PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy

Abstract

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

Keywords: MT: Bioinformatics; biomarkers for immunotherapy; immune-related prognostic signature; pathway analysis; somatic mutation; tumor mutational burden.

Graphical abstract

Figure 1

Flowchart of the PathwayTMB method.

Figure 2

Construction of the immune-related prognostic signature based on PTMB (A) Dot plot of differential PTMB pathways between deceased and alive samples (p < 0.01 and |log₂(FC)| > 1). (B) Plot of 10-fold cross-validation via minimum criteria for selection of the optimal value of tuning parameter. The two dotted vertical lines are drawn at the optimal values by minimum criteria (right) and 1-SE criteria (left). (C) Kaplan-Meier survival curves of OS comparing the three subgroups with different pathway mutation signature components from the melanoma training cohort. (D) Kaplan-Meier survival curves of OS comparing the IPSP-high and IPSP-low groups from the melanoma training cohort.

Figure 3

Prediction of clinical benefit with IPSP in the melanoma training cohort (A) Comparison of TMB among the three subgroups with different pathway mutation signature components. (B) Comparison of TMB between IPSP-high and IPSP-low groups. Statistical significance was tested by rank-sum Wilcoxon test. (C) Kaplan-Meier survival analysis of OS among patients within each of the four indicated subgroups (IPSP-high and TMB-low, IPSP-high and TMB-high, IPSP-low and TMB-low, IPSP-low and TMB-high) from the melanoma training cohort. (D) Comparison of the objective response rate between the IPSP-high and IPSP-low groups from the melanoma training cohort. (E) Comparison of the objective response rate between the TMB-high and TMB-low groups from the melanoma training cohort. Statistical significance was tested by chi-squared test. (F) ROC curves of the IPSP and TMB to predict immunotherapy response from the melanoma training cohort.

Figure 4

Validation of the predictive value of IPSP in the independent cohorts (A) Kaplan-Meier survival analysis of OS comparing the IPSP-high and IPSP-low groups from the Miao cohort. (B) Kaplan-Meier survival analysis of OS comparing the IPSP-high and IPSP-low groups from the Hugo cohort. (C) Kaplan-Meier survival analysis of OS comparing the IPSP-high and IPSP-low groups from TCGA-SKCM cohort. (D) Comparison of the objective response rate between the IPSP-high and IPSP-low groups from the Miao cohort. (E) Comparison of the objective response rate between the IPSP-high and IPSP-low groups from the Hugo cohort.

Figure 5

Correlation between IPSP score and immune-related features (A) GSEA plot of important pathways in comparison between the IPSP-high and IPSP-low groups. (B) Comparison of TIICs relative infiltrated abundance between the IPSP-high and IPSP-low groups. Statistical significance was tested by rank-sum Wilcoxon test.

Figure 6

Analysis of the pathway signatures in IPSP (A) Co-occurrence and mutual exclusivity plots among the three candidate mutation pathways in the IPSP signature. Statistical significance was tested by Fisher's exact test, '.' p < 0.05, '∗' p < 0.01. (B) Waterfall plot of the top 10 genes in terms of mutation rate were involved in each candidate mutation pathway. (C) Altered genes and their functional relationship in the JAK-STAT pathway. Shades of red indicate gene mutation frequency.

Figure 7

Application of the PathwayTMB method to the multi-cancers cohort (A) Kaplan-Meier survival analysis of OS comparing the IPSP-high and IPSP-low groups from the multi-cancers cohort. (B) Kaplan-Meier survival analysis of OS among patients within each of the four indicated subgroups (IPSP-high and TMB-low, IPSP-high and TMB-high, IPSP-low and TMB-low, IPSP-low and TMB-high) from the multi-cancers cohort. (C) Comparison of the objective response rate between the IPSP-high and IPSP-low groups from the multi-cancers cohort. (D) Comparison of the objective response rate between the TMB-high and TMB-low groups from the multi-cancers cohort. (E) ROC curves of the IPSP and TMB to predict immunotherapy response from the multi-cancers cohort.