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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
European Urology 2015 Apr;67(4):778-86
Surgery is a standard first-line therapy for men with intermediate- or high-risk prostate cancer. Clinical factors such as tumor grade, stage, and prostate-specific antigen (PSA) are currently used to identify those who are at risk of recurrence and who may benefit from adjuvant therapy, but novel biomarkers that improve risk stratification and that distinguish local from systemic recurrence are needed.
To determine whether adding the Decipher genomic classifier, a validated metastasis risk–prediction model, to standard risk-stratification tools (CAPRA-S and Stephenson nomogram) improves accuracy in predicting metastatic disease within 5 yr after surgery (rapid metastasis [RM]) in an independent cohort of men with adverse pathologic features after radical prostatectomy (RP).
Design, setting, and participants
The study population consisted of 169 patients selected from 2641 men who underwent RP at the Cleveland Clinic between 1987 and 2008 who met the following criteria: (1) preoperative PSA >20 ng/ml, stage pT3 or margin positive, or Gleason score ≥8; (2) pathologic node negative; (3) undetectable post-RP PSA; (4) no neoadjuvant or adjuvant therapy; and (5) minimum of 5-yr follow-up for controls. The final study cohort consisted of 15 RM patients and 154 patients as non-RM controls.
Outcome measurements and statistical analysis
The performance of Decipher was evaluated individually and in combination with clinical risk factors using concordance index (c-index), decision curve analysis, and logistic regression for prediction of RM.
Results and limitations
RM patients developed metastasis at a median of 2.3 yr (interquartile range: 1.7–3.3). In multivariable analysis, Decipher was a significant predictor of RM (odds ratio: 1.48;p = 0.018) after adjusting for clinical risk factors. Decipher had the highest c-index, 0.77, compared with the Stephenson model (c-index: 0.75) and CAPRA-S (c-index: 0.72) as well as with a panel of previously reported prostate cancer biomarkers unrelated to Decipher. Integration of Decipher into the Stephenson nomogram increased the c-index from 0.75 (95% confidence interval [CI], 0.65–0.85) to 0.79 (95% CI, 0.68–0.89).
Decipher was independently validated as a genomic metastasis signature for predicting metastatic disease within 5 yr after surgery in a cohort of high-risk men treated with RP and managed conservatively without any adjuvant therapy. Integration of Decipher into clinical nomograms increased prediction of RM. Decipher may allow identification of men most at risk for metastatic progression who should be considered for multimodal therapy or inclusion in clinical trials.
Use of Decipher in addition to standard clinical information more accurately identified men who developed metastatic disease within 5 yr after surgery. The results suggest that Decipher allows improved identification of the men who should consider secondary therapy from among the majority that may be managed conservatively after surgery.
Keywords: Prostate cancer, Prognosis, Clinical validation, Rapid metastasis, Genomic classifier.
Adverse pathology on a radical prostatectomy (RP) specimen, as defined by high Gleason grade, extraprostatic extension (EPE), seminal vesicle invasion, or lymph node positivity, is known to increase the risk of tumor recurrence. However, not all patients with one or more of these features are destined to develop life-threatening disease, as demonstrated by a recent analysis of almost 24 000 men that showed approximately 70% with one or more of these features had not died of prostate cancer (PCa) 15 yr after surgery  . Although some of these men received adjuvant therapy soon after surgery and others were treated at the time of progression, the low rate of metastatic disease in this and other large series with long-term follow-up suggests that many men with adverse pathology are cured by surgery alone and . Currently available tools such as nomograms based on clinical and pathologic factors have imperfect ability to identify men with metastatic progression who may benefit from adjuvant therapy, closer monitoring for disease recurrence with early initiation of salvage therapy, or participation in clinical trials  . The addition of biomarkers to clinical risk factors may help identify high-risk men who are at risk for metastatic disease within 5 yr following RP  . Decipher, a genomic classifier that uses a whole-transcriptome microarray assay from formalin-fixed paraffin embedded (FFPE) PCa specimens, has been developed and validated as a specific predictor of metastases and PCa-specific mortality following RP in several cohorts of men with adverse pathologic features, , , and . In this study, we aimed (1) to compare the performance of Decipher with standard risk-stratification tools (CAPRA-S and the Stephenson nomogram), (2) to assess performance and accuracy of adding Decipher to these tools for predicting metastatic disease within 5 yr after surgery in men with adverse pathologic features treated with RP, and (3) to compare Decipher with a panel of other reported gene biomarkers in predicting this end point.
