Prediction method for progression-free survival in patients with intermediate and advanced HCC after triple immunotherapy

Abstract

The present application discloses a method for predicting the progression-free survival of patients with intermediate and advanced HCC after triple immunotherapy. A personalized tumor mutation map is constructed from the sequencing data of the tumor tissues and paired leukocytes of patients with intermediate and advanced HCC. Based on the tumor mutation information in the personalized tumor mutation map, a gene risk score is calculated using a first model. Subsequently, with the gene risk score and clinical variables as independent variables, a total risk score is calculated using a second model. Risk stratification is performed according to the cut-off value of the total risk score to predict the probability of PFS at different times for patients receiving triple immunotherapy. The present invention can effectively predict the progression-free survival of patients with intermediate and advanced hepatocellular carcinoma after triple immunotherapy, thereby screening out patients who may potentially benefit.

Description

Technical Field

[0001] This application belongs to the field of bio-detection information analysis, and specifically relates to a method for predicting the progression-free survival of patients with advanced HCC after triple immunotherapy. Background Art

[0002] Liver cancer remains a huge challenge globally. Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, accounting for approximately 90% of all cases. Most liver cancer patients have lost the opportunity for surgical treatment at the time of diagnosis, and non-surgical local treatment and systemic treatment are the mainstays. Therefore, conversion therapy can enable patients with inoperable advanced-stage liver cancer to achieve downstaging to a resectable stage. In the IMbrave150 study, atezolizumab (an anti-PD-L1 inhibitor) and bevacizumab (an anti-VEGFA; anti-angiogenic agent) ("T+A" therapy) showed more significant survival benefits in the first-line treatment of patients with advanced HCC compared to traditional sorafenib. In addition, the current development of anti-angiogenic drugs combined with immunotherapy and interventional local chemotherapy (triple therapy) is rapid. Compared with the "T+A" therapy, it can further significantly improve the progression-free survival (PFS), and thus has become the main method for the conversion therapy of potentially resectable liver cancer.

[0003] However, not all patients can benefit from triple therapy. Clinical cohort data show that approximately 25% of patients with advanced HCC have a progression-free survival (PFS) of less than three months. Therefore, patients with advanced HCC are a heterogeneous group with different subgroups, and there are differences in the efficacy and prognosis of the corresponding triple therapy. This heterogeneity may be attributed to differences in tumor endogenous characteristics. Therefore, the current clinical challenge is how to predict and screen patients who can benefit from this therapy before treatment. In the existing technology, traditional clinical factors have no significant effect on predicting the efficacy and longer PFS of patients with advanced HCC. In addition, although there are a large number of studies and evidences in the current clinical technology or biomarkers (such as PD-1 / PD-L1, etc.) for predicting the efficacy of immunotherapy, there is a lack of relevant clinical evidence in predicting the efficacy of triple therapy for advanced HCC and screening the benefit population. On the other hand, the prediction of the efficacy of immunotherapy, such as the detection of PD-1 / PD-L1, often requires tissue biopsy, which has limitations such as tumor tissue heterogeneity and invasiveness. Therefore, it also leads to the barrenness of the existing technology in predicting the efficacy of HCC triple therapy.

[0004] Exploratory studies on predictive biomarkers for triple therapy are also underway, and a recent study highlighted potential predictive biomarkers in peripheral blood, such as CD4+ and CD8+ T cells. Clinical data of the present invention show that a high infiltration rate of T cells and DC cells in tumors in baseline biopsies is associated with a better prognosis. However, these studies have limitations such as low sample size and single center. Therefore, there are few effective biomarkers available for clinical practice. Current studies suggest that the cumulative effect of gene mutations in key biological signaling pathways may serve as a predictor of the efficacy of immune checkpoint inhibitors (ICI). The R language package "PMAPscore" can more comprehensively and effectively evaluate the gene mutation effect of signaling pathways by combining the cumulative effect and positional effect of gene mutations to assess the perturbation level of signaling pathway mutations. This algorithm has been applied to clinical cohorts of non-small cell lung cancer (NSCLC) and melanoma patients receiving ICI treatment, and relevant signaling pathways have been correspondingly identified to construct a prognostic model for predicting overall survival (OS). However, OS is easily affected by the post-line treatment method, and there are limitations in predicting the efficacy and prognosis after triple therapy. There is currently no effective biomarker or computational model for predicting the prognosis of triple therapy in patients with intermediate and advanced HCC. Additionally, progression-free survival (PFS) can more accurately reflect the clinical benefit of triple therapy in patients with intermediate and advanced HCC compared to predicting OS. Summary of the Invention

