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Abstract:
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In cancer translational research, there is a clinical need to identify clinically relevant cancer subtypes and driver molecular markers for precision medicine. Large-scale omics profiling studies such as The Cancer Genome Atlas (TCGA) project have generated vast amounts of genomic, transcriptomic, epigenomic and proteomic data, providing researchers a great resource for data mining and testing algorithms for integrative analysis. In an effort to reveal clinically meaningful cancer subtypes and subtype-specific omics features, we performed an integrative clustering (iCluster) analysis of the TCGA bladder cancer multi-omics data using the latest developed iCluster method. We identified integrative basal and differentiated subtypes for muscle-invasive bladder cancer (MIBC) and validated the subtypes and subtype-specific gene expression signatures using 8 independent public genomic data. We found that the MIBC subtypes were associated with altered immune signaling pathways and responded differently to neoadjuvant chemotherapy. This study demonstrated that iCluster analysis of multi-omics data could reveal cancer subtypes and pathways that could be targeted for precision medicine.
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