A Urine Exosome Gene Expression Panel Distinguishes between Indolent and Aggressive Prostate Cancers at Biopsy
2020; Lippincott Williams & Wilkins; Volume: 205; Issue: 2 Linguagem: Inglês
10.1097/ju.0000000000001374
ISSN1527-3792
AutoresIndu Kohaar, Yongmei Chen, Sreedatta Banerjee, Talaibek Borbiev, Huai‐Ching Kuo, Amina Ali, Lakshmi Ravindranath, Jacob Kagan, Sudhir Srivastava, Albert Dobi, Isabell A. Sesterhenn, Inger L. Rosner, Jennifer Cullen, Shiv Srivastava, György Petrovics,
Tópico(s)Prostate Cancer Treatment and Research
ResumoOpen AccessJournal of UrologyAdult Urology1 Feb 2021A Urine Exosome Gene Expression Panel Distinguishes between Indolent and Aggressive Prostate Cancers at Biopsy Indu Kohaar, Yongmei Chen, Sreedatta Banerjee, Talaibek Borbiev, Huai-Ching Kuo, Amina Ali, Lakshmi Ravindranath, Jacob Kagan, Sudhir Srivastava, Albert Dobi, Isabell A. Sesterhenn, Inger L. Rosner, Jennifer Cullen, Shiv Srivastava, and Gyorgy Petrovics Indu KohaarIndu Kohaar *Correspondence: E-mail Address: [email protected] Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland Co-inventors on a patent (patent pending), CPDR/Henry M. Jackson Foundation. More articles by this author , Yongmei ChenYongmei Chen Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author , Sreedatta BanerjeeSreedatta Banerjee Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author , Talaibek BorbievTalaibek Borbiev Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland More articles by this author , Huai-Ching KuoHuai-Ching Kuo Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland More articles by this author , Amina AliAmina Ali Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author , Lakshmi RavindranathLakshmi Ravindranath Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author , Jacob KaganJacob Kagan Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland More articles by this author , Sudhir SrivastavaSudhir Srivastava Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland More articles by this author , Albert DobiAlbert Dobi Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland More articles by this author , Isabell A. SesterhennIsabell A. Sesterhenn Joint Pathology Center, Silver Spring, Maryland More articles by this author , Inger L. RosnerInger L. Rosner Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author , Jennifer CullenJennifer Cullen Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author , Shiv SrivastavaShiv Srivastava Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Co-inventors on a patent (patent pending), CPDR/Henry M. Jackson Foundation. Equal contribution as senior authors. More articles by this author , and Gyorgy PetrovicsGyorgy Petrovics * E-mail Address: [email protected]. Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland Co-inventors on a patent (patent pending), CPDR/Henry M. Jackson Foundation. Equal contribution as senior authors. More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000001374AboutAbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Abstract Purpose: Prostate cancer is predominantly indolent at diagnosis with a small fraction (15% to 25%) representing aggressive subtype (Gleason score 7-10), which is prone to metastatic progression. It is critical to explore noninvasive assays for the early detection of this aggressive subtype, when it still can be treated effectively. Additionally, there is an emerging need to develop markers that perform equally well across races, as racial differences in the prevalence and mortality of prostate cancer has become evident. Materials and Methods: First catch, nondigital rectal examination urine specimens were collected from patients undergoing diagnostic biopsy. Total RNA was extracted from urinary exosomes and a quantitative expression assay protocol using droplet digital polymerase chain reaction was developed for detection of candidate genes in exosomal mRNAs from urine. Clinical performance for the gene expression assay was evaluated to predict high grade cancer (Gleason score 7-10) from low grade cancer (Gleason score 6) and cancer negative cases at biopsy. Assay performance was examined in combination with standard of care to determine improvement in model prediction. Results: In a racially diverse patient cohort a 2-gene panel (PCA3, PCGEM1), in combination with standard of care variables, significantly improved the prediction of high grade cancer at diagnosis compared to standard of care variables alone (AUC 0.88 vs 0.80, respectively, p=0.016). Decision curve analysis showed that there is a benefit of adopting the gene panel for detection of high grade cancer compared to standard