Methods for the identification and treatment of subjects suffering from cancer via car-t cell therapy stratification models

Abstract

The present disclosure pertains to methods for analyzing a sample to identify the presence or absence of particular T-cell populations. The presence or absence of such populations may be used to predict and / or monitor a subject's response to CAR-T cell therapy. The disclosure further relates to the treating of a subject with CAR-T cell therapy. Further disclosed are diagnostic kits for marker detection before and / or after initiating CAR-T cell therapy.

Description

[0001] METHODS FOR THE IDENTIFICATION AND TREATMENT OF SUBJECTS SUFFERING FROM CANCER VIA CAR-T CELL THERAPY STRATIFICATION MODELS TECHNICAL FIELD The present disclosure generally relates to methods of T-cell analysis relying on the detection of specific proteins. Such methods find application in, amongst other, predicting the response of a cancer patient to CAR-T cell therapy, and in following up such responses. The expression of the proteins can thus be used in determining which cancer patients are most likely to respond to CAR-T cell therapy. The proteins serving as biomarkers are individually and collectively highly performant in predicting the response of a cancer to CAR-T cell therapy. Also disclosed are methods of treatment based on the expression of the proteins before, during, or after treatment. Diagnostic kits are likewise part of the invention. BACKGROUND Immunotherapy has emerged as a promising approach for treating various diseases, leveraging the body's immune system to target and eliminate abnormal cells. Within the realm of immunotherapy, CAR-T cell therapy represents a groundbreaking advancement. Chimeric Antigen Receptors (CARs) are synthetic receptors that are engineered into T cells, enabling them to recognize specific antigens on target cells. This targeted recognition mechanism enhances the specificity and efficacy of T cells in eliminating diseased cells. The development of CAR-T cell therapy has undergone significant evolution since its inception. Early attempts focused on understanding basic receptor design, signal transduction pathways, and optimizing T cell activation. Subsequent innovations have addressed challenges such as off-target effects, limited persistence, and cytokine release syndrome. SUMMARY Various aspects for determining that a subject is suitable for CAR-T cell therapy are described. In one aspect, such method included determining whether the subject harbors a population of CD8+ T-cells which are TIM3+; CD45RO-, CCR7+, CD95+, CD28+, and CD27-; CD45RO+, CD27+, Lag3-, and TIM3+; CD45RO+, CD27+, Lag3-, and TIM3-; CD95-, CCR7-, and CD27-; CD45RO+, CD27-, TIM3+, and PD1+; and / or

[0002] 1 10877-10885-PCT CD45RO-, CD27-, CCR7+, and CD28- wherein when the subject harbors a population having at least one set of the markers listed, the subject is a suitable candidate for CAR-T cell therapy. In another aspect, such method included determining whether the subject harbors a population of CD4+ T-cells which are CD127+, CD45RO-, wherein when the subject harbors the population, the subject is a suitable candidate for CAR-T cell therapy In some embodiments, the subject suffers from a cancer. The cancer may be blood cancer such as, but not limited to, B cell acute lymphoblastic leukemia, Chronic lymphocytic leukemia, and Myeloma. In embodiments, the determining is performed by detecting the presence or absence of markers in the cells or on the surface of the cell. Such determining can be performed by any suitable method including, but not limited to, immunocytochemistry, flow cytometry, cell staining, cell panning, electrophoresis, blotting, PCR, etc. In a particular embodiment, the methods include determining whether the subject harbors a population of CD8+ T-cells which are TIM3+. In a particular embodiment, the methods include determining whether the subject harbors a population of CD8+ T-cells which are CD45RO-, CCR7+, CD95+, CD28+, and CD27. In a particular embodiment, the methods include determining whether the subject harbors a population of CD8+ T-cells which are CD45RO+, CD27+, Lag3-, and TIM3+. In a particular embodiment, the methods include determining whether the subject harbors a population of CD8+ T-cells which are CD45RO+, CD27+, Lag3-, and TIM3-. In a particular embodiment, the methods include determining whether the subject harbors a population of CD8+ T-cells which are CD95-, CCR7-, and CD27-. In an additional particular embodiment, the methods include determining whether the subject harbors a population of CD8+ T-cells which are CD45RO+, CD27-, TIM3+, and PD1+. In a further particular embodiment, the methods include determining whether the subject harbors a population of CD8+ T-cells which are CD45RO-, CD27-, CCR7+, and CD28-. Additional embodiments include treating the subject with CAR-T cell therapy. Included are methods where the detection of the population occurs before, during, and / or after the CAR-T cell therapy. The detection may be performed utilizing T-cells that were isolated from the subject before, during, or after treating the subject with CAR. In a particular embodiment, the detection may be performed utilizing T-cells that were isolated from the subject within two weeks of treating the subject with CAR-T cell therapy. In embodiments, the CAR-T cell therapy comprises T-cells engineered to express one or more chimeric antigen receptors (CARs) that specifically bind to a tumor antigen for use in cancer immunotherapy. In certain embodiments, the tumor antigen is selected from a group consisting of: TSHR, CD19, CD123, CD22, CD30, CD171, CS-1, CLL-1, CD33, EGFRvIII , GD2, GD3, BCMA,

[0003] 2 10877-10885-PCT Tn Ag, PSMA, ROR1, FLT3, FAP, TAG72, CD38, CD44v6, CEA, EPCAM, B7H3, KIT, IL-13Ra2, Mesothelin, IL-11Ra, PSCA, PRSS21, VEGFR2, LewisY, CD24, PDGFR-beta, SSEA-4, CD20, Folate receptor alpha, ERBB2 (Her2 / neu), MUC1, EGFR, NCAM, Prostase, PAP, ELF2M, Ephrin B2, IGF-I receptor, CAIX, LMP2, gp100, bcr-abl, tyrosinase, EphA2, Fucosyl GM1, sLe, GM3, TGS5, HMWMAA, o-acetyl-GD2, Folate receptor beta, TEM1 / CD248, TEM7R, CLDN6, GPRC5D, CXORF61, CD97, CD179a, ALK, Polysialic acid, PLAC1, GloboH, NY-BR-1, UPK2, HAVCR1, ADRB3, PANX3, GPR20, LY6K, OR51E2, TARP, WT1, NY-ESO-1, LAGE-1a, MAGE-A1, legumain, HPV E6,E7, MAGE A1, ETV6-AML, sperm protein 17, XAGE1, Tie 2, MAD-CT-1, MAD-CT-2, Fos-related antigen 1, p53, p53 mutant, prostein, survivin and telomerase, PCTA- 1 / Galectin 8, MelanA / MART1, Ras mutant, hTERT, sarcoma translocation breakpoints, ML-IAP, ERG (TMPRSS2 ETS fusion gene), NA17, PAX3, Androgen receptor, Cyclin B1, MYCN, RhoC, TRP-2, CYP1B1, BORIS, SART3, PAX5, OY-TES1, LCK, AKAP-4, SSX2, RAGE-1, human telomerase reverse transcriptase, RU1, RU2, intestinal carboxyl esterase, mut hsp70-2, CD79a, CD79b, CD72, LAIR1, FCAR, LILRA2, CD300LF, CLEC12A, BST2, EMR2, LY75, GPC3, FCRL5, and IGLL1. It should be understood that language used in the present disclosure has been principally selected for readability and instructional purposes, and not to limit the scope of the subject matter disclosed herein. BRIEF DESCRIPTION OF THE DRAWINGS Figures 1A and 1B illustrate a flow cytometry gating strategy to obtain a population of T cells comprising the following marker set: CD8+, CD45RO-, CCR7+, CD95+, CD28+, and CD27- (Population 1 (TCD_JO)). Figure 2 illustrates a flow cytometry gating strategy to obtain a population of T cells comprising the following marker set: CD8+, CCR7-, and CD95- (Population 2 (TCD_NV)). Figure 3 illustrates a flow cytometry gating strategy to obtain a population of T cells comprising the following marker set: CD8+, CD27-, and CD95- (Population 3 (TCD_OB)). Figures 4A and 4B illustrate a flow cytometry gating strategy to obtain a populations of T cells comprising the following marker sets: CD8+, CD27-, and CD95-, CD45RO+, CD27+, Lag3-, and TIM3+ (Population 4 (ICI_AKK)) or CD8+, CD27-, and CD95-, CD45RO+, CD27+, Lag3-, and TIM33 (Population 5 (ICI_AKN)). Figures 5A and 5B a flow cytometry gating strategy to obtain a populations of T cells comprising the following marker set: CD8+ and Tim3+( Populations 6 (ICI_AXS), 7 (ICI_BCD), 8 (ICI_BCJ), and 9 (ICI_BCP)).

