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BCRF Grantee Since


Area(s) of Focus

David Agus , MD

Professor of Medicine and Engineering
Keck School of Medicine and
Viterbi School of Engineering
Director, USC Center for Applied Molecular Medicine
Director, USC Westside Cancer Center
University of Southern California

Current Research

Computational modeling has shown promise in explaining aspects of cancer, but it has not yet demonstrated its potential in helping clinicians and patients in making treatment decisions. Significant progress has been made in creating elemental computational models that are combined with data from customized experiments in order to address specific cancer problems. Likewise, innovative experiments continue to shed light into the biology of breast cancer. However, there has been far less progress to date in sharing and recombining these data and computational models into larger, more comprehensive systems that can not only explain complex multiscale cancer dynamics, but also help patients and their clinicians to make breast cancer treatment choices.

The aim of Dr. Agus’s BCRF-funded research is to create a computational platform that will allow clinical teams to create automated, patient-tailored cancer simulators that dynamically combine the patient’s health records and clinical measurements with state-of-the-art computer models to predict their breast cancer progression under a variety of treatment options. Clinicians can then choose the treatment plan that best meets the patient’s needs. This system will be built upon a community science approach: shared systems of compatible computational cancer models that can exchange data with each other and data repositories in standardized formats. In this project, Dr. Agus will advance towards a long-term vision by creating and testing an online, searchable repository for cancer experimental and simulation data in standardized formats, an initial database of two well-characterized breast cell types (non-malignant and malignant), and methods for a detailed 3-D computational breast cancer model to access the repository and contribute new simulation results."

Mid-Year Summary

This project aims to create powerful tools that help computer modelers combine their software into sophisticated cancer simulators that can predict breast cancer progression in individual patients and help guide clinical decisions. In the past three months, Dr. Agus and team created a new data standard to record the properties of breast cancer cell lines under a variety of conditions, allowing them to be stored in a data repository as digital cell lines. They also created a format for recording simulation and experimental data in a standardized way in the data repository, paving the way for improved data mining, machine learning, and computer modeling. The researchers adapted a sophisticated cancer cell simulator to use the new format, and are preparing to add a variety of important breast cancer cell lines to the data repository. Much like biologists can perform experiments on standardized breast cancer cell lines and compare their results, computer modelers will soon be able to use digital cell lines in cancer simulations to share, combine, and improve their predictions.


Dr. David B. Agus is a professor of medicine and engineering at the University of Southern California (USC) Keck School of Medicine and Viterbi School of Engineering and heads the USC Westside Cancer Center and the USC Center for Applied Molecular Medicine. Dr. Agus leads a multidisciplinary team of researchers dedicated to the development and use of technologies to guide doctors in making health-care decisions tailored to individual needs, and directs a National Cancer Institute Physical Sciences in Oncology Center at USC. He is a medial oncologist and the co-founder of two personalized medicine companies, Navigenics and Applied Proteomics. Dr. Agus is an international leader in new technologies and approaches for personalized healthcare. His first book, The End of Illness, was published in 2012 and was a New York Times #1 best seller and also the subject of a PBS series.