Heterogeneous patterns of DNA methylation-based field effects in histologically normal prostate tissue from cancer patients

Prostate cancer (PC) diagnosis is based on histological evaluation of prostate needle biopsies, which have high false negative rates. Here, we investigated if cancer-associated epigenetic field effects in histologically normal prostate tissue may be used to increase sensitivity for PC. We focused on nine genes (AOX1, CCDC181 (C1orf114), GABRE, GAS6, HAPLN3, KLF8, MOB3B, SLC18A2, and GSTP1) known to be hypermethylated in PC. Using quantitative methylation-specific PCR, we analysed 66 malignant and 134 non-malignant tissue samples from 107 patients, who underwent ultrasound-guided prostate biopsy (67 patients had at least one cancer-positive biopsy, 40 had exclusively cancer-negative biopsies). Hypermethylation was detectable for all genes in malignant needle biopsy samples (AUC: 0.80 to 0.98), confirming previous findings in prostatectomy specimens. Furthermore, we identified a four-gene methylation signature (AOX1xGSTP1xHAPLN3xSLC18A2) that distinguished histologically non-malignant biopsies from patients with vs. without PC in other biopsies (AUC = 0.65; sensitivity = 30.8%; specificity = 100%). This signature was validated in an independent patient set (59 PC, 36 adjacent non-malignant, and 9 normal prostate tissue samples) analysed on Illumina 450 K methylation arrays (AUC = 0.70; sensitivity = 40.6%; specificity = 100%). Our results suggest that a novel four-gene signature may be used to increase sensitivity for PC diagnosis through detection of epigenetic field effects in histologically non-malignant prostate tissue samples.


Results
Detection of PC-specific hypermethylation in needle biopsy samples. By analysis of RP specimens, we have previously identified the eight genes AOX1, CCDC181 (C1orf114), GABRE, GAS6, HAPLN3, KLF8, MOB3B, and SLC18A2 as new common targets of aberrant promoter hypermethylation in PC [20][21][22] . Here, we initially tested if cancer-specific hypermethylation of these genes can be detected also in routinely processed sections of diagnostic prostate needle biopsies, where only limited amounts of FFPE tissue are available for DNA extraction and molecular analysis. For comparison, we included GSTP1, which is the most extensively studied candidate methylation marker for PC to date 40 .
The methylation level of each gene was analysed by qMSP in prostate needle biopsy samples from a total of 107 patients who underwent TRUS-guided prostate biopsy due to suspicion of PC. Out of 107 patients examined, 67 had at least one cancer positive biopsy, whereas the remaining 40 patients had exclusively cancer-negative biopsies. Based on histopathological diagnostic examination, prostate biopsy cores were divided into three sample subtypes: malignant (i.e. biopsies with histologically confirmed PC), non-malignant (NM; histologically non-malignant biopsies from patients with exclusively cancer-negative biopsies), and adjacent normal samples (AN; histologically normal biopsies from patients with PC in at least one other biopsy). Thus, the final biopsy set used for qMSP analysis included malignant samples from 48 patients, NM samples from 40 patients, and AN samples from 39 patients (Table 1; For further details, see Methods and Suppl. Fig. S1).
Next, we investigated if methylation levels in malignant biopsy samples were associated with PC aggressiveness as defined by the D' Amico risk score 41 . The D' Amico risk nomogram is based on serum PSA, Gleason score in biopsies, and cT stage, and is used for risk stratification at the time of diagnosis in order to guide treatment decisions 42 . For all nine genes, we found significantly higher methylation levels in malignant biopsy samples from high risk as compared to low risk patients (Fig. 3). Additional studies are needed to assess the potential prognostic value of our eight candidate methylation markers in diagnostic prostate biopsies; however, this is beyond the scope of the present study.
In summary, these results demonstrate that scarce amounts of FFPE prostate needle biopsy tissue, in this case leftover sections after routine histological examination, are sufficient for qMSP analysis of several candidate Scientific RepoRts | 7:40636 | DOI: 10.1038/srep40636 methylation marker genes. This further indicates that a future qMSP-based molecular diagnostic test may be developed as a relatively simple supplement to routine histological evaluation without the need for additional biopsies.
