Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis

@article{citeulike:2169987, abstract = {Cancer geneticists seek to identify genetic changes in tumor cells and to relate the genetic changes to tumor development. Because single changes can disrupt the cell cycle and promote other genetic changes, it is extremely hard to distinguish cause from effect. In this article we illustrate how 7 techniques from statistics, theoretical computer science, and phylogenetics can be used to infer and test possible models of tumor progression from single genome-wide descriptions of aberrations in a large sample of tumors. Specifically, we propose 4 tree models for tumor progression inferred from the large ovarian cancer data set described in the first 2 articles in this series. The models are derived from 2 different methods to select the non-random genetic aberrations and 2 different methods to infer the trees, given a set of events. Various aspects of the tree models are tested and extended by 5 methods: overall tests of independence, likelihood ratio tests, principal components analysis, directed acyclic graph modeling, and Bayesian survival analysis. All our methods lead to strikingly consistent conclusions about chromosomal breakpoints in ovarian adenocarcinoma, including (1) the non-random breakpoints in ovarian adenocarcinoma do not occur independently; (2) breakpoints in regions 1p3 and 11p1 are important early events and distinguish a class of tumors associated with poor prognosis; and (3) breakpoints in 1p1, 3p1, and 1q2 distinguish a class of ovarian tumors, and the breaks at 1p1 and 3p1 are associated with poor prognosis. Genes Chromosomes Cancer 28:106-120, 2000. {\copyright} 2000 Wiley-Liss, Inc.}, address = {Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland; Deutsches Krebsforschungszentrum, Abteilung Theoretische Bioinformatik, Heidelberg, Germany; Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California; Emmes Corporation, Potomac, Maryland; Departments of Medicine and Pathology, University of Arizona and Arizona Cancer Center, Tucson, Arizona; Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland; Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland}, author = {Simon, Richard and Desper, Richard and Papadimitriou, Christos H. and Peng, Amy and Alberts, David S. and Taetle, Raymond and Trent, Jeffrey M. and Sch\"{a}ffer, Alejandro A. }, citeulike-article-id = {2169987}, doi = {http://dx.doi.org/10.1002/(SICI)1098-2264(200005)28:1\%3C106::AID-GCC13\%3E3.0.CO;2-S}, journal = {Genes, Chromosomes and Cancer}, number = {1}, pages = {106--120}, posted-at = {2007-12-26 13:16:47}, priority = {2}, title = {Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis}, url = {http://dx.doi.org/10.1002/(SICI)1098-2264(200005)28:1\%3C106::AID-GCC13\%3E3.0.CO;2-S}, volume = {28}, year = {2000} }

TY - JOUR ID - citeulike:2169987 L3 - citeulike-article-id:2169987 TI - Chromosome abnormalities in ovarian adenocarcinoma: III. Using breakpoint data to infer and test mathematical models for oncogenesis JF - Genes, Chromosomes and Cancer VL - 28 IS - 1 SP - 106 EP - 120 AD - Biometric Research Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland; Deutsches Krebsforschungszentrum, Abteilung Theoretische Bioinformatik, Heidelberg, Germany; Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California; Emmes Corporation, Potomac, Maryland; Departments of Medicine and Pathology, University of Arizona and Arizona Cancer Center, Tucson, Arizona; Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland; Computational Biology Branch, National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland N2 - Cancer geneticists seek to identify genetic changes in tumor cells and to relate the genetic changes to tumor development. Because single changes can disrupt the cell cycle and promote other genetic changes, it is extremely hard to distinguish cause from effect. In this article we illustrate how 7 techniques from statistics, theoretical computer science, and phylogenetics can be used to infer and test possible models of tumor progression from single genome-wide descriptions of aberrations in a large sample of tumors. Specifically, we propose 4 tree models for tumor progression inferred from the large ovarian cancer data set described in the first 2 articles in this series. The models are derived from 2 different methods to select the non-random genetic aberrations and 2 different methods to infer the trees, given a set of events. Various aspects of the tree models are tested and extended by 5 methods: overall tests of independence, likelihood ratio tests, principal components analysis, directed acyclic graph modeling, and Bayesian survival analysis. All our methods lead to strikingly consistent conclusions about chromosomal breakpoints in ovarian adenocarcinoma, including (1) the non-random breakpoints in ovarian adenocarcinoma do not occur independently; (2) breakpoints in regions 1p3 and 11p1 are important early events and distinguish a class of tumors associated with poor prognosis; and (3) breakpoints in 1p1, 3p1, and 1q2 distinguish a class of ovarian tumors, and the breaks at 1p1 and 3p1 are associated with poor prognosis. Genes Chromosomes Cancer 28:106-120, 2000. © 2000 Wiley-Liss, Inc. AU - Simon, Richard AU - Desper, Richard AU - Papadimitriou, Christos AU - Peng, Amy AU - Alberts, David AU - Taetle, Raymond AU - Trent, Jeffrey AU - Schäffer, Alejandro PY - 2000/// UR - http://dx.doi.org/10.1002/(SICI)1098-2264(200005)28:1%3C106::AID-GCC13%3E3.0.CO;2-S ER -