Cervical cancer immunotherapy response prediction biomarker and application thereof

Abstract

The invention provides a biomarker combination for predicting cervical cancer immunotherapy response based on plasma immunoproteomics and application of the biomarker combination. By detecting the expression level of a specific protein biomarker combination in blood of a cervical cancer patient before and after immunotherapy and combining with a machine learning algorithm, a prediction scoring system is constructed, and the treatment response, prognosis and optimal treatment strategy of the patient are effectively analyzed and predicted. And meanwhile, implementation and development of blood-based immunochromatogram analysis are also facilitated.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of biotechnology, in particular to a biomarker combination for predicting the response of cervical cancer to immunotherapy based on plasma immunoproteomics and uses thereof. BACKGROUND

[0002] Cervical cancer (CC) is the fourth leading cause of cancer-related death in women worldwide. Despite the decline in incidence due to the increasing rate of human papillomavirus vaccination, there remains a significant burden, particularly in developing countries and susceptible populations.

[0003] Immune checkpoint inhibitors (ICIs) have emerged as promising treatment options, with the FDA approving pembrolizumab in combination with chemotherapy for persistent, recurrent, or metastatic cervical cancer with PD-L1 expression (CPS≥1). However, not all patients benefit equally from immunotherapy, necessitating reliable biomarkers to predict patient response to immunotherapy.

[0004] Current predictive biomarkers such as tumor mutational burden (TMB) and PD-L1 expression show limited clinical utility in cervical cancer. High TMB status (≥10 mutations per megabase) shows a modest objective response rate (ORR) of 29%, but is applicable to only 13% of patients. PD-L1 expression based on tumor tissue is found to yield inconsistent response rates, exacerbated by the spatial heterogeneity of PD-L1 expression, along with practical limitations of tumor biopsy, including its invasiveness and sampling bias.

[0005] In recurrent or metastatic cervical cancer, the key clinical challenge faced by biomarker-guided treatment decisions is limited by the frequent infeasibility of obtaining representative tissue specimens, necessitating blood-based liquid biopsies to enable indispensable systemic immune analysis. Plasma immunoproteomics can potentially provide the best reflection of systemic immunity due to its high content.

[0006] So far, there is still no effective tool to help determine which cervical cancer patients will benefit from immunotherapy, which treatment-induced immune response can be expected, and what is the best treatment combination, thus there is an urgent need for prognostic and predictive biomarkers related to the effectiveness of immunotherapy. SUMMARY

[0007] To solve the above problems, the present application provides a biomarker combination for predicting the response of cervical cancer immunotherapy based on plasma immunoproteomics and uses thereof. Specifically,

[0008] The first aspect of the present application provides the use of a reagent and / or device for detecting a marker combination in the preparation of a product for predicting the response and / or prognosis of cervical cancer immunotherapy; the marker combination comprises one or more selected from LYN, MetAP2, CD207 and exosome PD-L1.

[0009] In some embodiments, the marker combination comprises LYN, MetAP2, CD207 and exosome PD-L1.

[0010] In some embodiments, the marker combination consists of LYN, MetAP2, CD207 and exosome PD-L1.

[0011] In some embodiments, wherein the immunotherapy comprises immune checkpoint inhibitor therapy or VEGFR inhibitor therapy.

[0012] In some embodiments, the immune checkpoint inhibitor comprises anti-PD1 antibody, PD-L1 antibody and / or anti-CTLA-4 antibody.

[0013] In some embodiments, the immunotherapy is combined with chemotherapy and / or radiotherapy.

[0014] In some embodiments, wherein the reagent and / or device detect the protein level of LYN, MetAP2 and / or CD207 by Olink PEA.

[0015] In some embodiments, wherein the reagent and / or device analyze the level of exosome PD-L1 by proximity barcoding (PBA) technology.

[0016] In some embodiments, the exosome is an extracellular vesicle of the plasma of a cervical cancer patient.

[0017] In some embodiments, wherein the sample for detection is plasma, serum, cerebrospinal fluid or tissue lysate.

