Biomarker for predicting curative effect of gastric cancer and application of biomarker

Abstract

The invention discloses a biomarker for predicting the curative effect of gastric cancer and application, and belongs to the technical field of diagnosis and treatment of gastric cancer, the biomarker is derived from a composition of programmed cell death-ligand 1 of exosome and lactic acid, and the biomarker can be used for preparing a kit for predicting the curative effect of gastric cancer and a system for predicting the curative effect of gastric cancer. Tests prove that before a gastric cancer patient is treated, the exosome PD-L1 content is lower than 55.24 pg / ml, the exosome lactic acid content is lower than 3.68 ng / mu g, and a good curative effect prediction effect is shown when PD-1 immune blocking combined chemotherapy is accepted. The invention also provides a combined predictive factor which is formed aiming at two detection indexes of exosome PD-L1 and lactic acid and has a better gastric cancer curative effect prediction effect, and the combined predictive factor can be used for accurately predicting the curative effect of a gastric cancer patient after the gastric cancer patient receives first-line treatment. The clinical application value of the exosome PD-L1 and lactic acid as the combined marker in predicting the curative effect of the gastric cancer is further verified.

Description

Technical Field

[0001] This application relates to the technical field of gastric cancer diagnosis and treatment, and particularly relates to a biomarker for predicting the efficacy of gastric cancer and its application. Background Art

[0002] Gastric cancer is one of the most common malignant tumors in China. Its incidence rate ranks third among various tumors in China. Approximately 170,000 people die from gastric cancer every year, almost approaching 1 / 4 of the total number of deaths from all malignant tumors. Moreover, there are more than 20,000 new gastric cancer patients every year. Gastric cancer can occur in any part of the stomach, but it is more common in the antrum, especially on the lesser curvature side of the stomach. The average life expectancy of untreated patients is about 13 months. It is a disease that seriously threatens people's physical health. Common types of gastric cancer include gastric adenocarcinoma (GC), gastroesophageal junction adenocarcinoma (GEJAC), etc.

[0003] For advanced gastric cancer patients, the main treatment method is first-line treatment with programmed death receptor 1 (PD-1) blockade combined with chemotherapy, that is, immunotherapy with PD-1 monoclonal antibody drugs (such as nivolumab, pembrolizumab) for gastric cancer patients, combined with chemotherapy drugs (such as oxaliplatin, capecitabine). However, first-line treatment does not achieve consistent effects for all patients. In addition, after long-term repeated drug treatment, patients will develop drug resistance. Considering the drug tolerance of patients and the side effects brought by the re-selected chemotherapy drugs, the selection of treatment regimens after first-line treatment and the timely evaluation of efficacy are particularly important. Although programmed death-ligand 1 (PD-L1) is the most commonly used biomarker in tumor clinical diagnosis, it cannot be an accurate predictor of the efficacy of gastric cancer. Most gastric cancer patients do not experience clinical treatment benefits from PD-L1 as a efficacy biomarker.

[0004] Therefore, it is of great significance to develop biomarkers and personalized products for predicting the efficacy of gastric cancer. Summary of the Invention

[0005] Exosomes are small vesicles (30 - 150 nm) secreted by various cell types and carry bioactive molecules that mediate intercellular communication and regulate the immune system. Research has found that PD-L1 is expressed on exosomes, and the expression of PD-L1 in exosomes is associated with the inhibition of anti-tumor immune responses. Therefore, PD-L1 can predict the clinical response to PD-1 blockade; lactate in exosomes is involved in tumor metabolism and tumor microenvironment remodeling. Exosomes derived from tumors are rich in glycolytic enzymes, which can convert glucose into lactate in the tumor microenvironment. The increase in lactate levels can further promote the release of exosomes. In the embodiments of this application, the lactate levels in exosomes of patients with advanced gastric cancer were tested, and it was verified that the lactate levels have a strong correlation with the effects on peripheral blood T cells and the efficacy of immunotherapy. Therefore, PD-L1 and lactate can be used as a combined biomarker for predicting the treatment efficacy of gastric cancer patients.

