WO2020172591A1

WO2020172591A1 - A method to predict a patient's response to an anti-cancer drug from an expression level of a set of genes

Info

Publication number: WO2020172591A1
Application number: PCT/US2020/019313
Authority: WO
Inventors: Aaron Goldman; Pradip K. Majumder; Padhma D. Radhakrishnan; Biswanath MAJUMDER; Saravanan Thiyagarajan
Original assignee: Mitra Rxdx, Inc.
Priority date: 2019-02-22
Filing date: 2020-02-21
Publication date: 2020-08-27
Also published as: WO2020172592A1

Abstract

Provided are methods and kits for predicting a patient's response to an anti-cancer drug. The method comprises determining in a control biological sample an expression level of a set of genes to obtain a pre-treatment gene expression level, determining in a drug treated biological sample an expression level of the set of genes to obtain a post-treatment gene expression level, and comparing the post-treatment gene expression level to the pre-treatment gene expression level. If the post-treatment gene expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the anti-cancer drug and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the anti-cancer drug.

Description

A METHOD TO PREDICT A PATIENT’S RESPONSE TO AN ANTI-CANCER

DRUG FROM AN EXPRESSION LEVEL OF A SET OF GENES

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to and the benefit of U.S. Provisional Application No. 62/809,463, filed February 22, 2019, and U.S. Provisional Application No. 62/809,468, filed February 22, 2019, the contents of which are hereby incorporated by reference in their entireties.

Technical Field

[0001] This application pertains to prognostic and therapeutic methods involving predicting the response of a cancer patient to an anti-cancer drug based on a modulation of gene expression.

Background

[0002] Cancer is a highly heterogenous disease with significant inter-patient as well as intra-patient variability'. Given the toxicity profiles and high treatment cost of anti-cancer therapeutics, it is of utmost importance to identify subpopulation of patients who are most likely to respond to a given drug. Precision medicine has made significant headway in using biomarker guided strategies to match patients to drugs. However, the number of patients who harbor an actionable genetic mutation is low, and the fraction of patients actually benefitting from a therapy targeting such mutation is still lower (./ Clin. Oncol. 2015 Sep 1 ;33(25):2753- 62). Cancer immunotherapy has made significant progress in recent years, however as with many other blockbuster anti-cancer therapies (such as Avastin), clinical success is very unpredictable, largely owing to the lack of biomarkers that will .predict personalized response. For example, an anti-PD-1 drug, Nivolumab, has shown 20-30% response rates in patients with any degree of PD-L1 expression, but durable response has also been observed in patients with low or no detectable PD-L1 expression (Future Oncol. 2018; 14: 2415).

[0003] In the present disclosure, we have developed a biomarker panel based on the expression levels of a set of genes. These genes are modulated differentially in individual patients in response to an anti-cancer drug (such as an immune check point inhibitor). Based on the modulation of expression of the genes under drug pressure in a patient, the patient’s response or the durability of response to the anti-cancer drug can be predicted.

[0004] All references cited herein, including patent applications, patent publications, and scientific literature, are herein incorporated by reference in their entirety, as if each individual reference were specifically and individually indicated to be incorporated by reference.

Brief description

[0005] The invention relates to a method to predict a patient’s response to an anticancer drug, the method comprising, determining in a control biological sample, an expression level of a set of genes to obtain a pre-treatment gene expression level, determining in a drug treated biological sample, an expression level of the set of genes to obtain a posttreatment gene expression level, and comparing the post-treatment gene expression level with the pre-treatment gene expression level. The set of genes is at least 5 genes selected from a gene panel consisting of CASPl, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1. If the post-treatment gene expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the anti-cancer drug and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the anti-cancer drug.

[0006] In some embodiments, the invention relates to a method to predict a patient’s response to an anti-cancer drug, the method comprising, determining in a control biological sample, an expression level of a set of genes to obtain a pre-treatment gene expression level, determining in a drug treated biological sampl e, an expression level of the set of genes to obtain a post-treatment gene expression level, and comparing the post-treatment gene expression level with the pre-treatment gene expression level, wherein the anti-cancer drug is an immune checkpoint inhibitor and wherein the set of genes is CASP1, CCL3, CCL4,

CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1 and TNF. If the post-treatment gene expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the immune checkpoint inhibitor and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the immune checkpoint inhibitor. [0007] In some embodiments, the invention relates to a kit comprising a set of probes for detecting expression of each gene in the set of genes, wherein the set of genes is at least 5 genes selected from a gene panel consisting of CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRFl , STATl , STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1.

Description of figures

[0008] FIG. 1. Schematic of an embodiment for prediction of response using in-vivo drug treated biological sample and control biological sample.

[0009] FIG. 2. Schematic of an embodiment for prediction of response with in-vitro drug treated biological sample and control biological sample.

[0010] FIG. 3. Determination of differential gene expression levels and prediction of response for each patient treated with a PD-1 inhibitor using in-vivo drug treated biological sample and control biological sample.

[0011 ] FIG. 4A. Predicted response and clinical response for each patient with PD-1 inhibitor using in-vitro drug treated biological sample and control biological sample. Clinical responders are indicated by black bars and clinical non-responders are indicated by grey bars.

[0012] FIG. 4B. Confusion matrix for determination of positive and negative predictive values. Negative predictive value is 100% and positive predictive value is 71.4%.

[0013] FIG. 5. Determination of differential gene expression levels and prediction of response for each patient using in-vitro drug treated biological sample (tissue-section cultured in presence of PD-1 inhibitor, Nivolumab) and control biological sample (tissue-section cultured in absence of PD-1 inhibitor, Nivolumab).

Detailed description

[0014] The singular fonns“a”“an” and“the” include plural referents unless the context clearly dictates otherwise. Approximating language, as used herein throughout the specification and claims, may be applied to modify any quantitative representation that could permissibly vary without resulting in a change in the basic function to which it is related. Accordingly, a value modified by a term such as“about” is not to be limited to the precise value specified. Unless otherwise indicated, all numbers expressing quantities of ingredients, properties such as molecular weight, reaction conditions, so forth used in the specification and claims are to be understood as being modified in all instances by the term“about.” Reference to“about” a value or parameter herein includes (and describes) embodiments that are directed to that value or parameter per se. For example, description referring to“about X” includes description of“X.” Accordingly, unless indicated to the contrary, the numerical parameters set forth in the following specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the present invention. At the very least each numerical parameter should at least be construed in light of the number of reported significant digits and by applying ordinary rounding techniques.

[0015] It should be understood that, although exemplary' embodiments are illustrated in the figures and described below, the principles of the present disclosure may be implemented using any number of techniques, whether currently known or not. The present disclosure should in no way be limited to the exemplary implementations and techniques illustrated in the drawings and described below.

[0016] As used herein,“patient” refers to a cancer patient. The cancer can be a solid cancer or a hematological cancer. In some embodiments, the cancer is a cancer of head and neck. In some embodiments, the cancer of head and neck is, head and neck squamous cell carcinoma (HNSCC). In some embodiments, the patient is a head and neck cancer patient.

[0017] As used herein, a drug is an anti-cancer drug. An anti-cancer drug can be a single anti-cancer drug or a combination of anti-cancer drugs. Non-limiting examples of an anti-cancer drug includes a chemotherapeutic agent such as a cytostatic or a cytotoxic agent, a targeted anti-cancer agent (such as an antibody or small molecule drug targeting a signal transduction pathway in cancer), an immimotherapeutic agent, and the like. In some embodiments the anti-cancer drug is an immimotherapeutic agent. Examples of an immunotherapeutic agent includes but is not limited to an immune check point inhibitor (such as molecules directed to PD-1, PD-L1, or CTLA-4 etc.), an immune-stimulating agent (such as an agonistic antibody against OX-40), an adoptive cell therapy (such as CAR-T cell,

Tumor Infiltrating Lymphocyte etc.), a cancer vaccine, a cytokine (such as Interleukin-2, lnterferon-a etc.), and the like. In some embodiments, the anti-cancer drug is an immune checkpoint inhibitor. Non-limiting examples of immune checkpoint inhibitor include PD-1 inhibitor, PD-L1 inhibitor, CTLA-4 inhibitor and the like. In some embodiments, the immune checkpoint inhibitor is a PD-1 inhibitor, such as a small molecule or an antibody. In some embodiments, the small molecule or the antibody binds to PD-1 , thereby preventing the interaction of PD-1 with its ligand, PD-L1. In some embodiments, the PD-1 inhibitor is an antibody directed to PD-1, such as nivolumab or pembrolizumab. In some embodiments, an anti -cancer drug is a single anti -cancer drug such as an immune check point inhibitor (for example nivolumab or pembrolizumab). In some embodiments, an anti-cancer drug is a combination of two or more immune checkpoint inhibitors (for example, nivolumab and an antibody directed to CTLA-4, such as ipilimumab), or a combination of an immune checkpoint inhibitor (as example, nivolumab) with another class of anti-cancer drug (for example, cisplatin).

