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

Radka Stoyanova,Alan Pollack,Charles Lynne,Rosa Castillo,Christine Buerki,Matthew C Abramowitz,Nestor Parra,Adrian Ishkanian,Hussam Al-Deen Ashab,Oleksandr N Kryvenko,Elai Davicioni,Lucia L.C Lam,Mandeep Takhar,Nicholas Erho,Dipen J Parekh,Mohammed Alshalalfa,Sanoj Punnen,Robert J Gillies,Merce Jorda

doi:10.18632/oncotarget.10523

Radka Stoyanova, Alan Pollack + Show 17 more

Open Access

https://doi.org/10.18632/oncotarget.10523

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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.

Highlights

Treatment recommendations for prostate cancer patients are currently based on risk stratification using PSA, Gleason score (GS) and T-category, which typically categorize men as having low, intermediate, and high risk disease [1]
Genomic analyses and gene expression signatures, such as Decipher® (GenomeDx, San Diego, California) [4,5,6,7], Prolaris® Cell Cycle Progression (CCP) (Myriad Genetics, Salt Lake City, Utah) [8], Genomic Prostate Score® (GPS) (Genomic Health, Redwood City, CA) [9] have the potential to become integral to risk stratification and management
We describe for the first time the relationship between quantitative mpMRI and gene expression in prostate cancer samples from patients undergoing mpMRI-directed prostate biopsies

Summary

Introduction

Treatment recommendations for prostate cancer patients are currently based on risk stratification using PSA, Gleason score (GS) and T-category, which typically categorize men as having low, intermediate, and high risk disease [1]. The overtreatment of men with prostate cancer is a well-recognized problem and active surveillance has rapidly become a standard recommendation for many men with low risk disease [2]. Prostate tumor heterogeneity confounds the selection of men for active surveillance or definitive primary treatment because the determinate www.impactjournals.com/oncotarget lesion is missed in approximately 30% of cases. We propose the characterization of prostate “habitats”, and prostate cancer heterogeneity, through identification of distinct imaging characteristics using these sequences [10]. Quantification and characterization of these features have been found to reflect tumor molecular characteristics (radiogenomics) and, heterogeneity in solid tumors [12]

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