Patent Description:
Prostate cancer (PCa) is the most common non-cutaneous malignancy in men in the Western world. An estimated <NUM> million new cases were diagnosed in <NUM>, accounting for <NUM>% of all male cancers worldwide. Ireland is currently experiencing one of the highest incidences of PCa in Europe, with approximately <NUM>,<NUM> new cases diagnosed per annum, representing <NUM>% of all invasive cancers in men. With an ageing Western population and spread of Western culture (particularly diet), the global incidence is predicted to rise dramatically; the National Cancer Registry predicts the incidence in Ireland to rise by between <NUM>-<NUM>% by <NUM>.

It is often said that "most men die with and not because of their prostate cancer". This is explained by the fact that most prostate tumours have a slow, long natural trajectory, posing little likelihood of clinical manifestation, and deemed indolent in nature; <NUM>-year survival rates for PCa are close to <NUM>%. Nevertheless, a proportion of prostate tumours are highly aggressive, and are associated with the lethal form of the disease. Whilst PSA (prostate specific antigen) screening and improvements in treatments have reduced PCa mortality, this disease accounted for an ~<NUM>,<NUM> deaths in <NUM>, making it the <NUM>th leading cause of male cancer-related deaths worldwide. Identifying molecular correlates to discern between aggressive and indolent tumors at an early stage (whilst potentially curable), is one of the greatest unmet clinical needs in this field. This will become even more pressing as the differential between the total number of PCa cases diagnosed and the number of lethal PCa cases grows.

Early detection and diagnosis of PCa involves a combination of a PSA blood test, a digital rectal examination (DRE) and histological examination of transrectal ultrasound (TRUS)-guided biopsy cores, respectively. Several major problems confound the early detection of PCa. There are an estimated <NUM>-<NUM> million PSA tests performed worldwide every year, Widespread PSA testing has significantly increased PCa incidence and led to overtreatment of low-risk disease with little likelihood of clinical manifestation. A further problem with PSA is its poor tumour-specificity; its high false-positive rate means that two-thirds of men who undergo invasive TRUS-biopsy have no tumour diagnosed. There are an estimated <NUM> million prostate biopsies performed worldwide/annum. Unnecessary TRUS-biopsies create an enormous burden on our healthcare system and cause significant anxiety, trauma and co-morbidities for patients. Finally, TRUS-biopsies are needle biopsies that sample <<NUM>% of the prostate and can thus miss tumour foci or indeed miss high-grade aggressive tumours. Studies addressing the economic burden of cancer in the EU, have estimated costs for PCa diagnosis and treatment over the next <NUM> years per <NUM>,<NUM> men at €<NUM>,<NUM>,<NUM> (unscreened population) and €<NUM>,<NUM>,<NUM> (screened population), €<NUM>,<NUM>,<NUM> of which can be attributed to over-detected cancers.

Currently, there are no commercially available molecular diagnostics for PCa in widespread clinical practice. Progensa® (Gen-Probe) is a urine-based test of PCA3 gene expression performed after DRE, with FDA approval for use in men who have had ≥<NUM> previous negative biopsies and for whom a repeat biopsy would be recommended based on current standard of care. The test is used to guide the decision to perform a repeat biopsy only. Its prognostic value is debated and research efforts combining it with the fusion-transcript TMPRSS2-ERG are underway in an attempt to address this.

Prolaris® (Myriad Genetics) and oncotypeDX® Prostate Cancer Assay (Genomic Health) are two examples of prognostic gene expression signatures (<NUM> genes and <NUM> genes, respectively) that are analysed on biopsy tissues to aid prediction of PCa aggressiveness in conjunction with other clinical parameters (Gleason score, PSA). Both tests provide a more individualised risk-assessment of the underlying biology of the patient's tumour and are therefore aimed at guiding the decision between active surveillance and radical treatment in men diagnosed with PCa.

MDxHealth's product ConfirmMDx™ is a PCR-based assay, which measures methylation of a <NUM>-gene panel (GSTP1, RARβ, APC) in biopsy cores. It is positioned to distinguish patients with a true-negative prostate biopsy from those with occult cancer and akin to Progensa®, is used to guide the decision to perform a repeat biopsy. This same <NUM>-gene panel (ProCaM™) has also been investigated as a urine test to predict biopsy results for PCa, although these studies were inadequately powered. <CIT> describes methylation markers and different sets of methylation markers associated with prostate cancer. <CIT> discloses methods for assessing prostate cancer comprising diagnosis of prostate cancer; prognosis of prostate cancer; staging assessment of prostate cancer; prostate cancer aggressiveness classification using methylation markers.

