Study of Genetic and Epigenetic Alterations in Urine Samples As Diagnostic Markers for Prostate Cancer

ANTICANCER RESEARCH 33: 191-198 (2013)

Study of Genetic and Epigenetic Alterations in Urine Samples as Diagnostic Markers for Prostate Cancer

EFTHYMIOS DIMITRIADIS1, THEODOROS KALOGEROPOULOS2, STAMATIA VELAETI1, SOTIRIOS SOTIRIOU4, EVANGELOS VASSILIOU5, LOUKAS FASOULIS2, VASILIOS KLAPSAS2, MICHALIS SYNESIOU2, AIKATERINI APOSTOLAKI3, THEONI TRANGAS4 and NIKOLAOS PANDIS1

1Department of Genetics 2Urology and 3Pathology, St. Savvas Anticancer Hospital, Athens, Greece; 4Department of Biological Applications and Technologies, University of Ioannina, Ioannina, Greece; 5Department of Financial and Management Engineering, School of Business, University of Aegean, Chios, Greece

Abstract. Background: Early diagnosis of prostate cancer and Prostate cancer is one of the most frequently diagnosed types identification of new prognostic factors remain main issues in of non-skin cancer and a leading cause of cancer-related prostate cancer research. In this study, we sought to test a deaths for men worldwide (1). Diagnosis and management panel of cancer-specific markers in urine samples as an aid for are complicated by the lack of cancer-specific markers to early cancer diagnosis. Materials and Methods: Sedimented assist for diagnosis during the early stages of the disease, and urine samples of 66 candidates for needle biopsy were tested. to predict and monitor response to therapy. Real time-polymerase chain reaction (RT-PCR) was applied to The use of prostate-specific antigen (PSA) as a screening detect the expression of transmembrane protease serine-2 and and monitoring marker for prostate cancer is widespread (2). Ets-related gene fusion (TMPRSS2–ERG), Ets-related gene Although PSA monitoring has led to higher prostate cancer (ERG), prostate cancer antigen-3 (PCA3), and serine peptidase detection rates, it has also substantial drawbacks. PSA is inhibitor kazal type-1 (SPINK1) transcripts. For testing of the specific for tissues of prostatic origin but is not cancer-specific. methylation status of Glutahione S-tranferase P (GSTP1) and Serum PSA levels are often elevated in benign prostatic Ras association domain family member-1(RASSF1A) promoter hyperplasia and prostatitis. Thus, the PSA testing is associated region, methylation-specific PCR (MSP-PCR) was applied. with a significant false-positive rate, with a high proportion Results: Among the tested parameters, the presence of (>50%) of the resulting biopsies proving negative for cancer. TMPRSS2–ERG (OR=9.044, 95% CI=2.207-37.066, Furthermore, studies have also indicated that low levels of PSA p=0.002), as well as a positive test result for PCA3 do not preclude prostate cancer, and that 15% of men with PSA (OR=7.549, 95% CI=1,858-30,672, p=0.005) were associated 0-4 mg/ml developed prostate cancer (3). Moreover large with the subsequent diagnosis of prostate cancer. A randomized trials assigned modest effects upon mortality rates multivariable logistic regression including all the significantly of PSA screening during the first decade of follow-up (4, 5). associated variables [prostate-specific antigen (PSA), digital Thus the need for non-invasive methods that can accurately rectal examination (DRE), TMPRSS2-ERG and PCA3], yielded assist in the early detection of prostate cancer, still exists. To a model with area under the receiver-operating characteristic address this issue, additional markers have been investigated curve (AUC) =0.894 (95% CI=0.772-1.00). Conclusion: A including genetic and epigenetic alterations. Among those the multiplexed quantitative PCR analysis on sedimented urine, in most promising are: Genes that are specifically overexpressed conjunction with the results of serum PSA levels and DRE, has in prostate cancer cells, such as prostate cancer antigen-3 the potential to accurately foresee subsequent needle biopsy (PCA3), alpha methyl-CoA racemase (AMACR), serine outcomes. On the basis of the above, algorithms may be peptidase inhibitor kazal type1 (SPINK1) (6-8); prostate designed to guide decisions for needle biopsy. cancer-specific gene alterations, mainly the fusion genes involving transmembrane protease serine-2 (TMPRSS2) and E-twenty six (ETS) family members (9); and prostate cancer- specific methylation alterations of gene promoter regions Correspondence to: Efthymios Dimitriadis, Department of Genetics, Glutahione S-tranferase P (GSTP1), Ras association domain St. Savvas Anticancer Hospital, Alexandras Ave 171, 11522, Athens, Greece. E-mail: [email protected] family member 1(RASSF1A) (10, 11). Taking advantage of the fact that prostate cells can be Key Words: Prostatic neoplasms, carcinoma, gene expression, detected in blood and urine, prostate cancer-specific markers diagnostic markers, urine. can be tested through a urine or blood diagnostic test (12).