[0006] In view of the above inventive concept, this application provides a biomarker for predicting the efficacy of gastric cancer and its application. The biomarker includes programmed death-ligand 1 (PD-L1) and lactate derived from exosomes. By detecting the levels of PD-L1 and lactate in exosomes in the blood of gastric cancer patients, the efficacy of the PD-1 blockade combined chemotherapy treatment regimen for gastric cancer patients can be predicted, showing a better prediction effect than the comprehensive positive score (CPS) of tumors, and giving certain guiding significance to clinicians.

[0007] To achieve the above object, this application provides the following technical solutions:

[0008] In the first aspect, this application provides a biomarker for predicting the efficacy of gastric cancer, and the biomarker is a composition of programmed death-ligand 1 (PD-L1) and lactate derived from exosomes.

[0009] In the second aspect, this application provides the use of the aforementioned biomarker in the preparation of a kit for predicting the efficacy of gastric cancer.

[0010] Further, the kit includes a reagent for detecting PD-L1 and a reagent for detecting lactate.

[0011] In the third aspect, this application provides the use of the aforementioned biomarker in the preparation of a system for predicting the efficacy of gastric cancer.

[0012] Further, the system includes:

[0013] Data acquisition module: The data acquisition unit includes a PD-L1 data acquisition unit and a lactate acquisition unit; the PD-L1 data acquisition unit is used to acquire the first level value of PD-L1 in exosomes, and the lactate acquisition unit is used to acquire the second level value of lactate in exosomes;

[0014] Data analysis module: The data analysis module is used to standardize the horizontal value one and the horizontal value two, and evaluate them using the gastric cancer treatment efficacy prediction model to analyze the prediction probability value of the sample.

[0015] The present application provides a biomarker and its application for predicting the efficacy of gastric cancer. Compared with the prior art, the present application has at least the following beneficial effects:

[0016] The present application provides a combined biomarker for predicting the efficacy of gastric cancer. By detecting the levels of PD-L1 and lactate in the blood exosomes of gastric cancer patients, the efficacy of the first-line treatment regimen adopted for gastric cancer patients can be predicted and evaluated, so as to facilitate doctors to adjust the treatment plan for gastric cancer patients. Its detection rate is high, the detection is simple, and the prediction effect is highly accurate.

[0017] The present application provides the application of PD-L1 and lactate in exosomes as a combined biomarker in the preparation of a kit for predicting the efficacy of gastric cancer. The kit can effectively detect the level values of PD-L1 and lactate in gastric cancer patients, so as to well predict the treatment effect of gastric cancer patients receiving the first-line treatment regimen, and has high accuracy and sensitivity during use.

[0018] The present application provides the use of the aforementioned combined biomarker in the preparation of a system for predicting the efficacy of gastric cancer. The system includes a data acquisition module and an analysis module. The system standardizes the level values of PD-L1 and lactate in the obtained patient samples to ensure a smaller error range and more reliable results; by combining with the gastric cancer treatment efficacy prediction model for evaluation, the gastric cancer treatment efficacy prediction result can be accurately and quickly obtained, with high accuracy and credibility, and can be widely applied clinically. Description of the Drawings

[0019] Figure 1 It is the ROC curve graph provided by the embodiment of the present application for distinguishing effective and ineffective treatment patients by PD-L1 level.

[0020] Figure 2 It is the ROC curve graph provided by the embodiment of the present application for distinguishing effective and ineffective treatment patients by lactate level.

[0021] Figure 3 It is the ROC curve graph provided by the embodiment of the present application for distinguishing effective and ineffective treatment patients by the combined prediction factor. Detailed Embodiments

[0022] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0023] The "biomarkers" in this article refer to compounds and metabolites whose level changes are correlated with the occurrence and development of diseases, especially tumors. In other words, there are differences in the biomarker levels between samples from diseased patients and healthy individuals, and there are differences, especially significant differences, between at least one stage and the previous stage in different stages of diseased patients. Biomarkers can be recognized by, for example, antibodies (or their antigen-binding fragments) or other specific binding proteins. Specific biomarkers can also include their isotypes, preform, mature form, variants, their degradation forms (such as their fragments) and their metabolites.