It is an object of the subject invention to overcome at least one of the above-mentioned problems.

In contrast to these prior art technologies, the test presented herein (called epigenetic Cancer of the Prostate test in urine or epiCaPture) is an example of a "first in field" for urine diagnostics of potentially lethal, high-risk PCa. The panel of genes encompasses multiple dysregulated pathways in PCa, which is necessary to address the heterogeneity of the disease. These pathways include intracellular detoxification, the IGF axis, the Wnt axis and inflammation. The test presented herein addresses the unmet clinical needs confounding early detection of PCa. The test is a non-invasive DNA methylation test performed using urine or urine cell-sediment. It comprises a panel of at least <NUM> genes and an internal control gene. The test described herein offers significant commercial potential as a liquid biopsy for early non-invasive detection of high-risk, potentially lethal PCa. The data show that the test offers the unique advantages of i) better tumour-specificity than PSA, and ii) selective identification of high-risk PCa.

According to the invention, there is provided, as set out in the appended claims a method of determining the presence of high-risk prostate cancer in an individual, the method comprising a step of assaying a biological sample obtained from the individual for the presence of methylated regulatory DNA sequences as defined by SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM> and SEQ ID NO <NUM> and calculating a normalized index of methylation (NIM) score, wherein the presence of the methylated regulatory DNA sequences as defined by SEQ ID NOs <NUM> to <NUM>, and the calculated NIM score indicates a high-risk prostate cancer.

In one embodiment, the present invention discloses a method for determining an aggressive prostate cancer in an individual, the method comprising the step of assaying the biological sample obtained from the individual for the presence of at least one sequence selected from SEQ ID NO <NUM> and SEQ ID NO <NUM>, wherein detection of the methylated regulatory DNA sequences as defined by SEQ ID NOs <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM> and SEQ ID NO <NUM> and one sequence from SEQ ID NO <NUM> and SEQ ID NO <NUM> in a sample and calculating a NIM score indicates the presence of an aggressive prostate cancer, and wherein the sensitivity of the assay for detecting the methylated regulatory DNA sequences as defined by SEQ ID NOs <NUM> to <NUM> is at least <NUM>%.

In one embodiment, the at least one sequence selected from SEQ ID NOs <NUM> and <NUM> is SEQ ID NO: <NUM> prostate-specific antigen (PSA).

In one embodiment, the sample is urine or a urine derivative from the individual.

The two controls may be used in the method of the invention:.

The invention also relates to a kit for detecting the presence of prostate cancer in a sample from an individual, the kit comprising a control oligonucleotide as defined by SEQ ID NO <NUM>, <NUM> or <NUM>, or a variant thereof, and a set of oligonucleotides for detecting SEQ ID NOs <NUM> to <NUM>. In one embodiment, the kit further comprises an oligonucleotide for detecting the presence of PSA. In one embodiment, the set of oligonucleotides is defined by SEQ ID NOs. <NUM> to <NUM>. In one embodiment, the kit further comprises a support having at least one oligonucleotide selected from group SEQ ID No's <NUM> to <NUM> anchored thereon.

The kit preferably comprises a pair of forward and reverse oligonucleotide primers (SEQ ID NOs. <NUM> to <NUM>) designed to specifically hybridise with bisulfite modified hypermethylated DNA sequences at the regulatory regions of each specific gene as defined by SEQ ID NOs <NUM> to <NUM>; a fluorescently labelled oligonucleotide probe designed to specifically hybridise with bisulfite modified hypermethylated DNA sequences at the regulatory region of each specific gene (SEQ ID NO. <NUM> to <NUM>), a set of forward and reverse oligonucleotide primers and a fluorescently labelled probe to specifically hybridise with bisulfite modified DNA contained as part of the human ACTB gene, regardless of DNA methylation patterns of this gene (Positive control <NUM>), a qRT-PCR assay for the KLK3 gene (Positive control <NUM>) to control for the presence of prostate-derived nucleic acids in the bio-specimen, and a gBlock® synthetic gene fragments for construction of standard curves (SEQ ID NO. <NUM>, <NUM> or <NUM>), necessary for quantification of methylation levels at individual DNA sequences contained within the panel.

