Promoter DNA methylation patterns in oral, laryngeal and oropharyngeal anatomical regions are associated with tumor differentiation, nodal involvement and survival.

Differentially methylated regions (DMRs) can be used as head and neck squamous cell carcinoma (HNSCC) diagnostic, prognostic and therapeutic targets in precision medicine workflows. DNA from 21 HNSCC and 10 healthy oral tissue samples was hybridized to a genome-wide tiling array to identify DMRs in a discovery cohort. Downstream analyses identified differences in promoter DNA methylation patterns in oral, laryngeal and oropharyngeal anatomical regions associated with tumor differentiation, nodal involvement and survival. Genome-wide DMR analysis showed 2,565 DMRs common to the three subsites. A total of 738 DMRs were unique to laryngeal cancer (n=7), 889 DMRs were unique to oral cavity cancer (n=10) and 363 DMRs were unique to pharyngeal cancer (n=6). Based on the genome-wide analysis and a Gene Ontology analysis, 10 candidate genes were selected to test for prognostic value and association with clinicopathological features. TIMP3 was associated with tumor differentiation in oral cavity cancer (P=0.039), DAPK1 was associated with nodal involvement in pharyngeal cancer (P=0.017) and PAX1 was associated with tumor differentiation in laryngeal cancer (P=0.040). A total of five candidate genes were selected, DAPK1, CDH1, PAX1, CALCA and TIMP3, for a prevalence study in a larger validation cohort: Oral cavity cancer samples (n=42), pharyngeal cancer tissues (n=25) and laryngeal cancer samples (n=52). PAX1 hypermethylation differed across HNSCC anatomic subsites (P=0.029), and was predominantly detected in laryngeal cancer. Kaplan-Meier survival analysis (P=0.043) and Cox regression analysis of overall survival (P=0.001) showed that DAPK1 methylation is associated with better prognosis in HNSCC. The findings of the present study showed that the HNSCC subsites oral cavity, pharynx and larynx display substantial differences in aberrant DNA methylation patterns, which may serve as prognostic biomarkers and therapeutic targets.

Toward a gold standard for benchmarking gene set enrichment analysis.

MOTIVATION: Although gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected datasets and biological reasoning on the relevance of resulting enriched gene sets. RESULTS: We develop an extensible framework for reproducible benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization and detection of relevant processes. This framework incorporates a curated compendium of 75 expression datasets investigating 42 human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods, identifying significant differences in runtime and applicability to RNA-seq data, fraction of enriched gene sets depending on the null hypothesis tested and recovery of the predefined relevance rankings. We make practical recommendations on how methods originally developed for microarray data can efficiently be applied to RNA-seq data, how to interpret results depending on the type of gene set test conducted and which methods are best suited to effectively prioritize gene sets with high phenotype relevance. AVAILABILITY: http://bioconductor.org/packages/GSEABenchmarkeR. CONTACT: ludwig.geistlinger@sph.cuny.edu.

Global Alliance for Genomics and Health Meets Bioconductor: Toward Reproducible and Agile Cancer Genomics at Cloud Scale.

PURPOSE: Institutional efforts toward the democratization of cloud-scale data and analysis methods for cancer genomics are proceeding rapidly. As part of this effort, we bridge two major bioinformatic initiatives: the Global Alliance for Genomics and Health (GA4GH) and Bioconductor. METHODS: We describe in detail a use case in pancancer transcriptomics conducted by blending implementations of the GA4GH Workflow Execution Services and Tool Registry Service concepts with the Bioconductor curatedTCGAData and BiocOncoTK packages. RESULTS: We carried out the analysis with a formally archived workflow and container at dockstore.org and a workspace and notebook at app.terra.bio. The analysis identified relationships between microsatellite instability and biomarkers of immune dysregulation at a finer level of granularity than previously reported. Our use of standard approaches to containerization and workflow programming allows this analysis to be replicated and extended. CONCLUSION: Experimental use of dockstore.org and app.terra.bio in concert with Bioconductor enabled novel statistical analysis of large genomic projects without the need for local supercomputing resources but involved challenges related to container design, script archiving, and unit testing. Best practices and cost/benefit metrics for the management and analysis of globally federated genomic data and annotation are evolving. The creation and execution of use cases like the one reported here will be helpful in the development and comparison of approaches to federated data/analysis systems in cancer genomics.

Orchestrating a community-developed computational workshop and accompanying training materials.

The importance of bioinformatics, computational biology, and data science in biomedical research continues to grow, driving a need for effective instruction and education. A workshop setting, with lectures and guided hands-on tutorials, is a common approach to teaching practical computational and analytical methods. Here, we detail the process we used to produce high-quality, community-authored educational materials that are available for public consumption and reuse. The coordinated efforts of 17 authors over 10 weeks resulted in 15 workshops available as a website and as a 388-page electronic book. We describe how we utilized cloud infrastructure, GitHub, and a literate programming approach to robustly deliver hands-on tutorials to participants of the annual Bioconductor conference. The scripts, raw and published workshop materials, and cloud machine image are all openly available. Our approach uses free services and software and can be adapted by workshop organizers and authors in other contests with appropriate technical backgrounds.

High expression of SLCO2B1 is associated with prostate cancer recurrence after radical prostatectomy.

