FR3071615A1 - METHOD FOR ESTIMATING TREATMENT EFFECTIVENESS BY AN IL-6 ANTI-RECEPTOR AGENT IN A PATIENT WITH RHEUMATOID ARTHRITIS AND WHO HAS BEEN RESPONSE TO AT LEAST ONE BIOTHERAPY - Google Patents

Abstract

The present invention relates to a method for estimating the efficacy of treatment with an IL-6 anti-receptor agent in a patient with rheumatoid arthritis who has been in insufficient response to at least one prior biotherapy, comprising analyzing a biological sample of said patient for the expression of a set of biomarkers, the results of which make it possible to determine whether said agent is a treatment that will generate a response that is beneficial for said patient. The present invention also relates to a system for estimating the efficacy of said treatment in said patient comprising means for measuring or receiving the data, the level of expression of said biomarkers and means for processing these data configured to estimate said efficiency. said treatment in said patient.

Description

METHOD FOR ESTIMATING THE EFFECTIVENESS OF TREATMENT WITH AN IL-6 ANTIRECEPTOR AGENT IN A PATIENT WITH RHUMATOID POLYARTHRITIS WHO HAS BEEN IN INSUFFICIENT RESPONSE TO AT LEAST ONE BIOTHERAPY

FIELD OF THE INVENTION

The present invention relates to the treatment of rheumatoid arthritis. It relates more particularly to a method for estimating the efficacy of treatment with an IL-6 anti-receptor agent, in particular with Tocilizumab, in a patient suffering from one of rheumatoid arthritis and who has not had any positive therapeutic response to one or more previous treatment (s) by biotherapy, consisting in analyzing a biological sample of said patient with respect to the expression of a set of biomarkers, the correlation of the results obtained for this set biomarkers making it possible, in particular by comparison with reference values, to determine whether the anti-receptor agent for IL-6 is a promising treatment making it possible to lead to a beneficial response for said patient.

The present invention also relates to a system for estimating the effectiveness of said treatment in said patient comprising means for measuring or receiving data of the level of expression of said biomarkers and means for processing this data configured to estimate said efficiency of said treatment in said patient.

BACKGROUND OF THE INVENTION

Rheumatoid arthritis is a chronic inflammatory disease characterized by synovitis, joint damage, functional disability and a significant increase in mortality.

Early intervention using sDMARD (synthetic Disease-Modifÿing AntiRheumatic Drug) is now recognized as essential for preventing structural joint damage and progressive loss of function (Smolen et al., 2017). For patients who do not respond to treatment with sDMARD or who develop an inadequate response to these drugs over time, bDMARDs (biological DMARD) are an effective additive treatment option (Smolen et al., 2017). In clinical practice, the first choice of biological therapy is usually a TNF-a inhibitor. Response to treatment can vary significantly from patient to patient. About 30% to 40% of patients who start treatment with a TNF-a inhibitor subsequently develop an insufficient response to these drugs (Lipsky et al., 2000; Souto et al., 2016; Weinblatt et al., 1999 , 2003). Options for continuing treatment in patients with TNF-a (TNF-IR) deficiency include the use of a second biological agent. Given the number of bDMARD treatment options available to clinicians and their effectiveness in treating RA, switching between different bDMARDs is common practice (Zhang et al., 2011). This is why at present, the practitioner recommends, for most diseases, first-line treatment, then in case of insufficient response, second-line treatment, and so on. However, within this overall strategy, there is a debate about the relative effectiveness of using an alternative TNF-a inhibitor (cycle) or a biological agent with a different mode of action (switching). (Finckh et al., 2007; Smolen et al., 2016).

Today, the practitioner lacks objective elements capable of helping him in his therapeutic choice in order to be able to target and identify a priori the adequate treatment for his patient. A tool capable of providing the clinician with a score of probability of response or non-response to a treatment would be an important beneficial element.

Indeed, the time lost in bringing to light a possible therapeutic failure comes at the expense of the effectiveness of the therapeutic action and the well-being of the patient, which in some cases may have resulted in new symptoms or detrimental consequences in terms of general condition. In addition, these can be expensive treatments which can be cumbersome to put in place, which is completely unsatisfactory when finding a therapeutic failure.

Significant progress has been made in recent years in the diagnosis, management, treatment and monitoring of patients with chronic inflammatory diseases.

In terms of treatment of chronic inflammatory diseases, and in particular chronic inflammatory rheumatism, there are in particular biotherapies which consist of biological molecules, such as proteins, antibodies, having a therapeutic action. Some of them are already in use and others are under development.

Among chronic inflammatory diseases, rheumatoid arthritis (RA) is an autoimmune synovium disorder that is characterized by the proliferation of synoviocytes and the infiltration of inflammatory cells into the joint. Various cytokines play an important role in the regulation of inflammatory diseases.

The bDMARDs targeting tumor necrosis factor (TNF) or interleukin (IL) -6 for example have allowed considerable progress for the treatment of RA. Currently, 9 bDMARDs including the interleukin-6 inhibitor (IL-6) tocilizumab (TCZ), anti-CD20 rituximab (RTX), anti-interleukin-1 (IL-1) anakinra (ANK), anti-CD28 abatacept (ABA), and anti-TNFa (anti-tumor necrosis factor alpha agents adalimumab (ADA), etanercept (ETN), infliximab (IFX), golimumab (GOL), certolizumab pegol (CTP), are approved for the treatment of rheumatoid arthritis. However, the responses to each biological agent vary for each individual. Therefore, making an optimal choice of bDMARDs within a window of therapeutic opportunity is essential to obtain an efficacy of treatment which is very expensive. Indeed, the chances of success of a biological treatment diminish according to the increasing number of therapeutic failures by biotherapy (Rendas-Baum et al., 2011).

However, the practitioner lacks elements at his disposal to help him in his therapeutic choice. A tool capable of providing the clinician with a score of probability of response or non-response to a treatment would certainly be welcome.

In particular, there is currently a lack of very early biomarkers which can, among other things, provide guidance as to the possible response or non-response to a basic biological or conventional treatment.

These biomarkers use molecular biology and biochemistry. The hypothetico-deductive approach has reduced personalized medicine to a few biomarkers whose interest has been fixed a priori and which has not made it possible to exhaust the questions of early diagnosis or the theragnostic approach. Thus, the quest for the biomarker to predict THE response to biological treatment in chronic inflammatory diseases, and therefore in RA, is an illusion. The multiplicity of genetic or biochemical biomarkers associated with the correct response or clinical non-response to biological treatment makes the task difficult.

Genomics, transcriptomics, epigenetics and proteomics are complementary and non-redundant pillars in this perspective.

At the genomic level (Prajapati et al., 2011), the replication of the results on different cohorts is fragile. The hypothesis of several associated genes each having a low impact is preferred over that where few genes with each a significant effect would be involved. Its routine use remains complex.

