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WO2020144335A1 - Procédé pour prédire le besoin de thérapie pour des patients atteints d'un cancer - Google Patents

Procédé pour prédire le besoin de thérapie pour des patients atteints d'un cancer Download PDF

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WO2020144335A1
WO2020144335A1 PCT/EP2020/050553 EP2020050553W WO2020144335A1 WO 2020144335 A1 WO2020144335 A1 WO 2020144335A1 EP 2020050553 W EP2020050553 W EP 2020050553W WO 2020144335 A1 WO2020144335 A1 WO 2020144335A1
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Prior art keywords
therapy
markers
patient
cancer
cll
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Inventor
Pauline GONNORD
Salvatore VALITUTTI
Loïc YSEBAERT
Sébastien GADAT
Manon COSTA
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Centre National de la Recherche Scientifique CNRS
Institut National de la Sante et de la Recherche Medicale INSERM
Centre Hospitalier Universitaire de Toulouse
Universite Toulouse Capitole
Universite de Toulouse
Original Assignee
Centre National de la Recherche Scientifique CNRS
Institut National de la Sante et de la Recherche Medicale INSERM
Centre Hospitalier Universitaire de Toulouse
Universite Toulouse III Paul Sabatier
Universite Toulouse 1 Capitole
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Publication of WO2020144335A1 publication Critical patent/WO2020144335A1/fr
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57426—Specifically defined cancers leukemia
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5044—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics involving specific cell types
    • G01N33/5047—Cells of the immune system
    • G01N33/505—Cells of the immune system involving T-cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56966—Animal cells
    • G01N33/56972—White blood cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/54—Determining the risk of relapse
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/60—Complex ways of combining multiple protein biomarkers for diagnosis

Definitions

  • the invention relates to a method for identifying phenotypical signatures within CD8 + lymphocytes to predict the need for therapy of a patient suffering from a cancer comprising several steps.
  • CD8 + T cells and in particular cytotoxic T lymphocytes (CTL) are key components of the antitumor immune- surveillance. Accordingly, an increased CD8 + T cell infiltrate correlates with a better prognosis in various cancers (1).
  • therapeutic protocols designed to potentiate CTL responses against tumor cells are currently at the frontline of cancer clinical research (2) (3).
  • a better understanding of CD8 + T cells functional phenotype in cancer patients is becoming increasingly important.
  • the selective pressure of the immune system promotes tumor progression by selecting tumor variants that are fit to survive in an immunocompetent host (4).
  • a global remodeling of the CD8 + T cell compartment functional phenotype (beyond T cell exhaustion) in a process mirroring immuno- editing, could highlight the development of a new equilibrium at the whole organism scale occurring during disease progression.
  • monitoring the CD8 + T cell compartment phenotype might reveal the sculpturing of this compartment by the tumor and might provide tools to classify patients according to their disease evolution and need for therapy.
  • Chronic lymphocytic leukemia (CLL), a common adult leukemia characterized by the clonal expansion of B lymphocytes in the peripheral blood, lymphoid organs and bone marrow represents an excellent model to test such a hypothesis (5).
  • CLL Chronic lymphocytic leukemia
  • cellular partners such as CD8 + T cells and tumor B cells can interact within the three main tumor compartments (blood, bone marrow and lymph nodes) over prolonged time periods (6).
  • defined CD8 + T cell functional deficiencies have been described in CLL patients, including defective lytic synapse formation with tumor B cells and limited cytotoxic function (7) (8) .
  • the inventors undertook an unbiased approach for multi-dimensional characterization of CD8 + T cell phenotypic signature to investigate possible global CD8 + T cell phenotypic remodeling in CLL patients. They centered their study at the patient level so that multiple marker expression could be compared in multiple patients at the same time by implementing approaches for statistical multi-dimensional analysis of multicolor flow cytometry data sets.
  • CD8 + T cell phenotype is altered in CLL patients when compared to healthy donors and that major alterations are embedded within a limited number of functional markers.
  • the analysis also reveals a CD8 + T cell phenotypic signature in CLL patients that reflects disease progression towards therapy and is mainly due to imbalance in the memory compartment. They identify a phenotypic signature combining at least 2 unrelated markers that describes the evolution towards therapy within 6 months after phenotyping in fresh whole or frozen blood samples, which was never done before and which allow the stratification of patients.
  • memory compartment alteration appears to be an intrinsic feature of aggressive disease rather than the result of chronic immune system activation in CLL patients.
  • the invention relates to a method for identifying phenotypical signatures within CD8+ lymphocytes to predict the need for therapy of a patient suffering from a cancer comprising several steps.
  • the invention is defined by its claims
  • a first aspect of the invention relates to a method for identifying phenotypical signatures within CD8 + lymphocytes to predict the need for therapy of a patient suffering from a cancer comprising:
  • ii) Comparing the expression level of the markers determined at step i) with the background reference values obtained from unstained samples and calculate the percentage of cells expressing the markers above the background of the unstained sample. iii) Create a database with all the normalized marker expression values obtained on N patients for which the disease progression towards need for therapy within 4 to 8 months is known (learning cohort);
  • the calculation of a logistic regression and the calculation of the optimal threshold can be done to distinguish the patients that will evolve towards therapy.
  • the invention also relates to a method for identifying phenotypical signatures within CD8 + lymphocytes to predict the need for therapy of a patient suffering from a cancer comprising:
  • step iv) Calculate the logistic regression that describes the probability function of the treatment status of the patients as a function of the expression levels of the selected markers from step iv) and that goes from 0 (the patient is not treated) to 1 (the patient is treated);
  • the invention also relates to a method for identifying phenotypical signatures within CD8 + lymphocytes to predict the need for therapy of a patient suffering from a cancer comprising:
  • step iv) Calculate the logistic regression that describes the probability function of the treatment status of the patients as a function of the expression levels of the selected markers from step iv) and that goes from 0 (the patient is not treated) to 1 (the patient is treated);
  • the specific markers of CD8 + T lymphocytes are CM, EM, CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CD1 la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4, GAL-3.
  • the terms“N” denotes at least 10 patients.
  • the term“N” denotes at least of 10, 11, 12, 13, 14, 15, 16,
  • the supervised analysis can be performed for example by using Random Forest algorithm, Adaboost algorithm, Decision tree algorithm (CART), Linear Discriminant analysis, Topological data analysis, Support Vector Machine algorithm or Neural Networks.
  • the supervised analysis can be performed by using Random Forest algorithm, Adaboost algorithm or Decision tree algorithm.
  • the supervised analysis can be performed by using Random Forest algorithm.
  • the time wherein the disease will progress towards need for therapy can be of 6 months.
