Oncogenic Role of THOR, a Conserved Cancer/Testis Long Non-coding RNA.

Large-scale transcriptome sequencing efforts have vastly expanded the catalog of long non-coding RNAs (lncRNAs) with varying evolutionary conservation, lineage expression, and cancer specificity. Here, we functionally characterize a novel ultraconserved lncRNA, THOR (ENSG00000226856), which exhibits expression exclusively in testis and a broad range of human cancers. THOR knockdown and overexpression in multiple cell lines and animal models alters cell or tumor growth supporting an oncogenic role. We discovered a conserved interaction of THOR with IGF2BP1 and show that THOR contributes to the mRNA stabilization activities of IGF2BP1. Notably, transgenic THOR knockout produced fertilization defects in zebrafish and also conferred a resistance to melanoma onset. Likewise, ectopic expression of human THOR in zebrafish accelerated the onset of melanoma. THOR represents a novel class of functionally important cancer/testis lncRNAs whose structure and function have undergone positive evolutionary selection.

Expressed pseudogenes in the transcriptional landscape of human cancers.

Pseudogene transcripts can provide a novel tier of gene regulation through generation of endogenous siRNAs or miRNA-binding sites. Characterization of pseudogene expression, however, has remained confined to anecdotal observations due to analytical challenges posed by the extremely close sequence similarity with their counterpart coding genes. Here, we describe a systematic analysis of pseudogene "transcription" from an RNA-Seq resource of 293 samples, representing 13 cancer and normal tissue types, and observe a surprisingly prevalent, genome-wide expression of pseudogenes that could be categorized as ubiquitously expressed or lineage and/or cancer specific. Further, we explore disease subtype specificity and functions of selected expressed pseudogenes. Taken together, we provide evidence that transcribed pseudogenes are a significant contributor to the transcriptional landscape of cells and are positioned to play significant roles in cellular differentiation and cancer progression, especially in light of the recently described ceRNA networks. Our work provides a transcriptome resource that enables high-throughput analyses of pseudogene expression.

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome.

OBJECTIVE: We sought to comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. SUMMARY BACKGROUND DATA: Small studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intra-pancreatic microbial dysbiosis may drive malignant transformation. METHODS: Pancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n=20; IPMN, n=20; PDAC, n=19) and pancreatic cyst fluid (IPMN, n=30; SCA, n=10; MCN, n=10). Assessment of bacterial DNA by quantitative PCR and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) were analyzed to validate findings. RESULTS: Sequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade dysplasia (LGD) and high-grade dysplasia (HGD) IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in HGD cyst fluid. Decontamination analysis using the CPTAC database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. CONCLUSIONS: Our analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation.

Timing of Adjuvant Immunotherapy in Stage III Melanoma, Does it Matter?

BACKGROUND: The optimal time to initiate adjuvant immune checkpoint inhibitors (ICI) following resection remains undefined. Herein, we investigated the impact of time to adjuvant ICI on survival in patients with stage III melanoma. METHODS: Patients with resected stage III melanoma receiving adjuvant immune therapy were identified within a multi-institutional retrospective cohort. Patients were stratified by time to adjuvant ICI: within 6 weeks, 6-12 weeks, and greater than 12 weeks from surgery. Recurrence-free survival (RFS) was compared among time strata with Kaplan-Meier and Cox proportional hazards methods in the multi-institutional cohort. RESULTS: Altogether, 626 patients were identified within the multi-institutional cohort: 39% of patients initiated adjuvant ICI within 6 weeks, 42.2% within 6-12 weeks, and 18.8% greater than 12 weeks from surgery. In a multivariate Cox model, adjusting for histology, nodal tumor burden, and pathologic stage, we found that increased time to adjuvant ICI was associated with improved RFS. Patients who initiated adjuvant ICI within 6 weeks of surgery had worse RFS. These findings were preserved in a conditional landmark analysis and separate subgroups of patients with (1) new melanoma diagnoses, (2) occult stage III disease, and (3) those receiving anti-PD-1 monotherapy. CONCLUSIONS: Outcomes for patients with stage III melanoma are not compromised when adjuvant ICI is initiated beyond 6 weeks from resection. Additional work is needed to better understand the underlying mechanisms and implications of timing of adjuvant ICI on long-term outcomes.

Multidimensional Immunophenotyping of Intraductal Papillary Mucinous Neoplasms Reveals Novel T Cell and Macrophage Signature.