of care alone. Conclusions: This study highlights the potential for developing broadly applicable prostate cancer diagnostic biomarker panels for aggressive prostate cancer using our novel gene expression assay platform. Abbreviations and Acronyms CaP prostate cancer cDNA complimentary deoxyribonucleic acid ddPCR droplet digital PCR DRE digital rectal examination GS Gleason score PCR polymerase chain reaction PSA prostate specific antigen SOC standard of care In the United States 1 in 5 men are diagnosed with prostate cancer.1 Screening for early detection of CaP has been routinely performed by serum PSA testing and digital rectal examination, which is followed by CaP detection using transrectal ultrasound guided biopsy. Every year in the United States about 1 million men undergo transrectal ultrasound guided prostate biopsies primarily due to elevated serum PSA, of which approximately 70% do not detect CaP.2,3 Evidence suggests that most of the CaP detected are low grade tumors that will remain indolent for the patient’s lifetime.4 Randomized clinical trials have shown that the strongest evidence of CaP mortality reduction is in patients with GS 7-10 tumors, where benefits of radiotherapy or surgery were reported.5,6 Hence, optimal management of CaP is critically dependent on biofluid based biomarkers that would stratify patients with increased probability of harboring high grade tumors for “informed” biopsy procedure.7 These informed biopsies may address the 2 important clinical problems: early identification of aggressive CaP for early treatment, potentially lowering metastatic progression and death rate, and the significant reduction in repeated diagnostic biopsies in men without cancer or with likelihood of low grade tumors. The goals of this study were to present the development and optimization of a noninvasive, first catch, nonDRE urine based sensitive gene expression assay, and to define a CaP gene panel that can discriminate GS 7-10 from GS 6 (3+3) and benign disease before biopsy. In our military (DoD) patient cohort with equal access to health care we attempted to accomplish this in both Black (90) and White (181) patients. Materials and Methods Prostate Cancer Specimens The present study included archived tissue specimens collected under an institutional review board approved protocol (WU IRB No. 11-7487) from patients undergoing radical prostatectomy treatment at the Walter Reed National Military Medical Center. First catch, nonDRE urine specimens were collected from patients undergoing diagnostic biopsy from January 2016 to June 2019. The table and supplementary figure 1 (https://www.jurology.com) describe the patient characteristics of the cohort. The histopathological findings were blinded to the urine marker analyses. Descriptive statistics of patient cohort (271) stratified by race All Black White p Value No. (%) 271 (100) 90 (33.2) 181 (66.8) Age at biopsy: 0.02 Mean (SD) 61.8 (0.4) 60.7 (0.7) 62.4 (0.5) Median (range) 61.8 (43.7, 83.8) 60.6 (47.5, 81.5) 62.8 (43.7, 83.8) PSA at biopsy: 0.14 Mean (SD) 7.5 (0.4) 8.8 (1.1) 6.9 (0.3) Median (range) 5.6 (1.5, 71.5) 6.2 (1.5, 71.5) 5.5 (1.6, 31.8) Biopsy results: 0.32 Negative 129 (47.6) 39 (43.3) 90 (49.7) Positive 142 (52.4) 51 (56.7) 91 (50.3) Biopsy grade: 0.58 3+3 72 (50.7) 23 (45.1) 49 (54) 3+4 34 (23.9) 13 (25.5) 21 (23) 4+3/8-10 36 (25.4) 15 (29.4) 21 (23) Tissue mRNA Analysis All target genes were reverse transcribed from urine exosome RNA in a single reaction (Omniscript RT Kit, Qiagen) using gene specific pool of custom designed reverse primers (supplementary Appendix and supplementary table, https://www.jurology.com). The gene specific cDNA (100 pg final concentration) was pre-amplified using TaqMan® Pre-Amp Master Mix (Life Technologies) with forward and reverse primers (55 nM final concentration) for all target genes. The qPCR was performed on the diluted amplified product with the PCR conditions of 55C for 2 minutes followed by initial denaturation of 95C for 10 minutes and 50 cycles of 95C for 30 seconds, 56C for 1 minute and 72C for 1 minute. Expression was normalized and was analyzed according to the relative quantification method, as ΔCt=Ct GAPDH −Ct target. Fold difference between any 2 samples was calculated as fold=2^(ΔCt1 − ΔCt2). Urine Collection and Exosome Preparation Urine samples were collected in urine collection cups containing preservative (Assay Assure Genelock; Sierra Diagnostics LLC). Exosomes were isolated and mRNA was extracted from urine specimens using a commercial kit (Norgen Biotek Corp) as per manufacturer recommendation. Exosomes were characterized by nanoparticle tracking analysis and