Biomarkers for predicting response to immunotherapy and uses thereof

Abstract

In some embodiments, a set of biomarkers is provided, selected from one or more of the group consisting of ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP, and RNF213. The biomarker is used for predicting the response of a subject receiving immunotherapy to immunotherapy, has good performance in subjects suffering from different tumor types, has high universality, has the advantages of being low in cost, easy and convenient to use and the like, and has good application prospects. And the method is of great significance for predicting response to immunotherapy of cancers and personalized therapy.

Description

Technical Field

[0001] The present application relates to the field of biomedicine, and in particular to a biomarker for predicting response to immunotherapy and its application. Background Art

[0002] Immunotherapy is one of the effective treatments for tumors and is widely used in clinical practice. However, due to the large differences in the immune microenvironment of different patients, it is not certain whether a patient will respond to immunotherapy.

[0003] Existing biomarkers for predicting response to immunotherapy, such as tumor mutation burden (TMB), mismatch repair deficiency / microsatellite instability (dMMR / MSI), and the status of immune-related cells and gene expression levels in the tumor microenvironment, all have certain limitations, and their prediction results are often unstable due to factors such as tumor type, detection method, and patient characteristics.

[0004] Therefore, it is urgent to propose a biomarker that can combine accuracy and universality to predict the response of cancer patients to immunotherapy. Summary of the invention

[0005] To this end, the first aspect of the embodiments of the present application provides a group of biomarkers; the second aspect of the embodiments of the present application provides the use of biomarkers in the preparation of a kit for predicting a subject's response to immunotherapy; the third aspect of the embodiments of the present application provides a method for predicting a subject's response to immunotherapy based on biomarkers; the fourth aspect of the embodiments of the present application provides an apparatus for predicting a subject's response to immunotherapy based on biomarkers; the fifth aspect of the embodiments of the present application provides a system for predicting a subject's response to immunotherapy based on biomarkers; the sixth aspect of the embodiments of the present application provides a method for treating cancer based on biomarkers; the seventh aspect of the embodiments of the present application provides a computer program product.

[0006] An embodiment of one aspect of the present application provides a group of biomarkers selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP and RNF213.

[0007] Another aspect of the present application provides a use of a biomarker in preparing a kit for detecting a subject's response to immunotherapy, wherein the detection of a subject's response to immunotherapy comprises:

[0008] Acquiring nucleic acid expression profile data from a biological sample of the subject;

[0009] calculating an IFN / EMT score for the biological sample; and

[0010] determining the subject's response to immunotherapy according to the IFN / EMT score,

[0011] wherein the biomarker is selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP and RNF213; and

[0012] The IFN / EMT score being significantly higher than the reference level indicates that the subject is responsive to the immunotherapy.

[0013] In some embodiments, calculating the IFN / EMT score of the biological sample comprises:

[0014] extracting the transcriptome expression level of the biomarker from the nucleic acid expression profile data; and

[0015] The IFN / EMT score of the biological sample is calculated by regression analysis based on the transcriptome expression levels of the biomarkers.

[0016] In some embodiments, the IFN / EMT score is obtained by the following formula:

[0017] IFN / EMT score = -0.096559765 + (-0.010082384 × ITGB3 transcriptome expression level) + (-0.006223725 × HTRA1 transcriptome expression level) + (-0.0057209 × SERPINH1 transcriptome expression level) + (-0.001216867 × TIMP3 transcriptome expression level) + (-0.00069129 × ITGAV transcriptome expression level) + (-0.000611413 × COL11A1 transcriptome expression level) + (0.001477326 × ITGB7 transcriptome expression level) + (0.004623571 × CIITA transcriptome expression level) + (0.012179575 × ZBP1 transcriptome expression level) + (0.012582618 × GZMA transcriptome expression level) + (0.019644856 × IL18BP transcriptome expression level) + (0.036724799 × RNF213 transcriptome expression level).

[0018] In some embodiments, the biological sample is blood, tumor tissue, urine, saliva, or lymph fluid.

[0019] In some embodiments, the subject has or is suspected of having cancer.

[0020] In some embodiments, the cancer is gastric cancer, melanoma, lung cancer, esophageal squamous cell carcinoma, or urothelial carcinoma.