  • Random Forests algorithm As used herein, the term “Random Forests algorithm” or “RF” has its general meaning in the art and refers to classification algorithm. Random Forest is a decision-tree-based classifier that is constructed using an algorithm originally developed by Leo Breiman (Breiman L, "Random forests,” Machine Learning 2001, 45:5-32).
  • Adaboost algorithm has its general meaning in the art and refers to classification algorithm using an algorithm originally developed by Freund and Schapire (Freund Y. and Schapire R.E.“A decision-theoretic generalization of on-line learning and an application to boosting”. Journal of Computer and System Sciences, 55(1): 119—139, 1997).
  • support vector machine has its general meaning in the art and refers to a universal learning machine useful as a statistical tool for classification and using an algorithm developed by Cortes and Vapnik (Cortes C. and Vapnik V.N.“Support- vector networks” Machine Learning 1995, 20(3):273-297).
  • SI denotes the score of a given patient that is calculated with the markers expression levels using the formula of the logistic regression previously determined from the learning cohort. This score represent the probability of a given patient to evolve towards therapy within 4 to 8 months, particularly 6 months based on the phenotype of its CD8 + T lymphocyte compartment and particularly based on specific CD8 + T lymphocytes markers.
  • the term“learning cohort” denotes the cohort of patients that were phenotyped using the X markers from which the relevant markers of the need for therapy are learned using supervised learning algorithms and from which the logistic regression (probability function) is identified and fitted thanks to the phenotyping data.
  • the term“accuracy” denotes the statistical calculation of the fidelity of prediction of a method with reality.
  • the accuracy can be calculated from the confusion matrix using the formula: (True positive + True negative)/(True positive + False Positive + False negative + True negative).
  • True positive corresponds to the number of patients that are predicted positive for the considered outcome and are in reality positive.
  • False positive corresponds to the number of patients that are predicted positive but are in reality negative.
  • False negative corresponds to the number of patients that are predicted negative but are in reality positive.
  • True negative corresponds to the number of patients that are predicted negative and are in reality negative.
  • FI denotes another statistical calculation of the fidelity of prediction of a method with reality.
  • the FI measure can be calculated using the formula: 2 * ((precision * recall)/precision + recall)). Precision corresponds to: (True positive)/(True positive + False positive). Recall corresponds to: (True positive)/(True positive + False negative).
  • the term“optimal threshold” correspond to the threshold value of the SI score that will split the patients in 2 groups: SI above the threshold will corresponds to the group of patients that will evolve towards therapy within 4 to 8 months and particularly 6 months, SI below the threshold will corresponds to the group of patients that will not need therapy.
  • This threshold value of the SI score is qualified of optimal because it is calculated to obtain the value of SI that will give the best accuracy of prediction with the learning cohort.
  • the number of False positive and False negative are weighted differentially in order to minimize the amount of False negative (Patients that are predicted as not needing therapy but needing therapy in reality and thus won’t be treated when they should have been). This weight is given by applying a penalty (also called cost) to False negative that is twice as much important as the penalty of False positive.
  • background reference value denotes the minimal expression of a marker in CD8 + T lymphocyte.
  • Another aspect of invention relates to a method for predicting the need for therapy of a patient suffering from a cancer comprising: i) determining, in a sample obtained from the patient, the expression level of the markers of step iv) of the method described above ; ii) calculating the SI score of the patient, using the expression levels from step i), according to the logistic regression defined from a cancer learning cohort according to step v) of the method described above ; iii) comparing the SI score obtained for a given patient, to the optimal threshold value defined from the cancer learning cohort ; iv) providing a prognosis of evolution toward need for therapy within 4 to 8 months when the SI score determined at step ii) is higher than the optimal threshold value, or providing a prognosis of not needing therapy within 4 to 8 months when the SI score is lower than the optimal threshold.
  • the cancer may be selected in the group consisting of adrenal cortical cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, brain and central nervous system cancer, breast cancer, Castleman disease, cervical cancer, colorectal cancer, endometrial cancer, esophagus cancer, gallbladder cancer, gastrointestinal carcinoid tumors, Hodgkin's disease, non-Hodgkin's lymphoma, Kaposi's sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, liver cancer, lung cancer, mesothelioma, plasmacytoma, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngeal cancer, ovarian cancer, pancreatic cancer, penile cancer, pituitary cancer, prostate cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, skin cancer, stomach cancer, testicular
  • the cancer is a chronic lymphocytic leukemia (CLL).
  • CLL chronic lymphocytic leukemia
  • the markers of the invention are interne or surface markers.
  • “surface markers” denotes proteins express on the surface of CD8 + T lymphocytes.
  • “interne markers” denotes proteins express inside CD8 + T lymphocytes.
  • the marker are surface markers.
  • the invention also relates to a method for predicting the need for therapy of a patient suffering from a cancer comprising: i) determining, in a sample obtained from the patient, the expression level of markers expressed by the CD8+ T lymphocytes; ii) calculating the CD8+ T cell compartment SI score of the patient, using the expression levels from step i), according to the logistic regression defined from a cancer learning cohort; iii) comparing the S 1 score obtained for a given patient, to the optimal threshold value defined from the cancer learning cohort ; iv) providing a prognosis of evolution toward therapy within 4 to 8 months when the SI score determined at step ii) is higher than the optimal threshold value, or providing a prognosis of not needing therapy within 4 to 8 months when the SI score is lower than the optimal threshold.
  • the inventors have used the method described above to determine a set of marker specific of the chronic lymphocytic leukemia (CLL) and to determine the need for therapy of a specific patient.
  • CLL chronic lymphocytic leukemia
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM and EM.
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 markers selected among CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4, GAL-3.
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM and CXCR4.
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM, CXCR4 and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 26 markers selected among CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM- 1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1,
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM, CXCR4 and CXCR3.
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM, CXCR4 and CD25.
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM, CXCR4 and CCR5.
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM, CXCR4, CXCR3 and CD25.
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM, CXCR4, CXCR3 and CCR5.
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM, CXCR4, CCR5 and CD25
  • the invention also relates to a method for predicting the need for therapy of a patient suffering from a CLL comprising: i) determining, in a sample obtained from the patient, the expression level of the markers CM and EM expressed by the CD8 + T lymphocytes; ii) calculating the CD8 + T cell compartment SI score of the patient, using the expression levels from step i), according to the logistic regression defined from a cancer learning cohort; iii) comparing the S 1 score obtained for a given patient, to the optimal threshold value defined from the cancer learning cohort ; iv) providing a prognosis of evolution toward therapy within 4 to 8 months when the SI score determined at step ii) is higher than the optimal threshold value, or providing a prognosis of not needing therapy within 4 to 8 months when the SI score
  • the time wherein the disease will progress towards need for therapy can be of 6 months.
