Monitoring the Vascular Response and Resistance to Sunitinib in Renal Cell Carcinoma <i>In Vivo</i> with Susceptibility Contrast MRI.
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Antiangiogenic therapy is efficacious in metastatic renal cell carcinoma (mRCC). However, the ability of antiangiogenic drugs to delay tumor progression and extend survival is limited, due to either innate or acquired drug resistance. Furthermore, there are currently no validated biomarkers that predict which mRCC patients will benefit from antiangiogenic therapy. Here, we exploit susceptibility contrast MRI (SC-MRI) using intravascular ultrasmall superparamagnetic iron oxide particles to quantify and evaluate tumor fractional blood volume (fBV) as a noninvasive imaging biomarker of response to the antiangiogenic drug sunitinib. We also interrogate the vascular phenotype of RCC xenografts exhibiting acquired resistance to sunitinib. SC-MRI of 786-0 xenografts prior to and 2 weeks after daily treatment with 40 mg/kg sunitinib revealed a 71% (<i>P</i> < 0.01) reduction in fBV in the absence of any change in tumor volume. This response was associated with significantly lower microvessel density (<i>P</i> < 0.01) and lower uptake of the perfusion marker Hoechst 33342 (<i>P</i> < 0.05). The average pretreatment tumor fBV was negatively correlated (<i>R</i><sup>2</sup> = 0.92, <i>P</i> < 0.0001) with sunitinib-induced changes in tumor fBV across the cohort. SC-MRI also revealed suppressed fBV in tumors that acquired resistance to sunitinib. In conclusion, SC-MRI enabled monitoring of the antiangiogenic response of 786-0 RCC xenografts to sunitinib, which revealed that pretreatment tumor fBV was found to be a predictive biomarker of subsequent reduction in tumor blood volume in response to sunitinib, and acquired resistance to sunitinib was not associated with a parallel increase in tumor blood volume. <i>Cancer Res; 77(15); 4127-34. ©2017 AACR</i>.
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Cell Line, Tumor
Carcinoma, Renal Cell
Magnetic Resonance Imaging
Xenograft Model Antitumor Assays
Drug Resistance, Neoplasm
Image Processing, Computer-Assisted
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Cancer research, 2017, 77 (15), pp. 4127 - 4134