Purpose: The principle goal of our center is the development, validation and application of proteomics technologies for the diagnosis and management of disease. Our vision, as shown above, begins with patients of known outcomes. These patients will have biopsies of their tumor, and/or blood/plasma samples, interrogated by quantitative protein profiling techniques to discover and establish initial panels of markers of response to a given therapy. Once an initial panel has been constructed, it will direct the treatment course for patients. Samples from these patients may then be used to iteratively refine the existing marker panel and potentially to inform cancer biology and aid in the identification of new targets for the development of novel therapeutic agents. Towards this vision, we are actively applying existing proteomics techniques to clinical and biological samples to better understand the biology of cancer and other diseases. In addition, we are developing new technologies that will allow us to extract more information more reproducibly from biological and clinical samples. In particular, we are focusing on experimental techniques for sample enrichment and peptide labeling. Computationally, our focus is on the reliable and reproducible extraction and comparison of quality assurance and quantitative information from high-resolution mass spectrometric proteomics data. Finally, we are investigating computational linguistics methods for comparison of clinical annotations from multiple datasets.

History: In April 2009, Drs. Agus, Gross, Katz and Mallick initiated initiated the CAMM at USC. The Center has been implemented in response to the observation that some patients experience long latent periods between diagnosis and symptomatic progression while others are afflicted with an aggressive and rapidly fatal form of the disease and the similar observation that some patients experience a complete clinical response to therapy while others will exhibit complete resistance. A technology to stratify patients according to molecular abnormalities and then apply this understanding to predict treatment responsiveness would represent an important step towards improving the care of patients. The program includes team members with expertise spanning cancer biology, biochemistry, molecular biology, bioinformatics, computer science, electrical engineering, bioorganic chemistry, statistical physics and applied mathematics.