Check out the new video from PRIMR explaining Decipher Prostate
Physician Portal Pay Your Bill Questions? Call our billing team at 1.888.792.1601 and select option #3 Physician Portal Pay Your Bill

Extensively published in
leading scientific journals

Resources

Publications

Decipher Prostate Genomic Classifier

Post-Biopsy

  1. Awasthi S, et al. Genomic testing in localized prostate cancer can identify subsets of African-Americans with aggressive disease. J Natl Cancer Inst 2022.
  2. Parry, M. et al. 1358O Clinical qualification of transcriptome signatures for advanced prostate cancer (APC) starting androgen deprivation therapy (ADT) with or without abiraterone acetate and prednisolone (AAP): An ancillary study of the STAMPEDE AAP trial. Ann Oncol 2022; 33: S1161.
  3. Press BH, et al. Association Between a 22-feature Genomic Classifier and Biopsy Gleason Upgrade During Active Surveillance for Prostate Cancer. Eur Urol Open Sci 2022; 37: 113-119.
  4. Spratt DE, et al. Validation of the performance of the Decipher biopsy genomic classifier in intermediate-risk prostate cancer on the phase III randomized trial NRG Oncology/RTOG 0126. J Clin Oncol 2022; 40 (6_suppl): 269.
  5. Feng FY, et al. Association of Molecular Subtypes With Differential Outcome to Apalutamide Treatment in Nonmetastatic Castration-Resistant Prostate Cancer. JAMA Oncol 2021; 7: 1005-1014.
  6. Hamid AA et al. Transcriptional profiling of primary prostate tumor in metastatic hormone-sensitive prostate cancer and association with clinical outcomes: correlative analysis of the E3805 CHAARTED study. Ann Oncol 2021.
  7. Nguyen PL, et al. Validation of a 22-gene Genomic Classifier in the NRG Oncology/RTOG 9202, 9413 and 9902 Phase III Randomized Trials: A Biopsy-Based Individual Patient Meta-Analysis in High-Risk Prostate Cancer. Int J Radiat Oncol Biol Phys 2021; 111: S50.
  8. Punnen S, et al. Heterogeneity in Genomic Risk Assessment from Tissue Based Prognostic Signatures Used in the Biopsy Setting and the Impact of Magnetic Resonance Imaging Targeted Biopsy. J Urol 2021; 205: 1344-1351.
  9. Vince RA Jr. et al. Impact of Decipher Biopsy testing on clinical outcomes in localized prostate cancer in a prospective statewide collaborative. Prostate Cancer Prostatic Dis 2021.
  10. Goldberg H, et al. Clinical-genomic Characterization Unveils More Aggressive Disease Features in Elderly Prostate Cancer Patients with Low-grade Disease. Eur Urol Focus 2020.
  11. Feng FY, et al. Molecular determinants of outcome for metastatic castration-sensitive prostate cancer (mCSPC) with addition of apalutamide (APA) or placebo (PBO) to androgen deprivation therapy (ADT) in TITAN. J Clin Oncol 2020; 38: 5535-5535.
  12. Herlemann, A et al. Decipher identifies men with otherwise clinically favorable-intermediate risk disease who may not be good candidates for active surveillance. Prostate Cancer Prostatic Dis 2020; 23: 136-143.
  13. Tosoian JJ, et al. Performance of clinicopathologic models in men with high risk localized prostate cancer: impact of a 22-gene genomic classifier. Prostate Cancer Prostatic Dis 2020; 23: 646-653.
  14. Berlin A, et al. Genomic Classifier for Guiding Treatment of Intermediate-Risk Prostate Cancers to Dose-Escalated Image Guided Radiation Therapy Without Hormone Therapy. Int J Radiat Oncol Biol Phys 2019; 103(1): 84-91.
  15. Falagario UG, et al. Defining Prostate Cancer at Favorable Intermediate Risk: The Potential Utility of Magnetic Resonance Imaging and Genomic Tests. J Urol 2019; 202(1): 102-07.
  16. Kim HL, et al. Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance. Prostate Cancer Prostatic Dis 2019; 22(3): 399-405.
  17. Martin DT, et al. Prostate Cancer Genomic Classifier Relates More Strongly to Gleason Grade Group Than Prostate Imaging Reporting and Data System Score in Multiparametric Prostate Magnetic Resonance Imaging-ultrasound Fusion Targeted Biopsies. Urology 2019; 125: 64-72.
  18. Muralidhar V, et al. Genomic Validation of 3-Tiered Clinical Subclassification of High-Risk Prostate Cancer. Int J Radiat Oncol Biol Phys 2019; 105: 621-627.
  19. Purysko AS, et al. Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings. Eur Radiol 2019; 29(9): 4861-70.
  20. Van den Broeck T, et al. Validation of the Decipher Test for Predicting Distant Metastatic Recurrence in Men with High-risk Nonmetastatic Prostate Cancer 10 Years After Surgery. Eur Urol Oncol 2019; 2(5): 589-96.
  21. Xu MJ, et al. Genomic Risk Predicts Molecular Imaging-detected Metastatic Nodal Disease in Prostate Cancer. Eur Urol Oncol 2019; 2: 685-690.
  22. Beksac AT, et al. Multiparametric Magnetic Resonance Imaging Features Identify Aggressive Prostate Cancer at the Phenotypic and Transcriptomic Level. J Urol 2018; 200(6): 1241-49.
  23. Cooperberg MR, et al. The Diverse Genomic Landscape of Clinically Low-risk Prostate Cancer. Eur Urol 2018; 74(4): 444-52.
  24. Hu JC, et al. Clinical Utility of Gene Expression Classifiers in Men With Newly Diagnosed Prostate Cancer. JCO Precision Oncology 2018; 2: 1-15.
  25. Radtke JP, et al. Transcriptome Wide Analysis of Magnetic Resonance Imaging-targeted Biopsy and Matching Surgical Specimens from High-risk Prostate Cancer Patients Treated with Radical Prostatectomy: The Target Must Be Hit. Eur Urol Focus 2018; 4(4): 540-46.
  26. Spratt DE, et al. Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer. J Clin Oncol 2018; 36(6): 581-90.
  27. Klein EA, et al. Molecular Analysis of Low Grade Prostate Cancer Using a Genomic Classifier of Metastatic Potential. J Urol 2017; 197(1): 122-28.
  28. Nguyen PL, et al. Ability of a Genomic Classifier to Predict Metastasis and Prostate Cancer-specific Mortality after Radiation or Surgery based on Needle Biopsy Specimens. Eur Urol 2017; 72(5): 845-52.
  29. Nguyen PL, et al. Utilization of biopsy-based genomic classifier to predict distant metastasis after definitive radiation and short-course ADT for intermediate and high-risk prostate cancer. Prostate Cancer Prostatic Dis 2017; 20(2): 186-92.
  30. Klein EA, et al. Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk. Urology 2016; 90: 148-52.
  31. Knudsen BS, et al. Application of a Clinical Whole-Transcriptome Assay for Staging and Prognosis of Prostate Cancer Diagnosed in Needle Core Biopsy Specimens. J Mol Diagn 2016; 18(3): 395-406.
  32. Lee HJ, et al. Evaluation of a genomic classifier in radical prostatectomy patients with lymph node metastasis. Res Rep Urol 2016; 8: 77-84.
  33. Stoyanova R, et al. Association of multiparametric MRI quantitative imaging features with prostate cancer gene expression in MRI-targeted prostate biopsies. Oncotarget 2016; 7(33): 53362-76.

