Novel biomarkers to predict efficacy of cancer immunotherapy

Abstract

The present invention provides a new biomarker comprising a comprehensive score of T cell density in paired biopsy performed before and after treatment of a patient with cancer to inform potential outcomes in immunotherapy, including but not limited to clinical trials. The invention also provides methods for determining whether a cancer patient is likely to benefit from immunotherapy by using the biomarkers.

Description

Technical Field

[0001] The present invention relates to novel biomarkers for determining whether a patient suffering from cancer is likely to benefit from treatment using immunotherapy. Background Art

[0002] Over the past two decades, cancer immunotherapy, which involves harnessing the patient's own immune system for anti-cancer effects, has rapidly evolved. One of the earliest effective immunotherapies for melanoma and renal cell carcinoma was interleukin 2 (IL-2) [1]. More recently, the Nobel Prize-winning discovery of checkpoint molecules such as CTLA-4 and PD-1 / PD-L1 has led to the development of a new class of cancer immunotherapy that has transformed the landscape of cancer therapy [2]. These "checkpoint inhibitors" or CPIs (drugs that inhibit checkpoint molecules) have demonstrated unprecedented clinical efficacy against a variety of different cancer types [3]. There are currently numerous immunotherapy drugs and combination candidates being tested in clinical trials for different tumor types.

[0003] Clinical testing of immunotherapy drugs presents unique challenges that are different from traditional cytotoxicity. The response patterns, kinetics, and mechanisms of action of immunotherapy are fundamentally different from those of cytotoxic or tyrosine kinase inhibitors. Additionally, there are few pharmacodynamic biomarkers for immunotherapy candidates that can effectively measure the biological effects of the administered immunotherapy drug on the patient and inform the clinical benefit of such treatment [4]. Therefore, there is a need for markers that can provide early information about the potential outcome of clinical trials, in addition to reaching conventional endpoints such as overall response rate, progression-free survival, and overall survival at the end of the study period, and this need has not been met.

[0004] The work of Jerome Galon and others has highlighted the significance of T cell infiltration in the tumor microenvironment, particularly CD8+ T cells and their relationship to clinical prognosis [5]. There is now a global consensus that traditional classification of patients based on the AJCC / TNM system provides limited prognostic information, and the incorporation of immune-based classification or "immune score" is a necessary diagnostic and prognostic tool for clinical decision-making [6].

[0005] The present inventors have explored the density of CD8+ T cells as a putative early marker of clinical or therapeutic efficacy. Previously, Petrelli F et al. [7] have described three criteria for an ideal biomarker: 1) a direct association between pathogenesis, biomarker, and clinical endpoint; 2) changes in the biomarker should be associated with changes in the disease status of an individual patient; 3) an association between changes in the biomarker caused by a therapeutic intervention and the final clinical outcome within a trial. Using these criteria, the present inventors have examined changes in CD8+ T cell density in paired tumor biopsies taken before and after treatment in several different early immunotherapy trials. They have shown that a comprehensive decision rule based on CD8+ T cell density is associated with clinical outcomes at the individual patient level and the collective level for the study. Additionally, the present invention provides a specific cut-off threshold that can be used to predict the potential outcome of any immunotherapy treatment, including but not limited to early clinical trials, where the mechanism of action involves expanding CD8 T cells to mediate an anti-tumor response.

[0006] Accordingly, the present invention provides a framework for making decisions on whether to administer cancer immunotherapy in a patient or in a clinical study (such as in an early cancer immunotherapy clinical study) based on the use of selected biomarker data. As a prototype, we evaluated whether the density of CD8 T cells during treatment in the tumor microenvironment could be utilized to make these decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1: Forest plot of HR identifying total CD8 T cell density as the most consistent responder biomarker in the simlukafusp alfa study; (a) with baseline (BL) correction, (b) without BL correction.

[0008] Figure 2: In the simlukafusp alfa study, higher total CD8 T cell density is associated with better response. For each response category

[0009] (a) log FC and

[0010] (b) distribution of log OTD.

[0011] Figure 3: Derivation of the threshold separating higher and lower progression risks:

[0012] (a) True positive fraction and false positive fraction given by the ROC curve for FC show that the optimal threshold is 1.3 log FC

[0013] (b) True positive fraction and false positive fraction given by the ROC curve for OTD show that the optimal threshold is 6.7 log OTD

[0014] (c) Repeated landmark analysis of PFS showed that the lowest optimal threshold for log FC was 0.9

[0015] (d) Repeated landmark analysis of PFS showed that the lowest optimal threshold for log OTD was 6.2

[0016] Figure 4: In the training and validation datasets, extended PFS was associated with total CD8 T cell density as a response biomarker, with Log FC 0.9 fold change (4a) and 6.2 log OTD value (4b) thresholds.

[0017] Figure 5 : Distribution of the effect of above-threshold on tumor growth and shrinkage.

[0018] Figure 6 : The low correlation between log FC and log OTD of total CD8 T cell density highlights the potential of a method that combines both readings.

[0019] Figure 7 : Readings of data from an experiment where n = 15.

[0020] Figure 8 : Probability of success of decision rules for given true OTD and FC for studies of 5, 10, 20, 60 patients with paired biopsy data. SUMMARY OF THE INVENTION

[0021] In one embodiment, the present invention provides a method for determining whether a patient suffering from cancer is likely to benefit from treatment with immunotherapy, the method comprising

[0022] a) obtaining samples from the patient before treatment (at baseline) and after treatment,

[0023] b) determining the fold increase (FC) of CD8+ T cells and / or the density during treatment (OTD) of CD8+ T cells in the sample after treatment compared to the sample at baseline,

[0024] c) comparing the value obtained in b) with a threshold, wherein

[0025] if the value obtained in b) is higher than the threshold, the patient is likely to benefit from the treatment.