  • a step of v) communicating the result to the patient may be added to the method for predicting the need for therapy of a patient suffering from a chronic lymphocytic leukemia (CLL).
  • CLL chronic lymphocytic leukemia
  • the invention also relates to a method for predicting the need for therapy of a patient suffering from a CLL comprising: i) determining, in a sample obtained from the patient, the expression level of the markers CM, EM and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 markers selected in the group consisting in CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3 expressed by the CD8+ T lymphocytes; ii) calculating the CD8 + T cell compartment SI score of the patient, using the expression levels from step i), according to the logistic regression defined from a cancer learning cohort; iii) comparing the
  • the invention also relates to a method for predicting the need for therapy of a patient suffering from a CLL comprising: i) determining, in a sample obtained from the patient, the expression level of the markers CM, EM and CXCR4 expressed by the CD8+ T lymphocytes; ii) calculating the CD8 + T cell compartment SI score of the patient, using the expression levels from step i), according to the logistic regression defined from a cancer learning cohort; iii) comparing the SI score obtained for a given patient, to the optimal threshold value defined from the cancer learning cohort ; iv) providing a prognosis of evolution toward therapy within 4 to 8 months when the SI score determined at step ii) is higher than the optimal threshold value, or providing a prognosis of not needing therapy within 4 to 8 months when the SI score is lower than the optimal threshold value
  • the invention also relates to a method for predicting the need for therapy of a patient suffering from a CLL comprising: i) determining, in a sample obtained from the patient, the expression level of the markers CM, EM, CXCR4 and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 26 markers selected in the group consisting in CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3 expressed by the CD8+ T lymphocytes; ii) calculating the CD8 + T cell compartment SI score of the patient, using the expression levels from step i), according to the logistic regression defined from a cancer learning cohort; iii) comparing the SI score
  • the term“need for therapy” denotes the evolution of patients suffering from CLL to an advanced stage which require a treatment during the six months following the phenotypic analysis (i.e. the determination of the expression level of the markers according to the invention).
  • the invention relates to a method for predicting the need for therapy of a patient suffering from a chronic lymphocytic leukemia (CLL) comprising: i) determining, in a sample obtained from the patient, the expression level of CD8 + T lymphocytes expressing the markers CM and EM ii) calculating the CD8 + T cell compartment SI score of the patient; iii) comparing the S 1 score obtained for a given patient, to the optimal threshold value defined from the cancer learning cohort, iv) providing a prognosis of evolution toward therapy within 4 to 8 months when the SI score determined at step ii) is higher than the optimal threshold value, or providing a prognosis of not needing therapy within 4 to 8 months when the SI score is lower than the optimal threshold.
  • CLL chronic lymphocytic leukemia
  • the measurement of the expression level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 markers selected in the group consisting of CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3 expressed by the CD8 + T lymphocytes may be added to the method for predicting the need for therapy of a patient suffering from a chronic lymphocytic leukemia.
  • the invention relates to a method for predicting the need for therapy of a patient suffering from a chronic lymphocytic leukemia (CLL) comprising: i) determining, in a sample obtained from the patient, the expression level of CD8 + T lymphocytes expressing the markers CM, EM and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 markers selected in the group consisting in CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3; ii) calculating the CD8 + T cell compartment (SI) score of the patient; iii) comparing the SI score obtained for a given patient, to
  • the invention also relates to a method for predicting the need for therapy of a patient suffering from a chronic lymphocytic leukemia (CLL) comprising: i) determining, in a sample obtained from the patient, the expression level of CD8 + T lymphocytes expressing the markers CM, EM and CXCR4 ii) calculating the CD8 + T cell compartment SI score of the patient; iii) comparing the S 1 score obtained for a given patient, to the optimal threshold value defined from the cancer learning cohort and iv) providing a prognosis of evolution toward therapy within 4 to 8 months when the SI score determined at step ii) is higher than the optimal threshold value, or providing a prognosis of not needing therapy within 4 to 8 months when the S 1 score is lower than the optimal threshold.
  • CLL chronic lymphocytic leukemia
  • the measurement of the expression level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 26 markers selected in the group consisting of CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3 expressed by the CD8 + T lymphocytes may be added to the method for predicting the need for therapy of a patient suffering from a chronic lymphocytic leukemia.
  • the invention relates to a method for predicting the need for therapy of a patient suffering from a chronic lymphocytic leukemia (CLL) comprising: i) determining, in a sample obtained from the patient, the expression level of CD8 + T lymphocytes expressing the markers CM, EM, CXCR4 and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 26 markers selected in the group consisting in CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3; ii) calculating the CD8 + T cell compartment (SI) score of the patient; iii) comparing the SI score obtained for a given patient, to
  • Another aspect of the invention relates to a method for predicting the survival time of a patient suffering from a chronic lymphocytic leukemia (CLL) comprising: i) determining, in a sample obtained from the patient, the expression level of CD8 + T lymphocytes expressing the the markers CM and EM; ii) calculating the CD8 + T cell compartment (SI) score of the patient iii) comparing the expression level of the markers determined at step i) to the optimal threshold value defined from the cancer learning cohort and iv) providing a good prognosis when the SI score determined at step ii) is higher than the optimal threshold value, or providing a bad prognosis when the SI score is lower than the optimal threshold value.
  • CLL chronic lymphocytic leukemia
  • the measurement of the expression level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 markers in the group consisting of CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3 may be added to the method for predicting the survival time of a patient suffering from a chronic lymphocytic leukemia.
  • the invention relates to a method for predicting the survival time of a patient suffering from a chronic lymphocytic leukemia comprising i) determining in a sample obtained from the patient the expression level of the markers CM, EM and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 markers selected in the group consisting in CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3 ii) calculating the CD8 + T cell compartment (SI) score of the patient iii) comparing the expression level of the markers determined at step i) to the optimal threshold value defined from the cancer learning cohort and iv) providing
  • Another aspect of the invention relates to a method for predicting the survival time of a patient suffering from a chronic lymphocytic leukemia (CLL) comprising: i) determining, in a sample obtained from the patient, the expression level of CD8 + T lymphocytes expressing the the markers CM, EM and CXCR4 ii) calculating the CD8 + T cell compartment (SI) score of the patient iii) comparing the expression level of the markers determined at step i) to the optimal threshold value defined from the cancer learning cohort and iv) providing a good prognosis when the SI score determined at step ii) is higher than the optimal threshold value, or providing a bad prognosis when the SI score is lower than the optimal threshold value.
