Download Molecular Markers in Key Steroidogenic Pathways, Circulating

Published OnlineFirst November 27, 2012; DOI: 10.1158/1078-0432.CCR-12-2812
Clinical
Cancer
Research
Imaging, Diagnosis, Prognosis
Molecular Markers in Key Steroidogenic Pathways,
Circulating Steroid Levels, and Prostate Cancer Progression
Le
vesque1,2, Shu-Pin Huang3,4, Etienne
Audet-Walsh1, Louis Lacombe2, Bo-Ying Bao6,7, Yves Fradet2,
Eric
1
1
lanie Rouleau , Chao-Yuan Huang8, Chia-Cheng Yu5, Patrick Caron1, and
re , Me
Isabelle Laverdie
1
Chantal Guillemette
Abstract
Purpose: Prostate cancer is a heterogeneous genetic disease, and molecular methods for predicting
prognosis in patients with aggressive form of the disease are urgently needed to better personalize treatment
approaches. The objective was to identify host genetic variations in candidate steroidogenic genes affecting
hormone levels and prostate cancer progression.
Experimental Design: The study examined two independent cohorts composed of 526 Caucasian men
with organ-confined prostate cancer and 601 Taiwanese men on androgen–deprivation therapy. Caucasians
were genotyped for 109 haplotype-tagging single-nucleotide polymorphisms (SNP) in CYP17A1, ESR1,
CYP19A1, and HSD3B1, and their prognostic significance on disease progression was assessed using
Kaplan–Meier survival curves and Cox regression models. Positive findings, including previously identified
SRD5A1, SRD5A2, HSD17B2, HSD17B3, and HSD17B12 polymorphisms, were then explored in Taiwanese
men (n ¼ 32 SNPs). The influence of positive markers on the circulating hormonal levels was then appraised
in Caucasians using specific and sensitive mass spectrometry–based methods.
Results: After adjusting for known risk factors, variants of CYP17A1 (rs6162), HSD17B2 (rs4243229 and
rs7201637), and ESR1 (rs1062577) were associated with progressive disease in both cohorts. Indeed, the
presence of these variations was significantly associated with progression in Caucasians (HR, 2.29–4.10; P ¼
0.0014–2 107) and survival in Taiwanese patients [HR ¼ 3.74; 95% confidence interval (CI): 1.71–8.19,
P ¼ 0.009]. Remarkably, the CYP17A1 rs6162 polymorphism was linked to plasma dehydroepiandrosterone-sulfate (DHEA-S) levels (P ¼ 0.03), HSD17B2 rs7201637 with levels of dihydrotestosterone (P ¼
0.03), and ESR1 rs1062577 with levels of estrone-S and androsterone-glucuronide (P 0.05).
Conclusion: This study identifies, in different ethnic groups and at different disease stages, CYP17A1,
HSD17B2, and ESR1 as attractive prognostic molecular markers of prostate cancer progression. Clin Cancer
Res; 1–11. 2012 AACR.
Introduction
Prostate cancer is clearly a major public health concern
because it is the most common cancer in men and the
^ tel-Dieu
Authors' Affiliations: 1Pharmacogenomics Laboratory and 2L'Ho
bec, Centre Hospitalier Universitaire de Que
bec (CHUQ) Research
de Que
bec, Canada; 3DepartCenter, Faculty of Medicine, Laval University, Que
ment of Urology, Faculty of Medicine, College of Medicine, Kaohsiung
Medical University; 4Department of Urology, Kaohsiung Medical University
Hospital; 5Division of Urology, Department of Surgery, Kaohsiung Veterans
General Hospital, Kaohsiung; 6Department of Pharmacy, China Medical
University; 7Sex Hormone Research Center, China Medical University
Hospital, Taichung; and 8Department of Urology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei,
Taiwan
Note: Supplementary data for this article are available at Clinical Cancer
Research Online (http://clincancerres.aacrjournals.org/).
Corresponding Author: Chantal Guillemette, CHUQ Research Center, T3bec, Canada, G1V 4G2. Phone: 418-654-2296;
48, 2705 Boul. Laurier, Que
Fax: 418-654-2298; E-mail: [email protected]
doi: 10.1158/1078-0432.CCR-12-2812
2012 American Association for Cancer Research.
second leading cause of cancer-related death in North
America (1). It is well established that androgen hormones
play a central role in prostate cancer development and
progression. Indeed, androgen deprivation therapy (ADT)
is the cornerstone therapy for recurrent and metastatic
prostate cancer (2, 3). Furthermore, castration-resistant
prostate cancer (CRPC), once thought to be hormone
refractory, remains clearly driven by sex steroid hormones
(4). Even in a low-circulating testosterone environment,
cancer cells generate potent intracellular hormones to support cancer growth and proliferation (5). In addition,
several studies sustain the role of sex steroid formation by
prostate cancer cells that possess the enzymatic machinery
to convert precursors into more potent hormones (5–7).
This reinforces the need to block intracellular steroidogenesis of prostatic cells. In metastatic settings, this concept is
further reinforced with the advent of new compounds such
as abiraterone acetate and MDV-3100, which targets, respectively, the CYP17 androgens’ biosynthesis pathway and
the androgen receptor, both effective for CRPC (8–12).
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Published OnlineFirst November 27, 2012; DOI: 10.1158/1078-0432.CCR-12-2812
vesque et al.
Le
Translational Relevance
There are significant limitations in our ability to predict the progression and metastatic behavior of a
patient’s prostate cancer. Risk of disease progression
differs greatly between individuals, and the variability
in clinical outcome further emphasizes the need to find
novel markers of progression. Sex steroid hormones are
essential for the development and progression of prostate cancer. The purpose of this study was to assess the
prognostic value of common genetic variants in key
steroidogenic genes. Our findings support, in different
ethnic groups and at different disease stages, the importance of CYP17A1, HSD17B2, and ESR1 as attractive
prognostic molecular markers of prostate cancer progression. The use of understanding germline determinants of progression is that this could be used, along
with known and established clinical factors, to prognosticate clinical course. Poor prognostic genetic factors
could be used to select patients for future clinical trials
and identify subgroups of patients that would most
benefit from endocrine treatment. Therefore, our findings could lead to a more personalized approach to guide
patient therapy based on molecular markers.