Cancer field effects in histologically normal prostate biopsies. Methylation-based cancer field effects have previously been reported for GSTP1 in histologically normal prostate tissue samples from patients with PC 25,26,28 . The possible existence of such cancer field effects, however, remains to be investigated for AOX1, CCDC181 (C1orf114), GABRE, GAS6, HAPLN3, KLF8, MOB3B, and SLC18A2. To address this question, we performed qMSP analyses for all nine genes in histologically normal prostate biopsy samples from patients with cancer in other biopsies (AN, n = 39) vs. patients with exclusively cancer-negative biopsies (NM, n = 40). Although no significant differences in median methylation levels were seen between AN and NM biopsies for any of the nine genes tested (Fig. 4), a few highly methylated outliers were detected specifically in AN samples for AOX1, GAS6, HAPLN3, SLC18A2, and GSTP1 (Fig. 4), potentially reflecting cancer field effects.
Because these highly methylated outliers were relatively rare for each single gene, we tested if multi-gene methylation signatures might increase the sensitivity for detection of PC based on epigenetic field effects. For each gene, methylation levels were dichotomised at a cut-off that ensured 100% specificity for AN vs. NM samples. Then, all nine genes were combined into every possible two-gene model (n = 36 models in total) and samples scored as hypermethylated, if at least one of the genes in the model had a methylation level above this cut-off. The five two-gene models with the lowest p-values in χ 2 test for distinguishing AN vs. NM samples encompassed four genes: AOX1, HAPLN3, SLC18A2, and GSTP1 (Suppl . Table S1), hence, these were combined into a single four-gene model.
The combined four-gene model (AOX1xGSTP1xHAPLN3xSLC18A2) significantly distinguished AN from NM samples (p = 0.0001; χ 2 -test) based on detection of hypermethylation of at least one of the genes in AN tissue. Thus, at 100% fixed specificity, the four-gene methylation signature had 30.8% sensitivity for PC and was able to identify 12 out of 39 PC patients based solely on hypermethylation field effects in AN samples, while not detecting any of the 40 non-cancer patients with exclusively NM biopsies. Notably, the diagnostic accuracy of the four-gene model (AUC = 0.65; Fig. 5A) was superior to PSA (AUC = 0.47; Suppl. Fig. S2) in this patient set (p = 0.01). Importantly, exclusion of GSTP1 from the model gave highly similar results (AUC = 0.64; Fig. 5B and Suppl. Table S1), indicating that the discriminative power of the four-gene model was not simply driven by GSTP1 for which hypermethylation cancer field effects have previously been demonstrated in PC 25,26,28 . Furthermore, with an AUC of 0.64 the three gene model (AOX1xHAPLN3xSLC18A2) significantly outperformed (p = 0.006; χ 2 -test) the diagnostic accuracy of GSTP1 as a single marker (AUC 0.54). There were no significant differences in serum PSA levels between patients with high vs. low methylation in AN tissue for any of the multi-gene models (p = 0.63 (three-gene model) and p = 0.72 (four-gene model); Spearman's rank test) in this patient set.
