Organoids are tiny 3D structures cultured in the lab that recapitulate some of the structures of their organ of origin. They have been made from both healthy and malignant tissues, and have led to clinical applications, such as testing therapies for patients with cystic fibrosis.1
One of the big promises of organoids has been the testing of potential anticancer therapies on live, dividing structures that are isolated from tumor biopsies. However, there have been a number of issues with this model so far including cost, number of cells needed for successful organoid creation, and the time that would be required to grow such models. Now, a new study has shown that miniaturization of this method can enable testing of multiple drugs and combinations with results within a week of biopsy.2
“Originally we had a very low-throughput, old-school method to do this testing in organoids,” said Alice Soragni, PhD, group leader in the division of hematology-oncology at the David Geffen School of Medicine at UCLA, who is also the senior author of the paper. “Our bigger format required more cells, more reagents, and more work. The entire experiment to go through just a couple of conditions would take over half a day. It was very inefficient,” said Dr Soragni.
The new method grows organoids in a plastic plate that would fit in an outstretched palm, with 96 separate wells in it. The cancer biopsy sample is received from an oncologist, processed and suspended in a gel-like substance, which is formulated to contain proteins and nutrients in an extracellular matrix. A drop of this gel-cell mixture is then added to each well of the plate to make a ring structure, before being covered in nutrient media formulated to best support the type of cancer cells isolated. Within days, small organoid structures form in the rings and they are ready for drug testing.
“We test chemotherapies, including targeted kinase inhibitors at different concentrations and combinations. We also sequence every tumor, correlating our drug response data with the genomic data to see if we can figure out why seemingly similar tumors respond differently to the same drug,” said Dr Soragni.
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