WO2022157506A1

WO2022157506A1 - Method for treating rheumatoid arthritis

Info

Publication number: WO2022157506A1
Application number: PCT/GB2022/050172
Authority: WO
Inventors: Costantino Pitzalis; Myles J LEWIS
Original assignee: Queen Mary University Of London
Priority date: 2021-01-21
Filing date: 2022-01-21
Publication date: 2022-07-28
Also published as: WO2022157506A9; JP2024505175A; GB202100821D0; EP4281587A1

Abstract

A method for determining whether a Rheumatoid Arthritis (RA) patient is susceptible to treatment with a B cell targeted therapy, the method comprising the steps: (a) determining the level of one or more biomarker in one or more sample obtained from the patient, wherein the one or more biomarker is selected from Table 1; and (b) comparing the level of the one or more biomarker to one or more corresponding reference value; wherein the level of the one or more biomarker compared to the corresponding reference value is indicative of the susceptibility to treatment with a B cell targeted therapy.

Description

METHOD FOR TREATING RHEUMATOID ARTHRITIS

FIELD OF THE INVENTION

The invention relates to a method for determining whether a rheumatoid arthritis (RA) patient is susceptible to treatment with a B cell targeted therapy, such as Rituximab. The invention also relates to methods for treating RA patients that are determined to be susceptible or refractory to B cell targeted therapy.

BACKGROUND TO THE INVENTION

Inflammatory arthritis is a prominent clinical manifestation in diverse autoimmune disorders including rheumatoid arthritis (RA), psoriatic arthritis (PsA), systemic lupus erythematosus (SLE), Sjogren's syndrome and polymyositis.

RA is a chronic inflammatory disease that affects approximately 0.5 to 1% of the adult population in northern Europe and North America. It is a systemic inflammatory disease characterized by chronic inflammation in the synovial membrane of affected joints, which ultimately leads to loss of daily function due to chronic pain and fatigue. The majority of patients also experience progressive deterioration of cartilage and bone in the affected joints, which may eventually lead to permanent disability. The long-term prognosis of RA is poor, with approximately 50% of patients experiencing significant functional disability within 10 years from the time of diagnosis. Life expectancy is reduced by an average of 3-10 years.

Inflammatory bone diseases, such as RA, are accompanied by bone loss around affected joints due to increased osteoclastic resorption. This process is mediated largely by increased local production of pro-inflammatory cytokines, of which tumor necrosis factor-a (TNF-a) is a major effector.

In RA specifically, an immune response is thought to be initiated/perpetuated by one or several antigens presenting in the synovial compartment, producing an influx of acute inflammatory cells and lymphocytes into the joint. Successive waves of inflammation lead to the formation of an invasive and erosive tissue called pannus. This contains proliferating fibroblast-like synoviocytes and macrophages that produce proinflammatory cytokines such as TNF-a and interleukin-1 (IL-I). Local release of proteolytic enzymes, various inflammatory mediators, and osteoclast activation contributes to much of the tissue damage. There is loss of articular cartilage and the formation of bony erosions. Surrounding tendons and bursa may become affected by the inflammatory process. Ultimately, the integrity of the joint structure is compromised, producing disability. B cells are thought to contribute to the immunopathogenesis of RA, predominantly by serving as the precursors of autoantibody-producing cells but also as antigen presenting cells (APC) and pro-inflammatory cytokine producing cells. A number of autoantibody specificities have been identified including antibodies to Type II collagen and proteoglycans, as well as rheumatoid factors and most importantly anti citrullinated protein antibodies (ACPA). The generation of large quantities of antibody leads to immune complex formation and the activation of the complement cascade. This in turn amplifies the immune response and may culminate in local cell lysis.

Current standard therapies for RA which are used to modify the disease process and to delay joint destruction are known as disease modifying anti-rheumatic drugs (DMARDs). Methotrexate, leflunomide and sulfasalazine are traditional DMARDs and are often effective as first-line treatment.

Biologic agents designed to target specific components of the immune system that play a role in RA are also used as therapeutics. There are various groups of biologic treatments for RA including; TNF-a inhibitors (etanercept, infliximab and adalimumab), human IL-1 receptor antagonist (anakinra) and selective co-stimulation modulators (abatacept).

Anti-CD20 therapies are indicated for the treatment of RA in patients who have had an inadequate response to one or more DMARDs. In particular, rituximab is indicated for the treatment of moderate to severe RA in adult patients who have had an inadequate response to, or cannot tolerate, one or more TNF-a inhibitor therapies. Rituximab has been shown to be effective in the treatment of RA in patients refractory to treatment with anti-TNF therapy.

The Rituximab antibody is a genetically engineered chimeric murine/human monoclonal antibody directed against the CD20 antigen. Rituximab binds human complement and lyses lymphoid B-cell lines through complement-dependent cytotoxicity. Additionally, it has significant activity in assays for antibody-dependent cellular cyotoxicity. More recently, Rituximab has been shown to have anti-proliferative effects in tritiated thymidine-incorporation assays and to induce apoptosis directly. Other anti-CD19 and anti-CD20 antibodies have not been shown to have this activity.

Rituximab treatment has been shown to result in B cell depletion in peripheral blood, bone marrow and the synovium. However, not all patients refractory to treatment with anti-TNF therapy are responsive to Rituximab treatment. Current evidence on the efficacy of Rituximab relates primarily to rheumatoid factor, ACPA positive patients, although even within this population clinical responses are heterogeneous with only 60% achieving an ACR20 response within 6 months. Rituximab is associated with various safety issues, especially infusion-related adverse events and is also very expensive, costing approximately USD 10000 per treatment course.

Accordingly, there is a need for methods of predicting whether a given RA patient is likely to respond to a B cell targeted therapy, such as Rituximab treatment. There is also a need for methods of treating RA patients who are non-responsive to DMARD and/or anti-TNF therapy.

SUMMARY OF THE INVENTION

The present inventors have carried out the first biopsy-driven, multi-centre, randomised- controlled-trial (R4RA) comparing tocilizumab and rituximab in RA patients stratified for synovial B-cell status.

The inventors identified a panel of biomarkers that may be used in determining susceptibility to treatment with a B cell targeted therapy, such as Rituximab. These biomarkers may be applied to characterise RA patients as likely or not to respond to a B cell targeted therapy, such as Rituximab treatment. The biomarkers may therefore be used to direct patient treatment more effectively to such B cell targeted therapies or alternative therapies, such as tocilizumab treatment.

In one aspect, the invention provides a method for determining whether a Rheumatoid Arthritis (RA) patient is susceptible to treatment with a B cell targeted therapy, the method comprising the steps:

(a) determining the level of one or more biomarker in one or more sample obtained from the patient, wherein the one or more biomarker is selected from Table 1 ; and

(b) comparing the level of the one or more biomarker to one or more corresponding reference value; wherein the level of the one or more biomarker compared to the corresponding reference value is indicative of the susceptibility to treatment with a B cell targeted therapy.

In one aspect, the invention provides a method for selecting a therapy for a Rheumatoid Arthritis (RA) patient, the method comprising the steps:

(a) determining the level of one or more biomarker in one or more sample obtained from the patient, wherein the one or more biomarker is selected from Table 1 ; and (b) comparing the level of the one or more biomarker to one or more corresponding reference value; wherein the level of the one or more biomarker compared to the corresponding reference value is indicative of the patient’s susceptibility to treatment with a B cell targeted therapy.

In some embodiments, the level is a nucleic acid level. In some embodiments, the nucleic acid level is an mRNA level.

In some embodiments, the step of determining the level of one or more biomarker is performed by direct digital counting of nucleic acids, RNA-seq, RT-qPCR, qPCR, multiplex qPCR or RT- qPCR, microarray analysis, or a combination thereof.

In preferred embodiments, the step of determining the level of one or more biomarker is performed by RNA sequencing.

In some embodiments, the step of determining the level of the one or more biomarker comprises determining the level of gene expression of the one or more biomarker.

In preferred embodiments, the one or more sample is a synovial sample. In some embodiments, the sample is a synovial tissue sample or a synovial fluid sample.

In some embodiments, the sample is obtained by synovial biopsy, preferably ultrasound- guided synovial biopsy. In some embodiments, the synovial biopsy is obtained by an arthroscopic procedure.

In some embodiments, the level of one or more biomarker is determined in 2, 3, 4, 5, 6, 7, 8 or more samples obtained from the patient. In some embodiments, the level of one or more biomarker is determined in 6, 7, 8 or more samples obtained from the patient. In some embodiments, the level of one or more biomarker is determined in 6-8 samples obtained from the patient. In some embodiments, the level of one or more biomarker is determined in 6 samples obtained from the patient. In some embodiments, the samples obtained from the patient are pooled before determination of the level of one or more biomarker. In some embodiments, the level of one or more biomarker is determined in 1 sample obtained from the patient.

In some embodiments, (a) when the level of the one or more biomarker is greater than the corresponding reference value the patient is determined to be susceptible to treatment with the B cell targeted therapy; and/or (b) when the level of the one or more biomarker is less than the corresponding reference value the patient is determined to be resistant to treatment with a B cell targeted therapy.

In some embodiments, the level of the one or more biomarker compared to the corresponding reference value classifies the sample as B cell rich or B cell poor.

In some embodiments, (a) when the sample is B cell rich the patient is determined to be susceptible to treatment with a B cell targeted therapy; and/or (b) when the sample is B cell poor the patient is determined to be resistant to treatment with a B cell targeted therapy.

In some embodiments, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70 or all 71 biomarkers from Table 1.

In some embodiments, the one or more biomarkers comprise all 71 biomarkers from Table 1.

In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70 or all 71 biomarkers from Table 1.

In some embodiments, the one or more biomarkers consist of all 71 biomarkers from Table 1.

In some embodiments, the one or more biomarker comprises or consists of a biomarker selected from the group consisting of: CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA-DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5, WDFY4, FCER2, LCN10, CD19 and TNFRSF13B.

In some embodiments, the one or more biomarker comprises or consists of CXCR5. In some embodiments, the one or more biomarker comprises or consists of PTPRCAP. In some embodiments, the one or more biomarker comprises or consists of FCRLA. In some embodiments, the one or more biomarker comprises or consists of FCRL3. In some embodiments, the one or more biomarker comprises or consists of PNOC. In some embodiments, the one or more biomarker comprises or consists of CPNE5. In some embodiments, the one or more biomarker comprises or consists of POU2AF1. In some embodiments, the one or more biomarker comprises or consists of PAX5. In some embodiments, the one or more biomarker comprises or consists of CLEC17A. In some embodiments, the one or more biomarker comprises or consists of FCRL1. In some embodiments, the one or more biomarker comprises or consists of E2F5. In some embodiments, the one or more biomarker comprises or consists of IGLL5. In some embodiments, the one or more biomarker comprises or consists of STAP1. In some embodiments, the one or more biomarker comprises or consists of CLECL1. In some embodiments, the one or more biomarker comprises or consists of FAM177B. In some embodiments, the one or more biomarker comprises or consists of SNX22. In some embodiments, the one or more biomarker comprises or consists of MS4A1. In some embodiments, the one or more biomarker comprises or consists of HLA-DOB. In some embodiments, the one or more biomarker comprises or consists of TNFRSF17. In some embodiments, the one or more biomarker comprises or consists of TLR10. In some embodiments, the one or more biomarker comprises or consists of CD79A. In some embodiments, the one or more biomarker comprises or consists of FCRL5. In some embodiments, the one or more biomarker comprises or consists of CD79B. In some embodiments, the one or more biomarker comprises or consists of TMEM156. In some embodiments, the one or more biomarker comprises or consists of HLA-DRA. In some embodiments, the one or more biomarker comprises or consists of ZBTB32. In some embodiments, the one or more biomarker comprises or consists of HLA-DOA. In some embodiments, the one or more biomarker comprises or consists of RALGPS2. In some embodiments, the one or more biomarker comprises or consists of CD74. In some embodiments, the one or more biomarker comprises or consists of P2RX5. In some embodiments, the one or more biomarker comprises or consists of WDFY4. In some embodiments, the one or more biomarker comprises or consists of FCER2. In some embodiments, the one or more biomarker comprises or consists of LCN10. In some embodiments, the one or more biomarker comprises or consists of CD19. In some embodiments, the one or more biomarker comprises or consists of TNFRSF13B.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5 and PTPRCAP.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP and FCRLA.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA and FCRL3.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3 and PNOC. In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC and CPNE5.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5 and POU2AF1.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 and PAX5.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5 and CLEC17A.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A and FCRL1.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 and E2F5.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5 and IGLL5.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5 and STAP1.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 and CLECL1.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 and FAM177B.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B and SNX22. In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22 and MS4A1.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 and HLA-DOB.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB and TNFRSF17.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1, CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17 and TLR10.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10 and CD79A.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A and FCRL5.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5 and CD79B.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B and TMEM156.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156 and HLA- DRA.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA and ZBTB32.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32 and HLA-DOA.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA and RALGPS2.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2 and CD74.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74 and P2RX5.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5 and WDFY4.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1, FAM177B, SNX22, MS4A1, HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5, WDFY4 and FCER2.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5, WDFY4, FCER2 and LCN10.