However, despite the improvement in survival of some
patients with advanced prostate cancer, there are no available clinical prognostic molecular biomarkers of progression and mortality after treatment modalities such as radical
prostatectomy, radiotherapy, hormonal manipulation, and
chemotherapy.
In addition to tumor markers, common germline variations might determine tumor behavior or serve as informative biomarkers. In advanced disease state, studies have
shown the impact of HSD17B4 genetic variations (13) and
expression (14) relative to disease progression, and germline variations in SLCO2B1 and SLCO1B3 in response to
palliative ADT therapy (15). However, there is still a lack of
molecular markers better defining aggressive and lethal
prostate cancer. Recently, germline polymorphisms in
SRD5A and HSD17B genes as well as deletions of UGT2B
genes were shown to be associated with biochemical recurrence (BCR) in Caucasian and Taiwanese prostate cancer
patients after radical prostatectomy (16, 17). Such results
are in agreement with a meaningful effect of inherited
genetic variations in the androgenic pathway on prostate
cancer recurrence and progression, regardless of race. Here,
we sought to investigate the link between common germline variations in additional candidate steroidogenic genes
on cancer progression and mortality, and to evaluate their
relationships with circulating endogenous sex steroids.
Patients and Methods
Clinical data
The study included 526 Caucasians with localized
prostate cancer who underwent radical prostatectomy as
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Clin Cancer Res; 2012
initial therapy and 601 Taiwanese men on ADT, described
previously (16, 18). Briefly, in the Caucasian cohort, PSA
failure (n ¼ 130) after radical prostatectomy was defined
as values of 0.3 mg/L or more (16; Table 1). In the
Taiwanese ADT cohort (18), treatment modalities are
indicated in Table 2. PSA nadir was defined as the lowest
PSA value achieved during ADT treatment. The cause of
death was obtained by matching patients’ personal identification number with the official cause of death registry
provided by the Department of Health, Executive Yuan
(Taiwan, Republic of China). Prostate cancer-specific
mortality (PCSM) was defined as the interval from the
initiation of ADT to death from prostate cancer. The ACM
was defined as the period from the initiation of ADT to
death from any cause. Overall, 145 deaths were identified,
and 101 of them died from prostate cancer. Participants
provided written informed consent for the analysis of
their genome and the Institutional Review Board
approved the research protocol.
Genetic analysis
For genotyping, PCR amplifications were conducted on
genomic DNA and products were analyzed by Sequenom
iPLEX matrix-assisted laser desorption/ionization-time-offlight mass spectrometry. Polymorphisms in selected genes
were chosen to explain most of the haplotype diversity.
Indeed, a region covering all the exons, introns, and 5 kb of
each of the 50 and 30 untranslated regions of selected genes
was screened using a haplotype tagging SNPs (htSNP)
strategy to maximize coverage (16, 17).
A total of 112 SNPs were genotyped in Caucasians. Two
SNPs were excluded because of a missing genotype frequency more than 5% (rs8041933 in CYP19A1 and rs9340941 in
ESR1), and one SNP was excluded because of lack of genetic
diversity (rs11968373 in ESR1). All SNPs were in Hardy–
Weinberg equilibrium, except rs12900487, rs4441215, and
rs2470152 in CYP19A1 and rs2982894 in ESR1. SNPs
significantly associated with BCR, including those in SRD5A
and HSD17B genes (17, 19) were successfully genotyped in
Taiwanese men. There were not enough minor allele homozygotes to evaluate the impact of HSD17B2 rs1119933,
rs1364287, rs2955162, and rs8059915 SNPs (17). For both
populations, the genotype call rate was in average 98% or
more, and SNPs with a missing call rate 5% or more were
excluded (n ¼ 6). Negative controls were present for every
analysis and quality controls included 5% or more blind
duplicates.
Measurement of steroid levels
Plasma samples were available from the Caucasian
cohort collected on the morning of the surgical procedure. We excluded patients that received neoadjuvant
hormonal treatment and those with missing genotype
information for studied SNPs. Steroids were measured
by validated gas chromatography/mass spectrometry or
liquid chromatography/tandem mass spectrometry
methods (16, 20). Deuterated steroids were added to
samples and quality controls were included in each
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Published OnlineFirst November 27, 2012; DOI: 10.1158/1078-0432.CCR-12-2812
Biomarkers of Prostate Cancer Progression
Table 1. Clinical and pathologic characteristics
of the Caucasian population (n ¼ 526)
Characteristics
Localized
prostate cancer
Age at diagnosis, y
Mean
SD
Range
Follow-up median (months)
63.3
6.8
43.5–80.7
88.8
Biochemical recurrence (%)
PSA at diagnosis (ng/mL)
10
>10–20
>20
Pathologic Gleason score
6
7
8
Pathologic T stage
pT ¼ T2
pT ¼ T3
pT ¼ T4
Nodal invasion
N0
Nþ
Neoadjuvant hormonotherapy
Yes
No
Adjuvant hormonotherapy
Yes
No
Margin status
Negative
Positive
N (%)
130 (24.7)
362 (69)
103 (20)
56 (11)
158 (31)
244 (48)
107 (21)
313 (60)
195 (37)
13 (3)
481 (92)
44 (8)
31 (6)
495 (94)
30 (6)
496 (94)
368 (70)
154 (30)
Abbreviations: PSA, prostate-specific antigen; T, tumor; N,
node.
run. The lower limit of quantification is as follows:
testosterone (0.03 ng/mL), dihydrotestosterone (DHT;
0.005 ng/mL), ADT (0.025 ng/mL), ADT-glucuronide
(ADT-G; 1 ng/mL), and 3a-diol-3G and 3a-diol17G (0.25 ng/mL), DHEA (0.1 ng/mL), DHEA-S
(0.075 ug/mL), E1-S (0.075 ng/mL), E1 (0.005 ng/mL),
E2 (0.001 ng/mL), androstenedione (4-dione; 0.05 ng/
mL), and androstenediol (5-diol; 0.05 ng/mL). Coefficients of variation for intra- and interassays for these
methods were 10.0% or below, and accuracies for steroid
hormones were as follows: testosterone (100.5%), DHT
(103.5%), ADT (96.5%), ADT-G (99.5%), 3a-diol-3G
(89.2%), 3a-diol-17G (100.2%), DHEA (96.13%),
DHEA-S (99.9%), E1-S (100.45%), E1 (106.9%), E2
(103.15%), 4-dione (95.5%), and 5-diol (95.33%).
