Abstract IA002: Early-Onset Colorectal Cancer as a Model for Precision Cancer Prevention
Bibliographic record
Abstract
Abstract The rising incidence of early-onset colorectal cancer (EOCRC)—diagnosed before age 50—represents a sentinel trend for other early-onset cancers. While EOCRC is the most well-characterized early-onset malignancy, it also serves as a model for understanding shared mechanisms and prevention strategies across tumor types. Current prevention efforts emphasize earlier screening, yet early detection and interception alone cannot address the growing burden of early-onset cancers, particularly as carcinogenic processes may begin decades before diagnosis. Because early-onset cancers remain relatively rare, advancing risk stratification is critical to identify individuals most likely to benefit from early intervention. Accumulating evidence implicates early-life and mid-life exposures—including diet, obesity, sedentary behavior, and gut microbial dysbiosis—in shaping cancer susceptibility. These insights call for a life course prevention framework that integrates molecular and microbial biomarkers into precision risk models. Promising strategies include aspirin to block inflammation, GLP-1 receptor agonists to reverse obesity-related metabolic dysfunction, and dietary interventions—such as high-fiber and plant-forward diets—to favorably modulate the microbiome and reduce systemic inflammation. By viewing EOCRC as both a sentinel and prototype, we can move beyond early detection toward proactive, risk-targeted prevention—disrupting carcinogenic pathways before disease emerges and reimagining cancer control across the lifespan. Citation Format: Andrew T. Chan. Early-Onset Colorectal Cancer as a Model for Precision Cancer Prevention [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 IA002.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".