Molecular Sciences PCA3 and PCA3-Based Nomograms Improve Diagnostic

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Int. J. Mol. Sci. 2013, 14, 17767-17780; doi:10.3390/ijms140917767
OPEN ACCESS
International Journal of
Molecular Sciences
ISSN 1422-0067
www.mdpi.com/journal/ijms
Article
PCA3 and PCA3-Based Nomograms Improve Diagnostic
Accuracy in Patients Undergoing First Prostate Biopsy
Alain Ruffion 1,2, Marian Devonec 1,2, Denis Champetier 1, Myriam Decaussin-Petrucci 2,3,
Claire Rodriguez-Lafrasse 2,4, Philippe Paparel 1,2, Paul Perrin 1,2 and
Virginie Vlaeminck-Guillem 2,4,*
1
2
3
4
Department of Urology, University Hospital of Lyon Sud, Hospices Civils of Lyon,
Pierre Bénite 69495, France; E-Mails: [email protected] (A.R.);
[email protected] (M.D.); [email protected] chu-lyon.fr (D.C.);
[email protected] (P.P.); [email protected] (P.P.)
Medical Faculty of Lyon 1 University, Lyon 69000, France;
E-Mails: [email protected] (M.D.-P.);
[email protected] (C.R.-L.)
Department of Pathology, University Hospital of Lyon Sud, Hospices Civils of Lyon,
Pierre Bénite 69495, France
Medical Unit of Molecular Oncology and Transfer, Department of Biochemistry and Molecular
biology, University Hospital of Lyon Sud, Hospices Civils of Lyon, Pierre Bénite 69495, France
* Author to whom correspondence should be addressed;
E-Mail: [email protected];
Tel.: +33-47-886-2992; Fax: +33-47-886-6654.
Received: 21 July 2013; in revised form: 7 August 2013 / Accepted: 23 August 2013 /
Published: 29 August 2013
Abstract: While now recognized as an aid to predict repeat prostate biopsy outcome, the
urinary PCA3 (prostate cancer gene 3) test has also been recently advocated to predict
initial biopsy results. The objective is to evaluate the performance of the PCA3 test in
predicting results of initial prostate biopsies and to determine whether its incorporation into
specific nomograms reinforces its diagnostic value. A prospective study included
601 consecutive patients addressed for initial prostate biopsy. The PCA3 test was
performed before ≥12-core initial prostate biopsy, along with standard risk factor
assessment. Diagnostic performance of the PCA3 test was evaluated. The three available
nomograms (Hansen’s and Chun’s nomograms, as well as the updated Prostate Cancer
Prevention Trial risk calculator; PCPT) were applied to the cohort, and their predictive
Int. J. Mol. Sci. 2013, 14
17768
accuracies were assessed in terms of biopsy outcome: the presence of any prostate cancer
(PCa) and high-grade prostate cancer (HGPCa). The PCA3 score provided significant
predictive accuracy. While the PCPT risk calculator appeared less accurate; both Chun’s
and Hansen’s nomograms provided good calibration and high net benefit on decision curve
analyses. When applying nomogram-derived PCa probability thresholds ≤30%, ≤6% of
HGPCa would have been missed, while avoiding up to 48% of unnecessary biopsies. The
urinary PCA3 test and PCA3-incorporating nomograms can be considered as reliable tools
to aid in the initial biopsy decision.
Keywords: urine biomarker; nomogram; initial prostate biopsy; prostate cancer; prostate
cancer antigen 3
1. Introduction
The widespread use of the prostate-specific antigen (PSA) test proved to improve early diagnosis of
prostate cancer (PCa) [1]. The PSA test is nevertheless characterized by a poor specificity.
Non-malignant prostate pathologies, such as prostatitis, or benign prostate hyperplasia can also induce
increased PSA levels, resulting in a high proportion (up to 70%) of negative and eventual unnecessary
prostate biopsies. The poor PSA specificity also led to the overdiagnosis and, potentially, the
overtreatment of indolent PCas that do not evolve towards aggressive life-threatening cancers [1].