[0021] In some embodiments, the cancer is melanoma, gastric cancer, esophageal squamous cell carcinoma, and urothelial carcinoma.

[0022] Another aspect of the present application provides a system for predicting a subject's response to immunotherapy based on biomarkers, the system comprising:

[0023] Processor; and

[0024] A storage medium comprising program instructions executed by the processor, wherein the program instructions cause the processor to perform the following steps:

[0025] Acquiring nucleic acid expression profile data from a biological sample of the subject;

[0026] calculating an IFN / EMT score for the biological sample; and

[0027] determining the subject's response to immunotherapy according to the IFN / EMT score,

[0028] wherein the biomarker is selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP and RNF213; and

[0029] The IFN / EMT score being significantly higher than the reference level indicates that the subject is responsive to the immunotherapy.

[0030] In some embodiments, calculating the IFN / EMT score of the biological sample comprises:

[0031] extracting the transcriptome expression level of the biomarker from the nucleic acid expression profile data; and

[0032] The IFN / EMT score of the biological sample is calculated by regression analysis based on the transcriptome expression levels of the biomarkers.

[0033] In some embodiments, the IFN / EMT score is obtained by the following formula:

[0034] IFN / EMT score = -0.096559765 + (-0.010082384 × ITGB3 transcriptome expression level) + (-0.006223725 × HTRA1 transcriptome expression level) + (-0.0057209 × SERPINH1 transcriptome expression level) + (-0.001216867 × TIMP3 transcriptome expression level) + (-0.00069129 × ITGAV transcriptome expression level) + (-0.000611413 × COL11A1 transcriptome expression level) + (0.001477326 × ITGB7 transcriptome expression level) + (0.004623571 × CIITA transcriptome expression level) + (0.012179575 × ZBP1 transcriptome expression level) + (0.012582618 × GZMA transcriptome expression level) + (0.019644856 × IL18BP transcriptome expression level) + (0.036724799 × RNF213 transcriptome expression level).

[0035] In some embodiments, the biological sample is blood, tumor tissue, urine, saliva, or lymph fluid.

[0036] In some embodiments, the subject has or is suspected of having cancer.

[0037] In some embodiments, the cancer is gastric cancer, melanoma, lung cancer, esophageal squamous cell carcinoma, or urothelial carcinoma.

[0038] In some embodiments, the cancer is melanoma, gastric cancer, esophageal squamous cell carcinoma, and urothelial carcinoma.

[0039] The present invention provides a biomarker for predicting the response of a subject receiving immunotherapy to immunotherapy, and discloses a method for screening the biomarker and a method for predicting the response of a subject receiving immunotherapy to immunotherapy using the biomarker. The biomarker shows good predictive effects for subjects with different tumor types, has the advantages of high universality, low cost, and simple and convenient use, and is of great significance for predicting the response to immunotherapy of pan-solid tumors and personalized treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1is the differentially gene enriched signaling pathway, where n is the number of subjects, EMT is the epithelial-mesenchymal transition pathway, NES is the normalized enrichment score, and FDR is the false discovery rate.

[0042] Figure 2 IFN / EMT score box plot of biological samples from NCT04215471 in Example 1, where n is the number of subjects.

[0043] Figure 3 KM curves for recurrence-free survival and overall survival.

[0044] Figure 4 It is a box plot of IFN / EMT scores for each group of samples in Example 3, wherein mUC-Mariathasan-anti-PDL1 is a box plot of urothelial carcinoma sample scores, melanoma-Puch-anti-PD1 and melanoma-Gide anti-PD1 are box plots of melanoma sample scores, GC anti-PD1 is a box plot of gastric cancer sample scores, NSCLC-Jung anti-PD1 is a box plot of lung cancer sample scores, and ESCC-nICT anti-PD1 is a box plot of urothelial carcinoma sample scores; in the abscissa, CR is a significant response group, PR is a partial response group, SD is a non-response group (disease maintenance), PD is a non-response group (disease worsening), CR / PR is a combination of a significant response group and a partial response group, SD / PD is a combination of a non-response group (disease maintenance) and a non-response group (disease worsening), R is a response group, NR is a non-response group, and n is the number of subjects.

[0045] Figure 5 This is a schematic diagram of the process of predicting a subject's response to immunotherapy in one embodiment of the present application.

