Abstract 6437: Mutational patterns in normal tissue hold the key to understanding the cellular origins of cancer
Bibliographic record
Abstract
The processes that lead to cancer transformation, in particular the clonal evolution that precedes malignancy, remain incompletely understood. Somatic mutations accumulate throughout the life of a cell and are shaped by its epigenetic landscape. We hypothesize that integrating somatic mutational profiles with the epigenetic landscapes of normal cells can explain the distribution of mutations in cancer, provide insights into the relationships between normal and cancer cells, and thus shed light on the cellular origins of cancer. Our study integrates mutation data from more than 25 normal tissue types with epigenetic profiles from over 300 normal cell types. Using random forest regression, we show that mutational distributions in normal cells closely match the epigenetic landscapes of their corresponding cell types. For example, the mutational profile of normal breast tissue maps to the epigenomes of luminal progenitor cells, indicating that these mutations are predominantly acquired during the progenitor cell state. In contrast, mutations observed in ductal carcinoma in situ (DCIS) closely aligns with the epigenomes of mature luminal cells, reflecting a distinct pattern of mutation accumulation in the precancerous state compared to normal tissue. Building on this, we extended our analysis to cancer by comparing normal mutational profiles with data from 2,550 whole genomes spanning 32 cancer types. This reveals both close relationships and distinct origins of different cancer types and subtypes. For example, the different molecular subtypes of breast cancer are associated with specific cell types of the luminal cell lineage, with basal-like subtypes originating from luminal progenitors and all other subtypes (LumA, LumB, Her2) from luminal mature cells. Together, these findings demonstrate that both normal and cancer tissues retain a mutational profile ‘scar’ that reflects the ancestral cell state in which these mutations originally arose. This genomic ‘historical record’ of somatic mutations is a powerful tool for tracing the cellular origins of cancer and provides a framework for understanding how somatic evolution in normal tissues sets the stage for tumor initiation and subtype differentiation. With this approach, we are advancing our understanding of tumorigenesis and subtype differentiation, paving the way for more precise cancer diagnosis and tailored therapeutic strategies. Citation Format: Kirsten Kübler, Rosa Karlić, Mendy Miller, Chip Stewart, William D. Foulkes, Paz Polak, Peter F. Arndt, Gad Getz. Mutational patterns in normal tissue hold the key to understanding the cellular origins of cancer [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 6437.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".