Abstract B005: Comparative genomic analysis between early-onset and average-onset colorectal cancer patients at the Cleveland Clinic
Notice bibliographique
Résumé
Abstract The etiology underlying the alarming rise in early-onset colorectal cancer (EOCRC; age at diagnosis <50 years) remains largely unknown. Studies of the genomic and molecular landscapes of EOCRC tumors may elucidate unique mutational features potentially linked to novel environmental exposures and provide clues about driver events in cancer development and progression. To date, conflicting evidence in the literature exists regarding whether or not EOCRCs have distinct genomic and tumor microenvironment characteristics or have more aggressive disease biology than average-onset colorectal cancers (AOCRC; age at diagnosis ≥ 50 years). Here, we compared clinical characteristics and somatic genomic features of tumors from patients with EOCRC and AOCRC whose tumors underwent standard of care next generation sequencing at the Cleveland Clinic, a tertiary cancer care center, between 2019 and 2025. Sequencing was completed using the Caris (tumor only) or Tempus (paired tumor and normal) platforms, and data were compiled into a Cleveland Clinic-specific cBioPortal instance, linked to the local tumor registry, and analyzed on a deidentified basis. Five hundred forty-eight patients with a colorectal cancer diagnosis (112 EOCRC and 436 AOCRC) had data available through this platform. Of those, the average age at diagnosis was 60.6 (SD:13.4), and 44% were female. Eighty percent were of White race (13% Black) and 7.2% were of Hispanic ethnicity. Whole exome sequencing was conducted on a combination of primary tumors (62.2%) and metastatic lesions (37.7%). As anticipated, the majority of patients had metastatic disease (73%) at diagnosis, as compared to 5.7% stage I, 5.5% stage II, and 16% stage III. The overall proportion of rectal cancer was higher in EOCRC than AOCRC (35% vs. 27%); however, this difference was not statistically significant after adjusting for sex (P=0.17). Advanced stage disease at diagnosis was similar between average and early onset (90% vs. 88%; P=0.30), likely due to insurance coverage for clinical sequencing in this patient population. After adjusting for sex, age of onset was not associated with MSI-H status (P=0.50). No significant difference in tumor mutational burden was observed between EOCRC and AOCRC (P=0.50). In univariate analyses, EOCRC tumors were significantly enriched for mutations in KRAS (OR=2.01, 95% confidence interval (CI) 1.23-3.27, FDR P=0.036) and PIK3CA (OR=2.42, CI 1.18-4.85, FDR P=0.042). APC (OR=1.63, FDR P=0.078) and TP53 (OR=1.57, FDR P=0.089) showed trends toward higher prevalence of mutations among EOCRC tumors, while BRAF, SMAD4, LRP1B, and SYNE1 did not show significant differences between age groups. This analysis suggests potential differences in the frequency of common driver mutations between EOCRC and AOCRC cases with primarily advanced stage disease. Citation Format: Stephanie L. Schmit, Daniel Sobieski, Ying Ni, Jacob Mansell, Camila Gonzalez, Sarah McClaren, Alok A. Khorana, David Liska. Comparative genomic analysis between early-onset and average-onset colorectal cancer patients at the Cleveland Clinic [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 B005.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».