Post-Radical Prostatectomy

  1. Dal Pra A, et al. Validation of the Decipher Genomic Classifier in Patients receiving Salvage Radiotherapy without Hormone Therapy after Radical Prostatectomy – An Ancillary Study of the SAKK 09/10 Randomized Clinical Trial. Ann Oncol 2022; 33(9): 950-958.
  2. Lone Z, et al. Transcriptomic Features of Cribriform and Intraductal Carcinoma of the Prostate. Eur Urol Focus 2022; S2405-4569(22)00125-0.
  3. Ramotar M, et al. Subpathologies and genomic classifier for treatment individualization of post-prostatectomy radiotherapy. Urol. Oncol. 2022; 40 (1): 5.e1-5.e13.
  4. Feng FY, et al. Validation of a 22-Gene Genomic Classifier in Patients With Recurrent Prostate Cancer: An Ancillary Study of the NRG/RTOG 9601 Randomized Clinical Trial. JAMA Oncol 2021.
  5. Lee DI, et al. External validation of genomic classifier-based risk-stratification tool to identify candidates for adjuvant radiation therapy in patients with prostate cancer. World J Urol 2021.
  6. Li L, et al. A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI. EBioMedicine 2021; 63: 103163.
  7. Shahait M, et al. Impact of Decipher on use of post-operative radiotherapy: Individual patient analysis of two prospective registries. BJU Int 2021; 00: 1-8.
  8. Gore JL, et al. Clinical Utility of a Genomic Classifier in Men Undergoing Radical Prostatectomy: The PRO-IMPACT Trial. Pract Radiat Oncol 2020; 10: e82-e90.
  9. Howard LE, et al. Validation of a genomic classifier for prediction of metastasis and prostate cancer-specific mortality in African-American men following radical prostatectomy in an equal access healthcare setting. Prostate Cancer Prostatic Dis 2020; 23: 419-428.
  10. Kishan AU, et al. Transcriptomic Heterogeneity of Gleason Grade Group 5 Prostate Cancer. Eur Urol 2020; 78: 327-332.
  11. Marascio J, et al. Prospective study to define the clinical utility and benefit of Decipher testing in men following prostatectomy. Prostate Cancer Prostatic Dis 2020; 23: 295-302.
  12. Jambor I, et al. Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score. J Magn Reson Imaging 2020; 51: 1075-1085.
  13. Taylor AS, et al. Correlation between cribriform/intraductal prostatic adenocarcinoma and percent Gleason pattern 4 to a 22-gene genomic classifier. Prostate 2020; 80: 146-152.
  14. Martini A, et al. A transcriptomic signature of tertiary Gleason 5 predicts worse clinicopathological outcome. BJU Int 2019; 124(1): 155-62.
  15. Karnes RJ, et al. Validation of a Genomic Risk Classifier to Predict Prostate Cancer-specific Mortality in Men with Adverse Pathologic Features. Eur Urol 2018; 73(2): 168-75.
  16. Spratt DE, et al. Performance of a Prostate Cancer Genomic Classifier in Predicting Metastasis in Men with Prostate-specific Antigen Persistence Postprostatectomy. Eur Urol 2018;74(1):107-14.
  17. Dalela D, et al. Genomic Classifier Augments the Role of Pathological Features in Identifying Optimal Candidates for Adjuvant Radiation Therapy in Patients With Prostate Cancer: Development and Internal Validation of a Multivariable Prognostic Model. J Clin Oncol 2017; 35(18): 1982-90.
  18. Gore JL, et al. Decipher test impacts decision making among patients considering adjuvant and salvage treatment after radical prostatectomy: Interim results from the Multicenter Prospective PRO-IMPACT study. Cancer 2017; 123(15): 2850-59.
  19. Lobo JM, et al. Cost-effectiveness of the Decipher Genomic Classifier to Guide Individualized Decisions for Early Radiation Therapy After Prostatectomy for Prostate Cancer. Clin Genitourin Cancer 2017; 15(3): e299-e309.
  20. Spratt DE, et al. Individual Patient-Level Meta-Analysis of the Performance of the Decipher Genomic Classifier in High-Risk Men After Prostatectomy to Predict Development of Metastatic Disease. J Clin Oncol 2017; 35(18): 1991-98.
  21. Den RB, et al. Decipher correlation patterns post prostatectomy: initial experience from 2,342 prospective patients. Prostate Cancer Prostatic Dis 2016; 19(4): 374-79.
  22. Freedland SJ, et al. Utilization of a Genomic Classifier for Prediction of Metastasis Following Salvage Radiation Therapy after Radical Prostatectomy. Eur Urol 2016;70(4):588-96.
  23. Glass AG, et al. Validation of a Genomic Classifier for Predicting Post-Prostatectomy Recurrence in a Community Based Health Care Setting. J Urol 2016; 195(6): 1748-53.
  24. Ross AE, et al. Tissue-based Genomics Augments Post-prostatectomy Risk Stratification in a Natural History Cohort of Intermediate- and High-Risk Men. Eur Urol 2016; 69(1): 157-65.
  25. Ross AE, et al. Efficacy of post-operative radiation in a prostatectomy cohort adjusted for clinical and genomic risk. Prostate Cancer Prostatic Dis 2016; 19(3): 277-82.
  26. Badani KK, et al. Effect of a genomic classifier test on clinical practice decisions for patients with high-risk prostate cancer after surgery. BJU Int 2015; 115(3): 419-29.
  27. Cooperberg MR, et al. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur Urol 2015; 67(2): 326-33.
  28. Den RB, et al. Genomic classifier identifies men with adverse pathology after radical prostatectomy who benefit from adjuvant radiation therapy. J Clin Oncol 2015; 33(8): 944-51.
  29. Klein EA, et al. A genomic classifier improves prediction of metastatic disease within 5 years after surgery in node-negative high-risk prostate cancer patients managed by radical prostatectomy without adjuvant therapy. Eur Urol 2015; 67(4): 778-86.
  30. Lobo JM, et al. Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis. PLoS One 2015; 10(3): e0116866.
  31. Nguyen PL, et al. Impact of a Genomic Classifier of Metastatic Risk on Postprostatectomy Treatment Recommendations by Radiation Oncologists and Urologists. Urology 2015;86(1):35-40.
  32. Den RB, et al. Genomic prostate cancer classifier predicts biochemical failure and metastases in patients after postoperative radiation therapy. Int J Radiat Oncol Biol Phys 2014; 89(5): 1038-46.
  33. Michalopoulos SN, et al. Influence of a genomic classifier on post-operative treatment decisions in high-risk prostate cancer patients: results from the PRO-ACT study. Curr Med Res Opin 2014; 30(8): 1547-56.
  34. Ross AE, et al. A genomic classifier predicting metastatic disease progression in men with biochemical recurrence after prostatectomy. Prostate Cancer Prostatic Dis 2014;17(1):64-9.
  35. Badani K, et al. Impact of a genomic classifier of metastatic risk on postoperative treatment recommendations for prostate cancer patients: a report from the DECIDE study group. Oncotarget 2013; 4(4): 600-9.
  36. Erho N, et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS One 2013;8(6):e66855.
  37. Karnes RJ, et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J Urol 2013;190(6):2047-53.

