Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies

// Radka Stoyanova 1 , Alan Pollack 1 , Mandeep Takhar 2 , Charles Lynne 3 , Nestor Parra 1 , Lucia L.C. Lam 2 , Mohammed Alshalalfa 2 , Christine Buerki 2 , Rosa Castillo 4 , Merce Jorda 3, 5 , Hussam Al-deen Ashab 2 , Oleksandr N. Kryvenko 3, 5 , Sanoj Punnen 3 , Dipen J. Parekh 3 , Matthew C. Abramowitz 1 , Robert J. Gillies 6 , Elai Davicioni 2 , Nicholas Erho 2 , Adrian Ishkanian 1 1 Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA 2 Reserach and Development, GenomeDx Biosciences, Vancouver, BC, Canada 3 Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA 4 Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, USA 5 Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL, USA 6 Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, FL, USA Correspondence to: Radka Stoyanova, email: rstoyanova@med.miami.edu Keywords: prostate cancer, multiparametric MRI, MRI-targeted biopsies, gene expression, radiogenomics Received: February 26, 2016 Accepted: June 30, 2016 Published: July 11, 2016 ABSTRACT Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues. Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas ('habitats') were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy.