Author Correction: The evolutionary history of lethal metastatic prostate cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The architecture of clonal expansions in morphologically normal tissue from cancerous and non-cancerous prostates.

BACKGROUND: Up to 80% of cases of prostate cancer present with multifocal independent tumour lesions leading to the concept of a field effect present in the normal prostate predisposing to cancer development. In the present study we applied Whole Genome DNA Sequencing (WGS) to a group of morphologically normal tissue (n = 51), including benign prostatic hyperplasia (BPH) and non-BPH samples, from men with and men without prostate cancer. We assess whether the observed genetic changes in morphologically normal tissue are linked to the development of cancer in the prostate. RESULTS: Single nucleotide variants (P = 7.0 × 10-03, Wilcoxon rank sum test) and small insertions and deletions (indels, P = 8.7 × 10-06) were significantly higher in morphologically normal samples, including BPH, from men with prostate cancer compared to those without. The presence of subclonal expansions under selective pressure, supported by a high level of mutations, were significantly associated with samples from men with prostate cancer (P = 0.035, Fisher exact test). The clonal cell fraction of normal clones was always higher than the proportion of the prostate estimated as epithelial (P = 5.94 × 10-05, paired Wilcoxon signed rank test) which, along with analysis of primary fibroblasts prepared from BPH specimens, suggests a stromal origin. Constructed phylogenies revealed lineages associated with benign tissue that were completely distinct from adjacent tumour clones, but a common lineage between BPH and non-BPH morphologically normal tissues was often observed. Compared to tumours, normal samples have significantly less single nucleotide variants (P = 3.72 × 10-09, paired Wilcoxon signed rank test), have very few rearrangements and a complete lack of copy number alterations. CONCLUSIONS: Cells within regions of morphologically normal tissue (both BPH and non-BPH) can expand under selective pressure by mechanisms that are distinct from those occurring in adjacent cancer, but that are allied to the presence of cancer. Expansions, which are probably stromal in origin, are characterised by lack of recurrent driver mutations, by almost complete absence of structural variants/copy number alterations, and mutational processes similar to malignant tissue. Our findings have implications for treatment (focal therapy) and early detection approaches.

The evolutionary history of lethal metastatic prostate cancer.

Cancers emerge from an ongoing 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 interclonal cooperation between multiple subclones. Here 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 characterized 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.

Appraising the relevance of DNA copy number loss and gain in prostate cancer using whole genome DNA sequence data.

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.

Constitutively active androgen receptor splice variants AR-V3, AR-V7 and AR-V9 are co-expressed in castration-resistant prostate cancer metastases.

BACKGROUND: A significant subset of prostate cancer (PC) patients with a castration-resistant form of the disease (CRPC) show primary resistance to androgen receptor (AR)-targeting drugs developed against CRPC. As one explanation could be the expression of constitutively active androgen receptor splice variants (AR-Vs), our current objectives were to study AR-Vs and other AR aberrations to better understand the emergence of CRPC. METHODS: We analysed specimens from different stages of prostate cancer by next-generation sequencing and immunohistochemistry. RESULTS: AR mutations and copy number variations were detected only in CRPC specimens. Genomic structural rearrangements of AR were observed in 5/30 metastatic CRPC patients, but they were not associated with expression of previously known AR-Vs. The predominant AR-Vs detected were AR-V3, AR-V7 and AR-V9, with the expression levels being significantly higher in CRPC cases compared to prostatectomy samples. Out of 25 CRPC metastases that expressed any AR variant, 17 cases harboured expression of all three of these AR-Vs. AR-V7 protein expression was highly heterogeneous and higher in CRPC compared to hormone-naïve tumours. CONCLUSIONS: AR-V3, AR-V7 and AR-V9 are co-expressed in CRPC metastases highlighting the fact that inhibiting AR function via regions common to all AR-Vs is likely to provide additional benefit to patients with CRPC.

Genomic evolution shapes prostate cancer disease type.

The development of cancer is an evolutionary process involving the sequential acquisition of genetic alterations that disrupt normal biological processes, enabling tumor cells to rapidly proliferate and eventually invade and metastasize to other tissues. We investigated the genomic evolution of prostate cancer through the application of three separate classification methods, each designed to investigate a different aspect of tumor evolution. Integrating the results revealed the existence of two distinct types of prostate cancer that arise from divergent evolutionary trajectories, designated as the Canonical and Alternative evolutionary disease types. We therefore propose the evotype model for prostate cancer evolution wherein Alternative-evotype tumors diverge from those of the Canonical-evotype through the stochastic accumulation of genetic alterations associated with disruptions to androgen receptor DNA binding. Our model unifies many previous molecular observations, providing a powerful new framework to investigate prostate cancer disease progression.

