Abstract 1127: Differences in the clinical and genomic landscape in young onset (YO) gastroesophageal cancer (GEC): an analysis from AACR Project GENIE
Bibliographic record
Abstract
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".