Epigenetics has also provided very preliminary data for the moment (Duroux Richard et al., 2014; Krintel et al., 2016).

The study of the transcriptome makes it possible to identify a certain number of genes whose multiplicity makes their use difficult on a daily basis (Smith et al., 2013).

The proteomic approach has been real for about 10 years in Rheumatology for the theranostic sides (Obry etal., 2015; Trocmé etal., 2009).

Finally, the study of the serum metabolome remains complicated (Tataref al., 2016).

This predictive approach of the personalized or layered medicine type is very innovative in the field of chronic inflammatory rheumatism and would make it possible to prescribe the right treatment for the right patient at the right time, to limit the progression of the handicap by referring the patient as quickly as possible to treatment. to which he has the greatest chance of responding, and of avoiding prescribing treatments which, on the contrary, are associated with a low probability of response.

Tocilizumab (TCZ) is a humanized monoclonal antibody directed against the interleukin 6 receptor (anti-IL-6R) which inhibits the IL-6 signaling pathway by blocking its binding to its receptor (IL-6R). . Some studies have focused on highlighting biomarkers to predict the response to TCZ treatment in patients with RA. These studies mainly focus on the characterization of DNA and RNA biomarkers (Nakamura et al., 2016; Sanayama et al., 2014). However, DNA and RNA are subject to possible modifications (epigenetics, regulation of gene expression, splicing) linked to the environment before being translated into proteins which are the final effectors. The proteomic approach therefore makes it possible to minimize the possible variations between the level of expression of the biomarkers and the clinical results observed.

Another study reported that a low expression of the Osteopontin protein would be predictive of reminiscence at 1 year after treatment with TCZ (Izumi et al., 2015).

These studies did not use a biomarker combination strategy to improve specificity or sensitivity.

There is therefore a need to identify new methods and / or biomarkers making it possible to orient the practitioner in a personalized manner towards the treatment which is the most promising in terms of efficacy for a given patient suffering from chronic inflammatory diseases.

The present invention responds to this technical problem with regard to the response to a treatment with an IL-6 anti-reception agent for a patient suffering from rheumatoid arthritis and who has not had a positive therapeutic response to one or more previous treatment (s) by biotherapy; the inventors having identified a set of biological biomarkers whose level of expression detected in a biological sample taken from such a patient makes it possible to estimate the probability of an effective response to this treatment in this patient.

SUMMARY OF THE INVENTION

The present invention relates to a method for estimating the efficacy of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis and who has not had a sufficient therapeutic response to one or more previous treatment (s) by biotherapy, said method comprising:

a) in vitro measurement of the level of expression of at least two biomarkers chosen from the group consisting of S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4 ), Amyloid serum A (AAS), alphal antitrypsin (A1AT), C-reactive protein (CRP), tubulin beta-1 chain and alpha-enolase in a biological sample from said patient,

b) the estimation of said efficacy of treatment with an IL-6 anti-receptor agent in said patient as a function of each level of expression measured for a biomarker chosen from said group.

In particular, the method for estimating the efficacy of treatment with an IL-6 anti-receptor agent according to the invention comprises:

a) in vitro measurement of the level of expression of at least two biomarkers chosen from the group consisting of: S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 ( or C4), Amyloid serum A (AAS), alphal antitrypsin (A1AT), C-reactive protein (CRP), tubulin beta-1 chain and alpha-enolase in a biological sample from said patient, b1) comparison of the level of expression measured in step a) relative to that measured in a plurality of samples from patients suffering from rheumatoid arthritis and who have received treatment with an IL-6 anti-receptor agent for which the treatment effectiveness is known; said comparison being carried out using a statistical learning model using as input data the expression levels of at least two of the biomarkers measured in step a), b2) the estimation of said efficacy of treatment with an IL-6 anti-receptor agent in said patient as a function of the results determined by the model defined in step b1).

All of the biomarkers identified by the inventors are therefore particularly suitable for estimating the efficacy of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis.

The present invention further relates to a system for estimating the efficacy of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis and who has had an insufficient response to at least one treatment. anterior by biotherapy, said system comprising:

means for measuring or receiving data for measuring the level of expression of at least two biomarkers chosen from a group consisting of: S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4), Amyloid serum A (SAA), alphal antitrypsin (A1AT), Creative Protein (CRP), beta-1 chain of tubulin and alpha-enolase in a biological sample from said patient,

- measurement data processing means configured to estimate said processing efficiency in said patient as a function of each level of expression measured for a biomarker chosen from this group.

Preferably, the estimation method and the estimation system according to the invention make it possible to estimate the response to an agent which blocks the binding of IL-6 to its IL-6R receptor, and advantageously to the anti-IL-6 receptor agent Tocilizumab.

BRIEF DESCRIPTION OF THE FIGURES

Figure 1 represents the ROC (Receiver Operating Characteristic) curve obtained during the evaluation of model performance with the 3 variables S100A8 / A9, SAA and A1AT. It represents an example of the sensitivity of the test (on the ordinate) as a function of the complement of the specificity of the test: 1 - specificity (on the abscissa).

DETAILED DESCRIPTION OF THE INVENTION

The problem encountered in the field of the invention for the development of a robust predictive test consists first of all in identifying the biomarkers which, taken together, make it possible to obtain a relevant prediction with both specificity and high sensitivity.

Thus, according to a first aspect, the present invention relates to a method for estimating the effectiveness of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis and who has been in insufficient response to at least one prior treatment with biotherapy, said method comprising, or even consisting of:

a) in vitro measurement of the level of expression of at least two biomarkers chosen from the group consisting of S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4 ), Amyloid serum A (AAS), alphal antitrypsin (A1AT), C-reactive protein (CRP), tubulin beta-1 chain and alpha-enolase in a biological sample from said patient,

b) the estimation of said efficacy of treatment with an IL-6 anti-receptor agent in said patient as a function of each level of expression measured for a biomarker chosen from said group.

The inventors have in fact identified sets or combinations of relevant biomarkers for estimating the efficacy of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis and who has had an insufficient response to at least one prior treatment with biotherapy, namely at least two biomarkers chosen from the group consisting of S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4), Amyloid serum A (SAA), alphal antitrypsin (A1AT), C-reactive protein (CRP), tubulin beta-1 chain and alpha-enolase.

