Prostate Cancer Gene 3 (PCA3): Development and Internal Validation of a Novel Biopsy Nomogram

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1 EUROPEAN UROLOGY 56 (2009) available at journal homepage: Prostate Cancer Prostate Cancer Gene 3 (PCA3): Development and Internal Validation of a Novel Biopsy Nomogram Felix K. Chun a,1, *, Alexandre de la Taille c, Hendrik van Poppel d, Michael Marberger e, Arnulf Stenzl f, Peter F.A. Mulders g, Hartwig Huland b, Clement-Claude Abbou c, Alexander B. Stillebroer g, Martijn P.M.Q. van Gils g, Jack A. Schalken g, Yves Fradet h, Leonard S. Marks i, William Ellis j, Alan W. Partin k, Alexander Haese b,1, ** a Department of Urology, University Hospital Hamburg-Eppendorf, Hamburg, Germany b Martini Clinic, Prostate Cancer Center, University Hospital Eppendorf, Hamburg, Germany c Hopital Henri Mondor, Créteil, France d Universitair Ziekenhuis Gasthuisberg, Leuven, Belgium e University of Vienna, Vienna, Austria f Uniklinikum Tübingen, Tübingen, Germany g Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands h Department of Urology, Universite Laval, Quebec City, Quebec, Canada i Urological Sciences Research Foundation, Culver City, CA, USA j University of Washington Medical Center, Seattle, WA, USA k Department of Urology, Johns Hopkins University Medical Institution, Baltimore, MD, USA Article info Article history: Accepted March 4, 2009 Published online ahead of print on March 13, 2009 Keywords: Prostate biopsy Prostate cancer gene 3 Biomarker Prostate cancer Nomogram Risk assessment Abstract Background: Urinary prostate cancer gene 3 (PCA3) represents a promising novel marker of prostate cancer detection. Objective: To test whether urinary PCA3 assay improves prostate cancer (PCa) risk assessment and to construct a decision-making aid in a multi-institutional cohort with pre prostate biopsy data. Design, setting, and participants: PCA3 assay cut-off threshold analyses were followed by logistic regression models which used established predictors to assess PCa-risk at biopsy in a large multi-institutional data set of 809 men at risk of harboring PCa. Measurements: Regression coefficients were used to construct four sets of nomograms. Predictive accuracy (PA) estimates of biopsy outcome predictions were quantified using the area under the curve of the receiver operator characteristic analysis in models with and without PCA3. Bootstrap resamples were used for internal validation and to reduce overfit bias. The extent of overestimation or underestimation of the observed PCa rate at biopsy was explored graphically using nonparametric loss-calibration plots. Differences in PA were tested using the 1 Both authors contributed equally to the manuscript. * Corresponding author. Department of Urology, University Hospital Hamburg-Eppendorf, Martinistr. 52, Hamburg, Germany. ** Co-corresponding author. Martini Clinic, Prostate Cancer Center, University Hospital Hamburg- Eppendorf, Martinistr. 52, Hamburg, Germany. addresses: chun@uke.uni-hamburg.de (F.K. Chun), haese@uke.uni-hamburg.de (A. Haese) /$ see back matter # 2009 European Association of Urology. Published by Elsevier B.V. All rights reserved. doi: /j.eururo

2 660 EUROPEAN UROLOGY 56 (2009) Mantel-Haenszel test. Finally, nomogram-derived probability cut-offs were tested to assess the ability to identify patients with or without PCa. Results and limitations: PCA3 was identified as a statistically independent risk factor of PCa at biopsy. Addition of a PCA3 assay improved bootstrap-corrected multivariate PA of the base model between 2% and 5%. The highest increment in PA resulted from a PCA3 assay cut-off threshold of 17, where a 5% gain in PA (from 0.68 to 0.73, p = 0.04) was recorded. Nomogram probability derived risk cut-off analyses further corroborate the superiority of the PCA3 nomogram over the base model. Conclusions: PCA3 fulfills the criteria for a novel marker capable of increasing PA of multivariate biopsy models. This novel PCA3-based nomogram better identifies men at risk of harboring PCa and assists in deciding whether further evaluation is necessary. # 2009 European Association of Urology. Published by Elsevier B.V. All rights reserved. 