Speaker
Description
The sequencing of cancer biopsies revealed that cancer is multi-factorial diseases, which strongly vary between patients. This inter-patient variability poses a challenge for clinicians. A priori it is not clear which drug will be most beneficial for a specific patient. Here, we approach the problem of drug response prediction using mechanistic mathematical models. We develop a mathematical model describing several cancer associated signaling pathways. This model can be individualized using sequencing data. For statistical inference we develop a scalable approach facilitating the study of models with thousands of parameters. We the approach to analyse drug response data from the Cancer Cell Line Encyclopaedia for 7 drugs and 120 cell lines originating from five different tissues. These results demonstrate the potential of large-scale mechanistic modeling for drug selection in personalized therapy.