[0046] Figure 6 Schematic diagram of the process of calculating the IFN / EMT score of a biological sample in one embodiment of the present application. DETAILED DESCRIPTION

[0047] The present invention is further described in detail below in conjunction with specific embodiments. The examples provided are only for illustrating the present invention and are not intended to limit the scope of the present invention. The examples provided below can be used as a guide for further improvements by those of ordinary skill in the art and are not intended to limit the present invention in any way.

[0048] This application is made based on the following knowledge of the inventor:

[0049] At present, the main biomarkers for predicting the efficacy of immunotherapy are: Tumor Mutation Burden (TMB), mismatch repair deficiency / microsatellite instability (dMMR / MSI), and the status of immune-related cells and gene expression levels in the tumor microenvironment.

[0050] TMB refers to the number of somatic non-synonymous mutations per megabase in the gene region detected by targeted sequencing in tumor samples. TMB has been found to be correlated with ICI response in a variety of tumors such as urothelial carcinoma, melanoma, and non-small cell lung cancer. Subjects with higher TMB may be more likely to benefit from immunotherapy. The National Comprehensive Cancer Network (NCCN) has recommended TMB as a detection method for subjects with non-small cell lung cancer receiving immunotherapy.

[0051] dMMR / MSI is a special type of high TMB tumor. dMMR indicates a defect in DNA mismatch repair function, and MMR protein expression is negative. Any defect in MMR gene function can lead to dMMR. MSI refers to the phenomenon of microsatellite length changes caused by the increase or decrease of repeated fragments caused by dMMR during DNA replication, which cannot be repaired by the body. Several clinical trials (KEYNOTE-016, KEYNOTE-164, KEYNOTE-012, KEYNOTE-028, KEYNOTE-158) have shown that dMMR / MSI-High patients can have a lasting response to the anti-PD-1 monoclonal antibody drug pembrolizmab. In addition, neoantigen load may also be related to the patient's direct response to ICI.

[0052] The effect of immunotherapy can also be predicted by the expression profile of immune-related genes. Clinical studies on multiple tumors have shown that in first-line combined therapy, the expression of tumor PD-L1 is positively correlated with the patient's response to ICI or the patient's overall survival (OS). The expression level of PD-L1 has also become one of the markers for determining whether a patient is receptive to immunotherapy. Mariathasan et al. found that TGFβ shapes the tumor microenvironment by limiting T cell infiltration to inhibit anti-tumor immunity, and the expression level of TGFβ is negatively correlated with the efficacy of the anti-PD-1 monoclonal antibody drug atezolizumab (S. Mariathasan et al., TGFbeta attenuates tumor response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544-548 (2018)).

[0053] However, the existing immunotherapy efficacy markers have certain limitations: conventional TMB testing relies on whole-exome sequencing, with approximately 20,000 genes sequenced, and the cost is much higher than PD-L1 testing that relies on immunohistochemistry; although TMB calculated based on a small panel can reduce costs, the test results of different tumor types vary greatly, and sequencing methods using different detection platforms have a greater impact on the test results, making it difficult to set unified detection standards and thresholds for multiple tumors.

[0054] Although in some studies, the expression level of PD-L1 is positively correlated with the efficacy of immunotherapy, there are still a large number of clinical studies on solid tumors that have not found a strong correlation between it and ICI response, and some patients with negative PD-L1 expression can also benefit clinically from ICI (combination) treatment (K. Wu et al., The efficacy and safety of combination of PD-1 and CTLA-4 inhibitors: a meta-analysis. Exp Hematol Oncol 8, 26 (2019)). Other gene markers are also facing a similar situation as PD-L1. Therefore, the expression profile of genes related to immunotherapy in the tumor microenvironment cannot be used as an independent biomarker to comprehensively evaluate the efficacy.

[0055] To this end, the present application provides a group of biomarkers that are significantly correlated with the survival of the subject and evaluate the response to immunotherapy, and discloses a method for screening the biomarker and a method for predicting the response of the subject receiving immunotherapy to immunotherapy using the biomarker. The biomarker shows good predictive effect for subjects with different tumor types, has the advantages of high universality, low cost and simple and convenient use, and is of great significance for predicting the response to immunotherapy of pan-solid tumors and personalized treatment.