  • CLL chronic lymphocytic leukemia
  • the measurement of the expression level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 26 markers in the group consisting of CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM- 1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3 may be added to the method for predicting the survival time of a patient suffering from a chronic lymphocytic leukemia.
  • the invention relates to a method for predicting the survival time of a patient suffering from a chronic lymphocytic leukemia comprising i) determining in a sample obtained from the patient the expression level of the markers CM, EM, CXCR4 and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 26 markers selected in the group consisting in CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive,
  • SI CD8 + T cell compartment
  • the invention relates to a method for predicting the survival time of a patient suffering from a chronic lymphocytic leukemia comprising i) determining in a sample obtained from the patient the expression level of the markers CM, EM, CXCR4, CXCR3, CD25 and CCR5 ii) calculating the CD8 + T cell compartment SI score of the patient iii) comparing the expression level of the markers determined at step i) to the optimal threshold value defined from the cancer learning cohort and iv) providing a good prognosis when the score determined at step i) is higher than the optimal threshold value, or providing a bad prognosis when the score is lower than the optimal threshold value.
  • the invention in another aspect, relates to a method for predicting the need for therapy of a patient suffering from a cancer comprising : i) determining, in a sample obtained from the patient, the expression level of the markers expressed by the CD8+ T lymphocytes, ii) implementing a classification algorithm on data comprising the quantified plurality of markers expressed by the CD8+ T lymphocytes so as to obtain an algorithm output, iii) determining the probability that the patient will need for therapy from the algorithm output of step ii).
  • the markers expressed by CD8 + T cells whose expression level is determined in step i) are CM, EM and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 markers selected above CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM- 1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4, GAL-3.
  • the classification algorithm implemented in step ii) is Random Forest algorithm, Adaboost algorithm, Decision tree algorithm (CART), Linear Discriminant analysis, Topological data analysis, Support Vector Machine algorithm or Neural Networks.
  • Another aspect of the invention relates to a method to stratify a patient suffering from a chronic lymphocytic leukemia (CLL) according to its treatment need comprising determining, in a sample obtained from the patient, the expression level of the markers CM, EM.
  • CLL chronic lymphocytic leukemia
  • Another aspect of the invention relates to a method to stratify a patient suffering from a chronic lymphocytic leukemia (CLL) according to its treatment need comprising determining, in a sample obtained from the patient, the expression level of the markers CM, EM and at least
  • Another aspect of the invention relates to a method to stratify a patient suffering from a chronic lymphocytic leukemia (CLL) according to its treatment need comprising determining, in a sample obtained from the patient, the expression level of the markers CM, EM and CXCR4.
  • CLL chronic lymphocytic leukemia
  • Another aspect of the invention relates to a method to stratify a patient suffering from a chronic lymphocytic leukemia (CLL) according to its treatment need comprising determining, in a sample obtained from the patient, the expression level of the markers CM, EM, CXCR4 and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26 or 27 markers selected in the group consisting in CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD127, CCR4 and GAL-3.
  • CLL chronic lymphocytic leukemia
  • the terms“to stratify a patient suffering from a chronic lymphocytic leukemia according to its treatment need” denotes the possibility to determine a numerical value (score) that will reflect the disease progression stage toward treatment of the patient suffering from a CLL and thus determine the need for therapy of said patient.
  • CLL staging systems already exist (Binet or Rai classification, Scarfo L et al. CritRev Oncol Hematol, 2016) but the present invention relates to treatment need evaluation. If one consider the disease progression scale from diagnosis to time to first treatment, the present invention stratify the patients within this time frame.
  • the term“survival time” denotes the expected duration of time until death of a patient suffering from cancer or CLL.
  • the term“Good Prognosis” denotes a significantly more favourable probability of survival after patient treatment in the group of patients defined as“good prognosis” compared with the group of patients defined as“bad prognosis”. According to the invention, the term“Good Prognosis” also denotes a significantly more favourable probability of not needing treatment to survive in the group of patients defined as“good prognosis” compared with the group of patients defined as“bad prognosis”.
  • sample denotes, blood, fresh whole blood, peripheral-blood or peripheral blood mononuclear cell (PBMC).
  • PBMC peripheral blood mononuclear cell
  • the sample and more particularly the PBMC is not frozen.
  • the sample, and more particularly the PBMC is frozen.
  • the term "patient” refers to an individual who is being managed for a cancer like a CLL disease and who is susceptible to develop a cancer like a CLL at any stage of the disease like at more advanced Binet stage (B and C) (Scarfo L et al. Crit Rev Oncol Hematol, 2016).
  • the terms“markers (or biomarkers) of the invention” correspond to the proteins, or group of proteins CM, EM, CXCR4, CXCR3, CD25, CCR5, LAG-3, CXCR5, CD5, Perforin, CD58, B7-H3, ICAM-1, CD38, CDl la, CD57, CD 137, CD69, GzB, GzA, Naive, CTLA-4, BTLA, HLA2, PD1, EMRA, CD 127, CCR4 and GAL-3.
  • CM for“Central Memory” or CM marker denotes CD8 + T lymphocytes which express at their surfaces CD45RO, CCR7 and CD27 (CD45RO + , CCR7 + and CD27 + ) but not CD45RA (CD45RA ),
  • EM for“Effector Memory” or EM marker denotes CD8 + T lymphocytes which express at their surface CD45RO (CD45RO + ) but not CD45RA, CCR7 and CD27 (CD45RA-, CCR7 and CD27 ).
  • Naive or Naive marker denotes CD8 + T lymphocytes which express at their surfaces CD45RA, CCR7 and CD27 (CD45RA + , CCR7 + and CD27 + ) but not CD45RO (CD45RO ).
  • EMRA for“Effector Memory RA” or EMRA marker denotes CD8 + T lymphocytes which express at their surface CD45RA (CD45RA + ) but not CD45RO, CCR7 and CD27 (CD45RO , CCR7 and CD27 ).
  • CD45RA refers to a member of the protein tyrosine phosphatase (PTP) family. PTPs are known to be signaling molecules that regulate a variety of cellular processes including cell growth, differentiation, mitotic cycle, and oncogenic transformation. The sequence of said protein can be found under the Uniprot accession number P08575.
  • CD45RA denotes a specific isoform of CD45 corresponding to a splicing variant containing exon 4 but not exon 5 and 6.
  • the CD45RA antibody used to detect this isoform can detect glycosylated membrane proteins carrying exon 4 when expressed alone or together with exon 5 and 6.
