Abstract A002: Real-world clinicogenomic comparison of early- and average-onset gastric cancer
Bibliographic record
Abstract
Abstract In recent decades, there has been an unprecedented rise in gastric cancer among younger individuals, contrasting with a decline among older individuals. However, the biological underpinnings of gastric cancer in younger individuals remain poorly understood. We aimed to elucidate the clinicopathologic and genomic [HM1] characteristics of early-onset gastric cancer (EOGC) compared to average-onset gastric cancer (AOGC). We analyzed 311 patients using the multi-institutional prospective Oncology Research Information Exchange Network (ORIEN) database to compare demographic, clinicopathologic, genomic, and survival outcomes between EOGC (<50 years; N=72) vs AOGC (>50 years N=239). Genomic, germline, and RNA sequencing data were analyzed and compared between the cohorts. Mutational and immune signatures were also processed. EOGC patients exhibited significantly higher rates of pain at diagnosis (53% vs 30% p=0.001), but not anemia or reflux. EOGC patients were more likely to present with stage III/IV disease (70% vs 45% p=0.006) and diffuse/signet ring histology (47% vs 16% p=0.002). Consequently, OS was decreased in the younger cohort (HR 1.52; p=0.03). Somatic mutational load was decreased in young patients. Significantly mutated genes in the entire cohort included CDH1, ARID1A, TP53, PIK3CA, but CDH1 was more frequently mutated in the EOGC cohort (40% vs 18% p=0.09). Significant differences in RNA expression were observed, with upregulated epithelial mesenchymal transition (EMT), myogenesis, and apical junction. Immune deconvolution revealed a predominance of M2 macrophages, mast cells, and CD4+ T cell subsets, but no differences between cohorts. Early-onset gastric cancer has unique clinical and genomic features. Pathway dysregulation in EOGC may contribute to tumorigenesis and therapy resistance. This study underscores the necessity for further research into novel therapies, biomarker discovery, and early detection methodologies in younger individuals. Citation Format: Hannah McDonald, Abu Saleh Mosa Faisal, Chi Wang, Neelima Hosamani, Lilia Turcios, Irada Ibrahim-Zada, Leah Winer, Prakash Pandalai, Michael Cavnar, Mark Evers, Joseph Kim, Mautin Barry-Hundeyin. Real-world clinicogenomic comparison of early- and average-onset gastric 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 A002.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".