of one or more single nucleotide polymorphisms (SNPs) in an expressed sequence; and at least one clinical characteristic of previous subjects who responded and those who did not respond.

TNF-mediated conditions are currently treated by inhibiting TNF, particularly by administering anti-TNF agents (e.g., by anti-TNF therapy). Examples of anti-TNF agents approved for use in the United States include monoclonal antibodies such as adalimumab Certolizumab Infliximab and decoy circulating receptor fusion proteins, such as etanercept These agents are currently approved for the treatment of the indications according to the dosing regimens listed in Table 5:

Table 5

1. Administration by subcutaneous injection

2. Administration by intravenous infusion

The present disclosure provides technologies related to anti-TNF therapy, including those treatment regimens listed in Table 5. In some embodiments, the anti-TNF therapy is or includes the administration of infliximab Adalimumab Certolizumab Etanercept Or a biosimilar thereof. In some embodiments, the anti-TNF therapy is or includes administration of infliximab Adalimumab In some embodiments, the anti-TNF therapy is or includes the administration of infliximab In some embodiments, the anti-TNF therapy is or includes administration of adalimumab

In some embodiments, the anti-TNF therapy is or includes the administration of a biosimilar anti-TNF agent. In some embodiments, the anti-TNF agent is selected from infliximab biosimilars such as CT-P13, BOW015, SB2, and Ixifi ^™ , adalimumab biosimilars such as ABP 501 (AMGEVITA ^™ ), Adfrar, and Hulio ^™ , and etanercept biosimilars such as HD203, SB4 GP2015, and Intacept and combinations thereof.

In some embodiments, treatments such as juvenile idiopathic arthritis, psoriatic arthritis, rheumatoid arthritis, ankylosing spondylitis, pediatric Crohn's disease, ulcerative colitis, plaque psoriasis, hidradenitis suppurativa, and uveitis include dosing regimens of anti-TNF agents in Table 5. In some embodiments, anti-TNF agents include, for example, adalimumab in Table 5. In some embodiments, the dosing regimen of adalimumab includes, for example, an initial dose of up to 160 mg or more. In some embodiments, the dosing regimen of adalimumab includes, for example, a second dose of up to 80 mg or more. In some embodiments, the dosing regimen of adalimumab includes, for example, a maintenance dose of up to 40 mg or more every other week. In some embodiments, anti-TNF agents include, for example, certolizumab in Table 5. In some embodiments, the dosing regimen of certolizumab includes, for example, a first initial dose of up to 400 mg or more. In some embodiments, the dosing regimen of certolizumab includes, for example, a second initial dose of up to 400 mg or more in Week 2. In some embodiments, the dosing regimen of certolizumab includes, for example, a third initial dose of up to 400 mg at week 4. In some embodiments, the dosing regimen of certolizumab includes, for example, a maintenance dose of up to 200 mg or more every other week or a maintenance dose of up to 400 mg or more every four weeks. In some embodiments, the anti-TNF agent includes, for example, infliximab in Table 5. In some embodiments, the dosing regimen of infliximab includes, for example, a first initial dose of up to 5 mg/kg or more. In some embodiments, the dosing regimen of infliximab includes, for example, a second initial dose of up to 5 mg/kg or more at week 2. In some embodiments, the dosing regimen of infliximab includes, for example, a third initial dose of up to 5 mg/kg or more at week 6. In some embodiments, the dosing regimen of infliximab includes, for example, a maintenance dose of up to 5 mg/kg or more every 6 weeks or every 8 weeks. In some embodiments, the anti-TNF agent includes, for example, etanercept in Table 5. In some embodiments, the dosing regimen of etanercept includes, for example, an initial dose of up to 50 mg or more, twice a week for three months. In some embodiments, the dosing regimen of etanercept includes, for example, a maintenance dose of up to 50 mg or more per week. In some embodiments, the anti-TNF agent includes, for example, golimumab in Table 5. In some embodiments, the dosing regimen of golimumab includes, for example, a dose of up to 50 mg or more per month. In some embodiments, the dosing regimen of golimumab includes, for example, a first initial dose of up to 2 mg/kg. In some embodiments, the dosing regimen of golimumab includes, for example, a second initial dose of up to 2 mg/kg or more in week 2. In some embodiments, the dosing regimen of golimumab includes, for example, a maintenance dose of up to 2 mg/kg or more every 8 weeks.

In some embodiments, the present disclosure provides a method for treating a subject with an autoimmune disorder, the method comprising: administering an anti-TNF therapy to a subject who has been determined to be a responder by a classifier, the classifier being established to distinguish between responding prior subjects and non-responding prior subjects in a cohort who have received anti-TNF therapy; wherein the classifier is developed by evaluating: one or more genes whose expression levels are significantly correlated with clinical responsiveness or non-responsiveness (e.g., in a linear or non-linear manner); at least one of: the presence of one or more single nucleotide polymorphisms (SNPs) in the expressed sequences of the one or more genes; or at least one clinical feature of responding and non-responding prior subjects; and wherein the classifier is validated by an independent cohort different from the cohort that has received anti-TNF therapy.

In some embodiments, the subject has previously been administered anti-TNF therapy. In some embodiments, the subject has been administered anti-TNF therapy at least 1 month, at least 2 months, at least 3 months, at least 4 months, at least 5 months, or at least 6 months prior to said administration.

In some embodiments, the data derived from subjects in the cohort who have received anti-TNF therapy is of one type (e.g., microarray, RNAseq, etc.), and the data used to validate the classifier in the independent cohort is of a different type (e.g., microarray, RNAseq, etc.). Thus, in some embodiments, a classifier is built using microarray analysis derived from previous subjects who responded and did not respond. In some embodiments, the classifier is validated using RNAseq data derived from an independent cohort.

E. Disease, illness or condition

In general, the disclosure provided is useful in any situation where the administration of anti-TNF therapy is considered or implemented. In some embodiments, the technology provided can be used to diagnose or treat subjects with diseases, disorders or conditions associated with abnormal (e.g., elevated) TNF expression or activity. In some embodiments, the technology provided can be used to monitor subjects who are receiving or have received anti-TNF therapy. In some embodiments, the technology provided identifies whether a subject will respond to a given anti-TNF therapy. In some embodiments, the technology provided identifies whether a subject will develop resistance to a given anti-TNF therapy.

Therefore, the present disclosure provides technologies related to treating various disorders associated with TNF, including those listed in Table 5. In some embodiments, the subject suffers from a disease, disorder or condition selected from rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn's disease (adult or child), ulcerative colitis, inflammatory bowel disease, chronic psoriasis, plaque psoriasis, hidradenitis suppurativa, asthma, uveitis and juvenile idiopathic arthritis and combinations thereof. In some embodiments, the disease, disorder or condition is rheumatoid arthritis. In some embodiments, the disease, disorder or condition is psoriatic arthritis. In some embodiments, the disease, disorder or condition is ankylosing spondylitis. In some embodiments, the disease, disorder or condition is Crohn's disease. In some embodiments, the disease, disorder or condition is adult Crohn's disease. In some embodiments, the disease, disorder or condition is pediatric Crohn's disease. In some embodiments, the disease, disorder or condition is inflammatory bowel disease. In some embodiments, the disease, disorder or condition is ulcerative colitis. In some embodiments, the disease, disorder, or condition is chronic psoriasis. In some embodiments, the disease, disorder, or condition is plaque psoriasis. In some embodiments, the disease, disorder, or condition is hidradenitis suppurativa. In some embodiments, the disease, disorder, or condition is asthma. In some embodiments, the disease, disorder, or condition is uveitis. In some embodiments, the disease, disorder, or condition is juvenile idiopathic arthritis.