[0056] The inventors of this application screened a new biomarker based on mRNA expression profiles and comprehensively considered the subject groups with different degrees of response to immunotherapy to predict the subject's response to cancer immunotherapy. The operation is simple, labor-saving, and time-saving, and can achieve low-cost prediction, with higher clinical feasibility.

[0057] The first aspect of the present application provides a group of biomarkers selected from one or more of the following: ITGB3 (NCBI Gene: 3690), HTRA1 (NCBI Gene: 5654), SERPINH1 (NCBI Gene: 871), TIMP3 (NCBI Gene: 7078), ITGAV (NCBI Gene: 3685), COL11A1 (NCBI Gene: 1301), ITGB7 (NCBI Gene: 3695), CIITA (NCBI Gene: 4261), ZBP1 (NCBI Gene: 81030), GZMA (NCBI Gene: 3001), IL18BP (NCBI Gene: 10068) and RNF213 (NCBI Gene: 57674).

[0058] In a second aspect, the present application provides an application of a biomarker in preparing a kit for detecting a subject's response to immunotherapy. Figure 5 As shown, Figure 5 This is a schematic diagram of the process of predicting a subject's response to immunotherapy in one embodiment of the present application, which may specifically include the following steps S1 to S3.

[0059] S1: Obtain nucleic acid expression profile data from a biological sample of a subject.

[0060] S2: Calculate the IFN / EMT score of the biological sample.

[0061] S3: Determine the subject's response to immunotherapy based on the IFN / EMT score.

[0062] wherein the biomarker is selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP, and RNF213; and

[0063] An IFN / EMT score significantly higher than the reference level indicates that the subject is responding to immunotherapy.

[0064] In the present application example, the reference level is used to determine whether the subject responds or does not respond to immunotherapy. Based on the IFN / EMT score of the biological sample from the subject being significantly higher than the reference level, it is determined that the subject responds to immunotherapy.

[0065] In some embodiments, the reference level is determined based on the IFN / EMT scores of multiple reference samples. For example, the reference level can be the arithmetic mean, median, geometric mean, harmonic mean, weighted mean, square mean of the IFN / EMT scores of multiple reference samples.

[0066] In some embodiments, the reference sample is a subject with cancer who has received immunotherapy but was determined to be non-responsive to the immunotherapy.

[0067] In some embodiments, the immunotherapy is neoadjuvant immunotherapy.

[0068] In one embodiment, Figure 6 As shown, Figure 6 This is a schematic diagram of a process for calculating the IFN / EMT score of a biological sample in an embodiment of the present application, which may specifically include the following steps S201 to S202.

[0069] S201: Extracting transcriptome expression levels of biomarkers from nucleic acid expression data.

[0070] S202: Calculate the IFN / EMT score of the biological sample by regression analysis based on the transcriptome expression level of the biomarker.

[0071] In some embodiments, the IFN / EMT score is obtained by the following formula:

[0072] IFN / EMT score = -0.096559765 + (-0.010082384 × ITGB3 transcriptome expression level) + (-0.006223725 × HTRA1 transcriptome expression level) + (-0.0057209 × SERPINH1 transcriptome expression level) + (-0.001216867 × TIMP3 transcriptome expression level) + (-0.00069129 × ITGAV transcriptome expression level) + (-0.000611413 × COL11A1 transcriptome expression level) + (0.001477326 × ITGB7 transcriptome expression level) + (0.004623571 × CIITA transcriptome expression level) + (0.012179575 × ZBP1 transcriptome expression level) + (0.012582618 × GZMA transcriptome expression level) + (0.019644856 × IL18BP transcriptome expression level) + (0.036724799 × RNF213 transcriptome expression level).

[0073] In some embodiments, the biological sample is blood, tumor tissue, urine, saliva, or lymph fluid.

[0074] In some embodiments, the subject has or is suspected of having cancer.

[0075] In some embodiments, the cancer is gastric cancer, melanoma, lung cancer, esophageal squamous cell carcinoma, or urothelial carcinoma.

[0076] In some embodiments, the cancer is melanoma, gastric cancer, esophageal squamous cell carcinoma, and urothelial carcinoma.