Decipher GRID (All)

  1. Chakravarty D, et al. Association between Incidental Pelvic Inflammation and Aggressive Prostate Cancer. Cancers (Basel) 2022; 14(11): 2734.
  2. Kensler KH, et al. Variation in Molecularly Defined Prostate Tumor Subtypes by Self-identified Race. Eur Urol Open Sci 2022; 40: 19-26.
  3. Martini, A. et al. The Paradoxical Role of Body Mass Index in Patients with Muscle-invasive Bladder Cancer Receiving Neoadjuvant Immunotherapy. Eur Urol Oncol 2022; 5: 370-372.
  4. Qiu X, et al. MYC drives aggressive prostate cancer by disrupting transcriptional pause release at androgen receptor targets. Nat Commun 2022; 13: 2559.
  5. Pryma C, et al. Uroplakin II as a single marker for luminal versus basal molecular subtypes in muscle invasive urothelial carcinoma. Virchows Arch 2022; 481: 397-403.
  6. Shahait, et al. Does Perioperative Testosterone Predict Post-Prostatectomy Genomic Risk Score? J Urol 2022.
  7. Van den Broeck, et al. Antizyme Inhibitor 1 Regulates Matrikine Expression and Enhances the Metastatic Potential of Aggressive Primary Prostate Cancer. Mol Cancer Res 2022.
  8. Weiner AB, et al. High intratumoral plasma cells content in primary prostate cancer defines a subset of tumors with potential susceptibility to immune-based treatments. Prostate Cancer Prostatic Dis 2022;
  9. Yoshikawa Y, et al. Increased MYBL2 expression in aggressive hormone-sensitive prostate cancer. Mol Oncol 2022.
  10. Alshalalfa M, et al. Expression of ISL1 and its partners in prostate cancer progression and neuroendocrine differentiation. J Cancer Res Clin Oncol 2021.
  11. Awasthi S, et al. Comparative Genomics Reveals Distinct Immune-oncologic Pathways in African American Men with Prostate Cancer. Clin Cancer Res 2021; 27: 320-329.
  12. Chu CE, et al. Prostate-specific Membrane Antigen and Fluciclovine Transporter Genes are Associated with Variable Clinical Features and Molecular Subtypes of Primary Prostate Cancer. Eur Urol 2021.
  13. de Jong JJ, et al. Gene Expression Profiling of Muscle-Invasive Bladder Cancer With Secondary Variant Histology. Am J Clin Pathol 2021; 156: 895-905.
  14. Ding YC, et al. Prostate cancer in young men represents a distinct clinical phenotype: gene expression signature to predict early metastases. J Transl Genet Genom 2021; 5: 50-61.
  15. Grbesa I, et al. Reshaping of the androgen-driven chromatin landscape in normal prostate cells by early cancer drivers and effect on therapeutic sensitivity. Cell Rep 2021; 36: 109625.
  16. Krentel F, et al. A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer. Sci Rep 2021; 11: 5849.
  17. Liu D, et al. Tumor subtype defines distinct pathways of molecular and clinical progression in primary prostate cancer. J Clin Invest 2021; 131.
  18. Mukhopadhyay C, et al. G3BP1 inhibits Cul3(SPOP) to amplify AR signaling and promote prostate cancer. Nat Commun 2021; 12: 6662.
  19. Rayford W, et al. Comparative analysis of 1152 African-American and European-American men with prostate cancer identifies distinct genomic and immunological differences. Commun Biol 2021; 4: 670.
  20. Singh, et al. The long noncoding RNA H19 regulates tumor plasticity in neuroendocrine prostate cancer. Nat Commun 2021.
  21. Todenhofer T, et al. Evaluation of carbonic anhydrase IX as a potential therapeutic target in urothelial carcinoma. Urol Oncol 2021.
  22. Vandekerkhove G, et al. Plasma ctDNA is a tumor tissue surrogate and enables clinical-genomic stratification of metastatic bladder cancer. Nat Commun 2021; 12:184.
  23. Weiner AB, et al. A transcriptomic model for homologous recombination deficiency in prostate cancer. Prostate Cancer Prostatic Dis 2021.
  24. Weiner AB, et al. Plasma cells are enriched in localized prostate cancer in Black men and are associated with improved outcomes. Nat Commun 2021; 12: 935.
  25. Yoon J, et al. A comparative study of PCS and PAM50 prostate cancer classification schemes. Prostate Cancer Prostatic Dis 2021.
  26. Bahler CD, et al. Predictors of Prostate-specific Membrane Antigen (PSMA/FOLH1) Expression in a Genomic Database. Urology 2020; 144: 117-122.
  27. Ben-Salem S, et al. Diversity in Androgen Receptor Action Among Treatment-naive Prostate Cancers Is Reflected in Treatment Response Predictions and Molecular Subtypes. Eur Urol Open Sci 2020; 22: 34-44.
  28. Chipidza FE, et al. Development and Validation of a Novel TP53 Mutation Signature That Predicts Risk of Metastasis in Primary Prostate Cancer. Clin Genitourin Cancer 2020.
  29. Ferrari MG, et al. Identifying and treating ROBO1(-ve) /DOCK1(+ve) prostate cancer: An aggressive cancer subtype prevalent in African American patients. Prostate 2020; 80: 1045-1057.
  30. Mahal BA, et al. Genomic and clinical characterization of stromal infiltration markers in prostate cancer. Cancer 2020; 126(7): 1407-1412.
  31. Shoag J, et al. Prognostic value of the SPOP mutant genomic subclass in prostate cancer. Urol Oncol 2020; 38: 418-422.
  32. Tabrizi S, et al. Doublecortin Expression in Prostate Adenocarcinoma and Neuroendocrine Tumors. Int J Radiat Oncol Biol Phys 2020; 108: 936-940.
  33. Weiner AB, et al. Somatic HOXB13 Expression Correlates with Metastatic Progression in Men with Localized Prostate Cancer Following Radical Prostatectomy. Eur Urol Oncol 2020.
  34. Yamoah K, et al. Novel Transcriptomic Interactions Between Immune Content and Genomic Classifier Predict Lethal Outcomes in High-grade Prostate Cancer. Eur Urol 2020.
  35. Adams EJ, et al. FOXA1 mutations alter pioneering activity, differentiation and prostate cancer phenotypes. Nature 2019;571(7765):408-12.
  36. Alshalalfa M, et al. Characterization of transcriptomic signature of primary prostate cancer analogous to prostatic small cell neuroendocrine carcinoma. Int J Cancer 2019; 145: 3453-3461.
  37. Alshalalfa M, et al. Transcriptomic and Clinical Characterization of Neuropeptide Y Expression in Localized and Metastatic Prostate Cancer: Identification of Novel Prostate Cancer Subtype with Clinical Implications. Eur Urol Oncol 2019;2(4):405-12.
  38. Berglund AE, et al. Distinct transcriptional repertoire of the androgen receptor in ETS fusion-negative prostate cancer. Prostate Cancer Prostatic Dis 2019; 22(2): 292-302.
  39. Boufaied N, et al. Development of a predictive model for stromal content in prostate cancer samples to improve signature performance. J Pathol 2019; 249: 411-424.
  40. Cato L, et al. ARv7 Represses Tumor-Suppressor Genes in Castration-Resistant Prostate Cancer. Cancer Cell 2019; 35(3): 401-13 e6.
  41. Chen WS, et al. Novel RB1-Loss Transcriptomic Signature Is Associated with Poor Clinical Outcomes across Cancer Types. Clin Cancer Res 2019; 25(14): 4290-99.