[0004] 3 10877-10885-PCT Figures 6A and 6B a flow cytometry gating strategy to obtain a population of T cells comprising the following marker set: CD8+, CD45RO+, CD27-, TIM3+, and PD1+ (Population 10 (ICI_ALY)). Figures 7A and 7B a flow cytometry gating strategy to obtain a population of T cells comprising the following marker set: CD8+, CD45RO-, CD27-, CCR7+, and CD28- (Population 11 (TCD_NN)). Figures 8A and 8B illustrate illustrates a flow cytometry gating strategy to obtain a population of T cells comprising the following marker set: CD4+, CD45RO+,CD12- (Population 12 (TCD_IG)). Figure 9 plots the SHAP values to quantify the impacts of each feature to model prediction. Figure 10 illustrates the results of a feature ablation study performed with a separate model trained and evaluated with one feature excluded each time. Figure 11 provides a confusion matrix validating the results of the model against the 132 patients whose markers were determined pre-treatment. DETAILED DESCRIPTION Markers were used in multi-color flow cytometry panels to characterize the subpopulations (features) of T cells identified in the starting material of the patients where the frequency of the specific population may positively or negatively influence the cell therapy outcome, either by informing in vivo proliferative capacity and / or clinical response following administration of an autologous CAR-T cell product. It is poorly understood why some patients respond and others do not. Currently hypothesis include the impact of cell differentiation / exhaustion and “fit” cell manufacturing hypothesis (Fraietta et al. , Cohen et al), inhibitory pathways that heme tumors exploit to overcome immunosurveillance (Stadtmauer et al., Argawal et al.), and analyses from other clinical studies correlating T cell phenotype with sustained long-term responses (Melenhorst et al.). In work leading to the present disclosure, and in view of the art lacking clear and unambiguous biomarkers predictive for response subjects suffering from cancer to CAR-T cell therapy, there were identified sets of biomarkers of which are highly performant in predicting response of subjects suffering from cancer to CAR-T cell therapy. Further disclosed is the higher prognostic performance of the herein identified sets of biomarkers in predicting a positive response of response of subjects suffering from cancer to CAR-T cell therapy. The herein identified sets of biomarkers do not form an arbitrary choice from prior long lists of genes / proteins as the herein identified biomarker sets all were consistently selected for their high performance in predicting a positive response for subjects suffering from cancer to CAR-T cell therapy. The instant disclosure therefore in one aspect relates to a number of methods relating to identifying subjects suffering from cancer that are likely to respond to CAR-T cell therapy. Such methods include:

[0005] 4 10877-10885-PCT A) methods of selecting, identifying, choosing, or collecting (or, to the contrary, of refusing or rejecting) a subject for treatment with CAR-T cell therapy, comprising one or more steps of: a) determining in a sample obtained from the subject that the subject harbors (or does not harbor) a population of CD8+ T-cells comprising one or more of the following marker sets: TIM3+; CD45RO-, CCR7+, CD95+, CD28+, and CD27-; CD45RO+, CD27+, Lag3-, and TIM3+; CD45RO+, CD27+, Lag3-, and TIM3-; CD95-, CCR7-, and CD27-; CD45RO+, CD27-, TIM3+, and PD1+; and / or CD45RO-, CD27-, CCR7+, and CD28-; or b) determining in a sample obtained from the subject that the subject harbors (or does not harbor) a population of CD4+ T-cells comprising the following marker set: CD127+, CD45RO-; and electing, identifying, choosing, or collecting (or, to the contrary, of refusing or rejecting) the subject as suitable for treatment with for CAR-T cell therapy, when the subject harbors T- cells comprising (or not comprising) one or more of the marker sets; and B) methods of determining eligibility, susceptibility, qualification, suitability or acceptability of a subject for treatment with CAR-T cell therapy, comprising one or more steps of: a) determining in a sample obtained from the subject that the subject harbors a population of CD8+ T-cells comprising one or more of the following marker sets: TIM3+; CD45RO-, CCR7+, CD95+, CD28+, and CD27-; CD45RO+, CD27+, Lag3-, and TIM3+; CD45RO+, CD27+, Lag3-, and TIM3-; CD95-, CCR7-, and CD27-; CD45RO+, CD27-, TIM3+, and PD1+; and / or CD45RO-, CD27-, CCR7+, and CD28-; or b) determining in a sample obtained from the subject a population of CD4+ T-cells comprising the following marker set: CD127+, CD45RO-; and determining the subject having cancer to be eligible, susceptible, qualified, suited or acceptable for treatment with for CAR-T cell therapy when the subject harbors T-cells comprising one or more of the marker sets; and C) methods of determining prior to start, at start, or after start of CAR-T cell therapy in a subject the response of the subject to CAR-T cell therapy, comprising one or more steps of:

