Methods of evaluating the effect of pd-1 inhibitors in non-small cell lung cancer patients

Abstract

The application discloses a method for evaluating the effect of PD-1 inhibitor on non-small cell lung cancer patients, which is realized by detecting the PD-L1 expression level of ICI patients before and after treatment + The change value of PLT, the PLR of peripheral blood at the ICI treatment baseline, and the PD-L1 expression level at the ICI treatment baseline + The three parameters of PLT are analyzed by multiple logistic regression analysis to jointly construct a prediction model, and the effect of PD-1 inhibitor on non-small cell lung cancer patients is evaluated. The application can realize early and accurate prediction of the treatment response, and provides a new strategy for the clinical application of liquid biopsy in the evaluation of immunotherapy effect. It is helpful to provide new insights for the anti-tumor effect and drug resistance mechanism of ICI treatment, and these mechanisms have potential therapeutic value, and provide a new auxiliary scheme for discovering new therapeutic targets to overcome ICI treatment resistance.

Description

Technical Field

[0001] This invention relates to bioinformatics, and more specifically to a method for evaluating the effects of PD-1 inhibitors on patients with non-small cell lung cancer. Background Technology

[0002] The discovery of immune checkpoints and the clinical application of immune checkpoint inhibitors (ICIs) marked a milestone in the field of tumor immunotherapy. Immunotherapy, which blocks immune checkpoints and reactivates the anti-tumor immune response of immune cells, has made significant progress in the treatment of NSCLC (non-small cell lung cancer) patients. The use of immune checkpoint inhibitors has significantly prolonged patient survival; however, the continuous interaction and dynamic changes between tumor cells and various immune cells in the tumor microenvironment during tumor development limit the maximization of clinical benefits. The expression of PD-L1 in tumor cells is dynamically regulated by multiple factors such as cytokines, chemotherapy, and radiotherapy. Traditional tissue biopsies are insufficient for longitudinal monitoring of this dynamic process. Therefore, developing an efficient and accurate method to assess and predict the response to ICI treatment in NSCLC patients is crucial.

[0003] Chinese invention patent CN119152926B discloses a predictive model and its construction method for predicting the prognosis of lung cancer after neoadjuvant immunotherapy using primary tumors. It utilizes various spatial high-throughput sequencing bioinformatics technologies to analyze key immune cell subsets, topological features of the immune microenvironment, and functional cell subsets related to tertiary lymphosomes in the primary lesion after neoadjuvant immunotherapy. This model proposes a predictive model for the prognosis of NSCLC after neoadjuvant immunotherapy using the primary lesion, more accurately characterizing changes in the spatial tumor immune microenvironment after immunotherapy. It can more precisely predict the prognosis of NSCLC after neoadjuvant immunotherapy by identifying cellular targets that truly affect the immune response; however, data collection and statistical analysis are complex. Chinese invention patent application CN117219274A discloses a method for constructing a predictive model for immune myocarditis and an immune myocarditis prediction system. It obtains data from electronic medical records of non-myocarditis patients and myocarditis patients who used immune checkpoint inhibitors, performs logistic regression analysis on the processed data, and constructs a Nomogram predictive model for detecting immune myocarditis. However, it does not involve the prediction of immunotherapy in non-small cell lung cancer patients, and the predictive impact data differs. Summary of the Invention

[0004] To develop an efficient and accurate method for assessing and predicting the efficacy of PD-1 inhibitors in NSCLC patients, a first aspect of the present invention provides a method for assessing the efficacy of PD-1 inhibitors in non-small cell lung cancer patients, by measuring %PD-L1 levels before and after ICI treatment. +Changes in PLT, baseline peripheral blood PLR after ICI treatment, and baseline %PD-L1 after ICI treatment. + A predictive model was constructed by combining the three parameters of PLT using multivariate logistic regression analysis to evaluate the efficacy of PD-1 inhibitors in non-small cell lung cancer patients. The formula for the prediction model is as follows: z = β 0+ β 1 X 1 + β 2 X 2+ β 3 X 3 X 1: Changes in %PD-L1+ PLT before and after treatment; X 2: ICI treatment baseline %PD-L1 + PLT; X 3: ICI treatment of baseline peripheral blood PLR; B 0 =-0.2046，β 1 =0.1448 , β 2 =-0.01819 , β 3 =-0.006702； %PD-L1 levels before and after treatment in ICI patients + The change in PLT value is the change in the percentage of PD-L1 positive platelets in peripheral blood before and after ICI treatment in patients. The baseline peripheral blood PLR for ICI treatment is the ratio of platelets to lymphocytes in the baseline peripheral blood of patients receiving ICI treatment. The baseline %PD-L1 of ICI treatment + PLT represents the percentage of platelet-labeled antibody-containing cells in the baseline peripheral blood of patients receiving ICI treatment.

