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Harvey I. Pass

Bio: Harvey I. Pass is an academic researcher from New York University. The author has contributed to research in topics: Mesothelioma & Lung cancer. The author has an hindex of 108, co-authored 644 publications receiving 47456 citations. Previous affiliations of Harvey I. Pass include Uniformed Services University of the Health Sciences & Mesothelioma Applied Research Foundation.


Papers
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Journal ArticleDOI
01 Jan 2014-Nature
TL;DR: In this paper, the authors report molecular profiling of 230 resected lung adnocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses.
Abstract: Adenocarcinoma of the lung is the leading cause of cancer death worldwide. Here we report molecular profiling of 230 resected lung adenocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses. High rates of somatic mutation were seen (mean 8.9 mutations per megabase). Eighteen genes were statistically significantly mutated, including RIT1 activating mutations and newly described loss-of-function MGA mutations which are mutually exclusive with focal MYC amplification. EGFR mutations were more frequent in female patients, whereas mutations in RBM10 were more common in males. Aberrations in NF1, MET, ERBB2 and RIT1 occurred in 13% of cases and were enriched in samples otherwise lacking an activated oncogene, suggesting a driver role for these events in certain tumours. DNA and mRNA sequence from the same tumour highlighted splicing alterations driven by somatic genomic changes, including exon 14 skipping in MET mRNA in 4% of cases. MAPK and PI(3)K pathway activity, when measured at the protein level, was explained by known mutations in only a fraction of cases, suggesting additional, unexplained mechanisms of pathway activation. These data establish a foundation for classification and further investigations of lung adenocarcinoma molecular pathogenesis.