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The goal of the present study was to assess the diagnostic Table I. Sequence of primers, probes and annealing temperatures (TM). efficacy of a number of markers associated with malignant transformation when used individually or combined, in order Primer, probe Sequence TM to achieve a pre-biopsy prediction of prostate cancer and TMRSS2–ERGa2F cgc ggc agg aag cct ta 60˚C contribute to early diagnosis in a non-invasive manner. TMRSS2–ERGa2R tcc gta ggc aca ctc aaa caa c TMRSS2–ERG probe cag ttg tga gtg agg acc Materials and Methods PSA Fw gtc tgc ggc ggt gtt ctg 60˚C PSA Rw tgc cga ccc agc aag atc PSA probe cac agc tgc cca ctg cat cag ga Urine collection and DNA and RNA isolation. This study was PSA_SGfw gca tca gga aca aaa gcg tga 60˚C approved by the St. Savvas Anticancer Hospital Scientific PSA_SGRw cct gag gaa tcg att ctt cag Committee and informed consent was obtained from all participants. PCA3_SGFw cat ggt ggg aag gac ctg atg ata c 60˚C Urine samples were collected from 66 men who were admitted for PCA3_SGRw gat gtg tgg cct cag atg gta aag tc transrectal ultrasound (TRUS)-guided prostate biopsy at the SPINK1_SGFw caa aaa tct ggg cct tgc tga gaa c 60˚C Department of Urology in St. Savvas Anticancer Hospital on the SPINK1_SGRw agg cct cgc ggt gac ctg at basis of PSA level and/or abnormal DRE. At least 30 ml of urine ERG5-6_SGFw cgc aga gtt atc gtg cca gca gat 60˚C sample were collected from each patient immediately following a ERG5-6_SGRw cca tat tct ttc acc gcc cac tcc DRE. All patients included in this study were referred for prostate RASSFIA_methF gtg tta acg cgt tgc gta tc 56˚C biopsy for the first time. RASSFIA_methR aac ccc gcg aac taa aaa cga Urine was voided into sterile collection cups containing 5 ml of RASSFIA_unmethF ttt ggt tgg agt gtg tta atg tg 0.5 M EDTA. A minimum of 30 ml of the collected urine was RASSFIA_unmethR caa acc cca caa act aaa aac aa centrifuged at 5000 ×g for 20 min at 4˚C and the resulting cell pellet GSTP1_methF ttc ggg gtg tag cgg tcg t 56˚C was processed for DNA and RNA extraction. GSTP1_methR gcc cca ata cta aat cac gac g GSTP1_unmethF gat gtt tgg ggt gta gtg gtt gtt Total RNA and DNA was isolated using an RNA, DNA GSTP1_unmethR cca ccc caa tac taa atc aca aca extraction kit (NucleoSpin RNAXS, NucleoSpin RNA/DNA buffer set; Macherey-Nagel, Duren, Germany), according to the TMPRSS2: Transmembrane protease serine-2; ERG: Ets-related gene; manufacturer’s instructions. PSA: prostate-specific antigen; PCA3: prostate cancer antigen 3, SPINK1: serine peptidase inhibitor kazal type1; RASSF1A: Ras Quantitative reverse transcription-polymerase chain reaction association domain family member 1; GSTP: Glutahione S-tranferase (qRT-PCR). Quantitative RT-PCR was used to assess the P; SG: Syber Green. expression of four biomarkers [transmembrane protease serine2 and Ets related gene fusion (TMPRSS2–ERG), Ets related gene (ERG), PCA3, SPINK1] and the PSA transcript as control for prostate-derived cells. Total RNA (8 μl) was reverse-transcribed in 20 μl reactions using the SuperScript III First-strand Synthesis PCR conditions, as previously described (13, 14) (Table I). PCR Super Mix (Invitrogen, Carlsbad, California, USA) and random products were electrophoresed on 2.5% agarose gels and visualised hexamers as primers. with ethidium bromide staining. The cDNA was subjected to TaqMan PCR amplification for both the TMPRSS2–ERG fusion gene and PSA transcripts using primers Statistical analysis. Pre-biopsy clinical parameters were compared and PCR conditions, as described in Table I. between men diagnosed with prostate cancer vs. men without All PCR amplifications were performed in 20 μl reaction prostate cancer using Mann-Whitney tests for continuous variables mixture, containing 1× Platinum Quantitative PCR Super Mix and Fisher’s exact tests for categorical variables. Exact univariate (Invitrogen), 5 pmol of each primer and 5 pmol of TaqMan probes. logistic regression was used to examine the association between Syber Green qRT-PCR was applied for PCA3, ERG, SPINK1 and PSA levels, DRE outcome, PCA3 expression levels and PSA control expression using primers and PCR conditions described TMPRSS2–ERG fusion with the presence or absence of prostate in Table I. cancer upon prostate needle biopsy (PNB). All PCR amplifications were performed in 20μl reactions, The performance of each biomarker as a screening test was containing 1× Platinum Syber Green Quantitative PCR Super mix evaluated, and sensitivity, specificity, and the area under the (Invitrogen), and 5 pmol of each primer. receiver-operating characteristic curve (AUC), with 95% confidence The qPCR results for all the genes were calculated using intervals (CI), were calculated (15). A multivariable logistic RelQuant software (Roche Molecular Biochemicals, Mannheim, regression model predicting the diagnosis of prostate cancer on Germany) and are expressed as the ratio of the target gene/PSA biopsy was then developed using backward selection. The initial ×1000. The results were considered valid only if the PSA Ct was model contained PSA, DRE, PCA3 and TMPRSS2–ERG fusion as <35 cycles. potential predictor variables. The inclusion of PSA and PCA3 in the multivariable model was evaluated both as a continuous variable and Methylation-specific PCR (MSP). The extracted DNA was subjected as a categorical variable at a cut-off of 10 ng/ml and a ratio value of to sodium bisulphide modification using the EpiTec Bisulfite kit 30, respectively. Calibration was evaluated by calculating the AUC. (Qiagen, GmbH, Germany), following the manufacturer’s All analyses were carried out using SPSS ver. 17.0 (SPSS Inc., instructions. For the detection of methylated and unmethylated Chicago, Illinois, USA) at the 0.05 level of significance and two- GSTP1 and RASSF1A alleles MSP was applied using primers and sided p-values are reported.

