Application of combined marker in prediction of lifetime of tumor patient and treatment effect of immune checkpoint inhibitor

Abstract

The invention discloses an application of a combined marker in prediction of the lifetime of a tumor patient and the treatment effect of an immune checkpoint inhibitor, and provides a kit for predicting the lifetime of the tumor patient and the treatment effect of the immune checkpoint inhibitor by taking the ratio of ISG15 < + > macrophage to SPP1 < + > macrophage as the combined marker. The lifetime of a tumor object and the treatment effect of the tumor immune checkpoint inhibitor can be effectively predicted, so that the patient crowd sensitive to the treatment of the immune checkpoint inhibitor is effectively discriminated, the response rate of the treatment of the tumor immune checkpoint inhibitor is improved, and the tumor immune checkpoint inhibitor has good clinical transformation and application prospects.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically the application of combined biomarkers in predicting the survival of cancer patients and the efficacy of immune checkpoint inhibitor therapy. Background Technology

[0002] According to the latest statistics from the National Cancer Center in 2024, malignant tumors have become a major public health challenge in my country, ranking second in incidence and fourth in mortality among all diseases. Although immune checkpoint blockade therapies, represented by PD-1 / PD-L1 inhibitors, have made breakthrough progress in some cancer types, clinical research data shows that only about 20-30% of patients achieve a durable clinical response. In-depth analysis has revealed that the complex regulatory network of the tumor immunosuppressive microenvironment is a key mechanism leading to ICB therapy resistance, but a systematic understanding of its molecular regulatory axes is still lacking. Therefore, systematically elucidating the spatiotemporal dynamic evolution of the tumor immunosuppressive microenvironment and developing targeted intervention strategies has become an important scientific proposition for breaking through the current bottlenecks in immunotherapy.

[0003] As core regulatory cells of the tumor immunosuppressive microenvironment, tumor-associated macrophages (TAMs) exhibit significant phenotypic plasticity. Single-cell sequencing studies reveal that the M2 polarized subset promotes immunosuppression through multidimensional mechanisms; conversely, the M1 polarized subset activates anti-tumor immune responses by releasing pro-inflammatory factors such as TNF-α and IL-12 and enhancing antigen cross-presentation capabilities. This double-edged sword characteristic suggests that TAM subset balance may be a key regulatory node determining immunotherapy response. However, the traditional classification system based on the M1 / M2 binary polarization model has significant limitations: clinical cohort analyses show a lack of stable correlation between TAMs and key clinical indicators such as patient survival and treatment response rate.

[0004] Therefore, establishing a novel TAMs subgroup typing system based on multi-omics characteristics, developing biomarker combinations with prognostic predictive value, and designing precise targeted reprogramming strategies are of significant translational medical value for realizing personalized immunotherapy. Summary of the Invention

[0005] This invention provides the application of combined biomarkers in predicting the survival of cancer patients and the efficacy of immune checkpoint inhibitor therapy, thereby effectively identifying patient populations sensitive to immune checkpoint inhibitor therapy and improving the response rate of tumor immune checkpoint inhibitor therapy.

[0006] In view of this, the solution of the present invention is as follows: The first aspect of the invention is that it provides SG15 + Macrophages and SPP1+ Application of macrophage infiltration ratio detection reagent in the preparation of tumor prognosis survival prediction kit.

[0007] A second aspect of the invention is that it provides ISG15. + Macrophages and SPP1 + Application of macrophage infiltration level ratio detection reagent in the preparation of a kit for predicting the efficacy of tumor immune checkpoint inhibitor therapy.

[0008] In the applications described in the first or second aspect above, the detection reagents include immunoassay reagents or transcriptome assay reagents, which respectively correspond to the protein or gene expression levels in the sample.

[0009] Immunoassay reagents are used for immunohistochemical detection, including detection using immunofluorescence cytochemistry, immunoenzyme cytochemistry, or immunogold assay.

[0010] Furthermore, the immunoassay reagent includes ISG15 antibody, SPP1 antibody, and CD68 antibody and / or DAPI antibody.