In some embodiments, the disease, disorder, or condition is granuloma annulare, necrosis lipogenetic, hidradenitis suppurativa, pyoderma gangrenosum, Sweet syndrome, subcorneal pustular dermatosis, systemic lupus erythematosus, scleroderma, dermatomyositis, Behcet's disease, acute/chronic graft-versus-host disease, pityriasis rubra pilaris, Sjögren's syndrome, Wegener's granulomatosis, polymyalgia rheumatica, dermatomyositis, and pyoderma gangrenosum, and combinations thereof.

In addition, as noted, the present disclosure provides techniques that allow practitioners to reliably and consistently predict responses in a cohort of subjects. In particular, for example, in a given cohort of subjects, the response rate for some anti-TNF therapies is less than 35%. The techniques provided allow prediction of response rates (e.g., whether certain subjects will respond to a given treatment) with an accuracy of more than 65% in a cohort of subjects. In some embodiments, the methods and systems described herein predict 65% or more of non-responders (e.g., will not respond to anti-TNF therapy) subjects within a given cohort. In some embodiments, the methods and systems described herein predict 70% or more of non-responders (e.g., will not respond to anti-TNF therapy) subjects within a given cohort. In some embodiments, the methods and systems described herein predict 80% or more of non-responders (e.g., will not respond to anti-TNF therapy) subjects within a given cohort. In some embodiments, the methods and systems described herein predict 90% or more of non-responders (e.g., will not respond to anti-TNF therapy) subjects within a given cohort. In some embodiments, the methods and systems described herein predict 100% of non-responders (e.g., will not respond to anti-TNF therapy) subjects within a given cohort.

Computer control system

The present disclosure provides a computer control system programmed to implement the method of the present disclosure. Figure 10 shows a computer system 1001, which is programmed or otherwise configured to generate or develop an autoantibody spectrum or compare autoantibodies with a specific immune response spectrum. The computer system 1001 can adjust various aspects of the present disclosure, such as receiving or generating sequence reads, associating sequences with specific epitopes or autoantibodies, outputting results about the presence of autoantibodies or spectrum to a user, or the expected progression of a disease. The computer system 1001 can be an electronic device of the user or a computer system remotely located relative to the electronic device. The electronic device can be a mobile electronic device.

The computer system 1001 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 1005, which may be a single-core or multi-core processor, or multiple processors for parallel processing. The computer system 1001 also includes a memory or memory location 1010 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 1015 (e.g., a hard disk), a communication interface 1020 (e.g., a network adapter) for communicating with one or more other systems, and peripherals 1025, such as cache, other memory, data storage, or an electronic display adapter. The memory 1010, storage unit 1015, interface 1020, and peripherals 1025 communicate with the CPU 1005 via a communication bus (solid lines) such as a motherboard. The storage unit 1015 may be a data storage unit (or data repository) for storing data. The computer system 1001 may be operably coupled to a computer network ("network") 1030 by means of the communication interface 1020. The network 1030 may be the Internet, the internet or an extranet, or an intranet or an extranet in communication with the Internet. In some cases, the network 1030 is a telecommunications or data network. The network 1030 may include one or more computer servers, which may implement distributed computing, such as cloud computing. In some cases, with the help of the computer system 1001, the network 1030 may implement a peer-to-peer network, which may enable a device coupled to the computer system 1001 to act as a client or a server.

The CPU 1005 may execute a sequence of machine-readable instructions, which may be embodied in a program or software. These instructions may be stored in a memory location such as the memory 1010. These instructions may be directed to the CPU 1005, which may then program or otherwise configure the CPU 1005 to implement the methods of the present disclosure. Examples of operations performed by the CPU 1005 may include fetching, decoding, executing, and writing back.

CPU 1005 may be part of a circuit (e.g., an integrated circuit). One or more other components of system 1001 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

Storage unit 1015 can store files, such as drivers, libraries, and saved programs. Storage unit 1015 can store user data, such as user preferences and user programs. In some cases, computer system 1001 can include one or more additional data storage units external to computer system 1001, such as located on a remote server that communicates with computer system 1001 via an intranet or the Internet.

Computer system 1001 may communicate with one or more remote computer systems via network 1030. For example, computer system 1001 may communicate with a user's remote computer system. Examples of remote computer systems include a personal computer (e.g., a portable PC), a tablet computer or a tablet computer (e.g., a laptop computer). iPad, Galaxy Tab), phones, smartphones (e.g. iPhone, Android-enabled devices, ), or a personal digital assistant. A user can access the computer system 1001 via the network 1030.

The methods described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location of the computer system 1001 (e.g., memory 1010 or electronic storage unit 1015). The machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by the processor 1005. In some cases, the code may be retrieved from the storage unit 1015 and stored on the memory 1010 for ready access by the processor 1005. In some cases, the electronic storage unit 1015 may be excluded, and the machine executable instructions are stored on the memory 1010.

The code may be precompiled and configured for use with a machine having a processor suitable for executing the code, or may be compiled during runtime. The code may be provided in a programming language, which may be selected to enable the code to be executed in a precompiled or compiled manner.

Aspects of the systems and methods provided herein, such as computer system 1001, can be embodied in programming. Various aspects of the technology can be considered to be "products" or "articles" in the form of machine (or processor) executable code or related data generally carried or embodied in a type of machine-readable medium. Machine executable code can be stored on an electronic storage unit, such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. "Storage" type media can include any or all tangible memories of a computer, processor, etc., or its associated modules, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-temporary storage for software programming at any time. All or part of the software can sometimes communicate through the Internet or various other telecommunications networks. For example, such communication can enable software to be loaded from one computer or processor to another computer or processor, such as from a management server or host computer to a computer platform of an application server. Therefore, other types of media that can carry software elements include light waves, radio waves, and electromagnetic waves, such as through wired and optical landline networks and through various air links on the physical interface between local devices. The physical elements that carry such waves, such as wired or wireless links, optical links, etc., can also be considered to be the medium that carries the software. As used herein, unless limited to non-transitory tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

Thus, machine-readable media such as computer executable code may take a variety of forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any storage device in any computer, etc., such as can be used to implement the database shown in the accompanying drawings, etc. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wires and optical fibers, including wires that constitute a bus within a computer system. Carrier transmission media may take the form of electrical or electromagnetic signals, or sound or light waves, such as those waves generated during radio frequency (RF) and infrared (IR) data communications. Thus, common forms of computer-readable media include, for example: floppy disks, floppy disks, hard disks, magnetic tape, any other magnetic media, CD-ROM, DVD or DVD-ROM, any other optical media, punched card tape, any other physical storage medium with a pattern of holes, RAM, ROM, PROM and EPROM, FLASH-EPROM, any other memory chip or cartridge, a carrier that transmits data or instructions, a cable or link that transmits such a carrier, or any other medium from which a computer can read program code or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

The computer system 1001 may include or communicate with an electronic display 1035, which includes a user interface (UI) 1040 for providing, for example, selecting autoantibodies for analysis, interacting with charts that associate autoantibodies with specific generated profiles. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software when executed by the central processing unit 1005. The algorithms may, for example, calculate statistical measurements to identify autoantibodies and generate profiles or predict the efficacy and toxicity of treatments.

Example

Example 1 - Methods for predicting inadequate response to tumor necrosis factor-α inhibitors in rheumatoid arthritis Molecular signature response classifier.

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation that leads to joint destruction. After an inadequate response to synthetic disease-modifying antirheumatic drugs (csDMARDs) such as methotrexate, clinical guidelines recommend one of a number of targeted therapies with comparable efficacy and safety, including tumor necrosis factor-a inhibitors (TNFi), IL-6 inhibitors, Janus kinase (JAK) inhibitors, and B or T cell modulators. The abundance of treatment options emphasizes the need for precision medicine in the field of rheumatology. Because clinical guidelines do not recommend one treatment over another, treatment selection is often driven by administrative directives, and TNFi therapy remains the primary treatment for nearly 90% of RA patients. Matching the right targeted therapy to each patient to achieve the treat-to-target goal of low disease activity (LDA) or remission is a critical unmet medical need in RA.