[0024] Exosomes are biologically active lipid bilayer nanovesicles secreted by various cells in the human body. Exosomes mediate cell-to-cell signal transduction. They are widely distributed in body fluids such as saliva, plasma, and urine of tumor patients, and have advantages such as good stability, high content, and easy enrichment. They have become a reliable carrier for precision medicine and liquid biopsy. PD-L1 is a classical membrane-bound protein related to the inhibition of the immune system. By binding to PD-1, PD-L1 inhibits the anti-tumor function of T cells, thus enabling tumor cells to escape immune surveillance. PD-L1 can not only bind to PD-1 on the surface of immune cells, weakening the anti-tumor immunity in the tumor microenvironment, but also transfer PD-L1 to the surface of other cells with low or no expression of PD-L1, enabling it to play a role.

[0025] Lactic acid in exosomes is involved in tumor metabolism and the remodeling of the tumor microenvironment. On the one hand, the lactic acid released by tumor cells increases the acidification of the tumor microenvironment, promoting tumor progression. On the other hand, as an important immunosuppressive metabolite, lactic acid can affect the metabolism and function of immune cells, thereby regulating the tumor immune microenvironment and promoting tumor growth and metastasis. Tumor-derived exosomes are rich in glycolytic enzymes, which convert glucose into lactic acid in the tumor microenvironment, and the increase in lactic acid in the tumor microenvironment further promotes the release of exosomes. Based on this, in the embodiments of this application, blood samples were collected from HER2-negative advanced gastric adenocarcinoma patients before receiving first-line immunotherapy combined with chemotherapy, and exosome extraction, exosome PD-L1 detection, and exosome lactic acid detection were respectively performed, and it was verified that the levels of PD-L1 and lactic acid have a strong correlation with the metabolism and function of peripheral blood T cells and the efficacy of immunotherapy.

[0026] Therefore, in the embodiments of this application, the combination of PD-L1 and lactic acid in exosomes is used as a brand-new, convenient, and effective predictive biomarker, which can predict the efficacy of the PD-1 blockade combined with chemotherapy treatment regimen for gastric cancer patients, showing a better predictive effect than the comprehensive positive score (CPS) of tumors, and facilitating clinicians to accurately screen the optimal benefit population for first-line immunotherapy combined with chemotherapy.

[0027] Based on this, an embodiment of the present application provides a biomarker for predicting the efficacy of gastric cancer, and the biomarker is a composition of programmed death-ligand 1 (PD-L1) and lactic acid derived from exosomes.

[0028] Based on this, an embodiment of the present application provides the use of the foregoing biomarker in the preparation of a kit for predicting the efficacy of gastric cancer, and the kit includes reagents for detecting PD-L1 and reagents for detecting lactic acid.

[0029] In some embodiments, the detection methods using the reagents for detecting PD-L1 and the reagents for detecting lactic acid are at least one of ELISA method, immunoturbidimetry, and chemiluminescence method.

[0030] Based on this, an embodiment of the present application also provides the use of the foregoing biomarker in the preparation of a system for predicting the efficacy of gastric cancer.

[0031] In some embodiments, the system includes: (1) a data acquisition module: the data acquisition unit includes a PD-L1 data acquisition unit and a lactic acid acquisition unit; the PD-L1 data acquisition unit is used to acquire the first level value of PD-L1 in exosomes, and the lactic acid acquisition unit is used to acquire the second level value of lactic acid in exosomes; (2) a data analysis module: the data analysis module is used to perform normalization processing on the first level value and the second level value, and use a prediction model for the efficacy of gastric cancer to evaluate and analyze the prediction probability value of the sample.