In summary, our results support the existence of hypermethylation based field effects in PC and suggest a novel four-gene (AOX1xGSTP1xHAPLN3xSLC18A2) epigenetic cancer field effect signature for detection of (occult) PC. Validation of epigenetic cancer field effects by microarray analysis of surgical specimens. To further investigate the existence of epigenetic cancer field effects, we used Illumina 450 K methylation microarray data from an independent prostate tissue sample set of 51 PC patients and 9 controls without prostate cancer (bladder cancer patients) (Suppl . Table S2). At this stage, we analysed whole surgical specimens (prostatectomies) for which a complete histopathological evaluation had been performed, allowing us to map epigenetic cancer field effects in more detail, including from PC patients with verified multifocal disease. Thus, the total sample set used for 450 K analysis included 59 malignant (PC) and 36 adjacent normal (AN) prostate tissue samples from 51 PC patients (22 patient with multiple AN and/or PC samples; 19 patients with one PC sample, and 10 patients with one AN sample), as well as 9 normal (N) prostate tissue samples (Suppl . Table S2). While the majority of samples were macrodissected, four of the PC patients were analysed in more depth after laser microdissection of 1-2 PC foci (cancer samples, CAN), one proximal adjacent normal (PAN) sample (< 1 mm from PC), and one distant adjacent normal (DAN) sample (located > 3 mm from PC) (Suppl . Table S3 and Fig. 6a). For all genes, we focused specifically on DNA methylation levels within the promoter region, as also interrogated by qMSP (GAS6 was excluded as it had no probes on the array). Significant PC-specific hypermethylation was detected for all genes also in this patient sample set, and mean methylation levels were similar in AN and N samples (Suppl. Fig. S3 and Suppl. Table S4), corroborating our findings in the needle biopsy sample set ( Figs. 1 and 4). Next, for each probe, a cut-off for calling hypermethylation field effects in AN samples was defined as a β -value at least 0.1 higher than the maximum β -value for that particular probe in normal (N) samples. Furthermore, to avoid bias, only one AN sample was included in this analysis for each patient (i.e. PAN samples were excluded for the 4 patients also represented by a DAN sample. PAN samples were excluded rather than DAN samples due to the theoretically higher risk of contamination with neighbouring cancer cells in these samples). Using these criteria, we identified a total of 34 probes (CpG sites) for which methylation based field effects were detectable in AN tissue in at least one out of 32 patients analysed (Table 2). For each of the eight genes investigated, epigenetic cancer field effects were detected in a small subset (< 20%) of the patients (Table 2), consistent with our finding of heterogeneous and sporadic field effects in diagnostic needle specimens (Fig. 4). Importanty, when reversing the analysis, no probes passed the cut-off, i.e. no probe had a β -value in any of the normal samples that was at least 0.1 higher than the maximum β -value in AN samples ( Table 2). This result supports the validity of our findings and indicates that the increased methylation levels observed specifically in AN samples represent cancer-specific field effects rather than random variations in methylation levels between samples.
In the four patients (PC1-PC4) for whom we analysed both a proximal and a distant AN sample, we also observed highly heterogeneous patterns of epigenetic field effects. Thus, in one PC patient (PC1), hypermethylation field effects were almost exclusively detected in the PAN sample, raising the possibility of contamination by hypermethylated cancer cells from the neighboring PC foci (Fig. 6a,b, Suppl. Table S5). In contrast, however, more complex patterns of epigenetic field effects were detected in both DAN and PAN samples in the other three patients (PC2-4), suggesting the existence of a more generalised epigenetic field effect in these cases ( Fig. 6a,b, Suppl. Table S5).
Finally, to validate our novel four-and three-gene epigenetic field effect signatures ( Fig. 5a,b), we used 450 K methylation array data for the probe located in closest proximity to the qMSP assay used for each gene (for probe IDs, see legends to Fig. 5). The methylation signatures were analysed by the same approach as used for the biopsy (training) set. Thus, for each gene, methylation levels were dichotomised at a cut-off that ensured 100% specificity for AN vs. N samples, and each sample was then scored as hypermethylated, if at least one of the genes in the signature had a methylation level above this cut-off. The four-gene model (AOX1xGSTP1xHAPLN3xSLC18A2) had an AUC of 0.70 (sensitivity = 40.6% at 100% fixed specificity) and the three-gene model (AOX1xHAPLN3xSLC18A2) had a highly similar AUC of 0.69 (sensitivity = 37.5% at 100% fixed specificity) in the validation set ( Fig. 5c,d).
Notably, this result also confirmed that the diagnostic performance of our novel four-gene model is not simply driven by the inclusion of GSTP1. In comparison, GSTP1 as a single marker had an AUC of 0.55 in this sample set.