[0018] The second aspect of the present application provides a cervical cancer immunotherapy response and / or prognosis prediction system, comprising: (a) an input module for inputting detection data of protein levels of a marker combination of a detection object; the marker combination comprises LYN, MetAP2, CD207 and exosome PD-L1; (b) a processing module for calculating the input protein levels of the marker combination according to a predetermined scoring formula to obtain a score; and comparing the score with a cutoff value to obtain a discrimination result, wherein when the risk score is higher than the cutoff value, it is suggested that the detection object has a good response to immunotherapy or a good prognosis; and (c) an output module for outputting the prediction result.

[0019] In some embodiments, the protein levels of LYN, MetAP2 and CD207 are detected by Olink PEA.

[0020] In some embodiments, the exosome PD-L1 level is detected by proximity barcoding (PBA) technology.

[0021] In some embodiments, the immunotherapy comprises immune checkpoint inhibitor therapy or VEGFR inhibitor therapy.

[0022] In some embodiments, the immune checkpoint inhibitor comprises an anti-PD1 antibody, a PD-L1 antibody and / or an anti-CTLA-4 antibody.

[0023] In some embodiments, the scoring formula is 0.853xLYN-3.795xexosome PD-L1+0.455xMetAP2+1.353xCD207; preferably, the scoring formula is 0.853xLYN-3.795xexosome PD-L1+0.455xMetAP2+1.353xCD207; and preferably, the cutoff value is -20.871.

[0024] Compared with the prior art, the present application has the following beneficial effects:

[0025] The present application is based on a comprehensive analysis of cervical cancer patients receiving immunotherapy, determines a new marker combination for predicting the response of cervical cancer immunotherapy, and establishes a prediction model (PPRPScore system) based on the marker combination. The PPRPScore system shows strong prediction performance (AUC is 0.939 and 0.955, respectively) in the discovery cohort and the validation cohort, providing a reliable basis for clinical decision-making.

[0026] The prediction system provided by the application is based on a non-invasive detection method of plasma proteomics, which overcomes the limitations and spatial heterogeneity problems of tissue biopsy. At the same time, it is also helpful for the implementation and development of blood-based immune atlas analysis, and promotes the application of precision medicine in the immunotherapy of cervical cancer. BRIEF DESCRIPTION OF DRAWINGS

[0027] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments, when read in conjunction with the accompanying drawings:

[0028] Figure 1 Plasma immunoproteomics analysis reveals dynamic changes associated with immune therapy response in cervical cancer patients. Figure 1 A shows an integrated research strategy, combining tumor tissue analysis (mIHC and RNA-seq), systemic immune profiling (PEA and single-exosome PBA), and clinical outcome evaluation at the stages of surgery, immunotherapy, and follow-up; Figure 1 B shows the results of differential plasma protein expression analyzed by PEA technology. The upper panel presents the overall comparison of all patients, the middle panel shows the response-specific protein dynamics of partial remission (PR) and disease progression (PD) patients respectively, and the lower panel compares the protein expression changes before and after treatment. Figure 1 C shows the changes in plasma exosome protein expression analyzed by single-exosome PBA technology, with the same layout as Figure B. Figure 1 D shows that the plasma proteome UMAP dimensionality reduction analysis presents an obvious clustering pattern and patient heterogeneity, with a total of 21 clusters (numbered 0-20) marked in different colors. Figure 1 E shows a plasma protein consistency clustering heat map before treatment, which identifies 20 molecular clusters with unique feature profiles.

[0029] Figure 2 Response shows tumor microenvironment feature analysis reveals differential immune landscape associated with immune therapy response. Comparison of pathway enrichment analysis and mIHC analysis results of tumor transcriptome of PR and PD patients. Figure 2 A shows the comparison results of PR and PD patient tumor RNA-seq pathway enrichment analysis. Figure 2 B shows representative mIHC images of PR and PD patient tumor tissues. The upper panel presents the composite image of DAPI nuclear staining (blue) and immune markers; the lower panel shows single-channel staining of DAPI, CD4, CD8, PD-1, and PD-L1 respectively. The white box marks a representative area of immune cell infiltration. Scale bar = 100 μm. Figure 2 C shows the spatial quantitative analysis of immune cell populations and immune checkpoint molecule expression in the tumor microenvironment. Box plots compare the percentage distribution of different immune cell subpopulations and the expression level of checkpoint molecules in PR (red) and PD (blue) patients.

