Abstract

The routine use of cross-sectional imaging has resulted in a significant increase in the age-adjusted incidence of renal cell carcinoma (RCC). However, this has not translated into a decrease in cancer specific deaths, suggesting the possible over treatment of small, potentially indolent renal tumors. Thus, active surveillance of RCC has been proposed for small tumors. However, the natural history of these tumors remains unknown. Angiogenesis and tumor necrosis correlate with prognosis and metastatic potential in RCC. Inactivation of the VHL gene, HIF upregulation, and VEGF over- expression form the molecular basis of the enhanced angiogenesis associated with RCC. 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 RCC. The apparent diffusion coefficient (ADC) in RCC, measured with diffusion-weighted imaging (DWI), correlates with tumor cellularity at pathology. We seek to identify vascular and diffusion MRI measures in RCC in vivo that correlate to spatially-co-registered molecular alterations promoting angiogenesis and hypoxia and predict aggressive behavior. The spatial synchronization of various tissue-based analyses with in vivo alterations in tumor perfusion and hypoxia may help develop more robust imaging biomarkers to predict the biologic behavior of RCC. If successful, these imaging biomarkers will be immediately applicable to clinical practice and will help selecting patients for active surveillance.

Public Health Relevance

The aim of the proposed research is to investigate the correlation between in vivo magnetic resonance imaging (MRI) measures of blood flow and cellularity with the molecular alterations that promote angiogenesis and necrosis in renal cell carcinoma (RCC) and to correlate these to the biologic behavior of these tumors. Quantitative measures of blood flow and cellularity will be obtained with arterial spin labeling (ASL) and diffusion weighted imaging (DWI), respectively.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA154475-01
Application #
8020881
Study Section
Special Emphasis Panel (ZRG1-DTCS-A (81))
Program Officer
Henderson, Lori A
Project Start
2011-09-15
Project End
2016-07-30
Budget Start
2011-09-15
Budget End
2012-07-31
Support Year
1
Fiscal Year
2011
Total Cost
$312,789
Indirect Cost

  Institution

Name
University of Texas Sw Medical Center Dallas
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
800771545
City
Dallas
State
TX
Country
United States
Zip Code
75390

  Publications

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
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
Diaz de Leon, Alberto; Pedrosa, Ivan (2017) Imaging and Screening of Kidney Cancer. Radiol Clin North Am 55:1235-1250

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