A subset of RA patients has an adequate response to TNFi therapy: 50–70% achieve ACR20, 30–40% achieve ACR50, 15–25% achieve ACR70 responses, and 10–25% achieve remission. Many studies have attempted to identify biomarkers and develop models to predict response to TNFi therapy before treatment initiation. Failure to validate and reproduce the performance of these predictive biomarkers in new patient populations and clinical trials is a typical outcome. Different characteristics between patient populations, laboratory methods, procedures for generating molecular data, and other biases inherent in single-cohort retrospective blood studies have hampered advances in precision medicine not only in rheumatology but also in other medical specialties.

Using a novel network medicine approach to biomarker discovery, a blood-based molecular signature test was developed that combines next-generation RNA sequencing data with clinical features to predict the likelihood of an inadequate response to TNFi therapy in RA patients. Clinical validation of this molecular signature test in a subset of patients from the CERTAIN study showed that patients with a non-responder molecular signature were less likely to achieve ACR50 at 6 months.

Mapping disease-associated proteins onto the human interactome, a network graph of pairwise protein-protein interactions occurring in human cells, has yielded new insights into human disease biology and response to therapy. Through the identification of molecular biomarkers associated with RA biology discovered from human interactome analysis, this study demonstrated the insufficient TNFi response prediction performance of the Molecular Signature Response Classifier (MRSC) in two prospective observational clinical studies (CERTAIN study and NETWORK-004). Validation of the MSRC was performed in a total of 391 blood samples from patients who had not received targeted therapy and 113 blood samples from patients exposed to TNFi therapy.

method

patient

An outline of the study design Corrona is depicted in Figure 6. The CERTAIN study included: 345 RA patients PAXgene blood samples and clinical measurements, a comparative effectiveness study of initiation of biologics in RA patients. The CERTAIN study was nested within the Corona registry. Institutional Review Board or Ethics Committee approval was obtained prior to sample collection and study participation, and patients provided informed consent. CERTAIN is a comparative effectiveness study investigating biologic initiators. For these analyses, samples selected were from patients who were naive to targeted therapy at the time of sample collection and were initiating TNFi therapy. 92% (318/345) of patients were included in previous classifier training and validation analyses. Consistent with the inclusion criteria of the CERTAIN study, all patients had a clinical disease activity index (CDAI) greater than 10 at the time of initiation of biologic therapy. Clinical and molecular data were used for biomarker signature selection (100 patients) and within-cohort cross-validation (245 patients). Patients were randomized to these two separate analyses regardless of how each sample was used in previous studies.

NETWORK-004: Prior to enrollment, patients were identified as candidates for TNFi therapy by the treating rheumatologist. Eligible patients were >18 years of age, had active RA (CDAI >10, swollen joint count >4), and were receiving a stable dose of methotrexate (>15 mg/week) for >10 weeks prior to baseline. Hydroxychloroquine doses up to 400 mg/day or leflunomide doses up to 20 mg/day were permitted, as long as the dose was stable for at least 4 weeks prior to the baseline visit. Prednisone doses <10 mg/day were permitted, as long as the dose was stable for at least 2 weeks prior to baseline. Intra-articular or parenteral corticosteroids were prohibited <2 weeks prior to the first study procedure. The study was approved by the Copernicus Group Independent Review Committee (approval number 20191082) and local review committees (if required). All patients provided written informed consent. Dosage and treatment with TNFi therapy were at the discretion of the rheumatologist. At the 3-month follow-up, rheumatologists could make dose adjustments if deemed appropriate by clinical care. Initiation of a second TNFi therapy resulted in subject withdrawal. The COVID-19 pandemic caused a higher attrition rate than initially anticipated. Of the 273 RA patients enrolled, 168 completed the 24-week study. 146 patients had complete clinical and molecular data and were included in the analysis. Information on patients who withdrew from the study is available in Supplementary Table S1. PAXgene blood samples collected at the 3-month visit in 113 patients were analyzed as TNFi-exposed samples.

Clinical evaluation and response to TNFi therapy

Feature Selection Response Definition: Clinical outcome measures such as swollen and tender joint counts, patient and physician disease assessments have inherent variability. To identify subsets of patients in the training cohort who had been assigned responder and non-responder labels with high confidence, a Monte Carlo simulation approach was used to calculate a confidence outcome score for each patient. Patient clinical outcome data with at least 70% agreement between simulation and actual reported results were considered to have high confidence. High-confidence clinical outcomes for ACR and EULAR measures were used for feature selection.

The CERTAIN study examined baseline RNA sequencing data and clinical assessments to predict response to TNFi therapy at 3- and 6-month follow-up based on ACR, CDAI, and DAS28-CRP criteria.

The NETWORK-004 study examined clinical assessments collected at baseline, 3-month, and 6-month visits: 28 tender and swollen joint counts, patient's global assessment of pain, patient's global assessment of disease activity, CDAI score, health assessment questionnaire, and C-reactive protein (CRP). Rheumatoid factor (RF) and anti-cyclic citrullinated protein (anti-CCP) antibody serum status were recorded at baseline. PAXgene RNA Vascular was collected at each visit. RNA sequencing data at 3-month follow-up were used to predict response to TNFi therapy at 6-month follow-up based on ACR, CDAI, and DAS28-CRP criteria.

RNA preparation and sequencing analysis

RNA was extracted from whole blood in PAXgene RNA tubes using the Stable Blood PAXgene Tube RNA Isolation Kit MagMax ^™ (ThermoFisher Scientific) according to the manufacturer's instructions; 100-1000ng RNA was treated with the KAPARNA HyperPrep Kit and RiboErase (HMIR) beads. Samples were quantified using Agilent D1000 reagents. Libraries were sequenced at high uniform depth with a target of >7 million protein-coding reads. The CERTAIN cohort was sequenced using Illumina NextSeqDX 500 and NovaSeq 6000 instruments. NETWORK-004 samples were sequenced using an IlluminaNovaSeq 6000 instrument and a validated diagnostic assay was used in accordance with the Clinical Laboratory Improvement Amendments (CLIA). Sequence data were processed to determine gene expression across the genome. To be included in the analysis, samples had to have a TapeStation RIN>4, RNA concentration>10 ng/μL, sequencing library yield>10 nM, % Perfect Base Pair Index>85, % Bases above Phred score 30>75, Mean Quality Phred Score>30, Median Phred Score>25, and Lower Quartile Phred Score>10 for all bases.

Human interactome analysis and feature selection

To select transcript biomarker features, 100 samples were randomly selected from a cohort of 345 patients (CERTAIN study). Protein coding transcripts were ranked using a random forest algorithm through 96 rounds of 20% cross-validation computer experiments. The top 100 features ranked in 70/96 iterations were further analyzed by human interactome analysis to identify biologically relevant biomarkers. Biomarkers that overlapped ³⁵ with the RA disease module on the human interactome or had a large number of connections to the disease module were used in the final model. The significance of the connection was assessed using the hypergeometric test.

Predictive classification model training and validation

Samples not included in the biomarker feature selection were evaluated. Using machine learning, the transcripts identified in this study were integrated with previously described biomarkers to retrain the response classification model. To evaluate model performance, a feed-forward artificial neural network was used with 10-fold cross validation. Model construction was done using the MLPClassifier package provided in the Python machine learning library sklearn.

