Method for determining the ability of an individual to react to a stimulus

Abstract

The present invention relates to an in vitro or ex vivo method for determining the ability of an individual to react to a stimulus, based on the measurement of the expression of at least two different biomarkers selected from different lists among three lists of biomarkers, from a blood sample of said individual, said blood sample being incubated with said stimulus, as well as tools allowing to implement this method and the use of these tools.

Description

Technical Field

[0001] This invention relates to in vitro or ex vivo methods for determining an individual’s ability to respond to a stimulus, as well as tools that allow the implementation of such methods and uses of such tools, the methods being based on measurements of the expression of at least two different biomarkers selected from different lists of three biomarker lists derived from a blood sample of the individual cultured with the stimulus. Background Technology

[0002] The immune system is the body's defense system against things perceived as non-self, such as pathogens. Immune responses require very fine regulation and can sometimes be altered, especially in cases of inflammation, allergies, or autoimmune diseases (where the immune system is more active than normal) or diseases characterized by immunosuppression (where the immune system is less active than normal). This immunosuppression can have different origins, take many forms, and affect innate and / or adaptive immunity.

[0003] In particular, sepsis was identified as a health priority by the World Health Organization in 2017 and represents a global problem in terms of morbidity, mortality, and cost. It is estimated that 31.5 million people worldwide contract sepsis each year, of whom 6 million die from the disease, and 3 million develop conditions that lead to readmission. In patients with sepsis (also known as septic state), the post-infectious immune response is dysregulated, leading to multiple and life-threatening organ failure and dysfunction. This immune response is complex and evolves over time, accompanied by excessive pro-inflammatory and anti-inflammatory phenomena. All of these immune system disorders contribute to organ failure, immune system paralysis, and secondary infections. "Septic shock is a subtype of sepsis in which persistent hypotension is present despite adequate vasocongestion. In the initial stages of sepsis, inflammation, even an excessive inflammatory response (including cytokine shock), appears to dominate, leading to tissue damage and organ failure, particularly at the renal level. This is why clinical trials in the field of sepsis have long focused on anti-inflammatory therapy without conclusive results. Recent studies on the pathophysiology of sepsis have shown that patients with sepsis develop an anti-inflammatory or immunosuppressive response, which occurs concurrently with or following the initial inflammation in an attempt to counteract the excessive inflammatory response. Then, depending on the respective extent of the pro-inflammatory and anti-inflammatory responses, the patient may find themselves in a (potentially severe) state of immunosuppression. These immunocompromised patients are at high risk of nosocomial infections (or HAIs, hospital-acquired infections or healthcare-associated infections) and are susceptible to viral reactivation, and may benefit advantageously from immunostimulatory therapy. However, earlier studies in patients with septic shock have shown no benefit from this treatment. This may be due to the complexity of the pathophysiology of sepsis, including inter-individual variability in the immune response, or to the dynamics of the host response."

[0004] Therefore, stratification of patients based on their immunological characteristics appears crucial for their effective management. A diagnostic tool capable of accurately identifying immune system function and status is essential for adaptive and personalized treatment management. However, individuals with immune system disorders do not exhibit specific clinical symptoms; in particular, interpreting the host response in sepsis patients remains a challenge. Soluble or membrane biomarkers have been proposed, such as the expression of HLA-DR (human leukocyte antigen-D related) on the surface of monocytes (mHLA-DR) or CD88 expression in neutrophils, as well as counting lymphocytes or platelets, but these are each limited to individual cell populations, which may underestimate the overall immune contribution.

[0005] In certain clinical settings (e.g., latent tuberculosis), functional testing, or immune function testing (IFA, immune function assay), enables significant improvements in patient care. Functional testing directly measures the ability of one or more cell populations to respond to stimuli in contact with the cells, and has been used, for example, to study the energy of monocytes. The most common method is to measure TNFα at the protein level after in vitro stimulation with lipopolysaccharide (LPS), and in the clinical case of tuberculosis, to measure interferon-γ at the protein level after stimulation with Mycobacterium tuberculosis antigen. Functional testing has also been used as part of studies aimed at determining the limits of normal immune responses (i.e., in a “healthy” context) in response to different infectious challenges (Urrutia et al. (2016), Cell Reports 16:2777-2791).

[0006] Surprisingly, however, it has been found that functional tests based on the measurement of the expression of certain specific biomarkers (categorized into three lists from individual blood samples cultured with a stimulus) can determine an individual's (which may be a healthy individual or an individual with a disease, such as a patient with sepsis) ability to respond to a stimulus. In particular, these functional tests, with regard to dysfunction of innate and / or adaptive immune responses, can dynamically highlight the inter-patient heterogeneity of immune responses and thus capture the singularity of each patient's responsiveness, thereby inferring useful information regarding the patient's diagnosis, prognosis, and / or treatment management. The functional tests according to the invention can particularly highlight three categories of individuals: those exhibiting unchanged or slightly altered immune characteristics (cluster S1), those exhibiting strongly altered immune characteristics (cluster S2), and those with moderate immune characteristics (cluster S3). Individuals in cluster S2, whose immunity appears to have changed significantly and who have a greater probability of death, may benefit from more “aggressive” and / or earlier treatment interventions, while standard care is sufficient for individuals in cluster S1, whose immunity has changed slightly; and individualized treatments (e.g., IL-7, interferon-gamma) may be advantageously tested in individuals in cluster S3, whose immunity appears to be reversible. Summary of the Invention

[0007] Therefore, the present invention relates to an in vitro or ex vivo method for determining an individual's ability to respond to a stimulus, preferably for determining an individual's ability of the immune system to respond to a stimulus, said method comprising:

[0008] a) The step of culturing the blood sample of the individual together with the stimulant, and

[0009] b) The step of measuring the expression of at least two different biomarkers selected from at least two different lists of the following from the stimulated blood sample produced in step a):

