Plasma marker gpnmb for predicting the curative effect of esophageal squamous cell carcinoma immunotherapy and application thereof

Abstract

The present application relates to a kind of plasma markers GPNMB for predicting the curative effect of esophageal squamous cell carcinoma immunotherapy and its application, for screening predictive biomarker, we carry out plasma proteomics analysis, identify soluble glycoprotein non-metastatic melanoma protein B (sGPNMB) as the most significant circulating protein in treatment non-response patient is elevated, and through experiment determines, in humanized PDX model, the level of circulating GPNMB can predict PD-1 blocking curative effect, and inhibition GPNMB can enhance treatment effect.The method of the present application can realize the stratified management of esophageal squamous cell carcinoma patient, before carrying out immunotherapy, the plasma GPNMB level of patient is detected, can predict the subsequent treatment effect, and then screen out the patient suitable for immunotherapy and carry out treatment, ensure treatment effect, for the patient who is not suitable for immunotherapy can select other treatment scheme, realize the targeted effective treatment of different patients.

Description

Technical Field

[0001] This invention relates to the field of tumor treatment, specifically to a plasma biomarker, GPNMB, for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma and its application. Background Technology

[0002] Immune checkpoint inhibitors (ICIs) targeting PD-1 / PD-L1, CTLA-4, and emerging immune checkpoints such as LAG-3, TIGIT, and TIM-3 restore anti-tumor T-cell activity by blocking inhibitory signaling pathways. ICIs have significantly altered the treatment landscape for various cancers, including melanoma, non-small cell lung cancer, breast cancer, and gastrointestinal malignancies. In esophageal squamous cell carcinoma (ESCC), phase III clinical trials such as KEYNOTE-590, RATIONALE-306, and ORIENT-15 have demonstrated that combining PD-1 inhibitors with chemotherapy or chemoradiotherapy significantly improves pathological complete response (pCR) rates and survival outcomes. These results have established PD-1-based regimens as the standard neoadjuvant therapy strategy for locally advanced ESCC. However, only about 30% of ESCC patients achieve durable efficacy, with the majority exhibiting primary resistance or relapse after initial response. This heterogeneity in efficacy underscores the urgent need to develop reliable predictive biomarkers to optimize patient screening and treatment strategies.

[0003] Currently, biomarkers for predicting the efficacy of immunotherapy (ICI) are mainly divided into two categories. Tissue-derived biomarkers, including PD-L1 expression, tumor mutational burden (TMB), APOBEC mutation characteristics, and tumor-infiltrating T cell characteristics, have limited overall predictive ability. PD-L1 exhibits significant spatiotemporal heterogeneity in ESCC; the predictive performance of TMB is greatly affected by the detection platform and threshold setting; and standardized quantitative systems for T cell phenotypes are lacking. Progenitor-like exhausted CD8⁺ T cells are associated with treatment benefit, while terminally exhausted cells indicate poor prognosis. Blood-derived biomarkers, such as circulating tumor DNA, T cell receptor clonality, cytokines, and soluble proteins, have the advantages of being non-invasive and repeatable, but most are still in the exploratory stage, lacking clear mechanistic support and prospective validation. Therefore, we hypothesize that plasma proteomics may capture soluble mediators from the tumor microenvironment, thereby providing mechanistic clues to tumor-immune interactions and providing clinically feasible indicators for predicting the efficacy of immunotherapy. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a plasma biomarker for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma and its application.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: A plasma biomarker for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma, the biomarker being the soluble glycoprotein nonmetastatic melanoma protein B (sGPNMB).

[0006] The application of the plasma biomarker sGPNMB in predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma.

[0007] The application method involves predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma by detecting the level of soluble glycoprotein non-metastatic melanoma protein B in plasma, and the level of soluble glycoprotein non-metastatic melanoma protein B in plasma is negatively correlated with the efficacy of immunotherapy for esophageal squamous cell carcinoma.

