Abstract PR003: Exploring the exposome impact in early-onset colon and rectal cancer using methylation scores
Bibliographic record
Abstract
Abstract The incidence of early-onset colorectal cancer (eoCRC) has risen worldwide, yet the drivers of this trend remain poorly understood. Directly investigating the exposome as a possible risk factor has been hindered by limited data availability, particularly beyond standard lifestyle factors, such as an unhealthy diet and smoking. To enable the study of a greater variety of exposome factors, we investigated the possibility of using DNA methylation as an indirect biomarker for the exposome. We derived methylation scores (MSs) for 29 exposures, including lifestyle, air pollution, and pesticides, based on published epigenome-wide association studies. These MSs were used to compare eoCRC and later-onset CRC (loCRC) tumors in The Cancer Genome Atlas (TCGA) Colon Adenocarcinoma cohort (31 eoCRC vs. 100 loCRC), followed by replication through meta-analysis across nine independent studies (83 eoCRC vs. 272 loCRC). For exposures within the selected 29 factors where direct measurements were available, MSs consistently reflected associations with measured exposures in tumor or blood profiles. Moreover, our approach successfully identified established risk factors for eoCRC, such as lower educational attainment, smoking, and non-Mediterranean dietary patterns, further validating the proposed methodology. Besides validating our proposed methodology, we identified picloram, an herbicide classified as IARC Group 3, as a potential novel risk factor. To address the limited methylation data on picloram, we integrated transcriptomic profiles from pluripotent stem cell–derived cardiomyocytes exposed to picloram. Differentially expressed genes under exposure were used to construct a picloram-specific single-sample GSEA (ssGSEA) score in TCGA-COAD, which showed significant correlation with the picloram-MS. At the population level, we further linked U.S. county-level eoCRC incidence from the SEER program to picloram application estimates (NAWQA Project, 1992–2012). This relationship remained significant after adjusting for socioeconomic indicators and other pesticide use. Together, these findings demonstrate the utility of MSs as effective proxies in exposome research and demonstrate their ability to uncover novel environmental risk factors. Beyond confirming known contributors, our study highlights picloram as a candidate exposure associated with eoCRC, supported by evidence across methylation, transcriptomic, and population data. These results underscore exposome differences between eoCRC and loCRC and suggest opportunities for prevention through exposome modification and regulatory policy. Citation Format: Silvana C.E. Maas, Iosune Baraibar, Lea Lemler, Maria Butjosa-Espín, Odei Blanco Irazuegui, Josep Tabernero, Elena Elez, Jose A. Seoane. Exploring the exposome impact in early-onset colon and rectal cancer using methylation scores [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr PR003.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".