1. Introduction Development of biomarkers by genomic and proteomic high-throughput platforms has garnered great expectations of improving cancer screening, early detection, staging, and prognosis. Recently, the urinary prostate cancer gene 3 (PCA3) assay has shown promising results for prostate cancer (PCa) detection. This assay measures PCA3 messenger ribonucleic acid (mrna) and prostate-specific antigen (PSA) mrna concentrations in post digital rectal examination (post-dre) urine [1]. PCA3 is highly overexpressed (median: 66-fold) in malignant prostate tissue compared with benign and normal tissues [2]. Several studies demonstrated superior sensitivity and specificity of the PCA3 assay score over that of PSA level [3 5]. These findings could translate into improved identification of men at risk of harboring PCa and reduction in the number of unnecessary biopsies. Consequently, a urinary PCA3 assay was developed and was made available for clinical use as a Conformité Européenne (CE) marked product [1]. Beyond univariate and highly discriminatory ability, improvement of sensitivity and specificity, and confirmation of its independent predictor status, it is mandatory for a novel marker to increase the combined multivariate predictive accuracy (PA) of established risk factors. Furthermore, the increase in multivariate PA should not only be statistically significant but should also address a significant number of individuals. If these criteria are met, the novel marker may be considered clinically meaningful, and its application in clinical practice can be justified [6,7]. PCA3 has never been tested in a multivariate biopsy nomogram setting. To address this void, we tested several cut-off thresholds of urinary PCA3 assay scores in the largest reported PCA3 biopsy data set to date; we applied stringent analytic methods in addition to testing the multivariate independent status of PCA3;and we quantified the increment in PA related to its inclusion to established risk factors in risk models for biopsy outcome. 2. Materials and methods 2.1. Patient populations Data were collected from 1206 men subjected to 10 cores at initial or repeat prostate biopsy from two prospective, multicenter studies from Europe and North America. Men receiving medical therapy affecting PSA levels, men with symptoms of urinary tract infection, and men with a history of PCa or invasive treatment for benign prostatic hyperplasia (BPH) were not recruited for the studies. After exclusion of 397 men due to missing variables, 809 men remained in the cohort to develop a biopsy nomogram and to validate it internally. The respective independent ethics committees (IECs) approved the study protocol, and informed consent was obtained from all patients Clinical evaluation All men included on our study cohort had been referred for prostatic (re- )evaluation because of suspicious DRE results and/or abnormal PSA levels, and their medical data had complete information on age, PSA level, DRE, prostate volume, history of previous biopsy, and PCA3 assay score. DRE findings were classified as normal or suspicious. Ten or more core, systematic transrectal ultrasound (TRUS) guided biopsies were performed. TRUS-derived total prostate volume was calculated using the prolate ellipse formula (0.52 length width height) as described in Eskew et al [8]. No patient had a PSA level >50 ng/ml [9] Specimen collection and prostate cancer gene 3 assay procedure PSA levels were measured before DRE and TRUS. First-catch urine samples were collected following a DRE as described by Groskopf et al [1]. The urine sample was processed and tested to quantify PCA3-mRNA and PSA-mRNA concentrations using the Progensa PCA3 assay. The PCA3 assay score was calculated as (mrna PCA3)/(mRNA PSA) Biopsy specimens were evaluated by an experienced uropathologist at each site Statistical analyses First, the PCA3 assay score was explored with respect to possible cut-off values that could be more informative than the unaltered continuous variable format. A cut-off value was identified using the minimum p- value approach according to Mazumdar and Glassman [10]. Based on previous work, additional PCA3 assay score cut-off values of 24 [11] and 35 [4] were subsequently tested to determine the most informative model. Second, univariate and multivariate logistic regression models (LRMs) addressed the presence of PCa from biopsy. Base predictors were age, DRE, PSA, prostate volume, and history of previous biopsy. Within four distinct models, each was complemented with the PCA3 assay score, either coded as a continuously variable or according to three cut-off values. Multivariate regression coefficients were then used to construct four sets of nomograms. PA estimates of biopsy outcome predictions were quantified using the area under the curve (AUC) of the receiver

