Genomics has the potential to revolutionize the diagnosis and management of
cancer by offering an unprecedented comprehensive view of the molecular
underpinnings of pathology.  Computational analysis is essential to
transform the masses of generated data into a mechanistic understanding of
disease.  Here we review current research aimed at uncovering the module
organization and function of transcriptional networks and responses in
cancer.  We first describe how methods that analyze biological processes in
terms of higher-level modules can identify robust signatures of disease
mechanisms.  We then discuss methods that aim to identify the regulatory
mechanisms underlying these modules and processes.  Finally, we show how
comparative analysis, combining human data with model organisms, can lead to
more robust findings.  We conclude by discussing the challenges of
generlizing these methods from cells to tissues and the opportunities they
offer to improve cancer diagnosis and management.