Solute carrier organic anion (SLCO) gene families encode organic anion transport proteins, which are transporters that up-take a number of substrates including androgens. Among them, high expression of SLCO2B1 is known to associate with the resistance to androgen deprivation therapy in prostate cancer (PCa). We hypothesized that high expression of SLCO genes enhances PCa progression by promoting the influx of androgen. Here, we demonstrated the impact of the expression levels of SLCO2B1 on prognosis in localized PCa after radical prostatectomy (RP) utilizing 494 PCa cases in The Cancer Genome Atlas (TCGA). SLCO2B1 high expression group showed significantly worse Disease-free survival (DFS) after RP (p = 0.001). The expression level of SLCO2B1 was significantly higher in advanced characteristics including Gleason Score (GS ≤ 6 vs GS = 7; p = 0.047, GS = 7 vs GS ≥ 8; p = 0.002), pathological primary tumor (pT2 vs pT3/4; p < 0.001), and surgical margin status (positive vs negative; p = 0.013), respectively. There was a significant difference in DFS between these two groups only in GS ≥ 8 patients (p = 0.006). Multivariate analysis demonstrated that only SLCO2B1 expression level was an independent predictor for DFS after RP in GS ≥ 8. SLCO2B1 high expressed tumors in GS ≥ 8 not only enriched epithelial mesenchymal transition (EMT) related gene set, (p = 0.027), as well as Hedgehog (p < 0.001), IL-6/JAK/STAT3 (p < 0.001), and K-ras signaling gene sets (p < 0.001), which are known to promote EMT, but also showed higher expression of EMT related genes, including N-cadherin (p = 0.024), SNAIL (p = 0.001), SLUG (p = 0.001), ZEB-1 (p < 0.001) and Vimentin (p < 0.001). In conclusion, PCa with high expression of SLCO2B1 demonstrated worse DFS, which might be due to accelerated EMT.

A Panel of Novel Detection and Prognostic Methylated DNA Markers in Primary Non-Small Cell Lung Cancer and Serum DNA.

Purpose: To establish a novel panel of cancer-specific methylated genes for cancer detection and prognostic stratification of early-stage non-small cell lung cancer (NSCLC).Experimental Design: Identification of differentially methylated regions (DMR) was performed with bumphunter on "The Cancer Genome Atlas (TCGA)" dataset, and clinical utility was assessed using quantitative methylation-specific PCR assay in multiple sets of primary NSCLC and body fluids that included serum, pleural effusion, and ascites samples.Results: A methylation panel of 6 genes (CDO1, HOXA9, AJAP1, PTGDR, UNCX, and MARCH11) was selected from TCGA dataset. Promoter methylation of the gene panel was detected in 92.2% (83/90) of the training cohort with a specificity of 72.0% (18/25) and in 93.0% (40/43) of an independent cohort of stage IA primary NSCLC. In serum samples from the later 43 stage IA subjects and population-matched 42 control subjects, the gene panel yielded a sensitivity of 72.1% (31/41) and specificity of 71.4% (30/42). Similar diagnostic accuracy was observed in pleural effusion and ascites samples. A prognostic risk category based on the methylation status of CDO1, HOXA9, PTGDR, and AJAP1 refined the risk stratification for outcomes as an independent prognostic factor for an early-stage disease. Moreover, the paralog group for HOXA9, predominantly overexpressed in subjects with HOXA9 methylation, showed poor outcomes.Conclusions: Promoter methylation of a panel of 6 genes has potential for use as a biomarker for early cancer detection and to predict prognosis at the time of diagnosis. Clin Cancer Res; 23(22); 7141-52. ©2017 AACR.

Molecular Triage of Premalignant Lesions in Liquid-Based Cervical Cytology and Circulating Cell-Free DNA from Urine, Using a Panel of Methylated Human Papilloma Virus and Host Genes.

Clinically useful molecular tools to triage women for a biopsy upon referral to colposcopy are not available. We aimed to develop a molecular panel to detect cervical intraepithelial neoplasia (CIN) grade 2 or higher lesions (CIN2+) in women with abnormal cervical cytology and high-risk HPV (HPV+). We tested a biomarker panel in cervical epithelium DNA obtained from 211 women evaluated in a cervical cancer clinic in Chile from 2006 to 2008. Results were verified in a prospective cohort of 107 women evaluated in a high-risk clinic in Puerto Rico from 2013 to 2015. Promoter methylation of ZNF516, FKBP6, and INTS1 discriminated cervical brush samples with CIN2+ lesions from samples with no intraepithelial lesions or malignancy (NILM) with 90% sensitivity, 88.9% specificity, 0.94 area under the curve (AUC), 93.1% positive predictive value (PPV), and 84.2% negative predictive value (NPV). The panel results were verified in liquid-based cervical cytology samples from an independent cohort with 90.9% sensitivity, 60.9% specificity, 0.90 AUC, 52.6% PPV, and 93.3% NPV, after adding HPV16-L1 methylation to the panel. Next-generation sequencing results in HPV+ cultured cells, and urine circulating cell-free DNA (ccfDNA) were used to design assays that show clinical feasibility in a subset (n = 40) of paired plasma (AUC = 0.81) and urine (AUC = 0.86) ccfDNA samples obtained from the prospective cohort. Viral and host DNA methylation panels can be tested in liquid cytology and urine ccfDNA from women referred to colposcopy, to triage CIN2+ lesions for biopsy and inform personalized screening algorithms. Cancer Prev Res; 9(12); 915-24. ©2016 AACR.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update.

High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.