Abstract 852: Modeling the emergence of resistance to chemotherapeutics with virtual tumor
Authors: Frances A. Brightman, Eric Fernandez, David Orrell, Christophe Chassagnole
Journal: Cancer Research (2016)

Abstract Drug resistance is a major cause of treatment failure in cancer, and understanding and overcoming mechanisms of resistance is a key challenge in advancing cancer therapy. Although the progression from cytotoxic chemotherapy to drugs aimed at specific molecular targets has improved response rates and reduced adverse effects, in the majority of cases there is still no effective treatment for metastatic disease; resistance constrains the effectiveness of both conventional chemotherapy and targeted agents. Resistance arises from mutations in the genome of cancer cells and/or epigenetic changes. The problem is compounded by considerable intra- and inter-tumour genetic heterogeneity, dictated by the genetic background and history of each cancer cell. It is therefore not surprising that patients with apparently the same cancer can respond differently to the same treatment, and it is becoming increasingly clear that cancer should be managed through personalized medicine. Some steps have already been taken in this direction, such as the development of CancerDR (Cancer Drug Resistance Database), but the approach requires large-scale genomic profiling, which is unlikely to be widespread in clinical practice in the immediate future. In the interim, recent studies have shown that the emergence of drug-resistant disease can at least be delayed through treatment with novel dosing regimens. Physiomics has developed a ‘Virtual Tumour’ (VT) technology that can predict how a tumor will respond to drug exposure. This integrated PK/PD simulation platform can be used to optimize drug dosing and scheduling, and to design new combination therapies. The VT technology integrates pharmacokinetic and pharmacodynamic effects, and models the way individual cells behave within a tumor population. These agent-based methods are particularly suitable for modeling multiple cell populations, and representing the heterogeneity of a clinical tumor. Given the significance of cancer drug resistance, and the form that future cancer therapy is likely to take, Physiomics is actively engaged in developing personalized medicine solutions. As a first step, we have incorporated chemotherapeutic resistance into our VT platform. The VT has been extended by the addition of a resistance module, which has been developed and calibrated using data taken from the literature. This module captures the fundamental mechanism by which resistance arises. Through a case study also derived from the literature, we demonstrate that the extended VT can be applied to model the emergence of resistance in patient-derived xenografts. Furthermore, we show that the VT can be used to identify and optimize therapeutic strategies for delaying the emergence of drug resistance. Our enhanced VT capability represents the first step towards a ground-breaking tool for developing personalized treatment, which is set to revolutionize cancer therapy in the near future, especially for patients with resistant disease. Citation Format: Frances A. Brightman, Eric Fernandez, David Orrell, Christophe Chassagnole. Modeling the emergence of resistance to chemotherapeutics with virtual tumor. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 852.