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Clinical staging in patients with muscle invasive bladder cancer misses up to 25% of lymph node metastasis. These patients are at high risk for disease recurrence and improved clinical staging is critical to guide management.Whole transcriptome expression profiles were generated in 199 patients who underwent radical cystectomy and extended pelvic lymph node dissection. The cohort was divided randomly into a discovery set of 133 patients and a validation set of 66. In the discovery set features were identified and modeled in a KNN51 (K-nearest neighbor classifier 51) to predict pathological lymph node metastases. Two previously described bladder cancer gene signatures, including RF15 (15-gene cancer recurrence signature) and LN20 (20-gene lymph node signature), were also modeled in the discovery set for comparison. The AUC and the OR were used to compare the performance of these signatures.In the validation set KNN51 achieved an AUC of 0.82 (range 0.71-0.93) to predict lymph node positive cases. It significantly outperformed RF15 and LN20, which had an AUC of 0.62 (range 0.47-0.76) and 0.46 (range 0.32-0.60), respectively. Only KNN51 showed significant odds of predicting LN metastasis with an OR of 2.65 (range 1.68-4.67) for every 10% increase in score (p0.001). RF15 and LN20 had a nonsignificant OR of 1.21 (range 0.97-1.54) and 1.39 (range 0.52-3.77), respectively.The new KNN51 signature was superior to previously described gene signatures for predicting lymph node metastasis. If validated prospectively in transurethral resection of bladder tumor samples, KNN51 could be used to guide patients at high risk to early multimodal therapy.

Radical prostatectomy (RP) is a primary treatment option for men with intermediate- and high-risk prostate cancer. Although many are effectively cured with local therapy alone, these men are by definition at higher risk of adverse pathologic features and clinical disease recurrence. It has been shown that the Decipher test predicts metastatic progression in cohorts that received adjuvant and salvage therapy following RP.To evaluate the Decipher genomic classifier in a natural history cohort of men at risk who received no additional treatment until the time of metastatic progression.Retrospective case-cohort design for 356 men who underwent RP between 1992 and 2010 at intermediate or high risk and received no additional treatment until the time of metastasis. Participants met the following criteria: (1) Cancer of the Prostate Risk Assessment postsurgical (CAPRA-S) score ≥3; (2) pathologic Gleason score ≥7; and (3) post-RP prostate-specific antigen nadir0.2 ng/ml.The primary endpoint was defined as regional or distant metastases. Time-dependent receiver operating characteristic (ROC) curves, extension of decision curve analysis to survival data, and univariable and multivariable Cox proportional-hazards models were used to measure the discrimination, net benefit, and prognostic potential of genomic and pathologic risk factors. Cumulative incidence curves were constructed using Fine-Gray competing-risks analysis with appropriate weighting of the controls to account for the case-cohort study design.Ninety six patients had unavailable tumor blocks or failed microarray quality control. Decipher scores were then obtained for 260 patients, of whom 99 experienced metastasis. Decipher correlated with increased cumulative incidence of biochemical recurrence, metastasis, and prostate cancer-specific mortality (p0.01). The cumulative incidence of metastasis was 12% and 47% for patients with low and high Decipher scores, respectively, at 10 yr after RP. Decipher was independently prognostic of metastasis in multivariable analysis (hazard ratio 1.26 per 10% increase; p0.01). Decipher had a c-index of 0.76 and increased the c-index of Eggener and CAPRA-S risk models from 0.76 and 0.77 to 0.86 and 0.87, respectively, at 10 yr after RP. Although the cohort was large, the single-center retrospective design is an important limitation.In a patient population that received no adjuvant or salvage therapy after prostatectomy until metastatic progression, higher Decipher scores correlated with clinical events, and inclusion of Decipher scores improved the prognostic performance of validated clinicopathologic risk models. These results confirm the utility already reported for Decipher.The Decipher test improves identification of patients most at risk of metastatic progression and death from prostate cancer after radical prostatectomy.

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.