BACKGROUND: Intraductal papillary mucinous neoplasms (IPMN) are the only radiographically identifiable precursor to pancreatic adenocarcinoma, yet little is known about how these lesions progress to cancer. Inflammation has been associated with dysplastic progression; however, the cause and composition of this inflammation remains poorly characterized. We sought to comprehensively profile immune cell infiltration using parallel spatial transcriptomic and flow cytometric techniques. METHODS: Twelve patients with resected IPMN exhibiting both high-grade dysplasia (HGD) and low-grade dysplasia (LGD) were selected for spatial transcriptomics (NanoString GeoMx). Immune (CD45+), epithelial (PanCK+), and stromal (SMA+) compartments were analyzed separately using the GeoMx NGS Pipeline. An additional 11 patients resected for IPMN of varying degrees of dysplasia underwent immunophenotyping using flow cytometry (DURAClone IM). RESULTS: Spatial transcriptomics revealed that T cells represent the dominant immune cell within IPMN stroma, which was confirmed by flow cytometry (56%). Spatial profiling found that the T-cell infiltrate was significantly higher in regions of LGD compared with HGD (62% vs. 50%, p = 0.038). Macrophages were the only other immune cell type with > 10% abundance, yet conversely, were generally more abundant in regions of HGD compared to LGD (19% vs. 11%, p = 0.058). Correspondingly, immune cells within regions of HGD demonstrated transcriptional upregulation of genes associated with macrophage activity including secretion (CXCL1) and phagocytosis (C1QA, C1S, C4B). CONCLUSIONS: IPMN immune infiltrate is primarily composed of T cells and macrophages. Regions of HGD appear to be relatively deplete of T cells and show a trend toward macrophage enrichment compared with regions of LGD.

The role of the histone H3 variant CENPA in prostate cancer.

Overexpression of centromeric proteins has been identified in a number of human malignancies, but the functional and mechanistic contributions of these proteins to disease progression have not been characterized. The centromeric histone H3 variant centromere protein A (CENPA) is an epigenetic mark that determines centromere identity. Here, using an array of approaches, including RNA-sequencing and ChIP-sequencing analyses, immunohistochemistry-based tissue microarrays, and various cell biology assays, we demonstrate that CENPA is highly overexpressed in prostate cancer in both tissue and cell lines and that the level of CENPA expression correlates with the disease stage in a large cohort of patients. Gain-of-function and loss-of-function experiments confirmed that CENPA promotes prostate cancer cell line growth. The results from the integrated sequencing experiments suggested a previously unidentified function of CENPA as a transcriptional regulator that modulates expression of critical proliferation, cell-cycle, and centromere/kinetochore genes. Taken together, our findings show that CENPA overexpression is crucial to prostate cancer growth.

TACO produces robust multisample transcriptome assemblies from RNA-seq.

Accurate transcript structure and abundance inference from RNA sequencing (RNA-seq) data is foundational for molecular discovery. Here we present TACO, a computational method to reconstruct a consensus transcriptome from multiple RNA-seq data sets. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to improved reconstruction accuracy compared with other tools in its class. The tool is available at http://tacorna.github.io and can be readily incorporated into RNA-seq analysis workflows.

RNA biomarkers associated with metastatic progression in prostate cancer: a multi-institutional high-throughput analysis of SChLAP1.

BACKGROUND: Improved clinical predictors for disease progression are needed for localised prostate cancer, since only a subset of patients develop recurrent or refractory disease after first-line treatment. Therefore, we undertook an unbiased analysis to identify RNA biomarkers associated with metastatic progression after prostatectomy. METHODS: Prostate cancer samples from patients treated with radical prostatectomy at three academic institutions were analysed for gene expression by a high-density Affymetrix GeneChip platform, encompassing more than 1 million genomic loci. In a discovery cohort, all protein-coding genes and known long non-coding RNAs were ranked by fold change in expression between tumours that subsequently metastasised versus those that did not. The top ranked gene was then validated for its prognostic value for metastatic progression in three additional independent cohorts. 95% of the gene expression assays were done in a Clinical Laboratory Improvements Amendments certified laboratory facility. All genes were assessed for their ability to predict metastatic progression by receiver-operating-curve area-under-the-curve analyses. Multivariate analyses were done for the primary endpoint of metastatic progression, with variables including Gleason score, preoperative prostate-specific antigen concentration, seminal vesicle invasion, surgical margin status, extracapsular extension, lymph node invasion, and expression of the highest ranked gene. FINDINGS: 1008 patients were included in the study: 545 in the discovery cohort and 463 in the validation cohorts. The long non-coding RNA SChLAP1 was identified as the highest-ranked overexpressed gene in cancers with metastatic progression. Validation in three independent cohorts confirmed the prognostic value of SChLAP1 for metastatic progression. On multivariate modelling, SChLAP1 expression (high vs low) independently predicted metastasis within 10 years (odds ratio [OR] 2·45, 95% CI 1·70-3·53; p<0·0001). The only other variable that independently predicted metastasis within 10 years was Gleason score (8-10 vs 5-7; OR 2·14, 95% CI 1·77-2·58; p<0·0001). INTERPRETATION: We identified and validated high SChLAP1 expression as significantly prognostic for metastatic disease progression of prostate cancer. Our findings suggest that further development of SChLAP1 as a potential biomarker, for treatment intensification in aggressive prostate cancer, warrants future study. FUNDING: Prostate Cancer Foundation, National Institutes of Health, Department of Defense, Early Detection Research Network, Doris Duke Charitable Foundation, and Howard Hughes Medical Institute.

