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Supplementary Figure 8 (Relates to Figure 6) - SIK2 regulates a cell cycle network and a ER stress network related to cancer.

Supplementary Figure 2 (Relates to Figure 2) - SIK2 knock-down reduces proliferation and viability of LNCaP-Bic, C4-2 and DuCaP cells.

Supplementary Figure 1 (Relates to Figure 2) - Kinase-dead SIK2 and SIK2 knock-down reduce proliferation of LNCaP cells.

Supplementary Figure 7 (Relates to Figure 6) - Validation of microarray hits.

Supplementary Figure Legend

Supplementary Table S1. qRT-PCR primer sequences. When primer sequences were not from published studies, they were designed using Integrated DNA Technologies software.

Supplementary Figure 4 (Relates to Figure 4) - SIK2 localises at the centrosomes of SKOv3 and LNCaP cells.

Supplementary Figure 6 (Relates to Figure 5) - SIK2 regulates CREB1 activity via the phosphorylation of TORC2 and TORC3 and their sequestration in the cytoplasm.

Supplementary Figure 5. Validation of SIK2, TORC1, TORC2 and TORC3 antibodies by Western-Blot.

Supplementary Figure 3 (Relates to Figure 4) - SIK2 knock-down induces a cell cycle arrest in G1 phase and delays mitotic progression of LNCaP cells.

Pancreatic ductal adenocarcinoma (PDAC) is predicted to become the second leading cause of cancer-related mortality within the next decade, with limited effective treatment options and a dismal long-term prognosis for patients. Genomic profiling has not yet manifested clinical benefits for diagnosis, treatment or prognosis in PDAC, due to the lack of available tissues for sequencing and the confounding effects of low tumour cellularity in many biopsy specimens. Increasing focus is now turning to the use of minimally invasive liquid biopsies to enhance the characterisation of actionable PDAC tumour genomes. Circulating tumour DNA (ctDNA) is the most comprehensively studied liquid biopsy analyte in blood and can provide insight into the molecular profile and biological characteristics of individual PDAC tumours, in real-time and in advance of traditional imaging modalities. This can pave the way for identification of new therapeutic targets, novel risk variants and markers of tumour response, to supplement diagnostic screening and provide enhanced scrutiny in treatment stratification. In the roadmap towards the application of precision medicine for clinical management in PDAC, ctDNA analyses may serve a leading role in streamlining candidate biomarkers for clinical integration. In this review, we highlight recent developments in the use of ctDNA-based liquid biopsies for PDAC and provide new insights into the technical, analytical and biological challenges that must be overcome for this potential to be realised.

