Center for Applied Molecular Medicine


The Center for Applied Molecular Medicine (CAMM) is a multidisciplinary team of researchers dedicated to furthering the development and use of proteomic technologies to guide doctors in patient management decisions.

History of CAMM: In April 2009, Drs. Agus, Gross, Katz and Mallick 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.

David Agus, MD

David Agus, MD
Program Director
Professor of Medicine and Engineering; Director, Lawrence J. Ellison Institute for Transformative Medicine of USC; Director, USC Norris Westside Cancer Center; Director, USC Center for Applied Molecular Medicine, Keck School of Medicine of USC and USC Viterbi School of Engineering

Program Summary

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.