3 EUROPEAN UROLOGY 56 (2009) operator characteristic (ROC) analysis in models with and without PCA3. This method was selected to quantify increments in PA associated with the addition of PCA3 to all base predictor variables. Some 200 bootstrap resamples were used for internal validation of all accuracy estimates and to reduce overfit bias. The extent of overestimation or underestimation of the observed PCa rate at biopsy was explored graphically using nonparametric loss-calibration plots. Furthermore, differences in PA were tested for statistical significance using the Mantel-Haenszel test. Finally, various nomogram probability cut-offs were tested to assess the ability to identify patients with or without PCa. S-Plus Professional, v.1 (MathSoft Inc., Seattle, WA, USA) was used. All tests were two-sided with a significance level at Results Patient characteristics are shown in Table 1. PCa was detected in 316 men (39.1%). Median PCA3 score was 25.9 ( ). Mean and median PCA3 scores were significantly higher in the PCa versus the biopsy negative group (56.5 and 37.4 vs 34.6 and 19.5, respectively) ( p < 0.001). Mean and median ages were 65 yr and 66 yr, respectively ( p > 0.05). PSA levels ranged from 0.1 ng/ml to 48.5 ng/ml (mean: 7.4; median: 6.3). Most patients exhibited a normal DRE (n = 586, 72.4%) and a history of previous biopsy sessions (n = 569, 70.3%). High PSA level, suspicious DRE results, low prostate volume, previous biopsy sessions, and high PCA3 assay score were significantly associated with PCa at biopsy (all p < 0.001). In the PCA3 assay score cut-off analysis for PA, a score 17 or >17 was identified as the most statistically significant cut-off (Table 1). In all subsequent analyses, a continuously coded PCA3 assay score as well as PCA3 cut-off thresholds of 17, 24, and 35 were explored. Table 2 shows the univariate and multivariate LRMs predicting PCa at biopsy. Predictor variables were age, DRE result, PSA level, prostate volume, history of previous biopsy and/or biopsies, and PCA3 assay score. In univariate analyses (Table 2a), all variables were statistically significant predictors of PCa at biopsy ( p 0.02). In univariate analyses, PA estimates for age, DRE, PSA, prostate volume, and history of previous biopsy were 0.582, 0.576, 0.527, 0.619, and 0.523, respectively. Regardless of its PCA3 coding, PCA3 demonstrated the highest PA: The continuously coded PCA3 demonstrated the highest PA (0.679) followed by the PCA3 assay score cut-off threshold 17 (PCA3-17; 0.633), Table 1 Comparison of factors associated with prostate cancer between biopsy-negative and biopsy-positive men Variables Total cohort No prostate cancer Prostate cancer p value No. of patients (%) 809 (100) 493 (60.9) 316 (39.1) Age, yr 0.4 Mean Median Range PSA, ng/ml <0.001 Mean Median Range DRE <0.001 Suspicious, No. (%) 223 (27.6) 106 (21.5) 117 (37.0) Unsuspicious, No. (%) 586 (72.4) 387 (78.5) 199 (63.0) Total prostate volume, cm 3 <0.001 Mean Median Range Previous biopsy sessions <0.001 No, No. (%) 232 (28.7) 102 (20.7) 130 (41.1) Yes, No. (%) 577 (71.3) 391 (79.3) 186 (58.9) No. of biopsy cores 0.9 Mean Median Range PCA3 assay score <0.001 Mean Median Range , No. (%) 282 (34.9) 223 (45.2) 59 (18.7) >17, No. (%) 527 (65.1) 270 (54.8) 257 (81.3) 24, No. (%) 384 (47.5) 282 (57.2) 102 (32.3) >24, No. (%) 425 (52.5) 211 (42.8) 214 (67.7) 35, No. (%) 500 (61.8) 351 (71.2) 149 (47.2) >35, No. (%) 309 (38.2) 142 (28.8) 167 (52.8) PSA = prostate specific antigen; DRE = digital rectal examination; PCA3 = prostate cancer gene 3.

4 662 EUROPEAN UROLOGY 56 (2009) Fig. 1 (a) Prostate cancer gene 3 (PCA3) nomogram predicting cancer on prostate biopsy; (b) local regression nonparametric smoothing plots showing the calibration of the PCA3 nomogram; (c) local regression nonparametric smoothing plots showing the calibration of the base nomogram. Instructions for physicians: To obtain nomogram-predicted probability of prostate cancer, locate patient values at each axis. Draw a vertical line to the Point axis to determine how many points are attributed for each variable value. Sum the points for all variables. Locate the sum on the Total Points line to be able to assess the individual probability of cancer on prostate biopsy on the Probability of prostate cancer at biopsy line. Instructions for readers: Perfect predictions correspond to the 458 line. Points estimated below the 458 line correspond to nomogram overprediction, whereas points situated above the 458 line correspond to nomogram underprediction. A nonparametric, smoothed curve indicates the relationship between predicted probability and observed frequency of prostate cancer on initial biopsy. Vertical lines indicate the frequency distribution of predicted probabilities. PSA = prostatespecific antigen; DRE = digital rectal examination.

