Abstract PR004: Mutational signature profiling identifies a distinct subgroup of early-onset colorectal cancer associated with younger age at diagnosis, recent birth year and specific genomic features
Notice bibliographique
Résumé
Abstract Introduction: The cause/s of the increasing incidence of early-onset colorectal cancer (EOCRC) are unknown. Tumor mutational signatures provide a powerful genomic tool for discovering mutational processes associated with known or unknown etiologies. The aim was to identify subgroups of EOCRCs based on their tumor mutational signature profiles, then validate and genomically characterize these novel subgroups of EOCRC. Methods: Whole exome sequencing (WES) was performed on tumor and matched blood-derived DNA from 275 non-hereditary, mismatch repair proficient (MMRp) EOCRCs from the ANGELS and CCFR studies (age at diagnosis groups: 18-35yrs n=102; 36-45yrs n=128; 46-55yrs n=45). Single base substitution (SBS), insertion/deletion (ID) and doublet (DBS) tumor mutational signatures were calculated using COSMIC v3.4. An independent dataset comprising 1,716 whole-genome sequenced MMRp CRCs from the Genomics England (GEL)(including 240 EOCRCs) served as validation. Unsupervised dimensionality reduction in conjunction with hierarchical clustering was applied to identify signature-based clusters associated with common mutational processes, without reference to clinical features. Results: Hierarchical clustering identified nine subgroups in 275 EOCRCs and five in 1,716 CRCs from GEL (when compared with the largest subgroup from each study). A subgroup defined by dominant SBS89 and DBS8 signatures was present in both EOCRCs and GEL CRCs. In the ANGELS/CCFR EOCRCs, this subgroup comprised 14% and was associated with a younger age at diagnosis (aged 18-35 (24%) vs 36-45 (12%) vs 46-55 (0%) (p=0.006)), with those born in recent decades, (<1960 (0%) vs1960-1979 (9%) vs ≥1980 (24%) (p=0.001)), with a proximal location (proximal (24%), distal (12%), rectal (11%) (p=0.02)), and with the co-occurrence of multiple polyps at diagnosis (p=1x10-9). In the GEL CRCs, the SBS89/DBS8 subgroup was similarly significantly associated with younger age of diagnosis (≤45, p=4x10-10) and patients born more recently (≥1980, p=5x10-7). The SBS89/DBS8 EOCRC subgroup was associated with BRAF p.V600E mutation (55% vs 9%, p=1x10-8)) and high doublet somatic mutation count (mean=4.5 ± 3.0 vs 0.9 ±1.5; p=1x10-13) when compared with EOCRCs without SBS89/DBS8. These results were also observed in the GEL CRCs. Genomic characterization of the SBS89/DBS8 positive CRCs in GEL demonstrated significant differences in large-scale variants, including increased loss of heterozygosity events (17.7 ± 17.3 vs 10.8 ± 13.8; p=0.004). Conclusions: Tumor mutational signature profiling identified a distinct subgroup of CRCs associated with young age at diagnosis, more recent birth year and unique genomic features, that is characterized by high proportions of SBS89 and DBS8 mutational patterns, both of which currently have an unknown etiology. This distinct subgroup may contribute to the recent rising incidence in EOCRC and warrants further investigation to elucidate its underlying mechanisms. Citation Format: Peter Georgeson, Alysha Prisc, Jihoon E. Joo, Khalid Mahmood, Romy Walker, Mark Clendenning, Julia Como, Natalie Diepenhorst, Julie McDonald, Steven Gallinger, Robert Grant, Dylan E. O'Sullivan, Darren R. Brenner, Finlay A. Macrae, Christophe Rosty, Ingrid M. Winship, Mark A. Jenkins, Daniel D. Buchanan. Mutational signature profiling identifies a distinct subgroup of early-onset colorectal cancer associated with younger age at diagnosis, recent birth year and specific genomic features [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 PR004.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| 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 ».