Literature DB >> 29185869

Development and Validation of a Novel Integrated Clinical-Genomic Risk Group Classification for Localized Prostate Cancer.

Daniel E Spratt1, Jingbin Zhang1, María Santiago-Jiménez1, Robert T Dess1, John W Davis1, Robert B Den1, Adam P Dicker1, Christopher J Kane1, Alan Pollack1, Radka Stoyanova1, Firas Abdollah1, Ashley E Ross1, Adam Cole1, Edward Uchio1, Josh M Randall1, Hao Nguyen1, Shuang G Zhao1, Rohit Mehra1, Andrew G Glass1, Lucia L C Lam1, Jijumon Chelliserry1, Marguerite du Plessis1, Voleak Choeurng1, Maria Aranes1, Tyler Kolisnik1, Jennifer Margrave1, Jason Alter1, Jennifer Jordan1, Christine Buerki1, Kasra Yousefi1, Zaid Haddad1, Elai Davicioni1, Edouard J Trabulsi1, Stacy Loeb1, Ashutosh Tewari1, Peter R Carroll1, Sheila Weinmann1, Edward M Schaeffer1, Eric A Klein1, R Jeffrey Karnes1, Felix Y Feng1, Paul L Nguyen1.   

Abstract

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

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Year:  2017        PMID: 29185869      PMCID: PMC6530900          DOI: 10.1200/JCO.2017.74.2940

Source DB:  PubMed          Journal:  J Clin Oncol        ISSN: 0732-183X            Impact factor:   44.544


  26 in total

1.  The CAPRA-S score: A straightforward tool for improved prediction of outcomes after radical prostatectomy.

Authors:  Matthew R Cooperberg; Joan F Hilton; Peter R Carroll
Journal:  Cancer       Date:  2011-06-03       Impact factor: 6.860

2.  Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study.

Authors:  Jack Cuzick; Gregory P Swanson; Gabrielle Fisher; Arthur R Brothman; Daniel M Berney; Julia E Reid; David Mesher; V O Speights; Elzbieta Stankiewicz; Christopher S Foster; Henrik Møller; Peter Scardino; Jorja D Warren; Jimmy Park; Adib Younus; Darl D Flake; Susanne Wagner; Alexander Gutin; Jerry S Lanchbury; Steven Stone
Journal:  Lancet Oncol       Date:  2011-03       Impact factor: 41.316

3.  Trends in Management for Patients With Localized Prostate Cancer, 1990-2013.

Authors:  Matthew R Cooperberg; Peter R Carroll
Journal:  JAMA       Date:  2015-07-07       Impact factor: 56.272

4.  A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer.

Authors:  M W Kattan; J A Eastham; A M Stapleton; T M Wheeler; P T Scardino
Journal:  J Natl Cancer Inst       Date:  1998-05-20       Impact factor: 13.506

Review 5.  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.

Authors:  Daniel E Spratt; Kasra Yousefi; Samineh Deheshi; Ashley E Ross; Robert B Den; Edward M Schaeffer; Bruce J Trock; Jingbin Zhang; Andrew G Glass; Adam P Dicker; Firas Abdollah; Shuang G Zhao; Lucia L C Lam; Marguerite du Plessis; Voleak Choeurng; Zaid Haddad; Christine Buerki; Elai Davicioni; Sheila Weinmann; Stephen J Freedland; Eric A Klein; R Jeffrey Karnes; Felix Y Feng
Journal:  J Clin Oncol       Date:  2017-03-30       Impact factor: 44.544

6.  Long-term follow-up of a large active surveillance cohort of patients with prostate cancer.

Authors:  Laurence Klotz; Danny Vesprini; Perakaa Sethukavalan; Vibhuti Jethava; Liying Zhang; Suneil Jain; Toshihiro Yamamoto; Alexandre Mamedov; Andrew Loblaw
Journal:  J Clin Oncol       Date:  2014-12-15       Impact factor: 44.544

7.  Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort.

Authors:  Matthew R Cooperberg; Elai Davicioni; Anamaria Crisan; Robert B Jenkins; Mercedeh Ghadessi; R Jeffrey Karnes
Journal:  Eur Urol       Date:  2014-07-02       Impact factor: 20.096

8.  Prostate Cancer, Version 1.2016.

Authors:  James L Mohler; Andrew J Armstrong; Robert R Bahnson; Anthony Victor D'Amico; Brian J Davis; James A Eastham; Charles A Enke; Thomas A Farrington; Celestia S Higano; Eric M Horwitz; Michael Hurwitz; Christopher J Kane; Mark H Kawachi; Michael Kuettel; Richard J Lee; Joshua J Meeks; David F Penson; Elizabeth R Plimack; Julio M Pow-Sang; David Raben; Sylvia Richey; Mack Roach; Stan Rosenfeld; Edward Schaeffer; Ted A Skolarus; Eric J Small; Guru Sonpavde; Sandy Srinivas; Seth A Strope; Jonathan Tward; Dorothy A Shead; Deborah A Freedman-Cass
Journal:  J Natl Compr Canc Netw       Date:  2016-01       Impact factor: 11.908

Review 9.  Comparison of nomograms with other methods for predicting outcomes in prostate cancer: a critical analysis of the literature.