[0005] An object of the present invention is to provide a method for predicting the progression-free survival of patients with intermediate and advanced HCC after triple immunotherapy, so as to screen out patients who may potentially benefit.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for predicting the progression-free survival of patients with intermediate and advanced HCC after triple immunotherapy, comprising:

[0008] Constructing a personalized tumor mutation map from the sequencing data of the tumor tissue and paired leukocytes of patients with intermediate and advanced HCC;

[0009] Based on the tumor mutation information in the personalized tumor mutation map, calculating a gene risk score using a first model; then, using the gene risk score and clinical variables as independent variables, calculating a total risk score using a second model; and performing risk stratification according to the cut-off value of the total risk score to predict the probability of PFS at different times for patients receiving triple immunotherapy.

[0010] Among them, the first model and the second model are constructed using the retrospective cohort of patients with advanced HCC, including: constructing a personalized tumor mutation map based on the tumor tissue of the retrospective cohort patients before triple immunotherapy, calculating the perturbation level of the tumor mutation information in the signaling pathway by combining the cumulative effect and the positional effect of the mutant genes, screening the gene mutation perturbation pathways significantly associated with PFS, using the gene mutation perturbation pathways significantly associated with PFS as biomarkers, and constructing the first model based on regression analysis;

[0011] Using the calculation results of the first model and the clinical variables of the retrospective cohort patients as independent variables, a second model is established based on regression analysis, where the clinical variables are clinical risk factors significantly associated with PFS.

[0012] As a preferred implementation method, the gene mutation perturbation pathways significantly associated with PFS are screened through univariate Cox regression analysis and multivariate Cox stepwise regression analysis in sequence, and the first model is established.

[0013] As a preferred implementation method, using the calculation results of the first model and the clinical variables of the retrospective cohort patients as independent variables, a second model is established based on the nomogram.

[0014] As a preferred implementation method, the clinical risk factors significantly associated with PFS are screened through univariate Cox regression analysis and multivariate Cox stepwise regression analysis.

[0015] Furthermore, the clinical risk factors include aspartate aminotransferase level AST, Child-Pugh classification, and metastasis status.

[0016] As a preferred implementation method, the gene mutation perturbation pathways for constructing the first model are the Hedgehog signaling pathway, ErbB signaling pathway, and Focal adhesion signaling pathway.

[0017] Furthermore, the calculation formula of the first model is as follows:

[0018]

[0019] In the formula, Hedgehog, ErbB, and Focal adhesion are the perturbation values of the Hedgehog signaling pathway, ErbB signaling pathway, and Focal adhesion signaling pathway, respectively.

[0020] As a preferred implementation method, risk stratification is performed based on the median of the total risk score in the training data.

[0021] As a preferred implementation method, the calculation formula of the second model is as follows:

[0022]

[0023] In the formula, is the level of aspartate aminotransferase; Child-Pugh takes values of A grade = 0, B grade = 1, and C grade = 1; Metastasis is distant metastasis, and the value-taking method is metastasis = 1, no metastasis = 0. The hybrid model constructed by the present invention is verified by the data of the validation set, and the parameters have universality.

[0024] Another object of the present invention is to provide a computer program product, which includes a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of the above method are implemented.

