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Development of a multivariable risk model integrating urinary cell DNA methylation and cell‐free RNA data for the detection of significant prostate cancer

Abstract Background Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine‐derived cell‐free messenger RNA (cf‐RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. Methods Post‐digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf‐RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out‐of‐bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). Results As ExoMeth risk score (range, 0‐1) increased, the likelihood of high‐grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78‐2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver‐operator characteristic curve (AUC) = 0.89 (95% CI: 0.84‐0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87‐0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. Conclusion Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed.


| INTRODUCTION
Prostate cancer exhibits extreme clinical heterogeneity; 10-year survival rates following diagnosis approach 84%, yet prostate cancer is still responsible for 13% of all cancer deaths in men in the United Kingdom. 1 Coupled with the high rates of diagnosis, prostate cancer is more often a disease that men die with rather than from. This illustrates the need for clinically implementable tools able to selectively identify those men that can be safely removed from treatment pathways without missing those men harboring a disease that requires intervention.
An opportune point to intervene or supplement current clinical practices would be before an initial biopsy in men suspected of having prostate cancer, reducing costs to men, health care systems and providers alike. In current clinical practice men are selected for further investigations for prostate cancer if they have an elevated prostatespecific antigen (PSA) (≥4 ng/mL) and an adverse finding on digital rectal examination (DRE) or lower urinary tract symptoms; other factors such as age and ethnicity are also considered. [2][3][4] However, the rates of negative biopsies in men with a clinical suspicion of prostate cancer are overwhelming; a recent population-level study of 419 582 men from Martin et al 5 observed that 60% of all biopsies in the control arm of the Cluster-Randomised Trial of PSA Testing for Prostate Cancer (CAP) were negative for prostate cancer, similar to the rates observed by Donovan et al 6 as part of the ProtecT trial. 6 Needle biopsy is invasive, and not without complications: 44% of patients report pain, and detection of clinically insignificant disease can result in years of monitoring, causing patients undue stress. 4 Multiparametric magnetic resonance imaging (MP-MRI) has been developed as a triage tool to reduce the rates of negative biopsy and its use has become increasingly widespread since its validation. 7 However, MP-MRI is relatively expensive and has shown a high rate of interoperator and intermachine variability, leading to MP-MRI missing up to 28% of clinically significant diseases in practice. 4,[8][9][10] The interconnected nature of the male urological system makes it an ideal candidate for liquid biopsy and noninvasive biomarkers for prostate cancer. There is a sizeable interest in the development of such noninvasive tests and classifiers capable of reducing the rates of initial biopsy in men while retaining the sensitivity to detect aggressive disease. Single-gene or expression panels of few genes, such as the PCA3, 11 SelectMDx, 12 ExoDx Prostate(IntelliScore) 13 [16][17][18] However, due to their proposed implementation within current clinical pathways, these tests may not take into consideration the considerable economic, psychological, and societal costs of unnecessarily subjecting men with low volume, indolent disease to biopsy. [19][20][21] In 2012, the Movember Global Action Plan 1 (GAP1) initiative was launched, a collaborative effort between multiple institutes focusing on prostate cancer biomarkers in urine, plasma, serum, and extracellular vesicles. The prime aim of the GAP1 initiative was to develop a multimodal urine biomarker panel for the discrimination of disease state. The authors have previously published analyses from two of the GAP1 studies that measured differing molecular aspects within urine; epiCaPture assayed hypermethylation of urinary cell DNA, 22 and PUR assessed transcript levels in cell-free extracellular vesicle messenger RNA (cf-RNA) using NanoString. 23 Both of these tests were able to discriminate some level of clinically significant disease and exhibited differing characteristics; where epiCaPture was well suited to detecting the highest grade disease (Gs ≥8), PUR was better matched to the deconvolution of lower-risk and indolent disease, as detailed by its prognostic ability in active surveillance use.
With a suitable overlap in the numbers of patient samples analyzed by both methods, we hypothesized that these two methods could be complementary, and the integration of both data sets could result in a more holistic model with predictive ability greater than the sum of its parts, able to encapsulate the clinical heterogeneity of prostate cancer and reach the levels of accuracy and utility required for widespread adoption. In this study, we report the diagnostic accuracy of such an integrated model, determined by the ability to predict the 548 | presence of Gs ≥7 and Gs ≥4 + 3 disease on biopsy, both are critical distinctions, where patients with Gs ≥7 are recommended radical therapy, 4 while patients with Gs 4 + 3 have significantly worse outcomes than Gs 3 + 4 patients. 24 Mindful that many cancer biomarkers fail to translate to the clinic, the development of the presented model has been carried out adhering to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. 25 2 | MATERIALS AND METHODS

