AI-Powered 3D Bioprinting Platform Accelerates Cancer Drug Testing Using Patient-Derived Organoids
核心洞察
UCLA researchers developed a unified platform combining 3D bioprinting, label-free imaging, and AI to rapidly test cancer therapies on patient-derived tumor organoids.
The system continuously monitors organoid growth dynamics and drug responses at single-organoid resolution across thousands of samples without dyes or destructive assays.
The platform successfully measured drug responses in both established cancer cell lines and patient-derived tumor samples, detecting rare resistant populations.
A research team at the UCLA Health Jonsson Comprehensive Cancer Center (搜索) has developed a new platform that integrates 3D bioprinting, advanced imaging, and artificial intelligence to accelerate the identification of promising cancer therapies. Described in Nature Protocols, the system creates tiny, lab-grown replicas of patient tumors—known as organoids—and continuously tracks their response to different drugs, offering a potential pathway toward more personalized cancer treatment.
Addressing a Critical Bottleneck in Cancer Research
Tumor organoids have emerged as powerful tools for cancer research because they more closely resemble patient tumors than traditional laboratory models. However, many current systems struggle to combine biological accuracy with the speed, consistency, and scale required for larger studies or clinical application. The UCLA platform directly tackles this limitation by generating and analyzing large numbers of patient-derived tumor organoids while capturing detailed information about treatment response.
“Instead 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,” said Dr. Michael Teitell, director of the UCLA Health Jonsson Comprehensive Cancer Center (搜索), professor of pathology and laboratory medicine, and co-senior author of the study.
How the Platform Works
The unified workflow employs extrusion bioprinting to generate three-dimensional tumor organoids embedded in extracellular matrix constructs designed for high-throughput multiwell formats. These organoids are then continuously monitored using high-speed, label-free quantitative phase imaging, which tracks changes in biomass and growth dynamics to measure tumor fitness over time. Critically, the approach does not require dyes or destructive assays, which can alter cell behavior and limit the duration of observation.
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 enables researchers to quantify drug responses at single-organoid resolution across thousands of samples, providing a detailed view of tumor heterogeneity and differential treatment responses.
Validated Performance
The platform successfully measured how tumor organoids responded to drug treatment over time, both in established cancer cell lines and in a patient-derived tumor sample. Advanced imaging allowed researchers to continuously monitor organoid growth changes in response to a range of drugs, while artificial intelligence helped analyze large amounts of data and track responses at the level of individual organoids.
Dr. Teitell emphasized the platform's ability 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.”
Toward Personalized Cancer Treatment
The technology points toward a clinical 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.
The study's co-senior authors are Dr. Michael Teitell of UCLA and Alice Soragni of the University of Colorado School of Medicine. The first author is Bowen Wang, a postdoctoral fellow in the Teitell Laboratory. Additional authors include Peyton Tebon, Thang Nguyen, and Sara Sartini of UCLA, along with Graeme Murray, Daniel Guest, and Jason Reed of Virginia Commonwealth University's Massey Comprehensive Cancer Center. 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.