Cancer Histone Mutations Impact Binding and DNA Repair Processes, Leading to Increased Mutagenesis
Bibliographic record
Abstract
Abstract Histones are key epigenetic factors for regulating the accessibility and compaction of eukaryotic genomes, affecting the replication, repair, and expression of DNA. Recent studies have demonstrated that histone missense mutations can perturb normal histone function, promoting the development of phenotypically distinguishable cancers. However, most histone mutations observed in cancer patients remain enigmatic in their potential to promote cancer development. To assess the oncogenic potential of histone missense mutations, we have gathered whole-exome sequencing data for the tumors of over 12,000 patients. Overall, histone mutations occurred in about 16% of cancer patients, although specific cancer types showed substantially higher rates. Using a combination of genomic, structural, and biophysical analyses, we found several predominant modes of action, where cancer missense mutations in histones affected acidic patches and protein binding interfaces in a cancer-specific manner and targeted interaction sites with specific DNA repair proteins. Consistent with this finding, we observed a high tumour mutational burden in patients with histone mutations affecting interactions of DNA repair proteins. We also identified potential cancer driver mutations in several histone genes, including histone H4-a highly conserved histone without previously documented driver mutations.
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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 | 0.000 | 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) | 0.003 | 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".