In conclusion, we have trained and validated a novel three-gene and a novel four-gene methylation based cancer field effect signature highly specific to PC.

Discussion
In this study, we show that prostate cancer-specific hypermethylation of the eight genes AOX1, CCDC181 (C1orf114), GABRE, GAS6, HAPLN3, KLF8, MOB3B, and SLC18A2 can be detected by qMSP with very high sensitivity and specificity in scarce prostate needle biopsy samples taken at the time of diagnosis. This result provides proof of principle and could pave the way for development of methylation-based molecular diagnostic tests for PC in this clinically relevant context. Furthermore, we report the existence of heterogeneous and sporadic hypermethylation-based cancer field effects for all eight candidate genes (as well as for GSTP1) in adjacent non-malignant tissue samples from patients with PC in other biopsies and/or in adjacent non-malignant tissue samples from surgical prostatectomy specimens. Although field effects were detected in only a small proportion of the patients with PC, suggesting limited diagnostic potential of single genes, we trained a novel four-gene (AOX1xGSTP1xHAPLN3xSLC18A2) and a novel three-gene (AOX1xHAPLN3xSLC18A2) epigenetic cancer field effect signature that showed 30.8% and 28.2% sensitivity, respectively, at 100% fixed specificity, determined by qMSP analyses of non-malignant prostate biopsy samples from patients with vs. without PC in other biopsies. The four-and three-gene signatures were subsequently validated using 450 K data based on surgical prostatectomy specimens from an independent set of adjacent non-malignant vs. normal prostate tissue samples, resulting in 40.6% and 37.5% sensitivity, respectively, at 100% fixed specificity. Our results warrant further studies of these novel epigenetic cancer field effect signatures to assess their potential future clinical value for PC detection. Notably, while epigenetic field effects have been reported for GSTP1 in previous PC studies [24][25][26]28,29 , this is the first report of cancer field effects for AOX1, HAPLN3, and SLC18A2 in relation to PC.
Based on analyses of RP specimens, we and others have previously shown that AOX1, CCDC181, GABRE, GAS6, HAPLN3, KLF8, MOB3B, and SLC18A2 as well as GSTP1, are common targets (AUC > 0.90) of aberrant promoter hypermethylation in PC tissue samples 18,[20][21][22][23] . In this study, we obtained comparable AUCs, ranging from 0.79 (GABRE) to 0.98 (SLC18A2), from analyses of malignant vs. non-malignant diagnostic prostate needle biopsies, which not only corroborate the previous reports but also highlight the robustness of our qMSP assays, even for low input DNA samples. Moreover, the significant positive association between methylation levels in PC biopsies and D' Amico risk score for all candidate genes, is also in agreement with previous findings from RP specimens, where high methylation levels were generally associated with at least one adverse clinicopathological factor (high PSA, high Gleason score, positive surgical margins, and/or advanced pT-stage) 20,21,23,43 .
By analysis of surgical specimens from two large RP cohorts, we have previously demonstrated a significant independent prognostic potential for prediction of biochemical recurrence for GABRE and CCDC181 as PSA > 20 ng/mL and/or Gleason score 8-10 and/or cT2c-cT4. Higher methylation levels were significantly associated with higher risk score for all genes (p < 0.05 in Mann-Whitney U test). Normalisation to AluC4 was performed for all genes.
single methylation markers, as well as for a three-gene methylation signature including AOX1, CCDC181, and HAPLN3 14,20,21 . Hence, although this is beyond the scope of our present work, future studies should investigate if the prognostic potential of these top candidate prognostic methylation markers/signature can be transferred to prostate biopsies and thus potentially be used to guide treatment decisions at the time of diagnosis. Importantly, the results of our present study clearly demonstrate that it is possible to perform qMSP-based analysis of several candidate genes in parallel using only leftover biopsy tissue specimens after standard histopathological examination. This may further suggest that it would be relatively easy to incorporate such a test into routine clinical practice in the future.