[0005] This invention presents a prospective observational cohort study of NSCLC patients receiving PD-1 inhibitor therapy. Full-spectrum flow cytometry was used to measure the percentage of PD-L1-positive platelets before and after treatment, observing baseline results and dynamic changes. These findings were compared and correlated with histological immune checkpoint molecular assessments and long-term clinical follow-up of treatment response. A more efficient and accurate early prediction model for ICI treatment response in NSCLC patients was constructed, enabling early and precise prediction of treatment response and providing a new strategy for the clinical application of liquid biopsy in evaluating the efficacy of immunotherapy.

[0006] As one implementation, the prediction model has an AUC value ≥ 0.8865, sensitivity ≥ 79.2%, and specificity ≥ 89.7%.

[0007] As one implementation method, the %PD-L1 level of the ICI patients before and after treatment... + The change in PLT was obtained by measuring the proportion of PD-L1 positive platelets before and after treatment using full-spectrum flow cytometry.

[0008] In one implementation, the patient receiving ICI treatment is a patient who responds to the treatment.

[0009] As one implementation method, the method for constructing the prediction model includes the following steps: Collect peripheral venous blood samples and separate platelet plasma from the peripheral venous blood samples; Platelet plasma was subjected to routine blood tests and full-spectrum flow cytometry. Gating of the fluorescence signals output from the full-spectrum flow cytometer yielded %PD-L1. + PLT and TPS data; peripheral blood platelet count and lymphocyte count obtained from routine blood tests; Statistical analysis was performed on the obtained data. Spearman correlation analysis was used to calculate the correlation between pathological biopsy TPS (PD-L1 tumor cell positive percentage score) and peripheral blood %PD-L1+ PLT. Multivariate logistic regression analysis was used to obtain a predictive model for ICI treatment response in NSCLC patients.

[0010] As one implementation, the peripheral venous blood sample may be obtained from a single peripheral venous blood draw from a healthy volunteer, two peripheral venous blood draws from an NSCLC patient receiving ICI treatment, or two peripheral venous blood draws from an NSCLC patient not receiving ICI treatment.

[0011] In one implementation, the time interval between two peripheral venous blood samplings for the NSCLC patient receiving ICI treatment is two cycles of ICI treatment.

[0012] In one implementation, the two peripheral venous blood samples taken from the NSCLC patient receiving ICI treatment are: the first sample is taken the day before the patient receives the first ICI treatment, and the second sample is taken after the patient has received two cycles (42 days) of ICI treatment (i.e., the day before the planned third cycle of ICI treatment).

[0013] In one implementation, the two peripheral venous blood samples taken from the NSCLC patient who did not receive ICI treatment were collected the first time on the day before the patient was admitted to the hospital and scheduled to receive anti-tumor treatment, and the second time was collected 42 days after the first time.

[0014] As one embodiment, the method for separating platelet plasma from peripheral venous blood samples includes the following steps: placing the peripheral venous blood sample in a dipotassium ethylenediaminetetraacetate anticoagulant blood collection tube, sending it to the laboratory within 2 hours, centrifuging at high speed (200-400g) at room temperature for 4-6 minutes, and separating the upper layer to obtain platelet plasma.

[0015] As one implementation method, the method for separating platelet plasma from peripheral venous blood samples includes the following steps: placing the peripheral venous blood sample in a dipotassium ethylenediaminetetraacetate anticoagulant blood collection tube, sending it to the laboratory within 2 hours, centrifuging at 300g high speed at room temperature for 5 minutes, and separating the upper layer to obtain platelet plasma.