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Table 2. Clinical and pathologic characteristics
of the Asian population (n ¼ 601)
Characteristics
Age at diagnosis, y
Median (IQR)
73
>73
Follow-up median (months)
Mean (range)
Advanced
prostate cancer
73 (67–79)
320 (53.2)
281 (46.8)
39 (3–125)
N (%)
Disease progression
No ADT failure
ADT failure
PCSM
Alive
Dead of disease
ACM
Alive
Dead of any cause
Clinical stage at diagnosis
T1/T2
T3/T4/N1
M1
Gleason score at diagnosis
6
7
8
PSA at ADT initiation (ng/mL)
Median (IQR)
<34.6
34.6
PSA nadir (ng/mL)
Median (IQR)
<0.2
0.2
Time to PSA nadir, months
Median (IQR)
<10
10
Treatment modality
ADT as primary treatment
ADT for postradical
prostatectomy PSA failure
ADT for post RT PSA failure
Neoadjuvant/adjuvant ADT with RT
Others
184 (30.7)
415 (69.3)
499 (83.2)
101 (16.8)
455 (75.8)
145 (24.2)
189 (31.7)
184 (30.8)
224 (37.5)
194 (33.0)
180 (30.6)
214 (36.4)
35 (11–129)
287 (49.6)
292 (50.4)
0.18 (0.01–1.33)
301 (50.8)
292 (49.2)
10 (5–18)
293 (49.4)
300 (50.6)
333 (55.7)
68 (11.4)
18 (3.0)
125 (20.9)
54 (9.0)
Abbreviation: IQR, interquartile range.
Statistical analysis
For association of SNPs with progression and PCSM and
overall survival, individual htSNP was first considered using
a model based on 3 categories (genomic model), namely
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major allele homozygote, heterozygote, and minor allele
homozygote, because the function of most htSNPs remains
unknown. For rare homozygotes (frequency < 2%), minor
allele homozygotes were combined with the heterozygotes.
Cox regression was conducted on each SNP as an isolated
covariate, with adjustment for clinicopathologic variables,
as previously described (17, 19). In Caucasians, multiple
explanatory variables included known prognostic factors:
age at diagnosis, smoking status, pathologic stage, Gleason
score, PSA values, nodal and margins status, and neoadjuvant and adjuvant ADT (17, 19). In Asians, multiple explanatory variables included known prognostic factors: age at
diagnosis, clinical stage, Gleason score, PSA at ADT initiation, PSA nadir, time to PSA nadir, and treatment modality,
as defined (18). After analyses with the genomic model, a
secondary model was developed (recessive or dominant)
for each SNP. All covariables were treated as categorical
with 4% or less missing values for all covariables. To
estimate the cumulative impact of variations, the 3 categories subdivision was considered as a continuous variable. P
values were considered significant if <0.05 and false discovery rates (q values) were calculated to determine the
degree to which the tests were prone to false positives, using
the R QVALUE package (http://genomics.princeton.edu/
storeylab/qvalue/; ref. 21).
To adjust for differences in the absolute levels of sex
steroids, we calculated residuals of the natural logarithm
of the hormone level regressed on age at blood donation
and smoking status. The association with SNPs was conducted by regression of hormone residuals on each SNP
independently for 4 models: recessive, dominant, codominant, and additive with 1 degree of freedom for the first 3
models, and 2 degrees of freedom for the additive model.
We considered the association of a SNP with variation in
hormone levels to be significant if the P value was <0.05. To
facilitate the comparison between groups, we displayed the
hormone level as untransformed data by using geometric
mean and SEM. Statistical analyses were conducted using
SAS Statistical Software version 9.2 (SAS Institute) and using
PASW statistics version 17 (SPSS Inc.).
Results
A total of 109 htSNPs distributed across CYP17A1,
CYP19A1, HSD3B, and ESR1 were studied in Caucasians
(n ¼ 526) based on their role in hormone action, biosynthesis, and metabolism. Positive findings in these 4 genes,
combined with previously identified markers in SRD5A1,
SRD5A2, HSD17B2, HSD17B3, and HSD17B12 (17, 19)
were subsequently tested in the Taiwanese ADT cohort to
evaluate their potential impact on survival (n ¼ 32 SNPs).
Genetic analyses in Caucasians
Relative frequencies of SNPs in patients with cancer and
their corresponding HRs (95% CI) are shown in Table 3 and
Table 3. Association between htSNPs in candidate steroidogenic pathways and BCR in Caucasians
(n ¼ 526)
Cox regression analysesb
Heterozygous
SNP
CYP17A1
rs1004467
rs2486758
rs6162
rs743572
CYP19A1
rs1870050
rs2446404
ESR1
rs1062577
rs2982683
rs3003922
rs488133
rs9341016
Homozygous
Best-fitting genetic models
Major Minor MAF BCRa
No-BCRa
HR (95% CI)
P
HR (95% CI)
P
Model HR (95% CI)
P
TT
TT
AA
GG
CC
CC
GG
AA
7%
22%
59%
65%
1/11/118
9/55/65
65/49/16
66/47/16
1/58/332
19/123/250
143/204/46
150/199/43
0.50
1.82
0.71
0.69
0.052
0.002
0.27
0.24
0.69
1.50
1.19
1.17
0.72
0.29
0.60
0.60
Dom
Dom
Rec
Rec
0.52
1.77
1.54
1.56
0.051
0.003
0.022
0.020
AA
AA
CC
GG
6%
6%
0/20/110
0/18/112
2/40/351
1/39/345
Dom
Dom
2.03 (1.22–3.38) 0.006
2.04 (1.20–3.47) 0.008
TT
CC
GG
CC
TT
AA
TT
CC
TT
CC
11%
30%
20%
33%
4%
1/33/96
13/61/54
2/41/87
17/56/57
1/15/114
2/75/314
30/162/198 1.07 (0.72–1.58) 0.75
14/133/247 0.92 (0.62–1.37) 0.68
40/170/181 0.87 (0.58–1.29) 0.49
1/24/360
(0.25–1.01)
(1.24–2.68)
(0.39–1.31)
(0.38–1.27)
(0.09–5.15)
(0.71–3.19)
(0.66–2.13)
(0.65–2.11)
Dom
1.85 (0.98–3.51) 0.06 Rec
0.26 (0.06–1.09) 0.06 Rec
1.73 (0.97–3.09) 0.06 Rec
Dom
2.10
1.80
0.27
1.86
1.98
(0.27–1.00)
(1.22–2.57)
(1.07–2.24)
(1.07–2.26)
(1.38–3.22)
(0.98–3.29)
(0.06–1.11)
(1.08–3.20)
(1.15–3.39)
0.0006
0.06
0.07
0.026
0.013
NOTE: If there were too few individuals being minor-allele homozygotes, illustrated by shades of gray, they were combined with
heterozygotes. Significant associations in HSD17B2 and HSD17B3 were reported elsewhere (17). Bold, significant P values.
