Using gene expression from urine sediment to diagnose prostate cancer: development of a new multiplex mRNA urine test and validation of current biomarkers

During the last two decades, prostate-specific antigen (PSA) has been extensively used for prostate cancer (PCa) screening, detection and follow-up. The routine use of PSA has been the subject of continued controversy owing to its limited specificity, which derives from the fact that elevated serum levels of PSA occur in a variety of non-neoplastic conditions such as prostatitis and benign prostate hyperplasia (BPH) [1]. Furthermore, up to 27 % of men with PSA in the normal range (≤ 4 ng/ml) suffer from PCa [2]. The current gold standard method for diagnosis of PCa in patients with elevated serum PSA is non-targeted transrectal ultrasound-guided needle biopsy, which fails to detect PCa in approximately 20–30 % of cases [3]. Therefore, there is a need for additional non-invasive and more specific markers of early PCa that will permit the stratification of patients according to their risk of developing PCa and thus identify men who will require prostate biopsy.

A great improvement in high-throughput gene expression techniques has yielded several promising molecular biomarkers for PCa detection. Prostatic cells can be collected in urine after an intensive prostatic massage. In 2003, Hessels et al. for the first time used the prostate cancer antigen 3 (PCA3) for the identification of PCa in urine sediments obtained after prostatic massage [4]. Since then, several studies have assessed the diagnostic performance of this marker (reviewed in [5, 6]) and other individual transcripts [7, 8]. However, taking into account the heterogeneity of PCa, several authors have searched for a multiplex detection system of biomarkers, which has proved to outperform the diagnostic value of the individual markers [9‐12].

We have previously identified new putative mRNA markers for PCa diagnosis that can be extrapolated to post-prostatic massage (PPM) urine samples [13]. In the present study we aim to test several of those previously identified putative biomarkers in a large cohort of PPM-urine samples in order to develop an improved multiplex mRNA biomarker model for PCa diagnosis to be routinely used in the clinical setting. Furthermore, in our cohort we have validated the commercially available test based on urine PCA3 expression as well as the best performing mRNA panels of biomarkers reported in the literature [9‐12].

Currently, PSA is considered the most valuable tool in the early detection, staging and monitoring of PCa. However, as mentioned in the introduction, PSA has several limitations as a PCa diagnostic biomarker, especially in deciding the necessity of a prostate biopsy. Actually, PCa is detected in only about a third of patients with elevated serum PSA who undergo random prostate biopsy. Repeated biopsies reveal the presence of PCa in another 10–35 % of the cases [24]. Not only economic aspects but also anxiety, discomfort, and sometimes severe complications are associated with prostate biopsies. Therefore, the development of a non-invasive diagnostic tool for the early detection and screening of PCa as well as to increase the probability of detecting PCa at repeat biopsy, reducing the number of unnecessary biopsies, is needed in urological practice. Detection of aberrantly expressed transcripts in PCa cells shed into the urine after prostatic massage are promising biomarkers for the development of a reliable non-invasive PCa diagnostic method. In fact, several promising RNA-based urine PCa biomarkers are described in the literature, but only the PCA3 assay (Progensa) is approved by the FDA and currently is the only molecular diagnostic assay for PCa commercially available. However, PCA3 is not routinely used in the clinical setting mainly because clinicians feel that the increase in accuracy over serum PSA testing is not significant enough to warrant a biopsy. Furthermore, since PCa is a heterogeneous disease, it is reasonable that a combination of markers outperforms single marker detection. In this regard, several authors have described combinations of RNA-markers in urine samples but to our knowledge, none of them, except one [25], has been externally validated nor is currently used in the clinical setting. In the present work, we have developed a four-gene panel that outperforms those previously described in the literature. In addition, in our cohort we have validated PCA3 as well as the most promising panels of biomarkers described.

From our analysis, we have been able to identify six new candidates that independently predict PCa in PPM-urine samples, besides PCA3. This has been possible since we have explored target genes selected from previous PCa microarray data [13, 17] instead of analyzing only previously described prostate related biomarkers. Actually, all target genes explored were used to develop the four-gene set model that contains the previously described PCA3 gene and three new biomarkers: HIST1H2BG, SPP1 and ELF3. This model outperforms individual biomarkers and previously reported models in the literature. Although LOOCV indicates a certain degree of overfitting, all data obtained after cross validation corroborate the SN and SP for the final model. Moreover, the model performs well in the diagnostic PSA gray-zone (PSA 3–10 ng/ml) where a reduction in the number of unnecessary biopsies is necessary.

Notably, the three new biomarkers of the model had been previously associated with PCa. Alterations in expression of histone HIST1H2BG were associated with biochemical recurrence in PCa patients after radical prostatectomy [26]. The transcription factor ELF3 (E74-like factor 3), that acts as a negative modulator of androgen receptor transcriptional activity, was found underexpressed in PCa [27], according to our results. On the other hand, SPP1 (secreted phosphoprotein 1) encodes the protein osteopontin (OPN). Both, OPN RNA and protein have been found overexpressed in a number of human tumor types, including PCa [28]. In some cases, OPN overexpression has been shown to be associated directly with poor patient prognosis or with other indicators of poor prognosis. Thus, OPN has a dual interest, as a biomarker of malignancy as well as a candidate for testing as a poor prognostic factor. Even though in the present study we did not achieve statistical significance for SPP1, the addition of this gene to the model improved the AUC from 0.740 (HIST1H2BG, PCA3 and ELF3) to 0.763 (SPP1, HIST1H2BG, PCA3 and ELF3), indicating that effectively its expression adds information to the model.