visualized by Transmission Electron Microscopy (supplementary Appendix and supplementary fig. 2, https://www.jurology.com).8 Urine Assay A quantitative expression assay protocol using ddPCR was developed for the detection of candidate genes in exosomal mRNAs from urine, using gene specific cDNA synthesis with TaqMan pre-amplification (fig. 1). NKX2.3, HOXC4, COL10A1 and AMACR were excluded owing to their low sensitivity in urine (supplementary Appendix, https://www.jurology.com). Figure 1. Development of sensitive urine exosomal RNA based gene expression assay. A, tissue based mRNA analysis: Heatmap displaying mRNA expression levels assessed by qRT-PCR for 9 selected genes, DLX1, NKX2.3, HOXC4, COL10A1, PSGR and HOXC6 (lower panel); PCA3, AMACR and ERG3 (upper panel) in laser capture microdissected matched tumor normal (T/N) prostate tissue of 35 Black and 50 White patients. Column numbers represent individual patients (85), and rows represent genes of panel. Red squares indicate T/N ratio of fourfold or greater. White squares indicate T/N ratio less than fourfold. Green/blue tiles indicate cumulative expression of gene panel by patients. Pie charts (right) provide performance of PCA3, AMACR, ERG panel in upper panel, compared to 6-gene panel of DLX1, NKX2.3, COL10A1, HOXC4, PSGR and HOXC6 in lower panel, based on robust fourfold overexpression cutoff in tumor in 35 Black and 50 White patients. B, urine exosome characterization. Representative nanoparticle tracking analysis of exosomes isolated from urine. Exosome concentration showed peak at 73.8±0.4 nm. Values are mean±SD, and all values are representative of at least 3 replicates (left) and quantitative determination of exosomes by nanoparticle tracking analysis (right). C, urine assay platform. Schematic representation of assay workflow in patient urine derived exosome specimen. Assay was developed and optimized for SPDEF, PSGR, DLX1, HOXC6, PCGEM1, as well as PCA3 and ERG in regular (nonDRE) urine specimen. mRNA expression was normalized to prostate marker SPDEF and optimal cutoff was determined for each assay. D, assay optimization. Evaluation of mRNA copy numbers of selected genes in LNCaP and VCaP cell lines using ddPCR based TaqMan pre-amplification (input RNA 30 pg). Data represent average of triplicates (left) with representative RT-ddPCR titration readout (right). ddPCR data were analyzed using QuantaSoft software (BioRad). Final expression results were reported as Target/SPDEF ratios. Rigorous technical sample exclusion criteria were applied to triage samples with SPDEF mRNA at less than 10 copies per sample. Patients with serum PSA greater than 75 ng/ml with obviously high risk of CaP, or with prior positive biopsy were excluded. Statistical Analysis Chi-square testing was used to evaluate the associations of categorical clinicopathological variables across race and cancer status (positive vs negative). The Mann-Whitney U and Kruskal-Wallis tests were used to examine the differences in the distribution of urine marker expression across biopsy grade and cancer status. Multivariable ROC curve analysis was performed to examine association with study outcomes. CaP biopsy grade outcome was modeled as a function of 2 markers (PCA3, PCGEM1) dichotomized at an optimal cut point. The optimal cut point represents the Z score of original mRNA expression value. The 95% CI of cutoff was created using bootstrap method with 1,000 replicates, each marker was dichotomized at cutoff, noted as high vs low in further analysis. The cutoff was chosen a point value with the highest sensitivity which satisfy negative predictive value of 65% or greater and specificity of 35% or greater. The optimal cut points were: PCA3, 0.08307 (high vs low) and PCGEM1, 0.44558 (low vs high). Combined marker performance was examined in conjunction with SOC variables, including serum PSA (ng/ml), age at CaP diagnosis (years), and self-reported race (Black, White). Result and Discussion To define mRNA marker candidates for the detection of CaP in racially diverse populations, genes were selected or triaged based on prostate tissue derived mRNA expression data with the selection criteria of 1) overexpression in tumor tissue, and 2) robust expression levels (copy number) (fig. 1, A). NanoString and RNA-Seq data sets were used to select genes that were similarly overexpressed in tumors of both Black and White patients, and were further validated by qRT-PCR using laser capture microdissected tumor and normal epithelial cells from an independent cohort of patients (85) (fig. 1, A).9–11 A 6-gene signature (DLX1, HOXC4, NKX2-3, COL10A1, HOXC6 and PSGR) was identified, providing