[0010] - List S1: BST2, CCL20, CCL4, CCL8, CD209, CD3D, CD44, CD74, CD83, CLEC7A, CXCL10, CXCL2, CXCL9, DYRK2, FAM89A, HLA- DMB, HLA-DPB1, IFNG, IL1A, IRAK2, PTGS2, RARRES3, DDX58, SLAMF7, SRC, STAT2, STING, TNFA, TNFSF13B, ZBP1;

[0011] - List S2: ADGRE3, ARL14EP, BST2, C3, CCL2, CCL20, CCL8, CCNB1IP1, IL7R, CD209, CD3D, CD44, CD74, CD83, CDKN1A, CLEC7 A. CX3CR1, CXCL10, CXCL2, CXCL9, DYRK2, FAM89A, HLA-DMB, HLA-DPB1, HLA-DRA, IFITM1, IRAK2, SLAMF7, TGFB1;

[0012] - List S3: 121601901-HERV0116, BST2, C3, CCL20, CCL4, CCL8, CCR1, IL7R, CD209, CD44, CD74, CD83, CLEC7A, CX CL10, CXCL9, EIF2AK4, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DRA, IL1A, IL2, RARRES3, SLAMF7, STAT2.

[0013]

[0014]

[0015] Table 1. Chromosomal localization based on GRCh38 / hg38 biomarkers

[0016] In the context of this invention:

[0017] - The term "individual" refers to a human being, regardless of who he / she is (especially regardless of his / her health condition, whether he / she is a healthy individual or a sick individual). The term "patient" refers to an individual who has been in contact with a healthcare professional (e.g., a physician, general practitioner) or a medical facility (e.g., a hospital, especially an emergency room, resuscitation ward, intensive care unit, or continuing care unit). Patients are generally people who are ill, but can also be healthy people (e.g., an elderly person who is about to receive a vaccination);

[0018] - "Stimulants" refer to one or more molecules that can induce an immune response and allow for qualitative and / or quantitative assessment of an individual's immune response; in particular, they may be immunogens (or "challengers") or molecules used for therapeutic purposes;

[0019] - Determining an individual's "ability to respond to a stimulus" can have a variety of uses, including diagnosis (e.g., identifying an individual's immune status, which can be normal, inflammatory, or immunosuppressive) and prognosis (e.g., identifying individuals whose immune status may evolve—e.g., from normal to inflammatory, or vice versa, or even from immunosuppressive to inflammatory), for example, to adjust treatment management, or even to predict and / or monitor the effectiveness of the response to treatment.

[0020] - "Blood sample" refers to a whole blood sample or cell sample derived from blood (i.e., a sample obtained from blood and containing at least one cell type, such as peripheral blood mononuclear cells or PBMC samples);

[0021] - A "biomarker" or "marker" is an objectively measurable biological characteristic that represents a normal or pathological biological process or an indicator of a pharmacological response to a therapeutic intervention. It can be, in particular, a molecular biomarker, preferably detectable at the mRNA level. More specifically, the biomarker can be an endogenous biomarker or locus (e.g., a gene found in an individual's chromosomal material or HERV / human endogenous retrovirus) or an exogenous biomarker (e.g., a virus).

[0022] Preferably, in the method described above, the at least two different biomarkers are each selected from at least two different lists of the following:

[0023] - List S1-1: BST2, CCL20, CCL4, CCL8, CD209, CD3D, CD44, CD83, CXCL2, DYRK2, HLA-DMB, IFNG, IL1A, IRAK2, PTGS2, RARRES3, DDX58, SRC, STAT2, STING, TNFA, TNFSF13B, ZBP1;

[0024] - List S2-1: ADGRE3, ARL14EP, C3, CCL2, CCNB1IP1, IL7R, CD3D, CD44, CDKN1A, CLEC7A, CX3CR1, CXCL2, DYRK2, HLA-DMB, HLA-DRA, IFITM1, IRAK2, TGFB1;

[0025] - List S3-1:121601901-HERV0116, C3, CCR1, IL7R, CD44, CD74, CXCL10, CXCL9, EIF2AK4, HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DRA, IL1A, IL2, RARRES3, SLAMF7, STAT2.

[0026] More preferably, in the method described above, the at least two different biomarkers are each selected from at least two different lists of the following:

[0027] - List S1-2: CCL20, CCL4, CCL8, CD209, CD44, CD83, CXCL2, IFNG, IL1A, IRAK2, PTGS2, DDX58, SRC, STING, TNFA, TNFSF13B, ZBP1;

[0028] - List S2-2: ADGRE3, ARL14EP, CCL2, CCNB1IP1, IL7R, CDKN1A, CLEC7A, CX3CR1, DYRK2, IFITM1, TGFB1;

[0029] - List S3-2: 121601901-HERV0116, C3, CCR1, CXCL10, CXCL9, EIF2AK4, HLA-DMB, HLA-DPA1, IL2, SLAMF7.

[0030] Even more preferably, in the method described above, the at least two different biomarkers are each selected from at least two different lists of the following:

[0031] - List S1-3: IFNG, PTGS2, DDX58, SRC, STING, TNFA, TNFSF13B, ZBP1;

[0032] - List S2-3: ADGRE3, ARL14EP, CCL2, CCNB1IP1, CDKN1A, CX3CR1, IFITM1, TGFB1;

[0033] - List S3-3: 121601901-HERV0116, CCR1, EIF2AK4, HLA-DPA1, IL2.

[0034] Preferably, the method described above is an in vitro or ex vivo method for determining an individual's ability to respond to a stimulus, and more preferably, an in vitro or ex vivo method for determining an individual's ability to respond to a stimulus, the method comprising:

[0035] a) The step of culturing the blood sample of the individual together with the stimulant, and

[0036] b) Measuring the expression of at least three different biomarkers selected from the following in the stimulated blood sample produced in step a):

[0037] - List S1, List S2 and List S3;

[0038] - List S1-1, List S2-1 and List S3-1;

[0039] - Lists S1-2, S2-2, and S3-2; or

[0040] - Lists S1-3, S2-3, and S3-3.

