Cancer is a disease driven by mutations in the genome that alter the
structure, function, and regulation of genes.  These mutations range from
single letter changes in the DNA sequence to more drastic rearrangements,
gains, or losses of large pieces of DNA.  In some types of cancer these
large-scale alterations are directly implicated in the pathogenesis of
cancer and provide targets for cancer diagnostics and therapeutics.

I will describe computational methods for reconstructing tumor genome
architectures and analyzing rearrangements in tumor genomes at high
resolution using a technique called End Sequence Profiling (ESP).  These
methods are inspired by techniques in comparative genomics and view a tumor
genome as a rearranged version of the normal human genome.  In this
framework, both the human and tumor genomes are represented by permutations
and the problem is to find a parsimonious sequence of rearrangement
operations that transform one permutation into another. I will also
describe how computational analysis of ESP data suggests mechanisms that
produce complicated patterns of overlapping rearrangement and duplication
events that are observed in some tumor genomes.

Another experimental technique called array comparative genomic
hybridization (aCGH) has become indispensable in the identification of
duplicated and deleted segments of DNA in tumor genomes.  ESP provides an
effective complement to aCGH, and I will discuss how to combine data from
both types of experiments using network flow techniques in order to obtain
a comprehensive view of tumor genome architecture.  I will demonstrate the
application of these methods to ESP and aCGH data from breast cancers.
Finally, I will describe the implications of this work for the recently
proposed Cancer Genome Atlas, a genome project for cancer.