[0030] Figure 3 The display of correlation analysis reveals the dynamic correlation between plasma immunoproteomics and tumor microenvironment. Correlation heatmap between plasma immunoproteomics and tumor microenvironment immune cell infiltration. Figure 3 Figures 2A and 2B show the results of correlation analysis between plasma immunoproteomics (x-axis) (A: pre-treatment; B: post-treatment) and pre-treatment tumor microenvironment immune cell infiltration (y-axis). Plasma protein data derived from PEA (pink) and single exosome PBA (orange). Figure 3 Figure 2C shows the results of correlation analysis between pre-treatment tumor tissue gene mRNA expression level and pre-treatment plasma immunoproteomics. The upper panel shows the statistical indicators of correlation, and the lower panel presents the correlation coefficient distribution from positive value (blue bar) to negative value (red bar) in the form of column chart.

[0031] Figure 4 The display of post-treatment plasma exosome PD-L1 shows good predictive performance compared to immune checkpoint expression based on tumor tissue. Comparison of tumor tissue immune checkpoint molecule expression analysis in surgical pathology specimens and plasma exosome PD-L1 and PD-1 expression levels. Figure 4 Figure 3A shows the results of tumor tissue immune checkpoint molecule expression analysis based on surgical pathology specimens. The left panel is the comparison result of box plot of PD-L1 and PD-1 protein expression levels between PR and PD patients; the right panel is the corresponding receiver operating characteristic (ROC) curve, showing its predictive value for immunotherapy response. "ns" indicates no statistical difference. Figure 4 Figure 3B shows the expression levels of plasma exosome PD-L1 and PD-1 before (upper panel) and after (lower panel) immunotherapy. **p<0.01; "ns" indicates no statistical difference.

[0032] Figure 5 The display of pre-treatment plasma LYN levels shows good predictive performance for immunotherapy response. Comparative analysis of plasma LYN protein levels and ROC curve analysis. Figure 5 Figure 4A shows the results of comparative analysis of plasma LYN protein levels before and after immunotherapy. **p<0.01; "ns" indicates no statistical significance. Figure 5 Figure 4B shows the results of comparative analysis of plasma LYN levels between different response groups. **p<0.01; "ns" indicates no statistical significance. Figure 5 Figures 4C and 4D show the results of ROC curve analysis of plasma LYN levels before and after treatment. Figure 5 Figure 4E shows the results of correlation analysis between pre-treatment plasma LYN levels and tumor tissue immune checkpoint molecule expression (left: PD-L1; right: PD-1). Figure 5F shows the correlation analysis results of plasma LYN levels before (left panel) and after (right panel) immunotherapy with plasma exosome PD-L1 expression.

[0033] Figure 6 A shows the constructed pre-treatment plasma immunotherapy response prediction score (PPRPScore) system. Consensus clustering analysis, LASSO regression analysis and ROC curve evaluation of PPRPScore prediction performance. Figure 6 A shows the consensus clustering and cumulative distribution function analysis results of 30 plasma proteins before treatment. Figure 6 B shows the protein expression levels in the four identified clusters. Patients are grouped according to cluster attribution (clusters 1-4, marked with colored bars), and protein expression levels are represented by color intensity (red for high expression, blue for low expression). Figure 6 C shows the distribution of treatment response outcomes in the four consensus clusters. Figure 6 D shows the constructed pre-treatment plasma immunotherapy response prediction score (PPRPScore) system based on LASSO regression analysis, which includes four plasma proteins: LYN, exosome PD-L1, MetAP2 and CD207. Patients are divided into PPRPScore-high and PPRPScore-low groups with a cutoff value of -20.871. Figure 6 E and Figure 6 F shows that the PPRPScore system has good and stable prediction performance in both the discovery cohort and the validation cohort. Figure 6 G shows the comparison results of immunotherapy effectiveness based on the PPRPScore system. Figure 7 H shows the flowchart of clinical stratification analysis of patients using the PPRPScore system. Patients are divided into PPRPScore-high and PPRPScore-low groups, where PPRPScore-high patients may benefit from immunotherapy alone, while PPRPScore-low patients may need to combine chemotherapy, VEGFR inhibitors and other ICIs to improve treatment effectiveness.

