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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".