Many efforts are consequently made to develop new biomarkers that could complement PSA for early
PCa diagnosis. Urinary detection of prostate cancer gene 3 (PCA3), developed for a decade [2], is a
useful adjunct in predicting prostate biopsy outcome [3,4], particularly for patients with previous
negative biopsies [5]. In fact, studies have often included patients scheduled for either repeat or initial
biopsies [2,6–11]. Whether the PCA3 test could also be useful in guiding the initial biopsy decision [3]
has been specifically addressed only recently, with convincing results [12–15].
Nomograms are widely used to help physicians in decision guiding and proved to be more accurate
than the separate use of a marker. When reporting diagnostic performances of the urinary PCA3 test,
some authors also compared the diagnostic accuracy of base nomograms and nomograms, including
the PCA3 score. The addition of the PCA3 score always gave better accuracy, suggesting that the
PCA3 score is a strong independent predictor of biopsy results and should, therefore, be included in
nomograms [9,12,16–19]. To our knowledge, only four urinary PCA3-based nomograms have been
previously published. Two are proposed to all patients, whatever the medical history of previous
biopsies, and were externally validated: the updated version of the Prostate Cancer Prevention Trial
(PCPT) risk calculator (online available) and the graphically available nomogram published by
Chun et al. [20–22]. Another is specifically dedicated to patients scheduled for repeat biopsy [23],
while the last one, very recently published by Hansen et al. [14], has been developed for guiding the
initial biopsy decision. Both Hansen’s and Chun’s nomograms proved to provide significant clinical
benefit without missing a too important proportion of high-grade prostate cancer (HGPCa) [14,21].
Int. J. Mol. Sci. 2013, 14
17769
In this study, we therefore aimed to (1) evaluate the diagnostic performance of the urinary PCA3
test to predict the outcome of initial prostate biopsies; and (2) perform a head-to-head comparison of
the three urinary PCA3-based nomograms currently available for initial or mixed biopsy patients.
2. Results and Discussion
2.1. Characteristics of Our Validation Cohort
Urine samples were obtained from 601 consecutive patients addressed for initial prostate biopsy
and, 594 samples were informative for the PCA3 test (99%). Patients’ characteristics are summarized
in Table 1. Positive biopsies were observed in 276 patients (46%), including 128 patients with HGPCa
(Gleason score ≥ 7), i.e., 46% of all prostate cancer (PCa) diagnosed. See Table S1 for additional
pathological findings.
Table 1. Patient characteristics and initial biopsy results (n = 594).
No. of patients (%)
Age, year
Median
IQR
DRE
Unsuspicious, no. (%)
Suspicious, no. (%)
Familial history of PCa
No (%)
Yes (%)
Prostate volume *, mL
Median
IQR
Serum PSA, ng/mL
Median
IQR
≥2.5 ng/mL
≥4 ng/mL
≥10 ng/mL
Urinary PCA3 score
Median
IQR
≥35
<35
>21
≤21
Entire initial
biopsy cohort
594 (100)
No cancer at
initial biopsy
318 (54)
63
58–67
62
58–66
LGPCa at
initial biopsy
148 (25)
64
59–68
HGPCa at
initial biopsy
128 (22)
p-Value *
<0.0001
65
61–70
<0.001
519 (87)
75 (13)
293 (92)
25 (8)
136 (92)
12 (8)
90 (70)
38 (30)
0.428
505 (85)
89 (15)
273 (86)
45 (14)
121 (82)
27 (18)
111 (87)
17 (13)
<0.0001
39.5
28–50
42
32–55
37
29–48
30
24–47
5.9
4.7–7.9
579 (97)
525 (88)
70 (12)
6
4.6–7.6
308 (97)
279 (88)
32 (10)
5.7
4.6–7.4
143 (97)
133 (90)
17 (11)
6.1
5.1–8.9
128 (100)
113 (88)
21 (16)
30
15–65
265 (45)
329 (55)
364 (61)
230 (39)
18
10–42
90 (28)
228 (72)
138 (43)
180 (57)
48
25–94
93 (63)
55 (37)
117 (79)
31 (21)
52
26–100
82 (64)
46 (36)
109 (85)
19 (15)
0.012
0.119
0.799
0.170
<0.0001
<0.001
<0.001
PCa = prostate cancer; DRE = digital rectal examination; LGPCa = low-grade PCa; HGPCa = high-grade PCa
(Gleason score ≥ 7); PCA3 = prostate cancer antigen 3; PSA = prostate-specific antigen; IQR = interquartile
range; * comparison of the three groups: no cancer, LGPCa and HGPCa.
