dbCPM: a manually curated database for exploring the cancer passenger mutations

Area of Science:

• Genomics
• Bioinformatics
• Cancer Research

Background:

• Computational methods for predicting cancer mutation effects often lack comprehensive passenger mutation data.
• Existing datasets primarily focus on driver mutations, leaving a gap in understanding passenger mutations.

Purpose of the Study:

• To develop a literature-based database of Cancer Passenger Mutations (dbCPM) to serve as a benchmark dataset.
• To analyze patterns of missense passenger mutations and compare them with driver mutations.
• To evaluate the performance of existing cancer mutation effect prediction tools.

Main Methods:

• Manual curation of scientific literature to identify and compile passenger mutations.
• Development of the Cancer Passenger Mutations (dbCPM) database, including experimentally supported and putative mutations.
• Comparative analysis of missense passenger and driver mutations.
• Assessment of four cancer-focused mutation effect prediction algorithms.

Main Results:

• The dbCPM database contains 941 experimentally supported and 978 putative passenger mutations.
• Missense passenger mutations exhibit significant differences from driver mutations, with some mutations showing pleiotropic functions.
• Mutation effect predictors showed high true positive rates but low true negative rates, indicating a need for negative training data.

Conclusions:

• The dbCPM database is a valuable resource for improving and evaluating cancer mutation prediction algorithms.
• The findings highlight the distinct characteristics of passenger mutations and the need for better negative datasets in predictive modeling.
• dbCPM will advance cancer research by providing a benchmark for computational tools.