[0026] In another embodiment, the present invention provides a biomarker for use in the above method.

[0027] In another embodiment, the present invention provides a method for treating a patient suffering from cancer using a biomarker as defined herein.

[0028] In yet another embodiment, the present invention provides a biomarker as defined herein for monitoring the treatment of a patient suffering from cancer.

[0029] In yet another embodiment, the present invention provides a biomarker as defined herein for making a decision on whether to continue treatment of a patient suffering from cancer.

[0030] In yet another embodiment, the present invention provides a biomarker as defined herein for making a decision on whether to continue or stop a clinical study. Detailed Description

[0031] The association of T cells, specifically CD8+ T cells in tumors, with better clinical outcomes is not a new concept, including in cancer immunotherapy (16). Bocchialini et al.

[17] reported that in patients with thymic carcinoma, a high density of CD8+ tumor-infiltrating lymphocytes (TILs) was associated with improved recurrence-free rate and cause-specific survival. Similar results have also been reported for CD8+ TILs and improved overall survival and recurrence-free survival in oral squamous cell carcinoma [18, 19]. A report by Galon et al. showed that the correlation of immune cell infiltration in colorectal cancer with clinical outcomes was better compared to traditional histological staging

[20] .

[0032] The present inventors have analyzed the CD8 T cell density in paired biopsies in 8 Phase I and II studies to investigate the dose safety and clinical benefit of 5 investigational drugs (as monotherapy or in combination with atezolizumab, cetuximab, or bevacizumab). The new aspects of their work are essentially as follows: A) Using data from paired biopsies from the FAP-IL2v study as a training set, which showed that the fold change (FC) and the change in CD8+ T cell density during treatment (OTD) were associated with clinical outcomes. B) Validating the utility of FC and OTD as biomarkers for predicting clinical outcomes in various early immunotherapy trials that tested immuno-oncology (I-O) candidate drugs either independently or in combination with other drugs. C) Identifying thresholds for FC and OTD that can be used to separate potential responders from non-responders. D) Proposing a method for using FC or OTD in combination to inform both the clinical outcome of early clinical trials and the clinical outcome at the level of individual patients. Among other things, this method reduces the impact of the timing of biopsy sampling during treatment on determining the outcome of drug treatment.

[0033] Thus, the present invention solves the problem of providing effective biomarkers that can provide early information about outcomes and responses, for example, in (early) clinical trials of research, and in established cancer therapies using immunotherapy. In a clinical trial setting, it helps to conserve resources and effort that would otherwise be spent on clinical trials with a lower probability of success. More importantly, in a clinical trial setting as well as in therapies, it reduces the risk of harm to patients by limiting exposure to ineffective therapies and accelerating the switch to potentially more effective treatment modalities.

[0034] The present invention shows that there is a strong association between high levels of CD8 therapy (OTD) and the clinical benefit of treatment and between the increased level relative to baseline (FC) in CD8 therapy and the clinical benefit of treatment. While OTD or FC is qualitatively sufficient to inform the likelihood of clinical outcome, each of them requires a threshold to be set to draw conclusions about this information. The integrated biomarker according to the present invention is based on capturing the dynamic changes during a set study period by decomposing these two values. The expansion of CD8+ cells during treatment results in a positive FC and also leads to an increase in OTD due to the accumulation of CD8+ cells in the tumor environment. Using these two values reduces the need for precise timing of in-treatment biopsies at the moment of greatest change. Instead, OTD reflects the median change in CD8+ infiltration over a period of time. Equally important is the fact that depending on the mechanism of action of immunotherapy drugs (most of which rely on pre-existing immunity against the tumor), a minimum threshold of FC or OTD is required to inform potential clinical or treatment outcomes. As a corollary, a change in either of these two parameters can inform potential outcomes, where the predictive confidence is greatest when both parameters are considered simultaneously.

[0035] Thus, in one aspect, the present invention provides a new method for using changes in CD8+ cells as surrogate markers (the surrogate markers for providing early information about clinical outcomes to early I-O trials), and describes an algorithm that can be used to generalize the application of this method in different I-O drug trials for different cancer types. In another aspect, the present invention also provides the same method as the biomarker for obtaining early information on whether a patient with cancer benefits from treatment using immunotherapy.

[0036] Thus, in one aspect, the present invention provides a method for determining whether a patient with cancer is likely to benefit from treatment using immunotherapy.