As indicated above, the methods, assays and kits of the invention employ biomarkers (methylated regulatory DNA sequences of specific genes or oligonucleotides specific to those regulatory DNA sequences of those genes) as a means of assessing the risk of an aggressive or metastatic prostate cancer in an individual. In one preferred embodiment of the invention, the methods, assays, and kits may be employed as a clinical screening tool to assist in the identification of individuals with an aggressive form of or a high risk metastatic prostate cancer, especially symptomatic individuals, who should be subjected to more invasive investigations, such as a prostate biopsy. In this regard, it should be noted that many patients who present with symptoms of prostate cancer (i.e. the need to urinate frequently, difficulty in starting urination, weak or interrupted flow of urine, painful/burning urination; blood in the urine etc.) can turn out to be negative for prostate cancer, yet still have to undergo a prostate biopsy to reach that diagnosis. In this regard, the present invention provides a useful clinical decision making tool which can assist a clinician in identifying those symptomatic patients that are most at risk of having the cancer, thereby potentially reducing the numbers of patients who have to undergo a prostate biopsy needlessly.

In this specification, the term "biological sample" or "biological fluid" may be a sample obtained from an individual such as, for example, urine or urine cell-sediment, blood or a prostate tissue sample from a biopsy or a radical prostatectomy. In many cases, the individual will be a person suspected of having prostate cancer, or pre-disposed to developing prostate cancer as determined by other phenotypic, genotypic or hereditary traits.

In this specification, the term "prostate cancer status" when used with reference to an individual primarily refers to the risk of the individual having the cancer. Depending on the number of biomarkers detected in the individual, the assay and methods of the invention will assist a clinician is determining the risk that the individual is positive for prostate cancer. Thus, in one embodiment, the methods, assays and kits of the invention provide a means for screening male patients to identify those patients that should undergo further investigative procedures, such as a biopsy. However, the term also encompasses prognostic evaluation of the cancer, identification of predisposition to developing the cancer, staging of the cancer, and evaluation or monitoring of the progress of the cancer, in the individual. The latter evaluation is typically employed as a means of monitoring the effectiveness of a treatment for the cancer.

A "variant" of one of SEQUENCE ID No's <NUM> to <NUM> shall be taken to mean at least <NUM>% sequence identity, preferably at least <NUM>% sequence identity, more preferably at least <NUM>% sequence identity, and ideally at least <NUM>%, <NUM>%, <NUM>%, <NUM>% or <NUM>% sequence identity with the native sequence.

mRNA expression of the KLK3 gene (positive control <NUM> - SEQ ID NO. <NUM>) may be measured by any suitable method including, but not limited to, a Northern Blot or detection by hybridisation to a oligonucleotide probe. A variety of hybridization assays using a variety of technologies for hybridization and detection are available. For example, a TaqMan assay (PE Biosystems, Foster City, CA; See e.g., <CIT> and <CIT>,) is utilized. The assay is performed during a PCR reaction. The TaqMan assay exploits the <NUM>'-<NUM>' exonuclease activity of the AMPLITAQ GOLD DNA polymerase. A probe consisting of an oligonucleotide with a <NUM> '-reporter dye (e.g., a fluorescent dye) and a <NUM> '-quencher dye is included in the PCR reaction. During PCR, if the probe is bound to its target, the <NUM>'-<NUM>' nucleolytic activity of the AMPLITAQ GOLD polymerase cleaves the probe between the reporter and the quencher dye. The separation of the reporter dye from the quencher dye results in an increase of fluorescence. The signal accumulates with each cycle of PCR and can be monitored with a fluorometer.

DNA methylation may be measured by any suitable method, such as quantitative methylation specific PCR (PMID: <NUM>).

In other embodiments, reverse-transcriptase PCR (RT-PCR) is used to detect the expression of RNA where RNA is enzymatically converted to complementary DNA or "cDNA" using a reverse transcriptase enzyme. The cDNA is then used as a template for a PCR reaction. PCR products can be detected by any suitable method, including but not limited to, gel electrophoresis and staining with a DNA specific stain or hybridization to a labelled probe. In some embodiments, the quantitative reverse transcriptase PCR with standardized mixtures of competitive templates method described in <CIT>,<CIT>, and<CIT> is utilized.

In the specification, the term "high-risk prostate cancer", "high-risk disease" or "aggressive prostate cancer" or "metastatic prostate cancer" should be understood mean a prostate cancer that is categorised by the D'Amico Risk Stratification criteria. The D'Amico criteria are used to define low, intermediate and high-risk prostate cancer. For example, (i) Low risk: having a PSA less than or equal to <NUM>, a Gleason score less than or equal to <NUM>, or are in clinical stage T1-2a; (ii) Intermediate risk: having a PSA between <NUM> and <NUM>, a Gleason score of <NUM>, or are in clinical stage T2b; and (iii) High-risk: having a PSA more than <NUM>, a Gleason score equal or larger than <NUM>, or are in clinical stage T2c-3a. The terms high-risk, aggressive and metastatic can be used interchangeably. The terms high-risk and aggressive describe a cancer of high tumour grade (according to the Gleason scale, >=<NUM>) and a highly likelihood of metastasising.

