Analysis of somatic mutations across the kinome reveals loss-of-function mutations in multiple cancer types

AbstractIn this study we use somatic cancer mutations to identify important functional residues within sets of related genes. We focus on protein kinases, a superfamily of phosphotransferases that share homologous sequences and structural motifs and have many connections to cancer. We develop several statistical tests for identifying Significantly Mutated Positions (SMPs), which are positions in an alignment with mutations that show signs of selection. We apply our methods to 21,917 mutations that map to the alignment of human kinases and identify 23 SMPs. SMPs occur throughout the alignment, with many in the important A-loop region, and others spread between the N and C lobes of the kinase domain. Since mutations are pooled across the superfamily, these positions may be important to many protein kinases. We select eleven mutations from these positions for functional validation. All eleven mutations cause a reduction or loss of function in the affected kinase. The tested mutations are from four genes, including two tumor suppressors (TGFBR1 and CHEK2) and two oncogenes (KDR and ERBB2). They also represent multiple cancer types, and include both recurrent and non-recurrent events. Many of these mutations warrant further investigation as potential cancer drivers.</jats:p

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD

Prioritizing Potentially Druggable Mutations with dGene: An Annotation Tool for Cancer Genome Sequencing Data.

A major goal of cancer genome sequencing is to identify mutations or other somatic alterations that can be targeted by selective and specific drugs. dGene is an annotation tool designed to rapidly identify genes belonging to one of ten druggable classes that are frequently targeted in cancer drug development. These classes were comprehensively populated by combining and manually curating data from multiple specialized and general databases. dGene was used by The Cancer Genome Atlas squamous cell lung cancer project, and here we further demonstrate its utility using recently released breast cancer genome sequencing data. dGene is designed to be usable by any cancer researcher without the need for support from a bioinformatics specialist. A full description of dGene and options for its implementation are provided here

Applying the dGene list to CNVs in 46 breast cancer tumours.

<p><b>A,</b> 5421 CNVs were detected in 1752 druggable genes across the sample. The 20^th (0.7×) and 80^th (1.5×) percentiles served as cutoffs. <b>B,</b> Gains only (>1.5×). <b>C,</b> Losses only (<0.7×). <b>D,</b> Displaying PTEN family CNV values. <i>TPTE2</i> is the most frequently altered. Cutoffs are relaxed to <0.85× and >1.15× for display purposes.</p

Summary of the dGene list.

<p>The following references outline primary database construction: GPCRDB (Ref. 8; url: <a href="http://www.gpcr.org/7tm/" target="_blank">http://www.gpcr.org/7tm/</a>); MEROPS (Ref. 7; url: <a href="http://merops.sanger.ac.uk/" target="_blank">http://merops.sanger.ac.uk/</a>); KinBase (Ref. 11; url: kinase.com); NucleaRDB (Ref. 6; url: <a href="http://www.receptors.org/nucleardb/" target="_blank">http://www.receptors.org/nucleardb/</a>); Uniprot (Ref. 9; url: <a href="http://www.uniprot.org" target="_blank">www.uniprot.org</a>); Gene Ontology (Ref. 10; url: <a href="http://www.geneontology.org" target="_blank">www.geneontology.org</a>). All URLs valid as of 2/26/2013.</p

Rationale and process for construction of the dGene list.

<p><b>A</b>, Druggability serves as a rational screen in a hypothetical pipeline for reducing a raw gene list to an experimentally workable number. <b>B</b>, Lung cancer drugs in the pipeline classified by target type, with some target types considered broadly druggable and included in dGene. <b>C</b>, NHRs required a simple workflow. Russ <i>et al,</i> 2005 and NucleaRDB <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067980#pone.0067980-Vroling1" target="_blank">[6]</a> provided input. One gene mapped to neither the NCBI gene nor synonyms list. Six genes were identified in only one source and were manually checked against UniProt and Gene Ontology (GO) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067980#pone.0067980-Apweiler1" target="_blank">[9]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067980#pone.0067980-Blake1" target="_blank">[10]</a>. None could be confirmed as NHRs, leaving the final class with 48 members. <b>D</b>, The elaborated workflow for proteases is analogous to that of the NHRs and other classes. Because UniProt served as input, curation involved searching the primary literature in addition to querying GO.</p