Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies
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Radka Stoyanova1, Alan Pollack1, Mandeep Takhar2, Charles Lynne3, Nestor Parra1, Lucia L.C. Lam2, Mohammed Alshalalfa2, Christine Buerki2, Rosa Castillo4, Merce Jorda3,5, Hussam Al-deen Ashab2, Oleksandr N. Kryvenko3,5, Sanoj Punnen3, Dipen J. Parekh3, Matthew C. Abramowitz1, Robert J. Gillies6, Elai Davicioni2, Nicholas Erho2, Adrian Ishkanian1
1Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, USA
2Reserach and Development, GenomeDx Biosciences, Vancouver, BC, Canada
3Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
4Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, USA
5Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
6Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, FL, USA
Radka Stoyanova, email: firstname.lastname@example.org
Keywords: prostate cancer, multiparametric MRI, MRI-targeted biopsies, gene expression, radiogenomics
Received: February 26, 2016 Accepted: June 30, 2016 Published: July 11, 2016
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.
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