In the specification, the term "gBlock®" should be understood to mean a doublestranded DNA molecule of <NUM>-<NUM> bp in length. In this instance, the gBlock® is defined by SEQ ID NO. <NUM> and contains sequences for (A) an internal control ACTB, and the genes (B) GSTP1 (C), SFRP2, (D) IGFBP3, (E) IGFBP7, (F) APC and (G) PTGS2. The gBock® defined by SEQ ID NO: <NUM> was designed to contain the nucleotide sequences of bisulfite converted fully methylated internal control sequence (ACTB) and seven DNA regulatory sequences (LXN, MAGPIE-1B, DNAH10, ZMIZ1, CENPV and OR2L13). The gBlock® defined by SEQ ID NO. <NUM> was designed to contain the nucleotide sequences of bisulfite converted fully methylated internal control sequence (ACTB) and four DNA regulatory sequences (MTMR8, F3, CDH8 and GALNTL6).

Some of the uses of the invention include:.

Some of the advantages of the invention is to:.

The invention will be more clearly understood from the following description of an embodiment thereof, given by way of example only, with reference to the accompanying drawings, in which:-.

Logistic regression is a standard method for modelling the relationship between a binary variable, in this case high-risk versus low-risk prostate cancer, and a set of continuous or categorical variables. For this analysis, the variables used for prediction consist of gene methylation values, as well as patient variables age and PSA. Mathematically this relationship is expressed as <MAT> where pi is the probability that the nth patient is high-risk based on their methylation profile and clinical characteristics, which are represented by the Xmi's. The βm coefficients give the effect that each incremental change in methylation, age or PSA has on the log-odds of the patient being high risk of prostate metastasis.

Due to the cost of collection of biomarkers, and the general principle that simpler models lead to more robust predictions, one aim of the analysis is to choose the smallest number of predictor variables, the Xm's, which will yield the best performing prediction model.

A LASSO<NUM> logistic regression (discussed below), along with a standard logistic regression incorporating six genes were fit to the data. Logistic regression models for each of the separate genes were also fitted for comparison. All models were trained using repeated <NUM>-fold cross-validation with bootstrap resampling. The optimal cut-off value for prediction was then chosen using the entire data set.

A common problem with building a prediction model on the entire dataset is that the model will tend to over fit the current data set and will then underperform when new data is predicted from the model. In general, more complex models will tend to adapt to the training data and will not generalise as well to new data. Therefore sparser models are preferred.

In an ideal case a model is fitted to some training data and then its performance is estimated on an independent test set. A model can be selected by choosing the model that performs best on the test dataset which consists of new unseen observations. For small datasets, a single split of the data into testing and training sets is often not possible.

Cross-validation is a method for performing multiple random training-test splits of a dataset. The process is as follows:.

This method gives a more accurate assessment of the out-of-sample prediction performance of the models than simply fitting the model to the entire dataset. To further account for uncertainty in the cross-validation process, the dataset is bootstrapped, i.e. resample the entire dataset with replacement, and perform cross-validation on each bootstrap iteration. For the analysis here, K= <NUM> and <NUM> bootstrap iterations were used.

All of the data is used in the model-building and assessment stages. The model building process used aims to mitigate against any optimistic bias. Due to the relatively small number of high-risk cases, splitting the data is not an efficient option. Ideally a test or hold-out set of data which were not used in the training step should be used on which to test performance. This should be done at a later stage using an independently collected test data set.

The LASSO is a penalised regression method for building prediction models which mitigates against over-fitting on a data set. The LASSO is a method for both model selection and estimation. In standard logistic regression, the model parameters are fitted using iterative maximum likelihood. This estimates the parameters which fit the data the best, and as such can over-fit to the training data. Penalised regression methods add a penalty term to the estimation equation which penalises large values of the coefficients.

This is a form of shrinkage which can yield more robust results. In the case of LASSO, the penalty term shrinks some coefficients to zero, acting as a form of variable selection.

The strength of penalisation is determined by a parameter, λ. The optimal lambda value is found by running a further cross-validation iteration within each iteration of the outer cross-validation loop.