5 EUROPEAN UROLOGY 56 (2009) PCA3-24 (0.624) and PCA3-35 (0.620). The highest odds ratio of 3.6 (95% confidence interval [CI]: ) was recorded for PCA3-17. In multivariate models (Table 2b), all variables were independent predictors of PCa ( p 0.02). The combined multivariate PA of the base model was Addition of PCA3, regardless of its coding, improved bootstrap-adjusted multivariate PA of the base model from 2.3% to 4.6%. The highest increment in PA resulted from inclusion of the PCA3-17, for which a statistically significant 4.6% gain in PA, from to ( p = 0.04), was recorded. Fig. 1a shows the nomogram of regression coefficient based PCA3-17 demonstrating significantly higher accuracy compared with the nomogram devised from base predictor variables. Fig. 1b and c show calibration plots for the PCA3-17 nomogram and the base nomogram. On these calibration plots, the predicted probability of the nomogram is represented on the x-axis and the observed rate of biopsy-proven PCa is represented on the y-axis; close agreement with the 458 line indicates near-perfect prediction. Specifically, in the low- and high-risk prediction ranges, the performance of the PCA3-17 nomogram exhibits virtually perfect agreement, whereas the base model underestimates the risk of PCa. Table 3a and b show the effect of applying nomogramderived probabilities of PCa in the study population. If, for example, a nomogram-predicted probability cut-off of 20% is applied, patients whose assay scores fall below that cutoff would be qualified by the nomogram as being at low risk; patients whose assay scores are above that cut-off would be qualified as high risk of having PCa. The base nomogram correctly classified only 16.8% of patients below the threshold who did not harbor biopsy-confirmed PCa. Conversely, the PCA3-17 nomogram correctly classified substantially more (30.4%) patients who did not harbor biopsy-confirmed PCa below the threshold. At the other end of the scale, the base nomogram incorrectly classified 83.2% of patients with a negative biopsy above the threshold, falsely indicating high risk of PCa. Again, the PCA3-17 nomogram classified substantially fewer patients (69.5%) with a negative biopsy above the nomogram threshold, exposing fewer men to prostate biopsy if this novel nomogram had been used. Finally, at the nomogrampredicted probability cut-off of 20%, these figures translate into an equally high sensitivity of 95% versus 92% for the base nomogram versus the PCA3-17 nomogram. Conversely, regarding specificity, the PCA3-17 nomogram outperforms the base nomogram: At the cut-off threshold of 20% risk of harboring PCa, the difference in specificity was 16.8% for the base nomogram versus 30.4% for the PCA3-17 nomogram. Accordingly, negative and positive predictive values were in favor of the PCA3-17 nomogram. 4. Discussion In this study, we used the most stringent methodologic criteria suggested by Kattan, for which, in addition to demonstrating its independent predictor status, the candidate marker should enhance the overall PA of established predictors [6,7]. We added this methodology to the standard univariate and multivariate tests of the candidate marker PCA3. In univariate analyses predicting PCa at biopsy, all forms of PCA3 coding represented a statistically significant predictor (all p < 0.001), and they outperformed all other tested risk factors. Regardless of PCA3 coding, bootstrap-corrected PA of PCA3 assays was the highest among tested risk factors. The continuously coded PCA3 demonstrated the highest PA (0.68), followed by PCA3-17 (0.63), PCA3-24 (0.62), and PCA3-35 (0.62) (Table 2a). These findings match previously published results [4,5,12]. Marks et al demonstrated an AUC for PCA3 versus PSA of 0.68 versus 0.52 in 226 men with PSA levels 2.5 ng/ml [4]. Similar results were found for PSA ranges <2.5 ng/ml, for PSA ranges >10 ng/ml, and in initial and repeat biopsy settings by Deras et al [5]. In their study, the AUC of PCA3 versus PSA in an initial and repeat biopsy setting was 0.70 and 0.68 versus 0.62 and 0.55, respectively. Furthermore, Haese et al reported an AUC for PCA3 of 0.66 versus 0.58 for percent of free PSA [12]. In multivariate analyses, regardless of whether PCA3 was used as a continuously coded variable or whether PCA3-17, PCA3-24, or PCA3-35 were used, PCA3 invariably represented an independent risk factor of PCa (all p < 0.001). The combined multivariate bootstrap-corrected PA of established risk factors age, DRE, PSA, prostate volume, and history of previous biopsy (ie, base model) for predicting PCa was When PCA3 was added to the multivariate model, PA improved between 2% and 5%. PCA3-17 led to the highest increase in PA (5%, p = 0.04). This emphasizes the clinical potential of PCA3 assays to improve PCa prediction (Table 2b). Similar findings have been demonstrated by others [5,13]. Deras et al tested a multivariate model consisting of PSA level, prostate volume, and DRE result [5]. They found