[0016] As one implementation method, the full-spectrum flow cytometry detection method includes the following steps: Platelet plasma from healthy volunteers served as the control group, while platelet plasma from NSCLC patients served as the experimental group. Platelet-labeled antibodies, anti-CD41a, anti-CD42b, anti-CD61, and anti-CD62p, were added to the platelet plasma of the control group and the experimental group. Immune checkpoint molecular marker antibody anti-PD-L1 was added to the platelet plasma of the experimental group. The mixture was shaken and incubated in the dark. After incubation, wash, centrifuge, resuspend, shake, and then test. The cell population of peripheral blood platelets was determined for subsequent statistical analysis, enabling full-spectrum flow cytometry detection.

[0017] As one implementation method, the full-spectrum flow cytometry detection method specifically includes the following steps: 20 μL of platelet plasma from healthy volunteers was used as the control group, and 20 μL of platelet plasma from NSCLC patients was used as the experimental group. Platelet-labeled antibodies were added to the platelet plasma of the control group and the experimental group: anti-CD41a (PE) 5 μL, anti-CD42b (RB488) 5 μL, anti-CD61 (RB780) 5 μL, and anti-CD62p (APC-Cy7) 5 μL. Immune checkpoint molecular marker antibody anti-PD-L1 (PE) 5 μL was added to the platelet plasma of the experimental group. The mixture was shaken and mixed for 5 seconds and incubated in the dark for 15 minutes. After incubation, wash with 1-2 mL of PBS, centrifuge at 1050 g at room temperature for 5 min, resuspend in 500 µL of PBS, shake for 5 s, and then analyze. First, determine the approximate location of peripheral blood platelets based on the forward and lateral angle scattered light. Figure 1 A, Figure 2 A), and then platelet-specific antibodies against CD41a were used respectively. Figure 1B, Figure 2 B), Anti-CD42b ( Figure 1 C, Figure 2 C), Anti-CD61 ( Figure 1 D, Figure 2 D) Identify the platelet cell population; delineate the activated platelet cell population based on the platelet activation marker antibody anti-CD62p; since the immune checkpoint molecule PD-L1 is continuously expressed on platelets, use FMO gating to set the threshold range for PD-L1 positive platelet expression (D) Figure 1 EG), thereby determining the ratio of PD-L1 positive platelets to total platelets ( Figure 2 EG is used for subsequent statistical analysis to achieve full-spectrum flow cytometry detection.

[0018] As one implementation method, the %PD-L1 + PLT is the average percentage of CD41a(+)PD-L1(+) cell population, CD42b(+)PD-L1(+) cell population, and CD61(+)PD-L1(+) cell population in the platelet cell population.

[0019] Compared with the prior art, the present invention has the following beneficial effects: (1) The method described in this invention for evaluating the effect of PD-1 inhibitors on non-small cell lung cancer patients can provide a simpler, more feasible and efficient method than tumor tissue biopsy evaluation, and can realize long-term monitoring of the changes in adaptive immune response during the treatment cycle of patients.

[0020] (2) The method described in this invention for evaluating the effect of PD-1 inhibitors on non-small cell lung cancer patients helps to provide new insights into the anti-tumor effect and drug resistance mechanism of ICI treatment. These mechanisms have potential therapeutic value and provide new auxiliary solutions for discovering new therapeutic targets to overcome drug resistance in ICI treatment.

[0021] (3) The method of the present invention for evaluating the effect of PD-1 inhibitors on non-small cell lung cancer patients integrates %PD-L1 before and after treatment in ICI patients. + Changes in PLT, baseline peripheral blood PLR after ICI treatment, and baseline %PD-L1 after ICI treatment. + By constructing a platelet-related model based on three PLT parameters to predict the prognosis of ICI treatment in NSCLC patients, the anti-tumor effect of ICI treatment can be determined earlier and more accurately.

[0022] (4) The method for evaluating the effect of PD-1 inhibitors in patients with non-small cell lung cancer described in this invention, wherein the dynamic changes of platelet PD-L1 in liquid biopsy are expected to become an early warning or monitoring marker of the effect of immunotherapy, enhance its predictive value in the prognosis of ICI treatment, and further improve the anti-tumor mechanism of ICI.

