Grant Number:	5R01CA064364-08 Interpret this number
Primary Investigator:	Newton, Michael
Organization:	University Of Wisconsin Madison
Project Title:	Statistical Methods for Molecular Cancer Data
Fiscal Year:	2002

DESCRIPTION (Applicant's abstract): Variation is a fundamental property of data obtained in modern cancer research, be they genomic changes, gene expression abnormalities, or phenotypic data from gene mapping. Statistical methods arise from a logic that decomposes variation into that which is sporadic and that which may have some biological significance. The purpose of this research proposal is to develop statistical methods tailored to current and emerging data structures in cancer biology. If successful, this research will improve inference about cancer biology by enabling more efficient and robust extraction of information from the complex data that will be upon us. Four specific problems will be tackled. New microarray-based technologies have enabled DNA sequence copy number variations to be measured at very high resolution in cancer tumor cells, thus enhancing the characterization of suppressor genes and oncogenes. Sources of variation complicate inference. The first aim is to develop statistical methods for analyzing copy-number variation by extending existing models of allelic-imbalance data. New mathematical formulations and inference methods are proposed for this purpose. Microarray technology is also creating a wealth of data on gene expression in cancer cells. In Aim 2, hierarchical modeling methods are proposed to characterize the normal variation of these profiles, to enable comparison at various levels, such as among genes, or among microarrays, and to enable data reduction via nonparametric mixture modeling. The third aim concerns interval mapping methods which have for some time enabled the localization of genes in controlled animal experiments. Methods which are nonparametric in the phenotype distribution are highly robust, but available methods can lose too much information by working with sums of ranks. Sensitive nonparametric interval mapping methodology is proposed to enhance efficiency. Finally, phenotype-driven mutagenesis experiments based on quantitative phenotypes require statistical methods to efficiently screen mutagenized animals and to trace mutant genotypes through progeny testing and mapping. Parametric and nonparametric methods are proposed for this purpose. Developments on these four specific aims are linked by common biological features, by structural similarities in the statistical models, and in the computational issues raised by data analysis.




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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