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Bioconductor is a widely-used collection of R packages for the statistical analysis and comprehension of high-throughput genomic data. Bioconductor has strengths in sequence (RNA-seq, ChIP-seq, called variants, ...) and microarray (expression, methylation, copy number, ...) analysis, as well as significant facilities for flow cytometry, proteomics, and many other omics domains. The breadth of available facilities, coupled with principles of interoperability and reproducibility, make Bioconductor an ideal platform for integrative approaches to cancer genomics. This presentation outlines technical aspects of recent and forthcoming facilities to enable integrative cancer genomic analysis in Bioconductor. We discuss our own work to enable routine integration of large-scale consortium (e.g., ENCODE, Ensembl), annotation into analysis work flows, development within Biocondctor of facilities to manage multiple-assay experiments, and approaches to scaling R's in-memory model to large scale data sets. The presentation concludes with a brief overview of integrative approaches contributed to Bioconductor by our international contributors.

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