[0018] As used herein,“biological sample” refers to any tissue or biological material obtained from a patient. Non-limiting examples of tissue includes tumor tissue, skin tissue, hair follicles, bone marrow, blood etc. A biological material is a non-tissue, mostly acellular biological sample. Non-limiting examples of biological material includes saliva, sputum, pleural effusion, urine, sweat, serum, plasma, nail clippings and the like. It should be understood that a biological sample, without limitation, is any substance obtained from a patient in which a biomarker, such as the expression level of a gene, can be determined. In some embodiments, the biological sample is a tumor tissue obtained from a patient. In some embodiments, the biological sample is a blood sample obtained from a patient.

[0019] A drug treated biological sample is a tissue or a biological material that has been treated with an anti-cancer drug either in-vivo or in-vitro. A biological sample that has been treated with an anti-cancer drug in-vivo is also referred to as an in-vivo drug treated biological sample. A biological sample that has been treated with an anti-cancer drug in-vitro is also referred to as an in-vitro drug treated biological sample. A control biological sample is a tissue or a biological material that has not been treated with the anti-cancer drug either in- vivo or in-vitro In some embodiments, the anti-cancer drug is an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is a PD-1 inhibitor. In some embodiments, the PD-1 inhibitor is nivolumab or pembrolizumab.

[0020] In some embodiments a drug treated biological sample is a tissue or a biological material that has been treated with an anti-cancer drug in-vivo, that is an in-vivo drug treated biological sample. An example of such embodiment is where the biological sample is obtained from a patient after the patient is treated with an anti-cancer drug. The biological sample is obtained from the patient after the patient is treated with at least one dose of the anti-cancer drug. In some embodiments, the biological sample is obtained from the patient after the patient is treated with multiple doses of the anti-cancer drug. In some other embodiments, the biological sample is obtained from the patient after the patient has completed a treatment cycle with the anti-cancer drug. In some embodiments, a control biological sample is a tissue or a biological material that has not been treated with the anticancer drug in-vivo. An example of such embodiment is where the biological sample is obtained from the patient before the patient is treated with the anti-cancer drug. In some embodiments, the biological sample is a tumor tissue or blood. In some embodiments, a control biological sample is a tumor tissue obtained from a patient before the patient is treated with an anti-cancer drug and a drug treated biological sample is a tumor tissue obtained from the patient after the patient is treated with the anti-cancer drug. In some embodiments, a control biological sample is a blood sample obtained from a patient before the patient is treated with an anti-cancer drug and a drug treated biological sample is a blood sample obtained from the patient after the patient is treated with the anti-cancer drug.

[0021] In some embodiments a drug treated biological sample is a tissue or a biological material that has been treated with an anti-cancer drug in-vitro, that is an in-vitro drug treated biological sample. As an example of such embodiment, a drug treated biological sample is a tissue-section which is cultured in presence of the anti-cancer drug, wherein the tissue-section is a section of a tumor tissue obtained from a patient. In some embodiments, a control biological sample is a tissue-section which is not treated with an anti-cancer drug in- vitro. As an example of such embodiment, a control biological sample is a tissue-section which is not cultured in presence of the anti-cancer drug, wherein the tissue-section is a section of the tumor tissue obtained from the patient. A tissue-section which is not cultured in presence of an anti -cancer drug refers to a tissue-section which is not cultured in-vitro or a tissue-section which is cultured in-vitro, but in absence of the anti-cancer drug. In some embodiments, a control biological sample is a tissue-section which is cultured in absence of the anti-cancer drug. In some embodiments, a control biological sample is a tissue-section which is not cultured in-vitro. In such embodiments, the tissue-section may be processed for biomarker analysis, such as determination of the expression level of a gene, without the tissue-section being cultured prior to such analysis In some embodiments, the patient is not treated with the anti-cancer drug before the tumor tissue is obtained. In some embodiments, the patient is not treated with the anti-cancer drug for a minimum period of time before the tumor tissue is obtained. In some embodiments, the patient has not received a treatment with the anti-cancer drug at least 3 weeks before the tumor tissue is obtained.

[0022] Treating a patient with an anti-cancer drug refers to administering into the patient an anti-cancer drug. An anti-cancer drug can be administered by any means known to a person skilled in the art. Non-limiting examples of means of administering an anti -cancer drug includes intravenous administration, intramuscular administration, intrathecal administration, oral administration, and the like.

[0023] A biological sample can be obtained by any method known in the art, including but not limited to surgery, biopsy, aspiration, phlebotomy, thoracentesis, swab collection and the like.

[0024] A response can be determined by any means and/or criteria known to a person of ordinary skill in the art, such as but not limited to the RECIST or the WHO criteria. A response can be any one of tumor response (such as determined from a change in tumor size), overall survival (OS), progression-free survival (PFS), time to progression (TTP), recurrence, and the like. In some embodiments, a patient who is predicted to respond to an anti-cancer drug (responder or R), is predicted to achieve a complete response, a partial response, or have increased OS, PFS, or TTP, if treated with the anti-cancer drug. In some embodiments, a patient who is predicted to respond to an anti-cancer drug is predicted to have a complete or partial regression in tumor size, if treated with the anti-cancer drug. In some embodiments, a patient who is predicted not to respond to an anti-cancer drug (non-responder or NR), is predicted to have a progressive disease or have decreased OS, PFS, or TTP, if treated with the anti-cancer drug. In some embodiments, a patient who is predicted not to respond to an anti -cancer drug is predicted to have a progression in tumor size, if treated with the anticancer drug. An increased or decreased OS, PFS, and/or TTP can be predicted with respect to a median OS, a median PFS, and/or a median TTP for a particular anti-cancer drug. For example, a patient who is predicted to respond to an anti -cancer drug, is predicted to have an OS or PFS which is longer than the median OS or PFS respectively for that anti-cancer drug and a patient who is predicted not to respond to an anti-cancer drug, is predicted to have an OS or PFS which is shorter than the median OS or PFS respectively for that anti-cancer drug.

[0025] “Probe” as used herein means an oligonucleotide that is capable of specifically hybridizing under stringent hybridization conditions to a transcript expressed by a gene in a set of genes. The term“transcript” includes RNA transcribed from the gene, and/or specific spliced variants thereof and/or fragments of such RNA and spliced variants.

[0026] In some embodiments, the method of the disclosure relates to determining an expression level of a set of genes, wherein the set of genes is at least 5 genes (such as 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 genes) selected from a gene panel consisting of CASP1, CCL3, CCL4, CCL5, CCRl, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRFl, STAT1, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1. In some embodiments, the method of the disclosure relates to determining an expression level of a set of genes, wherein the set of genes is at least 15 genes selected from a gene panel consisting ofCASPl, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1 , STATl, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPPl. In some embodiments, the set of genes is CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, 1L12RB2, LTA, NFKBIA, PRF1, STATl and TNF.

