Research

Optimization of combination chemotherapy based on the calculation of network entropy for protein-protein interactions in breast cancer cell lines

¹ Laboratório de Modelagem de Sistemas Biológicos, National Institute for Science and Technology on Innovation in Neglected Diseases (INCT/IDN), Center for Technological Development in Health (CDTS), Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil
² Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, T6G 1Z2, AB, Canada
³ Department of Physics, University of Alberta, Edmonton, T6G 2E1, AB, Canada

^* e-mail: jackt@ualberta.ca

Received: 16 April 2015
Accepted: 20 July 2015
Published online: 11 August 2015

Abstract

Background

In this report, we show how entropy computation can be applied to the characterization of a protein-protein interaction networks to assist the selection of personalized chemotherapeutic strategy for cancer treatment.

Methods

With seven malignant (BT-20, BT-474, MDA-MB-231, MDA-MB-468, MCF-7, T-47D, ZR-75-1) and one healthy (MCF10A) cell lines, we combined interactome and transcriptome data as well as Shanon entropy computation to classify drugs according to their inhibitory potential and to identify the top-5 protein targets best suited for personalized chemotherapy.

Results

We have investigated breast cancer cell lines and found that the entropy of their protein interaction networks is negatively correlated with their sensitivity to target-specific drugs of high potency. This sensitivity is defined as half cell growth inhibition (GI50) with respect to drug administration. By contrast, we found no correlation for drugs that are either of low potency or with no specific molecular targets (cytotoxic). As a result, drugs can be divided into target specific and generally cytotoxic according to the GI50 they produce in malignant cell lines. By extrapolation, we predict that the inactivation of the top-5 up-regulated protein hubs by specific drugs will reduce the protein network entropy by ~2 %, on average, which is expected to substantially increase the benefit of a personalized chemo-therapeutic strategy for patient survival.

Conclusions

We propose several novel drug combinations using only the approved drugs for the inactivation of the target identified in this study with the purpose of increasing patient survival and lowering the deleterious side effects of cancer chemotherapy.