Challenges for the Optimization of Drug Therapy in the Treatment of Cancer

Abstract

Personalized medicine aims at identifying specific targets for treatment considering the gene expression profile of each patient individually. We discuss the challenges for personalized oncology to take off and present an approach based on hub inhibition that we are developing. That is, the subtraction of RNA-seq data of tumoral and non-tumoral surrounding tissues in biopsies allows the identification of up-regulated genes in tumors of patients. Targeting connection hubs in the subnetworks formed by the interactions between the proteins of up-regulated genes is a suitable strategy for the inhibition of tumor growth and metastasis in vitro. The most relevant protein targets may be further analyzed for drug repurposing by computational biology. The subnetworks formed by the interactions between the proteins of up-regulated genes allow the inference by Shannon entropy of the number of targets to be inhibited according to the tumor aggressiveness. There are common targets between tumoral tissues but many others are personalized at a molecular level. We also consider additional measures and more sophisticated modeling. This approach is necessary to improve the rational choice of therapeutic targets and the description of network dynamics. The modeling of attractors through Hopfield Network and ordinary differential equations are given here as examples.