Table 1 : Gene Panel

[0027] An expression level of a gene may be an absolute expression level of the gene or a normalized expression level of the gene. An expression level of a gene can be normalized by any method known in the art to obtain a normalized expression level. In some embodiments, the expression level is normalized by correcting the absolute expression level of a gene by comparing its expression to the expression of a control gene such as a housekeeping gene that is constitutively expressed. Non-limiting examples of genes for normalization include housekeeping genes such as the Actin gene ACTB, Lactate dehydrogenase A (LDHA), Ribosomal 18S gene, Phosphoglycerate kinase 1 (PGK1), Ubiquitin C (UBC), Transferrin receptor (TFRC) and b-Glucuronidase (GUSB). In some embodiments, nonnalization method further comprises removal of unwanted variation (RUV) method to correct the unwanted technical effects introduced in the dataset. Positive and negative controls (ERCC spike-in controls) which carry' unwanted variation in their expression are used to adjust for unwanted effects in the gene expression dataset. In some embodiments, a normalization of gene expression is done using housekeeping genes, followed by removal of unwanted variation. Removal of unwanted variation (RUV) can be done by any known method, for example by using any commercial or open source packages such as RUVSeq (Bioconductor) (Risso D, et al. (2014)“Normalization of RNA-seq data using factor analysis of control genes or samples.” Nature Biotechnology', 32(9), 896-902).

[0028] In some embodiments, a gene expression level is a single expression level determined from individual expression levels of each gene in a set of genes. For example, an expression level of a set of genes CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1 and TNF refers to a mean expression level or a median expression level determined from individual expression levels of CASP1, CCL3, CCL4, CCL5, CCRl , CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1,

STATl and TNF genes. In some embodiments, the individual expression levels are normalized to obtain normalized expression levels of each gene in the set of genes. A mean expression level or a median expression level is determined from the normalized expression levels. In some embodiments, the normalized expression levels can be transformed in any convenient way before determination of the mean expression level or the median expression level. In some embodiments, the normalized expression levels are converted to Z-scores. In some embodiments, the normalized expression levels are log transformed. In some embodiments, the Z-scores are further log transformed before determination of a mean or a median expression level. An expression level of the set of genes is determined in a control biological sample to obtain a pre-treatment gene expression level and an expression level of the set of genes is determined in a drug treated biological sample to obtain a post-treatment gene expression level. In some embodiments, the set of genes is at least 5 genes (such as 5,

6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 genes) selected from a gene panel consisting of CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1. In some embodiments, the set of genes is at least 15 genes selected from a gene panel consisting of CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRFl, STAT1, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3,

GATA3 and SPP1. In some embodiments, the set of genes is CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRFl, STAT1 and TNF.

[0029] In some embodiments, the method of the invention relates to determining individual expression levels of each gene in a set of genes. For example, an expression level of a set of genes CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRFl, STAT1 and TNF refers to individual expression levels of each of CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRFl, STAT1 and TNF gene. In some embodiments, the individual expression levels are normalized. In some embodiments, the normalized expression levels are transformed in any convenient way. In some embodiments, the expression levels are converted to Z-scores. In some embodiments, the expression levels are log transformed. In some embodiments, the Z- scores are further log transformed. Individual expression levels are determined in a control biological sample to obtain a pre-treatment gene expression level for each gene in a set of genes and individual expression levels are determined in a drug treated biological sample to obtain a post-treatment gene expression level for each gene in the set of gene. In such embodiments, comparing the post-treatment gene expression level with the pre-treatment gene expression level refers to comparing the expression level of a gene in the drug treated biological sample with the expression level of the same gene in the control biological sample. If the expression level of majority of the genes (such as greater than 50% of the genes) are increased in the drug treated biological sample compared to the control biological sample, the post-treatment gene expression level is considered to be greater than the pre-treatment gene expression level. If the expression level of majority of the genes (such as greater than 50% of the genes) are decreased in the drug treated biological sample compared to the control biological sample, the post-treatment gene expression level is considered to be less than the pre-treatment gene expression level. In some embodiments, the set of genes is at least 5 genes (such as 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 genes) selected from a gene panel consisting ofCASPl, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1, STAT4, INF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1. In some embodim ents, the set of genes is at least 15 genes selected from a gene panel consisting ofCASPl, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1. In some embodiments, the set of genes is CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1,

STAT1 and INF.

[0030] An expression level of a gene is determined using any method known to a person skilled in the art. In some embodiments, an expression level of a gene is determined by quantifying the level of a gene expression product such as a protein or an RNA. The gene expression product can be quantified in a biological sample, a secretion of the biological sample, or in a superatant of a culture medium used for culturing the biological sample. In some embodiments, the expression level of a gene is determined by quantifying the RNA level. A person skilled in the art will appreciate that a number of methods can be used to isolate RNA from a biological sample (such as a drug treated biological sample or a control biological sample). RNA can be extracted from fresh, frozen or fixed biological sample. In some embodiments, RNA can be extracted using any commercially available RNA extraction kits. In some embodiments, RN A is extracted from a tissue, such as a tumor tissue. In some embodiments, RNA is extracted from a blood sample. In some embodiments, the expression level of a gene is determined by quantifying the protein level. Non-limiting examples of methods for quantifying protein level includes western blot or enzyme-linked immunosorbent assay (ELISA).

[0031] Non-limiting examples of methods to determine an expression level of a gene from RNA include, quantitative reverse transcription-PCR (RT-qPCR), various quantitative isothermal amplification methods (for example nucleic acid sequence-based amplification (NASBA), reverse transcription loop-mediated isothermal amplification (RT-LAMP) and others), norther blot, microarray, RNA sequencing, or any other method for quantifying an expression level of a gene.

[0032] In some embodiments, a method to determine an expression level of a gene includes using the nComiter® Analysis System marketed by NanoString® Technologies (Seattle, Washington USA). This system, which is described by Geiss et al. Nature

Biotechnol. 2(3):317-325 (2008), utilizes a pair of probes, namely, a capture probe and a reporter probe, each comprising a 35- to 50-base sequence complementary to the transcript to be detected. The capture probe additionally includes a short common sequence coupled to an immobilization tag, e.g. an affinity tag that allows the complex to be immobilized for data collection. The reporter probe additionally includes a detectable signal or label, e.g. is coupled to a color-coded tag. Following hybridization, excess probes are removed from the sample, and hybridized probe/target complexes are aligned and immobilized via the affinity or otiier tag in a cartridge. The samples are then analyzed, for example using a digital analyzer or other processor adapted for this purpose. Generally, the color-coded tag on each transcript is counted and tabulated for each target transcript to yield the expression level of each transcript in the sample. This system allows measuring the expression levels of hundreds of unique gene transcripts in a single multiplex assay using capture and reporter probes designed by NanoString.

[0033] In some embodiments, the method relates to determining an expression level of a set of genes in a control biological sample to obtain a pre-treatment gene expression level, determining an expression level of the set of genes in a drug treated biological sample to obtain a post-treatment gene expression level, and comparing the post-treatment gene expression level with the pre-treatment gene expression level. In some embodiments, the set of genes is at least 5 genes selected from the gene panel CASP1, CCL3, CCL4, CCL5,

CCRl, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1. In some

embodiments, the set of genes is at least 15 genes selected from the gene panel CASP1,

CCL3, CCL4, CCL5, CCRl, CCR2, CCR5, CTLA4, IFNg, ILI2RB2, LTA, NFKBIA, PRF1, STAT1, STAT4, INF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1. In some embodiments, the set of genes is CASP1, CCL3, CCL4, CCL5, CCRl, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1 and TNF. If the post-treatment gene expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the anti-cancer drug and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the anti-cancer drug.

[0034] In some embodiments, a control biological sample (such as a blood sample or a tumor tissue) is obtained from a patient before the patient is treated with an anti-cancer drug. An expression level of a set of genes is determined in the control biological sample to obtain a pre-treatment gene expression level. The patient is then treated with the anti-cancer drug. In some embodiments, treating the patient with the anti-cancer drug refers to administering into the patient at least one dose of the anti-cancer drug. A drug treated biological sample is obtained from the patient after the patient is treated with the anti-cancer drug. An expression level of the same set of genes is determined in the drug treated biological sample to obtain a post-treatment gene expression level. If the post-treatment gene expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the anti-cancer drug and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the anti-cancer drug. In some embodiments, the method of determining the expression level of a set of genes comprises determining individual expression levels of each gene in the set of genes, normalizing the expression level of each gene to obtain a normalized expression level, converting the normalized expression level of each gene to a corresponding Z-score, and determining a median expression level from the Z-scores of all the genes in the set of genes. In some embodiments, the Z-scores are further log transformed before determination of the median expression level. In some embodiments, the method of the invention further comprises administering into the patient the anti -cancer drug if the post-treatment gene expression level is greater than the pre-treatment gene expression level. In some

embodiments, the method of the invention further comprises stopping the administration of the anti-cancer drug into the patient if the post-treatment gene expression level is less than the pre-treatment gene expression level. In some embodiments, the anti-cancer drug is an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is a PD-1 inhibitor. In some embodiments, the PD-1 inhibitor is nivolumab or pembrolizumab.