[0032] In some embodiments, the prediction model for the efficacy of gastric cancer of the data analysis module is: Y (combined prediction factor) = -2.304 + 0.013 * exosomal PD-L1 level value + 0.462 * exosomal lactic acid level value. When Y < -0.249, it is predicted that the treatment of gastric cancer is effective. When Y ≥ -0.249, it is predicted that the treatment of gastric cancer is ineffective or has a poor effect.

[0033] The specific research and implementation process of the present application is as follows:

[0034] 1. Subject inclusion

[0035] (1) From January 2022 to March 2023, a total of 68 patients with advanced gastric cancer were included in the embodiment of the present application, including 45 patients with gastric adenocarcinoma and 23 patients with esophagogastric junction adenocarcinoma. During the research process, tumor imaging was performed every 6 weeks, and the clinical response was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST v1.1).

[0036] 2. Subject treatment plan

[0037] Patients with pathologically diagnosed HER2-negative advanced gastric adenocarcinoma or esophagogastric junction adenocarcinoma were enrolled as subjects; the subjects were treated with at least 2 cycles of PD-1 blockade therapy combined with chemotherapy. Among them, the drug for PD-1 blockade therapy was nivolumab 360 mg, once every 3 weeks (1 cycle); or pembrolizumab 200 mg, once every 3 weeks; the chemotherapy treatment methods are shown in Table 1 below:

[0038] Table 1

[0039]

[0040]

[0041] 2. Detection of PD-L1 and lactate

[0042] (1) Exosome extraction: 5 mL of peripheral blood before treatment of 68 HER2-negative advanced gastric cancer patients receiving first-line immunotherapy combined with chemotherapy was collected in a tube containing EDTA. The peripheral blood samples were centrifuged at 1000 g for 10 minutes at 4°C, and the upper plasma was collected; the plasma samples were centrifuged at 3000 g for 15 minutes at 4°C to remove cell debris and dead cells; then 2 mL of plasma was taken and centrifuged at 12000 g for 30 minutes at 4°C, and the supernatant was collected and ultracentrifuged at 100000 g for 90 minutes (Beckman Coulter, Optima XE-100) at 4°C to precipitate exosomes; the precipitated exosomes were washed with PBS solution and ultracentrifuged again at 100000 g for 90 minutes at 4°C, and the obtained precipitate was the purified exosomes.

[0043] (2) The purified exosomes were resuspended in PBS, and the expression level of PD-L1 in exosomes was quantitatively detected using a PD-L1 detection kit (PD-L1 Human ELISA Kit, Abcam, ab277712), the lactate level in exosomes was quantitatively detected using a lactate detection kit (Lactate assay kit II Abcam, ab65330), and a BCA detection kit (P0010S, Beyotime, China) was used to quantify the protein concentration of exosomes.

[0044] 3. Detection results and analysis

[0045] (1) Detection results of PD-L1 and lactate levels

[0046] Table 2 shows the detection results of PD-L1 and lactate levels in exosomes of 68 advanced gastric cancer subjects:

[0047] Table 2

[0048]

[0049]

[0050] (2) Determination of Detection Threshold and Verification of ROC Curve

[0051] The ROC curve refers to the Receiver Operating Characteristic curve, which is a comprehensive index reflecting continuous variables of sensitivity and specificity. It reveals the mutual relationship between sensitivity (Sensiyivity) and specificity by means of a graphical method. By setting multiple different critical values for continuous variables, a series of sensitivities and specificities are calculated, and then with sensitivity as the ordinate and specificity (shown as 1 - specificity in some figures) as the abscissa, a curve is plotted. The larger the area under the curve (AUC), the higher the diagnostic accuracy. On the ROC curve, the point closest to the upper left of the coordinate graph is the critical value (detection threshold) with relatively high sensitivity and specificity.

