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General Departmental Seminar Series


A very very simple network model for cancer genome abnormalities

Michael Newton, Department of Biostatistics and Medical Informatics & Statistics,
University of Wisconsin

Wednesday, Oct 25, 2000, 4:00 pm

1221 CSSC, 1210 W. Dayton St.

ABSTRACT

A recurrent data analysis problem arising in cancer biology is to separate sporadic genomic abnormalities from those which are not sporadic and thus are likely to have some biological significance. When multiple genetic alterations are necessary for tumor development, selection forces acting on the tumor cell lineage induce stochastic dependence upon the abnormalities presented by an observed tumor cell. I will discuss a simple framework for generating probability models for data, and will demonstrate this `instability-selection' framework for several data types including loss of heterozygosity and copy number variations measured by comparative genomic hybridization. The main contribution I want to present is a joint distribution for correlated binary data based on a simple network representation of tumorigenesis.


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