Sex Differences in Oncogenic Mutational Processes
Bibliographic record
Abstract
Abstract Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here, we describe the first pan-cancer analysis of sex differences in whole genomes of 1,983 tumours of 28 subtypes from the ICGC Pan-Cancer Analysis of Whole Genomes project. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in cancer research. Sex disparities in cancer epidemiology include an increased overall cancer risk in males corresponding with higher incidence in most tumor types, even after adjusting for known risk factors 1,2 . Cancer mortality is also higher in males, due in part to better survival for female patients in many cancer types, including those of the colon and head & neck 3 . Interestingly, female colorectal cancer patients respond better to surgery 4 and adjuvant chemotherapy, though this is partially due to biases in tumour location and microsatellite instability 5 . Similarly, premenopausal female nasopharyngeal cancer patients have improved survival regardless of tumour stage, radiation or chemotherapy regimen 6 . There is a growing body of evidence for sex differences in cancer genomics 7-13 , but their molecular origins and clinical implications remain largely elusive. Previous studies have mostly focused on protein coding regions, leaving the vast majority of the genome unexplored. We hypothesized that there are uncharacterized sex differences in the non-coding regions of the genome. Using whole genome sequencing data from the Pan-cancer Analysis of Whole Genomes (PCAWG) project 14 , we performed a survey of sex-biased mutations in 1,983 samples (1,213 male, 770 female) from 28 tumour subtypes, excluding those of the sex organs ( Supplementary Table 1 ). We also excluded the X and Y chromosomes to focus on autosomal sex differences in cancers affecting both men and women, but there are known to be significant X-chromosome mutational differences between tumours arising in men and women 15 . Our analysis revealed sex differences in both genome-wide phenomena and in specific genes. These sex-biases occur not only at the pan-cancer level across all 1,983 samples, but also in individual tumour subtypes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".