[0006] 5 10877-10885-PCT a) determining in a sample obtained from the subject a population of CD8+ T-cells comprising one or more of the following marker sets: TIM3+; CD45RO-, CCR7+, CD95+, CD28+, and CD27-; CD45RO+, CD27+, Lag3-, and TIM3+; CD45RO+, CD27+, Lag3-, and TIM3-; CD95-, CCR7-, and CD27-; CD45RO+, CD27-, TIM3+, and PD1+; and / or CD45RO-, CD27-, CCR7+, and CD28-; or b) determining in a sample obtained from the subject a population of CD4+ T-cells comprising the following marker set: CD127+, CD45RO-; and determining prior to start, at start, or after start of CAR-T cell therapy the response of the subject to CAR-T cell therapy when the subject harbors T-cells comprising one or more of the marker sets; and D) methods of monitoring the response of a subject suffering from cancer to treatment with CAR-T cell therapy, comprising one or more steps of: a) determining in a sample obtained from the subject a population of CD8+ T-cells comprising one or more of the following marker sets: TIM3+; CD45RO-, CCR7+, CD95+, CD28+, and CD27-; CD45RO+, CD27+, Lag3-, and TIM3+; CD45RO+, CD27+, Lag3-, and TIM3-; CD95-, CCR7-, and CD27-; CD45RO+, CD27-, TIM3+, and PD1+; and / or CD45RO-, CD27-, CCR7+, and CD28-; or b) determining in a sample obtained from the subject a population of CD4+ T-cells comprising the following marker set: CD127+, CD45RO-; and determining the outcome of or the response to the CAR-T cell therapy. In any embodiment the subject may be suffering from cancer. In particular embodiments the cancer may be blood cancer such as, but not limited to, B cell acute lymphoblastic leukemia, Chronic lymphocytic leukemia, and Myeloma. Sample / prior to, at start, after or early after start of CAR-T cell therapy As indicated above, the methods can be performed on a sample, in particular a biological sample, obtained from the subject before or prior to starting CAR-T cell therapy. Alternatively, the methods can be performed on a (biological) sample obtained from the subject at the time point of starting / at the start of CAR-T cell therapy. In both cases, the therapeutic effects, if these would present

[0007] 6 10877-10885-PCT in the subject, of the CAR-T cell therapy have not yet taken off; in other words: the subject or the immune system of the subject has not yet reacted, adjusted or responded to the CAR-T cell therapy. Further alternatively, the methods can be performed on a (biological) sample obtained from the subject at the time point after start of the CAR-T cell therapy. In one embodiment, “after start” is early after start such as to mimic as closely as possible the situation of prior to or at start. In another embodiment, performing the methods on a (biological) sample obtained from the subject after start time point provides further information about the absence of presence of a response of the subject to the CAR-T cell therapy. In the latter case, the response of the subject to CAR-T cell therapy can be monitored and the methods are methods of monitoring the response of a subject having cancer to treatment with CAR-T cell therapy. More in particular, after start of the CAR-T cell therapy is referring to a time point (at when the biological sample was obtained from the subject) after administration of a first cycle of administration of CAR-T cell therapy. More in particular, early after start of CAR-T cell therapy is referring to a time point between just and up to one monty after the administration of CAR-T cell therapy, e.g. a time point between about 1 and 60 minutes, between about 1 minute and 2 hrs, between 1 minute and 6 hrs, between 1 minute and 12 hrs, between 1 minute and 24 hrs, between 1 to 24 hrs, between 1 to 2 days, between 1 to 3 days, between 1 to 4 days, between 1 to 5 days, between 1 to 6 days, between 1 to 7 days, between 1 to 9 days, between 1 to 15 days, between 1 to 20 days, between 7 to 15 days, at day 1, at day 2, at day 3, at day 4, at day 5, at day 6, at day 7, at day 8, at day 9, at day 10, at day 11, at day 12, at day 13, at day 14, at day 15, at day 16, at day 17, at day 18, at day 19 or at day 20 after the administration of the first cycle of CAR- T cell therapy. The “about 1 minute” (or around 1 minute) hereinabove refers to the closest feasible time point at which the biological sample can be obtained from the subject after administration of the first cycle of the CAR-T cell therapy; i.e. it can be less than 1 minute. In a particular embodiment, the sample is isolated from the subject within two weeks of treating the subject with CAR-T cell therapy. Further alternatively, the methods can be performed at multiple time points such as a first time prior to start of CAR-T cell therapy; followed by at least at one second time point such as at the start of CAR-T cell therapy and / or after starting CAR-T cell therapy. The sample in particular is a biological sample, more in particular a biological sample comprising T-cells from the subject. Such biological sample can be a solid sample (e.g. solid biopsy, or part of a surgically excised or removed tumor) or a fluid sample (e.g. a blood sample, lymph sample, or a bone marrow sample). If multiple samples are obtained from the subject, then, in one embodiment, the first sample can be a solid biopsy sample whereas the at least one second sample can be a liquid biopsy sample; or vice versa. In another embodiment, the sample obtained from the subject after start / early after start of CAR-T cell therapy is a blood sample. A biological sample, or shortly sample, as referred to herein is any sample taken from / obtained from a subject that can serve as source of detection of one or more of the populations

[0008] 7 10877-10885-PCT of T-cells described herein. Such biological samples include tumor samples (such as obtained upon tumor biopsy) and bodily fluid samples. Any of the above-listed methods subject of the current invention may entail / encompass / comprise / include detecting, determining, measuring, assessing or quantifying the expression level of one or more of the listed genes. In particular, any of the: - methods of selecting, identifying, choosing, or collecting (or, to the contrary, of refusing or rejecting) a subject for treatment with CAR-T cell therapy; or - methods of determining eligibility, susceptibility, qualification, suitability or acceptability of a subject for treatment with CAR-T cell therapy; or - methods of determining prior to start, at start, or after start of CAR-T cell therapy in a subject the response of the subject to CAR-T cell therapy; or - methods of monitoring the response of a subject suffering from cancer to treatment with CAR- T cell therapy; and may entail / encompass / comprise / include detecting, determining, measuring, assessing or quantifying the presence of one or more markers on or in a T-cell as included in the following embodiments: 1) in one embodiment, CD8; 2) in one embodiment, CD4; 3) in one embodiment, TIM3; 4) in one embodiment, CD45RO; 5) in one embodiment, CCR7; 6) in one embodiment, CD95; 7) in one embodiment, CD27; 8) in one embodiment, CD28; 9) in one embodiment, Lag3; 10) in one embodiment, PD1; 11) in one embodiment, CD127; 12) in one embodiment, CD8 and TIM3 13) in one embodiment, CD8, CD45RO, CCR7, CD27, CD28, and CD95; 14) in one embodiment, CD8, CD45RO, CD27, Lag3, and TIM3; 15) in one embodiment, CD8, CD95, CCR7, and CD27; 16) in one embodiment, CD8, CD45RO, CD27, TIM3, and PD1; 17) in one embodiment, CD8, CD45RO, CD27, CCR7, and CD28; 18) in one embodiment, CD4, CD127, and CD45RO; 19) in one embodiment, CD8, CD45RO, CCR7, CD27, CD28, CD95, and TIM3; 20) in one embodiment, CD8, CD95, CCR7, CD27, and TIM3; 21) in one embodiment, CD8, CD45RO, CD27, CCR7, CD28, and TIM3;

