Computational Methods Help Find New Patterns in Cancer Omics Data

SAN FRANCISCO (GenomeWeb) – In order to move past the idea of one tumor mutation correlating with one targeted therapy, researchers are turning toward computational tools to help make sense of the vast amounts of omics data and identify pathways and previously overlooked networks that correlate with drug response.

Sourav Bandyopadhyay, an assistant professor of bioengineering and therapeutic sciences at the University of California, San Francisco, recently described one such approach in a presentation at the BioData World West conference in San Francisco, California last month and in a follow-up interview. Specifically, his team developed a method to identify and score mutational networks associated with breast cancer and relevant therapies for those networks.

Similar work is being done by researchers at the Oregon Health Sciences University, where Laura Heiser and colleagues are looking to develop algorithms that can match common gene signature pathways and phenotypes across large cohorts and heterogeneous datasets.

Bandyopadhyay’s team developed modular analysis of genomic networks in cancer (MAGNETIC) after struggling to interpret experiments testing drugs on both cancer cell lines and actual tumors, work that the team described in a publication on the BioRxiv server last year.

Cell lines are great for screening drugs, but the

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