Interactive Web Resource Research Articles

Abstract 3606: Identification of oncogenic mutation hotspots via three-dimensional proximity

Abstract One of the greatest challenges in studying the genomic basis of human cancer is distinguishing the few mutations that directly contribute to tumorigenesis (“drivers”) from the many biologically neutral mutations (“passengers”). Existing methods can identify highly recurrent individual mutations or mutated genes, but capturing functional yet infrequent “long-tail” mutations remains challenging. While individually uncommon, these long-tail mutations are present in as many as one-third of cancer patients, and for many of them targeted therapies currently exist. Therefore, improved approaches to identify individual driver mutations in the long tail will facilitate our understanding of their potentially diverse biological and clinical significance. Some of these mutations can be identified as hotspots based on their recurrence on or around the same residue. Others may occur in different regions but are actually close in protein three-dimensional structures. We have developed a novel method that identifies mutations that significantly cluster together within three-dimensional proximity in protein structures, or 3D hotspots. We applied this method to a combined collection of mutation data of 21,000 sequenced tumor samples in 74 cancers. Our analysis confirmed many well-studied 3D hotspots of functional mutations in cancer genes such as KRAS, BRAF, IDH2, SMAD4, FBXW7, SPOP, RHOA, and PTPN11. Most of these genes have well known individual hotspots, but our analysis suggests that additional mutations in other residues of the protein are adjacent in the folded protein structure and may also be oncogenic. We also identified novel 3D hotspots in known cancer genes such as EP300, MAP2K1, and KDR, as well as several other genes of unknown significance that harbor 3D hotspots (e.g., DCC). Most of these hotspots are present in multiple cancers. To explore the functional consequences and potential translational significance of long-tail low-incidence mutations identified by our method, we assessed several MAP2K1 mutations in vitro. MAP2K1 (MEK1) is a critical effector of MAPK signaling, harbors conventional single-codon hotspots in several cancer types, and a number of selective MAPK pathway inhibitors are either FDA-approved or are being investigated in early-phase clinical trials. Our experiments confirmed that these mutations activate MAP2K1 and confer sensitivity to MAP2K1 inhibition. We have provided an implementation of the algorithm via an interactive web resource connecting to cBioPortal for Cancer Genomics (http://cbioportal.org/) for easy interpretation of cancer genomics data in the context of three-dimensional structures. By adding to cBioPortal's already powerful capacity of clinical decision support, our method and analysis provide a useful approach of interpretation, prioritization and extension of biologically significant and clinically actionable mutations in cancer. Citation Format: Jianjiong Gao, Matthew T. Chang, Brooke E. Sylvester, Hannah C. Johnsen, Sizhi P. Gao, S. Onur Sumer, David B. Solit, Barry S. Taylor, Nikolaus Schultz, Chris Sander. Identification of oncogenic mutation hotspots via three-dimensional proximity. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3606.

Decoding the complex genetic causes of heart diseases using systems biology.

The pace of disease gene discovery is still much slower than expected, even with the use of cost-effective DNA sequencing and genotyping technologies. It is increasingly clear that many inherited heart diseases have a more complex polygenic aetiology than previously thought. Understanding the role of gene-gene interactions, epigenetics, and non-coding regulatory regions is becoming increasingly critical in predicting the functional consequences of genetic mutations identified by genome-wide association studies and whole-genome or exome sequencing. A systems biology approach is now being widely employed to systematically discover genes that are involved in heart diseases in humans or relevant animal models through bioinformatics. The overarching premise is that the integration of high-quality causal gene regulatory networks (GRNs), genomics, epigenomics, transcriptomics and other genome-wide data will greatly accelerate the discovery of the complex genetic causes of congenital and complex heart diseases. This review summarises state-of-the-art genomic and bioinformatics techniques that are used in accelerating the pace of disease gene discovery in heart diseases. Accompanying this review, we provide an interactive web-resource for systems biology analysis of mammalian heart development and diseases, CardiacCode ( http://CardiacCode.victorchang.edu.au/ ). CardiacCode features a dataset of over 700 pieces of manually curated genetic or molecular perturbation data, which enables the inference of a cardiac-specific GRN of 280 regulatory relationships between 33 regulator genes and 129 target genes. We believe this growing resource will fill an urgent unmet need to fully realise the true potential of predictive and personalised genomic medicine in tackling human heart disease.

JCE VIPEr: An Inorganic Teaching and Learning Community

To facilitate the effective sharing of teaching tools among faculty and the development of deep subject knowledge in support of teaching, a group of faculty from predominantly undergraduate institutions has established a virtual community of inorganic chemists, IONiC (Interactive Online Network of Inorganic Chemists). Our mission is to enhance the inorganic chemistry classroom and laboratory experience for students and faculty members through the development of a vibrant, collaborative, and open community. VIPEr, the Virtual Inorganic Pedagogical Electronic Resource, is a Web site that forms the online home for our community. VIPEr is an interactive Web resource that combines a repository of learning objects (small instructional units such as an in-class activity, an exam problem, or a lab) with social networking tools (such as forums and comment threads) to form a rich virtual community of practice among inorganic chemistry faculty worldwide. Registration and access to all materials are freely available.

Proteopedia- a scientific 'wiki' bridging the rift between three-dimensional structure and function of biomacromolecules

Proteopedia is an interactive wiki-style web resource that presents 3D structural and functional information in a user-friendly manner and allows real-time community annotation.

ArrayProspector: a web resource of functional associations inferred from microarray expression data.

DNA microarray experiments have provided vast amounts of data which can be used for inferring gene function. However, most methods for predicting functional associations between genes from expression data are not suited to simultaneous analysis of multiple datasets, and a comprehensive resource of coexpression-based predictions is currently lacking. Here, we present an interactive web resource of gene associations predicted by applying a novel algorithm to all expression data in the Stanford Microarray Database. The underlying pre-computed database currently contains more than 200 000 high-confidence gene associations in 12 different species sampled from a broad taxonomic range. The resource allows every association to be inspected visually and can be accessed at http://www.bork.embl.de/ArrayProspector.