[0052] In order to evaluate the prediction critical values of exosomal PD - L1 and lactate for treatment responders, in the embodiments of the present application, the exosomal PD - L1 and lactate levels are respectively used as test variables, and the therapeutic efficacy of gastric cancer patients is used as the status variable. The status variable is set to 1 (effective is 0, ineffective is 1), and a Receiver Operating Characteristic (ROC) curve is created. The area under the curve (AUC) quantifies the performance of exosomal PD - L1 and lactate. The critical value (detection threshold) is the corresponding exosomal PD - L1 and lactate concentration level at which the sum of detection sensitivity and specificity is the largest.

[0053] Figure 1 The displayed ROC curve graph distinguishes effective and ineffective treatment patients by PD - L1 level. It can be seen from the figure that the detection threshold of PD - L1 is 55.237 pg / ml, its specificity value is 0.714, its sensitivity value is 0.758, and the AUC value representing diagnostic accuracy is the highest, which is 0.748.

[0054] In the embodiments of the present application, the detection threshold of PD - L1 is verified. The results show that for patients with pre - treatment exosomal PD - L1 level < 55.24 pg / ml, after treatment with first - line immunotherapy combined with chemotherapy, the median progression - free survival (mPFS) of the patients is 11.87 months (95% CI: 9.01 months, 13.79 months), which is much higher than the mPFS (5.87 months, 95% CI: 5.29 months, 8.37 months) of patients with exosomal PD - L1 ≥ 55.24 pg / ml.

[0055] Figure 2The displayed ROC curve graph distinguishes between patients with effective and ineffective treatments based on lactate levels. It can be seen from the graph that the detection threshold of lactate is 3.681 ng / μg, with a specificity value of 0.771 and a sensitivity value of 0.545. The AUC value representing diagnostic accuracy is the highest, at 0.706.

[0056] In the embodiments of this application, the detection threshold of exosomal lactate was verified. The results showed that for patients with exosomal lactate levels < 3.681 ng / μg before treatment, after first-line immunotherapy combined with chemotherapy, the median progression-free survival (mPFS) of the patients was 10.30 months (95% CI: 8.63 months, 12.37 months), which was much higher than the mPFS of patients with exosomal PD-L1 ≥ 3.681 ng / μg (5.10 months, 95% CI: 4.31 months, 7.71 months).

[0057] The level of exosomal lactate can be used as a marker to predict the treatment efficacy of gastric cancer patients. Due to the specific metabolic patterns of different subsets of T cells, lactate can inhibit the efflux of lactate in CD8+ T cells, increase intracellular acidification, inhibit the upregulation of nuclear factor of activated T cells (NFAT), thereby reducing the expression and translation of IFN-γ and inhibiting the production of cytotoxic cytokines. On the contrary, the immunosuppressive function and proliferative ability of Treg cells are resistant to different concentrations of lactate. At the same time, they can uptake lactate and participate in the tricarboxylic acid cycle as a metabolic substrate to produce phosphoenolpyruvate, further promoting the proliferation of Treg cells, generating an immunosuppressive microenvironment, and promoting the growth and metastasis of tumor cells. Tumor-derived exosomes are rich in glycolytic enzymes, which convert glucose into lactate in the tumor microenvironment. The increase in lactate in the tumor microenvironment further promotes the release of exosomes. Lactate is taken up by CD8+ T cells and Treg via exosomes, and exosomal PD-L1 can promote the uptake of lactate by binding to PD-1 on the cell surface, inhibiting the anti-tumor immune function of CD8+ T cells and promoting the immunosuppressive function of Treg cells, thereby forming an immunosuppressive microenvironment and resulting in ineffective treatment. Therefore, the plasma exosomal lactate level can be used as a marker to predict treatment efficacy.

[0058] (3) ROC curve analysis of combined multi-index diagnosis

[0059] In clinical diagnosis or screening for a certain disease, the diagnostic indicators related to the disease are not unique and definite, but involve multiple diagnostic indicators or inducer factors triggered by multiple indicators. Moreover, the diagnostic sensitivities of various physiological elements in the human body vary greatly for different aspects of the disease.