  42. Cheng A, et al. A four-gene transcript score to predict metastatic-lethal progression in men treated for localized prostate cancer: Development and validation studies. Prostate 2019; 79(14): 1589-96.
  43. Echevarria MI, et al. African American Specific Gene Panel Predictive of Poor Prostate Cancer Outcome. J Urol 2019;202(2):247-55.
  44. Feng Y, et al. Metagenomic and metatranscriptomic analysis of human prostate microbiota from patients with prostate cancer. BMC Genomics 2019;20(1):146.
  45. Gerke T, et al. Low Tristetraprolin Expression Is Associated with Lethal Prostate Cancer. Cancer Epidemiol Biomarkers Prev 2019;28(3):584-90.
  46. Hectors SJ, et al. Radiomics Features Measured with Multiparametric Magnetic Resonance Imaging Predict Prostate Cancer Aggressiveness. J Urol 2019;202(3):498-505.
  47. Hughes RM, et al. Asporin Restricts Mesenchymal Stromal Cell Differentiation, Alters the Tumor Microenvironment, and Drives Metastatic Progression. Cancer Res 2019;79(14):3636-50.
  48. Labbe DP, et al. High-fat diet fuels prostate cancer progression by rewiring the metabolome and amplifying the MYC program. Nat Commun 2019;10(4358).
  49. Mahal BA, et al. Prostate Cancer Genomic-risk Differences Between African-American and White Men Across Gleason Scores. Eur Urol 2019;75(6):1038-40.
  50. Ramnarine VR, et al. The evolution of long noncoding RNA acceptance in prostate cancer initiation, progression, and its clinical utility in disease management. Eur Urol 2019; 76: 546-559.
  51. Sjostrom M, et al. Clinicogenomic Radiotherapy Classifier Predicting the Need for Intensified Locoregional Treatment After Breast-Conserving Surgery for Early-Stage Breast Cancer. J Clin Oncol 2019; 37: 3340-3349.
  52. Sjostrom M, et al. Comprehensive transcriptomic profiling identifies breast cancer patients who may be spared adjuvant systemic therapy. Clin Cancer Res 2019; 26: 171-182.
  53. Spratt DE, et al. Transcriptomic heterogeneity of androgen receptor (AR) activity defines a de novo low AR-active subclass in treatment naive primary prostate cancer. Clin Cancer Res 2019; 25: 6721-6730.
  54. Zhao SG, et al. Clinical and Genomic Implications of Luminal and Basal Subtypes Across Carcinomas. Clin Cancer Res 2019; 25(8): 2450-57.
  55. Zhao SG, et al. The Immune Landscape of Prostate Cancer and Nomination of PD-L2 as a Potential Therapeutic Target. J Natl Cancer Inst 2019; 111(3): 301-10.
  56. Abou-Ouf H, et al. Validation of a 10-gene molecular signature for predicting biochemical recurrence and clinical metastasis in localized prostate cancer. J Cancer Res Clin Oncol 2018; 144(5): 883-91.
  57. Karnes RJ, et al. Development and Validation of a Prostate Cancer Genomic Signature that Predicts Early ADT Treatment Response Following Radical Prostatectomy. Clin Cancer Res 2018; 24(16): 3908-16.
  58. Liu D, et al. Impact of the SPOP Mutant Subtype on the Interpretation of Clinical Parameters in Prostate Cancer. JCO Precis Oncol 2018.
  59. Mahal BA, et al. Clinical and Genomic Characterization of Low-Prostate-specific Antigen, High-grade Prostate Cancer. Eur Urol 2018; 74(2): 146-54.
  60. Mo F, et al. Stromal Gene Expression is Predictive for Metastatic Primary Prostate Cancer. Eur Urol 2018; 73(4): 524-32.
  61. Rai R, et al. Epigenetic analysis identifies factors driving racial disparity in prostate cancer. Cancer Reports 2018;2(2):e1153.
  62. Ramnarine VR, et al. The long noncoding RNA landscape of neuroendocrine prostate cancer and its clinical implications. Gigascience 2018; 7(6).
  63. Rounbehler RJ, et al. Tristetraprolin Is a Prognostic Biomarker for Poor Outcomes among Patients with Low-Grade Prostate Cancer. Cancer Epidemiol Biomarkers Prev 2018; 27(11): 1376-83.
  64. Salami SS, et al. Transcriptomic heterogeneity in multifocal prostate cancer. JCI Insight 2018; 3(21).
  65. Sharma V, et al. Gene Expression Correlates of Site-specific Metastasis Among Men with Lymph Node Positive Prostate Cancer Treated With Radical Prostatectomy: A Case Series. Urology 2018; 112: 29-32.
  66. Todenhofer T, et al. Selective Inhibition of the Lactate Transporter MCT4 Reduces Growth of Invasive Bladder Cancer. Mol Cancer Ther 2018; 17(12): 2746-55.
  67. Torres A, et al. ETS2 is a prostate basal cell marker and is highly expressed in prostate cancers aberrantly expressing p63. Prostate 2018; 78(12): 896-904.
  68. Winters BR, et al. Mechanistic target of rapamycin (MTOR) protein expression in the tumor and its microenvironment correlates with more aggressive pathology at cystectomy. Urol Oncol 2018; 36(7): 342 e7-42 e14.
  69. Yang L, et al. Development and Validation of a 28-gene Hypoxia-related Prognostic Signature for Localized Prostate Cancer. EBioMedicine 2018; 31: 182-89.
  70. Alshalalfa M, et al. Low PCA3 expression is a marker of poor differentiation in localized prostate tumors: exploratory analysis from 12,076 patients. Oncotarget 2017; 8(31): 50804-13.
  71. Benzon B, et al. Correlation of B7-H3 with androgen receptor, immune pathways and poor outcome in prostate cancer: an expression-based analysis. Prostate Cancer Prostatic Dis 2017; 20(1): 28-35.
  72. Das R, et al. MicroRNA-194 Promotes Prostate Cancer Metastasis by Inhibiting SOCS2. Cancer Res 2017; 77(4): 1021-34.
  73. Flores IE, et al. Stress alters the expression of cancer-related genes in the prostate. BMC Cancer 2017; 17(1): 621.
  74. Guedes LB, et al. Analytic, Preanalytic, and Clinical Validation of p53 IHC for Detection of TP53 Missense Mutation in Prostate Cancer. Clin Cancer Res 2017; 23(16): 4693-703.
  75. Itkonen HM, et al. Lipid degradation promotes prostate cancer cell survival. Oncotarget 2017; 8(24): 38264-75.
  76. Kim H, et al. Transcriptome evaluation of the relation between body mass index and prostate cancer outcomes. Cancer 2017; 123(12): 2240-47.
  77. Kiss B, et al. Her2 alterations in muscle-invasive bladder cancer: Patient selection beyond protein expression for targeted therapy. Sci Rep 2017; 7: 42713.
  78. Labbe DP, et al. TOP2A and EZH2 Provide Early Detection of an Aggressive Prostate Cancer Subgroup. Clin Cancer Res 2017; 23(22): 7072-83.
  79. Liang Y, et al. LSD1-Mediated Epigenetic Reprogramming Drives CENPE Expression and Prostate Cancer Progression. Cancer Res 2017; 77(20): 5479-90.
  80. McNair C, et al. Cell cycle-coupled expansion of AR activity promotes cancer progression. Oncogene 2017; 36(12): 1655-68.
  81. Nouri M, et al. Therapy-induced developmental reprogramming of prostate cancer cells and acquired therapy resistance. Oncotarget 2017; 8(12): 18949-67.
  82. Pellegrini KL, et al. Evaluation of a 24-gene signature for prognosis of metastatic events and prostate cancer-specific mortality. BJU Int 2017; 119(6): 961-67.
  83. Seiler R, et al. An Oncofetal Glycosaminoglycan Modification Provides Therapeutic Access to Cisplatin-resistant Bladder Cancer. Eur Urol 2017; 72(1): 142-50.