[0041] More preferably, in step b) above, the following expressions are measured from the stimulated blood sample produced in step a):

[0042] - Select at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, or at least 46 different biomarkers from each of lists S1, S2, and S3;

[0043] - Select at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, or at least 45 different biomarkers from each of lists S1-1, S2-1, and S3-1;

[0044] - Select at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, or at least 38 different biomarkers from each of lists S1-2, S2-2, and S3-2; or

[0045] - Select at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, or at least 21 different biomarkers from each of lists S1-3, S2-3, and S3-3.

[0046] Two and three particularly preferred combinations of biomarkers for the above methods are disclosed in Table 2.

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] Table 2. Optimal combinations of two and three biomarkers

[0066] Preferably, the method described above, in all its embodiments, is applied to blood samples from patients, preferably hospital patients, more preferably patients in emergency rooms, resuscitation rooms, intensive care units, or continuous care units, even more preferably patients with trauma (preferably severe trauma), burns (preferably severe burns), who have undergone surgery (especially major surgery), or who are in a state of sepsis, and very particularly preferably patients in septic shock. A sepsis patient is defined as a patient suffering from at least one life-threatening organ failure due to an inappropriate host response to infection. Septic shock is a subtype of sepsis in which persistent hypotension is present despite adequate vasodilation.

[0067] Preferably, the method described above, in all its embodiments, is applied to a blood sample containing leukocytes. The blood sample may be, for example, a sample of peripheral blood mononuclear cells (or PBMCs), which consists of lymphocytes (B, T, and NK cells), dendritic cells, and monocytes, typically obtained via the Ficoll method well known to those skilled in the art. However, in a particularly advantageous manner, it is preferred to use a whole blood sample (i.e., containing all leukocytes, erythrocytes, platelets, and plasma) collected directly via a venous route (e.g., using tubes containing an anticoagulant) to minimize manipulation of the sample, preserve physiological cellular interactions between the different cell populations involved in the immune response, and better reflect the complexity of an individual's innate and adaptive immune responses. In particular, while PBMCs contain only mononuclear cells, whole blood also contains granulocytes (or polymorphonuclear cells). Using systems that allow for the standardization of procedures is particularly advantageous; in particular, semi-closed culture systems (e.g., test tubes) pre-filled with culture medium and target stimuli can be used. These systems are standardized, for example, containing well-defined stimuli (i.e., no inter-batch loading at the level of stimulus production, in terms of their properties / composition) and / or “batch” loading, thereby controlling the amount of stimuli within the test tube and having tube-to-tube reproducibility. Preferably, these tubes also allow for the collection of blood samples (which allows for cell stimulation at the time of collection), and more preferably, they allow for the collection of precise volumes of blood. An example of a standardized system is… test tube.

[0068] Blood samples may be collected at the request of a physician, for example, to determine whether an individual will respond to a vaccine injection. Samples may also be collected upon admission or during the course of the patient's development; in particular, for patients with sepsis or who have suffered trauma, samples may be collected especially during the first week following infection (i.e., sepsis or trauma) or septic shock (especially when the patient requires vasopressors and their lactate exceeds 2 mmol / L) (e.g., from day 3 to day 7, especially on day 3 / 4).

[0069] In the methods described above, in all embodiments thereof, the step of culturing an individual's blood sample with the stimulant can be performed at different temperatures (preferably 37°C) and at different incubation times (preferably from 1 hour to 48 hours; for example, incubation for 1 hour or less, 2 hours or less, 4 hours or less, 12 hours or less, 24 hours or less, or 48 hours or less). Short incubation times are particularly advantageous for clinical testing.

[0070] The stimulants used in the methods described above can have different properties in all their implementations.

[0071] According to one embodiment, the stimulant may contain one (or more) immunogenic molecules. In this embodiment, the method is particularly useful for determining diagnosis (particularly involving an individual's immune status), prognosis (particularly involving the evolution of an individual's immune status), and / or adjusting the individual's therapeutic care.

[0072] For example, an immunogenic stimulant may contain one or more molecules that can bind to:

[0073] - In one aspect, at least one type of antigen-presenting cell (APC), said APC may particularly be a type of innate immune cell (e.g., monocytes, macrophages, or dendritic cells) or a type of adaptive immune cell (e.g., B lymphocytes), and

[0074] - On the other hand, at least one type of adaptive immune cell (such as T lymphocytes).

[0075] Preferably, the stimulus comprises a superantigen-type molecule or a superantigen-like molecule. Superantigens are protein-based toxins that can be transmitted via both the hypervariable CDR4 region and the variable domain (V) of the T cell receptor. β The binding of the β chain of MHC and the binding of MHC class II (major histocompatibility complex II) molecules present on the surface of antigen-presenting cells (APCs) stimulate a large number of T lymphocytes. In antigen-presenting cells carrying MHC and their T cell receptors carrying V... β The forced interactions established between fragmented T lymphocytes lead to polyclonal activation of these T lymphocytes, regardless of their specificity to the presented peptide antigen. When a stimulant containing a superantigen-type molecule is used, the blood sample used in the method according to the invention contains T lymphocytes and antigen-presenting cells. Among superantigens of particular interest, superantigens produced by staphylococcal species and superantigens produced by streptococcal species may be specifically mentioned. Preferably, the stimulant contains at least one molecule selected from SEB (staphylococcal enterotoxin B) and SEA (staphylococcal enterotoxin A). Among molecules similar to superantigens, bispecific antibodies, for example, capable of binding to T lymphocytes on one hand and antigen-presenting cells on the other (e.g., capable of binding to V on T lymphocytes on one hand) may be mentioned. β On the one hand, antibodies can bind to MHC II molecules or TLR receptors on antigen-presenting cells.