[0011] Preferably, when the immunoassay reagent is used to detect the infiltration level of target macrophages, CD68 and DAPI-positive macrophages are used as the detection objects to detect the expression level of target proteins ISG15 or SPP1.

[0012] Furthermore, the transcriptome sequencing reagent is used to detect the expression levels of each gene in the ISG15 gene set and the SPP1 gene set in the sample, respectively; the transcriptome reagent also includes an R package; the R package assesses the infiltration level of the corresponding macrophages based on the expression levels of each gene in the two gene sets and calculates the ratio.

[0013] Preferably, the ISG15 gene set includes ISG15 and at least one of CXCL10, IFIT3, IFIT2 or CXCL9.

[0014] Preferably, the SPP1 gene set includes SPP1 and at least one of FBP1, MTIG, CHI3L1 or MMP12.

[0015] Preferably, the R package performs the following steps: S1. Sum the expression levels of each gene in the two gene sets, divide by the number of genes in the corresponding gene set, and take the mean to obtain the mean expression level of the two gene sets. S2. Calculate the mean and standard deviation of the two gene sets in different samples; S3. The mean expression levels of the two gene sets obtained in step S1 are normalized by z-score. S4. Calculate the ratio based on the standardized scores.

[0016] In the applications described in the first or second aspect above, the sample is selected from tissue specimen slices, tissue sequencing samples, or single-cell sequencing samples. The tissue specimen slices are used for immunoassay, and the tissue sequencing samples or single-cell samples are used for transcriptome sequencing.

[0017] In the applications described in the first or second aspect above, the tumor is selected from one of head and neck cancer, esophageal cancer, gastric cancer, liver cancer, bile duct cancer, pancreatic cancer, colorectal cancer, colon cancer, or rectal cancer.

[0018] This invention provides a method based on ISG15 + Macrophages and SPP1 + The assay protocol for macrophage infiltration level ratio (IS ratio, ISRatio) has been used to predict the prognostic survival of cancer patients and the efficacy of immune checkpoint inhibitor therapy, with the following outstanding benefits: 1. High accuracy This invention is the first to propose using the ratio of ISG15+ macrophage to SPP1+ macrophage infiltration level (ISRatio) as an indicator for assessing the prognosis and efficacy of immunotherapy in cancer patients. Compared with existing single biomarkers or traditional assessment methods, it significantly improves the accuracy and reliability of prediction. 2. Wide range of applications This detection indicator is widely applicable to various types of solid tumors, and has strong universality and adaptability. It can accurately distinguish the prognostic risks and treatment efficacy of patients with multiple diseases, thereby effectively avoiding the limitations of traditional predictive indicators, such as narrow applicability and insufficient predictive ability. 3. Guiding clinical decision-making This invention can effectively identify patient subgroups that are sensitive to immune checkpoint inhibitor therapy, helping clinicians to develop precise treatment plans in a timely manner, avoiding unnecessary drug use and treatment delays, maximizing clinical efficacy, and reducing the risk of adverse reactions. 4. Reduce medical costs By accurately predicting patients' survival time and sensitivity to immunotherapy, this invention can avoid blind treatment, reduce the waste of medical resources, effectively reduce the medical burden on patients and society, and significantly improve the efficiency of medical resource utilization. 5. Promote the development of personalized treatment This invention promotes the development of personalized medicine in the field of tumor treatment and helps to shift tumor treatment from empirical medicine to precision medicine. It has good clinical application prospects and promotion value.

[0019] In summary, this invention has significant technological innovation and clinical application value, and will play a positive role in improving the level of tumor diagnosis and treatment. Attached Figure Description

[0020] Figure 1 This is a schematic diagram showing the difference in infiltration levels of two macrophages detected by single-cell RNA-seq in Example 1 of the present invention.

[0021] Figure 2 This invention provides an example of analyzing ISG15 using various methods in Embodiment 1. + and SPP1 + The results showed differences in macrophage infiltration levels and cell function.

[0022] Figure 3 This is a forest plot of multifactor Cox regression analysis in Embodiment 1 of the present invention.