a consistent tumor expression signature in both Black and White patients. The marker set was further validated in silico in TCGA RNA-Seq data set (supplementary fig. 3, https://www.jurology.com).12 Toward the development of a urine based assay platform, a quantitative mRNA expression method (gene specific RT-PCR and pre-amplification followed by TaqMan based ddPCR) was optimized and developed for noninvasive early detection of candidate genes in regular urine (nonDRE) using exosomal RNA (fig. 1, B), based on the McKiernan et al method with modifications.13 Nanoparticle tracking analysis and transmission electron microscopy were used to characterize normal and cancer derived exosomes from the first catch, nonDRE urine specimens from urine (fig. 1, C, supplementary fig. 2 and supplementary Appendix, https://www.jurology.com). Average exosome size was 70 to 85 nm and the number of exosomes in 1 ml urine was 2×1011- 9×10.11 The candidate genes were based on the CaP tissue mRNA data as described earlier, with PCGEM1, PCA3 and ERG added based on published data from us and others.9,13–16 The 7 final candidate genes selected based on their expression in urine exosomal RNA (PCA3, ERG, HOXC6, PSGR, PCGEM1, DLX1, with a normalizing gene, SPDEF) were reverse transcribed in a single reaction using a gene specific primer pool, followed by pre-amplification and a PCR assay for the target genes, as modified from Klein17 and Knezevic et al (fig. 1, B and D).18 Analytical sensitivity with and without pre-amplification, as well as specificity and accuracy of CaP associated gene detection were measured in VCaP and LNCaP cell lines from ATCC (fig. 1, B). The assays were shown to accurately measure gene expression in a 300 to 0.3 pg RNA input range, demonstrating the ability of the assays to quantify cDNA from the limited amounts available from nonDRE urine specimens from patients before diagnostic biopsy procedure. Assay reproducibility was assessed by independent repeat runs with CV less than 10%. The final analysis for the 7 selected markers was performed in 271 patients with either initial or prior negative biopsy (see table, supplementary fig. 1, https://www.jurology.com). In our patient cohort 142 of the 271 patients (52.4%) were positive for cancer at biopsy, and 36 of the 142 patients (25.4%) had high grade tumors. Remarkably, the analysis showed that PCA3 and PCGEM1 levels were significantly different across biopsy grade (4+3, 8-10 vs 3+4 vs 3+3/biopsy negative; PCA3 levels increased with high grade, p=0.0002, and PCGEM1 expression levels decreased with high grade, p=0.01) (supplementary fig. 4, https://www.jurology.com). Most importantly, the addition of the 2-gene marker panel (PCA3, PCGEM1) to SOC variables significantly improved the prediction of high grade cancer in biopsy compared to SOC variables alone (AUC 0.88 vs 0.80, respectively, p=0.016) (fig. 2, supplementary fig. 5, https://www.jurology.com). Decision curve analysis showed that there was a robust net benefit of adopting the gene panel for detection of high grade cancer compared to SOC alone at any given threshold (fig. 2).19 Figure 2. Urine exosomal 2-gene panel (PCA3, PCGEM1) in combination with SOC predicts high grade prostate cancer at diagnostic biopsy. A, clinical performance of individual markers at optimized cut point. Cut point represents Z score of original mRNA expression value. 95% CI of cutoff was created using bootstrap method with 1,000 replicates, and each marker was dichotomized at cutoff, noted as high vs low in further analysis. Optimal cutoff was chosen point value with highest sensitivity among cut points which satisfy at least negative predictive value 65% or greater and specificity 35% or greater. B, AUC for performance of gene marker panel (PCA3, PCGEM1) plus SOC for predicting high grade cancer (4+3/8-10 vs 3+3/biopsy negative) at biopsy (left) and decision curve analysis depicting standardized net benefit of adopting 2-gene marker panel with SOC compared to SOC alone to predict high grade cancer at biopsy (right). Threshold probability is specific probability of having high grade CaP at which clinician would choose to perform biopsy. Highest curve at any given threshold is optimal decision making strategy to maximize net benefit. SOC includes patient age at CaP diagnosis, race and serum PSA at biopsy. In decision curve analysis, strategies for performing biopsy on all men (gray line) compared to no men (black solid line) are also shown. Logistic regression statistical model was used to compare model performance. PCGEM1 is a prostate tissue specific, androgen regulated long noncoding RNA which was discovered