[0037] The terms "cancer", "tumor / tumour", and "carcinoma" are used interchangeably herein to refer to cells that exhibit relatively abnormal, uncontrolled, and / or autonomous growth such that they exhibit an abnormal growth phenotype characterized by a significant loss of control of cell proliferation. Generally, the cells of interest for detection or treatment in the present application include pre-cancerous (e.g., benign) cells, malignant cells, pre-metastatic cells, metastatic cells, and non-metastatic cells. The teachings of the present disclosure may be relevant to any and all cancers. By way of only a few non-limiting examples, in some embodiments, the teachings of the present disclosure are applied to one or more cancers such as, for example, hematopoietic cancers, which include leukemia, lymphoma (Hodgkin and non-Hodgkin), myeloma, and myeloproliferative disorders; sarcoma, melanoma, adenoma, solid tissue carcinoma, oral squamous cell carcinoma, pharyngeal squamous cell carcinoma, laryngeal squamous cell carcinoma, and lung squamous cell carcinoma, liver cancer, genitourinary cancers (such as prostate cancer, cervical cancer, bladder cancer, uterine cancer, and endometrial cancer), and renal cell carcinoma, bone cancer, pancreatic cancer, skin cancer, cutaneous or intraocular melanoma, endocrine system cancers, thyroid cancer, parathyroid cancer, head and neck cancers, breast cancer, gastrointestinal cancers, and nervous system cancers, benign lesions (such as papilloma), and the like. In one aspect, the cancer according to the present invention is a solid tumor. In another aspect, the cancer according to the present invention is a hematological tumor. In yet another aspect, the cancer is a solid tumor selected from the group consisting of lung cancer (including non-small cell lung cancer), breast cancer, thyroid cancer, head and neck cancers, pancreatic cancer, prostate cancer, bladder cancer, colon cancer, esophageal cancer, ovarian cancer, gastric cancer, skin cancer, and colorectal cancer.

[0038] The term "immunotherapy" or "cancer immunotherapy" or "cancer immunotherapeutics" may be used interchangeably and is known to those of skill in the art (e.g., clinical oncologists). In one aspect, "cancer immunotherapy" refers to a therapeutic treatment that stimulates or restores the immune system's ability to fight cancer by inducing, enhancing, or suppressing an immune response. Cancer immunotherapy generates targeted immune activity against disease-specific antigens by increasing the immune cell recognition of the target or by reducing disease-associated immunosuppression. In one aspect of the present invention, "cancer immunotherapy" refers to any therapy in which the mechanism of action involves the expansion of CD8 T cells to mediate an anti-tumor response. In another aspect, the immunotherapy according to the present invention is selected from immune checkpoint inhibitors; T cell transfer therapies (CAR T cells); monoclonal, monospecific, or multispecific, preferably bispecific antibodies; cancer vaccines; or other immune system modulators such as, for example, cytokines. In one aspect, the immunotherapy is an approved therapy or an investigational therapy. In another aspect of the present invention, "cancer immunotherapy" refers to the "investigational medicinal product" disclosed in the appended working examples.

[0039] The immunotherapy according to the present invention may also include combination therapy. As used herein, the term "combination therapy" refers to those situations in which a subject is exposed to two or more treatment regimens (e.g., two or more therapeutic agents). In some embodiments, two or more agents may be administered simultaneously in a combined or separate dosage form. Alternatively, such agents may be administered sequentially, and the agents may also include overlapping dosing regimens. In some aspects, the combination partner is another immunotherapy drug. In one aspect, the drug for immunotherapy according to the present invention ("immunotherapy drug") is used in combination with atezolizumab, trastuzumab, cetuximab, or bevacizumab. In one aspect, atezolizumab, trastuzumab, cetuximab, or bevacizumab is administered alone according to their approved dosages, dosing regimens, and dosage forms.

[0040] As used herein, the term "patient" means one or more individuals suffering from cancer. In one aspect, the term "patient" means an individual suffering from cancer. In another aspect, the term "patient" means a group of individuals, such as, for example, a study group in a clinical trial. In this aspect, the comprehensive decision rule according to the present invention can be used to decide whether a given clinical trial involving, for example, immunotherapy should be continued, the protocol should be modified, or even stopped.

[0041] As used herein, the term "benefit from treatment" refers to attempting to alter the natural course of the disease of the treated individual and can be performed for prevention or as a clinical intervention that can be performed during the clinicopathological process. Such benefits include a cellular or biological response, complete remission, partial remission, stable disease (no progression or recurrence), or a response of a patient to treatment with a cancer immunotherapy drug or due to treatment with a cancer immunotherapy drug and subsequent recurrence. For example, an effective response can be a reduction in tumor size, progression-free survival, or overall survival. In one aspect of the present invention, if a patient shows improved progression-free survival (PFS) or partial or complete remission, then he / she benefits from treatment. In this aspect, the patient benefits in the "form" of, for example, PFS, as sometimes also used herein.

[0042] As used herein, "sample" means a body fluid sample, a sample of isolated cells, or a sample from a tissue or an organ. Body fluid samples can be obtained by well-known techniques and include samples of blood, plasma, serum, urine, lymph fluid, sputum, ascites, bronchoalveolar lavage fluid, or any other bodily secretion or derivative thereof. Tissue or organ samples can be obtained from any tissue or organ by, for example, biopsy. Isolated cells can be obtained from body fluids or tissues or organs by separation techniques such as centrifugation or cell sorting. For example, cell, tissue, or organ samples can be obtained from those cells, tissues, or organs that express or produce a biomarker. Samples can be frozen samples, fresh samples, fixed (e.g., formalin-fixed) samples, centrifuged and / or embedded (e.g., paraffin-embedded) samples, etc. Prior to assessing the amount of one or more markers in a sample, cell samples can of course be subjected to various well-known post-collection preparation and storage techniques (e.g., nucleic acid and / or protein extraction, fixation, storage, freezing, ultrafiltration, concentration, evaporation, centrifugation, etc.). Similarly, biopsy samples can also be subjected to post-collection preparation and storage techniques (e.g., fixation). In a preferred embodiment, the "sample" is a tumor biopsy sample from a patient.