For the final assessment of the LASSO model, models chosen in the resampling and cross-validation iterations were searched and selected as the final model, the most frequently occurring model. The performance assessments and model parameters were then based on the iterations where this model occurred. Regression coefficients were then obtained by averaging over these iterations.

The epiCaPture test (method described herein) is performed on urine or urine cell-sediment. The urine cell-sediment is obtained by centrifugation (<NUM>,000xg for <NUM> minutes) of a first-void urine sample (up to <NUM>) following a digital rectal examination (DRE). The DRE consists of three strokes per lobe of the prostate gland. Enough pressure is applied to the prostate to depress the surface approximately <NUM>, from the base toe the apex and from the lateral to the median line for each lobe. Total nucleic acid is extracted from the cell sediment using a standard silica-membrane based extraction protocol (using a Qiagen total nucleic acid isolation kit, or similar commercially available product). Purified DNA (100ng) is subject to bisulfite conversion (using a Qiagen epitect kit, or similar commercially available product).

Expression of the KLK3 gene is measured by qRT-PCR using a commercially available primer and probe assay (Assay ID Hs. <NUM> available from Integrated DNA Technologies). Positive expression of the KLK3 gene relative to a housekeeper gene (ACTB) indicates the presence of prostate cells in the urine sediment and validity of the urine sample for epiCaPture analysis.

A <NUM> bp synthetic gBlock® DNA sequence (IDT - SEQ ID NO: <NUM>) was designed to contain the nucleotide sequences of bisulfite converted fully methylated internal control sequence (ACTB) and six DNA regulatory sequences (GSTP1, SFRP2, IGFBP3, IGFBP7, APC, PTGS2):
<IMG>.

An <NUM> bp synthetic gBlock® DNA sequence (IDT - SEQ ID NO: <NUM>) was designed to contain the nucleotide sequences of bisulfite converted fully methylated internal control sequence (ACTB) and six DNA regulatory sequences (LXN, MAGPIE-1B, DNAH10, ZMIZ1, CENPV and OR2L13):
<IMG>
<IMG>.

A <NUM> bp synthetic gBlock® DNA sequence (IDT - SEQ ID NO: <NUM>) was designed to contain the nucleotide sequences of bisulfite converted fully methylated internal control sequence (ACTB) and four DNA regulatory sequences (MTMR8, F3, CDH8 and GALNTL6):
<IMG>.

Quantitative methylation specific PCR (qMSP) is performed, as previously described <NUM>-<NUM>. The PCR efficiency of each of the assays (internal control and <NUM> targets) was rigorously evaluated by performing <NUM> independent replicates (each with <NUM> technical replicates) over a <NUM>-log template concentration range (<FIG>). Bisulfite treated DNA is amplified in parallel TaqMan® PCR reactions performed with oligonucleotides specific for each of the target methylated DNA regulatory sequences (SEQ ID NOs <NUM> to <NUM>) and the endogenous control gene ACTB (SEQ ID NO: <NUM>). Samples are considered positively amplified when a comparative threshold cycle (CT) of <<NUM> was detected in at least two out of three replicates. A normalized index of methylation (NIM) was calculated, as previously described<NUM>, to determine the ratio of the normalized amount of methylated target to the normalized amount of ACTB in any given sample, by applying the formula: <MAT>.

Where TARGETsample is the quantity of fully methylated copies of each of the sequences being sampled in any individual sample, TARGETMC is the quantity of fully methylated copies of each of the sequences being sampled in a commercially available fully methylated bisulfite converted human DNA sample (Qiagen product number <NUM>), ACTB sample is the quantity of bisulfite modified templates in any individual sample and ACTB MC is the quantity of bisulfite modified templates in the universally methylated control DNA.

All genomic sequences for GSTP1, SFRP2, IGFBP3, IGFBP7, APC, PTGS-<NUM>, LXN, MAGPIE-1B, DNAH10, ZMIZ1, CENPV, OR2L13, and F3 were obtained from the UCSC Human Genome Browser (http://genome-euro.

Results from the study on <NUM> men (<FIG>) demonstrate that the invention can non-invasively discriminate high-risk (metastatic) PCa from low-risk (less chance of metastasis) disease and benign enlargement of the prostate (AUC=<NUM>). In this cohort, a high score (NIM><NUM>) had <NUM>% specificity for PCa, and greatly outperformed PSA, which yielded a PCa-specificity of only <NUM>%.