[0023] Figure 4 ISG15 in tissues of patients with good and poor prognoses in Example 1 of this invention + (Sky blue) and SPP1 + Representative immunofluorescence image of (green) macrophages.

[0024] Figure 5 In Embodiment 1 of the present invention, and Figure 4 The results shown correspond to the Kaplan-Meier survival curves.

[0025] Figure 6 The images show tumor growth curves and Kaplan-Meier survival curves for single-drug and combination therapies in Example 1 of this invention.

[0026] Figure 7 The results of single-cell blueprint analysis of the immunotherapy-sensitive and non-sensitive groups in Example 2 of this invention are shown.

[0027] Figure 8 The ISG15 difference between the head immunotherapy sensitive group and the non-sensitive group in Example 2 of this invention. + With SPP1 + Comparison results of differences in the proportion of macrophages.

[0028] Figure 9 SG15 in Embodiment 2 of the present invention + Macrophages, SPP1 + Forest plot of the predictive power of macrophages and IS ratio on immunotherapy sensitivity.

[0029] Figure 10 In Example 2 of this invention, ISG15 represents the difference between the immunotherapy-sensitive group and the non-sensitive group. + Macrophages, SPP1+ Differential analysis results of macrophages and IS ratio.

[0030] Figure 11 In Example 2 of this invention, patients in the immunotherapy-sensitive or non-sensitive groups were at ISG15. + (Sky blue) and SPP1 + (Green) Representative immunofluorescence images and percentage bar charts showing the distribution ratio of macrophages in high and low expression groups.

[0031] Figure 12 For the microsatellite stable (MSS) and microsatellite highly unstable (MSI-H) subtypes in CRC patients in Example 2 of this invention, in ISG15 + SPP1 + Bar chart showing the difference in the proportion of macrophages and IS Ratio high and low expression groups.

[0032] Figure 13 In Example 2 of this invention, after a CRC patient receives immunotherapy, ISG15 + Macrophages, SPP1 + Survival curves showing the survival differences between macrophages and groups with high and low IS Ratio expression.

[0033] Figure 14 This is a schematic diagram of the operation mechanism of the ISMacro software package (R package) in Embodiment 3 of the present invention.

[0034] Figure 15 This is a line graph of the area under the subject operating characteristic curve (ROC) at different time points in Example 3 of the present invention, as well as the results of decision curve analysis (DCA).

[0035] Figure 16 The ROC curve analysis results were used to verify the accuracy of the tumor prognosis model and the tumor immune checkpoint inhibitor therapy sensitivity prediction model in Example 3 of this invention. Detailed Implementation

[0036] The technical solution of the present invention will now be clearly and completely described in conjunction with preferred embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] In one embodiment, a combination of biomarkers for predicting the efficacy of tumor immune checkpoint inhibitor therapy is proposed, wherein the biomarker combination is ISG15. + Macrophages and SPP1 +The IS ratio (Invasive Stimulus Infiltration Ratio) is used to effectively predict the survival of tumor patients and the efficacy of tumor immune checkpoint inhibitor therapy, thereby effectively identifying patient populations sensitive to immune checkpoint inhibitor therapy and improving the response rate of tumor immune checkpoint inhibitor therapy. Furthermore, an R package for constructing an IS Ratio prediction model is provided for online clinical translation.

[0038] According to an embodiment of the present invention, the effectiveness of immune checkpoint inhibitor treatment for the subject is determined based on whether the subject meets the criteria of a high IS Ratio level group in the IS Ratio model.

[0039] In a preferred embodiment, by detecting ISG15 + Macrophages and SPP1 + The IS ratio can be obtained by calculating the ratio of macrophage infiltration level to the infiltration level. (ISG15) + Macrophages and SPP1 + The infiltration level of macrophages refers to the expression levels of the ISG15 and SPP1 gene sets in macrophages, respectively. The detection reagents include immunoassay reagents or transcriptome assay reagents, which correspond to the protein or gene expression levels in the sample, respectively.

