Grant Number:	5R29CA064364-05 Interpret this number
Primary Investigator:	Newton, Michael
Organization:	University Of Wisconsin Madison
Project Title:	Statistical Methods for Molecular Cancer Data
Fiscal Year:	1999

Molecular technology for studying the genome of human cells leads to large structured sets of categorical data. These data are used by cancer researchers to understand the complex and variable sequence of genetic changes that occur within cells of evolving tumors. The primary goal of the proposed research is to develop a statistical methodology that will assist oncologists in the analysis and interpretation of such data. In particular, statistical methods are proposed for the localization of genes associated with the cancer phenotype. A very common experiment, used in the study of diverse cancers, involves a panel of molecular markers either scattered throughout the genome or from a single chromosomal region. By comparing signals from normal and tumor cells, the oncologist can score each tumor-marker combination for loss of heterozygosity. Putative tumor suppressor genes may exist in regions commonly inactivated, and thus identifying such regions is of critical importance. Inference from marker data must account for various complexities: within tumor variation, dependence of response between nearby markers, the problem of multiple comparison, the known structural features of chromosomes like locations of fragile sites, the dependence of data from related cells, consequences of genetic instability like aneuploidy and background loss, and covariate information like levels of oncoproteins. The absence of statistical analysis, or the use of naive methods, is an inefficient use of valuable data, and may even lead to erroneous conclusions. The evolutionary nature of tumor growth suggests a natural form for a stochastic model of the changing genome--one based on genetic instability and selection. Such a model creates a framework for parametrizing the distribution of loss-of- heterozygosity data. Questions about the location and action of putative suppressor genes can be formulated as questions about components of the stochastic model, and thus classical inference procedures can be applied. Numerous technical questions arise about how and what to compute. Bayesian and profile likelihood strategies are proposed to estimate gene location given the model. Markov chain Monte Carlo methods are necessary to implement the Bayesian strategy, and predictive distributions will be studied to asses goodness of fit. Alternatively, bootstrap methods enable frequency calibration of profile likelihood as well as methods for model testing. Asymptotic analysis will give insight into the form of the non- standard likelihood surface. Computer simulation of the model will be useful both to study bias and variance properties of the proposed methods and as the basis for power calculations to design marker studies.




Enrichment of melanoma-associated T cells in 6-thioguanine-resistant T cells from metastatic melanoma patients.
Authors: Zuleger C.L. , Newton M.A. , Ma X. , Ong I.M. , Pei Q. , Albertini M.R. .
Source: Melanoma Research, 2019-05-23 00:00:00.0; , .
EPub date: 2019-05-23 00:00:00.0.
PMID: 31135600
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Statistical significance of optical map alignments.
Authors: Sarkar D. , Goldstein S. , Schwartz D.C. , Newton M.A. .
Source: Journal Of Computational Biology : A Journal Of Computational Molecular Cell Biology, 2012 May; 19(5), p. 478-92.
PMID: 22506568
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Longitudinal assessment of colonic tumor fate in mice by computed tomography and optical colonoscopy.
Authors: Durkee B.Y. , Shinki K. , Newton M.A. , Iverson C.E. , Weichert J.P. , Dove W.F. , Halberg R.B. .
Source: Academic Radiology, 2009 Dec; 16(12), p. 1475-82.
PMID: 19896065
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Statistical use of argonaute expression and RISC assembly in microRNA target identification.
Authors: Stanhope S.A. , Sengupta S. , den Boon J. , Ahlquist P. , Newton M.A. .
Source: Plos Computational Biology, 2009 Sep; 5(9), p. e1000516.
PMID: 19779550
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MicroRNA 29c is down-regulated in nasopharyngeal carcinomas, up-regulating mRNAs encoding extracellular matrix proteins.
Authors: Sengupta S. , den Boon J.A. , Chen I.H. , Newton M.A. , Stanhope S.A. , Cheng Y.J. , Chen C.J. , Hildesheim A. , Sugden B. , Ahlquist P. .
Source: Proceedings Of The National Academy Of Sciences Of The United States Of America, 2008-04-15 00:00:00.0; 105(15), p. 5874-8.
EPub date: 2008-04-15 00:00:00.0.
PMID: 18390668
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Fundamental differences in cell cycle deregulation in human papillomavirus-positive and human papillomavirus-negative head/neck and cervical cancers.
Authors: Pyeon D. , Newton M.A. , Lambert P.F. , den Boon J.A. , Sengupta S. , Marsit C.J. , Woodworth C.D. , Connor J.P. , Haugen T.H. , Smith E.M. , et al. .
