Abstract IA001: Integrative cancer population sciences to decipher the etiology of early-onset colorectal cancer
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».