Int. J. Mol. Sci. 2013, 14
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2.2. Diagnostic Performance of the Urinary PCA3 Test
By contrast with serum PSA, the PCA3 score did not correlate with prostate volume (Spearman r
coefficient = −0.0791, p = 0.054). The median urinary PCA3 score was significantly higher in the
patients with positive biopsies (Table 1). Patients with a PCA3 score ≥35 had a higher risk of positive
biopsies: 66% vs. 31% (p < 0.001); similarly, the risk was significantly higher using a cutoff of 21:
62% vs. 22% (p < 0.001) (Table 1). The risk of positive biopsies increased with increasing score
(Figure S1).
For predicting any PCa, the PCA3 score disclosed an area under the receiver operating curve (AUC)
of 0.743 (95%, CI 0.70–0.78) (Figure S2). At the usual cutoff of 35, sensitivity was 63%, with a
specificity of 72% and an accuracy of 68% (Table S2). Similar results were obtained for patients in the
PSA grey zone (4–10 ng/mL) with an AUC of 0.736 (95%, CI 0.69–0.78). For predicting HGPCa
(Gleason score ≥ 7), the PCA3 score AUC was 0.689 (95%, CI 0.64–0.74). Using Epstein criteria to
define HGPCa (>T1c, PSA density ≥ 0.15, Gleason score ≥ 7 and/or proportion of invaded cores
≥ 33%) [24], the PCA3 score AUC was 0.728 (95%, CI 0.69–0.77) with a significantly different
median (interquartile range, IQR) when compared to non-significant cancers: 51 (26–97) vs. 20
(11–48) (p < 0.0001).
Median serum PSA was significantly higher in patients with positive biopsies (Table 1). None of
the cutoffs, 2.5, 4 and 10 ng/mL, had a significant ability to predict biopsy results (Table 1). The AUC
of initial PSA was 0.517 (95%, CI 0.47–0.56), significantly lower than that of the PCA3 score
(p < 0.0001) (Figure S2). Similarly, at 0.562 (95%, CI 0.50–0.62), it was significantly lower than that
of the PCA3 score to predict HGPCa (p < 0.001).
Using univariate analysis, age, DRE findings, prostate volume and the PCA3 score, but not serum
PSA, were the predictors of any PCa and HGPCa (Table S3). In logistic regression models, all criteria,
including serum PSA, achieved independent predictor status and were included in a “base model” (age,
DRE findings, prostate volume and serum PSA) and additional models by adding PCA3 as either a
continuous or binary variable. We used the widely used cutoff of 35 [9,12] and the recently published
cutoff of 21 [14]. We found that the three models, including the PCA3 score, gave significantly higher
AUCs (≥0.780) than the base model (0.714) and provided a significantly better accuracy (≥71%) than
the base model (66%) in predicting any PCa (Table 2) or HGPCa (Table S4). Decision curve analysis
confirmed a higher benefit when adding the PCA3 score (either continuous or binary with a cutoff of
35) to the base model (Figure S3). The nomogram recently published by Hansen et al. [14] and
specifically proposed for initial prostate biopsy was applied to our whole cohort of 594 patients.