4,104 citations

Journal ArticleDOI
Peter Goldstraw1, Kari Chansky, John Crowley, Ramón Rami-Porta2, Hisao Asamura3, Wilfried Ernst Erich Eberhardt4, Andrew G. Nicholson1, Patti A. Groome5, Alan Mitchell, Vanessa Bolejack, David Ball6, David G. Beer7, Ricardo Beyruti8, Frank C. Detterbeck9, Wilfried Eberhardt4, John G. Edwards10, Françoise Galateau-Salle11, Dorothy Giroux12, Fergus V. Gleeson13, James Huang14, Catherine Kennedy15, Jhingook Kim16, Young Tae Kim17, Laura Kingsbury12, Haruhiko Kondo18, Mark Krasnik19, Kaoru Kubota20, Antoon Lerut21, Gustavo Lyons, Mirella Marino, Edith M. Marom22, Jan P. van Meerbeeck23, Takashi Nakano24, Anna K. Nowak25, Michael D Peake26, Thomas W. Rice27, Kenneth E. Rosenzweig28, Enrico Ruffini29, Valerie W. Rusch14, Nagahiro Saijo, Paul Van Schil23, Jean-Paul Sculier30, Lynn Shemanski12, Kelly G. Stratton12, Kenji Suzuki31, Yuji Tachimori32, Charles F. Thomas33, William D. Travis14, Ming-Sound Tsao34, Andrew T. Turrisi35, Johan Vansteenkiste21, Hirokazu Watanabe, Yi-Long Wu, Paul Baas36, Jeremy J. Erasmus22, Seiki Hasegawa24, Kouki Inai37, Kemp H. Kernstine38, Hedy L. Kindler39, Lee M. Krug14, Kristiaan Nackaerts21, Harvey I. Pass40, David C. Rice22, Conrad Falkson5, Pier Luigi Filosso29, Giuseppe Giaccone41, Kazuya Kondo42, Marco Lucchi43, Meinoshin Okumura44, Eugene H. Blackstone27, F. Abad Cavaco, E. Ansótegui Barrera, J. Abal Arca, I. Parente Lamelas, A. Arnau Obrer45, R. Guijarro Jorge45, D. Ball6, G.K. Bascom46, A. I. Blanco Orozco, M. A. González Castro, M.G. Blum, D. Chimondeguy, V. Cvijanovic47, S. Defranchi48, B. de Olaiz Navarro, I. Escobar Campuzano2, I. Macía Vidueira2, E. Fernández Araujo49, F. Andreo García49, Kwun M. Fong, G. Francisco Corral, S. Cerezo González, J. Freixinet Gilart, L. García Arangüena, S. García Barajas50, P. Girard, Tuncay Göksel, M. T. González Budiño51, G. González Casaurrán50, J. A. Gullón Blanco, J. Hernández Hernández, H. Hernández Rodríguez, J. Herrero Collantes, M. Iglesias Heras, J. M. Izquierdo Elena, Erik Jakobsen, S. Kostas52, P. León Atance, A. Núñez Ares, M. Liao, M. Losanovscky, G. Lyons, R. Magaroles53, L. De Esteban Júlvez53, M. Mariñán Gorospe, Brian C. McCaughan15, Catherine J. Kennedy15, R. Melchor Íñiguez54, L. Miravet Sorribes, S. Naranjo Gozalo, C. Álvarez de Arriba, M. Núñez Delgado, J. Padilla Alarcón, J. C. Peñalver Cuesta, Jongsun Park16, H. Pass40, M. J. Pavón Fernández, Mara Rosenberg, Enrico Ruffini29, V. Rusch14, J. Sánchez de Cos Escuín, A. Saura Vinuesa, M. Serra Mitjans, Trond Eirik Strand, Dragan Subotic, S.G. Swisher22, Ricardo Mingarini Terra8, Charles R. Thomas33, Kurt G. Tournoy55, P. Van Schil23, M. Velasquez, Y.L. Wu, K. Yokoi 
Imperial College London1, University of Barcelona2, Keio University3, University of Duisburg-Essen4, Queen's University5, Peter MacCallum Cancer Centre6, University of Michigan7, University of São Paulo8, Yale University9, Northern General Hospital10, University of Caen Lower Normandy11, Fred Hutchinson Cancer Research Center12, University of Oxford13, Memorial Sloan Kettering Cancer Center14, University of Sydney15, Sungkyunkwan University16, Seoul National University17, Kyorin University18, University of Copenhagen19, Nippon Medical School20, Katholieke Universiteit Leuven21, University of Texas MD Anderson Cancer Center22, University of Antwerp23, Hyogo College of Medicine24, University of Western Australia25, Glenfield Hospital26, Cleveland Clinic27, Icahn School of Medicine at Mount Sinai28, University of Turin29, Université libre de Bruxelles30, Juntendo University31, National Cancer Research Institute32, Mayo Clinic33, University of Toronto34, Sinai Grace Hospital35, Netherlands Cancer Institute36, Hiroshima University37, City of Hope National Medical Center38, University of Chicago39, New York University40, Georgetown University41, University of Tokushima42, University of Pisa43, Osaka University44, University of Valencia45, Good Samaritan Hospital46, Military Medical Academy47, Fundación Favaloro48, Autonomous University of Barcelona49, Complutense University of Madrid50, University of Oviedo51, National and Kapodistrian University of Athens52, Rovira i Virgili University53, Autonomous University of Madrid54, Ghent University55
TL;DR: The methods used to evaluate the resultant Stage groupings and the proposals put forward for the 8th edition of the TNM Classification for lung cancer due to be published late 2016 are described.

2,826 citations

Journal ArticleDOI
14 Sep 2012-Cell
TL;DR: Exome and genome sequences and whole-genome sequence analysis revealed frequent structural rearrangements, including in-frame exonic alterations within EGFR and SIK2 kinases, which are attractive targets for biological characterization and therapeutic targeting of lung adenocarcinoma.

1,631 citations

Journal ArticleDOI
Peter J. Campbell1, Gad Getz2, Jan O. Korbel3, Joshua M. Stuart4  +1329 moreInstitutions (238)
06 Feb 2020-Nature
TL;DR: The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.
Abstract: Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1,2,3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10,11,12,13,14,15,16,17,18.