  84. Torres A, et al. Comprehensive Determination of Prostate Tumor ETS Gene Status in Clinical Samples Using the CLIA Decipher Assay. J Mol Diagn 2017; 19(3): 475-84.
  85. Tsai HK, et al. Gene expression signatures of neuroendocrine prostate cancer and primary small cell prostatic carcinoma. BMC Cancer 2017; 17(1): 759.
  86. Tse BWC, et al. Neuropilin-1 is upregulated in the adaptive response of prostate tumors to androgen-targeted therapies and is prognostic of metastatic progression and patient mortality. Oncogene 2017; 36(24): 3417-27.
  87. Urbanucci A, et al. Androgen Receptor Deregulation Drives Bromodomain-Mediated Chromatin Alterations in Prostate Cancer. Cell Rep 2017; 19(10): 2045-59.
  88. Wahl DR, et al. Pan-Cancer Analysis of Genomic Sequencing Among the Elderly. Int J Radiat Oncol Biol Phys 2017; 98(4): 726-32.
  89. Wei L, et al. Intratumoral and Intertumoral Genomic Heterogeneity of Multifocal Localized Prostate Cancer Impacts Molecular Classifications and Genomic Prognosticators. Eur Urol 2017; 71(2): 183-92.
  90. White NM, et al. Multi-institutional Analysis Shows that Low PCAT-14 Expression Associates with Poor Outcomes in Prostate Cancer. Eur Urol 2017; 71(2): 257-66.
  91. Zhao SG, et al. Associations of Luminal and Basal Subtyping of Prostate Cancer With Prognosis and Response to Androgen Deprivation Therapy. JAMA Oncol 2017; 3(12): 1663-72.
  92. Evans JR, et al. Patient-Level DNA Damage and Repair Pathway Profiles and Prognosis After Prostatectomy for High-Risk Prostate Cancer. JAMA Oncol 2016; 2(4): 471-80.
  93. Faisal FA, et al. Racial Variations in Prostate Cancer Molecular Subtypes and Androgen Receptor Signaling Reflect Anatomic Tumor Location. Eur Urol 2016; 70(1): 14-17.
  94. Hu BR, et al. AXIN2 expression predicts prostate cancer recurrence and regulates invasion and tumor growth. Prostate 2016; 76(6): 597-608.
  95. Hurley PJ, et al. Germline Variants in Asporin Vary by Race, Modulate the Tumor Microenvironment, and Are Differentially Associated with Metastatic Prostate Cancer. Clin Cancer Res 2016; 22(2): 448-58.
  96. Johnson MH, et al. SPINK1 Defines a Molecular Subtype of Prostate Cancer in Men with More Rapid Progression in an at Risk, Natural History Radical Prostatectomy Cohort. J Urol 2016; 196(5): 1436-44.
  97. Kim H, et al. Potential Impact on Clinical Decision Making via a Genome-Wide Expression Profiling: A Case Report. Urol Case Rep 2016; 9: 51-54.
  98. Spans L, et al. Genomic and epigenomic analysis of high-risk prostate cancer reveals changes in hydroxymethylation and TET1. Oncotarget 2016; 7(17): 24326-38.
  99. You S, et al. Integrated Classification of Prostate Cancer Reveals a Novel Luminal Subtype with Poor Outcome. Cancer Res 2016; 76(17): 4948-58.
  100. Zhao SG, et al. Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis. Lancet Oncol 2016; 17(11): 1612-20.
  101. Zhao SG, et al. The Landscape of Prognostic Outlier Genes in High-Risk Prostate Cancer. Clin Cancer Res 2016; 22(7): 1777-86.
  102. Alshalalfa M, et al. Clinical and genomic analysis of metastatic prostate cancer progression with a background of postoperative biochemical recurrence. BJU Int 2015; 116(4): 556-67.
  103. Alshalalfa M, et al. Evolving transcriptomic fingerprint based on genome-wide data as prognostic tools in prostate cancer. Biol Cell 2015; 107(7): 232-44.
  104. Goodwin JF, et al. DNA-PKcs-Mediated Transcriptional Regulation Drives Prostate Cancer Progression and Metastasis. Cancer Cell 2015; 28(1): 97-113.
  105. Hurley PJ, et al. Androgen-Regulated SPARCL1 in the Tumor Microenvironment Inhibits Metastatic Progression. Cancer Res 2015; 75(20): 4322-34.
  106. Jager W, et al. Patient-derived bladder cancer xenografts in the preclinical development of novel targeted therapies. Oncotarget 2015; 6(25): 21522-32.
  107. Tomlins SA, et al. Characterization of 1577 primary prostate cancers reveals novel biological and clinicopathologic insights into molecular subtypes. Eur Urol 2015; 68(4): 555-67.
  108. Tsai H, et al. Cyclin D1 Loss Distinguishes Prostatic Small-Cell Carcinoma from Most Prostatic Adenocarcinomas. Clin Cancer Res 2015; 21(24): 5619-29.
  109. Yamoah K, et al. Novel Biomarker Signature That May Predict Aggressive Disease in African American Men With Prostate Cancer. J Clin Oncol 2015; 33(25): 2789-96.
  110. Zhao SG, et al. High-throughput transcriptomic analysis nominates proteasomal genes as age-specific biomarkers and therapeutic targets in prostate cancer. Prostate Cancer Prostatic Dis 2015; 18(3): 229-36.
  111. Chakravarty D, et al. The oestrogen receptor alpha-regulated lncRNA NEAT1 is a critical modulator of prostate cancer. Nat Commun 2014; 5: 5383.
  112. Kaushik AK, et al. Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer. J Proteome Res 2014; 13(2): 1088-100.
  113. Lalonde E, et al. Tumour genomic and microenvironmental heterogeneity for integrated prediction of 5-year biochemical recurrence of prostate cancer: a retrospective cohort study. Lancet Oncol 2014; 15(13): 1521-32.
  114. Prensner JR, et al. RNA biomarkers associated with metastatic progression in prostate cancer: a multi-institutional high-throughput analysis of SChLAP1. Lancet Oncol 2014; 15(13): 1469-80.
  115. Prensner JR, et al. The IncRNAs PCGEM1 and PRNCR1 are not implicated in castration resistant prostate cancer. Oncotarget 2014; 5(6): 1434-8.
  116. Prensner JR, et al. The long noncoding RNA SChLAP1 promotes aggressive prostate cancer and antagonizes the SWI/SNF complex. Nat Genet 2013; 45(11): 1392-8.
  117. Wiseman SM, et al. Whole-transcriptome profiling of thyroid nodules identifies expression-based signatures for accurate thyroid cancer diagnosis. J Clin Endocrinol Metab 2013; 98(10): 4072-9.
  118. Erho N, et al. Transcriptome-wide detection of differentially expressed coding and non-coding transcripts and their clinical significance in prostate cancer. J Oncol 2012; 2012: 541353.
  119. Hurley PJ, et al. Secreted protein, acidic and rich in cysteine-like 1 (SPARCL1) is down regulated in aggressive prostate cancers and is prognostic for poor clinical outcome. Proc Natl Acad Sci U S A 2012;109(37):14977-82.
  120. Knudsen ES, et al. Progression of ductal carcinoma in situ to invasive breast cancer is associated with gene expression programs of EMT and myoepithelia. Breast Cancer Res Treat 2012; 133(3): 1009-24.
  121. Vergara IA, et al. Genomic "Dark Matter" in Prostate Cancer: Exploring the Clinical Utility of ncRNA as Biomarkers. Front Genet 2012; 3: 23.
  122. Abdueva D, et al. Quantitative expression profiling in formalin-fixed paraffin-embedded samples by affymetrix microarrays. J Mol Diagn 2010; 12(4): 409-17.