Abbreviations: Dom, dominant model; Rec, recessive model.
a
The number represents minor allele homozygotes, heterozygotes, and major allele homozygotes, respectively.
b
Multivariate models included PSA at diagnosis, pathologic Gleason score, pathologic T stage, age at diagnosis, neoadjuvant therapy,
smoking status, adjuvant therapy, surgical margins status, and nodal invasion status. The major allele homozygotes were considered
as the reference group, with a fixed HR ¼ 1.00.
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Biomarkers of Prostate Cancer Progression
Supplementary Table S1. After adjustments for known
clinicopathologic variables, 4 of the 6 htSNPs in CYP17A1
were significantly associated with progression, 4 of 37 in
CYP19A1, 3 of 57 in ESR1, and none in HSD3B1. Of note,
significant associations were observed for CYP17A1,
involved in one of the initial steps in hormone biosynthesis
immediately downstream the action of the P450 side chain
cleavage (P450scc) gene, which transforms cholesterol
into C21-steroids. The CYP17A1 variant rs6162G and
rs2486758C were associated with progression with HR
values of 1.54 (95% CI, 1.07–2.24; P ¼ 0.022; q ¼ 0.18)
and 1.77 (95%CI, 1.22–2.57; P ¼ 0.003; q ¼ 0.065),
respectively. Two additional variants in genes involved in
the estrogen biosynthetic pathway merit attention. The
rs1870050C variant in CYP19A1, encoding the aromatase
enzyme and being the rate-limiting step involved in the
estrogens biosynthesis from androgens, was associated
with a higher risk of progression (HR 2.03; 95%CI, 1.22–
3.38; P ¼ 0.006; q ¼ 0.10). In addition, the rs1062577A
minor allele of the ESR1 gene, which encodes for the
estrogen receptor alpha, was also associated with an
increased risk of recurrence (HR ¼ 2.10; 95% CI, 1.38–
3.22; P ¼ 0.0006; q ¼ 0.02).
Genetic analyses in Taiwanese ADT cohort
In agreement with the observations obtained in the
Caucasian cohort, positive findings were also revealed for
CYP17A1 in the Taiwanese ADT cohort. The CYP17A1
rs6162G variant was associated with mortality with a HR
value of 1.69 (95% CI, 1.03–2.78; P ¼ 0.037; q ¼ 0.17).
Interestingly, the rs4243229A in the HSD17B2 gene, the
product of which inactivates sex steroids, was notably
associated with an increased risk of death (HR 1.75; 95%
CI, 1.22–2.51; P ¼ 0.002; q ¼ 0.03). Moreover, the ESR1
rs1062577A minor allele was similarly associated with
worse outcome with an increased risk of mortality with a
HR of 1.45 (95% CI, 1.02–2.07; P ¼ 0.038; q ¼ 0.17).
Interestingly, the variant rs2257157C of HSD17B3,
involved in hormone bioactivation, was also associated
with an increased risk of death (HR 1.60, 95% CI: 1.11–
Table 4. Association between htSNPs in candidate steroidogenic pathways and all-cause mortality
following ADT in the Asian cohort (n ¼ 601)
All-cause mortality
ACM
SNP
CYP17A1
rs1004467
rs2486758
rs6162
rs743572
CYP19A1
rs1870050
rs2446404
ESR1
rs1062577
rs2982683
rs3003922
rs488133
rs9341016
HSD17B2
rs4243229
rs7201637
rs9934209
HSD17B3
rs10739847
rs1810711
rs2257157
Heterozygotes
Homozygotes
Best-fitting genetic models
Major Minor MAF Dead
Alive
HR (95% CI)
P
HR (95% CI)
P
Model HR (95% CI)
P
TT
TT
AA
GG
CC
CC
GG
AA
33%
22%
47%
47%
15/59/59
7/47/80
34/66/34
32/65/36
40/196/177
17/148/246
85/207/120
84/206/117
0.94
0.96
1.24
1.13
0.75
0.81
0.35
0.59
0.78
1.87
1.69
1.53
0.42
0.12
0.037
0.10
DOM
REC
REC
REC
0.91
1.90
1.49
1.42
0.58
0.10
0.06
0.10
AA
AA
CC
GG
29%
3%
7/51/76
0/7/127
39/174/200 0.72 (0.50–1.04) 0.08
1/29/382
0.57 (0.26–1.25) 0.16
DOM
DOM
0.70 (0.49–1.00) 0.048
0.55 (0.24–1.28) 0.17
TT
CC
GG
CC
TT
AA
TT
CC
TT
CC
24%
18%
46%
7%
14%
9/52/72
2/41/91
24/69/38
1/16/117
2/36/93
23/144/249
11/127/275
75/226/107
0/54/356
12/85/310
1.42 (0.98–2.06) 0.06
0.90 (0.61–1.32) 0.58
1.11 (0.73–1.68) 0.63
1.68 (0.82–3.41) 0.15
0.99 (0.24–4.08) 0.99
1.10 (0.64–1.88) 0.73
1.00 (0.67–1.49) 0.99
1.52 (0.37–6.34) 0.56
DOM
REC
DOM
DOM
REC
1.45
1.03
1.11
0.66
1.52
GG
TT
GG
AA
AA
CC
20%
18%
44%
9/48/76 15/123/273 1.73 (1.19–2.51) 0.004 1.96 (0.87–4.38) 0.10
4/33/97 13/130/270 0.65 (0.43–0.98) 0.041 1.13 (0.41–3.10) 0.82
25/68/41 77/211/126 1.06 (0.70–1.59) 0.79
1.21 (0.72–2.03) 0.47
DOM
DOM
REC
1.75 (1.22–2.51) 0.002
0.68 (0.46–1.02) 0.06
1.17 (0.74–1.84) 0.50
AA
GG
TT
TT
TT
CC
41%
41%
35%
22/60/50 73/191/144 1.03 (0.70–1.52) 0.89
0.95 (0.56–1.62) 0.85
21/66/47 60/214/137 0.87 (0.59–1.29) 0.50
1.08 (0.64–1.83) 0.76
14/76/44 52/180/181 1.72 (1.17–2.52) 0.005 1.14 (0.61–2.15) 0.68
REC
REC
DOM
0.94 (0.58–1.51) 0.79
1.18 (0.73–1.88) 0.50
1.60 (1.11–2.33) 0.013
(0.65–1.37)
(0.66–1.38)
(0.80–1.92)
(0.73–1.74)
(0.43–1.42)
(0.85–4.11)
(1.03–2.78)
(0.93–2.52)
(0.63–1.29)
(0.87–4.13)
(0.99–2.25)
(0.93–2.17)
(1.02–2.07)
(0.25–4.21)
(0.74–1.64)
(0.38–1.14)
(0.37–6.32)
0.038
0.97
0.62
0.14
0.56
NOTE: For SNPs in HSD17B2 (rs1119933, rs1364287, rs2955162, and rs8059915), frequency of minor-allele homozygotes was too low
to compute HRs in the recessive model as previously described in Caucasians (17). P < 0.05 are indicated in bold. HRs were adjusted for
age, TNM staging, Gleason score, PSA at ADT initiation, PSA nadir, time to PSA nadir, and treatment modality as described in the
Materials and Methods section. When the frequency of rare minor-allele homozygotes was 2% or less, they were combined with
heterozygotes. For these SNPs, values were not computed for heterozygotes or minor-allele homozygotes, and corresponding boxes
are in gray. Bold, significant P values.
