Towards a global cancer knowledge network: dissecting the current international cancer genomic sequencing landscape.

BACKGROUND: While next generation sequencing has enhanced our understanding of the biological basis of malignancy, current knowledge on global practices for sequencing cancer samples is limited. To address this deficiency, we developed a survey to provide a snapshot of current sequencing activities globally, identify barriers to data sharing and use this information to develop sustainable solutions for the cancer research community. METHODS: A multi-item survey was conducted assessing demographics, clinical data collection, genomic platforms, privacy/ethics concerns, funding sources and data sharing barriers for sequencing initiatives globally. Additionally, respondents were asked as to provide the primary intent of their initiative (clinical diagnostic, research or combination). RESULTS: Of 107 initiatives invited to participate, 59 responded (response rate = 55%). Whole exome sequencing (P = 0.03) and whole genome sequencing (P = 0.01) were utilized less frequently in clinical diagnostic than in research initiatives. Procedures to identify cancer-specific variants were heterogeneous, with bioinformatics pipelines employing different mutation calling/variant annotation algorithms. Measurement of treatment efficacy varied amongst initiatives, with time on treatment (57%) and RECIST (53%) being the most common; however, other parameters were also employed. Whilst 72% of initiatives indicated data sharing, its scope varied, with a number of restrictions in place (e.g. transfer of raw data). The largest perceived barriers to data harmonization were the lack of financial support (P < 0.01) and bioinformatics concerns (e.g. lack of interoperability) (P = 0.02). Capturing clinical data was more likely to be perceived as a barrier to data sharing by larger initiatives than by smaller initiatives (P = 0.01). CONCLUSIONS: These results identify the main barriers, as perceived by the cancer sequencing community, to effective sharing of cancer genomic and clinical data. They highlight the need for greater harmonization of technical, ethical and data capture processes in cancer sample sequencing worldwide, in order to support effective and responsible data sharing for the benefit of patients.

Epigenetic profiling demarcates molecular subtypes of muscle-invasive bladder cancer.

Muscle-invasive bladder cancer (MIBC) is a heterogeneous disease that often recurs despite aggressive treatment with neoadjuvant chemotherapy and (radical) cystectomy. Basal and luminal molecular subtypes have been identified that are linked to clinical characteristics and have differential sensitivities to chemotherapy. While it has been suggested that epigenetic mechanisms play a role in defining these subtypes, a thorough understanding of the biological mechanisms is lacking. This report details the first genome-wide analysis of histone methylation patterns of human primary bladder tumours by chromatin immunoprecipitations and next-generation sequencing (ChIP-seq). We profiled multiple histone marks: H3K27me3, a marker for repressed genes, and H3K4me1 and H3K4me3, which are indicators of active enhancers and active promoters. Integrated analysis of ChIP-seq data and RNA sequencing revealed that H3K4 mono-methylation demarcates MIBC subtypes, while no association was found for the other two histone modifications in relation to basal and luminal subtypes. Additionally, we identified differentially methylated H3K4me1 peaks in basal and luminal tumour samples, suggesting that active enhancers play a role in defining subtypes. Our study is the first analysis of histone modifications in primary bladder cancer tissue and provides an important resource for the bladder cancer community.

Dynamic metabolomic data analysis: a tutorial review.

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a 'dynamic' method. Some of the methods are illustrated with real-life metabolomics examples.