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5, WDFY4, FCER2, LCN10 and CD19

In some embodiments, the one or more biomarker comprises or consists of the biomarkers CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5, WDFY4, FCER2, LCN10, CD19 and TNFRSF13B.

In some embodiments, the B cell targeted therapy is B cell depletion therapy.

In some embodiments, the B cell targeted therapy is selected from the group consisting of: rituximab, ocrelizumab, veltuzumab, ofatumumab and epratuzumab. In some embodiments, the B cell targeted therapy is an anti-CD20 antibody or an anti-CD22 antibody. In some embodiments, the B cell targeted therapy is selected from the group consisting of rituximab, ocrelizumab, veltuzumab, obinutuzumab, ibritumomab tiuxetan, ofatumumab, and epratuzumab.

In preferred embodiments, the B cell targeted therapy is rituximab.

In some embodiments, a patient determined to be resistant to treatment with the B cell targeted therapy is determined to be suitable for treatment with an agent that downregulates IL-6 mediated signalling. In some embodiments, the agent that downregulates IL-6 mediated signalling is tocilizumab, sarilumab, satralizumab, or siltuximab. In some embodiments, the agent is an anti-IL-6 antibody. In some embodiments, the agent is an IL-6 receptor antagonist. In some embodiments, the agent is tocilizumab.

In some embodiments, the RA patient is refractory to DMARD and/or anti-TNF therapy. In some embodiments, the method further comprises: (a) administering to the patient a B cell targeted therapy when the patient is determined to be susceptible to treatment with a B cell targeted therapy; or (b) administering to the patient an IL-6 receptor antagonist when the patient is determined to be resistant to treatment with a B cell targeted therapy.

In another aspect, the invention provides a kit for use in the method of the invention.

In another aspect, the invention provides a method for treating Rheumatoid Arthritis (RA), the method comprising administering to a patient an effective amount of a B cell targeted therapy, wherein the patient is determined to be susceptible to treatment with a B cell targeted therapy by the method of the invention.

In another aspect, the invention provides a method for treating Rheumatoid Arthritis (RA), the method comprising administering to a patient an effective amount of an IL-6 receptor antagonist, wherein the patient is determined to be resistant to treatment with a B cell targeted therapy by the method of the invention.

DESCRIPTION OF THE DRAWINGS

FIGURES 1-3

A minimum of 6 synovial biopsies were paraffin embedded en masse and sections stained for Hematoxylin and Eosin (H&E), and immune-histochemical markers CD20 (B cells), CD3 (T cells), CD138 (plasma cells) and CD68 (macrophages) as previously described (Humby F et al. PLoS Med 2009; 6: e1 ; Kraan MC et al. Arthritis Rheum 2002; 46: 2034-8). Sections underwent semi-quantitative scoring (0-4) to determine expression of CD20+ B cells, CD3+ T cells, CD138+ plasma cells and CD68+ lining (I) and sub lining (si) macrophages (Figure 1) adapted from a previously described score (Kraan MC et al. Arthritis Rheum 2002; 46: 2034- 8; Rivellese F et al. Arthritis Rheumatol 2020; 72: 714-25). H&E stained slides also underwent evaluation to determine the level of synovitis. If CD20+ve cells were identified staining for CD21 (follicular dendritic cells, FDC) was also performed as previously described (Humby F et al. PLoS Med 2009; 6: e1). Patients were classified as B-cell rich or B-cell poor in the NHS pathology laboratory of Barts Health NHS Trust by a consultant pathologist (HR) followed by an independent histological classification in the rheumatology research laboratories at QMUL by a second expert in synovial pathology (GT), according to a validated algorithm shown in Figure 2. Synovial tissue with a CD20 score >2 and with CD20+ B cell aggregates were classified as B cell rich as previously described (Kraan MC et al. Arthritis Rheum 2002; 46: 2034-8). Synovial tissue with CD20 score <2 were classified as B cell poor (Kraan MC et al. Arthritis Rheum 2002; 46: 2034-8). Any discrepancies in classification were resolved through mutual agreement. Patients in which definite synovial tissue could not be identified were classified as “unknown”. B-cell rich samples were further classified as germinal centre (GC)+ve if CD21+ FDC networks were subsequently identified (Figure 3). As predefined in the study protocol only patients classified as B-cell rich or B-cell poor were included in the primary analysis of the trial with examination of the GC+ve cohort to be undertaken as part of a subsequent exploratory analysis.

FIGURE 4 - Heat map of RNA-seq B cell module gene expression across whole cohort

Samples are ranked by RNA-seq B cell module score from lowest to highest demonstrating reclassification of patients into RNA-seq B cell poor and rich categories. Top tracks show original histology class, CD20 and CD138 histology scores. GC: germinal centre as classified by histology.

Synovial tissue from 162 patients was available for RNA extraction and RNA-sequencing. A minimum of 6 synovial samples per patient were immediately immersed in RNA-Later and RNA extracted using either Phenol/Chloroform or via a Zymo Direct-zol™ RNA MicroPrep - Total RNA/miRNA Extraction kit. All RNA samples were transferred to Genewiz for RNA sequencing by Illumina HiSeq. 184 paired-end RNA-seq samples of 50 million reads of 150 base pairs were trimmed to remove the Illumina adaptors using bbduk from the BBMap package version 37.93 using the default parameters. Transcripts were then quantified using Salmon version 0.13.1 (22) and an index generated from the Gencode release 29 transcriptome following the standard operating procedure. Tximport version 1.13.10 was used to aggregate the transcript level expression data to genes, counts were then subject to variance stabilising transform (VST) using the DESEQ2 version 1.25.9 package. Following exclusion of patients classified histologically as GC+ (n=9) 153 patients remained. One patient was withdrawn before IMP was administered and 28 were excluded following RNAseq quality control or due to poor mapping. Therefore, 124 patients had RNAseq data available for subsequent analysis. Patients were classified as B cell poor/rich according to a B cell-specific gene module derived from analysis of FANTOM5 gene expression data. As no pre-determined cut-off points for B cell transcript classification were found in the literature and to avoid potential bias, patients were classified as B cell poor/rich according to the median transcript module value as shown in the figure.

FIGURE 5 - Testing the cut-off of the RNA-Seq B cell module for defining B cell poor/rich in R4RA

Plot showing how the risk ratio (y axis) for Tocilizumab (TCZ) vs Rituximab (RTX) for CDAI 50% responders at week 16 in B cell poor (blue, above) and B cell rich (red, below) groups varies if the RNA-seq B cell module cut-off point is varied (x axis). Vertical grey line shows median RNA-seq B module score corresponding to original analysis of study using median B cell score as cut-off. At the median, the lower bound for the 95% Cl for B cell poor is significantly >1.0 confirming significant result for B cell poor, but not for B cell rich. Dashed horizontal lines show range over which statistical significance is maintained.

FIGURE S

Schematic of clinical trial design.

FIGURE ?

Patient disposition.

DETAILED DESCRIPTION OF THE INVENTION

The terms “comprising”, “comprises” and “comprised of’ as used herein are synonymous with “including” or “includes”; or “containing” or “contains”, and are inclusive or open-ended and do not exclude additional, non-recited members, elements or steps. The terms “comprising”, “comprises” and “comprised of” also include the term “consisting of”.

Rheumatoid arthritis (RA)

Rheumatoid arthritis (RA) is a chronic, systemic inflammatory disorder that may affect many tissues and organs, but principally attacks synovial joints. It is a disabling and painful condition, which can lead to substantial loss of functioning and mobility if not adequately treated.

The disease process involves an inflammatory response of the synovium, secondary to massive immune cell infiltration and proliferation of synovial cells, excess synovial fluid, and the development of fibrous tissue (pannus) in the synovium that attacks the cartilage and subchondral bone. This often leads to the destruction of articular cartilage and the formation of bone erosions with secondary ankylosis (fusion) of the joints. RA can also produce diffuse inflammation in the lungs, the pericardium, the pleura, the sclera, and also nodular lesions, most commonly in subcutaneous tissue. RA is considered a systemic autoimmune disease as autoimmunity plays a pivotal role in its chronicity and progression.

A number of cell types are involved in the aetiology of RA, including T cells, B cells, monocytes, macrophages, dendritic cells and synovial fibroblasts. Autoantibodies known to be associated with RA include those targeting Rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA). RA therapy

A typical patient with newly diagnosed RA is often treated initially with nonsteroidal antiinflammatory drugs and disease-modifying anti-rheumatic drugs (DMARDs), such as hydroychloroquine, sulfasalazine, leflunomide or methotrexate (MTX), alone or in combinations. Patients who do not respond to general DMARDs may be termed DMARD- refractory.

DMARD-refractory patients are traditionally often progressed to biological therapeutic agents, for example TNF-a antagonists such as Adalimumab, Etanercept, Golimumab and Infliximab. Patients who do not respond to TNF-a antagonist therapy may be termed TNF-a antagonistrefractory or inadequate responders (ir).

The method of the invention may be performed on a sample from a RA patient who has previously been determined to be refractory to DMARD-therapy and/or TNF-a antagonist therapy. The method may also be performed on a sample from a RA patient unable to tolerate TNF-a antagonist therapy.

B cell targeted therapy

The method of the invention may determine an RA patient as being susceptible to treatment with a B cell targeted therapy.

The term “B cell targeted therapy”, as used herein, may refer to the administration of an agent that interferes with or inhibits the development and/or function of B cells. The B cell targeted therapy may cause B cell depletion or the inhibition of B cell development and maturation. Advantageously, the B cell targeted therapy is directed against B cells in all stages of development other than undifferentiated stem cells and terminally differentiated antibodyproducing plasma cells.

The agent may be a small molecule drug, such as a Bruton's tyrosine kinase (BTK) inhibitor or other agent which targets B cell signalling pathways.

Direct depletion of B cells may be performed through the use of antibodies, such as monoclonal antibodies (mAbs), directed against cell surface markers (e.g. CD20 and CD22). Such antibodies bind to the target antigen and kill the cell by initiating a mixture of apoptosis, complement dependent cytotoxicity (CDC), and antibody-dependent cell-mediated cellular cytotoxicity (ADCC). The B cell targeted therapy used in the invention may be an agent directed against CD20, for example Rituximab, Ocrelizumab, Veltuzumab or Ofatumumab, or an agent directed against CD22 such as Epratuzumab. In some embodiments, the B cell targeted therapy is an anti- CD20 antibody. In some embodiments, the B cell targeted therapy is an anti-CD22 antibody. In some embodiments, the B cell targeted therapy is rituximab, ocrelizumab, veltuzumab, ofatumumab, obinutuzumab, ibritumomab tiuxetan, or epratuzumab. In some embodiments, the B cell targeted therapy is rituximab. In some embodiments, the B cell targeted therapy is ocrelizumab. In some embodiments, the B cell targeted therapy is veltuzumab. In some embodiments, the B cell targeted therapy is ofatumumab. In some embodiments, the B cell targeted therapy is obinutuzumab. In some embodiments, the B cell targeted therapy is ibritumomab tiuxetan. In some embodiments, the B cell targeted therapy is epratuzumab.

