Patient-derived micro-organospheres enable clinical precision oncology.

Abstract

Patient-derived xenografts (PDXs) and patient-derived organoids (PDOs) have been shown to model clinical response to cancer therapy. However, it remains challenging to use these models to guide timely clinical decisions for cancer patients. Here, we used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of micro-organospheres (MOSs) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology. A clinical study of recently diagnosed metastatic colorectal cancer (CRC) patients using an MOS-based precision oncology pipeline reliably assessed tumor drug response within 14 days, a timeline suitable for guiding treatment decisions in the clinic. Furthermore, MOSs capture original stromal cells and allow T cell penetration, providing a clinical assay for testing immuno-oncology (IO) therapies such as PD-1 blockade, bispecific antibodies, and T cell therapies on patient tumors.

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Citation

Published Version (Please cite this version)

10.1016/j.stem.2022.04.006

Publication Info

Ding, Shengli, Carolyn Hsu, Zhaohui Wang, Naveen R Natesh, Rosemary Millen, Marcos Negrete, Nicholas Giroux, Grecia O Rivera, et al. (2022). Patient-derived micro-organospheres enable clinical precision oncology. Cell stem cell, 29(6). pp. 905–917.e6. 10.1016/j.stem.2022.04.006 Retrieved from https://hdl.handle.net/10161/31495.

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Scholars@Duke

Shen

Xiling Shen

Adjunct Professor in the Department of Pathology

Dr. Shen’s research interests lie at precision medicine and systems biology. His lab integrates engineering, computational and biological techniques to study cancer, stem cells, microbiota and the nervous system in the gut. This multidisciplinary work has been instrumental in initiating several translational clinical trials in precision therapy. He is the director of the Woo Center for Big Data and Precision Health (DAP) and a core member of the Center for Genomics and Computational Biology (GCB).


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