Comparison with the actual biopsy results confirmed a strong correlation between prediction and
pathological findings (Figure S4): the observed proportion of positive biopsies increased with the
calculated PCa risk (p < 0.001). The nomogram provided a 70% predicted accuracy and an AUC of
0.764 (95%, CI 0.726–0.802).
Int. J. Mol. Sci. 2013, 14
17771
Table 2. Multivariate analysis evaluating performance of logistic regression models to predict initial prostate biopsies.
Multivariate analysis
Base model
+ continuous PCA3 score
Base model
Base model
+ PCA3 cutoff of 21
Base model
+ PCA3 cutoff of 35
OR (95% CI)
p-Value
OR (95% CI)
p-Value
OR (95% CI)
p-Value
OR (95% CI)
p-Value
Age, year
1.08 (1.05–1.11)
<0.001
1.05 (1.02–1.09)
<0.001
1.06 (1.02–1.09)
<0.001
1.05 (1.02–1.08)
0.001
DRE
Prostate volume, cm3
Serum PSA, ng/mL
Urinary PCA3 score
AUC
IC 95%
p-Value *
PA
IC 95%
Increment in PA *
p-Value *
1.09 (1.03–1.15)
0.004
0.96 (0.95–0.97)
<0.001
1.10 (1.03–1.17)
0.004
0.714
(0.672–0.755)
66%
(62%–70%)
-
1.08 (1.02–1.15)
0.006
0.96 (0.95–0.97)
<0.001
1.10 (1.02–1.18)
0.008
1.01 (1.01–1.01)
<0.001
0.780
(0.743–0.818)
p < 0.0001
72%
(68%–76%)
+6%
1.08 (1.02–1.15)
0.008
0.96 (0.95–0.98)
<0.001
1.08 (1.01–1.16)
0.027
5.00 (3.36–7.45)
<0.001
0.781
(0.744–0.818)
p < 0.0001
71%
(68%–75%)
+5%
1.09 (1.03–1.16)
0.004
0.96 (0.95–0.97)
<0.001
1.09 (1.02–1.17)
0.015
4.21 (2.88–6.15)
<0.001
0.780
(0.742–0.817)
p < 0.0001
73%
(69%–76%)
+7%
p = 0.033
p = 0.060
p = 0.017
AUC = area under the receiver operating curve; CI = confidence interval; DRE = digital rectal examination (suspicious vs. unsuspicious); OR = odds ratio; PA = predictive
accuracy (proportion of well-classified patients according to the best automatically calculated cutoff); PSA = prostate-specific antigen; PCA3 = prostate cancer antigen 3;
*: when comparing to the base model.
Int. J. Mol. Sci. 2013, 14
17772
2.3. Head-to-Head Comparisons of the 3 Available Urinary PCA3-Based Nomograms
For head-to-head comparison of the three nomograms, we excluded patients <55 (age ≥55 is
required for the PCPT risk calculator) and, therefore, evaluated 536 patients. For the three nomograms,
AUCs were ≥0.730 and predictive accuracies, ≥67% (Figure S5). No statistically significant difference
was observed when comparing these performances. Calibration plot curves are provided in Figure 1, as
a representation of the PCa probability predicted by the nomogram and the actual observed proportion
of positive initial biopsies. Although it did not attain perfect calibration, Hansen’s nomogram was
slightly better calibrated than the two others. To compare the predictive net benefit of the three
nomograms, we used decision curve analysis (Figure 2). Chun’s and Hansen’s nomograms disclosed
the highest net benefits, while the Hansen one provided the lowest underestimation rate. As expected, a
net reduction of unnecessary biopsies was observed using each of the three nomograms, while missing
a few HGPCa. When applying nomogram-derived PCa probability thresholds ≤30%, the PCPT risk
calculator would have missed only 5% of any PCa and 3% of HGPCa, but only 22% of the biopsies
would have been avoided in patients without PCa. Using Chun’s or Hansen’s nomograms, initial
biopsy could be avoided up to 48% or 43% of the patients without PCa, respectively, while missing
≤11% of any PCa and only ≤6% of HGPCa (Table 3). Similar results were obtained when defining
significant cancers, according to Epstein criteria. Using thresholds ≤ 30% and the PCPT risk
calculator, Chun’s and Hansen’s nomograms would have missed three to 7% of significant cancers.
