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      Age-related cancer mutations associated with clonal hematopoietic expansion

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          Abstract

          Several genetic alterations characteristic of leukemia and lymphoma have been detected in the blood of individuals without apparent hematological malignancies. We analyzed blood-derived sequence data from 2,728 individuals within The Cancer Genome Atlas, and discovered 77 blood-specific mutations in cancer-associated genes, the majority being associated with advanced age. Remarkably, 83% of these mutations were from 19 leukemia/lymphoma-associated genes, and nine were recurrently mutated ( DNMT3A, TET2, JAK2, ASXL1, TP53, GNAS, PPM1D, BCORL1 and SF3B1). We identified 14 additional mutations in a very small fraction of blood cells, possibly representing the earliest stages of clonal expansion in hematopoietic stem cells. Comparison of these findings to mutations in hematological malignancies identified several recurrently mutated genes that may be disease initiators. Our analyses show that the blood cells of more than 2% of individuals (5–6% of people older than 70 years) contain mutations that may represent premalignant, initiating events that cause clonal hematopoietic expansion.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Heng Li, Richard Durbin (2009)
          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
          Article 0 comments Cited 10154 times – based on 0 reviews     Review now
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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)
            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              Cancer genome landscapes.

              Bert Vogelstein, Nickolas Papadopoulos, Victor E Velculescu … (2013)
              Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. For most cancer types, this landscape consists of a small number of "mountains" (genes altered in a high percentage of tumors) and a much larger number of "hills" (genes altered infrequently). To date, these studies have revealed ~140 genes that, when altered by intragenic mutations, can promote or "drive" tumorigenesis. A typical tumor contains two to eight of these "driver gene" mutations; the remaining mutations are passengers that confer no selective growth advantage. Driver genes can be classified into 12 signaling pathways that regulate three core cellular processes: cell fate, cell survival, and genome maintenance. A better understanding of these pathways is one of the most pressing needs in basic cancer research. Even now, however, our knowledge of cancer genomes is sufficient to guide the development of more effective approaches for reducing cancer morbidity and mortality.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9502015
                Journal ID (pubmed-jr-id): 8791
                Journal ID (nlm-ta): Nat Med
                Journal ID (iso-abbrev): Nat. Med.
                Title: Nature medicine
                ISSN (Print): 1078-8956
                ISSN (Electronic): 1546-170X
                Publication date Nihms-submitted: 23 January 2015
                Publication date (Electronic): 19 October 2014
                Publication date (Print): December 2014
                Publication date PMC-release: 01 June 2015
                Volume: 20
                Issue: 12
                Pages: 1472-1478
                Affiliations
                [1 ]The Genome Institute, Washington University in St. Louis, MO 63108, USA
                [2 ]Department of Medicine, Washington University in St. Louis, MO 63108, USA
                [3 ]Brown School Master of Public Health Program, Washington University in St. Louis, St. Louis, MO 63130, USA
                [4 ]Department of Genetics, Washington University in St. Louis, MO 63108, USA
                [5 ]Department of Mathematics, Washington University in St. Louis, MO 63108, USA
                [6 ]Siteman Cancer Center, Washington University in St. Louis, MO 63108, USA
                Author notes
                [# ]Corresponding Author: Li Ding, Ph.D., The Genome Institute, Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63108, lding@ 123456genome.wustl.edu
                [*]

                These authors contributed equally to this work.

                Article
                Manuscript ID: NIHMS630249
                DOI: 10.1038/nm.3733
                PMC ID: 4313872
                PubMed ID: 25326804
                SO-VID: a7d748f3-cad0-4fc5-8d6b-2378df739905
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                ScienceOpen disciplines: Medicine

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