Method for assessing diseases associated with a loss of p53 function in subjects in need thereof

Abstract

The present invention relates to a method for predicting the survival time and / or the disease severity in a subject suffering from a disease associated with a loss of p53 function. Here, the inventors established a 13-gene functional p53 score from a sample collection involving genes downregulated upon p53-silencing and they showed that the score directly predicts the survival time in subjects. Moreover, the inventors showed that this method works with the commonly methods used in the prior art for sequencing genes and measuring their expression. Thus, the present invention relates to a method for use in the prevention and / or the treatment of diseases associated with a loss of p53 function in a subject in need thereof.

Description

[0001] METHOD FOR ASSESSING DISEASES ASSOCIATED WITH A LOSS OF P53 FUNCTION IN SUBJECTS IN NEED THEREOF

[0002] FIELD OF THE INVENTION

[0003] The invention is in the field of medicine, in particular diseases associated with a loss of p53 function.

[0004] BACKGROUND OF THE INVENTION

[0005] Multiple Myeloma (MM), is a yet not curable cancer of plasma cells (Raab M.S et al., 2009, The Lancet). In 2020, about 175 000 people were diagnosed and 117 000 people died from MM (ASCO, « Multiple Myeloma Statistic », 2023, Cancer.net). Thus, the mortality rate of this disease is still high and it is important to stratify risk of resistance of patients in order to propose adapted therapies. Significant progress has been made in the treatment of patients with MM mainly thanks to the introduction of antibody or cell-based therapies (Moreau P et al., 2019, The Lancet). Although most patients have an excellent response to treatment, patients with known high-risk features, like dell7p or Iq gain, still have an insufficient response rate (Thakurta A et al., 2019, Blood, Corre J et al., 2021, Blood & Rajkumar S.V et al., 2022, Am. J. HematoT). The diagnosis, the monitoring of MM progression and the evaluation of patient survival consist mainly of assessing symptoms and analyzing blood and urine samples. However, these methods lack precision and require an interpretation of the results. Therefore, it is important to find others methods to better assess patient stage and survival. At the biological level, MM is a very heterogeneous disease with several well-known hallmarks of resistance. Among them, two genes, respectively TP 53 (dell7p / mutation) and MCL1 (Iq gain), are associated with resistance. Loss of p53 function induced by gene deletion (dell7p) and / or mutation is not directly druggable, although it induces targetable neo- vulnerabilities like viral permissiveness or loss of cell cycle control (Lok A et al., 2018, Blood & Botrugno O.A et al., 2019, Haematologica). The most frequent hit is monoallelic chromosomal deletion, although biallelic hits are more frequent in very advanced disease like secondary plasma cell leukemia. Moreover, whole genome sequencing of samples from 386 patients recently identified that frequency of TP53 biallelic losses increased in refractory / resistant patients when compared to patients at diagnosis, emphasizing that complete p53 inactivation plays a major role in resistance to treatment (Ansari-Pour N et al., 2023, Blood). SUMMARY OF THE INVENTION

[0006] The present invention relates to a method for predicting the survival time and / or the disease severity in a subject suffering from a disease associated with a loss of p53 function. The invention also relates to a method for evaluating patient survival or disease progression in a subject suffering from a disease with a loss of p53 function.

[0007] The present invention is defined by the claims. The following detailed description, figures and examples do not fall under the scope of the present invention and are present for understanding and illustration purposes only.

[0008] DETAILED DESCRIPTION OF THE INVENTION

[0009] In order to establish a strict p53-dependent gene expression profile, p53 negative clones were derived from two TP53+ / + and two TP53- / mut t(4;14) human myeloma cell lines (HMCLs) using CRISPR / Cas9 technology. From the 17 dysregulated genes shared between the 6 clones from the two TP53+ / + myeloma cell lines, the inventors established a 13-gene functional p53 score from a single sample collection, involving genes downregulated upon p53- silencing. This functional score allowed to segregate clones, myeloma cell lines, patients samples according to the presence or absence of hit in TP53 gene i.e., chromosomal deletion and / or mutation. The score also distinguished 1,105 cell lines from hematological and solid cancers according to reported hits (mutation and / or deletion) in TP53. Moreover, the 13-gene score was predictive of overall survival in two independent cohorts of patients. Among the 13 genes, the inventors showed that p53-regulated BAX expression correlated to, and directly impacted, the MCL1 BH3 mimetic S63845 sensitivity of myeloma cells by modulating the amount of MCL1-BAX complexes. Resistance to cell death induced by p53-silencing was overcome by combining S63845 with venetoclax, although the efficacy was significantly lower in p53-deficient versus -proficient clones and patients’ samples. Finally, by using single cell RNA sequencing (scRNAseq), the inventors further showed in patients bone marrow samples that myeloma cells surviving to the BH3 mimetic combination had an about 3-fold lower p53 score. Therefore, this data established a functional p53 score and showed that p53-mediating BAX expression greatly impacts mitochondria-mediated cell death in myeloma cells. Moreover, the inventors showed that this method works with the commonly methods used in the prior art for sequencing genes and measuring their expression. Therefore, the inventors found a score which directly defines subject survival. Applications of the invention

[0010] • Prognostic applications

[0011] A first object of the invention relates to the method according to the invention for predicting the survival time and / or the disease severity in a subject suffering from a disease associated with a loss of p53 function comprising i) determining in a sample obtained from the patient the expression level of at least one gene selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIG AR, ii) comparing said expression level determined at step i) with a predetermined reference value and iii) concluding that the method provides a good prognostic when the level of gene expression is higher than the predetermined reference value, or provides a bad prognostic when the level of gene expression is lower than the predetermined reference value.

[0012] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level at least 8 genes among the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

[0013] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1 A, DDB2, APOBEC3H, RPS27L, PHLDA3 and EDA2R.

[0014] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

[0015] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B and TIGAR.

[0016] As used herein, the term “predicting” relates to anticipating the presence and / or the progress of the disease as well as the survival time and / or the survival rate of the subject. As used herein, the term “good prognostic” indicates a subject without a progress of the disease and / or with a good survival time and / or with a good survival rate. In the same way, “bad prognostic” indicates a subject with a constant progress of the disease and / or with an increased progress of the disease and / or a with bad survival time and / or with a bad survival rate. As used herein, the term “survival time” refers to the percentage of people in a study or treatment group who are still alive for a certain period of time after they were diagnosed with or started treatment for a disease, such as the disease according to the invention. The survival time rate is often stated as a five-year survival rate, which is the percentage of people in a study or treatment group who are alive five years after their diagnosis or the start of treatment. As used herein and according to the invention, the term “survival time” can regroup the term OS.

[0017] As used herein, the term “Overall survival (OS)” refers to the time from diagnosis of a disease such as the disease according to the invention until death from any cause. The overall survival rate is often stated as a two-year survival rate, which is the percentage of people in a study or treatment group who are alive two years after their diagnosis or the start of treatment.

[0018] As used herein, the term “disease associated with a loss of p53 function” refers to any disease involving a decrease of the p53 activity. Loss of p53 function may be induced by a mutation, a p53-silencing and / or a gene deletion. In particular, “disease associated with a loss of p53 function” refers to cancers associated with a loss of p53 function. In particular, “disease associated with a loss of p53 function” refers to Multiple Myeloma and / or Hematological cancer. The “disease associated with a loss of p53 function” can also refer to diseases inducing a high cancer predisposition such as Li-Fraumeni syndrome or clonal hematopoiesis.

[0019] As used herein, the term "cancer" or "tumors" refers to or describes the pathological condition in mammals that is typically characterized by unregulated cell growth.

[0020] As used herein, the term “Hematological cancer refers to blood cell cancers including Hodgkin's lymphoma, non-Hodgkin's lymphoma, acute leukemia, chronic lymphocytic leukemia and Multiple Myeloma.

[0021] As used herein, the term “Multiple Myeloma” or “MM” refers to a bone marrow cancer affecting immune cells, in particular plasma cells. MM comprises all patient profiles including those with high-risk features like dell7p and / or Iq gain.

[0022] In one embodiment, disease associated with a loss of p53 function is a cancer. In one embodiment, disease associated with a loss of p53 function is a hematological cancer. In one embodiment, disease associated with a loss of p53 function is Multiple Myeloma cancer.

[0023] “p53” or “TP53” or “Tumor protein P53” refers to a tumor suppressor transcription factor that binds to DNA to activate more than 1 000 genes and that regulates many cellular functions such as autophagy or apoptosis. It is the most frequently mutated gene in human cancers and is located on the short arm of chromosome 17. As used herein, the term “patient” or “subject” refers to a mammal, such as a feline, a canine, an equine or a primate. In a preferred embodiment, the “patient” or the “subject” is a human. The subject may as well be afflicted by the disease, as he may be healthy. By “healthy”, it is herein intended to mean that the subject is not afflicted by the disease, whether or not he is afflicted by another disease.

[0024] In one embodiment, the subject is a resistant patient. As used herein, the term “resistant patient” or “treatment-resistant patient” refers to a subject who does not respond favorably to drugs, pharmaceutically compositions and / or any therapeutic treatments.

[0025] As used herein, the term “sample” relates to sample obtained from the subject. The sample can be blood, peripheral-blood, serum, plasma, circulating cells, sample obtained from biopsy. In particular, the sample can be mononuclear cells or plasma cells. In particular, the sample may be a set cells or a single cell. In particular, sample come from patients included / enrolled in MYRACLE or MMRF-coMMpass cohort.

[0026] As used herein, the term “normal sample” or “healthy sample” refers to a sample from healthy donors. As used herein, the term “wild-type sample”, refers to a patient sample without mutation and / or deletion in TP53 locus in the tumor cells. As used herein, the term “pathological sample” or “mutated sample” or “unhealthy sample” refers to any biological sample that contains a mutation, a cell, a tumor or any biological change inducing diseases or metabolic dysfunctions.

[0027] As used herein, the term “reference value” relates to a value known compared with the value measured in the method of the present invention. The reference value used in the method of the invention is preferably a cut-off value. It is well within the skills of the person skilled in the art to establish such a cut-off value for the statistical method of the invention, depending on the required level of selectivity and specificity for the diagnosis and benefit / risk balance associated with the disease. Such a determination may be carried out either experimentally or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and / or clinical conditions, as would be recognized by a person of ordinary skilled in the art. In the present invention, the reference value relates to the expression level of the genes in wild type conditions wherein the value indicates the expression level of the genes. In one embodiment, the reference value is a an absolute value or a relative value. In one embodiment, the reference value is determined from a set cells or a single cell sample in wildtype condition.

[0028] In one embodiment, the method according to the invention is an ex vivo method. In one embodiment, the method according to the invention is an in vitro method. • Stratification applications

[0029] Another object of the invention relates to the method according to the invention for stratifying patients suffering from a disease associated with a loss of p53 function comprising i) determining in a sample obtained from the patient the expression level of at least one gene selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR, ii) comparing said expression level determined at step i) with a predetermined reference value and iii) concluding that the method provides a low level of gravity when the level of gene expression is higher than the predetermined reference value, or provides a high level of gravity when the level of gene expression is lower than the predetermined reference value.

[0030] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level at least 8 genes among the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

[0031] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1 A, DDB2, APOBEC3H, RPS27L, PHLDA3 and EDA2R.

[0032] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

[0033] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B and TIGAR.

[0034] As used herein, the term “stratify” consists in, depending on the level of expression of the genes according to the invention, classifying the patients with a good or bad prognosis between them and in adapting the treatment to the patient according to their biological characteristics.

[0035] As used herein, the term “level of gravity” refers to the disease stage and / or the risk of death. In particular, “high level of gravity” refers to a subject with an advanced disease stage and / or a high risk of death. In the same way, “low level of gravity” refers to a subject with an early stage of the disease and / or a low risk of death. • Monitoring applications

[0036] Another object of the invention relates to the method according to the invention for monitoring disease progression and / or treatment efficacy in a subject suffering from a disease associated with a loss of p53 function comprising i) determining in a sample obtained from the patient the expression level of at least one gene selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIG AR, ii) comparing said expression level determined at step i) with a predetermined reference value and iii) concluding that the method provides good monitoring when the level of gene expression is higher than the predetermined reference value, or provides a bad monitoring when the level of gene expression is lower than the predetermined reference value.

[0037] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level at least 8 genes among the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

[0038] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1 A, DDB2, APOBEC3H, RPS27L, PHLDA3 and EDA2R.

[0039] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

[0040] In one embodiment, the step i) of the method according to the invention comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B and TIGAR.