1
0.8
0.6
0.4
0.2
0.4
0.6
0.8
Actual probability
Chun’s nomogram
0.2
0
0.4
0.6
0.8
1
0
0.2
c
0.4
0.4
0.6
0.8
Hansen’s nomogram
0
0.6
Predicted probability
1
Predicted probability
Actual probability
0.2
0.2
0
0
Actual probability
b
Updated PCA3-incorporating
PCPT risk calculator
0
a
1
Figure 1. Calibration plots within external validation cohort using the three available
urinary PCA3-based nomograms (n = 536 patients): (a) PCA3, including updated Prostate
Cancer Prevention Trial risk calculator [20]; (b) Chun’s nomogram [21]; and (c) Hansen’s
nomogram [14].
0.2
0.4
0.6
Predicted probability
0.8
1
0.8
1
Int. J. Mol. Sci. 2013, 14
17773
Table 3. Numbers of biopsies performed and detection rates of any prostate cancer and high-grade prostate cancer (Gleason score ≥ 7),
according to the three urinary PCA3-based nomograms-derived probability cut-offs.
Nomogram
PCPT [20]
Chun [21]
Hansen [14]
Biopsies
performed
Biopsies not
performed a
n (%)
n (%)
Biopsies not
performed in men
without PCa b
n (%)
Any PCa
detected c
Any PCa
missed
NPV for PCa
prediction
HGPCa
detected
HGPCa
missed
n (%)
n (%)
%
n (%)
n (%)
NPV for
HGPCa
prediction
%
None
536 (100)
0 (0)
0 (0)
256 (100)
0 (0)
100
122 (100)
0 (0)
100
10
531 (99)
5 (1)
5 (2)
256 (100)
0 (0)
100
122 (100)
0 (0)
100
20
512 (96)
24 (4)
22 (8)
254 (99)
2 (1)
92
121 (99)
1 (1) *
98
30
462 (86)
40
376 (70)
74 (14)
66 (22)
244 (95)
12 (5)
84
118 (97)
4 (3) *
95
160 (30)
123 (44)
219 (86)
37 (14)
77
108 (89)
14 (11) **
93
50
275 (51)
261 (49)
184 (66)
179 (70)
77 (30)
71
90 (74)
32 (26) ***
88
10
516 (96)
20 (4)
17 (6)
253 (99)
3 (1)
85
122 (100)
0 (0)
100
20
462 (86)
74 (14)
63 (23)
245 (96)
11 (4)
85
121 (99)
1 (1) *
98
30
375 (70)
161 (30)
134 (48)
229 (89)
27 (11)
83
115 (94)
7 (6) *
96
40
342 (64)
194 (36)
154 (55)
216 (84)
40 (16)
79
108 (89)
14 (11) *
93
50
249 (46)
287 (54)
204 (73)
173 (68)
83 (32)
71
93 (76)
29 (24) ****
90
10
524 (98)
12 (2)
11 (4)
255 (99.6)
1 (0.4)
92
122 (100)
0 (0)
100
20
466 (87)
70 (13)
66 (22)
248 (97)
8 (3)
89
121 (99)
1 (1) *
98
30
390 (73)
146 (27)
119 (43)
229 (89)
27 (11)
82
115 (94)
7 (6) *
96
40
345 (64)
191 (36)
149 (53)
214 (84)
42 (16)
78
108 (89)
14 (11) *
93
50
292 (54)
244 (46)
179 (64)
191 (75)
65 (25)
73
97 (80)
25 (20) **
90
Probability
cutoff (%)
a
PCa = prostate cancer; NPV = negative predictive value; HGPCa = high-grade prostate cancer (Gleason score ≥ 7); biopsies that would have not been performed if the test, considered
negative under the corresponding probability cutoff, has been used to decide biopsy or not; b part of the number of biopsies not performed (see a) in the subgroup of patients in whom biopsies
were eventually revealed to be negative; percentage is indicative of specificity; c percentage is indicative of sensitivity; * all men had a Gleason score = 7; ** two men had a Gleason score of
4 + 4; the other men had a Gleason score = 7; *** one man had a Gleason score of 5 + 4; three men had a Gleason score of 4 + 4; the other men had a Gleason score = 7; **** three men had
a Gleason score of 4 + 4; the other men had a Gleason score = 7.