[0034] Figure 7 A forest plot showing Cox regression analysis and Kaplan-Meier survival curves of plasma proteomic biomarkers associated with PFS. Figure 7 A shows the univariate (left) and multivariate (right) Cox regression analysis results of pre-treatment plasma protein biomarkers associated with progression-free survival (PFS). The hazard ratio (HR) and its 95% confidence interval (CI) are shown in the figure, and the dashed vertical axis corresponds to a risk ratio of 1.0. Figure 7 B shows the Kaplan-Meier survival curve of the pre-treatment prognostic biomarker LYN. Figure 7C. Kaplan-Meier survival curves of pre-treatment prognostic biomarker MetAP2. Figure 7 D. Results of univariate (left) and multivariate (right) Cox regression analysis of post-treatment plasma protein biomarkers associated with PFS. Hazard ratios and their 95% confidence intervals are shown in the figure, with a vertical axis of the dashed line corresponding to a hazard ratio of 1.0. Figure 7 E. Kaplan-Meier survival curves of post-treatment prognostic biomarker exosomal PD-L1. Figure 1 F. Kaplan-Meier survival curves of post-treatment prognostic biomarker MUC16. DETAILED DESCRIPTION

[0035] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of the present application.

[0036] Unless otherwise defined, technical terms or scientific terms used herein should be interpreted as is normally used by one of ordinary skill in the art to which the present application pertains.

[0037] The present application is based on a comprehensive analysis of 47 cervical cancer patients receiving immunotherapy, including a discovery cohort (n=17) and a validation cohort (n=30). Through techniques such as multiplex immunohistochemistry (mIHC), RNA sequencing, PEA, and single exosome PBA, the relationship between plasma immunoproteomics and immunotherapy response was systematically analyzed.

[0038] Experimental methods of Example 1

[0039] 1.1 Cervical cancer patients

[0040] Forty-seven cervical cancer patients receiving immunotherapy were included in the study, including a discovery cohort (n=17) and a validation cohort (n=30). Patients were classified as partial remission (PR, ≥30% reduction in target lesions) or progressive disease (PD, ≥20% increase in lesions / new lesions) according to the RECIST v1.1 standard. There were no significant differences in patient basic clinical characteristics between and within cohorts, confirming the homogeneity of patient distribution between study groups.

[0041] 1.2 Blood sample collection and processing

[0042] For all cervical cancer patients, approximately 2-5 mL of peripheral venous blood was collected into EDTA tubes per patient, then centrifuged at 3000 rpm for 15 minutes to extract plasma, and the extracted plasma was frozen at -80°C for subsequent immuno-proteomic determination.

[0043] 1.3 Proximity Extension Assay (PEA)

[0044] Proximity Extension Assay (PEA) was performed using Olink Proteomics Target 96 Oncology II assay plates. This method is based on antibody pairs coupled to single-stranded oligonucleotide DNA barcodes. When bound to the target protein, the antibody pairs generate double-stranded DNA amplicons, which can subsequently be quantified to infer protein levels. All analyses were performed using the recommended internal controls and inter-plate variability was adjusted by intensity normalization. Protein expression levels were reported as normalized protein expression (NPX) values - a relative, log2 transformed unit.

[0045] 1.4 Exosome purification and proximity barcode assay (PBA)

[0046] Exosomes were isolated by differential centrifugation. Briefly, a sequential three-step centrifugation process was performed: 300 x g for 10 minutes, 2000 x g for 10 minutes, and 10,000 x g for 30 minutes to remove cells, apoptotic bodies, cellular debris, and aggregated biomolecules from the plasma. Subsequently, exosomes were pelleted using an Optima XPN ultracentrifuge equipped with a 32Ti rotor at 100,000 x g for 70 minutes.

[0047] Extracellular vesicles (EV) samples from cervical cancer patient plasma were analyzed using the PBA technology. The assay plate containing 112 antibodies was coupled to oligonucleotides carrying unique protein tags, unique molecular tags, and universal primer binding sequences. PBA enables the detection of target biomarkers at the single-exosome level.

