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      Somatic Genomics and Clinical Features of Lung Adenocarcinoma: A Retrospective Study

      research-article
      1 , 1 , 1 , 2 , 1 , 3 , 4 , 1 , 3 , 5 , 6 , 7 , 1 , 3 , 1 , 3 , 1 , 3 , 1 , 3 , 1 , 3 , 8 , 1 , 2 , 1 , 3 , 9 , 10 , 11 , 4 , 4 , 12 , 12 , 13 , 1 , 1 , 14 , 15 , 14 , 14 , 15 , 1 , 1 , *
      PLoS Medicine
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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer and has a high risk of distant metastasis at every disease stage. We aimed to characterize the genomic landscape of LUAD and identify mutation signatures associated with tumor progression.

          Methods and Findings

          We performed an integrative genomic analysis, incorporating whole exome sequencing (WES), determination of DNA copy number and DNA methylation, and transcriptome sequencing for 101 LUAD samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. We detected driver genes by testing whether the nonsynonymous mutation rate was significantly higher than the background mutation rate and replicated our findings in public datasets with 724 samples. We performed subclonality analysis for mutations based on mutant allele data and copy number alteration data. We also tested the association between mutation signatures and clinical outcomes, including distant metastasis, survival, and tumor grade. We identified and replicated two novel candidate driver genes, POU class 4 homeobox 2 ( POU4F2) (mutated in 9 [8.9%] samples) and ZKSCAN1 (mutated in 6 [5.9%] samples), and characterized their major deleterious mutations. ZKSCAN1 was part of a mutually exclusive gene set that included the RTK/RAS/RAF pathway genes BRAF, EGFR, KRAS, MET, and NF1, indicating an important driver role for this gene. Moreover, we observed strong associations between methylation in specific genomic regions and somatic mutation patterns. In the tumor evolution analysis, four driver genes had a significantly lower fraction of subclonal mutations (FSM), including TP53 (p = 0.007), KEAP1 ( p = 0.012), STK11 ( p = 0.0076), and EGFR ( p = 0.0078), suggesting a tumor initiation role for these genes. Subclonal mutations were significantly enriched in APOBEC-related signatures ( p < 2.5×10 −50). The total number of somatic mutations ( p = 0.0039) and the fraction of transitions ( p = 5.5×10 −4) were associated with increased risk of distant metastasis. Our study’s limitations include a small number of LUAD patients for subgroup analyses and a single-sample design for investigation of subclonality.

          Conclusions

          These data provide a genomic characterization of LUAD pathogenesis and progression. The distinct clonal and subclonal mutation signatures suggest possible diverse carcinogenesis pathways for endogenous and exogenous exposures, and may serve as a foundation for more effective treatments for this lethal disease. LUAD’s high heterogeneity emphasizes the need to further study this tumor type and to associate genomic findings with clinical outcomes.

          Abstract

          Maria Teresa Landi and colleagues report genomic tumor data for a cohort of patients with lung adenocarcinoma, focusing on implications for tumor initiation and distant metastasis.

          Author Summary

          Why Was This Study Done?
          • Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer and causes more than half a million deaths worldwide annually.

          • Genomic studies of LUAD can shed light on tumor initiation and progression and identify potential targets for treatment.

          What Did the Researchers Do and Find?
          • We performed an integrative genomic analysis, incorporating whole exome sequencing (WES), DNA copy number and DNA methylation determination, and transcriptome sequencing in 101 LUAD samples. We replicated major findings using public genomic resources and combined all existing genomic data for an overall analysis of 825 LUAD samples.

          • We identified two novel driver genes and characterized the driver events and types of mutations that have a stronger role in tumor initiation versus tumor progression.

          • We found strong associations between DNA methylation and somatic mutation patterns.

          • The total number of somatic mutations and the fraction of C→T transitions were associated with increased risk of distant metastasis.

          What Do These Findings Mean?
          • We characterized LUAD genomic architecture and linked major genomic features with clinical outcomes.

          • Tobacco smoking-related mutations appear to have a stronger role in tumor initiation, while mutations associated with endogenous processes are more prominent at a later stage of tumor development and are associated with tumor progression.

