Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Cancer genomes accumulate passenger mutations from various endogenous and exogenous sources.
• Mutational signatures characterize distinct mutagenic processes, crucial for understanding tumorigenesis.

Purpose of the Study:

• To investigate network-level associations between mutational signatures and cancer pathways.
• To identify functional pathways correlated with mutational signature strengths and those potentially causing specific signatures.

Main Methods:

• A network-based approach was employed, treating mutational signatures as cancer phenotypes.
• Integer linear programming was used to identify mutated pathways.
• Analysis was performed on a breast cancer dataset.

Main Results:

• Pathways associated with mutational signatures were identified at both expression and mutation levels.
• Distinct etiologies were found for APOBEC-related and clock-like mutational signatures.
• Clustered versus dispersed APOBEC mutations and two age-related signatures showed differential pathway correlations.

Conclusions:

• This study establishes a novel network-level association between mutational signatures and dysregulated pathways.
• Findings offer new insights into cancer mutagenic processes.
• Identified pathways and subnetworks can inform personalized drug therapy development.