[0040] In a preferred embodiment, the immunoassay reagent is used for immunohistochemical detection, including detection using immunofluorescence cytochemistry, immunoenzyme cytochemistry, or immunogold immunoassay. Tissue specimen sections can be selected as test samples.

[0041] In a preferred embodiment, the immunoassay reagent includes ISG15 antibody, SPP1 antibody, and CD68 antibody and / or DAPI antibody. When the immunoassay reagent is used to detect the infiltration level of target macrophages, CD68- and DAPI-positive macrophages are used as the detection targets.

[0042] In a preferred embodiment, the transcriptome sequencing reagent is used to detect the expression levels of each gene in the ISG15 gene set and the SPP1 gene set in the sample, respectively. The transcriptome reagent also includes an R package. The R package assesses the infiltration level of the corresponding macrophages based on the expression levels of each gene in the two gene sets and calculates the ratio. The ISG15 gene set includes ISG15 and at least one of CXCL10, IFIT3, IFIT2, or CXCL9, preferably ISG15, CXCL10, IFIT3, IFIT2, and CXCL9. The SPP1 gene set includes SPP1 and at least one of FBP1, MTP1G, CHI3L1, or MMP12, preferably SPP1, FBP1, MTP1G, CHI3L1, and MMP12.

[0043] In a preferred embodiment, the sample used for transcriptome sequencing is a single-cell sample, preferably a fresh tissue sample.

[0044] According to an embodiment of the present invention, the method for calculating the IS Ratio of the R package is as follows: (1) First, the GSVA method was used to evaluate ISG15. + Macrophages and SPP1 + The infiltration level of macrophages is calculated using the following formula.

[0045] in: It is a sample China Gene Collection GSVA score; It is a sample Zhonggen The level of expression; It is a gene set The number of genes in a gene set. For the ISG15 gene set and the SPP1 gene set.

[0046] (2) The GSVA score for each gene set is standardized using the Z-score, as shown in the following formula.

[0047] in: This is the original GSVA score; It is a gene set The average GSVA score across all samples; It is a gene set Standard deviation across all samples.

[0048] (3) Calculate the IS Ratio of the standardized score for each sample, using the following formula:

[0049] in: It is ISG15 + GSVA score after macrophage standardization It is SPP1 + GSVA score after macrophage standardization.

[0050] In a preferred embodiment, the tumor includes, but is not limited to, one of head and neck cancer, esophageal cancer, gastric cancer, liver cancer, bile duct cancer, pancreatic cancer, colorectal cancer, colon cancer, or rectal cancer; the immune checkpoint inhibitor is a PD-1 blocker or a PD-L1 blocker, including peptides, monoclonal antibodies, or chemically synthesized small molecule inhibitors.

[0051] In some embodiments, the IS ratio was verified to be significantly superior to simply quantifying ISG15 in predicting overall survival in cancer patients. + Or SPP1 + Macrophage abundance. Compared with monotherapy, combination therapy can more effectively inhibit tumor growth and significantly prolong animal survival, indicating that the IS ratio prediction survival model has good predictive effect, and improving the IS ratio can be used as a clinically meaningful treatment strategy to effectively inhibit tumor progression and improve the prognosis of cancer patients.

[0052] In some embodiments, the IS ratio was validated as a predictor of the therapeutic efficacy of tumor immune checkpoint inhibitors. Tumor patients with higher IS ratios showed better clinical responses to immune checkpoint inhibitor therapy, and the IS ratio was even higher in the immune checkpoint inhibitor-sensitive group. A high IS ratio was associated with a higher disease control rate (DCR), and patients with higher IS ratios exhibited significantly improved progression-free survival.

[0053] In one embodiment, the IS ratio can be used to predict the survival of tumor patients and the efficacy of tumor immune checkpoint inhibitor therapy. A reference value can be used as a standard, such as a cutoff value. The IS ratio relative to the reference value is used to predict the survival of tumor patients or the efficacy of tumor immune checkpoint inhibitor therapy. The cutoff value is determined by the critical IS ratio that yields the minimum p-value from sample data. Tumor patients with an IS ratio higher than this cutoff value show better clinical response after receiving immune checkpoint inhibitor therapy, and the IS ratio in the immune checkpoint inhibitor sensitive group is also higher than the cutoff value. An IS ratio higher than the cutoff value indicates a higher disease control rate and a significantly improved progression-free survival.