[0041] As used herein, the term “monitoring” relates to assessing the progress of the disease, in particular by biological measures associated to the disease. It also relates to assessing of the treatment efficacy in subject in need thereof. In particular, “good monitoring” refers to a decrease of the disease progress and / or an effective treatment. In the same way, “bad monitoring” refers to no decrease of the disease progress, an ineffective treatment and / or a treatment-resistant patient. The result of such a monitoring method proves very useful to the practitioner for deciding on the continuation of the treatment, or the replacement or complementation thereof with a different treatment.

[0042] The score

[0043] Another object of the invention relates to a method for obtaining a score comprising i) determining reference values by calculating gene expression level in wild type condition of all genes belonging to the genome, ii) determining in a sample obtained from the patient the expression level of at least one gene selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR, iii) obtaining a score with an algorithmic method, iv) comparing said score obtained at the step iii) with the reference value calculated at the step i) wherein a score higher than a reference value indicates a good prognostic, a low level of gravity and / or a good monitoring; and a score lower than a reference value indicates a bad prognostic, a high level of gravity and / or a bad monitoring.

[0044] In one embodiment, the gene expressions calculated at the step i) and ii) are calculated from a cell set or a single-cell sample.

[0045] In one embodiment, the algorithmic method at the step iii) is the singscore package in R or the AddModule Score function of Seurat package in R as described in Foroutan M et al., 2018, BMC Bioinformatics.

[0046] Singscore method and the AddModule Score method are known in the prior art and commonly used by the person skilled in the art (Bhuva D.D et al., 2019, FlOOORes,' Pyatnitskiy M.A et al., 2021, Biology, Deng C et al., 2023, Front Immunol,' Liu J et al., 2023, EPMAJ & Mei Y et al., 2023, Cancer Med, Decombis S et al, 2023, Blood).

[0047] In one embodiment, a score obtained at the step iv) lower than 0,36 indicates a bad prognostic, a high level of gravity and / or a bad monitoring.

[0048] In one embodiment, a score obtained at the step iv) lower than 0,2 indicates a bad prognostic, a high level of gravity and / or a bad monitoring.

[0049] In one embodiment, the step ii) of the method for obtaining the score comprises determining in sample obtained from the patient the expression level of 8 genes among the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

[0050] In one embodiment, the step ii) of the method for obtaining the score comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1 A, DDB2, APOBEC3H, RPS27L, PHLDA3 and EDA2R.

[0051] In one embodiment, the step ii) of the method for obtaining the score comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

[0052] In one embodiment, the step ii) of the method for obtaining the score comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B and TIGAR.

[0053] As used herein, the term “wild type condition” refers to physiological condition. In particular, “wild type condition” refers to a genome without mutation and / or deletion in TP53 locus. In the present invention, the gene expressions obtained in wild type condition are used as reference values.

[0054] As used herein, the term “reference value” refers to a value known compared with the value of the gene expression of the present invention. The reference value used in the method of the invention is preferably a cut-off value. It is well within the skills of the person skilled in the art to establish such a cut-off value for the statistical method of the invention, depending on the required level of selectivity and specificity for the diagnosis and benefit / risk balance associated with the disease. Such a determination may be carried out either experimentally or theoretically. A threshold value can also be arbitrarily selected based upon the existing experimental and / or clinical conditions, as would be recognized by a person of ordinary skilled in the art. Reference values used for comparison of the expression levels may comprise “cutoff” or “threshold” values that may be determined as described herein. Each reference (“cutoff’) value for the genes of the invention level may be predetermined by carrying out a method comprising the steps of: a) providing a collection of samples from patients suffering of a disease of the invention; b) determining the level of the genes of the invention for each sample contained in the collection provided at step a); c) ranking the samples according to said level d) classifying said samples in pairs of subsets of increasing, respectively decreasing, number of members ranked according to their expression level, e) providing, for each sample provided at step a), information relating to the actual clinical outcome for the corresponding ill patient; f) for each pair of subsets of samples, obtaining a Kaplan Meier percentage of survival curve; g) for each pair of subsets of samples calculating the statistical significance (p value) between both subsets h) selecting as reference value for the level, the value of level for which the p value is the smallest.

[0055] In particular, the reference value is a relative value. In particular, the reference value is determined from a set cells or a single cell sample.

[0056] For example the score has been assessed for 100 samples of 100 patients. The 100 samples are ranked according to their expression level. Sample 1 has the highest expression level and sample 100 has the lowest expression level. Afirst grouping provides two subsets: on one side sample Nr 1 and on the other side the 99 other samples. The next grouping provides on one side samples 1 and 2 and on the other side the 98 remaining samples etc., until the last grouping: on one side samples 1 to 99 and on the other side sample Nr 100. According to the information relating to the actual clinical outcome for the corresponding ill patient, Kaplan Meier curves are prepared for each of the 99 groups of two subsets. Also for each of the 99 groups, the p value between both subsets was calculated. The reference value is selected such as the discrimination based on the criterion of the minimum p value is the strongest. In other terms, the expression level corresponding to the boundary between both subsets for which the p value is minimum is considered as the reference value. It should be noted that the reference value is not necessarily the median value of expression levels. In routine work, the reference value (cut-off value) may be used in the present method to discriminate samples of interest for the studied disease and therefore the corresponding patients. Kaplan-Meier curves of percentage of survival as a function of time are commonly used to measure the fraction of patients living for a certain amount of time after treatment and are well known by the man skilled in the art. The man skilled in the art also understands that the same technique of assessment of the expression level of a protein should of course be used for obtaining the reference value and thereafter for assessment of the expression level of a protein of a patient subjected to the method of the invention. Such predetermined reference values of expression level may be determined for any genes of the invention defined above. In the present invention, the reference value relates to the expression of the gene of interest in wild type condition. The expression of genes as reference value as well as the expression level of the interest genes may be measured by RNA-sequencing, DGE-sequencing, GEP, Single-Cell RNA-sequencing (scRNASeq) or qPCR.

[0057] As used herein, the term “normalization” refers to a measure by correcting the absolute expression level of a gene by comparing its expression to the expression of a gene that is not relevant for determining the disease stage of the patient. In the present invention, the normalization comprises a correction of the gene expression of BAX, MDM2, TNFRSF10B, FDXR, CDKN1A, DDB2, RRM2B, APOBEC3C, APOBEC3H, RPS27L, TIGAR, PHLDA3 and / or EDA2R by the gene expression obtained in wild type conditions. As used herein, the term “score” refers to a value obtained by the function of the invention. In the present invention, the “score” is calculated as described in Foroutan M et al., 2018, BMC Bioinformatics. Bulk RNA-seq, DGE-seq or GEP p53 score have been calculated via a rank-based gene set scoring method with the singscore package in R. scRNASeq p53 score was calculated using the AddModule Score function of Seurat package in R as described in Foroutan M et al., 2018, BMC Bioinformatics. The function of the invention can include other variables than those described above. These variables can in particular be relative to other biological markers and / or physical characteristics of the subject. According to the invention, a score higher indicates a good prognostic, a low level of gravity and / or a good monitoring; and a score lower than a reference value indicates a bad prognostic, a high level of gravity and / or a bad monitoring. The result of which based on score, owing to its very good reliability, provides a very helpful information for the physician for making his clinical diagnosis and, where appropriate, prescribing steps to be taken for the management of the disease. It can help early identification of asymptomatic patients with high risk of disease progression. More generally, it advantageously allows a better monitoring and global disease management. In the present invention, the score is suitable for predicting the survival time of a subject suffering from a loss of p53 function. "Risk" relates to the probability that an event will occur over a specific time period, and can mean a subject's "absolute" risk or "relative" risk. Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low-risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed. "Risk evaluation" or "evaluation of risk" in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event or disease state may occur, the rate of occurrence of the event or conversion from one disease state to another. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of the disease, such as cellular population determination in peripheral tissues, in serum or other fluid, either in absolute or relative terms in reference to a previously measured population.

[0058] Biomarkers of the invention

[0059] In the present invention, biomarkers may be BAX, MDM2, CDKN1A, DDB2, AP0BEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, AP0BEC3C and / or TIGAR. All the biomarkers included in the method of the invention are well-known by the person skilled in the art, and have been described in the prior art as associated with a loss of p53 function. As used herein, the term “biomarker” or “biological marker” relates to any detectable product that is synthesized upon the expression of a specific gene, and thus includes gene-specific mRNA, cDNA and protein. The various biological markers names specified herein correspond to their internationally recognized acronyms that are usable to get access to their complete amino acid and nucleic acid sequences, including their complementary DNA (cDNA) and genomic DNA sequences. Illustratively, the corresponding amino acid and nucleic acid sequences of each of the biological markers specified herein may be retrieved, on the basis of their acronym names, that are also termed herein "gene symbols", in the GenBank or EMBL sequence databases. All gene symbols listed in the present specification correspond to the GenBank nomenclature. Their DNA (cDNA and gDNA) sequences, as well as their amino acid sequences are thus fully available to the one skilled in the art from the GenBank database, notably at the following Website address : "http: / / www.ncbi.nlm.nih.gov / ". Of course variant sequences of the biological markers may be employed in the context of the present invention, those including but not limited to functional homologues, paralogues or orthologues of such sequences.

[0060] Table!: list of the 13 genes of the invention use as markers (here “the genes of the invention”). Abreviations used : NAD PH : Nicotinamide adenine dinucleotide phosphate.

[0061] Detection and measurement of the gene expression level

[0062] Measuring the expression level of the genes of the invention (see the table 1) can be done by measuring the gene expression level of them or by measuring the level of the protein of these genes and can be performed by a variety of techniques well known in the art. Typically, the expression level of a gene may be determined by determining the quantity of mRNA. Methods for determining the quantity of mRNA are well known in the art. For example the nucleic acid contained in the samples (e.g., cell or tissue prepared from the patient) is first extracted according to standard methods, for example using lytic enzymes or chemical solutions or extracted by nucleic-acid-binding resins following the manufacturer's instructions. The extracted mRNA is then detected by hybridization (e. g., Northern blot analysis, in situ hybridization) and / or amplification (e.g., RT-qPCR). Other methods of Amplification include ligase chain reaction (LCR), transcription-mediated amplification (TMA), strand displacement amplification (SDA) and nucleic acid sequence based amplification (NASBA). Nucleic acids having at least 10 nucleotides and exhibiting sequence complementarity or homology to the mRNA of interest herein find utility as hybridization probes or amplification primers. It is understood that such nucleic acids need not be identical, but are typically at least about 80% identical to the homologous region of comparable size, more preferably 85% identical and even more preferably 90-95% identical. In certain embodiments, it will be advantageous to use nucleic acids in combination with appropriate means, such as a detectable label, for detecting hybridization. Typically, the nucleic acid probes include one or more labels, for example to permit detection of a target nucleic acid molecule using the disclosed probes. In various applications, such as in situ hybridization procedures, a nucleic acid probe includes a label (e.g., a detectable label).