Breast cancer affects one in seven women worldwide during their lifetime. Widespread mammographic screening programs and education campaigns allow for early detection of the disease, often during its asymptomatic phase. Current practice in treatment and recurrence monitoring is based primarily on pathological evaluations but can also encompass genomic evaluations, both of which focus on the primary tumor. Although breast cancer is one of the most studied cancers, patients still recur at a rate of up to 15% within the first 10 years post-surgery. Local recurrence was originally attributed to tumor cells contaminating histologically normal (HN) tissues beyond the surgical margin, but advances in technology have allowed for the identification of distinct aberrations that exist in the peri-tumoral tissues themselves. One leading theory to explain this phenomenon is the field cancerization theory. Under this hypothesis, tumors arise from a field of molecularly altered cells that create a permissive environment for malignant evolution, which can occur with or without morphological changes. The traditional histopathology paradigm dictates that molecular alterations are reflected in the tissue phenotype. However, the spectrum of inter-patient variability of normal breast tissue may obfuscate recognition of a cancerized field during routine diagnostics. In this review, we explore the concept of field cancerization focusing on HN peri-tumoral tissues: we present the pathological and molecular features of field cancerization within these tissues and discuss how the use of peri-tumoral tissues can affect research. Our observations suggest that pathological and molecular evaluations could be used synergistically to assess risk and guide the therapeutic management of patients. © 2022 The Authors. The Journal of Pathology published by John WileySons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Background The utility of circulating tumour DNA (ctDNA) for longitudinal tumour monitoring in pancreatic ductal adenocarcinoma (PDAC) has not been explored beyond mutations in the KRAS proto-oncogene. Here, we aimed to characterise and track patient-specific somatic ctDNA variants, to assess longitudinal changes in disease burden and explore the landscape of actionable alterations. Methods We followed 3 patients with resectable disease and 4 patients with unresectable disease, including 4 patients with ≥ 3 serial follow-up samples, of whom 2 were rare long survivors (> 5 years). We performed whole exome sequencing of tumour gDNA and plasma ctDNA (n = 20) collected over a ~ 2-year period from diagnosis through treatment to death or final follow-up. Plasma from 3 chronic pancreatitis cases was used as a comparison for analysis of ctDNA mutations. Results We detected > 55% concordance between somatic mutations in tumour tissues and matched serial plasma. Mutations in ctDNA were detected within known PDAC driver genes (KRAS, TP53, SMAD4, CDKN2A), in addition to patient-specific variants within alternative cancer drivers (NRAS, HRAS, MTOR, ERBB2, EGFR, PBRM1), with a trend towards higher overall mutation loads in advanced disease. ctDNA alterations with potential for therapeutic actionability were identified in all 7 patients, including DNA damage response (DDR) variants co-occurring with hypermutation signatures predictive of response to platinum chemotherapy. Longitudinal tracking in 4 patients with follow-up > 2 years demonstrated that ctDNA mutant allele fractions and clonal trends were consistent with CA19-9 measurements and/or clinically reported disease burden. The estimated prevalence of ‘stem clones’ was highest in an unresectable patient where changes in ctDNA dynamics preceded CA19-9 levels. Longitudinal evolutionary trajectories revealed ongoing subclonal evolution following chemotherapy. Conclusion These results provide proof-of-concept for the use of exome sequencing of serial plasma to characterise patient-specific ctDNA profiles, and demonstrate the sensitivity of ctDNA in monitoring disease burden in PDAC even in unresectable cases without matched tumour genotyping. They reveal the value of tracking clonal evolution in serial ctDNA to monitor treatment response, establishing the potential of applied precision medicine to guide stratified care by identifying and evaluating actionable opportunities for intervention aimed at optimising patient outcomes for an otherwise intractable disease.

Simple Summary There is a tremendous amount of gene expression information available for prostate cancer, but very few tools exist to combine the disparate datasets generated across sample types and technical platforms. We present a method of integrating different types of expression data from different study cohorts to increase analytic power, and improve our understanding of the molecular changes underlying the development and progression of prostate cancer from normal to advanced disease. Using this approach, we identified nine additional disease stage-specific candidate genes with prognostic significance, which were not identified in any one study alone. We have developed a free online tool summarizing our results, and making the complete combined dataset available for further translational research. Abstract Next-generation sequencing of primary tumors is now standard for transcriptomic studies, but microarray-based data still constitute the majority of available information on other clinically valuable samples, including archive material. Using prostate cancer (PC) as a model, we developed a robust analytical framework to integrate data across different technical platforms and disease subtypes to connect distinct disease stages and reveal potentially relevant genes not identifiable from single studies alone. We reconstructed the molecular profile of PC to yield the first comprehensive insight into its development, by tracking changes in mRNA levels from normal prostate to high-grade prostatic intraepithelial neoplasia, and metastatic disease. A total of nine previously unreported stage-specific candidate genes with prognostic significance were also found. Here, we integrate gene expression data from disparate sample types, disease stages and technical platforms into one coherent whole, to give a global view of the expression changes associated with the development and progression of PC from normal tissue through to metastatic disease. Summary and individual data are available online at the Prostate Integrative Expression Database (PIXdb), a user-friendly interface designed for clinicians and laboratory researchers to facilitate translational research.