[0043] As used herein, "determining the fold change (FC) of CD8+ T cells and / or the on-treatment density (OTD) of CD8+ T cells" can be performed according to methods known to those skilled in the art

[10] . In one aspect, the determination can be performed using ROC curve analysis

[21] or landmark analysis

[22] . In another aspect, the determination can be performed as described in the attached working examples.

[0044] In one embodiment, the present invention provides a method for determining whether a patient suffering from cancer is likely to benefit from treatment using immunotherapy, the method comprising

[0045] a) obtaining a sample from the patient before treatment (at baseline) and after treatment,

[0046] b) determining the fold change (FC) of CD8+ T cells and / or the on-treatment density (OTD) of CD8+ T cells in the sample after treatment compared to the sample at baseline,

[0047] c) comparing the value obtained in b) with a threshold, wherein

[0048] if the value obtained in b) is higher than the threshold, the patient is likely to benefit from the treatment.

[0049] In one embodiment, the above method is an in vitro method.

[0050] In yet another embodiment, the present invention provides any of the above methods, wherein the sample is a tumor biopsy sample from the patient.

[0051] In yet another embodiment, the present invention provides any of the above methods, wherein the cancer is a solid tumor.

[0052] In yet another embodiment, the present invention provides any of the above methods, wherein the cancer is selected from lung cancer (including non-small cell lung cancer), breast cancer, thyroid cancer, head and neck cancer, pancreatic cancer, prostate cancer, bladder cancer, colon cancer, and colorectal cancer.

[0053] In yet another embodiment, the present invention provides any of the above methods, wherein the patient's benefit from the treatment is one of partial remission (PR), complete remission (CR), or longer progression-free survival (PFS).

[0054] In yet another embodiment, the present invention provides any of the above methods, wherein if the threshold for the fold increase in CD8+ T cells compared to the baseline is ≥ 0.9 on a logarithmic scale, as determined by repeated landmark analysis, then the patient may benefit in the form of PFS.

[0055] In yet another embodiment, the present invention provides any of the above methods, wherein if the threshold for the density of CD8+ T cells during treatment compared to the baseline is ≥ 6.2 (about 500 cells / mm 2 ), as determined by repeated landmark analysis, then the patient may benefit in the form of PR, CR, and / or PFS.

[0056] In yet another embodiment, the present invention provides any of the above methods, wherein a patient may benefit from immunotherapy if the following conditions are met

[0057] - The threshold for the fold increase in CD8+ T cells compared to the baseline is ≥

[0058] 0.9, as determined by ROC curve analysis, and

[0059] - The threshold for the density of CD8+ T cells during treatment compared to the baseline is ≥

[0060] 6.2 (about 500 cells / mm 2 ), as determined by repeated landmark analysis.

[0061] In yet another embodiment, the present invention provides any of the above methods, wherein if the threshold for the fold increase in CD8+ T cells compared to the baseline is ≥ 1.3 on a logarithmic scale, and the threshold for the density of CD8+ T cells during treatment compared to the baseline is ≥ 6.7 on a logarithmic scale, as determined by ROC curve analysis, then the patient may benefit in the form of PR or CR.

[0062] In yet another embodiment, the present invention provides any of the above methods, wherein a patient may benefit in the form of PFS if the threshold for fold increase in CD8+ T cells is ≥ 0.9 on a logarithmic scale compared to baseline, and the threshold for on-treatment density of CD8+ T cells is ≥ 6.2 on a logarithmic scale compared to baseline, as determined by repeated landmark analysis.

[0063] In yet another embodiment, the assays described in

[10] are used to obtain the thresholds as defined herein. In a preferred embodiment, the thresholds as defined herein are obtained using the CD8 / Ki67 assay described in

[10] or in the attached working examples.

[0064] In yet another embodiment, the present invention provides any of the above methods for use in early phase clinical trials.

[0065] In yet another embodiment, the present invention provides any of the above methods for use in monitoring the treatment of a patient with cancer, wherein the treatment involves immunotherapy.

[0066] In yet another embodiment, the present invention provides any of the above methods for making a decision on whether to continue treating a patient with cancer with immunotherapy.

[0067] In yet another embodiment, the present invention provides a biomarker as defined herein for making a decision on whether to continue or stop a clinical study.

[0068] In yet another embodiment, the present invention provides a biomarker for determining whether a patient with cancer is likely to benefit from treatment with immunotherapy, wherein the biomarker is characterized by

[0069] - a fold increase (FC) in CD8+ T cells in a sample obtained from the patient after treatment compared to a sample from the same patient at baseline; and / or

[0070] - the on-treatment density (OTD) of CD8+ T cells.

[0071] In yet another embodiment, the present invention provides a biomarker for use as described above, wherein the biomarker is characterized by

[0072] - a fold increase (FC) in CD8+ T cells in a sample obtained from the patient after treatment compared to a sample from the same patient at baseline; or

[0073] - the on-treatment density (OTD) of CD8+ T cells.

[0074] In yet another embodiment, the present invention provides a biomarker for use as described above, wherein the biomarker is characterized by

[0075] - a fold change (FC) in CD8+ T cells in a sample obtained from the patient after treatment, compared to a sample from the same patient at baseline; and

[0076] - the on-treatment density (OTD) of CD8+ T cells.

[0077] In yet another embodiment, the present invention provides a biomarker for use as described in any of the foregoing embodiments, wherein the fold change (FC) in CD8+ T cells and / or the on-treatment density (OTD) of CD8+ T cells is higher than their corresponding values detected at baseline.