Abbreviations: DOM, dominant model; REC, recessive model.
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Le
2.33; P ¼ 0.013). Conversely, the variant rs1870050C in
CYP19A1 was linked with a lower risk of death (HR 0.70;
95% CI, 0.49–1.00; P ¼ 0.048; q ¼ 0.17; Table 4). Most of
these associations were also significant for PCSM in Taiwanese men (Supplementary Table S2).
Cumulative effects of adverse genotypes
When the cumulative effects of the adverse genotypes
were investigated, significant associations with progression
and overall survival were observed for patients with
unfavorable genotypes in CYP17A1, ESR1, and HSD17B2
in both Caucasian and Taiwanese men (Fig. 1). In Caucasians, the cumulative model includes CYP17 rs6162, the
presence of one HSD17B2 risk allele (rs1364287,
rs8059915, rs4243229, rs2955162, and rs1119933) and
ESR1 rs1062577. In Taiwanese men, in addition to
CYP17A1 and ESR1 variants, only the HSD17B2 alleles
(rs4243229 and rs7201637) were included in the model
because of very low frequency of the other SNPs in this
population.
Relationship with endogenous sex-steroid hormones
levels
The relationship of prognostic markers positive in both
cohorts with circulating sex steroids was then assessed in
the Caucasian cohort. Remarkably, CYP17A1 (rs6162),
HSD17B2 (rs7201637), and ESR1 (rs1062577) variations
were associated with significant changes in plasma steroid
levels. The CYP17A1 rs6162 variation is associated with a
20% difference in DHEA-S levels (P ¼ 0.031). Although
homozygotes for the minor allele are rare in Caucasians for
the HSD17B2 rs7201637 variation (n ¼ 2), these patients
display 50% lower 5-DIOL and DHT levels. The ESR1
rs1062577 variant correlated with reduced levels of E1-S
and ADT-G. Additional hormone data in relation to genetic
polymorphisms in CYP17A1, HSD17B2, and ESR1 are
provided in Table 5.
Discussion
There are clear unmet oncological needs to better predict
prostate cancer progression, especially lethal prostate cancer, and so far, no prognostic markers fill this gap in
knowledge. Host genetic variations in the well-characterized hormone biosynthetic and degradation pathways have
not been studied systematically in terms of molecular
markers associated with prostate cancer progression and
none have been validated at different stages of the disease
(13, 18, 22). Here, we exposed several common inherited
variations in CYP17A1, HSD17B2, and ESR1 associated
with prostate cancer progression in 2 independent cohorts
of patients and further showed their biologic association
with plasma hormone levels.
The HSD17B2 gene is involved in steroids interconversion and controls C21, C19-, and C18-steroid bioavailability for nuclear receptor bindings and actions (Fig. 2; refs. 23,
24). Remarkably, variants in this gene, although being at
different frequencies in the 2 ethnic groups studied herein,
persist as prognostic markers associated with BCR, disease
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Clin Cancer Res; 2012
Figure 1. Impact of germline variations in CYP17A1, ESR1, and HSD17B2
with prostate cancer progression. Prognostic SNPs located in ESR1,
CYP17A1, and HSD17B2 were combined in relation to BCR in
Caucasians (A) and all-cause mortality in Asians under ADT (B). Log rank
(LR) P values are shown in each frame and HRs for each category are
shown under both panels.
progression, and mortality following ADT [herein and ref.