[0077] The third aspect of the present application provides a method for predicting a subject's response to immunotherapy based on biomarkers, including the process of predicting a subject's response to immunotherapy in the second aspect of the present application.

[0078] The fourth aspect embodiment of the present application proposes a device for predicting a subject's response to immunotherapy based on biomarkers, comprising an information acquisition unit, a calculation unit and a determination unit. The information acquisition unit, the calculation unit and the determination unit together implement the process of predicting a subject's response to immunotherapy in the above-mentioned second aspect embodiment.

[0079] In one embodiment, the device includes: an information acquisition unit for acquiring nucleic acid expression profile data of a biological sample from a subject; a calculation unit for calculating an IFN / EMT score of the biological sample; and a determination unit for determining the subject's response to immunotherapy based on the IFN / EMT score, wherein the biomarkers are selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP and RNF213; and an IFN / EMT score significantly higher than a reference level indicates that the subject responds to the immunotherapy.

[0080] In some embodiments, the computing unit further comprises: a biomarker extraction module for extracting the transcriptome expression level of the biomarker from the nucleic acid expression profile data; and an IFN / EMT score calculation module for calculating the IFN / EMT score of the biological sample through regression analysis based on the transcriptome expression level of the biomarker.

[0081] In some embodiments, the IFN / EMT score is obtained by the following formula:

[0082] IFN / EMT score = -0.096559765 + (-0.010082384 × ITGB3 transcript expression level) + (-0.006223725 × HTRA1 transcript expression level) + (-0.0057209 × SERPINH1 transcript expression level) + (-0.001216867 × TIMP3 transcript expression level) + (-0.00069129 × ITGAV transcript expression level) + (-0.000611413 × COL11A1 transcript expression level) + (0.001477326 × ITGB7 transcript expression level) + (0.004623571 × CIITA transcript expression level) + (

[0083] 0.012179575×ZBP1 transcriptome expression level)+(0.012582618×GZMA transcriptome expression level)+(0.019644856×IL18BP transcriptome expression level)+(0.036724799×RNF213 transcriptome expression level).

[0084] The fifth aspect embodiment of the present application proposes a system for predicting a subject's response to immunotherapy based on biomarkers, comprising a processor and a storage medium, wherein the storage medium contains program instructions executed by the processor, and when the processor executes the program instructions, the process of predicting a subject's response to immunotherapy in the above-mentioned second aspect embodiment is implemented.

[0085] The sixth aspect of the present application proposes a method for treating cancer based on biomarkers, including the process of predicting the subject's response to immunotherapy in the above-mentioned second aspect.

[0086] In some embodiments, the method includes: obtaining nucleic acid expression profile data from a biological sample from a subject; calculating an IFN / EMT score for the biological sample; determining the subject's response to immunotherapy based on the IFN / EMT score; and based on the subject's response to immunotherapy, performing immunotherapy on the subject, wherein the biomarkers are selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP and RNF213; and an IFN / EMT score significantly higher than a reference level indicates that the subject responds to immunotherapy.

[0087] A seventh aspect of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the process of predicting a subject's response to immunotherapy in the second aspect of the present application.

[0088] It should be noted that the aforementioned explanation of the process of predicting a subject's response to immunotherapy is also applicable to the method for predicting a subject's response to immunotherapy based on biomarkers, the device for predicting a subject's response to immunotherapy based on biomarkers, the system for predicting a subject's response to immunotherapy based on biomarkers, the method for treating cancer based on biomarkers, and the computer program product in the above-mentioned embodiments, which will not be repeated here.

[0089] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0090] Unless otherwise specified, the quantitative tests in the following examples were repeated three times and the results were averaged. Example 1 Screening of biomarkers for predicting response to immunotherapy

[0091] 1. Sample Preparation

[0092] The samples were from 25 subjects with esophageal squamous cell carcinoma who received two doses of adebelimumab (20 mg / kg, intravenous injection, Q3W) in a Phase Ib trial (NCT04215471) and underwent surgery. The subjects met the following criteria:

[0093] 1) 18 years and above.

[0094] 2) Eastern Cooperative Oncology Group (ECOG) physical status

[0095] (Performance status, PS) score is 0 or 1.

[0096] 3) The presence of at least one measurable lesion (according to RECIST 1.1 standard)

[0097] 4) No anti-PD-1 or PD-L1 treatment within two years

[0098] 5) No meningeal metastasis or autoimmune disease, and no other previous malignant tumors.