In addition, future biopsy-based studies could include analysis of KLF8 and SLC18A2 for which our previous study in two large patient cohorts showed that a higher methylation level in PC tissue samples from RP specimens was associated with early biochemical recurrence in univariate analyses 20,23 . Finally, we note that GAS6 and MOB3B methylation did not show significant prognostic value in our previous study of two large RP cohorts 20 , while the potential prognostic value of GSTP1 hypermethylation has been evaluated by multiple research groups, however with conflicting results 14 .
The ability to distinguish morphologically normal/non-malignant tissue from cancer tissue in prostate needle biopsies based on DNA methylation analysis probably has limited clinical utility for diagnostic purposes. In contrast, detection of molecular cancer field effects that are not microscopically visible to the pathologist could increase sensitivity for occult PC and ensure early diagnosis of potentially aggressive tumours. PC associated field effects have previously been detected at various molecular levels, including RNA 30,31 , protein 33,34 and DNA (mutations) 32 . Here, we focused specifically on epigenetic field effects since aberrant DNA methylation has been found to be an early and highly recurrent event in PC 16 . The existence of methylation based PC field effects has previously been reported based on single gene 25,26,44,45 as well as genome-wide techniques, including MethylPlex-next-generation sequencing 36 , pyrosequencing 37 , and whole genome bisulphite sequencing 38 . While genomewide approaches may be preferred in the discovery phase, subsequent development of gene-specific qMSP assays (as used in the present study) will allow easier translation into future clinical use, due to their relative simplicity, low cost, and compatibility with standard real-time PCR equipment available in most if not all molecular diagnostic laboratories.
Based on qMSP analysis of malignant and non-malignant prostate needle biopsy specimens, we developed and validated a novel four-gene (AOX1xGSTP1xHAPLN3xSLC18A2) epigenetic field effect signature for PC that showed more than 30% sensitivity for PC at 100% specificity. More specifically, the signature was able to identify 12 out of 39 patients with PC based solely on detection of epigenetic field effects in morphologically non-malignant prostate biopsies. Seven of these 12 patients presented with PSA < 10 ng/ml, suggesting that our novel four-gene signature could potentially assist in the detection of PC also in patients with PSA in this lower range. However, further studies are needed to investigate this and to assess whether our epigenetic field effect signature can be used to guide repeat biopsy decisions. The superior performance of multi-gene panels over single markers for detection of PC-associated field effects, as found here, is consistent with a previous report by Brikun et al. who suggested a minimum of five hypermethylation markers for detection of occult PC in histologically benign biopsy cores 46 . Hence, future studies should also investigate, if inclusion of additional genes (e.g. APC, RARB2, and/or RASSF1, see below) may improve the diagnostic performance of our novel epigenetic field effect signature.
Prior to the present study, the most extensively studied multi-gene signatures for detection of DNA methylation-based field effects in diagnostic prostate needle biopsies include various combinations of the four genes APC, RARB2, GSTP1, and RASSF1 [24][25][26][27][28][29]47 . Most notably, this has led to a commercially available test (ConfirmMDx for Prostate Cancer; MDx Health) with a reported negative predictive value of 90% in confirming negative biopsies based on qMSP analysis of APC, GSTP1, and RASSF1 39 . Since repeat biopsies were not available for our study, we cannot estimate a negative predictive value of our four-gene signature for direct comparison. However, despite reports of relatively high sensitivities (68% and 62%, respectively) for the APC, GSTP1, and RASSF1 field effect signature in two large clinical studies (MATLOC and DOCUMENT), it was at the expense of specificity (64% in both studies) 28,29 . Furthermore, the DOCUMENT study reported an AUC of 0.63 for APC, GSTP1, and RASSF1 to distinguish non-malignant prostate needle biopsy samples from patients who later had a positive biopsy vs. those who did not 29 , which is highly similar to the AUCs found in the present study for our four-gene and three-gene epigenetic signatures (0.65 and 0.64, respectively).