Statistical analysis

Statistical analyses were performed using Python 3.7.6 and R version 3.6.1. Continuous data were summarized with mean, standard deviation, median, minimum, maximum, and number of evaluable observations. Categorical variables were summarized with frequency counts and percentages. Confidence intervals (CIs) were determined using the t distribution for continuous data (exact method for categorical variables) where appropriate. All tests were performed in a two-sided setting. Hypothesis tests were performed at a two-sided 0.05 significance level unless otherwise stated. All attempts were made to limit missing data. No attempt was made to impute missing data.

result

Identification of molecular signatures of non-response to TNFi therapy using the human interactome

Transcripts predicting inadequate response to TNFi therapy at 6 months according to ACR50 and EULAR response definitions (see Methods) were identified using machine learning from baseline blood sample data from 100 patients randomly selected from the Corona CERTAIN study who had not received RA targeted therapy. To ensure that the transcripts reflected RA disease biology, the proteins encoded by the selected transcripts were mapped to the human interactome map of pairwise protein-protein interactions to identify transcripts significantly associated with the RA disease module (p value < 0.05) (Figure 1). The TNFi therapy response signature overlapped with the same network neighborhood of the human interactome consisting of RA disease-associated proteins. These signatures included proteins associated with RA pathology, including JAK3 and interleukin-1β (IL-1B). The molecular signature of non-response to TNFi therapy included 23 features: 19 RNA transcripts and 4 clinical features (Table 6):

Table 6

MSRC In-cohort Validation

MSRC was tested by intra-cohort cross-validation between baseline blood samples from an independent cohort of 245 patients who had never received targeted therapy from the Corona CERTAIN study (Table 7). This resulted in AUC values of 0.63 to 0.67 for ACR50, ACR70, CDAI, and DAS28 responses six months after the start of treatment (Figure 2A and Table 8). Significant differences in the log-likelihood ratios of model scores were observed between patients with and without non-responsive molecular features (p < 0.001) (Figure 2B). In addition, in patients lacking non-responsive molecular features, a greater proportion of patients achieved LDA or remission at 6 months according to CDAI and DAS28-CRP (Figure 2C).

Table 7

Table 8

Validation of MSRC in a prospective observational clinical study in samples of patients who had not received targeted therapy

To further validate the ability of MSRC to predict the likelihood of an inadequate response to TNFi therapy, patient samples were prospectively collected in a multicenter observational clinical study. After clinical and molecular data quality review, 146 patients completed the 24-week study and were included in the analysis. These patients were primarily female (78.8%) and white (80.1%), with a median age of 58 years (Table 7). TNFi therapy selection was at the discretion of the prescribing physician and represented all five treatment options in the category (adalimumab 32.9%, certolizumab 8.9%, etanercept 21.2%, infliximab 12.3%, and golimumab 24.7%). Non-response molecular features were detected at baseline in 44.5% (65/146) of patients.

Based on the primary endpoint of ACR50 response to TNFi therapy at 6 months, the MSRC stratified patients according to the likelihood of having an inadequate response to TNFi therapy with an AUC of 0.64 (Figure 3A) and an odds ratio of 4.1 (95% CI: 2.0-8.3; p-value 0.0001) (Table 9).

Other endpoints included ACR50 response at 3 months and response to treatment at 3 and 6 months, as assessed by ACR70, DAS28-CRP remission (<2.4) or LDA (<2.9), and CDAI remission (<2.8) or LDA (<10). MSRC stratified patients according to the likelihood of inadequate response and response criteria at both time points, with AUC values ranging from 0.59 to 0.74 (Figure 3A-3B) and significant odds ratios of 3.0 to 9.1 (p-value <0.01) (Table 7). The odds ratios describing whether patients with non-responder molecular features failed to achieve ACR70 or DAS28-CRP remission were significant at 6 months (p-value <0.0001), but not at 3 months (p-values of 0.07 and 0.34, respectively). For all response criteria except DAS28-CRP remission at 3 months, significant differences in model scores were observed between patients with and without non-response molecular features (p-value < 0.002) (Figures 3B-3C). In addition, a greater proportion of patients achieved remission and LDA at 6 months according to CDAI and DAS28-CRP definitions were observed in patients without non-response molecular features (Figures 3E-3F).

Table 9

AUC Odds ratio (95% CI; p value) Prospective observational, primary ACR50, 3 months 0.67 3.5 (1.6-7.7; 0.002) ACR50, 6 months 0.64 4.1(2.0-8.3; 0.0001) ACR70, 3 months 0.67 2.6 (1.0-6.7; 0.07) ACR70, 6 months 0.72 9.1 (3.5-24.2; <0.0001) CDAI LDA, 3 months 0.65 3.0 (1.4-6.1; 0.005) CDAI LDA, 6 months 0.68 3.6 (1.8-7.2; 0.0002) CDAI remission, 3 months 0.70 3.4 (1.3-8.7; 0.01) CDAI remission, 6 months 0.74 8.4 (3.0-23.9; <0.0001) DAS28-CRP LDA, 3 months 0.69 3.3 (1.5-7.1; 0.002) DAS28-CRP LDA, 6 months 0.67 3.4 (1.7-6.8; 0.0007) DAS28-CRP remission, 3 months 0.59 1.5 (0.7-3.2; 0.34) DAS28-CRP remission, 6 months 0.74 5.2 (2.5-11.1; <0.0001) Prospective observational, TNFi exposure ACR50, 6 months 0.67 3.3 (1.5-7.4; <0.0001) ACR70, 6 months 0.76 7.3 (2.3-23.3; 0.00004) CDAI LDA, 6 months 0.74 7.5 (3.2-17.6; <0.0001) CDAI remission, 6 months 0.84 25.4 (3.2-200.6; <0.0001) DAS28-CRP LDA, 6 months 0.71 6.2 (2.6-14.9; <0.0001) DAS28-CRP remission, 6 months 0.65 2.0(0.9-4.6; 0.1)

Validation of MSRC in a prospective observational clinical study in TNFi-exposed patient samples

Of the patients who completed the 24-week study, RNA blood samples at 3 months were available for 113 patients. Using the same MSRC as the analysis of patients who had not received targeted therapy, 3-month patient samples were used to predict inadequate response to TNFi therapy. The molecular signatures in these TNFi-exposed samples stratified patients according to inadequate response to treatment, with AUC values ranging from 0.65 to 0.84 (Figure 4A). Non-responsive molecular signatures were detected for 40.7% (46/113) of TNFi-exposed patients, and significant differences in model scores were observed between patients with and without non-responsive molecular signatures (p-value <0.012) (Figure 4B). This corresponds to a significant odds ratio of 3.3-25.4 in patients with molecular signatures who failed to respond to treatment based on all criteria except DAS28-CRP remission (Table 9).

discuss

Many targeted therapy options are available for RA, but therapy selection is a challenge because these options have similar treatment outcomes. Precision medicine tools are greatly needed to determine which patients have disease biology that is amenable to each targeted therapy. The blood-based MSRC described analyzes RNA sequencing data along with clinical features to accurately identify patients who have not received targeted therapy before and those who have been exposed to TNFi who are unlikely to have an adequate response to TNFi therapy. Among patients starting their first targeted therapy, those with a non-responder molecular signature were three to nine times less likely to have an adequate response to TNFi therapy (Table 7). When patients were tested after at least three months of receiving TNFi therapy, patients with a non-responder molecular signature were 25 times less likely to achieve remission. In addition, the non-responder molecular signature predicted an inadequate response to TNFi therapy based on multiple clinically validated measures including ACR50, ACR70, DAS28-CRP, and CDAI. The MSRC can inform provider decision making at multiple occasions in the care pathway, such as before initial therapy selection or when targeted TNFi therapy has not achieved treatment goals and a second therapy or dose escalation is being considered. By validating multiple response target definitions, the MSRC fits into multiple practice scenarios, making it easy to understand, act upon, and operationalize in clinical settings.

