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Background Unsupervised learning methods, such as Hierarchical Cluster Analysis, are commonly used for the analysis of genomic platform data. Unfortunately, such approaches ignore the well-documented heterogeneous composition of prostate cancer samples. Our aim is to use more sophisticated analytical approaches to deconvolute the structure of prostate cancer transcriptome data, providing novel clinically actionable information for this disease. Methods We apply an unsupervised model called Latent Process Decomposition (LPD), which can handle heterogeneity within individual cancer samples, to genome-wide expression data from eight prostate cancer clinical series, including 1,785 malignant samples with the clinical endpoints of PSA failure and metastasis. Results We show that PSA failure is correlated with the level of an expression signature called DESNT (HR = 1.52, 95% CI = [1.36, 1.7], P = 9.0 × 10−14, Cox model), and that patients with a majority DESNT signature have an increased metastatic risk (X2 test, P = 0.0017, and P = 0.0019). In addition, we develop a stratification framework that incorporates DESNT and identifies three novel molecular subtypes of prostate cancer. Conclusions These results highlight the importance of using more complex approaches for the analysis of genomic data, may assist drug targeting, and have allowed the construction of a nomogram combining DESNT with other clinical factors for use in clinical management.

The Prostate Urine Risk (PUR) biomarker is a four-group classifier for predicting outcome in patients prior to biopsy and for men on active surveillance. The four categories correspond to the probabilities of the presence of normal tissue (PUR-1), D’Amico low-risk (PUR-2), intermediate-risk (PUR-3), and high-risk (PUR-4) prostate cancer. In the current study we investigate how the PUR-4 status is linked to Gleason grade, prostate volume, and tumor volume as assessed from biopsy (n = 215) and prostatectomy (n = 9) samples. For biopsy data PUR-4 status alone was linked to Gleason Grade group (GG) (Spearman’s, ρ = 0.58, p &lt, 0.001 trend). To assess the impact of tumor volume each GG was dichotomized into Small and Large volume cancers relative to median volume. For GG1 (Gleason Pattern 3 + 3) cancers volume had no impact on PUR-4 status. In contrast for GG2 (3 + 4) and GG3 (4 + 3) cancers PUR-4 levels increased in large volume cancers with statistical significance observed for GG2 (p = 0.005, Games-Howell). These data indicated that PUR-4 status is linked to the presence of Gleason Pattern 4. To test this observation tumor burden and Gleason Pattern were assessed in nine surgically removed and sectioned prostates allowing reconstruction of 3D maps. PUR-4 was not correlated with Gleason Pattern 3 amount, total tumor volume or prostate size. A strong correlation was observed between amount of Gleason Pattern 4 tumor and PUR-4 signature (r = 0.71, p = 0.034, Pearson’s). These observations shed light on the biological significance of the PUR biomarker and support its use as a non-invasive means of assessing the presence of clinically significant prostate cancer.

Simple Summary Prostate cancer is a disease responsible for a large proportion of all male cancer deaths but there is a high chance that a patient will die with the disease rather than from. Therefore, there is a desperate need for improvements in diagnosing and predicting outcomes for prostate cancer patients to minimise overdiagnosis and overtreatment whilst appropriately treating men with aggressive disease, especially if this can be done without taking an invasive biopsy. In this work we develop a test that predicts whether a patient has prostate cancer and how aggressive the disease is from a urine sample. This model combines the measurement of a protein-marker called EN2 and the levels of 10 genes measured in urine and proves that integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy. Abstract The objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (n = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0–1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85–0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC = 0.84 (95% CI: 0.78–0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

The evolutionary progression from primary to metastatic prostate cancer is largely uncharted, and the implications for liquid biopsy are unexplored. We infer detailed reconstructions of tumor phylogenies in ten prostate cancer patients with fatal disease, and investigate them in conjunction with histopathology and tumor DNA extracted from blood and cerebrospinal fluid. Substantial evolution occurs within the prostate, resulting in branching into multiple spatially intermixed lineages. One dominant lineage emerges that initiates and drives systemic metastasis, where polyclonal seeding between sites is common. Routes to metastasis differ between patients, and likely genetic drivers of metastasis distinguish the metastatic lineage from the lineage that remains confined to the prostate within each patient. Body fluids capture features of the dominant lineage, and subclonal expansions that occur in the metastatic phase are non-uniformly represented. Cerebrospinal fluid analysis reveals lineages not detected in blood-borne DNA, suggesting possible clinical utility., The evolutionary progression from primary to metastatic prostate cancer is largely uncharted, and the implications for liquid biopsy are unexplored. Here, the authors use deep genomic sequencing and histopathological information to trace tumor evolution both within the prostate and during metastasis in ten men.