[0008] The beneficial effects of this invention are as follows: To screen and predict biomarkers, we conducted plasma proteomics analysis and identified soluble glycoprotein nonmetastatic melanoma protein B (sGPNMB) as the most significantly elevated circulating protein in treatment-unresponsive patients. In the humanized PDX model, circulating GPNMB levels can predict the efficacy of PD-1 blockade, and inhibiting GPNMB can enhance the therapeutic effect.

[0009] The method of this invention can realize stratified management of patients with esophageal squamous cell carcinoma. Before immunotherapy, the plasma GPNMB level of patients can be tested to predict the subsequent treatment effect, thereby screening out patients who are suitable for immunotherapy and ensuring the treatment effect. For patients who are not suitable for immunotherapy, other treatment options can be selected, so as to achieve targeted and effective treatment for different patients. Attached Figure Description

[0010] Figure 1 To identify proteomic volcano plots of plasma samples from non-responders and responders of esophageal squamous cell carcinoma patients who received neoadjuvant chemotherapy and immunotherapy in a cohort; yellow dots (n=41) represent proteins upregulated in non-responders, and blue dots (n=12) represent proteins upregulated in responders; Figure 2 This is a schematic diagram of plasma GPNMB levels in patients resistant to immunotherapy; Figure A shows plasma GPNMB levels in the discovery cohort (n=91), validation cohort 1 (n=86), validation cohort 2 (n=114), validation cohort 3 (n=43), and validation cohort 4 (n=27); data are expressed as mean ± standard error. P Value through Student's t The calculations were performed; Figure B shows the proportion of responders and non-responders stratified according to plasma GPNMB levels in each cohort, and the number of patients is marked. Figure 3This diagram illustrates the predictive performance of plasma GPNMB levels on immunotherapy response across all cohorts. Figure A shows the ROC curve illustrating the predictive performance of plasma GPNMB levels on immunotherapy response; Figure B is a forest plot showing the odds ratio, 95% confidence interval, and p-value of univariate logistic regression analysis and meta-analysis of the predictive features. Figure 4 The correlation between plasma GPNMB levels and tumor burden after immunotherapy was investigated. Figure A shows the percentage change in plasma GPNMB levels and tumor size from baseline in the discovery cohort (n=91), validation cohort 1 (n=86), and combined cohort (n=177). Figure B shows the correlation between plasma GPNMB levels and residual tumor size after treatment in the discovery cohort (n=91), validation cohort 1 (n=86), and combined cohort (n=177). P-values ​​and r-values ​​were determined using the Pearson correlation test. Figure 5 A schematic diagram illustrating the correlation between plasma GPNMB levels and prognosis in immunotherapy patients; Kaplan-Meier curves of overall survival stratified according to high and low plasma GPNMB levels in validation cohort 1 (n=86), validation cohort 2 (n=114), and pooled cohort (n=200); P-values ​​were determined by log-rank test; hazard ratios and 95% confidence intervals were calculated using the Cox proportional hazards model. Figure 6 This diagram illustrates the dynamic changes in plasma GPNMB levels and their correlation with immunotherapy response. Figure A shows the plasma GPNMB levels of 6 patients in validation cohort 2 before and after treatment; Figure B shows the plasma GPNMB levels of responders and non-responders in validation cohort 2 before and after treatment. Data are expressed as mean ± standard error. P Value through Student's t Calculations were performed; Figure C shows the plasma GPNMB levels before and after treatment in 6 patients in validation cohort 5; Figure D shows the plasma GPNMB levels before and after treatment in responders and non-responders in validation cohort 5; data are expressed as mean ± standard error. P Value through Student's t Verification calculation; Figure 7This diagram illustrates the correlation between dynamic changes in plasma GPNMB levels and survival outcomes in patients receiving immunotherapy. Figure A shows the correlation between the percentage change in plasma GPNMB levels relative to baseline and the optimal percentage change in tumor size relative to baseline in validation cohorts 2 (n=6) and 3 (n=6). P-values ​​and r-values ​​were determined using Pearson correlation analysis. Figure B shows the survival and mortality ratios among patients grouped according to changes in plasma GPNMB levels before and after treatment (increase vs. decrease) in validation cohorts 2 (n=6) and 3 (n=6). The number of patients is indicated in the figure; P-values ​​were calculated using Fisher's exact test. Figure C shows the Kaplan-Meier curves of overall survival stratified by changes in plasma GPNMB levels before and after treatment in validation cohorts 2 (n=6) and 5 (n=6). P The values ​​were determined by the log-rank test; the hazard ratio and 95% confidence interval were calculated using the Cox proportional hazards model. Figure 8 Humanized PDX models were used to demonstrate that circulating GPNMB predicts the efficacy of PD-1 inhibitors; Figure A shows a schematic diagram of the correlation analysis between GPNMB immunofluorescence scores and serum GPNMB levels in 20 esophageal squamous cell carcinoma PDX tumors; selected representative PDXs are highlighted; P The values ​​and r-values ​​were determined by the Pearson correlation test; Figure B is a schematic diagram of the endpoint tumor weight of PDX tumors in NOG mice (n=5 per group); data are expressed as mean ± standard error. P The values ​​were calculated using one-way ANOVA and Tukey post-hoc test; Figure C is a bar chart showing the number of GPNMB carriers. low or GPNMB high Schematic diagram of GPNMB levels in PDX tumor tissue interstitial fluid and serum of humanized mice with PDX tumors. P Value through Student's t Calculation of test results; Figure D is a schematic diagram of humanized mice carrying a PDX model and receiving anti-human PD-1 treatment; Figure E is GPNMB. low and GPNMB high PDX tumors reached approximately 150 mm in NOG and humanized mice. 3 A comparative diagram showing the time required to produce a volume; data are expressed as mean ± standard error. P Value through Student's t The calculation was performed; the F-plot is a schematic diagram of the average tumor growth inhibition rate (n=4 per group) in the humanized PDX model treated with anti-human PD-1; the data are expressed as mean ± standard error. P Value through Student's tTest calculations; G-plot is a schematic diagram of the mean endpoint tumor weight (n=4 per group) in the humanized PDX model treated with anti-human PD-1; data are expressed as mean ± standard error; P Value through Student's t Verification calculation.