[0023] (5) The method described in this invention for evaluating the effect of PD-1 inhibitors on non-small cell lung cancer patients can achieve early and accurate prediction of treatment response, providing a new strategy for the clinical application of liquid biopsy in the evaluation of immunotherapy efficacy. Attached Figure Description

[0024] Figure 1 This is a scatter plot of flow cytometry data for the control group. Figure 1 middle: Figure 1 A represents the approximate location of peripheral blood platelets as defined by forward and side-scattered light. Figure 1 B represents the platelet cell population identified by the platelet-specific antibody anti-CD41a; Figure 1 C represents the platelet cell population identified by the platelet-specific antibody anti-CD42b; Figure 1 D represents the platelet cell population identified by the platelet-specific antibody anti-CD61; Figure 1 EG sets a threshold range for PD-L1 positive platelet expression in the FMO circle.

[0025] Figure 2 Scatter plot of flow cytometry data for the experimental group; Figure 2 middle: Figure 2 A represents the approximate location of peripheral blood platelets as defined by forward and side-scattered light. Figure 2 B represents the platelet cell population identified by the platelet-specific antibody anti-CD41a; Figure 2 C represents the platelet cell population identified by the platelet-specific antibody anti-CD42b; Figure 2 D represents the platelet cell population identified by the platelet-specific antibody anti-CD61; Figure 2 EG represents the range of the ratio of PD-L1 positive platelets to the total platelet count.

[0026] Figure 3 The prediction performance graph of the prediction model constructed for Example 1; Figure 4 The prediction performance of the ROC curve constructed for Comparative Example 1. Detailed Implementation

[0027] Anti-CD41a (PE), anti-CD42b (RB488), anti-CD61 (RB780), and anti-CD62p (APC-Cy7) were purchased from Shanghai Yuanqi Biomedical Technology Co., Ltd., China.

[0028] Anti-PD-L1 (PE) was purchased from Shanghai Yuanqi Biopharmaceutical Technology Co., Ltd., China.

[0029] Phosphate-buffered saline (PBS) was purchased from Corning, USA.

[0030] Example 1 A method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer, using %PD-L1 levels before and after ICI treatment. + Changes in PLT, baseline peripheral blood PLR after ICI treatment, and baseline %PD-L1 after ICI treatment. + A predictive model was constructed by combining the three parameters of PLT using multivariate logistic regression analysis to evaluate the efficacy of PD-1 inhibitors in non-small cell lung cancer patients. The formula for the prediction model is as follows: z = β 0+ β 1 X 1 + β 2 X 2+ β 3 X 3 X 1: Changes in %PD-L1+ PLT before and after treatment X 2: ICI treatment baseline %PD-L1 + PLT, X 3: ICI treatment of baseline peripheral blood PLR B 0 =-0.2046，β 1 =0.1448 , β 2 =-0.01819 , β 3 =-0.006702； %PD-L1 levels before and after treatment in ICI patients + The change in PLT value is the change in the percentage of PD-L1 positive platelets in peripheral blood before and after ICI treatment in patients. The baseline peripheral blood PLR for ICI treatment is the ratio of platelets to lymphocytes in the baseline peripheral blood of patients receiving ICI treatment. The baseline %PD-L1 of ICI treatment + PLT represents the percentage of platelet-labeled antibody-containing cells in the baseline peripheral blood of patients receiving ICI treatment. See details... Figure 3 .

[0031] %PD-L1 levels before and after treatment in ICI patients + The change in PLT (Δ%PD-L1+ PLT) was obtained by measuring the proportion of PD-L1 positive platelets in patients before and after treatment using full-spectrum flow cytometry.

[0032] The patients who received ICI treatment were those who responded to the treatment.

[0033] The baseline %PD-L1 of ICI treatment + PLT is the percentage of PD-L1 in NSCLC patients before ICI treatment. + PLT.

[0034] The method for constructing the prediction model includes the following steps: Collect peripheral venous blood samples and separate platelet plasma from the peripheral venous blood samples; Platelet plasma was subjected to routine blood tests and full-spectrum flow cytometry. Gating of the fluorescence signals output from the full-spectrum flow cytometer yielded %PD-L1. + PLT and TPS data; peripheral blood platelet count and lymphocyte count obtained from routine blood tests; Statistical analysis was performed on the obtained data. Spearman correlation analysis was used to calculate the correlation between pathological biopsy TPS results and peripheral blood %PD-L1+ PLT. Multivariate logistic regression analysis was used to obtain a predictive model for ICI treatment response in NSCLC patients.