Widespread mammographic screening programs and improved self-monitoring allow for breast cancer to be detected earlier than ever before. Breast-conserving surgery is a successful treatment for select women. However, up to 40% of women develop local recurrence after surgery despite apparently tumor-free margins. This suggests that morphologically normal breast may harbor early alterations that contribute to increased risk of cancer recurrence. We conducted a comprehensive transcriptomic and proteomic analysis to characterize 57 fresh-frozen tissues from breast cancers and matched histologically normal tissues resected proximal to (p

Castrate‐resistant prostate cancer (CRPC) is poorly characterized and heterogeneous and while the androgen receptor (AR) is of singular importance, other factors such as c‐Myc and the E2F family also play a role in later stage disease. HES6 is a transcription co‐factor associated with stem cell characteristics in neural tissue. Here we show that HES6 is up‐regulated in aggressive human prostate cancer and drives castration‐resistant tumour growth in the absence of ligand binding by enhancing the transcriptional activity of the AR, which is preferentially directed to a regulatory network enriched for transcription factors such as E2F1. In the clinical setting, we have uncovered a HES6‐associated signature that predicts poor outcome in prostate cancer, which can be pharmacologically targeted by inhibition of PLK1 with restoration of sensitivity to castration. We have therefore shown for the first time the critical role of HES6 in the development of CRPC and identified its potential in patient‐specific therapeutic strategies.

Thomas Whitington and colleagues present a detailed characterization of gene regulatory mechanisms underlying prostate cancer susceptibility. Their analysis highlights key mechanisms altered by disease-associated SNPs, including widespread disruption of ternary complexes involving AR, FOXA1 and HOXB13. Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.

Prostate cancer predisposition has been extensively investigated in European populations, but there have been few studies of other ethnic groups. To investigate prostate cancer susceptibility in the under-investigated Chinese population, we performed single-nucleotide polymorphism (SNP) array analysis on a cohort of Chinese cases and controls and then meta-analysis with data from the existing Chinese prostate cancer genome-wide association study (GWAS). Genotyping 211,155 SNPs in 495 cases and 640 controls of Chinese ancestry identified several new suggestive Chinese prostate cancer predisposition loci. However, none of them reached genome-wide significance level either by meta-analysis or replication study. The meta-analysis with the Chinese GWAS data revealed that four 8q24 loci are the main contributors to Chinese prostate cancer risk and the risk alleles from three of them exist at much higher frequencies in Chinese than European populations. We also found that several predisposition loci reported in Western populations have different effect on Chinese men. Therefore, this first extensive single-nucleotide polymorphism study of Chinese prostate cancer in comparison with European population indicates that four loci on 8q24 contribute to a great risk of prostate cancer in a considerable large proportion of Chinese men. Based on those four loci, the top 10% of the population have six- or two-fold prostate cancer risk compared with men of the bottom 10% or median risk respectively, which may facilitate the design of prostate cancer genetic risk screening and prevention in Chinese men. These findings also provide additional insights into the etiology and pathogenesis of prostate cancer.

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

Obtaining access to robust,well-annotated human genomic datasets is an important step in demonstrating the relevance of experimental findings and, often, in generating the hypotheses that led to those experiments being conducted in the first place. We recently published data from the CamCaP Study Group which comprised two cohorts of men with prostate cancer who had undergone prostatectomy in Cambridge, UK and Stockholm, Sweden (Ross-Adams et al., 2015).We considered how we might best share our output with those who wish to interrogate the data with their own ideas, gene lists and clinical questions. Werecognised that finding, down-loading, pre-processing and assimilating any such dataset into a usable format is daunting andmay put off many researchers.We also felt that interrogation tools generated to date (e.g. cBioPortal) lack functionality as they either cover too many organ types, or are limited in the extent, precision and tumour-site specificity of their clinical annotation. We therefore determined to produce an accessible web-based platform that would permit straightforward interrogation of these datasets with individual gene identifiers or gene sets. Furthermore, we decided to include additional ‘publicly-accessible’ human prostate cancer sets in order to increase the number of samples available and provide a degree of validation of any observations made across independent cohorts. We included a number of prominent publicly available sets with both gene expression and copy number data leading to a cohort of almost 500 men (Ross-Adams et al., 2015; Taylor et al., 2010; Grasso et al., 2012). We also included a small landmark series of expression data (Varambally et al., 2005). These studies are summarised in Table 1. We plan to include additional studies in the app as well-annotated datasets become publicly available.