Int. J. Mol. Sci. 2013, 14
17774
Figure 2. Decision curve analysis of predicting prostate cancer on initial prostate biopsy
using the three available urinary PCA3-based nomograms (n = 536 patients).
2.4. Discussion
Consequent efforts have been made to provide algorithms that could accurately evaluate the actual
risk of PCa, while deciding whether prostate biopsies have to be performed. Several studies
even disclosed the improvement of predictive accuracy when adding a PCA3 score to a
previously published nomogram [25] or incorporating it in a new one [9,12,16–19]. Two urinary
PCA3-based nomograms were available for external use. They are not specifically devoted to
patients scheduled for initial biopsy, but this medical history has to exist in order to obtain a risk
calculation [20,21]. Recently, Hansen et al. [14] developed a novel, internally validated, urinary
PCA3-based nomogram, specifically for men scheduled for initial prostate biopsies. Similarly to the
two others, this nomogram significantly improved the accuracy of biopsy outcome prediction.
To compare these three available nomograms in predicting the outcome of initial prostate biopsies,
we took the opportunity to use a large French single-institution patient cohort. We first characterized
this cohort and checked that the diagnostic performance of urinary PCA3 test, as assessed using both
univariate and multivariate analyses, was quite similar to that observed in previous studies [12–15].
Performance was conserved in the PSA grey zone of 4–10 ng/mL. The PCA3 score proved to be an
independent predictor of initial biopsy outcome, as previously observed [6–15]. The addition of the
PCA3 score to a base model, including classical prediction factors (age, DRE findings, prostate
volume and total PSA), proved, again, a significant increase in predictive accuracy [9,12,16–19].
In our whole cohort of 594 patients, Hansen’s nomogram, which uses the PCA3 score as a binary
variable around a cutoff of 21, gave results slightly inferior to that observed in the princeps study. We
found an AUC of 0.764 (95%, CI 0.726–0.802) as compared to the reported one: 0.807 (95%,
CI 0.768–0.828) [14]. These results, along with those obtained using calibration curves and decision
curve analyses (Figures 1 and 2) nevertheless suggest that the present study could be considered as the
Int. J. Mol. Sci. 2013, 14
17775
claimed [26] external validation study of Hansen’s nomogram. Our cohort is, however, monocenter
and exclusively composed of French patients. Even if the strong correlation between our results and
the published ones underlines the nomogram’s robustness, results have therefore to be generalized with
caution until verifications have been performed in other populations.
As a limitation of their study, Hansen et al. [26] acknowledged the lack of comparison between
their nomogram and other existing predictive tools. Direct comparison between Chun’s nomogram and
the PCA3-updated PCPT risk calculator has previously been performed in a cohort of 218 patients
scheduled for either initial or repeat biopsies [27]. The two nomograms were found to be equivalent in
terms of predictive accuracy, proportion of saved biopsies and proportion of missed cancers, but
Chun’s nomogram provided better overall calibration and a higher net benefit on decision curve
analyses. In the present study, although we found no statistical differences between the three
nomograms when comparing AUCs and predictive accuracies, we observed a trend towards the
lessened diagnostic performance of the PCPT risk calculator. This was also underlined using both
calibration plot curves and decision curve analyses. The reasons why PCPT seems less accurate remain
to be determined, but at least two intrinsic particularities can be noticed: the PCA3 score is used as a
continuous variable, and prostate volume is not included as a prediction factor. Using the PCA3 score
as a binary variable around a cutoff seems indeed to display a better predictive accuracy [12,14,21].