[0035] The peripheral venous blood samples were obtained from one peripheral venous blood sample from a healthy volunteer, two peripheral venous blood samples from an NSCLC patient receiving ICI treatment, and two peripheral venous blood samples from an NSCLC patient not receiving ICI treatment.

[0036] The two peripheral venous blood samples taken from NSCLC patients undergoing ICI treatment were: the first sample was taken the day before the patient received the first ICI treatment, and the second sample was taken after the patient received two cycles (42 days) of ICI treatment (i.e., the day before the planned third cycle of ICI treatment).

[0037] For the NSCLC patients who did not receive ICI treatment, the first peripheral venous blood sample was taken the day before the patient was scheduled to receive anti-tumor treatment after admission, and the second sample was taken 42 days after the first sample.

[0038] The method for separating platelet plasma from peripheral venous blood samples includes the following steps: placing the peripheral venous blood sample in a dipotassium ethylenediaminetetraacetate anticoagulant blood collection tube, sending it to the laboratory within 2 hours, centrifuging at 300g high speed at room temperature for 5 minutes, and separating the upper layer to obtain platelet plasma.

[0039] The method for full-spectrum flow cytometry detection specifically includes the following steps: 20 μL of platelet plasma from healthy volunteers was used as the control group, and 20 μL of platelet plasma from NSCLC patients was used as the experimental group. Platelet-labeled antibodies were added to the platelet plasma of the control group and the experimental group: anti-CD41a (PE) 5 μL, anti-CD42b (RB488) 5 μL, anti-CD61 (RB780) 5 μL, and anti-CD62p (APC-Cy7) 5 μL. Immune checkpoint molecular marker antibody anti-PD-L1 (PE) 5 μL was added to the platelet plasma of the experimental group. The mixture was shaken and mixed for 5 seconds and incubated in the dark for 15 minutes. After incubation, wash with 1-2 mL of PBS, centrifuge at 1050 g at room temperature for 5 min, resuspend in 500 µL of PBS, shake for 5 s, and then analyze. First, determine the approximate location of peripheral blood platelets based on the forward and lateral angle scattered light. Figure 1 A, Figure 2 A), and then platelet-specific antibodies against CD41a were used respectively. Figure 1 B, Figure 2 B), Anti-CD42b ( Figure 1 C, Figure 2 C), Anti-CD61 ( Figure 1 D, Figure 2 D) Identify the platelet cell population; delineate the activated platelet cell population based on the platelet activation marker antibody anti-CD62p; since the immune checkpoint molecule PD-L1 is continuously expressed on platelets, use FMO gating to set the threshold range for PD-L1 positive platelet expression (D) Figure 1 EG), thereby determining the ratio of PD-L1 positive platelets to total platelets ( Figure 2 EG is used for subsequent statistical analysis to achieve full-spectrum flow cytometry detection.

[0040] The %PD-L1 + PLT is the average percentage of CD41a(+)PD-L1(+) cell population, CD42b(+)PD-L1(+) cell population, and CD61(+)PD-L1(+) cell population in the platelet cell population.

[0041] The prediction model had an AUC of 0.8865 [0.7929, 0.9801], with an optimal cutoff value that showed a sensitivity of 79.2% and a specificity of 89.7%, P < 0.0001. (See attached image) Figure 3 Internal validation was performed using the Bootstrap resampling method, with 500 resampling estimations. The optimistic bias was 0.0236, and the corrected AUC and confidence interval were 0.8629 [0.7817, 0.9690].

[0042] Comparative Example 1 A method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer includes calculating the platelet / lymphocyte ratio based on platelet and lymphocyte counts, and constructing ROC curves. See details below. Figure 4 .

[0043] The data for constructing the ROC curve is obtained in the same way as in Example 1.

[0044] The AUC value of the ROC curve was 0.6437 [0.4912, 0.7961], the sensitivity was 83.3%, and the specificity was 55.2%.