X-chromosome inactivation results in dosage equivalence between the X chromosome in males and females; however, over 15% of human X-linked genes escape silencing and these genes are enriched on the evolutionarily younger short arm of the X chromosome. The spread of inactivation onto translocated autosomal material allows the study of inactivation without the confounding evolutionary history of the X chromosome. The heterogeneity and reduced extent of silencing on autosomes are evidence for the importance of DNA elements underlying the spread of silencing. We have assessed DNA methylation in six unbalanced X-autosome translocations using the Illumina Infinium HumanMethylation450 array. Two to 42% of translocated autosomal genes showed this mark of silencing, with the highest degree of inactivation observed for trisomic autosomal regions. Generally, the extent of silencing was greatest close to the translocation breakpoint; however, silencing was detected well over 100 kb into the autosomal DNA. Alu elements were found to be enriched at autosomal genes that escaped from inactivation while L1s were enriched at subject genes. In cells without the translocation, there was enrichment of heterochromatic features such as EZH2 and H3K27me3 for those genes that become silenced when translocated, suggesting that underlying chromatin structure predisposes genes towards silencing. Additionally, the analysis of topological domains indicated physical clustering of autosomal genes of common inactivation status. Overall, our analysis indicated a complex interaction between DNA sequence, chromatin features and the three-dimensional structure of the chromosome.

Background An early report on the molecular subtyping of muscle-invasive bladder cancer (MIBC) by gene expression suggested that response to neoadjuvant chemotherapy (NAC) varies by subtype. Objective To investigate the ability of molecular subtypes to predict pathological downstaging and survival after NAC. Design, setting, and participants Whole transcriptome profiling was performed on pre-NAC transurethral resection specimens from 343 patients with MIBC. Samples were classified according to four published molecular subtyping methods. We developed a single-sample genomic subtyping classifier (GSC) to predict consensus subtypes (claudin-low, basal, luminal-infiltrated and luminal) with highest clinical impact in the context of NAC. Overall survival (OS) according to subtype was analyzed and compared with OS in 476 non-NAC cases (published datasets). Intervention Gene expression analysis was used to assign subtypes. Outcome measurements and statistical analysis Receiver-operating characteristics were used to determine the accuracy of GSC. The effect of GSC on survival was estimated by Cox proportional hazard regression models. Results and limitations The models generated subtype calls in expected ratios with high concordance across subtyping methods. GSC was able to predict four consensus molecular subtypes with high accuracy (73%), and clinical significance of the predicted consensus subtypes could be validated in independent NAC and non-NAC datasets. Luminal tumors had the best OS with and without NAC. Claudin-low tumors were associated with poor OS irrespective of treatment regimen. Basal tumors showed the most improvement in OS with NAC compared with surgery alone. The main limitations of our study are its retrospective design and comparison across datasets. Conclusions Molecular subtyping may have an impact on patient benefit to NAC. If validated in additional studies, our results suggest that patients with basal tumors should be prioritized for NAC. We discovered the first single-sample classifier to subtype MIBC, which may be suitable for integration into routine clinical practice. Patient summary Different molecular subtypes can be identified in muscle-invasive bladder cancer. Although cisplatin-based neoadjuvant chemotherapy improves patient outcomes, we identified that the benefit is highest in patients with basal tumors. Our newly discovered classifier can identify these molecular subtypes in a single patient and could be integrated into routine clinical practice after further validation.

Fifteen-year-old adolescents (N = 109) in a longitudinal study of child development were recruited to examine differences in DNA methylation in relation to parent reports of adversity during the adolescents’ infancy and preschool periods. Microarray technology applied to 28,000 cytosine–guanine dinucleotide sites within DNA derived from buccal epithelial cells showed differential methylation among adolescents whose parents reported high levels of stress during their children’s early lives. Maternal stressors in infancy and paternal stressors in the preschool years were most strongly predictive of differential methylation, and the patterning of such epigenetic marks varied by children’s gender. To the authors’ knowledge, this is the first report of prospective associations between adversities in early childhood and the epigenetic conformation of adolescents’ genomic DNA.

