Abstract 1397: Quantitative MR imaging measures predict intratumoral molecular heterogeneity in clear cell renal cell carcinoma

• Published in: Cancer research (Chicago, Ill.) Ill.), 2019-07, Vol.79 (13_Supplement), p.1397-1397
• Main Authors: Udayakumar, Durga, Zhang, Ze, Dwivedi, Durgesh, Xi, Yin, Wang, Tao, Madhuranthakam, Ananth, Kapur, Payal, Hajibeigi, Asghar, Joyce, Allison, Yousuf, Qurratulain, Fulkerson, Michael, Leon, Alberto Diaz de, Lewis, Matthew, Cadeddu, Jeffrey, Bagrodia, Aditya, Margulis, Vitali, Brugarolas, James, Pedrosa, Ivan
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2019-1397

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