For non-invasively distinguishing men who have prostate cancer form those who do not (or more strictly speaking, men with a positive biopsy from men with a negative biopsy), the best combination of biomarkers is GSTP1 used in conjunction with PSA. This is calculated using a LASSO model (Table <NUM>, <FIG>). This achieved a positive predictive value (PPV) of <NUM>%, with a negative predictive value (NPV) of <NUM>%, with a sensitivity and specificity for prostate cancer of <NUM>% and <NUM>%, respectively. The combination of six methylated DNA regulatory sequences (as defined by SEQ ID NOs <NUM> to <NUM>) in the method described herein also performs well at non-invasive detecting prostate cancer: PPV = <NUM>%, NPV = <NUM>%, sensitivity = <NUM>% and specificity = <NUM>%.

However, as stated already, the dilemma for prostate cancer detection is not in the ability to detect the entire spectrum of disease, for which PSA is already adequately doing, but to specifically detect high-risk disease with high likelihood to metastasise. For predicting high-risk prostate cancer according to D'Amico criteria<NUM>, the LASSO, which is the selection method used here, determines that GSTP1 and IGFBP3 are the best fit (Table <NUM>, <FIG>). This combination delivers a PPV <NUM>% of and NPV of <NUM>% for high-risk disease, with a sensitivity and specificity both at <NUM>%. The combination of all <NUM> genes, performs slightly less well, delivering a sensitivity of <NUM>% and a specificity of <NUM>%, for high risk disease. The method described herein (and derivations of it) outperforms current clinical practice (PSA), which in this cohort was found to have a sensitivity of <NUM>% and specificity of only <NUM>% (at the 4ng/ml cut-off) for high-risk disease.

The Gleason grading system is the strongest prognostic indicator for prostate cancer. It is a histological grading system based on the glandular pattern of the tumour. A Gleason score is obtained by the addition of the primary and secondary grades. The presence of Gleason grade <NUM> or higher, or a Gleason score of <NUM> or higher predicts a poor prognosis.

For predicting tumours with a high Gleason score (>=<NUM>), the combination of all <NUM> biomarkers outlined above outperforms all biomarkers assessed individually (Table <NUM>, <FIG>), with a PPV of <NUM>%, a NPV of <NUM>% and a sensitivity and specificity of <NUM>% and <NUM>%, respectively. Combining all six markers with PSA gives some improvement again, with a sensitivity and specificity of <NUM>% and <NUM>%.

The cohort size was increased from <NUM> men to <NUM> men.

epiCaPture was performed on the cohort of <NUM> men, consisting of <NUM> biopsy-positive men and <NUM> biopsy-negative men. The age and PSA characteristics of the cohort are presented in Table <NUM>. Although the biopsy-positive group were significantly older and had a significantly higher median PSA level (<NUM> versus <NUM>), there is considerable overlap in the range of ages and PSA levels for both groups (<FIG>). Indeed, the mean and median PSA levels for the biopsy-negative group are above the 4ng/ml threshold widely used for indicating need for prostate-biopsy. The biopsy-positive cohort were considered in terms of risk-group stratification (according to the D'Amico criteria), which encompasses tumour grade (Gleason score), PSA level and clinical stage) and tumour grade stratification (Table <NUM>, <FIG>).

Each of the <NUM> gene panel was analysed individually in each patient, and a normalised index of methylation (NIM) score was generated for each gene (<FIG>). Different approaches were studied to determine the best performing method to (<NUM>) discriminate biopsy positive from biopsy negative and (<NUM>) selectively detect high-risk and high-grade disease. The performance of individual genes versus different combinations was studied using LASSO and tree mathematical models. In each instance, the performance of an NIM threshold (equations <NUM> to <NUM> below) produced the best performance indices (positive and negative predictive power) (Table <NUM>-<NUM>). The NIM equation normalises for the amount if input bisulfite modified DNA present in the sample and calculates the proportion of the target sequence which is methylated relative to a <NUM>% fully methylated DNA sequence.

NIM threshold for discriminating biopsy positive from biopsy negative was determined as <NUM>: <MAT>.

Data from the <NUM> men show that for the <NUM> gene panel, the NIM threshold for detecting high-risk/high-grade disease was determined as <NUM> across the <NUM> gene panel.

