New AI platform tracks cancer treatment responses in tumor organoids

Why it matters 

Tumor organoids have become powerful tools for cancer research because they more closely resemble patient tumors than traditional laboratory models. However, many current systems still struggle to combine biological accuracy with the speed, consistency, and scale needed for larger studies or clinical use. This study addresses that challenge by creating a platform that can generate and analyze large numbers of patient-derived tumor organoids while capturing detailed information about how they respond to treatment.

What the study did

To analyze the resulting datasets, the platform incorporates automated image reconstruction, deep learning-based segmentation, and machine learning-based tracking of individual organoid responses to therapy. This allows researchers to quantify drug responses at single-organoid resolution across thousands of samples, providing a detailed view of tumor heterogeneity and differences in how tumors respond to therapy.

What they found

Dr. Michael Teitell, director of the UCLA Health Jonsson Comprehensive Cancer Center, professor of pathology and laboratory medicine and co-senior author of the studyInstead of asking whether a drug works on average for a large number of tumor cells, we can now determine which specific organoids respond and which do not, and, ultimately, have an approach to determine the underlying reasons for unique response profiles. This allows us to measure drug responses across thousands of individual organoids, detect rare resistant tumor populations, track growth and treatment responses over time, and better predict which therapies may work for a particular patient."

What this means for patients

The technology points to a potential approach in which doctors could test cancer drugs on a patient's own tumor cells before treatment begins. By helping researchers identify which therapies are most likely to work for a particular tumor, the method could support more personalized treatment decisions, particularly for patients with rare and hard-to-treat cancers.

About the researchers

The study's co-senior authors are Dr. Michael Teitell, director of the UCLA Health Jonsson Comprehensive Cancer Center and professor of pathology and laboratory medicine, and Alice Soragni of the University of Colorado School of Medicine. The first author is Bowen Wang, a postdoctoral fellow in the Teitell Laboratory. Other authors include Peyton Tebon, Thang Nguyen and Sara Sartini of UCLA, and Graeme Murray, Daniel Guest and Jason Reed of Virginia Commonwealth University's Massey Comprehensive Cancer Center.

Funding

The work was funded in part by grants from the Air Force Office of Scientific Research, the Department of Defense, the National Science Foundation, and the National Institutes of Health.

Source:

University of California - Los Angeles Health Sciences

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