          • Our findings highlight the complexity and heterogeneity of LUAD. In addition to new driver genes, we found some tumors with no exonic mutations in known lung cancer driver genes. This suggests that there are further drivers (genetic or epigenetic) to be identified, and larger numbers of samples need to be studied to fully capture LUAD genomic characteristics.

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          Most cited references29

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          RET, ROS1 and ALK fusions in lung cancer.

          Through an integrated molecular- and histopathology-based screening system, we performed a screening for fusions of anaplastic lymphoma kinase (ALK) and c-ros oncogene 1, receptor tyrosine kinase (ROS1) in 1,529 lung cancers and identified 44 ALK-fusion-positive and 13 ROS1-fusion-positive adenocarcinomas, including for unidentified fusion partners for ROS1. In addition, we discovered previously unidentified kinase fusions that may be promising for molecular-targeted therapy, kinesin family member 5B (KIF5B)-ret proto-oncogene (RET) and coiled-coil domain containing 6 (CCDC6)-RET, in 14 adenocarcinomas. A multivariate analysis of 1,116 adenocarcinomas containing these 71 kinase-fusion-positive adenocarcinomas identified four independent factors that are indicators of poor prognosis: age ≥ 50 years, male sex, high pathological stage and negative kinase-fusion status.
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            Assessing the significance of chromosomal aberrations in cancer: methodology and application to glioma.

            Comprehensive knowledge of the genomic alterations that underlie cancer is a critical foundation for diagnostics, prognostics, and targeted therapeutics. Systematic efforts to analyze cancer genomes are underway, but the analysis is hampered by the lack of a statistical framework to distinguish meaningful events from random background aberrations. Here we describe a systematic method, called Genomic Identification of Significant Targets in Cancer (GISTIC), designed for analyzing chromosomal aberrations in cancer. We use it to study chromosomal aberrations in 141 gliomas and compare the results with two prior studies. Traditional methods highlight hundreds of altered regions with little concordance between studies. The new approach reveals a highly concordant picture involving approximately 35 significant events, including 16-18 broad events near chromosome-arm size and 16-21 focal events. Approximately half of these events correspond to known cancer-related genes, only some of which have been previously tied to glioma. We also show that superimposed broad and focal events may have different biological consequences. Specifically, gliomas with broad amplification of chromosome 7 have properties different from those with overlapping focalEGFR amplification: the broad events act in part through effects on MET and its ligand HGF and correlate with MET dependence in vitro. Our results support the feasibility and utility of systematic characterization of the cancer genome.
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              • Article: not found

              Characterizing the cancer genome in lung adenocarcinoma.

              Somatic alterations in cellular DNA underlie almost all human cancers. The prospect of targeted therapies and the development of high-resolution, genome-wide approaches are now spurring systematic efforts to characterize cancer genomes. Here we report a large-scale project to characterize copy-number alterations in primary lung adenocarcinomas. By analysis of a large collection of tumours (n = 371) using dense single nucleotide polymorphism arrays, we identify a total of 57 significantly recurrent events. We find that 26 of 39 autosomal chromosome arms show consistent large-scale copy-number gain or loss, of which only a handful have been linked to a specific gene. We also identify 31 recurrent focal events, including 24 amplifications and 7 homozygous deletions. Only six of these focal events are currently associated with known mutations in lung carcinomas. The most common event, amplification of chromosome 14q13.3, is found in approximately 12% of samples. On the basis of genomic and functional analyses, we identify NKX2-1 (NK2 homeobox 1, also called TITF1), which lies in the minimal 14q13.3 amplification interval and encodes a lineage-specific transcription factor, as a novel candidate proto-oncogene involved in a significant fraction of lung adenocarcinomas. More generally, our results indicate that many of the genes that are involved in lung adenocarcinoma remain to be discovered.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                6 December 2016
                December 2016
                : 13
                : 12
                : e1002162
                Affiliations
                [1 ]Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
                [2 ]Advanced Biomedical Computing Center, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, Maryland, United States of America
                [3 ]Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, Maryland, United States of America
                [4 ]Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, Maryland, United States of America
                [5 ]Division of Thoracic Surgery, Fondazione IRCCS Ca’ Granda—Ospedale Maggiore Policlinico, Milan, Italy
                [6 ]Division of Thoracic Surgery, Istituto Clinico Humanitas, Rozzano, Milan, Italy
                [7 ]Thoracic Surgery Unit, Community Hospital, Brescia, Italy
                [8 ]Information Management Services, Inc., Rockville, Maryland, United States of America
                [9 ]Center for Psychiatric Genetics, Department of Psychiatry and Behavioral Sciences, North Shore University Health System Research Institute, University of Chicago Pritzker School of Medicine, Evanston, Illinois, United States of America
                [10 ]Department of Physiology & Cancer Center, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
                [11 ]Division of Genome Biology, National Cancer Center Research Institute, Tokyo, Japan
                [12 ]Departments of Surgery and of Biochemistry and Molecular Biology, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
                [13 ]Prostate Cancer UK/Movember Centre of Excellence for Prostate Cancer Research, Centre for Cancer Research and Cell Biology, Queen’s University, Belfast, United Kingdom
                [14 ]Epidemiology Unit, Fondazione IRCCS Ca’ Granda—Ospedale Maggiore Policlinico, Milan, Italy
                [15 ]Department of Clinical Sciences and Community Health, Universita’ degli Studi di Milano, Milan, Italy
                MSKCC, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: MTL JS.

