Abstract 3121: Exercise and tumor genomic landscapes in 2,879 patients with cancer
Bibliographic record
Abstract
Abstract Approximately two-thirds of cancer diagnoses globally are attributed to modifiable lifestyle factors such as smoking, diet and inactivity. Conversely, regular exercise is linked to decreased risk of multiple cancers. However, the underlying molecular mechanisms are not fully elucidated. Specifically, how exercise impacts tumor genomic landscapes has not been considered. To address this gap, we integrated clinical annotation of exercise exposure with tumor mutational profiling of 2,879 patients with cancer. Exercise exposure was evaluated by validated questionnaire and tumor genomic profiling of ≥ 505 commonly mutated cancer genes was performed using the Memorial Sloan Kettering Cancer Center Integrated Mutation Profiling of Actional Cancer Targets (MSK-IMPACT) assay. Among the most represented cancer types were breast (n = 623), lung (n = 524), endometrial (n = 260), colorectal (n = 217) and prostate (n = 176). We found that exercise influences the genomic landscapes of tumors in a cancer-type specific manner. For instance, breast, colorectal and lung cancers from exercising patients had significantly lower tumor mutation burden (TMB) compared with non-exercisers. TMB of melanoma was significantly higher in exercisers versus non-exercisers. A pan-cancer analysis showed that mutations in SMAD3 were more frequent among exercisers, while mutations in STK11, KEAP1 and RBM10 were more frequent among non-exercisers. Overall, this study shows that exercise regulates tumor genomic landscapes and does so in a cancer-site specific manner. Citation Format: Brandon L. Tsai, Lydia Y. Liu, Stefan E. Eng, Whitney P. Underwood, Catherine P. Lee, Joshua W. Bliss, Paul C. Boutros, Lee W. Jones. Exercise and tumor genomic landscapes in 2,879 patients with cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3121.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".