Background Understanding the heterogeneous genotypes and phenotypes of prostate cancer is fundamental to improving the way we treat this disease. As yet, there are no validated descriptions of prostate cancer subgroups derived from integrated genomics linked with clinical outcome. Methods In a study of 482 tumour, benign and germline samples from 259 men with primary prostate cancer, we used integrative analysis of copy number alterations (CNA) and array transcriptomics to identify genomic loci that affect expression levels of mRNA in an expression quantitative trait loci (eQTL) approach, to stratify patients into subgroups that we then associated with future clinical behaviour, and compared with either CNA or transcriptomics alone. Findings We identified five separate patient subgroups with distinct genomic alterations and expression profiles based on 100 discriminating genes in our separate discovery and validation sets of 125 and 103 men. These subgroups were able to consistently predict biochemical relapse (p = 0.0017 and p = 0.016 respectively) and were further validated in a third cohort with long-term follow-up (p = 0.027). We show the relative contributions of gene expression and copy number data on phenotype, and demonstrate the improved power gained from integrative analyses. We confirm alterations in six genes previously associated with prostate cancer (MAP3K7, MELK, RCBTB2, ELAC2, TPD52, ZBTB4), and also identify 94 genes not previously linked to prostate cancer progression that would not have been detected using either transcript or copy number data alone. We confirm a number of previously published molecular changes associated with high risk disease, including MYC amplification, and NKX3-1, RB1 and PTEN deletions, as well as over-expression of PCA3 and AMACR, and loss of MSMB in tumour tissue. A subset of the 100 genes outperforms established clinical predictors of poor prognosis (PSA, Gleason score), as well as previously published gene signatures (p = 0.0001). We further show how our molecular profiles can be used for the early detection of aggressive cases in a clinical setting, and inform treatment decisions. Interpretation For the first time in prostate cancer this study demonstrates the importance of integrated genomic analyses incorporating both benign and tumour tissue data in identifying molecular alterations leading to the generation of robust gene sets that are predictive of clinical outcome in independent patient cohorts., Highlights • Integrated genomic profiling of 259 men with prostate cancer Cambridge discovery cohort and Stockholm validation cohort • 100-feature gene set that reliably differentiates five subgroups (iClusters) of prostate cancer • Prognostic gene signature that predicts relapse-free survival Studies in other cancers have shown the advantage of integrated genomic approaches in stratifying disease (e.g. Curtis et al., 2012, breast cancer) but such approaches have not yet been undertaken in prostate cancer. In this study we conducted a comprehensive integrated analysis of a Cambridge discovery and Stockholm validation cohort to stratify men into 5 molecular clusters of varying clinical risk based on copy number and gene expression profiling of 100 key genes. The study then showed that we could predict disease relapse based on a refined subgroup of this gene set and showed the superiority of this ‘signature’ compared to other available signatures. The study introduces to the prostate cancer research community 259 men who have been profiled with copy number, transcript expression, TMPRSS2:ERG gene fusion status and tissue microarray data.

