We propose to study patients who are candidates for active surveillance (AS) to identify imaging and gene expression signatures that distinguish indolent from aggressive prostate cancer and to better understand the mechanisms underlying progression. We will apply novel MRI techniques (i) for quantitative multiparametric MRI (MP-MRI) findings to define """"""""habitats"""""""" within the prostate;(ii) to guide prostate biopsies to MP-MRI defined lesions and determine histopathologic associations with habitats;(iii) to develop signatures based on high throughput analysis of imaging features (radiomics);(iv) to relate biopsy oligonucleotide gene expression signatures to inform on the molecular characteristics associated with imaging signatures (radiogenomics);and (v) develop models of progression (conversion to treatment) that incorporate clinical, histopathologic, imaging signatures and gene expression signatures. A Phase II AS trial of prostate cancer patients is designed to acquire MP-MRI, prostate tissue and biofluids at yearly intervals to relate to MP-MRI results and the primary endpoint of progression. The techniques that we propose have the potential to better identify indolent versus aggressive disease, thereby reducing the effects of overdiagnosis.
The Specific Aims are:
Aim 1. To assess the overall rate and temporal distribution of progression in men undergoing MP-MRI assessments and directed prostate biopsies for AS in a prospective Phase II trial.
Aim 2. To establish MP-MRI habitats and use radiomics analysis of MP-MRI features to develop signatures related to adverse histopathologic parameters and patient progression.
Aim 3. To molecularly characterize the MP-MRI-directed prostate biopsies obtained, develop a gene expression signature of indolent versus aggressive prostate cancers, and relate this information to the radiomics-derived signatures. We propose that quantitative MP-MRI parameters will be representative of histopathologic and molecular parameters and be an important adjunct to defining risk of progression and, consequently, reduce the rate of unnecessary biopsies.

Public Health Relevance

Multiparametric MRI (MP-MRI) in combination with MP-MRI-directed prostate biopsies will be used to define prostate cancer patients who are suitable candidates for active surveillance (AS). We will apply novel MRI techniques for identification of habitats based on quantitative MP-MRI parameters and then associate imaging features to the development of signatures (radiomics) for distinguishing indolent from aggressive disease. These attributes will be further evaluated by gene expression profiling of tumor biopsy material to better associate molecular abnormalities with radiomics-derived subvolumes and understand the mechanisms underlying progression.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Mazurchuk, Richard V
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University of Miami School of Medicine
Schools of Medicine
Coral Gables
United States
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Stoyanova, Radka; Chinea, Felix; Kwon, Deukwoo et al. (2018) An Automated Multiparametric MRI Quantitative Imaging Prostate Habitat Risk Scoring System for Defining External Beam Radiation Therapy Boost Volumes. Int J Radiat Oncol Biol Phys 102:821-829
Velasquez, Maria C; Chinea, Felix M; Kwon, Deukwoo et al. (2018) The Influence of Ethnic Heterogeneity on Prostate Cancer Mortality After Radical Prostatectomy in Hispanic or Latino Men: A Population-based Analysis. Urology 117:108-114
Tschudi, Yohann; Pollack, Alan; Punnen, Sanoj et al. (2018) Automatic Detection of Prostate Tumor Habitats using Diffusion MRI. Sci Rep 8:16801
Soodana-Prakash, Nachiketh; Stoyanova, Radka; Bhat, Abhishek et al. (2018) Entering an era of radiogenomics in prostate cancer risk stratification. Transl Androl Urol 7:S443-S452
Padgett, Kyle R; Swallen, Amy; Pirozzi, Sara et al. (2018) Towards a universal MRI atlas of the prostate and prostate zones : Comparison of MRI vendor and image acquisition parameters. Strahlenther Onkol :
Yang, Fei; Ford, John C; Dogan, Nesrin et al. (2018) Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol 7:445-458
Chinea, Felix M; Patel, Vivek N; Kwon, Deukwoo et al. (2017) Ethnic heterogeneity and prostate cancer mortality in Hispanic/Latino men: a population-based study. Oncotarget 8:69709-69721
Chang, Yu-Cherng Channing; Ackerstaff, Ellen; Tschudi, Yohann et al. (2017) Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI. Sci Rep 7:9746
Chinea, Felix M; Lyapichev, Kirill; Epstein, Jonathan I et al. (2017) Understanding PSA and its derivatives in prediction of tumor volume: Addressing health disparities in prostate cancer risk stratification. Oncotarget 8:20802-20812
Parra, Nestor Andres; Pollack, Alan; Chinea, Felix M et al. (2017) Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness. Front Oncol 7:259

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