2. Materials and methods
The Cleveland Clinic institutional review board reviewed and approved the research protocol under which this validation study was conducted. The study met the PRoBE  and REMARK  criteria for prospective blinded evaluation and analysis of prognostic biomarkers.
2.1. Patient cohort
Tumor tissue and clinicopathologic data for this study were obtained from 184 patients selected from all 2641 men who underwent RP at the Cleveland Clinic between 1987 and 2008 who met the following criteria: (1) preoperative prostate-specific antigen (PSA) >20 ng/ml, stage pT3 or margin positive, and no clinical or radiographic evidence of metastasis or pathologic Gleason score ≥8; (2) pathologic node-negative disease; (3) undetectable post-RP PSA; (4) no neoadjuvant or adjuvant therapy; and (5) a minimum of 5-yr follow-up for those who remained metastasis free. Fifteen patients with inadequate tumor cell content, insufficient extracted RNA, or microarray assay that failed quality control were excluded from analysis (Supplementary Fig. 1), leaving a final study cohort of 169 patients that included 15 men with metastatic progression <5 yr after RP, as defined by positive computed tomography (CT) or bone scan, and 154 controls, including 120 nonmetastatic patients and 34 with metastasis >5 yr after RP. A total of 165 patients (98%) had pT3 disease or positive margins, and 146 (86%) had pathologic Gleason score ≥7, including 41 with Gleason score ≥8 ( Table 1 ). Median follow-up for censored patients was 7.8 yr (range: 5.1–19.3).
|No. patients (%)||169 (100)|
|Race, n (%)|
|Patient age, yr|
|Median (range)||62 (42–74)|
|Year of surgery|
|Median (range)||1997 (1987–2008)|
|Preoperative PSA, ng/ml|
|Median (range)||6.54 (0.1–66.6)|
|Pathologic Gleason score, n (%)|
|Extraprostatic extension, n (%)|
|Seminal vesicle invasion, n (%)|
|Surgical margins, n (%)|
|Follow-up of censored patients, yr|
|Median (range)||7.8 (5.1–19.3)|
|Time to rapid metastasis, yr|
|Median (range)||2.3 (0.8–5)|
|Time to late metastasis, yr|
|Median (range)||9.3 (5.1–17.8)|
IQR = interquartile range; PSA = prostate-specific antigen; Q = quartile.
2.2. Specimen selection and processing
Following histopathologic re-review of FFPE tumor blocks from each case by an expert genitourinary pathologist (C.M.-G.), the RP block with the highest Gleason score (independent of volume) was selected as the index lesion for sampling. Next, 2 × 0.6-mm diameter tissue biopsy punch tool cores were used to sample the primary Gleason grade of the index lesion, and these samples were placed in a microfuge tube for processing. RNA extraction and microarray expression data generation were performed, as described previously and . Briefly, RNA was amplified and labeled using the Ovation WTA FFPE system (NuGen, San Carlos, CA, USA) and hybridized to Human Exon 1.0 ST microarrays (Affymetrix, Santa Clara, CA, USA).
Following microarray quality control using the Affymetrix Power Tools packages  , probe set summarization and normalization was performed by the SCAN algorithm, which normalizes each batch individually by modeling and removing probe- and array-specific background noise using only data from within each array  . The expression values for the 22 prespecified biomarkers that constitute Decipher were extracted from the normalized data matrix and entered into the random forests algorithm that was locked with defined tuning and weighting parameters, as reported previously, , and . The Decipher read-out is a continuous risk score between 0 and 1, with higher scores indicating a greater probability of metastasis  .
2.3. Calculation of nomogram scores and combined clinical–genomic prediction models
CAPRA-S scores were calculated, as described previously  , and Stephenson 5-yr survival probabilities were calculated using the online prediction tool  . The scores attributed to CAPRA-S are derived using a point-based system with seven variables, whereas the Stephenson nomogram attributes risk scores to exactly the same predictors from the locked Cox regression coefficients and relies on eight variables. For comparison purposes, the Stephenson probabilities were subtracted from 1 (1 − Stephenson probability); higher probabilities reflected greater risk of cancer recurrence.
