Cancer is a complex genetic disease that results from a combination of genetic and environmental perturbations to biomolecular networks that typically maintain a homeostatic balance between normal cellular functional states, such as proliferation, apoptosis, and differentiation. Using genome-wide genomic and proteomic measurements of cancer cells (primary tumors, distant metastases, cell lines treated with chemotherapeutic drugs, etc.), coupled with computational approaches, we can gain insights into cellular dysfunction underlying cancer onset, progression, and metastasis.
At the same time, accurate and early diagnostic markers are critical to the prevention and treatment of cancers. Genome-wide measurements of cancer tissues, combined with statistical pattern recognition and machine learning approaches, allow us to determine sets of informative genes or proteins whose measurements may be used for prognosis or diagnosis. Examples include: distinguishing subtypes or different stages of a cancer, determining whether a cancer has metastatic potential, predicting survival or the likelihood of a successful response to a therapy.