Deep sequencing reveals distinct patterns of DNA methylation in prostate cancer.

Beginning with precursor lesions, aberrant DNA methylation marks the entire spectrum of prostate cancer progression. We mapped the global DNA methylation patterns in select prostate tissues and cell lines using MethylPlex-next-generation sequencing (M-NGS). Hidden Markov model-based next-generation sequence analysis identified ∼68,000 methylated regions per sample. While global CpG island (CGI) methylation was not differential between benign adjacent and cancer samples, overall promoter CGI methylation significantly increased from ~12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues, respectively (P-value < 2 × 10(-16)). We found distinct patterns of promoter methylation around transcription start sites, where methylation occurred not only on the CGIs, but also on flanking regions and CGI sparse promoters. Among the 6691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer-specific, including numerous novel DMRs. A novel cancer-specific DMR in the WFDC2 promoter showed frequent methylation in cancer (17/22 tissues, 6/6 cell lines), but not in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested an epigenetic mechanism for alternate transcription start site utilization, and these modifications segregated into distinct regions when present on the same promoter. Finally, we observed differences in repeat element methylation, particularly LINE-1, between ERG gene fusion-positive and -negative cancers, and we confirmed this observation using pyrosequencing on a tissue panel. This comprehensive methylome map will further our understanding of epigenetic regulation in prostate cancer progression.

RNASTAR: an RNA STructural Alignment Repository that provides insight into the evolution of natural and artificial RNAs.

Automated RNA alignment algorithms often fail to recapture the essential conserved sites that are critical for function. To assist in the refinement of these algorithms, we manually curated a set of 148 alignments with a total of 9600 unique sequences, in which each alignment was backed by at least one crystal or NMR structure. These alignments included both naturally and artificially selected molecules. We used principles of isostericity to improve the alignments from an average of 83%-94% isosteric base pairs. We expect that this alignment collection will assist in a wide range of benchmarking efforts and provide new insight into evolutionary principles governing change in RNA structural motifs. The improved alignments have been contributed to the Rfam database.

Digital spatial profiling of intraductal papillary mucinous neoplasms: Toward a molecular framework for risk stratification.

The histopathologic heterogeneity of intraductal papillary mucinous neoplasms (IPMN) complicates the prediction of pancreatic ductal adenocarcinoma (PDAC) risk. Intratumoral regions of pancreaticobiliary (PB), intestinal (INT), and gastric foveolar (GF) epithelium may occur with either low-grade dysplasia (LGD) or high-grade dysplasia (HGD). We used digital spatial RNA profiling of dysplastic epithelium (83 regions) from surgically resected IPMN tissues (12 patients) to differentiate subtypes and predict genes associated with malignancy. The expression patterns of PB and GF lesions diverged from INT, suggesting that PB and GF arise from a common lineage. Transcriptional dysregulation within PB lesions mirrored that of PDAC, whereas INT and GF foci did not. Tumor necrosis factor/nuclear factor κB (TNF-NFκB) and cell cycle (cycling S and cycling G2-M) programs occurred with relative prominence in PB and INT subtypes, respectively. Together, this study delineates markers of high-risk IPMN and insights into malignant progression.

Oculus: faster sequence alignment by streaming read compression.

BACKGROUND: Despite significant advancement in alignment algorithms, the exponential growth of nucleotide sequencing throughput threatens to outpace bioinformatic analysis. Computation may become the bottleneck of genome analysis if growing alignment costs are not mitigated by further improvement in algorithms. Much gain has been gleaned from indexing and compressing alignment databases, but many widely used alignment tools process input reads sequentially and are oblivious to any underlying redundancy in the reads themselves. RESULTS: Here we present Oculus, a software package that attaches to standard aligners and exploits read redundancy by performing streaming compression, alignment, and decompression of input sequences. This nearly lossless process (> 99.9%) led to alignment speedups of up to 270% across a variety of data sets, while requiring a modest amount of memory. We expect that streaming read compressors such as Oculus could become a standard addition to existing RNA-Seq and ChIP-Seq alignment pipelines, and potentially other applications in the future as throughput increases. CONCLUSIONS: Oculus efficiently condenses redundant input reads and wraps existing aligners to provide nearly identical SAM output in a fraction of the aligner runtime. It includes a number of useful features, such as tunable performance and fidelity options, compatibility with FASTA or FASTQ files, and adherence to the SAM format. The platform-independent C++ source code is freely available online, at http://code.google.com/p/oculus-bio.