The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using entirely different approaches indicate genes fundamental for controlling cancer progression. For prostate cancer, we found two sets of signatures that had significant overlaps suggesting important genes (p &lt, 10&minus, 34 for paired overlaps, hypergeometrical test). These overlapping signatures defined a core set of genes linking hormone signalling (HES6-AR), cell cycle progression (Prolaris) and a molecular subgroup of patients (PCS1) derived by Non Negative Matrix Factorization (NNMF) of control pathways, together designated as SIG-HES6. The second set (designated SIG-DESNT) consisted of the DESNT diagnostic signature and a second NNMF signature PCS3. Stratifications using SIG-HES6 (HES6, PCS1, Prolaris) and SIG-DESNT (DESNT) classifiers frequently detected the same individual high-risk cancers, indicating that the underlying mechanisms associated with SIG-HES6 and SIG-DESNT may act together to promote aggressive cancer development. We show that the use of combinations of a SIG-HES6 signature together with DESNT substantially increases the ability to predict poor outcome, and we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways that has implication for therapeutic design.

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 naive 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.

Urine from patients with prostate cancer (PCa) contains gene transcripts that have been used for PCa diagnosis and prognosis. Historically, patient urine samples have been collected after a digital rectal examination of the prostate, which was thought necessary to boost the levels of prostatic secretions in the urine. We herein describe methodology that allows urine to be collected by patients at home and then posted to a laboratory for analysis. RNA yields and quality were comparable to those for post digital rectal examination urine, and there was improved sensitivity for the detection of TMPRSS2:ERG transcripts by RT-PCR. The At-Home collection protocol has opened up the potential to perform large-scale PCa studies without the inconvenience, cost, discomfort and expense of patients having to visit the clinic.

Sphingosine kinases 1 and 2 (SK1 and SK2) are proto-oncogenic isozymes expressed in many human tumors and associated with chemoresistance and poor prognosis. They are well-recognized therapy targets and their inhibition was shown to induce tumor volume reduction and chemosensitization in multiple cancer models. Oncogenic signaling is extremely complex and often cross-regulated. Designing molecular therapies and their combinations requires rational approaches to avoid redundant targeting or developing resistance. In this study, we have performed RNA transcriptome microarray analysis of two breast and two prostate metastatic cancer cell lines treated with siRNAs targeting SK1 or SK2. In prostate cancer cell lines SK1 knockdown (KD) has significantly changed expression of several genes including downregulation of NSUN2, G3BP2 and upregulation of ETS1. SK2 KD also affected expression of multiple genes including downregulation of CAPZA1 NSUN3 and ADPGK and upregulation of VDAC1, IBTK, ETS1, and MKNK2. Similarly, in breast cancer cells SK1 KD led to downregulation of NSUN2, NFATC3, CDK2, and G3BP2 and upregulation of GTF2B, TTC17, and RAB23. SK2 KD in breast cancer cells has decreased expression of ITGAV and CAPZA1 and increased expression of GTF2B and ST13. Gene-set enrichment analysis of known biochemical pathways showed that in prostate and breast cell lines SKs KD have altered multiple pathways. SK1 KD altered chromatin assembly, regulation of G1/S transition and mitosis, Wnt and MAP kinase signaling and cell motility. SK2 KD altered RAS protein signal transduction, regulation of MAP kinase and serine/threonine kinase activity, cell motility, small GTPase mediated signal transduction and phosphatidylinositol 3-kinase (PI3K) signaling. Through genome-wide microarray analysis, we have identified important molecular pathways affected by SK1 and SK2 KD. It appears that while KD of both genes leads to a decrease in individual pro-tumorigenic genes, there is a universal cellular response resulting in upregulation of several known pro-survival and pro-tumorigenic pathways such as MAPK, RAS, and PI3K, which may mediate cancer resistance to anti-SKs therapies. Our data point out to the potential advantage of certain molecular therapy combinations in targeting prostate and breast cancer. Further signaling studies are required to confirm the individual involvement of identified pathways.

