Abstract 1397: Quantitative MR imaging measures predict intratumoral molecular heterogeneity in clear cell renal cell carcinoma
Authors: Durga Udayakumar, Ze Zhang, Durgesh Dwivedi, Yin Xi, Tao Wang, Ananth Madhuranthakam, Payal Kapur, Asghar Hajibeigi, Allison Joyce, Qurratulain Yousuf, Michael Fulkerson, Alberto Diaz de Leon, Matthew Lewis, Jeffrey Cadeddu, Aditya Bagrodia, Vitali Margulis, James Brugarolas, Ivan Pedrosa
Journal: Cancer Research (2019)

Abstract Clear cell renal cell carcinoma (ccRCC) is primarily driven by mutation in von Hippel-Lindau (VHL) leading to constitutive hypoxia inducible factors (HIFs) upregulation promoting angiogenesis. ccRCC is the most aggressive and common histological subtype of kidney cancer. It is characterized by high pathologic and molecular intra-tumor heterogeneity (ITH), a reflection of the genetic branched evolution during the tumor development. The overall molecular complexity in ccRCC may be underestimated with limited tissue samples in percutaneous biopsies. Non-invasive imaging methods that can provide quantitative spatial information on those varying features in the whole tumor may be a valuable tool for predicting tumor progression and therapy outcome. In this work we aim to understand the predictive value of quantitative Magnetic Resonance Imaging (MRI) measures of tumor vascularity as a noninvasive tool to identify molecular heterogeneity in ccRCC. In this IRB approved, prospective, HIPAA compliant study, 62 ccRCC patients underwent 3T multi-parametric MRI: T2-weighted (T2W), dynamic contrast-enhanced (DCE), and arterial spin labeled (ASL) MRI. All tumors were manually segmented with a region of interest (ROI) drawn on the central slice of the tumor. A grey-level co-occurrence matrix (GLCM) was constructed for each ROI and Haralick texture features were extracted. After surgery, 182 snap frozen samples from 49 tumors were subjected to RNA extraction, library preparation and mRNA sequencing using established protocols (Admerahealth, NJ). Spearman correlation coefficient between first- and second-order MRI statistics, including Haralick texture features, and gene expression levels were calculated. Gene ontology (GO) analysis was performed to identify the biological pathways associated with imaging features. Entropy, a measure of ITH, was correlated with standard deviation of normalized gene expression levels in multiple samples obtained from the same tumor. False discovery rate (FDR), q-values &amp;lt;0.05 were considered statistically significant. GO analysis of the top positively correlated genes with ASL-MRI and DCE-MRI measures of tumor perfusion indicated enrichment of immune system and cellular metabolic processes (q&amp;lt;0.05). ASL-MRI perfusion levels correlated positively with 81 HIF2 specific target genes (p&amp;lt;0.05). Gene set Enrichment Analysis (GSEA, Broad Institute, MA) indicated that correlated HIF2 target genes overlapped with key hallmark Molecular Signature database (MSigDB) gene sets, including G2M checkpoint, MTORC1 signaling, and glycolysis. Entropy of the DCE-MRI images correlated with heterogeneity in both metabolic processes, and expression of HIF1/2 target genes. Our study has set the initial framework for utilizing quantitative radiomics to assess the association of the imaging phenotype in ccRCC with specific molecular signatures. Citation Format: Durga Udayakumar, Ze Zhang, Durgesh Dwivedi, Yin Xi, Tao Wang, Ananth Madhuranthakam, Payal Kapur, Asghar Hajibeigi, Allison Joyce, Qurratulain Yousuf, Michael Fulkerson, Alberto Diaz de Leon, Matthew Lewis, Jeffrey Cadeddu, Aditya Bagrodia, Vitali Margulis, James Brugarolas, Ivan Pedrosa. Quantitative MR imaging measures predict intratumoral molecular heterogeneity in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1397.