Prostate Cancer Biomarker and Imaging Validation Alliance: Emory University, University of Alabama Birmingham, and University of Texas Southwestern

We hypothesize that new biomarkers to refine selecting men for prostate biopsy, together with imaging innovations to refine biopsy targeting, will enhance prostate cancer early detection by reducing unnecessary biopsy and over-detection of indolent disease. With this goal, we assembled biospecimen and imaging data via rigorous SOP's in pre-biopsy cohorts designed to avoid bias. We provided specimens and guidance on PRoBE adherence to EDRN Labs, NIH Consortia (eg SPOREs) and industry and co-led, in prior cycles, EDRN studies that facilitated FDA approval of the Prostate Health Index (phi) and urinary PCA3. In the current cycle, we enrolled 3,263 subjects and distributed 27,072 biospecimens to 25 PI's leading to 56 publications. Our multicenter phase III validation of an algorithm combining urinary PCA3 and TMPRSS2:Erg (T2:Erg) measurement validated the cost-effectiveness of this biomarker combination. We then showed significant benefit of sequentially testing blood phi followed by conditional urine PCA3 and T2:Erg testing. Further, we partnered with a commercial collaborator having expertise in bringing tissue RNA assays to regulatory approval and clinical use, to complete whole transcriptome urinary RNA expression analysis in a multi-center case-control cohort of 587 men, resolving a 33-gene predictive model that significantly improved prediction of aggressive prostate cancer compared to PSA, clinical factors, or urinary PCA3 and T2:Erg. To advance prostate cancer imaging, we completed a phase II validation study using the radiotracer fluciclovine (FACBC) in PET-CT to characterize aggressiveness of prostate cancer at initial diagnosis (to our knowledge, this was first completed American validation study of FACBC PET-CT for primary, untreated, early stage prostate cancer). For this renewal, we expand our inclusiveness by adding UAB and UTSW (which, with Emory, enrolled the majority of African-Americans in EDRN's Prostate MRI trial). For this resubmission, two biomedical engineering investigators expert in Artificial Intelligence (AI) have joined our CVC, who bring preliminary data supporting a rigorously developed, retrospectively validated deep-learning MRI AI nomogram to predict PCa on biopsy that is poised for prospective validation. Based on the combined preliminary data in urinary RNA biomarkers, MRI AI, and PET imaging from our expanded CVC team, we now propose the following Aims: 1) To validate, by nested case-control study using PROBE design, the performance of a 33-gene urinary RNA panel in predicting aggressive prostate cancer on biopsy; 2) To validate a deep learning-based nomogram using MRI AI to predict aggressive prostate cancer on biopsy 3) To evaluate the performance of PET-MRI in the detection of clinically significant prostate cancer; 4) To conduct, with CISNET, cost-effectiveness evaluation of the impact of these new biomarker, imaging and detection techniques. Finally, we commit to continuing to serve as a Collaborative Resource for the EDRN, through leadership and participation in Set-Aside and Core Collaborative Studies and provision of biospecimens and blinded clinical data to EDRN BCC's and other collaborating biomarker labs.