[0025] The present invention uses an NGS panel targeting cancer-related genes and the R package "PMAPscore" to screen out key signaling pathways related to the triple therapy for intermediate and advanced liver cancer. In the training cohort, three signaling pathways were screened out to establish the first model, and combined with clinical risk factors, a second model for evaluating comprehensive risk was constructed. Subsequently, the risk score was verified in a prospective internal cohort, an external immunotherapy cohort, and an external non-immunotherapy cohort, and the universality of the hybrid model was determined. Compared with the prior art, the beneficial effect of the present application is that it can specifically and robustly divide intermediate and advanced HCC patients receiving triple therapy or PD-1 inhibitor therapy into two different subgroups with high or low disease progression risks. The hybrid model has important clinical significance for the clinical decision-making and personalized treatment of intermediate and advanced HCC patients. Description of the Drawings

[0026] Figure 1 is the flowchart of the present invention.

[0027] Figure 2 Integrates the PFS-related signaling pathways into GeneScore to evaluate the impact of gene mutations on the prognosis of intermediate and advanced HCC patients, where: (A) uses univariate Cox survival analysis to analyze the relationship between all 123 signaling pathways and PFS in a retrospective HCC cohort treated with triple therapy; (B) the forest plot shows five signaling pathways related to PFS (p < 0.1); (C) the forest plot of three signaling pathways related to PFS shown by using multivariate Cox stepwise regression analysis (p < 0.1); (D) the GeneScore distribution of advanced HCC patients treated with triple therapy; (E) the heat map of the perturbation scores of three signaling pathways for each HCC sample: blue: high perturbation score. White: low perturbation score.

[0028] Figure 3 is the construction of the prognosis model for intermediate and advanced HCC patients treated with triple therapy.

[0029] Figure 4 The Kaplan-Meier survival curves of PFS for the low GeneScore group and the high GeneScore group.

[0030] Figure 5 It is the evaluation and validation of the prognostic model for patients with intermediate and advanced HCC treated with triple therapy. Among them, (A) Kaplan-Meier survival curves based on the OverallScore calculated by the mixed model, with high / low groups divided according to the median value of OverallScore; (B) Different response situations of high / low Overallscore advanced HCC patients after triple therapy: CR, complete remission; PR, partial remission; SD, stable disease; PD, disease progression; (C) Kaplan-Meier survival curves of 26 advanced HCC patients in the validation set receiving triple therapy, with OverallScore calculated by the second model and the cut-off value the same as that of the training set.

[0031] Figure 6 It is the evaluation and validation of the prognostic model for patients with intermediate and advanced HCC treated with triple therapy. Among them, (A) ROC curves of the mixed model predicting PFS at 9 months, 18 months, and 24 months; (B) AUC of the mixed model predicting the change of PFS over time at different time points; (C) Calibration plots of the mixed model predicting PFS rates at 6 months, 9 months, and 12 months, with the predicted probability and actual probability of PFS plotted on the x-axis and y-axis respectively. The dashed line represents the ideal state diagram, and the solid line represents the actual observed state diagram.

[0032] Figure 7 It is to explore the potential molecular mechanisms of triple therapy for HCC patients from the aspects of biological processes and immune microenvironment; among them, (A)-(C) The top 20 mutated genes in the low / high GeneScore groups: (A) Gene mutation volcano plot; (B) Mutation frequency of CNTTB1 gene (C).

[0033] Figure 8 It is to explore the potential molecular mechanisms of triple therapy for HCC patients from the aspects of biological processes and immune microenvironment; among them, (A) Gene set enrichment analysis (GSEA) of the low GeneScore group and the high GeneScore group, with significant enrichment of angiogenesis and hypoxia pathways in the low GeneScore group; (B) Immune maps of the cancer immune cycle in the low / high GeneScore groups, IGS1, presence of T cell immune environment in the tumor; IGS2, tumor antigenicity; IGS3, initiation and activation; IGS4, transport and infiltration; IGS5, tumor antigen recognition; IGS6 to IGS8, inhibitory factors preventing the killing of cancer cells.

[0034] Figure 9Explore the potential molecular mechanisms of triple therapy for HCC patients from the aspects of biological processes and immune microenvironment; among them, (A) GSEA comparison between the low GeneScore group and the high GeneScore group, and significant enrichment of antigen processing and presentation, BCR signaling pathway, and chemokine gene sets in the low GeneScore group; (B) Representative immunofluorescence images of CD3+ and CD4+ in tumor tissues of low / high GeneScore patients. Detailed implementation manners

[0035] The technical solutions of the present invention will be further elaborated below in conjunction with the accompanying drawings and detailed implementation manners.