an increment in AUC from 0.67 for the base model versus 0.75 for the base model with PCA3 score. In contrast to our study, they did not investigate the incremental value of different PCA3 variants in multivariate models. In fact, we further carried on analyses and constructed a regression-based nomogram to predict PCa risk on an individual basis (Fig. 1a). This novel PCA3-17 nomogram was 0.73 accurate after internal validation, and it compares favorably with other recent biopsy nomograms [14 16]. Ankerst et al tested urinary PCA3 in the Prostate Cancer Prevention Trial (PCPT) risk calculator [13]. Compared with our results and previous diagnostic prediction tools, which range between 0.73 and 0.76, they demonstrated a lower overall AUC of 0.70 after addition of urinary PCA3 assay to the six PCPT risk-calculator base predictors (PSA level, DRE result, family history of PCa, biopsy history, age, and African American race) which was significantly higher than the established PCPT risk calculator (0.70 vs 0.65; p < 0.05). Unlike our analyses, neither their AUC comparison between the multivariate model, including PCA3 versus PCA3 assay alone (0.70 vs 0.67), nor PCA3 assay alone versus PSA demonstrated statistically significantly differences ( p > 0.05). Moreover, clinical decision-making aids should be simple to access, and many risk factors make their use impractical in busy clinical routine. The PCPT risk

6 664 Table 2 Univariable and multivariable logistic regression models to predict presence of cancer at prostate biopsy Variables Univariable models OR (95% CI); p value PA (a) Univariable logistic regression models. Age 1.04 ( ); < DRE suspicious vs unsuspicious 2.15 ( ); < PSA level 1.04 ( ); Prostate volume 0.98 ( ); < Repeat biopsy sessions vs initial biopsy 1.69 ( ); PCA3 assay score continuously coded 1.01 ( ); < PCA3 assay score >17 vs ( ); < PCA3 assay score >24 vs ( ); < PCA3 assay score >35 vs ( ); < Variables y Base model y Base model + continuously coded PCA3 Multivariable models y Base model + PCA3-17 y Base model + PCA3-24 y Base model + PCA3-35 OR (95% CI); p value OR (95% CI); p value OR (95% CI); p value OR (95% CI); p value OR (95% CI); p value (b) Multivariable logistic regression models Age 1.05 ( ); < ( ); < ( ); ( ); ( ); <0.001 DRE suspicious vs unsuspicious 1.84 ( ); < ( ); ( ); < ( ); < ( ); <0.001 PSA level 1.06 ( ); < ( ); ( ); ( ); < ( ); <0.001 Prostate volume 0.98 ( ); < ( ); < ( ); < ( ); < ( ); <0.001 Repeat biopsy sessions vs initial biopsy 2.05 ( ); ( ); ( ); ( ); ( ); 0.02 PCA3 assay score 1.01( ); < ( ); < ( ); < ( ); <0.001 Predictive accuracy Increment in predictive accuracy (%) [Mantel Haenszel Test] 2.3 ( p = 0.3) 4.6 ( p = 0.04) 3.4 ( p = 0.1) 3.4 ( p = 0.1) EUROPEAN UROLOGY 56 (2009) PA = predictive accuracy; OR = odds ratio; CI = confidence interval; DRE = digital rectal examination; PSA = prostate specific antigen; PCA3 = prostate cancer gene 3; PCA3-17 = PCA3 assay score cut-off threshold of 17; PCA3-24 = PCA3 assay score cut-off threshold of 24; PCA3-35 = PCA3 assay score cut-off threshold of 35. y Base model: age, DRE result, PSA level, total prostate volume, previous biopsy sessions.

7 EUROPEAN UROLOGY 56 (2009) Table 3 Analysis of nomogram-derived cut-offs used to determine the absence of prostate cancer (PCa; n = 493) versus the presence of PCa (n = 316) Nomogram-derived probability of PCa, % No. of patients below probability threshold without PCa (true negatives), % Negative predictive value, % No. of patients above probability threshold without PCa (false positives), % Positive predictive value, % (a) Analysis of base-nomogram derived cut-offs < < < < < < Nomogram-derived probability of PCa, % No. of patients below probability threshold without PCa (true negatives), % Negative predictive value, % No. of patients above probability threshold without PCa (false positives), % Positive predictive value, % (b) Analysis of PCA3-nomogram derived cut-offs < < < < < < calculator including PCA3 assay relies on seven risk factors versus five risk factors in our study. Computational access is needed to apply the PCPT risk calculator to future patients. Conversely, our nomogram is easy to access in a paperbased format. Last, as correctly stated by Ankerst et al, their nomogram applies primarily to screening patients as opposed to referral patients in our cohort. Taken together, our findings demonstrate that, following most stringent criteria, PCA3 does fulfill the characteristics of an independent and informative marker; thus PCA3 can be termed a novel diagnostic marker of PCa [6,7]. Moreover, it is important to note that within our cohort, commonly applied standard risk factors of PCa (age, DRE result, prostate volume, and biopsy history) failed to achieve satisfactory PA (0.68). This finding corroborates previous recent studies that show standard risk factors of PCa lose their PA in men at risk for PCa, [14,17], and this finding further corroborates the need for