The combined Decipher plus CAPRA-S and Decipher plus Stephenson models were trained for the RM end point and locked on an independent data set to avoid overfitting, as described previously  .
2.4. Comparison with prostate cancer gene biomarkers
The performance of Decipher was compared with an unrelated set of previously reported genes includingCHGA and ;ETV1andERG and ; Ki-67 (MKI67) and ;AMACR and ;GSTP1 and ;SPOP  ;FOXP1  ;FLI1  ;PGRMC1  ;MSMB  ;SRD5A2  ; andEZH2, AR, TP53, PTEN, RB1, AURKA, andAURKAB  . Each gene was mapped to its associated Affymetrix core transcript cluster if available; otherwise, the extended transcript cluster was used ( http://www.affymetrix.com/analysis/index.affx ). SCAN-summarized expression values for the individual genes were used in tests for significance  .
2.5. Statistical analyses
Statistical analyses were performed in R v3.0 (R Foundation, Vienna, Austria), and all statistical tests were two-sided using a 5% significance level. The end point of interest, rapid metastasis (RM), was defined as metastasis within 5 yr after RP, as shown by positive CT or bone scan. All study participants were blinded to the outcome and clinical data prior to data analysis. Analytic procedures for validation were applied following guidelines and recommendations for evaluation of prognostic tests and .
Discrimination of the clinical risk factors, Decipher, and other biomarkers was established according to Harrell's concordance index (c-index)  . Harrell's c-index gives the probability that, in a randomly selected pair of patients in which one patient experiences the event (ie, RM), the patient who experiences the event had the worse predicted outcome according to the predictive model.
Multivariable logistic regression analysis evaluated the independent prognostic ability of Decipher in comparison to clinical risk factors, the Stephenson nomogram, and CAPRA-S. To ensure the robustness of the multivariable model involving clinical risk factors for rare events such as RM, a penalized least absolute shrinkage and selection operator (LASSO) regression method for sparse data was used to identify the most predictive variables and . Moreover, to determine the net benefit, decision curve analyses (DCAs) were used  . DCAs provide a theoretical relationship between the threshold probability of the event (RM) and the relative value of false-positive and false-negative rates to evaluate the net benefit of a prediction model.
Among the 169 patients, 15 experienced rapid metastasis (RM; metastasis within 5 yr of surgery) and 34 had late metastasis (LM; metastasis >5 yr after surgery). All clinical and pathologic factors at the time of treatment were not significantly different between the groups ( Table 2 ). Metastasis occurred at a median of 2.3 yr (interquartile range [IQR]: 1.7–3.3) in those with RM compared with 9.3 yr (IQR: 8.3–11.6) in the LM group. More patients in the LM group (28 of 34, 82.4%) received secondary therapy at the time of biochemical recurrence and prior to metastasis than those with RM (4 of 15, 26.6%) (p < 0.0019). Median PCa-specific mortality (PCSM) occurred at 7.3 yr in the RM group; LM patients had a PCSM rate of 25% by 15 yr after RP ( Fig. 1 ). The key difference between these groups was time to development of initial metastases, as no significant difference in PCSM rate was observed between the two groups after the development of metastasis (p = 0.87) (Supplementary Fig. 2).
|Variables||Rapid metastasis||Late metastasis||p value|
|No. patients (%)||15 (30.6)||34 (69.4)|
|Race, n (%)|
|White||13 (86.7)||32 (94.2)||0.4634 *|
|Black||2 (13.3)||1 (2.9)|
|Asian||0 (0)||1 (2.9)|
|Other||0 (0)||0 (0)|
|Patient age, yr|
|Median (range)||60 (54–73)||63 (42–74)||0.6170 †|
|Year of surgery|
|Median (range)||1995 (1988–2008)||1992 (1987–2003)||0.1447|
|Preoperative PSA (ng/ml)|
|Median (range)||9 (1.79–19.20)||9.6 (3.6–66.6)||0.6726|
|Pathologic Gleason score, n (%)|
|≤7||7 (46.7)||15 (44.1)||1 *|
|>7||8 (53.3)||19 (55.9)|
|Extraprostatic extension, n (%)|
|2 (13.3)||3 (8.8)||0.6354 *|
|Seminal vesicle invasion, n (%)|
|10 (66.7)||18 (52.9)||0.5327 *|
|Surgical margins, n (%)|
|8 (53.3)||20 (58.8)||0.7621 *|
|Treatment modality at relapse, n (%)|
|RTx||1 (6.7)||2 (5.9)||0.0019 *|
|HTx||2 (13.3)||11 (32.4)|
|RTx + HTx||1 (6.7)||14 (41.2)|
|Other||0 (0)||1 (2.9)|
|No treatment||11 (73.3)||6 (17.6)|
* Fisher exact test.