Summary Background Clinical prognostic groupings for localised prostate cancers are imprecise, with 30–50% of patients recurring after image-guided radiotherapy or radical prostatectomy. We aimed to test combined genomic and microenvironmental indices in prostate cancer to improve risk stratification and complement clinical prognostic factors. Methods We used DNA-based indices alone or in combination with intra-prostatic hypoxia measurements to develop four prognostic indices in 126 low-risk to intermediate-risk patients (Toronto cohort) who will receive image-guided radiotherapy. We validated these indices in two independent cohorts of 154 (Memorial Sloan Kettering Cancer Center cohort [MSKCC] cohort) and 117 (Cambridge cohort) radical prostatectomy specimens from low-risk to high-risk patients. We applied unsupervised and supervised machine learning techniques to the copy-number profiles of 126 pre-image-guided radiotherapy diagnostic biopsies to develop prognostic signatures. Our primary endpoint was the development of a set of prognostic measures capable of stratifying patients for risk of biochemical relapse 5 years after primary treatment. Findings Biochemical relapse was associated with indices of tumour hypoxia, genomic instability, and genomic subtypes based on multivariate analyses. We identified four genomic subtypes for prostate cancer, which had different 5-year biochemical relapse-free survival. Genomic instability is prognostic for relapse in both image-guided radiotherapy (multivariate analysis hazard ratio [HR] 4·5 [95% CI 2·1–9·8]; p=0·00013; area under the receiver operator curve [AUC] 0·70 [95% CI 0·65–0·76]) and radical prostatectomy (4·0 [1·6–9·7]; p=0·0024; AUC 0·57 [0·52–0·61]) patients with prostate cancer, and its effect is magnified by intratumoral hypoxia (3·8 [1·2–12]; p=0·019; AUC 0·67 [0·61–0·73]). A novel 100-loci DNA signature accurately classified treatment outcome in the MSKCC low-risk to intermediate-risk cohort (multivariate analysis HR 6·1 [95% CI 2·0–19]; p=0·0015; AUC 0·74 [95% CI 0·65–0·83]). In the independent MSKCC and Cambridge cohorts, this signature identified low-risk to high-risk patients who were most likely to fail treatment within 18 months (combined cohorts multivariate analysis HR 2·9 [95% CI 1·4–6·0]; p=0·0039; AUC 0·68 [95% CI 0·63–0·73]), and was better at predicting biochemical relapse than 23 previously published RNA signatures. Interpretation This is the first study of cancer outcome to integrate DNA-based and microenvironment-based failure indices to predict patient outcome. Patients exhibiting these aggressive features after biopsy should be entered into treatment intensification trials. Funding Movember Foundation, Prostate Cancer Canada, Ontario Institute for Cancer Research, Canadian Institute for Health Research, NIHR Cambridge Biomedical Research Centre, The University of Cambridge, Cancer Research UK, Cambridge Cancer Charity, Prostate Cancer UK, Hutchison Whampoa Limited, Terry Fox Research Institute, Princess Margaret Cancer Centre Foundation, PMH-Radiation Medicine Program Academic Enrichment Fund, Motorcycle Ride for Dad (Durham), Canadian Cancer Society.

Two independent regions within HNF1B are consistently identified in prostate and ovarian cancer genome-wide association studies (GWAS); their functional roles are unclear. We link prostate cancer (PC) risk SNPs rs11649743 and rs3760511 with elevated HNF1B gene expression and allele-specific epigenetic silencing, and outline a mechanism by which common risk variants could effect functional changes that increase disease risk: functional assays suggest that HNF1B is a pro-differentiation factor that suppresses epithelial-to-mesenchymal transition (EMT) in unmethylated, healthy tissues. This tumor-suppressor activity is lost when HNF1B is silenced by promoter methylation in the progression to PC. Epigenetic inactivation of HNF1B in ovarian cancer also associates with known risk SNPs, with a similar impact on EMT. This represents one of the first comprehensive studies into the pleiotropic role of a GWAS-associated transcription factor across distinct cancer types, and is the first to describe a conserved role for a multi-cancer genetic risk factor.

Localized prostate cancer is clinically heterogeneous, despite clinical risk groups that represent relative prostate cancer-specific mortality. We previously developed a 100-locus DNA classifier capable of substratifying patients at risk of biochemical relapse within clinical risk groups.The 100-locus genomic classifier was refined to 31 functional loci and tested with standard clinical variables for the ability to predict biochemical recurrence (BCR) and metastasis.Four retrospective cohorts of radical prostatectomy specimens from patients with localized disease were pooled, and an additional 102-patient cohort used to measure the 31-locus genomic classifier with the NanoString platform.The genomic classifier scores were tested for their ability to predict BCR (n=563) and metastasis (n=154), and compared with clinical risk stratification schemes.The 31-locus genomic classifier performs similarly to the 100-locus classifier. It identifies patients with elevated BCR rates (hazard ratio=2.73, p0.001) and patients that eventually develop metastasis (hazard ratio=7.79, p0.001). Combining the genomic classifier with standard clinical variables outperforms clinical models. Finally, the 31-locus genomic classifier was implemented using a NanoString assay. The study is limited to retrospective cohorts.The 100-locus and 31-locus genomic classifiers reliably identify a cohort of men with localized disease who have an elevated risk of failure. The NanoString assay will be useful for selecting patients for treatment deescalation or escalation in prospective clinical trials based on clinico-genomic scores from pretreatment biopsies.It is challenging to determine whether tumors confined to the prostate are aggressive, leading to significant undertreatment and overtreatment. We validated a test based on prostate tumor DNA that improves estimations of relapse risk, and that can help guide treatment planning.