by our laboratory.20 It is significantly overexpressed in Black patients with CaP, and functionally involved in CaP development. PCA3, formerly known as DD3 is also a prostate tissue specific long noncoding RNA, which was discovered by differential display experimental approach.9 It is highly expressed in CaP, independent of the prostate size and serum PSA. The PCA3 urine test, a U.S. Food and Drug Administration approved test, which measures PCA3 mRNA in urine samples after DRE, has been demonstrated as a useful diagnostic tool to aid in guiding biopsy decision among men with prior negative prostate biopsies.7 However, the ability of PCA3 to predict tumor aggressiveness across race, specifically in Black patients, has not been explored. The ExoDx™ Prostate Test is the only other regular (nonDRE) urine exosome based assay that measures ERG and PCA3, along with SPDEF control, in conjunction with SOC variables to determine the risk of GS 7 or higher grade cancer on initial biopsy (AUC 0.73-0.77).13,21 However, these reports have limitation in addressing the clinical utility of the test in Black men due to insufficient number of Black patients in their cohorts. We anticipate that our assay, when combined with SOC variables and current biopsy decision models, will reduce unnecessary biopsies in larger racially heterogeneous populations. This approach is promising for the identification and prioritization of patients for biopsy procedure, who are likely to harbor clinically significant CaP among both Black and White patients. One of the limitations of the study is that our patient cohort has limited generalizability compared to other U.S. cohorts. It is a single institution study under Department of Defense with uniqueness for the racial diversity, equal access health care setting, and military health care beneficiary population.22 Future effort will include validation study in an independent patient cohort. In addition, we will evaluate combined role of magnetic resonance imaging and the marker panel along with SOC for predicting clinically significant CaP at biopsy in Black and White men including patients enrolled under active surveillance. Conclusion We have developed an assay platform and identified a prostate cancer gene panel which, in combination with standard of care variables, significantly improved the prediction of clinically significant prostate cancer in a racially diverse patient population. Additionally, the assay may reduce the burden of unnecessary repeat biopsies. References 1. : Cancer statistics, 2020. CA Cancer J Clin 2020; 70: 7. Google Scholar 2. : Addressing the need for repeat prostate biopsy: new technology and approaches. Nat Rev Urol 2015; 12: 435. Google Scholar 3. : All change in the prostate cancer diagnostic pathway. Nat Rev Clin Oncol 2020; 17: 372. Google Scholar 4. : When is prostate cancer really cancer?Urol Clin North Am 2014; 41: 339. Google Scholar 5. : Screening and prostate cancer mortality: results of the European Randomised Study of Screening for Prostate Cancer (ERSPC) at 13 years of follow-up. Lancet 2014; 384: 2027. Google Scholar 6. : Radical prostatectomy versus observation for localized prostate cancer. N Engl J Med 2012; 367: 203. Google Scholar 7. : A rich array of prostate cancer molecular biomarkers: opportunities and challenges. Int J Mol Sci 2019; 20: 1813. Google Scholar 8. : Evaluation of optimal extracellular vesicle small RNA isolation and qRT-PCR normalisation for serum and urine. J Immunol Methods 2016; 429: 39. Google Scholar 9. : Elevated expression of PCGEM1, a prostate-specific gene with cell growth-promoting function, is associated with high-risk prostate cancer patients. Oncogene 2004; 23: 605. Google Scholar 10. : A novel genomic alteration of LSAMP associates with aggressive prostate cancer in African American men. EBioMedicine 2015; 2: 1957. Google Scholar 11. : Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies ERG as the strongest independent predictor of recurrence. Oncotarget 2019; 10: 6466. Google Scholar 12. The Cancer Genome Atlas Research Network: The molecular taxonomy of primary prostate cancer. Cell 2015; 163: 1011. Google Scholar 13. : A novel urine exosome gene expression assay to predict high-grade prostate cancer at initial biopsy. JAMA Oncol 2016; 2: 882. Google Scholar 14. : A molecular signature of PCA3 and ERG exosomal RNA from non-DRE urine is predictive of initial prostate biopsy result. Prostate Cancer Prostatic Dis 2015; 18: 370. Google Scholar 15. : PCA3 score before radical prostatectomy predicts extracapsular extension and tumor volume. J Urol 1975; 180: 2008. Google Scholar 16. : Evaluation of