1,600 citations

Journal ArticleDOI
TL;DR: Analysis of tumor samples from multiple independent patient cohorts and array-based comparative genomic hybridization suggest that MET amplification occurs independently of EGFRT790M mutations and that MET may be a clinically relevant therapeutic target for some patients with acquired resistance to gefitinib or erlotinib.
Abstract: In human lung adenocarcinomas harboring EGFR mutations, a second-site point mutation that substitutes methionine for threonine at position 790 (T790M) is associated with approximately half of cases of acquired resistance to the EGFR kinase inhibitors, gefitinib and erlotinib. To identify other potential mechanisms that contribute to disease progression, we used array-based comparative genomic hybridization (aCGH) to compare genomic profiles of EGFR mutant tumors from untreated patients with those from patients with acquired resistance. Among three loci demonstrating recurrent copy number alterations (CNAs) specific to the acquired resistance set, one contained the MET proto-oncogene. Collectively, analysis of tumor samples from multiple independent patient cohorts revealed that MET was amplified in tumors from 9 of 43 (21%) patients with acquired resistance but in only two tumors from 62 untreated patients (3%) (P = 0.007, Fisher's Exact test). Among 10 resistant tumors from the nine patients with MET amplification, 4 also harbored the EGFRT790M mutation. We also found that an existing EGFR mutant lung adenocarcinoma cell line, NCI-H820, harbors MET amplification in addition to a drug-sensitive EGFR mutation and the T790M change. Growth inhibition studies demonstrate that these cells are resistant to both erlotinib and an irreversible EGFR inhibitor (CL-387,785) but sensitive to a multikinase inhibitor (XL880) with potent activity against MET. Taken together, these data suggest that MET amplification occurs independently of EGFRT790M mutations and that MET may be a clinically relevant therapeutic target for some patients with acquired resistance to gefitinib or erlotinib.

1,587 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
01 Jun 1990-Cell
TL;DR: A model for the genetic basis of colorectal neoplasia that includes the following salient features is presented, which may be applicable to other common epithelial neoplasms, in which tumors of varying stage are more difficult to study.

11,576 citations

Journal ArticleDOI
TL;DR: This timely monograph is a distillation of knowledge of hepatitis B, C and D, based on a review of 1000 studies by a small group of scientists, and it is concluded that hepatitis D virus cannot be classified as a human carcinogen.
Abstract: Viral hepatitis in all its forms is a major public health problem throughout the world, affecting several hundreds of millions of people. Viral hepatitis is a cause of considerable morbidity and mortality both from acute infection and chronic sequelae which include, in the case of hepatitis B, C and D, chronic active hepatitis and cirrhosis. Hepatocellular carcinoma, which is one of the 10 commonest cancers worldwide, is closely associated with hepatitis B and, at least in some regions of the world, with hepatitis C virus. This timely monograph is a distillation of knowledge of hepatitis B, C and D, based on a review of 1000 studies by a small group of scientists. (It is interesting to note in passing that some 5000 papers on viral hepatitis are published annually in the world literature.) The epidemiological, clinical and experimental data on the association between infection with hepatitis B virus and primary liver cancer in humans are reviewed in a readable and succinct format. The available information on hepatitis C and progression to chronic infection is also evaluated and it is concluded (perhaps a little prematurely) that hepatitis C virus is carcinogenic. However, it is concluded that hepatitis D virus, an unusual virus with a number of similarities to certain plant viral satellites and viroids, cannot be classified as a human carcinogen. There are some minor criticisms: there are few illustrations and some complex tabulations (for example, Table 6) and no subject index. A cumulative cross index to IARC Monographs is of little value and occupies nearly 30 pages. This small volume is a useful addition to the overwhelming literature on viral hepatitis, and the presentation is similar to the excellent World Health Organisation Technical Reports series on the subject published in the past. It is strongly recommended as a readable up-to-date summary of a complex subject; and at a cost of 65 Sw.fr (approximately £34) is excellent value. A J ZUCKERMAN

11,533 citations