Precision medicine has improved patient outcomes in oncology and hematology by matching treatment options to the patient’s unique biology. However, even in these areas, predicting drug response, particularly from the blood, remains a challenging technical problem. Furthermore, machine learning and statistical methods tend to overfit to characteristics and attributes of the study population. Unlike oncology, reliance on DNA analysis and biopsy of disease tissue is not readily accepted in RA patient care, as synovial biopsies outside of clinical research are rare and DNA sequence variants provide limited actionable information in RA. Studies from the AMPLE, AVERT, GO-BEFORE, and GO-FORWARD trials used baseline disease assessments such as DAS28, RAPID3, CDAI, or SDAI to predict radiographic progression or magnetic resonance imaging-detected synovitis in response to treatment with targeted therapies. Comparable AUC values (0.54–0.72) were reported, but odds ratios (1.01–1.65) were lower than those observed in this study (3.0–25.4). Furthermore, the odds ratios were consistent between the cross-validated CERTAIN cohort and the prospective NETWORK-004 cohort in this study, indicating that the MSRC is reproducible and generalizable across studies and patient populations. As demonstrated by the results of this study, the technical challenges surrounding the development of precision medicine tools underscore the importance of evaluating biomarkers relevant to disease biology and developing novel approaches, such as network-based approaches.

The network-based approach used in this study revealed new connections in disease biology. A survey of 248 U.S.-based rheumatologists suggests that rheumatologists may welcome precision medicine advances in the field of autoimmune diseases and may find value in this predictive drug response test. When rheumatologists received sample MSRC results indicating no response, selection of TNFi therapy dropped by more than 80% (from 79.8% to as low as 11.3%). In addition, the majority of rheumatologists surveyed reported that the test results would increase their confidence in prescribing decisions, improve medical decisions, and change their treatment choices. Modeling treatment selection guided by precision medicine tools that predict response to TNFi therapy to improve response rates to targeted therapies and save healthcare costs.

RNA transcripts in the MSRC assess seemingly different aspects of disease biology, although these aspects are unified in the same network neighborhood on the human interactome and capture the different biology of RA and the response to TNFi therapy. The proteins encoded by these transcripts affect biological processes, including cellular homeostasis of adaptive and innate immune cells, production of TNF-α and other secreted signaling molecules, synovitis, and bone destruction (Figure 5). TNF-a biology is reliably captured in the MSRC, and features involve the production and release of TNF-a (e.g., COMMD5) and upstream or downstream TNF-a signaling events (e.g., NOTCH1). The identification of molecular features expressed in circulating blood cells suggests that direct assessment of joint physiology or biochemistry may not be necessary to assess synovial phenotype or response to treatment. The MSRC is rooted in RA disease biology and is easily generalized to molecular phenotypes of independent patient cohorts in blinded studies.

in conclusion

The MSRC validation involved analysis of RNA sequencing data derived from blood samples of 391 RA patients treated with TNFi therapy from two independent studies and patient populations, reproducing for the first time the predictive power of a molecular biomarker using seemingly different RA biology. These findings suggest that directly assessing joint physiology or biochemistry may not be important for predicting response. In patients who had not received targeted therapy and those exposed to TNFi, those with a non-response molecular signature were less likely to respond to TNFi therapy at 3 or 6 months, as assessed by ACR50, ACR70, DAS28-CRP, and CDAI. When providers use the MSRC test results to stratify patients for treatment, patients with a non-response molecular signature to TNFi therapy could be directed to alternative therapies to avoid the expense and potential toxicity without possible benefit. Those who lack this signature could continue on TNFi therapy and potentially achieve higher remission rates relative to the unstratified population.

Example 2 - RNA for predicting non-response to tumor necrosis factor inhibitor therapy in patients with rheumatoid arthritis Clinical lifespan of feature groups

As a potentially debilitating autoimmune disease, rheumatoid arthritis (RA) is a clinical manifestation that involves joint deterioration and chronic inflammation. Although there is no cure for the disease, patients with RA do have a variety of available therapies to alleviate symptoms and prevent joint destruction. Treatment guidelines indicate that early therapeutic intervention is important to delay permanent loss of joint function that accompanies tissue structural damage. Once a patient is diagnosed with RA, the first course of therapy applied may be synthetic disease-modifying antirheumatic drugs (csDMARDs), with methotrexate as a first-line option. According to treatment guidelines, patients with RA who do not adequately control symptoms with csDMARDs may be offered a variety of other therapies, including targeted agents that inhibit interleukin 6 (IL-6), Janus kinases (JAKs), and tumor necrosis factor λ (TNF). Although targeted therapies are considered the next step in treatment beyond csDMARDs, no one targeted therapy is recommended over another in these circumstances, and the choice of therapy may depend on nonclinical selection factors. This is demonstrated by the fact that >80% of biologic-naïve RA patients with symptoms that are not adequately controlled by csDMARDs are subsequently directed to anti-TNF therapy.

In a large subpopulation of patients who have an inadequate response to csDMARDs and subsequently initiate anti-TNF therapy, approximately 75-90% of these RA patients do not achieve the intended treatment goal of low disease activity (LDA) or remission in the American College of Rheumatology (ACR) guidelines. In RA patients who have just started a TNF inhibitor (TNFi), a 20% improvement in symptoms (ACR20) is seen in 50-70% of patients, a 50% improvement in ACR score (ACR50) is observed in 30-40% of patients, and 15-25% of patients achieve a 70% improvement (ACR70). It has been reported that no more than about 10-25% of RA patients who have not received biologic therapy who initiate TNFi therapy are able to achieve remission of RA symptoms, indicating that the widespread use of TNFi therapy has not met the need for precision medicine to reduce the potential for treatment cycling. Given the degenerative nature of RA over time, reducing delays in reaching treatment goals could provide quality of life improvements for RA patients who do not respond to anti-TNF therapy.

In the current context of TNFi use, previous studies have shown that RA patients who begin such treatment are receiving a treatment regimen that is suboptimal for their specific biology. This results in a significant amount of money being wasted in the healthcare system to cover the cost of TNFi therapy for patients who would not benefit from treatment, and it also delays directing these RA patients to therapies with alternative mechanisms of action that may be more appropriate for their specific circumstances. Given that the current situation is unhelpful for most RA patients and healthcare resource efficiency, the introduction of a validated biomarker panel that can predict patient non-responsiveness to TNFi could be very beneficial and well accepted by rheumatologists. A proprietary biomarker panel and prediction algorithm called PrismRA, developed by Scipher Medicine, analyzes 19 RNA transcripts, an anti-cyclic citrullinated peptide (anti-CCP) laboratory test, and 3 clinical measures (BMI, sex, and patient global assessment) and has been shown to predict response to anti-TNF therapy in RA patients who have not received biologics. Currently, it is unknown how long PrismRA results remain valid after a patient begins TNFi treatment. The current study was designed to evaluate the stability of PrismRA predictions over the time course of TNFi therapy, with the aim of determining the long-term clinical significance of the PrismRA score at both the population and individual levels.

Study Group

The demographics of the patient population evaluated herein are summarized in Table 10. A total of 452 whole blood samples and accompanying clinical measurements were obtained from 330 patients with rheumatoid arthritis. Samples were collected from RA patients after they started anti-TNF therapy. All patients in this study had not received RA biologics before starting a course of TNFi. Blood samples from RA patients were collected 3 months or 6 months after the start of TNFi, and a cross-sectional analysis was performed on patients who provided samples at both time points. In the patient population, 94 patients provided samples only 3 months after the start of TNFi, 114 patients provided samples only at the 6-month time point, and 122 patients provided samples at both the 3-month and 6-month time points. The overlay between the three patient groups and the two sample collection time points is shown in Figure 7. All patients participating in this study provided informed consent, and institutional review board approval was obtained before any sample collection or study participation by the patient. The choice of TNFi therapy and associated doses for all patients was at the discretion of the rheumatologist.