[0009] 8 10877-10885-PCT 22) in one embodiment, CD4, CD127, CD45RO, and TIM3; 23) in one embodiment, CD8, CD45RO, CCR7, CD27, CD28, CD95, and Lag3; 24) in one embodiment, CD8, CD95, CCR7, CD27, and Lag3; 25) in one embodiment, CD8, CD45RO, CD27, CCR7, CD28, and Lag3; 26) in one embodiment, CD4, CD127, CD45RO, and Lag3; 27) in one embodiment, CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, and Lag3; 28) in one embodiment, CD8, CD95, CCR7, CD27, TIM3, and Lag3; 29) in one embodiment, CD8, CD45RO, CD27, CCR7, CD28, TIM3, and Lag3; 30) in one embodiment, CD4, CD127, CD45RO, TIM3, and Lag3; 31) in one embodiment, CD8, CD45RO, CCR7, CD27, CD28, CD95, PD1, and TIM3; 32) in one embodiment, CD8, CD95, CCR7, CD27, PD1, and TIM3; 33) in one embodiment, CD8, CD45RO, CD27, CCR7, CD28, PD1, and TIM3; 34) in one embodiment, CD4, CD127, CD45RO, PD1, and TIM3; 35) in one embodiment, CD8, CD45RO, CCR7, CD27, CD28, CD95, PD1, and Lag3; 36) in one embodiment, CD8, CD95, CCR7, CD27, PD1, and Lag3; 37) in one embodiment, CD8, CD45RO, CD27, CCR7, CD28, PD1, and Lag3; 38) in one embodiment, CD4, CD127, CD45RO, PD1, and Lag3; 39) in one embodiment, CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, PD1, and Lag3; 40) in one embodiment, CD8, CD95, CCR7, CD27, TIM3, PD1, and Lag3; 41) in one embodiment, CD8, CD45RO, CD27, CCR7, CD28, TIM3, PD1, and Lag3; 42) in one embodiment, CD4, CD127, CD45RO, TIM3, PD1, and Lag3; 43) in one embodiment, CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, and PD1. CAR-T cell therapy “CAR-T cell therapy,” as used herein, refers to the killing of cancer cells using a T cell genetically modified to express a chimeric antigen receptor (CAR) that binds to the cancer cells, resulting in activation of the patient’s immune system to kill the cancer cells. CAR-T cell therapy can be particularly useful in treating blood cancers such as acute lymphoblastic leukemia (ALL), non - Hodgkin lymphoma (NHL), CD 19 malignancies, myeloma or other B cell- related or hematologic malignancies, or in treating solid tumors, such as ovarian cancer. All methods described herein may include an additional step of treating the subject with CAR- T cell therapy. Included are methods where the detection of the population occurs before, during, and / or after the CAR-T cell therapy. The detection may be performed utilizing T-cells that were isolated from the subject before, during, or after treating the subject with CAR. In a particular embodiment, the detection may be performed utilizing T-cells that were isolated from the subject within two weeks of treating the subject with CAR-T cell therapy.

[0010] 9 10877-10885-PCT In embodiments, the CAR-T cell therapy comprises T-cells engineered to express one or more chimeric antigen receptors (CARs) that specifically bind to a tumor antigen for use in cancer immunotherapy. In certain embodiments, the tumor antigen is selected from a group consisting of: TSHR, CD19, CD123, CD22, CD30, CD171, CS-1, CLL-1, CD33, EGFRvIII, GD2, GD3, BCMA, Tn Ag, PSMA, ROR1, FLT3, FAP, TAG72, CD38, CD44v6, CEA, EPCAM, B7H3, KIT, IL-13Ra2, Mesothelin, IL-11Ra, PSCA, PRSS21, VEGFR2, LewisY, CD24, PDGFR-beta, SSEA-4, CD20, Folate receptor alpha, ERBB2 (Her2 / neu), MUC1, EGFR, NCAM, Prostase, PAP, ELF2M, Ephrin B2, IGF-I receptor, CAIX, LMP2, gp100, bcr-abl, tyrosinase, EphA2, Fucosyl GM1, sLe, GM3, TGS5, HMWMAA, o-acetyl-GD2, Folate receptor beta, TEM1 / CD248, TEM7R, CLDN6, GPRC5D, CXORF61, CD97, CD179a, ALK, Polysialic acid, PLAC1, GloboH, NY-BR-1, UPK2, HAVCR1, ADRB3, PANX3, GPR20, LY6K, OR51E2, TARP, WT1, NY-ESO-1, LAGE-1a, MAGE-A1, legumain, HPV E6,E7, MAGE A1, ETV6-AML, sperm protein 17, XAGE1, Tie 2, MAD-CT-1, MAD-CT-2, Fos-related antigen 1, p53, p53 mutant, prostein, survivin and telomerase, PCTA- 1 / Galectin 8, MelanA / MART1, Ras mutant, hTERT, sarcoma translocation breakpoints, ML-IAP, ERG (TMPRSS2 ETS fusion gene), NA17, PAX3, Androgen receptor, Cyclin B1, MYCN, RhoC, TRP-2, CYP1B1, BORIS, SART3, PAX5, OY-TES1, LCK, AKAP-4, SSX2, RAGE-1, human telomerase reverse transcriptase, RU1, RU2, intestinal carboxyl esterase, mut hsp70-2, CD79a, CD79b, CD72, LAIR1, FCAR, LILRA2, CD300LF, CLEC12A, BST2, EMR2, LY75, GPC3, FCRL5, and IGLL1. Detection of Markers In embodiments, the determining is performed by detecting the presence or absence of markers in the cells or on the surface of the cell. Such determining can be performed by any suitable method including, but not limited to, immunocytochemistry, flow cytometry, cell staining, cell panning, electrophoresis, blotting, PCR, etc. In a further aspect, provided herein are kits, such as diagnostic kits or companion diagnostic kits, such as for use in any of the above described methods of, wherein such kits are comprising the tools to detect the presence or absence of at least one marker as described herein, such one or more of CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, PD1, CD4, or CD127 (combinations thereof having been elaborately described hereinabove). In one specific embodiment, such kits / diagnostic kits / companion diagnostic kits are including the tools for detecting the status of, in total, at most 1000 markers, at most 950 markers, at most 900 markers, at most 850 markers, at most 800 markers, at most 750 markers, at most 700 markers, at most 650 markers, at most 600 markers, at most 550 markers, at most 500 markers, at most 450 markers, at most 400 markers, at most 350 markers, at most 300 markers, at most 250 markers, or at most 225, 200, 175, 150, 125, 111, 110, 105, 100, 95, 90, 85, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66, 65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49, 48, 47, 46, 45, 44,