[0099] All biopsy and surgically removed tumor samples were visually examined by two experienced pathologists. Local tumors were collected within 30 minutes after esophagoscopy and surgery and quickly frozen in liquid nitrogen. Tumor specimens were dissected on a clean operating table with a new sterile scalpel, and the tumor was completely cut without damaging the integrity of the entire specimen, avoiding areas of obvious necrosis, fibrosis, or bleeding, and tumor samples were extracted.

[0100] 2. Sample RNA Extraction, Library Construction and Sequencing

[0101] Tumor samples extracted in step 1 above were sequenced separately. RNA concentration was quantified using Qubit RNA HS Assay Kit (Cat#Q32852, Thermo Fisher Scientific), and RNA quality and quantity were assessed using Agilent RNA 6000pico Kit (Cat#5067-1513, Agilent). RNA library samples were prepared using MGI rRNA removal kit (Cat#1000005953, MGI) and MGIEasy RNA library preparation kit (Cat#1000006383, MGI) according to the manufacturer's protocol. The concentration of the library was quantified using Qubit dsDNA HS Assay Kit (Cat#Q32851, Thermo Fisher Scientific), and the quality of the library was assessed using Agilent DNA 1000Kit (Cat#5067-1504, Agilent). The library was sequenced on an MGISEQ2000 sequencer (MGI, China) with a read length of 100 bp. The raw sequencing data were firstly removed from the adapters and low-quality reads, aligned using STAR (v2.5.1b58), and duplicate reads were filtered using the Picard tool. The gene expression profile was obtained by standard RNA process analysis.

[0102] 3. Regression Analysis

[0103] According to the residual tumor ratio of the subjects, the subjects were divided into a response group (residual ratio ≤ 33%) and a non-response group (residual ratio > 33%). Deseq2 (v1.30.1) was used to perform differential expression analysis on the response group and the non-response group, and Log2 (fold change) > 1 and P < 0.05 were used as thresholds to screen differentially expressed genes.

[0104] ClusterProfiler (v3.18.1) was used to perform pathway enrichment analysis on differentially expressed genes, and Hallmark genes obtained from the Molecular Signature Database (MSigDB v7.4) were used to perform Q-value tests on gene set enrichment analysis. After Kolmogorov-Smirnov correction for multiple testing, only pathways with Q-values ​​< 0.05 were retained. Figure 1 , the following three pathways were screened out:

[0105] HALLMARK_INTERFERON_ALPHA_RESPONSE(IFN-αresponse)

[0106] HALLMARK_INTERFERON_GAMMA_RESPONSE(IFN-γresponse)

[0107] HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION(EMT)

[0108] The genes involved in these three pathways were intersected with the differentially expressed genes, and finally 134 candidate genes were obtained.

[0109] According to the residual tumor ratio of each subject, the tumor regression ratio (1-tumor residual ratio) was obtained, and glmnet (v4.1-7) was used to extract characteristic genes from 134 candidate genes using sparse linear regression (elastic regression). The elastic regression algorithm combines two regularization methods, Lasso regression and ridge regression, and uses two parameters α and λ to control the penalty term. Hyperparameter tuning was performed using the RandomSearchCV method of python to determine the final values ​​of α and λ. Finally, we obtained twelve biomarkers associated with response to immunotherapy: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP, RNF213 and their corresponding regression coefficients, where each coefficient is shown in Table 1 below. The IFN / EMT score of each sample was calculated using the following regression equation:

[0110] IFN / EMT score = -0.096559765 + (-0.010082384 × ITGB3 transcript expression level) + (-0.006223725 × HTRA1 transcript expression level) + (-0.0057209 × SERPINH1 transcript expression level) + (-0.001216867 × TIMP3 transcript expression level) + (-0.00069129 × ITGAV transcript expression level) + (-0.000611413 × COL11A1 transcript expression level) + (0.001477326 × ITGB7 transcript expression level) + (0.004623571 × CIITA transcript expression level) + (

[0111] 0.012179575×ZBP1 transcriptome expression level)+(0.012582618×GZMA transcriptome expression level)+(0.019644856×IL18BP transcriptome expression level)+(0.036724799×RNF213 transcriptome expression level).

