PO-467 Integrative modelling to understand and predict cancer drug response

Introduction Response to cancer therapeutics, whether in cell lines or patients, is variable and often difficult to predict. While many oncogenic drivers and drug resistance mechanisms have been described, understanding of how the interplay of these factors impacts response is still limited, precluding implementation in clinical practice. Material and methods To better understand the mechanisms behind the variability in drug response in breast cancer, we profiled both cell lines and tumour samples, in the absence of drug treatment, for the presence of mutations, copy number aberrations, mRNA and protein expression as well as protein phosphorylation. Response of the cell lines to several drugs in the RTK/PI3K/MAPK pathways was also determined. The molecular characteristics together with response data were used to construct a knowledge-based, Bayesian computational model that integrates all data types and estimates the relative contribution of the various drug sensitivity mechanisms. Using a set of patient samples, with known response to a combination treatment of trastuzumab, paclitaxel and carboplatin in a neoadjuvant setting (TRAIN trial), we have constructed a preliminary model to explain the observed variability in pathological complete response (pCR). Upon further refinement, the predictive ability of this model will be tested using an independent validation set of patient samples. Results and discussions Our model of regulatory signalling is able to explain most of the variability observed in drug response in cell lines. It also identified cell lines with an unexplained response, and provided an opportunity for us to search for novel explanatory factors. Among others, we found that the 4E-BP1 protein expression level – and not just the extent of its phosphorylation – is a determinant of mTOR inhibitor sensitivity, which we validated experimentally. Extending the modelling approach to focus on a predictive clinical application, specifically response to chemotherapy combined with trastuzumab treatment in a neoadjuvant setting for HER2 +breast cancer, we have constructed a preliminary model able to explain part of the variability in pCR, the refinement and validation of which will be presented. Conclusion Combining molecular characterisation with integrative modelling can be used to systematically test and extend our understanding of the variability in anticancer drug response. Such approaches pave the way for establishing effective personalised treatments.