Abstract 3817: Using gene-environment interactions to explore pathways for colorectal cancer risk
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) is a major public health concern, with incidence rates increasing over the past decades particularly among younger adults. The identification of novel intervention targets for CRC prevention becomes imperative. In the present study we explored patterns of genes and pathways underlying the observed associations using estimates from genome-wide interaction studies (GWIS) of 15 exposures with established or putative CRC risk. GWIS estimates were derived from a pool of 36 primary studies including up to 38, 578 CRC cases and 49, 658 controls. The 15 risk factors included body mass index (BMI), height, physical activity, smoking, type 2 diabetes, hormone replacement therapy (HRT), and intake of non-steroidal anti-inflammatory drugs (NSAIDs) including aspirin, alcohol, calcium, fiber, folate, fruits, processed meat, red meat, and vegetables. We conducted pathway-environment interaction analysis for CRC risk to identify associated pathways using the adaptive combination of Bayes Factors (ADABF) framework. The pathway findings were further investigated by exploring the relevance of the enriched genes for CRC using publicly available resources [hallmarks of cancer, Open Targets Platform (OTP)]. Using the ADABF, a total of 1, 973 pathways were enriched out of the 2, 950 analyzed for at least one exposure. Additionally, within the enriched pathways, 1, 227 genes showed evidence of interaction with at least one exposure. A high overlap of associated genes was observed between the three exposures with higher number of associated genes: BMI (n=768), followed by smoking (n=223) and NSAIDs (n=173), while for remaining exposures the number of enriched genes ranged from 3 to 27. Data were available for 811/1, 227 genes in the OTP, of which an overall association score >0.05 was found for 241 coding genes. Fifty percent of the genes (617/1, 227) mapped to at least one hallmark of cancer, most of which (388/617) pertained to the Sustaining Proliferative Signaling hallmark. Our findings reflect previously established pathways for CRC risk, such as mitogen-activated protein kinase (MAPK), Notch, PI3K/AKT, transforming growth factor-β (TGF-β), Wnt, and participating genes, for BMI, NSAIDs, and smoking, and highlight the emerging importance of several less studied genes (such as argonaute RISC component, UDP-glucuronosyltransferase, and taste receptor coding genes). Common pathways were found for several combinations of exposures (mostly for BMI, NSAIDs, and smoking), potentially suggesting common underlying mechanisms. The results of the present analysis can be used in future investigations, and, if confirmed, may aid in elucidating the etiological associations and inform personalized CRC prevention strategies. Citation Format: Emmanouil Bouras, Ren Yu, Andre E. Kim, Georgios Markozannes, Neil Murphy, Demetrius Albanes, Laura N. Anderson, Elizabeth L. Barry, Hermann Brenner, Peter T. Campbell, Robert Carreras-Torres, Andrew T. Chan, Jenny Chang-Claude, Iona Cheng, Matthew A. Devall, Niki Dimou, David A. Drew, Stephen B. Gruber, Andrea Gsur, Li Hsu, Jeroen R. Huyghe, Temitope O. Keku, Anshul Kundaje, Loïc Le Marchand, Li Li, Brigid M. Lynch, Victor Moreno, John Morrison, Christina C. Newton, Nikos Papadimitriou, Andrew J. Pellatt, Anita R. Peoples, Paul D. Pharoah, Elizabeth A. Platz, Conghui Qu, Joel Sanchez Mendez, Robert E. Schoen, Mariana C. Stern, Claire E. Thomas, Caroline Y. Um, Pavel Vodicka, Veronika Vymetalkova, Emily White, Alicja Wolk, Anna H. Wu, Marc J. Gunter, W. James Gauderman, Ulrike Peters, Marina Evangelou, Konstantinos K. Tsilidis. Using gene-environment interactions to explore pathways for colorectal cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3817.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".