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.