Publications by authors named "Runjun D Kumar"

9 Publications

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A deep intronic variant is a common cause of OTC deficiency in individuals with previously negative genetic testing.

Mol Genet Metab Rep 2021 Mar 8;26:100706. Epub 2021 Jan 8.

National Urea Cycle Disorders Foundation, Pasadena, CA, USA.

Pathogenic variants in non-coding regions of genes encoding enzymes or transporters of the urea cycle can lead to urea cycle disorders (UCDs). However, not all commercially available testing platforms interrogate these regions. Here, we used a gene panel based on massively parallel sequencing (MPS) in 10 individuals with clinical or pedigree-based evidence of a proximal UCD but without a molecular confirmation of the diagnosis. We identified causal variant(s) in 5 of 10 individuals, including in 3 of 7 individuals in whom prior molecular testing was unrevealing. We show that a deep-intronic pathogenic variant in , c.540+265G>A, is an important cause of ornithine transcarbamylase (OTC) deficiency.
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http://dx.doi.org/10.1016/j.ymgmr.2020.100706DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809430PMC
March 2021

Facial Nerve Palsy in a Child With Fever of Unknown Origin.

Clin Pediatr (Phila) 2020 05 16;59(4-5):516-518. Epub 2020 Jan 16.

Baylor College of Medicine, Houston, TX, USA.

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http://dx.doi.org/10.1177/0009922819900967DOI Listing
May 2020

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

Sci Rep 2017 07 25;7(1):6418. Epub 2017 Jul 25.

Division of Oncology, Department of Medicine, Washington University School of Medicine, 660S Euclid Ave, St. Louis, MO, 63110, USA.

In 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.
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http://dx.doi.org/10.1038/s41598-017-06366-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5527104PMC
July 2017

Unsupervised detection of cancer driver mutations with parsimony-guided learning.

Nat Genet 2016 10 12;48(10):1288-94. Epub 2016 Sep 12.

Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.

Methods are needed to reliably prioritize biologically active driver mutations over inactive passengers in high-throughput sequencing cancer data sets. We present ParsSNP, an unsupervised functional impact predictor that is guided by parsimony. ParsSNP uses an expectation-maximization framework to find mutations that explain tumor incidence broadly, without using predefined training labels that can introduce biases. We compare ParsSNP to five existing tools (CanDrA, CHASM, FATHMM Cancer, TransFIC, and Condel) across five distinct benchmarks. ParsSNP outperformed the existing tools in 24 of 25 comparisons. To investigate the real-world benefit of these improvements, we applied ParsSNP to an independent data set of 30 patients with diffuse-type gastric cancer. ParsSNP identified many known and likely driver mutations that other methods did not detect, including truncation mutations in known tumor suppressors and the recurrent driver substitution RHOA p.Tyr42Cys. In conclusion, ParsSNP uses an innovative, parsimony-based approach to prioritize cancer driver mutations and provides dramatic improvements over existing methods.
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http://dx.doi.org/10.1038/ng.3658DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5328615PMC
October 2016

Statistically identifying tumor suppressors and oncogenes from pan-cancer genome-sequencing data.

Bioinformatics 2015 Nov 25;31(22):3561-8. Epub 2015 Jul 25.

Division of Oncology, Department of Medicine, Washington University School of Medicine.

Motivation: Several tools exist to identify cancer driver genes based on somatic mutation data. However, these tools do not account for subclasses of cancer genes: oncogenes, which undergo gain-of-function events, and tumor suppressor genes (TSGs) which undergo loss-of-function. A method which accounts for these subclasses could improve performance while also suggesting a mechanism of action for new putative cancer genes.

Results: We develop a panel of five complementary statistical tests and assess their performance against a curated set of 99 HiConf cancer genes using a pan-cancer dataset of 1.7 million mutations. We identify patient bias as a novel signal for cancer gene discovery, and use it to significantly improve detection of oncogenes over existing methods (AUROC = 0.894). Additionally, our test of truncation event rate separates oncogenes and TSGs from one another (AUROC = 0.922). Finally, a random forest integrating the five tests further improves performance and identifies new cancer genes, including CACNG3, HDAC2, HIST1H1E, NXF1, GPS2 and HLA-DRB1.

Availability And Implementation: All mutation data, instructions, functions for computing the statistics and integrating them, as well as the HiConf gene panel, are available at www.github.com/Bose-Lab/Improved-Detection-of-Cancer-Genes.

Contact: rbose@dom.wustl.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv430DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4757952PMC
November 2015

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

Nat Genet 2014 Aug 22;46(8):826-36. Epub 2014 Jun 22.

Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy (affiliated institute of the University of Lübeck, Lübeck, Germany).

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.
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http://dx.doi.org/10.1038/ng.3014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4124521PMC
August 2014

DGIdb: mining the druggable genome.

Nat Methods 2013 Dec 13;10(12):1209-10. Epub 2013 Oct 13.

1] The Genome Institute, Washington University School of Medicine, St. Louis, Missouri, USA. [2] Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA. [3].

The Drug-Gene Interaction database (DGIdb) mines existing resources that generate hypotheses about how mutated genes might be targeted therapeutically or prioritized for drug development. It provides an interface for searching lists of genes against a compendium of drug-gene interactions and potentially 'druggable' genes. DGIdb can be accessed at http://dgidb.org/.
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http://dx.doi.org/10.1038/nmeth.2689DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3851581PMC
December 2013

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

PLoS One 2013 27;8(6):e67980. Epub 2013 Jun 27.

Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America ; Computational and Systems Biology Program, Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri, United States of America.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0067980PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694871PMC
October 2017