[0078] In yet another embodiment, the present invention provides a biomarker for use as described in any of the foregoing embodiments, wherein the value of the fold change (FC) in CD8+ T cells or the on-treatment density (OTD) of CD8+ T cells is detected by ROC curve analysis or repeated landmark analysis. In a preferred embodiment, the value of the fold change (FC) in CD8+ T cells or the on-treatment density (OTD) of CD8+ T cells is detected as described in the attached working examples.

[0079] In yet another embodiment, the present invention provides a biomarker for use as described in any of the foregoing embodiments, wherein the patient's benefit from the treatment is one of partial remission (PR), complete remission (CR), or longer progression-free survival (PFS).

[0080] In yet another embodiment, the present invention provides a biomarker for use as described in any of the foregoing embodiments, wherein if the threshold for the fold change in CD8+ T cells is ≥ 0.9 on a logarithmic scale compared to baseline, as determined by repeated landmark analysis, the patient may benefit in the form of PFS.

[0081] In yet another embodiment, the present invention provides a biomarker for use as described in any of the foregoing embodiments, wherein if the threshold for the on-treatment density of CD8+ T cells is ≥ 6.2 on a logarithmic scale compared to baseline (about 500 cells / mm 2 ), as determined by repeated landmark analysis, the patient may benefit in the form of PR, CR, and / or PFS.

[0082] In yet another embodiment, the present invention provides a biomarker for use as described in any of the foregoing embodiments, wherein if the following conditions are met, the patient may benefit from immunotherapy

[0083] - The threshold for the fold increase in CD8+ T cells is ≥

[0084] 0.9 on a logarithmic scale compared to the baseline, as determined by ROC curve analysis, and

[0085] - The threshold for the median density during treatment of CD8+ T cells is ≥

[0086] 6.2 (about 500 cells / mm 2 ), as determined by repeated landmark analysis.

[0087] In yet another embodiment, the present invention provides a biomarker for use as described in any of the foregoing embodiments, wherein if the threshold for the fold increase in CD8+ T cells is ≥ 1.3 on a logarithmic scale compared to the baseline, and the threshold for the median density during treatment of CD8+ T cells is ≥ 6.7 on a logarithmic scale compared to the baseline, as determined by ROC curve analysis, then the patient may benefit in the form of a PR or CR.

[0088] In yet another embodiment, the present invention provides a biomarker for use as described in any of the foregoing embodiments, wherein if the threshold for the fold increase in CD8+ T cells is ≥ 0.9 on a logarithmic scale compared to the baseline, and the threshold for the median density during treatment of CD8+ T cells is ≥ 6.2 on a logarithmic scale compared to the baseline, as determined by repeated landmark analysis, then the patient may benefit in the form of a PFS.

[0089] In yet another embodiment, the present invention provides a biomarker as defined in any of the above embodiments for use in early stage clinical trials.

[0090] In yet another embodiment, the present invention provides a biomarker as defined in any of the above embodiments for monitoring the treatment of a patient suffering from cancer, wherein the treatment involves immunotherapy.

[0091] In yet another embodiment, the present invention provides a biomarker as defined in any of the above embodiments for making a decision on whether to continue treating a patient suffering from cancer with immunotherapy.

[0092] In yet another embodiment, the present invention provides a method for monitoring the efficacy of immunotherapy in a patient suffering from cancer, the method comprising: measuring and calculating the value of FC and / or OTD for CD8 T cells as defined in any of the above embodiments; comparing the value with the corresponding value at the baseline; and if the value is higher than the threshold also defined herein, continuing the immunotherapy. In one embodiment, the method is an in vitro method.

[0093] In yet another embodiment, the present invention provides a method for treating a cancer patient with immunotherapy, the method comprising: measuring and calculating the value of FC and / or OTD for CD8 T cells as defined in any one of the above embodiments; comparing the value with the corresponding value at baseline; and if the value is higher than a threshold also defined herein, continuing the immunotherapy.

[0094] In yet another embodiment, the present invention provides an immunotherapy for treating a patient suffering from cancer, wherein a patient is selected for treatment or treatment with the immunotherapy is continued when the value of FC and / or OTD for CD8 T cells is higher than a threshold as defined above (when compared with their corresponding values at baseline).

[0095] For clarity of understanding, the present invention will now be further illustrated by some working examples. The description and examples should not be regarded as limiting the scope of the present invention. The disclosures of all patents and scientific literature cited herein are hereby expressly incorporated by reference in their entirety.

[0096] Examples

[0097] Materials and Methods

[0098] Investigational drugs used:

[0099] Simlukafusp alfa (FAP-IL2v), Emactuzumab (EMAC), Selicrelumab (CD40), Cergutuzumab Amunaleukin (CEA-IL2v), and an in-house investigational bispecific antibody against FAP-41BBL.

[0100] Study:

[0101] The analysis was based on samples and data collected from a multicenter open-label phase I dose-escalation study and a phase II multicenter open-label basket trial (study identifiers: NCT02627274 (FAP-IL2v), NCT03063762 (FAP-IL2v), NCT03386721 (FAP-IL2v), NCT02323191 (EMAC), NCT02665416 (CD40), NCT02304393 (CD40), NCT02350673 (CEA-IL2v), NCT04826003 (FAP-41BBL)).

[0102] The training cohort consisted entirely of matched biopsies from an earlier study of Simlukafusp alfa (FAP-IL2v). Simlukafusp alfa (FAP-IL2v) is an immunocytokine that consists of: an antibody against fibroblast activation protein alpha (FAP); and an IL-2 variant that has retained affinity for IL-2Rβγ>IL-2Rβγ and binding to IL-2Rα is abrogated [8]. The validation set consisted of data and samples from earlier studies.