[0060] In the embodiments of the present application, a binary Logistic regression model is established for two detection indexes, PD-L1 and lactic acid, and a predicted probability or a combined prediction factor is formed as the only analysis index, and a ROC curve is established for analysis.

[0061] Through binary Logistic regression analysis, an evaluation model Y (combined prediction factor) for predicting the therapeutic efficacy of gastric cancer is obtained: Y = -2.304 + 0.013 * exosomal PD-L1 level value + 0.462 * exosomal lactic acid level value. Taking the combined prediction factor as the test variable and the therapeutic efficacy of gastric cancer patients as the state variable, with the state variable value set to 1 (0 for effective and 1 for ineffective), a receiver operating characteristic (ROC) curve is created.

[0062] Figure 3 The displayed ROC curve uses the combined prediction factor to distinguish between patients with effective and ineffective treatments. It can be seen from the figure that the critical value of the combined prediction factor is -0.249, its specificity value is 0.657, its sensitivity value is 0.848, and the AUC value representing diagnostic accuracy is the highest, at 0.789. Therefore, the accuracy of predicting the therapeutic efficacy of gastric cancer through the combined prediction factor is higher than that using PD-L1 and lactic acid alone as detection indexes.

[0063] In the embodiments of the present application, the combined prediction value is verified. The results show that for patients with a combined prediction factor Y < -0.249 before treatment, after first-line immunotherapy combined with chemotherapy, the median progression-free survival (mPFS) of the patients is 13.83 months (95% CI: 9.85 months, 14.47 months), which is much higher than the mPFS of patients with a combined prediction factor Y ≥ -0.249 (5.5 months, 95% CI: 5.13 months, 8.32 months).

[0064] As mentioned above, the above are only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A biomarker for predicting the efficacy of gastric cancer treatment, characterized in that: The biomarker is a combination of programmed cell death-ligand 1 and lactic acid derived from exosomes.

2. The biomarker according to claim 1, characterized in that The biomarker is used to predict the efficacy of first-line treatment for patients with gastric cancer.

3. The biomarker according to claim 1, characterized in that The first-line treatment regimen includes PD-1 immune blocking therapy and chemotherapy including oxaliplatin and fluorouracil.

4. The biomarker according to claim 1, characterized in that The gastric cancer is gastric adenocarcinoma and / or gastroesophageal junction adenocarcinoma.

5. Use of the biomarker according to claim 1 in preparing a kit for predicting the therapeutic effect of gastric cancer, characterized in that: The kit includes a reagent for detecting PD-L1 and a reagent for detecting lactic acid.

6. The use according to claim 5, characterized in that: The detection method of the reagent for detecting PD-L1 and the reagent for detecting lactic acid is at least one of ELISA, immunoturbidimetry, and chemiluminescence.

7. Use of the biomarker according to any one of claims 1 to 4 in preparing a system for predicting the therapeutic efficacy of gastric cancer.

8. The use according to claim 7, characterized in that The system for predicting the therapeutic effect of gastric cancer comprises: Data acquisition module: the data acquisition unit includes a PD-L1 data acquisition unit and a lactate acquisition unit; the PD-L1 data acquisition unit is used to obtain a level value 1 of PD-L1 in the exosomes, and the lactate acquisition unit is used to obtain a level value 2 of lactate in the exosomes; Data analysis module: The data analysis module is used to standardize the level values ​​1 and 2, and use the gastric cancer efficacy prediction model to evaluate and analyze the predicted probability values ​​of the samples.

9. The use according to claim 8, characterized in that The model for predicting the efficacy of gastric cancer is: combined prediction factor = -2.304 + 0.013 * exosome PD-L1 level + 0.462 * exosome lactate level; when Y < -0.249, it is predicted that the treatment of gastric cancer is effective, and when Y ≥ -0.249, it is predicted that the treatment of gastric cancer is ineffective or has a poor effect.

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