Abstract 3881: Comprehensive analysis of somatic mutagenesis in normal tissues across 19,643 samples reveals insights into clonal evolution and tumorigenesis

The detection of somatic mutagenesis in normal tissues has transformed our understanding of clonal evolution, revealing a mosaic of somatic mutations, including those in cancer driver genes, that influence cellular behavior and tissue homeostasis. However, key questions remain about how these mutational patterns contribute to tumorigenesis and the role of cancer driver mutations in the transition from normal to malignant states. To address this, we performed a combined computational analysis of the largest normal-tissue DNA-sequencing and clinical dataset to date, spanning 21 published datasets, 37 tissue types, 521 patients, and 19,643 samples, and integrated these with precancer and cancer tissue data to identify tissue-specific mutational changes potentially critical for tumorigenesis. We identified tissue-specific differences in mutation burden, with epithelial tissues exhibiting significantly higher mutation rates than other tissue categories (p<2x10-16). These differences were further reflected in the mutational signatures: UV-light signatures dominated sun-exposed epithelial tissues, while gastrointestinal epithelial tissues showed mutation burden driven by intrinsic factors. Regression analysis revealed that, alongside aging, mutations in specific driver genes (e.g., ARID2, ATM, TP53), but not others, were selectively associated with higher mutation rates, highlighting nuanced contributions to tissue mutagenesis. Further MutSig2CV and dN/dS analyses identified positively selected driver genes with distinct patterns based on tumorigenic pathways: tumors that develop through normal-to-dysplastic sequences (e.g., colon cancer) shared driver genes across all progression stages, reflecting clonal evolution within the same tissue lineage. In contrast, tumors arising from metaplastic processes (e.g., esophageal adenocarcinoma) shared mutated driver genes between cancer and precancer metaplastic tissues, but not normal tissues, suggesting selective pressures emerge during metaplasia. Across the cohort, most driver genes were highly mutated in cancer and present at lower mutation frequency in normal tissues. In contrast, similar to NOTCH1 mutations, CHEK2 mutations were significantly enriched in the normal esophagus but absent in cancer, hinting at potential tumor-suppressive effects. Taken together, our large-scale analysis has allowed us to detect previously unrecognized phenomena through increased statistical power. The analysis of normal tissue mutagenesis underscores its potential for predicting tissue-specific cancer risks. In addition, our integration of normal tissue data into the cBioPortal platform provides an important resource to support and advance future cancer research. Citation Format: Leonie Stockschläder, Eldar Abdullaev, Siddharth Annaldasula, Ino de Bruijn, Baby Anusha Satravada, Ritika Kundra, Rima Madupuri, Manuela Benary, Dieter Beule, Tim H. Coorens, Regine Armann, Nikolaus Schultz, Chris Sander, Kirsten Kubler. Comprehensive analysis of somatic mutagenesis in normal tissues across 19,643 samples reveals insights into clonal evolution and tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3881.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-3881
Primary Topic
Science, Research, and Medicine
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 3881: Comprehensive analysis of somatic mutagenesis in normal tissues across 19,643 samples reveals insights into clonal evolution and tumorigenesis

Siddharth Annaldasula, Ritika Kundra, Chris Sander, Kirsten Kübler et al.
Cancer Research
Science, Research, and Medicine
article

Abstract 3881: Comprehensive analysis of somatic mutagenesis in normal tissues across 19,643 samples reveals insights into clonal evolution and tumorigenesis

Siddharth Annaldasula, Ritika Kundra, Chris Sander, Kirsten Kübler, Nikolaus Schultz, Baby A. Satravada, Tim Coorens, Dieter Beule, Ino de Bruijn, Manuela Benary, Eldar T. Abdullaev, Leonie Stockschläder, Regine Armann, Rima Madupuri
article en

Abstract

The detection of somatic mutagenesis in normal tissues has transformed our understanding of clonal evolution, revealing a mosaic of somatic mutations, including those in cancer driver genes, that influence cellular behavior and tissue homeostasis. However, key questions remain about how these mutational patterns contribute to tumorigenesis and the role of cancer driver mutations in the transition from normal to malignant states. To address this, we performed a combined computational analysis of the largest normal-tissue DNA-sequencing and clinical dataset to date, spanning 21 published datasets, 37 tissue types, 521 patients, and 19,643 samples, and integrated these with precancer and cancer tissue data to identify tissue-specific mutational changes potentially critical for tumorigenesis. We identified tissue-specific differences in mutation burden, with epithelial tissues exhibiting significantly higher mutation rates than other tissue categories (p<2x10-16). These differences were further reflected in the mutational signatures: UV-light signatures dominated sun-exposed epithelial tissues, while gastrointestinal epithelial tissues showed mutation burden driven by intrinsic factors. Regression analysis revealed that, alongside aging, mutations in specific driver genes (e.g., ARID2, ATM, TP53), but not others, were selectively associated with higher mutation rates, highlighting nuanced contributions to tissue mutagenesis. Further MutSig2CV and dN/dS analyses identified positively selected driver genes with distinct patterns based on tumorigenic pathways: tumors that develop through normal-to-dysplastic sequences (e.g., colon cancer) shared driver genes across all progression stages, reflecting clonal evolution within the same tissue lineage. In contrast, tumors arising from metaplastic processes (e.g., esophageal adenocarcinoma) shared mutated driver genes between cancer and precancer metaplastic tissues, but not normal tissues, suggesting selective pressures emerge during metaplasia. Across the cohort, most driver genes were highly mutated in cancer and present at lower mutation frequency in normal tissues. In contrast, similar to NOTCH1 mutations, CHEK2 mutations were significantly enriched in the normal esophagus but absent in cancer, hinting at potential tumor-suppressive effects. Taken together, our large-scale analysis has allowed us to detect previously unrecognized phenomena through increased statistical power. The analysis of normal tissue mutagenesis underscores its potential for predicting tissue-specific cancer risks. In addition, our integration of normal tissue data into the cBioPortal platform provides an important resource to support and advance future cancer research. Citation Format: Leonie Stockschläder, Eldar Abdullaev, Siddharth Annaldasula, Ino de Bruijn, Baby Anusha Satravada, Ritika Kundra, Rima Madupuri, Manuela Benary, Dieter Beule, Tim H. Coorens, Regine Armann, Nikolaus Schultz, Chris Sander, Kirsten Kubler. Comprehensive analysis of somatic mutagenesis in normal tissues across 19,643 samples reveals insights into clonal evolution and tumorigenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3881.

Cancer ResearchVol. 85(8_Supplement_1)
Broad Institute (US), Memorial Sloan Kettering Cancer Center (US), Harvard University (US), Kettering University (US), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE)
Openalex Percentile: Top 88%
Science, Research, and Medicine
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.