Whereas GSTP1 has been linked with cellular protection from the by-products of oxidative stress 48 , little is known about the possible function of AOX1, HAPLN3, and SLC18A2 in PC. AOX1 has been shown to be involved in degradation of the Imidazo[1,2-a]pyrimidine moiety of a specific androgen receptor antagonist, suggesting a possible association with drug sensitivity 49,50 . There are no previous reports of HAPLN3 function in relation to cancer, but based on sequence similarity with other proteins, it has been suggested that HAPLN3 might stabilise the hyaluronan:chondroitin sulfate proteoglycan complex that is important for e.g. extracellular matrix structure 51 . The SLC18A2 gene encodes a synaptic vesicular amine transporter protein that has been extensively studied in the central nervous system 52 , while its possible function in PC development and/or progression remains to be investigated.
There are some limitations to our study. Our qMSP results from the biopsy set were based on only one medium-sized patient cohort. Nevertheless, we were able to train novel epigenetic field effect signatures that Results are shown for probes associated with the CpG islands also analysed by qMSP (see Suppl. Table S5 for further details). Black boxes highlight hypermethylation based field effects, as defined by a beta-value in a DAN/PAN sample that is at least 0.1 higher than the maximum beta-value detected for that particular probe in the normal (N) samples. validated successfully in an independent patient set based on 450 K data from radical prostatectomy specimens. Despite our use of different patient sample types (biopsies vs. surgical specimen) and distinct methods for methylation analysis (qMSP vs 450 K arrays), the novel epigenetic field effect signatures performed equally well in both the training and the validation set, suggesting that they are robust. However, future studies including larger numbers of patients are needed to validate our findings and determine transferable threshold values. Ideally, such future studies should include patients referred to initial as well as repeat prostate biopsy due to suspicion of PC.
In conclusion, our results showed that scarce prostate biopsy tissue sections, leftover after routine histopathological diagnostic procedures, are sufficient for methylation analyses of several candidate genes in parallel. Furthermore, we found frequent and highly prostate cancer-specific hypermethylation of AOX1, CCDC181, GABRE, GAS6, HAPLN3, KLF8, MOB3B, and SLC18A2 in diagnostic needle biopsy samples. We also identified and validated a novel four-gene (AOX1xGSTP1xHAPLN3xSLC18A2) epigenetic field effect signature with over 30% sensitivity for PC at 100% fixed specificity. Future studies should investigate if this novel epigenetic field effect signature can be used to increase sensitivity for (occult) PC in a routine clinical setting and/or be used to guide the need for repeat biopsy. If successful, implementation of such a test could help to limit the number of unnecessary repeat biopsies. Finally, to pave the way for non/minimally-invasive diagnostic tests, future studies are also needed to investigate if hypermethylation of AOX1, CCDC181, GABRE, GAS6, HAPLN3, KLF8 Table 2. Epigenetic cancer field effects in patient sample set analysed on 450 K arrays. N: normal prostate tissue samples from cystoprostatectomy patients from the 450 K set. AN: adjacent normal prostate tissue samples (PAN samples excluded for the four patients also represented by a DAN sample). Field effects AN > N: the total number of patients with a field effect detected for a given probe based on the criteria: Any AN β -value at least 0.1 higher than the maximum β -value for that particular probe in N. N > AN: The number of N samples with hypermethylation compared to AN samples (any N sample with a β -value at least 0.1 higher than the maximum β -value for that particular probe in AN samples). Field effects detected in ≤10% of the patients are marked in bold. Field effects detected in 10-25% of the patients are marked in bold and italics. and/or SLC18A2 can be detected also in urine and/or blood (plasma/serum) samples from PC patients, as has been reported for GSTP1 53 .

Methods
Clinical Samples. Two patient sample sets were used in this study for qMSP and 450 K analysis, respectively.
