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EP-4373974-B1 - BIOMARKERS FOR THERAPY RESPONSE AFTER IMMUNOTHERAPY

EP4373974B1EP 4373974 B1EP4373974 B1EP 4373974B1EP-4373974-B1

Inventors

  • KHMELEVSKIY, Andrey

Dates

Publication Date
20260513
Application Date
20220720

Claims (15)

  1. A method for determining and/or predicting the response or resistance to treatment with an immunotherapeutic agent that is an immune checkpoint inhibitor or a combination of immune checkpoint inhibitors in a subject diagnosed with cancer, said method comprising analyzing the expression level of the biomarkers CD247, LAX1, and IKZF3 in a sample of the subject wherein the sample contains or is suspected to contain tumor cells.
  2. The method of claim 1, wherein the immunotherapeutic agent is selected from the group consisting of a PD-1 targeting agent, a PD-L1 targeting agent, a CTLA-1 targeting agent, and a combination thereof.
  3. The method according to claims 1 or 2 wherein the sample is a tumor tissue sample or a fluid sample comprising tumor cells; preferably wherein the sample is a tumor tissue sample or a blood sample comprising circulating tumor cells.
  4. The method of any one of claims 1 to 3 further comprising analyzing the expression level of the biomarkers CD3G and ITGAL in the sample of the subject.
  5. The method according to any of the preceding claims wherein the expression level of at least 2, preferably at least 4, even more preferably all of the biomarkers CD3E, TIGIT, PIK3CD, IGHV1.3, and IGHV4.4 is analyzed in a sample of the subject.
  6. The method according to any of the preceding claims, wherein the expression level of the biomarkers is compared to a corresponding reference value or threshold value that is characteristic of a subject with a known response to treatment with the immunotherapeutic agent.
  7. The method according to any of the preceding claims wherein on the basis of the expression level of the biomarkers a risk score is obtained, said risk score representing the likelihood for the response to the immunotherapeutic agent.
  8. The method according to claim 7, wherein the risk score is obtained and calculated using a pre-trained machine learning model and the expression level of the biomarkers as input values.
  9. The method according to any of the preceding claims wherein the response is selected from the RECIST 1.1 response criteria.
  10. Use of a kit comprising: - means for measuring the expression level of the biomarkers CD247, LAX1, and IKZF3 in a sample of the subject; - optionally, means for measuring the expression level of the biomarkers CD3G and ITGAL in a sample of the subject; - optionally, means for measuring the expression level of the biomarkers CD3E, TIGIT, PIK3CD, IGHV1.3, IGHV4.4 in a sample of the subject; - optionally, a reference value or threshold value for the biomarkers CD247, LAX1, and IKZF3; - optionally, a reference value or threshold value for the biomarkers CD3G and ITGAL ; - optionally, a reference value or threshold value for the biomarkers CD3E, TIGIT, PIK3CD, IGHV1.3, IGHV4.4, in the method according to any one of the claims 1 to 9.
  11. A computer-implemented method for monitoring, predicting or determining the response of a subject diagnosed with cancer to treatment with an immunotherapeutic agent that is an immune checkpoint inhibitor or a combination of immune checkpoint inhibitors, the method comprising: (a) providing quantified expression levels of the biomarkers CD247, LAX1, and IKZF3 in a sample of the subject wherein the sample contains or is suspected to contain tumor cells; (b) optionally providing quantified expression levels of the biomarkers CD3G and ITGAL in a sample of the subject wherein the sample contains or is suspected to contain tumor cells; (c) normalizing the quantified expression levels whereby normalization occurs via comparison with data obtained from corresponding assessments and expression levels from a reference set; (d) classifying whether the normalized values of step (c) exceed a predetermined threshold; (e) obtaining a risk score of said normalized values, wherein said risk score is calculated using a pre-trained machine learning model, and wherein the risk score represents the likelihood for the response to the immunotherapeutic agent by the subject.
  12. The method according to claim 11 wherein the immunotherapeutic agent is selected from the group consisting of a PD-1 targeting agent, a PD-L1 targeting agent, a CTLA-4 targeting agent, and a combination thereof.
  13. The method according to claim 11 or 12 wherein the sample is a tumor tissue sample or a fluid sample comprising tumor cells; preferably wherein the sample is a tumor tissue sample or a blood sample comprising circulating tumor cells.
  14. The method according to any of the preceding claims 1 to 9, or 11 to 13, or the use of claim 10 wherein the biomarker is a protein coding gene, in particular wherein the expression level of the biomarker is the RNA-based expression level of the protein coding gene.
  15. The method according to any of the preceding claims 1 to 9, or 11 to 14, or the use of claim 10 or 14 wherein the subject is diagnosed with melanoma; preferably diagnosed with stage II, stage III or stage IV melanoma.