[0011] 10 10877-10885-PCT 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20 ,19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9 , 8, 7, 6, 5, 4, 3, 2 markers, or at most 1 marker; in any case including at least one selected marker as identified herein, i.e. at least on marker selected from CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, PD1, CD4, or CD127. Alternatively, such kits are including the tools for detecting the status of 1 to 10 markers, of 1 to 20 markers, of 1 to 50 markers, of 1 to 30 markers, of 1 to 50 markers, of 1 to 60 markers, of 1 to 70 markers, of 1 to 80 markers, of 1 to 90 markers, of 1 to 100 markers, of 1 to 150 markers, of 1 to 200 markers, of 1 to 300 markers, of 1 to 400 markers, of 1 to 500 markers, of 1 to 600 markers, of 1 to 700 markers, of 1 to 800 markers, of 1 to 900 markers, or of 1 to 1000 markers; in any case including at least one selected marker as identified herein, i.e. at least on marker selected from CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, PD1, CD4, or CD127. In particular, the tools of a kit / diagnostic kit / companion diagnostic kit may comprise, besides optionally e.g. reagents, enzymes, reaction vessels and kit inserts, antibodies capable of detecting the status of an envisaged marker. In particular, the antibodies specifically bind to the marker or in the immediate vicinity of marker (e.g. to another antigen that is found in complex with the marker). Determining if a marker is present or absent Determining the presence or absence of a biomarker as listed above (i.e. any of CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, PD1, CD4, or CD127) can be defined in some alternative ways. In one embodiment, the presence or absence of a particular marker refers to a pre-determined threshold. Typically such thresholds are defined after collecting a set of data points for a given marker X (i.e., any of CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, PD1, CD4, or CD127) as determined in a suitable number of samples. As outlined in the Examples herein, the presence or absence of a marker as listed above (i.e., any of CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, PD1, CD4, or CD127) has been determined in samples from a large number of subjects. From these, suitable thresholds can be determined. The presence or absence of a maker X (i.e., any of CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, PD1, CD4, or CD127) as determined in any of the above methods can alternatively be compared with the level at a similar time-point of the same marker X in a subject or set of subjects known as (subsequent) responder(s) (or non-responder(s)) to the CAR-T cell therapy (the control subject(s)). If the amount of marker X in the test subject is (roughly / about) equal to the level in a responding control subject, or is higher than the level in a non-responding control subject, then the marker is present. If the expression level of marker X in the test subject is (roughly / about) equal to the level in a non-responding control subject, or is lower than the level in a non-responding control subject, then the marker is absent.

[0012] 11 10877-10885-PCT In particular, the presence or absence of a marker X is determined by normalization relative to e.g. a housekeeping gene or set of housekeeping genes. Any diagnostic kit or device designed to operate according to any of the above-listed methods (see further) therefore includes the option / possibility to determine, assess, measure, quantify expression of one or more household genes in addition to the means to determine, assess, measure, quantify the presence or absence of one or more of the above-listed markers relating to CAR-T cell therapy (i.e., any one individual, or any one combination of CD8, CD45RO, CCR7, CD27, CD28, CD95, TIM3, Lag3, PD1, CD4, or CD127). EXAMPLES The present invention is further described in the following examples, which are offered by way of illustration and are not intended to limit the invention in any manner. Example 1: Identification of Populations having Marker Sets (Features) Cryopreserved mononuclear-enriched apheresis (APH) samples, were collected from subjects diagnosed with relapsed / refractory hematological malignancies (ie. acute lymphoblastic leukemia, chronic lymphocytic leukemia, acute myeloid leukemia, multiple myeloma, non hodgkins lymphoma). Samples were obtained from 132 cancer patients prior to treatment with CAR-T cell therapy and from 71 cancer patents after treatment CAR-T cell therapy and frozen. Of the 132 patients that were prior to treatment, post treatment 81 of the 132 were identified as responders and 51 of the 132 were identified as non-responders. The category of responders was formed by combining the patients with a label of complete responder ("CR") or partial responder ("PR") as "Best Overall Response", whereas the category of non-responders was formed by combing the patients labeled progressive disease / relapse ("PD / Rel") or non-responder ("NR"). The samples were thawed, and co-stained with a cocktail of fluorochrome-conjugated antibodies via multi-color panels and staining data acquired with a BD Fortessa. Analysis was performed manually via FlowJo software or FCS Express. Nonviable cells and / or monocytes were excluded from the downstream analysis and the composition of starting material for each subject was fated on various subsets of viable CD3+ T cells, with respect to lineages (ie. CD4, CD8, Eomes), cell differentiation markers (ie. CD45RO, CD27, CD28, CD95, CD127, CCR7), activation / functional status (ie. Ki67, HLA-DR, Granzyme B, KLRG1), inhibitory / exhaustion / dysfunction (ie. PD1, TIM- 3, LAG-3, CTLA-4). All potential maker combinations provided 2011 permutations that identified distinctive T cell subsets in the “pre-manufacture” features were used to input into the machine learning models to determine value in predicting post-infusion outcomes. The following examples of populations of T-cells were identified: Population 1 (TCD_JO)

[0013] 12 10877-10885-PCT Subjects harboring a population of T-cell with the following marker profile CD8+, CD45RO- , CCR7+, CD95+, CD28+, and CD27- were found to have correlated with favorable outcomes in CAR-T cell therapy. The gating strategy depicted in Figures 1A and 1B was used to identify T cells of this population. CD8+ TCSM in general (Q5 + CD95+) were positively correlated with favorable outcomes in CAR-T cell therapy for several disease indications. This flow cytometry analysis assessed the differentiation profile within the CD8+ Stem Memory T Cells (TSCM) subsets based on CD27 and CD28 expression. TCSM will be double positive and then will lose CD27 and / or CD28 during differentiation. The majority of the TCSM in this subject is Q9 (TSCM that express CD27 but have lost CD28). However, the selected population #1 is Q11, or TCD_JO, which identifies the frequency of TCSM that downregulated CD27 but retain CD28. Cells of this phenotype should still be capable of self-renewal due to retention of surface CD28. Increased frequency of Q11 and Q10 compared to Q9, Q12 may correlate with improved long-term responses. Population 2 (TCD_NV) Subjects harboring a population of T-cell with the following marker profile CD8+, CCR7-, and CD95- were identified. The gating strategy depicted in Figure 2 was used to identify T cells of this population. Readout is the proportion of viable CD8+ T cells that do not express CD95 or CCR7. Absence of CCR7 and CD95 excludes TCSM and central memory T cells (TCM). However, this gating does not distinguish between effector memory and effector cells or differentiation state. CD95 may be downregulated due to activation, or loss of CD95 may enable these cells to persist for a long time (and, if the TEM portion are not as differentiated, they would be able to self-renew additional TEM as well as generate additional effector T cells). Population 3 (TCD_OB) Subjects harboring a population of T-cell with the following marker profile CD8+, CD27-, and CD95- were identified. The gating strategy set depicted in Figure 3 was used to identify T cells of this population. Readout is the proportion of viable CD8+ T cells that do not express or have downregulated CD95 or CD27. Loss of CD27 occurs during T cell differentiation and correlates with reduced capacity for self-renewal. CD95 is induced when naïve T cells are exposed to cognate antigen. Gating includes effectors T cells and terminally differentiated T cells that may have long-term persistence. Populations 4 (ICI_AKK) and 5 (ICI_AKN) Subjects harboring a populations of T-cell with the following marker profile CD8+, CD27-, and CD95-, CD45RO+, CD27+, Lag3-, and TIM3+ (Population 4) or CD8+, CD27-, and CD95-,