the ETS-related gene mRNA in urine for the detection of prostate cancer. Clin Cancer Res 2010; 16: 1572. Google Scholar 17. : A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur Urol 2014; 66: 550. Google Scholar 18. : Analytical validation of the Oncotype DX prostate cancer assay—a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genomics 2013; 14: 690. Google Scholar 19. : Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26: 565. Google Scholar 20. : PCGEM1, a prostate-specific gene, is overexpressed in prostate cancer. Proc Natl Acad Sci U S A 2000; 97: 12216. Google Scholar 21. : A prospective adaptive utility trial to validate performance of a novel urine exosome gene expression assay to predict high-grade prostate cancer in patients with prostate-specific antigen 2-10ng/ml at initial biopsy. Eur Urol 2018; 74: 731. Google Scholar 22. : Predicting prostate cancer progression as a function of ETS-related gene status, race, and obesity in a longitudinal patient cohort. Eur Urol Focus 2018; 4: 818. Google Scholar Supported by NCI/EDRN IAA No. ACN 17005-001-0000011 (ShS and ILR). The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions or policies of Uniformed Services University of the Health Sciences (USUHS), The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Defense (DoD), or the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. No direct or indirect commercial, personal, academic, political, religious or ethical incentive is associated with publishing this article. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.© 2020 The Author(s). Published on behalf of the American Urological Association, Education and Research, Inc.FiguresReferencesRelatedDetails Volume 205Issue 2February 2021Page: 420-425Supplementary Materials Advertisement Copyright & Permissions© 2020 The Author(s). Published on behalf of the American Urological Association, Education and Research, Inc.Keywordsprostatic neoplasmsdiagnosisbiomarkersMetricsAuthor Information Indu Kohaar Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland *Correspondence: E-mail Address: [email protected] Co-inventors on a patent (patent pending), CPDR/Henry M. Jackson Foundation. More articles by this author Yongmei Chen Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author Sreedatta Banerjee Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author Talaibek Borbiev Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland More articles by this author Huai-Ching Kuo Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland More articles by this author Amina Ali Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author Lakshmi Ravindranath Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author Jacob Kagan Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland More articles by this author Sudhir Srivastava Cancer Biomarkers Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland More articles by this author Albert Dobi Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland More articles by this author Isabell A. Sesterhenn Joint Pathology Center, Silver Spring, Maryland More articles by this author Inger L. Rosner Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author Jennifer Cullen Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland More articles by this author Shiv Srivastava Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Co-inventors on a patent (patent pending), CPDR/Henry M. Jackson Foundation. Equal contribution as senior authors. More articles by this author Gyorgy Petrovics Center for Prostate Disease Research, John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, Maryland Henry Jackson Foundation for the Advancement of Military Medicine (HJF), Bethesda, Maryland * E-mail Address: [email protected]. Co-inventors on a patent (patent pending), CPDR/Henry M. Jackson Foundation. Equal contribution as senior authors. More articles by this author Expand All Supported by NCI/EDRN IAA No. ACN 17005-001-0000011 (ShS and ILR). The contents of this publication are the sole responsibility of the author(s) and do not necessarily reflect the views, opinions or policies of Uniformed Services University of the Health Sciences (USUHS), The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., the Department of Defense (DoD), or the Departments of the Army, Navy, or Air Force. Mention of trade names, commercial products, or organizations does not imply endorsement by the U.S. Government. No direct or indirect commercial, personal, academic, political, religious or ethical incentive is associated with publishing this article. Advertisement Advertisement PDF DownloadLoading ...
Referência(s)