Development of a streamlined NGS-based TCGA classification scheme for gastric cancer and its implications for personalized therapy
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View abstract on PubMed
Summary
This summary is machine-generated.A new next-generation sequencing (NGS) method efficiently classifies gastric cancer (GC) subtypes, mirroring The Cancer Genome Atlas (TCGA) classification. This approach aids in predicting prognosis and immunotherapy response for gastric cancer patients.
Area Of Science
- Oncology
- Genomics
- Molecular Biology
Background
- The Cancer Genome Atlas (TCGA) identified four prognostic gastric cancer (GC) subtypes: EBV-positive, MSI-high, genomically stable (GS), and chromosomal instability (CIN).
- TCGA classification's complexity limits its clinical utility.
- There is a need for efficient methods to classify GC subtypes for prognostic and predictive value.
Purpose Of The Study
- To develop a next-generation sequencing (NGS)-based method for efficient GC classification.
- To establish this NGS method as a practical surrogate for TCGA classification.
- To correlate GC subtypes with prognosis and immunotherapy efficacy.
Main Methods
- Retrospective analysis of two independent GC cohorts (3DMed and Korean).
- Development of an NGS classification method using 733 cancer-related genes and 4 EBV-encoded genes in the 3DMed cohort (n=765).
- Validation of NGS subtypes' correlation with immune checkpoint inhibitor (ICI) response in the Korean cohort (n=55).
Main Results
- The NGS method identified EBV (5.2%), MSI (4.6%), GS (30.6%), and CIN (59.6%) subtypes in the 3DMed cohort.
- MSI subtype showed high mutation events, tumor mutational burden (TMB), and PD-L1 expression.
- EBV and MSI subtypes demonstrated superior response to ICIs (100% disease control) compared to GS (62.9%) and CIN (12.5%) subtypes in the Korean cohort.
Conclusions
- The developed NGS method effectively classifies GC subtypes, serving as a practical alternative to TCGA classification.
- This NGS approach provides valuable insights into the mutational landscape of GC.
- The classification can optimize patient-specific treatment strategies and predict immunotherapy response.
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