A Statistical Approach to Modeling Genomic Aberrations in Cancer Cells
Whereas most cells in the body carry the normal complement of 23 chromosome pairs, the cells within a cancerous tumor very often present highly abnormal genomic structure. Deletions, amplifications, rearrangements and mutations are common at various scales and are highly variable amongst tumors, as indicated by molecular technologies which enable ever better measurement. It is an important statistical problem to separate sporadic abnormalities from those that may not be sporadic and that may have some biological significance. We discuss a modeling strategy for genomic aberration data which allows us to to infer combinations of aberrations that together increase the chance that a precancerous cell will have a descendant tumor lineage. The likelihood component involves a network of pathway structures. Markov chain Monte Carlo is used to sample from the space of these oncogenic networks. We illustrate the methodology with comparative genomic hybridizations from a recent breast cancer study, and we derive a likelihood formula for the larger class of tree-like networks.