[0063] As used herein, the term “detectable label” refers to a molecule or material that can be used to produce a detectable signal that indicates the presence or concentration of the probe (particularly the bound or hybridized probe) in a sample. Thus, a labelled nucleic acid molecule provides an indicator of the presence or concentration of a target nucleic acid sequence (e.g., genomic target nucleic acid sequence) (to which the labelled uniquely specific nucleic acid molecule is bound or hybridized) in a sample. A label associated with one or more nucleic acid molecules (such as a probe generated by the disclosed methods) can be detected either directly or indirectly. A label can be detected by any known or yet to be discovered mechanism including absorption, emission and / or scattering of a photon (including radio frequency, microwave frequency, infrared frequency, visible frequency and ultra-violet frequency photons). Detectable labels include coloured, fluorescent, phosphorescent and luminescent molecules and materials, catalysts (such as enzymes) that convert one substance into another substance to provide a detectable difference (such as by converting a colourless substance into a coloured substance or vice versa, or by producing a precipitate or increasing sample turbidity), haptens that can be detected by antibody binding interactions, and paramagnetic and magnetic molecules or materials. Particular examples of detectable labels include fluorescent molecules (or fluorochromes). Numerous fluorochromes are known to those of skill in the art, and can be selected, for example from Life Technologies (formerly Invitrogen), e.g., see, The Handbook — A Guide to Fluorescent Probes and Labelling Technologies). Examples of particular fluorophores that can be attached (for example, chemically conjugated) to a nucleic acid molecule (such as a uniquely specific binding region) are provided in U.S. Pat. No. 5,866, 366 to Nazarenko et al., such as 4-acetamido-4'-isothiocyanatostilbene-2,2' disulfonic acid, acridine and derivatives such as acridine and acridine isothiocyanate, 5-(2'-aminoethyl) aminonaphthalene- 1 -sulfonic acid (EDANS), 4-amino -N- [3 vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate (Lucifer Yellow VS), N-(4-anilino-l- naphthyl)maleimide, antllranilamide, Brilliant Yellow, coumarin and derivatives such as coumarin, 7-amino-4-methylcoumarin (AMC, Coumarin 120), 7-amino-4- trifluoromethylcouluarin (Coumarin 151); cyanosine; 4',6-diarninidino-2-phenylindole (DAPI); 5',5"dibromopyrogallol-sulfonephthalein (Bromopyrogallol Red); 7 -diethylamino -3 (4'-isothiocyanatophenyl)-4-methylcoumarin; diethylenetriamine pentaacetate; 4,4'- diisothiocyanatodihydro-stilbene-2,2'-disulfonic acid; 4,4'-diisothiocyanatostilbene-2,2'- disulforlic acid; 5-[dimethylamino] naphthalene- 1 -sulfonyl chloride (DNS, dansyl chloride); 4-(4'-dimethylaminophenylazo)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl- 4'-isothiocyanate (DABITC); eosin and derivatives such as eosin and eosin isothiocyanate; erythrosin and derivatives such as erythrosin B and erythrosin isothiocyanate; ethidium; fluorescein and derivatives such as 5-carboxyfluorescein (FAM), 5-(4,6diclllorotriazin-2- yDaminofluorescein (DTAF), 2'7'dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein, fluorescein isothiocyanate (FITC), and QFITC Q(RITC); 2',7'-difluorofluorescein (OREGON GREEN®); fluorescamine; IR144; IR1446; Malachite Green isothiocyanate; 4- methylumbelliferone; ortho cresolphthalein; nitrotyrosine; pararosaniline; Phenol Red; B- phycoerythrin; o-phthaldialdehyde; pyrene and derivatives such as pyrene, pyrene butyrate and succinimidyl 1 -pyrene butyrate; Reactive Red 4 (Cibacron Brilliant Red 3B-A); rhodamine and derivatives such as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), lissamine rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, rhodamine green, sulforhodamine B, sulforhodamine 101 and sulfonyl chloride derivative of sulforhodamine 101 (Texas Red); N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA); tetramethyl rhodamine; tetramethyl rhodamine isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives. Other suitable fluorophores include thiol-reactive europium chelates which emit at approximately 617 nm (Heyduk and Heyduk, Analyt. Biochem. 248:216-27, 1997; J. Biol. Chem. 274:3315-22, 1999), as well as GFP, LissamineTM, diethylaminocoumarin, fluorescein chlorotriazinyl, naphthofluorescein, 4,7-dichlororhodamine and xanthene (as described in U.S. Pat. No. 5,800,996 to Lee et al.) and derivatives thereof. Other fluorophores known to those skilled in the art can also be used, for example those available from Life Technologies (Invitrogen; Molecular Probes (Eugene, Oreg.)) and including the ALEXA FLUOR® series of dyes (for example, as described in U.S. Pat. Nos. 5,696,157, 6, 130, 101 and 6,716,979), the BODIPY series of dyes (dipyrrometheneboron difluoride dyes, for example as described in U.S. Pat. Nos. 4,774,339, 5,187,288, 5,248,782, 5,274,113, 5,338,854, 5,451,663 and 5,433,896), Cascade Blue (an amine reactive derivative of the sulfonated pyrene described in U.S. Pat. No. 5,132,432) and Marina Blue (U.S. Pat. No. 5,830,912). In addition to the fluorochromes described above, a fluorescent label can be a fluorescent nanoparticle, such as a semiconductor nanocrystal, e.g., a QUANTUM DOTTM (obtained, for example, from Life Technologies (QuantumDot Corp, Invitrogen Nanocrystal Technologies, Eugene, Oreg.); see also, U.S. Pat. Nos. 6,815,064; 6,682,596; and 6,649, 138). Semiconductor nanocrystals are microscopic particles having size-dependent optical and / or electrical properties. When semiconductor nanocrystals are illuminated with a primary energy source, a secondary emission of energy occurs of a frequency that corresponds to the handgap of the semiconductor material used in the semiconductor nanocrystal. This emission can he detected as coloured light of a specific wavelength or fluorescence. Semiconductor nanocrystals with different spectral characteristics are described in e.g., U.S. Pat. No. 6,602,671. Semiconductor nanocrystals that can he coupled to a variety of biological molecules (including dNTPs and / or nucleic acids) or substrates by techniques described in, for example, Bruchez et al., Science 281 :20132016, 1998; Chan et al., Science 281 :2016-2018, 1998; and U.S. Pat. No. 6,274,323. Formation of semiconductor nanocrystals of various compositions are disclosed in, e.g., U.S. Pat. Nos. 6,927, 069; 6,914,256; 6,855,202; 6,709,929; 6,689,338; 6,500,622; 6,306,736; 6,225,198; 6,207,392; 6,114,038; 6,048,616; 5,990,479; 5,690,807; 5,571,018; 5,505,928; 5,262,357 and in U.S. Patent Publication No. 2003 / 0165951 as well as PCT Publication No. 99 / 26299 (published May 27, 1999). Separate populations of semiconductor nanocrystals can he produced that are identifiable based on their different spectral characteristics. For example, semiconductor nanocrystals can he produced that emit light of different colours based on their composition, size or size and composition. For example, quantum dots that emit light at different wavelengths based on size (565 nm, 655 nm, 705 nm, or 800 nm emission wavelengths), which are suitable as fluorescent labels in the probes disclosed herein are available from Life Technologies (Carlshad, Calif.). Additional labels include, for example, radioisotopes (such as 3 H), metal chelates such as DOTA and DPTA chelates of radioactive or paramagnetic metal ions like Gd3+, and liposomes. Detectable labels that can he used with nucleic acid molecules also include enzymes, for example horseradish peroxidase, alkaline phosphatase, acid phosphatase, glucose oxidase, beta-galactosidase, betaglucuronidase, or beta-lactamase. Alternatively, an enzyme can he used in a metallographic detection scheme. For example, silver in situ hybridization (SISH) procedures involve metallographic detection schemes for identification and localization of a hybridized genomic target nucleic acid sequence. Metallographic detection methods include using an enzyme, such as alkaline phosphatase, in combination with a water-soluble metal ion and a redox-inactive substrate of the enzyme. The substrate is converted to a redox-active agent by the enzyme, and the redox active agent reduces the metal ion, causing it to form a detectable precipitate. (See, for example, U.S. Patent Application Publication No. 2005 / 0100976, PCT Publication No. 2005 / 003777 and U.S. Patent Application Publication No. 2004 / 0265922). Metallographic detection methods also include using an oxido-reductase enzyme (such as horseradish peroxidase) along with a water soluble metal ion, an oxidizing agent and a reducing agent, again to form a detectable precipitate. (See, for example, U.S. Pat. No. 6,670,113). Probes made using the disclosed methods can be used for nucleic acid detection, such as ISH procedures (for example, fluorescence in situ hybridization (FISH), chromogenic in situ hybridization (CISH) and silver in situ hybridization (SISH)) or comparative genomic hybridization (CGH). In situ hybridization (ISH) involves contacting a sample containing target nucleic acid sequence (e.g., genomic target nucleic acid sequence) in the context of a metaphase or interphase chromosome preparation (such as a cell or tissue sample mounted on a slide) with a labelled probe specifically hybridisable or specific for the target nucleic acid sequence (e.g., genomic target nucleic acid sequence). The slides are optionally pretreated, e.g., to remove paraffin or other materials that can interfere with uniform hybridization. The sample and the probe are both treated, for example by heating to denature the double stranded nucleic acids. The probe (formulated in a suitable hybridization buffer) and the sample are combined, under conditions and for sufficient time to permit hybridization to occur (typically to reach equilibrium). The chromosome preparation is washed to remove excess probe, and detection of specific labelling of the chromosome target is performed using standard techniques. For example, a biotinylated probe can be detected using fluorescein-labelled avidin or avidin-alkaline phosphatase. For fluorochrome detection, the fluorochrome can be detected directly, or the samples can be incubated, for example, with fluorescein isothiocyanate (FITC)-conjugated avidin. Amplification of the FITC signal can be effected, if necessary, by incubation with biotin- conjugated goat antiavidin antibodies, washing and a second incubation with FITC-conjugated avidin. For detection by enzyme activity, samples can be incubated, for example, with streptavidin, washed, incubated with biotin-conjugated alkaline phosphatase, washed again and pre-equilibrated (e.g., in alkaline phosphatase (AP) buffer). For a general description of in situ hybridization procedures, see, e.g., U.S. Pat. No. 4,888,278. Numerous procedures for FISH, CISH, and SISH are known in the art. For example, procedures for performing FISH are described in U.S. Pat. Nos. 5,447,841; 5,472,842; and 5,427,932; and for example, in Pirlkel et al., Proc. Natl. Acad. Sci. 83:2934-2938, 1986; Pinkel et al., Proc. Natl. Acad. Sci. 85:9138- 9142, 1988; and Lichter et al., Proc. Natl. Acad. Sci. 85:9664-9668, 1988. CISH is described in, e.g., Tanner et al., Am. l. Pathol. 157: 1467-1472, 2000 and U.S. Pat. No. 6,942,970. Additional detection methods are provided in U.S. Pat. No. 6,280,929. Numerous reagents and detection schemes can be employed in conjunction with FISH, CISH, and SISH procedures to improve sensitivity, resolution, or other desirable properties. As discussed above probes labelled with fluorophores (including fluorescent dyes and QUANTUM DOTS®) can be directly optically detected when performing FISH. Alternatively, the probe can be labelled with a nonfluorescent molecule, such as a hapten (such as the following non-limiting examples: biotin, digoxigenin, DNP, and various oxazoles, pyrrazoles, thiazoles, nitroaryls, benzofurazans, triterpenes, ureas, thioureas, rotenones, coumarin, courmarin-based compounds, Podophyllotoxin, Podophyllotoxin-based compounds, and combinations thereof), ligand or other indirectly detectable moiety. Probes labelled with such non-fluorescent molecules (and the target nucleic acid sequences to which they bind) can then be detected by contacting the sample (e.g., the cell or tissue sample to which the probe is bound) with a labelled detection reagent, such as an antibody (or receptor, or other specific binding partner) specific for the chosen hapten or ligand. The detection reagent can be labelled with a fluorophore (e.g., QUANTUM DOT®) or with another indirectly detectable moiety, or can be contacted with one or more additional specific binding agents (e.g., secondary or specific antibodies), which can be labelled with a fluorophore. In other examples, the probe, or specific binding agent (such as an antibody, e.g., a primary antibody, receptor or other binding agent) is labelled with an enzyme that is capable of converting a fluorogenic or chromogenic composition into a detectable fluorescent, colored or otherwise detectable signal (e.g., as in deposition of detectable metal particles in SISH). As indicated above, the enzyme can be attached directly or indirectly via a linker to the relevant probe or detection reagent. Examples of suitable reagents (e.g., binding reagents) and chemistries (e.g., linker and attachment chemistries) are described in U.S. Patent Application Publication Nos. 2006 / 0246524; 2006 / 0246523, and 2007 / 01 17153. It will be appreciated by those of skill in the art that by appropriately selecting labelled probespecific binding agent pairs, multiplex detection schemes can he produced to facilitate detection of multiple target nucleic acid sequences (e.g., genomic target nucleic acid sequences) in a single assay (e.g., on a single cell or tissue sample or on more than one cell or tissue sample). For example, a first probe that corresponds to a first target sequence can he labelled with a first hapten, such as biotin, while a second probe that corresponds to a second target sequence can be labelled with a second hapten, such as DNP. Following exposure of the sample to the probes, the bound probes can he detected by contacting the sample with a first specific binding agent (in this case avidin labelled with a first fluorophore, for example, a first spectrally distinct QUANTUM DOT®, e.g., that emits at 585 nm) and a second specific binding agent (in this case an anti-DNP antibody, or antibody fragment, labelled with a second fluorophore (for example, a second spectrally distinct QUANTUM DOT®, e.g., that emits at 705 nm). Additional probes / binding agent pairs can he added to the multiplex detection scheme using other spectrally distinct fluorophores. Numerous variations of direct, and indirect (one step, two step or more) can he envisioned, all of which are suitable in the context of the disclosed probes and assays. Probes typically comprise single-stranded nucleic acids of between 10 to 1000 nucleotides in length, for instance of between 10 and 800, more preferably of between 15 and 700, typically of between 20 and 500. Primers typically are shorter single-stranded nucleic acids, of between 10 to 25 nucleotides in length, designed to perfectly or almost perfectly match a nucleic acid of interest, to be amplified. The probes and primers are “specific” to the nucleic acids they hybridize to, i.e. they preferably hybridize under high stringency hybridization conditions (corresponding to the highest melting temperature Tm, e.g., 50 % formamide, 5x or 6x SCC. SCC is a 0.15 M NaCl, 0.015 M Na-citrate). The nucleic acid primers or probes used in the above amplification and detection method may be assembled as a kit. Such a kit includes consensus primers and molecular probes. A preferred kit also includes the components necessary to determine if amplification has occurred. The kit may also include, for example, PCR buffers and enzymes; positive control sequences, reaction control primers; and instructions for amplifying and detecting the specific sequences. In one embodiment, the methods of the invention comprise the steps of providing total RNAs extracted from cumulus cells and subjecting the RNAs to amplification and hybridization to specific probes, more particularly by means of a quantitative or semi-quantitative RT-PCR (or q RT-PCR). In one embodiment, the expression level is determined by DNA chip analysis. Such DNA chip or nucleic acid microarray consists of different nucleic acid probes that are chemically attached to a substrate, which can be a microchip, a glass slide or a microsphere-sized bead. A microchip may be constituted of polymers, plastics, resins, polysaccharides, silica or silica-based materials, carbon, metals, inorganic glasses, or nitrocellulose. Probes comprise nucleic acids such as cDNAs or oligonucleotides that may be about 10 to about 60 base pairs. Expression level of a gene may be expressed as absolute expression level or normalized expression level. Typically, expression levels are normalized by correcting the absolute expression level of a gene by comparing its expression to the expression of a gene that is not a relevant for determining the disease stage of the patient, e.g., a housekeeping gene that is constitutively expressed. Suitable genes for normalization include housekeeping genes such as the actin gene ACTB, ribosomal 18S gene, GUSB, PGK1, TFRC, GAPDH, TBP and ABLE This normalization allows the comparison of the expression level in one sample, e.g., a patient sample, to another sample, or between samples from different sources.