[0112] Table 1 Regression equation coefficients

[0113] feature coefficient intercept -0.096559765 ITGB3 -0.010082384 HTRA1 -0.006223725 SERPINH1 -0.0057209 TIMP3 -0.001216867 ITGAV -0.00069129 COL11A1 -0.000611413 ITGB7 0.001477326 CIITA 0.004623571 ZBP1 0.012179575 GZMA 0.012582618 IL18BP 0.019644856 RNF213 0.036724799

[0114] Example 2 Validation of biomarkers

[0115] (1) Samples from subjects in the Phase Ib trial (NCT04215471) in Example 1 were used to verify the accuracy of the markers and corresponding regression equations obtained in Example 1 in predicting the response of subjects to neoadjuvant immunotherapy.

[0116] The regression equation was used to calculate the IFN / EMT score of each subject's biological sample. Figure 2 It can be seen that the IFN / EMT scores of biological samples of the response group subjects were significantly higher than those of the non-response group.

[0117] It shows that this biomarker is significantly correlated with the subject's response to neoadjuvant immunotherapy and can be used to predict the subject's response to immunotherapy.

[0118] (2) The recurrence-free survival (RFS) and overall survival (OS) of the subjects in the Phase Ib trial (NCT04215471) in Example 1 were recorded and plotted into survival curves. The survival curve results showed that subjects with high IFN / EMT scores had longer RFS and OS ( Figure 3 ), indicating that this biomarker can also be used for the prognosis of immunotherapy in subjects.

[0119] Example 3 Validation of biomarkers

[0120] The immunotherapy data in six published articles were used to verify the predictive effect of the biomarkers in Example 1 of the present application, including gastric cancer (ST Kim et al., Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat Med 24, 1449-+ (2018)), melanoma (TN Gide et al., Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1 / Anti-CTLA-4 Combined Therapy. Cancer Cell 35, 238-+ (2019) and C. Cui et al., Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma. NPJ Genom Med 6, 7 (2021)), lung cancer (H. Jung et al., DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load. Nat Commun 10, 4278 (2019)), esophageal squamous cell carcinoma (JM Xun et al., Clinical and biomarker analyses of sintilimab versus chemotherapy as second-line therapy for advanced or metastatic esophageal squamous cell carcinoma: a randomized, open-label phase 2 study (ORIENT-2). Nat Commun 13, (2022)) and urothelial carcinoma (S. Mariathasan et al., TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544-548 (2018)).

[0121] The IFN / EMT scores of biological samples in each group of immunotherapy data were calculated separately (the response group and non-response group were defined according to the standards in the citation). The results showed that in each group of data, the IFN / EMT scores of the response group (R) were higher or significantly higher than those of the non-response group (NR). The results are shown in Figure 4 .

[0122] Example 4 Validation of biomarkers

[0123] Further, the gene expression profiles and subject efficacy information tables of 26 subjects receiving neoadjuvant immunotherapy (ESCC-nICT) (GQ Zhang et al., Multi-omics analysis uncovers tumor ecosystem dynamics during neoadjuvant toripalimab plus nab-paclitaxel and S-1for esophageal squamous cell carcinoma: a single-center, open-label, single-arm phase 2 trial. Ebiomedicine 90, (2023)) were obtained using an external database, and the expression information of twelve genes including ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP, and RNF213 were extracted from them. The gene expression profile data of the subjects was processed using the regression equation developed in Example 1 to obtain the IFN / EMT score of each sample, which shows the degree of response of the subject to immunotherapy. The results are as follows: Figure 4 As shown in (ESCC-nICTanti-PD1), the IFN / EMT scores of patients in the response group were significantly higher than those in the non-response group.

[0124] This example shows that the biomarker is generally applicable to predicting the response of subjects with various solid tumors to immunotherapy, and has the advantages of simple operation, labor saving, short time consumption, low-cost prediction, and higher clinical feasibility.

[0125] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0126] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A panel of biomarkers selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP, and RNF213.