[0054] Example 1: Constructing ISG15 + Macrophages compared to SPP1 + Macrophage (IS Ratio) prediction model predicts survival of cancer patients

[0055] 1. Construct an IS Ratio prediction model and validate the IS ratio prediction survival model on multiple public datasets.

[0056] We compiled bulk RNA sequencing (bulk RNA-seq) data from 4,904 samples across 30 studies and assessed the infiltration of M1 macrophages, M2 macrophages, and monocytes using various methods, including MCP-counter, CIBERSORT, TIMER, XCell, EPIC, QUANTISEQ, and CIBERSORT-ABS. (ISG15) + and SPP1 + Macrophage infiltration was quantified using the “GSVA” (v1.52.3)R package based on the following marker genes: ISG15, CXCL10, IFIT3, IFIT2, and CXCL9 (for ISG15). + Macrophages); and SPP1, FBP1, MTT1G, CHI3L1 and MMP12 (for SPP1). + Macrophages). ISG15 was confirmed to be present in both tumor and normal tissues. + and SPP1 + Macrophage infiltration and functional differences, results as follows Figure 1-2 As shown. Figure 1 This shows the difference in macrophage infiltration levels between "mono_macrophage_C1" and "macrophage_C5" as detected by single-cell RNA-seq (mono_macrophage_C1 represents ISG15). + Macrophages, macrophage_C5 represents SPP1 + macrophages); Figure 2 Figure A shows the enrichment analysis of macrophages “mono_macrophage_C1” and “macrophage_C5” (the upper part of the figure is a volcano plot of differentially expressed genes in macrophages “mono_macrophage_C1” and “macrophage_C5”, and the lower part of the figure shows the enrichment analysis function of macrophages “mono_macrophage_C1” and “macrophage_C5”). Figure 2 B-bubble thermograms show the expression of the marker genes “mono_macrophage_C1” and “macrophage_C5” in macrophages; Figure 2 C shows ISG15 + and SPP1 + Representative gene set of macrophages; Figure 2 D illustrates the detection of ISG15 in tumor tissue using large-scale RNA-seq. + and SPP1 + Results of differences in macrophage infiltration levels.

[0057] We collected the following datasets from the TCGA database (https: / / portal.gdc.cancer.gov / ): Head and Neck Cancer (HNSC), Esophageal Cancer (ESCA), Gastric Cancer (STAD), Liver Cancer (LIHC), Bile Duct Cancer (CHOL), Pancreatic Cancer (PAAD), Colorectal Cancer (CRC), Colon Cancer (COAD), and Rectal Cancer (READ). First, single-sample gene set enrichment analysis (ssGSEA) was used to analyze datasets from the TCGA-HNSC (518 HNSC patients), TCGA-ESCA (182 ESCA patients), TCGA-STAD (384 STAD patients), TCGA-LIHC (364 LIHC patients), TCGA-CHOL (36 CHOL patients), TCGA-PAAD (178 PAAD patients), TCGA-COAD (270 COAD patients), and TCGA-READ (92 READ patients) cohorts, as well as datasets from the GEO-HNSC (270 and 97 HNSC patients), GEO-ESCA (179 ESCA patients), GEO-STAD (300 and 192 STAD patients), GEO-LIHC (162 and 81 LIHC patients), and GEO-CHOL (30 CHOL patients) cohorts. For the cohort datasets, including the GEO-PAAD (66 and 63 PAAD patients) cohort and the GEO-CRC (579 and 232 CRC patients) cohort, we used the single-sample gene set enrichment analysis (ssGSEA) algorithm of the R software package "GSVA" to evaluate and quantify the ISG15 of each patient sample. + Or SPP1 + Macrophage infiltration levels were analyzed and then correlated with patient overall survival. Multivariate Cox regression analysis and forest plots were performed. Figure 3 As shown, Figure 3 A, Figure 3 B-3C displays ISG15 based on TCGA and GEO analyses, respectively. + Macrophages, SPP1 + The correlation between macrophages and overall survival (OS) in patients with gastrointestinal tumors. This demonstrates the effectiveness of using ISG15 alone. + Or SPP1 + The degree of macrophage infiltration did not show a stable and consistent prognostic value, which may be related to the complexity and dynamic characteristics of TAM in the tumor microenvironment.