192 Dimitriadis et al: Cancer-specific Markers in Urine Samples in Early Cancer Diagnosis

Table II. Pre-biopsy clinical parameters and biomarker status according to diagnosis on prostate needle biopsy.

Variable No prostate cancer (n=52) Prostate cancer (n=14) **p-Value

PSA (ng/ml) 8.17 (5.8-10.9) 8.64 (6.8-16.4) 0.301 PCA3 value* 19.85 (7.3-35.8) 43.75 (29.1-148.7) 0.008 SPINK1 value* 14.5 (4.2-72.7) 29.35 (4.1-98.8) 0.490 ERG value* 2.77 (0.7-12.1) 5.35 (1.5-131.5) 0.355 Suspicious DRE 20 (38.5%) 11 (78.6%) 0.008 TMPRSS2–ERG positive 15 (28.8%) 11 (78.6%) 0.001 GSTP Methylation positive 12 (23.1%) 6 (42.9%) 0.129 RASSF1A Methylation positive 15 (28.8%) 6 (42.9%) 0.246

*Target gene/PSA ×1000. **N (%) compared using Fisher’s exact tests, or median (inter-quartile range). PSA: prostate-specific antigen; PCA3: prostate cancer antigen-3, SPINK1: serine peptidase inhibitor kazal type-1; ERG: Ets-related gene; TMPRSS2: transmembrane protease serine-2; GSTP: Glutahione S-tranferase P; RASSF1A: Ras association domain family member 1.

