The routine use of cross-sectional imaging has resulted in a dramatic increase in the age-adjusted incidence of renal cell carcinoma (RCC) over the last decades. However, this has not translated into a decrease in cancer specific deaths, which suggests over treatment of potentially indolent renal tumors. Thus, active surveillance (AS) of RCC is now accepted as a management option for renal tumors, particularly in patients with comorbidities. Although AS in larger tumors has been reported to be safe (i.e. very low risk of metastasis), the natural history of these tumors remains unknown and percutaneous biopsies may be limited in assessing tumor grade due to intrinsic heterogeneity. Tumor angiogenesis and lipogenesis have been correlated with prognosis and metastatic potential in clear cell RCC (ccRCC), the most common and aggressive type of RCC. Inactivation of the VHL gene, HIF upregulation, and VEGF over-expression form the molecular basis of the enhanced angiogenesis associated with ccRCC. More recently, progress has been made in recognizing the distinct role of HIF-1 and HIF-2 transcription factors in tumor progression and inhibition of HIF-2, the main driver of angiogenesis, is now been tested in humans. Similarly, upregulation of lipogenic enzymes has been recognized as an aggressive metabolic phenotype in ccRCC. Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI) method for measuring blood flow by manipulating the signal from inflowing arterial blood. ASL blood flow correlates tightly to vascularity in ccRCC. Dixon-based techniques have been extensively validated for quantification of hepatic lipids. Diffusion weighted imaging (DWI) provides an indirect non-invasive estimate of tumor cellularity. We seek to identify cellularity, vascular, and lipid MRI measures in ccRCC in vivo that correlate to spatially-co-localized molecular alterations promoting angiogenesis and lipogenesis and predict aggressive behavior. The spatial co-localization of various tissue-based analyses with in vivo alterations in tumor perfusion and lipogenesis may help develop more robust imaging biomarkers to predict the biologic behavior of ccRCC. If successful, these imaging biomarkers will be immediately applicable to clinical practice and will help selecting patients for active surveillance thus decreasing the number of unnecessary surgeries.
The aim of the proposed research is to investigate the correlation between in vivo magnetic resonance imaging (MRI) measures of cellularity, blood flow and fat accumulation with the recently discovered molecular alterations in the angiogenic and lipogenic pathways that are associated to aggressive behavior in clear cell renal cell carcinoma (RCC). Quantitative measures of cellularity, blood flow and fat fraction will be obtained with diffusion weighted imaging (DWI), arterial spin labeling (ASL) and Dixon-based MRI techniques, respectively.
|Kay, Fernando U; Canvasser, Noah E; Xi, Yin et al. (2018) Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology. Radiology 287:543-553|
|Chen, Xi; Zhou, Zhiguo; Hannan, Raquibul et al. (2018) Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model. Phys Med Biol 63:215008|
|Wang, Tao; Lu, Rong; Kapur, Payal et al. (2018) An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors. Cancer Discov 8:1142-1155|
|Wang, Xinzeng; Pirasteh, Ali; Brugarolas, James et al. (2018) Whole-body MRI for metastatic cancer detection using T2 -weighted imaging with fat and fluid suppression. Magn Reson Med 80:1402-1415|
|Krajewski, Katherine M; Pedrosa, Ivan (2018) Imaging Advances in the Management of Kidney Cancer. J Clin Oncol :JCO2018791236|
|Kay, Fernando U; Pedrosa, Ivan (2018) Imaging of Solid Renal Masses. Urol Clin North Am 45:311-330|
|Courtney, Kevin D; Bezwada, Divya; Mashimo, Tomoyuki et al. (2018) Isotope Tracing of Human Clear Cell Renal Cell Carcinomas Demonstrates Suppressed Glucose Oxidation In Vivo. Cell Metab 28:793-800.e2|
|Dwivedi, Durgesh Kumar; Chatzinoff, Yonatan; Zhang, Yue et al. (2018) Development of a Patient-specific Tumor Mold Using Magnetic Resonance Imaging and 3-Dimensional Printing Technology for Targeted Tissue Procurement and Radiomics Analysis of Renal Masses. Urology 112:209-214|
|Xi, Yin; Yuan, Qing; Zhang, Yue et al. (2018) Statistical clustering of parametric maps from dynamic contrast enhanced MRI and an associated decision tree model for non-invasive tumour grading of T1b solid clear cell renal cell carcinoma. Eur Radiol 28:124-132|
|Diaz de Leon, Alberto; Pedrosa, Ivan (2017) Imaging and Screening of Kidney Cancer. Radiol Clin North Am 55:1235-1250|
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