Identifying the optimal treatment strategy for cancer is an important challenge, particularly for complex diseases like epithelial ovarian cancer (EOC) that are prone to recurrence. In this study we developed a quantitative, multivariate model to predict the extent of ovarian cancer cell death following treatment with an ErbB inhibitor (canertinib, CI-1033). A partial least squares regression model related the levels of ErbB receptors and ligands at the time of treatment to sensitivity to CI-1033. In this way, the model mimics the clinical problem by incorporating only information that would be available at the time of drug treatment. The full model was able to fit the training set data and was predictive. Model analysis demonstrated the importance of including both ligand and receptor levels in this approach, consistent with reports of the role of ErbB autocrine loops in EOC. A reduced multi-protein model was able to predict CI-1033 sensitivity of six distinct EOC cell lines derived from the three subtypes of EOC, suggesting that quantitatively characterizing the ErbB network could be used to broadly predict EOC response to CI-1033. Ultimately, this systems biology approach examining multiple proteins has the potential to uncover multivariate functions to identify subsets of tumors that are most likely to respond to a targeted therapy.