investigation of novel markers to improve PCa prediction. Our results suggest that the PCA3 assay is capable of diminishing this loss in PA. Specifically, addition of the PCA3 assay is related to an increase in PA of 5%. Recently, Shariat et al [18] added nine different, experimental, novel diagnostic markers to a multivariate model and demonstrated a gain in PA of 15% (72% to 87%, p < 0.001). Therefore, the increment of +5% related to one single marker (PCA3) is clearly remarkable and clinically significant. Furthermore, it may be expected that, in the context of the ongoing PSA screening debate with decreasing cancer-specific mortality [19] and growing PCa awareness, physicians will be faced with increasing numbers of men who are potential candidates for prostatic evaluation. Klotz et al recently calculated for the year 2007 that if the PSA threshold had been lowered to 2.5 ng/ml 2.74 million American men between yr would have faced a biopsy indication [20]. Assuming a 30% detection rate, roughly 1.97 million men would have been left with elevated PSA levels. Such numbers demonstrate the clinical value of a novel, highly specific, PCa detection marker such as PCA3 and its potential as an individual risk stratification tool to select men for prostatic evaluation. Therefore, health and/or economic expenses are reduced as well as patient concerns (anxiety, discomfort, pain, and complications associated with prostate biopsies) [12]. Finally, in the PCa-staging scenario for which discrimination between significant and insignificant disease is under debate [21], PCA3 assay may be valuable in identifying patients for active surveillance or definitive therapy. This hypothesis is supported by work suggesting a correlation between tumor volume and PCA3 assay score [22]. After confirmation of PCA3 as a novel marker, we assessed risk factors of PCa of our cohort to construct a novel nomogram. Within this process, we determined 17 to be the most significant PCA3 cut-off value, and we tested variants of the risk factor PCA3 coded linearly or PCA3-17 and compared those to published PCA3 assay thresholds of 24 and 35 [4,11]. Our findings differ from results reported by Marks et al, who suggested that PCA3-35 is the most efficacious [4]. These differences may be explained by the relatively small sample size available to the authors; our findings were based on a study cohort that was four times larger. Nevertheless, we may hypothesize that, analogous to PSA levels, the PCA3 assay score represents a continuum of risk that is expressed in our univariate analysis: The continuously coded PCA3 score had the highest PA (0.68), which was as high as the base multivariate model; however, after controlling for other factors, the effect of continuously coded PCA3 disappeared. Instead, the multivariate results suggested a cut-off of 17 as the most informative PCA3 threshold to use for nomogram construction. The advantage of considering PCA3 is further corroborated by graphical exploration and nomogram probability cut-off analysis between the novel nomogram versus the base model

8 666 EUROPEAN UROLOGY 56 (2009) (Fig. 1b and c, Table 3). Obviously, the increase of 5% in PA related to inclusion of the PCA3 assay improved detection of PCa in those men at lowest and highest risk of PCa, respectively. Using a cut-off probability of 20% (Table 3), for example, the PCA3 nomogram increased the rate of truenegative individuals from 17% to 30% and decreased the number of false-positive individuals from 83% to 70%. This is further corroborated by equally high sensitivity and substantially higher specificity of the PCA3-17 nomogram versus the base nomogram according to increasing nomogram-predicted PCa risk probability analyses (Table 3). Taken together, our PCA3 nomogram seems reliable to select men with PCa more accurately but also to identify those for whom further work may be spared. Several limitations may apply to our findings. Although this study involves one of the largest PCA3 biopsy-verified patient cohorts to date, our findings are still based on a relatively small sample size. It needs to be acknowledged that statistical models such as nomograms depend heavily on their development data. Therefore, PCA3 discrepancies in PA between continuously coded PCA3 and PCA3-17 between univariate and multivariate analyses may be biased by the limited number of investigated subjects and must be corroborated in further validation studies. Furthermore, due to missing data, we were unable to compare performance of other established models such as the PCPT risk calculator or the initial biopsy nomogram by Nam et al to determine the most accurate model out of a wide array of published biopsy nomograms [16,23]. It may be argued that the prostate volume risk factor [24] does not represent a readily available risk factor because TRUS may be necessary; however, for those scheduled for repeat biopsy, TRUS-derived prostate volume is available. Actually, this may