[0058] This suggests that relying on a single TAM subtype as a stable prognostic predictor may not be sufficient. To address this issue, we propose the concept of the "IS ratio," defined as the ratio of ISG15 in tumor tissue. + Macrophages and SPP1 + The proportion of macrophage infiltration. For example... Figure 3 The IS ratio results shown in A-3C demonstrate a significantly higher predictive performance than any single indicator. Therefore, the IS ratio, as a novel prognostic indicator, can be widely applied to predict the prognosis of patients with various malignant tumors.

[0059] 2. Clinical and animal validation of the IS ratio prediction survival model

[0060] To validate the predictive efficacy of the IS ratio prediction model in cancer patients at the clinical level, we collected paraffin-embedded specimens from 112 patients with esophageal cancer, 42 with colorectal cancer, 78 with gastric cancer, and 90 with liver cancer. Tumor samples were cut into 4 μm thin sections for multicolor immunofluorescence. Primary antibodies were purchased from Proteintech: Anti-SPP1, Anti-ISG15, Anti-CD68, and DAPI antibodies. Secondary antibody incubation and immunoperoxidase staining were then performed. Image-ProPlus 6.0 was used for IHC scoring. IHC scoring is a histological scoring method for processing multicolor immunofluorescence results, converting the number of positive cells and their staining intensity in each section into corresponding numerical values, achieving semi-quantitative tissue staining. Cell staining intensity was scored on a 4-point scale: negative (0 points), weakly positive (1 point), positive (2 points), and strongly positive (3 points). The positive cell percentage score is graded into four levels: 0% ≤ positive cell percentage ≤ 25%, 1 point; 25% < positive cell percentage ≤ 50%, 2 points; 50% < positive cell percentage ≤ 75%, 3 points; 75% < positive cell percentage ≤ 100%, 4 points. IHC score = cell staining intensity score × positive cell percentage score. A score indicating positivity for all three indicators (SPP1, CD68, and DAPI) is defined as the patient's SPP1 score. + Macrophage infiltration score, defined as a score in which all three indicators (ISG15, CD68, and DAPI) are positive. + Macrophage infiltration score, results as follows Figure 4 As shown, Figure 4 The Chinese label is: Esophageal cancer (ESCA, Figure 4 A) Colorectal cancer (CRC) Figure 4 B) Stomach cancer (STAD, Figure 4 C) and hepatocellular carcinoma (LIHC, Figure 4 D). ISG15 +Macrophage infiltration score compared to SPP1 + Macrophage infiltration score was denoted as IS ratio. Results showed that the IS ratio was significantly superior to simply quantifying ISG15 in predicting overall survival in cancer patients. + Or SPP1 + Methods for macrophage abundance ( Figure 5 Marked as: Esophageal cancer (ESCA, Figure 5 A) Colorectal cancer (CRC) Figure 5 B) Stomach cancer (STAD, Figure 5 C) and hepatocellular carcinoma (LIHC, Figure 5 D).

[0061] To validate the predictive performance of the IS ratio-based survival model in animals, we constructed subcutaneous xenograft models (gastric, hepatic, and colorectal cancer) in Balb / c mice and induced ISG15 using regular intraperitoneal injections of IFN-β (1 μg / mouse, every 3 days). + Macrophages and anti-SPP1 antibody (2.5 mg / mouse, once a week) were used to inhibit SPP1. + Macrophages were used for single-agent or combination therapy. Results showed that, compared to single-agent therapy, combination therapy was more effective in inhibiting tumor growth and significantly prolonged animal survival. Figure 6 A shows tumor growth curves for IFN-β, anti-SPP1 antibody (anti-SPP1) monotherapy, and their combination therapy; 6B shows Kaplan-Meier survival curves for the IFN-β, anti-SPP1 monotherapy, and their combination therapy groups. These data indicate that the IS ratio prediction survival model has good predictive power, and improving the IS ratio may be a clinically meaningful treatment strategy that can effectively inhibit tumor progression and improve the prognosis of cancer patients.