Rituximab is a chimeric mouse/human immunoglobulin G1 (lgG1) monoclonal antibody to CD20 that stimulates B cell destruction upon binding to CD20. Rituximab depletes CD20 surface-positive naive and memory B cells from the blood, bone marrow and lymph nodes via mechanisms which include antibody-dependent cellular cytotoxicity (ADCC), complement dependent cytotoxicity (CDC). It does not affect CD20-negative early B cell lineage precursor cells and late B lineage plasma cells in the bone marrow.

Ocrelizumab is a humanized anti-CD20 monoclonal antibody that causes CD20+ B cell depletion following binding to CD20 via mechanisms including ADCC and CDC.

Veltuzumab is a humanized, second-generation anti-CD20 monoclonal antibody that causes CD20+ B cell depletion following binding to CD20 via mechanisms including ADCC and CDC.

Ofatumumab is a human monoclonal lgG1 antibody to CD20 and may inhibit early-stage B lymphocyte activation. Ofatumumab targets a different epitope located closer to the N- terminus of CD20 compared to the epitope targeted by rituximab and includes an extracellular loop, as it binds to both the small and large loops of the CD20 molecule. Ofatumumab stimulates B cell destruction through ADCC and CDC pathways.

Epratuzumab is a humanized monoclonal lgG1 antibody to CD22. It contains a murine sequence comprising 5-10% of the molecule, the remainder being human framework sequences. Epratuzumab binds to the CD22 third extracellular domain (epitope B), without blocking the ligand binding site, with measured affinity of Kd = 0.7 nm. In vitro studies showed epratuzumab induces CD22 phosphorylation by binding to its surface. It results in modulation, mostly negative, of BCR activation.

IL-6 mediated signalling The present invention may determine an RA patient to be suitable for treatment with an agent which downregulates interleukin-6 (IL-6) signalling.

IL-6 is a cytokine that provokes a broad range of cellular and physiological responses, including inflammation, hematopoiesis and oncogenesis by regulating cell growth, gene activation, proliferation, survival, and differentiation. It is able to directly influence B cell activation state and late stage differentiation towards plasma cells.

IL-6 signals through a receptor composed of two different subunits, an alpha subunit that produces ligand specificity and GP (Glycoprotein) 130, a receptor subunit shared in common with other cytokines in the IL-6 family. Binding of IL-6 to its receptor initiates cellular events including activation of JAK (Janus Kinase) kinases and activation of Ras-mediated signalling. Activated JAK kinases phosphorylate and activate STAT transcription factors, particularly STAT3 and SHP2. Phosphorylated STAT3 then forms a dimer and translocates into the nucleus to activate transcription of genes containing STAT3 response elements. STAT3 is essential for GP130-mediated cell survival and G1 to S cell-cycle-transition signals. Both c- Myc and Pirn have been identified as target genes of STAT3 and together can compensate for STAT3 in cell survival and cell-cycle transition. SHP2 links cytokine receptor to the Ras/MAP (Mitogen-Activated Protein) kinase pathway and is essential for mitogenic activity.

The Ras-mediated pathway, acting through SHC, GRB2 (Growth Factor Receptor Bound protein-2) and SOS1 (Son of Sevenless-1) upstream and activating MAP kinases downstream, activates transcription factors such as Elk1 and NF-IL-6 (C/EBP-P) that can act through their own cognate response elements in the genome.

In addition to JAK/STAT and Ras/MAP kinase pathways, IL-6 also activates PI3K (Phosphoinositide-3 Kinase). The PI3K/Akt/NF-KappaB cascade activated by IL-6, functions cooperatively to achieve the maximal anti-apoptotic effect of IL-6 against TGF-p. The anti- apoptotic mechanism of PI3K/Akt is attributed to phosphorylation of the BCL2 family member BAD (BCL2 Associated Death Promoter) by Akt. The phosphorylated BAD is then associated with 14-3-3, which sequesters BAD from BCLXL, thereby promoting cell survival. Regulating the BCL2 family member is also considered as one of the anti-apoptotic mechanisms of STAT3, which may be capable of inducing BCL2 in pro-B cells. The termination of the I L-6- type cytokine signalling is through the action of tyrosine phosphatases, proteasome, and JAK kinase inhibitors SOCS (Suppressor of Cytokine Signaling), PIAS (Protein Inhibitors of Activated STATs), and internalization of the cytokine receptors via GP130.

An agent which downregulates IL-6 signalling may interfere with or inhibit any of the above stages involved in IL-6 mediated signalling such that IL-6 signalling and responses are diminished. For example, the agent may be an IL-6 receptor antagonist such as Tocilizumab, which is a humanized monoclonal antibody against the IL-6 receptor. An IL-6 receptor antagonist refers to an agent that reduces the level of IL-6 that is able to bind to the IL-6 receptor.

In some embodiments, the agent that downregulates IL-6 mediated signalling is tocilizumab, sarilumab, satralizumab, or siltuximab. In some embodiments, the agent that downregulates IL-6 mediated signalling is an IL-6 receptor antagonist. In some embodiments, the IL-6 receptor antagonist is tocilizumab, sarilumab, or satralizumab. In some embodiments, the IL- 6 receptor antagonist is tocilizumab. In some embodiments, the IL-6 receptor antagonist is sarilumab. In some embodiments, the IL-6 receptor antagonist is satralizumab. In some embodiments, the agent that downregulates IL-6 mediated signalling is an anti-IL-6 antibody. In some embodiments, the anti-IL-6 antibody is siltuximab.

Tocilizumab is a humanized monoclonal lgG1 antibody against the IL-6 receptor that binds to soluble and membrane-bound IL-6 receptor. Tocilizumab inhibits the induction of biological activity due to IL-6 in cells that have expressed both membrane-bound IL-6 receptor and gp130 molecules, and also inhibits the induction of biological activity due to IL-6/IL-6 receptor complex formation in cells that express gp130 alone. Furthermore, since it has the capacity to dissociate IL-6/IL-6 receptor complexes that have already formed, it is able to block IL-6 signal transduction.

B cells

B cells play a central role in the pathogenesis of RA.

Immature B cells are produced in the bone marrow. After reaching the lgM⁺ immature stage in the bone marrow, these immature B cells migrate to secondary lymphoid tissues (such as the spleen, lymph nodes) where they are called transitional B cells, and some of these cells differentiate into mature B lymphocytes and possibly plasma cells.

B cells may be defined by a range of cell surface markers which are expressed at different stages of B cell development and maturation (see table below). These B cell markers may include CD19, CD20, CD22, CD23, CD24, CD27, CD38, CD40, CD72, CD79a and CD79b, CD138 and immunoglobulin (Ig).

Immunoglobulins (Ig) are glycoproteins belonging to the immunoglobulin superfamily which recognise foreign antigens and facilitate the humoral response of the immune system. Ig may occur in two physical forms, a soluble form that is secreted from the cell, and a membranebound form that is attached to the surface of a B cell and is referred to as the B cell receptor (BCR). Mammalian Ig may be grouped into five classes (isotypes) based on which heavy chain they possess. Immature B cells, which have never been exposed to an antigen, are known as naive B cells and express only the IgM isotype in a cell surface bound form. B cells begin to express both IgM and IgD when they reach maturity - the co-expression of both these immunoglobulin isotypes renders the B cell “mature” and ready to respond to antigen. B cell activation follows engagement of the cell bound antibody molecule with an antigen, causing the cell to divide and differentiate into an antibody producing plasma cell. In this activated form, the B cell starts to produce antibody in a secreted form rather than a membrane-bound form. Some daughter cells of the activated B cells undergo isotype switching to change from IgM or IgD to the other antibody isotypes, IgE, IgA or IgG, that have defined roles in the immune system.

CD19 is expressed by essentially all B-lineage cells and regulates intracellular signal transduction by amplifying Src-family kinase activity.

CD20 is a mature B cell-specific molecule that functions as a membrane embedded Ca²⁺ channel. Expression of CD20 is restricted to the B cell lineage from the pre-B-cell stage until terminal differentiation into plasma cells.

CD22 functions as a mammalian lectin for a2,6-linked sialic acid that regulates follicular B-cell survival and negatively regulates signalling.

CD23 is a low-affinity receptor for IgE expressed on activated B cells that influences IgE production. CD24 is a GPI-anchored glycoprotein which was among the first pan-B-cell molecules to be identified.

CD27 is a member of the TNF-receptor superfamily. It binds to its ligand CD70, and plays a key role in regulating B-cell activation and immunoglobulin synthesis. This receptor transduces signals that lead to the activation of NF-KB and MAPK8/JNK.

CD38 is also known as cyclic ADP ribose hydrolase. It is a glycoprotein that also functions in cell adhesion, signal transduction and calcium signalling and is generally a marker of cell activation.

CD40 serves as a critical survival factor for germinal centre (GC) B cells and is the ligand for CD154 expressed by T cells.

CD72 functions as a negative regulator of signal transduction and as the B-cell ligand for Semaphorin 4D (CD100).

CD79a/CD79b dimer is closely associated with the B-cell antigen receptor, and enables the cell to respond to the presence of antigens on its surface. The CD79a/CD79b dimer is present on the surface of B-cells throughout their life cycle, and is absent on all other healthy cells.

CD138 is also known as Syndecan 1. Syndecans mediate cell binding, cell signalling and cytoskeletal organisation. CD138 may be useful as a cell surface marker for plasma cells.

In some embodiments, the method further comprises a step of analysing the presence of B cells in one or more sample (preferably a synovial sample) from the RA patient and determining if the RA patient is B cell rich or B cell poor by histological analysis. This analysis may involve determining the presence of cells expressing one or more of the markers detailed in the table above.

The presence of B cells may be determined by analysing the level and pattern of B cells.

The histological identification of RA patients who are B cell rich or B cell poor may be performed by using a system for grading lymphocytic aggregates known to those skilled in the art, for example as disclosed in the Examples herein.

For example, sections may undergo semi-quantitative scoring (0-4) to determine expression of CD20+ B-cells, CD3+ T cells, CD138+ plasma cells and CD68+ lining (I) and sub lining (si) macrophages (see, for example, Figure 1) as previously described and validated (Rivellese F et al. Arthritis Rheumatol 2020; 72: 714-25; Kraan MC et al. Rheumatology 2000; 39: 43-9; Krenn V et al. Histopathology 2006; 49: 358-64). Patients may be classified histologically as B-cell-rich or B-cell-poor according to the algorithm as shown in Figure 2. Synovial tissue with a CD20 score <2 may be classified histologically as B-cell-poor, while tissues with CD20 score >2 and with CD20+ B-cell aggregates may be classified histologically as B-cell-rich.

Response to therapies in RA patients

Methods of assessing a subject’s response to a therapy for rheumatoid arthritis are known in the art and would be familiar to a skilled person.

Byway of example, well known measures of disease activity in RA include the Disease Activity Score (DAS), a modified version DAS28, and the DAS-based ELILAR response criteria.

The assessment of response to a therapy for rheumatoid arthritis may use the Clinical Disease Activity Index (CDAI), for example as disclosed in the Examples herein.

Other measures of assessment of response to a therapy for rheumatoid arthritis include CDAI- remission, DAS28(ESR)/(CRP) moderate/good EULAR-response, DAS28(ESR)/(CRP) low- disease-activity, DAS28(ESR)/(CRP) remission and patient reported outcomes, such as fatigue, for example as disclosed in the Examples herein.

Biomarkers

The present invention provides a method for determining whether a Rheumatoid Arthritis (RA) patient is susceptible to treatment with a B cell targeted therapy, the method comprising the steps:

In some embodiments, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12,

13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37,

38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62,

63, 64, 65, 66, 67, 68, 69, 70 or all 71 biomarkers from Table 1.

In some embodiments, the one or more biomarkers comprise all 71 biomarkers from Table 1. In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12,

63, 64, 65, 66, 67, 68, 69, 70 or all 71 biomarkers from Table 1. In some embodiments, the one or more biomarkers consist of all 71 biomarkers from Table 1.