represent a strength of the PCA3 nomogram because it is applicable not only to the initial biopsy setting but also to the repeat biopsy setting. Nevertheless, further studies are warranted to replicate our findings and to validate externally the performance of this novel risk-stratification tool. 5. Conclusions In conclusion, PCA3 was identified as a statistically independent and informative novel marker that is capable of increasing the PA of multivariate biopsy models. We constructed a novel PCA3-based individual risk stratification tool to identify men at risk of harboring PCa. It may assist patients and clinicians in deciding whether further prostatic evaluations are necessary. Author contributions: Felix K. Chun and Alexander Haese 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: Chun, Haese. Acquisition of data: Chun, Haese, de la Taille, van Poppel, Marberger, Stenzl, Mulders, Huland, Abbou, Stillebroer, van Gils, Schalken, Fradet, Marks, Ellis, Partin. Analysis and interpretation of data: Chun, Haese. Drafting of the manuscript: Chun, Haese. Critical revision of the manuscript for important intellectual content: Chun, Haese, de la Taille, van Poppel, Marberger, Stenzl, Mulders, Huland, Abbou, Stillebroer, van Gils, Schalken, Fradet, Marks, Ellis, Partin. Statistical analysis: Chun. Obtaining funding: None. Administrative, technical, or material support: None. Supervision: None. Other (specify): None. Financial disclosures: I certify 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: None. Funding/Support and role of the sponsor: Gen-Probe, Inc provided funding and/or support for the following aspects of the study: design and conduct, data collection, management, analysis, interpretation of data, preparation, and review and approval of the manuscript. References [1] Groskopf J, Aubin SM, Deras IL, et al. APTIMA PCA3 molecular urine test: development of a method to aid in the diagnosis of prostate cancer. Clin Chem 2006;52: [2] Hessels D, Klein Gunnewiek JMT, van Oort I, et al. DD3 PCA3 -based molecular urine analysis for the diagnosis of prostate cancer. 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J Urol 2002;168: [10] Mazumdar M, Glassman JR. Categorizing a prognostic variable: review of methods, code for easy implementation and applications to decision-making about cancer treatments. Stat Med 2000;19: [11] Chun FK, Haese A, de la Taille A, et al. Performance analysis of different PCA3 cut-offs. J Urol 2008;179:705. [12] Haese A, de la Taille A, van Poppel H, et al. Clinical utility of the PCA3 urine assay in European men scheduled for repeat biopsy. Eur Urol 2008;54: [13] Ankerst DP, Groskopf J, Day JR, et al. Predicting prostate cancer risk through incorporation of prostate cancer gene 3. J Urol 2008; 180:1303 8, discussion [14] Chun FK-H, Briganti A, Graefen M, et al. Development and external validation of an extended 10-core biopsy nomogram. Eur Urol 2007;52: [15] Chun FK, Briganti A, Graefen M, et al. Development and external validation of an extended repeat biopsy nomogram. J Urol 2007;177:510 5.

9 EUROPEAN UROLOGY 56 (2009) [16] Nam RK, Toi A, Klotz LH, et al. Assessing individual risk for prostate cancer. J Clin Oncol 2007;25: [17] Canto EI, Singh H, Shariat SF, et al. Effects of systematic 12-core biopsy on the performance of percent free prostate specific antigen for prostate cancer detection. J Urol 2004;172: [18] Shariat SF, Karam JA, Walz J, et al. Improved prediction of disease relapse after radical prostatectomy through a panel of preoperative blood-based biomarkers. Clin Cancer Res 2008;14: [19] Jemal A, Siegel R, Ward E, et al. Cancer statistics, CA Cancer J Clin 2008;58: [20] Klotz L. Active surveillance for prostate cancer: for whom? J Clin Oncol 2005;23: [21] Chun FK, Haese A, Ahyai SA, et al. Critical assessment of tools to predict clinically insignificant prostate cancer at radical prostatectomy in contemporary men. Cancer 2008;113: [22] Nakanishi H, Groskopf J, Fritsche HA, et al. PCA3 molecular urine assay correlates with prostate cancer tumor volume: implication in selecting candidates for active surveillance. J Urol 2008;179:1804 9, discussion [23] Thompson IM, Ankerst DP, Chi C, et al. Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. J Natl Cancer Inst 2006;98: [24] Briganti A, Chun FK, Suardi N, et al. Prostate volume and adverse prostate cancer features: fact not artifact. Eur J Cancer 2007; 43: Editorial Comment on: Prostate Cancer Gene 3 (PCA3): Development and Internal Validation of a Novel Biopsy Nomogram Alberto Briganti Department of Urology, Vita-Salute University San Raffaele, Milan, Italy briganti_alberto@yahoo.it The introduction of prostate-specific antigen (PSA) into clinical practice has drastically changed the clinical approach and management of prostate cancer (PCa). Since the late 1990s, the utility of PSA in predicting the presence of PCa at biopsy has been corroborated by numerous studies [1 3]. But although PSA can be considered