Somatic alterations contributing to metastasis of a castration-resistant prostate cancer.

Metastatic castration-resistant prostate cancer (mCRPC) is a lethal disease, and molecular markers that differentiate indolent from aggressive subtypes are needed. We sequenced the exomes of five metastatic tumors and healthy kidney tissue from an index case with mCRPC to identify lesions associated with disease progression and metastasis. An Ashkenazi Jewish (AJ) germline founder mutation, del185AG in BRCA1, was observed and AJ ancestry was confirmed. Sixty-two somatic variants altered proteins in tumors, including cancer-associated genes, TMPRSS2-ERG, PBRM1, and TET2. The majority (n = 53) of somatic variants were present in all metastases and only a subset (n = 31) was observed in the primary tumor. Integrating tumor next-generation sequencing and DNA copy number showed somatic loss of BRCA1 and TMPRSS2-ERG. We sequenced 19 genes with deleterious mutations in the index case in additional mCRPC samples and detected a frameshift, two somatic missense alterations, tumor loss of heterozygosity, and combinations of germline missense SNPs in TET2. In summary, genetic analysis of metastases from an index case permitted us to infer a chronology for the clonal spread of disease based on sequential accrual of somatic lesions. The role of TET2 in mCRPC deserves additional analysis and may define a subset of metastatic disease.

Genetic markers associated with early cancer-specific mortality following prostatectomy.

BACKGROUND: This study sought to identify novel effectors and markers of localized but potentially life-threatening prostate cancer (PCa), by evaluating chromosomal copy number alterations (CNAs) in tumors from patients who underwent prostatectomy and correlating these with clinicopathologic features and outcome. METHODS: CNAs in tumor DNA samples from 125 patients in the discovery cohort who underwent prostatectomy were assayed with high-resolution Affymetrix 6.0 single-nucleotide polymorphism microarrays and then analyzed using the Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm. RESULTS: The assays revealed 20 significant regions of CNAs, 4 of them novel, and identified the target genes of 4 of the alterations. By univariate analysis, 7 CNAs were significantly associated with early PCa-specific mortality. These included gains of chromosomal regions that contain the genes MYC, ADAR, or TPD52 and losses of sequences that incorporate SERPINB5, USP10, PTEN, or TP53. On multivariate analysis, only the CNAs of PTEN (phosphatase and tensin homolog) and MYC (v-myc myelocytomatosis viral oncogene homolog) contributed additional prognostic information independent of that provided by pathologic stage, Gleason score, and initial prostate-specific antigen level. Patients whose tumors had alterations of both genes had a markedly elevated risk of PCa-specific mortality (odds ratio = 53; 95% CI = 6.92-405, P = 1 × 10(-4)). Analyses of 333 tumors from 3 additional distinct patient cohorts confirmed the relationship between CNAs of PTEN and MYC and lethal PCa. CONCLUSIONS: This study identified new CNAs and genes that likely contribute to the pathogenesis of localized PCa and suggests that patients whose tumors have acquired CNAs of PTEN, MYC, or both have an increased risk of early PCa-specific mortality.

Nonsense-mediated decay microarray analysis identifies mutations of EPHB2 in human prostate cancer.

The identification of tumor-suppressor genes in solid tumors by classical cancer genetics methods is difficult and slow. We combined nonsense-mediated RNA decay microarrays and array-based comparative genomic hybridization for the genome-wide identification of genes with biallelic inactivation involving nonsense mutations and loss of the wild-type allele. This approach enabled us to identify previously unknown mutations in the receptor tyrosine kinase gene EPHB2. The DU 145 prostate cancer cell line, originating from a brain metastasis, carries a truncating mutation of EPHB2 and a deletion of the remaining allele. Additional frameshift, splice site, missense and nonsense mutations are present in clinical prostate cancer samples. Transfection of DU 145 cells, which lack functional EphB2, with wild-type EPHB2 suppresses clonogenic growth. Taken together with studies indicating that EphB2 may have an essential role in cell migration and maintenance of normal tissue architecture, our findings suggest that mutational inactivation of EPHB2 may be important in the progression and metastasis of prostate cancer.

