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Analysis of alterations in the human cancer genome

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dc.contributor.advisor Gad Getz and Matthew Meyerson. en_US
dc.contributor.author Carter, Scott L. (Scott Lambert) en_US
dc.contributor.other Harvard University--MIT Division of Health Sciences and Technology. en_US
dc.date.accessioned 2012-01-12T19:28:52Z
dc.date.available 2012-01-12T19:28:52Z
dc.date.copyright 2011 en_US
dc.date.issued 2011 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/68452
dc.description Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2011. en_US
dc.description Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references. en_US
dc.description.abstract Aneuploidy, an abnormal complement of chromosomes, is present in approximately 90% of human malignancies. Despite over 100 years of research, many questions remain regarding the contribution of aneuploidy to the cancer phenotype. In this thesis, we develop computational methods to infer the presence and specific patterns of aneuploidy across thousands of primary cancer tissue specimens. We then combine these inferences with clinical and genomic features of the cancer samples to refine our understanding of both the clinical implications of aneuploidy, and how it evolves in various human cancers. We identified a signature of chromosomal instability from specific genes whose expression was consistently correlated with aneuploidy in several cancer types, and which was predictive of poor clinical outcome multiple cancer types. Current genomic characterization techniques measure somatic alterations in a cancer sample in units of genomes (DNA mass). The meaning of such measurements is highly dependent on the tumors purity and its overall ploidy; they are hence complicated to interpret and compare across samples. Ideally, copy-number should be measured in copies-per-cancer-cell. Such measurements are straightforward to interpret and, for alterations that are fixed in the cancer cell population, are simple integer values. We develop two computational methods to infer tumor purity and malignant cell ploidy directly from allelic analysis of DNA. First we describe HAPSEG, a probabilistic method to interpret bi-allelic marker data in cancer samples in order to produce genome-wide estimates of homologue specific copy-ratios. Second, we describe ABSOLUTE, a method that infers purity, ploidy, and absolute copy-numbers from the estimates produced by HAPSEG. In addition, ABSOLUTE can analyze point mutations to detect subclonal heterogeneity and somatic homozygosity. We used ABSOLUTE to analyze ovarian cancer data and discovered that 54% of somatic point mutations were, in fact, subclonal. In contrast, mutations occurring in key tumor suppressor genes, TP53 and NF1 were predominantly clonal and homozygous. Analysis of absolute allelic copy-number profiles from 3,155 cancer specimens revealed that genome-doubling events are common in human cancer, and likely occur in already aneuploid cells in many cancer types. By correlating genome-doubling status with mutation data, we found that homozygous mutations in NF1 occurred predominantly in non-doubled samples. This finding suggests that genome doubling influences the pathways of tumor progression, with recessive inactivation being less common after genome doubling. en_US
dc.description.statementofresponsibility by Scott L. Carter. en_US
dc.format.extent 218 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Harvard University--MIT Division of Health Sciences and Technology. en_US
dc.title Analysis of alterations in the human cancer genome en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Harvard University--MIT Division of Health Sciences and Technology. en_US
dc.identifier.oclc 769119965 en_US


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