[0035] In some embodiments, the drug treated biological sample is a tissue-section which is cultured in presence of the anti-cancer drug, wherein the tissue-section is a section of a tumor tissue obtained from a patient. A tissue-section which is cultured in presence of an anti-cancer drug is also referred to as a drug treated tissue-section. The control biological sample is a tissue-section which is not cultured in presence of the anti-cancer drug, wherein the tissue-section is a section of the tumor tissue obtained from the patient. A tissue-section which is not cultured in presence of the anti -cancer drug, is a tissue section which is not cultured in-vitro or a tissue section which is cultured in-vitro, but in absence of the anticancer drug. In some embodiments, the control biological sample is a tissue-section which is not cultured in-vitro. In some embodiments, the control biological sample is a tissue-section which is cultured in-vitro, but in absence of the anti-cancer drug. In some embodiments, the control biological sample is a tissue-section which is cultured in-vitro in absence of the anticancer drug and in presence of a vehicle control. In some embodiments, where the drug is a targeted antibody (such as nivolumab or pembrolizumab), a vehicle control can comprise an isotype control. A tissue-section which is not cultured in presence of a drug is also referred to as a control tissue-section. An expression level of a set of genes is determined in the control tissue-section to obtain a pre-treatment gene expression level and an expression of the set of genes is determined in the drug treated tissue-section to obtain a post-treatment gene expression level. In some embodiments, the method of determining the expression level of a set of genes comprises determining individual expression levels of each gene in the set of genes, normalizing the expression level of each gene to obtain a normalized expression level, converting the normalized expression level of each gene to a corresponding Z-score, and determining a median expression level from the Z-scores of all the genes in the set of genes. In some embodiments, the Z-scores are further log transformed before determination of the median expression level. If the post-treatment gene expression level is greater than the pretreatment gene expression level, the patient is predicted to respond to the anti-cancer drug and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the anti-cancer drug. In some embodiments, the method of the invention further comprises administering into the patient the anti-cancer drug if the post-treatment gene expression level is greater than the pre-treatment gene expression level. In some embodiments, the anti-cancer drug is an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is a PD-1 inhibitor. In some embodiments, the PD-1 inhibitor is nivolumab or pembrolizumab.

[0036] A tumor tissue is obtained from a patient by surgery or by biopsy. In some embodiments, a tissue-section having a thickness of about 100 mm to about 3000 mm is used for culturing in-vitro. In some embodiments, the tissue-section having a volume of about 0.2 cm³ to about 0.5 cm³ is used for culturing in-vitro. In some embodiments the tissue-sections are non-homogenized sections, that is the tissue-sections are not dissociated mechanically, chemically or enzymatically into single cell suspension prior to culture in-vitro. The tissue- sections retain the intratumoral heterogeneity, signaling pathways, immune contexture, and the stromal compartment of the native tumor tissue. In some embodiments, the tissue- sections are dissociated mechanically, chemically or enzymatically into single cell suspension prior to culture in-vitro.

[0037] A tissue-section is cultured in-vitro using any in-vitro culture technique known to a person skilled in the art. An extracellular matrix (ECM) composition is coated on a platform to obtain an ECM composition-coated platform. In some embodiments, the tissue-sections are cultured on the ECM composition-coated platform. An ECM composition can be any ECM composition used in the culture of tumor tissue. The ECM composition can be an off-the-shelf ECM composition, such as Matrigel™ (Coming Inc., USA) or a customized ECM composition. In some embodiments, the components of the ECM composition are specific for the cancer type and grade of the tumor tissue. In some embodiments, the components of the ECM composition are selected by subjecting a sample of a tumor tissue to one or more assays to identify components of the ECM present in the tumor tissue (example of assays include mass spectrometry, such as liquid chromatography- mass spectrometry (LCMS)). In some embodiments, the ECM composition comprises ECM components identified from a sample of bone marrow. In some embodiments, the ECM composition comprises ECM components i dentified from a sample of blood plasma. In some embodiments, the ECM composition comprises ECM components identified from an autologous sample (e.g., the tumor tissue is derived from the same individual as the sample from which the ECM components are identified). In some embodiments, the ECM composition comprises ECM components identified from a heterologous sample (e.g., the tumor tissue is derived from a different individual than the sample from which the ECM components are identified). In some embodiments, the ECM composition is a customized and defined composition comprising one or more of collagen 1, collagen 3, collagen 4, collagen 6, Fibronectin, Vitronectin, Cadherin, Filamin A, Vimentin, Osteopontin, Laminin, Decorin, and Tenascin C. In some embodiments, the ECM composition comprises at least three components selected from a group consisting of collagen 1, collagen 3, collagen 4, collagen 6, Fibronectin, Vitronectin, Cadherin, Filamin A, Vimentin, Osteopontin, Laminin, Decorin, and Tenascin C. We have previously established and optimized an ECM

composition for culturing tumor tissue-sections that mimics the native human tumor environment (see US Patent No. 2014/0228246, incorporated herein in its entirety).

[0038] The platform can be any platform used in cell culture, including but not limited to, a plate, base, flask, dish, petri-plate and petri-dish. The platform can be made of any material suitable for being coated with the ECM composition. In some embodiments, the platfomi is coated with the ECM composition by depositing a liquid mixture comprising the ECM composition on the platform and allowing the liquid mixture to dry. In some embodiments, the liquid mixture is an aqueous mixture. In some embodiments, the liquid mixture is allowed to dry at a temperature at least about 25 °C. In some embodiments, the platfonn is washed with an appropriate solution (for example a buffer, such as PBS) following coating with the ECM composition. In some embodiments, the substrate has been stored at a temperature no greater than about 4 °C prior to combination a culture medium.

[0039] Tissue-sections can be cultured in any culture medium known to a person skilled in the art. Illustrative example of culture medium include without limitation, DMEM (Dulbecco's Modified Eagle Medium) or RPMI 1640 (Roswell Park Memorial Institute Medium).

[0040] In some embodiments, one or more of serum, plasma and peripheral blood nuclear cells (PBNC) are added to the ECM composition-coated platfonn to obtain a tumor microenvironment platform. In some embodiments, the tissue-sections are cultured in-vitro on the tumor microenvironment platform. Culturing a tissue-section on a tumor

microenvironment platfomi refers to culturing the tissue-section on an ECM composition- coated platform in the presence of one or more of serum, plasma and PBNC. There is no limitation on the order of addition of the tissue-section, or one or more of serum, plasma and PBNC. In some embodiments, one or more of serum, plasma and PBNC is obtained from the patient, that is one or more of serum, plasma and PBNC is autologous to the patient. In some embodiments, one or more of serum, plasma and PBNC is not obtained from the patient, that is one of one or more of serum, plasma and PBNC is heterologous to the patient. One or more of serum, plasma and PBNC can be obtained from the patient or from any other individual by any method known to a person of ordinaiy skill in the art. We have previously established and optimized a tumor microenvironment platform for culturing tumor tissue- sections that mimics the native human tumor environment (see US Patent No. 2014/0228246, incorporated herein in its entirety). In some embodiments, a tumor microenvironment platform comprises an ECM composition-coated platform and at least one of serum, plasma and PBNC. In some embodiments, at least one of serum, plasma and PBNC is autologous to said patient In some embodiments, the ECM composition comprises at least three components selected from a group consisting of collagen 1, collagen 3, collagen 4, collagen 6, Fibronectin, Vitronectin, Cadherin, Filamin A, Vimentin, Osteopontin, Laminin, Decorin, and Tenascin C.

[0041] A tissue-section can be cultured for any period of time, such as for 12 hours, 24 hours, 48 hours, 72 hours, 96 hours, 120 hours, or 240 hours. In some embodiments, according to any of the methods described herein, culturing of the tissue-sections is carried out at a temperature ranging from about 30° C to about 40° C, such as at about 37° C. In some embodiments, culturing of the tumor tissue is carried out at about 5% CO2. The expression level of a gene can be determined at the end of the culture period or at any predetermined timepoint during the culture period. The expression level of a gene can be determined by quantifying a gene expression product on the tissue-sections or in a supernatant of the culture medium used for culturing the tissue-sections.

