Abstract 1508: Chromatin accessibility of primary human cancers ties regional mutational processes with tissues of origin
Bibliographic record
Abstract
Abstract Cancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. However, the underlying mechanisms of this mutagenesis and its functional and genetic determinants remain poorly understood. Somatic regional mutagenesis is known to correlate with DNA replication timing and chromatin accessibility; however, these studies have used epigenetic information from common cell lines while the epigenomes of primary human cancers remain uncharacterized in this context. Here we model megabase-scale mutation frequencies of single nucleotide variants (SNVs) in thousands of whole cancer genomes using hundreds of genome-wide profiles of chromatin accessibility and replication timing spanning primary cancer samples, normal tissues, and cell lines. Using a machine learning framework, we show that CA profiles of primary cancers, rather than those of normal cells, predict regional mutagenesis and single base substitution (SBS) signatures in most cancer types. Thus the majority of passenger mutations follow the epigenetic landscapes of transformed cells of matching cancer sites and tissues. Mutational signatures associated with carcinogen exposure, as well as signatures of unknown origin, show the strongest associations with epigenomes while associations with endogenous signatures are weaker. In most cases, overall mutation burden and SBS signatures inversely correlate with chromatin accessibility, however certain signatures are instead enriched in chromatin-accessible elements. Lastly, the genomic regions with excess mutations unexplained by the epigenetic profiles in our models likely represent a combination of localized mutagenesis and positive selection of functional oncogenic mutations. Our computational models highlight an enrichment of known driver mutations, point out novel intergenic regions with putative non-coding drivers, and indicate an over-representation of developmental and lineage-specific genes in these frequently mutated genomic regions. These results characterize the complex interplay of mutational processes, genome function, and somatic evolution in cancer and its tissues of origin, and also outline strategies for distinguishing driver and passenger mutations. Citation Format: Jüri Reimand, Oliver Ocsenas. Chromatin accessibility of primary human cancers ties regional mutational processes with tissues of origin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1508.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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 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".