Abstract 4126: Quantification of step-specific selection reveals ordered acquisition of driver mutations in colon cancer

Abstract Colorectal cancer is a leading cause of cancer mortality worldwide. Colon adenocarcinoma develops through sequential evolutionary steps originating in adenomatous polyps. Many driver mutations of colon cancer and their relations have been hypothesized. However, quantitative timing and strength of selection across specific stages of progression remains unknown. Somatic single nucleotide variants from colon adenomas and adenocarcinomas were analyzed using a step-specific likelihood model that treats adenoma formation and malignant progression as consecutive steps within a shared evolutionary trajectory. Analyses were stratified by POLE/POLD1 mutation and DNA mismatch repair (MMR) status to evaluate background-specific effects. We identified mutations under positive selection during initial formation of adenomas and later in malignant progression to adenocarcinomas, and also quantified how each successive driver changes selection on the next. Many trends of step-specific selection emerged that were consistent across POLE/POLD1-mutant and MMR-deficient tumors, such as high early selection on APC and strong late selection on CTNNB1 and TP53. However, step-specific selection varied across the steps of progression in mutants of other genes, including KRAS, BRAF, and FBXW7. Inferred evolutionary trajectories point to early disruption of WNT and TGF-β signaling, followed by later acquisition of TP53, MAPK, and PI3K pathway mutations. These findings provide a data-driven, high-resolution temporal sequence of the selective pressures shaping tumorigenesis. They identify potential biomarkers for early detection, and reveal how polymerase and mismatch-repair backgrounds alter evolutionary paths by purely altering gene- and site-specific mutation rates. Consistency with experimentally identified early drivers (APC, KRAS) and late drivers (TP53) validates the approach and demonstrates its suitability for extending step-specific analyses to many additional cancer types. Ultimately, such high-resolution evolutionary trajectories enable precision-medicine targeted drug treatments to be exquisitely tailored to individual patients and facilitate treatments that not only target extant variants but also anticipate additional therapies that could be useful to forestall or prevent the next driver mutation and consequent burgeoning tumor burden. Citation Format: Kira A. Glasmacher, Jeffrey Peter Townsend, . Quantification of step-specific selection reveals ordered acquisition of driver mutations in colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4126.

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Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-4126
Primary Topic
Cancer Genomics and Diagnostics
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article
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article

Abstract 4126: Quantification of step-specific selection reveals ordered acquisition of driver mutations in colon cancer

Kira A. Glasmacher, Jeffrey Peter Townsend
Cancer Research
Cancer Genomics and Diagnostics
article

Abstract 4126: Quantification of step-specific selection reveals ordered acquisition of driver mutations in colon cancer

Kira A. Glasmacher, Jeffrey Peter Townsend
article en

Abstract

Abstract Colorectal cancer is a leading cause of cancer mortality worldwide. Colon adenocarcinoma develops through sequential evolutionary steps originating in adenomatous polyps. Many driver mutations of colon cancer and their relations have been hypothesized. However, quantitative timing and strength of selection across specific stages of progression remains unknown. Somatic single nucleotide variants from colon adenomas and adenocarcinomas were analyzed using a step-specific likelihood model that treats adenoma formation and malignant progression as consecutive steps within a shared evolutionary trajectory. Analyses were stratified by POLE/POLD1 mutation and DNA mismatch repair (MMR) status to evaluate background-specific effects. We identified mutations under positive selection during initial formation of adenomas and later in malignant progression to adenocarcinomas, and also quantified how each successive driver changes selection on the next. Many trends of step-specific selection emerged that were consistent across POLE/POLD1-mutant and MMR-deficient tumors, such as high early selection on APC and strong late selection on CTNNB1 and TP53. However, step-specific selection varied across the steps of progression in mutants of other genes, including KRAS, BRAF, and FBXW7. Inferred evolutionary trajectories point to early disruption of WNT and TGF-β signaling, followed by later acquisition of TP53, MAPK, and PI3K pathway mutations. These findings provide a data-driven, high-resolution temporal sequence of the selective pressures shaping tumorigenesis. They identify potential biomarkers for early detection, and reveal how polymerase and mismatch-repair backgrounds alter evolutionary paths by purely altering gene- and site-specific mutation rates. Consistency with experimentally identified early drivers (APC, KRAS) and late drivers (TP53) validates the approach and demonstrates its suitability for extending step-specific analyses to many additional cancer types. Ultimately, such high-resolution evolutionary trajectories enable precision-medicine targeted drug treatments to be exquisitely tailored to individual patients and facilitate treatments that not only target extant variants but also anticipate additional therapies that could be useful to forestall or prevent the next driver mutation and consequent burgeoning tumor burden. Citation Format: Kira A. Glasmacher, Jeffrey Peter Townsend, . Quantification of step-specific selection reveals ordered acquisition of driver mutations in colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4126.

Cancer ResearchVol. 86(7_Supplement)
Yale University (US)
Good health and well-being
Openalex Percentile: Top 10%
Cancer Genomics and Diagnostics
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