Table III. Performance of prostate-specific antigen (PSA), digital rectal examination (DRE), prostate cancer antigen (PCA3) and transmembrane protease serine-2 and Ets-related gene (TMPRSS2–ERG) fusion gene in predicting prostate cancer diagnosis following prostate needle biopsy (PNB): Univariate analyses.

Predictor PNB Result Sensitivity (%) Specificity (%) AUC (95% CI) Univariate p-Value

Positive Negative

PSA ng/ml 36 73 0.544 (0.370-0.718) 0.521 ≥10 5 14 <10 9 38 DRE 79 61 0.701 (0.551-0.850) 0.013 Normal 3 32 Abnormal 11 20 TMPRSS2–ERG fusion 79 71 0.749 (0.604-0.893) 0.002 Positive 11 15 Negative 3 37 PCA3 * 79 67 0.729 (0.583-0.876) 0.005 Low (≤30) 3 35 High(>30) 11 17

*PCA3/PSA ×1000.

Results (both p<0.01). The other pre-biopsy clinical variables did not differ significantly between men found upon biopsy to have The mean age of men included in this study was 66.3 (range prostate cancer and those free of cancer. 45-83) years. The pre-biopsy clinical parameters and The predicted classification of the presence or absence of biomarker status of 66 men are presented in Table II. prostate cancer on biopsy using PSA as a binary variable and Fourteen (21%) men received diagnosis of prostate cancer the experimental urine biomarkers is as shown in Table II. upon biopsy and 52 (79%) men were found to be free of Twenty-six (39%) men had positive TMPRSS2–ERG fusion prostate cancer. For 10 of the cancer-positive patients, the status, while 28 (42%) men tested with a high level of urine Gleason score was 6 (3+3), it was7 (3+4) in 3, and one had PCA3, and 31 (47%) men had an abnormal DRE. In a Gleason score of 10 (5+5). univariate logistic analysis of these biomarkers, men The univariate analysis of the pre-biopsy clinical diagnosed with prostate cancer were more likely to have a parameters revealed that men diagnosed with prostate cancer positive test for TMPRSS2–ERG fusion (OR=9.044, 95% had significantly higher PCA3 levels compared to men free CI=2.207-37.066; p=0.002), a positive test result for PCA3 of prostate cancer (p<0.01). In addition, significant (OR=7.549, 95% CI=1.858-30.672; p=0.005) and an associations were found between prostate cancer and the abnormal test result for DRE (OR=5.867, 95% CI 1.456- TMPRSS2–ERG fusion gene, positivity and abnormal DRE 23.636; p=0.013).

193 ANTICANCER RESEARCH 33: 191-198 (2013)

Table IV. Clinical application of the multivariate model in selecting patients for biopsy.

Biopsy selection scenario*

PSA DRE TMPRSS2–ERG fusion PCA3 Probability of cancer at biopsy Less restricted More Restricted

<10 Normal Positive <30 0.10962 Biopsy No biopsy <10 Normal Positive ≥30 0.28850 Biopsy Biopsy <10 Normal Negative <30 0.02094 No biopsy No biopsy <10 Normal Negative ≥30 0.06580 No biopsy No biopsy <10 Abnormal Positive <30 0.31993 Biopsy Biopsy <10 Abnormal Positive ≥30 0.60774 Biopsy Biopsy <10 Abnormal Negative <30 0.07555 No biopsy No biopsy <10 Abnormal Negative ≥30 0.21206 Biopsy Biopsy ≥10 Normal Positive <30 0.15874 Biopsy No biopsy ≥10 Normal Positive ≥30 0.38278 Biopsy Biopsy ≥10 Normal Negative <30 0.03168 No biopsy No biopsy ≥10 Normal Negative ≥30 0.09725 No biopsy No biopsy ≥10 Abnormal Positive <30 0.41848 Biopsy Biopsy ≥10 Abnormal Positive ≥30 0.70323 Biopsy Biopsy ≥10 Abnormal Negative <30 0.11110 Biopsy No biopsy ≥10 Abnormal Negative ≥30 0.29160 Biopsy Biopsy

*Probability >10.34% for less restricted biopsy selection scenario and >18.52% for more restricted biopsy selection, scenario with sensitivity/specificity of 92.9/60.0% and 85.7/78.8%, respectively. PSA: Prostate-specific antigen; DRE: digital rectal examination; TMPRSS2–ERG: transmembrane protease serine-2 and Ets-related fusion gene; PCA3: prostate cancer antigen-3.