In particular, the method for estimating the effectiveness of treatment with an IL-6 anti-receptor agent according to the invention comprises:

a) in vitro measurement of the level of expression of at least two biomarkers chosen from the group consisting of: S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 ( or C4), Amyloid serum A (AAS), alphal antitrypsin (A1AT), C-reactive protein (CRP), tubulin beta-1 chain and alpha-enolase in a biological sample from said patient, b1) comparison of the level of expression measured in step a) relative to that measured in a plurality of samples from patients suffering from rheumatoid arthritis and who have received treatment with an IL-6 anti-receptor agent for which the treatment effectiveness is known; said comparison being carried out using a statistical learning model using as input data the expression levels of at least two of the biomarkers measured in step a), b2) the estimation of said efficacy of treatment with an IL-6 anti-receptor agent in said patient as a function of the results determined by the model defined in step b1).

Measuring the level of expression of the particular combinations of these particular biomarkers and their analysis in particular using a statistical learning model makes it possible to obtain a relevant estimate of the prediction of response to an IL receptor agent. -6 for a patient with rheumatoid arthritis.

As regards step a) of the method according to the invention, the relevant biomarkers identified by the inventors are defined below.

S100A8 (also called MRP8, calgranulin A) and S100A9 (MRP14, calgranulin B) are calcium binding proteins (EF hand motifs) belonging to the S100 protein family. The S100A8 / A9 proteins are inflammatory proteins mainly secreted by neutrophils and described for several years as actors in the development of chronic inflammatory rheumatism (Baillet, 2010; Baillet et al., 2010). In particular, the S100A8 / A9 proteins appear as biomarkers for monitoring the response to etanercept biotherapy (Drynda et al., 2004) but may have a predictive character for response to bDMARDs (Choi et al., 2015; Obry et al ., 2014, 2014) such as etanercept, adalimumab, infliximab or rituximab. The authors, unlike the strategy developed by the inventors, did not use a strategy of combining biomarkers to improve specificity or sensitivity. In addition, the case of tocilizumab was not addressed in their study

Enolase is a metalloenzyme involved in glycolysis by catalyzing the conversion of 2-phospho-D-glycerate (2PG) to phosphoenolpyruvate (PEP). Enolase has three subunits, α, β and y which can combine to form five different isoenzymes. Alpha enolase (non-neuronal enolase (NNE) or enolase 1 (ENO1)) is one of these isoenzymes formed by an αα homodimer expressed in all kinds of tissues such as the liver, brain, kidney, spleen and fat tissue. The involvement of alpha enolase in rheumatoid arthritis is not clearly described. Some studies mention the presence of autoantibodies directed against it in the serum of RA patients (Gerstner et al., 2016; Saulot et al., 2002).

Galectins play a key role in many pathological conditions, including metastasis of tumor cells, autoimmune diseases and inflammation. This family of proteins has a restricted expression distribution which is tissue specific, as well as developmentally regulated, throughout embryogenesis. An increased expression of galectin 3 has been associated with an inflammatory process, as well as the development and progression of tumors. Galectin-3-binding protein (also known as lectin galactoside-binding soluble 3 binding protein (LGALS3BP) or Mac-2-binding protein (M2BP), originally described as a macrophage marker, is a secreted glycoprotein suggested to have a role in host defense and tumor invasion LGALS3BP contains binding sites not only for galectin 3, but also for type V and VI collagens, β integrins and fibronectin Therefore, LGALS3BP attached to the cell membrane may be involved in both extracellular matrix fixation and the destructive process in RA. Few studies have documented the relationship between LGALS3BP and rheumatoid arthritis. however, galectin 3 and LGALS3BP are not only involved in inflammation, but also contribute to the activation of synovial fibroblasts and may represent new mar disease activity in RA (Ohshima et al., 2003).

The C4b complement binding protein (C4BP) is a protein involved in the complement system where it acts as an inhibitor. It inhibits the action of classical pathways and lectins, more particularly C4. It also has the ability to link C3b. Excessive or misdirected activation of the complement contributes to the pathogenesis of inflammatory diseases such as rheumatoid arthritis (Swaak et al., 1987). The use of the recombinant protein C4BP in two mouse RA models reduces the severity of the disease (Blom et al., 2009).

CRP (C-reactive protein), alpha 1 antitrypsin, complement C4 and amyloid serum A (SAA) are markers of inflammatory status, the use of which is very widespread in clinical practice and in particular in the monitoring of chronic inflammatory rheumatism ( Buisseret et al., 1977; Connolly et al., 2012; Drynda et al., 2004; Kokubun et al., 2005; Vogt et al., 2017). They belong to a category of proteins involved in the acute phase responses during inflammation. For example, during the acute phase, circulating SAA levels increase from 100 to 1000 times. These cytokines, in turn, signal stromal cells for the synthesis of secondary inflammatory mediators such as interleukin 6 (IL-6), interleukin 8 (IL-8) or even chemo-attractive monocytic proteins. It has been suggested that SAA levels better reflect disease activity in inflammatory joint disease than the rate of sedimentation and CRP traditionally used to estimate the score of disease activity. The level of C4 complement expression appears lower in RA patients responding to Rituximab at 12 months (Conigliaro et al., 2016).

Tubulin is the structural protein of microtubules, a major constituent of the cytoskeleton. It is composed of 2 alpha and beta subunits. The tubulin beta-1 chain (TUBB1) is one of the 10 isoforms of the beta subunit. The link between TUBB1 and rheumatoid arthritis has not been clearly established.

Preferably, the method of estimating the treatment efficiency according to the invention is based on the in vitro measurement of the level of expression of at least three of the nine biomarkers mentioned above, of at least four, of at least five, at least six, at least seven, at least eight of the biomarkers of the above-mentioned group or of the nine biomarkers of the above-mentioned group, namely S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4), Amyloid serum A (SAA), alphal antitrypsin (A1AT), C-reactive protein (CRP), tubulin beta-1 chain and alphaenolase.

Advantageously, the method of the invention comprises in vitro measurement of the level of expression of at least two biomarkers chosen from S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4), Amyloid serum A (AAS), alphal antitrypsin (A1AT), C-reactive protein (CRP), and in particular at least three, at least four, at least five, at least six of these biomarkers or these seven biomarkers.

The preferred embodiments of the method for estimating the treatment efficiency according to the invention include the in vitro measurement of the level of expression of the particular combinations of the following biomarkers from the list of the nine biomarkers mentioned above.

- at least S100A8 / A9 and A1AT or at least S100A8 / A9 and SAA,

- at least S100A8 / A9, SAA and A1AT or at least S100A8 / A9, SAA and LGALS3BP,

- at least S100A8 / A9, SAA, A1AT and C4BP, or at least S100A8 / A9, SAA, CRP and C4BP,

- at least S100A8 / A9, SAA, LGALS3BP, C4 and C4BP or at least S100A8 / A9, SAA, LGALS3BP, CRP and C4BP.

These sets provide the most relevant results in terms of estimating treatment effectiveness.

The method according to the invention makes it possible to estimate the effectiveness of treatment with an IL-6 anti-receptor agent in a patient suffering from rheumatoid arthritis who has been in insufficient response to at least one prior treatment with a biotherapy.

