Genomic and Proteomic Characterisation of Gastric Cancer

Project Description

This project aims to find more effective treatments and novel therapeutic regimens for gastric cancer patients. It will consist of two parts and 2,000 patients in total:

  1. discovery study: investigate the proteogenomic landscape of gastric cancer from a clinically well-annotated cohort, to define molecular subtype and corresponding therapeutic implications with the current chemo-based regimen and novel target/immune therapies;
  2. validation study: The developed multi- modal predictive signatures for chemo- response will be validated using samples collected from two clinical trials (a phase III and a phase II/III). The novel targeted therapies based on molecular features will be validated using gastric cancer organoids, a pre-clinical model under development.

Project Goals and Expected Outcomes

Gastric cancer is the fifth most common cancer and the third leading cause of cancer deaths worldwide. Systemic chemotherapy is still the backbone therapeutic option for this poor prognosis cancer, thus gaining an exhaustive molecular understanding of GC is crucial to develop better therapeutic strategies and improve patient outcome. We will provide a full picture of the genomic landscape of gastric cancer by utilizing multi-omics profiling approaches and aim to develop a novel molecular classification of gastric cancer associated with clinical outcome, and identify novel therapeutic strategies based on molecular insights of each subtype.

What gaps in existing knowledge will be addressed by the study?

Genomic studies of gastric cancer have demonstrated the potential of personalized medicine, e.g., tumours with high microsatellite instability (MSI) are found to be associated with resistance to chemotherapy but are sensitive to PD-1 immunotherapy. Despite these advances, few genomic findings have been translated into clinical practices, which urges us to design this study. We organized an International multi-disciplinary team and will apply multi-omics approaches to high-quality clinical trial samples, to identify clinical-grade biomarkers and evidence for novel therapeutic strategies, which could help improve patient outcome for this poor prognosis cancer.

For more details, please visit the ICGC-ARGO website.

Jianmin Wu
Center for Cancer Bioinformatics

ICGC-ARGO Scientific Planning Committee