We use genetic, genomic, and proteomic data to understand and model the biology of cancer and to develop methods to effectively deploy therapeutic agents. See research interests
Our lab is interested in using genetic, genomic, and proteomic data to understand and model the biology of cancer and to develop methods to effectively deploy therapeutic agents in the age of molecularly guided medicine.
My group participates in numerous collaborations to understand the effects of genome aberrations in cancer. These efforts include development of new methodologies for identifying changes in the cancer genome, systematic integration of multiple genomic data types (copy number, expression, and mutation) to better understand the process by which cancer develops, and the application of cell line systems as models for the genetic heterogeneity within cancers.
Modeling of Signaling Systems
The group is developing methods for computationally modeling the signaling that occurs in cancer cells. The idea behind the current work is to create Bayesian estimates of signaling systems from both dynamic and static signaling processes.
Immune System Therapies
It has been observed that the survival of breast cancer patients is related to the presence of immune system signatures and T cell infiltrates in a patient’s tumors. The hypothesis is that stimulating the immune system can be an effective mode of therapy. We aim to develop approaches to stimulate the natural immune response in breast cancer patients.