[0103] Assessment:

[0104] Tumor responses in solid tumors were assessed according to RECIST 1.1 [9] criteria, every 6 or 8 weeks in the first year and then every 12 weeks. Analysis of tumor biopsies was based on fresh tissue (when available) or archival tissue. The schedule during treatment depended on the study. Trial details are provided in Supplementary Table 1.

[0105] Immunohistochemical staining and digital image analysis:

[0106] Staining for immune cell infiltration and digital image analysis for quantifying cellular immune cell infiltration were performed as previously described

[10] . Briefly, 2.5-μm thick sections were stained using Ventana Discovery Ultra, Discovery XT, or Benchmark XT automated stainers (Ventana Medical Systems, Tucson, AZ) and NEXES version 10.6 software for single- and double-color assays for CD11b / CD14, CD11b / CD15, CD8 / Ki67, ARG1, and FOXP3. Chromogenic reactions were performed using appropriate conjugated secondary antibodies and Discovery Purple, Discovery Yellow, or OptiView DAB detection kits (Ventana Medical Systems). Immunohistochemically stained slides were digitally scanned at 20X magnification using a high-throughput iScan HT (Ventana Medical Systems). Whole-slide images were analyzed using HALO software (IndicaLabs) tools. Total cell counts, annotated areas, and cell object XY coordinates were extracted for tumor, invasive margin, and normal regions of interest (ROIs).

[0107] Statistical analysis:

[0108] The analysis included all patients treated with the investigational drug who had biopsies at baseline and during the study. The values of total CD8 (baseline, on-treatment density (OTD), and fold change (FC) relative to baseline) were log-transformed for the analysis. Categorical tables and receiver operating characteristic (ROC) curve analysis were used to analyze the ORR. The Youden index was used to determine the optimal threshold for the ORR endpoint

[11] . A landmark cox PH hazard model was used to perform the analysis of PFS (landmark time was 60 days, during which most on-treatment biopsies were performed)

[12] . The C-index

[13] was used as a measure of the association between PFS and OTD or FC. For the analysis of FC, the cox PH model included the baseline value and the OTD value. A Stein model

[14] with OT or FC as covariates was used to perform the effect of OTD and FC on tumor kinetics. The data were divided into a training dataset (simlukafusp alfa study) and a validation dataset (other studies). Threshold determination was performed on the training dataset, and threshold performance was evaluated on the validation dataset.

[0109] Example 1: Establishment and Validation of Thresholds

[0110] First, the impact of baseline correction of the on-treatment density of CD8 on the risk of progression was evaluated in the training dataset (see below). This analysis identified that among the 6 different biomarkers in the tumor microenvironment, the total CD8 density (MKI67+CD8A+)+(MKI67-CD8A+), followed by the density of cytotoxic T cells (PRF1+CD3+), had the best correlation with a reduced risk of progression (see Figure 1).

[0111] Then the associations of both the FC (fold change relative to baseline) and OTD (on-treatment density) of total CD8 T cells with clinical response were established. Figures 2 and 1 show the results of these analyses. For both FC and OTD, the value was higher for responder patients (complete response (CR), partial response (PR)) compared to other response categories.

[0112]

[0113] Table 1: Mean log FC and log OTD for each response category

[0114] For progression-free survival (PFS), an association was observed between FC and OTD, which was given by performing a landmark analysis using the logarithms of both FC and OTD as covariates. For OTD, this association seemed stronger than for FC (see Table 2).

[0115] <![CDATA Measurement > <![CDATA HR(95% CI) > <![CDATA C-index > <![CDATA FC > <![CDATA 0.86 (0.73 to 1.025) > <![CDATA 0.53 > <![CDATA OTD > <![CDATA 0.75(0.61-0.92) > <![CDATA 0.63 >

[0116] Note: In the translation of , there seems to be an error in the original text where it says "Figures 2 and 1", which should probably be "Figures 2 and Table 1" for consistency with the context. The translation has been done as per the provided text.Table 2: Association between PFS, FC, and OTD.

[0117] Using ROC curve analysis (for the ratio of CR / PR) and repeated landmark analysis for PFS, threshold definitions were achieved on the training dataset. The AUC under the ROC curve for FC was 0.63, with the optimal threshold being 1.3 log FC. For log OTD, the AUC was 0.78, with the corresponding optimal threshold = 6.7. For PFS, the lowest optimal threshold for log FC was 0.9, and log OTD = 6.2 (see Figure 3). The thresholds derived from the response rate (PR / CR) and PFS analyses were different, and the inventors chose to rely on the threshold obtained from the PFS analysis. The rationale for this choice is that the PFS analysis is a finer-grained clinical endpoint, and PFS is more representative of clinical efficacy in immunotherapy than the ratio of PR / CR [4].

[0118] Using these defined thresholds, Figure 4 and Table 3 show the differences between the groups (below and above the threshold) in the training and validation datasets. The PFS HR (95% CI) for FC was 0.60 (0.36 - 1.0) for the training dataset and 0.72 (0.46 - 1.10) for the validation dataset. For OTD, this value was 0.58 (0.36 - 0.93) for the training dataset and 0.64 (0.41 - 0.98) for the validation dataset. For both FC and OTD, Table 3 indicates that non-responders were mostly classified into the below-threshold group (on both the training and validation datasets), while most responders were classified into the above-threshold group (on both the training and validation datasets).