Recent studies have identified a major locus for risk for coronary artery disease and myocardial infarction on chromosome 9p21.3. Stroke, in particular, ischemic stroke caused by atherosclerotic disease, shares common mechanisms with myocardial infarction. We investigated whether the 9p21 region contributes to ischemic stroke risk.In an initial screen, 15 single nucleotide polymorphisms (SNPs) covering the critical genetic interval on 9p21 were genotyped in samples from Southern Germany (1,090 cases, 1,244 control subjects) and the United Kingdom (758 cases, 872 control subjects, 3 SNPs). SNPs significantly associated with ischemic stroke or individual stroke subtypes in either of the screening samples were subsequently genotyped in 2,528 additional cases and 2,189 additional control subjects from Europe and North America.Genotyping of the screening samples demonstrated associations between seven SNPs and atherosclerotic stroke (all p0.05). Analysis of the full sample confirmed associations between six SNPs and atherosclerotic stroke in multivariate analyses controlling for demographic variables, coronary artery disease, myocardial infarction, and vascular risk factors (all p0.05). The odds ratios for the lead SNP (rs1537378-C) were similar in the various subsamples with a pooled odds ratio of 1.21 (95% confidence interval, 1.07-1.37) under both fixed- and random-effects models (p = 0.002). The point estimate for the population attributable risk is 20.1% for atherosclerotic stroke.The chromosome 9p21.3 region represents a major risk locus for atherosclerotic stroke. The effect of this locus on stroke appears to be independent of its relation to coronary artery disease and other stroke risk factors. Our findings support a broad role of the 9p21 region in arterial disease.

Prostate cancer is the most common cancer in men, resulting in over 10 000 deaths/year in the UK. Sequencing and copy number analysis of primary tumours has revealed heterogeneity within tumours and an absence of recurrent founder mutations, consistent with non-genetic disease initiating events. Using methylation profiling in a series of multi-focal prostate tumours, we identify promoter methylation of the transcription factor HES5 as an early event in prostate tumourigenesis. We confirm that this epigenetic alteration occurs in 86–97% of cases in two independent prostate cancer cohorts (n=49 and n=39 tumour–normal pairs). Treatment of prostate cancer cells with the demethylating agent 5-aza-2′-deoxycytidine increased HES5 expression and downregulated its transcriptional target HES6, consistent with functional silencing of the HES5 gene in prostate cancer. Finally, we identify and test a transcriptional module involving the AR, ERG, HES1 and HES6 and propose a model for the impact of HES5 silencing on tumourigenesis as a starting point for future functional studies.

Salt-inducible kinase 2 (SIK2) is a multifunctional kinase of the AMPK family that plays a role in CREB1-mediated gene transcription and was recently reported to have therapeutic potential in ovarian cancer. The expression of this kinase was investigated in prostate cancer clinical specimens. Interestingly, auto-antibodies against SIK2 were increased in the plasma of patients with aggressive disease. Examination of SIK2 in prostate cancer cells found that it functions both as a positive regulator of cell-cycle progression and a negative regulator of CREB1 activity. Knockdown of SIK2 inhibited cell growth, delayed cell-cycle progression, induced cell death, and enhanced CREB1 activity. Expression of a kinase-dead mutant of SIK2 also inhibited cell growth, induced cell death, and enhanced CREB1 activity. Treatment with a small-molecule SIK2 inhibitor (ARN-3236), currently in preclinical development, also led to enhanced CREB1 activity in a dose- and time-dependent manner. Because CREB1 is a transcription factor and proto-oncogene, it was posited that the effects of SIK2 on cell proliferation and viability might be mediated by changes in gene expression. To test this, gene expression array profiling was performed and while SIK2 knockdown or overexpression of the kinase-dead mutant affected established CREB1 target genes; the overlap with transcripts regulated by forskolin (FSK), the adenylate cyclase/CREB1 pathway activator, was incomplete. Implications: This study demonstrates that targeting SIK2 genetically or therapeutically will have pleiotropic effects on cell-cycle progression and transcription factor activation, which should be accounted for when characterizing SIK2 inhibitors. Mol Cancer Res; 13(4); 620–35. ©2014 AACR.

