We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas. We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may impact patient survival. These results suggest a systematic approach to understand the noncoding genome in cancer to advance diagnosis and therapy.

Background: Muscle-invasive bladder cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, but the diversity of their subtype sets impedes their clinical application. Objective: To achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes. Design, setting, and participants: We used 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts. Outcome measurements and statistical analysis: We performed a network-based analysis of six independent MIBC classification systems to identify a consensus set of molecular classes. Association with survival was assessed using multivariable Cox models. Results and limitations: We report the results of an international effort to reach a consensus on MIBC molecular subtypes. We identified a consensus set of six molecular classes: luminal papillary (24%), luminal nonspecified (8%), luminal unstable (15%), stroma-rich (15%), basal/squamous (35%), and neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics, including outcomes. We provide a single-sample classifier that assigns a consensus class label to a tumor sample’s transcriptome. Limitations of the work are retrospective clinical data collection and a lack of complete information regarding patient treatment. Conclusions: This consensus system offers a robust framework that will enable testing and validation of predictive biomarkers in future prospective clinical trials. Patient summary: Bladder cancers are heterogeneous at the molecular level, and scientists have proposed several classifications into sets of molecular classes. While these classifications may be useful to stratify patients for prognosis or response to treatment, a consensus classification would facilitate the clinical use of molecular classes. Conducted by multidisciplinary expert teams in the field, this study proposes such a consensus and provides a tool for applying the consensus classification in the clinical setting.

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The molecular landscape in non-muscle-invasive bladder cancer (NMIBC) is characterized by large biological heterogeneity with variable clinical outcomes. Here, we perform an integrative multi-omics analysis of patients diagnosed with NMIBC (n = 834). Transcriptomic analysis identifies four classes (1, 2a, 2b and 3) reflecting tumor biology and disease aggressiveness. Both transcriptome-based subtyping and the level of chromosomal instability provide independent prognostic value beyond established prognostic clinicopathological parameters. High chromosomal instability, p53-pathway disruption and APOBEC-related mutations are significantly associated with transcriptomic class 2a and poor outcome. RNA-derived immune cell infiltration is associated with chromosomally unstable tumors and enriched in class 2b. Spatial proteomics analysis confirms the higher infiltration of class 2b tumors and demonstrates an association between higher immune cell infiltration and lower recurrence rates. Finally, the independent prognostic value of the transcriptomic classes is documented in 1228 validation samples using a single sample classification tool. The classifier provides a framework for biomarker discovery and for optimizing treatment and surveillance in next-generation clinical trials.

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BackgroundMuscle-invasive bladder cancer (MIBC) is a heterogeneous disease, and gene expression profiling has identified several molecular subtypes with distinct biological and clinicopathological characteristics. While MIBC subtyping has primarily been based on messenger RNA (mRNA), long non-coding RNAs (lncRNAs) may provide additional resolution.MethodsLncRNA expression was quantified from microarray data of a MIBC cohort treated with neoadjuvant chemotherapy (NAC) and radical cystectomy (RC) (n = 223). Unsupervised consensus clustering of highly variant lncRNAs identified a four-cluster solution, which was characterized using a panel of MIBC biomarkers, regulon activity profiles, gene signatures, and survival analysis. The four-cluster solution was confirmed in The Cancer Genome Atlas (TCGA) cohort (n = 405). A single-sample genomic classifier (GC) was trained using ridge-penalized logistic regression and validated in two independent cohorts (n = 255 and n = 94).ResultsNAC and TCGA cohorts both contained an lncRNA cluster (LC3) with favorable prognosis that was enriched with tumors of the luminal-papillary (LP) subtype. In both cohorts, patients with LP tumors in LC3 (LPL-C3) were younger and had organ-confined, node-negative disease. The LPL-C3 tumors had enhanced FGFR3, SHH, and wild-type p53 pathway activity. In the TCGA cohort, LPL-C3 tumors were enriched for FGFR3 mutations and depleted for TP53 and RB1 mutations. A GC trained to identify these LPL-C3 patients showed robust performance in two validation cohorts.ConclusionsUsing lncRNA expression profiles, we identified a biologically distinct subgroup of luminal-papillary MIBC with a favorable prognosis. These data suggest that lncRNAs provide additional information for higher-resolution subtyping, potentially improving precision patient management.Electronic supplementary materialThe online version of this article (10.1186/s13073-019-0669-z) contains supplementary material, which is available to authorized users.

Motivation Transcriptional networks are models that allow the biological state of cells or tumours to be described. Such networks consist of connected regulatory units known as regulons, each comprised of a regulator and its targets. Inferring a transcriptional network can be a helpful initial step in characterizing the different phenotypes within a cohort. While the network itself provides no information on molecular differences between samples, the per-sample state of each regulon, i.e. the regulon activity, can be used for describing subtypes in a cohort. Integrating regulon activities with clinical data and outcomes would extend this characterization of differences between subtypes. Results We describe RTNsurvival, an R/Bioconductor package that calculates regulon activity profiles using transcriptional networks reconstructed by the RTN package, gene expression data, and a two-tailed Gene Set Enrichment Analysis. Given regulon activity profiles across a cohort, RTNsurvival can perform Kaplan-Meier analyses and Cox Proportional Hazards regressions, while also considering confounding variables. The Supplementary Information provides two case studies that use data from breast and liver cancer cohorts and features uni- and multivariate regulon survival analysis. Availability and implementation RTNsurvival is written in the R language, and is available from the Bioconductor project at http://bioconductor.org/packages/RTNsurvival/. Supplementary information Supplementary data are available at Bioinformatics online.