"Insufficient response to at least one prior treatment with biotherapy" means a patient who has not presented a positive therapeutic response to one or more previous treatment (s) with a bDMARD (biological Disease-Modifÿing AntiRheumatic Drug). As part of the treatment of chronic inflammatory diseases, current therapeutic strategies are used to reduce the activity of rheumatism and the response to treatment is generally judged at 6 months. Regarding rheumatoid arthritis, the response to treatment is determined by the evolution of rheumatism activity according to the EULAR (European League Against Rheumatism) response. The EULAR response takes into account the activity of rheumatism which is evaluated by the DAS 28 (Disease Activity Score 28) as well as its variation. The DAS is a composite score calculated on the basis of the number of painful joints on 28 joints, the VAS (Analog Visual Scale) and a biological inflammatory parameter: VS (speed of sedimentation) or CRP, as described for example by Wells et al., 2009 and Fransen and van Riel, 2005. The EULAR response is defined as a function of the DAS28 score at time T and the difference between DAS28 at time T and the initial DAS28, i.e. before treatment. In the context of the present invention, the term “insufficient response to treatment” means in particular a response

EULAR with a DAS28 at 6 months greater than 3.2 or a variation of DAS28 at 6 months and DAS28 before treatment less than or equal to 1.2.

In other words, “an insufficient response” can also be defined by a variation of DAS28 at 6 months and of DAS28 before treatment less than or equal to 0.6. Conversely, according to the invention "a sufficient response" is defined by a DAS28 at 6 months less than or equal to 3.2 associated with a variation of DAS28 at 6 months and DAS28 before treatment greater than 1.2. Consequently, according to the invention, a so-called moderate response according to the EULAR response is considered to be an insufficient response.

Biotherapy is understood to mean therapy using the use of a bDMARD. DMARDs are a category of drugs defined by their use in rheumatoid arthritis to slow the progression of the disease. There are several types of DMARDs classified as follows:

- synthetic (sDMARD) which include conventional synthetic (csDMARDs) and targeted synthetic (tsDMARDs). CsDMARD are traditional medicines such as methotrexate, sulfasalazine, leflunomide, hydroxychloroquine, gold salts etc. TsDMARD are drugs that have been developed to target a particular molecular structure.

- biological (bDMARDs) which include the original DMARDs (boDMARD) and biosimilars (bsDMARD). bsDMARDs are those which have the same primary, secondary and tertiary structure as the original (boDMARD) and have an efficacy and safety similar to that of the original protein.

Preferably, the method according to the invention makes it possible to estimate the effectiveness of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis and who has been in insufficient response to at least previous treatment with biotherapy chosen from Etanercept, Adalimumab, Infliximab, Abatacept, Rituximab, Certolizumab, Golimumab, etc.

According to the invention, the method makes it possible to estimate the effectiveness of treatment with an anti-IL-6 receptor agent. Such an agent can be defined as being an agent which is capable of inhibiting the IL-6 signaling pathway, in particular by blocking, directly or indirectly, the binding of IL-6 to its IL-6R receptor . Among these agents, there may be mentioned in particular bDMARD Tocilizumab.

In a particularly advantageous manner, the method according to the invention makes it possible to estimate the effectiveness of treatment with an anti-receptor agent for IL-6 which is Tocilizumab.

Any biological sample consisting of a biological fluid can be used within the framework of the invention to measure in vitro the level of expression of the biomarkers and combinations of biomarkers mentioned above, and among which there may be mentioned in particular the synovial fluid, serum, plasma, saliva, urine, etc.

According to a preferred embodiment, the level of expression of the biomarkers and combinations of biomarkers mentioned above is measured in vitro on a serum sample from the patient for whom it is sought to estimate the effectiveness of treatment with an anti- IL-6 receptor.

Advantageously, the method for estimating the treatment efficiency according to the invention comprises in step a) in vitro measurement of the level of expression of the biomarkers or combinations of protein biomarkers.

Particularly preferred embodiments of the method according to the invention are the following, each being to be applied to the combinations of biomarkers defined above, namely at least two biomarkers chosen from the group consisting of S100A8 / A9, Galectin-3-binding protein ( or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4), Amyloid serum A (SAA), alphal antitrypsin (A1AT), Creative Protein (CRP), tubulin beta-1 chain and alpha-enolase:

Estimation of the efficacy of Tocilizumab treatment in a patient with rheumatoid arthritis;

Estimation of the efficacy of Tocilizumab treatment in a patient with rheumatoid arthritis, including the measurement of the level of protein expression; Estimation of the efficacy of Tocilizumab treatment in a patient suffering from rheumatoid arthritis who has had an insufficient response to at least one previous treatment with a biotherapy chosen from Etanercept, Adalimumab, Infliximab, Abatacept, Rituximab, Certolizumab or golimumab.

Estimation of the efficacy of Tocilizumab treatment in a patient suffering from rheumatoid arthritis who has had an insufficient response to at least one previous treatment with a biotherapy chosen from Etanercept, Adalimumab, Infliximab, Abatacept, Rituximab, Certolizumab or Golimumab, including measuring the level of protein expression.

In step b1) of the estimation method according to the invention, the level of expression of the biomarkers or combinations of biomarkers measured in step a) described above is compared with that measured in a plurality of samples of rheumatoid arthritis patients who have received treatment with an anti-IL-6 receptor agent for which the efficacy of treatment is known.

This comparison is made using a statistical learning model using as input data the expression levels of at least two of the biomarkers measured in step a). To do this, any statistical learning model can be used, and in particular the models obtained by logistic regression methods, discriminant analysis, neural networks, learning by decision trees, machines support vectors (SVM), or aggregation of models.

Preferably, in the method for estimating the effectiveness of treatment with an IL-6 anti-receptor agent according to the invention, the expression levels of each biomarker measured in step a) are used to obtain a score related to the estimation of the treatment efficiency in said patient, said score being compared with at least a predetermined threshold so as to classify the prognosis among a plurality of classes. In this embodiment, it is possible in particular to use a plurality of classes which comprises at least two classes including a class of non-response to treatment with said anti-IL-6 receptor agent, and preferably which comprises, see consists of , two classes including a class of non-response to treatment with said IL-6 anti-receptor agent. The two classes can, for example, be obtained from patients known as “non-responders”, in whom treatment with the anti-IL-6 receptor is ineffective and from patients known as “responders” in whom treatment with anti-IL-6 -IL-6 receptor is effective. Other classes can be envisaged to refine the analyzes, in particular in addition to those of the so-called "non-responders" patients, such as for example "moderate responders" and "good responders" to treatment, the qualifications of moderate or good corresponding to the assessments. usual practitioners specialized in the treatment of rheumatoid arthritis. Still in this embodiment, the estimation of the effectiveness of treatment in the patient suffering from rheumatoid arthritis and who has had an insufficient response to at least one prior treatment with biotherapy comprises comparing said score with a predetermined threshold at - below which poor efficiency is predicted and above which good efficiency is predicted.

