Abstract 102: Pan-cancer analysis of sex differences in somatic mutation profiles
Bibliographic record
Abstract
Abstract Sex influences the clinical and pathological characteristics of a number of cancers including those of the lung, colon and kidney. Men and women differ in incidence rate, response to treatment and survival - even after controlling for confounding factors such as smoking status, age and weight. Some gene-specific sex differences have been identified in these cancers, but there has not been a rigorous, genome-wide analysis of the mutational differences in tumors between men and women. We fill this gap, providing a comprehensive assessment of sex-associated differences in cancer genomics. We leverage resources from the International Cancer Genome Consortium-The Cancer Genome Atlas pan-cancer project, which includes genomic, transcriptomic and epigenomic profiles of 25,000 tumours and whole genome sequences of 5,000 tumours. Using this data, we analyzed mutational differences between the sexes in 15 tumor types. We use both univariate (t-test, proportions test) and multivariate (multinomial logistic regression) statistical techniques, along with bioinformatics and pathway analyses. We found distinct sex-associated genomic differences in the copy number aberration (CNA) profiles of six tumor types and in pan-cancer analysis. We observe higher genome instability in male-derived tumors and sex-biased mutations in specific genes and across genomic intervals. Using multivariate modeling to control for confounding variables reinforces the significance of sex in these genomic regions and also reveals additional sex-biased CNAs. Ongoing work with mRNA abundance data suggests an association between sex-differences in CNAs and mRNA abundance both in cis and in trans. Further investigation into affected genes and pathways may reveal fundamental differences in how tumors develop in men and women and provide explanations for reported clinical and pathological sex differences. Citation Format: Constance H. Li, Syed Haider, Paul C. Boutros. Pan-cancer analysis of sex differences in somatic mutation profiles. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 102.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".