[0048] 1.5 Multiplexed immunohistochemistry (mIHC) and RNA sequencing (RNA-seq)

[0049] mIHC analysis was performed on 4-millimeter-thick formalin-fixed paraffin-embedded (FFPE) tissue sections. Sequential immunostaining was performed using primary antibodies against CD4, CD8, PD-L1, and PD-1. The Quantitative assessment of cell density and biomarker co-localization analysis were performed using the image analysis platform.

[0050] Total RNA was extracted from tumor tissue samples using the RNeasy Mini kit. Double-end sequencing libraries were constructed using the TruSeq RNA Sample Preparation kit. The resulting libraries were sequenced on the Illumina NovaSeq 6000 platform.

[0051] 1.6 Statistical analysis and predictive model construction

[0052] Normality of variables was assessed using the Shapiro-Wilk normality test. For two-group comparisons, normally distributed variables were analyzed using Student's t-test, and non-normally distributed variables were analyzed using the Wilcoxon rank-sum test. Correlation coefficients were calculated using the Spearman correlation method.

[0053] Pre-therapy proteomic profiles were identified using K-means unsupervised clustering analysis. The consensus clustering algorithm was implemented through the ConsensusClusterPlus R package. The pre-therapy plasma response predictive score (PPRScore) system was developed using the glmnet R package with least absolute shrinkage and selection operator (LASSO) regression.

[0054] Results of Example 2

[0055] 2.1 Dynamic changes in plasma immunoproteomics correlate with immunotherapy response

[0056] Plasma samples collected from cervical cancer patients before and after immunotherapy were subjected to immunoproteomic analysis by PEA and single-exosome PBA detection Figure 1 A). Comparison of protein levels in plasma samples before and after treatment revealed dynamic changes in plasma immunoproteomics after immunotherapy. The dynamic differences in plasma protein expression profiles between PR and PD patients were visualized by volcano plots Figure 1 B, 1C). Among the differentially expressed proteins, pre-treatment LYN levels showed the most significant difference between response groups. Plasma LYN levels decreased significantly in PR patients after immunotherapy, accompanied by changes in the expression of proteins such as LYPD3, EGF, SYND, SCAMP3, PVRL4, MetAP2, and VEGFA; while in PD patients, MUC-16 was significantly upregulated after treatment Figure 1 B). PBA analysis provided supplementary information on the dynamics of plasma exosomal proteins: MICA and CD26 expression differed significantly in PR patients, and LRP2 and SIGLEC10 also showed differential expression in PD patients Figure 1C). Post-treatment comparison between responder groups identified multiple differentially expressed exosomal proteins, including PD-L1, GPA33, NPHS1, EGFR, CD26, SIGLEC8, CDH11, SLC12A1, AMIGO1, ADIPOQ, and ITGA1 Figure 1 C). UMAP dimensionality reduction analysis showed that plasma proteome presented a clear clustering tendency, and there was significant heterogeneity between patients Figure 1 D). Further consensus clustering analysis of pre-treatment plasma proteins identified 20 molecular clusters with unique signature profiles, with proteins such as NPHS1, LAG3, ADIPOQ, CD3E, L1CAM, and CD8A showing high consistency within their respective clusters Figure 2 E).

[0057] 2.2 Tumor microenvironment is associated with patient immune therapy response

[0058] Comparative analysis of tumor transcriptome of patients with different response performance was performed to clarify the local characteristics of tumor. The results showed that there was significant enrichment of differential pathways, including MAPK signaling pathway, immune regulation signaling pathway (such as TGF-β and IL-4 / IL-13 pathway) and microenvironment remodeling pathway (such as extracellular matrix degradation and GPCR ligand binding) Figure 2 A), indicating that the differences in immune status before treatment may be the reason for different efficacy.

