Personalized cancer therapy with single cell analysis of heterogeneous tumors

Researchers in Prof. Sylvia Plevritis' laboratory have developed an algorithm designed to optimize cancer combination therapy for individual patients by analyzing distinct single-cell responses from heterogeneous tumors. This technology, called “DRUG-NEM”, can incorporate data from Mass Cytometry Time-of-Flight (CyTOF), single-cell RNA-seq, or any single-cell imaging data to identify subpopulations of cells. Then, it creates a nested drug network based on their effects derived from intracellular signaling changes associated with a desired phenotype such as cell death that may be different between these subpopulations. Finally, it systematically scores potential drug combinations to identify or prioritize strategies that will be both economically and clinically sustainable - to maximize the effects with a minimal number of drugs. This algorithm and computational framework can be used to integrate real-time testing of therapeutic agents in cancer clinical trials to provide precise, personalized treatment.

Stage of Research
The inventors used the DRUG-NEM computational framework to individualize drug combinations based on CyTOF data generated from a panel of targeted single drugs applied to single samples. They demonstrated that DRUG-NEM can identify optimal targeted drug combinations for cervical tumor cell lines and primary leukemia samples.