Nineteen (29%) men had elevated serum PSA (cut-off of Table III shows the efficacy of the latter model to support 10 ng /ml) but were not more likely to have prostate cancer the decision to advise a patient to undergo prostate biopsy. In on biopsy, as compared to men with PSA <10 ng /ml a less restricted biopsy selection scenario at high sensitivity (OR=1.508, 95% CI=0.431-5.280; p=0.521). (92.9%), individuals with either a TMPRSS2–ERG fusion or Although PCA3, TMPRSS2–ERG fusion and DRE had the an abnormal DRE and either PSA or PCA3 at a high level highest sensitivity of 79% in predicting the presence of will be recommended for prostate biopsy. However, with the prostate cancer, PSA was more specific (specificity=73%). more restricted biopsy selection scenario at high sensitivity TMPRSS2–ERG fusion had the greatest discriminatory value, and specificity (85.75 and 78.8%, respectively), a positive with an AUC of 0.75 (95% CI=0.604-0.893), whereas the TMPRSS2–ERG fusion and either abnormal DRE or a high performance of serum binary PSA at a cut-off of 10 ng/ml level of PCA3 are required simultaneously in order to make a (AUC=0.544, 95% CI=0.370-0.718) was not as effective. recommendation for prostate biopsy. A predictive model including only those biomarkers that showed significant association with malignancy on univariate Discussion analysis and PSA was constructed. A multivariable logistic regression, where PSA and PCA3 are considered as binary The performance of serum PSA as a biomarker for prostate variables (at cut-offs of 10 ng /ml and ratio of 30, respectively), cancer diagnosis is undermined by several limitations, yielded a model wherein DRE and TMPRSS2–ERG fusion including a relatively high proportion of false-positive and were both significant (p-values=0.028 and 0.005, AUC=0.818, false-negative test results because PSA is expressed in 95% CI=0.690-0.946). Furthermore, a multivariable logistic normal as well as in cancerous cells. Hence, there is a need regression including all the variables, where PSA and PCA3 to identify biomarkers that are more specific for prostate were considered as continuous variables, yielded a model with cancer, which are easily tested and that may thereby improve AUC=0.894, 95% CI=0.772-1.000). When the parameters PSA, prospects for prostate cancer screening. DRE, TMPRSS2–ERG fusion and PCA3 were removed from Research during the past five years has provided a large the model one at a time the p-values were=0.039, 0.046, 0.001 inventory of candidate biomarkers that might prove to be and 0.079l, respectively. Finally, the discriminatory ability of a more selective and potentially more useful than individual multivariate model combining all the variables as binary biomarkers when combined in a multiplex model (16, 17). variables was slightly weaker than the model where the PSA The parameters tested in this study were chosen either and PCA3 were considered as continuous variables because they are being used in routine screening (DRE, (AUC=0.852, 95% CI=0.723-0.980). serum PSA), or have been recently approved for diagnostic

