Abstract 1127: Differences in the clinical and genomic landscape in young onset (YO) gastroesophageal cancer (GEC): an analysis from AACR Project GENIE
Notice bibliographique
Résumé
Abstract Background: Although GEC is most common in the elderly population, there is a rising incidence of YO GEC defined as occurring in patients ≤50 years of age. The clinical and genomic differences between YO and older onset disease remain unclear. We aim to comprehensively explore the clinical and genomic landscape of YO GEC and compare with older onset disease (>50 years) to elucidate potential molecular differences between the two age groups that may guide therapeutic approaches in the future. Methods: Data from the Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (AACR-GENIE) registry (version 16.1 for somatic mutation analysis and version 13.1 for survival analysis) to study patients diagnosed with GEC was queried. Patients with GEC were stratified by age at time of tumor sequencing. cBioPortal was used to analyze the differences in genetic mutation frequencies and differences were examined using chi-squared test. Overall survival (OS) between the groups was calculated using the Kaplan Meir method. Results: The AACR-GENIE registry included 5933 samples from 5553 patients; 892 (16%) patients were ≤50 years old and 4661 (84%) were >50 years old. This cohort includes patients across all stages of disease. 64% were male in YO group versus 73% in group > 50 years. 11% of patients in YO group were of Asian race compared with 6% in older group. The older onset group demonstrated a significantly higher gene altered frequency (p<0.001). At a sample level analysis, significant genomic alterations between both age cohorts were identified in 21 genes. In particular, there were only two genes, CDH1 (p<0.001) and CCNE1 (p<0.001), found to be significantly observed more frequently in YO patients compared with the older onset group. These mutations have been associated with hereditary gastric cancer syndromes, aggressive disease and poorer survival. Amongst the 19 genes significantly observed more frequently in the older onset group was the hereditary APC gene p<0.001 and TP53 (<0.001). Potentially targetable markers such as CDKN2A (p<0.001), KRAS (p<0.001), FGF4 (p<0.001), and FGFR3 (p<0.001) were also significantly observed more frequently in patients >50 years old. In the earlier analysis of GENIE (Version 13.1) with 2874 patients median OS (all stages) in YO GEC was 37 months (26.6-59.5) versus 28.9 months (26.3- 32.5) (95% CI) in older onset patients, however this was not significant (p=0.057). Conclusions: The GENIE analysis provides a comprehensive landscape of the clinical and genomic data in patients with YO and older onset GEC. Fewer somatic alterations were observed in YO GEC. Further exploration of these results and updated survival are vital to better understand the biological mechanisms and differences observed and to pinpoint potential biomarkers for diagnostic assays and personalized therapy. Citation Format: Ronan Andrew McLaughlin, Yvonne Bach, Esraa Mahmoud, Nadia Ghazali, Sheeraz Ali, Harry Harvey, Zac Coyne, Ekaterina Kosyachkova, Carly Barron, Hiroko Aoyama, Sokaina Akhtar, Raymond Jang, Eric Chen, Kevin Wang, Lucy Ma, Sangeetha Kalimuthu, Aruz Mesci, Rebecca Wong, Elliot Wakeam, Jonathan Yeung, Philippe Bedard, Elena Elimova. Differences in the clinical and genomic landscape in young onset (YO) gastroesophageal cancer (GEC): an analysis from AACR Project GENIE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1127.
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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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 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) | 0,002 | 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 ».