PATOPA

PATOPA estimates the temporal sequence of pathway-level somatic mutations during carcinogenesis by integrating functional mutation annotations to distinguish driver from passenger events.

Key Features:

• Pathway-Level Analysis: Aggregates gene mutations into functional pathways to assess the order of mutational events at the pathway level.
• Functional Annotation Integration: Leverages functional annotations to weight mutations and prioritize driver versus passenger mutations based on impact on tumor progression.
• Probabilistic Framework: Uses a probabilistic model to compute probabilities that one pathway is mutated before, after, or simultaneously with another (A>B, A
• Maximum Likelihood Estimation: Estimates model parameters and infers temporal order using maximum likelihood methods.

Scientific Applications:

• Simulation validation: Validated through simulation studies to evaluate performance under controlled conditions.
• TCGA whole exome sequencing analysis: Applied to whole exome sequencing data from The Cancer Genome Atlas (TCGA) to infer pathway mutation order in tumor cohorts.
• Cancer-type studies: Used in analyses of colorectal and lung cancers to generate biological insights into carcinogenesis and potential therapeutic targets.

Methodology:

Computes pathway-level mutation order probabilities using a probabilistic model that integrates functional annotations and estimates parameters via maximum likelihood estimation to infer A>B, A