[0036] Some tools and existing methods used in the examples are described as follows:

[0037] (1) Screening of signaling pathways and fitting of the first model (Genescore calculation model)

[0038] Perform univariate Cox regression analysis using the survival analysis R package to screen for signaling pathways related to prognosis. Then use multivariate Cox stepwise regression analysis to screen for candidate signaling pathways that can be used to establish the first model (prognostic risk score Genescore calculation model).

[0039] (2) Gene set enrichment analysis and TME immune microenvironment analysis

[0040] Gene set enrichment analysis (GSEA) is performed using the clusterProfiler software package. Gene sets for Lenvatinib-targeted signaling pathways, such as angiogenesis and hypoxia, are from the MSigDB database, while the immune-related gene list is from the Immport database (https: / / www.immport.org / shared / genelists). Use single-sample GSEA in the GSVA package to quantify the abundance of tumor-infiltrating immune cells (TIICs). The anti-tumor immune response (cancer immune cycle) between different risk groups is also evaluated.

[0041] (3) Construction and validation of the second model (Overallscore calculation model)

[0042] Based on the gene risk score Genescore characteristic signaling pathways and clinical factors, use the RMS software package to construct the Overallscore calculation model. Evaluate the prediction feasibility and reliability of the Overallscore calculation model through calibration and ROC curves. Then evaluate the clinical performance and net benefit of the Overallscore calculation model through decision curve analysis (DCA).

[0043] (4) Immunofluorescence

[0044] Paraffin-embedded hepatocellular carcinoma tissue sections were immunofluorescently stained according to the standard protocol. The immunofluorescent antibody was anti-CD3 antibody (Dako, Cat#A0452 IR503), and the cell nuclei were counterstained with 4'-6'-diamidino-2-phenylindole (DAPI). The labeled slides were scanned using the TissueFACS SL plus S system (TissueGnostics, Vienna, Austria; acquisition software: TissueFAXS SL V7.1.120) and analyzed using the advanced image analysis software StrataQuest (V7.1.1.129). Images from different channels were overlaid to visualize different labels in the same tissue area. To identify the tumor area, the analysis software was used to automatically identify areas with distinct morphological features of the tumor tissue. Each staining included positive and negative controls to ensure the reliability of the results.

[0045] (5) Statistical analysis

[0046] All statistical analyses were performed using R version 4.1.1 software. The Wilcoxon test was used for the comparative analysis between two continuous variables, while the Fisher exact tests were used for the comparative analysis between categorical variables. The Kaplan-Meier method was used to construct the survival curve for the prognostic analysis of categorical variables, and the log-rank test was used for the statistical analysis. A p value < 0.05 was considered statistically significant.

[0047] Example 1

[0048] This example specifically illustrates the construction method of a hybrid model for predicting PFS after triple immunotherapy in patients with intermediate and advanced HCC.

[0049] An 82-patient retrospective cohort of intermediate and advanced HCC patients was used as the training set for model construction. All 82 patients received a triple therapy of chemotherapy combined with anti-angiogenic drugs and immunotherapy. The enrolled patients mainly received the treatment regimen of HAIC combined with lenvatinib and tislelizumab. The detailed treatment plan is as follows: HAIC regimen: Oxaliplatin (130 mg / m 2 , from 0 hour to 2 hours, day 1), folinic acid (200 mg / m 2 , from 2 hours to 4 hours, day 1), fluorouracil (400 mg / m 2 , fluorouracil within 15 minutes, followed by 2400 mg / m 2Fluorouracil, 46 hours, on the 1st and 2nd days). After HAIC administration, the catheter and sheath were removed. All patients received intravenous infusion of tislelizumab 101 and oral lenvatinib (12 mg / 60 kg; 8 mg / 60 kg or less) every three weeks. The methods for obtaining tumor tissues and paired white blood cells are conventional techniques and are not limited in this example.

