Abstract IA001: Integrative cancer population sciences to decipher the etiology of early-onset colorectal cancer
Bibliographic record
Abstract
Abstract The rise in early-onset cancer has been a growing global concern. Our recent global analysis using the Cancer Incidence in Five Continents and World Health Organization (WHO) mortality databases showed that steeper increases in early-onset cancers compared with later-onset cancers were observed in colorectal cancer, cervical cancer, pancreatic cancer, and multiple myeloma among females and in prostate cancer, colorectal cancer, and kidney cancer among males after 2000 in more than 5 countries. Our analysis also showed significant increases in both the incidence and mortality of uterine cancer (5 countries) and colorectal cancer (3 countries in females and 5 countries in males). We also observed strong positive correlations between the increasing obesity prevalence among young populations (aged 20-49 years) and the rising incidence of early-onset obesity-related cancers in many countries; however, even after adjusting for obesity prevalence, we still observed increasing trends of early-onset cancers. Such data might indicate that the increase in early-onset cancers is (at least partly) driven by shifts in risk factor exposure among younger generations. Tumor tissue analyses can provide valuable insights into the pathogenesis of early-onset cancer. We have shown that, compared to later-onset colorectal cancer (CRC), early-onset CRC tends to exhibit CIMP-negative/low phenotype, BRAF-wildtype, tumoral LINE-1 hypomethylation (an indicator of genomic DNA hypomethylation), immunosuppressed microenvironmental features, and pks + Escherichia coli enrichment / colibactin-induced mutational signatures. We further integrated these tumor profiles into large-scale prospective cohort studies. Our integrative research demonstrated that dietary scores related to insulin resistance were strongly associated with increased incidence of pks + E. coli-high colorectal cancer (CRC) but not with that of pks + E. coli-low or pks + E. coli-negative CRC in prospective cohort studies (P for heterogeneity <0.001). Similarly, our study showed that long-term excessive alcohol intake was associated with increased incidence of CRC with tumor LINE-1 hypomethylation (but not cancer with LINE-1 high-level methylation) (P for heterogeneity <0.001). Combined findings on exposures and early-onset CRC-associated tumor profiles suggest that these exposures may promote the development of early-onset CRC through related molecular changes. Therefore, the integrative approach that can link long-term exposures with early-onset tumor characteristics improves our understanding of the etiology of early-onset CRC. In conclusion, our integrative descriptive and molecular epidemiologic studies demonstrated significant heterogeneity in early-onset cancers by cancer types, countries, and molecular subtypes, highlighting a need for further studies to investigate potential biological, environmental, and lifestyle factors contributing to the rising burden of specific early-onset cancer types. Citation Format: Tomotaka Ugai. Integrative cancer population sciences to decipher the etiology of early-onset colorectal cancer [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 IA001.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".