  • CD45RO refers to a member of the protein tyrosine phosphatase (PTP) family. PTPs are known to be signaling molecules that regulate a variety of cellular processes including cell growth, differentiation, mitotic cycle, and oncogenic transformation. The sequence of said protein can be found under the Uniprot accession number P08575.
  • CD45RO denotes a specific isoform of CD45 corresponding to a splicing variant from which exon 4, 5 and 6 were removed.
  • the CD45RO antibody used to detect this isoform can only detect glycosylated membrane proteins that do not express the amino acid portion corresponding to exon 4, 5 and 6.
  • CXCR4 for“C-X-C chemokine receptor type 4” refers to Ga protein-coupled receptor in the CXC chemokine receptor family specific for stromal-derived- factor-1 (SDF-1 also called CXCL12), a molecule endowed with potent chemotactic activity for lymphocytes.
  • CXCR4 is one of several chemokine receptors that HIV can use to infect CD4+ T cells. HIV isolates that use CXCR4 are traditionally known as T-cell tropic isolates. The sequence of said protein can be found under the Uniprot accession number P61073.
  • the term“CXCR4 marker” denote CD8 + T lymphocytes which express at their surface CXCR4.
  • CD27 refers to a receptor which is a member of the TNF- receptor superfamily. This receptor is required for generation and long-term maintenance of T cell immunity. It binds to ligand CD70, and plays a key role in regulating B-cell activation and immunoglobulin synthesis.
  • the sequence of said protein can be found under the Uniprot accession number P26842.
  • the term“CD27 marker” denote CD8 + T lymphocytes which express at their surface CD27.
  • CXCR3 for“C-X-C chemokine receptor type 3” refers to a Ga protein-coupled receptor in the CXC chemokine receptor family.
  • CXCR3 is expressed primarily on activated T lymphocytes and NK cells, and some epithelial cells and regulates leukocytes trafficking.
  • CXCR3 and CCR5 are preferentially expressed on Thl cells, whereas Th2 cells favor the expression of CCR3 and CCR4.
  • the sequence of said protein can be found under the Uniprot accession number P49682.
  • the term“CXCR3 marker” denote CD8 + T lymphocytes which express at their surface CXCR3.
  • CCR7 for“C-C chemokine receptor type 7” refers to a G protein-coupled receptor in the CC chemokine receptor family. This receptor was identified as a gene induced by the Epstein-Barr virus (EBV), and is thought to be a mediator of EBV effects on B lymphocytes. This receptor is expressed in various lymphoid tissues, activates B and T lymphocytes and regulates their trafficking. CCR7 has been shown to stimulate dendritic cell maturation. The sequence of said protein can be found under the Uniprot accession number P32248. According to the invention, the term“CCR7 marker” denote CD8 + T lymphocytes which express at their surfaces CCR7.
  • EBV Epstein-Barr virus
  • CD58 refers to a cell adhesion molecule expressed on Antigen Presenting Cells (APC), particularly macrophages but also on T lymphocytes. It binds to CD2 (LFA-2) on T cells and is important in strengthening the adhesion between the T cells and Professional Antigen Presenting Cells.
  • APC Antigen Presenting Cells
  • LFA-2 LFA-2
  • CD8 + T lymphocytes which express at their surfaces CD58.
  • CCR5 for“C-C chemokine receptor type 5” refers to a G protein-coupled receptor in the CC chemokine receptor family mostly expressed in the immune system cells where it acts as a receptor for chemokines. This is the process by which T cells are attracted to specific tissue and organ targets. The sequence of said protein can be found under the Uniprot accession number P51681. According to the invention, the term“CCR5 marker” denote CD8 + T lymphocytes which express at their surfaces CCR5.
  • Perforin refers to a pore forming cytolytic protein found in the granules of cytotoxic T lymphocytes (CTLs) and NK cells. Upon degranulation, perforin binds to the target cell's plasma membrane, and oligomerises in a Ca2+ dependent manner to form pores on the target cell.
  • CTLs cytotoxic T lymphocytes
  • NK cells cytotoxic T lymphocytes
  • the sequence of said protein can be found under the Uniprot accession number P14222.
  • the term“Perforin marker” denote CD8 + T lymphocytes which express at their surfaces the Perforin.
  • CXCR5 for“C-X-C chemokine receptor type 5” refers to a Ga protein-coupled receptor in the CXC chemokine receptor family.
  • CXCR5 marker denote CD8 + T lymphocytes which express at their surface CXCR5.
  • CD 127 marker denote CD8+ T lymphocytes which express at their surfaces CD127, also known as interleukin-7 receptor-a.
  • CD 137 marker denote CD8+ T lymphocytes which express at their surfaces CD137, also known as tumor necrosis factor receptor superfamily member 9.
  • CD25 marker denote CD8+ T lymphocytes which express at their surfaces CD25, also known as interleukin-2-receptor subunit alpha.
  • B7-H3 also known as CD276, refers to an immune checkpoint molecule, member of the B7 and CD28 families. B7-H3 participate in the regulation of T-cell mediated immune response. According to the invention, the term“B7-H3 marker” denote CD8+ T lymphocytes which express at their surfaces B7-H3
  • CTLA-4 for“Cytotoxic T-lymphocyte-Associated Protein 4” refers to a CD28 homologue expressed on T cells which regulates the amplitude of the early stages of T-cell activation.
  • CTLA-4 marker denotes CD8+ T lymphocytes which express at their surfaces CTLA-4.
  • CD38 marker denote CD8 + T lymphocytes which express at their surfaces CD38, also known as cyclic ADP ribose hydrolase 1.
  • Gal-3 marker denotes CD8 + T lymphocytes which express at their surfaces galectin 3 (Gal-3).
  • the term“GzA marker” denotes CD8 + T lymphocytes which express at their surfaces granzyme A (GzA).
  • the term“GzB marker” denotes CD8 + T lymphocytes which express at their surfaces granzyme B (GzB).
  • HLA-II marker denotes CD8 + T lymphocytes which express at their surfaces human leukocyte antigen II (HLA-II).
  • LAG-3 for“Lymphocyte-activation gene 3” refers to a cell surface protein expressed on activated T cells, NK cells, B cells and plasmacytoid dentritic cells.
  • LAG-3 marker denotes CD8 + T lymphocytes which express at their surfaces LAG-3.
  • CD57 marker denotes CD8 + T lymphocytes which express at their surfaces CD57, also known as“Beta-l-3-Glucuronyltransferase 1”.
  • CD69 marker denotes CD8 + T lymphocytes which express at their surfaces CD69, also known as early T-cell activation antigen P60.
  • CDl la marker denotes CD8 + T lymphocytes which express at their surfaces CD1 la, also known as integrin alpha-L.