(17)]. In Taiwanese men, the HSD17B2 rs4243229A [a
variant more frequent in Asians than Caucasians; MAF;
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Hormone
SNP
A/G
G/A
T/C
T/C
A/G
G/A
T/C
T/C
C/T
G/A
G/A
T/A
G/C
C/T
G/A
G/A
T/A
G/C
C/T
G/A
G/A
T/A
G/C
C/T
G/A
G/A
T/A
G/C
C/T
T/A
T/C
C/T
T/A
T/C
54/223/189
51/217/196
404/59/2
289/147/28
54/226/187
51/220/194
404/60/2
289/149/27
251/185/26
276/160/26
453/12
403/56/2
177/213/74
231/173/25
254/149/26
420/12
376/50/2
169/196/66
251/187/26
276/162/26
455/12/
405/56/2
178/215/73
252/187/26
277/162/26
456/12
406/56/2
178/215/74
213/204/48
366/97/2
425/32/2
214/202/47
364/96/3
423/32/2
Homozygote
minor
Homozygote
major
1 minor
allele
1 minor
allele
Homozygote
minor
Secondary model (dominant) Secondary model (recessive)
1.83 0.15
1.82 0.16
1.61 0.07
1.66 0.07
0.97 0.09
0.94 0.08
0.80 þ 0.03
0.85 0.03
527.27 18.51
521.75 17.54
519.42 14.08
511.71 15.22
509.41 22.82
0.63 0.04
0.63 0.04
0.63 0.03
0.63 þ 0.03
0.63 0.03
309.07 9.47
306.61 8.93
307.24 7.16
303.08 7.56
295.38 11.32
17.68 1.27
17.39 1.16
17.92 0.75
18.03 þ 0.83
18.21 1.81
30.81 2.75
31.27 1.73
30.72 1.52
0.42 0.03
0.42 0.02
0.40 0.02
1.55 0.09
1.57 0.09
1.63 0.16
1.51 0.11
0.78 0.04
0.78 0.04
0.81 þ 0.09
0.71 0.05
503.22 21.81
513.68 23.52
454.39 81.06
569.64 34.01
518.69 19.84
0.62 0.03
0.63 0.03
0.66 0.10
0.61 þ 0.05
0.65 0.05
303.47 11.23
301.46 12.42
326.29 36.17 j
347.53 19.12
315.20 10.70
18.07 0.48
18.80 0.70
20.39 2.21
17.32 þ 0.93
17.65 0.45
29.92 1.30
27.40 1.62
27.04 3.38
0.38 0.03
0.34 0.03
0.35 0.07
1.83 0.15
1.82 0.16
1.61 0.07
1.66 0.07
0.97 0.09
0.94 0.08
0.80 þ 0.03
0.85 0.03
527.27 18.51
521.75 17.54
519.42 14.08
199.24 122.38 511.71 15.22
533.07 37.28 509.41 22.82
0.69 0.08
0.63 0.04
0.67 0.08
0.63 0.04
0.63 0.03
0.78 þ 0.09
0.63 þ 0.03
0.58 0.04
0.63 0.03
321.31 32.76 309.07 9.47
360.06 30.57 306.61 8.93
307.24 7.16
157.65 33.73 303.08 7.56
313.98 16.10 295.38 11.32
19.46 2.86
17.68 1.27
19.21 1.81
17.39 1.16
17.92 0.75
12.33 þ 0.24
18.03 þ 0.83
18.34 0.85
18.21 1.81
30.98 2.50
30.81 2.75
27.39 7.18
31.27 1.73
17.57 5.38
30.72 1.52
0.40 0.05
0.42 0.03
0.33 0.08
0.42 0.02
0.82 0.15
0.40 0.02
1.63 0.10
1.63 0.09
0.70 0.06
1.78 0.36
0.79 0.06
0.79 0.05
0.44 þ 0.03
0.87 0.25
524.48 76.08
510.49 75.01
1.59 0.06
1.59 0.07
1.58 0.16
1.55 0.11
0.79 0.03
0.79 0.03
0.79 þ 0.09
0.74 0.06
505.79 21.23
513.23 22.70
454.39 81.06
549.38 34.26
522.36 17.55
0.63 0.03
0.64 0.03
0.66 0.10
0.61 þ 0.04
0.63 0.04
305.59 10.62
308.96 11.56
326.29 36.17
338.19 19.17
314.89 8.96
18.24 0.55
18.86 0.65
20.39 2.21
17.11 þ 0.91
17.82 0.40
30.12 1.16
27.40 1.59
26.37 3.23
0.38 0.02
0.34 0.03
0.37 0.06
1.60 0.08
1.61 0.08
1.61 0.06
1.61 0.06
0.82 0.04
0.81 0.04
0.80 þ 0.03
0.80 0.03
516.93 14.10
518.77 14.05
517.63 13.87
518.45 13.99
514.46 14.97
0.63 0.03
0.63 0.03
0.63 0.02
0.63 þ 0.03
0.64 0.03
306.67 7.23
304.69 7.25
307.71 7.03
308.16 7.05
306.06 7.79
17.85 0.76
17.90 0.77
17.98 0.73
17.94 þ 0.74
17.90 0.86
30.37 1.54
30.42 1.41
30.45 1.43
0.40 0.02
0.40 0.02
0.40 0.02
Pb
0.166
0.168
0.157
0.280
0.094
0.133
0.433
0.029
0.629
0.984
0.350
0.028
0.938
0.638
0.772
0.804
0.704
0.131
0.824
0.159
0.709
0.007
0.580
0.521
0.085
0.273
0.417
0.590
0.834
0.088
0.173
0.452
0.054
0.278
0.089
0.071
0.759
0.322
0.031
0.045
0.927
0.027
0.378
0.888
0.350
0.325
0.807
0.880
0.611
0.804
0.805
0.779
0.769
0.722
0.709
0.062
0.297
0.415
0.027
0.273
0.441
0.453
0.935
0.027
0.123
0.241
0.017
0.565
0.233
0.688
0.588
0.794
0.164
0.944
0.439
0.188
0.030
0.725
0.322
0.406
0.458
0.048
0.653
0.057
0.023
0.742
0.390
0.521
0.779
0.990
0.054
0.392
0.710
0.672
0.198
0.495
0.892
0.882
Codomin. Add. Dom. Reces.
0.170
0.262
0.947
0.141
0.309
0.403
0.875
0.008
0.336
0.942
0.350
199.24 122.38 0.145
533.07 37.28 0.997
0.69 0.08
0.571
0.67 0.08
0.840
0.804
0.78 þ 0.09
0.682
0.58 0.04
0.248
321.31 32.76 0.609
360.06 30.57 0.578
0.709
157.65 33.73 0.020
313.98 16.10 0.446
19.46 2.86
0.717
19.21 1.81
0.060
0.273
12.33 þ 0.24
0.588
18.34 0.85
0.304
30.98 2.50
0.678
27.39 7.18
0.029
17.57 5.38
0.220
0.40 0.05
0.221
0.33 0.08
0.024
0.82 0.15
0.351
1.63 0.10
1.63 0.09
0.70 0.06
1.78 0.36
0.79 0.06
0.79 0.05
0.44 þ 0.03
0.87 0.25
524.48 76.08
510.49 75.01
Mean SEMa Mean SEMa Mean SEMa Mean SEMa Mean SEMa Mean SEMa Mean SEMa
Heterozygote
Genomic model
NOTE: Data are presented for individuals on whom we had genotyping, and plasma assay data. Bold, significant P values.