[0011] Figure 9 A schematic diagram illustrating how plasma GPNMB levels predict patient responsiveness to immunotherapy and survival prognosis. Detailed Implementation

[0012] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0013] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: To screen predictors of ESCC immunotherapy efficacy, we analyzed pretreatment plasma samples from patients receiving neoadjuvant chemotherapy combined with immunotherapy (NCIT, PD-1 inhibitor combined with chemotherapy) and validated candidate predictors in multiple independent cohorts. High-throughput plasma proteomics analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS / MS) on 91 ESCC patients in the discovery cohort (from the Cancer Hospital of the Chinese Academy of Medical Sciences), quantifying a total of 1,342 proteins (Figure 1). Differential analysis showed that, compared with responders, 41 proteins were upregulated and 12 proteins were downregulated in non-responders (log2FC>0.2 or <–0.2). P <0.05; Figure 1). Among them, GPNMB was the most statistically significant differentially expressed protein (log2FC = 0.38, P = 8.6 × 10 -5 (Figure 1).

[0014] Subsequently, we used ELISA to detect plasma GPNMB levels in several independent validation cohorts (Validation Cohort 1: 86 cases, Cancer Hospital of Chinese Academy of Medical Sciences; Validation Cohort 2: 114 cases, Cancer Hospital of Harbin Medical University; Validation Cohort 3: 43 cases, Cancer Hospital of Chinese Academy of Medical Sciences; Validation Cohort 4: 27 cases, Cancer Hospital of Chinese Academy of Medical Sciences). In all cohorts, plasma GPNMB levels in non-responders were significantly higher than those in responders (Figure 2, A), and patients with high plasma GPNMB levels were significantly enriched in the non-responder group (Figure 2, B).