[0064] In one embodiment, the gene expressions according to all methods of the present invention are calculated from a cell set or a single-cell sample.

[0065] In one embodiment according to all methods of the present invention, the expression level of the genes is determined by RNA-sequencing, DGE-sequencing, GEP, Single-Cell RNA-sequencing (scRNASeq) or qPCR.

[0066] In one embodiment according to all methods of the present invention, the expression level of the genes is determined from a Single-Cell RNA-sequencing. Therapeutic applications

[0067] Another object of the invention relates to a method for treating a subject with a bad prognostic assessed by the method according to the invention comprising the administration to said subject at least one active agent.

[0068] In one embodiment, the active agent is an anti-cancer agent and / or an agent targeting p53 (Nishikawa S et al., 2023, Cancers').

[0069] Anti-cancer agent can be selected among all agents used for treating cancer or cancer- associated diseases. In particular, anti-cancer agent can be selected among the group used for treating Myeloma, MM and / or hematological cancer and comprising steroids, corticosteroids, chemotherapy agents such as melphalan or cyclophosphamide, agents used in the stem cell transplant, radiotherapy agents or CD38 antibodies. Steroid and corticosteroid agents comprise prednisone, dexamethasone and bisphosphonate. Chemotherapy agents comprise targeted therapy agents including proteasome inhibitors such as bortezomib, carfilzomib or ixazomib. Chemotherapy agents also comprise immunotherapy agents including antibodies such as daratumumab, isatuximab or elotuzumab as well as immunomodulator agents such as thalidomide, lenalidomide or pomalidomide. Agents targeting p53 may be agents which restore and / or stabilize the wtp53 conformation such as PEITC (phenethyl isothiocyanate) or ATO (arsenic trioxide / Trisenox). Agents targeting p53 may be agents which induce degradation or depletion of missense mutp53 such as HSP90 inhibitor (ganetespib / STA-9090), Atorvastatin, ATO / trisenox or Vorinostat / Zolinza / SAHA. Agents targeting p53 may be agents which induce cell death such as Weel inhibitor (adavosertib / AZD1775 / MK-1775), Lamivudine (3TC / Epivir / Zeffix / DELSTRIGO) or Zoledronic acid (ZA / Reclast / Zometa) and atorvastatin. Agents targeting p53 may comprise inhibitors of retrotransposon activated upon p53 deficiency and inhibitors of YAP / TAZ function.

[0070] As used herein, the terms "therapeutic" or “treatment” refer to curative or disease modifying treatment, including treatment of subjects at risk of contracting the disease or suspected to have contracted the disease as well as subjects who are ill or have been diagnosed as suffering from a disease or medical condition, and includes suppression of clinical relapse. The treatment may be administered to a subject having a medical disorder or who ultimately may acquire the disorder, in order to prevent, cure, delay the onset of, reduce the severity of, or ameliorate one or more symptoms of a disorder or recurring disorder, or in order to prolong the survival of a subject beyond that expected in the absence of such treatment. Preferably, the individual to be treated is a human or non-human mammal (such as a rodent, a feline, a canine or a primate) affected or likely to be affected by the disease. Preferably, the individual is a human. By "therapeutic regimen" is meant the pattern of treatment of an illness, e.g., the pattern of dosing used during therapy. A therapeutic regimen may include an induction regimen and a maintenance regimen. The phrase "induction regimen" or "induction period" refers to a therapeutic regimen (or the portion of a therapeutic regimen) that is used for the initial treatment of a disease. The general goal of an induction regimen is to provide a high level of drug to a subject during the initial period of a treatment regimen. An induction regimen may employ (in part or in whole) a "loading regimen", which may include administering a greater dose of the drug than a physician would employ during a maintenance regimen, administering a drug more frequently than a physician would administer the drug during a maintenance regimen, or both. The phrase "maintenance regimen" or "maintenance period" refers to a therapeutic regimen (or the portion of a therapeutic regimen) that is used for the maintenance of a subject during treatment of an illness, e.g., to keep the subject in remission for long periods of time (months or years). A maintenance regimen may employ continuous therapy (e.g., administering a drug at a regular intervals, e.g., weekly, monthly, yearly, etc.) or intermittent therapy (e.g., interrupted treatment, intermittent treatment, treatment at relapse, or treatment upon achievement of a particular predetermined criteria [e.g., disease manifestation, etc.]). As used herein, the term “therapeutically effective amount” refers to an amount effective, at dosages and for periods of time necessary, to achieve a desired therapeutic result. The therapeutically effective amount, the time of administration, route of administration, and the duration of the treatment may vary according to factors well known in the medical art such as the disease state, age, sex, and weight. A therapeutically effective amount is also one in which any toxic or detrimental effects are outweighed by the therapeutically beneficial effects. The efficient dosages and dosage regimens depend on the disease or condition to be treated and may be determined by the persons skilled in the art. A physician having ordinary skill in the art may readily determine and prescribe the effective amount required. For example, the physician could start doses at levels lower than that required in order to achieve the desired therapeutic effect and gradually increase the dosage until the desired effect is achieved. However, the daily dosage of the products may be varied over a wide range from 0.01 to 1,000 mg per adult per day. Preferably, the compositions contain 0.01, 0.05, 0.1, 0.5, 1.0, 2.5, 5.0, 10.0, 15.0, 25.0, 50.0, 100, 250 and 500 mg of the active ingredient for the symptomatic adjustment of the dosage to the subject to be treated. A medicine typically contains from about 0.01 mg to about 500 mg of the active ingredient, preferably from 1 mg to about 100 mg of the active ingredient. An effective amount of the drug is ordinarily supplied at a dosage level from 0.0002 mg / kg to about 20 mg / kg of body weight per day, especially from about 0.001 mg / kg to 7 mg / kg of body weight per day.

[0071] Pharmaceutical composition

[0072] Another object of the invention relates to a pharmaceutical composition comprising an active agent for treating diseases associated with a loss of p53 function in a subject with a bad prognostic assessed by the method according to the invention.

[0073] In one embodiment, the active agent is an anti-cancer agent. In one embodiment, the pharmaceutical composition according to the invention comprises a pharmaceutically acceptable carrier. In one embodiment, the composition of the invention may be combined with pharmaceutically acceptable excipients, and optionally sustained-release matrices, such as biodegradable polymers, to form therapeutic compositions.

[0074] As used herein, the term “pharmaceutically” or “pharmaceutically acceptable” refers to molecular entities and compositions that do not produce an adverse, allergic or other untoward reaction when administrated to a mammal, especially a human, as appropriate. A pharmaceutically acceptable carrier or excipient refers to a non-toxic solid, semi-solid or liquid filler, diluents, encapsulating material or formulation auxiliary of any type. The composition of the present invention may e.g. be formulated for a topical, oral, intranasal, parenteral, intravenous, intramuscular, intraperitoneal or subcutaneous administration and the like. The uses are adjusted to provide the optimum desired response (e.g., a therapeutic response). Preferably, the composition of the invention may be formulated for any mode of administration suitable for the treatment of cancer associated with a loss of p53 function, in particular Myeloma, MM and / or hematological cancer. The pharmaceutical compositions may contain vehicles which are pharmaceutically acceptable for a formulation capable of being injected. These may be in isotonic, sterile, saline solutions (monosodium or disodium phosphate, sodium, potassium, calcium or magnesium chloride and the like or mixtures of such salts), or dry, especially freeze-dried compositions. In particular, these may be in organic solvent such as DMSO, ethanol which upon addition, depending on the case, of sterilized water or physiological saline permit the constitution of injectable solutions. The form of the composition, the route of administration, the dosage and the regimen naturally depend upon the condition to be treated, the severity of the illness, the age, weight, and sex of the subject, etc. The daily dosage of the composition may be varied over a wide range from 0.01 to 1,000 mg per adult per day. Preferably, the compositions contain 0.01, 0.05, 0.1, 0.5, 1.0, 2.5, 5.0, 10.0, 15.0, 25.0, 50.0, 100, 250 and 500 mg of the active ingredient for the symptomatic adjustment of the dosage to the patient to be treated. A medicament typically contains from about 0.01 mg to about 500 mg of the active ingredient, preferably from 1 mg to about 100 mg of the active ingredient. An effective amount of the drug is ordinarily supplied at a dosage level from 0.0002 mg / kg to about 20 mg / kg of body weight per day, especially from about 0.001 mg / kg to 10 mg / kg of body weight per day. The specific dose level and frequency of dosage for any particular patient may be varied and will depend upon a variety of factors including the activity of the specific compound employed, the metabolic stability, and length of action of that compound, the age, the body weight, general health, sex, diet, mode and time of administration, rate of excretion, drug combination, the severity of the particular condition, and the host undergoing therapy. In some embodiments, the composition of the present invention is administered by slow continuous infusion over a long period, such as more than 24 hours, in order to minimize any unwanted side effects.

[0075] Kits

[0076] Another object of the invention relates to kits for performing the method according to the invention, wherein said kits comprise means for measuring the expression level of the genes of the invention in the sample obtained from the patient.

[0077] The kits may include probes, primers macroarrays or microarrays as above described. For example, the kit may comprise a set of probes as above defined, usually made of DNA, and that may be pre-labelled. Alternatively, probes may be unlabelled and the ingredients for labelling may be included in the kit in separate containers. The kit may further comprise hybridization reagents or other suitably packaged reagents and materials needed for the particular hybridization protocol, including solid-phase matrices, if applicable, and standards. Alternatively, the kit of the invention may comprise amplification primers that may be prelabelled or may contain an affinity purification or attachment moiety. The kit may further comprise amplification reagents and also other suitably packaged reagents and materials needed for the particular amplification protocol.

