A First-Generation Multiplex Biomarker Analysis of Urine for the Early Detection of Prostate Cancer Bharathi Laxman, 1,2 David S. Morris, 1,3 Jianjun Yu, 1,2,4 Javed Siddiqui, 1,2 Jie Cao, 1,2 Rohit Mehra, 1,2,5 Robert J. Lonigro, 1,5 Alex Tsodikov, 1,6 John T. Wei, 1,3,5 Scott A. Tomlins, 1,2 and Arul M. Chinnaiyan 1,2,3,4,5 1 Michigan Center for Translational Pathology, 2 Department of Pathology, 3 Department of Urology, 4 Bioinformatics Program, and 5 Comprehensive Cancer Center, University of Michigan Medical School; and 6 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan Abstract Although prostate-specific antigen (PSA) serum level is currently the standard of care for prostate cancer screening in the United States, it lacks ideal specificity and additional biomarkers are needed to supplement or potentially replace serum PSA testing. Emerging evidence suggests that monitor- ing the noncoding RNA transcript PCA3 in urine may be useful in detecting prostate cancer in patients with elevated PSA levels. Here, we show that a multiplex panel of urine transcripts outperforms PCA3 transcript alone for the detection of prostate cancer. We measured the expression of seven putative prostate cancer biomarkers, including PCA3 ,in sedimented urine using quantitative PCR on a cohort of 234 patients presenting for biopsy or radical prostatectomy. By univariate analysis, we found that increased GOLPH2, SPINK1 , and PCA3 transcript expression and TMPRSS2:ERG fusion status were significant predictors of prostate cancer. Multi- variate regression analysis showed that a multiplexed model, including these biomarkers, outperformed serum PSA or PCA3 alone in detecting prostate cancer. The area under the receiver-operating characteristic curve was 0.758 for the multiplexed model versus 0.662 for PCA3 alone (P = 0.003). The sensitivity and specificity for the multiplexed model were 65.9% and 76.0%, respectively, and the positive and negative predictive values were 79.8% and 60.8%, respectively. Taken together, these results provide the framework for the development of highly optimized, multiplex urine biomarker tests for more accurate detection of prostate cancer. [Cancer Res 2008;68(3):645–9] Introduction Serum prostate-specific antigen (PSA) has been used extensively to screen for prostate cancer in the United States based on early studies showing that PSA levels >4 ng/mL have predictive value for detecting prostate cancer (1, 2). Although PSA testing has led to a dramatic increase in prostate cancer detection (3), PSA has substantial drawbacks. For example, PSA is often elevated in benign conditions, such as benign prostatic hyperplasia and prostatitis, likely accounting for the poor specificity of the PSA test, which has been reported to be only 20% at a sensitivity of 80% (4). Further, the Prostate Cancer Prevention Trial showed that even in patients with PSA levels <4 ng/mL, >15% had biopsy- detectable prostate cancer (5). Together, this supports the identification and characterization of prostate cancer biomarkers that could supplement PSA. Numerous promising prostate cancer biomarkers have been identified, including genes specific for prostate cancer, such as AMACR (6) and PCA3 (7), and recurrent gene fusions involving TMPRSS2 and ETS family members (such as TMPRSS2:ERG ; ref. 8). As prostate cells can be detected in the urine of men with prostate cancer, urine-based diagnostic tests have the advantage of being noninvasive. Although urine-based testing for PCA3 expression has already been documented in large screening programs (9), the feasibility of testing based on other markers has not been rigorously evaluated. Importantly, single marker tests, such as those based on PCA3 , ignore the heterogeneity of cancer development and may only capture a proportion of cancer cases. To overcome this limitation, multiplexing, or combining, bio- markers for cancer detection can improve testing characteristics (10, 11). In this study, we sought to explore a multiplexed urine- based diagnostic test for prostate cancer. Materials and Methods Urine collection, RNA isolation, amplification, and quantitative PCR. Samples were obtained from 276 patients with informed consent following a digital rectal exam before either needle biopsy (n = 216) or radical prostatectomy (n = 60) at the University of Michigan Health System with Institutional Review Board approval (Supplementary Table S1). The digital rectal examination was done by systematically applying mild digital pressure over the entire palpated surface. Initial voided urine was then collected in urine collection cups containing DNA/RNA preservative (Sierra Diagnostics LLC). Isolation of RNA from urine and TransPlex whole transcriptome amplification (WTA) were as described (12). Quantitative PCR (qPCR) was used to detect seven prostate cancer biomarkers (AMACR, ERG, GOLPH2, PCA3, SPINK1, TFF3 , and TMPRSS2:ERG ) and the control transcripts PSA and GAPDH from WTA-amplified cDNA essentially as described (12, 13). The primer sequences for ERG (exon5_6; ref. 8), GAPDH (14), AMACR (15), and PSA (16) were previously described and for other biomarkers are listed in Supplementary Table S2. Threshold levels were set during the exponential phase of the qPCR using Sequence Detection Software version 1.2.2 (Applied Biosystems), with the same baseline and threshold set for each plate, to generate threshold cycle ( C t ) values for all genes for each sample. Analysis. qPCR was performed on WTA cDNA from urine collected from 111 biopsy-negative patients and 165 patients with prostate cancer (105 biopsy-positive patients and 60 prostatectomy patients). Samples that had PSA C t values of >28 were excluded to ensure sufficient prostate cell collection, leading to 105 biopsy negative and 152 samples from patients with prostate cancer in the analysis. We used raw ÀDC t (to stabilize the variance of testing variables) as opposed to testing markers against control (2 ÀDC t ). TMPRSS2:ERG was dichotomized as a binary variable to reflect the Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/). B. Laxman, D.S. Morris, J. Yu, and S.A. Tomlins contributed equally to this work. Requests for reprints: Arul M. Chinnaiyan, University of Michigan Medical School, 1400 East Medical Center Drive, 5316 CCGC, Ann Arbor, MI 48109-0602. Phone: 734- 615-4062; Fax: 734-615-4498; E-mail: arul@umich.edu. I2008 American Association for Cancer Research. doi:10.1158/0008-5472.CAN-07-3224 www.aacrjournals.org 645 Cancer Res 2008; 68: (3). February 1, 2008 Priority Report Research. on November 3, 2015. © 2008 American Association for Cancer cancerres.aacrjournals.org Downloaded from