journal contribution
posted on 2023-03-31, 18:46 authored by Samira A. Brooks, Amir H. Khandani, Julia R. Fielding, Weili Lin, Tiffany Sills, Yueh Lee, Alexandra Arreola, Mathew I. Milowsky, Eric M. Wallen, Michael E. Woods, Angie B. Smith, Mathew E. Nielsen, Joel S. Parker, David S. Lalush, W. Kimryn Rathmell Standard uptake values (SUV) and ccRCC subtype classifications for PET/MR samples
Funding
UNC Biological Research Imaging Center and NIH
National Institute of Environmental Health Sciences
Howard Hughes Medical Cancer Institute of the NIH
UNC Translational Pathology Laboratory is supported, in part, by grants from the NCI and UNC University Cancer Research Fund (UCRF)
American Cancer Society (grant MRSG-13-154-01-CPPB) and the Urology Care Foundation/Astellas
History
ARTICLE ABSTRACT
Purpose: Clear cell renal cell carcinoma (ccRCC) has recently been redefined as a highly heterogeneous disease. In addition to genetic heterogeneity, the tumor displays risk variability for developing metastatic disease, therefore underscoring the urgent need for tissue-based prognostic strategies applicable to the clinical setting. We have recently employed the novel PET/magnetic resonance (MR) image modality to enrich our understanding of how tumor heterogeneity can relate to gene expression and tumor biology to assist in defining individualized treatment plans.Experimental Design: ccRCC patients underwent PET/MR imaging, and these images subsequently used to identify areas of varied intensity for sampling. Samples from 8 patients were subjected to histologic, immunohistochemical, and microarray analysis.Results: Tumor subsamples displayed a range of heterogeneity for common features of hypoxia-inducible factor expression and microvessel density, as well as for features closely linked to metabolic processes, such as GLUT1 and FBP1. In addition, gene signatures linked with disease risk (ccA and ccB) also demonstrated variable heterogeneity, with most tumors displaying a dominant panel of features across the sampled regions. Intriguingly, the ccA- and ccB-classified samples corresponded with metabolic features and functional imaging levels. These correlations further linked a variety of metabolic pathways (i.e., the pentose phosphate and mTOR pathways) with the more aggressive, and glucose avid ccB subtype.Conclusions: Higher tumor dependency on exogenous glucose accompanies the development of features associated with the poor risk ccB subgroup. Linking these panels of features may provide the opportunity to create functional maps to enable enhanced visualization of the heterogeneous biologic processes of an individual's disease. Clin Cancer Res; 22(12); 2950–9. ©2016 AACR.
