Comprehensive molecular profiling of 5,175 esophagogastric cancer (EGC) patients identified distinct molecular signatures for early-onset (< 50 years of age, EOEGC, n=530) versus average-onset (AOEGC, n=4,645) tumors.
EOEGC has increased frequency of CDH1 mutations, ARHGAP26 fusions, enrichment of epithelial mesenchymal transition (EMT) and angiogenesis pathways, decreased MAPK pathway activity, decreased frequency of TMB-high and dMMR/MSI-H, and a unique immune cell infiltrate with decreased M1 macrophages and increased M2 macrophages.
These unique differential characteristics present therapeutic opportunities but also demonstrate the limitations of currently approved therapies in this subset of patients.
An Artificial Intelligence (AI) model integrating multiscale foundation model histopathology representations with molecular and clinical features predicts early and late distant recurrence in TAILORx
Abstract We developed and validated IICM+, a multimodal model integrating clinicopathologic variables, transcriptomic features, and multiscale histopathology-derived image representations to predict distant recurrence in hormone… […]
Optimization of first-line treatment selection in advanced pancreatic adenocarcinoma using artificial intelligence
Abstract Improved clinical outcomes are reported for patients with advanced pancreatic adenocarcinoma (PDAC) treated with first-line FOLFIRINOX/NALIRIFOX, but elderly patients with comorbidities are more often… […]
