stratification and clinical decision making for selected
patients with newly diagnosed disease.
Author contributions:
Stephen K. Van Den Eeden had full access to all the
data in the study and takes responsibility for the integrity of the data and
the accuracy of the data analysis.
Study concept and design:
Van Den Eeden, Quesenberry, Zhang, Lawrence,
Febbo, Presti.
Acquisition of data:
Han, Tsiatis, Shan, Leimpeter, Zhang, Presti.
Analysis and interpretation of data:
Van Den Eeden, Lu, Zhang, Shan,
Lawrence, Febbo, Presti.
Drafting of the manuscript:
Van Den Eeden, Lawrence, Lu, Febbo, Presti.
Critical revision of the manuscript for important intellectual content:
Van
Den Eeden, Lu, Lawrence, Febbo, Quesenberry, Presti.
ce:italic>Statistical analysis: Lu, Zhang
[1_TD$DIFF]
, Quesenberry, Shan.
Obtaining funding:
None.
Administrative, technical, or material support:
Van Den Eeden, Lawrence.
Supervision:
Van Den Eeden, Lawrence.
Other:
None.
Financial disclosures:
Stephen K. Van Den Eeden certi
fi
es that all
con
fl
icts of interest, including speci
fi
c
fi
nancial interests and relation-
ships and af
fi
liations relevant to the subject matter or materials
discussed in the manuscript (eg, employment/af
fi
liation, grants or
funding, consultancies, honoraria, stock ownership or options, expert
testimony, royalties, or patents
fi
led, received, or pending), are the
following: Stephen K. Van Den Eeden received funding from Genomic
Health Inc. to conduct this study. He has received research support from
Abbott Molecular and the National Institutes of Health. Ruixiao Lu is a
full-time employee of and has stock in Genomic Health, Inc. Nan Zhang
was a full-time employee of and has stock in Genomic Health, Inc.
Charles P. Quesenberry Jr, received salary support for this study from the
research grant from Genomic Health Inc. He has received research
support from the National Institutes of Health. Jun Shan received salary
support for this study from the research grant from Genomic Health Inc.
Jeong S. Han received salary support for this study from the research
grant from Genomic Health Inc. Athanasios C. Tsiatis is a full-time
employee of and has stock in Genomic Health, Inc. Amethyst D.
Leimpeter received salary support for this study from the research grant
fromGenomic Health Inc. H. Jeffrey Lawrence is a paid consultant for and
has stock in Genomic Health, Inc. Phillip G. Febbo is a full-time employee
of and has stock in Genomic Health, Inc. Joseph C. Presti received salary
support for this study from the research grant from Genomic Health Inc.
Funding/Support and role of the sponsor:
This study was sponsored by
Genomic Health, Inc., which was involved in the design and conduct of
the study; collection, management, analysis, and interpretation of the
data; and preparation, review, and approval of the manuscript. Genomic
Health, Inc. performed the genomic testing and analyses described and
provided
fi
nancial support to KPNC.
Acknowledgements:
The authors acknowledge Agnes Nemeth, Kevin
Chew, Hargita Kaplan, Cindy Loman, Greg Jones, Rahul Peethala, Kenny
Wong, Dejan Knezevic, Aditi Dedhia, Rory O
’
Brien, and Keith Gran from
Genomic Health, Inc. and Daniel Fernandez, Deborah Burman, Judith
Morse, Erica Kereszi, and Marilyn Foley at KPNC for their logistical and
technical support, and Emily Burke for providing assistance in the
preparation of the manuscript.
Appendix A. Supplementary data
Supplementary data associated with this article can be
found, in the online version, at
http://dx.doi.org/10.1016/j. eururo.2017.09.013.
References
[1]
Sartori DA, Chan DW. Biomarkers in prostate cancer: what ’ s new? Curr Opin Oncol 2014;26:259 – 64.[2]
Davis JW. Novel commercially available genomic tests for prostate cancer: a roadmap to understanding their clinical impact. BJU Int 2014;114:320 – 2.
[3]
National Comprehensive Cancer Network practice guidelines in oncology. Prostate Cancer v2 2017.
[4]
Simon RM, Paik S, Hayes DF. Use of archived specimens in evalua- tion of prognostic and predictive biomarkers. J Natl Cancer Inst 2009;101:1446 – 52.[5]
Febbo PG, Ladanyi M, Aldape KD, et al. NCCN Task Force report: evaluating the clinical utility of tumor markers in oncology. J Natl Compr Canc Netw 2011;9(Suppl 5):S1 – 32.
[6]
Knezevic D, Goddard AD, Natraj N, et al. Analytical validation of the oncotype DX prostate cancer assay — a clinical RT-PCR assay opti- mized for prostate needle biopsies. BMC Genomics 2013;14:690.[7]
Klein EA, Cooperberg MR, Magi-Galluzzi C, et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersam- pling. Eur Urol 2014;66:550 – 60.
[8]
Cullen J, Rosner IL, Brand TC, et al. A biopsy-based 17-gene Genomic Prostate Score predicts recurrence after radical prostatectomy and adverse surgical pathology in a racially diverse population of men with clinically low- and intermediate-risk prostate cancer. Eur Urol 2015;68:123 – 31.
[9]
Albala D, Kemeter MJ, Febbo PG, et al. Health economic impact and prospective clinical utility of Oncotype DX Genomic Prostate Score. Rev Urol 2016;18:123 – 32.[10]
Badani KK, Kemeter MJ, Febbo PG, et al. The impact of a biopsy based 17-gene Genomic Prostate Score on treatment recommendations in men with newly diagnosed clinically localized prostate cancer who are candidates for active surveillance. Urol Pract 2015;2:181 – 9.
[11]
Hu CY, Xing Y, Cormier JN, Chang GJ. Assessing the utility of cancer- registry-processed cause of death in calculating cancer-speci fi c survival. Cancer 2013;119:1900 – 7.[12]
Gray RJ. Weighted analyses for cohort sampling designs. Lifetime Data Anal 2009;15:24 – 40.
[13]
McShane LM, Altman DG, Sauerbrei W, et al. Reporting recommen- dations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005;97:1180 – 4.
[14]
Epstein JI, Allsbrook Jr WC, Amin MB, Egevad LL. ISUP Grading Committee. The 2005 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carci- noma. Am J Surg Pathol 2005;29:1228–42.
[15]
Therneau TM, Grambsch PM. Modeling survival data: extending the Cox model. New York, NY: Springer; 2000.[16]
Hochberg Y, Tamhane AC. Multiple comparison procedures. New York, NY: Wiley; 1987.[17]
Therneau TM, Grambsch PM, Fleming TR. Martingale-based resi- duals for survival models. Biometrika 1990;77:47 – 160.[18]
Schoenfeld D. Residuals for the proportional hazards regression model. Biometrika 1982;69:239 – 41.[19]
Can fi eld SE, Kibel AS, Kemeter MJ, Febbo PG, Lawrence HJ, Moul JW. A guide for clinicians in the evaluation of emerging molecular diagnostics for newly diagnosed prostate cancer. Rev Urol 2014;16:172 – 80.
[20]
Cuzick J, Swanson GP, Fisher G, et al. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: a retrospective study. Lancet Oncol 2011;12:245 – 55.[21]
Bishoff JT, Freedland SJ, Gerber L, et al. Prognostic utility of the cell cycle progression score generated from biopsy in men treated with prostatectomy. J Urol 2014;192:409 – 14.
E U R O P E A N U R O L O GY 7 3 ( 2 0 18 ) 12 9
–
13 8
137




