|Grant Number:||5R01CA064364-11 Interpret this number|
|Primary Investigator:||Newton, Michael|
|Organization:||University Of Wisconsin-Madison|
|Project Title:||Statistical Methods for Molecular Cancer Data|
DESCRIPTION (provided by applicant): Challenging questions are raised by the measurement of genomic and transcriptional aberrations in cancer cells. In a broad sense, answering these questions will further our understanding of cancer biology, guide more sensitive diagnosis, and ultimately improve therapy. These questions present clear opportunities for the development of statistical methods, since many sources of variation affect the measurements, and these need to be properly accommodated so that relevant biological signals can be detected. The four specific aims of the proposed project react to the demand for better statistical methods to study dependencies in multivariate profiles of genomic aberration and to study patterns of differential gene expression. Motivated by recent innovations in the stochastic modeling of genomic aberrations, the first three specific aims will extend and fully evaluate the model-based instability-selection approach to data analysis. The fourth aim focuses on new methodology to characterize patterns of differential gene expression among multiple groups. The proposed research merges advanced stochastic modeling techniques and tools from statistical computing to provide useful statistical methods for oncologists who study cancer at the molecular level.
Statistical significance of optical map alignments.
Authors: Sarkar D, Goldstein S, Schwartz DC, Newton MA
Source: J Comput Biol, 2012 May;19(5), p. 478-92.
EPub date: 2012 Apr 16.
Longitudinal assessment of colonic tumor fate in mice by computed tomography and optical colonoscopy.
Authors: Durkee BY, Shinki K, Newton MA, Iverson CE, Weichert JP, Dove WF, Halberg RB
Source: Acad Radiol, 2009 Dec;16(12), p. 1475-82.
Statistical use of argonaute expression and RISC assembly in microRNA target identification.
Authors: Stanhope SA, Sengupta S, den Boon J, Ahlquist P, Newton MA
Source: PLoS Comput Biol, 2009 Sep;5(9), p. e1000516.
EPub date: 2009 Sep 25.
MicroRNA 29c is down-regulated in nasopharyngeal carcinomas, up-regulating mRNAs encoding extracellular matrix proteins.
Authors: Sengupta S, den Boon JA, Chen IH, Newton MA, Stanhope SA, Cheng YJ, Chen CJ, Hildesheim A, Sugden B, Ahlquist P
Source: Proc Natl Acad Sci U S A, 2008 Apr 15;105(15), p. 5874-8.
EPub date: 2008 Apr 4.
Fundamental differences in cell cycle deregulation in human papillomavirus-positive and human papillomavirus-negative head/neck and cervical cancers.
Authors: Pyeon D, Newton MA, Lambert PF, den Boon JA, Sengupta S, Marsit CJ, Woodworth CD, Connor JP, Haugen TH, Smith EM, Kelsey KT, Turek LP, Ahlquist P
Source: Cancer Res, 2007 May 15;67(10), p. 4605-19.
Genes involved in DNA repair and nitrosamine metabolism and those located on chromosome 14q32 are dysregulated in nasopharyngeal carcinoma.
Authors: Dodd LE, Sengupta S, Chen IH, den Boon JA, Cheng YJ, Westra W, Newton MA, Mittl BF, McShane L, Chen CJ, Ahlquist P, Hildesheim A
Source: Cancer Epidemiol Biomarkers Prev, 2006 Nov;15(11), p. 2216-25.
A statistical test of the hypothesis that polyclonal intestinal tumors arise by random collision of initiated clones.
Authors: Newton MA, Clipson L, Thliveris AT, Halberg RB
Source: Biometrics, 2006 Sep;62(3), p. 721-7.
Genome-wide expression profiling reveals EBV-associated inhibition of MHC class I expression in nasopharyngeal carcinoma.
Authors: Sengupta S, den Boon JA, Chen IH, Newton MA, Dahl DB, Chen M, Cheng YJ, Westra WH, Chen CJ, Hildesheim A, Sugden B, Ahlquist P
Source: Cancer Res, 2006 Aug 15;66(16), p. 7999-8006.
Lack of association between EBV and breast carcinoma.
Authors: Perrigoue JG, den Boon JA, Friedl A, Newton MA, Ahlquist P, Sugden B
Source: Cancer Epidemiol Biomarkers Prev, 2005 Apr;14(4), p. 809-14.
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 KA, Tochacek M, Schaffer BS, Reindl TM, Murrin CR, Lachel CM, VanderWoude EA, Pennington KL, Flood LA, Bynote KK, Meza JL, Newton MA, Shull JD
Source: Genetics, 2004 Dec;168(4), p. 2113-25.
Detecting differential gene expression with a semiparametric hierarchical mixture method.
Authors: Newton MA, Noueiry A, Sarkar D, Ahlquist P
Source: Biostatistics, 2004 Apr;5(2), p. 155-76.
On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles.
Authors: Kendziorski CM, Newton MA, Lan H, Gould MN
Source: Stat Med, 2003 Dec 30;22(24), p. 3899-914.
Inferring the location and effect of tumor suppressor genes by instability-selection modeling of allelic-loss data.
Authors: Newton MA, Lee Y
Source: Biometrics, 2000 Dec;56(4), p. 1088-97.