By applying this model (NIM_SUM ><NUM>), epiCaPture has a comparable sensitivity for high-grade prostate cancer (>=Gleason score <NUM>) compared with the predicate test, PSA (Table <NUM>, <FIG>). In this cohort of men, epiCaPture detected <NUM>% of men with high-grade disease, as compared with <NUM>% detected by PSA. The specificity and negative predictive value (Table <NUM>, Table <NUM>) of epiCaPture is far superior to PSA. Almost <NUM>% of men with a negative biopsy tested negative for epiCaPture. Comparably, only <NUM>% of the <NUM> men with a negative biopsy did not have an elevated PSA. This high false-positive rate (<NUM>%) of PSA is the reason why so many men undergo unnecessary biopsy.

Individual genes (targets) varied in their ability to discriminate presence of prostate cancer (biopsy-positive) from absence (biopsy-negative), ranging from a sensitivity of <NUM>% (Gene <NUM>) to <NUM>% (Gene <NUM>) (Table <NUM>). Increasing the number of markers, for example, the best <NUM> or <NUM> or all <NUM>, improved the sensitivity of urinary detection of prostate cancer to <NUM>%, <NUM>% and <NUM>%, respectively.

However, summing the NIM across the gene panel and applying an NIM sum threshold of ><NUM> improved the sensitivity to <NUM>% of men with prostate cancer. The positive and negative predictive values for prostate cancer by applying an NIM sum threshold ><NUM> are <NUM>% and <NUM>%, respectively.

Individual genes (targets) varied in their ability to detect high-risk prostate cancers, ranging from a sensitivity of <NUM>% (Gene <NUM>) to <NUM>% (Gene <NUM>) (Table <NUM>). Increasing the number of markers, for example, the best <NUM> or <NUM> or all <NUM>, does not markedly improve the accuracy of detecting high-risk prostate cancer, over individual markers, which can be attributed to the molecular heterogeneity of prostate cancer.

However, summing the NIM across the panel of best <NUM> or best <NUM> or applying an NIM sum threshold of <NUM> improved the sensitivity to <NUM>% and <NUM>%, respectively. The positive and negative predictive value for high-risk prostate cancer by applying an NIM sum threshold ><NUM> are <NUM>% and <NUM>%, respectively.

Individual genes also varied in their ability to detect high-grade (Gleason score >=<NUM>) prostate cancers, ranging from a sensitivity of <NUM>% (Gene <NUM> and <NUM>) to <NUM>% (Gene <NUM> and <NUM>) (Table <NUM>). Increasing the number of markers, for example, the best <NUM> or <NUM> or all <NUM>, does not markedly improve the accuracy of detecting high-grade prostate cancer, over individual markers, which can be attributed to the molecular heterogeneity of prostate cancer.

However, summing the NIM across the panel of best <NUM> or best <NUM> or applying an NIM sum threshold of <NUM> improved the sensitivity to <NUM>% and <NUM>%, respectively. The positive and negative predictive values for high-grade prostate cancer by applying an NIM sum threshold ><NUM> are <NUM>% and <NUM>%, respectively.

By applying this model (NIM_SUM><NUM>), epiCaPture has a comparable sensitivity for high-grade prostate cancer (>=Gleason score <NUM>) compared with the predicate test, PSA (Table <NUM>, <FIG>). In this cohort of men, epiCaPture detected <NUM>% of men with high-grade disease, as compared with <NUM>% detected by PSA. The specificity and negative predictive value (Table <NUM>, Table <NUM>) of epiCaPture is far superior to PSA. Almost <NUM>% of men with a negative biopsy tested negative for epiCaPture. Comparably, only <NUM>% of the <NUM> men with a negative biopsy did not have an elevated PSA. This high false-positive rate (<NUM>%) of PSA is the reason why so many men undergo unnecessary biopsy.

Quantitative analysis of DNA methylation at six gene loci in prostate tissues and urine samples indicates that high levels of methylation detected in high-risk tumour tissues can be measured in urine as a surrogate. Examples of this are shown for five of the six gene panel, Target <NUM> (GSTP1; <FIG>), Target <NUM> (SFRP2; <FIG>), Target <NUM> (IGFBP3; <FIG>), Target <NUM> (IGFBP7; <FIG>) and Target <NUM> (APC; <FIG>).

Quantitative analysis of DNA methylation at the seven of the remaining ten gene loci that were analysed in prostate tissues indicates that high levels of methylation detected in high-risk tumour tissues can be measured. The genes were analysed on <NUM> independent cohorts of prostate tissue samples and all show consistent patterns of significant methylation in high-risk/aggressive prostate cancer. Examples of this are shown for Target <NUM> (LXN; <FIG>), Target <NUM> (MAGPIE-1B; <FIG>), Target <NUM> (DNAH10; <FIG>), Target <NUM> (ZMIZ1; <FIG>), Target <NUM> (CENPV; <FIG>), Target <NUM> (OR2L13; <FIG>) and Target <NUM> (F3; <FIG>). The details of the three different cohorts used for the study relating to those genes listed above are provided below.