[0050] Secondly, formalin-fixed paraffin-embedded (FFPE) tumor tissue samples were used to detect somatic single nucleotide variants (SNVs) and insertion or deletion mutations. Library sequencing data of the patient's tumor tissue and paired white blood cell controls were obtained, and using this library data, a personalized tumor mutation map of the patient was constructed. When detecting somatic single nucleotide variants (SNVs) and insertion / deletion (indel) mutations through FFPE tumor samples, the implementation of the present invention does not limit the specific method, and those skilled in the art can directly use existing methods to complete. For example, in this example, a black-PREP FFPE DNA kit (Analytik Jena, Germany) was used to isolate DNA from FFPE fragments. The control group used a Tiangen whole blood DNA kit (Tiangen) to extract DNA from peripheral blood lymphocytes. Whole blood (1600 g) was centrifuged at room temperature for 10 min to obtain lymphocytes. A Covaris M220 focused ultrasound instrument (Covaris) was used to fragment genomic DNA (150 - 200 bp fragments), and an Illumina platform KAPA HTP library preparation kit (KAPA Biosystems) was used to construct a DNA library. Subsequently, the DNA library (NimbleGen SeqCap EZ library; Roche) was used to capture the sequences of major tumor-related genes with a 769-gene panel and sequenced using an Illumina HiSeq X-Ten sequencer. The above were all carried out according to the manufacturer's instructions. Somatic SNVs were filtered and analyzed by VarScan2 (v2.4.2), and eligible variants needed to meet the following filtering criteria: (i) sequencing coverage: control > 50x, tumor > 100x; (ii) mutant allele frequency > 2%; (iii) mutant allele read count > 2%; (iv) SNVs and Indels were located in the exon region; (v) allele frequency in the ExAC database or gnomAD database < 0.5%. The final mutation list was used to calculate the PMPscore through the R package "PMAPscore".

[0051] First, the PMAPscore package was used to calculate the signal pathway perturbation score from the DNA mutation data of the baseline biopsy tissues of the patients in the retrospective cohort (who had not received triple therapy). Subsequently, the survival analysis R package was used to perform univariate Cox regression analysis on a total of 123 signal pathways ( Figure 2 in A), and 5 pathways with a p-value of less than 0.1 for the correlation with PFS were selected ( Figure 2 in B), which was statistically significant. Further, multivariate Cox stepwise regression analysis was performed on these 5 pathways, and finally 3 pathways were determined ( Figure 2 in C), namely the Hedgehog signaling pathway, the ErbB signaling pathway, and the Focal adhesion signaling pathway, which together contained 31 related genes and were incorporated into the final scoring system for predicting PFS ( Figure 2 in C), to construct a signal pathway-based biomarker model, Genescore. The personalized mutation list of the patients was imported into the Genescore calculation model to calculate the gene risk score.

[0052] The calculation formula for the gene risk score Genescore is: Genescore = ∑(Coefi × Expi), where Coefi and Expi represent the risk coefficient and the characteristic signal pathway perturbation value respectively, and the risk coefficient is obtained by fitting the retrospective cohort data.

[0053] In this embodiment, the calculation formula is as follows:

[0054]

[0055] In the formula, Hedgehog, ErbB, and Focal adhesion are the perturbation values of the Hedgehog signaling pathway, the ErbB signaling pathway, and the Focal adhesion signaling pathway respectively.

[0056] According to the Genescore value grouping, Genescore ≤ 0: low risk; Genescore > 0: high risk.

[0057] According to the above calculation formula, each patient obtains their unique gene risk score Genescore, and can be classified into high-risk patients and low-risk patients based on the cut-off value = 0 ( Figure 2 in D). In the high-risk group, the perturbation scores of the Hedgehog signaling pathway and the Focal adhesion signaling pathway are higher. Conversely, in the low-risk group, the perturbation score of the ErbB signaling pathway is higher ( Figure 2 in E). Among the patients in the training set, the PFS of 30 high-risk patients (36.6%) was shorter than that of 52 low-risk patients (63.4%) at this cut-off value.Figure 4 ) The result of this Genescore calculation model indicates that the progression-free survival (PFS) of low-risk patients receiving triple therapy is prolonged compared to that of high-risk patients, suggesting a potentially better prognosis.