Histone H3K9 methylation is required for DNA methylation and silencing of repetitive elements in plants and filamentous fungi. In mammalian cells however, deletion of the H3K9 histone methyltransferases (HMTases) Suv39h1 and Suv39h2 does not affect DNA methylation of the endogenous retrovirus murine leukaemia virus, indicating that H3K9 methylation is dispensable for DNA methylation of retrotransposons, or that a different HMTase is involved. We demonstrate that embryonic stem (ES) cells lacking the H3K9 HMTase G9a show a significant reduction in DNA methylation of retrotransposons, major satellite repeats and densely methylated CpG-rich promoters. Surprisingly, demethylated retrotransposons remain transcriptionally silent in G9a(-/-) cells, and show only a modest decrease in H3K9me2 and no decrease in H3K9me3 or HP1alpha binding, indicating that H3K9 methylation per se is not the relevant trigger for DNA methylation. Indeed, introduction of catalytically inactive G9a transgenes partially 'rescues' the DNA methylation defect observed in G9a(-/-) cells. Taken together, these observations reveal that H3K9me3 and HP1alpha recruitment to retrotransposons occurs independent of DNA methylation in ES cells and that G9a promotes DNA methylation independent of its HMTase activity.

Background Despite salvage radiation therapy (SRT) for recurrent prostate cancer (PCa) after radical prostatectomy (RP), some patients still progress to metastases. Identifying these men would allow them to undergo systemic therapy including testing novel therapies to reduce metastases risk. Objective To test whether the genomic classifier (GC) predicts development of metastatic disease. Design, setting, and participants Retrospective multi-center and multi-ethnic cohort study from two academic centers and one Veterans Affairs Medical Center in the United States involving 170 men receiving SRT for recurrent PCa post-RP. Outcome measurements and statistical analysis Time from SRT to development of metastatic disease tested using Cox regression, survival c-index, and decision curve analysis. Performance of GC was compared to the Cancer of the Prostate Risk Assessment Score and Briganti risk models based on these metrics. Results and limitations With a median 5.7 yr follow-up after SRT, 20 patients (12%) developed metastases. On multivariable analysis, for each 0.1 unit increase in GC (scaled from 0 to 1), the hazard ratio for metastasis was 1.58 (95% confidence interval 1.16–2.17; p =0.002). Adjusting for androgen deprivation therapy did not materially change the results. The c-index for GC was 0.85 (95% confidence interval 0.73–0.88) versus 0.63–0.65 for published clinico-pathologic risk models. The 5-yr cumulative incidence of metastasis post-SRT in patients with low, intermediate, and high GC scores was 2.7%, 8.4%, and 33.1%, respectively ( p Conclusions While validation in larger, prospectively collected cohorts is required, these data suggest GC is a strong predictor of metastases among men receiving SRT for recurrent PCa post-RP, accurately identifying men who are excellent candidates for systemic therapy due to their very high-risk of metastases. Patient summary Genomic classifier and two clinico-pathologic risk models were evaluated on their ability to predict metastases among men receiving salvage radiation therapy for recurrent prostate cancer. Genomic classifier was able to identify candidates for further therapies due to their very high-risk of metastases.

Of the 386,300 urinary bladder cancer cases diagnosed annually worldwide, nearly 30% present with disease invading the muscle layer of the bladder (1). Although radical cystectomy can improve cancer-specific outcomes, long-term prognosis continues to be compromised by the high risk for recurrence, which occurs in 40% to 50% of patients (2,3). Postcystectomy recurrence of urothelial carcinoma of the bladder (UCB) is ultimately fatal in 85% to 95% of patients (4,5). Contemporary adjuvant chemotherapy shows modest success in delaying or preventing UCB recurrence and is associated with substantial toxicity (6). Identification of candidates at high risk for recurrence who may benefit most from adjuvant chemotherapy is currently based on standard clinicopathologic criteria (7). Even when combined as multivariable nomograms, these metrics do not fully account for the diverse clinical behavior of muscle-invasive UCB (8,9). It is now recognized that biomarker panels that reflect the biological heterogeneity of UCB can better identify patients who need aggressive therapy than single molecular markers alone (10,11). Unbiased and pathway-specific approaches have been previously used to identify prognostic molecular signatures in UCB (12–19). However, such panels have not been implemented clinically because of several shortcomings: signature development on underpowered and clinically heterogeneous cohorts thereby potentially resulting in data overfitting, short follow-up and use of non-disease-specific endpoints, reliance on fresh tumor tissues, and limited validation. To address the clinical need for accurate and reproducible identification of patients with aggressive disease postcystectomy, we performed unbiased transcriptome-wide expression profiling on a cohort of uniformly-treated patients with muscle-invasive and/or pathologically node-positive (hereafter referred to as clinically high-risk) UCB, the largest such effort in this disease stage to date. This resulted in the discovery of a 15-marker genomic signature that robustly identifies patients at greatest risk for recurrence. The performance of this signature was improved by addition of clinicopathologic variables. The prognostic potential of the locked signature was confirmed by blinded independent validation, and was shown to outperform previously reported gene signatures.