Benign prostate tissue was obtained from radical cystoprostatectomy or trans-urethral resection of the prostate, from men with no clinical or histopathological evidence of prostate cancer. Precursor lesions proliferative inflammatory atrophy (PIA) and high grade prostatic intra-epithelial neoplasia (HGPIN), (HGPIN) and primary tumours (indolent (PCI) and aggressive (PCA)) were all obtained from radical prostatectomy specimens. PCI was defined as Gleason <NUM>, pT2 disease, with a pre-operative PSA <<NUM> ng/ml and no evidence of biochemical or clinical recurrence (<NUM>-year follow-up). PCA was defined as primary Gleason ≥<NUM>, pT3 disease, with evidence of biochemical or clinical recurrence. Metastatic lesions were obtained from visceral metastases (liver and or lymph node), obtained during rapid autopsy. All patient samples were obtained retrospectively with ethical approval granted by the associated institutions: benign (St. James's Hospital (SJH), Ireland; Adelaide and Meath Hospital incorporating the National Children's Hospital (AMNCH), Ireland); PIA (SJH); HGPIN (AMNCH); PCI (SJH; Mater Misericordiae (MM), Ireland; Beaumont Hospital (BH), Ireland); PCA (SJH; MM; BH); PCM (University of Washington, USA).

In each case, H&E slides were reviewed by a consultant pathologist, who identified and marked the relevant target areas. Six serial <NUM> sections were cut from the respective formalin fixed paraffin embedded (FFPE) blocks and mounted onto PEN membrane glass slides (Life Technologies) for laser capture microdissection (LCM). The sixth section was H&E stained and reviewed to ensure a consistent percentage of target cells. LCM was performed to enrich for target epithelia as previously described, using the Arcturus XT system (Life Technologies). DNA and total RNA were isolated from LCM caps (harboring microdissected tissue) in parallel, using the QIAamp DNA micro kit (Qiagen) and RecoverAll Total Nucleic Acids isolation kit (Ambion), respectively.

A retrospective cohort of radical prostatectomy cases was used to validate potentially prognostic differentially methylated regions identified in cohort <NUM>. All patient samples were obtained retrospectively with ethical approval granted by the associated institutions: benign (SJH, AMNCH) and tumor (SJH, MM and BH). Tumor samples were assigned as low-risk (Gleason score <NUM>+<NUM>, pT2; n=<NUM>); significant (Gleason score <NUM>, pT2; n=<NUM>); or high-risk (Gleason score ≥ <NUM>+<NUM>, pT3; n=<NUM>), based on histopathological review of radical prostatectomy specimens. For control purposes, histologically benign prostate tissues (n=<NUM>) were procured from radical prostatectomy or trans-urethral resection of the prostate. Tumor and benign foci were marked by a consultant histopathologist (SPF, BL) and targeted macro-dissection with a scalpel was carried out on four serial <NUM> sections. DNA and total RNA were isolated using the RecoverAll Total Nucleic Acids Isolation kit (Ambion).

In June <NUM>, The Cancer Genome Atlas (TCGA) database was mined for HM450k data for patient specimens corresponding to low-risk (n=<NUM>), significant (n=<NUM>) and high-risk (n=<NUM>) PCa as defined already for cohort <NUM>. Histologically benign HM450k data were also retrieved (n=<NUM>). For each sample, raw *. IDAT files were extracted and processed through an abridged run of RnBeads (including pre-filtering, BMIQ normalization and post-filtering). β-values for probes contained within the <NUM> potentially prognostic DMRs were extracted and a mean DMR β-value was calculated for each sample. Methylation differences between cohorts were assessed using an unpaired T test with Welch's correction. Significance was ascribed as P<<NUM>.

Claim 1:
A method of determining the presence of high-risk prostate cancer in an individual, the method comprising a step of assaying a biological sample obtained from the individual for the presence of methylated regulatory DNA sequences as defined by SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM>, SEQ ID NO <NUM> and SEQ ID NO <NUM> and calculating a normalized index of methylation (NIM) score, wherein the presence of the methylated regulatory DNA sequences as defined by SEQ ID NOs <NUM> to <NUM>, and the calculated NIM score indicates a high-risk prostate cancer.