Damage and Misrepair Signatures: Compact Representations of Pan-cancer Mutational Processes
Bibliographic record
Abstract
Mutational signatures of single-base substitutions (SBSs) characterize somatic mutation processes which contribute to cancer development and progression. However, current mutational signatures do not distinguish the two independent steps that generate SBSs: the initial DNA damage followed by erroneous repair. To address this modelling gap we developed DAMUTA, a hierarchical Bayesian probabilistic model that infers separate signatures for each process, and captures their sample-specific interaction. We applied DAMUTA to 18,974 pan-cancer whole genome sequencing mutation catalogues from 23 cancer types and show that tissue-specificity in mutation patterns is driven largely by variability in damage processes. We also show that misrepair processes are predictive of DNA damage response deficiencies. Unlike existing approaches, DAMUTA distinguishes damage from misrepair contributions, and we demonstrate significant improvements over a mutational-burden baseline or signatures from the COSMIC database. Our analysis reveals a shared pan-cancer pattern of early clonal transition-mutations which shifts to a more uniform substitution pattern consistent with increased reliance on translesion synthesis for damage tolerance. DAMUTA thus generates a compact set of signatures which resolves redundancies of current signature models, disentangles the effects of DNA damage and misrepair processes, and facilitates improved stratification of tumours, while providing a framework towards a unified pan-cancer model of the cellular response to DNA damage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".