Sunitinib Treatment Enhances Metastasis of Innately Drug-Resistant Breast Tumors

Abstract

Antiangiogenic therapies have failed to confer survival benefits in patients with metastatic breast cancer (mBC). However, to date, there has not been an inquiry into the roles for acquired versus innate drug resistance in this setting. In this study, we report roles for these distinct phenotypes in determining therapeutic response in a murine model of mBC resistance to the antiangiogenic tyrosine kinase inhibitor sunitinib. Using tumor measurement and vascular patterning approaches, we differentiated tumors displaying innate versus acquired resistance. Bioluminescent imaging of tumor metastases to the liver, lungs, and spleen revealed that sunitinib administration enhances metastasis, but only in tumors displaying innate resistance to therapy. Transcriptomic analysis of tumors displaying acquired versus innate resistance allowed the identification of specific biomarkers, many of which have a role in angiogenesis. In particular, aquaporin-1 upregulation occurred in acquired resistance, mTOR in innate resistance, and pleiotrophin in both settings, suggesting their utility as candidate diagnostics to predict drug response or to design tactics to circumvent resistance. Our results unravel specific features of antiangiogenic resistance, with potential therapeutic implications. Cancer Res; 77(4); 1008-20. ©2016 AACR.

Figure 1. Sunitinib treated 4T1 tumours display both acquired and innate resistance.

A, Tumour growth curves from the initial sunitinib drug trials, with endpoint set at 1300 mm³ (mean ± SEM ). Measurements begin one week after tumour inoculation and on the day sunitinib treatment began. Subsequent experimental endpoints were set based on these growth curves and their intersections with this data are shown. B, histogram plot showing the distribution of tumour sizes at day 8 of treatment. Sunitinib treated tumours exceeding 250 mm³ in size were identified as falling into the non-responsive cohort. Sunitinib treatment significantly retards growth of responsive tumours. C & D, Kaplan-Meier comparative analysis of time to endpoint of tumours grown to 600 mm³ and 1300 mm³. Log ranks statistical analysis of significant results is shown. E, spider plot of the growth curves of all tumours used in the experiment. Throughout this figure n-numbers are as shown and tumour cohorts are coloured as follows (Responsive – blue, Non-responsive – red, Untreated – green).

Figure 2. Sunitinib treatment reduces vascularity and impacts vascular patterning of 4T1 tumours.

A, representative images of tumours from each cohort and experimental endpoint, showing macroscopic vascular patterning, not to scale. Avascular regions marked with arrows. B, bar chart of vascular density of tumours from each cohort and experimental endpoint, determined by the average percentage of PECAM immunofluorescent staining across 10 fields of view (mean ± SEM, Mann-Whitney, ** p<0.01, * p<0.05, n-numbers: Day 9, responsive (RE)=9, non-responsive 1 (NR 1)=3, non-responsive 2 (NR 2)=3, untreated (UT)=15; 600mm³, RE=9, NR 1=2, NR 2=4, UT=15; 1300mm³, RE=13, NR=7, UT=18.). C, representative images of immunofluorescent PECAM-1 staining in the core and outer regions of tumours from each cohort of tumours, taken at 600 mm³. D, bar chart of vascular density in the core and outer regions of tumours from each cohort, taken at 600 mm³, determined by the average percentage of PECAM immunofluorescent staining across 5 fields of view (mean ± SEM, statistics and n-numbers as in B).

Figure 3. Sunitinib treatment enhances 4T1 tumour metastasis, but only in the innately resistant setting.

A, representative image of liver, spleen and lung whole organs undergoing bioluminescent imaging by the IVIS (overlay of blue-green-red colouring represents bioluminescence of increasing intensity. B, representative images of H&E staining of 4T1 tumour and metastasis in spleen, lungs and liver (metastasis marked by white arrows). Scale bar = 100 μm. C, correlation between metastatic burden, as determined by measurement of average metastatic area across 10 fields of view, in organs stained by H&E and by whole organ bioluminescent pixel density, in spleen, liver and lung tissues (mean ± SEM, n=10). D, distribution plots of bioluminescence from each organ and cohort at the 1300 mm³ endpoint. Cohorts are coloured as follows (Responsive – blue, Non-responsive – red, Untreated – green). The level of background auto-fluorescence measured by imaging of organs from mice with no tumour is also displayed (dashed line). Statistical analysis: Mann-Whitney, *** p<0.001, * p<0.05. E, longitudinal bioluminescent quantification of primary and secondary lung tumour development, generated by bioluminescent imaging of the whole mouse with regions of interest drawn over the tumour and lung areas (mean ± SEM, n-numbers as displayed).

Figure 4. Aquaporin is significantly enriched in the vessels of responsive tumours over those of untreated and non-responsive tumours.

A, RTqPCR for the relative expression of the six genes of interest in endothelial isolates from untreated, responsive and non-responsive tumours harvested at 9 days, 600 mm³ and 1300 mm³ (mean expression ±SEM, *** p<0.001, * p<0.05, NS – Not Significant, Mann-Whitney). B, representative images of AQP1 staining in untreated, responsive and non-responsive tumours by immunofluorescence (IF). Black and white split channel and colour merged channel images of tumours triple stained by IF for DAPI (nuclei, blue), PECAM-1 (vessels, red) and AQP1 (green). C, quantitation of pixel density of staining by IF for AQP1 standardised to PECAM-1 staining (mean ± SEM, *** p<0.001, * p<0.05, Mann-Whitney, n-numbers: Day 9, responsive (R)=5, non-responsive (NR)=5, untreated (UT)=10; 600mm³, R=4, NR=6, UT=10; 1300mm³, R=12, NR=7, UT=17, 10 fields of view each).