[0076] Alternatively, it can be a stimulant that directly activates T lymphocytes, preferably selected from antibodies that recognize and activate T lymphocyte surface receptors to trigger activation signals at the T lymphocyte level. More preferably, these antibodies are physically and / or chemically bound to each other, and even more preferably, bound by coupling on a polymer, by coupling on beads, or by coupling between them. They can be, for example, anti-CD3 antibodies (e.g., Muromonab-CD3, marketed under the name Orthoclone OKT3), preferably associated with anti-CD28, anti-CD2, and / or anti-CD137 / TNFRSF9 antibodies.

[0077] It could also be an imidazoquinoline-type stimulant, a nucleoside structural analogue that includes a ring in its structure and has a low molecular weight. This type of stimulant produces antiviral and antitumor effects in vivo. One example of an imidazoquinoline-type stimulant that can be mentioned is retimote (R848), which binds to human TLR7 and TLR8 on dendritic cells, or more generally on antigen-presenting cells (or APCs (NF-responsive-KB-dependent)). Direct effects on T lymphocytes have also been described (Smits et al. (2008), Oncologist 13(8):859-875).

[0078] According to another embodiment, the stimulant may comprise, preferably substantially, the following: molecules (especially drugs or drug candidates) for therapeutic purposes, more preferably molecules with immunomodulatory effects (especially molecules with immunostimulatory or anti-inflammatory effects). Examples include IL-7 or interferon-γ. In this embodiment, the method is particularly suitable for predicting and / or monitoring the efficacy of the response to said molecules for therapeutic purposes.

[0079] Measuring the expression (or expression level) of a biomarker involves quantifying at least one expression product of that biomarker. Within the scope of this invention, the expression product of a biomarker is any biomolecule produced by the expression of that biomarker. More specifically, the expression product of the biomarker can be an RNA transcript. "Transcript" refers to RNA transcribed from a biomarker, particularly messenger RNA (mRNA). More specifically, a transcript is RNA produced through gene transcription, followed by post-transcriptional modifications in the form of precursor RNA.

[0080] Therefore, preferably, in all embodiments of the method described above, the expression of the biomarker is measured at the level of RNA or mRNA transcripts. In the context of this invention, the expression levels of one or more RNA transcripts of the same biomarker can be measured. The determination of the quantity of multiple transcripts can be performed sequentially or simultaneously according to methods well known to those skilled in the art. The detection of mRNA transcripts can be performed by direct methods, by any method known to those skilled in the art that can determine the presence of the transcript in a sample, or by indirect detection of the transcript, after the transcript has been converted to DNA, or after the transcript has been amplified, or after DNA amplification obtained after the transcript has been converted to DNA. Many methods exist for detecting nucleic acids (see, for example, Kricka et al., Clinical Chemistry, 1999, n°45(4), pp.453-458; Relier GH et al., DNA Probes, 2nd edition, Stockton Press, 1993, sections 5 and 6, pp.173-249). The expression of biomarkers can be measured, in particular, by reverse transcription-polymerase chain reaction or RT-PCR, preferably by quantitative RT-PCR or RT-qPCR (e.g., using...). The measurement is performed using technologies such as sequencing (preferably high-throughput sequencing) or hybridization techniques (e.g., using hybridization microarrays or other methods). (Type of technology) for measurement. Technologies that allow multiplexing (e.g.) or ) is the preferred option.

[0081] In the context of this invention, the measurement of expression levels allows for the determination of the amount of one or more transcripts present in a test sample, or the derivation of such amounts. For example, the value derived from this amount can be an absolute concentration, calculated using a calibration curve obtained from serial dilutions of amplicon solutions of known concentrations. It can also correspond to a value of a normalized and calibrated amount, such as CNRQ (Calibrated Normalized Relative Amount, (Hellemans et al. (2007), Genome Biology 8(2):R19)), which integrates the values ​​of a reference sample, calibrator, and one or more housekeeping genes (also called reference genes). Examples of reference genes include PPIB, PPIA, GLYR1, RANBP3, HPRT1, 18S, GAPDH, RPLPO, and ACTB genes.

[0082] Preferably, in the method described above, in all embodiments thereof, the expression of the biomarker is normalized relative to the expression of one or more of the following reference genes: HPRT1, DECR1, and TBP; in particular, the geometric mean of the three genes HPRT1, DECR1, and TBP can be used for normalization.

[0083] Preferably, the method as described above, in all its embodiments, may further include a step of measuring the expression of those same biomarkers as measured in the stimulated blood sample from a control blood sample without stimulation (i.e., blood sample cultured under the same conditions as the stimulated blood sample but without the stimulant). More preferably, the method includes a step of calculating the ratio of the expression (preferably normalized expression) of each biomarker in the stimulated blood sample to the expression (preferably normalized expression) of the same biomarker in the control blood sample. Even more preferably, the method includes a step of transforming the ratio obtained by transforming the base logarithm to 10, and possibly transforming it into a reduced central variable.

[0084] This invention also relates to a kit comprising tools (preferably primers and / or probes) for amplifying and / or detecting at least two different biomarkers, said biomarkers being selected from at least two different lists:

[0085] - List S1, S2, and S3;

[0086] - List S1-1, S2-1 and S3-1;

[0087] - List S1-2, S2-2, and S3-2; or

[0088] - List S1-3, S2-3 and S3-3;

[0089] Preferably, the tool (preferably primers and / or probes) comprises amplifying and / or detecting at least three different biomarkers, each selected from the following three lists:

[0090] - List S1, S2, and S3;

[0091] - List S1-1, S2-1 and S3-1;

[0092] - List S1-2, S2-2, and S3-2; or

[0093] - List S1-3, S2-3 and S3-3;

[0094] More preferably, it includes tools for amplifying and / or detecting the following (preferably primers and / or probes):

[0095] - Select at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, at least 45, or at least 46 different biomarkers from each of lists S1, S2, and S3;

