Identifying individualized risk subpathways reveals pan-cancer molecular classification based on multi-omics data.

Area of Science:

• Oncology
• Bioinformatics
• Systems Biology

Background:

• Cancer is a heterogeneous disease driven by dysregulated pathways.
• Identifying patient-specific risk pathways is crucial for understanding tumorigenesis and heterogeneity.
• Current methods lack approaches for mining individualized cancer risk subpathway regions.

Purpose of the Study:

• To develop and validate an individualized cancer risk subpathway identification method.
• To apply the method to a large cohort of cancer samples to identify key oncogenic regions.
• To establish a pan-cancer molecular classification based on risk subpathway dysregulation profiles.

Main Methods:

• Integration of multi-omics data to develop the InCRiS method.
• Application of InCRiS to nearly 3000 samples across 9 TCGA cancer types.
• Analysis of dysregulated subpathways to identify key oncogenic regions and nominate candidate genes.

Main Results:

• The InCRiS method demonstrated robustness and reliability in identifying individualized cancer risk subpathways.
• Several key dysregulated subpathway regions with potential oncogenic roles were identified.
• A pan-cancer molecular classification revealed 11 subtypes (InCRiS subtypes) with significantly different clinical outcomes.
• Specific subpathway regions and key genes (e.g., CTNNB1, EP300, PRKDC) were associated with different subtypes, offering insights into tumor pathogenesis.

Conclusions:

• The InCRiS method provides a valuable resource for studying cancer heterogeneity.
• The identified subpathway dysregulation profiles and subtypes offer insights into tumor mechanisms.
• The study nominates novel therapeutic targets for precise cancer treatment.