Grant Number:	4R01CA097346-07 Interpret this number
Primary Investigator:	Siegmund, Kimberly
Organization:	University Of Southern California
Project Title:	Statistical Models in Epigenomics
Fiscal Year:	2011

DESCRIPTION (provided by applicant): Our primary objective is to utilize the "molecular clock hypothesis" to develop mathematical models that will allow us to study how cancers grow and spread. Human cancer growth cannot be directly observed and the overall goal is to develop an approach that can retrospectively reconstruct tumor progression by "reading" the ancestry surreptitiously written within genomes by replication errors. Sequences are commonly used to reconstruct the genealogy of species and individuals, and we propose to translate this general molecular phylogeny approach to human cancers. We will use DNA methylation data, an epigenetic modification of DNA that is replicated at cell division. As direct calculation can be either impractical or infeasible, we propose to use rejection algorithms, a simulation-based approach. This general framework will allow us to estimate the age of a tumor, the age of a metastasis, the methylation error rate, and whether the metastasis is derived from a specific population of cells from the primary cancer. Our aims are motivated by ongoing studies at the Norris Comprehensive Cancer Center at the University of Southern California. Specifically, we propose to: 1. Develop methods that will allow us to estimate parameters characterizing the growth of cancer using 5' to 3' DNA methylation patterns and validate these models using clinical data from patients and experimental data from cancer cell lines. The models will address the following biological problems: a. Estimate the number of cancer stem cells based on the types of ancestral trees inferred from the methylation patterns; b. Evaluate tumor heterogeneity, e.g. identify different subpopulations of cells in the left and right side of the tumor; c. Estimate tumor age and the rate at which methylation errors occur. 2. Extend the models developed in Aim 1 to include additional complexities. We propose to address the following: a. Modeling multiple gene regions within a single ancestral tree; b. Modeling autosomal genes (diploid genomes); c. Modeling multiple tissues (primary tumor and metastasis), to answer questions about whether the cell populations are the same age, or if one is younger and derived from the other; We will apply the methods developed in Aims 1-2 to DNA methylation patterns observed in primary tumors of the colon and distant metastasis in humans. PUBLIC HEALTH RELEVANCE: Cancer is the second leading cause of death in the United States in 2005, as reported by the National Center for Health Statistics. Its treatment relies on understanding how cancers grow and spread. We propose to develop mathematical models that can retrospectively reconstruct tumor histories, allowing us to address important biological questions about the growth and spread of cancer.




Identifying Differential Transcription Factor Binding In Chip-seq
Authors: Wu D.Y. , Bittencourt D. , Stallcup M.R. , Siegmund K.D. .
Source: Frontiers In Genetics, 2015; 6, p. 169.
PMID: 25972895
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Ancestral Inference In Tumors: How Much Can We Know?
Authors: Zhao J. , Siegmund K.D. , Shibata D. , Marjoram P. .
Source: Journal Of Theoretical Biology, 2014-10-21 00:00:00.0; 359, p. 136-45.
PMID: 24907673
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A Panel Of Three Markers Hyper- And Hypomethylated In Urine Sediments Accurately Predicts Bladder Cancer Recurrence
Authors: Su S.F. , de Castro Abreu A.L. , Chihara Y. , Tsai Y. , Andreu-Vieyra C. , Daneshmand S. , Skinner E.C. , Jones P.A. , Siegmund K.D. , Liang G. .
Source: Clinical Cancer Research : An Official Journal Of The American Association For Cancer Research, 2014-04-01 00:00:00.0; 20(7), p. 1978-89.
PMID: 24691641
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Non-specific Filtering Of Beta-distributed Data
Authors: Wang X. , Laird P.W. , Hinoue T. , Groshen S. , Siegmund K.D. .
Source: Bmc Bioinformatics, 2014; 15, p. 199.
PMID: 24943962
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Low-level Processing Of Illumina Infinium Dna Methylation Beadarrays
Authors: Triche T.J. , Weisenberger D.J. , Van Den Berg D. , Laird P.W. , Siegmund K.D. .
Source: Nucleic Acids Research, 2013 Apr; 41(7), p. e90.
