Geographic variation of mutagenic exposures in kidney cancer genomes
Bibliographic record
Abstract
ABSTRACT International differences in the incidence of many cancer types indicate the existence of carcinogen exposures that have not been identified by conventional epidemiology yet potentially make a substantial contribution to cancer burden 1 . This pertains to clear cell renal cell carcinoma (ccRCC), for which obesity, hypertension, and tobacco smoking are risk factors but do not explain its geographical variation in incidence 2 . Some carcinogens generate somatic mutations and a complementary strategy for detecting past exposures is to sequence the genomes of cancers from populations with different incidence rates and infer underlying causes from differences in patterns of somatic mutations. Here, we sequenced 962 ccRCC from 11 countries of varying incidence. Somatic mutation profiles differed between countries. In Romania, Serbia and Thailand, mutational signatures likely caused by extracts of Aristolochia plants were present in most cases and rare elsewhere. In Japan, a mutational signature of unknown cause was found in >70% cases and <2% elsewhere. A further mutational signature of unknown cause was ubiquitous but exhibited higher mutation loads in countries with higher kidney cancer incidence rates (p-value <6 × 10 −18 ). Known signatures of tobacco smoking correlated with tobacco consumption, but no signature was associated with obesity or hypertension suggesting non-mutagenic mechanisms of action underlying these risk factors. The results indicate the existence of multiple, geographically variable, mutagenic exposures potentially affecting 10s of millions of people and illustrate the opportunities for new insights into cancer causation through large-scale global cancer genomics.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".