Chromatin accessibility of primary human cancers ties regional mutational processes and signatures with tissues of origin
Bibliographic record
Abstract
Somatic mutations in cancer genomes are associated with DNA replication timing (RT) and chromatin accessibility (CA), however these observations are based on normal tissues and cell lines while primary cancer epigenomes remain uncharacterised. Here we use machine learning to model megabase-scale mutation burden in 2,500 whole cancer genomes and 17 cancer types via a compendium of 900 CA and RT profiles covering primary cancers, normal tissues, and cell lines. CA profiles of primary cancers, rather than those of normal tissues, are most predictive of regional mutagenesis in most cancer types. Feature prioritisation shows that the epigenomes of matching cancer types and organ systems are often the strongest predictors of regional mutation burden, highlighting disease-specific associations of mutational processes. The genomic distributions of mutational signatures are also shaped by the epigenomes of matched cancer and tissue types, with SBS5/40, carcinogenic and unknown signatures most accurately predicted by our models. In contrast, fewer associations of RT and regional mutagenesis are found. Lastly, the models highlight genomic regions with overrepresented mutations that dramatically exceed epigenome-derived expectations and show a pan-cancer convergence to genes and pathways involved in development and oncogenesis, indicating the potential of this approach for coding and non-coding driver discovery. The association of regional mutational processes with the epigenomes of primary cancers suggests that the landscape of passenger mutations is predominantly shaped by the epigenomes of cancer cells after oncogenic transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".