[0062] Example 2: The IS ratio prediction model can effectively predict the efficacy of immune checkpoint inhibitor therapy in cancer patients.

[0063] 1. Validation of IS ratio on multiple datasets to predict the efficacy of immune checkpoint inhibitor therapy in cancer patients.

[0064] We analyzed single-cell RNA sequencing (scRNA-seq) data from cancer patients who received immunotherapy, including those with head and neck cancer, esophageal cancer, and colorectal cancer, as well as ascites single-cell data from clinically diagnosed colorectal or gastric cancer patients. We used the single-sample gene set enrichment analysis (ssGSEA) algorithm from the R software package "GSVA" to assess and quantify the ISG15 of each patient sample. + Or SPP1 + Macrophage infiltration level, ISG15 + Or SPP1+ The ratio of macrophage infiltration levels, denoted as the IS ratio, was analyzed for correlation with the efficacy of immune checkpoint inhibitors in patients. Results showed that compared to using ISG15 alone... + Or SPP1 + Patients with predominantly macrophage infiltration and tumors with a high IS ratio showed better clinical responses to immune checkpoint inhibitor therapy (single-cell blueprint analysis results of immunotherapy-sensitive and non-sensitive groups are shown in the figure). Figure 7 Between the vaccine-sensitive group and the non-sensitive group, ISG15 + With SPP1 + Comparison of differences in macrophage proportions, as follows Figure 8 As shown in Figure 8, the cancer types are labeled as: head and neck squamous cell carcinoma (HNSC, Figure A), esophageal cancer (ESCA, Figure B), ascites (ASC, Figure C), and colorectal cancer (CRC, Figure D). The IS rate was higher in the immune checkpoint inhibitor-sensitive group. Furthermore, batch sequencing data from multiple immunotherapy cohorts, including head and neck cancer (102 cases), esophageal cancer (30 cases), and gastric cancer (78 cases), were analyzed. Figure 9 The forest map shown illustrates ISG15 + Macrophages, SPP1 + The predictive power of macrophages and IS ratio on the sensitivity of HNSC (Figure A, B), ESCA (Figure C), and STAD (Figure D) to immunotherapy; Figure 10 For the difference between the immunotherapy-sensitive group and the non-sensitive group, ISG15 + Macrophages, SPP1 + Differential analysis of macrophages and IS ratio further supports this conclusion.

[0065] 2. Clinical validation of the IS Ratio model for predicting the efficacy of immune checkpoint inhibitors in cancer patients.

[0066] To validate the predictive efficacy of the IS ratio prediction model in clinical settings for cancer patients, we used multicolor immunofluorescence results from the aforementioned clinical colorectal cancer (42 cases) and ascites patients (68 cases), and evaluated ISG15 using the methods described above. + SPP1 + Macrophage infiltration degree and IS ratio. Figure 11 The image shows immunotherapy-sensitive and non-sensitive groups in patients with ascites (top) and colorectal cancer (bottom) at ISG15. + (Sky blue) and SPP1 + (Green) Representative immunofluorescence images and percentage bar charts showing the distribution ratio of macrophages with high and low expression levels. A comparison revealed that, compared to ISG15 alone... +and SPP1 + Macrophage infiltration level, high IS ratio is associated with higher disease control rate (DCR). Meanwhile, Figure 12 The percentage bar chart shown illustrates the microsatellite stable (MSS) and microsatellite highly unstable (MSI-H) subtypes in CRC patients at ISG15. + SPP1 + The difference in the proportion of macrophages and IS ratio between the high and low expression groups indicates that colorectal cancer patients with higher IS ratios have a higher proportion of MSI-H. Figure 13 The Kaplan-Meier survival curves show that ISG15 levels were lower in CRC patients after immunotherapy. + Macrophages, SPP1 + The survival difference between macrophage and IS ratio high and low expression groups showed a significant improvement in progression-free survival (PFS), further highlighting the important value of IS ratio in clinical stratification and treatment strategy decision-making in immunotherapy populations.