[0042] In some embodiments, the method further comprises perfonning one or more in-vitro assays on the control tissue-section and the drug treated tissue-section. In some embodiments, the one or more in-vitro assays are selected from a group consisting of a cell viability' assay, a cell death assay, a cell proliferation assay, a tumor morphology assay, a tumor stroma content assay, a cell metabolism assay, a senescence assay, a cytokine profile assay, an enzyme activity assay, a tumor and stromal cell expression assay, and any combination thereof. In some embodiments, the assay for cell viability include MTT assay, WST assay, ATP uptake assay and glucose uptake assay. In some embodiments, the assay for cell proliferation and metabolism include, Ki-67 assay, PCNA (proliferating nuclear cell antigen) assay, ATP/ADP ratio assay, and glucose uptake assay. In some embodiments, the assay for cell death include, for example, lactose dehydrogenase (LDH) assay, activated Caspase-3 assay, activated Caspase 8 assay, Nitric Oxide Synthase assay, and TUNEL assay. In some embodiments, the assay for senescence include, for example, senescence-associated beta-galactosidase staining. In some embodiments, the assay for tumor morphology and tumor stroma include, for example, haemaotxylin & eosin staining (H&E) for tumor cell content, size of the tumor cells, ratio of viable cells/dead cells, ratio of tumor cells/normal cells, tumor/macrophage ratio, nuclear size, density, and integrity, apoptotic bodies, and mitotic figures. In some embodiments, the in-vitro assay is an immunohistochemical assay, including multiplexed immunohistochemical assays, such as for evaluating simultaneous activity/infiltration of immune cells and/or signaling/activity components. In some embodiments, the in-vitro assay is a quantitative or qualitative assay including, for example, ELISA, blotting (e.g., Western, Norther, or Southern blot), LC/MS, bead based assay, immune-depletion assay, and chromatographic assay.

[0043] In some embodiments, an in-vitro assay readout is obtained from each of the one or more in-vitro assays. In some embodiments, an in-vitro assay readout is an input in a predictive model. A prediction of response or non-response is generated by the predictive model. In some embodiments, an in-vitro assay readout is obtained from each of the one or more in-vitro assays performed on a control tissue-section and an in-vitro assay readout is obtained from each of the one or more in-vitro assays performed on a drug treated tissue- section. In some embodiments, an in-vitro assay readout is a numeric value. In some embodiments, an assessment score is determined for each of the one or more in-vitro assays, wherein an assessment score is a ratio of an in-vitro assay readout obtained from an in-vitro assay performed on a drug treated tissue-section to an in-vitro assay readout obtained from an in-vitro assay perfonned on a control tissue-section. The method further comprises multiplying each assessment score with a corresponding weightage coefficient to obtain a weighted assessment score. The weighted assessment scores from one or more in-vitro assays are combined to obtain a sensitivity index. The sensitivity index provides a prediction of response or non-response. In some embodiments, the sensitivity index is generated by the predictive model on inputting the in-vitro assay readouts into the predictive model. In some embodiments, all the steps are performed as a computer-implemented method. In some embodiments, the sensitivity index is generated such that a sensitivity index value above a threshold value predicts response (such as a complete response or a partial response) and a sensitivity index value below the threshold value predicts no response (such as a progressive disease) in the patient. In some embodiments, the sensitivity index is generated such that a sensitivity index value above an upper threshold value predicts complete response, a sensitivity index value betw een the upper threshold value and a lower threshol d value predicts partial response, and a sensitivity index value below the lower threshold value predicts non response in the patient. In some embodiments, the method further comprises generating a prediction of response or non-response from the predictive model using the in- vitro assay readouts as inputs.

[0044] In some embodiments, if the post-treatment gene expression level is greater than the pre-treatment gene expression level, and the predictive model generates a prediction of response, the patient is predicted to respond to the anti-cancer drug. In such embodiments, the method of the invention further comprises administering into the patient the anti-cancer drug. In some embodiments, if the post-treatment gene expression level is less than the pretreatment gene expression level, and the predictive model generates a prediction of nonresponse, the patient is predicted not to respond to the anti-cancer drug. In such

embodiments, the anti-cancer drug is not administered into the patient. In some

embodiments, if the post-treatment gene expression level is greater than the pre-treatment gene expression level, but the predictive model generates a prediction of non-response, the predictive model is updated with the post-treatment and pre-treatment gene expression levels as additional inputs. Similarly, if in some embodiments, the post-treatment gene expression level is less than the pre-treatment gene expression level, but the predictive model generates a prediction of response, the predictive model is updated with the post-treatment and pretreatment gene expression levels as additional inputs. [0045] In some embodiments, the method of the invention relates to a method of treatment of a cancer patient. In some embodiments, the method comprises obtaining from the patient a tumor tissue. The method comprises determining in a control tissue-section, an expression level of a set of genes to obtain a pre-treatment gene expression level, wherein the control tissue-section is a section of the tumor tissue which is not cul tured in presence of an anti-cancer drug. The method further comprises determining in a drug treated tissue-section, an expression level of the set of genes to obtain a post-treatment gene expression level, wherein the drug treated tissue-section is a section of the tumor tissue which is cul tured in presence of the anti-cancer drug. In some embodiments, the control tissue-section and the drug treated tissue-section are cultured on a tumor microenvironment platform. In some embodiments, the tumor microenvironment platform comprises an ECM composition-coated platform and at least one of serum, plasma and PBNC. In some embodiments, at least one of serum, plasma and PBNC is autologous to said patient. In some embodiments, the ECM composition comprises at least three components selected from a group consisting of collagen 1, collagen 3, collagen 4, collagen 6, Fibronectin, Vitronectin, Cadherin, Filamin A, Vimentin, Osteopontin, Laminin, Decorin, and Tenascin C. If the post-treatment expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the anti-cancer drug and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the anti-cancer drug. In some embodiments, the method of the invention comprises administering into the patient the anti-cancer drug if the post-treatment gene expression level is greater tiian the pre- treatment gene expression level. In some embodiments, the anti-cancer drug is an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is a PD-1 inhibitor. In some embodiments, the PD-1 inhibitor is nivolumab or pembrolizumab.

[0046] In some aspect, the invention provides a method of treatment of a cancer patient. In some aspect, a method of treatment includes determ ining whether or not to continue treatment with an anti-cancer drug or determining whether or not to change the dose of an anti-cancer drug. In some embodiments, a control biological sample (such as a blood sample or a tumor tissue) is obtained from a patient before the patient is treated with an anticancer drug. An expression level of a set of genes is determined in the control biological sample to obtain a .pre-treatment gene expression level. The patient is then treated with the anti -cancer drug. In some embodiments, treating the patient with the anti-cancer drug comprises administering into the patient at least one dose of the anti -cancer drug. In some embodiments, treating the patient with the anti -cancer drug comprises administering into the patient multiple doses of the anti-cancer drug. In some embodiments, treating the patient with the anti-cancer drug comprises administering into the patient multiple doses of the anticancer drug to complete at least one treatment cycle. A drug treated biological sample (such as a blood sample or a tumor tissue) is obtained from the patient after the patient is treated with the anti -cancer drug. An expression level of the set of genes is determined in the drug treated biological sample to obtain a post-treatment gene expression level. If the posttreatment gene expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the anti -cancer drug and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the anti-cancer drug. In some embodiments, the method of the invention further comprises administering into the patient the anti-cancer drug if the post-treatment gene expression level is greater than the pre-treatment gene expression level. In some embodiments, the method of the invention further comprises stopping the administration of the anti-cancer drug into the patient if the post-treatment gene expression level is less than the pre-treatment gene expression level. In some embodiments, the method comprises changing the dose of the anti-cancer drug if the post-treatment gene expression level is less than the pre-treatment gene expression level. In some embodiments, the anti-cancer drug is an immune checkpoint inhibitor. In some embodiments, the immune checkpoint inhibitor is a PD-1 inhibitor. In some embodiments, the PD-1 inhibitor is nivolumab or pembrolizumab.