Table 1.

In some embodiments, the present invention provides a method for determining whether a Rheumatoid Arthritis (RA) patient is susceptible to treatment with a B cell targeted therapy, the method comprising the steps:

(a) determining the level of one or more biomarker in one or more sample obtained from the patient, wherein the one or more biomarker is selected from Table 17; and

In some embodiments, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, or all 35 biomarkers from Table 17.

In some embodiments, the one or more biomarkers comprise all 35 biomarkers from Table 17.

In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, or all 35 biomarkers from Table 17.

In some embodiments, the one or more biomarkers consist of all 35 biomarkers from Table 17.

In some embodiments, where the one or more biomarkers comprise or consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, or 34 biomarkers from Table 17, the biomarkers can be any combination of the biomarkers set forth in Table 17. By way of example, where the one or biomarkers comprise 2 biomarkers from Table 17, the 2 biomarkers can be CXCR5 and FCRLA; or the 2 biomarkers from Table 17 can be CXCR5 and FCRL3; or the 2 biomarkers from Table 17 can be PTPRCAP and FCRLA; and the like.

In some embodiments, the 2 biomarkers from Table 17 comprise or consists of CXCR5 and PTPRCAP. In some embodiments, the 3 biomarkers from Table 17 comprise or consists of CXCR5, PTPRCAP and FCRLA. In some embodiments, the 4 biomarkers from Table 17 comprise or consists of CXCR5, PTPRCAP, FCRLA and FCRL3. In some embodiments, the 5 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3 and PNOC. In some embodiments, the 6 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC and CPNE5. In some embodiments, the 7 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5 and POU2AF1. In some embodiments, the 8 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 and PAX5. In some embodiments, the 9 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5 and CLEC17A. In some embodiments, the 10 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A and FCRL1. In some embodiments, the 11 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 and E2F5. In some embodiments, the 12 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5 and IGLL5. In some embodiments, the 13 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5 and STAPI . In some embodiments, the 14 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 and CLECL1. In some embodiments, the 15 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 and FAM177B. In some embodiments, the 16 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B and SNX22. In some embodiments, the 17 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22 and MS4A1. In some embodiments, the 18 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 and HLA-DOB. In some embodiments, the 19 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB and TNFRSF17. In some embodiments, the 20 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17 and TLR10. In some embodiments, the 21 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10 and CD79A. In some embodiments, the 22 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A and FCRL5. In some embodiments, the 23 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5 and CD79B. In some embodiments, the 24 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B and TMEM156. In some embodiments, the 25 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156 and HLA- DRA. In some embodiments, the 26 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA and ZBTB32. In some embodiments, the 27 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA- DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32 and HLA-

DOA. In some embodiments, the 28 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA and RALGPS2. In some embodiments, the 29 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2 and CD74. In some embodiments, the 30 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-

DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA- DOA, RALGPS2, CD74 and P2RX5. In some embodiments, the 31 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5 and WDFY4. In some embodiments, the 32 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5, WDFY4 and FCER2. In some embodiments, the 33 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1, E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1, HLA-

DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA- DOA, RALGPS2, CD74, P2RX5, WDFY4, FCER2 and LCN10. In some embodiments, the 34 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1 , PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5, WDFY4, FCER2, LCN10 and CD19. In some embodiments, the 35 biomarkers comprise or consists of CXCR5, PTPRCAP, FCRLA, FCRL3, PNOC, CPNE5, POU2AF1, PAX5, CLEC17A, FCRL1 , E2F5, IGLL5, STAP1 , CLECL1 , FAM177B, SNX22, MS4A1 , HLA-DOB, TNFRSF17, TLR10, CD79A, FCRL5, CD79B, TMEM156, HLA- DRA, ZBTB32, HLA-DOA, RALGPS2, CD74, P2RX5, WDFY4, FCER2, LCN10, CD19 and TNFRSF13B.

Table 17.

(a) determining the level of one or more biomarker in one or more sample obtained from the patient, wherein the one or more biomarker is selected from Table 18; and

In some embodiments, the one or more biomarker comprises or consists of HTR3A. In some embodiments, the one or more biomarker comprises or consists of COL19A1. In some embodiments, the one or more biomarker comprises or consists of FOXP1. In some embodiments, the one or more biomarker comprises or consists of HLA-DMB. In some embodiments, the one or more biomarker comprises or consists of BLNK. In some embodiments, the one or more biomarker comprises or consists of MARCH1. In some embodiments, the one or more biomarker comprises or consists of HLA-DPB1. In some embodiments, the one or more biomarker comprises or consists of IL4R. In some embodiments, the one or more biomarker comprises or consists of CIITA. In some embodiments, the one or more biomarker comprises or consists of CD180. In some embodiments, the one or more biomarker comprises or consists of STX7. In some embodiments, the one or more biomarker comprises or consists of DRAM2. In some embodiments, the one or more biomarker comprises or consists of TLK2. In some embodiments, the one or more biomarker comprises or consists of TAPT 1. In some embodiments, the one or more biomarker comprises or consists of TNFRSF13C. In some embodiments, the one or more biomarker comprises or consists of BACH2. In some embodiments, the one or more biomarker comprises or consists of LISP6NL. In some embodiments, the one or more biomarker comprises or consists of SPIB. In some embodiments, the one or more biomarker comprises or consists of BLK. In some embodiments, the one or more biomarker comprises or consists of SNX2. In some embodiments, the one or more biomarker comprises or consists of PLEKHF2. In some embodiments, the one or more biomarker comprises or consists of FCRL2. In some embodiments, the one or more biomarker comprises or consists of VPREB3. In some embodiments, the one or more biomarker comprises or consists of BANK1. In some embodiments, the one or more biomarker comprises or consists of BTLA. In some embodiments, the one or more biomarker comprises or consists of CD22. In some embodiments, the one or more biomarker comprises or consists of ZNF860. In some embodiments, the one or more biomarker comprises or consists of CD40. In some embodiments, the one or more biomarker comprises or consists of WDR11 . In some embodiments, the one or more biomarker comprises or consists of SMC6. In some embodiments, the one or more biomarker comprises or consists of RFX5. In some embodiments, the one or more biomarker comprises or consists of FAM129C. In some embodiments, the one or more biomarker comprises or consists of CD72. In some embodiments, the one or more biomarker comprises or consists of CNR2. In some embodiments, the one or more biomarker comprises or consists of TCL1A. In some embodiments, the one or more biomarker comprises or consists of TCL1 B.

In some embodiments, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or all 36 biomarkers from Table 18.

In some embodiments, the one or more biomarkers comprise all 36 biomarkers from Table 18.

In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or all 36 biomarkers from Table 18.

In some embodiments, the one or more biomarkers consist of all 36 biomarkers from Table 18.

In some embodiments, where the one or more biomarkers comprise or consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, or 35 biomarkers from Table 18, the biomarkers can be any combination of the biomarkers set forth in Table 18. By way of example, where the one or biomarkers comprise 2 biomarkers from Table 18, the 2 biomarkers can be HTR3A and FOXP1 ; or the 2 biomarkers from Table 18 can be HTR3A and HLA-DMB; or the 2 biomarkers from Table 18 can be COL19A1 and FOXP1 ; and so forth. In some embodiments, the 2 biomarkers from Table 18 comprise or consist of HTR3A and COL19A1. In some embodiments, the 3 biomarkers from Table 18 comprise or consist of HTR3A, COL19A1, and FOXP1. In some embodiments, the 4 biomarkers from Table 18 comprise or consist of HTR3A, COL19A1, FOXP1, and HLA-DMB. In some embodiments, the 5 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, and BLNK. In some embodiments, the 6 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, and MARCH1. In some embodiments, the 7 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, and HLA-DPB1. In some embodiments, the 8 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , and IL4R. In some embodiments, the 9 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, and CIITA. In some embodiments, the 10 biomarkers comprise or consist of HTR3A, COL19A1 , FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, and CD180. In some embodiments, the 11 biomarkers comprise or consist of HTR3A, COL19A1 , FOXP1 , HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, and STX7. In some embodiments, the 12 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA- DMB, BLNK, MARCH1, HLA-DPB1, IL4R, CIITA, CD180, STX7, and DRAM2. In some embodiments, the 13 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA- DMB, BLNK, MARCH1, HLA-DPB1, IL4R, CIITA, CD180, STX7, DRAM2, and TLK2. In some embodiments, the 14 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, and TAPT1. In some embodiments, the 15 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1 , HLA-DMB, BLNK, MARCH1, HLA-DPB1, IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, and TNFRSF13C. In some embodiments, the 16 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, and BACH2. In some embodiments, the 17 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA- DMB, BLNK, MARCH1, HLA-DPB1, IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, and USP6NL. In some embodiments, the 18 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, and SPIB. In some embodiments, the 19 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1 , TNFRSF13C, BACH2, USP6NL, SPIB, and BLK. In some embodiments, the 20 biomarkers comprise or consist of HTR3A, COL19A1 , FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, and SNX2. In some embodiments, the 21 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, and PLEKHF2. In some embodiments, the 22 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, and FCRL2. In some embodiments, the 23 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, and VPREB3. In some embodiments, the 24 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, and BANK1. In some embodiments, the 25 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1, and BTLA. In some embodiments, the 26 biomarkers comprise or consist of HTR3A, COL19A1 , FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1 , BTLA, and CD22. In some embodiments, the 27 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1 , TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1 , BTLA, CD22, and ZNF860. In some embodiments, the 28 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1, BTLA, CD22, ZNF860, and CD40. In some embodiments, the 29 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA- DMB, BLNK, MARCH1, HLA-DPB1, IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1, BTLA, CD22, ZNF860, CD40, and WDR11. In some embodiments, the 30 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1 , BTLA, CD22, ZNF860, CD40, WDR11 , and SMC6. In some embodiments, the 31 biomarkers comprise or consist of HTR3A, COL19A1 , FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1 , BTLA, CD22, ZNF860, CD40, WDR11 , SMC6, and RFX5. In some embodiments, the 32 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1 , TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1 , BTLA, CD22, ZNF860, CD40, WDR11 , SMC6, RFX5, and FAM129C. In some embodiments, the 33 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA- DMB, BLNK, MARCH1, HLA-DPB1, IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1, BTLA, CD22, ZNF860, CD40, WDR11 , SMC6, RFX5, FAM129C, and CD72. In some embodiments, the 34 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA- DMB, BLNK, MARCH1, HLA-DPB1, IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1, BTLA, CD22, ZNF860, CD40, WDR11 , SMC6, RFX5, FAM129C, CD72, and CNR2. In some embodiments, the 35 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA- DMB, BLNK, MARCH1, HLA-DPB1, IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1, TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1, BTLA, CD22, ZNF860, CD40, WDR11 , SMC6, RFX5, FAM129C, CD72, CNR2, and TCL1A. In some embodiments, the 36 biomarkers comprise or consist of HTR3A, COL19A1, FOXP1, HLA-DMB, BLNK, MARCH1, HLA-DPB1 , IL4R, CIITA, CD180, STX7, DRAM2, TLK2, TAPT1 , TNFRSF13C, BACH2, USP6NL, SPIB, BLK, SNX2, PLEKHF2, FCRL2, VPREB3, BANK1 , BTLA, CD22, ZNF860, CD40, WDR11 , SMC6, RFX5, FAM129C, CD72, CNR2, TCL1A, and TCL1B.

Table 18.

Determining the level of one or more biomarkers

Methods for determining biomarker levels are well known in the art and would be familiar to the skilled person.

For example, the level of a biomarker may be determined by measuring gene expression for the biomarker gene (for example, using RTPCR) or by detecting the protein product of the biomarker gene (for example, using an immunoassay).

In some embodiments, the step of determining the levels of the one or more biomarkers comprises determining the levels of gene expression of the one or more biomarkers.

In some embodiments, the level of the one or more biomarkers is determined by direct digital counting of nucleic acids (e.g. by Nanostring), RNA-seq, RT-qPCR, qPCR, multiplex qPCR or RT-qPCR, microarray analysis, or a combination thereof.