  • ICAM-1 marker denotes CD8 + T lymphocytes which express at their surfaces ICAM-1, also known as Intercellular Adhesion Molecule 1.
  • CD5 marker denotes CD8 + T lymphocytes which express at their surfaces CD5, also known as lymphocyte antigen Tl.
  • the term“PD1”, for Programmed cell death protein 1” refers to a cell surface receptor that belongs to the immunoglobulin family. The interaction between PD1 and its two ligands regulates the induction and maintenance of peripheral tolerance and protect tissues from autoimmune attack.
  • the term“PD1 marker” denotes CD8 + T lymphocytes which express at their surfaces PD1.
  • the term“BTLA”, for“B- and T- lymphocyte attenuator” refers to a lymphocyte inhibitory receptor which inhibits lymphocytes during immune response.
  • the term“BTLA marker” denotes CD8+ T lymphocytes which express at their surfaces BTLA.
  • CCR4 for“C-C chemokine receptor type 4” refers to a G protein-coupled receptor in the CC chemokine receptor family.
  • the term“CCR4 marker” denote CD8 + T lymphocytes which express at their surfaces CCR4.
  • an optional step of statistical classifier or any machine learning algorithm can be added at the end of the methods of the invention.
  • a step of classification of the patients with the k-neighbours algorithm see for example Fix, E. et al 1951
  • principal component analysis see for example Pearson K. 1901 "On Lines and Planes of Closest Fit to Systems of Points in Space”. Philosophical Magazine. 2 (11): 559-572
  • support vector machine see for example Cortes C. et al 1995. "Support-vector networks”. Machine Learning. 20 (3): 273-297
  • neural networks see for example Rosenblatt, F. 1958. "The Perceptron: A Probabilistic Model For Information Storage And Organization In The Brain”.
  • Psychological Review. 65 (6): 386-408 or any classifier
  • the expression level of the markers listed above may also be measured by measuring the protein expression level of these markers and can be performed by a variety of techniques well known in the art.
  • protein expression level may be measured for example by capillary electrophoresis-mass spectroscopy technique (CE-MS), flow cytometry, mass cytometry or ELISA performed on the sample.
  • CE-MS capillary electrophoresis-mass spectroscopy technique
  • the“level of protein” or the“protein level expression” means the quantity or concentration of said protein.
  • the protein is expressed at the cell surface for markers whose function is linked to their correct plasma membrane expression or total expression for markers whose function is not limited to membrane expression.
  • the “level of protein” means the quantitative measurement of the proteins expression relative to a negative control.
  • Such methods comprise contacting a sample with a binding partner capable of selectively interacting with proteins present in the sample.
  • the binding partner is generally an antibody that may be polyclonal or monoclonal, preferably monoclonal.
  • the presence of the protein can be detected using standard electrophoretic and immunodiagnostic techniques, including immunoassays such as competition, direct reaction, or sandwich type assays.
  • immunoassays such as competition, direct reaction, or sandwich type assays.
  • assays include, but are not limited to, Western blots; agglutination tests; enzyme-labeled and mediated immunoassays, such as ELISAs; biotin/avidin type assays; radioimmunoassays; Immunoelectrophoresis; immunoprecipitation, capillary electrophoresis- mass spectroscopy technique (CE-MS).etc.
  • the reactions generally include revealing labels such as fluorescent, chemioluminescent, radioactive, enzymatic labels or dye molecules, or other methods for detecting the formation of a complex between the antigen and the antibody or antibodies reacted therewith.
  • the aforementioned assays generally involve separation of unbound protein in a liquid phase from a solid phase support to which antigen-antibody complexes are bound.
  • Solid supports which can be used in the practice of the invention include substrates such as nitrocellulose (e. g., in membrane or microtiter well form); polyvinylchloride (e. g., sheets or microtiter wells); polystyrene latex (e.g., beads or microtiter plates); polyvinylidine fluoride; diazotized paper; nylon membranes; activated beads, magnetically responsive beads, and the like.
  • an ELISA method can be used, wherein the wells of a microtiter plate are coated with a set of antibodies against the proteins to be tested. A sample containing or suspected of containing the marker protein is then added to the coated wells. After a period of incubation sufficient to allow the formation of antibody-antigen complexes, the plate(s) can be washed to remove unbound moieties and a detectably labeled secondary binding molecule is added. The secondary binding molecule is allowed to react with any captured sample marker protein, the plate is washed and the presence of the secondary binding molecule is detected using methods well known in the art.
  • Mass spectrometry-based quantification methods may be used. Mass spectrometry-based quantification methods may be performed using either labelled or unlabelled approaches [DeSouza and Siu, 2012] Mass spectrometry -based quantification methods may be performed using chemical labeling, metabolic labeling or proteolytic labeling. Mass spectrometry-based quantification methods may be performed using mass spectrometry label free quantification, a quantification based on extracted ion chromatogram (EIC) and then profile alignment to determine differential level of polypeptides.
  • EIC extracted ion chromatogram
  • a mass spectrometry-based quantification method particularly useful can be the use of targeted mass spectrometry methods as selected reaction monitoring (SRM), multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), data independent acquisition (DIA) and sequential window acquisition of all theoretical mass spectra (SWATH) [Moving target Zeliadt N 2014 The Computer;Liebler Zimmerman Biochemistry 2013 targeted quantitation pf proteins by mass spectrometry; Gallien Domon 2015 Detection and quantification of proteins in clinical samples using high resolution mass spectrometry. Methods v81 pl5-23 ; Sajic, Liu, Aebersold, 2015 Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications. Proteomics Clin Appl v9 p 307-21]
  • the mass spectrometry-based quantification method can be the mass cytometry also known as cytometry by time of flight (CYTOF) (Bandura DR, Analytical chemistry, 2009).
  • CYTOF cytometry by time of flight
  • the mass spectrometry-based quantification is used to do peptide and/or protein profiling can be use with matrix-assisted laser desorption/ionisation time of flight (MALDI-TOF), surface-enhanced laser desorption/ionization time of flight (SELDI-TOF; CLINPROT) and MALDI Biotyper apparatus [Solassol, Jacot, Lhermitte, Boulle, Maudelonde, Mange 2006 Clinical proteomics and mass spectrometry profiling for cancer detection. Journal: Expert Review of Proteomics V3, 13, p311-320 ; FDA K130831]
  • MALDI-TOF matrix-assisted laser desorption/ionisation time of flight
  • SELDI-TOF surface-enhanced laser desorption/ionization time of flight
  • MALDI Biotyper apparatus Solassol, Jacot, Lhermitte, Boulle, Maudelonde, Mange 2006 Clinical proteomics and mass spectrometry profiling for cancer detection. Journal:
  • Methods of the invention may comprise a step consisting of comparing the proteins and fragments concentration in circulating cells with a control value.
