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Personalized cancer therapy with single cell analysis of heterogeneous tumors


Stanford Reference:

16-087


Abstract


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.

Applications


  • Rationally-designed cancer clinical trials:
    • integrate real time testing of therapeutic agents to stratify patients based on single cell responses to targeted single drugs
    • incorporate data from Mass Cytometry Time-of-Flight (CyTOF), single-cell RNA-seq, or any single-cell imaging data

Advantages


  • Precise, personalized treatment:
    • integrates single-cell data to account for intratumoral heterogeneity to reduce the risk of drug resistance and patient relapse
    • predicts cancer drug combination responses to minimize number of drugs while maximizing response
    • potential to minimize cost and toxicity

Publications



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Innovators & Portfolio



Date Released

 2/13/2017
 

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Anne Kopf-Sill, Licensing Associate
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Related Keywords


oncology companion diagnostics   companion diagnostics   single cell analysis   personalized medicine   Mass cytometry   high-throughput, single cell, cytology   healthcare software   flow cytometry software   CyTOF   cancer combination therapy   software: bioinformatics   software: optimization   
 

   

  

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