The qMSP set (training): Formalin-fixed and paraffin embedded (FFPE) diagnostic needle biopsies from 176 patients undergoing ultrasound-guided prostate biopsy due to suspicion of PC, were obtained from Department of Pathology, Aarhus University Hospital after routine histopathological examination. As part of standard diagnostic procedures, consecutive 3-μ m sections of individual biopsy cores were mounted on glass slides (3-4 sections on each slide from the same biopsy). The total number of cores per patient ranged from 4-24 (with 10 cores being most common) equally representing the left and the right side of the prostate. Leftover sections were stored at − 80 °C until used in the present study. For DNA extraction, due to scarce amounts of tissue, biopsy sections were systematically pooled for each patient based on an anatomical left/right separation and a histopathological malignant/non-malignant separation (Suppl. Fig. S1). This led to three sample types: malignant (PC) tissue samples from cancer-positive biopsies, adjacent non-malignant (AN) biopsy samples from patients with PC in at least one other biopsy, and non-malignant (NM) biopsy samples from patients with exclusively cancer-negative biopsies. After exclusion of 31 patients with PIN and/or inflammation in at least one biopsy, a total of 402 samples (114 NM, 109 AN, and 179 malignant samples) from 145 patients were selected for this study. Another 202 samples were excluded from further analysis, due to either low DNA yield, insufficient DNA quality resulting in failed QC of reference genes and/or detection of PC at repeat biopsy within 18 months. The final set consisted of 200 biopsy tissue samples (75 NM, 59 AN, and 66 malignant samples) from 107 patients (Suppl. Fig. S1). Clinicopathological information is provided in Table 1.
The 450 K set (validation): Prostate tissue samples from radical prostatectomy specimens from 51 PC patients who underwent radical prostatectomy for histologically verified PC and from 9 control patients who underwent radical cystoprostatectomy due to bladder cancer (histopathologically confirmed to not have PC  . Table S3), who were examined in-depth after lasermicrodissection (LMD) of as many morphologically different, geographically separated PC foci and/ or AN samples as possible using whole FFPE prostatectomy specimens. For these 4 PC patients, non-malignant tissue samples were further classified as either distant AN (DAN) or proximal AN (PAN) based on physical distance to the nearest PC focus (>3 mm vs. <1 mm). A total of 14 samples (4 DAN, 4 PAN, and 6 CAN) were included from these 4 patients (Fig. 6a). The total sample set used for 450 K analysis included 59 malignant and 36 adjacent normal prostate tissue samples from 51 PC patients as well as 9 normal (N) prostate tissue samples (Suppl. Table S2).
The study was approved by the Central Denmark Region Committees on Biomedical Research Ethics and The Danish Data Protection Agency. Informed written consent was obtained from all patients. The study was carried out in accordance with the approved guidelines.
DNA Purification from Tissue. Biopsy tissue sections were adjusted to room temperature for 20 min, deparaffinised in xylene (20 min), and rehydrated in EtOH (99% EtOH for 20 min, 96% EtOH for 10 min, and 70% EtOH for 3 min). Tissue sections were scraped into 1.5 ml tubes and genomic DNA extracted using the QIAamp DNA FFPE Tissue Kit (Qiagen) with the following modifications: Deparaffination was performed as described above and incubation in ATL buffer with proteinase K at 56 °C was extended from 1 to 16 hours. Genomic DNA was extracted from macrodissected fresh-frozen and FFPE prostatectomy specimens using the PUREGENE DNA purification kit (Gentra systems) with proteinase K treatment, and the miRNeasy FFPE kit (Qiagen)/RNeasy plus mini kit (Qiagen), respectively, as described previously 21,54 . In addition, FFPE prostatectomy specimens from 4 PC patients were used for laser microdissection (LMD). Tissue sections (5 μ m) were mounted on polyethylene naphthalate membrane glass, deparaffinised and rehydrated in xylene/EtOH, and stained with a 0.25% w/v solution of cresyl violet (Sigma-Aldrich) in 99.99% EtOH. The Veritas Microdissection Instrument (Arcturus) was used for LMD of individual DAN/PAN/CAN areas from 10-20 serial sections and genomic DNA extracted using the Allprep DNA/RNA FFPE Kit (Qiagen) or the QIAamp DNA FFPE Tissue Kit (Qiagen).