Preferably, the method for estimating the efficacy of treatment with an IL-6 anti-receptor agent according to the invention uses in step b1) a learning model based on a prior analysis of samples from a cohort of patients treated with an IL-6 anti-receptor agent with good response to treatment and patients treated with an IL-6 anti-receptor agent with poor response to treatment. In this embodiment, the learning model is preferably based on a prior analysis which includes the application of a learning method and selection of variables. Advantageously, logistic regression will be used as a learning and variable selection method.

Still in this embodiment based on a prior analysis which includes the application of a learning and variable selection method, the expression levels of the biomarkers or combinations of biomarkers measured in step a) are weighted in function of the prior analysis of the cohort comprising patients treated with an anti-IL-6 receptor agent having good responses to treatment and patients treated with an IL-6 anti-receptor agent having bad responses responses to treatment to derive the score related to the estimation of treatment effectiveness.

Still in this embodiment based on a prior analysis which includes the application of a learning method and selection of variables, the method for estimating the treatment efficiency according to the invention can use a method of learning by decision tree. According to this embodiment, the expression levels of the biomarkers or combinations of biomarkers measured in step a) are compared with a reference value at each node of the tree.

The reference values can be obtained by analyzing the level of expression of the biomarkers or combinations of biomarkers in biological samples from a set of patients suffering from rheumatoid arthritis before treatment so as to have a set of data on the expression levels of biomarkers associated with each biological sample from each patient.

These reference values can change over time depending on the results obtained with other patients who complete the number of results used to define the threshold value.

According to a second aspect, the invention also relates to a system for estimating the effectiveness of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis and who has had an insufficient response to at least one prior treatment with biotherapy, said system comprising:

means for measuring or receiving data for measuring the level of expression of at least two biomarkers chosen from a group consisting of: S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4), Amyloid serum A (SAA), alphal antitrypsin (A1AT), Creative Protein (CRP), beta-1 chain of tubulin and alpha-enolase in a biological sample from said patient,

- measurement data processing means configured to estimate said processing efficiency in said patient as a function of each level of expression measured for a biomarker chosen from this group.

Among the means for measuring the level of expression of the biomarkers or combinations of biomarkers chosen, mention may in particular be made of reagents specific to each of the biomarkers such as enzymes, substrates or antibodies which can be used in methods, such as among others nephelometry, chemiluminescence, immunoturbidimetry, flow cytometry, ELISA, etc., but also physical means such as methods of analysis in mass spectrometry for example.

The system according to the invention can also contain means for receiving measurement data, thus making it possible to estimate, for said patient, the effectiveness of response to treatment with the anti-IL-6 receptor agent, from data supplied, for example, by a practitioner who measured the level of expression of the biological biomarkers as described above.

The reception means may in particular comprise transmission / reception means for exchanging with a remote server by means of a communication network such as an intranet network or the secure internet network. The device can also include input means such as a keyboard.

The data processing means can in particular call on database management, code instructions, the development of software comprising an algorithmic brick, an interface to allow the user to consult the results. .. These various elements can be recorded on a storage medium such as a hard disk, a CD ROM, a USB key, or any other storage medium known to those skilled in the art.

They can be implemented by a device which can be fixed or mobile. The device is for example a personal computer, a mobile phone, an electronic tablet, or any other type of terminal known to those skilled in the art.

As a variant, the system may also include transmission means for transmitting, still via intranet or internet for example, the results of the estimation of the effectiveness of the treatment in the patient concerned.

According to another advantageous variant, the system according to the invention comprises means for receiving the efficiency data obtained, in order to complete and enrich the reference values in view of the processing result obtained with regard to the level of expression of the selected biomarkers.

EXAMPLES

Materials & Methods

Development of the predictive model:

The link between the response to biotherapy and each variable is analyzed through a logistic regression model on a data set. The variable to be explained is the correct answer at 6 months.

First, a pre-selection of the variables to be included in the multivariate model is carried out. For this, the predictive capacity of each explanatory variable is analyzed individually. Biomarkers are analyzed quantitatively and qualitatively. A variable selection method is implemented to keep only the relevant variables which will then be introduced into a multivariate model. Biomarkers are preselected if they have:

o In quantitative form, a p-value <0.25 or an AUC> 0.60 o In qualitative form, a p-value <0.05 and an AUC> 0.65

The criteria chosen are deliberately broad to include the significant variables, but also the variables showing trends in the multivariate model.

The preselected biomarkers present relevant trends for analysis. Multivariate models with the different possible combinations of these biomarkers are constructed, and the AUC calculated. Models with an AUC> 0.75 are considered relevant and are kept.

The models thus constructed allow the assay results of each of the specific biomarkers to be weighted in order to obtain a probability of response. The coefficients of each model make it possible to calculate from the dosage values of each patient an associated probability of response. The performance characteristics (AUC (area under the curve), sensitivity and specificity, VPP (positive predictive value) and VPN (negative predictive value)) of each model are calculated to define its relevance.

Cross validation is performed to validate the selection of variables and models. For this, the cohort is divided into two datasets: a learning game representing 2/3 of the cohort, and a validation game representing 1/3 of the cohort. This division is carried out randomly 1000 times to analyze different samples.

An AUC> 0.70 is considered acceptable discrimination, an AUC> 0.80 demonstrates a very good capacity for discrimination. A threshold probability level is set to calculate specificity and sensitivity. This optimal threshold is determined based on the Youden index. At this threshold, patients can be classified according to the following table:

Responder Non responder

Positive test (proba> threshold) VP FP

Negative test (proba <threshold) FN VN • VP (true positive) represents the number of responding individuals with a positive test, • FP (false positive) represents the number of non-responding individuals with a positive test, • FN (false negative) represents the number of responding individuals with a negative test, • VN (true negative) represents the number of non-responding individuals with a negative test. Sensitivity, or the probability that the test will be positive if the patient is a responder, is measured

VP in patients only. It is given by: -----.

Specificity is measured in non-patients only. The specificity, or the probability of obtaining a negative test in non-responders is given by:

The sensitivity of the test measures its ability to give a positive result when the patient is a responder. Specificity measures the ability of the test to give a negative result when the patient is a non-responder.

The positive predictive value (PPV) is the probability that the patient will respond when the

VP test is positive. VPP = _{vp + pp} . The negative predictive value (VPN) is the probability that the patient will not respond when the test is negative. VPN = _wn ^vn _cnt .