Germline mutations and sequence variants of the macrophage scavenger receptor 1 gene are associated with prostate cancer risk.

Deletions on human chromosome 8p22-23 in prostate cancer cells and linkage studies in families affected with hereditary prostate cancer (HPC) have implicated this region in the development of prostate cancer. The macrophage scavenger receptor 1 gene (MSR1, also known as SR-A) is located at 8p22 and functions in several processes proposed to be relevant to prostate carcinogenesis. Here we report the results of genetic analyses that indicate that mutations in MSR1 may be associated with risk of prostate cancer. Among families affected with HPC, we identified six rare missense mutations and one nonsense mutation in MSR1. A family-based linkage and association test indicated that these mutations co-segregate with prostate cancer (P = 0.0007). In addition, among men of European descent, MSR1 mutations were detected in 4.4% of individuals affected with non-HPC as compared with 0.8% of unaffected men (P = 0.009). Among African American men, these values were 12.5% and 1.8%, respectively (P = 0.01). These results show that MSR1 may be important in susceptibility to prostate cancer in men of both African American and European descent.

Fanconi anemia pathway regulation by FANCI in prostate cancer.

Prostate cancer is one of the leading causes of death among men worldwide, and thus, research on the genetic factors enabling the formation of treatment-resistant cancer cells is crucial for improving patient outcomes. Here, we report a cell line-specific dependence on FANCI and related signaling pathways to counteract the effects of DNA-damaging chemotherapy in prostate cancer. Our results reveal that FANCI depletion results in significant downregulation of Fanconi anemia (FA) pathway members in prostate cancer cells, indicating that FANCI is an important regulator of the FA pathway. Furthermore, we found that FANCI silencing reduces proliferation in p53-expressing prostate cancer cells. This extends the evidence that inactivation of FANCI may convert cancer cells from a resistant state to an eradicable state under the stress of DNA-damaging chemotherapy. Our results also indicate that high expression of FA pathway genes correlates with poorer survival in prostate cancer patients. Moreover, genomic alterations of FA pathway members are prevalent in prostate adenocarcinoma patients; mutation and copy number information for the FA pathway genes in seven patient cohorts (N = 1,732 total tumor samples) reveals that 1,025 (59.2%) tumor samples have an alteration in at least one of the FA pathway genes, suggesting that genomic alteration of the pathway is a prominent feature in patients with the disease.

Identification of long noncoding RNAs with aberrant expression in prostate cancer metastases.

Prostate cancer (PCa) is the second-most common cause of male cancer-related death in western industrialized countries, and the emergence of metastases is a key challenge in the treatment of PCa. Accumulating studies have shown that long noncoding RNAs (lncRNAs) play an important role in the regulation of diverse cellular and molecular processes during the development and progression of cancer. Here, we utilized a unique cohort of castration-resistant prostate cancer metastases (mCRPC) and corresponding localized tumors and RNA sequencing (RNA-seq). First, we showed that patient-to-patient variability accounted for most of the variance in lncRNA expression between the samples, suggesting that genomic alterations in the samples are the main drivers of lncRNA expression in PCa metastasis. Subsequently, we identified 27 lncRNAs with differential expression (DE-lncRNAs) between metastases and corresponding primary tumors, suggesting that they are mCRPC-specific lncRNAs. Analyses of potential regulation by transcription factors (TFs) revealed that approximately half of the DE-lncRNAs have at least one binding site for the androgen receptor in their regulatory regions. In addition, TF enrichment analysis revealed the enrichment of binding sites for PCa-associated TFs, such as FOXA1 and HOXB13, in the regulatory regions of the DE-lncRNAs. In a cohort of prostatectomy-treated prostate tumors, four of the DE-lncRNAs showed association with progression-free time and two of them (lnc-SCFD2-2 and lnc-R3HCC1L-8) were independent prognostic markers. Our study highlights several mCRPC-specific lncRNAs that might be important in the progression of the disease to the metastatic stage and may also serve as potential biomarkers for aggressive PCa.

Cancer origin tracing and timing in two high-risk prostate cancers using multisample whole genome analysis: prospects for personalized medicine.