[0078] The invention will be further illustrated by the following figures and examples. However, the examples and figures should not be interpreted in any way as limiting the scope of the present invention. FIGURES

[0079] Figure 1 : A 13-gene functional p53 score established from isogenic TP53 CRISPR / Cas9 HMCLs . (A) Number of genes significantly differentially expressed between TP53+ / +or TP53~ / mutand TP53~ ~ clones. The graph represents the number of genes significantly downregulated or upregulated in TP 53 ~ NCI-H929, XG7, NAN3, JIM3 clones compared to their respective TP53+ / +or TP53~ / mutclones (FDR<0.05). Genes were classified according to their unknown or known p53 transactivation: early-direct, late-direct, late-indirect and not regulated by p53 (Andrysik Z Genome Res 2017). Expression profile was performed by DGE- Seq in triplicate wells. Schema of the functional p53 score construction. Sixteen downregulated genes in TP53~ ~ clones were shared between NCI-H929 and XG7, and 13 known as early-direct p53 regulated genes were selected for establishing a p53 functional score. (B) The p53 score segregates the clones according to their TP53 status. The score was calculated in TP53+ / +or TP53~ / mutanciTP53~l~ NCI-H929, XG7, JIM3 and NAN3 clones under constitutive (left panel) or 24h-nutlin3a (2000nM for XG7 clones, 10 OOOnM for all other clones) culture (right panel). Statistical analyses were performed using the Mann-Whitney test. Blue, red, and orange represent TP53+ / +, TP53' ' and 77753-mi,lalcdHMCLs, respectively. (C) The p53 score segregates HMCLs according to their TP 53 status. The score was calculated in 18 HMCLs characterized by DGE-seq. TP53 sequencing was performed on cDNA and p53 expression was determined by western blotting. Statistical analyses were performed using the Kruskal-Wallis test with multiple comparisons. (D) The p53 score is functional in 1,105 cancer cell lines with different TP 53 status. The score was calculated in 1,105 cancer cell lines from DepMap and analyzed according to the presence of TP53 deletion and / or mutation (left panel) and to the number of TP53 deletion or mutation (right panel). Statistical analyses were performed using the Kruskal- Wallis test with multiple comparisons. **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05

[0080] Figure 2 : p53 score in HMCLs according to TP53 status and 14q32 translocation and in 1,105 cancer cell lines. (A & B) Expression profile was performed using microarray (Moreaux J, Haematologica 2011). Score was calculated with 12 probe sets because of the lack of APOBEC3H specific probe set. (A) HMCLs were segregated according to TP53 status, wildtype versus abnormal (mutation and lack of expression). (B) HMCLs were segregated according to myeloma genomic group MS - t(4; 14)-, MF - t(14; 16) or -t(14;20), CCND1 - t(ll;14)-, other and TP53 status (wildtype or abnormal, Moreaux J Haematologica 2011). Statistical analysis was performed using the Mann-Whitney test.

[0081] Figure 3 : The score is functional in 2 cohorts of patients. (A) The score discriminates patient samples according to TP53 status. The score was calculated in 38 patient samples 1 characterized by DGE-seq. Presence of dell7p was performed by FISH on CD 138+ myeloma cells. TP53 sequencing was performed on RT-PCR cDNA products from purified myeloma cells. VAF thresholds were set up at <-0.5 for deletion and >0.3 for mutation. Statistical significance was determined using the Kuskal-Wallis test **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05 (B) The score was calculated in 684 MMRF-coMMpass patient samples displaying or not deletion and / or mutation in TP53 characterized by RNASeq. VAF thresholds were set up at <-0.5 for deletion and >0.3 for mutation. Statistical significance was determined using the Kuskal-Wallis test **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05 (C) Overall survival of 764 MMRF-coMMpass patients according to p53 score. The 4 groups were determined using the K-means method, the score value increasing from Group 1 to Group 4. Statistical significance was determined using the Log-rank (Mantel-Cox) test. (D) Overall survival of 684 patients according to deletion and or mutation in TP53. Statistical significance was determined using the Log-rank (Mantel-Cox) test.

[0082] Figure 4 : BCL2 family expression profile of clones and HMCLs according to TP53 status. (A & B) BCL2 family profile in NCI-H929, XG7, JIM3 and NAN3 TP53+ / +, TP53 / mand TP53~Aclones, respectively (A) and 3 TP53+ / +HMCLs i.e., NAN9, NCI-H929, XG7, and 5 TP53 / mor TP53 HMCLs i.e., JIM3, LP1, NAN8, NAN3 and 0PM2 (B) was performed by western blotting. Graphs represent the quantification of BAX and BAK over actin. Statistical analyses were performed using the Wilcoxon paired-signed rank test in clones (A) and the Mann-Whitney test in HMCLs (B).

[0083] Figure 5 : TP53 loss impairs response to MCL1 BH3 mimetic S63845. (A) CRISPR / Cas9-mediated TP 53 inactivation increased LDso BH3 mimetic specific to MCL1 S63845 values. LDso S63845 values in doxy- (bulk transduced cells non exposed to doxycycline), TP53+ / +and TP53' ' clones in NCLH929 and XG7. Statistical analysis was performed using the Mann-Whitney test. (B) CRISPR / Cas9-mediated TP53 inactivation decreased priming to MCL1. Priming to MCL1 was assessed by assessing cytochrome C release induced by using MSI peptide. Cytochrome-C staining was performed using anti-cytochrome- C mAb and analyzed by flow cytometry. Graphs represent the mean ± SD of 3 to 4 independent experiments. Statistical analysis was performed using the Wilcoxon matched pairs signed-rank test. (C) LDso values for S63845 were significantly lower in TP53wtHMCLs. LD50 values for S63845, Venetoclax and A1155463 were analyzed in 11 TP53wtand 20 TP53AbnHMCLs. Each point represents one HMCL, median values are indicated. For Al 155463 and Venetoclax, LD50 was arbitrarily set up at 20,000 nM and 10,000 nM when it did not reach 10,000nM or 5,000 nM, respectively. Statistical analyses were performed using the Mann-Whitney test. **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05

[0084] Figure 6 : Lack of impact of TP53 silencing on global mitochondrial priming in NCI-H929 and XG7. Global mitochondrial priming was measured in isogenic NCI-H929 and XG7 TP53+ / +and TPSS ^ clones by cytochrome c release using BIM and BMF peptides. Graphs represent 3 to 4 independent experiments. Statistical analysis was performed using the Mann Whitney test.

[0085] Figure 7 : Role of BAK and BAX in the response to S63845. BAX , but not BAK, silencing inhibited S63845-induced cell death. XG7 TP53+ / +#1 cells were transiently transfected with siCont, siBAX, siBAK, or both, prior to treatment with increasing doses of S63845. The graph represents the mean ± SD of 3 independent experiments. Statistical analyses were performed using the unpaired t-test.

[0086] Figure 8 : BAX expression governs the response to MCL1 BH3-mimetic S63845. (A) BAX, but not BAK1, expression correlated with sensitivity to S63845 in NCI-H929 and XG7 clones. Expression of BAX and BAK1 (mean DGE-Seq values) were plotted against LDso S63845 values. Correlation was assessed using the Spearman test. (B) BAX, but not BAK1, expression correlated with sensitivity to S63845 in HMCLs. Expression of BAX and BAK1 (microarray) in 30 HMCLs was plotted against LDso S63845 value. Correlation was assessed using the Spearman test. (C) TP53 CRISPR / Cas9 increased MCL1 / BAK and decreased MCL1 / BAX complexes. For IP assays, cells were lysed in 1 % digitonin-containing buffer and lysates were pre-cleared with protein A conjugated to sepharose beads. For MCL1 IP assay, 0.700 mg of protein lysate was incubated overnight with an agarose-conjugated mAb anti- MCL1 from Santa Cruz Biotechnology. After immunoblotting, MCL1, BAK and BAX levels were determined in the immunoprecipitated fraction and BAX / MCL1 et BAK / MCL1 ratios were calculated to evaluate the abundance of both complexes. One experiment out of 3 is represented. The graph represents the quantities of BAX and BAK bound to MCL1 in TP53+ / +and TPSS ^ NCI-H929 and XG7 cells (3 independent experiments). Statistical analyses were performed using the Mann-Whitney test.

[0087] **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05

[0088] Figure 9 : CRISPR / Cas9-mediated TP53 inactivation impacts combination of S63845 and venetoclax. Combination of S63845 and venetoclax was synergistic in TP53+ / +and TP53 ' NCI-H929 and XG7 clones. Clones were cultured for 24h with increasing concentrations of S63845 and venetoclax clones as indicated in the figure. BLISS scores were calculated using synergyfmder.com. The figure represents the mean of 3 independent experiments. (A) Combination of S63845 and venetoclax was efficient in TP53+ / +(left parts) and TP53 ' (right parts) clones in both NCI-H929 and XG7 cell lines. The 12 clones were cultured for 24h with 10 nM (NCI-H929) or 20 nM (XG7) S63845 combined with 300 nM venetoclax (upper panel and lower left panel). CRISPR / Cas9-mediated BAX (right part), but not BAK (left part), inactivation inhibited responses to S63845 and venetoclax combination (lower panel, right). The 6 clones were cultured for 24h with 10 nM (NCI-H929) S63845 combined with 300 nM venetoclax. The graphs represent 3 independent experiments. Statistical analyses were performed using the Mann-Whitney or Wilcoxon matched pairs signed-rank tests. (B) Gain of lq21, but not dell7p, impacted response to S63845. Response to 25 nM S63845 or 300 nM venetoclax was assessed in 71 myeloma samples, characterized by FISH for both lq21 gain and dell7p. The graphs represent cell death induced by each BH3 mimetic in function of 1 q21 gain or dell7p. Statistical analyses were performed using the Mann-Whitney test. (C) Deletion of 17p had a trend to decrease the synergy between S63845 and venetoclax. The graphs represent cell death induced by 25 nM S63845 combined with 300 nM venetoclax depending on Iq or dell7p (left, middle panels), and the difference between observed cell death (25 nM S63845 combined with 300 nM venetoclax) minus expected cell death (sum of cell death induced by 25 nM S63845 and by 300 nM venetoclax) depending on dell7p. Cell death was assessed after 24h using flow cytometry. Statistical analyses were performed using the Mann-Whitney test. **** p<0.0001, *** p<0.001, ** p<0.01, * p<0.05

[0089] Figure 10 : Impact of t(ll;14) in the response to BH3 mimetics combination. (A) Death response in 42 MM samples without t(l 1 ; 14) to S63845, venetoclax or their combination in function of the presence of dell7p or lq21 gain. (B) Death response in 18 MM samples t(ll;41) to S63845, venetoclax or their combination in function of the presence of dell7p or 1 q21 gain. Statistical analysis was performed using the Mann Whitney test.

[0090] Figure 11 : ROC curves of p53 score specificity and sensitivity

[0091] Specificity and sensitivity of score thresholds were analyzed using ROC curves in the 1,105 cell lines from DepMap website and in the 684 patients from the MMRF-coMMpass cohort and. A,B: 13 -gene score in 1,105 cell lines and MMRF-coMMpass cohort, respectively. C,D: 8-gene score in in 1,105 cell lines and MMRF-coMMpass cohort, respectively.

[0092] Figure 12 : The 8- and 13-gene p53 scores have similar efficacy to identify cancer cells with TP53 hits

[0093] The statistical significance of the 13-gene (A) or 8-score (B) values on the overall survival was determined using the log-rank (Mantel-Cox) test in 764 patients in the MMRF-CoMMpass cohort (the number of patient group was established using Nbclust package in R and k-means method to assign patients to the group). The number of patients in each group is indicated over time (days).

[0094] EXAMPLES

[0095] Material & Methods

[0096] Human myeloma cell lines (HMCLs) and patients’ samples

[0097] Samples from patients with MM came from the MYRACLE cohort (NCT03807128) (Benaniba L et al., 2019, BMC Cancer). Isolation of mononuclear cells, myeloma cells, assessment of chromosomal Iq gain or 17p loss, cDNA TP53-sequencing and characterization of human myeloma cell lines (HMCLs) was reported previously (Gomez-Bougie P et al., 2018, Blood Tessoulin B et al., 2014, Blood Tessoulin B et al., 2018, J Hematol Oncol Moreaux J et al., 2011, Haematological & Maiga S et al., 2015, Cytometry A).

[0098] Reagents and antibodies

[0099] BH3 mimetics S63845, Venetoclax and A1155463 were purchased from Chemietek (Indiana Polis, ID, USA), and Selleck Chemicals (Houston, TX, USA). Anti-CD138-PE monoclonal antibody came from Beckman Coulter (Villepinte, France), and Annexin V-APC from Immunotools (Friesoythe, Germany). Anti-cytochrome-C mAb came from BD Bioscience (Le Pont de Claix, France). Anti-MCLl and anti-BCLXL Abs were purchased from Santa Cruz Biotechnology (Heidelberg, Germany). Anti-BAK mAb and anti-BAX pAb came from Cell Signaling (Ozyme, Saint-Cyr 1’Ecole, France), anti-BIM and anti-ACTIN from Millipore (Molsheim, France), anti-BCL2 from BD Transduction Laboratories (Le Pont de Claix, France), and anti-p53 was purchased from Oncogene Science (Life Technologies, Paris, France).

