The "Virtual Patient" system: Modeling cancer using deep sequencing technologies for personalized cancer treatment

Abstract

Cancer is a complex disease involving many different genomic and epigenomic changes, mutations, copy number changes, loss of heterozygosity etc. These differences cause tumors with the same pathological classification to respond very differently to the drugs, making therapy decisions difficult. As a result of intensive research in the field of oncology much information is available on molecular interactions and pathways involved in cancer onset and progression. In addition recent increases in the capacity of next-generation sequencing systems will provide huge amounts of genome, epigenome and transcriptome data, making it feasible to apply deep sequencing in the clinic to characterize tumor/patient samples. Both, the complexity of disturbances in interaction networks of biological processes in cancer and the new molecular information generated by this sequence analysis urgently require the development of systems that are able to derive clinically relevant predictions from all available data. The "Virtual Patient" modeling system combines general information available about cancer relevant pathways with the individual (genome, transcriptome) information available on the individual tumor/patient to generate models able to predict the effects and side effects of individual drugs or drug combinations. This opens the way to experiment with the response of the model of the individual patient to different therapy options in the computer, offering new routes to improve oncological practice, reduce health costs but also to accelerate the development and the approval process for new drugs in this area. © 2012 The Author(s).