[0119]

[0120] Table 3: Classification table for response on training (top) and validation (bottom)

[0121] In addition to PFS and response, tumor growth kinetics were also used to evaluate the effectiveness of the thresholds. Figure 5 The distribution of parameters capturing the effects above the threshold was provided. In this figure and for the growth parameter, a distribution centered on the left indicates that patients above the threshold will have lower tumor growth than patients below the threshold. For shrinkage, a distribution centered on the right indicates that patients above the threshold will have greater tumor shrinkage. It can be seen that for FC, the effect of total CD8 on tumor growth and shrinkage was not significant, where for patients above the threshold, there was no difference in growth, but shrinkage increased for the validation dataset and decreased for the training dataset. For the OTD endpoint, the effect of total CD8 was more obvious, where for patients above the threshold, growth was significantly smaller and shrinkage was higher.

[0122] Collectively, these results support the conclusion that high values of FC and OTD are associated with improved clinical outcomes at the patient level.

[0123] Example 2: Correlation between Decision Rules and Clinical Outcomes

[0124] In the next step, decision rules based on FC and / or OTD were investigated, and the results of the decision rules were correlated with the clinical outcomes evaluated by the study team for each individual study or cohort (for the Phase II study of FAP-IL2v). The decision rule could be based on the mean of log FC or log OTD. In this case, the confidence level was defined as the posterior probability that the true log FC or log OTD was above the threshold given the observed data on the study. In the calculation of the posterior probability, it was assumed that log FC or log OTD was normal, with the variance given in Table 4. The variance was derived from the pooled training and validation datasets, although sensitivity analysis showed that the variability was similar across the datasets. The higher the confidence level, the higher the likelihood of improved clinical outcomes (as established by the patient-level associations described above). A second option was to define the decision rule based on the number of patients observed above the threshold. The confidence level was then defined as the following posterior probability: the posterior probability that the number of patients above the threshold was higher than the target rate. The target rate was set a priori as the expected number of patients with a sufficient increase in total CD8 T cells and was defined based on preclinical experiments.

[0125] Measurement Sd log FC 1.4 log OTD 1.44

[0126] Table 4: Standard deviations observed on our dataset and used in the definition of the confidence level

[0127] Derive decision rules from the following confidence levels: positive results, where the confidence level is above 75%; indeterminate results, where the confidence level is between 50% and 75%; and negative results, where the confidence level is below 50%. These levels are the standard for Bayesian decision-making in early clinical trials (see, for example, Fisch et al.

[15] ). Two decision rules were evaluated for FC at the study level in Table 5. In this table, the studies for the training dataset and the validation dataset are ranked according to the confidence level. The shade of color indicates a confidence level > 75% (light gray), between 50% and 75% (gray), and < 50% (dark gray). The frames indicate the cohorts in which a clinical decision can be made (sufficient sample size of 10 to 100 patients). The light gray frames highlight the cohorts with positive clinical efficacy. The gray frames highlight the cohorts with marginal (intermediate) efficacy, i.e., not sufficient for further development but above the standard of care (SOC). The dark gray frames indicate drugs with insufficient clinical efficacy (see also the legend in Table 5). Compare the two decision rules: the rule based on the geometric mean of FC and clinical efficacy and the rule based on the number of patients above this threshold, which requires the specification of a target rate. The geometric mean of log FC and clinical efficacy produced more consistent results, where the selected cohorts were all ranked higher in the light gray frames, followed by the gray frames, and then the dark gray frames.

[0128] For these reasons, we chose to use the rule based on the geometric mean of FC to focus on further evaluating the decision rules.

[0129] P[log FC > threshold]

[0130]

[0131] P[Nb subj > target Nb subj]

[0132]

[0133] Table 5: There is a strong association between the clinical assessment (frames; response and PFS) and the confidence level derived as the geometric mean of the fold change from the total CD8 T cell density (shading) (upper panel). In the 20 evaluated patient cohorts, this degree of association with the number of patients above the threshold is less (lower panel). FC decision rule and clinical efficacy. For the number of patients above the threshold, the target rate of patients above the threshold was set based on an expected log fold increase of 0.9, resulting in a target rate of 0.5.

[0134] The decision rule can be applied to the mean log OTD and is given in Table 6. Although there is a better association between total CD8 and clinical efficacy at the patient level compared to log FC, the decision rule seems weaker in the cohort where there is inconsistent decision-making in terms of clinical efficacy.

[0135] P[log OTD > threshold]

[0136]

[0137] Table 6: There is a good association between clinical assessment (framework; response and PFS) and the confidence level derived from the density of total CD8 T cell density treatment (shaded) as the geometric mean in 20 evaluated patient cohorts, but to a lesser extent than the fold change (above the threshold). Log OTD decision rule

[0138] Figure 6 The correlation between log OTD and log FC is provided. Acknowledging the fact that log FC includes log OTD in its definition, a moderate correlation between log FC and log OTD is observed, indicating that both endpoints may be relevant for deriving a more robust decision rule.

[0139] Both the log OTD endpoint and the log FC endpoint can be combined in various ways. It is chosen to combine the OTD and log FC decision rules rather than using a score with a log baseline value. The benefit is that the decision rule will make full use of FC itself rather than through OTD. The drawback is that it relies on the correlation between the distribution of log FC and the distribution of log OTD, which can be estimated from the dataset used.

[0140] From the joint distribution of log FC and log OTD, two methods can be used to derive a combined decision rule: it can be considered that both log FC and log OTD need to be above the threshold to obtain a positive result, or either log FC or log OTD alone is sufficient to obtain a positive result. These two methods are shown on our dataset in Table 7.

