Modular Analysis of Genomic NETworks In Cancer (MAGNETIC) is a novel algorithm that performs functional network analysis of molecular profiling data to identify tumor biomarkers and link them to therapies.
Large-scale tumor sequencing efforts and machine learning methods have focused on identification of molecular aberrations in human cancers and exploiting these to better tailor therapies for patients. However, such efforts are yet to prove successful in predicting tumor response to a majority of drugs. This new algorithm provides a powerful way of integrating data across various molecular profiling platforms to identify tumor biomarkers, suggest treatment options, and potentially predict therapeutic outcomes.
Researchers at the University of California, San Francisco have developed a robust algorithm that integrates various omics data (or patient and cell line data) across cancers to identify a set of functional networks or gene modules that have coordinated activities across patients and can inform new therapeutic targets and biomarkers. This method is robust to differences between the tumor microenvironments in patients versus cell lines and reveals a new approach to connect tumor genotype to therapy in a clinically relevant manner. The algorithm has been tested using relevant breast cancer data, but should have wide application across cancer subtypes.
ADVANTAGES
For commercial development
Proof of Concept
Under CDA/NDA
Cancer biology, Pathways, Networks, Systems biology, Bioinformatics, Gene Regulatory Networks, Protein Interaction Mapping