TN + FN

Results:

On our learning cohort (51 RA patients), the models constructed have the characteristics presented in the table below. All combinations including at least 2 of the 7 biomarkers among A1AT; S100A8 / A9; C4BP; C4; CRP; LGALS3BP; SAA bring relevant results:

-Table presenting the combinations with AUOQ.75:

NOT S10A8 / A9 SAA LGALS3B P o A1AT CRP C4BP AUC 2 X X 0.826 X X 0.819 X X 0.805 X X 0.796 X X 0.779 X X 0.773 X X 0.765 X X 0.764 X X 0.760 X X 0.757 3 X X X 0.890 X X X 0,889 X X X 0,880 X X X 0.877 X X X 0.868 X X X 0.868 X X X 0.867 X X X 0.861 X X X 0.855 X X X 0.853 X X X 0,850 X X X 0.846 X X X 0.841 X X X 0,830 X X X 0,830 X X X 0,830 X X X 0.827 X X X 0.816 X X X 0.815 X X X 0.813 X X X 0.809 X X X 0.808 X X X 0.808 X X X 0.805 X X X 0.804 X X X 0.790 X X X 0.781 X X X 0.774 X X X 0.768 X X X 0.766 X X X 0,762 X X X 0.757 X X X 0.757 4 X X X X 0.929 X X X X 0.927 X X X X 0.922 X X X X 0.921 X X X X 0.921 X X X X 0.920 X X X X 0.915 X X X X 0.913

NOT S10A8 / A9 SAA LGALS3B P O A1AT CRP C4BP AUC X X X X 0.910 X X X X 0.903 X X X X 0.903 X X X X 0.893 X X X X 0.888 X X X X 0.887 X X X X 0.886 X X X X 0.884 X X X X 0.881 X X X X 0.879 X X X X 0.876 X X X X 0.875 X X X X 0.875 X X X X 0.870 X X X X 0.863 X X X X 0.857 X X X X 0,850 X X X X 0.847 X X X X 0.836 X X X X 0.835 X X X X 0,830 X X X X 0.824 X X X X 0.824 X X X X 0.819 X X X X 0.816 X X X X 0.796 X X X X 0.779 5 X X X X X 0.955 X X X X X 0,952 X X X X X 0,946 X X X X X 0.944 X X X X X 0.943 X X X X X 0.939 X X X X X 0.936 X X X X X 0.935 X X X X X 0.930 X X X X X 0.924 X X X X X 0.921 X X X X X 0.918 X X X X X 0.910 X X X X X 0.909 X X X X X 0.902 X X X X X 0.893 X X X X X 0.890 X X X X X 0.882 X X X X X 0,880 X X X X X 0.847 X X X X X 0.838

Taking for example the 3-variable model S100A8 / A9, SAA, and A1AT, the characteristics obtained are presented in the table below:

3-variable model: S100A8 / A9, SAA and A1AT AUC 0890 Sensitivity 0.68 Specificity 0.94 PPV 0.96 VPN 0.58

The corresponding ROC curve is shown in Figure 1.

REFERENCES

Baillet, A. (2010). [S100A8, S100A9 and S100A12 proteins in rheumatoid arthritis]. Rev. Med. Internal 31, 458-461.

Baillet, A., Trocmé, C., Berthier, S., Arlotto, M., Grange, L., Chenau, J., Quêtant, S., Sève, M., Berger, F., Juvin, R., et al. (2010). Synovial fluid proteomic fingerprint: S100A8, S100A9 and S100A12 proteins discriminate rheumatoid arthritis from other inflammatory joint diseases. Rheumatol. Oxf. Engl. 49, 671-682.

Blom, A.M., Nandakumar, K.S., and Holmdahl, R. (2009). C4b-binding protein (C4BP) inhibits development of experimental arthritis in mice. Ann. Rheum. Dis. 68, 136-142.

Buisseret, P.D., Pembrey, M.E., and Lessof, M.H. (1977). alpha 1 -antitrypsin phenotypes in rheumatoid arthritis and ankylosing spondylitis. Lancet Lond. Engl. 2, 1358-1359.

Choi, I.Y., Gerlag, D.M., Herenius, M.J., Thurlings, R.M., Wijbrandts, C.A., Foell, D., Vogl, T., Roth, J., Tak, P.P., and Holzinger, D. (2015). MRP8 / 14 serum levels as a strong predictorof response to biological treatments in patients with rheumatoid arthritis. Ann. Rheum. Dis. 74, 499-505.

Conigliaro, P., Triggianese, P., Chimenti, M.S., Lucchetti, R., Kroegler, B., and Perricone, R. (2016). Serological markers associated with disease activity in patients with rheumatoid arthritis treated with rituximab. J. Int. Med. Res. 44, 53-57.

Connolly, M., Mullan, R.H., McCormick, J., Matthews, C., Sullivan, O., Kennedy, A., FitzGerald, O., Poole, A.R., Bresnihan, B., Veale, D.J., et al. (2012). Acute-phase amyloid serum A regulates tumor necrosis factor a and matrix turnover and predicts disease progression in patients with inflammatory arthritis before and after biology therapy. Arthritis Rheum. 64, 1035-1045.

Drynda, S., Ringel, B., Kekow, M., Kühne, C., Drynda, A., Glocker, M.O., Thiesen, H.-J., and Kekow, J. (2004). Proteome analysis reveals disease-associated marker proteins to differentiate RA patients from other inflammatory joint diseases with the potential to monitor anti-TNFalpha therapy. Pathol. Res. Pract. 200, 165-171.

Duroux-Richard, I., Pers, Y.-M., Fabre, S., Ammari, M., Baeten, D., Cartron, G., Touitou, I., Jorgensen, C., and Apparailly, F. (2014). Circulating miRNA-125b is a potential biomarker predicting response to rituximab in rheumatoid arthritis. Picks Inflamm. 2014, 342524. Finckh, A., Ciurea, A., Brulhart, L., Kyburz, D., Moller, B., Dehler, S., Revaz, S., Dudler, J., Gabay, C., and Physicians of the Swiss Clinical Quality Management Program for Rheumatoid Arthritis (2007). B cell depletion may be more effective than switching to an alternative anti-tumor necrosis factor agent in rheumatoid arthritis patients with inadequate response to anti-tumor necrosis factor agents. Arthritis Rheum. 56, 1417-1423.

Fransen, J., and van Riel, P.L.C.M. (2005). The Disease Activity Score and the EULAR response criteria. Clin Exp Rheumatol. 23 (Suppl. 39): S93-S99

Gerstner, C., Dubnovitsky, A., Sandin, C., Kozhukh, G., Uchtenhagen, H., James, EA, Ronnelid, J., Ytterberg, AJ, Pieper, J., Reed, E., et al . (2016). Functional and Structural Characterization of a Novel HLA-DRB1 * 04: 01-Restricted α-Enolase T Cell Epitope in Rheumatoid Arthritis. Forehead. Immunol. 7, 494.