In preferred embodiments, the level of one or more biomarker is determined by RNA sequencing

In some embodiments, the level is a protein level.

In some embodiments, the level of the one or more biomarkers is determined by an immunoassay, liquid chromatography-mass spectrometry (LC-MS), nephelometry, aptamer technology, or a combination thereof.

In some embodiments, the level of the one or more biomarkers is an average of the level of the one or more biomarkers. In some embodiments, the average of the level of the one or more biomarkers is an average of a normalised level of the one or more biomarkers. In some embodiments, the level of the one or more biomarkers is a median of the level of the one or more biomarkers. In some embodiments, the median of the level of the one or more biomarkers is a median of a normalised level of the one or more biomarkers.

In some embodiments, the level of the one or more biomarkers is the level of the one or more biomarkers normalised to a reference gene.

Reference values

The method of the invention may comprise the step of comparing the level of one or more biomarker to one or more corresponding reference value.

As used herein, the term “reference value” may refer to a level against which another level (e.g. the level of one or more biomarker disclosed herein) is compared (e.g. to make a diagnostic (e.g. predictive and/or prognostic) and/or therapeutic determination).

For example, the reference value may be derived from level(s) in a reference population (preferably the median level in a reference population), for example the population of patients disclosed in the Examples herein; a reference sample; and/or a pre-assigned value (e.g. a cut-off value which was previously determined to significantly separate a first subset of individuals who are susceptible to treatment with a B cell targeted therapy and a second subset of individuals who are resistant to treatment with a B cell targeted therapy).

In some embodiments, the cut-off value may be the median or mean (preferably median) level in the reference population. In some embodiments, the reference level may be the top 40%, the top 30%, the top 20%, the top 10%, the top 5% or the top 1 % of the expression level in the reference population.

The reference value may, for example, be based on a mean or median level of the biomarker in a control population of subjects, e.g. 5, 10, 100, 1000 or more subjects (who may be age- and/or gender-matched, or unmatched to the test subject).

In certain embodiments the reference value may have been previously determined, or may be calculated or extrapolated without having to perform a corresponding determination on a control sample with respect to each test sample obtained.

The increase in the level of the one or more biomarker compared to the corresponding reference value (when the level of the one or more biomarker is greater than the corresponding reference value) may, for example, be an increase in the level of at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98% or 99% or greater relative to the reference value. The increase in the level of the one or more biomarker compared to the corresponding reference value (when the level of the one or more biomarker is greater than the corresponding reference value) may, for example, be an increase in the level of at least about 1.1x, 1.2x, 1.3x, 1.4x, 1.5x, 1.6x, 1.7x, 1.8x, 1.9x, 2x, 2.1x, 2.2x, 2.3x, 2.4x, 2.5x, 2.6x, 2.7x, 2.8x, 2.9x, 3x, 3.5x, 4x, 4.5x, 5x, 6x, 7x, 8x, 9x, 10x, 15x, 20x, 30x, 40x, 50x, 100x, 500x or 1000x relative to the reference value.

The decrease in the level of the one or more biomarker compared to the corresponding reference values (when the level of the one or more biomarker is less than the corresponding reference value) may, for example, be a decrease in the level of at least about 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98% or 99% or greater relative to the reference value.

Sample

The method of the invention may be carried out on one or more sample obtained from a subject, for example a patient with RA.

“Sample” may refer to any biological sample taken from a patient. Samples include blood, plasma, serum, tissue, cells, and the like.

In some embodiments, the one or more sample is a blood sample.

In preferred embodiments, the one or more sample is a synovial sample. In some embodiments, the synovial sample is a synovial tissue sample or a synovial fluid sample.

Samples may be obtained from a joint of a subject, for example from a biopsy. Samples may be obtained from a synovial tissue sample from a subject.

As used herein, the term “synovial sample” refers to a sample derived from a synovial joint. Typically, the synovial sample will be derived from a synovial joint of a RA patient. A synovial sample may be a synovial tissue biopsy and the synovial joint may display active inflammation at the time the sample is taken.

Methods for obtaining samples, such as synovial tissue samples are well known in the art and would be familiar to the skilled person. For example, techniques such as ultrasound (US)- guided biopsies may be used to obtain tissue samples.

In some embodiments, the sample is obtained by synovial biopsy, preferably ultrasound- guided synovial biopsy. Patient

In preferred embodiments, the patient is a human.

In preferred embodiments the patient is an adult human. In some embodiments, the patient may be a child or an infant.

In preferred embodiments, the RA patient is refractory to DMARD and/or anti-TNF therapy

Antibodies

The term “antibody” is used herein to relate to an antibody or a functional fragment thereof. By functional fragment, it is meant any portion of an antibody which retains the ability to bind to the same antigen target as the parental antibody.

As used herein, “antibody” means a polypeptide having an antigen binding site which comprises at least one complementarity determining region (CDR). The antibody may comprise 3 CDRs and have an antigen binding site which is equivalent to that of a domain antibody (dAb). The antibody may comprise 6 CDRs and have an antigen binding site which is equivalent to that of a classical antibody molecule. The remainder of the polypeptide may be any sequence which provides a suitable scaffold for the antigen binding site and displays it in an appropriate manner for it to bind the antigen. The antibody may be a whole immunoglobulin molecule or a part thereof such as a Fab, F(ab)’2, Fv, single chain Fv (ScFv) fragment or Nanobody. The antibody may be a conjugate of the antibody and another agent or antibody, for example the antibody may be conjugated to a polymer (e.g. PEG), toxin or label. The antibody may be a bifunctional antibody. The antibody may be non-human, chimeric, humanised or fully human.

Methods of treatment

The invention also provides a method for treating Rheumatoid Arthritis (RA), the method comprising administering to a patient an effective amount of a B cell targeted therapy, wherein the patient is determined to be susceptible to treatment with a B cell targeted therapy by the method of the invention.

In some embodiments, the invention provides methods of treating RA in a patient in need thereof by administering to the patient an effective amount of a B cell targeted therapy wherein the patient has a level of one or more biomarkers greater than the level one or more corresponding reference values, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 1. In some embodiments, the invention provides methods of treating RA in a patient in need thereof comprising: (i) detecting an increased level of one or more biomarkers, relative to the level of one or more corresponding reference values, in a sample obtained from the patient, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 1 ; and (ii) administering to the patient an effective amount of a B cell targeted therapy.

In some embodiments, the one or more biomarkers comprise 1 biomarker from Table 1 (including embodiments thereof). In some embodiments, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70 or all 71 biomarkers from Table 1 (including embodiments thereof). In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, 50, 51 , 52, 53, 54, 55, 56, 57, 58, 59, 60, 61 , 62, 63, 64, 65, 66, 67, 68, 69, 70 or all 71 biomarkers from Table 1 (including embodiments thereof).

In some embodiments, the invention provides methods of treating RA in a patient in need thereof by administering to the patient an effective amount of a B cell targeted therapy wherein the patient has a level of one or more biomarkers greater than the level one or more corresponding reference values, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 17.

In some embodiments, the invention provides methods of treating RA in a patient in need thereof comprising: (i) detecting an increased level of one or more biomarkers, relative to the level of one or more corresponding reference values, in a sample obtained from the patient, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 17; and (ii) administering to the patient an effective amount of a B cell targeted therapy.

In some embodiments of the methods described herein, the one or more biomarkers comprise 1 biomarker from Table 17 (including embodiments thereof). In some embodiments of the methods described herein, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, or all 35 biomarkers from Table 17 (including embodiments thereof). In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, or all 35 biomarkers from Table 17 (including embodiments thereof).

In some embodiments, the invention provides methods of treating RA in a patient in need thereof, the method comprising administering to the patient an effective amount of a B cell targeted therapy, wherein the patient has a level of one or more biomarkers greater than the level of one or more corresponding reference values, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 18.

In some embodiments, the invention provides methods of treating RA in a patient in need thereof comprising: (i) detecting an elevated level of one or more biomarkers, relative to the level of one or more corresponding reference values, in a sample obtained from the patient, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 18; and (ii) administering to the patient an effective amount of a B cell targeted therapy.

In some embodiments of the methods described herein, the one or more biomarkers comprise 1 biomarker from Table 18 (including embodiments thereof). In some embodiments of the methods described herein, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or all 36 biomarkers from Table 18 (including embodiments thereof). In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or all 36 biomarkers from Table 18 (including embodiments thereof).

In some embodiments of the methods described herein, the sample is a blood sample or a synovial sample. In some embodiments, the sample is a blood sample. In some embodiments, the sample is synovial sample. In some embodiments, the synovial sample is synovial fluid or synovial tissue.

In some embodiments of the methods described herein, the B cell targeted therapy is an anti- CD20 antibody or an anti-CD22 antibody. In some embodiments, the B cell targeted therapy is an anti-CD20 antibody. In some embodiments, the B cell targeted therapy is an anti-CD22 antibody. In some embodiments, the B cell targeted therapy is rituximab, ocrelizumab, veltuzumab, ofatumumab, obinutuzumab, ibritumomab tiuxetan, or epratuzumab. In some embodiments, the B cell targeted therapy is rituximab. In some embodiments, the B cell targeted therapy is ocrelizumab. In some embodiments, the B cell targeted therapy is veltuzumab. In some embodiments, the B cell targeted therapy is ofatumumab. In some embodiments, the B cell targeted therapy is obinutuzumab. In some embodiments, the B cell targeted therapy is ibritumomab tiuxetan. In some embodiments, the B cell targeted therapy is epratuzumab.

The invention also provides a method for treating Rheumatoid Arthritis (RA), the method comprising administering to a patient an effective amount of an IL-6 receptor antagonist, wherein the patient is determined to be resistant to treatment with a B cell targeted therapy by the method of the invention. In some embodiments, the invention also provides a method for treating RA, the method comprising administering to a patient an effective amount of agent that downregulates IL-6 mediated signalling (e.g., an IL-6 receptor antagonist or an anti-IL-6 antibody), wherein the patient is determined to be resistant to treatment with a B cell targeted therapy by the method of the invention.

In some embodiments, the invention provides methods of treating RA in a patient in need thereof by administering to the patient an effective amount of an agent that downregulates IL- 6 mediated signalling, wherein the patient has a level of one or more biomarkers lower than the level one or more corresponding reference values, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 1.

In some embodiments, the invention provides methods of treating RA in a patient in need thereof comprising: (i) detecting an decreased level of one or more biomarkers, relative to the level of one or more corresponding reference values, in a sample obtained from the patient, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 1 ; and (ii) administering to the patient an effective amount of an agent that downregulates IL-6 mediated signalling.

In some embodiments, the invention provides methods of treating RA in a patient in need thereof by administering to the patient an effective amount of an agent that downregulates IL- 6 mediated signalling wherein the patient has a level of one or more biomarkers less than the level of one or more corresponding reference values, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 17.

In some embodiments, the invention provides methods of treating RA in a patient in need thereof comprising: (i) detecting a decreased level of one or more biomarkers, relative to one or more corresponding reference values, in a sample obtained from the patient, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 17; and (ii) administering to the patient an effective amount of an agent that downregulates IL-6 mediated signalling.

In some embodiments of the methods described herein, the one or more biomarkers comprise 1 biomarker from Table 17 (including embodiments thereof). In some embodiments, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, or all 35 biomarkers from Table 17 (including embodiments thereof). In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, or all 35 biomarkers from Table 17 (including embodiments thereof).

In some embodiments, the invention provides methods of treating RA in a patient in need thereof by administering to the patient an effective amount of agent that downregulates IL-6 mediated signalling wherein the patient has a level of one or more biomarkers less than one or more corresponding reference values, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 18.

In some embodiments, the invention provides methods of treating RA in a patient in need thereof comprising: (i) detecting a decreased level of one or more biomarkers, relative to one or more corresponding reference values, in a sample obtained from the patient, wherein the one or more biomarker comprises or consists of a biomarker selected from the biomarkers in Table 18; and (ii) administering to the patient an effective amount of an agent that downregulates IL-6 mediated signalling.