194 Dimitriadis et al: Cancer-specific Markers in Urine Samples in Early Cancer Diagnosis use (PCA3), or were found – in most previous studies – to SPINK1 overexpression in urine sediments was previously be significantly associated with prostate cancer. shown to be associated with prostate cancer detection but Among them are the recurrent fusion genes involving the was not found to be significantly associated in this study androgen-regulated gene TMPRSS2 and members of the ETS (28). This may be due to sample variability or differences in family transcription factors (ERG, ETV1, and ETV4). the methods used, as there was no whole-transcriptome TMPRSS2–ERG is by far the most common subtype of ETS amplification step in our study. fusion accounting for approximately 85% of all ETS fusion- Whereas TMPRSS2–ERG fusion was significantly positive samples (18, 19). One consequence of the presence associated with the detection of prostate cancer on biopsy, of fusion gene is the overexpression of ERG, assigning ERG ERG overexpression was not, suggesting that cells from expression a putative role as a prostate cancer biomarker. other tissues may also contribute to ERG transcripts in urine, PCA3 is a non-coding RNA and its expression is restricted thus masking any differences. solely to prostate (it not being expressed in any other normal Neither GSTP1 nor RASSF1A promoter hypermethylation, human tissue nor any other tumour type). PCA3 RNA is found in previous studies to be specific markers for prostate highly overexpressed in 95% of tumours compared to normal cancer in tissues and urine sediments, were found to be or benign hyperplastic prostate tissue (6, 20). statistically significant predictors for prostate cancer The expression of SPINK1 has been found to be increased detection in this study (11, 29). The sample heterogeneity in prostate cancer. It was also shown that SPINK1 is and differences in method sensitivity may account for the exclusively expressed in tumours without the TMPRSS2–ERG observed discrepancies. fusion (7). However, the main issue addressed in this study is the Methylation of regulatory sequences at the GSTP1 and feasibility of using urine sediment-based tests for prostate RASSF1A gene loci is a common molecular alteration in cancer screening. prostate cancer and it has also been demonstrated in high- In this study, we detected TMPRSS2–ERG fusion in post grade PIN, but not in normal prostate tissue (21). DRE urine sediments in 11 out of 14 prostate cancer-positive Some genes that are overexpressed, hypermethylated, or cases on biopsy, whereas 37 out of 52 biopsy-negative mutated in prostate cancer tissues may be detectable in urine. patients were TMPRSS2–ERG negative, resulting in a Prostate cancer cells that are ex-foliated or which enter the specificity of 71% and a sensitivity of 79%. This findings are circulation even during early stages of tumour growth might in agreement with all the previous studies that assessed the display characteristics of cancer that is either likely to use of urine-detected TMPRSS2–ERG fusion gene as a metastasize or remain indolent. prostate cancer-specific marker (17-19). Recent studies have shown that all these markers can be At this point, the TMPRSS2–ERG fusion gene reliably detected in the urine of patients collected after outperformed all the other markers tested in this study DRE, which has the advantage of being a non-invasive namely PCA3 (specificity 67%, sensitivity 79%), PSA procedure (22, 23). (specificity 73%, sensitivity 36%), ERG (specificity 23%, Although urine-based testing for PCA3 expression has sensitivity 88.6%), RASSF1A methylation (specificity 69.8%, already been documented in large screening programs, the sensitivity 38.4%), GSTP1 methylation (specificity 75.4%, feasibility of testing based on other markers has not been sensitivity 38.4%). rigorously evaluated. More importantly, single marker tests, The performance of the individual tests in prostate such as those based on PSA or PCA3, ignore the cancer prediction at biopsy was found to be improved when heterogeneity of cancer development and the innate all the significant cancer-associated markers (DRE, PCA3, heterogeneity within tumour cells and may only detect a TMPRSS2–ERG) were combined with serum PSA. In a proportion of cancer cases (24). To overcome this limitation, multivariate regression analysis where PSA and PCA3 were multiplexing, or combining biomarkers for cancer detection considered as continuous variables, DRE, PSA, PCA3 and can improve the performance of a test in predicting the TMPRSS2–ERG fusion were significantly associated with biopsy result (25-27). prostate cancer detection on biopsy. The discriminatory In this study we used the simplest procedure so far ability of a multivariate model combining all the markers described for simultaneous extraction of RNA and DNA and as binary variables (AUC=0.852, 95% CI=0.723-0.980) real-time PCR without any kind of target enrichment. (PSA at cut of 10 ng/ml and PCA3 at 30) was found to All the markers were first evaluated by univariate analysis exceed that of PSA-alone (AUC=0.544, 95% CI=0.370- with DRE (p=0.008), PCA3 (p=0.008) and TMPRSS2–ERG 0.718) DRE-alone (AUC=0.701, 95% CI=0.551-0.850) and (p=0.001) showing significant association with biopsy- both combined (AUC=0.724, 95% CI=0.557-0.891). confirmed prostate cancer. Serum PSA levels before biopsy Our findings demonstrate the value of and support the were not associated with subsequent prostate cancer efficacy of combining the detection of TMPRSS2–ERG detection in this cohort (p=0.301) fusion gene and of PCA3 score with the routinely used DRE