[0096] - Select at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, at least 38, at least 39, at least 40, at least 41, at least 42, at least 43, at least 44, or at least 45 different biomarkers from each of lists S1-1, S2-1, and S3-1;

[0097] - Select at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, at least 21, at least 22, at least 23, at least 24, at least 25, at least 26, at least 27, at least 28, at least 29, at least 30, at least 31, at least 32, at least 33, at least 34, at least 35, at least 36, at least 37, or at least 38 different biomarkers from each of lists S1-2, S2-2, and S3-2; or

[0098] - Select at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 16, at least 17, at least 18, at least 19, at least 20, or at least 21 different biomarkers from each of lists S1-3, S2-3, and S3-3;

[0099] - The kit is characterized in that all amplification and / or detection tools of the kit allow the detection and / or amplification of a total of up to 100 (preferably up to 90, 80, 70, 60, 50, 40, 30, 20, 10, or 5) biomarkers.

[0100] Therefore, the kit may also include, for example, tools for amplifying and / or detecting one or more housekeeping genes. The kit may also include positive control tools that can assess the quality of RNA extraction, any amplification, and / or hybridization methods.

[0101] The term "primer" or "amplification primer" refers to a nucleotide fragment that may consist of 5 to 100 nucleotides, preferably 15 to 30 nucleotides, and has specificity for hybridization with a target nucleotide sequence under the conditions determined for initiating an enzymatic polymerization reaction, such as in an enzymatic amplification reaction of a target nucleotide sequence. A "primer pair" is commonly used, consisting of two primers.

[0102] When it is necessary to amplify multiple different biomarkers (e.g., genes), it is preferable to use multiple different primer pairs, each of which preferably has the ability to specifically hybridize with different biomarkers.

[0103] The term "probe" or "hybridization probe" refers to a nucleotide fragment typically consisting of 5 to 100 nucleotides, preferably 15 to 90 nucleotides, and even more preferably 15 to 35 nucleotides, which exhibits hybridization specificity under defined conditions for forming a hybridization complex with a target nucleotide sequence. The probe also includes a reporter molecule (e.g., a fluorophore, enzyme, or any other detection system) that will allow the detection of the target nucleotide sequence. In this invention, the target nucleotide sequence may be a nucleotide sequence contained in messenger RNA (mRNA) or a nucleotide sequence contained in complementary DNA (cDNA) obtained by reverse transcription of said mRNA. When it is necessary to target multiple different biomarkers (e.g., genes), it is preferable to use multiple different probes, each preferably having the ability to hybridize specifically with a different biomarker.

[0104] The term "hybridization" refers to a process in which, under appropriate conditions, two nucleotide fragments with sufficiently complementary sequences, such as a hybridization probe and a target nucleotide fragment, are able to form a double helix with stable and specific hydrogen bonds. A nucleotide fragment "capable of hybridizing" with a polynucleotide is a fragment capable of hybridizing with said polynucleotide under hybridization conditions, which can be determined in a known manner under each condition. The hybridization conditions are determined by stringency (i.e., the stringency of the operating conditions). Hybridization is more specific because it is performed under higher stringency. The stringency is specifically defined based on the base composition of the probe / target double helix and the degree of mismatch between the two nucleic acids. Stringency can also be a function of reaction parameters, such as the concentration and type of ions present in the hybridization solution, the nature and concentration of the denaturing agent, and / or the hybridization temperature. The stringency of the conditions for the hybridization reaction that must be performed depends on the hybridization probe used. All of these data are well known, and the appropriate conditions can be determined by those skilled in the art. Typically, depending on the length of the hybridization probe used, the temperature for the hybridization reaction is between about 20 and 70°C in a 0.5 to 1 M salt solution, particularly between 35 and 65°C. Then, the step of detecting the hybridization reaction is performed.

[0105] The term "enzymatic amplification reaction" refers to the process of producing multiple copies of a target nucleotide fragment through the action of at least one enzyme. Such amplification reactions are well known to those skilled in the art, and the following techniques may be specifically mentioned: PCR (polymerase chain reaction), LCR (ligase chain reaction), RCR (repair chain reaction), 3SR (self-persistent sequence replication) as described in patent application WO-A-90 / 06995, NASBA (nucleic acid sequence-based amplification), TMA (transcription-mediated amplification) as described in patent US-A-5,399,491, and LAMP (loop-mediated isothermal amplification) as described in patent US6410278. When the enzymatic amplification reaction is PCR, we will speak more specifically of RT-PCR (RT stands for "reverse transcription"), where the amplification step is preceded by a reverse transcription step of messenger RNA (mRNA) into complementary DNA (cDNA), and when the PCR is quantitative, it is either qPCR or RT-qPCR.

[0106] The present invention also relates to the following uses:

[0107] -Amplification and / or detection tools (preferably primers and / or probes) as previously described in kits according to the invention in all its embodiments; preferably, tools (preferably primers and / or probes) for amplifying and / or detecting at least two different biomarkers, said biomarkers being selected from at least two different lists of lists S1 to S3 (or S1-1 to S3-1, or S1-2 to S3-2, or S1-3 to S3-3), more preferably, tools for amplifying and / or detecting at least three different biomarkers, said biomarkers being selected from each of three lists S1 to S3 (or S1-1 to S3-1, or S1-2 to S3-2, or S1-3 to S3-3), or

[0108] - A kit containing such amplification and / or detection tools, preferably, wherein all amplification and / or detection tools of the kit allow the detection and / or amplification of a total of up to 100 (preferably up to 90, preferably up to 80, preferably up to 70, preferably up to 60, preferably up to 50, preferably up to 40, preferably up to 30, preferably up to 20, preferably up to 10, preferably up to 5) biomarkers, and optionally, the kit contains tools for amplifying and / or detecting one or more housekeeping genes and / or positive control tools capable of assessing the quality of RNA extraction, the quality of any amplification and / or hybridization method, to determine an individual's ability to respond to a stimulus, preferably the individual's immune system's ability to respond to a stimulus. Attached Figure Description

[0109] Figure 1 Biomarkers that contribute most to the difference in response between healthy individuals and patients with septic shock after stimulation with SEB. (A) Principal component analysis (PCA) of responses (stimulation samples / control samples) in 10 healthy individuals (circles) and 30 patients with septic shock (triangles) after stimulation with SEB. Each individual (donor, D) is labeled with their number. The percentage of difference explained by each principal component (PC) axis and the total difference are indicated. The vector position of each individual is plotted. The most important variables are graphically represented in (B) (representing 20% ​​of the total weight of the PC1 and PC2 variables).

