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David B. Agus, MD
Professor of Medicine and Engineering
Founding Director and CEO
Lawrence J. Ellison Institute for Transformative Medicine
University of Southern California, Keck School of Medicine
Los Angeles, California
Goal: To use advanced computational tools to predict which HER2-positive breast cancers will respond to HER2-targeted therapy.
Impact: HER2-positive breast tumors are variable and do not always respond to targeted treatment. Dr. Agus is using machine learning on tissue sample images to predict whether HER2-positive breast cancers will respond to HER2-targeted therapy and improve personalized treatment plans for patients.
What’s next: He and his team will use artificial intelligence (AI) techniques to explore the microscopic tissue patterns of the tumors to predict tumor response to HER2-targeted therapy.
In his earlier BCRF-supported research, Dr. Agus developed AI tools to determine breast cancer subtype based on tissue architecture. Now, Dr. Agus aims to apply machine learning to predict how HER2-positive tumors will respond to targeted treatment. Personalized HER2-targeted therapy response prediction could save many breast cancer patients from ineffective treatment and side effects and determine the best treatment plan as early as possible.
Full Research Summary
Research area: To explore the ability of artificial intelligence-enhanced digital pathology analysis of HER2-positive breast cancer specimens to improve outcome predictions.
Impact: Patients who receive HER2-targeted therapy do not always benefit from it, and instead suffer possible side effects and miss the opportunity to receive more appropriate treatment. Dr. Agus’ goal is to distinguish tumors that are likely to respond to targeted therapy from those that are not through the use of machine learning on tissue samples. His previous BCRF-supported research focused on using machine learning to discern breast cancer subtypes from microscopic images of tumors. Dr. Agus will advance these techniques to determine the best treatment for individual patients.
Current investigation: He and his colleagues are investigating the three-dimensional architecture of HER2-positive breast tumors and how this is related to outcome. They are now employing a machine learning algorithm to analyze tumor samples for predictors of treatment response.
What he’s learned so far: Dr. Agus and his colleagues have previously demonstrated the capability to create machine learning classifiers for molecular marker status in breast cancer from diagnostic images. His work leverages “tissue fingerprints” that are based on small image patches to extract the relevant biology, upon which they build classifiers of breast cancer subtype. The tissue fingerprint method has led to greater molecular classification ability for breast cancer and provides the basis for Dr. Agus’ current research in HER2-targeted therapy response prediction.
What’s next: The team will use the AI algorithm they have developed to predict clinical outcomes to HER2-targeted therapy from diagnostic images.
David Agus is professor of medicine and engineering at the University of Southern California, where he is the founding CEO of USC’s Lawrence J. Ellison Institute for Transformative 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 medical oncologist and an international leader in new technologies and approaches for personalized healthcare. He serves as a CBS News contributor. Dr. Agus’ first book, The End of Illness, was published in 2012 and is a New York Times #1 and international best seller, and subject of a PBS special. His second book, New York Times best-selling A Short Guide to a Long Life, was published January 2014, and his newest book The Lucky Years: How to thrive in the brave new world of health, also a New York Times bestseller was published in 2016. He is a 2017 recipient of the Ellis Island Medal of Honor.