[0058] Furthermore, through univariate Cox regression analysis and multivariate Cox stepwise regression analysis, clinical risk factors significantly associated with PFS were screened out. Three clinical characteristics, namely AST, Child-Pugh, and metastasis, were selected. Combining with Genescore, a total of four risk factors were used to construct a nomogram calculation model for calculating the overall risk score Overallscore ( Figure 3 ) The calculation formula is as follows:

[0059]

[0060] In the formula, AST is the level of aspartate aminotransferase; Child-Pugh takes the value of A grade = 0, B grade = 1, C grade = 1 according to the classification; Metastasis is distant metastasis, and the value is 1 for metastasis and 0 for non-metastasis.

[0061] Taking the median value of Overallscore, 0.8, as the cut-off value, Overallscore ≤ 0.8: low risk; Overallscore > 0.8: high risk.

[0062] To enhance the convenience of clinical application, a calculation tool for predicting PFS of HCC triple immunotherapy that is convenient for clinicians to use can be developed. In this tool, by inputting the patient's AST, Child-Pugh, Metastasis, and mutation gene list, Genescore, Overallscore can be automatically calculated and the PFS prediction result can be generated.

[0063] Example 2

[0064] The overallscore model was validated based on a prospective internal validation cohort.

[0065] According to the OverallScore cut-off value (=0.8), HCC patients were divided into high / low OverallScore groups. Survival analysis confirmed that the PFS of patients in the low OverallScore group was significantly better than that in the high-score group (hazard ratio, 5; 95% CI, 2.66 - 9.38; p < 0.001; Figure 5 as shown in A). The objective response rate (ORR) of patients with a low overall score was 68.3%, higher than that of high-risk patients in the HCC triple therapy cohort (54%) ( Figure 5 as shown in B).

[0066] Furthermore, 26 advanced HCC patients who received triple therapy recruited prospectively were used as a validation cohort to verify the reliability of this model. In the validation cohort, after triple therapy, the PFS of HCC patients in the low OverallScore group was more excellent than that in the high OverallScore group (HR, 2.8; 95% CI, 0.84 - 9.3; P = 0.046; Figure 5 C). The results showed that OverallScore combining signaling pathway perturbation and clinical risk factors could effectively predict the PFS of advanced HCC patients after HAIC combined with lenvatinib and tislelizumab treatment.

[0067] Example 3

[0068] The prediction accuracy of the hybrid model was verified by calculating the AUC and performing calibration.

[0069] The ROC results showed that the AUCs of the hybrid model for predicting PFS at 9 months, 18 months, and 24 months were 0.863, 0.876, and 0.897 respectively ( Figure 6 A). The AUC for predicting PFS within two years was 0.846 ( Figure 6 B), thus verifying that the hybrid model had excellent predictive ability. The calibration plots for predicting PFS at 6, 9, and 12 months highly overlapped with the ideal model and showed good performance ( Figure 6 C). In addition, the DCA plot showed that the hybrid model had clinical practicability and high benefits in predicting PFS at 6, 9, and 12 months, demonstrating its superior clinical application predictive ability. Based on the hybrid model, HCC patients were divided into high-risk and low-risk groups, and survival analysis confirmed that the PFS of patients in the low-risk group was significantly longer than that in the high-risk group.

[0070] Example 4

[0071] Explore Genescore-related biological processes and gene signaling pathways

[0072] The present invention explored the biological processes and gene signaling pathways related to the gene risk score Genescore from multiple levels. First, at the DNA mutation level, the gene mutation map ( Figure 7 A) showed that the TP53 mutation frequencies in the low GeneScore group and the high GeneScore group were 60.4% (32 / 53 cases) and 40% (12 / 30 cases) respectively. However, the mutation frequency of CTNNB1 in the low-score group was 3.8% (2 / 53 cases), much lower than 53.3% (17 / 30 cases) in the high-score group; Figure 7 B and C).