Moreover, Hansen et al. [14] considered as crucial the inclusion of prostate volume within their
nomogram, and this criteria was also part of Chun’s one [21]. When checking the application of the
urinary PCA3 test in a multivariate regression model, we also found prostate volume to be an
independent predictor of biopsy outcome. It is worthy to note that the PCA3 thresholds used in our
study and the previously published ones are not always the same: 17 for Chun et al. [21], 21 for
Hansen et al. [14] and 35 for de la Taille et al. [12] and our study. The FDA retained the threshold of
25 for men subjected to repeat biopsy. No consensual threshold is currently available for men
subjected to initial biopsy, but it could be assumed that the lower one is the chosen threshold; the
lowest being the risk of missing (significant) PCas.
One could question the head-to-head analyses between Hansen’s nomogram and nomograms not
specifically devoted to the initial biopsy decision. However, the fact that Chun’s nomogram provided
the best AUC and predictive accuracy suggests that they can reliably be compared. In this regard,
calibration plot curves and decision curve analyses, of strong interest when comparing diagnostic
markers and nomograms, also showed that comparison is not as biased as expected, since the higher
net benefit was observed with Chun’s nomogram (Figure 2), while Hansen’s one provided the
calibration curve nearest to the perfect prediction diagonal (Figure 1). These analyses showed that
application of either nomogram to our cohort would have induced a reduction in the number of
prostate biopsies performed, although lesser than expected, as confirmed by Table 3. Very similarly to
the reported one, the increase in predictive accuracy is about 6% when adding PCA3 and can be
considered, together with the intrinsic value of predictive accuracy (about 70%), as poorly significant
in clinical practice. However, we found that at a probability threshold of 30%, Chun’s and Hansen’s
nomogram would have saved 30% and 27% of biopsies, respectively, while missing only 6% of
HGPCa, as defined by a biopsy Gleason sum ≥7. These results are quite similar to that previously
observed in Hansen’s princeps paper (36% of saved biopsies and 2% of missed HGPCa) and in the
head-to-head comparison between the two other available PCA3-based nomograms [27]. While
Int. J. Mol. Sci. 2013, 14
17776
Chun’s nomogram [21] would have saved 28% of biopsies, while missing 3.7% of HGPCa,
PCA3 including the PCPT risk calculator [20] would have saved 18% of biopsies, while missing no
HGPCa [27]. We mainly used in the present paper the definition of HGPCa used by the authors of the
nomograms [14,21], that is, the one only based on a Gleason sum ≥7. In fact, up to 50% of the patients
with a biopsy Gleason sum at six eventually disclosed upgrading to a Gleason of seven or more at
prostatectomy [28]. Of interest, when applying Epstein criteria to identifying significant cancers [24],
the diagnostic performance of the PCA3 score (AUC = 0.728) was at least as high as that observed
when using a Gleason sum as the sole criterion (AUC = 0.689). It is likely that the good performance
of the PCA3 test using the Epstein criteria is related to the fact that this classification takes into
account tumor volume estimation, a variable known to be predicted by the PCA3 score [29]. In our
experience, PCA3-incorporating nomograms also reduce the number of useless biopsies without
missing more than 7% of Epstein-defined significant PCas, a result underlying PCA3 test robustness.
3. Experimental Section
3.1. Patients and Study Design
Between January, 2008, and January, 2013, consecutive patients scheduled in the Department of
Urology of our secondary institution were included to have an initial prostate biopsy, because of
elevated serum PSA (≥4 ng/mL) and/or suspicious digital rectal examination (DRE), whatever the PSA
levels. Patients with serum PSA ≥20 ng/mL were eventually excluded. The institutional review board
approved this study, and all patients provided informed written consent to participate. Excluded were
patients with previous prostate biopsy (biopsies) (whatever the results), any medical therapy affecting
serum PSA levels and/or previous prostate surgery for benign hypertrophy (BPH).