Clinical evaluation and response to anti-TNF therapy

Clinical response to anti-TNF therapy was assessed at baseline, 3-month, and 6-month visits based on criteria defined for ACR, Clinical Disease Activity Index (CDAI), and Disease Activity Score 28 using C-reactive protein (DAS28-CRP). ACR measurements of ACR50 and ACR70 were defined as when an individual demonstrated ≥50% or ≥70% improvement in 28 tender joint counts, 28 swollen joint counts, and at least three of the five clinical values used to assess disease status in RA patients. Whole blood samples in PAXgene RNA blood tubes were collected at each visit. Rheumatoid factor (RF) and anti-cyclic citrullinated protein (anti-CCP) antibody serum status measurements were established at the patient's baseline sampling point. Variables used to assess disease status in RA patients included the Health Assessment Questionnaire Disability Index, Patient Global Assessment, Provider Global Assessment, CRP and anti-CCP levels, and patient-reported pain. Clinical assessment data were also used to determine whether patients met the clinical thresholds for CDAI low disease activity (CDAI-LDA), CDAI remission (CDAI-R), DAS28-CRP low disease activity (DAS28-CRP-LDA), and DAS28-CRP remission (DAS28-CRP-R).

RNA isolation, preparation, and sequencing analysis

PAX-gene blood RNA tubes are used to collect blood samples for total RNA isolation. Stable blood PAXgene tube RNA isolation kit MagMax ^TM from Thermo Fisher Scientific was used for RNA sample preparation according to the manufacturer's protocol. RNA in the mass range of 100-1000ng was processed using the KAPA RNA HyperPrep kit with RiboErase (HMR) beads. The quality of the collected RNA was assessed using the Agilent Bioanalyzer (Agilent Bioanalyzer) automated electrophoresis platform, while RNA quantification was performed using the NanoDrop ND-8000 spectrophotometer. RNA samples were sequenced using the Illumina NovaSeq6000 platform and a diagnostic assay validated by the Clinical Laboratory Improvement Amendments (CLIA). Gene expression across the entire genome was determined by processed sequence data. To be included in sample analysis, RNA samples were required to have a TapeStation RIN>4, RNA concentration ≥10 ng/μL, sequencing library yield ≥10 nM, perfect base pair index percentage >85, percentage of bases exceeding Phred score 30 >75, mean quality Phred score >30, median Phred score >25, and lower quartile Phred score >10 for all bases in the RNA molecules.

TNFi response prediction model

Using a panel of 23 selected biomarkers, a TNFi therapy response classification model was trained using 245 samples collected from RA patients. Model building was performed using the MLPClassifier provided in the Python machine learning library sklearn.

Statistical analysis

The performance of the PrismRA biomarker panel was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). The MSRS model cutoff for odds ratio calculation was selected based on previous validation results. Odds ratios were calculated. Python 3.7.6 was used to perform all statistical analyses and data processing procedures. All values that could be classified as continuous data were presented as mean, standard deviation, median, minimum, maximum, and appropriate observation counts. For categorical variables, frequency counts and percentages were used to summarize individual values. To determine confidence intervals (CIs), t distribution was used to obtain CIs for continuous data, while exact methods were used to determine CIs for categorical variables. Unless otherwise stated, two-sided tests were performed at a significance level of 0.05 in all cases.

PrismRA maintains performance throughout the duration of anti-TNF exposure

Table 10 shows the demographics of the patient population evaluated in this study. A total of 452 samples collected from 330 RA patients who recently started TNF therapy were evaluated. Samples were collected at 3 months after TNF initiation or 6 months after TNF initiation. Figure 7 shows the overlay of patients who provided samples at the 3-month and 6-month time points. Of the 330 patients in this study, 94 patients provided samples only at the 3-month time point, 122 patients provided samples at both the 3-month and 6-month time points, and 114 patients provided samples only at the 6-month time point.

Table 10

Using the patient data collected at two time points, molecular signature response classifier (MSRC) is used to predict the therapeutic response to TNF therapy. Using seven different clinically acceptable response definitions (ACR20, ACR50, ACR70, CDAI-R, CDAI-LDA, DAS28-CRP-R and DAS28-CRP-LDA), after sample collection+3 months and+6 months, the patient response to TNF therapy is assessed. See Materials and Methods for more details. Fig. 8 shows the ROC curve produced by comparing the treatment results of MSRC score with+3 months and+6 months.

Comparable performance was observed when using 3-month data (FIG. 8a/b) compared to predictions using 6-month data (FIG. 8c/d). Across the various response definitions, the AUC ranged from 0.66-0.73 when using 3-month data, and from 0.67-0.75 when using 6-month data. Similar performance was observed when model predictions were compared to treatment response results at +3 months (FIG. 8a/c) and +6 months (FIG. 8b/d) after the data collection time, with AUC ranges of 0.67-0.79 and 0.66-0.76, respectively. Table 11 summarizes the odds ratios observed in each sample and endpoint definition. For all models evaluated, statistically significant differences in the distribution of scores between responders and non-responders were observed (p<0.001).

Table 11

The stability of MSRC prediction is further evaluated by comparing the model performance between 122 patients providing 3 month samples and 6 month samples (Fig. 9). In order to ensure consistent results, response is evaluated after TNF therapy starts + 9 months (+ 6 months from 3 month sample collection time point and + 3 months from 6 month sample collection time point). In various response definitions, when using TNF to start the data collected after 3 months, AUC ranges from 0.66-0.74, and when using TNF to start the data collected after 6 months, AUC ranges from 0.65-0.73. In both cases, it is observed that there is a statistically significant difference (p < 0.001) in the distribution of MSRC scores between responders and non-responders.

PrismRA predictions are stable over the entire time course of anti-TNF exposure

To assess the longitudinal stability of PrismRA response predictions on an individual basis, we first investigated the stability of the response outcome signature in the 122 patients for whom data at 3 and 6 months were available. Table 12 details the degree of agreement between the outcomes when data collected at 3 and 6 months after TNF initiation are considered. On average, results at +3 months were consistent 73.9% of the time, and results at +6 months were consistent 80.9% of the time, across endpoint definitions. Among patients with discordance, the proportions of nonresponders to responders were similar to those of nonresponders to responders. For results at +3 months, there was an average of 14% change from nonresponders to responders and 12% change from responders to nonresponders. For results at +6 months, there was an 8.5% change from nonresponders to responders and 10.5% change from responders to nonresponders.

Table 12

The stability of MSRC predictions over the entire TNF therapy time course was assessed by comparing predictions made using data collected three months after the start of TNF therapy with predictions made using data collected six months after the start of TNF therapy. Of the 122 patients for whom both 3-month and 6-month data were available, 97 patients (81.5%) had consistent class predictions between the two time points, while 22 patients (18.5%) had different predictions between the two time points. Of the 18.5% that changed, 9 changed from non-responders to responders and 12 changed from responders to non-responders.

discuss

Biologic therapies for rheumatoid arthritis can target a range of different targets (TNF, IL-6, and JAK) and have roughly equivalent benefits when patients respond to therapy, yet the most common biologic therapy choice among rheumatologists is TNFi. Without additional clinical guidance, the primary use of TNFi is certain to continue, meaning that 90% of patients who do not have an adequate response to csDMARDs will receive a drug that has a 70% chance of failing to reach target threshold for RA treatment. However, these negative outcomes could be mitigated by implementing clinical panels that can determine whether a patient will respond to TNFi therapy. The PrismRA predictive biomarker panel has been clinically validated in previous studies to successfully classify RA patients as TNFi responders or non-responders.

One goal of this study was to determine the efficacy of PrismRA MSRC across the time course of TNF therapy. At the population level, model performance was good and reflected previous validation results. MSRC was able to discriminate nonresponders from responders, with an AUC range of 0.61-0.69 when using 3-month data and an AUC range of 0.64-0.73 when using 6-month data. This is in good agreement with the observations of Mellors et al., where the biomarker response group successfully identified TNFi nonresponders with an odds ratio of 6.57. Furthermore, previous validation of MSRC by Cohen et al. successfully stratified patients’ likelihood of being a TNFi nonresponder, with an odds ratio of 4.1 based on the ACR50 response endpoint at 6 months.