[0073] Subsequently, gene set enrichment analysis (GSEA) showed that hallmark angiogenesis and hallmark hypoxia were significantly enriched in the low GeneScore group ( Figure 8 in A). As a first-line treatment for unresectable HCC, lenvatinib targets multiple tyrosine kinases and inhibits angiogenesis. This may be one of the important reasons for the good prognosis of triple therapy for low-risk advanced HCC. First, at the DNA mutation level, in the volcano plot showing the odds ratio of all genes, the mutation frequency of CNNTB1 in the triple therapy response group was significantly lower than that in the non-response group, while the mutation frequencies of other genes were basically the same. Subsequently, gene set enrichment analysis (GSEA) found that hallmark features related to angiogenesis and hypoxia were significantly enriched in the response group. Analysis of the tumor immune environment suggested that the tumor immunogenicity of the response group was stronger.

[0074] Furthermore, analysis of the tumor immune environment suggested that the tumor immunogenicity of the low-risk group was stronger ( Figure 8 in B).

[0075] Further GESA analysis showed that antigen processing and presentation, BCR signaling pathway, and chemokine signaling pathway were significantly enriched in the response group, indicating that the immune environment of the response group was more active ( Figure 9 in A). Furthermore, immunofluorescence analysis confirmed that the cell contents of CD3+ and CD4+ were both higher in the response group ( Figure 9 in B). In addition, the results of gene set variation analysis (GSVA) of 28 immune subtypes suggested that the contents of memory effector CD4+, activated CD4+, and Th2 cells in the response group were significantly higher than those in the non-response group. This multi-level exploration revealed the biological processes and gene signaling pathways related to the risk score.

Claims

1. A method for predicting progression-free survival of patients with advanced HCC after triple immunotherapy, characterized in that: include: Construct a personalized tumor mutation map based on the sequencing data of tumor tissues and paired leukocytes from patients with advanced HCC; Based on the tumor mutation information in the personalized tumor mutation map, the first model is used to calculate the genetic risk score; Then, the total risk score was calculated using the second model with genetic risk score and clinical variables as independent variables; Risk stratification was performed based on the cutoff value of the total risk score to predict the probability of PFS of patients receiving triple immunotherapy at different times; Among them, the first model and the second model are constructed with a retrospective cohort of patients with advanced HCC as training data, including: constructing a personalized tumor variation map based on tumor tissues of patients in the retrospective cohort before triple immunotherapy, calculating the disturbance level of tumor mutation information in the signal pathway by combining the cumulative effect and position effect of mutant genes, screening gene mutation disturbance pathways significantly associated with PFS, and using the gene mutation disturbance pathways significantly associated with PFS as the Hedgehog signal path, ErbB Signaling pathways and Focal adhesion The signaling pathway gene mutation perturbation pathway is a biomarker, and the first model is constructed based on regression analysis as follows: ； In the formula, Hedgehog , ErbB , Focal adhesion They are Hedgehog , ErbB , Focal adhesion Signaling pathway perturbation value; The calculation results of the first model and the clinical variables of the retrospective cohort patients were used as independent variables, and the second model was established based on regression analysis as follows: ； In the formula, AST is the aspartate aminotransferase level; Child-Pugh According to the classification, the values ​​are A=0, B=1, and C=1; Metastasis For remote transfer, the value is transferred = 1, not transferred = 0.

2. The method according to claim 1, characterized in that The gene mutation perturbation pathway significantly associated with PFS was screened through univariate Cox regression analysis and multivariate Cox stepwise regression analysis, and the first model was established.

3. The method according to claim 1, characterized in that The calculation results of the first model and the clinical variables of the retrospective cohort patients were used as independent variables, and the second model was established based on the nomogram.

4. The method according to claim 1, characterized in that: Univariate Cox regression analysis and multivariate Cox stepwise regression analysis were used to screen out clinical risk factors that were significantly associated with PFS as clinical variables.

5. The method according to claim 1, characterized in that Risk stratification is performed based on the median of the total risk score in the training data.

6. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method described in any one of claims 1 to 5 are implemented.

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