Quantitative Methylation Specific PCR. The full procedure is described in Supplementary Methods.
Briefly, DNA was bisulphite converted, pre-amplified, and used for qMSP analysis. All qMSP reactions were run in triplicates. Chromosomal locations of qMSP assays are illustrated in Suppl. Fig. S4. The AluC4 assay 55 (Alu-element based normalisation assay for qMSP) is methylation insensitive and was included for normalisation and quality control. To assess the limit of detection for all candidate genes, we used dilution series (10 ng DNA in total) based on decreasing amounts of methylated control DNA (CpGenome Universal Methylated DNA (Millipore)) diluted into unmethylated control DNA (Whole Genome Amplified DNA). Except for KLF8, all qMSP assays performed robustly in up to 200× excess unmethylated DNA, corresponding to detection of < 20 methylated copies. The qMSP assay for KLF8 performed robustly in up to 50× excess unmethylated DNA, corresponding to detection of < 80 methylated copies. (StataCorp LP), which was used for all subsequent data analysis. The following QC steps were performed: Samples with quantification cycles (C q ) > 35 for AluC4 were excluded from further analysis. Furthermore, for candidate methylation markers, single outliers more than 2 C q different from the other 2 replicates were excluded, except in cases where C q -values from 2 of the 3 replicates exceeded 35. Here all three values were kept. C q -values > 40 were considered undetermined and the quantity was set to 0. The quantity was also set to 0 for patient samples with a C q -value higher than the C q -value for WGA (whole-genome amplified DNA; used as unmethylated control) in the same qMSP run. For normalisation, the mean quantity for each candidate gene was divided by the mean quantity for the reference AluC4. Finally, for each patient, the sample with the highest methylation level for each tissue sample type (i.e. NM, AN, or malignant, respectively) was selected to represent that patient, resulting in 40 NM, 39 AN, and 48 malignant samples from a total of 107 biopsy patients for the final analyses.
Differences in methylation levels were assessed using Mann Whitney U tests. Furthermore, for each gene, methylation levels were dichotomised (high/low) based on receiver operator characteristics (ROC) curve analyses with specificity set to 100%. To generate methylation-based multi-gene models for epigenetic cancer field effects, all possible combinations (n = 36) of two-gene models were constructed from the dichotomised data. Based on these results, we also build a four-gene and a three-gene model, relying on the same principle. Thus, for all multi-gene models, patients were classified as belonging to the high-methylation group, if they had high methylation for at least one of the genes in the model. The diagnostic potential of all multi-gene models was assessed using χ 2 -tests. P-values were corrected for multiple testing using Bonferroni correction.
Data from 450 K arrays was analysed in R 56 where both QC and comparison of methylation levels between samples groups were performed, as described by Morris et al. 57 . The methylation level for each probe/CpG site was represented by a β -value, ranging from 0 (fully unmethylated) to 1 (fully methylated). For each gene, probes that passed QC and were located in the promoter associated CpG island analysed by qMSP were selected for further analysis (63 probes). For determination of cancer associated hypermethylation, Mann-Whitney U tests were performed for N samples vs. CAN samples and p-values were adjusted for multiple testing using Bonferroni correction. For analysis of methylation-based cancer field effects, probes with a mean β -value above 0.6 in normal prostate tissue samples were excluded, resulting in a total of 58 probes for further analysis. For each probe, a cut-off for calling hypermethylation field effects in AN samples was defined as a β -value at least 0.1 higher than the maximum β -value for that particular probe in normal (N) samples. For validation of the three-gene and four-gene methylation based field effect signatures, we used β -values for the probe located in closest proximity to the qMSP assay for each gene which had a demonstrated field effect in the 450 K sample set (SLC18A2: cg00498305; HAPLN3: cg03628719; GSTP1: cg02659086; AOX1: cg22953017). All analyses of multi-gene models in the validation set were performed in Stata 12.1, as described above for the training (qMSP) set.