A pooled mutational analysis identifies ionizing radiation-associated mutational signatures conserved between mouse and human malignancies
Bibliographic record
Abstract
Single nucleotide variants (SNVs) identified in cancer genomes can be de-convolved using non-negative matrix factorization (NMF) into discrete trinucleotide-based mutational signatures indicative of specific cancer-causing processes. The stability of NMF-generated mutational signatures depends upon the numbers of variants available for analysis. In this work, we sought to assess whether data from well-controlled mouse models can compensate for scarce human data for some cancer types. High quality sequencing data from radiotherapy-induced cancers is particularly scarce and the mutational processes defining ionizing radiation (IR)-induced mutagenesis in vivo are poorly defined. Here, we combine sequencing data from mouse models of IR-induced malignancies and human IR-induced malignancies. To determine whether the signatures identified from IR-exposed subjects can be differentiated from other mutagenic signatures, we included data from an ultraviolet radiation (UV)-induced human skin cancer and from a mouse model of urethane-induced cancers. NMF distinguished all three mutagens and in the pooled analysis IR was associated with mutational signatures common to both species. These findings illustrate the utility of pooled analysis of mouse and human sequencing data.
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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 | 0.001 |
| 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 | 0.000 |
| 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".