BACKGROUND: Prostate cancer (PrCa) genomic heterogeneity causes resistance to therapies such as androgen deprivation. Such heterogeneity can be deciphered in the context of evolutionary principles, but current clinical trials do not include evolution as an essential feature. Whether or not analysis of genomic data in an evolutionary context in primary prostate cancer can provide unique added value in the research and clinical domains remains an open question. METHODS: We used novel processing techniques to obtain whole genome data together with 3D anatomic and histomorphologic analysis in two men (GP5 and GP12) with high-risk PrCa undergoing radical prostatectomy. A total of 22 whole genome-sequenced sites (16 primary cancer foci and 6 lymph node metastatic) were analyzed using evolutionary reconstruction tools and spatio-evolutionary models. Probability models were used to trace spatial and chronological origins of the primary tumor and metastases, chart their genetic drivers, and distinguish metastatic and non-metastatic subclones. RESULTS: In patient GP5, CDK12 inactivation was among the first mutations, leading to a PrCa tandem duplicator phenotype and initiating the cancer around age 50, followed by rapid cancer evolution after age 57, and metastasis around age 59, 5 years prior to prostatectomy. In patient GP12, accelerated cancer progression was detected after age 54, and metastasis occurred around age 56, 3 years prior to prostatectomy. Multiple metastasis-originating events were identified in each patient and tracked anatomically. Metastasis from prostate to lymph nodes occurred strictly ipsilaterally in all 12 detected events. In this pilot, metastatic subclone content analysis appears to substantially enhance the identification of key drivers. Evolutionary analysis' potential impact on therapy selection appears positive in these pilot cases. CONCLUSIONS: PrCa evolutionary analysis allows tracking of anatomic site of origin, timing of cancer origin and spread, and distinction of metastatic-capable from non-metastatic subclones. This enables better identification of actionable targets for therapy. If extended to larger cohorts, it appears likely that similar analyses could add substantial biological insight and clinically relevant value.

BACOM: in silico detection of genomic deletion types and correction of normal cell contamination in copy number data.

MOTIVATION: Identification of somatic DNA copy number alterations (CNAs) and significant consensus events (SCEs) in cancer genomes is a main task in discovering potential cancer-driving genes such as oncogenes and tumor suppressors. The recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale with high resolution. However, existing copy number analysis methods are oblivious to normal cell contamination and cannot distinguish between contributions of cancerous and normal cells to the measured copy number signals. This contamination could significantly confound downstream analysis of CNAs and affect the power to detect SCEs in clinical samples. RESULTS: We report here a statistically principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately estimate genomic deletion type and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. We tested the proposed method on two simulated datasets, two prostate cancer datasets and The Cancer Genome Atlas high-grade ovarian dataset, and obtained very promising results supported by the ground truth and biological plausibility. Moreover, based on a large number of comparative simulation studies, the proposed method gives significantly improved power to detect SCEs after in silico correction of normal tissue contamination. We develop a cross-platform open-source Java application that implements the whole pipeline of copy number analysis of heterogeneous cancer tissues including relevant processing steps. We also provide an R interface, bacomR, for running BACOM within the R environment, making it straightforward to include in existing data pipelines. AVAILABILITY: The cross-platform, stand-alone Java application, BACOM, the R interface, bacomR, all source code and the simulation data used in this article are freely available at authors' web site: http://www.cbil.ece.vt.edu/software.htm.

The Movember Global Action Plan 1 (GAP1): Unique Prostate Cancer Tissue Microarray Resource.

BACKGROUND: The need to better understand the molecular underpinnings of the heterogeneous outcomes of patients with prostate cancer is a pressing global problem and a key research priority for Movember. To address this, the Movember Global Action Plan 1 Unique tissue microarray (GAP1-UTMA) project constructed a set of unique and richly annotated tissue microarrays (TMA) from prostate cancer samples obtained from multiple institutions across several global locations. METHODS: Three separate TMA sets were built that differ by purpose and disease state. RESULTS: The intended use of TMA1 (Primary Matched LN) is to validate biomarkers that help determine which clinically localized prostate cancers with associated lymph node metastasis have a high risk of progression to lethal castration-resistant metastatic disease, and to compare molecular properties of high-risk index lesions within the prostate to regional lymph node metastases resected at the time of prostatectomy. TMA2 (Pre vs. Post ADT) was designed to address questions regarding risk of castration-resistant prostate cancer (CRPC) and response to suppression of the androgen receptor/androgen axis, and characterization of the castration-resistant phenotype. TMA3 (CRPC Met Heterogeneity)'s intended use is to assess the heterogeneity of molecular markers across different anatomic sites in lethal prostate cancer metastases. CONCLUSIONS: The GAP1-UTMA project has succeeded in combining a large set of tissue specimens from 501 patients with prostate cancer with rich clinical annotation. IMPACT: This resource is now available to the prostate cancer community as a tool for biomarker validation to address important unanswered clinical questions around disease progression and response to treatment.