a reliable and useful marker in PCA diagnosis, it is limited by a (relatively) low specificity. This limitation results in the exposure of a certain number of patients to unnecessary prostate biopsies and urgently needs to be overcome in view of the upcoming results of PSA screening studies. Indeed, if an association between PCa screening and disease mortality is demonstrated, an increasing number of patients is expected to be referred for prostatic evaluation. Moreover, of those patients who are diagnosed with PCa, not all will be affected by life-threatening disease. Patient stratification with regard to PCa risk and aggressiveness is necessary in the clinical decision-making process. It is clear that such an important approach cannot be done on the basis of PSA alone. In the field of PCa diagnosis, other key variables such as digital rectal examination result, prostate volume, percent of free PSA, and familiarity of PCa are routinely considered in conjunction with PSA for determining the need for prostate biopsy [1 4]. Despite the combination of all of these parameters, the accuracy of multivariable models in predicting the presence of PCa has never reached 100% [5]. Therefore, something else is needed to improve our ability to detect PCa at biopsy. In this context, urinary PCa gene 3 (PCA3) has been shown to be an accurate predictor of PCa at biopsy [6 8]. Its sensitivity and specificity has been demonstrated to be superior even to PSA [7,8]. Moreover, Chun et al also demonstrated that inclusion of PCA3 in multivariable models was able to improve significantly the predictive accuracy of models predicting presence of PCa at first or repeat biopsy [9]. The authors also developed and internally validated the first nomogram aimed at predicting the probability of PCa to include PCA3 as covariate. This tool showed a bootstrap-corrected 73% accuracy using a PCA3 cut-off threshold of 17, and it awaits external validation. Despite these very interesting results supporting the importance of PCA3 in PCa diagnosis, some key questions still need to be answered. First, despite a significant association between urinary PCA3 levels and PCa risk, the normal ranges of PCA3 in the population without PCa are still unknown. Hopefully, these data will be available soon from the results of an ongoing multicenter trial. Second, controversies exist with regard to the most significant cutoff value of PCA3 that should be considered as an indicator for prostate biopsy. Different PCA3 cut-offs have been proposed in different studies [6,8,9]. This issue must be fully addressed in future larger studies in which different cut-offs should be tested and given separately for patients submitted to either first or repeat biopsy. As for PSA, use of the same PCA3 cut-off (if any) is unlikely in clinical practice for patients who are considered for a first biopsy compared with patients who previously submitted to one or more negative biopsy sessions. Third, it would be interesting to test the role and the performance characteristics of PCA3 according to various PSA strata, such as patients with low and intermediate PSA levels, for whom the added value of a novel marker would be even more important. While waiting for these data, we can determine that PCA3 represents a novel and promising marker in the diagnosis and staging of PCa. References [1] Carlson GD, Calvanese CB, Partin AW. An algorithm combining age, total prostate-specific antigen (PSA), and percent free PSA to predict prostate cancer: results on 4298 cases. Urology 1998; 52: [2] Chun FK-H, Briganti A, Graefen M, et al. Development and external validation of an extended 10-core biopsy nomogram. Eur Urol 2007;52:

10 668 EUROPEAN UROLOGY 56 (2009) [3] Karakiewicz PI, Benayoun S, Kattan MW, et al. Development and validation of a nomogram predicting the outcome of prostate biopsy based on patient age, digital rectal examination and serum prostate specific antigen. J Urol 2005;173: [4] Walz J, Haese A, Scattoni V, Steuber T. Percent free prostatespecific antigen (PSA) is an accurate predictor of prostate cancer risk in men with serum PSA 2.5 ng/ml and lower. Cancer 2008;113: [5] Chun FK-H, Graefen M, Briganti A, et al. Initial biopsy outcome prediction head-to-head comparison of a logistic regressionbased nomogram versus artificial neural network. Eur Urol 2007;51: [6] Haese A, de la Taille A, van Poppel H, et al. Clinical utility of the PCA3 urine assay in European men scheduled for repeat biopsy. Eur Urol 2008;54: [7] Deras IL, Aubin SM, Blase A, et al. PCA3: a molecular urine assay for predicting prostate biopsy outcome. J Urol 2008;179: [8] Marks LS, Fradet Y, Deras IL, et al. PCA3 molecular urine assay for prostate cancer in men undergoing repeat biopsy. Urology 2007;69: [9] Chun FK, de la Taille A, van Poppel H, et al. Prostate cancer gene 3 (PCA3): development and internal validation of a novel biopsy nomogram. Eur Urol 2009;56: DOI: /j.eururo DOI of original article: /j.eururo