[0047] In another aspect, the invention provides a kit for assaying a biological sample to detemiine an expression level of a set of genes. The kit comprises a set of probes for detecting expression of each gene in the set of genes. The kit comprises, for each target transcript in the set of genes, at least one probe for the target transcript. In some

embodiments, the set of genes is at least 5 genes selected from a gene panel consisting of CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STATl, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1. In some embodiments, the set of genes is CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STATl. In other embodiments, the kit may also comprise a second set of probes for detecting expression of a set of normalization genes. The normalization gene set consists of 10 to 1000 genes, e.g., this gene set may consist of at least any of 25, 50, 75, 100, 150, 200, 300, 400, 500, 600, 700, 800 or 900 genes.

Exemplary Embodiments [0048] Among the embodiments provided herein are:

Embodiment 1: A method to predict a patient’s response to an anti-cancer drug, the method comprising, determining in a control biological sample, an expression level of a set of genes to obtain a pre-treatment gene expression level; determining in a drug treated biological sample, an expression level of the set of genes to obtain a post-treatment gene expression level, wherein the set of genes is at least 5 genes selected from a gene panel consisting of CASPl, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1, STAT4, INF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1; and comparing the post-treatment gene expression level with the pretreatment gene expression level, wherein if the post-treatment gene expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the anticancer drug and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the anti-cancer drug.

Embodiment 2: The method of embodiment 1, wherein the control biological sample is a tissue or a biological material obtained from the patient, wherein the tissue or the biological material is obtained from the patient before the patient is treated with the anti-cancer drug. Embodiment 3: The method as in any one of embodiments 1 and 2, wherein the drug treated biological sample is a tissue or a biological material obtained from the patient, wherein the tissue or the biological material is obtained from the patient after the patient is treated with the anti-cancer drug.

Embodiment 4: The method of embodiment 1, wherein the control biological sample is a tissue-section which is not cultured in presence of the anti-cancer drug, wherein the tissue- section is a section of a tumor tissue obtained from the patient.

Embodiment 5: The method as in any one of embodiments 1 and 4, wherein the control biological sample is a tissue-section which is cultured in absence of the anti-cancer drug. Embodiment 6: The method as in any one of embodiments 1 , 4 and 5, wherein the drug treated biological sample is a tissue-section which is cultured in presence of the anti-cancer drug, wherein the tissue-section is a section of the tumor tissue obtained from the patient. Embodiment 7: The method as in any one of embodiments 1, 4, 5, and 6, wherein the drug treated biological sample and the control biological sample are cultured on a tumor microenvironment platform, wherein the control biological sample is a tissue-section which is cultured in absence of the anti-cancer drug, wherein the tissue-section is a section of a tumor tissue obtained from the patient and wherein the drug treated biological sample is a tissue-section which is cultured in presence of the anti-cancer drug, wherein the tissue-section is another section of the tumor tissue obtained from the patient.

Embodiment 8: The method as in any one of embodiments 1, 4, 5, 6, and 7 further comprising performing one or more in-vitro assays on the drug treated biological sample and the control biological sample, wherein the one or more in-vitro assays are selected from a group consisting of a cell viability assay, a cell death assay, a cell proliferation assay, a tumor morphology assay, a tumor stroma content assay, a cell metabolism assay, a senescence assay, a cytokine profile assay, an enzyme activity assay, a tumor and stromal cell expression assays, and any combination thereof.

Embodiment 9: The method as in any one of embodiments 7 and 8, wherein the tumor microenvironment platfomi comprises an ECM composition-coated platform and at least one of serum, plasma and peripheral blood nuclear cells, wherein at least one of serum, plasma and peripheral blood nuclear cells is autologous to said patient.

Embodiment 10: The method as in any one of embodiments 1-9, wherein the set of genes is CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STATl and INF.

Embodiment 11: The me thod as in any one of embodiments 1-10, wherein the anti-cancer drug is an immune checkpoint inhibitor.

Embodiment 12: The method as in any one of embodiments 1-11, wherein the anti-cancer drug is a PD-1 inhibitor.

Embodiment 13: The method as in any one of embodiments 1-12, wherein the anti-cancer drug is nivolumab or pembrolizumab.

Embodiment 14: The method as in any one of embodiments 1-13, wherein the patient is a head and neck cancer patient.

Embodiment 15: The method as in any one of embodiments 1-14, further comprising administering into the patient the anti-cancer drug if the post-treatment gene expression level is greater than the pre-treatment gene expression level.

Embodiment 16: A method to predict a patient’s response to an anti-cancer drug, the method comprising, determining in a control biological sample, an expression level of a set of genes to obtain a pre-treatment gene expression level; determining in a drug treated biological sample, an expression level of the set of genes to obtain a post-treatment gene expression level, wherein the anti-cancer drug is an immune checkpoint inhibitor, and wherein the set of genes is CASP1, CCL3, CCL4, CCL5, CCRl, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, ST ATI and TNF; and comparing the post-treatment gene expression level with the pre-treatment gene expression level, wherein if the post-treatment gene expression level is greater than the pre-treatment gene expression level, the patient is predicted to respond to the immune checkpoint inhibi tor and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the immune checkpoint inhibitor.

Embodiment 17: The method of embodiment 16, wherein the control biological sample is a tissue or a biological material obtained from the patient, wherein the tissue or the biological material is obtained from the patient before the patient is treated with the immune checkpoint inhibitor.

Embodiment 18: The method as in any one of embodiments 16 and 17, wherein the drug treated biological sample is a tissue or a biological material obtained from the patient, wherein the tissue or the biological material is obtained from the patient after the patient is treated with the immune checkpoint inhibitor.

Embodiment 19: The method of embodiment 16, wherein the control biological sample is a tissue-section which is cultured in absence of the immune checkpoint inhibitor, wherein the tissue-section is a section of a tumor tissue obtained from the patient.

Embodiment 20: The method as in any one of embodiments 16 and 19, wherein the drug treated biological sample is a tissue-section which is cultured in presence of the immune checkpoint inhibitor, wherein the tissue-section is a section of the tumor tissue obtained from the patient.

Embodiment 21: The method as in any one of embodiments 16, 19, and 20, wherein the drag treated biological sample and the control biological sample are cultured on a tumor microenvironment platform, wherein the control biological sample is a tissue -section which is cultured in absence of the anti-cancer drag, wherein the tissue-section is a section of a tumor tissue obtained from the patient and wherein the drug treated biological sample is a tissue-section which is cultured in presence of the anti-cancer drug, wherein the tissue -section is another section of the tumor tissue obtained from the patient.

Embodiment 22: The method as in any one of embodiments 16, 19, 20, and 21 further comprising performing one or more in-vitro assays on the drug treated biological sample and the control biological sample, wherein the one or more in-vitro assays are selected from a group consisting of a cell viability assay, a cell death assay, a cell proliferation assay, a tumor morphology assay, a tumor stroma content assay, a cell metabolism assay, a senescence assay, a cytokine profile assay, an enzyme activity assay, a tumor and stromal cell expression assays, and any combination thereof.

Embodiment 23: The method as in any one of embodiments 21 and 22, wherein the tumor microenvironment platform comprises an ECM composition-coated platform and at least one of serum, plasma and peripheral blood nuclear cells, wherein at least one of serum, plasma and peripheral blood nuclear cells is autologous to said patient.

Embodiment 24: The method as in any one of embodiments 16-23, wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

Embodiment 25: The method as in any one of embodiments 16-24, wherein the immune checkpoint inhibitor is nivolumab or pembrolizumab.

Embodiment 26: The method as in any one of embodiments 16-25, wherein the patient is a head and neck cancer patient.

Embodiment 27: The method as in any one of embodiments 16-26, further comprising administering into the patient the immune checkpoint inhibitor, if the post-treatment gene expression level is greater than the pre-treatment gene expression level.

Embodiment 28: A kit comprising a set of probes for detecting expression of each gene in the set of genes, wherein the set of genes is at least 5 genes selected from a gene panel consisting of CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1.

Embodiment 29: The kit of embodiment 28, wherein the set of genes is CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1,

STATl and TNF.

Examples:

[0049] The following examples are intended only to illustrate methods ad embodiments in accordance with the invention, and as such should not be constmed as imposing limitations upon the claims.

