Sex Differences in Cancer Driver Genes and Biomarkers
Bibliographic record
Abstract
Abstract Cancer differs significantly between men and women; even after adjusting for known epidemiologic risk factors, the sexes differ in incidence, outcome, and response to therapy. These differences occur in many but not all tumor types, and their origins remain largely unknown. Here, we compare somatic mutation profiles between tumors arising in men and in women. We discovered large differences in mutation density and sex biases in the frequency of mutation of specific genes; these differences may be associated with sex biases in DNA mismatch repair genes or microsatellite instability. Sex-biased genes include well-known drivers of cancer such as β-catenin and BAP1. Sex influenced biomarkers of patient outcome, where different genes were associated with tumor aggression in each sex. These data call for increased study and consideration of the molecular role of sex in cancer etiology, progression, treatment, and personalized therapy. Significance: This study provides a comprehensive catalog of sex differences in somatic alterations, including in cancer driver genes, which influence prognostic biomarkers that predict patient outcome after definitive local therapy. Cancer Res; 78(19); 5527–37. ©2018 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".