Purpose Liquid biopsies that noninvasively detect molecular correlates of aggressive prostate cancer (PCa) could be used to triage patients, reducing the burdens of unnecessary invasive prostate biopsy and enabling early detection of high-risk disease. DNA hypermethylation is among the earliest and most frequent aberrations in PCa. We investigated the accuracy of a six-gene DNA methylation panel (Epigenetic Cancer of the Prostate Test in Urine [epiCaPture]) at detecting PCa, high-grade (Gleason score greater than or equal to 8) and high-risk (D’Amico and Cancer of the Prostate Risk Assessment] PCa from urine. Patients and Methods Prognostic utility of epiCaPture genes was first validated in two independent prostate tissue cohorts. epiCaPture was assessed in a multicenter prospective study of 463 men undergoing prostate biopsy. epiCaPture was performed by quantitative methylation-specific polymerase chain reaction in DNA isolated from prebiopsy urine sediments and evaluated by receiver operating characteristic and decision curves (clinical benefit). The epiCaPture score was developed and validated on a two thirds training set to one third test set. Results Higher methylation of epiCaPture genes was significantly associated with increasing aggressiveness in PCa tissues. In urine, area under the receiver operating characteristic curve was 0.64, 0.86, and 0.83 for detecting PCa, high-grade PCa, and high-risk PCa, respectively. Decision curves revealed a net benefit across relevant threshold probabilities. Independent analysis of two epiCaPture genes in the same clinical cohort provided analytical validation. Parallel epiCaPture analysis in urine and matched biopsy cores showed added value of a liquid biopsy. Conclusion epiCaPture is a urine DNA methylation test for high-risk PCa. Its tumor specificity out-performs that of prostate-specific antigen (greater than 3 ng/mL). Used as an adjunct to prostate-specific antigen, epiCaPture could aid patient stratification to determine need for biopsy.

This work was funded by the Bob Champion Cancer Trust, The Masonic Charitable Foundation successor to The Grand Charity, The King Family, and The University of East Anglia. We acknowledge support from Movember, from Prostate Cancer UK, Callum Barton, and from The Andy Ripley Memorial Fund. We would like to acknowledge the support of the National Institute for Health Research which funds the Cambridge Bio-medical Research Centre, Cambridge UK. Background : A critical problem in the clinical management of prostate cancer is that it is highly heterogeneous. Accurate prediction of individual cancer behaviour is therefore not achievable at the time of diagnosis leading to substantial overtreatment. It remains an enigma that, in contrast to breast cancer, unsupervised analyses of global expression profiles have not currently defined robust categories of prostate cancer with distinct clinical outcomes. Objective: To devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches. Design, setting, and participants: Our analyses are based on the hypothesis that previous attempts to classify prostate cancer have been unsuccessful because individual samples of prostate cancer frequently have heterogeneous compositions. To address this issue, we applied an unsupervised Bayesian procedure called Latent Process Decomposition to four independent prostate cancer transcriptome datasets obtained using samples from prostatectomy patients and containing between 78 and 182 participants. Outcome measurements and statistical analysis: Biochemical failure was assessed using log-rank analysis and Cox regression analysis. Results and limitations: Application of Latent Process Decomposition identified a common process in all four independent datasets examined. Cancers assigned to this process (designated DESNT cancers) are characterized by low expression of a core set of 45 genes, many encoding proteins involved in the cytoskeleton machinery, ion transport, and cell adhesion. For the three datasets with linked prostate-specific antigen failure data following prostatectomy, patients with DESNT cancer exhibited poor outcome relative to other patients (p = 2.65 × 10−5, p = 4.28 × 10−5, and p = 2.98 × 10−8). When these three datasets were combined the independent predictive value of DESNT membership was p = 1.61 × 10−7 compared with p = 1.00 × 10−5 for Gleason sum. A limitation of the study is that only prediction of prostate-specific antigen failure was examined. Conclusions: Our results demonstrate the existence of a novel poor prognosis category of human prostate cancer and will assist in the targeting of therapy, helping avoid treatment-associated morbidity in men with indolent disease. Patient Summary: Prostate cancer, unlike breast cancer, does not have a robust classification framework. We propose that this failure has occurred because prostate cancer samples selected for analysis frequently have heterozygous compositions (individual samples are made up of many different parts that each have different characteristics). Applying a mathematical approach that can overcome this problem we identify a novel poor prognosis category of human prostate cancer called DESNT. Publisher PDF

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials. Postprint

Cancers emerge from an on-going Darwinian evolutionary process, often leading to multiple competing subclones within a single primary tumour1-4. This evolutionary process culminates in the formation of metastases, which is the cause of 90% of cancer-related deaths5. However, despite its clinical importance, little is known about the principles governing the dissemination of cancer cells to distant organs. Although the hypothesis that each metastasis originates from a single tumour cell is generally supported6-8, recent studies using mouse models of cancer demonstrated the existence of polyclonal seeding from and inter-clonal cooperation between multiple subclones9,10. In this study, we sought definitive evidence for the existence of polyclonal seeding in human malignancy and to establish the clonal relationship among different metastases in the context of androgen-deprived metastatic prostate cancer. Using whole genome sequencing, we characterised multiple metastases arising from prostate tumours in ten patients. Integrated analyses of subclonal architecture revealed the patterns of metastatic spread in unprecedented detail. Metastasis-to-metastasis spread was found to be common, either through de novo monoclonal seeding of daughter metastases or, in five cases, through the transfer of multiple tumour clones between metastatic sites. Lesions affecting tumour suppressor genes usually occur as single events, whereas mutations in genes involved in androgen receptor signalling commonly involve multiple, convergent events in different metastases. Our results elucidate in detail the complex patterns of metastatic spread and further our understanding of the development of resistance to androgen deprivation therapy in prostate cancer.