In some embodiments of the methods described herein, the one or more biomarkers comprise 1 biomarker from Table 18 (including embodiments thereof). In some embodiments, the one or more biomarkers comprise 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or all 36 biomarkers from Table 18 (including embodiments thereof). In some embodiments, the one or more biomarkers consist of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or all 36 biomarkers from Table 18 (including embodiments thereof). In some embodiments of the methods described herein, the sample is a blood sample or a synovial sample. In some embodiments, the sample is a blood sample. In some embodiments, the sample is synovial sample. In some embodiments, the synovial sample is synovial fluid or synovial tissue.

The terms “treating” or “treatment” are well-known in the art and may include any approach for obtaining beneficial or desired results in a subject’s condition, including clinical results. Beneficial or desired clinical results can include, but are not limited to, alleviation or amelioration of one or more symptoms or conditions, diminishment of the extent of a disease, stabilizing (i.e., not worsening) the state of disease, prevention of a disease’s transmission or spread, delay or slowing of disease progression, amelioration or palliation of the disease state, diminishment of the reoccurrence of disease, and remission, whether partial or total and whether detectable or undetectable. In other words, "treatment" as used herein may include any cure, amelioration, or prevention of a disease. Treatment may prevent the disease from occurring; inhibit the disease’s spread; relieve the disease’s symptoms, fully or partially remove the disease’s underlying cause, shorten a disease’s duration, or do a combination of these things.

"Treating" and "treatment" as used herein may include prophylactic treatment. Treatment methods include administering to a subject a therapeutically effective amount of an active agent (e.g., a B cell targeted therapy or an agent that downregulates IL-6 mediated signaling). The administering step may consist of a single administration or may include a series of administrations. The length of the treatment period depends on a variety of factors, such as the severity of the condition, the age of the patient, the concentration of active agent, the activity of the compositions used in the treatment, or a combination thereof. It will also be appreciated that the effective dosage of an agent used for the treatment or prophylaxis may increase or decrease over the course of a particular treatment or prophylaxis regime. Changes in dosage may result and become apparent by standard diagnostic assays known in the art. In some instances, chronic administration may be required. For example, the compositions are administered to the subject in an amount and for a duration sufficient to treat the patient. In embodiments, the treating or treatment is not prophylactic treatment.

The term “prevent” refers to a decrease in the occurrence of disease symptoms in a patient. As indicated above, the prevention may be complete (no detectable symptoms) or partial, such that fewer symptoms are observed than would likely occur absent treatment.

A “effective amount” may be an amount sufficient for a compound (e.g., a B cell targeted therapy, an agent that downregulates IL-6 mediated signaling) to accomplish a stated purpose relative to the absence of the compound (e.g. achieve the effect for which it is administered, treat a disease, reduce a signaling pathway, or reduce one or more symptoms of a disease or condition). An example of an “effective amount” is an amount sufficient to contribute to the treatment, prevention, or reduction of a symptom or symptoms of a disease, which could also be referred to as a “therapeutically effective amount.” A “reduction” of a symptom or symptoms (and grammatical equivalents of this phrase) means decreasing of the severity or frequency of the symptom(s), or elimination of the symptom(s). A “prophylactically effective amount” of a drug is an amount of a drug that, when administered to a subject, will have the intended prophylactic effect, e.g., preventing or delaying the onset (or reoccurrence) of an injury, disease, pathology or condition, or reducing the likelihood of the onset (or reoccurrence) of an injury, disease, pathology, or condition, or their symptoms. The full prophylactic effect does not necessarily occur by administration of one dose, and may occur only after administration of a series of doses. Thus, a prophylactically effective amount may be administered in one or more administrations. The exact amounts will depend on the purpose of the treatment, and will be ascertainable by one skilled in the art using known techniques (see, e.g., Lieberman, Pharmaceutical Dosage Forms (vols. 1-3, 1992); Lloyd, The Art, Science and Technology of Pharmaceutical Compounding (1999); Pickar, Dosage Calculations (1999); and Remington: The Science and Practice of Pharmacy, 20th Edition, 2003, Gennaro, Ed., Lippincott, Williams & Wilkins).

The term "administering" may mean oral administration, administration as a suppository, topical contact, intravenous, parenteral, intraperitoneal, intramuscular, intralesional, intrathecal, intranasal or subcutaneous administration, or the implantation of a slow-release device, e.g., a mini-osmotic pump, to a subject. Administration may be by any route, including parenteral and transmucosal (e.g., buccal, sublingual, palatal, gingival, nasal, vaginal, rectal, or transdermal). Parenteral administration includes, e.g., intravenous, intramuscular, intra-arteriole, intradermal, subcutaneous, intraperitoneal, intraventricular, and intracranial. Other modes of delivery include, but are not limited to, the use of liposomal formulations, intravenous infusion, transdermal patches, etc. In embodiments, the administering does not include administration of any active agent other than the recited active agent.

Kits

The present invention also provides a kit suitable for performing the method as disclosed herein. In particular, the kit may comprise reagents suitable for detecting the biomarkers disclosed herein, or a biomarker combination as disclosed herein.

The kit may also comprise instructions for use.

The kit may also comprise a B cell targeted therapy or an agent that downregulates IL-6 signalling.

The skilled person will understand that they can combine all features of the invention disclosed herein without departing from the scope of the invention as disclosed.

Preferred features and embodiments of the invention will now be described by way of nonlimiting examples.

The practice of the present invention will employ, unless otherwise indicated, conventional techniques of chemistry, biochemistry, molecular biology, microbiology and immunology, which are within the capabilities of a person of ordinary skill in the art. Such techniques are explained in the literature. See, for example, Sambrook, J., Fritsch, E.F. and Maniatis, T. (1989) Molecular Cloning: A Laboratory Manual, 2nd Edition, Cold Spring Harbor Laboratory Press; Ausubel, F.M. et al. (1995 and periodic supplements) Current Protocols in Molecular Biology, Ch. 9, 13 and 16, John Wiley & Sons; Roe, B., Crabtree, J. and Kahn, A. (1996) DNA Isolation and Sequencing: Essential Techniques, John Wiley & Sons; Polak, J.M. and McGee, J.O’D. (1990) In Situ Hybridization: Principles and Practice, Oxford University Press; Gait, M.J. (1984) Oligonucleotide Synthesis: A Practical Approach, IRL Press; and Lilley, D.M. and Dahlberg, J.E. (1992) Methods in Enzymology: DNA Structures Part A: Synthesis and Physical Analysis of DNA, Academic Press. Each of these general texts is herein incorporated by reference.

EXAMPLES

EXAMPLE 1

METHODS Trial design

We conducted a phase IV open label randomised control trial in 19 Centres across 5 European countries: UK, Belgium, Italy, Portugal, Spain. The study was conducted in compliance with the Declaration of Helsinki, International Conference on Harmonisation Guidelines for Good Clinical Practice and local country regulations. The final protocol, amendments and documentation of consent were approved by the institutional review board of each study centre or relevant independent ethics committees. The trial was supported by an unrestricted grant from the National Institute for Health Research (NIHR).

Patients

Patients aged 18 years or over, fulfilling 2010 ACR/EULAR classification criteria for RA16 who were eligible for treatment with rituximab therapy according to UK NICE guidelines (failing or intolerant to csDMARD therapy and at least one biologic therapy (excluding trial I MPs) (National Institute for Health and Clinical Excellence. Adalimumab, etanercept, infliximab, rituximab and abatacept for the treatment of rheumatoid arthritis after the failure of a TNF inhibitor. 2010 https://www.nice.org.uk/guidance/ta195 (accessed June 6, 2020)) were eligible for recruitment to the study and identified through rheumatology outpatient clinics at each study site. A complete list of the inclusion and exclusion criteria is provided Table 2). All patients provided written informed consent.

Interventions

Synovial Biopsy: Patients underwent a synovial biopsy of a clinically active joint at entry to the trial performed according to local expertise as either US-guided or arthroscopic procedure, as previously described (Kelly S et al. Ann Rheum Dis 2015; 74: 611-7; Kraan MC et al. Arthritis Rheum 2002; 46: 2034-8). Six-eight biopsies were immediately fixed in 4% paraformaldehyde for paraffin embedding and a further six immersed in 10:1 v:v of RNA-Later (Ambion) for later RNA extraction and shipped to the NHS pathology laboratory of Barts Health NHS Trust for further processing and central evaluation, as per Protocol Standard Operating Procedure (SOP).

Histological analysis: a minimum of 6 synovial biopsies were paraffin embedded en masse and sections stained for Hematoxylin and Eosin (H&E), and immune-histochemical markers (Humby F et al. PLoS Med 2009; 6: e1 ; Rivellese F et al. Arthritis Rheumatol 2020; 72: 714- 25) (Figure 1). Sections underwent semi-quantitative scoring (0-4) to determine expression of CD20+ B-cells, CD3+ T cells, CD138+ plasma cells and CD68+ lining (I) and sub lining (si) macrophages (Figure 1) adapted from a previously described and validated score (Rivellese F et al. Arthritis Rheumatol 2020; 72: 714-25; Kraan MC et al. Rheumatology 2000; 39: 43- 9; Krenn V et al. Histopathology 2006; 49: 358-64). Patients were classified as B-cell-rich or B-cell-poor histologically in the NHS pathology laboratory of Barts Health NHS Trust by a consultant pathologist (HR) followed by an independent evaluation by a second expert in synovial pathology (GT) according to a validated algorithm (Figure 2). Synovial tissue with a CD20 score <2 were classified as B-cell-poor, while tissues with CD20 score >2 and with CD20+ B-cell aggregates were classified as B-cell-rich. Any discrepancies in classification were resolved through mutual agreement. Patients in which definite synovial tissue could not be identified were classified as “unknown”. B-cell-rich samples were further classified as germinal centre (GC)+ve if CD21+ve FDC networks were present (Figure 3). As predefined in the study protocol only patients classified as B-cell-rich or B-cell-poor were included in the primary analysis of the trial presented herein.

RNA-seq analysis: A minimum of 6 synovial samples per patient were immediately immersed in RNA-Later and RNA extracted (Rivellese F et al. Rheumatoid Arthritis: Relationship to Disease Stages and Drug Exposure. Arthritis Rheumatol 2020; 72: 714-25) and sequenced at Genewiz according to their SOP. 184 paired-end RNA-seq samples of 150 base pairs were trimmed to remove the Illumina adaptors using bbduk from the BBMap (package version 37.93) using default parameters. Transcripts were quantified using Salmon version 0.13.123 and an index generated from the Gencode (release-29) transcriptome following the SOP. Tximport (version-1.13.10) was used to aggregate the transcript level expression data to genes, counts were then subject to variance stabilising transform (VST) using the DESEQ2 version-1.25.9 package (Love Ml et al. Genome Biol 2014; 15: 550). Patients were classified as B-cell-poor/rich according to a B-cell-specific gene module derived from analysis of FANTOM5 gene expression data (FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest ARRRR, Kawaji H, et al. Nature 2014; 507: 462-70). As no pre-determined cut-off points for B-cell transcript classification were found in the literature, to avoid potential bias, patients were classified as B-cell poor/rich according to the median transcript module value (Figure 4). In addition, to assess the performance of the RNA-seq B-cell poor/rich stratification method, post trial analysis, we conducted a further evaluation of the RNA-Seq FANTOMO5 B-cell module by varying the cut-off between B cell rich and poor to determine whether the median value was optimal. As shown in Figure 5, it was confirmed, that the FANTOM5 B-cell module median values performed optimally as varying cut-offs across a 20% range made no difference to results of the study based on the primary outcome measure.

Randomisation and masking At week 0, patients were randomised to receive rituximab or tocilizumab stratified into 4 blocks according to histological classification of baseline synovial biopsy (B-cell-poor, B-cell-rich, GC+ or unknown) and by site (Queen Mary University of London vs all other sites) using an interactive web response system. Patients were randomised within blocks (1 :1), with random block size of 6 and 4. The randomisation list and allocation algorithm were prepared by the trial statistician and securely embedded with the application code so that it was not accessible to end-users. The programmer was responsible for implementing the allocation algorithm into the randomisation database. The Trial Manager and trial management TEAM staff were responsible for checking patient eligibility and performing the randomisation procedure centrally. The randomisation result was sent electronically to all the clinical trial site staff by the R4RA trial office except the named joint assessor (research nurse/assistant) at each site, who remained blinded to study drug allocation. All site teams remained blinded to histological subtypes throughout the duration of the study.

