Lapatinib and poziotinib overcome ABCB1-mediated paclitaxel resistance in ovarian cancer

Abstract

Conventional frontline treatment for ovarian cancer consists of successive chemotherapy cycles of paclitaxel and platinum. Despite the initial favorable responses for most patients, chemotherapy resistance frequently leads to recurrent or refractory disease. New treatment strategies that circumvent or prevent mechanisms of resistance are needed to improve ovarian cancer therapy. We established in vitro paclitaxel-resistant ovarian cancer cell line and organoid models. Gene expression differences in resistant and sensitive lines were analyzed by RNA sequencing. We manipulated candidate genes associated with paclitaxel resistance using siRNA or small molecule inhibitors, and then screened the cells for paclitaxel sensitivity using cell viability assays. We used the Bliss independence model to evaluate the anti-proliferative synergy for drug combinations. ABCB1 expression was upregulated in paclitaxel-resistant TOV-21G (q < 1x10-300), OVCAR3 (q = 7.4x10-156) and novel ovarian tumor organoid (p = 2.4x10-4) models. Previous reports have shown some tyrosine kinase inhibitors can inhibit ABCB1 function. We tested a panel of tyrosine kinase inhibitors for the ability to sensitize resistant ABCB1-overexpressing ovarian cancer cell lines to paclitaxel. We observed synergy when we combined poziotinib or lapatinib with paclitaxel in resistant TOV-21G and OVCAR3 cells. Silencing ABCB1 expression in paclitaxel-resistant TOV-21G and OVCAR3 cells reduced paclitaxel IC50 by 20.7 and 6.2-fold, respectively. Furthermore, we demonstrated direct inhibition of paclitaxel-induced ABCB1 transporter activity by both lapatinib and poziotinib. In conclusion, lapatinib and poziotinib combined with paclitaxel synergizes to inhibit the proliferation of ABCB1-overexpressing ovarian cancer cells in vitro. The addition of FDA-approved lapatinib to second-line paclitaxel therapy is a promising strategy for patients with recurrent ovarian cancer.

Fig 1. Ovarian cancer cell lines exhibit varied responses to paclitaxel treatment.

Paclitaxel-resistant (PacR) and parental control ovarian cancer cell lines, TOV-21G and OVCAR3, were treated with serially diluted doses of paclitaxel for 96 hours in vitro. Cell viability is displayed at each concentration tested relative to untreated cells for the control (black) and PacR (red) cell lines of (A) TOV-21G and (B) OVCAR3 cells. Dose response curves were fit to the data and IC₅₀ values were calculated using four-parameter log-logistic models. (C) Ovarian tumor organoid cell lines’ paclitaxel EC₅₀ values. Resistant lines are shown in gold and sensitive lines are shown in black.

Fig 2. ABCB1 overexpression among recurrent gene expression differences tracking with paclitaxel resistance in ovarian cancer.

(A) Supervised hierarchical clustering of 229 common transcripts phenotypically segregated 111 down-regulated and 118 up-regulated transcripts in PacR cells (orange bar) from control cells (black bar). (B) mRNA expression levels measured by RNAseq in control and PacR cells were compared within cell lines for EGFR, ERBB2, ERBB3, ERBB4, ABCB1 and ABCG2 (* q < 0.05). (C) ABCB1 mRNA expression levels measured by RNAseq compared in paclitaxel-sensitive and -resistant ovarian cancer organoids. Statistical significance determined by exact test (edgeR).

Fig 3. Lapatinib and poziotinib demonstrate anti-proliferative synergy when combined with paclitaxel.

(A) A panel of TKIs were screened for anti-proliferative synergy when combined with paclitaxel on TOV-21G-control and–PacR derivative cell lines. The ABCB1-specific inhibitor, elacridar, was included as a positive control. Average Bliss synergy scores across drug combinations (minimum of 3 independent experiments) are summarized as averages +/- standard error of the mean. Unpaired two-tailed t-tests were performed for each drug (control vs. PacR; * p < 0.05; ** p < 0.005; *** p < 0.0005). (B) Representative surface response model plots of synergy analyses using a Bliss independence model for paclitaxel in combination with lapatinib are depicted for control and PacR TOV-21G and (C) OVCAR3 cell lines.

Fig 4. Differential regulation of ABCB1 expression by EGFR in ovarian cancer cell lines.

(A) Dose response curves following siRNA knockdown of EGFR, ERBB2 or ERBB4 in TOV-21G-control and–PacR cells indicating cell viability after 96 hours of exposure to paclitaxel. (B) Paclitaxel IC50 in OVCAR3-control and -PacR cells following knock-down of EGFR, ERBB2, and ERBB4 expression. Bar plots depict average IC50 +/- standard error of the mean (SEM). ANOVA p < 0.0001. Bars not sharing subscripts are significantly different (Tukey’s p < 0.05). (C) ABCB1 and EGFR expression measured with real-time PCR 72 hours after siEGFR transfection in OVCAR3 cells. Relative expression was compared using one-way ANOVA (ABCB1 p = 0.001; EGFR p < 0.0001) and Tukey’s Multiple Comparison Tests (* p < 0.05, ** p < 0.01, *** p < 0.001).

Fig 5. ABCB1 overexpression is sufficient for paclitaxel resistance in ovarian cancer.

Cell viability following paclitaxel treatment was examined after silencing ABCB1 expression in TOV-21G and OVCAR3 cells. IC₅₀ values were compared across non-target siRNA (siNTC) and ABCB1 siRNA (siABCB1) transfected cells using unpaired two-tailed t-tests. * p < 0.05; ** p < 0.005.

Fig 6. Lapatinib and poziotinib directly inhibit ABCB1-mediated paclitaxel transport.

ABCB1 ATPase activity was measured in untreated (baseline) and drug-treated recombinant human ABCB1 membranes and normalized to baseline activity. Verapamil served as positive control. Box and whisker plots depict the median activity, interquartile range (boxes) with whiskers extending to data points up to 1.5x the interquartile range (IQR). Outlier values greater than 1.5x IQR are shown as closed circles. Statistical significance was determined using Kruskal-Wallis test followed by Conover-Iman tests for pair-wise comparisons.