Andrew D. Yates

Bio: Andrew D. Yates is an academic researcher from European Bioinformatics Institute. The author has contributed to research in topics: Ensembl & Ensembl Genomes. The author has an hindex of 22, co-authored 44 publications receiving 8619 citations. Previous affiliations of Andrew D. Yates include Novartis Institute for Tropical Diseases & Wellcome Trust.

Patterns of somatic mutation in human cancer genomes

Abstract: Cancers arise owing to mutations in a subset of genes that confer growth advantage. The availability of the human genome sequence led us to propose that systematic resequencing of cancer genomes for mutations would lead to the discovery of many additional cancer genes. Here we report more than 1,000 somatic mutations found in 274 megabases (Mb) of DNA corresponding to the coding exons of 518 protein kinase genes in 210 diverse human cancers. There was substantial variation in the number and pattern of mutations in individual cancers reflecting different exposures, DNA repair defects and cellular origins. Most somatic mutations are likely to be 'passengers' that do not contribute to oncogenesis. However, there was evidence for 'driver' mutations contributing to the development of the cancers studied in approximately 120 genes. Systematic sequencing of cancer genomes therefore reveals the evolutionary diversity of cancers and implicates a larger repertoire of cancer genes than previously anticipated.

GENCODE reference annotation for the human and mouse genomes.

Abstract: The accurate identification and description of the genes in the human and mouse genomes is a fundamental requirement for high quality analysis of data informing both genome biology and clinical genomics. Over the last 15 years, the GENCODE consortium has been producing reference quality gene annotations to provide this foundational resource. The GENCODE consortium includes both experimental and computational biology groups who work together to improve and extend the GENCODE gene annotation. Specifically, we generate primary data, create bioinformatics tools and provide analysis to support the work of expert manual gene annotators and automated gene annotation pipelines. In addition, manual and computational annotation workflows use any and all publicly available data and analysis, along with the research literature to identify and characterise gene loci to the highest standard. GENCODE gene annotations are accessible via the Ensembl and UCSC Genome Browsers, the Ensembl FTP site, Ensembl Biomart, Ensembl Perl and REST APIs as well as https://www.gencodegenes.org.

Lung cancer: intragenic ERBB2 kinase mutations in tumours.

Abstract: The protein-kinase family is the most frequently mutated gene family found in human cancer and faulty kinase enzymes are being investigated as promising targets for the design of antitumour therapies. We have sequenced the gene encoding the transmembrane protein tyrosine kinase ERBB2 (also known as HER2 or Neu) from 120 primary lung tumours and identified 4% that have mutations within the kinase domain; in the adenocarcinoma subtype of lung cancer, 10% of cases had mutations. ERBB2 inhibitors, which have so far proved to be ineffective in treating lung cancer, should now be clinically re-evaluated in the specific subset of patients with lung cancer whose tumours carry ERBB2 mutations.

Ensembl 2021

Abstract: The Ensembl project (https://www ensembl org) annotates genomes and disseminates genomic data for vertebrate species We create detailed and comprehensive annotation of gene structures, regulatory elements and variants, and enable comparative genomics by inferring the evolutionary history of genes and genomes Our integrated genomic data are made available in a variety of ways, including genome browsers, search interfaces, specialist tools such as the Ensembl Variant Effect Predictor, download files and programmatic interfaces Here, we present recent Ensembl developments including two new website portals Ensembl Rapid Release (http://rapid ensembl org) is designed to provide core tools and services for genomes as soon as possible and has been deployed to support large biodiversity sequencing projects Our SARS-CoV-2 genome browser (https://covid-19 ensembl org) integrates our own annotation with publicly available genomic data from numerous sources to facilitate the use of genomics in the international scientific response to the COVID-19 pandemic We also report on other updates to our annotation resources, tools and services All Ensembl data and software are freely available without restriction

Somatic Mutations of the Protein Kinase Gene Family in Human Lung Cancer

Abstract: Protein kinases are frequently mutated in human cancer and inhibitors of mutant protein kinases have proven to be effective anticancer drugs. We screened the coding sequences of 518 protein kinases (approximately 1.3 Mb of DNA per sample) for somatic mutations in 26 primary lung neoplasms and seven lung cancer cell lines. One hundred eighty-eight somatic mutations were detected in 141 genes. Of these, 35 were synonymous (silent) changes. This result indicates that most of the 188 mutations were "passenger" mutations that are not causally implicated in oncogenesis. However, an excess of approximately 40 nonsynonymous substitutions compared with that expected by chance (P = 0.07) suggests that some nonsynonymous mutations have been selected and are contributing to oncogenesis. There was considerable variation between individual lung cancers in the number of mutations observed and no mutations were found in lung carcinoids. The mutational spectra of most lung cancers were characterized by a high proportion of C:G > A:T transversions, compatible with the mutagenic effects of tobacco carcinogens. However, one neuroendocrine cancer cell line had a distinctive mutational spectrum reminiscent of UV-induced DNA damage. The results suggest that several mutated protein kinases may be contributing to lung cancer development, but that mutations in each one are infrequent.

The Pfam protein families database

Abstract: Pfam is a widely used database of protein families and domains. This article describes a set of major updates that we have implemented in the latest release (version 24.0). The most important change is that we now use HMMER3, the latest version of the popular profile hidden Markov model package. This software is approximately 100 times faster than HMMER2 and is more sensitive due to the routine use of the forward algorithm. The move to HMMER3 has necessitated numerous changes to Pfam that are described in detail. Pfam release 24.0 contains 11,912 families, of which a large number have been significantly updated during the past two years. Pfam is available via servers in the UK (http://pfam.sanger.ac.uk/), the USA (http://pfam.janelia.org/) and Sweden (http://pfam.sbc.su.se/).

Signatures of mutational processes in human cancer

Abstract: All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.

Comprehensive genomic characterization defines human glioblastoma genes and core pathways

Abstract: Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas ( TCGA) pilot project aims to assess the value of large- scale multi- dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas - the most common type of primary adult brain cancer - and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol- 3- OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.

An Integrated Genomic Analysis of Human Glioblastoma Multiforme

Abstract: Glioblastoma multiforme (GBM) is the most common and lethal type of brain cancer. To identify the genetic alterations in GBMs, we sequenced 20,661 protein coding genes, determined the presence of amplifications and deletions using high-density oligonucleotide arrays, and performed gene expression analyses using next-generation sequencing technologies in 22 human tumor samples. This comprehensive analysis led to the discovery of a variety of genes that were not known to be altered in GBMs. Most notably, we found recurrent mutations in the active site of isocitrate dehydrogenase 1 (IDH1) in 12% of GBM patients. Mutations in IDH1 occurred in a large fraction of young patients and in most patients with secondary GBMs and were associated with an increase in overall survival. These studies demonstrate the value of unbiased genomic analyses in the characterization of human brain cancer and identify a potentially useful genetic alteration for the classification and targeted therapy of GBMs.