Source: Cancer Research, 2007-05-15 00:00:00.0; 67(10), p. 4605-19.
PMID: 17510386
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Genes involved in DNA repair and nitrosamine metabolism and those located on chromosome 14q32 are dysregulated in nasopharyngeal carcinoma.
Authors: Dodd L.E. , Sengupta S. , Chen I.H. , den Boon J.A. , Cheng Y.J. , Westra W. , Newton M.A. , Mittl B.F. , McShane L. , Chen C.J. , et al. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2006 Nov; 15(11), p. 2216-25.
PMID: 17119049
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A statistical test of the hypothesis that polyclonal intestinal tumors arise by random collision of initiated clones.
Authors: Newton M.A. , Clipson L. , Thliveris A.T. , Halberg R.B. .
Source: Biometrics, 2006 Sep; 62(3), p. 721-7.
PMID: 16984313
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Genome-wide expression profiling reveals EBV-associated inhibition of MHC class I expression in nasopharyngeal carcinoma.
Authors: Sengupta S. , den Boon J.A. , Chen I.H. , Newton M.A. , Dahl D.B. , Chen M. , Cheng Y.J. , Westra W.H. , Chen C.J. , Hildesheim A. , et al. .
Source: Cancer Research, 2006-08-15 00:00:00.0; 66(16), p. 7999-8006.
PMID: 16912175
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Polyclonality Of Familial Murine Adenomas: Analyses Of Mouse Chimeras With Low Tumor Multiplicity Suggest Short-range Interactions
Authors: Thliveris A.T. , Halberg R.B. , Clipson L. , Dove W.F. , Sullivan R. , Washington M.K. , Stanhope S. , Newton M.A. .
Source: Proceedings Of The National Academy Of Sciences Of The United States Of America, 2005-05-10 00:00:00.0; 102(19), p. 6960-5.
PMID: 15870186
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Lack of association between EBV and breast carcinoma.
Authors: Perrigoue J.G. , den Boon J.A. , Friedl A. , Newton M.A. , Ahlquist P. , Sugden B. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2005 Apr; 14(4), p. 809-14.
PMID: 15824148
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Genetic determination of susceptibility to estrogen-induced mammary cancer in the ACI rat: mapping of Emca1 and Emca2 to chromosomes 5 and 18.
Authors: Gould K.A. , Tochacek M. , Schaffer B.S. , Reindl T.M. , Murrin C.R. , Lachel C.M. , VanderWoude E.A. , Pennington K.L. , Flood L.A. , Bynote K.K. , et al. .
Source: Genetics, 2004 Dec; 168(4), p. 2113-25.
PMID: 15611180
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Detecting differential gene expression with a semiparametric hierarchical mixture method.
Authors: Newton M.A. , Noueiry A. , Sarkar D. , Ahlquist P. .
Source: Biostatistics (oxford, England), 2004 Apr; 5(2), p. 155-76.
PMID: 15054023
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On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles.
Authors: Kendziorski C.M. , Newton M.A. , Lan H. , Gould M.N. .
Source: Statistics In Medicine, 2003-12-30 00:00:00.0; 22(24), p. 3899-914.
PMID: 14673946
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On Differential Variability Of Expression Ratios: Improving Statistical Inference About Gene Expression Changes From Microarray Data
Authors: Newton M.A. , Kendziorski C.M. , Richmond C.S. , Blattner F.R. , Tsui K.W. .
Source: Journal Of Computational Biology : A Journal Of Computational Molecular Cell Biology, 2001; 8(1), p. 37-52.
PMID: 11339905
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Inferring the location and effect of tumor suppressor genes by instability-selection modeling of allelic-loss data.
Authors: Newton M.A. , Lee Y. .
Source: Biometrics, 2000 Dec; 56(4), p. 1088-97.
PMID: 11129465
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A Resistant Genetic Background Leading To Incomplete Penetrance Of Intestinal Neoplasia And Reduced Loss Of Heterozygosity In Apcmin/+ Mice
Authors: Shoemaker A.R. , Moser A.R. , Midgley C.A. , Clipson L. , Newton M.A. , Dove W.F. .
Source: Proceedings Of The National Academy Of Sciences Of The United States Of America, 1998-09-01 00:00:00.0; 95(18), p. 10826-31.
PMID: 9724789
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On The Statistical Analysis Of Allelic-loss Data
Authors: Newton M.A. , Gould M.N. , Reznikoff C.A. , Haag J.D. .
Source: Statistics In Medicine, 1998-07-15 00:00:00.0; 17(13), p. 1425-45.
PMID: 9695190
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