[0100] Generation of CRISPR / cas9 clones

[0101] NCI-H929, XG7, NAN3 and JIM3 HMCLs were stably transduced with lentivirus expressing mCherry-Cas9 (FUCas9Cherry gifted from Marco Her old- Addgene plasmid # 70182), prior to second infection with lentivirus containing GFP and doxycycline-inducible sgRNA (FgHltUTG gifted from Marco Herold- Addgene plasmid # 70183). The sgRNA sequences were (SEQ ID NO: 1 : 5'-AGATGGCCATGGCGCGGACG(CGG)-3') for TP53, (SEQ ID NO: 2 : 5’-GCCATGCTGGTAGACGTGTA-3’) for BAK1, and (SEQ ID NO: 3 : 5’- AGTAGAAAAGGGCGACAACC-3’) for BAX (Lopez J et al., 2016, Nat Commuri). GFP positive cells were sorted, sgRNA expression was induced with doxycycline and cells were cloned (0.3 cell / well). Control clones were obtained from mCherry+GFP+ cells (containing sgTP53) not treated with doxycycline. To increase proportion of TP53- / - clones, mCherry+ / GFP+ NCI-H929 and XG7 were additionally treated 48h with Nutlin3a (10 microM) prior to cloning. TP53- / -, BAK1- / - or BAX- / - clones were selected based on the lack of protein expression and gene sequencing.

[0102] Cell death and BH3 profiling assays

[0103] Cell death was assessed by Annexin V staining (HMCLs) or loss of CD138 staining (patient samples) using flow cytrometry (Gomez-Bougie P et al., 2018, Blood Tessoulin B et al., 2014, Blood & Surget S et al., 2012, Cancer Res). BH3 profiling was performed as previously reported (Dousset C et al., 2017, Br J Haematol).

[0104] RNAseq and scRNAseq

[0105] Genomic profiling of HMCLs, clones or purified myeloma cells was determined with RNA sequencing (3’-Digital gene expression sequencing, DGE-Seq, GSE245163). For scRNASeq, MM bone marrow mononuclear cells were cultured overnight in RPMI1640, containing 7%FCS and 3 ng / ml IL6, with or without a combination of BH3 mimetics prior to scRNASeq analysis (10X, Chromium). Bulk RNA-seq, DGE-Seq or GEP p53 score was calculated via a rank-based gene set scoring method with the singscore package in R (Foroutan M et al., 2018, BMC Bioinformatics). scRNASeq p53 score was calculated using the AddModule Score function of Seurat package in R.

[0106] Survival analysis

[0107] Time-to-event survival curves were estimated with the use of the Kaplan-Meier method. Patients were divided into four groups based on the signature score (NbClust package and K- means methods). Probabilities of OS were compared in groups using the log-rank test and Cox proportional-hazard regression (Prism, GraphPad software).

[0108] Whole transcriptome sequencing

[0109] 3 ’seq-RNA Profiling protocol (DGE-Seq) was performed according to Charpentier et al (Charpentier E et al., 2021, Protocole Exchange). The mRNA poly(A) tails were tagged with universal adapters, well-specific barcodes and unique molecular identifiers (UMIs) during template-switching reverse transcriptase. Barcoded cDNAs from multiple samples were then pooled, amplified and tagmented using a transposon-fragmentation approach, which enriches for 3 'ends of cDNA. A library of 350-800 bp length was run on Illumina NovaSeq 6000 using NovaSeq 6000 SP Reagent Kit 100 cycles (ref #20027464). Raw fastq pairs used for analysis matched the following criteria: the 16 bases of the first read corresponded to 6 bases for a designed well-specific barcode and 10 bases for a unique molecular identifier (UMI). The second read (58 bases) corresponded to the captured poly(A) RNAs sequence. We performed demultiplexing of these fastq pairs according to the sample sheet to generate one single-end fastq for all samples. These fastq files were then aligned with bwa to the reference mRNA sequences and the mitochondrial genomic sequence, both available on UCSC. DGE profiles were generated by parsing the alignment files (.bam) and counting for each sample the number of unique UMIs associated with each RefSeq genes. Reads aligned on multiple genes, containing more than one mismatch with the reference sequence or reads containing a poly A pattern were discarded. Finally, a matrix containing the expression of all genes on all samples was produced. The absolute abundance of mRNAs expression values was used for determining gene expression analysis. Normalization was performed by using DESeq2, normalized counts were transformed with vst (variance stabilized transformation) function from DESeq library. Differentially expressed genes were identified using edgeR.

[0110] Single-cell RNA-sequencing

[0111] Raw sequence filtering, normalization, and clustering

[0112] Control and BH3 mimetics treated bone marrow mononuclear cells were successively depleted of dead cells (dead cell removal kit) and erythroid cells (CD235a-conjugated beads) by immunomagnetic sorting (Miltenyi Biotec, Paris, France). A total of 15,000 cells (with a viability superior to 80%) were loaded per lane on the 10X Chromium device and processed for complementary DNA synthesis and library preparation according to manufacturer’s protocol using 3’ v2 / v3 or v3.1 chemistry (10X Genomics, Paris, France). Libraries were sequenced on NovaSeq 6000 to a mean depth of 45,000 reads / cell as paired-end, 28bp Read 1, 90bp Read 2 (GenoA, UMS Biocore, SFR Bonamy, Nantes). Sample demultiplexing and genome alignment were performed using the Cell Ranger Software Suite (v3.0.2, 10X Genomics) and refdata- cellranger-GRCh38 3.0.0, respectively. Data analysis was performed using the R package Seurat (v.4.3.0). For each sample, the SoupX (version 1.5.2) package was used to remove cell- free mRNA contamination. Cells that have a total count of UMI below 1000, less than 600 expressed genes, or with high percentage of mitochondrial reads (three standard deviation above the median) which were mainly cells entering apoptosis were removed. An additional quality control step was performed to remove potential doublets, by using scds (v.1.6.0) and scDblFinder (v.1.4.0) R package. Myeloma cells were identified by positive and negative selections. Normal bone marrow cells were identified and excluded (singleR v.1.4.1, downsampled late 2020 version of the Single-Cell Tumor Immune Atlas, TICAtlas_downsampled_1000.rds, available at https: / / doi.org / 10.5281 / zenodo.4263972) and cells having a plasma cell profile (with 5% or more UMI mapping to immunoglobin genes) were selected. In order to exclude normal remaining cells, we calculated two signature score per cell based on gene expression using the AddModule Score function (T / NK signature : IL32, CD69, CCL5, CD52, BTG1, S100A4, RPS27, TMSB10, RPL31, ACTB, KLRB1, GZMK, CD3D, IL7R, NKG7, and a light chain signature : IGKC, IGLC1, IGLC2, IGLC3, IGLC4, IGLC5, IGLC6, IGLC7): thresholds were determined to exclude these cells (two MAD above the median for NK / T signature, and three MAD below the median for the light chain signature). To exclude remaining normal plasma cells, we merged control and BH3 mimetics treated samples with plasma cells from normal bone marrow samples (merge function) and excluded “myeloma cells” that clustered with plasma cells. Then we identified highly variable genes (FindVariableFeature, nfeatures=2000, excluding immunoglobulin genes), and scaled the expression matrix with ScaleData function. Principal component analysis (PC A) was performed using the RunPC A function. We used the first 30 principal components to perform unsupervised clustering analysis. We excluded cells clustering with normal cells for a resolution of 0.05, and manual curation was performed to exclude remaining cells having passed the lineage negative selection. The copy number variation profiles were inferred from scRNA-seq data, using inferCNV of the Trinity CTAT Project, https: / / github.com / broadinstitute / inferCNV (v.1.9.1, default parameters for lOx Genomics data, cluster_by_groups=TRUE) and compared to plasma cells from normal bone marrow samples. Following individual sample analysis, we performed a dataset merge to all the samples into a single dataset. We identified genes with significant variation across the dataset (FindVariableFeature, nfeatures=2000, excluding immunoglobulin genes) and a PCA analysis was performed on the 30 principal components for unsupervised clustering analysis. Samples were classified according to the myeloma molecular classification established by Zhan et al : a score was calculated for each cell based on the gene expression list for each molecular subgroup..

[0113] 13 -gene 53 score

[0114] Bulk RNA-seq, DGESeq and GEP p53 scores were calculated with singscore package in R. scRNASeq p53 score was calculated using the AddModule Score function in Seurat package in R. ROC curves and AUC (aera under the curve) were calculated in two sets of data (cell lines and patient samples) using R package to determine thresholds with best sensitivity / sensitivity according to the total number of annotated genes, pseudo genes , noncoding RNA .

[0115] 8-gene 53 score

[0116] A functional 8-gene p53 score, based on the ranked expression of 8 p53-target genes i.e., APOBEC3H, BAX, CDKN1A, DDB2, EDA2R, MDM2, PHLDA3, and RPS27L, was calculated using the rank-based gene set scoring method with the singscore package in R, as previously described, with minor adaptations (the 8-gene score was adapted from the 13 -gene score).

[0117] EXAMPLE 1

[0118] Results

[0119] Silencing TP53 identifies shared p53-regulated genes that provide a functional p53 score

[0120] To assess direct p53 impact in myeloma cells, TP53~ ~ cells were derived after CRISPR / Cas9 genome editing from t(4; 14) HMCL, 2 TP53+'+NCI-H929 and XG7 and 2 TP 53' / mutJIM3 TP53~m^cand NAN3 TP5Tm). Three control (77G3 # I ,2,3 or 7F5J- / mut#l,2,3) and 3 TPSS ^ clones (7P53_ / ’#1,2,3) were selected for each cell line (Data not shown). Including TP53, 84 and 77 genes were differentially expressed between TP53+ +and TPSS ^ clones in NCI-H929 and XG7, respectively (FDR<0.05, Data not shown): 56% and 68% of these genes have been characterized elsewhere as p53-regulated genes (Fischer M et al., 2017, Oncogene) i.e., early-direct or late p53 targets, with or without proximal p53 binding, in response to Nutlin3a in cell lines (Andrysik Z et al., 2017, Genome Res), (Fig. 1A). Several genes were differentially expressed between TP53'lrmAand TP53 ' cells, but none, excepting TP53 itself, was shared between the HMCLs. We next focused on the genes whose regulation was shared between the two TP53+l+HMCLs: 16 genes (including TP 53) were downregulated in TP 53 ~ clones and 1 was upregulated, (Data not shown). We selected 13 genes with decreased expression in TP53 ' clones and known as “early direct” for creating a functional p53 score (Data not shown) (Foroutan M et al., 2018, BMC Bioinformatics). Notably, only five of these genes were found differentially expressed between 16 TP 53'"1and 27 Z 53abnormalHMCLs using microarray profiling (Data not shown). The 13 genes are located on 12 different chromosome arms, excluding direct impact of myeloma-recurrent chromosomal abnormalities on expression (Data not shown). The score discriminated TP53~ ~ and TP53+ +clones from NCI-H929 and XG7 but not TP53" and TP53'm[Acells from JIM3 and NAN3 (Fig. IB). We also characterized the genes differentially expressed between TP53+ +and TP53~ ~ clones after Nutlin3a treatment: excluding genes with Nutlin3a-induced modification of expression in TP53~ ~ clones (i.e., MDM2-dependent and p53-independent regulation), 825 genes (397 up, 428 down in TP53+ +versus TP53~ ~ cells) were commonly regulated in NCI-H929 and XG7, including the 16 downregulated genes (Data not shown). As expected, Nutlin3a increased p53 score only in TP53+ +clones (Fig. IB). The score did not discriminate 8 HMCLs expressing a mutated p53 protein from 4 expressing no p53 protein, underscoring that it identifies p53 loss-of-function whatever its origin (Fig. 1C). In HMCLs profiled using microarray, the score segregated 16 TP53'''1from 27 7P53abnormalHMCLs (p<0.0001), whatever their myeloma-specific 14q32 translocation background (Fig. 2A, Fig. 2B). Moreover, in 1, 105 cancer cell lines, the score was significantly associated with TP53 deletion, mutation or both (Fig. ID), cell lines with biallelic hits having the lowest score (Fig. ID & Data not shown), whatever the cell ontology and the gender (Data not shown). A ROC analysis of the score distribution in these 1,105 cell lines (the total number of annotated genes, pseudo genes, non-coding RNA was about 20K) identified a threshold of 0.2 that segregated cell lines according to functionality of p53 pathway i.e., normal versus abnormal (Fig. 11 A).