Decipher Bladder

  1. Basile, G. et al. Neoadjuvant pembrolizumab and radical cystectomy in patients with muscle-invasive urothelial bladder cancer: 3-year median follow-up update of PURE-01 trial. Clin Cancer Res 2022.
  2. Chou, J. et al. TROP2 Expression Across Molecular Subtypes of Urothelial Carcinoma and Enfortumab Vedotin-resistant Cells. Eur Urol Oncol 2022.
  3. Chu CE, et al. Heterogeneity in NECTIN4 expression across molecular subtypes of urothelial cancer mediates sensitivity to enfortumab vedotin. Clin Cancer Res 2021; 27 (18): 5123-5130.
  4. Krentel, F. et al. A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer. Sci Rep 2021; 11: 5849.
  5. Lotan, Y. et al. Patients With Muscle Invasive Bladder Cancer with Non-luminal Subtype Derive Greatest Benefit from Platinum Based Neoadjuvant Chemotherapy. J Urol 2021: 101097JU0000000000002261.
  6. Necchi A, et al. Molecular subtyping and immune-gene signatures identify a subset of early bladder tumors as candidates for single-agent immune-checkpoint inhibition. Urol Oncol 2021; 39: 734 e711-734 e717.
  7. Necchi, A. et al. Molecular Characterization of Residual Bladder Cancer after Neoadjuvant Pembrolizumab. Eur Urol 2021.
  8. Bandini, M et al. Does the administration of preoperative pembrolizumab lead to sustained remission post-cystectomy? First survival outcomes from the PURE-01 study*. Ann Oncol 2020; 31: 1755-1763.
  9. de Jong JJ, et al. A Genomic Classifier for Predicting Clinically Aggressive Luminal Bladder Tumors with Higher Rates of Pathological Upstaging. J Urol 2020; 204: 239-246.
  10. de Jong JJ, et al. Distribution of Molecular Subtypes in Muscle-invasive Bladder Cancer Is Driven by Sex-specific Differences. Eur Urol Oncol 2020.; 3 (4): 420-423.
  11. Grivas P, et al. Validation of a neuroendocrine-like classifier confirms poor outcomes in patients with bladder cancer treated with cisplatin-based neoadjuvant chemotherapy. Urol Oncol 2020; 38: 262-268.
  12. Necchi A, et al. Can Patients with Muscle-invasive Bladder Cancer and Fibroblast Growth Factor Receptor-3 Alterations Still Be Considered for Neoadjuvant Pembrolizumab? A Comprehensive Assessment from the Updated Results of the PURE-01 Study. Eur Urol Oncol 2020.
  13. Necchi A, et al. Impact of Molecular Subtyping and Immune Infiltration on Pathological Response and Outcome Following Neoadjuvant Pembrolizumab in Muscle-invasive Bladder Cancer. Eur Urol 2020.
  14. Batista da Costa J, et al. Molecular Characterization of Neuroendocrine-like Bladder Cancer. Clin Cancer Res 2019;25(13):3908-20.
  15. de Jong JJ, et al. Long non-coding RNAs identify a subset of luminal muscle-invasive bladder cancer patients with favorable prognosis. Genome Med 2019;11(1):60.
  16. Efstathiou JA, et al. Impact of Immune and Stromal Infiltration on Outcomes Following Bladder-Sparing Trimodality Therapy for Muscle-Invasive Bladder Cancer. Eur Urol 2019;76(1):59-68.
  17. Genitsch V, et al. Morphologic and genomic characterization of urothelial to sarcomatoid transition in muscle-invasive bladder cancer. Urol Oncol 2019;37(9):573 e19-73 e29.
  18. Grivas P, et al. Validation of a neuroendocrine-like classifier confirms poor outcomes in patients with bladder cancer treated with cisplatin-based neoadjuvant chemotherapy. Urol Oncol 2019.
  19. Lotan Y, et al. Molecular Subtyping of Clinically Localized Urothelial Carcinoma Reveals Lower Rates of Pathological Upstaging at Radical Cystectomy Among Luminal Tumors. Eur Urol 2019;76(2):200-06.
  20. Narayan VM, et al. Genomic Analysis and Treatment Response of a Bladder Urothelial Carcinoma With Sarcomatoid Variant Histology. Clin Genitourin Cancer 2019;17(5):e888-e92.
  21. Seiler R, et al. Divergent Biological Response to Neoadjuvant Chemotherapy in Muscle-invasive Bladder Cancer. Clin Cancer Res 2019;25(16):5082-93.
  22. Seiler R, et al. Impact of Molecular Subtypes in Muscle-invasive Bladder Cancer on Predicting Response and Survival after Neoadjuvant Chemotherapy. Eur Urol 2017;72(4):544-54.
  23. Seiler R, et al. Prediction of Lymph Node Metastasis in Patients with Bladder Cancer Using Whole Transcriptome Gene Expression Signatures. J Urol 2016;196(4):1036-41.
  24. Mitra AP, et al. Discovery and validation of novel expression signature for postcystectomy recurrence in high-risk bladder cancer. J Natl Cancer Inst 2014;106(11):dju290.