Aims Assessing whether next-generation DNA sequencing (NGS) can be used to screen prostate cancer for multiple gene alterations in men routinely diagnosed with this disease and/or who are entered into clinical trials. Previous studies are limited and have reported only low success rates. Methods We marked areas of cancer on H&E-stained sections from formalin-fixed needle biopsies, and used these as templates to dissect cancer-rich tissue from adjacent unstained sections. DNA was prepared using a Qiagen protocol modified to maximise DNA yield. The DNA was screened simultaneously for mutations in 365 cancer-related genes using an Illumina HiSeq 2000 NGS platform. Results From 63 prostate cancers examined, 59 (94%) of the samples yielded at least 30 ng of DNA, the minimum amount of DNA considered suitable for NGS analysis. Patients in the D9Amico high-risk group yielded an average of 1033 ng, intermediate-risk patients 401 ng, and low-risk patients 97 ng. NGS of eight samples selected from high-risk and intermediate-risk groups gave a median exon read depth of 962 and detected TMPRRS2-ERG fusions, as well as a variety of mutations including those in the SPOP , TP53 , ATM , MEN1 , NBPF10 , NCOR2 , PIK3CB and MAP2K5 (MEK5) genes. Conclusions Using the methods presented here, NGS technologies can be used to screen a high proportion of patients with prostate cancer for mutations in cancer-related genes in tissue samples opening up its general use in the context of clinical trials or routine diagnosis.

A variety of models have been proposed to explain regions of recurrent somatic copy number alteration (SCNA) in human cancer. Our study employs Whole Genome DNA Sequence (WGS) data from tumor samples (n = 103) to comprehensively assess the role of the Knudson two hit genetic model in SCNA generation in prostate cancer. 64 recurrent regions of loss and gain were detected, of which 28 were novel, including regions of loss with more than 15% frequency at Chr4p15.2-p15.1 (15.53%), Chr6q27 (16.50%) and Chr18q12.3 (17.48%). Comprehensive mutation screens of genes, lincRNA encoding sequences, control regions and conserved domains within SCNAs demonstrated that a two-hit genetic model was supported in only a minor proportion of recurrent SCNA losses examined (15/40). We found that recurrent breakpoints and regions of inversion often occur within Knudson model SCNAs, leading to the identification of ZNF292 as a target gene for the deletion at 6q14.3-q15 and NKX3.1 as a two-hit target at 8p21.3-p21.2. The importance of alterations of lincRNA sequences was illustrated by the identification of a novel mutational hotspot at the KCCAT42, FENDRR, CAT1886 and STCAT2 loci at the 16q23.1-q24.3 loss. Our data confirm that the burden of SCNAs is predictive of biochemical recurrence, define nine individual regions that are associated with relapse, and highlight the possible importance of ion channel and G-protein coupled-receptor (GPCR) pathways in cancer development. We concluded that a two-hit genetic model accounts for about one third of SCNA indicating that mechanisms, such haploinsufficiency and epigenetic inactivation, account for the remaining SCNA losses., Author summary Cancer is a genetic disease where changes in DNA cause alterations in the control of cellular systems leading to unchecked growth. Copy number changes, including duplications, amplifications, and deletions, are a common type of DNA change observed in cancer cells but it is not always clear which of the changes are important in driving cancer development. We have examined this class of genetic alteration in prostate cancer by DNA sequencing the whole genome in 103 cancers. 64 recurrent copy number changes were detected, of which 28 were new. For genetic losses our study comprehensively assessed the role of a model called the “Knudson two-hit genetic model” where alterations in both alleles of a gene is required to generate functional alterations. This model was only supported a minor proportion of recurrent deletions (15/40). This observation indicates that other mechanisms, such haploinsufficiency and epigenetic inactivation, may account for the majority of deletions. Our studies highlight several novel changes including those in non-coding lincRNA sequences, the identification ZNF292 as a target gene for a recurrent deletion on chromosome 6, and the common Knudson deletions at the NKX3.1 loci on chromosome 8.