[0059] To further evaluate the spatial immune landscape in tumor tissue, the inventors used multiplex immunohistochemistry (mIHC) technology to compare the immune cell infiltration patterns and immune checkpoint expression characteristics between PR and PD groups by CD4, CD8, PD-1 and PD-L1 staining Figure 2 B). Quantitative spatial analysis showed that the proportion of CD4+T cells, CD8+T cells, CD4+PD-1+T cells, total PD-1+cells and PD-L1+cells in PR patients showed an increasing trend compared with PD patients, but did not reach statistical significance. Notably, the proportion of CD8+PD-1+T cells in the tumor microenvironment of PR patients was significantly higher than that of PD patients (p=0.025, Figure 3 C), suggesting that CD8+PD-1+T cells with reactivation potential that existed before treatment may contribute to effective immune therapy response.

[0060] 2.3 Plasma immune proteome is associated with tumor microenvironment immune cell infiltration

[0061] To investigate the correlation between systemic immune response and the local tumor microenvironment (TME), the inventors analyzed the correlation between plasma immunoproteome and TME immune cell infiltration. The study found a dynamic association between plasma proteins and the tumor-infiltrating immune cell population. Notably, the plasma level of Lyn protein exhibited different correlation patterns with the TME immune cell population before and after immunotherapy. Before treatment, LYN levels were positively correlated with the proportion of CD4+PD-1+ T cells in the TME, but this correlation turned into a significant negative correlation after treatment. Furthermore, both before and after treatment, plasma LYN levels maintained a significant positive correlation with the proportion of CD8+ T cells in the TME, suggesting that LYN may play a role in the dynamic regulation of CD8+ T cells. For CD8+PD-1+ T cells, plasma LYN levels were significantly positively correlated with their proportion in the TME before treatment, but this correlation turned into a negative correlation after treatment. Figure 3 (A, 3B). The aforementioned shift in correlation may reflect a remodeling of the interaction between systemic immunity and the TME, with immunotherapeutic interventions inducing a reshaping of the immune state. Figure 4 C shows the correlation analysis results between pre-treatment tumor tissue gene mRNA expression levels and pre-treatment plasma immunoproteome. The top figure shows the statistical indicators of the correlation, and the bottom figure presents the correlation coefficient distribution from positive values ​​(blue bars) to negative values ​​(red bars) in bar chart form.

[0062] 2.4 Post-treatment plasma exosome PD-L1 levels predict immunotherapy response

[0063] To assess the correlation between immune checkpoint molecule expression and treatment response, the inventors analyzed the expression levels of PD-L1 and PD-1 in tumor tissues of patients with partial response (PR) and progressive disease (PD). The analysis showed that PD-L1 expression levels in PR patients' tumor tissues tended to be higher than those in PD patients, but the difference was not statistically significant; there was also no significant difference in PD-1 expression between the two groups. ROC curve analysis indicated that both PD-L1 and PD-1 showed limited predictive value, with the area under the curve (AUC) for PD-L1 being 0.612 and the AUC for PD-1 being 0.591. Figure 4 A).

[0064] Further study was made on the dynamic changes of plasma exosome PD-L1 and PD-1 levels before and after immunotherapy. Before treatment, the plasma exosome PD-L1 level of PD patients showed a trend of being higher than that of PR patients, but there was no significant difference; after treatment, the exosome PD-L1 level of PD patients was significantly higher than that of PR patients (p = 0.0048). ROC analysis showed that the predictive performance of exosome PD-L1 was significantly improved after treatment (AUC = 0.621 before treatment, AUC = 0.909 after treatment). In contrast, the exosome PD-1 level showed no significant difference between the response groups before and after treatment. ROC analysis suggested that the PD-1 level before treatment had certain predictive value (AUC = 0.712), while the predictive performance decreased after treatment (AUC = 0.591) Figure 1 B).

[0065] 2.5 Plasma LYN level before treatment predicts immunotherapy response

[0066] In the comparison of plasma immunoproteome before treatment of patients with different responses, ten proteins showed significant differences (p < 0.05), among which the difference in LYN level before treatment was the most significant Figure 5 B). In PR patients, the plasma LYN level decreased significantly after immunotherapy (p < 0.01); in contrast, the LYN level of PD patients showed a significant increase after treatment, although it was not statistically significant Figure 5 A). Comparison before treatment showed that the plasma LYN level of PR patients was significantly higher than that of PD patients (p = 0.0048, Figure 5 B); however, the test results after treatment showed that this trend was reversed, and the LYN level of PR patients was lower than that of PD patients, but the difference was not statistically significant Figure 5 B). ROC analysis showed that the plasma LYN protein level before treatment showed excellent predictive performance in distinguishing responders from non-responders, with an AUC of 0.909 Figure 5 C); in contrast, the predictive accuracy of LYN level after treatment decreased, with an AUC of 0.621 Figure 5 D).

