Abstract C124: Proteomic and phenotype derived model for selection of combinations of targeted therapies in ovarian cancer.
Authors: Hays John L. , Polley Eric C. , David Jason J. , Lu Joyce , Leone Anna M. , Mehta Saawan , Kohn Elise C. 
Published: August 2014

Combinations of molecular targeted therapies may be more effective due to collective inhibition at multiple points within a signaling network, but selecting the right targets has been elusive. We have used a partial least squares regression (PLS) statistical model to identify key functional proteins in four ovarian cancer cell lines (HEYA8, OVCAR8, CAOV3, SKOV3). We measured the changes in 85 proteins by reverse phase protein arrays (RPPA) when these four cell lines were treated with seven different kinase inhibitors (AZD6244, XL147, vandetinib, dasatinib, rapamycin, PF0573228, sunitinib). We also measured how these drugs modulated five different phenotypic endpoints: (1) a novel optimized co-culture differentiation assay with tumor cells and primary human microvascular endothelial cells (HMVECs), (2) mitochondrial activity as a surrogate for cytotoxicity using an XTT assay, and the production of (3) VEGF, (4) bFGF, and (5) EGF by tumor cells. We then applied PLS regression to the relationship between the proteins measured by RPPA and in vitro phenotypes to define important driver signaling proteins. We identified 14 proteins in this analysis: EGFR, p27, mTOR, p38, HER3, ERalpha, JNK, PRAS40, CHK2, TAZ, YAP, CHK1, AKT, and AMPK. Nine of these proteins have readily available inhibitors in various stages of clinical development: EGFR (BIBW2992), mTOR (MLN0128), p38 (SB203580), HER3 (AZD8931), ERalpha (tamoxifen), JNK (SP600125), CHK2 (AZD7762), CHK1 (LY2603618), AKT (MK2206). We examined these nine agents against all four cell lines with XTT assays. The mean IC50 was significantly less for the inhibitors targeting PLS selected driving proteins when compared to the original seven inhibitors tested across all four cell lines (Mean IC50 2.2x10−5M vs. 3.2 x 10−4M, p=0.0025). Similarly, while TORC1 inhibition with temsirolimus has been clinically tested in ovarian cancer, the PLS algorithm suggested that inhibitors targeting both TORC1 and TORC2 signaling might be more effective. MLN0128 (TORC1/2 inhibitor) was significantly more effective than sirolimus in HEYA8 (&gt;5x10−4M vs. 8.9x10−7M, p=0.002), SKOV3 (1.4x10−5M vs 1.4 x10−8M, p=0.0006), CAOV3 (5.2x10−6M vs. 8.3x10−8M, p=0.018) and OVCAR8 (8.8x10−7M vs. 5.8x10−8M, p=0.0012) cell lines when comparing IC50 values for an XTT assay. The four most active compounds (MLN0128, AZD7762, LY2603618 and MK2206) across all four cell lines were selected for further testing. These compounds demonstrate good single agent activity as well as pair-wise synergism in all four cell lines. The PLS method provides a unique and predictive algorithm to examine interactions between proteomics and phenotypic endpoints and has yielded driver signaling events that will guide novel target combinations for phase I examination in ovarian cancer.

Citation Information: Mol Cancer Ther 2013;12(11 Suppl):C124.

Citation Format: John L. Hays, Eric C. Polley, Jason J. David, Joyce Lu, Anna M. Leone, Saawan Mehta, Elise C. Kohn. Proteomic and phenotype derived model for selection of combinations of targeted therapies in ovarian cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr C124.