The present study confirms that PCA3 can successfully discriminate PCa from controls in randomly selected patients with variable PSA levels (PSA = 0.94–365 ng/ml) [29, 30]. A limitation of most studies based on urinary biomarkers is that the negative PCa patient group consists of patients who have undergone prostate biopsy for suspected PCa with a negative result, but in fact, 20–30 % of such patients will be diagnosed with PCa at a later date [3]. To overcome this limitation, our control group consisted of patients without suspected PCa (PSA < 4.0 ng/ml), thus minimizing the risk of including subjects with PCa in the control group. Moreover, there is no uniform methodological protocol for urinary transcript quantification in the reported studies. For instance, some studies use a multiplex cDNA preamplification step before qPCR transcript quantification [16, 31], while others use a Whole Transcriptome Amplification [10, 32] or even in some studies cDNA is not preamplifed [11]. Also different gene expression normalization methods are used [4, 11, 16, 18, 31]. Thus, it is notable that despite this methodological heterogeneity and the inherent limitations of the sample source (PPM-urine contains different cell types, including renal tubular cells, urothelial cells, prostate cells, etc.… and the proportion of prostate tumor cells in each subject is different), we and the vast majority of the groups identify PCA3 as an independent predictor for PCa diagnosis, making it the most reliable individual biomarker to date.

However, combining urinary biomarkers in a panel has shown higher diagnostic accuracy than PCA3 alone. Regarding this, we have been able to validate some of the previously reported panels of biomarkers [9‐12] in our cohort and to develop a new urinary panel of biomarkers that improves serum PSA and previously reported panels of biomarkers. On the contrary, we could not validate differences between control and cancer population for the TMPRSS2-ERG status. This is in all probability due to the methodological approach used here, since others using the same methodology as us (RT-qPCR using the same gene expression assay as us; Hs03063375_ft ) to evaluate TMPRSS2-ERG status also did not find differences between cancer and control urines [33] while other authors using Southern blot [9] or transcription-mediated amplification [32] were able to find such differences.

Of concern, neither the FDA approved PCA3 test alone, or in combination with other biomarkers, is being routinely used in the clinical setting. This is most likely because the addition of urine biomarkers to the current clinical diagnostic tools only shows a limited improvement in the PCa diagnosis accuracy and does not provide sufficient value to affect biopsy decision making. In fact, recently the Evaluation of Genomic Applications in Practice and Prevention Working Group (EWG) has found insufficient evidence to recommend PCA3 testing not only for deciding to conduct initial biopsies for PCa at risk men (e.g. previously elevated PSA test or suspicious digital rectal examination) but also for deciding when to rebiopsy previously biopsy-negative patients for PCa. Furthermore, the EWG did not find convincing evidence to recommend PCA3 testing in men with PCa positive-biopsies to determine whether the disease is indolent or aggressive, in order to develop an optimal treatment plan [34]. Thus, even though many efforts have been made in the last decade to identify urine biomarkers that determine men at high risk of PCa and whether the disease is indolent or aggressive in men with PCa, the results do not seem convincing for clinicians.

We acknowledge that our study has several limitations. First it resides in the relatively low sample size of the studied cohort. This was because 18 % of urine samples collected could not be evaluated (informative specimen rate of 82 %). Although some improvements in the methodological process would be desirable to decrease the percentage of fails, this percentage is in the range of those described by other authors who quantify gene expression in PPM urine samples (informative specimen rates 56 to 92 %) [10‐12, 16, 30, 31]. However, sample collection can be repeated if necessary. It could also be argued that we arbitrarily selected the 42 target genes, while the list of differentially expressed genes in PCa is much larger. In this regard, we have tried to include the biomarkers according to previous studies, as being either detectable in urine or appropriate for combined models, and genes highly differentially expressed in PCa tissue samples. We are also aware that we should test the performance of our four-gene expression signature in a real clinical scenario by analyzing patients who undergo prostate biopsy for suspected PCa, even though this study will have the limitation of false negative biopsies, which account for 20–30 % of men at risk of PCa [3]. Lastly, future validation studies are needed to further improve the performance of this test by examination of larger and independent cohorts.

We report a four-gene expression signature with higher diagnostic accuracy than PCA3, the only non-invasive commercially available urinary biomarker, to predict individuals at risk of PCa. Moreover, our four-gene expression signature outperforms previously reported panels of biomarkers for PCa detection. Taken together, these results suggest that new biomarkers can be successfully combined with PCA3, resulting in improvements in PCa detection. However, further sources of new non-invasive biomarkers that enable physicians to accurately predict any PCa at initial prostate biopsy and aggressive PCa should be explored.