[0067] The inventors further explored the relationship between plasma LYN level and expression pattern of immune checkpoint molecules. In tumor tissues, the plasma LYN level before treatment was significantly positively correlated with tumor PD-L1 expression (R = 0.58, p = 0.015, Figure 5 E), while there was no significant correlation with tumor PD-1 expression (R = 0.35, p = 0.17, Figure 1F) For plasma exosomal PD-L1, no significant correlation was observed with plasma LYN levels either before or after immunotherapy (R=0.09, p=0.73; R=0.39, p=0.12, respectively). These results collectively suggest that plasma LYN protein levels before treatment can serve as a potential biomarker to predict immunotherapy response.

[0068] 2.6 Pre-treatment plasma immunotherapy response prediction score (PPRP Score) system

[0069] Based on the results of PR vs. PD group pre-treatment plasma proteome comparison ( Figure 6 C), the inventors selected 30 proteins with p<0.1 significance level for consensus clustering analysis. By comprehensive evaluation of Consensus Cumulative Distribution Function (CDF) plot, delta area plot and cluster matrix pattern ( Figure 6 A, 6B), four clusters were finally determined, among which cluster 2 was associated with significantly improved immunotherapy response ( Figure 6 C).

[0070] To further improve clinical applicability, the inventors utilized the protein expression data obtained from PEA and single exosome PBA analysis to construct a pre-treatment plasma immunotherapy response prediction score (PPRP Score) system through LASSO regression. The system first adopted K-means unsupervised clustering analysis to identify pre-treatment proteomic features and classify patients, performed consensus clustering algorithm (repeated 1,000 times to ensure stability) through ConsensusClusterPlus R package to determine the optimal cluster number, and then applied LASSO regression through glmnet R package to screen four plasma proteins (LYN, MetAP2, CD207 and exosomal PD-L1) at the optimal lambda value, finally established the PPRP Score system for predicting immunotherapy response. The PPRP Score system showed good prediction performance in both discovery cohort and validation cohort (AUC=0.939 and 0.955, Figure 6 E, 6F), with -20.871 as the cutoff value ( Figure 6 D). According to PPRP Score stratification (high-score group vs. low-score group, Figure 6 D) showed that high-score patients were significantly associated with better immunotherapy response, while low-score group tended to have poorer clinical outcomes ( Figure 6 D and 6G).

[0071] The inventors' results showed that PPRPScore could facilitate the response stratification of cervical cancer patients, both identifying the population that might benefit from immunotherapy and helping to find potential non-responders. For the latter, treatment strategies combining chemotherapy, VEGFR inhibitors and other ICIs might improve their clinical outcomes Figure 7 H).

[0072] 2.7 Prognostic value of plasma immunoproteomics

[0073] The inventors further evaluated the prognostic value of plasma protein biomarkers in cervical cancer patients receiving immunotherapy before and after treatment. Through univariate and multivariate Cox regression analysis, multiple biomarkers were identified to be significantly associated with progression-free survival (PFS) (A and 7D). Pre-treatment analysis showed that higher plasma levels of LYN and MetAP2 before immunotherapy could be predictive of good clinical outcomes. Kaplan-Meier survival curves showed that patients with high pre-treatment expression of these proteins had significantly prolonged PFS (p<0.0001, p=0.0019; Figure 7 B and 7C). Conversely, post-treatment evaluation found that plasma MUC16 and exosomal PD-L1 were poor prognostic markers, with high expression associated with shorter PFS (p<0.0001, p=0.0027; Figure 7 E and 7F). ​ Notably, although plasma exosomal PD-L1 before treatment was included in the PPRPScore system for response evaluation, its prognostic significance for PFS was mainly reflected in post-treatment samples. This difference suggests that early treatment response and sustained disease control might involve different immunological mechanisms, or reflect the initial tumor immunogenicity and adaptive immune escape processes in cervical cancer immunotherapy, respectively.