Background: Androgen receptor (AR)-gene amplification, found in 20–30% of castration-resistant prostate cancer (CRPCa) is proposed to develop as a consequence of hormone-deprivation therapy and be a prime cause of treatment failure. Here we investigate AR-gene amplification in cancers before hormone deprivation therapy. Methods: A tissue microarray (TMA) series of 596 hormone-naive prostate cancers (HNPCas) was screened for chromosome X and AR-gene locus-specific copy number alterations using four-colour fluorescence in situ hybridisation. Results: Both high level gain in chromosome X (⩾4 fold; n=4, 0.7%) and locus-specific amplification of the AR-gene (n=6, 1%) were detected at low frequencies in HNPCa TMAs. Fluorescence in situ hybridisation mapping whole sections taken from the original HNPCa specimen blocks demonstrated that AR-gene amplifications exist in small foci of cells (⩽600 nm, ⩽1% of tumour volume). Patients with AR gene-locus-specific copy number gains had poorer prostate cancer-specific survival. Conclusion: Small clonal foci of cancer containing high level gain of the androgen receptor (AR)-gene develop before hormone deprivation therapy. Their small size makes detection by TMA inefficient and suggests a higher prevalence than that reported herein. It is hypothesised that a large proportion of AR-amplified CRPCa could pre-date hormone deprivation therapy and that these patients would potentially benefit from early total androgen ablation.

Background: The natural history of prostate cancer is highly variable and difficult to predict. We report on the prognostic value of phosphatase and tensin homologue (PTEN) loss in a cohort of 675 men with conservatively managed prostate cancer diagnosed by transurethral resection of the prostate. Methods: The PTEN status was assayed by immunohistochemistry (PTEN IHC) and fluorescent in situ hybridisation (PTEN FISH). The primary end point was death from prostate cancer. Results: The PTEN IHC loss was observed in 18% cases. This was significantly associated with prostate cancer death in univariate analysis (hazard ratio (HR)=3.51; 95% CI 2.60–4.73; P=3.1 × 10−14). It was highly predictive of prostate cancer death in the 50% of patients with a low risk score based on Gleason score, PSA, Ki-67 and extent of disease (HR=7.4; 95% CI 2.2–24.6; P=0.012) ), but had no prognostic value in the higher risk patients. The PTEN FISH loss was only weakly associated with PTEN IHC loss (κ=0.5). Both PTEN FISH loss and amplification were univariately predictive of death from prostate cancer, but this was not maintained in the multivariate analyses. Conclusion: In low-risk patients, PTEN IHC loss adds prognostic value to Gleason score, PSA, Ki-67 and extent of disease.

We show protein kinase C-zeta (PKC-ζ) to be a novel predictive biomarker for survival from prostate cancer (P < 0.001). We also confirm that transcription of the PRKC-ζ gene is crucial to the malignant phenotype of human prostate cancer. Following siRNA silencing of PRKC-ζ in PC3-M prostate cancer cells, stable transfectant cell line si-PRKC-ζ-PC3-M(T1-6) is phenotypically nonmalignant in vitro and in vivo. Genome-wide expression analysis identified 373 genes to be differentially expressed in the knockdown cells and 4 key gene networks to be significantly perturbed during phenotype modulation. Functional interconnection between some of the modulated genes is revealed, although these may be within different regulatory pathways, emphasizing the complexity of their mutual interdependence. Genes with altered expression following PRKC-ζ knockdown include HSPB1, RAD51, and ID1 that we have previously described to be critical in prostatic malignancy. Because expression of PRKC-ζ is functionally involved in promoting the malignant phenotype, we propose PKC-ζ as a novel and biologically relevant target for therapeutic intervention in prostate cancer.

BACKGROUND: The discovery of ERG/ETV1 gene rearrangements and PTEN gene loss warrants investigation in a mechanism-based prognostic classification of prostate cancer (PCa). The study objective was to evaluate the potential clinical significance and natural history of different disease categories by combining ERG/ETV1 gene rearrangements and PTEN gene loss status. METHODS: We utilised fluorescence in situ hybridisation (FISH) assays to detect PTEN gene loss and ERG/ETV1 gene rearrangements in 308 conservatively managed PCa patients with survival outcome data. RESULTS: ERG/ETV1 gene rearrangements alone and PTEN gene loss alone both failed to show a link to survival in multivariate analyses. However, there was a strong interaction between ERG/ETV1 gene rearrangements and PTEN gene loss (Po0.001). The largest subgroup of patients (54%), lacking both PTEN gene loss and ERG/ETV1 gene rearrangements comprised a ‘good prognosis’ population exhibiting favourable cancer-specific survival (85.5% alive at 11 years). The presence of PTEN gene loss in the absence of ERG/ETV1 gene rearrangements identified a patient population (6%) with poorer cancer-specific survival that was highly significant (HR ¼4.87, Po0.001 in multivariate analysis, 13.7% survival at 11 years) when compared with the ‘good prognosis’ group. ERG/ETV1 gene rearrangements and PTEN gene loss status should now prospectively be incorporated into a predictive model to establish whether predictive performance is improved. CONCLUSIONS: Our data suggest that FISH studies of PTEN gene loss and ERG/ETV1 gene rearrangements could be pursued for patient stratification, selection and hypothesis-generating subgroup analyses in future PCa clinical trials and potentially in patient management.