Nucleotides that are essential but not conserved; a sufficient L-tryptophan site in RNA.

Conservation is often used to define essential sequences within RNA sites. However, conservation finds only invariant sequence elements that are necessary for function, rather than finding a set of sequence elements sufficient for function. Biochemical studies in several systems-including the hammerhead ribozyme and the purine riboswitch-find additional elements, such as loop-loop interactions, required for function yet not phylogenetically conserved. Here we define a critical test of sufficiency: We embed a minimal, apparently sufficient motif for binding the amino acid tryptophan in a random-sequence background and ask whether we obtain functional molecules. After a negative result, we use a combination of three-dimensional structural modeling, selection, designed mutations, high-throughput sequencing, and bioinformatics to explore functional insufficiency. This reveals an essential unpaired G in a diverse structural context, varied sequence, and flexible distance from the invariant internal loop binding site identified previously. Addition of the new element yields a sufficient binding site by the insertion criterion, binding tryptophan in 22 out of 23 tries. Random insertion testing for site sufficiency seems likely to be broadly revealing.

ChimeraScan: a tool for identifying chimeric transcription in sequencing data.

SUMMARY: Next generation sequencing (NGS) technologies have enabled de novo gene fusion discovery that could reveal candidates with therapeutic significance in cancer. Here we present an open-source software package, ChimeraScan, for the discovery of chimeric transcription between two independent transcripts in high-throughput transcriptome sequencing data. AVAILABILITY: http://chimerascan.googlecode.com CONTACT: cmaher@dom.wustl.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Analysis of the androgen receptor-regulated lncRNA landscape identifies a role for ARLNC1 in prostate cancer progression.

The androgen receptor (AR) plays a critical role in the development of the normal prostate as well as prostate cancer. Using an integrative transcriptomic analysis of prostate cancer cell lines and tissues, we identified ARLNC1 (AR-regulated long noncoding RNA 1) as an important long noncoding RNA that is strongly associated with AR signaling in prostate cancer progression. Not only was ARLNC1 induced by the AR protein, but ARLNC1 stabilized the AR transcript via RNA-RNA interaction. ARLNC1 knockdown suppressed AR expression, global AR signaling and prostate cancer growth in vitro and in vivo. Taken together, these data support a role for ARLNC1 in maintaining a positive feedback loop that potentiates AR signaling during prostate cancer progression and identify ARLNC1 as a novel therapeutic target.

Genome-Wide STAT3 Binding Analysis after Histone Deacetylase Inhibition Reveals Novel Target Genes in Dendritic Cells.

STAT3 is a master transcriptional regulator that plays an important role in the induction of both immune activation and immune tolerance in dendritic cells (DCs). The transcriptional targets of STAT3 in promoting DC activation are becoming increasingly understood; however, the mechanisms underpinning its role in causing DC suppression remain largely unknown. To determine the functional gene targets of STAT3, we compared the genome-wide binding of STAT3 using ChIP sequencing coupled with gene expression microarrays to determine STAT3-dependent gene regulation in DCs after histone deacetylase (HDAC) inhibition. HDAC inhibition boosted the ability of STAT3 to bind to distinct DNA targets and regulate gene expression. Among the top 500 STAT3 binding sites, the frequency of canonical motifs was significantly higher than that of noncanonical motifs. Functional analysis revealed that after treatment with an HDAC inhibitor, the upregulated STAT3 target genes were those that were primarily the negative regulators of proinflammatory cytokines and those in the IL-10 signaling pathway. The downregulated STAT3-dependent targets were those involved in immune effector processes and antigen processing/presentation. The expression and functional relevance of these genes were validated. Specifically, functional studies confirmed that the upregulation of IL-10Ra by STAT3 contributed to the suppressive function of DCs following HDAC inhibition.

The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression.

Molecular classification of cancers into subtypes has resulted in an advance in our understanding of tumour biology and treatment response across multiple tumour types. However, to date, cancer profiling has largely focused on protein-coding genes, which comprise <1% of the genome. Here we leverage a compendium of 58,648 long noncoding RNAs (lncRNAs) to subtype 947 breast cancer samples. We show that lncRNA-based profiling categorizes breast tumours by their known molecular subtypes in breast cancer. We identify a cohort of breast cancer-associated and oestrogen-regulated lncRNAs, and investigate the role of the top prioritized oestrogen receptor (ER)-regulated lncRNA, DSCAM-AS1. We demonstrate that DSCAM-AS1 mediates tumour progression and tamoxifen resistance and identify hnRNPL as an interacting protein involved in the mechanism of DSCAM-AS1 action. By highlighting the role of DSCAM-AS1 in breast cancer biology and treatment resistance, this study provides insight into the potential clinical implications of lncRNAs in breast cancer.