[0074] Table 1. Core biomarkers of PPRPScore system

[0075]

[0076] The biomarker combination based on plasma immunoproteomics and the PPRPScore system provided by the present invention show strong predictive performance in predicting the response of cervical cancer immunotherapy. The invention overcomes the limitations of traditional tissue-based biomarkers, providing a non-invasive, accurate and reliable patient stratification tool for clinicians, which helps to achieve precision medicine in cervical cancer immunotherapy.

[0077]

[0078] ​The foregoing merely illustrates the principles of the application and application of its leading features. This application is not limited to the illustrative embodiments shown and described herein. Rather, this application is capable of operating within a further range of conditions and environments than those specifically described herein, and further modifications can be made without departing from the spirit or scope of the application. Accordingly, the description is to be construed as illustrative only and not restrictive of the broad disclosure or application of the application. The specification and drawings are, accordingly, to be regarded simply as an illustration of the broadest aspects of the application and in no way limiting of its scope. Any drawing reference designations in the claims are to be construed in accordance with their use in the specification and drawing and not for purposes of limitation of any claim.

[0079] Furthermore, it should be understood that although the description has been set forth in the context of implementations, the present description is not only directed to each individual implementation, but also each implementation individually, and any combination of the implementations, as would be understood by those skilled in the art.

Claims

1. Use of reagents and / or devices for detecting a marker combination in the manufacture of a product for predicting immunotherapy response and / or prognosis of cervical cancer; the marker combination comprises one or more selected from LYN, MetAP2, CD207 and exosome PD-L1.

2. The use according to claim 1, wherein the marker combination comprises LYN, MetAP2, CD207 and exosome PD-L1.

3. The use according to claim 1 or 2, wherein the immunotherapy comprises immune checkpoint inhibitor therapy or VEGFR inhibitor therapy; preferably, the immune checkpoint inhibitor comprises anti-PD1 antibody, PD-L1 antibody and / or anti-CTLA-4 antibody.

4. The use according to any one of claims 1-3, wherein the reagents and / or devices detect protein levels of LYN, MetAP2 and / or CD207 by Olink PEA.

5. The use according to any one of claims 1-4, wherein the reagents and / or devices analyze exosome PD-L1 levels by proximity barcoding (PBA) technology; preferably, the exosome is an extracellular vesicle of plasma of a cervical cancer patient.

6. The use according to any one of claims 1-5, wherein the sample for detection is plasma, serum, cerebrospinal fluid or tissue lysate.

7. A cervical cancer immunotherapy response and / or prognosis prediction system, characterized by, The system comprises: (a) an input module for inputting detection data of protein levels of a marker combination of a detection object; the marker combination comprises LYN, MetAP2, CD207 and exosome PD-L1; (b) a processing module for calculating a score according to a predetermined scoring formula for the input protein levels of the marker combination, and comparing the score with a cutoff value to obtain a discrimination result, wherein when the risk score is higher than the cutoff value, it indicates that the detection object has a good response to immunotherapy or a good prognosis; and (c) an output module for outputting the prediction result.

8. The prediction system according to claim 7, wherein the protein levels of LYN, MetAP2 and CD207 are detected by Olink PEA; and / or the exosome PD-L1 level is detected by proximity barcoding (PBA) technology.

9. The prediction system of claim 7 or 8, wherein, The immunotherapy comprises immune checkpoint inhibitor therapy or VEGFR inhibitor therapy; preferably, the immune checkpoint inhibitor comprises anti-PD1 antibody, PD-L1 antibody and / or anti-CTLA-4 antibody.

10. The detection system according to any one of claims 7-9, wherein, The scoring formula is 0.853 x LYN - 3.795 x exosome PD-L1 + 0.455 x MetAP2 + 1.353 x CD207; preferably, the scoring formula is 0.853 x LYN - 3.795 x exosome PD-L1 + 0.455 x MetAP2 + 1.353 x CD207; and preferably, the cutoff value is -20.871.

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