[0015] ROC curves indicate that plasma GPNMB has good predictive value for the efficacy of immunotherapy. Figure 3Figure A in the table shows that univariate logistic regression analysis and meta-analysis indicate that plasma GPNMB is a risk factor for immunotherapy resistance. Figure 3 (Figure B in the diagram).

[0016] Further analysis revealed that plasma GPNMB levels were correlated with the rate of tumor change before and after treatment (Figure 4, A) and the size of residual tumor after treatment (Figure 4, A). Figure 4 (Figure B in the diagram) showed a significant positive correlation. Furthermore, esophageal squamous cell carcinoma patients with higher plasma GPNMB levels had a worse prognosis. Figure 5 ).

[0017] Longitudinal analysis showed that paired plasma samples from 6 patients in validation cohort 2 revealed a significant decrease in GPNMB levels in responders after treatment, while levels remained elevated or increased further in non-responders. Figure 6 (See Figures A and B in the original text). The external independent validation cohort (6 cases, Tongji Hospital, Wuhan) also showed the same trend (…). Figure 6 (Figures C and D in the diagram).

[0018] Furthermore, a sustained increase in GPNMB during treatment was significantly associated with disease progression and shorter overall survival. Figure 7 (AC diagram in the figure). This indicates that plasma GPNMB is both a baseline predictor and a dynamic monitoring biomarker during treatment.

[0019] To further validate the role of GPNMB in immunotherapy in vivo, we utilized a previously established library of patient-derived xenograft (PDX) models (n=20) from esophageal squamous cell carcinoma. We selected six representative PDX models for functional comparisons. Figure 8 (See Figure A in the table). PDX-01, PDX-12, and PDX-17 showed high GPNMB expression, while PDX-02, PDX-08, and PDX-16 showed low expression. In immunodeficient mice, tumor growth in these models was comparable (…). Figure 8 (See Figure B in the figure). However, in humanized mice, tumors with high GPNMB expression of PDX secreted significantly higher levels of human soluble GPNMB into serum and tumor stroma compared to tumors with low GPNMB expression of PDX. Figure 8 (Figure C in the diagram) Its growth rate is significantly faster and the tumor weight is also larger. Figure 8 The D–E plots in the figure indicate that GPNMB primarily promotes disease progression through immune evasion rather than the intrinsic proliferative capacity of tumor cells. Consistent with this, in humanized mice, PDX tumors with high GPNMB expression exhibited resistance to anti-PD-1 monotherapy, while PDX tumors with low GPNMB expression remained sensitive. Figure 8(F–G diagram in the figure), which establishes that GPNMB is both a functional mediator of resistance to immune checkpoint inhibitors and a biomarker for predicting their resistance.

[0020] The method of this invention can realize stratified management of patients with esophageal squamous cell carcinoma. Before immunotherapy, the plasma GPNMB level of patients can be tested to predict the subsequent treatment effect, thereby screening out patients who are suitable for immunotherapy and ensuring the treatment effect. For patients who are not suitable for immunotherapy, other treatment options can be selected, so as to achieve targeted and effective treatment for different patients.

[0021] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A plasma biomarker for predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma, characterized in that, The biomarker is the soluble glycoprotein nonmetastatic melanoma protein B (sGPNMB).

2. The application of the plasma biomarker sGPNMB as described in claim 1 in predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma.

3. The application according to claim 2, characterized in that, The application method involves predicting the efficacy of immunotherapy for esophageal squamous cell carcinoma by detecting the level of soluble glycoprotein non-metastatic melanoma protein B in plasma, and the level of soluble glycoprotein non-metastatic melanoma protein B in plasma is negatively correlated with the efficacy of immunotherapy for esophageal squamous cell carcinoma.

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