[0110] Figure 2Multivariate cluster analysis following SEB stimulation. Ten healthy individuals and 30 patients with septic shock were treated as a group to differentiate gene expression profiles. Responses to SEB stimulation revealed three groups or clusters (S1; n=16, S2; n=11, and S3; n=12) using a PAM method with relevant distance (score index = 31). Dendrograms were based on the distance between individuals at the centroid of each cluster found by the PAM method. Higher intensity (closer to black) gray levels on the heatmap (or thermal map) indicated higher values ​​of biomarker expression ratios or fold changes (stimulus / control samples), while lower intensity (closer to white) gray levels indicated lower values ​​of biomarker expression ratios or fold changes (stimulus / control samples). A value of 10,000 Ab / c was used as a threshold for high and low mHLA-DR levels. HLA-DR: Human leukocyte antigen DR.

[0111] Figure 3 Distribution of protein TNFα secretion following stimulation with SEB, LPS, and mHLA-DR via defined clusters. On days 3–4 post-septic shock, (A) protein TNFα secretion was measured ex vivo 24 hours after LPS stimulation in healthy individuals (circles) and septic shock patients (squares), and (B) mHLA-DR was measured by flow cytometry only in septic shock patients (squares). Mortality (non-surviving individuals) is represented by triangles, while nosocomial infections are represented by empty squares. Clusters defined after SEB stimulation were obtained using a PAM method with relevant distances. **p<0.001; ***p<0.0001. SEB: Staphylococcal enterotoxin B, LPS: Lipopolysaccharide, mHLA-DR: Monocyte-Human Leukocyte Antigen DR.

[0112] The invention is illustrated in a non-limiting manner by way of the following examples. Example

[0113] Materials and methods

[0114] Group of tested individuals

[0115] This clinical study was approved by the regional ethics committee (Comité de Protection des Hommes Sud-Est II, No. 11236) and registered with the French Ministry of Research (Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation; DC-2008-509) and the National Commission for Information Protection (Commission Nationale de l'Informatique et des Libertés). The study, conducted on septic shock patients admitted to the intensive care unit of Edouard Herriot Hospital (Hospices Civils de Lyon, Lyon, France), was part of a large study on intensive care unit-related immune dysfunction (NCT02803346).

[0116] Patients with septic shock were prospectively included. Septic shock was defined according to the Sepsis-3 consensus of the Society of Critical Care Medicine and the European Society of Critical Care Medicine (Singer et al. (2016), JAMA 315:801-10): patients with infection or suspected infection who do not have hypovolemia, require vasopressors, and have a serum lactate concentration greater than 2 mmol / L (i.e., the criteria for defining septic shock in patients with sepsis). Exclusion criteria were age under 18 years and presence of developmental dysplasia or known immunosuppressive disease. Data collected at admission included demographic characteristics (age, sex) and site of primary infection; initial severity was assessed at admission using the Simplified Severity Index (IGS II; range: 0–163). Information on deaths during ICU stay was collected, and severity at 24 hours post-admission was assessed using the Continuous Organ Failure Assessment (SOFA) score (range: 0–24). Laboratory data were also collected during the follow-up period, including monocyte HLA-DR (mHLA-DR) values ​​and measurements of TNFα protein secretion after LPS stimulation.

[0117] At the same time, blood samples from healthy individuals (or healthy volunteers) are obtained from the national blood service (French blood agency) and used immediately.

[0118] Immune function test

[0119] Cultured in TruCulture tubes

[0120] Heparinized whole blood (1 mL) collected from septic shock patients or healthy individuals on days 3–4 following the onset of septic shock was dispensed into pre-warmed TruCulture tubes (Myriad Rbm, Austin, Texas, USA) containing only culture medium (“control sample”) or medium containing SEB (400 ng / mL). These tubes were then placed in a dry block incubator and incubated at 37°C for 24 hours. After incubation, the cell pellet was resuspended in 2 mL of TRI In LS (Sigma-Aldrich, Deisenhofen, Germany), vortex for 2 minutes at room temperature and let stand for 10 minutes, then store at room temperature -80°C.

[0121] Measuring the expression of biomarkers

[0122] For TruCulture cell precipitation, RNA processing, and detection, the protocol was followed according to Urrutia et al. (2016), Cell Reports 16, 2777-2791. RNA generated by TruCulture stimulation and stored in TRI... The cell pellet in the LS (Sigma-Aldrich) was thawed with agitation. Prior to processing, the thawed sample was centrifuged (3000g for 5 minutes at 4°C) to precipitate cell debris generated during Trizol lysis. For extraction, a modified protocol using the NucleoSpin 96 RNA Tissue Kit (Macherey-Nagel GmbH & Co. KG, Düren, Germany) was followed using a vacuum system. Briefly, 600 μl of clear lysate obtained by Trizol lysis was transferred to a tube pre-filled with 900 μl of 100% ethanol.

[0123] The mixture was transferred to a silica column, washed with buffers MW1 and MW2, and RNA was eluted with 30 μL of RNase-free water. Nanostring technology was used for mRNA detection of a group of 46 biomarkers (Table 3) – a hybridization-based multiplex assay characterized by the absence of amplification; 300 ng RNA was hybridized with the probe for 18 hours at 67 °C using a thermal cycler (Biometra, Tprofessional TRIO, Analytick Jena AG, Jena, Germany).

