Integrated genomic profiling and modelling for risk stratification in patients with advanced oesophagogastric adenocarcinoma

2020 
Objective Prognosis of patients with advanced oesophagogastric adenocarcinoma (mEGAC) is poor and molecular determinants of shorter or longer overall survivors are lacking. Our objective was to identify molecular features and develop a prognostic model by profiling the genomic features of patients with mEGAC with widely varying outcomes. Design We profiled 40 untreated mEGACs (20 shorter survivors 36 months) with whole-exome sequencing (WES) and RNA sequencing and performed an integrated analysis of exome, transcriptome, immune profile and pathological phenotypes to identify the molecular determinants, developing an integrated model for prognosis and comparison with The Cancer Genome Atlas (TCGA) cohorts. Results KMT2C alterations were exclusively observed in shorter survivors together with high level of intratumour heterogeneity and complex clonal architectures, whereas the APOBEC mutational signatures were significantly enriched in longer survivors. Notably, the loss of heterozygosity in chromosome 4 (Chr4) was associated with shorter survival and ‘cold’ immune phenotype characterised by decreased B, CD8, natural killer cells and interferon-gamma responses. Unsupervised transcriptomic clustering revealed a shorter survivor subtype with distinct expression features (eg, upregulated druggable targets JAK2, MAP3K13 and MECOM). An integrated model was then built based on clinical variables and the identified molecular determinants, which significantly segregated shorter and longer survivors. All the above features and the integrated model have been validated independently in multiple TCGA cohorts. Conclusion This study discovered novel molecular features prognosticating overall survival in patients with mEGAC and identified potential novel targets in shorter survivors.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    38
    References
    7
    Citations
    NaN
    KQI
    []