OBJECTIVE To integrate the mapping of ERG alterations with the collection of expression microarray (EMA) data, as previous EMA analyses have failed to consider the genetic heterogeneity and complex patterns of ERG alteration frequently found in cancerous prostates. MATERIALS AND METHODS We determined genome-wide expression levels with GeneChip Human Exon 1.0 ST arrays (Affymetrix, Santa Clara, CA, USA) using RNA prepared from 35 specimens of prostate cancer from 28 prostates. RESULTS The expression profiles showed clustering, in unsupervised hierarchical analyses, into two distinct prostate cancer categories, with one group strongly associated with indicators of poor clinical outcome. The two categories are not tightly linked to ERG status. By analysis of the data we identified a subgroup of cancers lacking ERG rearrangements that showed an outlier pattern of SPINK1 mRNA expression. There was a major distinction between ERG rearranged and non-rearranged cancers that involves the levels of expression of genes linked to exposure to beta-oestradiol, and to retinoic acid. CONCLUSIONS Expression profiling of prostate cancer samples containing single patterns of ERG alterations can provide novel insights into the mechanism of prostate cancer development, and support the view that factors other than ERG status are the major determinants of poor clinical outcome.

Prostate cancer is the most frequently diagnosed male cancer, and its clinical outcome is difficult to predict. The disease may involve the inappropriate expression of genes that normally control the proliferation of epithelial cells in the basal layer and their differentiation into luminal cells. Our aim was to identify novel basal cell markers and assess their prognostic and functional significance in prostate cancer. RNA from basal and luminal cells isolated from benign tissue by immunoguided laser-capture microdissection was subjected to expression profiling. We identified 112 and 267 genes defining basal and luminal populations, respectively. The transcription factor TEAD1 and the ubiquitin ligase c-Cbl were identified as novel basal cell markers. Knockdown of either marker using siRNA in prostate cell lines led to decreased cell growth in PC3 and disrupted acinar formation in a 3D culture system of RWPE1. Analyses of prostate cancer tissue microarray staining established that increased protein levels of either marker were associated with decreased patient survival independent of other clinicopathological metrics. These data are consistent with basal features impacting on the development and clinical course of prostate cancers.

© 2015 Nature America, Inc. All rights reserved.Genome-wide DNA sequencing was used to decrypt the phylogeny of multiple samples from distinct areas of cancer and morphologically normal tissue taken from the prostates of three men. Mutations were present at high levels in morphologically normal tissue distant from the cancer, reflecting clonal expansions, and the underlying mutational processes at work in morphologically normal tissue were also at work in cancer. Our observations demonstrate the existence of ongoing abnormal mutational processes, consistent with field effects, underlying carcinogenesis. This mechanism gives rise to extensive branching evolution and cancer clone mixing, as exemplified by the coexistence of multiple cancer lineages harboring distinct ERG fusions within a single cancer nodule. Subsets of mutations were shared either by morphologically normal and malignant tissues or between different ERG lineages, indicating earlier or separate clonal cell expansions. Our observations inform on the origin of multifocal disease and have implications for prostate cancer therapy in individual cases.

The mapping of around 100 single nucleotide polymorphism (SNP) susceptibility variants for prostate cancer represents a remarkable technological achievement driven by large multicentre collaborations and the lower cost of genomic technologies. The high degree of statistical certainty for each SNP locus in duplicate or even triplicate experiments provides a series of waypoints defining a genetic landscape that accounts for an estimated 33% of familial risk. A critical question addressed by Kearns et al [1] in this issue of European Urology is whether variant SNP status has relevance to patient outcome and clinical management beyond simple higher risk of disease incidence. The authors assess the relationship between prostate cancer upgrading in surgical and active surveillance (AS) cohorts and the 23 single SNP variants identified using the Collaborative Oncological Gene-environment Study iCOGS chip [2]. The surgical cohort consisted of 950 patients who were categorised into those who continued to have Gleason score (GS) 6 disease (nonupgraded) and those who had higher-grade disease (GS 7, upgraded) following surgical pathology. The AS cohort consisted of 205 low-risk patients initially diagnosed as GS 6 with PSA