                • Data curation: AR DC.

                • Formal analysis: JS XH SR MW CN LS.

                • Funding acquisition: MTL SC PAB NEC.

                • Investigation: BTL JD QC DM CM ILO IM ACP DC AP MA GP PAB SJC.

                • Methodology: JS XH BZ SR MW CN LS MHG.

                • Project administration: MTL SC PAB.

                • Resources: AP MA GP TK NEC MTL.

                • Software: XH MW LS WZ CN HB PL AR.

                • Supervision: MTL SC PAB.

                • Validation: SAB KJ AJB JB BH TK.

                • Visualization: XH SR CN.

                • Writing – original draft: JS MTL.

                • Writing – review & editing: JS XH BZ SR MW CN SAB AP MA GP KJ WZ AJB JB BH AR HB BTL LS JD PL TK QC DM CM ILO IM NEC MHG ACP DC PAB SJC MTL.

                Author information
                http://orcid.org/0000-0001-6349-0172
                http://orcid.org/0000-0003-0588-5314
                http://orcid.org/0000-0003-2640-2422
                http://orcid.org/0000-0003-4510-5167
                http://orcid.org/0000-0001-6997-360X
                http://orcid.org/0000-0002-0467-3064
                http://orcid.org/0000-0001-8014-4888
                http://orcid.org/0000-0002-0155-7092
                http://orcid.org/0000-0002-8495-1545
                http://orcid.org/0000-0001-5729-6488
                Article
                PMEDICINE-D-16-01366
                10.1371/journal.pmed.1002162
                5140047
                27923066
                21fd3414-4fae-49a2-9ebf-99c9b3707321

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 29 April 2016
                : 23 September 2016
                Page count
                Figures: 6, Tables: 1, Pages: 24
                Funding
                This work was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, United States Department of Health and Human Services, Bethesda, MD. CM and ILO were supported by NIH grant (1 R01 HL114094, NHLBI: http://www.nhlbi.nih.gov/). ILO was supported by the Thomas G. Labrecque Foundation ( https://tglfoundation.com/), and in part by the Norris Comprehensive Cancer Center core grant, award number P30CA014089 from the National Cancer Institute ( https://www.nih.gov/about-nih/what-we-do/nih-almanac/national-cancer-institute-nci). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Custom metadata
                All whole exome sequencing, RNA sequencing, methylation, and SNP array data files will be made available from the dbGaP database (accession number: phs001169.v1.p1). To have access to the data, users need approval from a NIH Data Access Committee that will verify whether the requests meet the criteria for access to these data. Contact details for the NIH Data Access Committee are: Margaret Tucker, Director, Human Genetics Program, DCEG, NCI, NIH, nci_cgems_dac@ 123456mail.nih.gov .

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