| Sample processing and analysis
Urine samples were processed according to the Movember GAP1 standard operating procedure (Supporting Information Methods). Hypermethylation at the 5′-regulatory regions of six genes (GSTP1, SFRP2, IGFBP3, IGFBP7, APC, and PTSG2) in urinary cell-pellet DNA was assessed using a quantitative methylation-specific polymerase chain reaction (qPCR) as described by O'Reilly et al. 22 cf-RNA was isolated and quantified from urinary extracellular vesicles using NanoString technology, with 167 gene-probes (Table S1), as described in Connell et al, 23 with the modification that NanoString data were normalized according to NanoString guidelines using NanoString internal positive controls, and log 2 transformed. Clinical variables considered were serum PSA, age at sample collection, DRE impression, and urine volume collected.  however, these genes did not add additional information in preliminary analysis and were not included in further analyses (data not shown).

| Comparator models
To evaluate potential clinical utility, additional models were trained as comparators using subsets of the available variables across the patient population: a clinical standard of care (SoC) model was trained by incorporating age, PSA, T-staging, and clinician DRE impression; a model using only the available DNA methylation probes (methylation, n = 6); and a model only using NanoString gene probe information (ExoRNA, n = 167). The fully integrated ExoMeth model was trained by incorporating information from all of the above variables (n = 177). Each set of variables for comparator models were independently selected via the bootstrapped Boruta feature selection process described above to select the most optimal subset of variables possible for each predictive model.

| Model construction
All models were trained via the random forest algorithm, 29

| Statistical evaluation of model predictivity
Area under the receiver-operator characteristic curve (AUC) metrics were produced using the pROC package, 31 with confidence intervals calculated via 1000 stratified bootstrap resamples. Density plots of model risk scores and all other plots were created using the ggplot2 package. 32 Cumming estimation plots and calculations were produced using the dabestr package 33 and 1000 bootstrap resamples were used to visualize robust effect size estimates of model predictions.
Decision curve analysis (DCA) 34 examined the potential net benefit of using the developed comparator models in the clinic.
Standardized net benefit (sNB) was calculated with the rmda package 35 and presented throughout our DCAs as it is a more directly interpretable metric compared to a net benefit. 36 To ensure DCA was representative of a more general population, the prevalence of Gs within the ExoMeth cohort were adjusted via stratified bootstrap resampling to match those observed in a population of 219 439 men that were in the control arm of the CAP Trial, 5 as described in Connell et al. 23 Briefly, of the biopsied men within this CAP cohort, 23.6% were Gs 6, 8.7% Gs 7, and 7.1% Gs ≥8, with 60.6% of biopsies showing no evidence of cancer. These ratios were used to perform stratified bootstrap sampling with replacement of the Movember cohort to produce a "new" data set of 197 samples with risk scores from each comparator model. sNB was then calculated for this resampled data set, and the process repeated for a total of 1000 resamples with replacement. The mean sNB for each risk score and the "treat-all" options over all iterations were used to produce the presented figures to account for variance in resampling. The net reduction in biopsies, based on the adoption of models vs the default T A B L E 2 Boruta-derived features positively selected for each model Transcript targets  In the SoC comparator model, only PSA and age were selected as important predictors. All methylation probes were selected as important in both the independent Methylation model and the ExoMeth models (Table 2). Twelve NanoString gene-probes were selected for the NanoString model, notably containing both variants of the ERG gene probe and TMPRSS2/ERG fusion gene probe, alongside PCA3. All features within the ExoMeth model were also selected in one of the comparator models.   Table S1. The full list of variables in each model is available in Table 2

