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<p>Reliable estimates for the number of cancer patients with a specific mutation can help quantify the size of the population that could potentially benefit from a targeted therapy. We adapt our previously developed approach for estimating gene-level mutation abundances to estimate mutation-specific (e.g., KRAS G12C) abundances by combining United States cancer epidemiology and genomic data. We demonstrate the approach by obtaining population-level estimates for all acquired somatic missense mutations that create a de novo cysteine residue. We find that approximately 14% of non-epidemiological informed estimates are more than twice the epidemiological informed estimate. Non-epidemiologically informed pan-cancer estimation of mutation rates may not be representative of the number of cancer patients with a specific mutation. Our study suggests that epidemiological and genomic information should be combined when estimating the population level abundance of specific pathogenic mutations.</p>

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Integrating epidemiology and genomics data to estimate the prevalence of acquired cysteine drug targets in the U.S. cancer patient population

  • 期刊论文

  • 发表日期：

    2025年3月5日

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  • Adith S. Arun,
  • David Liarakos,
  • Gaurav Mendiratta,
  • Jacob Kim,
  • George Goshua,
  • Peter Olson,
  • Edward C. Stites

摘要

Reliable estimates for the number of cancer patients with a specific mutation can help quantify the size of the population that could potentially benefit from a targeted therapy. We adapt our previously developed approach for estimating gene-level mutation abundances to estimate mutation-specific (e.g., KRAS G12C) abundances by combining United States cancer epidemiology and genomic data. We demonstrate the approach by obtaining population-level estimates for all acquired somatic missense mutations that create a de novo cysteine residue. We find that approximately 14% of non-epidemiological informed estimates are more than twice the epidemiological informed estimate. Non-epidemiologically informed pan-cancer estimation of mutation rates may not be representative of the number of cancer patients with a specific mutation. Our study suggests that epidemiological and genomic information should be combined when estimating the population level abundance of specific pathogenic mutations.