195 ANTICANCER RESEARCH 33: 191-198 (2013) and serum PSA findings to formulate a clinical algorithm Ahmadie H, Eastham JA, Eggener SE, Fine SW, Hotakainen K, guiding the decision for prostate biopsy. Stenman UH, Tsodikov A, Gerald WL, Lilja H, Reuter VE, The multistep model is illustrated in Table IV, suggesting Kantoff PW, Scardino PT, Rubin MA, Bjartell AS and Chinnaiyan AM: The role of SPINK1 in ETS rearrangement- two possible selection scenarios: a less restricted one with a negative prostate cancers. Cancer Cell 13(6): 519-528, 2008. sensitivity and specificity at 92.9% and 60%, respectively, 8 Luo J, Zha S, Gage WR, Dunn TA, Hicks JL, Bennett CJ, Ewing and a more restricted one with a lower sensitivity of 85.7% CM, Platz EA, Ferdinandusse S, Wanders RJ, Trent JM, Isaacs but a much higher specificity of 78.8%. 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22 Laxman B, Tomlins SA, Mehra R, Morris DS, Wang L, 27 Rigau M, Ortega I, Mir MC, Ballesteros C, Garcia M, Llauradó Helgeson BE, Shah RB, Rubin MA, Wei JT and Chinnaiyan M, Colás E, Pedrola N, Montes M, Sequeiros T, Ertekin T, AM: Noninvasive detection of TMPRSS2:ERG fusion transcripts Majem B, Planas J, Ruiz A, Abal M, Sánchez A, Morote J, in the urine of men with prostate cancer. Neoplasia 8(10): 885- Reventós J and Doll A: A three-gene panel on urine 888, 2006. increasesPSA specificity in the detection of prostate cancer. 23 Salami SS, Schmidt F, Laxman B, Regan MM, Rickman DS, Prostate 71(16): 1736-1745, 2011. Scherr D, Bueti G, Siddiqui J, Tomlins SA, Wei JT, Chinnaiyan 28 Rice KR, Chen Y, Ali A, Whitman EJ, Blase A, Ibrahim M, AM, Rubin MA and Sanda MG: Combining urinary detection of Elsamanoudi S, Brassell S, Furusato B, Stingle N, Sesterhenn TMPRSS2:ERG and PCA3 with serum PSA to predict diagnosis IA, Petrovics G, Miick S, Rittenhouse H, Groskopf J, McLeod of prostate cancer. Urol Oncol 25 May 2011, in press. DG and Srivastava S: Evaluation of the ETS-related gene mRNA 24 Powell AA, Talasaz AH, Zhang H, Coram MA, Reddy A, Deng in urine for the detection of prostate cancer. Clin Cancer Res G, Telli ML, Advani RH, Carlson RW, Mollick JA, Sheth S, 16(5): 1572-1576, 2010. Kurian AW, Ford JM, Stockdale FE, Quake SR,Pease RF, 29 Fontenete S, Silva J, Teixeira AL, Ribeiro R, Bastos E, Pina F Mindrinos MN, Bhanot G, Dairkee SH, Davis RW and Jeffrey and Medeiros R: Controversies in using urine samples for S: Single-cell profiling of circulating tumor cells: Transcriptional prostate cancer detection: PSA and PCA3 expression analysis. heterogeneity and diversity from breast cancer cell lines. PLoS Int Braz J Urol 37(6): 719-726, 2011. One 7(5): e33788, 2012. 25 Laxman B, Morris DS, Yu J, Siddiqui J, Cao J, Mehra R, Lonigro RJ, Tsodikov A, Wei JT, Tomlins SA and Chinnaiyan AM: A first-generation multiplex biomarker analysis of urine for the early detection of prostate cancer. Cancer Res 68(3): 645- 649, 2008. 26 Wang R, Chinnaiyan AM, Dunn RL, Wojno KJ and Wei JT: Received September 25, 2012 Rational approach to implementation of prostate cancer antigen Revised November 9, 2012 3 into clinical care. Cancer 115(17): 3879-3886, 2009. Accepted November 9, 2012

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