† Wilcoxon rank sum test.
HTx = hormone therapy; IQR = interquartile range; PSA = prostate-specific antigen; Q = quartile; RTx = radiation therapy.
The median Decipher score for all patients was 0.35 (range: 0.03–0.91; IQR: 0.22–0.59), with mean scores of 0.58 (IQR: 0.44–0.91), 0.54 (IQR: 0.33–0.87), and 0.33 (IQR: 0.18–0.41) for RM, LM, and control (nonmetastatic) patients, respectively. Decipher score was moderately correlated with pathologic Gleason score (Spearman ρ = 0.47). Decipher score was also moderately correlated with CAPRA-S score (which includes PSA and pathologic stage in addition to Gleason score; Spearman ρ = 0.35) but further stratified patients within each CAPRA-S risk category ( Fig. 2 ). Among the 47 patients with multiple adverse pathology features that had the highest CAPRA-S scores (corresponding to >50% probability of recurrence by this model), 15 (32%) were classified as Decipher low risk (according to previously reported cut points  ), and only one of these patients developed RM. Conversely, for the 23 patients with Gleason score 6 disease (19 with positive margins and 4 with EPE), all but 2 (who had borderline scores) were classified in the Decipher low-risk group, and none of these patients developed metastasis. Finally, in the subset of patients with metastasis, median Decipher score was 0.31 (IQR: 0.26–0.39; not different from controls) for those patients with primary Gleason grade 3 and 0.64 (IQR: 0.54–0.91) for those with primary Gleason pattern ≥4.
As measured by Harrell's c-index for RM, Decipher (c-index: 0.77; 95% confidence interval [CI], 0.66–0.87) outperformed individual clinicopathologic variables (Supplementary Table 1) including the original pathologic Gleason score alone (c-index: 0.68; 95% CI, 0.52–0.84), expert-reviewed Gleason score regraded according to 2005 International Society of Urological Pathology criteria (c-index: 0.71; 95% CI, 0.59–0.84), CAPRA-S (c-index: 0.72; 95% CI, 0.60–0.84), and the Stephenson nomogram (c-index: 0.75; 95% CI, 0.65–0.85). The highest c-index was obtained by the combination of Decipher plus the Stephenson nomogram (c-index: 0.79; 95% CI, 0.68–0.89) ( Fig. 3 A). Moreover, among patients with low-risk Decipher scores based on cut points previously described  , 95% had RM-free survival, falling to 82% RM-free survival for those with high-risk scores.
In a series of multivariable logistic regression analyses, Decipher was found to be the only significant variable for predicting RM ( Table 3 ). When adjusting for clinical risk factors, for every 0.1-unit increase in Decipher score, the odds of RM increased by 48% (odds ratio: 1.48; 95% CI, 1.07–2.05;p = 0.018). In multivariable models including only Decipher score and either the Stephenson nomogram or CAPRA-S, Decipher outperformed both ( Table 3 ). To ensure the robustness of the multivariable model, penalized LASSO regression for sparse data and rare events (ie, 15 RM events) was used to confirm the results of the logistic regression model and further demonstrate that Decipher is the most important variable for predicting RM ( Fig. 3 B). Even with large values of the penalty parameter λ, Decipher had a nonzero coefficient and is the first variable to enter the model, confirming its significance in predicting RM in multivariable analysis despite the few RM events. Following Decipher, the next two variables entering the model were EPE and high Gleason score (≥8) ( Fig. 3 B).
|Variables||Odds ratio (95% CI)||p value|
|Model 1||Patient age, yr||0.98 (0.89–1.08)||0.6524|
|Year of surgery||1.01 (0.89–1.15)||0.8498|
|log2(Preoperative PSA)||1.33 (0.7–2.52)||0.3809|
|Pathologic Gleason score >7 (ref.: ≤7)||2.03 (0.55–7.53)||0.2875|
|Extraprostatic extension||2.64 (0.44–15.94)||0.2907|
|Seminal vesicle invasion||0.76 (0.17–3.31)||0.7115|
|Surgical margins||1.37 (0.42–4.44)||0.5971|
|Decipher *||1.48 (1.07–2.05)||0.0179|
|Model 2||Stephenson nomogram *||1.14 (0.86–1.52)||0.3581|
|Decipher *||1.46 (1.08–1.97)||0.0136|
|Model 3||CAPRA-S †||1.21 (0.92–1.58)||0.1786|
|Decipher *||1.43 (1.07–1.91)||0.0162|
* Decipher and Stephenson are reported per 0.1-unit increase.