There is increasing evidence that multiple chromosomal rearrangements occur in prostate cancer. PTEN loss is considered to be a key event in prostate carcinogenesis but the mechanisms of loss remain to be fully elucidated. We hypothesised that gross rearrangements may exist that cause disruption of the PTEN gene in the absence of genomic deletion. We therefore designed a novel fluorescence in situ hybridisation (FISH) assay with probes overlying regions 3' and 5' of PTEN and a third probe overlying the gene. We aimed to identify both genomic deletions and gross rearrangements of PTEN that would be overlooked by previously reported single-probe FISH assays. We proceeded to evaluate a tissue microarray with radical prostatectomy and trans-urethral resection of the prostate specimens from 187 patients. We identified PTEN genomic loss in 45/150 (30%) radical prostatectomy patients and 16/37 (43%) trans-urethral resection of the prostate patients. Importantly, our assay detected novel chromosomal alterations in the PTEN gene (characterised by splitting of FISH signals) in 13 tumours (6.9% of all prostate cancers; 21% of PTEN-lost cancers). All PTEN-rearranged tumours had genomic loss at the other allele and had no expression of PTEN by immunohistochemistry. PTEN-rearranged tumours were significantly more likely to have an underlying ERG rearrangement. Our assay differentiated loss of the probe overlying PTEN in isolation or in combination with either one of or both the probes overlying the 3' and 5' regions. This gave an indication of the size of genomic loss and we observed considerable inter-tumoural heterogeneity in the extent of genomic loss in PTEN-lost tumours. In summary, gross rearrangements of the PTEN locus occur in prostate cancer and can be detected by a 'break-apart' FISH assay. This observation could explain the absence of PTEN protein expression in a subgroup of tumours previously classified as having heterozygous genomic loss using single-probe traditional FISH assays.

Active surveillance provides a unique opportunity to study biomarkers of prostate cancer behaviour, although only small volumes of tumor tissue are typically available. We have evaluated a technique for constructing tissue microarrays (TMAs) from needle biopsies for assessing immunohistochemical markers in localized prostate cancer managed by active surveillance. TMAs were constructed from diagnostic prostate biopsies for 60 patients with localized prostatic adenocarcinoma in a prospective cohort study of active surveillance. Radical treatment was recommended for a prostate-specific antigen (PSA) velocity greater than 1 ng ml(-1) per year or adverse histology in repeat biopsies, defined as Gleason score > or =4+3 or >50% of cores involved. Sections from the TMAs were stained with H&E, P63/AMACR and Ki-67. Time to radical treatment was analysed with respect to clinical characteristics and Ki-67 LI. At a median follow up of 36 months, 25/60 (42%) patients had received radical treatment. On univariate analysis, PSA density (P=0.001), Gleason score (P=0.001), clinical T stage (P=0.01), Ki-67 LI (P=0.02) and initial PSA (P=0.04) were associated with time to radical treatment. On multivariate analysis, PSA density (P=0.01), Ki-67 LI (P=0.03) and Gleason score (P=0.04) were independent determinants of progression to radical treatment. TMAs constructed from prostate needle biopsies can be used to assess immunohistochemical markers in localized prostate cancer managed by active surveillance. Ki-67 LI merits further study as a possible biomarker of early prostate cancer behaviour.

BACKGROUND. Recent evidence suggests that prostate stem cells in benign and tumor tissue express the cell surface marker CD133, but these cells have not been well characterized. The aim of our study was to gene expression profile CD133-expressing cells. METHODS. We analyzed CD133-positive (CD133(+)) and -negative (CD133(-)) sub-populations of high-integrin expressing epithelial cells isolated from benign human prostate tissue and hormone-refractory prostate cancer (HRPC). RESULTS. CD133(+) cells freshly isolated from benign prostate tissue exhibited an expression profile characteristic of a putative stem/progenitor cell population, with transcripts involved in biological processes ranging from development and ion homeostasis to cell communication. The profile of CD133(-) cells was consistent with that of a transit amplifying population, suggesting up-regulated proliferation and metabolism. Comparison of benign populations to those from HRPC showed some similarities between CD133(+) profiles but also revealed significant differences that provide a tumor-specific pattern, which included evidence of increased metabolic activity and active proliferation. Subsequently, we demonstrated protein expression of a number of candidate genes in these cell populations and in benign tissue. In a novel observation we also found expression of some of these markers in prostate tumors, including the oligodendrocyte lineage transcription factor OLIG1. CONCLUSIONS. This study provides a unique genome-wide molecular signature of CD133(+) and CD133(-) human prostate epithelial cells. This will provide a valuable resource for prostate stem cell biology research and the identification of novel therapeutic targets for the treatment of prostate cancer.

