A statistical method to discover significant combinations
of genetic aberrations associated with cancer using
comparative genomic hybridization profiles
A statistical method to discover significant combinations
of genetic aberrations associated with cancer using
comparative genomic hybridization profiles
M.A. Newton
October 2001 Technical Report 148 , Department of Biostatistics
and Medical Informatics, UW Madison
Abstract:
I introduce a model-based statistical methodology for the analysis
of copy-number variations in cancer genomes measured by comparative
genomic hybridization. The stochastic
model involves random genomic instability in an unobserved
progenitor cell followed by selection
of cell lineages in which oncogenic pathways have been opened.
I investigate sampling properties of the model and describe
Markov chain Monte Carlo methodology for model fitting. A double-Polya-urn
prior is introduced to characterize prior information about the
oncogenic pathway structure. The methodology is tested and used
to reanalyze genomic aberrations from 116 renal cell carcinomas.
In addition to point estimates of the underlying oncogenic pathways,
the methodology produces posterior probabilities that any given aberration
is relevant to oncogenesis and pairwise posterior probabilities that
pairs of aberrations reside on a common pathway. It infers the
set of sporadic aberrations, and provides a model-based clustering
of all measured aberrations. From the model one can compute the
posterior probability that a tumor followed any one of the oncogenic
pathways, thus also providing a model-based clustering of the tumors.
Limitations and possible extensions of the methodology are discussed.
Key words: correlated binary data;
genetic instability; Markov chain Monte Carlo; model-based clustering;
oncogenic pathways; selection
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