2. Use of a biomarker in the preparation of a kit for detecting a subject's response to immunotherapy, characterized in that: The detecting of the subject's response to immunotherapy comprises: Acquiring nucleic acid expression profile data from a biological sample of the subject; calculating an IFN / EMT score for the biological sample; and determining the subject's response to immunotherapy according to the IFN / EMT score, wherein the biomarker is selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP and RNF213; and The IFN / EMT score being significantly higher than the reference level indicates that the subject is responsive to the immunotherapy.

3. The use according to claim 2, characterized in that: The calculating of the IFN / EMT score of the biological sample comprises: extracting the transcriptome expression level of the biomarker from the nucleic acid expression profile data; and The IFN / EMT score of the biological sample is calculated by regression analysis based on the transcriptome expression levels of the biomarkers.

4. The use according to claim 3, characterized in that: The IFN / EMT score is obtained by the following formula: IFN / EMT score = -0.096559765 + (-0.010082384 × ITGB3 transcriptome expression level) + (-0.006223725 × HTRA1 transcriptome expression level) + (-0.0057209 × SERPINH1 transcriptome expression level) + (-0.001216867 × TIMP3 transcriptome expression level) + (-0.00069129 × ITGAV transcriptome expression level) + (-0.000611413 × COL11A1 transcriptome expression level) + (0.001477326 × ITGB7 transcriptome expression level) + (0.004623571 × CIITA transcriptome expression level) + (0.012179575 × ZBP1 transcriptome expression level) + (0.012582618 × GZMA transcriptome expression level) + (0.019644856 × IL18BP transcriptome expression level) + (0.036724799 × RNF213 transcriptome expression level).

5. The use according to any one of claims 2 to 4, characterized in that The biological sample is blood, tumor tissue, urine, saliva or lymph fluid.

6. The use according to any one of claims 2 to 5, characterized in that The subject suffers from or is suspected of suffering from cancer; preferably, the cancer is gastric cancer, melanoma, lung cancer, esophageal squamous cell carcinoma or urothelial carcinoma, more preferably, the cancer is melanoma, gastric cancer, esophageal squamous cell carcinoma and urothelial carcinoma.

7. A system for predicting a subject's response to immunotherapy based on biomarkers, the system comprising: processor; and A storage medium comprising program instructions executed by the processor, wherein the program instructions cause the processor to perform the following steps: Acquiring nucleic acid expression profile data from a biological sample of the subject; calculating an IFN / EMT score for the biological sample; and determining the subject's response to immunotherapy according to the IFN / EMT score, wherein the biomarker is selected from one or more of the following: ITGB3, HTRA1, SERPINH1, TIMP3, ITGAV, COL11A1, ITGB7, CIITA, ZBP1, GZMA, IL18BP and RNF213; and The IFN / EMT score being significantly higher than the reference level indicates that the subject is responsive to the immunotherapy.

8. The system according to claim 7, characterized in that The calculating of the IFN / EMT score of the biological sample comprises: extracting the transcriptome expression level of the biomarker from the nucleic acid expression profile data; and calculating the IFN / EMT score of the biological sample based on the transcriptome expression level of the biomarker by regression analysis; Preferably, the IFN / EMT score is obtained by the following formula: IFN / EMT score = -0.096559765 + (-0.010082384 × ITGB3 transcriptome expression level) + (-0.006223725 × HTRA1 transcriptome expression level) + (-0.0057209 × SERPINH1 transcriptome expression level) + (-0.001216867 × TIMP3 transcriptome expression level) + (-0.00069129 × ITGAV transcriptome expression level) + (-0.000611413 × COL11A1 transcriptome expression level) + (0.001477326 × ITGB7 transcriptome expression level) + (0.004623571 × CIITA transcriptome expression level) + (0.012179575 × ZBP1 transcriptome expression level) + (0.012582618 × GZMA transcriptome expression level) + (0.019644856 × IL18BP transcriptome expression level) + (0.036724799 × RNF213 transcriptome expression level).

9. The system according to claim 7 or 8, characterized in that: The biological sample is blood, tumor tissue, urine, saliva or lymph fluid.

10. The system according to any one of claims 7 to 9, characterized in that The subject suffers from or is suspected of suffering from cancer; preferably, the cancer is gastric cancer, melanoma, lung cancer, esophageal squamous cell carcinoma or urothelial carcinoma; more preferably, the cancer is melanoma, gastric cancer, esophageal squamous cell carcinoma and urothelial carcinoma.

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