A multivariate model of ErbB network composition predicts ovarian cancer cell response to canertinib

Abstract

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.

Bioinformatics and variability in drug response: a protein structural perspective

“…For instance, multivariate systems biology approaches that consider multiple proteins, signalling pathways, cell types and tissues can accurately recapitulate and predict therapeutic responses [310][311][312][313]. Recently, there has been a call for the inclusion of interpatient variability into these models [312].…”

Using Partial Least Squares Regression to Analyze Cellular Response Data

“…To demonstrate how PLSR can be used to analyze a systems-level problem, research utilizing PLSR to identify a predictive relationship between the abundance of members of the epidermal growth factor receptor family ErbB signaling network and sensitivity to an ErbB inhibitor in ovarian cancer cells (OvCa) (17). Although receptors of the ErbB type and their ligands are present in many ovarian cancers, only a subset of patients have responded to ErbB-targeted therapies in clinical trials (18).…”

“…Although prior studies have identified univariate biomarkers (for example overexpression of specific genes in the pathway or mutations in those genes) that predict response to these drugs in other tumor types, these markers have not been able to predict therapeutic response in ovarian cancer. Because the ErbB system is composed of four receptors that homo- and hetero-dimerize in response to stimulation by a family of thirteen ligands, Prasasya, et al utilized PLSR to examine if the quantitative balance of the abundance of these ErbB network components could be used to determine which ovarian cancer cells would be sensitive to canertinib (CI-1033), a pan-ErbB inhibitor (17) (Slide 18).…”

Synergistic Chemoimmunotherapy of Epithelial Ovarian Cancer Using ErbB-Retargeted T Cells Combined with Carboplatin

“…Instead, they act as hubs within a ligand-driven network in which all possible ErbB homo-and/ or heterodimers may occur (15). Substantial evidence points toward the operation of complex integrated ErbB signaling within EOC (16). Coexpression of two or more ErbB receptors is prevalent in this tumor, often together with autocrine ErbB ligand production (16)(17)(18)(19)(20).…”

“…Substantial evidence points toward the operation of complex integrated ErbB signaling within EOC (16). Coexpression of two or more ErbB receptors is prevalent in this tumor, often together with autocrine ErbB ligand production (16)(17)(18)(19)(20). Furthermore, a variety of ErbB heterodimers has been shown to enhance proliferation of EOC tumor cells (20,21).…”