A Probabilistic Approach to Estimate the Temporal Order of Pathway Mutations Accounting for Intra-Tumor Heterogeneity

Area of Science:

• Oncology
• Computational Biology
• Genomics

Background:

• Cancer develops through accumulating somatic mutations in key biological pathways.
• Understanding the sequence of these mutations is vital for cancer research and therapy development.
• Existing computational methods often overlook intra-tumor heterogeneity (ITH), limiting their accuracy in determining mutation order.

Purpose of the Study:

• To develop a novel computational approach for accurately estimating the temporal order of pathway mutations during tumorigenesis.
• To address the limitations of current methods by incorporating intra-tumor heterogeneity (ITH).
• To leverage pathway and functional annotation information for improved mutation ordering.

Main Methods:

• Proposed PATOPAI, a probabilistic method for estimating pathway mutation order.
• Incorporated ITH, pathway, and functional annotation data into the model.
• Utilized a maximum likelihood approach to determine probable mutation sequences consistent with tumor phylogeny.

Main Results:

• PATOPAI successfully estimates the temporal order of pathway mutations.
• The method effectively incorporates ITH information for more accurate sequencing.
• Demonstrated utility on whole exome sequencing data from The Cancer Genome Atlas (TCGA) across multiple cancer types.

Conclusions:

• PATOPAI offers an improved approach to understanding cancer development by accurately ordering pathway mutations.
• Accounting for ITH is crucial for precise reconstruction of mutational timelines in cancer.
• The method has broad applicability for analyzing cancer genomics data and identifying therapeutic targets.