The predictive performance of the +3 month and +6 month results remained consistent regardless of when samples were collected throughout the anti-TNF therapy timeline. The stability of the PrismRA results suggests that the biomarker panel can be used at any time during TNFi therapy while still providing valid predictions about a patient's response to therapy over the next 3 to 6 months. Rather than providing valid treatment guidance only at the start of treatment and changing over time, this study shows the long-term validity of this biomarker panel over the time course of TNFi therapy.

The results of this study do have some degree of variability, as can be inferred from the concordance rates of approximately 74% for the +3 month results and approximately 81% for the +6 month results. The occurrence of false predictions or patients switching between responders and non-responders on consecutive measurements highlights the importance of understanding the natural outcome variability to properly characterize model performance.

Among the discordant results, the proportion of patients who switched from responders to nonresponders was approximately the same as the proportion of patients who switched from nonresponders to responders. On average, the proportion of nonresponders who switched to responders was 14% at the +3-month time point and 8.5% at the +6-month time point, while the proportion of responders who switched to nonresponders was found to be 12% at the +3-month time point and 10.5% at the +6-month time point. Because patients who are nonresponders on anti-TNF therapy at the time of testing have a smaller chance of switching to responder status, some clinicians may prefer to keep nonresponders on TNFi just in case they eventually respond. However, the time-sensitive and debilitating nature of RA disease progression may warrant a clinical decision to prioritize the chances of success over waiting for ineffective therapy to achieve eventual remission. The American College of Rheumatology adjusted its recommendations for bDMARD-based anti-TNF therapy in the 2021 guidelines, in which patients who do not achieve target improvement should be switched to a bDMARD from a different drug class rather than a different bDMARD in the same class. Among alternatives to TNF inhibitors, options such as inhibitors of IL-6, IL-6 receptor, and JAK have been reported to be roughly as effective as TNF inhibitors (29–36). In addition, other classes of bDMARDs have been reported to remain effective in patients with RA even after nonresponse to anti-TNF therapy, so patients who may transition from responders to nonresponders after initial TNFi therapy may still have access to biologics as an effective treatment option (37–39). The updated ACR recommendations and the availability of effective alternatives to TNFi therapy have reinforced the motivation to stratify patients into responders or nonresponders to TNFi therapy so that time is not wasted trying therapies with poorer response rates in the population or continuing on medications that patients are no longer responding to. In a study of the perceived clinical utility of the PrismRA panel, Pappas et al. found that an MSRC that could categorize a patient’s TNFi response would likely be well received by rheumatologists. Of the 248 clinicians surveyed, 92% felt that the test results would increase their confidence in making decisions about treatment for their RA patients, and approximately 80% of the rheumatologists surveyed agreed that this type of biomarker panel could improve medical decision making.

The MSRC demonstrated a high level of agreement when predictions made using 3-month data were compared to predictions made using 6-month data. Comparisons between measurements made at these two time points maintained agreement of >81% in all cases. These data suggest that even if the MSRC is given at different points during the TNFi time course, this group will still produce consistent results. The results confirm the validity and utility of using the MSRC to identify nonresponders to TNFi therapy at the time of treatment selection (24,26,40–43), and this longitudinal study suggests that physicians may or may not retest their patients after initial testing. Accordingly, patients are recommended to undergo PrismRA testing when a change in prescription is warranted due to disease progression or side effects of current therapy. Further studies measuring intervals for PrismRA testing are planned to better define patient testing recommendations.

Knowing that the PrismRA test has consistent accuracy can avoid further waste and inefficiency in indicating repeat testing or indeterminate results, and can realize cost savings by placing patients on the most effective therapy based on their molecular profile. Bergman et al described an approximation of the cost savings from predicting TNF inhibitor nonresponse with PrismRA in clinical decision making. When modeling the cost of standard-of-care biologic therapy compared with RA patients treated with PrismRA stratification over 12 months, a 22% reduction in the cost of ineffective therapy and a 5% reduction in overall RA treatment costs were obtained. In the Medicare-eligible population, these savings equate to a reduction of $6668 per patient per year in spending on ineffective therapy. Given that PrismRA stratification directly reduces ineffective therapy and provides indirect value by slowing disease progression, the multiple benefits that may be available for RA reinforce the value of advancing precision medicine in the clinical arena.

Certain non-limiting embodiments of the subject matter described therein have been described previously. It should therefore be understood that the embodiments described in this specification are merely illustrative of the subject matter reported therein. Reference to details of the illustrated embodiments is not intended to limit the scope of the claims, which themselves recite those features deemed essential.

It is contemplated that the systems and methods of the claimed subject matter cover variations and adaptations developed using information from the embodiments described therein. One of ordinary skill in the relevant art may adapt, modify, or both the systems and methods described therein.

Throughout the specification, when a system is described as having, including, or comprising specific components, or when a method is described as having, including, or comprising specific steps, it is contemplated that there are additional systems consisting essentially of or consisting of the enumerated components encompassed by the invention, and there are methods consisting essentially of or consisting of the enumerated processing steps encompassed by the invention.

It should be understood that the order of steps or the order in which certain actions are performed is not important, as long as any embodiment of the subject matter described therein remains operable. In addition, two or more steps or actions may be performed simultaneously.

Although preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Now, those skilled in the art will conceive of many variations, changes and substitutions without departing from the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be adopted in practicing the present invention. The appended claims are intended to define the scope of the present invention, and thus encompass methods and structures within the scope of these claims and their equivalents.

Claims (45)

