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Predictive biomarkers are important for selecting appropriate patients for particular treatments. Comprehensive genomic, transcriptomic, and pharmacological data provide clues for understanding relationships between biomarkers and drugs. However, it is still difficult to mine biologically meaningful biomarkers from multi-omics data. Here, we developed an approach for mining multi-omics cell line data by integrating joint non-negative matrix factorization (JNMF) and pathway signature analyses to identify candidate biomarkers. The JNMF detected known associations between biomarkers and drugs such as  BRAF  mutation with PLX4720 and  HER2  amplification with lapatinib. Furthermore, we observed that tumours with both  BRAF  mutation and MITF activation were more sensitive to BRAF inhibitors compared to tumours with  BRAF  mutation without MITF activation. Therefore, activation of the BRAF/MITF axis seems to be a more appropriate biomarker for predicting the efficacy of a BRAF inhibitor than the conventional biomarker of  BRAF  mutation alone. Our biomarker discovery scheme represents an integration of JNMF multi-omics clustering and multi-layer interpretation based on pathway gene signature analyses. This approach is also expected to be useful for establishing drug development strategies, identifying pharmacodynamic biomarkers, in mode of action analysis, as well as for mining drug response data in a clinical setting.

Biomarker discovery by integrated joint non-negative matrix factorization and pathway signature analyses