Abbreviations: Codomin, codominant model; Add, additive model; Dom, dominant model; Reces, recessive model.
a
Hormone levels are expressed as geometric mean and SEM.
b
P values from linear regression of the residuals on the SNP under each model.
rs6162
rs743572
rs 1004467
rs2486758
DHEA-S (mg/mL) rs6162
rs743572
rs 1004467
rs2486758
HSD17B2 5-diol (pg/mL)
rs1119933
rs2955162
rs4243229
rs7201637
rs8059915
4-dione (ng/mL) rs1119933
rs2955162
rs4243229
rs7201637
rs8059915
DHT (pg/mL)
rs1119933
rs2955162
rs4243229
rs7201637
rs8059915
E2 (pg/mL)
rs 1119933
rs2955162
rs4243229
rs7201637
rs8059915
ESR1
ADT-G (ng/mL) rs488133
rs1062577
rs9341016
E1-S (ng/mL)
rs488133
rs1062577
rs9341016
CYP17A1 DHEA (ng/mL)
Gene
Major/
Minor
Homozygote
allele N (0, 1, 2) major
Table 5. Association between positive htSNP markers and circulating steroid hormone levels in the Caucasian cohort
Published OnlineFirst November 27, 2012; DOI: 10.1158/1078-0432.CCR-12-2812
Biomarkers of Prostate Cancer Progression
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vesque et al.
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Figure 2. Simplified schematic
representation of sex–steroid
biosynthesis pathways. 5-DIOL,
androst-5-ene-3b, 17bdiol;
A-DIONE, androstanedione;
DHEA, dehydroepiandrosterone;
DHEA-S, DHEA-sulfate; DHT,
dihydrotestosterone; E1, estrone;
E2, estradiol; TESTO,
testosterone.
20% vs. 1%; ref. (17)] was associated with shorter survival
after adjustment for known prognostic factors and clearly
defines a subgroup of patients at higher risk of progression
and mortality. Although the impact of this polymorphism
could not be assessed precisely on the steroid hormonal
profile due to its lower frequency in Caucasians (no minor
allele homozygote), other HSD17B2 markers influenced
steroid hormone levels in patients with cancer, further
reinforcing their potential role in cancer progression.
On the basis of our preliminary data and those of others
(24–27), we can speculate that genetic variations associated
with progression in the HSD17B2 gene might lead to a
loss-of-function in favor of the reductive pathway and
accumulation of potent androgens and estrogens, thereby
potentially modifying the course of the disease. Inversely,
the observation of an association for the protective
HSD17B2 rs7201637 with lower DHT in available homozygotes, suggests a more efficient inactivation of potent
hormone by the 17bHSD type 2 enzyme, thereby plausibly
reducing progression risk in these patients. Therefore, these
positive markers, or any functional polymorphism in strong
linkage, may significantly impair gene expression/function
with subsequent modification in hormone levels that favor
cell proliferation, and ultimately the survival of the patient.
Together, data sustain a critical role of the HSD17B2 pathway as an important determinant in prostate cancer aggressiveness, progression, and survival (24–28), and suggest
that targeting this pathway and/or potentially the reverse
HSD17B3/5 pathway (29, 30) might be beneficial for anticancer therapy (Fig. 2).
Significant associations were also observed in both
cohorts for SNPs in CYP17A1 and ESR1. CYP17A1 is one
of the early steps in the biosynthesis of sex steroids and the
molecular target of abiraterone that improves survival after
docetaxel treatment in patients with CRPC (8). The
CYP17A1 rs6162 variation was associated with prostate
cancer progression and mortality herein, further reinforcing
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Clin Cancer Res; 2012
the importance of this critical pathway in cancer progression at numerous disease stages and in different ethnic
groups. In support, 3 previous studies have looked at
the impact of CYP17A1 on prostate cancer outcomes
(31–33). In agreement with our findings, in these studies,
the presence of polymorphisms in CYP17A1 was also
associated with disease progression and mortality in
Caucasians and Asians (31–33; Table 6). Overall, our
data combined with previous observations (31–33) clearly support a major role of CYP17A1 on cancer progression
after diagnosis. Moreover, we showed that the presence of
CYP17A1 SNPs is further associated with altered levels of
circulating DHEA-S in Caucasians, which likely modify
steroid precursor levels available for intracrine conversion
to more potent hormones in tissues and prostatic cells. It
is unclear whether this modification in DHEA-S levels is
caused by an altered enzyme activity or expression in
target cells and/or feedback mechanism on the hypothalamic–pituitary–adrenal axis or any other unknown physiologic process. Interestingly, germline variations of
CYP17A1, associated with outcomes herein and in previous studies (31, 32), are mostly located in the promoter
region and exon 1 raising the possibility of a functional
impact on transcriptional activity of the gene. Further
study is clearly needed to decipher the molecular mechanism(s) underlying this pathophysiologic association.
Ryan and colleagues showed that baseline levels of androstenedione might be predictive of ketoconazole response
in metastatic patients (34). Therefore, in view of data
presented here, the high frequency of the CYP17A1
rs6162 (50%) and its association with circulating
DHEA-S levels, it would be relevant to evaluate, in future
studies, the predictive and prognostic impact of CYP17A1
variations on abiraterone response and outcome.
Moreover, the ESR1 rs1062577 variant associated with
progression also seems to affect the circulating hormone
profile, with carriers of the minor allele showing reduced
Clinical Cancer Research
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Biomarkers of Prostate Cancer Progression
Table 6. Summary of current data supporting CYP17A1 SNPs as germline prognostic molecular markers of
prostate cancer progression
Sample size (n)
Ethnic background
Disease stage
Treatment
CYP17A1
rs6162(G)
rs743572 (A)
rs10883783 (T)
Wright and
colleagues (32)
Hamada and
colleagues (31)
Yamada and
colleagues (30)
This
study
598
Caucasian
Localized (RP/radiation/ADT)
222
Caucasian
Advanced (CRPC)
214
Asian Japanese
Advanced (ADT)
526
Caucasian
Localized (RP)
601
Asian Taiwanese
Advanced (ADT)
—
—
"
—
"
"
n/s
"
"
n/s
"
"
n/s
a
"
NOTE: Bold, significant P values.
Abbreviations: RP, radical prostatectomy; n/s, nonsignificant; —, not evaluated.
a
Trend.
systemic E1-S and ADT-G levels. A previous report suggests
that E1-S is associated with prostate cancer aggressiveness,
being positively associated with margin status, stage, and
PSA levels (35). Accordingly, we also observed a correlation
between E1-S and margin status, with higher levels being
associated positive margins (P ¼ 0.007, not shown).