Associations between single nucleotide polymorphisms (snps) at 5p15 and multiple cancer types have been reported. we have previously shown evidence for a strong association between prostate cancer (prca) risk and rs2242652 at 5p15, intronic in the telomerase reverse transcriptase (tert) gene that encodes tert. to comprehensively evaluate the association between genetic variation across this region and prca, we performed a fine-mapping analysis by genotyping 134 snps using a custom illumina iselect array or sequenom massarray iplex, followed by imputation of 1094 snps in 22 301 prca cases and 22 320 controls in the practical consortium. multiple stepwise logistic regression analysis identified four signals in the promoter or intronic regions of tert that independently associated with prca risk. gene expression analysis of normal prostate tissue showed evidence that snps within one of these regions also associated with tert expression, providing a potential mechanism for predisposition to disease. © The author 2013. Published by Oxford University Press.

One of the central goals of human genetics is to discover the genes and pathways driving human traits. To date, most of the common risk alleles discovered through genome-wide association studies (GWAS) map to nonprotein-coding regions. Because of our relatively poorer understanding of this part of the genome, the functional consequences of trait-associated variants pose a considerable challenge. To identify the genes through which risk loci act, we hypothesized that the risk variants are regulatory elements. For each of 12 known risk polymorphisms, we evaluated the correlation between risk allele status and transcript abundance for all annotated protein-coding transcripts within a 1-Mb interval. A total of 103 transcripts were evaluated in 662 prostate tissue samples [normal ( n = 407) and tumor ( n = 255)] from 483 individuals [European Americans ( n = 233), Japanese ( n = 127), and African Americans ( n = 123)]. In a pooled analysis, 4 of the 12 risk variants were strongly associated with five transcripts ( NUDT11 , MSMB , NCOA4 , SLC22A3 , and HNF1B ) in histologically normal tissue ( P ≤ 0.001). Although associations were also observed in tumor tissue, they tended to be more attenuated. Previously, we showed that MSMB and NCOA4 participate in prostate cancer pathogenesis. Suppressing the expression of NUDT11 , SLC22A3 , and HNF1B influences cellular phenotypes associated with tumor-related properties in prostate cancer cells. Taken together, the data suggest that these transcripts contribute to prostate cancer pathogenesis.

Background Paraoxonase (PON) has anti-atherogenic activity due to its protective function against low density lipoprotein (LDL) oxidation. Alteration of enzyme activity due to polymorphisms in the PON genes may influence the development of atheroma and thus affect stroke risk. Three PON genes (PON1, PON2 and PON3) have been identifiedand mapped to chromosome 7. Methods We looked at the distribution of paraoxonase polymorphisms and haplotype arrangement in 397 Caucasian ischaemic stroke patients and 405 controls. We investigated 6 different common single nucleotide polymorphisms (SNP) in PON genes; two substitutions in PON1 ["A/G": Gln (Q)/Arg (R)] at codon 192 and ["T/A": Leu (L)/Met (M)] at codon 55, two in PON2 at codon 311 ["G/A": Cys (C)/Ser (S)] and codon 148 ["C/G": Ala (A)/Gly (G)] and two SNPs, both "A" to "G" substitutions, in PON3 – intronic rs2074353, which we designated PON3-1 and [Ala (A)/Ala (A)] at codon 99, designated as PON3-3. Dynamic Allele Specific Hybridisation (DASH) was used as the genotyping assay. Haplotype analysis was performed using both PHASE and EHPLUS programs. Results Genotype and allele frequencies were similar in cases and controls. Lipid profiles were not influenced by PON genotype. Haplotype frequencies for the six loci (PON2-148, PON2-311, PON3-3, PON3-1, PON1-55 and PON1-192) were estimated. Comparison of the two programs showed a significant difference in haplotype arrangements with EHPLUS (p-value = 0.005) but not with PHASE Ver.2 (p-value = 0.12). The 112211 (1 = frequent allele, 2 = rare allele) haplotype arrangement was commoner in cases than controls (p = 0.015), and the 111121 haplotype was commoner in controls (p = 0.006). Conclusion Our study did not identify a role for individual paraoxonase gene polymorphisms in the pathogenesis of ischaemic stroke. Findings of haplotype differences should be confirmed in large scale studies. The importance of using a well-validated haplotype analysis program is also underlined.