  • concentration of protein refers to an amount or a concentration of a transcription product, for instance the proteins of the invention.
  • a level of a protein can be expressed as nanograms per microgram of tissue or nanograms per milliliter of a culture medium, for example.
  • relative units can be employed to describe a concentration.
  • concentration of proteins may refer to fragments of the proteins of the invention.
  • the detection of the level of the markers of the invention can be performed by flow cytometry.
  • the method consists of determining the percentages of CD8+ lymphocytes expressing the markers of the invention.
  • Expression levels may be expressed as absolute expression level or normalized expression level of the marker.
  • Optimal threshold values can be determined as explained below and as explained in the examples part and by statistical methods well known in the state of art.
  • the“background” as used in the context of the invention may be predetermined by carrying out a method comprising the steps of:
  • a second aspect of the invention relates to a chemotherapeutic compound for use in the treatment of a cancer in a patient with need for therapy as described above.
  • the invention relates to a chemotherapeutic compound for use in the treatment of a cancer in a patient with a bad prognosis as described above.
  • the invention in another embodiment, relates to a method for treating a cancer in a patient with need for therapy as described above comprising administering to said subject in need of a chemotherapeutic compound.
  • the invention in another embodiment, relates to a method for treating a cancer in a patient with a bad prognosis as described above comprising administering to said subject with need of a chemotherapeutic compound.
  • the cancer is a CLL.
  • chemotherapeutic compounds may be selected in the group consisting in: fludarabine, gemcitabine, capecitabine, methotrexate, taxol, taxotere, mercaptopurine, thioguanine, hydroxyurea, cytarabine, cyclophosphamide, ifosfamide, nitrosoureas, platinum complexes such as cisplatin, carboplatin and oxaliplatin, mitomycin, dacarbazine, procarbizine, etoposide, teniposide, campathecins, bleomycin, doxorubicin, idarubicin, daunorubicin, dactinomycin, plicamycin, mitoxantrone, L-asparaginase, epimbicm, 5-fluorouracil, taxanes such as docetaxel and paclitaxel, leucovorin, levamisole, ir
  • additional anticancer agents may be selected from, but are not limited to, one or a combination of the following class of agents: alkylating agents, plant alkaloids, DNA topoisomerase inhibitors, anti-folates, pyrimidine analogs, purine analogs, DNA antimetabolites, taxanes, podophyllotoxin, hormonal therapies, retinoids, photosensitizers or photodynamic therapies, angiogenesis inhibitors, antimitotic agents, isoprenylation inhibitors, cell cycle inhibitors, actinomycins, bleomycins, anthracyclines, MDR inhibitors and Ca2+ ATPase inhibitors.
  • chemotherapeutic compounds used to treat CLL can be chlorambucil (alkylating agent) alone or in combination with anti-CD20, fludarabine, pentostatin, and cladribine (2-CdA), CHOP (cyclophosphamide, doxorubicin, vincristine, prednisone), CAP (cyclophosphamide, doxorubicin, prednisone), Bendamustine (4-[5-[bis(2- chloroethyl)amino]-l-methylbenzimidazol-2-yl]butanoic acid, monoclonal antibodies : anti- CD20 (Rituximab, Ofatumumab, Obinutuzumab) or anti-CD52 (Alemtuzumab), molecular agents targeting B cell receptor and chemokine receptor signaling like idelalisib (PI3K-delta isoform specific inhibitor), ibrutinib (Bu
  • treatment refers to both prophylactic or preventive treatment as well as 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.
  • 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.
  • maintenance regimen 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.]).
  • the patient has already a cancer (for example a CLL).
  • a third aspect of the invention relates to a therapeutic composition
  • a therapeutic composition comprising a chemotherapeutic compound according to the invention for use in the treatment of CLL in a patient with need for therapy as described above.
  • the invention relates to a therapeutic composition
  • a therapeutic composition comprising a chemotherapeutic compound according to the invention for use in the treatment of CLL in patient with a bad prognosis as described above.
  • Any therapeutic agent of the invention may be combined with pharmaceutically acceptable excipients, and optionally sustained-release matrices, such as biodegradable polymers, to form therapeutic compositions.
  • “Pharmaceutically” or “pharmaceutically acceptable” refers to molecular entities and compositions that do not produce an adverse, allergic or other untoward reaction when administered 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, diluent, encapsulating material or formulation auxiliary of any type.
  • compositions for example, 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 patient, etc.
  • compositions of the invention can be formulated for a topical, oral, intranasal, parenteral, intraocular, intravenous, intramuscular or subcutaneous administration and the like.
  • the pharmaceutical compositions contain vehicles which are pharmaceutically acceptable for a formulation capable of being injected.
  • vehicles which are pharmaceutically acceptable for a formulation capable of being injected.
  • These may be in particular 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 which upon addition, depending on the case, of sterilized water or physiological saline, permit the constitution of injectable solutions.
  • the doses used for the administration can be adapted as a function of various parameters, and in particular as a function of the mode of administration used, of the relevant pathology, or alternatively of the desired duration of treatment.
  • other pharmaceutically acceptable forms include, e.g. tablets or other solids for oral administration; time release capsules; and any other form currently can be used.
  • FIGURES are a diagrammatic representation of FIGURES.
  • Figure 1 Supervised analysis of phenotypic expression data defines the markers that are important in detecting patients who will be treated.
  • A- Parameters correlating with“need for therapy” as ranked by Random Forest analysis are ranked according to normalized Gini index of their importance (Random Forest importance).
  • FIG. 1 CD8 + T cell compartment signature associated with need for therapy allows to score CLL patients on the basis of their CD8 + T cell compartment
  • A- Parameters correlating with need for therapy as ranked by random forest analysis The parameters are ranked according to the Gini index of their importance (Random Forest importance). Dotted lines highlight the six selected parameters for CD8 + T cell compartment signature chosen of the basis of the analysis shown in panels B and C.
  • Fluorescence distributions were acquired by flow cytometry on a BD FORTESSA cytometer. Fluorescence compensations, gating and selection of cells of interest (CD 19-, CD3+, CD4-, CD8 + , alive) were performed using FlowJo software (Tree Star, Inc, Ashland, Ore) and fluorescence data files corresponding to CD8 + T cells only were exported as csv files.