Izumi, K., Kaneko, Y., Hashizume, M., Yoshimoto, K., and Takeuchi, T. (2015). Baseline Sérum Osteopontin Levels Predict the Clinical Effectiveness of Tocilizumab but Not Infliximab in Biologie-Naïve Patients with Rheumatoid Arthritis: A Single-Center Prospective Study at 1 Year (the Keio First-Bio Cohort Study). PLoS ONE 10.

Kokubun, M., Imafuku, Y., Okada, M., Ohguchi, Y., Ashikawa, T., Yamada, T., and Yoshida, H. (2005). Amyloid A serum (SAA) concentration varies among rheumatoid arthritis patients estimated by SAA / CRP ratio. Clin. Chim. Acta Int. J. Clin. Chem. 360, 97-102.

Krintel, SB, Dehlendorff, C., Hetland, ML, H0rslev-Petersen, K., Andersen, KK, Junker, P., P0denphant, J., Ellingsen, T., Ahlquist, P., Lindegaard, HM, et al . (2016). Prediction of treatment response to adalimumab: a double-blind placebo-controlled study of circulating microRNA in patients with early rheumatoid arthritis. Pharmacogenomics J. 16, 141-146.

Lipsky, P.E., van der Heijde, D.M., St Clair, E.W., Furst, D.E., Breedveld, F.C., Kalden, J.R., Smolen, J.S., Weisman, M., Emery, P., Feldmann, M., et al. (2000). Infliximab and methotrexate in the treatment of rheumatoid arthritis. Anti-Tumor Necrosis Factor Trial in Rheumatoid Arthritis with Concomitant Therapy Study Group. N. Engl. J. Med. 343, 15941602.

Nakamura, S., Suzuki, K., lijima, H., Hata, Y., Lim, CR, Ishizawa, Y., Kameda, H., Amano, K., Matsubara, K., Matoba, R., and al. (2016). Identification of baseline gene expression signatures predicting therapeutic responses to three biology agents in rheumatoid arthritis: a retrospective observational study. Arthritis Res. Ther. 18, 159.

Obry, A., Lequerré, T., Hardouin, J., Boyer, O., Fardellone, P., Philippe, P., Le Loët, X., Cosette, P., and Vittecoq, O. (2014). Identification of S100A9 as biomarker of responsiveness to the methotrexate / etanercept combination in rheumatoid arthritis using a proteomic approach. PloS One 9, e115800.

Obry, A., Hardouin, J., Lequerré, T., Jarnier, F., Boyer, O., Fardellone, P., Philippe, P., Marcelli, C., Loët, XL, Vittecoq, O., and al. (2015). Identification of 7 Proteins in Sera of RA

Patients with Potential to Predict ETA / MTX Treatment Response. Theranostics 5, 1214-1224.

Ohshima, S., Kuchen, S., Seemayer, CA, Kyburz, D., Hirt, A., Klinzing, S., Michel, BA, Gay, RE, Liu, F.-T., Gay, S., et al. (2003). Galectin 3 and its binding protein in rheumatoid arthritis. Arthritis Rheum. 48, 2788-2795.

Prajapati, R., Plant, D., and Barton, A. (2011). Genetic and genomic predictors of anti-TNF response. Pharmacogenomics 12, 1571-1585.

Rendas-Baum, R., Wallenstein, G.V., Koncz, T., Kosinski, M., Yang, M., Bradley, J., and Zwillich, S.H. (2011). Evaluating the efficacy of sequential biology therapies for rheumatoid arthritis patients with an inadequate response to tumor necrosis factor-α inhibitors. Arthritis Res. Ther. 13, R25.

Sanayama, Y., Ikeda, K., Saito, Y., Kagami, S.-l., Yamagata, M., Furuta, S., Kashiwakuma, D., Iwamoto, I., Umibe, T., Nawata, Y., et al. (2014). Prediction of therapeutic responses to tocilizumab in patients with rheumatoid arthritis: biomarkers identified by analysis of gene expression in peripheral blood mononuclear cells using genome-wide DNA microarray. Arthritis Rheumatol. Hoboken NJ 66, 1421-1431.

Saulot, V., Vittecoq, O., Charlionet, R., Fardellone, P., Lange, C., Marvin, L., Machour, N., Le Loët, X., Gilbert, D., and Tron, F (2002). Presence of autoantibodies to the glycolytic enzyme alpha-enolase in sera from patients with early rheumatoid arthritis. Arthritis Rheum. 46, 1196—1201.

Smith, S.L., Plant, D., Eyre, S., and Barton, A. (2013). The potential use of expression profiling: implications for predicting treatment response in rheumatoid arthritis. Ann. Rheum. Dis. 72, 1118-1124.

Smolen, JS, Burmester, G.-R., Combe, B., Curtis, JR, Hall, S., Haraoui, B., van Vollenhoven, R., Cioffi, C., Ecoffet, C., Gervitz, L ., et al. (2016). Head-to-head comparison of certolizumab pegol versus adalimumab in rheumatoid arthritis: 2-year efficacy and safety results from the randomized EXXELERATE study. Lancet Lond. Engl. 388, 2763-2774.

Smolen, JS, Landewé, R., Bijlsma, J., Burmester, G., Chatzidionysiou, K., Dougados, M., Nam, J., Ramiro, S., Voshaar, M., van Vollenhoven, R., et al. (2017). EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifÿing antirheumatic drugs: 2016 update. Ann. Rheum. Dis. 76, 960-977.

Souto, A., Maneiro, J.R., and Gômez-Reino, J.J. (2016). Rate of discontinuation and drug survival of biology therapies in rheumatoid arthritis: a systematic review and meta-analysis of drug registries and health care databases. Rheumatol. Oxf. Engl. 55, 523-534.

Swaak, A.J., Van Rooyen, A., Planten, O., Han, H., Hattink, O., and Hack, E. (1987). An analysis of the levels of complement components in the synovial fluid in rheumatic diseases. Clin. Rheumatol. 6, 350-357.

Tatar, Z., Migne, C., Petera, M., Gaudin, P., Lequerre, T., Marotte, H., Tebib, J., Pujos Guillot, E., and Soubrier, M. (2016). Variations in the metabolome in response to disease activity of rheumatoid arthritis. BMC Musculoskelet. Disord. 77, 353.

Trocmé, C., Marotte, H., Baillet, A., Pallot-Prades, B., Garin, J., Grange, L., Miossec, P., Tebib, J., Berger, F., Nissen, MJ , et al. (2009). Apolipoprotein A-l and platelet factor 4 are biomarkers for infliximab response in rheumatoid arthritis. Ann. Rheum. Dis. 68, 1328-1333. Vogt, L.M., Talens, S., Kwasniewicz, E., Scavenius, C., Struglics, A., Enghild, J.J., Saxne, T., and Blom, A.M. (2017). Activation of Complément by Pigment Epithelium-Derived Factor in Rheumatoid Arthritis. J. Immunol. Baltim. Md 1950 199, 1113-1121.

