Evolution of genomic instability in metastatic cancer.
Bibliographic record
Abstract
12008 Background: Although metastasis underlies up to 90% of cancer-related mortality, genomic instability and mutation signatures are mostly studied in primary tumours. Mutation signatures are patterns of somatic mutation resulting from specific mutational processes (i.e. tobacco/UV exposure) and often evolve over time. Recent studies suggest that certain mutation signatures may predict chemotherapy response. Understanding mutational processes in metastatic cancers could uncover actionable targets and refine the understanding of progression and drug resistance. Methods: As part of the BC Cancer Agency Personalized Oncogenomics Project, mutation signatures were deciphered from 571 metastatic whole genomes from 12 cancer types totalling 13,249,678 somatic mutations. We created a novel Bayesian hiearchical model named SignIT (github.com/eyzhao/SignIT) to track temporal evolution of mutation signatures. Using real and simulated data, we showed that SignIT decomposes signatures and their temporal evolution more accurately than comparable methods. Previous chemotherapy treatments were catalogued for all patients by retrospective review. Results: We discovered 21 distinct mutation signatures, including 9 novel signatures (numbered M1-M9). Mutational processes associated with aging and cigarette smoke were early-arising. Signature 17 and M2 were consistently late-arising across cancer types and metastatic sites. Prior treatment with platinum-based chemotherapy was associated with depression of the homologous recombination deficiency signature 3 (p = 0.03). Platinum exposure was also associated with late elevation of signature 17. Conclusions: To date, this is the largest study of metastatic cancer whole genomes. Our findings revealed 9 novel mutation signatures, including potential markers of late disease and metastasis. We also observed temporal evolution of mutation signatures correlated with chemotherapy exposures. The association of decreasing signature 3 activity with platinum exposure suggests the restoration of homologous recombination as a resistance mechanism. These findings highlight the complexity of metastatic cancers, and the variety of factors which impact their mutagenesis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".