Abstract 1509: In silico prediction of the clinical response to the mTOR inhibitor everolimus using a Boolean model: validation from a cohort of the SHIVA trial
Authors: Verlingue Loic , Calzone Laurence , Kamal Maud , Servant Nicolas , Belin Lisa , Barillot Emmanuel , Le Tourneau Christophe 
Published: July 2016

Abstract
Introduction:
Around half of currently marketed molecularly targeted agents (MTAs) in oncology have a companion diagnostic predictive biomarker that is usually a single molecular alteration (e.g. V600E BRAF mutation for the BRAF inhibitor vemurafenib), whereas around half of MTAs lack a predictive biomarker of efficacy (e.g. mTOR inhibitors). We aimed at modeling mathematically the response to the mTOR inhibitor everolimus taking into account coexisting molecular alterations and validating the model on clinical data from the everolimus cohort of the SHIVA trial (Lancet Oncol. 2015).
Material and methods:
We constructed the molecular signaling network corresponding to the most frequent altered genes found in different cancer types. We translated this network in a Boolean model and simulated it stochastically. We also developed a method to simulate the pharmacodynamics of everolimus. A function comprising the probabilities of the phenotypes of the model (cell cycling, apoptosis, senescence and quiescence) has been used to predict the tumor growth kinetics from the molecular profiles. We evaluated whether the in silico model could be validated on clinical data from 45 patients with various tumor types treated with everolimus in the SHIVA trial following the identification of a molecular alteration involving the PI3K/AKT/mTOR pathway. The correlation between the reported progression-free survival (PFS) in the SHIVA trial and the PFS predicted by the model was evaluated.
Results:
There were 34 unique associations of alterations detected by target sequencing, comparative genomic hybridization array and immunohistochemistry in the cohort of 45 patients. We found a non-linear relation between the predicted PFS from the model and the reported PFS from the trial (Pearson correlation = 0.91, p value = 1e-17). The two patients with the best PFS, one harboring a metastatic germinal tumor and the other a metastatic breast cancer, are precisely predicted by the model. Similarly, the model was able to predict efficiently the resistant tumors to everolimus.
Conclusion:
We were able to predict the response to everolimus and confirmed these predictions in data reported in the SHIVA trial using an in silico model of the molecular determinant of tumor growth. By this mean, we provide a novel approach to predict treatment response from the association of molecular alterations.
Citation Format: Loic Verlingue, Laurence Calzone, Maud Kamal, Nicolas Servant, Lisa Belin, Emmanuel Barillot, Christophe Le Tourneau. In silico prediction of the clinical response to the mTOR inhibitor everolimus using a Boolean model: validation from a cohort of the SHIVA trial. [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 1509.