Somatic Mutation Trajectories Define Prognostically Distinct Subtypes and Shape the Tumor Microenvironment in Gastric...

Area of Science:

• Oncology
• Genomics
• Computational Biology

Background:

• Gastric cancer (GC) exhibits molecular heterogeneity, but current classifications overlook mutation accumulation order.
• Understanding temporal mutation patterns is crucial for identifying prognostically distinct GC subtypes.

Purpose of the Study:

• Reconstruct somatic mutation trajectories in GC using the SuStaIn algorithm.
• Identify distinct GC subtypes based on mutation order.
• Examine transcriptomic and microenvironmental features associated with these trajectories.

Main Methods:

• Applied the Subtype and Stage Inference (SuStaIn) algorithm to TCGA-STAD somatic mutation data.
• Performed stage-correlated gene expression analysis and tumor microenvironment (TME) characterization using EcoTyper and scRNA-seq deconvolution.
• Evaluated clinical relevance in external cohorts and predicted drug sensitivity.

Main Results:

• Identified two evolutionary trajectories: Accelerated Path (AP, 65%) and Gradual Path (GP, 35%).
• AP shows earlier TP53 mutations, worse survival, SCN4A downregulation, and a stromal-enriched TME.
• GP exhibits later TP53 mutations, SDHD-correlated expression, and higher inferred immune cell scores.

Conclusions:

• Two distinct mutational ordering patterns in GC correlate with divergent transcriptomic features, TME, and clinical outcomes.
• The AP subtype is linked to early TP53 mutations and a stromal-enriched microenvironment, while GP is associated with later TP53 mutations and higher immune cell scores.
• Findings require prospective validation and offer hypothesis-generating insights for personalized GC therapy.