Reid M. Pinchback

MOTIVATION: Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets. RESULTS: We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site. AVAILABILITY AND IMPLEMENTATION: MSigDB is freely available for non-commercial use at http://www.broadinstitute.org/msigdb.

Abstract Comprehensive genomic characterization of cancer is proceeding at a rapidly accelerating pace, mainly due to the expanded use of massively parallel sequencing. Despite the promise of cancer genomics, many cancer drugs still fail in the clinic due to nonresponsive patients and this translates into a significant unmet medical need. Accurate predictions of which patients are more likely to respond to drugs in development could speed clinical trials and personalize treatments. Here we propose the use of a compendium of experimentally tractable cancer model systems, ∼1000 human genomically-annotated cancer cell lines (at the level of gene expression, DNA copy number alterations and mutations), coupled with pharmacological profiling, to systematically link genetic and transcriptional features to drug response. This resource, the Cancer Cell Line Encyclopedia (CCLE), is available online at www.broadinstitute.org/ccle. Through computational predictive modeling we have both rediscovered molecular features that predict response to several drugs and also uncovered a number of novel potential biomarkers of sensitivity and resistance to targeted agents and chemotherapy drugs. For instance, we have found that response to topoisomerase 1 inhibitors seem to be driven by expression of a single gene. We have also observed that tissue lineage is a key predictor for sensitivity to certain compounds, providing rationale for clinical trials of these drugs in particular cancer types. Our cell line-based platform provides a valuable tool for the development of personalized cancer medicine, revealing critical tumor dependencies and helping to stratify patients for clinical trials. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5455. doi:10.1158/1538-7445.AM2011-5455

Using your favourite document preparation package for typesetting mathematical expressions — to include in papers and theses, for example — can be tedious at the best of times. It is particularly frustrating when the desired expressions already exist somewhere else, such as in an active Maple session, but are not in any kind of format that is both transferable and typographically useful. Here, the concepts of denotational semantics are applied to the problem of exporting Maple expressions to FrameMaker via MIF (Maker Interchange Format) files.

A powerful way to discover key genes playing causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Here, we report high-resolution analyses of somatic copy-number alterations (SCNAs) from 3131 cancer specimens, belonging largely to 26 histological types. We identify 158 regions of focal SCNA that are altered at significant frequency across multiple cancer types, of which 122 cannot be explained by the presence of a known cancer target gene located within these regions. Several gene families are enriched among these regions of focal SCNA, including the BCL2 family of apoptosis regulators and the NF-κB pathway. We show that cancer cells harboring amplifications surrounding the MCL1 and BCL2L1 anti-apoptotic genes depend upon expression of these genes for survival. Finally, we demonstrate that a large majority of SCNAs identified in individual cancer types are present in multiple cancer types.