INTRODUCTION AND OBJECTIVES: Nearly half of muscleinvasive bladder cancer patients succumb to their disease following cystectomy. Selecting appropriate candidates for adjuvant therapy is currently based on clinical parameters with limited predictive power. Here we aimed to develop and validate a genomic-based signature that can better identify patients at high risk of recurrence following radical cystectomy. METHODS: A set of neoadjuvant chemotherapy-naive patients who underwent radical cystectomy for muscle-invasive urothelial carcinoma of the bladder from 1998 to 2004 was used as the study population. Whole transcriptomes of archival tumors were profiled using 1.4 million-feature oligonucleotide microarrays. A discovery cohort (n 1⁄4 133) was used to develop a genomic classifier (GC) for predicting recurrence. A multivariable classifier based on typical clinical covariates (CC) was also developed for the same endpoint. Finally, CC and GC were combined into a genomic-clinical classifier (G-CC). Performances of GC, CC and G-CC were compared to a post-cystectomy nomogram from the International Bladder Cancer Nomogram Consortium (IBCNC). Classifier performance was assessed by area under receiver-operating characteristic curves (AUCs) in the discovery cohort and an independent validation cohort (n 1⁄4 66). RESULTS: A 15-gene GC was developed on the discovery cohort (median follow-up, 9.3 years) with AUC 1⁄4 0.88 for predicting recurrence, which was higher than any individual clinical variable, and the IBCNC (AUC 1⁄4 0.73) and CC (AUC 1⁄4 0.81) nomograms. When genomic markers were combined with IBCNC (G-IBCNC) and CC (G-CC), AUCs of the models increased to 0.89 and 0.93, respectively. When the locked classifiers were blindly applied to the validation cohort (median follow-up, 10.8 years), GC retained its superior performance compared to individual clinical variables (AUC 1⁄4 0.77). Further, addition of GC to clinical nomograms improved their performance (IBCNC vs. G-IBCNC, 0.73 vs. 0.82; CC vs. G-CC, 0.78 vs. 0.86). G-CC high-risk patients had elevated recurrence probabilities (p < 0.001), with multivariable analysis indicating that the genomic component was the most significant predictor of recurrence (p 1⁄4 0.005). When compared to prior signatures, GC showed the best performance in validation. CONCLUSIONS: We present a validated G-CC that shows superior performance over clinical models for predicting post-cystectomy cancer recurrence. Such transcriptomic approaches can identify robust biological predictors of post-cystectomy outcomes.

Epigenetics is emerging as an attractive mechanism to explain the persistent genomic embedding of early-life experiences. Tightly linked to chromatin, which packages DNA into chromosomes, epigenetic marks primarily serve to regulate the activity of genes. DNA methylation is the most accessible and characterized component of the many chromatin marks that constitute the epigenome, making it an ideal target for epigenetic studies in human populations. Here, using peripheral blood mononuclear cells collected from a community-based cohort stratified for early-life socioeconomic status, we measured DNA methylation in the promoter regions of more than 14,000 human genes. Using this approach, we broadly assessed and characterized epigenetic variation, identified some of the factors that sculpt the epigenome, and determined its functional relation to gene expression. We found that the leukocyte composition of peripheral blood covaried with patterns of DNA methylation at many sites, as did demographic factors, such as sex, age, and ethnicity. Furthermore, psychosocial factors, such as perceived stress, and cortisol output were associated with DNA methylation, as was early-life socioeconomic status. Interestingly, we determined that DNA methylation was strongly correlated to the ex vivo inflammatory response of peripheral blood mononuclear cells to stimulation with microbial products that engage Toll-like receptors. In contrast, our work found limited effects of DNA methylation marks on the expression of associated genes across individuals, suggesting a more complex relationship than anticipated.