[0121] The 13-gene functional p53 score is prognostic for myeloma patients’ survival

[0122] In 38 samples from MYRACLE patients characterized for both dell7p and TP 53 mutation, the p53 score significantly discriminated samples with dell7p and TP 53 mutation (n=8) from samples with dell7p only (n=6, p=0.0183), and from samples without hits (n=24, p=0.0003, Fig. 3A & Data not shown). In 684 samples from MMRF-CoMMpass patients with known TP53 status, 19 displayed deletion and mutation, 34 deletion and 5 mutation (deletion or mutation was considered when LOH or VAF was inferior to -0.5 or superior to 0.3, respectively). Samples with dell7p and mutation had the lowest score (p<0.0001) and a lower score than samples with only dell7p (p=0.0002, Fig. 3B). Since TP53 hits are known to impair overall survival (Corre J et al., 2021, Blood), the p53 score was calculated in 764 patients at diagnosis from the MMRF-CoMMpass cohort , with RNAseq expression at baseline and overall survival. K-means method segregated patient samples into 4 groups (Data not shown). Overall survival analysis showed that the group 1 with the lowest values had significantly reduced overall survival, p=0.015, (Fig. 3 C. Noteworthy, 90% of samples with double hit were in groups 1 (53%) and 2 (37%) while samples with dell7p only were more equally distributed among the 4 groups (p=0.003, Data not shown). Samples with TP53 hit(s) constituted 74% of group 1 and only 5% to 10% of groups 2,3 and 4, respectively (Data not shown). Patients with TP53 mutation and dell7p, and not with dell7p only, had lower overall survival, the median was 870 days versus not reached in the other groups (Fig. 3D, p=0.004). A ROC analysis of the score distribution in this set of 684 patients (the total number of annotated genes, pseudo genes, noncoding RNA was about 39K) identified a threshold of 0.36 that segregated samples according to functionality of p53 pathway i.e., normal versus abnormal (Fig. 11B). These results collectively showed that this score identified samples with double hit in TP53 and provided prognosis in two independent cohorts. We further compared this score to another p53 functional score, used for the clinical interpretation of germline TP53 variants in Li-Fraumeni syndrome, that assesses the increased expression of 10 p53 target genes (none shared with our 13-gene score, Data not shown) in doxorubicin-treated PBMCs. This score did not distinguish XG7 or NCI-H929 TP53'1' clones from TP53+ +clones in the absence of nutlin3a (Data not shown). It also didn’t segregate cell lines or patient samples according to their TP53 status (Data not shown). A 8-gene score (based on the expression of APOBEC3H, BAX, CDKN1A, DDB2, EDA2R MDM2, PHLDA3, RPS27L) derived from the 13-gene was even more efficient than the 13-gene score in both the 1,105 cell lines and the 684 patients (Fig 11C & Fig 11D, respectively).

[0123] TP53-silencing impairs mitochondrial MCL1 priming

[0124] As expected, p53-silencing in TP53+ +cells, but not in TP53~mMcells, increased resistance to melphalan (LDso values increased from 11.3 to 16.6 microM, p=0.0011 and from 18.7 to 28.3 microM, p=0.004 in NCI-H929 and XG7, respectively), and to Nutlin3a (LDso values were around 2 mM in TP53+ +clones, while no cell death was induced at 10 microM in TP53~ ~ clones (Data not shown) (Surget. S et al., 2014, BMC Cancer & Surget S et al., 2014, Leak Lymphoma) . The 13 genes involved in the p53 score are known to be involved in the cell cycle, DNA duplication and repair (CDKN1A, DDB2, RRM2B), defense against viruses or ROS (APOBEC3C, APOBEC3H, RPS27L, TIGAR), apoptosis and mitochondrial metabolism (BAX, TNFRSF10B, FDXR), signaling pathways (PHLDA3, EDA2R), and in p53 regulation (MDM2). Given the major role of BAX in apoptosis, we focused on the impact of its expression on mitochondrial fitness. We confirmed that BAX expression was decreased in TPSS ^ clones derived from TP53+ +and unchanged in those derived from TP53~ / mutHMCLs (Fig. 4A). As previously reported, TP53~ / mutHMCLs also under expressed BAX when compared to TP53vHMCLs (Fig. 4B) (Moreaux J et al., 2011, Haematologica Surget S et al., 2012, Cancer Res & Surget. S et al., 2014, BMC Cancer). Noteworthy, BAX was the only BCL2-family protein displaying modified expression in both NCI-H929 and XG7 TPSS ^ clones. To assess the mitochondrial impact of p53 loss, we used BH3 mimetics specific to MCL1 (S63845), BCL2 (venetoclax) and BCLXL (Al 155463), and performed BH3 profiling (Gomez-Bougie P et al., 2018, Blood & Seiller C et al., 2020, Cell Death Dis). Silencing p53wlin NCI-H929 and XG7 (but not p53mutin JIM3 and NAN3) induced a resistance to S63845: LDso mean values in clones increased from 6 to 40 nM (p=0.0002) and from 29 to 138nM, (p<0.0001), in NCI-H929 and XG7, respectively (Fig. 5A). Impact of p53 on response to venetoclax or A1155463 was not assessed, cell lines being resistant to both. Global mitochondrial priming, i.e., response to BIM or BMF peptides, was unchanged for both NCI-H929 and XG7 TP53'1' clones (Fig. 6), whereas cytochrome-C release in response to MSI peptide specific to MCL1 was significantly decreased in both NCI-H929 and XG7 TP53~l~ clones (Fig. 5B), in keeping with reduced sensitivity to S63845. In 31 HMCLs, S63845, but not venetoclax or A1155463, LDso values were also significantly lower in 11 TP53"' HMCLs compared to 20 TP53AbnHMCLs (median value was 12.5 nM versus 77.50 nM, p=0.026, Fig. 5C). These data collectively showed that TP53 status strongly impacted priming to MCL1 in HMCLs.

[0125] BAX expression controls response to MCLl-specific BH3 mimetic

[0126] To determine the respective role of BAX and BAK in the response to S63845, we transiently silenced BAK1. BAX or both in TP53+ +and TP53~ ~ XG7 cells (Figure 7). By contrast to BAK1 silencing that did not significantly impaired response to S63845, BAX silencing (with or without BAK1 silencing) strongly inhibited cell death: death inhibition was 87%±5% cells treated with 25 nM S63845, respectively. CRISPR / Cas9-mediated inactivation of BAX or BAK1 in NCLH929 cells confirmed that BAX, but not BAK, expression was essential for response to low doses of S63845 (Figure 9A low right panel): compared with control clones, / MA inactivation induced 10-fold increase in LDso (mean values 64 nM versus 6nM, p<0.0001) whereas BAK1 inactivation did not induce significant LDso changes (6.7 nM versus 6nM). Of note, BAX'1' clones appeared slightly more resistant to S63845 than TP53~ ~ clones (mean values 64nM versus 40nM, p=0.015), the latter having heterogeneous LDso values and BAX expression (Fig. 4). We next assessed the impact of BAX expression on S63845 sensitivity in myeloma cells. BAX expression inversely correlated with S63845 LDso values in TP53+ +and TP53~ ~ clones (r=-0.8521, p=0.0009, Spearman test), while BAK1 expression demonstrated no significant correlation (Fig. 8A). In 30 HMCLs, S63845 LDso values also displayed an inverse correlation with BAX expression (r=-0.5011, p=0.0048), but not with BAK1 (p=0.6, Fig. 8B). None other BCL2 family members displayed significant correlation with S63845 LDso values in both clones and HMCLs (Data not shown). These results prompted us to directly assess the amount of MCL1 complexed with BAX and BAK. MCL1 immunoprecipitation showed that TP53~ ~ clones displayed strong decreased amount of MCL1 / BAX complexes (median reduction 3.07, p=0.008) and slight increased amount of MCL1 / BAK (median increase 1.32, p=0.026) when compared to TP53+ +clones (Fig. 8C). S63845 induced the disruption of MCL1-BAX and MCL1-BAK complexes, and BAX-MCL1 complexes were disrupted at lower S63845 concentrations than BAK-MCL1 complexes (Data not shown). These data showed that p53 loss reduced the amount of MCL1-BAX complexes.

[0127] TP53 or BAX silencing impaired response to BH3 mimetics combination

[0128] Because combination of the BH3 mimetics S63845 with venetoclax was reported to be synergistic in myeloma cells, we wondered how p53 loss would impact its efficacy (Seiller C et al., 2020, Cell Death Dis'). Combination was indeed synergistic at high doses in TP53+I+and TP53" clones, BLISS score was 11.7=1=2.1 and 16.3=1=1.9 in TP53+ / +#1 and TPS^#! NCI-H929 clones and 13.6±6.1 and 14.6±2.8 in TP53+ / +#1 and TP53''#! XG7 clones, respectively (Data not shown). However, at the same doses, maximum cell death induced by combination was significantly lower in TP53~ ~ clones versus TP53+ +clones (mean values 59% ±15% versus 79%±3.2% in NCI-H929, p<0.0001, and 28%±6.5% versus 61%±11% in XG7 cells, p<0.01, Fig. 9A). Noteworthy, combination was more efficient in NCI-H929 cells than in XG7 cells, which have very low BCL2 expression, Fig. 4. Finally, BAX but not BAK1 invalidation in NCI- H929 cells, inhibited the response to combination, confirming the major role of BAX in BH3 mimetics response (Fig. 9A). We next assessed death response to S63845, venetoclax and their combination in myeloma cells from 71 consecutive patients at diagnosis, progression or relapse. Median values of cell death induced by 25 nM S63845, 300 nM venetoclax or combo were 21%, 18% and 81%, respectively (Data not shown). Presence of Iq gain positively impacted response to S63845 (cell death was 13% in 33 no Iq samples versus 28.5% in 38 Iq samples, p=0.0152), as previously reported, but did not impact response to venetoclax or to the combination, and presence of dell7p did not impact responses to BH3 mimetics (Fig. 9B & Fig. 9C) (Slomp A et al., 2019, Blood Adv & Ziccheddu B et al., 2021, Clin Cancer Res). Whatever the presence of 1 q21 or dell7p, combination was effective in 97% of the samples i.e., additive (-10%< observed death minus expected death <10%) in 10 samples (14%) and supra additive to synergistic (observed death minus expected death> 10%) in 59 samples (83%). Supra additivity to synergy of combination was mainly found in samples without t(ll;14), t(l 1 ; 14) samples having a high response rate to venetoclax alone (Fig. 10A & Fig. 10B). As observed in NCI-H929 and XG7 clones, combination had a trend to be less efficient in the dell7p samples when compared to the no dell7p samples: median of observed minus expected value was 11% (range -10 to 55) versus 23.5% (range -14 to 85), respectively, p=0.09, Fig. 9C. Of note, the dell7p impact on BH3 combination efficacy was rather restricted to samples without t(l 1; 14), median of observed minus expected value was 13% (range -10 to 55) in 10 samples dell7p versus 34% (range -12 to 85) in 32 samples with no dell7p, p=0.0457, Fig. 10A.

[0129] Myeloma cells resistant to BH3 mimetics displayed a lower p53 score at single-cell level

[0130] Given the p53 impact on the response to BH3 mimetics and the heterogeneity of myeloma cells in patients, we used scRNAseq to determine whether cells surviving to BH3 mimetics would have a lower p53 score. Bone marrow (n=23) or pleural effusion (n=l) mononuclear cells from 24 patients (Data not shown) were cultured overnight with or without BH3 mimetics combination, and 15,000 living cells were separated and profiled using lOXChromium. Myeloma cells were identified as described in Supplemental methods section. Figures show the 83,191 myeloma cells from the 24 control (55,114 control cells) and BH3- mimetics treated samples (28,077 treated cells) and their molecular classification: 21 samples were classified as CD-I, CD-2, HY, LB, MF, MS and samples belonging to the same molecular subgroup were grouped together (Data not shown) (Zhan F et al., 2006, Blood). The median myeloma number analyzed per sample was 2,284 cells and 643 cells in control and BH3 mimetics-treated conditions, respectively (Data not shown). In 15 of 24 samples, proportion of cells in paired clusters was significantly modified upon BH3 mimetics treatment (data not shown). Dell7p status of clusters was determined using InferCNV, clusters with dell7p being shown in red (Data not shown). The p53 score median value in clusters was compared between paired control and treated samples (when cluster contained at least 10 cells in both conditions): in BH3 mimetics treated samples, the score was unchanged in 51 clusters (66.2%), inferior in 25 clusters (32.5%), and superior in 1 cluster (1.3%). Change in score was not similar in clusters with or without dell7p (p=0.035, Chi-2 test), i.e., it decreased in 35.4 % of clusters with no dell7p (23 out of 65) versus 16.7% of clusters with dell7p (2 out of 12) (Data not shown). The p53 score value was compared between control and treated cells: the p53 score was 3.13-fold inferior in treated cells versus control cells (median values were -0.009612 and -0.03004, respectively p<0.0001 (Data not shown). Discussion