Patient Recruitment

[0050] HNSCC patients were recruited from multiple hospitals with approval from appropriate ethics committees. Patient-consented tumor biopsies or surgical tissues, in addition to blood specimens (~ 10 ml) were obtained. Drugs

[0051] The anti-PD-1 antibodies, Nivoluniab (Opdivo, Bristol Myers Squibb) and Pembrolizumab (Keytruda, Merck Sharp & Dohme Corp), were stored in aliquots at - 80 °C for one-time use. Nivolumab, Pembrolizumab and Isotype control (Ultra-LEAFTM Purified Human IgG4 Isotype Control, Biolegend) were used at the concentration of 132 mg/ml, 65.7 mg/ml and 132 mg/ml, respectively.

In-vitro Culture

[0052] Tumor tissues were dissected into tissue -sections approximately 2-4 mm³ thickness using MaCwin tissue chopper. Tissue-sections were randomized to preserve the heterogeneity in culture. Tissue-sections were maintained in customized ECM composition- coated platforms. Peripheral blood nucleated cells (PBNCs) were isolated from blood of patients by density-gradient centrifugation using Histopaque® (Sigma-Aldrich, St. Louise, MO, USA). The PBNCs were co-cultured with tissue-sections. Autologous plasma was added to the culture at the concentration of 2%. All experiments were performed either in triplicates or quadruplicates contingent on the sample size received. Culture plates were incubated for 72 hours at 37 °C in a humidified incubator containing (5% CO₂) with gently rocking.

Control tissue-sections were cultured in presence of isotype control and drug treated tissue- sections were cultured in presence of Nivolumab or Pembrolizumab. Culture media and drugs were replenished every 24 h. Culture plates were observed under the inverted phase contrast microscope to ensure maintenance of tissue integrity.

Harvesting Tissue Fragments and Culture Supernatants

[0053] Superatants of the culture medium used for culturing tissue-sections were collected at 0 h (baseline) & 72 h post-culture in the presence of protease and phosphatase inhibitors and stored at -80 °C until further analysis. Similarly, tissue-sections were collected at 0 hour (baseline) & 16 hours of culture in RNAlater (Ambion, Thermo-Fisher Scientific, USA) and processed for downstream analysis.

RNA extraction and NanoString Gene Expression Analysis

[0054] Total RNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissue-sections, four sections, each 6 mm thick), using RNeasy FFPE RNA isolation Kit (Qiagen, Valencia, CA, USA), according to the manufacturer’s instructions. The PanCancer Immune Panel was used to profile 770 genes. NanoString probe hybridization was perfonned at either NanoString headquarters (Seattle, WA) or at Mitra Biotech (Woburn, MA). RNA hybridized with probes were run either on the SPRINT or MAX machines. Raw counts obtained for each sample were calibrated and normalized using nSolver software version 4.0 (NanoString Technologies). Positive control normalization using six positive control probes was performed on NanoString codeset. Housekeeping mean normalization was performed using two housekeeping genes included in the codeset. The nonnalized expression levels of genes CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRFl, STAT1 and TNF were obtained and converted to Z-scores. A median expression level was determined from the Z-scores of all the genes in the set of genes to obtain an expression level of the set of genes. The expression level of the set of genes was determined in a control biological sample to obtain a pre-treatment gene expression level and the expression level of the set of genes was determined in a drug treated biological sample to obtain a post-treatment gene expression level.

Bioinformatics and Statistics:

RUV Normalization

[0055] Removal of Unwanted Variation (RUV) method was used to correct the unwanted technical effects introduced in the dataset through NanoString sequencing.

Positive and negative controls (ERCC spike-in controls) carry imwanted variation in their expression and were used to adjust for unwanted effects in the gene expression dataset using R Bioconductor package RUVSeq (vl.14.0). The RUVg procedure was used to remove k factors (k=l) of unwanted variation before expression analysis.

Change in Variance Calculations

[0056] The variance calculation for control (such as control tissue-section) and treatment groups (such as drug treated tissue-section) was performed to detect the change in variance of a gene signature between samples after drag pressure. Data was transfonned to log2 Z-scores to obtain mean of 0 and standard deviation of 1. The variance across all patient samples for each gene signature within a treatment was calculated and vehicle variance was subtracted to obtain the change in variance. To calculate the same in Cytokines, Flow Cytometry and IHC datasets a small value of 0.1 was added to the data before log2 transformation to prevent infinite values after log, keeping the rest of the analysis the same. Differential gene expression analysis

[0057] Expression counts from nSolver were adjusted for libraiy size using the R package DESeq2 (vl.20.0) Prefiltering of low-count genes was performed to keep the genes with minimum of 5 counts in at least two samples. The resulting genes were then used to determine differentially expressed genes between groups. Unless otherwise noted, genes with a log2 fold change above 1 and padj< 0.05 were classified as upregulated and genes with a log2 fold change below 1 and padj < 0.05 were classified as downregulated. Genes found differentially expressed in DESeq2 analysis were used to perfonn PCA using ClustVis (https://biit.cs.ut.ee/clustvis/) and FactoMineR version 1.41.

Gene ontology pathway analyses

[0058] Ingenuity Pathway Analysis (IP A, version 2.3) (QIAGEN Inc.,

https://www.qiagenbioinformatics.com/products/ingenuitypathway-analysis) and Reactome (https://reactome.org/) were used to determine pathways and other associations connecting lists of significantly expressed (p-value <0.05) transcripts.

Statistical Analysis

[0059] Unless otherwise noted, one-sided Student’s T-test was used to compare between two groups. One-way or Two-way ANOVA followed by Turkey post-hoc test or Bonferroni correction, respectively, was performed to compare between multiple groups using GraphPad Prism V7.0, (GraphPad Software, La Jolla California

In-vitro Assays

Cell Viability Assay

[0060] Cell Counting Kit-8 (CCK-8, Dojindo Inc), a colorimetric assay, was used to measure cell viability. Briefly, one tenth volume of CCK-8 solution (20 mL) was added to 200 mL of superatant of culture media and incubated at 37 °C for 3 to 4 h in a humidified incubator (5%, C02). The resulting incubation media was collected and absorbance was measured at 450 nm using a microplate reader (Bio-Rad).

Glucose utilization

[0061] Superatant of culture medium (2 mL) was added to 200 mL of Erba Enzyme Reagent in 96 well-plate. The plate was incubated with mild shaking for 5 min at room temperature. Absorbance was measured at 505 nm in multimode plate reader (BioRad). ATP Utilization

[0062] Tissue-section was gently lysed and subjected for the extraction. 12.5m1 of tissue lysates were mixed with 12.5m1 of Lu-Lu mixer (DCS Bioluminescence Kit, #TCA- LITE) and luminescence reading was taken immediately. The data are normalized with total protein concentration (DC protein assay reagent, BioRad).

Release of LDH Assay

[0063] Supernatant of culture medium (25 mL) was added to equal volume of freshly- prepared enzyme mix (LDH assay buffer, NAD+, Lactic acid, 1NT, Diaphorase, Cayman Chemical Company). Absorbance was measured at 490 nm in multimode plate reader after 30 min incubation at room temperature with mild shaking.

Tumor Morphology

[0064] Histol ogical evaluation of tumors before and after culture was performed. A portion of the tissue-section was fixed in 10% formalin immediately after the surgery/biopsy and embedded in paraffin (TO baseline). Similarly, drug treated tumor-sections were fixed in 10% formalin and embedded in paraffin. Four-micron thick sections were stained with H&E. Tumor content and tumor-stroma area (%) in three fields per section for triplicates were assessed by two independent experts. Additionally, area of necrosis and inflammation were evaluated in drug treated tissue-sections (post-culture) with respect to the corresponding controls. Caspase-3 and Ki67 by IHC were also quantitatively assessed using similar methods as described here and above.

Multiplex Cytokine Analysis

[0065] The tissue culture supernatants (25 ml) were processed to measure the secreted profile of cytokine analytes and incubated with 25 ml of beads for 1 hr and 25 ml biotinylated detection antibody for 30 min. The complex was spiked with 25 ml of Streptavidin-PE and analyzed for cytokine profiling using Luminex200 (Luminex, USA) platform. The cell-free supernatant (25 ml) was run on one or multiple Millipore Milliplex plates, customized for the analytes selected. For each plate, a set of standard curves was run to ensure accurate evaluation of the concentration of each analyte and the integrity of the assay. Each plate was read on the Luminex 200. Concentrations of each analyte was interpolated from their respective standard curve using the Milliplex Analyst software (Millipore, USA). Data from multiple plates were compiled and analyte fold changes, relative to vehicle controls, was calculated using an appropriate graphing and statistical software.