[0124] After removing excess probes, the samples were purified in an nCounter Prep Station (NanoString Technologies, Seattle, WA, USA) and immobilized on the inner surface of the sample tube 2-3 times. The sample tubes were then transferred and imaged on an nCounter digital analyzer (NanoString Technologies), where color scales of 46 biomarkers were counted and tabulated.

[0125]

[0126]

[0127]

[0128] Table 3. Used for Target biomarkers and their accession numbers (or chromosome locations)

[0129] Generation of normalized data

[0130] Each sample was analyzed in a separate multiplex reaction, with each reaction containing eight negative probes and six serial concentrations of positive control probes. Negative control analysis was performed to determine the background for each sample. Data were imported into nSolver analysis software (version 4.0, NanoString Technologies) for quality control and data normalization.

[0131] The first standardization step, using internal positive controls, allows for the correction of potential sources of variation related to the technology platform. To this end, we calculated the mean background noise level for all samples as the median of all six negative probes + 3 standard deviations. This value was set for each sample below the background noise level.

[0132] Then, the geometric mean of the positive probes for each sample is calculated. The scale factor for a sample is the ratio of the sample's geometric mean to all geometric means. For each sample, all gene values ​​are divided by the corresponding scale factor.

[0133] Finally, to normalize the differences in the amount of introduced RNA, the same method as that used for normalization with the positive control was employed, except that the geometric mean of the three housekeeping genes (HPRT1 (NM_000194.1), DECR1 (NM_001359.1), and TBP (NM_001172085.1)) was calculated.

[0134] These genes were selected from six candidate genes included in a custom gene pool using the NormFinder method, an established method for identifying stable intra- and inter-group housekeeping genes. Results are expressed as expression ratios (or “fold changes”). TruCulture tubes containing SEB failed quality control and were therefore not included in the analysis.

[0135] mHLA-DR expression was measured by flow cytometry.

[0136] HLA-DR expression on the surface of circulating monocytes (mHLA-DR) in peripheral whole blood collected in EDTA tubes on days 3–4 following the onset of septic shock was assessed by flow cytometry (NAVIOS; Beckman-Coulter, Brea, CA, USA). Results are expressed as the number of antibodies bound per cell (Ab / C).

[0137] Protein detection

[0138] For patients with septic shock and healthy individuals, TNFα protein in TruCulture tube supernatant was quantified using the ELLA nanofluidic system (Biotechne, Minneapolis, MI, USA) according to the manufacturer's instructions. Results are expressed in pg / ml.

[0139] Statistical analysis

[0140] Results are presented as median and interquartile range [IQR] for continuous variables. Parametric data were analyzed using ANOVA, and nonparametric data were analyzed using the Kruskal-Wallis test. GraphPad was used. Statistical analysis was performed using software (version 5; GraphPad, La Jolla, CA, USA) and R (version 3.5.1). An adjusted p-value <0.05 was considered statistically significant. Principal component analysis (PCA) was performed using Genomics Suite 7 (Partek, St. Louis, MO, USA).

[0141] Create a cluster

[0142] The data was transformed to 10 using a base logarithm, centered, and simplified. Two distance matrices and one correlation matrix were constructed on the data, and 10 clustering methods (hierarchical, kmeans, diana, fanny, som, model, sota, pam, clara, and agnes) were tested. For each method, clusters from k=3 to k=18 were tested. The optimal clustering method was selected using seven metrics combining internal measures (connectivity, profile width, and Dunn index) and stability (average proportion of non-overlapping clusters (APN), average distance (AD), average distance between means (ADM), and quality factor (FOM)). The most stable method for SEB was selected: the PAM method (scoring index = 31) using the correlation matrix.

[0143] result

[0144] Diversity of responses to stimulation with SEB

[0145] To determine biomarkers that primarily contribute to quantitative changes in the response to SEB stimulation in healthy individuals and patients with septic shock. Figure 1 A) These biomarkers are represented graphically, and weights of the biomarkers explaining the differences are obtained. Differences in SEB response in the first component PC1 (39%) ( Figure 1 Among the largest contributors in component B), RARRES3 and STAT2 were found to be most strongly expressed in individuals on the right side of the component, while IL1A, CXCL2, and IFNG were more strongly expressed in individuals on the opposite side. Regarding the second component PC2 (19%), the variation was “primarily” caused by an element of human endogenous retrovirus or HERV (121601901-HERV0116), but also by SLAMF7, CCL4, C3, and CXCL10.

[0146]

[0147]

[0148] Table 4. Weights of biomarkers that caused the greatest differences between the first (PC1) and second (PC2) components of the SEB stimulation in the two populations. For each component, biomarkers were ranked from highest to lowest weight (absolute value).

[0149] Immune function testing as a stratification tool for sepsis patients

[0150] By considering these two groups (healthy individuals and patients), we performed unsupervised classification (clustering) of the entire molecular genome to identify gene motifs. Healthy individuals were clustered together after SEB stimulation, showing a high degree of homogeneity in their immune responses. During SEB stimulation, six patients were grouped with healthy individuals (n=16, cluster S1), while other patients were divided into two nearly equal groups (n=11 for cluster S2 and n=12 for cluster S3). Figure 2 The donor composition for each cluster is shown in Table 5.

[0151]

[0152] Table 5. Individual composition of clusters obtained after SEB stimulation (per donor). Healthy individuals are shown in italics, non-survivors in bold, and those who have developed nosocomial infections are underlined. D: Donor

[0153] Then bivariate analysis was performed between clusters and biological or clinical parameters.

[0154] For SEB stimulation, mHLA-DR (adjusted p = 0.0131) and TNFα protein secretion after LPS stimulation (adjusted p ≤ 0.0001; Table 6) were found to be statistically significant.