| DISCUSSION
The accurate discrimination of disease state in men before a confirmatory initial biopsy would mark a significant development and impact large numbers of men suspected of harboring prostate cancer. Up to 75% of men with a raised PSA (≥4 ng/mL) are negative for prostate cancer on biopsy. 4,5,37 This has resulted in concentrated research efforts to address this problem noninvasively and the development of several biomarker panels capable of detecting Gleason ≥3 + 4 disease with superior accuracy to current clinically implemented methods. [11][12][13]23 However, in each of these examples, only a single quantification method or biological process is assayed and with the molecular heterogeneity of prostate cancer considered, 38 a more holistic approach is necessary.
It is becoming apparent from published data that urine can contain a wealth of useful cancer biomarkers within RNA, DNA, cellfree DNA, DNA methylation, and proteins. 14,22,23,39,40 However, the analyses presented here are, to the author's knowledge, the first attempt to integrate such biomarker information within the same samples for the detection of prostate cancer before biopsy. There has recently been reported that a combination of miRNA and methylation markers can be used to predict outcome following radical prostatectomy. 41 Our results show an improved diagnostic marker can be produced from the synergistic relationship of information derived from different urine fractions in men suspected to have prostate cancer. The methylation of six previously identified genes 22 was quantified via methylation-specific qPCR, while the transcript levels of 167 cf-RNAs were quantified using NanoString technology.

CONFLICT OF INTERESTS
A patent application has been filed by the authors for the present work. The authors declare that there are no conflict of interests.

AUTHOR CONTRIBUTIONS
SPC drafted the manuscript and conceived, designed, and performed the statistical analyses. ER, AT, FZ, and BB were involved in sample collection and methylation analyses at their respective institutes. MW, RH, and F I G U R E 6 Net percentage reduction in biopsies, as calculated by decision curve analysis (DCA) measuring the benefit of adopting different risk models for aiding the decision to biopsy patients who would otherwise undergo biopsy by current clinical guidelines. The x-axis details the range of accepted risk a clinician or patient may accept before deciding to biopsy. Panels show the percentage reduction in biopsies based upon the detection of varying levels of disease severity: A, detection of Gleason ≥4 + 3; B, detection of Gleason ≥3 + 4; C, any cancer. Colored lines show differing comparator models; Blue: biopsy all patients with a PSA greater than 3 ng/mL; Orange: biopsy patients by according the to the SoC model; Green: biopsy patients based on the Methylation model; Purple: biopsy patients based on the ExoRNA model; Red: biopsy patients based on a the ExoMeth model. To assess the benefit of adopting these risk models in a non-PSA screened population we used data available from the control arm of the Cluster-Randomised Trial of PSA Testing for Prostate Cancer (CAP) study. 5 DCA curves were calculated from 1000 bootstrap resamples of the available data to match the distribution of disease reported in the CAP trial population. Mean sNB from these resampled DCA results are used to calculate the potential reductions in biopsy rates shown here. See Section 2 for full details [Color figure can be viewed at wileyonlinelibrary.com] RM were involved in sample collection and NanoString analyses as well as development of clinical methodologies. DSB, JC, ASP, and CSC had joint and equal contributions to senior authorship and were contributors in writing the manuscript. All authors read and approved the manuscript.
All authors critiqued the manuscript for intellectual content.

DATA AVAILABILITY STATEMENT
All data and code required to quantitatively reproduce these analyses can be found at https://github.com/UEA-Cancer-Genetics-Lab/ ExoMeth. The repository has been set up with a binder instance for ease of use, requiring no local computation or installation of dependencies.

ETHICAL STATEMENT
Sample collections and processing were ethically approved in their