Data from The Cytidine Deaminase APOBEC3G Contributes to Cancer Mutagenesis and Clonal Evolution in Bladder Cancer
Bibliographic record
Abstract
Abstract Mutagenic processes leave distinct signatures in cancer genomes. The mutational signatures attributed to APOBEC3 cytidine deaminases are pervasive in human cancers. However, data linking individual APOBEC3 proteins to cancer mutagenesis in vivo are limited. Here, we showed that transgenic expression of human APOBEC3G promotes mutagenesis, genomic instability, and kataegis, leading to shorter survival in a murine bladder cancer model. Acting as mutagenic fuel, APOBEC3G increased the clonal diversity of bladder cancer, driving divergent cancer evolution. Characterization of the single-base substitution signature induced by APOBEC3G in vivo established the induction of a mutational signature distinct from those caused by APOBEC3A and APOBEC3B. Analysis of thousands of human cancers revealed the contribution of APOBEC3G to the mutational profiles of multiple cancer types, including bladder cancer. Overall, this study dissects the mutagenic impact of APOBEC3G on the bladder cancer genome, identifying that it contributes to genomic instability, tumor mutational burden, copy-number loss events, and clonal diversity. Significance: APOBEC3G plays a role in cancer mutagenesis and clonal heterogeneity, which can potentially inform future therapeutic efforts that restrict tumor evolution. See related commentary by Caswell and Swanton, p. 487
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".