Description

FIELD OF THE INVENTION The invention relates to the field of diagnostics to guide cancer immunotherapy. More specifically, the invention provides methods and kits for predicting and/or determining the response of a subject to treatment with an immunotherapeutic agent. BACKGROUND TO THE INVENTION Cancer is a major public health problem. In 2015, 1.3 million people died from cancer in the European Union, which equated to more than one quarter (25.4%) of the total number of deaths. Many treatments have been devised for various cancers. Immune checkpoint inhibitors (ICls) have changed the treatment landscape for many tumor types, particularly in the metastatic setting. The development of these ICls targeting cytotoxic T-lymphocyte antigen-4 (CTLA-4) and programmed cell death-1 (PD-1/PD-L1) has significantly improved the treatment of a variety of cancers, like melanoma and non-small cell lung carcinoma (NSCLC). ICls enhance antitumor immunity by blocking the prototypical immune checkpoint receptors that exist both on immune cells and on tumor cells and exhibit negative regulation of T-cell function. Although these inhibitors can lead to remarkable responses, patients with cancer respond differently to an ICI treatment. Some patients quickly improve and are able to overcome the disease, counting for about 25% to 30% of the treated patients, but 50% of the treated patients see no durable benefits. Even more alarming, a growing number of studies shows that immunotherapy may accelerate tumor progression in a significant subset of patients ranging from 4% to 29% across multiple histologies and lead to so-called hyperprogression. Currently available diagnostics are not efficient enough to prognostically identify which patients are going to respond to immunotherapy, such as ICI therapy, or to identify which patients will experience hyperprogressive disease or be non-responders to the ICI therapy. Notably, at best the accuracy of available biomarkers or sets of biomarkers falls below 52-58% and it does not provide much insight into the biological mechanism underlying the non-responsiveness to ICls. For example, WO2014151006 provides biomarkers for patient selection and prognosis in cancer. However, this patent application is limited to predicting the responsiveness of an individual with a disease to treatment with a PD-L1 axis binding antagonist. WO2019012147 proposes a radiomics based biomarker for detecting the presence and density of tumor infiltrating CD8 T-cells to prognose the survival and/or the treatment efficiency of cancer patients treated with immunotherapy such as anti-PD-1/PD-L1 monotherapy. US20180107786 discloses a method for generating an immune score based on tumor infiltrating lymphocytes, T-cell receptor signaling and mutation burden. WO2021030627 provides biomarkers for predicting a patient's response to checkpoint inhibitor therapy. Accordingly, there is a need for assays capable of characterizing an immunological tumor microenvironment for companion diagnostics development and therapeutic decisions. In particular, there is a high need for biomarkers to identify responders and non-responders to immunotherapy treatment. SUMMARY OF THE INVENTION The inventors have addressed the challenges for predicting and/or determining the response of a subject diagnosed with cancer to treatment with an immunotherapeutic agent by identifying biomarkers that are able to predict the response of the subject to the immunotherapeutic agent based on the RECIST response criteria. With the present invention, the inventors thus identified biomarkers that are able to predict whether a subject will respond, partially respond or not respond to treatment with an immunotherapeutic agent, more specifically with an immune checkpoint inhibitor or a combination of immune checkpoint inhibitors. Accordingly, a first aspect of the present invention relates to methods for determining and/or predicting the response to treatment with an immunotherapeutic agent that is an immune checkpoint inhibitor or a combination of immune checkpoint inhibitors in a subject diagnosed with cancer, and wherein said methods are based on the analysis of the expression level of the biomarkers CD247, LAX1, and IKZF3 in a sample of the subject wherein the sample contains or is suspected to contain tumor cells. More specifically, the methods are based on the analysis of the RNA expression level of the protein coding genes, and wherein on the basis of said expression level it is predicted whether the subject will respond to an immunotherapeutic agent. In an embodiment, the expression level of the biomarkers CD247, LAX1 and IKZF3 is analyzed in a sample of the subject that contains or is suspected to contain tumor cells, and the expression level of these biomarkers is used to determine and/or predict the response to treatment with an immunotherapeutic agent in the subject. In still a further embodiment, the expression level of the biomarkers CD247, LAX1 and IKZF3