1. A method of treating a subject having an autoimmune disorder, the method comprising: administering an anti-TNF therapy to the subject, wherein the subject has been determined to be a responder by a classifier established to distinguish between responding prior subjects and non-responding prior subjects in a cohort who have received the anti-TNF therapy; Wherein the classifier is developed by evaluating: One or more genes whose expression levels significantly correlate with clinical responsiveness or non-responsiveness; and At least one of the following: the presence of one or more single nucleotide polymorphisms (SNPs) in the expressed sequences of the one or more genes; or at least one clinical characteristic of the responding prior subject and the non-responding prior subject; and wherein said classifier is validated by an independent cohort different from a cohort that has received said anti-TNF therapy; and The one or more genes include: ALPL, ATRAAID, BCL6, CDK11A, CFLAR, COMMD5, GOLGA1, IL1B, IMPDH2, JAK3, KLHDC3, LIMK2, NOD2, NOTCH1, SPINT2, SPON2, STOML2, TRIM25 or ZFP36. 2. The method of claim 1, wherein the subject has previously been administered the anti-TNF therapy. 3. The method of claim 2, wherein the subject has been administered the anti-TNF therapy for at least one month, at least two months, at least three months, at least four months, at least five months, or at least six months prior to the administering. 4. The method of claim 3, wherein the previously administered anti-TNF therapy is different from the anti-TNF therapy administered in response to the classifier. 5. The method of claim 1, wherein the classifier identifies 60% or more non-responders in the treatment-naïve cohort. 6. The method of claim 5, wherein the classifier identifies 60% or more non-responders in a treatment-naïve cohort of at least 350 subjects. 7. The method of claim 1, wherein the one or more genes are characterized by their topological properties when mapped onto a human interactome map. 8. The method of claim 1, wherein the SNP is identified with reference to the human genome. 9. The method of claim 1, wherein the one or more genes include: ALPL, BCL6, CDK11A, CFLAR, IL1B, JAK3, LIMK2, NOD2, NOTCH1, TRIM25 or ZFP36. 10. The method of claim 1, wherein the at least one clinical characteristic is selected from the group consisting of: body mass index (BMI), gender, age, race, prior therapy, disease duration, C-reactive protein level, anti-ring melon The presence of amino acid peptides, the presence of rheumatoid factor, patient global evaluation, treatment response rate (eg, ACR20, ACR50, ACR70), and combinations thereof. 11. The method of claim 1, wherein the anti-TNF therapy comprises administration of infliximab, adalimumab, etanercept, certolizumab, goliluma, or biosimilars thereof. 12. The method of claim 1, wherein the autoimmune disorder is selected from the group consisting of rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn's disease, ulcerative colitis, chronic psoriasis disease, hidradenitis suppurativa, multiple sclerosis and juvenile idiopathic arthritis. 13. The method of claim 1, wherein the classifier is established using microarray analysis derived from the responding prior subject and the non-responding prior subject. 14. The method of claim 1, wherein the SNPs are selected from Table 3. 15. The method of claim 1, wherein the response is verified in the subject by statistical analysis of clinical characteristics. 16. The method of claim 15, wherein the statistical analysis of clinical characteristics analyzes changes in clinical characteristics following receipt of anti-TNF therapy. 17. The method of claim 15, wherein the statistical analysis of clinical characteristics analyzes changes in one or more of ACR50, ACR70, CDAI LDA, CDAI remission, DAS28-CRP LDA, or DAS28-CRP remission. 18. The method of claim 15, wherein the statistical analysis is Monte Carlo analysis. 19. The method of claim 1, wherein the classifier includes all of the following genes and clinical features: ALPL, ATRAAID, BCL6, CDK11A, CFLAR, COMMD5, GOLGA1, IL1B, IMPDH2, JAK3, KLHDC3, LIMK2, NOD2, NOTCH1, SPINT2, SPON2, STOML2, TRIM25, ZFP36, BMI, gender, patient overall status, assessment and anti-CCP. 20. The method of claim 1, wherein the method is an automated computer-implemented method. 21. A non-transitory computer-readable storage medium encoded with a computer program, the computer program comprising instructions executable by at least one processor to create an improved sample classification application, the non-transitory computer-readable storage medium comprising: at least one classifier stored on the medium, wherein the classifier is capable of distinguishing between subjects who respond to anti-TNF therapy and subjects who do not respond; Wherein the classifier is developed by evaluating: One or more genes whose expression levels are significantly correlated with clinical responsiveness or non-responsiveness to anti-TNF therapy; At least one of the following: the presence of one or more single nucleotide polymorphisms (SNPs) in the expressed sequence of the one or more genes; or at least one clinical characteristic of the prior responding subject and the prior non-responding subject; and wherein said classifier is validated by an independent cohort different from a cohort that has received said anti-TNF therapy; and The one or more genes include: ALPL, ATRAAID, BCL6, CDK11A, CFLAR, COMMD5, GOLGA1, IL1B, IMPDH2, JAK3, KLHDC3, LIMK2, NOD2, NOTCH1, SPINT2, SPON2, STOML2, TRIM25 or ZFP36. 22. The non-transitory computer-readable storage medium of claim 21, further comprising a software module configured to receive gene expression data derived from a blood sample from the subject. 23. The non-transitory computer-readable storage medium of claim 22, further comprising a software module for applying the classifier to the gene expression data. 24. The non-transitory computer-readable storage medium of claim 23, further comprising a software module for outputting a classification of the sample using the classifier, wherein the classification classifies the blood sample as Either from subjects who responded to anti-TNF therapy or from subjects who did not respond to anti-TNF therapy. 25. The non-transitory computer-readable storage medium of claim 21, wherein the subject has previously been administered the anti-TNF therapy. 26. The non-transitory computer-readable storage medium of claim 25, wherein at least one month, at least two months, at least three months, at least four months, at least five months, or at least six months precedes said administration. Months, the subject has been administered the anti-TNF therapy. 27. The non-transitory computer-readable storage medium of claim 21, wherein the classifier identifies 60% or more non-responders in the treatment-naïve cohort. 28. The non-transitory computer-readable storage medium of claim 27, wherein the classifier identifies 60% or more non-responders in a treatment-naïve cohort of at least 350 subjects. 29. The non-transitory computer-readable storage medium of claim 21, wherein the one or more genes are characterized by their topological properties when mapped onto a human interactome map. 30. The non-transitory computer-readable storage medium of claim 21, wherein the SNP is identified with reference to the human genome. 31. The non-transitory computer-readable storage medium of claim 21, wherein the one or more genes include: ALPL, BCL6, CDK11A, CFLAR, IL1B, JAK3, LIMK2, NOD2, NOTCH1, TRIM25 or ZFP36 . 32. The non-transitory computer-readable storage medium of claim 21, wherein the at least one clinical characteristic is selected from: body mass index (BMI), gender, age, race, prior therapy, disease duration, C Reactive protein levels, presence of anti-cyclic citrullinated peptides, presence of rheumatoid factor, global patient evaluation, treatment response rate (eg, ACR20, ACR50, ACR70), and combinations thereof. 33. The non-transitory computer-readable storage medium of claim 21, wherein the anti-TNF therapy comprises administration of infliximab, adalimumab, etanercept, certolizumab, goliluma, or its biosimilars. 34. The non-transitory computer-readable storage medium of claim 21, wherein the classifier is established using microarray analysis derived from the responding prior subject and the non-responding prior subject. 35. The non-transitory computer-readable storage medium of claim 21, wherein the SNP is selected from Table 3. 36. The non-transitory computer-readable storage medium of claim 21, wherein the response is verified in the subject by statistical analysis of clinical characteristics. 37. The non-transitory computer-readable storage medium of claim 36, wherein the statistical analysis of clinical characteristics analyzes changes in clinical characteristics following receipt of anti-TNF therapy. 38. The non-transitory computer-readable storage medium of claim 36, wherein the statistical analysis of the clinical characteristics analyzes one of ACR20, ACR50, ACR70, CDAILDA, CDAI remission, DAS28-CRP LDA or DAS28-CRP remission. or multiple changes. 39. The non-transitory computer-readable storage medium of claim 36, wherein the statistical analysis is a Monte Carlo analysis. 40. The non-transitory computer-readable storage medium of claim 21, wherein the classifier includes any of the following genes or clinical features: ALPL, ATRAAID, BCL6, CDK11A, CFLAR, COMMD5, GOLGA1, IL1B, IMPDH2, JAK3 , KLHDC3, LIMK2, NOD2, NOTCH1, SPINT2, SPON2, STOML2, TRIM25, ZFP36, BMI, gender, overall patient status, assessment, or anti-CCP. 41. A method of treating a subject having an autoimmune disorder, the method comprising: Determining the subject as a responder by a classifier established to distinguish prior subjects in the cohort who responded who have received anti-TNF therapy from prior subjects who have not responded; Wherein the classifier is developed by evaluating: One or more genes whose expression levels significantly correlate with clinical responsiveness or non-responsiveness; and At least one of the following: the presence of one or more single nucleotide polymorphisms (SNPs) in the expressed sequence of the one or more genes; or at least one clinical characteristic of the responding prior subject and the non-responding prior subject; and wherein said classifier is validated by an independent cohort different from a cohort that has received said anti-TNF therapy; and The one or more genes include: ALPL, ATRAAID, BCL6, CDK11A, CFLAR, COMMD5, GOLGA1, IL1B, IMPDH2, JAK3, KLHDC3, LIMK2, NOD2, NOTCH1, SPINT2, SPON2, STOML2, TRIM25 or ZFP36. 42. The method of claim 41, wherein the subject has previously been administered the anti-TNF therapy. 43. The method of claim 42, wherein the subject has been were administered the anti-TNF therapy. 44. A kit comprising the non-transitory computer-readable storage medium of claim 21. 45. The kit of claim 44, further comprising instructions describing how to execute the classifier.