[0014] 13 10877-10885-PCT CD45RO+, CD27+, Lag3-, and TIM33 (Population 5) were identified. The gating strategy depicted in Figures 4A and 4B was used to identify T cells of this population. Memory CD8 T cell subsets with varied tumor inhibition based on expression of inhibitory receptors, TIM3 and LAG3. Readout is the proportion of viable CD8+ memory T cells (45RO / CD27 does not distinguish between Central and Effector Memory subsets) that, for population #4 express TIM3 but not LAG3, and for population #5 do not express TIM3 or LAG3. Some tumors express ligands for inhibitory receptors including TIM3, LAG3, PD1, etc. Expression of the receptor can negatively impact the ability of the T cell to respond to tumor. Co-expression of several inhibitory receptors on T cells may further reduce the ability to response. In this subject, the majority of memory T cells are #5, and lack Lag3 and Tim, suggesting that the T cells were more capable of responding to tumors that expressed the Lag3 and TIm3 ligands. Populations 6 (ICI_AXS), 7 (ICI_BCD), 8 (ICI_BCJ), and 9 (ICI_BCP) Subjects harboring a populations of T-cell with the following marker profile CD8+ and TIM3+ (Populations 6-9) were identified. Population #6 ICI_AXS 8+ / To Tim3 / PD1: Tim3+ Population #7 ICI_BCD 8+ / To CTLA-4 / Tim3: Tim3+ Population #8 ICI_BCJ 8+ / To GrB / Tim3: Tim3+ Population #9 ICI_BCP 8+ / To Lag3 / Tim3: Tim3+ The gating strategy depicted in Figures 5A and 5B was used to identify T cells of this population. Bulk CD8+ T cells that express TIM3. 4 different ways of identifying the same population. Expression of inhibitory markers on bulk CD4 and CD8 T cells may be biomarkers for predicting outcomes to anti-tumor therapies in the absence of defining T cell subsets via known differentiation, exhaustion, and senescent phenotypes. Granzyme B is a lytic granule that can facilitate CTL functional activity. Readout is the proportion of viable CD8+ T cells that express TIM3 and would potentially be susceptible to inhibition by tumors that express ligands for TIM3. Increased frequency of these cells may result in poorer outcomes in patients whose tumors express TIM3 ligands. Populations #6, #7, #8, #9 should be redundant= same sample gated on Tim3+ but based on expression patterns with other receptors (#6, co-expression of PD1; #7, co-expression of CTLA4; #8, co- expression of GzB; #9 co-expression of Lag3). Should score comparably to each other in the models. Population 10 (ICI_ALY) Subjects harboring a population of T-cell with the following marker profile CD8+, CD45RO+, CD27-, TIM3+, and PD1+ were identified. The gating strategy set depicted in Figures 6A and 6B was used to identify T cells of this population.

[0015] 14 10877-10885-PCT Effector Memory and Effector CD8 T cell subsets that co-express TIM3 and PD1 inhibitory receptors. Readout is the proportion of viable CD8+ TEM and Teffector cells that express Tim3 and PD1 on the surface. As described for population #4 and #5, some tumors express ligands for inhibitory receptors including TIM3, LAG3, PD1, etc. Expression of the receptor can negatively impact the ability of the T cell to respond to tumor. Co-expression of several inhibitory receptors on T cells may further reduce the ability to response. In this subject, the 26% of the TEM / Teff cells co-express TIM3 and PD1 and may be inhibited by tumors that express the ligands for these receptors. It is important to note that in this subject, the TEM / Teff population is less than 5% of total CD8+ T cells, and a quarter of those fall into the TIM3+PD1+ quadrant. The population #10 represents less than 2% of the total CD8+ T cells for this subject. Population 11 (TCD_NN) Subjects harboring a population of T-cell with the following marker profile CD8+, CD45RO- , CD27-, CCR7+, and CD28- were identified. The gating strategy set depicted in Figures 7A and 7B was used to identify T cells of this population. Non-effector CD8+ TSCM cells that have lost CD27, CD28 but retain CCR7 and ability to home to secondary lymphoid structures. Readout is the proportion of viable CD8+ T cells that are CD45ROneg, CD27neg, CD28neg, but CCR7pos. Q16 gating does not distinguish between Effector T cells and TSCM, and represents less than 10% of the CD8+ T cells from this subject. Differentiated Effector T cells are further defined by the lack of CCR7 expression (Q4) and represent the majority of the cells in the Q16 gate for this subject. The presence of CCR7 on a very small portion of Q16 are non-Effectors (population #11, Q3). The lack of costimulatory receptors CD27 and CD28 may impact the ability of this subset to be sufficiently activated and respond to target antigen and / or self- renew. Population #11 represents <1% of the total CD8+ T cells in this subject. Population 12 (TCD_IG) Subjects harboring a population of T-cell with the following marker profile CD4+, CD45RO- , CD127+ were identified. The gating strategy set depicted in Figures 8A and 8B was used to identify T cells of this population. Naïve and / or TSCM CD4+ T cells. Readout is the proportion of viable CD4+ T cells that lack CD45RO but express IL-7 receptor, CD127. This gating in Q91 does not distinguish between naïve T cells and TSCM. The majority of the CD4+ T cells in this subject are CD45RO+CD127neg and likely TEM or Terminally differentiated effector cells (Q89). In this subject, selected population #12 (TCD_IG) represents <1% of total CD4+ T cells. Example 2: Machine Learning to Identify Marker Sets Correlated with Positive Outcomes from CAR- T Cell Therapy

[0016] 15 10877-10885-PCT Machine Learning (ML) models were developed that predict how a patient will respond to T- cell therapy at various points along the therapy regimen. The model makes its prediction prior to beginning the manufacturing process. The model was trained on disparate data including demographics, flow cytometry on the blood sample, cytokine analysis, and qPCR. Data science experiments involving feature selection and feature combination were performed to address multi- collinearity, and Shapley Value plots of feature importance have resulted in the determination of biomarkers. While 2011 features were analyzed in the development of the pre-manufacturing model, a greatly reduced set of about 12 features were chosen to form the signatures that comprised the input to the machine learning model. The data for the pre-manufacturing model represents a snapshot in time that describes the state of the patient at baseline, prior to possibly beginning cell therapy. The data may have been collected at regular intervals or may be irregular and was in need of interpolation for comparing to other patient data for modeling purposes. The pre-manufacturing model is used to make a go / no-go decision on whether to recruit a patient into a trial. The system for developing model consisted of 4 steps: data acquisition, data preprocessing, feature selection, and modeling. First, data acquisition collected data from each patient into a standard format. Second, data preprocessing involved gating the flow cytometry data for the pre-manufacturing model as set forth in Example 1. Third, feature selection identified informative and non-redundant inputs to the modeling. Fourth, modeling trained a machine learning algorithm such as a Random Forest or XGBoost to predict the label of patient response given the input features. The 2011 features obtained from the flow cytometry data were winnowed to 13 by identifying informative and non-redundant features. Mutual information was used to reduce the set of features to 1162 members. That subset was then reduced further to 13 based on principal component analysis. SHAP values were calculated to quantify the importance and impact of each feature to the model prediction. SHAP values were calculated to quantify the impact of each feature to the model prediction. Figure 9 shows that SHAP values of the 13 features. 12 features were selected for the pre- manufacturing model after an ablation study was performed on the features, with the features ordered based on the overall impact from top to bottom. On the seven flow cytometric features, a feature ablation study was performed with a separate model trained and evaluated with one feature excluded each time. As shown in Figure 10, excluding any of the top 5 features resulted in a remarkable drop in performance (measured in RoC AUC), while the impact of excluding TCD_IG was relatively trivial and excluding group_193 did not reduce the performance. Leave-one-out-validation with bootstrapping was performed on 132 patients for the pre- manufacturing model. A non-bootstrap Leave-one-out validation showed 82% accuracy and 0.85

[0017] 16 10877-10885-PCT ROC AUC for the pre-manufacturing model, but bootstrapping the data produced a distribution where the mean accuracy was 80% and mean ROC AUC is 0.84. Figure 11 provides a confusion matrix validating the results of the model against the 132 patients whose markers were determined pre-treatment. As can be seen therein, the model is better than 82% accurate at predicting responders to CAR-T cell therapy. While this invention has been described in certain embodiments, the present invention can be further modified within the spirit and scope of this disclosure. This application is therefore intended to cover any variations, uses, or adaptations of the invention using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains and which fall within the limits of the appended claims.