[0141] P[FC and OTD > threshold]

[0142]

[0143] P[FC or OTD > threshold]

[0144]

[0145] Table 7: Combining the readings from fold change and total CD8 T cell density increases the association with clinical assessment (framework; response and PFS). A composite decision rule was reached that requires both log FC and log OTD to be above the threshold for a positive result (upper panel), or only one endpoint to be above the threshold for a positive result (lower panel).

[0146] Table 7 shows that the requirement for both log FC and log OTD to be above the threshold is too stringent and results in a low confidence level obtained by this combined decision rule. In contrast, the decision rule (whereby log FC or log OTD above the threshold is associated with better clinical outcomes) improves the confidence interval. The lower panel of Table 7 shows that the decision rule based on log FC or log OTD above the threshold is more consistent with clinical assessment compared to the rule requiring both log FC and log OTD to be above the threshold.

[0147] Therefore, the data suggest a combined decision rule based on the log FC endpoint or the log OTD endpoint.

[0148] Given this decision rule, and assuming a sample size of 15 patients with paired biopsies, Figure 7 the readings from an experiment are given. The figure shows the decisions derived from the data observed in the experiment.

[0149] One can also calculate the probability of success (i.e., confidence level > 0.75) for a given true log OTD and log FC. This calculation accounts for the fact that even the true values are never observed in reality, and only the random experiments are observed. Figure 8 The probability of success for studies with 5, 10, 20, 60 patients with paired biopsies is shown. The figure shows that the probability of success increases when the true value is above the threshold (upper right quadrant) and decreases when the true value is below the threshold (lower left quadrant).

[0150] References

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Claims

1. A method for determining whether a patient suffering from cancer is likely to benefit from treatment with immunotherapy, the method comprising a) obtaining samples from the patient before treatment (at baseline) and after treatment, b) determining the fold change (FC) of CD8+ T cells and / or the on-treatment density (OTD) of CD8+ T cells in the sample after treatment compared to the sample at baseline, c) comparing the value obtained in b) with a threshold, wherein if the value obtained in b) is higher than the threshold, the patient is likely to benefit from the treatment.

2. The method according to claim 1, wherein the method is an in vitro method.

3. The method according to claim 1 or 2, wherein step b) comprises determining the fold change of CD8+ T cells or the on-treatment density of CD8+ T cells in the sample after treatment compared to the sample at baseline 4. The method according to any one of claims 1 to 3, wherein the sample is a tumor biopsy sample of the patient.

5. The method according to any one of claims 1 to 4, wherein the cancer is a solid tumor.

6. The method according to claim 5, wherein the solid tumor is selected from lung cancer (including non-small cell lung cancer), breast cancer, thyroid cancer, head and neck cancer, pancreatic cancer, prostate cancer, bladder cancer, colon cancer, and colorectal cancer.

7. The method according to any one of claims 1 to 6, wherein the patient's benefit from treatment is one of partial remission (PR), complete remission (CR), or longer progression-free survival (PFS).

8. The method according to any one of claims 1 to 7, wherein if the threshold for the fold change of CD8+ T cells is ≥ 0.9 on a logarithmic scale compared to baseline, as determined by repeated landmark analysis, the patient is likely to benefit in the form of PFS.

9. The method according to any one of claims 1 to 7, wherein if the threshold for the on-treatment density of CD8+ T cells is ≥ 6.2 on a logarithmic scale compared to baseline (about 500 cells / mm 2 ), as determined by repeated landmark analysis, the patient is likely to benefit in the form of PR, CR, and / or PFS.

10. The method according to any one of claims 1 to 7, wherein if the following conditions are met, the patient is likely to benefit from immunotherapy - The threshold of the fold increase for CD8+ T cells is ≥ 0.9 on a logarithmic scale compared to the baseline, as determined by ROC curve analysis, and - The threshold of the in-treatment density for CD8+ T cells is ≥ 6.2 on a logarithmic scale compared to the baseline (about 500 cells / mm 2 ), as determined by repeated landmark analysis.

11. The method according to any one of claims 1 to 7, wherein if the threshold of the fold increase for CD8+ T cells is ≥ 1.3 on a logarithmic scale compared to the baseline, and the threshold of the in-treatment density for CD8+ T cells is ≥ 6.7 on a logarithmic scale compared to the baseline, as determined by ROC curve analysis, then the patient may benefit from PR or CR.

12. The method according to any one of claims 1 to 7, wherein if the threshold of the fold increase for CD8+ T cells is ≥ 0.9 on a logarithmic scale compared to the baseline, and the threshold of the in-treatment density for CD8+ T cells is ≥ 6.2 on a logarithmic scale compared to the baseline, as determined by repeated landmark analysis, then the patient may benefit in the form of PFS.

13. The method according to any one of claims 1 to 12, which is used for early-phase clinical trials.

14. The method according to any one of claims 1 to 12, which is used for monitoring the treatment of a patient with cancer, wherein the treatment involves immunotherapy.

15. The method according to any one of claims 1 to 12, which is used for making a decision on whether to continue treating a patient with cancer with immunotherapy.

16. A biomarker for use in the method according to any one of claims 1 to 15, wherein the biomarker is characterized by - the fold increase (FC) of CD8+ T cells in a sample obtained from the patient after treatment compared to a sample from the same patient at baseline; and / or - the in-treatment density (OTD) of CD8+ T cells.

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