  • PBMC peripheral Blood Mononuclear Cells
  • CD8 + T cells were isolated using Whole Blood CD8 microbeads (Miltenyi Biotec SAS) from 1-2 ml of fresh patient or healthy donor blood. Normal B cells or B-CLL cells were isolated using RosetteSep human B cell enrichment cocktail (STEMCELL technologies). EBV transformed B cells (JY) were used as conventional target cells. Target cells were either unpulsed or pulsed with a bacterial superantigen cocktail (100 ng/mL TSST-1, SEC 1, SEB, SEA, SEE) for 1 h 30 at 37 °C in RPME5% FCS/Hepes and washed. CD8 + T cells were conjugated with target cells at different Effector: Target cell ratios for 18 h.
  • a bacterial superantigen cocktail 100 ng/mL TSST-1, SEC 1, SEB, SEA, SEE
  • CD8 + T cells were labeled before conjugation with 1 mM CMFDA (Molecular Probes) for 20 min at 37 °C. Immediately before FACS analysis, 7-Amino-actinomycin D (7-AAD) was added to each sample to stain dead cells.
  • CMFDA Molecular Probes
  • cytokine proteins FITC mouse anti-human IFNy antibody, BV421 mouse anti-human TNFa antibody, PECy7 mouse anti -human MIRIb antibody and PE rat anti -human IL2 antibody all from BD biosciences
  • the percentage of positive cells was calculated after applying a threshold corresponding to unstimulated cells.
  • the fold increase MFI Median Fluorescence Intensity was calculated by dividing the MFI of CD8 + cytokine + cells by the MFI of the total CD8 population for the considered cytokine.
  • the first step of data processing was to define which numerical values would be extracted from fluorescence distributions obtained by each antibody used in multiparametric analysis.
  • the unsupervised analysis of the multivariate data was conducted on the scaled data in order to avoid parasite effects due to the presence of percentage values (between 0 and 1) and larger values as age.
  • the clustering was done using hierarchical clustering on Euclidean distance of scaled data using Ward method.
  • PCA Principal component Analysis
  • FactoMineR Flexible component Analysis
  • the no information rate corresponds to the larger class (cluster) in the data set.
  • the p-value obtained thus reflects the probability to be wrong when accepting the hypothesis that the tested classification do better than a classification that would always predict the most common class.
  • SI score a score (defined as SI score)
  • hClust hierarchical clustering algorithm
  • PC A Principal Component Analysis
  • the markers correlating the most with this first dimension, and thus responsible for the difference between the individuals, are indicators of relevant biological functions of CD8 + T cells such as: migration and adhesion (CXCR4, CDl la, CCR7, CD58), lytic function (GzB, GzA, perforin), cell activation and differentiation (CD57, CD 127, CD45RA, CD45RO, CD27) (data not shown). While adhesion molecule and lytic molecule expression correlated positively with dimension 1, chemokine receptor and activation/differentiation molecule expression negatively correlated with dimension 1 (data not shown).
  • CMV infection has been associated with CLL, and CMV specific expansion of CD8 + T cells in CLL patients has been reported to be more pronounced than in age-matched healthy individuals (14).
  • CD8 + T cell memory compartment correlates with need for therapy as
  • CLL is an indolent disease and some patients can live for years without therapy, predicting the potential need of treatment before uncontrolled tumor progression is of major interest. Since we described a CLL CD8 + T cell phenotypic imprinting that is strong enough to cluster CLL patients and healthy donors, we asked whether this signature could also classify patients on the basis of their need for therapy. We selected progression towards therapy as a readout rather than established prognostic markers since the decision to treat is a turning point of the disease that could be associated with observable phenotypical changes among CD8+ T cells. We used a similar strategy of hClust/PCA analysis to generate clusters of patients.
  • RF Random Forest
  • CM, EM, and CXCR4 The three first markers of the RF analysis (CM, EM, and CXCR4) were effective to distinguish the patients that evolve towards therapy from the one who don’t (data not shown).
  • the expression profile of the differentiation markers associated with evolution towards therapy was also observed, to a lesser extent, in patients with more advanced Binet stage (B and C) and patients with unmutated IGVH genes (data not shown).
  • the calculated score (defined as SI score) represents the probability of being in one of the two states (“treated” or“untreated”) according to the phenotypic marker expression values and the coefficients applied in the regression.
  • SI score represents the probability of being in one of the two states (“treated” or“untreated”) according to the phenotypic marker expression values and the coefficients applied in the regression.
  • a frozen validation cohort confirms the existence of CD8 + T cell phenotype imprinting
  • CD8 + T cell scores of these patients were computed using the same logistic regression method we described on the fresh cohort.
  • Figure 2C We observed that increasing the number of markers taken into account in the logistic regression to 3 markers (CM, EM, CXCR4) (but not above) did improve the accuracy or the F-measure.
  • CD8 + T cell memory compartment alteration can be detected early after disease diagnosis

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Abstract

La présente invention concerne la prédiction du besoin de thérapie pour des patients atteints d'un cancer. Dans le cas présent, les inventeurs ont eu une approche sans biais pour la caractérisation multidimensionnelle de la signature phénotypique des lymphocytes T CD8+ pour étudier le remodelage phénotypique éventuel des lymphocytes T CD8+ globaux chez des patients CLL. Les résultats indiquent que le phénotype de lymphocytes T CD8+ est modifié chez des patients CLL lorsqu'il est comparé à des donneurs sains et que des modifications majeures sont incorporées dans un nombre limité de marqueurs fonctionnels. Ils identifient une signature phénotypique associant au moins 2 marqueurs non liés qui décrivent l'évolution vers la thérapie dans les 6 mois après le phénotypage dans des échantillons de sang entier frais ou congelé, ce qui n'avait jamais été effectué auparavant et permet la stratification de patients. De façon intéressante, l'altération du compartiment mémoire semble être une caractéristique intrinsèque d'une maladie agressive plutôt que le résultat de l'activation chronique du système immunitaire chez des patients CLL. L'invention concerne un procédé d'identification de signatures phénotypiques dans des lymphocytes CD8+ pour prédire le besoin de thérapie pour un patient atteint d'un cancer, comprenant plusieurs étapes.
PCT/EP2020/050553 2019-01-11 2020-01-10 Procédé pour prédire le besoin de thérapie pour des patients atteints d'un cancer Ceased WO2020144335A1 (fr)

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WO2013192339A1 (fr) * 2012-06-19 2013-12-27 The Regents Of The University Of California Biomarqueurs pour la phase d'échappement à la réponse immunitaire anti-tumorale (immunoediting)

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WO2013192339A1 (fr) * 2012-06-19 2013-12-27 The Regents Of The University Of California Biomarqueurs pour la phase d'échappement à la réponse immunitaire anti-tumorale (immunoediting)

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