Claims

1. A method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer, characterized in that, %PD-L1 levels before and after ICI treatment in patients + Changes in PLT, baseline peripheral blood PLR after ICI treatment, and baseline %PD-L1 after ICI treatment. + A predictive model was constructed by combining the three parameters of PLT using multivariate logistic regression analysis to evaluate the efficacy of PD-1 inhibitors in non-small cell lung cancer patients. The formula for the prediction model is as follows: z = β 0+ β 1 X 1 + β 2 X 2+ β 3 X 3 X 1: Changes in %PD-L1+ PLT before and after treatment; X 2: ICI treatment baseline %PD-L1 + PLT; X 3: ICI treatment of baseline peripheral blood PLR; B 0 =-0.2046，β 1 =0.1448 ， β 2 =-0.01819 ， β 3 =-0.006702； %PD-L1 levels before and after treatment in ICI patients + The change in PLT value is the change in the percentage of PD-L1 positive platelets in peripheral blood before and after ICI treatment in patients. The baseline peripheral blood PLR for ICI treatment is the ratio of platelets to lymphocytes in the baseline peripheral blood of patients receiving ICI treatment. The baseline %PD-L1 of ICI treatment + PLT represents the percentage of platelet-labeled antibody-containing cells in the baseline peripheral blood of patients receiving ICI treatment.

2. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 1, characterized in that, The prediction model has an AUC value ≥ 0.8865, sensitivity ≥ 79.2%, and specificity ≥ 89.7%.

3. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 1, characterized in that, %PD-L1 levels before and after treatment in ICI patients + The change in PLT was obtained by measuring the proportion of PD-L1 positive platelets before and after treatment using full-spectrum flow cytometry.

4. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 1, characterized in that, The patients receiving ICI treatment include those who respond to the treatment.

5. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 1, characterized in that, The method for constructing the prediction model includes the following steps: Collect peripheral venous blood samples and separate platelet plasma from the peripheral venous blood samples; Platelet plasma was subjected to routine blood tests and full-spectrum flow cytometry. Gating of the fluorescence signals output from the full-spectrum flow cytometer yielded %PD-L1. + PLT and TPS data; peripheral blood platelet count and lymphocyte count obtained from routine blood tests; Statistical analysis was performed on the obtained data. Spearman correlation analysis was used to calculate the correlation between pathological biopsy TPS results and peripheral blood %PD-L1+ PLT. Multivariate logistic regression analysis was used to obtain a predictive model for ICI treatment response in NSCLC patients.

6. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 5, characterized in that, The peripheral venous blood samples were obtained from one peripheral venous blood sample from a healthy volunteer, two peripheral venous blood samples from an NSCLC patient receiving ICI treatment, and two peripheral venous blood samples from an NSCLC patient not receiving ICI treatment.

7. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 6, characterized in that, The time interval between two peripheral venous blood samplings for NSCLC patients receiving ICI treatment is equivalent to two cycles of ICI treatment.

8. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 6, characterized in that, The method for separating platelet plasma from peripheral venous blood samples includes the following steps: placing the peripheral venous blood sample in a dipotassium ethylenediaminetetraacetate anticoagulant blood collection tube, sending it to the laboratory within 2 hours, centrifuging at high speed (200-400g) at room temperature for 4-6 minutes, and separating the upper layer to obtain platelet plasma.

9. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 6, characterized in that, The method for full-spectrum flow cytometry detection includes the following steps: Platelet plasma from healthy volunteers served as the control group, while platelet plasma from NSCLC patients served as the experimental group. Platelet-labeled antibodies anti-CD41a, anti-CD42b, anti-CD61, and anti-CD62p were added to the platelet plasma of the control group and the experimental group. Immune checkpoint molecular marker antibody anti-PD-L1 was added to the platelet plasma of the experimental group. The mixture was shaken and incubated in the dark. After incubation, wash, centrifuge, resuspend, shake, and then test. The cell population of peripheral blood platelets was determined for subsequent statistical analysis, enabling full-spectrum flow cytometry detection.

10. The method for evaluating the efficacy of PD-1 inhibitors in patients with non-small cell lung cancer according to claim 6, characterized in that, The %PD-L1 + PLT is the average percentage of CD41a(+)PD-L1(+) cell population, CD42b(+)PD-L1(+) cell population, and CD61(+)PD-L1(+) cell population in the platelet cell population.

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