Trial procedures

Following synovial biopsy and subsequent randomisation, rituximab (Mabthera-Roche) as two 1000mg infusions at an interval of 2 weeks or tocilizumab (RoActemra-Roche) infused at a dose of 8mg/kg at 4-weekly intervals was administered at baseline. Both drugs were obtained from hospital stocks. Patients were followed up at 4-weekly intervals throughout the 48-week trial treatment period where RA disease activity measurements and safety data were collected (Figure 6). Clinical outcomes up to week 16 only are presented herein.

Outcomes

The study was powered to test superiority of tocilizumab over rituximab in the B-cell-poor population at 16 weeks. The primary end-point was defined as difference in Clinical Disease Activity Index (CDAI) (Aletaha D et al. Arthritis Res Ther 2005; 7: R796-806) >50% improvement at 16 weeks from baseline between tocilizumab and rituximab treated groups.

Primary efficacy analysis evaluated the number of patients meeting primary end-point: CDAI>50% improvement from baseline. Patients could also be deemed non-responders, as pre-defined in the Protocol, if they achieved CDAI improvement > 50% from baseline but did not reached low disease activity CDAI<10.1 , defined from hereon for simplicity CDAI major- treatment-response (CDAI-MTR), thus, a supplementary efficacy analysis in line with the International Council for Harmonization (ICH) Guidelines (2019) was carried out to evaluated the number of patients meeting CDAI-MTR. In addition, as pre-defined in the Protocol the main aim of the trial was to test both cellular and molecular signatures in the synovial tissue, CDAI>50% improvement and CDAI-MTR were evaluated in patients classified according to the RNA-seq methodology described above.

The study was not powered to evaluate comparative efficacy of either drug in the B-cell-rich cohort, however, assessment of CDAI>50% response and CDAI-MTR at 16 weeks was also carried out as a supplementary analysis where the response rate of rituximab was compared to tocilizumab.

Additional secondary efficacy analyses included: CDAI-remission, DAS28(ESR)/(CRP) moderate/good EULAR-response, DAS28(ESR)/(CRP) low-disease-activity, DAS28(ESR)/(CRP) remission and patient reported outcomes such as fatigue are defined in the Table 3.

The incidence and severity of treatment and procedure emergent adverse events were monitored throughout the study; adverse event coding was performed according to the Medical Dictionary for Regulatory Activities (MedDRA), version.22. The causality and expectedness of all serious adverse events in relation to the trial treatment was assessed by the PI (or delegated medic), according to the SAE definition. If an SAE related to the treatment was unexpected, then it was considered a Suspected Unexpected Serious Adverse Reaction (SUSAR). All SAEs up to week 48 (and accounting for an additional 30-days window) were reported by relatedness and using the MedDRA “Lowest-level-term” classification. All AEs up to week 48 (+30 days) were reported using the MedDRA “System-organ-class” classification. Recurrent events (events that occurred more than once in the same participant) were considered as 1 event

Sample size

A sample size of 82 B-cell-poor patients was assessed to provide 90% power to detect a 35% difference (assuming 55% response rate to Tocilizumab and 20% in Rituximab determined in previously conducted pilot study) in the proportion of patients who were deemed as responders by the primary endpoint. The assumed proportions of B-cell-poor, B-cell-rich and GC+ recruited patients were 60%, 35% and 5% respectively. After estimating for 10% ungradable biopsy samples and a 5% dropout rate, we estimated a total of 160 patients would be required to recruit 82 patients in the B cell poor group. No power calculation was conducted on the B-cell-rich population.

Statistical Analyses The primary endpoint and other binary endpoints were analysed using a Chi-square or Fisher's exact test as appropriate. For continuous outcomes, an analysis of covariance (ANCOVA) was performed with treatment as factor and baseline value as the continuous covariate. When the assumptions for the ANCOVA were not met, non-parametric ANCOVA was used. Changes from baseline within groups were analysed through paired Wilcoxon test.

Although the study was not powered to evaluate comparative efficacy of either drug in the B- cell-rich group, we tested whether rituximab was as efficacious as tocilizumab as a secondary analysis. The analysis of the interaction between treatments and pathotypes was conducted through the likelihood ratio test between two nested logistic regression models: one with pathotype and treatment as covariates and the other with pathotype, treatment and their interaction as covariates.

All efficacy analyses were performed in the intention-to-treat (ITT) population and then on the per protocol (PP) set to assess the robustness of the results. The PP population included all subjects from ITT who did not have any major protocol violations. The list of deviations that exclude a subject from PP was reviewed at a classification meeting prior to data lock. Safety analyses were carried out on the safety analysis set (ITT, including only participants who received at least one dose of the trial medication), where patients were analysed according to their actual treatment in case this differed from the scheduled treatment (randomised or switched). Missing values when assuming MAR were imputed using Multiple Imputation by Chained Equations (MICE) and implemented using R package “Amelia” 1.7.5.

All statistical analyses were carried using R, version:3.5.1. The trial was registered on the ISRCTN database (ref: ISRCTN97443826). An independent Data-Monitoring-Ethics- Committee met on a 6-monthly basis during the trial to review the accruing trial data and assess whether there were any safety issues, and to make recommendations to the Trial- Steering-Committee.

RESULTS

Two hundred-twelve (212) patients were screened, 190 were consented and 164 underwent randomisation. The first patient visit was on 28-02-2013 and the last patient visit was on 17- 01-2019. The trial ended as recruitment targets were reached, 83 patients were randomised to receive rituximab and 81 tocilizumab. 161 patients, 82 rituximab and 79 tocilizumab, received investigational-medicinal-product (IMP); 99% (81/82) of rituximab and 92% (73/79) of tocilizumab treated patients completed treatment to primary endpoint at week-16 (Figure 7). The largest proportion of patients (38%, 62/164) was recruited at Barts Health NHS Trust (Table 4). Baseline characteristics, disease activity and histological groups were balanced across the treatment groups (Table 5 and Table 6). Most patients were female (80%) and the majority were seropositive for rheumatoid-factor (RF) (74%) and/or anti-citrullinated-peptide-antibodies (ACPA) (80%). Median disease duration was 9 years (IQR:4,19). Disease activity was high with a mean DAS28(ESR) of 5.8 (SD:1.2). 49% (79/161) patients were classified as B-cell- poor, 40% (64/161) as B-cell-rich, 6% (9/161) as GC+ and 6% (9/161) as unknown. Of the 49% (79/161) of patients, classified as B-cell-poor histologically, who received IMP 48% (38/79) patients were randomised to rituximab and 52% (41/79) to tocilizumab (Figure 7).

At 16 weeks in the B-cell-poor population no statistically significant difference was observed in primary outcome (Table 7), CDAI>50% improvement from baseline response rates (rituximab: 17/38=44.7% vs tocilizumab:23/41=56.1%, Differenced 1.4%, 95%CI:-10.6-33.3) between rituximab and tocilizumab treatment groups. A predefined supplementary analysis of CDAI MTR (Table 7), however, did reach statistical significance (rituximab:9/38=23.7% vs tocilizumab: 19/41 =46.3%, Difference:22.7%, 95% 01:2.3-43.0). In addition, throughout a number of secondary endpoints in the B-cell-poor population the response rates in the tocilizumab treated patients were higher (Table 7) including: CDAI-remission, DAS28(ESR)- moderate/good EULAR-response, DAS28(ESR)-low-disease-activity, DAS28(ESR)- remission, DAS28(CRP)-moderate/ good ELILAR response, DAS28(CRP)-low-disease- activity and DAS28(CRP)-remission.

Other week-16 secondary endpoints also favoured tocilizumab including trends for greater falls in DAS28(ESR/CRP) and CDAI (Table 7). Quality of life outcome measures (FACIT/SF36 scores) also demonstrated higher levels of improvement between baseline and 16 weeks in tocilizumab treated patients (Table 7). We observed little difference in HAQ scores between IMP groups (Table 7). Importantly per protocol analyses were consistent with ITT outcomes (Table 8).

We next examined clinical outcomes comparing treatment groups in patients categorized as B-cell-poor according to RNAseq molecular classification: 65/124. We observed a significantly higher response rate in the tocilizumab compared to rituximab group both for CDAI>50% improvement, the primary outcome measure (rituximab: 12/33=36.4% vs tocilizumab:20/32=62.5%, Difference^.1 %, 95% CI:2.3-49.6) and CDAI-MTR

(rituximab:4/33=12.1% vs tocilizumab: 16/32=50.0%, Difference:37.9%, 95%CI:17.3-58.5). A number of secondary outcomes including ELILAR DAS28-ESR/CRP good/moderate response, DAS28-ESR/CRP-low-disease-activity (S3.2) and DAS28-ESR-remission (<2.6) (Table 7) also favoured tocilizumab. Similarly, to the histopathological classification, we also observed trends for larger falls in CDAI and DAS28-ESR/CRP between baseline and 16- weeks in the tocilizumab vs rituximab groups (Table 7) and trends for greater improvements in quality of life measures (FACIT/SF36 MCS and PCS) in tocilizumab treated patients (Table 7). Per protocol analyses were consistent with the ITT results (Table 9).

Genes used in the RNAseq biomarker panel are shown in Table 16. The 35 genes marked “TRUE” were determined as most informative for patient stratification. The P value generally reflects stratification ability, although certain genes having relatively low P values were excluded (marked “FALSE”), because they are more informative for the gene rich group (equivalent to B cell rich).

We next analysed patients who were classified as B-cell-rich, using both the histopathological 40% (64/161) and RNA-seq 47% (59/124) classification. 52% (33/64) patients were randomised to rituximab and 48% (31/64) to tocilizumab (Figure 7). Although the study was not powered for the comparative analysis of the histologically-classified B-cell-rich group we observed similar week-16 response rates between the two biologic agents for the majority of endpoints analysed including CDAI>50% improvement (rituximab: 13/33=39.4% vs tocilizumab:16/31=51.6%, Difference: 12.2%, 95%CI:-12-36.5) and CDAI-MTR

(rituximab:5/33=15.2% vs tocilizumab:11/31=35.5%, Difference:20.3%, 95%CI:-0.5-41.1) (Table 10). Similar effects were seen through a number of additional secondary endpoints (Table 10). Importantly, in comparison to the analysis in the B-cell-poor cohort we saw minimal difference in quality of life measures (FACIT/SF36) between rituximab and tocilizumab treated groups (Table 10). Per protocol analyses were consistent with the ITT results (Table 11).

The 16-week outcomes were then evaluated between treatment groups in patients classified as RNAseq-classified B-cell-rich (n=59). No statistically significant difference between rituximab and tocilizumab treated patients was observed for CDAI>50% improvement, CDAI- MTR (Table 10) and the majority of secondary efficacy endpoints evaluated (Table 10 and Table 12).

Logistic regression analysis showed no evidence of an interaction between IMP and histologically-defined B-cell subgroups for primary endpoint but a statistically significant interaction between RNA-seq-defined B-cell subgroup and IMP (p=0.049) was observed when using CDAI MTR, suggesting that the difference between rituximab and tocilizumab was statistically different between RNA-seq B-cell-rich and B-cell-poor stratified groups. When we evaluated differences in CDAI>50% improvement response rates to rituximab between patients classified histologically as B-cell-rich or B-cell-poor, however, we saw no statistically significant differences in outcome (Fisher’s exact test p=0.81). In patients treated with rituximab (n=82) we saw no statistically significant difference in CDAI>50% response rates between those classified as ACPA+ve and ACPA-ve (responders 44.8% 30/67 and 46.3% 7/15, respectively, p-value=0.89) and no statistically significant difference between patients classified as RF+ve and RF-ve (responders 43.8% 28/64 and 50% 9/9, respectively, p-value=0.63) (Table 13). We also saw no statistically significant difference in response rates according to RF/ACPA seropositive patients treated with tocilizumab (Table 13).