PMID: 23476028
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Dna Methylation In The Arginase-nitric Oxide Synthase Pathway Is Associated With Exhaled Nitric Oxide In Children With Asthma
Authors: Breton C.V. , Byun H.M. , Wang X. , Salam M.T. , Siegmund K. , Gilliland F.D. .
Source: American Journal Of Respiratory And Critical Care Medicine, 2011-07-15 00:00:00.0; 184(2), p. 191-7.
PMID: 21512169
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Modeling Measurement Error In Tumor Characterization Studies
Authors: Rakovski C. , Weisenberger D.J. , Marjoram P. , Laird P.W. , Siegmund K.D. .
Source: Bmc Bioinformatics, 2011-07-13 00:00:00.0; 12, p. 284.
PMID: 21752297
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Statistical Approaches For The Analysis Of Dna Methylation Microarray Data
Authors: Siegmund K.D. .
Source: Human Genetics, 2011 Jun; 129(6), p. 585-95.
PMID: 21519831
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Dna Methylation Changes In Atypical Adenomatous Hyperplasia, Adenocarcinoma In Situ, And Lung Adenocarcinoma
Authors: Selamat S.A. , Galler J.S. , Joshi A.D. , Fyfe M.N. , Campan M. , Siegmund K.D. , Kerr K.M. , Laird-Offringa I.A. .
Source: Plos One, 2011; 6(6), p. e21443.
PMID: 21731750
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High Dna Methylation Pattern Intratumoral Diversity Implies Weak Selection In Many Human Colorectal Cancers
Authors: Siegmund K.D. , Marjoram P. , Tavaré S. , Shibata D. .
Source: Plos One, 2011; 6(6), p. e21657.
PMID: 21738754
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Unique Dna Methylation Patterns Distinguish Noninvasive And Invasive Urothelial Cancers And Establish An Epigenetic Field Defect In Premalignant Tissue
Authors: Wolff E.M. , Chihara Y. , Pan F. , Weisenberger D.J. , Siegmund K.D. , Sugano K. , Kawashima K. , Laird P.W. , Jones P.A. , Liang G. .
Source: Cancer Research, 2010-10-15 00:00:00.0; 70(20), p. 8169-78.
PMID: 20841482
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Hormone Therapy, Dna Methylation And Colon Cancer
Authors: Wu A.H. , Siegmund K.D. , Long T.I. , Cozen W. , Wan P. , Tseng C.C. , Shibata D. , Laird P.W. .
Source: Carcinogenesis, 2010 Jun; 31(6), p. 1060-7.
PMID: 20064828
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Using Dna Methylation Patterns To Infer Tumor Ancestry
Authors: Hong Y.J. , Marjoram P. , Shibata D. , Siegmund K.D. .
Source: Plos One, 2010; 5(8), p. e12002.
PMID: 20711251
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Modeling Dna Methylation In A Population Of Cancer Cells
Authors: Siegmund K.D. , Marjoram P. , Shibata D. .
Source: Statistical Applications In Genetics And Molecular Biology, 2008; 7(1), p. Article 18.
PMID: 18597664
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Statistical Methods For Evaluating Dna Methylation As A Marker For Early Detection Or Prognosis
Authors: Alonzo T.A. , Siegmund K.D. .
Source: Disease Markers, 2007; 23(1-2), p. 113-20.
PMID: 17325431
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Modeling Exposures For Dna Methylation Profiles
Authors: Siegmund K.D. , Levine A.J. , Chang J. , Laird P.W. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2006 Mar; 15(3), p. 567-72.
PMID: 16537717
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Cluster Analysis For Dna Methylation Profiles Having A Detection Threshold
Authors: Marjoram P. , Chang J. , Laird P.W. , Siegmund K.D. .
Source: Bmc Bioinformatics, 2006; 7, p. 361.
PMID: 16872497
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A Comparison Of Cluster Analysis Methods Using Dna Methylation Data
Authors: Siegmund K.D. , Laird P.W. , Laird-Offringa I.A. .
Source: Bioinformatics (oxford, England), 2004-08-12 00:00:00.0; 20(12), p. 1896-904.
PMID: 15044245
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