A previous extensive evaluation of ESR1 genetic variations
was also conducted on cancer aggressiveness and efficacy
of ADT; however, to our knowledge, the rs1062577
variant was not specifically evaluated in previous studies
(28, 36). Another group reported that this common SNP
(rs1062557) might affect the binding capacity of miR-186
based on in silico analysis (37).
Interestingly, the HSD17B3 rs2257157 SNP was associated with cancer-related death in Taiwanese men on ADT,
whereas the CYP19A1 rs1870050 SNP was associated with
better survival. This variation is located in the promoter
region of CYP19A1 encoding the aromatase enzyme, a ratelimiting step in estrogens production from androgens.
Remarkably, the CYP19A1 rs1870050 was associated with
progression in Caucasians in agreement with previous association displaying a shorter time to progression in a cohort
of men on ADT composed mainly of Caucasians (13).
Differences in outcome between populations are perhaps
linked to different haplotypes tagged by these SNPs in
diverse ethnic groups. Nevertheless, validation of these 2
latter markers in cohorts with similar clinical and pathologic characteristics is still required to fully ascertain their
role in cancer progression.
The importance of inherited genetic variations in the
steroidogenic pathways is not surprising because they contribute to the maintenance and bioavailability of active
hormones in multiple target organs despite hormonal therapy (5, 6). Nevertheless, most of these htSNPs, or their
associated SNPs, have not been biochemically studied and
their functionality still remains undefined. As such, limitations of the study are mainly related to the scarcity of the
functional data for positive markers and lack of additional
cohorts for definitive validation at each disease stage.
www.aacrjournals.org
Strengths of the study include a significant number of
patients, a candidate gene approach, the high plausibility
of the association based on the biologic function of selected
candidate pathways, repeated associations in 2 genetically
divergent ethnic groups, an impact on circulating steroid
levels associated with positive markers, and clinical endpoints including both progression and survival. Finally,
results further underscore the need to examine the potential
relationship between prognostic markers and tissue hormone levels to better understand their physiologic consequence and contribution to cancer progression.
Data clearly emphasizes the biologic significance of the
HSD17B2, CYP17A, and ESR1 pathways in prostate cancer
progression and provides promising prognostic candidates
for explaining differences in clinical outcomes. The assessment of host genetic variations in key steroidogenic pathways, such as those identified herein, represent additional
evidence that sex steroids are likely involved throughout all
stages of disease progression including a castrate environment. Furthermore, findings support the seed and soil
hypothesis in which germline variations in steroidogenic
pathways influence the hormonal microenvironment to
which cancer cells are exposed and a subsequent impact on
prostate cancer proliferation, recurrence, and progression.
These molecular markers may improve prognostication of
prostate cancer, lead to a better patient stratification in
future clinical trials, targeting therapeutic interventions to
optimize hormonal manipulation in patients with a high risk
of recurrence, and more likely to benefit from treatment.
However, before such translational advance is made, additional investigations are required to fully characterize the
underlying biologic mechanisms driving the positive associations of inherited germline variations in those key genes
on steroid hormone levels, progression, and survival. In
conclusion, in combination with tumor changes, the major
challenge will be to decipher precisely which sets of inherited
gene combinations are associated with favorable and adverse
outcomes to ultimately personalize the management of
prostate cancer.
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vesque et al.
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Disclosure of Potential Conflicts of Interest
Levesque, C. Guillemette, L. Lacombe, and Y. Fradet have been named
E.
inventors on a patent application owned by Laval University on related work
that preceded this study. No potential conflicts of interest were disclosed by
the other authors.
Acknowledgments
The authors thank the genomics, particularly Marc-Andre Rodrigue,
and the statistical service platforms, particularly Sun Makosso-Kallyth, of
the CRCHUQ (Quebec, Canada), as well as the National Center for
Genome Medicine [National Science Council (NSC), Taiwan], for technical support.
Authors' Contributions
Levesque, S. Huang, Y. Fradet, C. Guillemette
Conception and design: E.
Levesque, C. Guillemette
Development of methodology: E.
Acquisition of data (provided animals, acquired and managed patients,
Levesque, S. Huang, E.
Audet-Walsh, L.
provided facilities, etc.): E.
Lacombe, B. Bao, Y. Fradet, I. Laverdiere, M. Rouleau, C. Huang, P. Caron,
C. Guillemette
Analysis and interpretation of data (e.g., statistical analysis, biosta Levesque, S. Huang, E.
Audet-Walsh, I.
tistics, computational analysis): E.
Laverdiere, M. Rouleau, C. Guillemette
Levesque, E.
Writing, review, and/or revision of the manuscript: E.
Audet-Walsh, L. Lacombe, B. Bao, Y. Fradet, I. Laverdiere, M. Rouleau, C.
Guillemette
Administrative, technical, or material support (i.e., reporting or orga Levesque, L. Lacombe, C. Huang,
nizing data, constructing databases): E.
C. Guillemette
Levesque, C. Guillemette
Study supervision: E.
Grant Support
This work was supported by Canadian research grants from the Prostate
Cancer Research Foundation of Canada (to E. Levesque), Cancer Research
Society (to C. Guillemette), and the Canada Research Chair Program (to C.
Guillemette). This work was also supported by the NSC (Taiwan, Republic of
China; grants NSC-98-2320-B-039-019-MY3, NSC-99-2314-B-037-018MY3, and NSC-100-2314-B-039-009-MY3), China Medical University (grant
CMU99-COL-13), and Kaohsiung Medical University Hospital (grant
KMUH99-9R12 and grant KMUH100-0R42).
The costs of publication of this article were defrayed in part by the
payment of page charges. This article must therefore be hereby marked
advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate
this fact.
Received August 29, 2012; revised October 30, 2012; accepted November
5, 2012; published OnlineFirst November 27, 2012.
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Published OnlineFirst November 27, 2012; DOI: 10.1158/1078-0432.CCR-12-2812
Molecular Markers in Key Steroidogenic Pathways,
Circulating Steroid Levels, and Prostate Cancer Progression
Éric Lévesque, Shu-Pin Huang, Étienne Audet-Walsh, et al.
Clin Cancer Res Published OnlineFirst November 27, 2012.
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