† CAPRA-S is per point increase in the CAPRA score.
CI = confidence interval; PSA = prostate-specific antigen.
Decipher is the only significant variable in models adjusted for clinical risk factors (model 1), Stephenson nomogram (model 2), and CAPRA-S score (model 3).
DCA further demonstrated increased discrimination of risk models that incorporated the genomic information captured by Decipher. Across a range of RM threshold probabilities from 5% to 25%, DCA showed that Decipher resulted in the highest net benefit compared withtreat allortreat nonescenarios (ie, in which no prediction model is required or used) and both the Stephenson and CAPRA-S clinical models ( Fig. 3 C). A combined Decipher plus Stephenson model and a combined Decipher plus CAPRA-S model each resulted in higher net benefits than individual models, demonstrating the potential clinical utility of Decipher in identifying patients with high-risk pathologic features at highest risk of RM. Because few men in this cohort developed metastatic disease even with conservative management after RP, DCA was then used to determine the number oftrue negativepatients that did not develop metastatic disease who could be spared unnecessary treatment compared with the scenario in which all patients with adverse pathology would be treated (eg, adjuvant radiation as per current guidelines). The number of interventions that could be reduced per 100 patients tested with the Decipher and Stephenson model predictions across a range of threshold probabilities for metastasis-free survival that would trigger intervention was calculated (Supplementary Fig. 3). Finally, Decipher also outperformed a panel of 19 individual biomarkers in predicting RM (Supplementary Table 2; Supplementary Fig. 4 and 5).
Recent randomized clinical trials have demonstrated the efficacy and survival benefit of RP over watchful waiting, particularly for men with intermediate- and high-risk disease and . The vast majority of men treated with surgery for PCa will have excellent metastasis- and disease-free survival. However, men with so-called adverse pathology that includes high-grade or non–organ-confined disease are, by current clinical practice guidelines, considered at risk for disease recurrence, metastasis, and cause-specific mortality  . Despite this recognized risk and level 1 evidence of clinical benefit for adjuvant radiotherapy, and unlike common practice for other prevalent cancers such as breast and colon, the use of adjuvant therapy after RP is generally deferred in the absence of detectable PSA. This approach is largely based on the belief that even in high-risk patients, as defined by adverse pathology, undetectable PSA soon after surgery indicates a low risk of developing lethal disease rapidly; it is also based on the desire to limit exposure to potentially harmful secondary therapy.
In this study, we investigated whether a genomic classifier can identify a subset of patients with a highly lethal form of metastatic disease (characterized by median onset at about 2 yr and 50% cause-specific mortality by 7 yr) who are otherwise indistinguishable by standard clinical and pathologic features, including undetectable PSA postoperatively, from those with a more indolent clinical course (ie, no recurrence or metastasis >5 yr with only 25% PCSM at 15 yr). Identification of such patients is clinically relevant because those at highest risk for early metastasis and death are most likely to benefit from effective adjuvant therapy. Notably, all of the RM patients had preoperative PSA <20 ng/ml and were clinical stage T1 or T2, and nearly half had only Gleason 7 disease on final pathology. All achieved undetectable PSA after RP, but only 27% received secondary treatment prior to the onset of metastasis. In contrast, 82% of the late metastasis group received treatment prior to metastasis. The development of novel biomarkers to identify the subset with rapidly lethal disease is clearly needed  because they are a clinically homogenous group (ie, all had one adverse pathology feature or more) for which nomograms are recognized to have reduced discrimination ability  . As such, on univariable analysis, no clinical or pathologic risk factors could be used to distinguish RM patients from patients with no or late metastasis. Consequently, it is not surprising that we observed that the Decipher genomic classifier remained the only independent variable on multivariable analyses when compared with both individual clinical risk factors or with commonly used clinical risk-assessment tools (CAPRA-S and the Stephenson nomogram). More important, DCA demonstrated that the clinical utility of Decipher in this cohort may have the most impact with its ability to identify (beyond nomograms) more patients who, despite having multiple adverse pathology findings, have a high probability of metastasis-free survival even when managed conservatively after surgery. Finally, the biologic robustness of Decipher was demonstrated by the observation that it outperforms other biomarkers, includingSPOP, Ki-67, andFOXP1, that have been associated with aggressive disease.
