Genomic based personalized chemotherapy analysis to support decision systems for breast cancer

Abstract

Personalized approach to anti-cancer therapy necessitates the adaptation of standardized guidelines for chemotherapy schedules to individual cancer patients. We introduce a methodology, namely Personalized Relevance Parameterization (PReP-G), based on the genomic data of breast cancer patients to compute time course of drug efficacy on tumor progression. The pharmacodynamic (PD) parameters of transit compartmental systems are computed to quantify the drug efficacy and kinetics of cell death. We integrate the genetic information of 74 breast cancer related genes for 78 patients with clinical t-stage of 3 from the I-SPY 1 TRIAL with the tumor volume measurements from NBIA database into our PReP-G model to compute tumor growth and shrinkage parameters. The performance of the method is evaluated for the breast cancer cell lines of BT-474, MDA-MB-435 and MDA-MB-231 for a given chemotherapy, where the anti-cancer agents Doxorubicin and Cyclophosphamide are administered to animal models and the change of tumor size is measured in time. We compare our results from PReP-G model with the experimental measurements. The consistency between computed results and the volume measurements is encouraging to develop personalized tumor growth models and decision support systems based on genetic data.