Quantitative glycoproteomic analysis of optimal cutting temperature-embedded frozen tissues identifying glycoproteins associated with aggressive prostate cancer.

Prostate cancer is the most common malignancy in men in the United States, and one in seven men with prostate cancer dies of the disease. A major issue of prostate diagnosis is that there is no good method to reliably distinguish aggressive prostate cancer from nonaggressive prostate cancer. This leads to significant unnecessary suffering among prostate cancer patients and massive unnecessary health care expenditures. In this study, we aim to identify glycoproteins associated with aggressive prostate cancer using optimal cutting temperature (OCT)-embedded frozen tissues obtained from patients with known clinical outcome. To eliminate the interference of mass spectrometric analysis by the compounds in OCT and identify extracellular proteins that are likely to serve as biomarkers in body fluids, we employed glycoproteomic analysis using solid-phase extraction of glycopeptides, which allowed the immobilization of glycopeptides to solid support and removal of OCT from sample proteins before releasing the glycopeptides from the solid support for mass spectrometry analysis. Tumor tissues were cryostat microdissected from four cases of aggressive and four cases of nonaggressive prostate tumors, and glycopeptides were isolated and labeled with iTRAQ reagents before the samples were analyzed with LTQ Orbitrap Velos. From the aggressive prostate cancer tissues, we identified the overexpression of three glycoproteins involved in an extracellular matrix remodeling and further examined two glycoproteins, cathepsin L and periostin, using Western blot and immunohistochemistry analyses. This is the first proteomic study to identify proteins potentially associated with aggressive prostate cancer using OCT-embedded frozen tissues. Further study of these proteins will be needed to understand the roles of extracellular matrix proteins in cancer progression and their potential clinical utility in improving diagnosis of aggressive prostate cancer.

Detection and verification of glycosylation patterns of glycoproteins from clinical specimens using lectin microarrays and lectin-based immunosorbent assays.

Aberrant glycosylation is a fundamental characteristic of progression of diseases such as cancer. Therefore, characterization of glycosylation patterns of proteins from disease tissues may identify changes specific to the disease development and improve diagnostic performance. Thus, analysis strategies with sufficient sensitivity for evaluation of glycosylation patterns in clinical specimens are needed. Here, we describe an analytical strategy for detection and verification of glycosylation patterns. It is based on a two-phase platform including a pattern discovery phase to identify the glycosylation changes using high-density lectin microarrays and a verification phase by developing lectin-based immunosorbent assays using the identified lectins. We evaluated the analytical performance of the platform using the glycoprotein standard and found that the lectin microarray could detect specific bindings of glycoprotein to lectins at the nanogram level and the lectin-based immunosorbent assay could be used for verification of protein glycosylation. We then applied the approach to the analysis of glycosylation patterns of two glycoproteins, which are highly expressed in prostate cancer in our prior studies, prostate specific antigen (PSA) and membrane metallo-endopeptidase (MME), from aggressive (AC) and nonaggressive prostate cancer (NAC) tissues. The observed differences in glycosylation patterns of PSA and MME may represent a significant clinical importance and could be used to develop multiplex assays for diagnosis of aggressive prostate cancer.

Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning.

Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the histological images and poses challenges for automated nucleus detection. Here, we studied the effect of histopathological sample fixation on the accuracy of a deep learning based nuclei detection model trained with hematoxylin and eosin stained images. We experimented with training data that includes three methods of fixation; PAXgene, formalin and frozen, and studied the detection accuracy results of various convolutional neural networks. Our results indicate that the variability introduced during sample preparation affects the generalization of a model and should be considered when building accurate and robust nuclei detection algorithms. Our dataset includes over 67 000 annotated nuclei locations from 16 patients and three different sample fixation types. The dataset provides excellent basis for building an accurate and robust nuclei detection model, and combined with unsupervised domain adaptation, the workflow allows generalization to images from unseen domains, including different tissues and images from different labs.