[0018] 17 10877-10885-PCT REFERENCES Agarwal, S., Aznar, M. A., Rech, A. J., Good, C. R., Kuramitsu, S., Da, T., Gohil, M., Chen, L., Hong, S.-J. A., Ravikumar, P., Rennels, A. K., Salas-Mckee, J., Kong, W., Ruella, M., Davis, M. M., Plesa, G., Fraietta, J. A., Porter, D. L., Young, R. M., & June, C. H. (2023). Deletion of the inhibitory co- receptor CTLA-4 enhances and invigorates chimeric antigen receptor T cells. Immunity, 56(10), 2388- 2407.e9. doi:10.1016 / j.immuni.2023.09.001. Cohen, A. D., Garfall, A. L., Stadtmauer, E. A., Melenhorst, J. J., Lacey, S. F., Lancaster, E., Vogl, D. T., Weiss, B. M., Dengel, K., Nelson, A., Plesa, G., Chen, F., Davis, M. M., Hwang, W.-T., Young, R. M., Brogdon, J. L., Isaacs, R., Pruteanu-Malinici, I., Siegel, D. L., Levine, B. L., June, C. H., & Milone, M. C. (2019). B cell maturation antigen-specific CAR T cells are clinically active in multiple myeloma. Journal of Clinical Investigation, 129(6), 2210-2221. doi:10.1172 / JCI126397. Fraietta, J. A., Lacey, S. F., Orlando, E. J., Pruteanu-Malinici, I., Gohil, M., Lundh, S., Boesteanu, A. C., Wang, Y., O'Connor, R. S., Hwang, W.-T., Pequignot, E., Ambrose, D. E., Zhang, C., Wilcox, N., Bedoya, F., Dorfmeier, C., Chen, F., Tian, L., Parakandi, H., Gupta, M., Young, R. M., Johnson, F. B., Kulikovskaya, I., Liu, L., Xu, J., Kassim, S. H., Davis, M. M., Levine, B. L., Frey, N. V., Siegel, D. L., Huang, A. C., Wherry, E. J., Bitter, H., Brogdon, J. L., Porter, D. L., June, C. H., & Melenhorst, J. J. (2018). Determinants of response and resistance to CD19 chimeric antigen receptor (CAR) T cell therapy of chronic lymphocytic leukemia. Nature Medicine, 24(5), 563-571. doi:10.1038 / s41591-018- 0010-1. Melenhorst, J. J., Chen, G. M., Wang, M., Porter, D. L., Chen, C., Collins, M. A., Gao, P., Bandyopadhyay, S., Sun, H., Zhao, Z., Lundh, S., Pruteanu-Malinici, I., Nobles, C. L., Maji, S., Frey, N. V., Gill, S. I., Loren, A. W., Tian, L., Kulikovskaya, I., Gupta, M., Ambrose, D. E., Davis, M. M., Fraietta, J. A., Brogdon, J. L., Young, R. M., Chew, A., Levine, B. L., Siegel, D. L., Alanio, C., Wherry, E. J., Bushman, F. D., Lacey, S. F., Tan, K., & June, C. H. (2022). Decade-long leukaemia remissions with persistence of CD4+ CAR T cells. Nature, 602(7897), 503-509. doi:10.1038 / s41586- 021-04390-6. Stadtmauer, E. A., Fraietta, J. A., Davis, M. M., Cohen, A. D., Weber, K. L., Lancaster, E., Mangan, P. A., Kulikovskaya, I., Gupta, M., Chen, F., Tian, L., Gonzalez, V. E., Xu, J., Jung, I.-Y., Melenhorst, J. J., Plesa, G., Shea, J., Matlawski, T., Cervini, A., Gaymon, A. L., Desjardins, S., Lamontagne, A., Salas-Mckee, J., Fesnak, A., Siegel, D. L., Levine, B. L., Jadlowsky, J. K., Young, R. M., Chew, A., Hwang, W.-T., Hexner, E. O., Carreno, B. M., Nobles, C. L., Bushman, F. D., Parker, K. R., Qi, Y.,

[0019] 18 10877-10885-PCT Satpathy, A. T., Chang, H. Y., Zhao, Y., Lacey, S. F., & June, C. H. (2020). CRISPR-engineered T cells in patients with refractory cancer. Science, 367(6481), eaba7365. doi:10.1126 / science.aba7365.

[0020] 19 10877-10885-PCT

Claims

What Is Claimed Is:

1. A method of determining whether a subject is a suitable candidate for CAR-T cell therapy, the method comprising: determining whether the subject harbors a population of CD8+ T-cells which are TIM3+; CD45RO-, CCR7+, CD95+, CD28+, and CD27-; CD45RO+, CD27+, Lag3-, and TIM3+; CD45RO+, CD27+, Lag3-, and TIM3-; CD95-, CCR7-, and CD27-; CD45RO+, CD27-, TIM3+, and PD1+; and / or CD45RO-, CD27-, CCR7+, and CD28-. wherein when the subject harbors the population, the subject is a suitable candidate for CAR-T cell therapy.

2. A method according to claim 1, wherein the subject suffers from a cancer.

3. The method according to claim 1, wherein the subject suffers from a blood cancer.

4. The method according to claim 3, wherein the blood cancer is selected from the group consisting of B cell acute lymphoblastic leukemia, Chronic lymphocytic leukemia, and Myeloma.

5. The method according to claim 1, wherein the determining step is determined by flow cytometry.

6. The method according to claim 1, wherein the determining is determining whether the subject harbors a population of CD8+ T-cells which are TIM3+.

7. The method according to claim 1, wherein the determining comprises determining whether the subject harbors a population of CD8+ T-cells which are CD45RO-, CCR7+, CD95+, CD28+, and CD27-.

8. The method according to claim 1, wherein the determining comprises determining whether the subject harbors a population of CD8+ T-cells which are CD45RO+, CD27+, Lag3-, and TIM3+.20 10877-10885-PCT9. The method according to claim 1, wherein the determining comprises determining whether the subject harbors a population of CD8+ T-cells which are CD45RO+, CD27+, Lag3-, and TIM3-.

10. The method according to claim 1, wherein the determining comprises determining whether the subject harbors a population of CD8+ T-cells which are CD95-, CCR7-, and CD27-.