Immunohistochemistry

[0066] Tissue-sections were deparaffinized followed by rehydration and soaked in Antigen Unmasking Solution (Vector Labs) for 10 minutes followed by retrieval. Following protein blocking, FFPE tissue sections were incubated with appropriate primary antibodies (anti-Ki-67, Dako, envision kit, 1:400, and anti-caspase 3c (rabbit) from CST, 1 :600 dilution). Validated positive and negative controls were included for every IHC assay. Each IHC result was evaluated by two independent experts and any differences in observation both experts came to a consensus as described previously. Changes in the frequency of proliferating or dividing population of tumor cells in the explant slices were evaluated using Ki-67 or caspase 3c scoring (i.e. number of Ki-67 or caspase3c positive cells per section (mean of triplicates) for each treatment compared to matched untreated control from a total of 100 cells. A compatible secondary antibody (100 mL) was incubated for an optimized time period in humidified condition (Signal stain(R) Boost IHC detection reagent HRP Rabbit,

Cell Signaling Technology- 8114s or Signal stain(R) Boost IHC detection reagent HRP Mouse - Cell Signaling Technology- 8125s wherever applicable). Staining was visualized with freshly prepared DAB + Chromogen. The Dako Envision Kit (cat# K5007) was used only for Ki-67 which includes a secondary antibody and DAB substrate. For all other markers, the DAB detection system (Vector Lab) was used. Slides were counterstained in Harris’ hematoxylin (Merck-6092530121730), dehydrated through graded ethanol solutions, cleared in xylene and cover slipped. IHC slides were examined using a light microscope (DM2500, Leica, USA) and quantified by percentage scoring and intensity scoring. All slides were examined independently by two experienced histopathologists in a blinded fashion. Representative images were captured in 200X magnification using Leica’s inbuilt camera (DFC 450 C).

Claims

CLAIMS We claim:

1. A method to predict a patient’s response to an anti-cancer drug, the method comprising:

i. determining in a control biological sample, an expression level of a set of genes to obtain a pre-treatment gene expression level;

ii. detennining in a drug treated biological sample, an expression level of the set of genes to obtain a post-treatment gene expression level,

wherein the set of genes is at least 5 genes selected from a gene panel consisting of CASPl, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STATl, STAT4, INF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1,

and

iii. comparing the post-treatment gene expression level with the pre-treatment gene expression level,

wherein if the post-treatment gene expression level is greater than the pretreatment gene expression level, the patient is predicted to respond to the anticancer drug and if the post-treatment gene expression level is less than the pretreatment gene expression level, the patient is predicted not to respond to the anti-cancer drug.

2. The method of claim 1, wherein the control biological sample is a ti ssue or a biological material obtained from the patient, wherein the tissue or the biological material is obtained from the patient before the patient is treated with the anti-cancer drug.

3. The method of claim 2, wherein the drug treated biological sample is a tissue or a biological material obtained from the patient, wherein the tissue or the biological material is obtained from the patient after the patient is treated with the anti-cancer drug.

4. The method of claim 1, wherein the control biological sample is a tissue-section which is not cultured in presence of the anti-cancer drug, wfterein the tissue-section is a section of a tumor tissue obtained from the patient.

5. The method of claim 4, wherein the control biological sample is a tissue-section which is cultured in absence of the anti-cancer drug.

6. The method of claim 5, wherein the drug treated biological sample is a tissue-section which is cul tured in presence of the anti-cancer drug, wherein the tissue-section is a section of the tumor tissue obtained from the patient.

7. The method of claim 6, wherein the drug treated biological sample and the control biological sample are cultured on a tumor microenvironment platform.

8. The method of claim 7, further comprising perfonning one or more in-vitro assays on the drug treated biological sample and the control biological sample, wherein the one or more in-vitro assays are selected from a group consisting of a cell viability assay, a cell death assay, a cell proliferation assay, a tumor morphology' assay, a tumor stroma content assay, a cell metabolism assay, a senescence assay, a cytokine profile assay, an enzyme activity assay, a tumor and stromal cell expression assays, and any combination thereof.

9. The method of claim 7, wherein the tumor microenvironment platform comprises an ECM composition-coated platform and at least one of serum, plasma and peripheral blood nuclear cells, wherein at least one of serum, plasma and peripheral blood nuclear cells is autologous to said patient.

10. The method of claim 1, wherein the set of genes is CASPl, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1 and TNF.

11. The method of claim 1, wherein the anti-cancer drug is an immune checkpoint inhibitor.

12. The method of claim 11, wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

13. The method of claim 12, wherein the PD-1 inhibitor is nivolumab or pembrolizumab.

14. The method as in any one of claims 1-13, wherein the patient is a head and neck cancer patient.

15. The method of claim 1, further comprising administering into the patient the anti- cancer drug if the post-treatment gene expression level is greater than the pre-treatment gene expression level.

16. A method to predict a patient’s response to an anti-cancer drug, the method comprising:

ii. determining in a drug treated biological sample, an expression level of the set of genes to obtain a post-treatment gene expression level,

wherein the anti-cancer drug is an immune checkpoint inhibitor, and wherein the set of genes is CASPl, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1 and INF, and

wherein if the post-treatment gene expression level is greater than the pretreatment gene expression level, the patient is predicted to respond to the immune checkpoint inhibi tor and if the post-treatment gene expression level is less than the pre-treatment gene expression level, the patient is predicted not to respond to the immune checkpoint inhibitor.

17. The method of claim 16, wherein the control biological sample is a tissue or a biological material obtained from the patient, wherein the tissue or the biological material is obtained from the patient before the patient is treated with the immune checkpoint inhibitor.

18. The method of claim 17, wherein the drug treated biological sample is a tissue or a biological material obtained from the patient, wherein the tissue or the biological material is obtained from the patient after the patient is treated with the immune checkpoint inhibitor.

19. The method of claim 16, wherein the control biological sample is a tissue-section which is cultured in absence of the immune checkpoint inhibitor, wherein the tissue-section is a section of a tumor tissue obtained from the patient.

20. The method of claim 19, wherein the drug treated biological sample is a tissue-section which is cultured in presence of the immune checkpoint inhibitor, wherein the tissue-section is a section of the tumor tissue obtained from the patient.

21. The method of claim 20, wherein the drug treated biological sample and the control biological sample are cultured on a tumor microenvironment platform.

22. The method of claim 21, further comprising performing one or more in-vitro assays on the drug treated biological sample and the control biological sample, wherein the one or more in-vitro assays are selected from a group consisting of a cell viability assay, a cell death assay, a cell proliferation assay, a tumor morphology assay, a tumor stroma content assay, a cell metabolism assay, a senescence assay, a cytokine profile assay, an enzyme activity assay, a tumor and stromal cell expression assays, and any combination thereof.

23. The method of claim 21, wherein the tumor microenvironment platform comprises an ECM composition-coated platform and at least one of serum, plasma and peripheral blood nuclear cells, wherein at least one of serum, plasma and peripheral blood nuclear cells is autologous to said patient.

24. The method of claim 16, wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

25. The method of claim 24, wherein the PD-1 inhibitor is nivolumab or pembrolizumab.

26. The method as in any one of claims 16-25, wherein the patient is a head and neck cancer patient.

27. The method of claim 16, further comprising administering into the patient the immune checkpoint inhibitor, if the post-treatment gene expression level is greater than the pretreatment gene expression level.

28. A kit comprising a set of probes for detecting expression of each gene in the set of genes, wherein the set of genes is at least 5 genes selected from a gene panel consisting of CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1 , STATl, STAT4, TNF, CD38, TBX21, CLU, CD55, CSF2, CXCR3, GATA3 and SPP1.

29. The kit of claim 28, wherein the set of genes is CASP1, CCL3, CCL4, CCL5, CCR1, CCR2, CCR5, CTLA4, IFNg, IL12RB2, LTA, NFKBIA, PRF1, STAT1 and TNF.