Weinblatt, M.E., Kremer, J.M., Bankhurst, A.D., Bulpitt, K.J., Fleischmann, R.M., Fox, R.I., Jackson, C.G., Lange, M., and Burge, D.J. (1999). A trial of etanercept, a recombinant tumor necrosis factor receptor: Fc fusion protein, in patients with rheumatoid arthritis receiving methotrexate. N. Engl. J. Med. 340, 253-259.

Weinblatt, M.E., Keystone, E.C., Furst, D.E., Moreland, L.W., Weisman, M.H., Birbara, C.A., Teoh, L.A., Fischkoff, S.A., and Chartash, E.K. (2003). Adalimumab, a fully human anti-tumor necrosis factor alpha monoclonal antibody, for the treatment of rheumatoid arthritis in patients taking concomitant methotrexate: the ARMADA trial. Arthritis Rheum. 48, 35-45.

Wells, G., Becker, J-C., Teng, J., Dougados, M., Schiff, M., Smolen, J., Aletaha, D., and van Riel, P.L.C.M. 2009). Validation of the 28-joint Disease Activity Score (DAS28) and European League Against Rheumatism response criteria based on C-reactive protein against disease progression in patients with rheumatoid arthritis, and comparison with the DAS28 based on erythrocyte sedimentation rate. Ann Rheum Dis 2009; 68: 954-960.

Zhang, J., Shan, Y., Reed, G., Kremer, J., Greenberg, J.D., Baumgartner, S., and Curtis, J.R. (2011). Thresholds in disease activity for switching biologics in rheumatoid arthritis patients: experience from a large U.S. cohort. Arthritis Care Res. 63, 1672-1679.

Claims (15)

1. Method for estimating the effectiveness of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis and who has had an insufficient response to at least one prior treatment with biotherapy, said process comprising: a) in vitro measurement of the level of expression of at least two biomarkers chosen from the group consisting of S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4 ), Amyloid serum A (AAS), alphal antitrypsin (A1AT), C-reactive protein (CRP), tubulin beta-1 chain and alpha-enolase in a biological sample from said patient, b) the estimation of said efficacy of treatment with an IL-6 anti-receptor agent in said patient as a function of each level of expression measured for a biomarker chosen from said group. 2. Method for estimating the efficacy of treatment with an anti-IL-receptor agent 6 according to claim 1, characterized in that step b) comprises: b1) comparing the level of expression measured in step a) with that measured in a plurality of samples from patients suffering from rheumatoid arthritis and who have received treatment with an anti-IL receptor agent -6 for which the treatment efficiency is known; said comparison being carried out using a statistical learning model using as input data the expression levels of at least two of the biomarkers measured in step a), b2) the estimation of said efficacy of treatment with an IL-6 anti-receptor agent in said patient as a function of the results determined by the model defined in step b1). 3. Method for estimating the effectiveness of treatment with an IL-6 anti-receptor agent according to any one of claims 1 or 2, characterized in that the expression levels of each measured biomarker are used to obtain a score related to the estimation of the treatment efficiency in said patient, said score being compared with at least a predetermined threshold so as to classify the prognosis among a plurality of classes. 4. Method for estimating the effectiveness of treatment with an anti-IL-6 receptor agent according to claim 3, characterized in that said plurality of classes comprises at least two classes including a class of non-response to treatment by said anti-IL-6 receptor agent. 5. Method for estimating the effectiveness of treatment with an IL-6 anti-receptor agent according to any one of claims 1 to 4, characterized in that the estimation of said treatment efficiency in said patient comprises comparing said score with a predetermined threshold below which poor efficiency is predicted; and above which good efficiency is predicted. 6. Method for estimating the effectiveness of treatment with an IL-6 anti-receptor agent according to any one of claims 1 to 5, in which the learning model is based on a prior analysis of samples from a cohort of patients treated with an IL-6 anti-receptor agent with good response to treatment and patients treated with an IL-6 anti-receptor agent with poor response to treatment. 7. Method for estimating the efficacy of treatment with an IL-6 anti-receptor agent according to claim 6, in which said prior analysis comprises the application of a learning method and selection of variables . 8. Method for estimating the effectiveness of treatment with an IL-6 anti-receptor agent according to claim 7, in which said method of learning and selecting variables is logistic regression. 9. Method for estimating the efficacy of treatment with an IL-6 anti-receptor agent according to any one of claims 6 to 8, in which said expression levels are weighted as a function of l prior analysis of said cohort to derive said score. 10. Method for estimating the effectiveness of treatment with an anti-IL-6 receptor agent according to any one of claims 7 to 9, in which said learning method comprises a decision tree in which each node corresponds to a comparison of the level of expression measured in step a) with a reference value. 11. Method for estimating the effectiveness of treatment with an anti-IL-6 receptor agent according to any one of the preceding claims, characterized in that said agent is capable of inhibiting the signaling pathway of the 'IL-6, in particular by blocking the binding of IL6 to its IL-6R receptor, and preferably said agent is Tocilizumab. 12. Method for estimating the effectiveness of treatment with an anti-IL-6 receptor agent according to any one of the preceding claims, characterized in that the said patient was in insufficient response to at least one previous treatment by a biotherapy chosen from Etanercept, Adalimumab, Infliximab, Abatacept, Rituximab, Certolizumab, and Golimumab. 13. Method for estimating the effectiveness of treatment with an IL-6 anti-receptor agent according to any one of the preceding claims, characterized in that the biological sample consists of a sample of biological fluid , and preferably the serum. 14. Method for estimating the effectiveness of treatment with an anti-IL-6 receptor agent according to any one of the preceding claims, characterized in that the biomarker (s) whose expression level is measured at step a) is / are protein biomarker (s). 15. System for estimating the effectiveness of treatment with an anti-IL-6 receptor agent in a patient suffering from rheumatoid arthritis and who has had an insufficient response to at least one prior treatment with biotherapy, said treatment system comprising: means for measuring or receiving data for measuring the level of expression of at least two biomarkers chosen from a group consisting of: S100A8 / A9, Galectin-3-binding protein (or LGALS3BP), C4b binding protein (or C4BP), Complement C4 (or C4), Amyloid serum A (SAA), alphal antitrypsin (A1 AT), Creative Protein (CRP), tubulin beta-1 chain and alpha-enolase in a biological sample from said patient, - measurement data processing means configured to estimate said processing efficiency in said patient as a function of each level of expression measured for a biomarker chosen from this group. 1/1