Safety data up to 48week are summarised in Table 14 and Table 15. Occurrence of adverse- events (rituximab:76/108=70.4% vs tocilizumab:94/117=80.3%, Difference: 10.0%, 95%CI:- 1.3-21.2) and serious-adverse-events (rituximab:8/108=7.4% vs tocilizumab: 12/117=10.3%, Difference: 2.8%, 95%CI:-4.5-10.2) was not significantly different between treatment groups. One death due to suicide was reported in the rituximab group. No malignancies were reported within the 48-week trial period. Two patients in the rituximab-treated group (corneal melt, reported as a SLISAR, and suicide) and three patients in the tocilizumab group (pleural effusion, chest pain and cytokine release syndrome) discontinued IMP because of serious- adverse-events. There were three patients who underwent randomisation but did not receive study drug and no serious adverse events were reported in these patients. Importantly, there were no serious adverse events reported related to synovial biopsy.

DISCUSSION

Rituximab remains an important therapeutic option for RA patients, however clinical response remains heterogeneous with only 30% of anti-TNF-ir patients achieving an ACR50 response rates at 6-months (Cohen SB et al. Arthritis Rheum 2006; 54: 2793-806), while the mechanism of response/non-response remains unknown. Thus, understanding such mechanisms is critical to avoid unnecessary exposure to a potentially toxic drug and delay in bringing disease under control. As over 50% of RA patients show low/absent B-cell infiltration in the main disease tissue (joint-synovium), the R4RA trial was designed and independently supported by the UK National Institute for Health Research (NIHR) to determine whether target expression levels of specific cellular (CD20+ve/B-cells) and molecular signatures (B-cell associated) in the synovial tissue can provide a mechanistic explanation for drug mode-of-action and treatment response.

In this first biopsy-based, multi-centre, randomised control trial in RA, we tested the hypothesis that, in patients stratified for low/absent synovial-biopsy CD20+ve/B-cells, the target for rituximab, tocilizumab, a specific IL6-Receptor inhibitor would be superior. Using the histological B-cell-poor (CD20+ve/low) classification there was no statistically significant difference between the two treatment arms for the primary endpoint: CDAI>50% improvement. However, tocilizumab was superior to rituximab in the proportion of patients achieving low disease activity, defined as CDAI>50% improvement and CDAI<10.1 major treatment response (CDAI-MTR).

In addition, when patients were classified as B-cell-poor/rich by RNA-seq B-cell molecular module, both primary endpoint (CDAI>50%) and CDAI-MTR reached statistical significance. Moreover, although logistic regression analysis showed no evidence of an interaction between IMP and histologically-defined B-cell subgroups for primary endpoint, a statistically significant interaction between RNA-seq defined B-cell subgroups and IMP (p=0.049) was observed when using CDAI-MTR (p=0.049), providing evidence that the difference between rituximab and tocilizumab was statistically different between molecularly defined RNA-seq B-cell-poor and B-cell-rich stratified groups.

The reasons for the histological and RNA-seq differences are likely to relate to the sensitivity of the classification technique. CD20 staining was evaluated at 3 cutting levels on a minimum of 6 biopsies as recommended for use in clinical trials and reported to be representative of the whole joint tissue (Orr C et al. Nat Rev Rheumatol 2017; 13: 463-75). However, though the semi-quantitative score used for balanced stratification (prior to randomization) had been validated both against digital image analysis (DIA) and the transcript levels determined using the FANTOM5-derived B-cell related gene set (Rivellese F et al. Arthritis Rheumatol 2020; 72: 714-25; FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest ARRRR, Kawaji H, et al. A promoter-level mammalian expression atlas. Nature 2014; 507: 462-70), since no published “gold standard” was available, the cut-off of 0-1 for B-cell-poor and 2-4 for B-cell- rich was set arbitrarily on the basis of a previous pilot and potentially not at an optimal level for the whole trial. Furthermore, considering that these cut-offs were determined by physically counting <20 CD20+ve B-cells (B-cell-poor) or >20 CD20+ve B-cells (B-cell-rich), the counting of one B-cell either way of the divide would determine the opposite allocation with the potential for misclassification.

The RNA-seq B-cell-poor/rich classification, on the other hand, was determined by applying a FANTOM 5-derived module to include 73 genes to the RNA-seq of 6 pooled homogenized biopsies that provide a more integrated measure (expression of 30,000 genes) of pathobiological processes within the entire active joint and arguably a more precise estimate of the number not only of mature CD20+ve B-cells but also of B-cells at different stages of differentiation e.g. plasma blast/pre-plasma cells. As these latter subsets, both in the peripheral blood and synovial tissue, have been shown to influence response to rituximab (Dass S et al. Arthritis Rheum 2008; 58: 2993-9; Thurlings RM et al. Ann Rheum Dis 2008; 67: 917-25; Hogan VE et al. Ann Rheum Dis 2012; 71 : 1888-94; Teng YKO et al. Ann Rheum Dis 2009; 68: 1011-6), the RNA-seq classification clearly appears to be more sensitive. In addition, the application of RNA-seq classification overcame a number of limitations of the histological classification including the relatively subjective assessment of synovial B-cell infiltration by histopathology with an objective method using the transcript expression levels median value of a B-cell gene set module. Importantly, in a post trial analysis it was confirmed, that the FANTOM5 B-cell module median values performed optimally as varying cut-offs across a 20% range made no difference to results of the study based on the primary outcome measure.

Notably, in the RNA-seq B-cell-poor classified patients, tocilizumab was significantly superior to rituximab not only in relation to the primary endpoint (CDAI>50% improvement) and CDAI- MTR but also in most of the secondary endpoints considered, indicating a closer association with a broad range of outcome measures. Vice versa, in the RNA-seq B-cell-rich classified population the efficacy of rituximab overlapped with tocilizumab supporting the concept that target expression levels in the disease tissue are important mechanistically in determining non-response (low/absent) versus response (medium/high). Namely, in the B-cell-poor patients tocilizumab is more efficacious as inhibiting non-B-cell dependent pathways e.g. IL6, while in B-cell-rich patients as both tocilizumab and rituximab modulate B-cell function both drugs are similarly efficacious.

This study also highlighted the potential importance of the synovial biopsy in relationship to clinical response and RF/ACPA serological status, as no statistically significant difference in clinical response rates to rituximab or tocilizumab were observed between RF and/or ACPA positive and negative patients. Thus, it is possible that the synovial biopsy may be more sensitive than serology in stratifying patients to rituximab therapy, as while there is a strong association between RF/ACPA positivity and B-cell-rich synovitis (Humby F et al. Ann Rheum Dis 2019; 78: 761-72) there are RF/ACPA positive patients with low/absent B-cells in the joint. However, these results must be interpreted with caution considering the small number of seronegative patients included in the trial with a potential for a false-negative finding (type-2 error) while most studies, though not all, have reported better response to rituximab in RF/ACPA+ve patients (Isaacs JD et al. Ann Rheum Dis 2013; 72: 329-36).

Regarding safety, although higher number of serious-adverse-events and adverse-events in patients treated with tocilizumab were observed, these appeared largely unrelated to study drug and there was no statistically significant difference. Importantly there were no serious- adverse-events related to the synovial biopsy supporting previous data confirming safety of minimally-invasive ultrasound-guided procedure performed by rheumatologists. In conclusion, we report herein the results from the first pathobiology-driven, stratified, multicentre randomised controlled trial in rheumatoid arthritis which demonstrated that while the histological classification of RA synovial tissue was insensitive in determining treatment response in the primary analysis, the RNA-seq stratification showed significant associations with clinical responses and in patients with low/absent B-cell lineage expression signature (the target for rituximab) tocilizumab was superior to rituximab both in the primary end-point (CDAI>50%) and the number of patients reaching low disease activity, major treatment response CDAI-MTR [only 12% (4/33) in the rituximab group, four times as many 50% (16/32) in the tocilizumab group], as well as most secondary outcomes. In patients presenting with a B-cell-rich synovium, on the other hand, rituximab was as effective as tocilizumab. These results support the notion that disease tissue target expression levels are important to inform treatment response.

Table 2

Table 3

Table 4

Table 5

Table 5 (continued)

Table 6

Table 6 (continued)

Table 7

Table 8

Table 9

Table 10

Table 11

Table 12

Table 13

Table 14

Table 15

Table 16.

All publications mentioned in the above specification are herein incorporated by reference. Various modifications and variations of the disclosed methods, products and uses of the invention will be apparent to the skilled person without departing from the scope and spirit of the invention. Although the invention has been disclosed in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the disclosed modes for carrying out the invention, which are obvious to the skilled person are intended to be within the scope of the following claims.

Claims

1 . A method for determining whether a Rheumatoid Arthritis (RA) patient is susceptible to treatment with a B cell targeted therapy, the method comprising the steps:

2. The method of claim 1 , wherein the level is a nucleic acid level, optionally wherein the nucleic acid level is an mRNA level.

3. The method of claim 1 or 2, wherein the step of determining the level of one or more biomarker is performed by direct digital counting of nucleic acids, RNA-seq, RT-qPCR, qPCR, multiplex qPCR or RT-qPCR, microarray analysis, or a combination thereof.

4. The method of any preceding claim, wherein the step of determining the level of one or more biomarker is performed by RNA sequencing.

5. The method of claim 1 , wherein the step of determining the level of the one or more biomarker comprises determining the level of gene expression of the one or more biomarker.

6. The method of any preceding claim, wherein the one or more sample is a synovial sample.

7. The method of any preceding claim, wherein: (a) when the level of the one or more biomarker is greater than the corresponding reference value the patient is determined to be susceptible to treatment with the B cell targeted therapy; and/or (b) when the level of the one or more biomarker is less than the corresponding reference value the patient is determined to be resistant to treatment with a B cell targeted therapy.

77 The method of any preceding claim, wherein the level of the one or more biomarker compared to the corresponding reference value classifies the sample as B cell rich or B cell poor. The method of any preceding claim, wherein: (a) when the sample is B cell rich the patient is determined to be susceptible to treatment with a B cell targeted therapy; and/or (b) when the sample is B cell poor the patient is determined to be resistant to treatment with a B cell targeted therapy. The method of any preceding claim, wherein the B cell targeted therapy is B cell depletion therapy. The method of any preceding claim, wherein the B cell targeted therapy is selected from the group consisting of: rituximab, ocrelizumab, veltuzumab, ofatumumab and epratuzumab. The method of any preceding claim, wherein the B cell targeted therapy is rituximab. The method of any preceding claim, wherein a patient determined to be resistant to treatment with the B cell targeted therapy is determined to be suitable for treatment with an agent that downregulates IL-6 mediated signalling. The method of any preceding claim, wherein the agent is an IL-6 receptor antagonist. The method of claim 13 or 14, wherein the agent is tocilizumab. The method of any preceding claim, wherein the RA patient is refractory to DMARD and/or anti-TNF therapy. The method of any preceding claim, wherein the method further comprises: (a) administering to the patient a B cell targeted therapy when the patient is determined to be susceptible to treatment with a B cell targeted therapy; or (b) administering to the patient an IL-6 receptor antagonist when the patient is determined to be resistant to treatment with a B cell targeted therapy. A kit for use in the method of any preceding claim. The kit of claim 18, wherein the kit comprises reagents suitable for detecting the one or more biomarkers. The kit of claim 18 or 19, wherein the kit comprises reagents for RNA sequencing.

78 The kit of any one of claims 18-20, which comprises probes or antibodies for detecting the one or more biomarkers. The kit of any one of claims 18-21 which is in the form of a microchip or microarray. A method for treating Rheumatoid Arthritis (RA), the method comprising: (a) administering to a patient an effective amount of a B cell targeted therapy, wherein the patient is determined to be susceptible to treatment with a B cell targeted therapy by the method of any one of claims 1-17; or

(b) administering to a patient an effective amount of an IL-6 receptor antagonist, wherein the patient is determined to be resistant to treatment with a B cell targeted therapy by the method of any one of claims 1-17.

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