Background Measurement of genome-wide DNA methylation (DNAm) has become an important avenue for investigating potential physiologically-relevant epigenetic changes. Illumina Infinium (Illumina, San Diego, CA, USA) is a commercially available microarray suite used to measure DNAm at many sites throughout the genome. However, it has been suggested that a subset of array probes may give misleading results due to issues related to probe design. To facilitate biologically significant data interpretation, we set out to enhance probe annotation of the newest Infinium array, the HumanMethylation450 BeadChip (450 k), with >485,000 probes covering 99% of Reference Sequence (RefSeq) genes (National Center for Biotechnology Information (NCBI), Bethesda, MD, USA). Annotation that was added or expanded on includes: 1) documented SNPs in the probe target, 2) probe binding specificity, 3) CpG classification of target sites and 4) gene feature classification of target sites. Results Probes with documented SNPs at the target CpG (4.3% of probes) were associated with increased within-tissue variation in DNAm. An example of a probe with a SNP at the target CpG demonstrated how sample genotype can confound the measurement of DNAm. Additionally, 8.6% of probes mapped to multiple locations in silico. Measurements from these non-specific probes likely represent a combination of DNAm from multiple genomic sites. The expanded biological annotation demonstrated that based on DNAm, grouping probes by an alternative high-density and intermediate-density CpG island classification provided a distinctive pattern of DNAm. Finally, variable enrichment for differentially methylated probes was noted across CpG classes and gene feature groups, dependant on the tissues that were compared. Conclusion DNAm arrays offer a high-throughput approach for which careful consideration of probe content should be utilized to better understand the biological processes affected. Probes containing SNPs and non-specific probes may affect the assessment of DNAm using the 450 k array. Additionally, probe classification by CpG enrichment classes and to a lesser extent gene feature groups resulted in distinct patterns of DNAm. Thus, we recommend that compromised probes be removed from analyses and that the genomic context of DNAm is considered in studies deciphering the biological meaning of Illumina 450 k array data.

Polycomb-mediated gene silencing and DNA methylation underlie many epigenetic processes important in normal development as well as in cancer. An interaction between EZH2 of the Polycomb repressive complex 2 (PRC2), which trimethylates lysine 27 on Histone 3 (H3K27me3), and all three DNA methyltransferases (DNMTs) has been reported, implicating a role for PRC2 in directing DNA methylation. Interestingly, however, the majority of H3K27me3 marked genes lack DNA methylation in ES cells, indicating that EZH2 recruitment may not be sufficient to promote DNA methylation. Here, we employed a Gal4DBD/gal4UAS-based system to directly test if EZH2 binding at a defined genomic site is sufficient to promote de novo DNA methylation in a murine erythroleukaemia cell line. Targeting of a Gal4DBD-EZH2 fusion to an intergenic transgene bearing a gal4 binding-site array promoted localized recruitment of Suz12 and Bmi1, subunits of PRC2 and PRC1, respectively, and deposition of H3K27me3. Further analysis of the H3K27me3-marked site revealed the persistence of H3K4me2, a mark inversely correlated with DNA methylation. Strikingly, while Dnmt3a was also recruited in an EZH2-dependent manner, de novo DNA methylation of the transgene was not observed. Thus, while targeting of EZH2 to a specific genomic site is sufficient for recruitment of Dnmt3a, additional events are required for de novo DNA methylation.

Background: Ethnic differences in human DNA methylation have been shown for a number of CpG sites, but the genome-wide patterns and extent of these differences are largely unknown. In addition, whether the genetic control of polymorphic DNA methylation is population-specific has not been investigated. Results: Here we measure DNA methylation near the transcription start sites of over 14, 000 genes in 180 cell lines derived from one African and one European population. We find population-specific patterns of DNA methylation at over a third of all genes. Furthermore, although the methylation at over a thousand CpG sites is heritable, these heritabilities also differ between populations, suggesting extensive divergence in the genetic control of DNA methylation. In support of this, genetic mapping of DNA methylation reveals that most of the population specificity can be explained by divergence in allele frequencies between populations, and that there is little overlap in genetic associations between populations. These population-specific genetic associations are supported by the patterns of DNA methylation in several hundred brain samples, suggesting that they hold in vivo and across tissues. Conclusions: These results suggest that DNA methylation is highly divergent between populations, and that this divergence may be due in large part to a combination of differences in allele frequencies and complex epistasis or gene × environment interactions.