[0155] As expected, due to the classification with healthy individuals, the six patients in cluster S1 showed the highest median mHLA-DR (10938Ab / C, IQR: [9456-14642]) and the highest TNFα protein concentration (10938Ab / C, IQR: [9456-14642]) after LPS stimulation.

[0156] The only significant difference between clusters S1 and S2 was the median concentration of TNFα protein after LPS stimulation (p<0.0001). Cluster S2 showed the lowest median TNFα protein level among the three clusters.

[0157] Comparing clusters S1 and S3, significant differences were found between the two parameters (p<0.001). After LPS stimulation, cluster S3 exhibited a moderate median TNFα protein concentration among the three clusters, while mHLA-DR showed the lowest median level. Figure 3 ).

[0158] Furthermore, we observed that among the 20 patients with at least one comorbidity (out of 30 patients), 10 (50%) belonged to S3, accounting for 83.3% of the cluster.

[0159] Similarly, of the five patients who did not survive, the four who died before day 28 (80%) belonged to cluster S2, accounting for 36% of that cluster, while the five who died late in the hospital belonged to cluster S3 (Table 6). It should be noted that the only patient who developed a hospital-acquired infection belonged to cluster S2.

[0160]

[0161] SOFA: Sequential Organ Failure Assessment

[0162] CCI: Charson Comorbidity Index

[0163] HLA-DR: Human Leukocyte Antigen DR

[0164] Ab / C: Antibodies bound to each cell

[0165] TNFα: Tumor Necrosis Factor α

[0166] LPS: Lipopolysaccharide

[0167] IQR: Interquartile Range

[0168] *: Parameters specifically measured for patients with septic shock.

[0169] Table 6. Bivariate analysis of clusters S1, S2, and S3 during SEB stimulation for clinical and biological parameters. Six parameters are represented when statistical analysis is performed between clusters S1 (n=16 or n=6 when no information is available for healthy individuals), S2 (n=11), and S3 (n=12) defined using the PAM method with correlation distance. p-values ​​are adjusted for multiple test outputs. The presence of comorbidities is affirmative when at least one of the following comorbidities is present in the patient: chronic lung disease, heart failure, myocardial infarction, ulcer, diabetes, renal failure, or malignant solid tumor.

[0170] Therefore, the developed immune function test can demonstrate that if the immune response is homogeneous in healthy individuals, then the immune response in patients with septic shock is heterogeneous, and this heterogeneity lies in the adaptive arm of the immune response. Unlike other patients, those grouped with healthy individuals in cluster S1 have more “normal” / “healthy” immune profiles. A priori, these patients do not require any special vigilance; standard care is sufficient. Patients in cluster S2 correspond to “severe” patients characterized by high mortality. These patients appear to have severely impaired immunity and present a greater probability of death; these patients could benefit from more “aggressive” and / or earlier treatment interventions. Finally, the third group (patients in cluster S3) corresponds to patients with a moderate to severe phenotype who may exhibit some degree of immune recovery. Therefore, these patients whose immunity appears to be recoverable may be the target of personalized treatment (e.g., IL-7, interferon-γ). Therefore, these results demonstrate that the immune function test developed in the context of this invention enables patient stratification, with reference markers (or gold standards) that are generally accepted by the scientific community, such as mHLA-DR or even TNF-α.

Claims

1. The use of a tool for amplifying and / or detecting at least three different biomarkers in the preparation of a kit for determining an individual's ability to respond to a stimulus, characterized in that, The at least three different biomarkers are selected from each of the following three lists: - List S1-3: IFNG、PTGS2、DDX58、SRC、STING、TNFA、TNFSF13B、ZBP1; - List S2-3: ADGRE3、ARL14EP、CCL2、CCNB1IP1、CDKN1A、CX3CR1、IFITM1、TGFB1; - List S3-3: 121601901-HERV0116、CCR1、EIF2AK4、HLA-DPA1、IL2, Furthermore, the at least three different biomarkers are not a combination of TNFSF13B, TGFB1, and HLA-DPA1; The characteristic is that the irritant contains a staphylococcal enterotoxin B (SEB) molecule and the individual is a patient in a state of sepsis; and Its characteristic is that the stimulant allows for the direct activation of T lymphocytes; The ability to determine an individual's response to stimuli includes: a) The step of culturing the blood sample of the individual together with the stimulant; b) The step of measuring the expression of the at least three different biomarkers from the stimulated blood sample generated in step a); c) Perform statistical analysis on the data obtained in step b), and perform stratification of the individuals.

2. The use according to claim 1, characterized in that, The individual in question was a patient with septic shock.

3. The use according to claim 1 or 2, characterized in that, The blood sample mentioned is a whole blood sample.

4. The use according to claim 1 or 2, characterized in that, The expression of the biomarkers was measured at the messenger RNA (mRNA) level.

5. The use according to claim 1 or 2, characterized in that, The expression of the biomarkers was measured by RT-PCR.

6. The use according to claim 5, wherein the RT-PCR is RT-qPCR.

7. The use according to claim 1 or 2, characterized in that, The expression of the biomarkers was measured by sequencing.

8. The use according to claim 1 or 2, characterized in that, The expression of the biomarkers was measured by hybridization.

9. The use according to claim 1 or 2, characterized in that, The expression of the biomarker is normalized relative to the expression of one or more housekeeping genes.

10. The use according to claim 1 or 2, characterized in that, The determination of an individual's ability to respond to a stimulus includes the steps of measuring the expression of the same biomarkers from unstimulated control blood samples as from stimulated blood samples.

11. The use according to claim 10, characterized in that, Determining an individual's ability to respond to a stimulus includes the step of calculating the ratio of the expression of each biomarker in the stimulated blood sample to the expression of the same biomarker in a control blood sample.

12. The use according to claim 11, wherein the expression is a normalized expression.

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