Cancers emerge from an on-going Darwinian evolutionary process, often leading to multiple competing subclones within a single primary tumour. This evolutionary process culminates in the formation of metastases, which is the cause of 90% of cancer-related deaths. However, despite its clinical importance, little is known about the principles governing the dissemination of cancer cells to distant organs. Although the hypothesis that each metastasis originates from a single tumour cell is generally supported, recent studies using mouse models of cancer demonstrated the existence of polyclonal seeding from and inter-clonal cooperation between multiple subclones. In this study, we sought definitive evidence for the existence of polyclonal seeding in human malignancy and to establish the clonal relationship among different metastases in the context of androgen-deprived metastatic prostate cancer. Using whole genome sequencing, we characterised multiple metastases arising from prostate tumours in ten patients. Integrated analyses of subclonal architecture revealed the patterns of metastatic spread in unprecedented detail. Metastasis-to-metastasis spread was found to be common, either through de novo monoclonal seeding of daughter metastases or, in five cases, through the transfer of multiple tumour clones between metastatic sites. Lesions affecting tumour suppressor genes usually occur as single events, whereas mutations in genes involved in androgen receptor signalling commonly involve multiple, convergent events in different metastases. Our results elucidate in detail the complex patterns of metastatic spread and further our understanding of the development of resistance to androgen deprivation therapy in prostate cancer. Citation Format: Gunes Gundem, Peter Van Loo, Barbara Kremeyer, Ludmil B. Alexandrov, Jose M.C. Tubio, Elli Papaemmanuil, Daniel S. Brewer, Heini Kallio, Gunilla Högnäs, Matti Annala, Kati Kivinummi, Victoria Goody, Calli Latimer, Sarah O'Meara, Kevin J. Dawson, William Isaacs, Michael R. Emmert-Buck, Matti Nykter, Christopher Foster, Zsofia Kote-Jarai, Douglas Easton, Hayley C. Whitaker, David E. Neal, Colin S. Cooper, Rosalind A. Eeles, Tapio Visakorpi, Peter J. Campbell, Ultan McDermott, David C. Wedge, G. S. Bova. The evolutionary history of lethal metastatic prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 956. doi:10.1158/1538-7445.AM2015-956

Background Biomarkers are urgently required to dissect prostate cancer (PrCa) inter-patient disease heterogeneity to improve treatment and accelerate drug development. We analyzed blood mRNA expression arrays to identify metastatic castration resistant PrCa (CRPC) with poorer outcome. Methods Whole blood was collected into PAXgeneTM tubes from CRPC patients and PrCa patients selected for active surveillance (AS). In Stage I (derivation test-set) 69 CRPC patients were used as cases and 31 AS patients as controls; in Stage II (validation-set) 70 CRPC patients were evaluated. Whole blood RNA from patients in Stage I was hybridised to Affymetrix U133plus2 microarrays. Expression profiles were analysed using Bayesian Latent Process Decomposition (LPD) to identify RNA expression profiles associated with CRPC subgroups and prognosis. A reduced gene signature was then derived using Random Forest algorithm and later verified (Stage I) and validated (Stage II) by qRT-PCR studies. All p values were corrected for false discovery rate. Results LPD analyses of the mRNA expression data divided the evaluable patients in stage-I (n = 94) into 4 groups. LPD1 and LPD2 consisted almost entirely of CRPC patients (14/14; 17/18); LPD3 and LDP4 comprised similar proportions of CRPC patients (15/31; 12/21) vs controls (AS patients). LPD1 group patients had features of worse prognosis CRPC and poorer overall survival (OS) than CRPC patients in other LPD groups (p = 0.00007). A 9 gene signature (TERF2IP, TMCC2, SNCA, GABARAPL2, RIOK3, TFDP1, HMBS, SLC4A1, STOM) classified patients into this LPD1 group with a very low misclassification rate (1.2%). After verification of the signature by qRT-PCR, LPD1 membership was associated with worse OS in the derivation CRPC cohort (10.7 vs 25.6 months, p = 0.00001). Importantly, the prognostic value of this signature was confirmed in the validation CRPC cohort, where LPD1 membership was also associated to poor OS (9.2 vs 21.6 months, p = 0.001), and remained an independent prognostic factor in multivariable-analyses for both cohorts. The LPD1 signature was associated with bone marrow disruption (mobilization of early erythroid cells) and decreased immune response. Conclusion Gene expression signatures derived from whole Blood genome profiling identify CRPC patients with very poor outcomes. Disclosure All authors have declared no conflicts of interest.

4577 Background: Prostate cancer (PCa) is the commonest male cancer in the Western world. Its biology and prognosis are heterogeneous and improved tools are needed to predict outcome more reliably....

Background: The discovery of ERG/ETV-1-gene rearrangements and PTEN-gene loss suggests their use in a mechanism-based prognostic classification of prostate cancer (PCa). Purpose: To evaluate the potential clinical significance and natural history of different disease categories by combining ERG/ETV-1-gene rearrangements and PTEN-gene loss status. Methods: We utilized fluorescence in situ hybridization (FISH) assays to detect PTEN-gene loss and ERG/ETV-1-gene rearrangements in 308 conservatively managed PCa patients with survival outcome data. Results: ERG/ETV-1-gene rearrangements alone and PTEN-gene loss alone each failed to show a link to survival in multivariate analyses. However, there was a strong interaction between ERG/ETV-1-gene rearrangements and PTEN-gene loss (p Conclusions: Our data suggest that FISH studies of PTEN-gene loss and ERG/ETV-1-gene rearrangements could be pursued for patient stratification, selection and hypothesis-generating sub-group analyses in future PCa clinical trials and potentially in patient management. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 662.