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    Individual factors significantly alter cancer driver gene selection. Our new method, DiffDriver, identifies these context-dependent selection forces, improving accuracy and reducing false positives in cancer research.

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    Area of Science:

    • Genomics
    • Cancer Biology
    • Computational Biology

    Background:

    • Somatic mutations drive cancer progression, influenced by individual factors like germline genetics and environment.
    • These individual factors, termed 'contexts,' can alter the selective advantage of driver mutations.
    • Current methods struggle to identify differential selection due to sparse mutations and complex mutational processes.

    Purpose of the Study:

    • To develop a statistical method, DiffDriver, for identifying associations between tumor contexts and selection strength on driver genes.
    • To improve the power and reduce false positives in detecting differential selection of cancer driver genes.

    Main Methods:

    • Developed DiffDriver, a statistical method accounting for mutation rate variations and leveraging sequence functional information.
    • Evaluated DiffDriver's performance through simulations against existing methods.
    • Applied DiffDriver to identify differential selection across various tumor contexts.

    Main Results:

    • DiffDriver demonstrated reduced false positives and increased power in simulations.
    • 33% of driver genes exhibited differential selection in at least one studied context.
    • Identified significant heterogeneity in selection strength driven by individual-level factors.

    Conclusions:

    • Individual-level factors create substantial heterogeneity in selection pressures on cancer driver genes.
    • DiffDriver provides new insights into context-dependent forces driving tumorigenesis.
    • Findings highlight the importance of considering tumor context in understanding cancer evolution.