Motif Disruption Domains Lead To Cancer Gene Expression Rewiring
Bibliographic record
Abstract
Abstract Somatic mutations accumulate in non-coding regions of the genome during tumorigenesis, but their functional characterization presents a challenge. Somatic non-coding mutations rarely overlap among patients, which necessitates large sample sizes to detect associations. We analysed somatic mutations called from whole-genome sequencing (WGS) and RNA sequencing (RNAseq) from 3000 tumors across the Pan-Cancer Analysis of Whole Genomes to identify and functionally characterize mutation accumulation and its impact on gene dysregulation in cancer. We identified 1.5 million motif disruption domains (MDDs) across 40 cancer types, which we characterized as pan-cancer targets for recurrent mutation accumulation. These MDDs deregulate gene expression in cancer-specific and pan-cancer patterns by disrupting transcription factor binding sites in regulatory and insulator elements. Disruption is most recurrent across individuals at MDDs in conserved open chromatin, revealing potential drivers. This accumulation of somatic variants targeting regulatory and structural elements in MDDs generates gene expression dysregulation during tumorigenesis.
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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) | 0.002 | 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".