3.2. Biochemical Assays and Nomograms
After standardized DRE and before initial prostate biopsy, the first voided urine was collected as
previously described, and PCA3 and PSA RNA quantification was performed as recommended using
the Progensa™ PCA3 Assay (Hologic® Gen-Probe) [29,30]. The three nomograms used are
summarized in Table 4. The updated PCPT calculator risk, including the PCA3 score [20], is available
online (http://deb.uthscsa.edu/URORiskCalc/Pages/calcs.jsp), allowing PCa risk calculation. The
probability of PCa was graphically calculated from Chun’s [21] and Hansen’s nomograms [14].
3.3. Prostate Biopsies
Prostate volume was measured by TRUS (trans-rectal ultrasonography) using the elliptical formula.
A TRUS-guided prostate biopsy was performed with at least 12 cores (there are possible additional
cores from suspicious areas according to DRE and/or TRUS findings). Histological examinations were
performed according to international standards by experienced pathologists unaware of the PCA3
test results.
Int. J. Mol. Sci. 2013, 14
17777
Table 4. The three compared urinary PCA3-based nomograms used in the present study.
Race a
PCPT [20]
Chun [21]
Hansen [14]
+
Age
(year)
Serum
PSA
(ng/mL)
Family
history
(yes/no)
+b
+
+
+
+c
+d
+
DRE e
+
+
+
Negative previous
biopsy(ies)
(yes/no)
+
+
Prostate
volume
(cm3)
Urinary
PCA3
score f
+
+
+
+
+
a
African American, Caucasian, Hispanic or other (accounting only for HGPCa risk calculation); b ≥55 years
(younger patients are excluded); c ≤50 ng/mL; d ≤20 ng/mL; e suspicious vs. unsuspicious; f Progensa® PCA3
assay. A “+” is indicated when the corresponding criterion (race, age, PSA…) is used to calculate the prostate
cancer risk in the nomogram.
3.4. Statistics
Continuous variables were expressed as medians and interquartile ranges (IQR). They proved to be
normally distributed using the Skewness and Kurtosis test, except for patient age. The parametric
Student’s t-test and non-parametric Wilcoxon U test were performed to determine differences for
continuous variables. The chi-square test was used to compare proportions. Univariate and multivariate
logistic regression models addressed the presence of any PCa at the initial prostate biopsy. Areas under
receiver operating curves (AUC) were used to address discriminative properties of the tested variables
and to identify the best cutoff, compared using Hanley’s test. Predictive accuracies were defined as the
proportion of patients correctly classified using an individual marker or the regression models. The
extent of over- and under-estimation of the observed vs. predicted PCa probability was explored
graphically using regression smoothing plot curves. The relationship between the threshold probability
of biopsy outcome and the relative value of false-positive and false negative results was examined
using decision curve analyses to determine the net benefit of the predictive models [31]. Data were
analyzed using software package STATA®v11.0 (College Station, TX, USA), with p <0.05 considered
to be statistically significant.
4. Conclusions
In conclusion, we provided valuable data to consider the urinary PCA3 score as a useful adjunct to
predict the results of initial prostate biopsies. Either Chun’s (dedicated to either initial or repeat
biopsies) or Hansen’s nomograms can be considered of added value when considering the issue of
initial prostate biopsy. Conversion of these nomograms into an online available calculator is likely to
be beneficial in clinical practice.
Acknowledgments
We thank N. Gobeaux, M. Vinet, J.L. Campos-Fernandes, S. Genevoix, R. Lardon, E. Briant,
F.X. Buttin, J.G. Lopez and M. Goris for their contributions in patient recruitment and M. Cottancin
and B. Grangier for their technical contributions.
Int. J. Mol. Sci. 2013, 14
17778
Conflicts of Interest
The authors declare no conflict of interest.
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