[0067] Example 3: Constructing the IS Ratio Prediction Model (R Package)

[0068] To facilitate clinical translation, we developed a prediction model called "ISMacro," which is an R package and is now available on GitHub (https: / / github.com / Limitation1994 / ISMacro). A diagram illustrating its operation is shown below. Figure 14 As shown.

[0069] ISMacro uses three consecutive steps: (1) dropout imputation using ALRA, (2) quantification of IS Ratio using methods such as VISION, AUCell, GSVA or ssGSEA, and (3) customizable data visualization (e.g., block diagrams, UMAP, dot plots).

[0070] The results were obtained by analyzing line plots of the area under the receiver operating characteristic (ROC) curve (AUC) at different time points and decision curves. Figure 15 As shown, the results confirm that ISMacro can accurately predict the prognosis of patients with esophageal cancer, gastric cancer, liver cancer, and colorectal cancer, as well as the outcome of immunotherapy for patients with colorectal cancer and ascites. Figure 16 The ROC curve analysis shown demonstrates the accuracy of our constructed tumor prognostic model and tumor immune checkpoint inhibitor therapy sensitivity prediction model. Therefore, ISMacro exhibits high robustness in both prognostic prediction and immunotherapy efficacy, which is beneficial for its application in clinical research.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. ISG15 + Macrophages and SPP1 + Application of macrophage infiltration ratio detection reagent in the preparation of tumor prognosis survival prediction kit.

2. ISG15 + Macrophages and SPP1 + Application of macrophage infiltration level ratio detection reagent in the preparation of a kit for predicting the efficacy of tumor immune checkpoint inhibitor therapy.

3. The application according to claim 1 or 2, characterized in that, The detection reagents include immunoassay reagents or transcriptome assay reagents, which correspond to the protein or gene expression levels in the sample, respectively.

4. The application according to claim 3, characterized in that, The immunoassay reagents include ISG15 antibody, SPP1 antibody, CD68 antibody and / or DAPI antibody.

5. The application according to claim 4, characterized in that, When the immunoassay reagent is used to detect the infiltration level of target macrophages, CD68 and DAPI-positive macrophages are used as the detection subjects to detect the expression level of target proteins ISG15 or SPP1.

6. The application according to claim 3, characterized in that, The transcriptome sequencing reagent is used to detect the expression levels of each gene in the ISG15 gene set and the SPP1 gene set in the sample, respectively; the transcriptome reagent also includes an R package; the R package assesses the infiltration level of the corresponding macrophages based on the expression levels of each gene in the two gene sets and calculates the ratio.

7. The application according to claim 6, characterized in that, The ISG15 gene set includes ISG15 and at least one of CXCL10, IFIT3, IFIT2 or CXCL9; And / or, the SPP1 gene set includes SPP1, and at least one of FBP1, MTT1G, CHI3L1 or MMP12.

8. The application according to claim 6, characterized in that, The R package performs the following steps: S1. Sum the expression levels of each gene in the two gene sets, divide by the number of genes in the corresponding gene set, and take the mean to obtain the mean expression level of the two gene sets. S2. Calculate the mean and standard deviation of the two gene sets in different samples; S3. The mean expression levels of the two gene sets obtained in step S1 are normalized by z-score. S4. Calculate the ratio based on the standardized scores.

9. The application according to claim 3, characterized in that, The samples were selected from tissue specimen slices, tissue sequencing samples, or single-cell sequencing samples.

10. The application according to claim 1 or 2, characterized in that, The tumor is selected from one of the following: head and neck cancer, esophageal cancer, gastric cancer, liver cancer, bile duct cancer, pancreatic cancer, colon cancer, or rectal cancer.

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