ProgModule: A novel computational framework to identify mutation driver modules for predicting cancer prognosis and immunotherapy response

Abstract

Background: Cancer originates from dysregulated cell proliferation driven by driver gene mutations. Despite numerous algorithms developed to identify genomic mutational signatures, they often suffer from high computational complexity and limited clinical applicability.

Methods: Here, we presented ProgModule, an advanced computational framework designed to identify mutation driver modules for cancer prognosis and immunotherapy response prediction. In ProgModule, we introduced the Prognosis-Related Mutually Exclusive Mutation (PRMEM) score, which optimizes the balance between exclusive mutation coverage and the incorporation of mutation combination mechanisms critical for cancer prognosis.

Results: Applying to BLCA and HNSC cohorts, ProgModule successfully identified driver modules that stratify patients into distinct prognostic subgroups, and the combination of these modules could serve as an effective prognostic biomarker. Extending our method to diverse cancers, ProgModule presented robust prognostic performance and stability across model parameters, including stopping criteria and network topology. Moreover, our analysis suggested that driver modules can predict immunotherapeutic benefit more effectively than existing signatures. Further analyses based on published CRISPR data indicated that genes within these modules may serve as potential therapeutic targets.

Conclusions: Altogether, ProgModule emerges as a powerful tool for identifying mutation driver modules as prognostic and immunotherapy response biomarkers, and genes within these modules may be used as potential therapeutic targets for cancer, offering new insights into precision oncology.

Keywords: Driver module; Immune checkpoint inhibitors; Mutually exclusive mutation; Prognostic biomarkers; Therapeutic targets.

Fig. 1

The flowchart of the ProgModule method. (A) Elucidation of the ProgModule for identification of prognosis-related mutually exclusive modules; (B) Application of ProgModule, encompassing the prediction of cancer prognosis and immunotherapy response

Fig. 2

Applying ProgModule to the BLCA cohort. (A) The landscape of candidate modules for BLCA (simplified schematic to highlight the internal PPI structure of each module). (B) The detailed information of module M5; (C) The enriched KEGG pathways of genes involved in module M5; (D) The waterfall plot of the module M6; (E) The heatmap for the Cox regression P values of candidate modules

Fig. 3

Comparing the prognostic ability of modules versus individual genes. (A) Comparing the prognostic ability of candidate modules versus individual genes in BLCA; (B) Kaplan-Meier survival analysis of OS comparing the M15-Mutant and WT groups; (C) Kaplan-Meier survival analysis of OS comparing the TFE3-Mutant and WT groups; (D) Kaplan-Meier survival analysis of OS comparing the M16-Mutant and WT groups; (E) Kaplan-Meier survival analysis of OS comparing the BCAR1-Mutant and WT groups; (F) Kaplan-Meier survival curve of risk model in TCGA-BLCA; (G) The ROC curve of risk model to predict patient OS in TCGA-BLCA; (H) The Kaplan-Meier survival curve of risk model in ICGC-BLCA; (I) The ROC curve of risk model to predict patient OS in ICGC-BLCA

Fig. 4

Applying ProgModule to the HNSC cohort. (A) The landscape of candidate modules for HNSC. (B) The detailed information of the module M10; (C) The enriched KEGG pathways of genes involved in the module M10; (D) The waterfall plot of genes within the module M10; (E) The heatmap for the Cox regression P values of candidate modules; (F) Kaplan-Meier survival analysis of OS comparing the M10-Mutant and WT groups; (G) The Kaplan-Meier survival curve of risk model in TCGA-HNSC; (H) The Kaplan-Meier survival curve of risk model in ICGC-HNSC

Fig. 5

The modules identified by ProgModule were cancer-specific. (A) Applying the cancer-specific model to other cancer type datasets, each pixel represents the number of modules identified in one cancer type that can predict patient prognosis in another cancer type. (B) The number of conserved modules across all cancer types. (C) Heatmap showing the number of overlapping Gene Ontology (GO) terms between cancer types. The color scale represents the number of shared GO terms, with darker colors indicating a higher number of overlaps. (F) The number of overlapped KEGG pathways between two cancer types

Fig. 6

ProgModule can be used to predict the clinical outcomes of patients receiving immunotherapy. (A) The enriched KEGG pathways of genes involved in all candidate modules in the Van Allen cohort. (B) Circle plot depicting the impact on melanoma overall survival of five candidate module mutations. (C) Kaplan-Meier survival analysis of OS comparing the High-risk and Low-risk groups from the Van Allen cohort. (D) Comparison of ORR between the High-risk and Low-risk groups in the Van Allen cohort. (E) Kaplan-Meier survival analysis of OS comparing the High-risk and Low-risk groups from the Miao cohort. (F) Comparison of ORR between the High-risk and Low-risk groups in the Miao cohort. (G) Compared the performance of our method with other published immunotherapy biomarkers based on C-index and MCC. (H) Heatmap depicting the Z score of seven candidate genes in the top 10% of ranked genes across different CRISPR datasets