[0131] In our work, we performed CRISPR / Cas9 7 / <53-silencing to study the direct genomic and functional impact of p53 removal in myeloma cells. Given the myeloma genomic heterogeneity, we chose 2 HMCLs belonging to the same subgroup, the t(4; 14) group. Using RNAseq, 17 genes were significantly regulated upon p53 silencing in both TP53+ +HMCLs, 16 down (including TP53) and 1 up. Using microarray, only 5 genes were under expressedMDM2, CDKN1A, DDB2, FDXR, and PHLDA3) and 1 overexpressed (CDKN2A) in TP 53^" HMCLs when compared with TP53vHMCLs: by contrast to the 5 under expressed genes, CDKN2A expression was not modified in isogenic clones. CDKN2A has never been reported as regulated by p53 and its overexpression in TPSS^ HMCLs is, in fact, related to CDKN2A / ARF locus loss in some TP53vHMCLs (Tessoulin B et al., 2018, J Hematol Oncol), which is a common hit in TP53"Acancer cells. This difference illustrates the major interest of isogenic cells for providing a specific p53 transcriptome. As expected, none of the genes impacted by p53-silencing in both NCI-H929 and XG7 were found modified in JIM3 or NAN3 TP53' ' clones, excepting TP53, showing that R248C or R273Q mutation had no residual p53 transcriptomic activity. Although the expression of several genes was found modified in either JIM3 or NAN3 TP53~ ~ clones, the modest changes instead suggested non-specific regulation. To establish a functional p53 score, we kept the 13 under expressed genes in both NCI-H929 and XG7 TP53'1' clones, known as p53 target genes: these genes were identified as early- transactivated upon p53 activation, highlighting their high sensitivity to the amount of p53 (Andrysik Z et al., 2017, Genome Res)' . This score segregated TIG 3 from TP53' ' or TP53'mcells or HMCLs, underscoring its efficacy for identifying p53 loss-of-function whatever its origin i.e., lack of expression or mutation, and whatever the genomic background. In 1,105 cancer cell lines, the score also distinguished cells with mono- or bi-allelic TP53 hit , deletion and / or mutation, from cells without any TP53 abnormalities. Monoallelic deletion or mutation was associated with a low score, and cells displaying biallelic hit had an even lower score. In patient cells, samples with low score were mostly dell7p and TP 53 mutated in MYRACLE and MMRF-coMMpass. Indeed, in MYRACLE and MMRF-coMMpass cohorts, TP 53 deletion alone had only a trend to impact the score (p=0.13 and p=0.18 in MYRACLE and MMRF- coMMpass, respectively). This discrepancy between patient cells and cell lines might be explained by the low number of dell7p samples in patient cohorts and / or by the clonal proportion (100% of cells in cell lines but not in patient samples) or by the bi- versus monoallelic deletion, the bi-allelic deletion being more frequent in cell lines than in patient cells. Thus, this functional p53 score is highly efficient in identifying samples with double hit both in cell lines and patient samples. Although, the score will not identify samples with TP53 hit in minor subpopulations by contrast to FISH or NGS, it could define the functional impact of mutation. Nevertheless, the score also identified few samples lacking any TP53 hit suggesting the existence of other p53 pathway dysregulations that merit further investigation. Of interest, the score was also heterogeneous at single cell level, suggesting that it identified cells with low to high activation of p53 pathway. Thus, this functional p53 score is highly efficient in identifying samples with double hits both in cell lines and patient samples. Interestingly, the score was predictive of overall survival in two cohorts of patient: it indeed identified 3 to 5% of patients with a low p53 score which is consistent with the low occurrence with mutation / bi- allelic hit in TP53 gene (Ansari -Pour N et al., 2023, Blood & Chng W.J et al., 2007, Leukemia). In the MMRF-coMMpass cohort, which is like MYRACLE a real-life cohort, only dell7p and mutation was predictive of overall survival and p53 score indeed identified patients with double-hit (53% of double-hit samples were in group 1). By contrast, deletion alone was not associated with significant lower score in good agreement with the lack of significance on overall survival when deletion and mutation were separately considered. The p53 score involves a combination of genes that has not been used in other p53 score either derived from shTP53 HMCLs or from MMRF-CoMMpass study (Ziccheddu B et al., 2021, Clin Cancer Res Teoh P.J et al., 2014, Leukemia & De Ramon C et al., 2022, Br J Haematol). Our p53 functional score did also not share any gene with the “Li-Fraumeni” functional p53 score that was unable to discriminate 1,105 cell lines, 12 clones and 38 patient samples according to their respective TP53 status. As expected, TP53 silencing in XG7 and NCI-H929 HMCLs increased resistance to melphalan and Nutlin3a. We didn’t assess functional impact of one allele deletion in isogenic cell lines because both alleles were inactivated in all clones. However, Munawar and collaborators previously demonstrated that the loss of one allele significantly impaired drug sensitivity in isogenic AM01 cells (Munawar U et al., 2019, Sci Rep). To investigate the functional impact of p53-dependent BAX decrease on CRISPR / Cas9 TPSS^, we focused on mitochondrial fitness. Regarding BCL2-family proteins, only the expression of BAX was found significantly modified in TPSS ^ clones: expression of two other known p53 target genes i.e., PMAIP1 (NOXA) and BBC3 (PUMA), was not decreased in TP53'1' clones in the absence of stress. Indeed, transactivation of these genes requires a higher amount of p53, as illustrated by their activation by nutlin3a that induced p53 accumulation (Andrysik Z et al., 2017, Genome Res Surget S et al., 2014, BMC Cancer & Surget S et al., 2014, Leuk Lymphoma). Although BAX and BAK are considered as redundant, only BAX expression is directly regulated by p53. To decipher its role we used BH3 mimetics that directly target mitochondria without inducing genomic or signaling stress, in contrast to DNA-damaging drugs like melphalan. In MM, sensitivity to BH3 mimetics is primarily associated with genomic heterogeneity, t(l 1 ; 14) cells being quite sensitive to BCL2 BH3 mimetics, t(4; 14) to MCL1 BH3 mimetics (Gomez-Bougie P et al., 2018, Blood). NCI-H929 and XG7 were sensitive to S63845 but resistant to venetoclax, and removal of p53 induced about 5-fold increase in S63845 LDso values. Transient or permanent silencing of BAX or BAK1 in XG7 or NCI-H929 demonstrated that BAX (not BAK) expression was essential for the death response to low doses of S63845, whatever the level of BAK expression. These results clearly demonstrated that BAX decrease in TP53~ ~ clones greatly impacted mitochondrial fitness, in spite of a slight increase in the amount of MCL1 / BAK complexes: indeed MCL1 IP assays showed that the amount of MCL1-BAX was greatly decreased in TP53'1' clones, and that low concentrations of S63845 totally disrupted MCL1-BAX, and partly MCL1-BAK complexes. Although BAX and BAK affinity for MCL1 are quite similar, BAK has been reported to display a higher affinity than BAX that could favor the release of BAX under low concentrations of S63845 (Chonghaile T.N et al., 2011, Science & Kale J et al., 2018, Cell Death Differ). These results showed that BAK is not requisite for a response to low or intermediate doses of S63845. Moreover, the impact of p53 and BAX (but not BAK) on MCL1 -sensitivity was also found in HMCLs: S63845 LDso values were correlated with TP 53 status and with BAX expression level. Overall, these results show that BAK and BAX are not completely redundant, and that p53-induced BAX expression is a limiting factor in mitochondrial apoptosis. Most TP53"' HMCLs being resistant to BCL2 or BCLXL inhibitors, p53 / BAX impact on other BH3 mimetics response could not be assessed in myeloma. Nonetheless, in other hematological malignancies loss of TP53 was also reported as impairing response to BCL2 or MCL1 BH3 mimetic (Thijssen R et al., 2021, Blood Liu T et al., 2021, Clin Cancer Res & Carter B.Z et al., 2023, Blood Cancer J). In CLL, efficacy of venetoclax with obinutuzumab was efficient but reported inferior in patients with TP53 hit compared with patients without TP53 hit (Brem E.A et al., 2022, Blood Adv). Although of BCL2 and MCL1 BH3 mimetics combination was more efficient in TP53+ +clones, it overcame the resistance induced by p53 loss in both NCI-H929 and XG7 TP53~ ~ clones and was highly efficient in 69% dell7p patient samples (9 of 13). At single cell level, we showed in 24 paired control and treated samples that the 13-gene p53 score was heterogeneous: cells surviving to BH3 mimetics combination in vitro mainly displayed either a decreased or unchanged p53 score, suggesting again that cells with high p53 score were more sensitive to BH3 mimetics. In summary, we established a functional p53 score that identifies myeloma cells with biallelic TP53 invalidation, demonstrated that p53-regulated BAX is critical for optimal cell response to BH3 mimetics, and showed that BH3 mimetic combination is efficient in vitro for patients with biallelic TP53 invalidation, for whom there is still an unmet medical need.

[0132] EXAMPLE 2

[0133] The 8-gene functional p53 score is prognostic for myeloma patients’ survival

[0134] The inventors show that a functional 8-gene p53 score derived from the 13-gene score can be used for assessing the overall survival in patients in the MMRF-CoMMpass cohort (Fig. 12). Therefore, the 8-gene functional score can be used for predicting the survival time and / or the disease severity in a subject suffering from disease associated with a loss of p53 function, in particular Multiple Myeloma cancer and / or a hematological cancer.

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Claims

CLAIMS1. A method for predicting the survival time and / or the disease severity in a subject suffering from a disease associated with a loss of p53 function comprising i) determining in a sample obtained from the patient the expression level of at least one gene selected in the group consisting in BAX, MDM2, CDKN1 A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR, ii) comparing said expression level determined at step i) with a predetermined reference value and iii) concluding that the method provides a good prognostic when the level of gene expression is higher than the predetermined reference value, or provides a bad prognostic when the level of gene expression is lower than the predetermined reference value.

2. A method for stratifying patients suffering from a disease associated with a loss of p53 function comprising i) determining in a sample obtained from the patient the expression level of at least one gene selected in the group consisting in BAX, MDM2, CDKN1 A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR, ii) comparing said expression level determined at step i) with a predetermined reference value and iii) concluding that the method provides a low level of gravity when the level of gene expression is higher than the predetermined reference value, or provides a high level of gravity when the level of gene expression is lower than the predetermined reference value.

3. The method according to claims 1 to 2 wherein the step i) of the method comprises determining in sample obtained from the patient the expression level at least 8 genes among the group consisting in BAX, MDM2, CDKN1 A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

4. The method according to claims 1 to 2 wherein the step i) of the method comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3 and EDA2R.

5. The method according to claims 1 to 2 wherein the step i) of the method comprises determining in sample obtained from the patient the expression level of the genesselected in the group consisting in BAX, MDM2, CDKN1A, DDB2, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

6. The method according to claims 1 to 2 wherein the step i) of the method comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B and TIGAR.

7. A method for obtaining a score comprising i) determining reference values by calculating gene expression level in wild type condition of all genes belonging to the genome, ii) determining in a sample obtained from the patient the expression level of at least one gene selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR, iii) obtaining a score with an algorithmic method, iv) comparing said score obtained at the step iii) with the reference value calculated at the step i) wherein a score higher than a reference value indicates a good prognostic, a low level of gravity and / or a good monitoring; and a score lower than a reference value indicates a bad prognostic, a high level of gravity and / or a bad monitoring.

8. The method according to claim 7 wherein the step ii) of the method comprises determining in sample obtained from the patient the expression level of 8 genes among the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3, EDA2R, TNFRSF10B, FDXR, RRM2B, APOBEC3C and TIGAR.

9. The method according to claim 7 wherein the step ii) of the method comprises determining in sample obtained from the patient the expression level of the genes selected in the group consisting in BAX, MDM2, CDKN1A, DDB2, APOBEC3H, RPS27L, PHLDA3 and EDA2R.

10. The method according to claims 7 to 9 wherein a score lower than 0,36 indicates a good prognostic, a low level of gravity and / or a good monitoring; and a score lower than a reference value indicates a bad prognostic, a high level of gravity and / or a bad monitoring.

11. The method according to claims 1 to 10 wherein the gene expression is calculated from a cell set or a single-cell sample.

12. Amethod for treating a subject with a bad prognostic assessed by the method according to claims 1 to 11 comprising the administration to said subject at least one active agent.

13. A pharmaceutical composition for treating diseases associated with a loss of p53 function in a subject with a bad prognostic assessed by the method according to claims I to 11.

14. The method or the pharmaceutical composition for use according to claims 1 to 13 wherein the disease associated with a loss of p53 function is a Multiple Myeloma cancer and / or a hematological cancer.

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