Authors:  Shahrokh F Shariat; Pierre I Karakiewicz; Nazareno Suardi; Michael W Kattan
Journal:  Clin Cancer Res       Date:  2008-07-15       Impact factor: 12.531

10.  A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling.

Authors:  Eric A Klein; Matthew R Cooperberg; Cristina Magi-Galluzzi; Jeffry P Simko; Sara M Falzarano; Tara Maddala; June M Chan; Jianbo Li; Janet E Cowan; Athanasios C Tsiatis; Diana B Cherbavaz; Robert J Pelham; Imelda Tenggara-Hunter; Frederick L Baehner; Dejan Knezevic; Phillip G Febbo; Steven Shak; Michael W Kattan; Mark Lee; Peter R Carroll
Journal:  Eur Urol       Date:  2014-05-16       Impact factor: 20.096

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  35 in total

Review 1.  Cellular and Molecular Mechanisms Underlying Prostate Cancer Development: Therapeutic Implications.

Authors:  Ugo Testa; Germana Castelli; Elvira Pelosi
Journal:  Medicines (Basel)       Date:  2019-07-30

2.  Early biochemical predictors of survival in intermediate and high-risk prostate cancer treated with radiation and androgen deprivation therapy.

Authors:  Mira A Patel; Marisa Kollmeier; Sean McBride; Daniel Gorovets; Melissa Varghese; Luanna Chan; Andrea Knezevic; Zhigang Zhang; Michael J Zelefsky
Journal:  Radiother Oncol       Date:  2019-06-06       Impact factor: 6.280

Review 3.  Conceptual review of key themes in treating prostate cancer in older adults.

Authors:  Ramy Sedhom; Arjun Gupta
Journal:  J Geriatr Oncol       Date:  2019-11-05       Impact factor: 3.599

4.  Prostate cancer: Genomic information improves risk prediction.

Authors:  Diana Romero
Journal:  Nat Rev Clin Oncol       Date:  2017-12-19       Impact factor: 66.675

5.  Prostate cancer: Genomic information improves risk prediction.

Authors:  Diana Romero
Journal:  Nat Rev Urol       Date:  2017-12-19       Impact factor: 14.432

6.  Optimizing Time to Treatment to Achieve Durable Biochemical Disease Control after Surgery in Prostate Cancer: A Multi-Institutional Cohort Study.

Authors:  Shivanshu Awasthi; Travis Gerke; Jong Y Park; Francis A Asamoah; Vonetta L Williams; Angelina K Fink; Rajesh Balkrishnan; David I Lee; S Bruce Malkowicz; Priti Lal; Jasreman Dhillon; Julio M Pow-Sang; Timothy R Rebbeck; Kosj Yamoah
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2018-11-09       Impact factor: 4.254

7.  Epigenetic analysis identifies factors driving racial disparity in prostate cancer.

Authors:  Richa Rai; Shalini S Yadav; Heng Pan; Irtaza Khan; James O'Connor; Mohammed Alshalalfa; Elai Davicioni; Emanuela Taioli; Olivier Elemento; Ashutosh K Tewari; Kamlesh K Yadav
Journal:  Cancer Rep (Hoboken)       Date:  2018-12-13

8.  Correlation between cribriform/intraductal prostatic adenocarcinoma and percent Gleason pattern 4 to a 22-gene genomic classifier.

Authors:  Alexander S Taylor; Todd M Morgan; David G Wallington; Arul M Chinnaiyan; Daniel E Spratt; Rohit Mehra
Journal:  Prostate       Date:  2019-11-18       Impact factor: 4.104

9.  Age dependence of modern clinical risk groups for localized prostate cancer-A population-based study.

Authors:  Minh-Phuong Huynh-Le; Tor Åge Myklebust; Christine H Feng; Roshan Karunamuni; Tom Børge Johannesen; Anders M Dale; Ole A Andreassen; Tyler M Seibert
Journal:  Cancer       Date:  2020-01-03       Impact factor: 6.860

Review 10.  Harnessing cell-free DNA: plasma circulating tumour DNA for liquid biopsy in genitourinary cancers.

Authors:  Manuel Caitano Maia; Meghan Salgia; Sumanta K Pal
Journal:  Nat Rev Urol       Date:  2020-03-17       Impact factor: 14.432

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