Computational Tool for the Design of Personalized Drug Cocktails, Based on Intra-Tumor Subpopulations
A computational approach to map intra-tumor subpopulations for the rational design of individualized cancer drug cocktails Project ID : 6-2019-7741
Summary of the technology
A computational approach to map intra-tumor subpopulations for the rational design of individualized cancer drug cocktails
Project ID : 6-2019-7741
Description of the technology
Cancer research is moving into the frontiers of how to assign the correct drug(s) to a given patient. Cancer clinicians and researchers commonly search for overexpression of oncogenic biomarkers to assign a targeted treatment, but lack important information regarding the interplay between these markers and the response to therapy of the tumor-specific altered network. Furthermore certain cancer types, such as triple negative breast cancer, do not allow for the development of targeted drugs due to tremendous heterogeneity. Thus cytotoxic chemotherapy and/or radiotherapy remains the standard treatment of those cancers.
We propose a novel strategy, based on information theory resolves the intra-tumor molecular heterogeneity on the single cell level and allows to rationally design patient-specific treatments. The approach breaks down tumors into the subpopulations, and altered protein networks associated with each subpopulation. Targeting central proteins from the main altered networks, associated with each subpopulation, is essential in order to disturb the altered signaling in each tumor. Moreover, these networks can be used to rationally plan personalized sensitization of tumors to chemotherapy and/or radiotherapy.
The approach addresses individual patients, and in this way is superior to more common statistical analyses that divide the patient population into categories and then assign each patient to a predefined group.