Unlike PSA measurements for disease monitoring, which may not indicate true disease aggressiveness until long after surgery, results of a genomic classifier derived from tumor sampled at the time of prostatectomy are available soon after surgery and thus may be used immediately for clinical decision making, including early institution of adjuvant radiotherapy or more frequent monitoring for recurrence with earlier institution of salvage therapy. Such a tool would also address the need to accurately identify patients at high risk of recurrence for clinical trials of newer adjuvant therapies, thereby enriching the event rate and potentially allowing smaller and shorter trials. The results of this study suggest that Decipher can be used as a standard tool to better risk-stratify patients based on clinical and pathologic risk factors. Recent studies show that Decipher influences physician postoperative decision making in the adjuvant setting and lowers the discrepancy in decision making between urologists and radiation oncologists, , and .
This study has several strengths. First, it meets both PRoBE and REMARK criteria for the prospective validation of biomarkers. Second, all results were derived and reported blind to clinical outcome. Third, it used a robust and extensively validated expression assay. Fourth, there was long clinical follow-up in the censored and LM patients. The results build on other recent studies using commercially available genomic or gene expression platforms that have shown value in improving clinical decision making for men with early stage PCa, , and .
There are also several limitations in this study. There were few patients with RM in this cohort and further validation may be needed, although penalized LASSO regression demonstrated the robustness of these results even after taking the rarity of RM events into account. The information on extent of positive margin was available only for about one-third of those patients with positive margins and limited us from determining whether the performance of Decipher is different among those with focal versus extensive margin positivity. In addition, tumor specimens for this study were sampled only from the primary Gleason pattern present in RP specimens, although the specified method for the commercial Decipher assay is to sample tumor cells with the highest Gleason grade of the index lesion. Ongoing studies by this group aim to more fully characterize the performance and consistency of genomic signatures among primary, secondary, and tertiary lesions and gain a better understanding of the relationship between pathologic and genomic heterogeneity and more detailed assessment of multifocality.
This study is the first to show that genomic information such as the Decipher metastasis signature encoded in the primary tumor can be used to detect tumors with the highest biological potential for rapid metastatic disease after RP, thereby identifying patients that may benefit from adjuvant or other multimodal therapy. Future studies will be required to determine whether knowledge of this information can be used to improve quality of life and oncologic outcomes with existing therapies or whether these patients require new therapeutic approaches.
Author contributions:Eric A. Klein 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:Klein, Buerki, Davicioni.
Acquisition of data:Klein, Magi-Galluzzi, Haddad.
Analysis and interpretation of data:Klein, Davicioni, Kattan, Haddad, Choeurng, Yousefi, Stephenson.
Drafting of the manuscript:Klein, Davicioni, Haddad, Choeurng, Yousefi.
Critical revision of the manuscript for important intellectual content:Klein, Kattan, Stephenson, Buerki, Davicioni, Magi-Galluzzi.
Statistical analysis:Li, Kattan, Yousefi, Choeurng.
Administrative, technical, or material support:Klein.
Financial disclosures:Eric A. Klein certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: Christine Buerki, Voleak Choeurng, Elai Davicioni, Zaid Haddad, and Kasra Yousefi are employees of GenomeDx Biosciences.
Funding/Support and role of the sponsor:GenomeDx Biosciences Inc. was involved in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; and the preparation, review, and approval of the manuscript.
Acknowledgment statement:The authors acknowledge Dr. Darby Thompson (EMMES Canada) and Mercedeh Ghadessi (GenomeDx) for useful comments relating to study design and analysis and Marguerite du Plessis (GenomeDx) for her assistance in conducting the study.
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a Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
b GenomeDx Biosciences, Vancouver, British Columbia, Canada
c Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
d Anatomic Pathology, Cleveland Clinic, Cleveland, OH, USA
© 2014 European Association of Urology, Published by Elsevier B.V.