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.

Future of Medicine: Models in Predictive Diagnostics and Personalized Medicine

“…The PyBios system uses a Monte Carlo approach to simulate and predict the elements that cannot be measured yet. In a current pilot project (2010-2013) on melanoma patients in Germany (TREAT20) the PyBios modeling approach is used for the first time in a clinical setting [52]. The cancer model used in TREAT20 is based on the publications by Hanahan and Weinberg 2001 (with an update in 2011) [53,54] representing the signalling pathways relevant in cancer.…”

Network and systems biology: essential steps in virtualising drug discovery and development

“…To be able to model interacting cells in sufficient molecular complexity, we have developed a series of models of human signalling and other regulatory pathways (ModCell TM ) on PyBioS, an object-oriented modelling platform (http:// pybios.molgen.mpg.de) [64][65][66][67], based on information on signalling and other pathways, as well as functional, kinetic and drug data from the scientific literature and databases (Fig. 2).…”

Big Data – kihívások és kockázatok

“…Hans Lehrach, a Max Plank Molekuláris Genetikai Intézet kutatójának víziója egy olyan, anatómiai, fi ziológi-ai, molekuláris biológiai adatokon és teljesgenom-szekvencián alapuló számítógépes modell kialakítása minden egyes betegről, amelynek segítségével az orvosok személyre szabott terápiát nyújthatnak a betegnek [37]. Ha továbbgondoljuk ezt a víziót, nemsokára a súlyos betegségek már kialakulásuk előtt felismerhetővé válnak, és lehetővé válik a célzott, egyénre szabott prevenció vagy a korai fázisban történő kezelés.…”