Reviews Including Decipher

  1. Ahmed, M. E. et al. Prognostic Role of RNA Expression Molecular Biomarkers in Prostate and Bladder Cancers. Eur Urol Focus 2022; 8: 663-666.
  2. Sutera, P. et al. Genomic biomarkers to guide precision radiotherapy in prostate cancer. Prostate 2022; 82 Suppl 1, S73-S85.
  3. Banerjee et al. A review on the role of tissue-based molecular biomarkers for active surveillance. World J Urol 2021.
  4. Jairath, NK et al. A Systematic Review of the Evidence for the Decipher Genomic Classifier in Prostate Cancer. Eur Urol 2021; 79: 374-383.
  5. Nowroozi, A et al. Adjuvant vs. salvage Radiation Therapy after Radical Prostatectomy: Role of Decipher(R) in the Era of Personalized Medicine. Urol J 2021.
  6. Vandekerkhove, G et al. Plasma ctDNA is a tumor tissue surrogate and enables clinical-genomic stratification of metastatic bladder cancer. Nat Commun 2021; 12: 184.
  7. Bronimann, S et al. An overview of current and emerging diagnostic, staging and prognostic markers for prostate cancer. Expert Rev Mol Diagn 2020; 20: 841-850.
  8. Duffy MJ. Biomarkers for prostate cancer: prostate-specific antigen and beyond. Clin Chem Lab Med 2020;58(3):326-39.
  9. Jalanko, T et al. Genomic Subtyping in Bladder Cancer. Curr Urol Rep 2020; 21: 9.
  10. McKay RR, et al. Recent Advances in the Management of High-Risk Localized Prostate Cancer: Local Therapy, Systemic Therapy, and Biomarkers to Guide Treatment Decisions. Am Soc Clin Oncol Educ Book 2020;40:1-12.
  11. Necchi, A et al. Converging Roads to Early Bladder Cancer. Eur Urol 2020; 78: 127-130.
  12. Norris, JM et al. Genetic landscape of prostate cancer conspicuity on multiparametric MRI: a protocol for a systematic review and bioinformatic analysis. BMJ Open 2020; 10: e034611.
  13. Alam S, et al. Prostate cancer genomics: comparing results from three molecular assays. Can J Urol 2019;26(3):9758-62.
  14. Al Hussein Al Awamlh B, et al. Genomics and risk stratification in high-risk prostate cancer. Nat Rev Urol 2019;16(11):641-42.
  15. Al-Salama ZT. Apalutamide: A Review in Non-Metastatic Castration-Resistant Prostate Cancer. Drugs 2019;79(14):1591-98.
  16. Cozar JM, et al. The role of miRNAs as biomarkers in prostate cancer. Mutat Res 2019;781:165-74.
  17. Creed JH, et al. Commercial Gene Expression Tests for Prostate Cancer Prognosis Provide Paradoxical Estimates of Race-Specific Risk. Cancer Epidemiol Biomarkers Prev 2019.
  18. Eggener SE, et al. Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline. J Clin Oncol 2019:JCO1902768.
  19. Fine ND, et al. Genomic Classifiers for Treatment Selection in Newly Diagnosed Prostate Cancer. BJU Int 2019.
  20. Goldenberg SL, et al. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol 2019;16(7):391-403.
  21. Kohaar I, et al. A Rich Array of Prostate Cancer Molecular Biomarkers: Opportunities and Challenges. Int J Mol Sci 2019;20(8).
  22. McCormick BZ, et al. Biochemical recurrence after radical prostatectomy: Current status of its use as a treatment endpoint and early management strategies. Indian J Urol 2019;35(1):6-17.
  23. Schulster M. Bladder Cancer Academy 2019 Selected Summaries. Rev Urol 2019;21(1):23-28.
  24. Vince RA, Jr., et al. Tissue-based genomics: which test and when. Curr Opin Urol 2019;29(6):598-604.
  25. Zumsteg ZS, Spratt DE. Precision Medicine for Localized Prostate Cancer: Time to Move Beyond NCCN Risk Stratification? Int J Radiat Oncol Biol Phys 2019;103(1):92-94.
  26. Carneiro A, et al. Are localized prostate cancer biomarkers useful in the clinical practice? Tumour Biol 2018;40(9):1010428318799255.
  27. Cucchiara V, et al. Genomic Markers in Prostate Cancer Decision Making. Eur Urol 2018;73(4):572-82.
  28. da Costa JB, et al. Molecular tumor heterogeneity in muscle invasive bladder cancer: Biomarkers, subtypes, and implications for therapy. Urol Oncol 2018.
  29. Gadzinski AJ, Cooperberg MR. Prostate Cancer Markers. Cancer Treat Res 2018;175:55-86.
  30. Kornberg Z, et al. Genomic biomarkers in prostate cancer. Transl Androl Urol 2018;7(3):459-71.
  31. Kristiansen G. Markers of clinical utility in the differential diagnosis and prognosis of prostate cancer. Mod Pathol 2018;31(S1):S143-55.
  32. Lamy PJ, et al. Prognostic Biomarkers Used for Localised Prostate Cancer Management: A Systematic Review. Eur Urol Focus 2018;4(6):790-803.
  33. Loeb S, Tosoian JJ. Biomarkers in active surveillance. Transl Androl Urol 2018;7(1):155-59.
  34. Olleik G, et al. Evaluation of New Tests and Interventions for Prostate Cancer Management: A Systematic Review. J Natl Compr Canc Netw 2018;16(11):1340-51.
  35. Kim SP, et al. Physician attitudes about genetic testing for localized prostate cancer: A national survey of radiation oncologists and urologists. Urol Oncol 2018;36(11):501 e15-01 e21.