11. The method according to claim 1, wherein the determining comprises determining whether the subject harbors a population of CD8+ T-cells which are CD45RO+, CD27-, TIM3+, and PD1+.

12. The method according to claim 1, wherein the determining comprises determining whether the subject harbors a population of CD8+ T-cells which are CD45RO-, CD27-, CCR7+, and CD28-.

13. The method according to any of the preceding claims, further comprising treating the suitable candidate with CAR-T cell therapy.

14. The method according to claim 13, wherein the determining is performed utilizing T-cells isolated from the subject before treating the suitable candidate with CAR-T cell therapy.

15. The method according to claim 13, wherein the determining is performed utilizing T-cells isolated from the subject after treating the suitable candidate with CAR-T cell therapy.

16. The method according to claim 15, wherein the T-cells are isolated from the subject within two weeks of treating the suitable candidate with CAR-T cell therapy 17. The method according to any of claims 13-16, the CAR-T cell therapy comprises T-cells engineered to express one or more chimeric antigen receptors (CARs) that specifically binds to a tumor antigen for use in cancer immunotherapy.

18. The method according to claim 17, wherein the tumor antigen is selected from a group consisting of: TSHR, CD19, CD123, CD22, CD30, CD171, CS-1, CLL-1, CD33, EGFRvIII , GD2, GD3, BCMA, Tn Ag, PSMA, ROR1, FLT3, FAP, TAG72, CD38, CD44v6, CEA, EPCAM, B7H3, KIT, IL-13Ra2, Mesothelin, IL-11Ra, PSCA, PRSS21, VEGFR2, LewisY, CD24, PDGFR-beta, SSEA-4, CD20, Folate receptor alpha, ERBB2 (Her2 / neu), MUC1, EGFR, NCAM, Prostase, PAP, ELF2M, Ephrin B2, IGF-I receptor, CAIX, LMP2, gp100,21 10877-10885-PCTbcr-abl, tyrosinase, EphA2, Fucosyl GM1, sLe, GM3, TGS5, HMWMAA, o-acetyl-GD2, Folate receptor beta, TEM1 / CD248, TEM7R, CLDN6, GPRC5D, CXORF61, CD97, CD179a, ALK, Polysialic acid, PLAC1, GloboH, NY-BR-1, UPK2, HAVCR1, ADRB3, PANX3, GPR20, LY6K, OR51E2, TARP, WT1, NY-ESO-1, LAGE-1a, MAGE-A1, legumain, HPV E6,E7, MAGE A1, ETV6-AML, sperm protein 17, XAGE1, Tie 2, MAD- CT-1, MAD-CT-2, Fos-related antigen 1, p53, p53 mutant, prostein, survivin and telomerase, PCTA-1 / Galectin 8, MelanA / MART1, Ras mutant, hTERT, sarcoma translocation breakpoints, ML-IAP, ERG (TMPRSS2 ETS fusion gene), NA17, PAX3, Androgen receptor, Cyclin B1, MYCN, RhoC, TRP-2, CYP1B1, BORIS, SART3, PAX5, OY-TES1, LCK, AKAP-4, SSX2, RAGE-1, human telomerase reverse transcriptase, RU1, RU2, intestinal carboxyl esterase, mut hsp70-2, CD79a, CD79b, CD72, LAIR1, FCAR, LILRA2, CD300LF, CLEC12A, BST2, EMR2, LY75, GPC3, FCRL5, and IGLL1.

19. A method of determining whether a subject is a suitable candidate for CAR-T cell therapy, the method comprising determining whether the subject harbors a population of CD4+ T- cells which are CD127+, CD45RO-, wherein when the subject harbors the population, the subject is a suitable candidate for CAR-T cell therapy.

20. A method according to claim 19, wherein the subject suffers from a cancer.

21. The method according to claim 19, wherein the subject suffers from a blood cancer.

22. The method according to claim 21, wherein the blood cancer is selected from the group consisting of B cell acute lymphoblastic leukemia, Chronic lymphocytic leukemia, and Myeloma.

23. The method according to claim 19, wherein the determining step is determined by flow cytometry.

24. The method according to any of the preceding claims, further comprising treating the suitable candidate with CAR-T cell therapy.

25. The method according to claim 24, wherein the determining is performed utilizing T-cells isolated from the subject before treating the suitable candidate with CAR-T cell therapy.22 10877-10885-PCT26. The method according to claim 24, wherein the determining is performed utilizing T-cells isolated from the subject after treating the suitable candidate with CAR-T cell therapy.

27. The method according to claim 25, wherein the T-cells are isolated from the subject after within two weeks of treating the suitable candidate with CAR-T cell therapy 28. The method according to any of claims 24-27, the CAR-T cell therapy comprises T-cells engineered to express one or more chimeric antigen receptors (CARs) that specifically binds to a tumor antigen for use in cancer immunotherapy.

29. The method according to claim 28, wherein the tumor antigen is selected from a group consisting of: TSHR, CD19, CD123, CD22, CD30, CD171, CS-1, CLL-1, CD33, EGFRvIII , GD2, GD3, BCMA, Tn Ag, PSMA, ROR1, FLT3, FAP, TAG72, CD38, CD44v6, CEA, EPCAM, B7H3, KIT, IL-13Ra2, Mesothelin, IL-11Ra, PSCA, PRSS21, VEGFR2, LewisY, CD24, PDGFR-beta, SSEA-4, CD20, Folate receptor alpha, ERBB2 (Her2 / neu), MUC1, EGFR, NCAM, Prostase, PAP, ELF2M, Ephrin B2, IGF-I receptor, CAIX, LMP2, gp100, bcr-abl, tyrosinase, EphA2, Fucosyl GM1, sLe, GM3, TGS5, HMWMAA, o-acetyl-GD2, Folate receptor beta, TEM1 / CD248, TEM7R, CLDN6, GPRC5D, CXORF61, CD97, CD179a, ALK, Polysialic acid, PLAC1, GloboH, NY-BR-1, UPK2, HAVCR1, ADRB3, PANX3, GPR20, LY6K, OR51E2, TARP, WT1, NY-ESO-1, LAGE-1a, MAGE-A1, legumain, HPV E6,E7, MAGE A1, ETV6-AML, sperm protein 17, XAGE1, Tie 2, MAD- CT-1, MAD-CT-2, Fos-related antigen 1, p53, p53 mutant, prostein, survivin and telomerase, PCTA-1 / Galectin 8, MelanA / MART1, Ras mutant, hTERT, sarcoma translocation breakpoints, ML-IAP, ERG (TMPRSS2 ETS fusion gene), NA17, PAX3, Androgen receptor, Cyclin B1, MYCN, RhoC, TRP-2, CYP1B1, BORIS, SART3, PAX5, OY-TES1, LCK, AKAP-4, SSX2, RAGE-1, human telomerase reverse transcriptase, RU1, RU2, intestinal carboxyl esterase, mut hsp70-2, CD79a, CD79b, CD72, LAIR1, FCAR, LILRA2, CD300LF, CLEC12A, BST2, EMR2, LY75, GPC3, FCRL5, and IGLL1.23 10877-10885-PCT

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