  36. Spratt DE, et al. A Systematic Review and Framework for the Use of Hormone Therapy with Salvage Radiation Therapy for Recurrent Prostate Cancer. Eur Urol 2018;73(2):156-65.
  37. Teo MY, et al. Drug development for noncastrate prostate cancer in a changed therapeutic landscape. Nat Rev Clin Oncol 2018;15(3):150.
  38. Tilki D, Evans CP. The Decipher Genomic Classifier Independently Improves Prognostication for Patients After Prostatectomy. Eur Urol 2018;73(2):176-77.
  39. Alford AV, et al. The Use of Biomarkers in Prostate Cancer Screening and Treatment. Rev Urol 2017;19(4):221-34.
  40. Clinton TN, et al. Tissue-based biomarkers in prostate cancer. Expert Rev Precis Med Drug Dev 2017;2(5):249-60.
  41. Colicchia M, et al. Genomic tests to guide prostate cancer management following diagnosis. Expert Rev Mol Diagn 2017;17(4):367-77.
  42. Dall'Era M, Evans C. Genomic and Biological Markers to Select Treatment for Patients with Prostate Cancer: Choose Wisely, My Friend. J Urol 2017;197(1):8-9.
  43. Kretschmer A, Tilki D. Biomarkers in prostate cancer - Current clinical utility and future perspectives. Crit Rev Oncol Hematol 2017;120:180-93.
  44. Kretschmer A, et al. [Molecular biomarkers and prognostic factors for prostate cancer]. Urologe A 2017;56(7):933-44.
  45. Loeb S, Ross AE. Genomic testing for localized prostate cancer: where do we go from here? Curr Opin Urol 2017;27(5):495-99.
  46. Reichard CA, Klein EA. Clinical and molecular rationale to retain the cancer descriptor for Gleason score 6 disease. Nat Rev Urol 2017;14(1):59-64.
  47. Spratt DE. Performance and Utility of Prognostic Genomic Biomarkers After Prostatectomy: Decipher-ing the Data. J Clin Oncol 2017;35(25):2977-78.
  48. Dalela D, et al. Contemporary Role of the Decipher(R) Test in Prostate Cancer Management: Current Practice and Future Perspectives. Rev Urol 2016;18(1):1-9.
  49. De Marzo AM, et al. Premalignancy in Prostate Cancer: Rethinking What we Know. Cancer Prev Res 2016;9(8):648-56.
  50. Gaudreau PO, et al. The Present and Future of Biomarkers in Prostate Cancer: Proteomics, Genomics, and Immunology Advancements. Biomark Cancer 2016;8(Suppl 2):15-33.
  51. Martin NE. New developments in prostate cancer biomarkers. Curr Opin Oncol 2016;28(3):248-52.
  52. Morlacco A, Karnes RJ. Early salvage radiation therapy post-prostatectomy: key considerations. Future Oncol 2016;12(22):2579-87.
  53. Moschini M, et al. Incorporation of tissue-based genomic biomarkers into localized prostate cancer clinics. BMC Med 2016;14:67.
  54. Na R, et al. Clinically available RNA profiling tests of prostate tumors: utility and comparison. Asian J Androl 2016;18(4):575-9.
  55. Pisansky TM. Salvage Radiotherapy for Postoperative Biochemical Failure of Prostate Cancer: The Path Toward Personalized Medicine. Eur Urol 2016;70(4):597-98.
  56. Reiter RE. Risk stratification of prostate cancer 2016. Scand J Clin Lab Invest Suppl 2016;245:S54-9.
  57. Ross AE, D'Amico AV, Freedland SJ. Which, when and why? Rational use of tissue-based molecular testing in localized prostate cancer. Prostate Cancer Prostatic Dis 2016;19(1):1-6.
  58. Ross AE, et al. Utility of Risk Models in Decision Making After Radical Prostatectomy: Lessons from a Natural History Cohort of Intermediate- and High-Risk Men. Eur Urol 2016;69(3):496-504.
  59. Sharma P, Zargar-Shoshtari K, Pow-Sang JM. Biomarkers for prostate cancer: present challenges and future opportunities. Future Sci OA 2016;2(1):FSO72.
  60. Stoyanova R, et al. Prostate cancer radiomics and the promise of radiogenomics. Transl Cancer Res 2016;5(4):432-47.
  61. Zhuang L, Johnson MT. How Precisely Can Prostate Cancer Be Managed? Int Neurourol J 2016;20(Suppl 2):S120-30.
  62. Bostrom PJ, et al. Genomic Predictors of Outcome in Prostate Cancer. Eur Urol 2015;68(6):1033-44.
  63. Davis J. Use of genomic markers to risk stratify men with prostate cancer. Trends in Urology & Men's Health 2015;6(3):36-39.
  64. Falzarano SM, et al. Novel biomarkers and genomic tests in prostate cancer: a critical analysis. Minerva Urol Nefrol 2015;67(3):211-31.
  65. Marrone M, et al. A 22 Gene-expression Assay, Decipher(R) (GenomeDx Biosciences) to Predict Five-year Risk of Metastatic Prostate Cancer in Men Treated with Radical Prostatectomy. PLoS Curr 2015;7.
  66. Nguyen HG, Welty CJ, Cooperberg MR. Diagnostic associations of gene expression signatures in prostate cancer tissue. Curr Opin Urol 2015;25(1):65-70.
  67. Spahn M, et al. What is the Need for Prostatic Biomarkers in Prostate Cancer Management? Curr Urol Rep 2015;16(10):70.
  68. Davis JW. Novel commercially available genomic tests for prostate cancer: a roadmap to understanding their clinical impact. BJU Int 2014;114(3):320-2.

Please enter your name & email address below to download your PDF: