Researchers at UC San Francisco announced on June 12 that they have developed a robotic platform to systematically identify, track, and treat cancer cells that survive initial drug treatments. These surviving "persister" cells are genetically identical to the tumor but are rare and difficult to isolate, making them a challenge for ongoing cancer therapy.
The team built the system to address the problem of persister cells seeding new tumors, which often forces patients into repeated cycles of testing and treatment. The platform enables scientists to observe thousands of miniature tumors simultaneously in laboratory conditions. According to Xiaoxiao "Vany" Sun, PhD, first author of the paper and assistant researcher in the UCSF Department of Pharmaceutical Chemistry, "A few years ago, people were still asking whether persister cells were real. Now we can find them and test ideas for how to eliminate them."
The findings were published in Science Advances on June 12. The researchers gathered 94 drug candidates previously identified by other laboratories as potential therapies against persister cells. They tested each candidate at different doses on persisters from two types of lung cancer treated with standard therapies—a process requiring thousands of experiments that was made feasible by their automated system.
Inside controlled incubators, stacks of 384-well plates held thousands of mini tumors while a robotic arm moved plates between stations for drug application and imaging. One station used sound waves for precise dosing; others stained tumors with antibodies or captured microscopic images.
Of all drugs tested, nine consistently weakened persister cells across samples from different treatment backgrounds. This suggests shared vulnerabilities among these resilient tumor cells that could be targeted by future therapies.
Steve Altschuler, PhD, professor of Pharmaceutical Chemistry at UCSF and co-senior author, said, "We expected each tumor to behave as its own special case. Instead, we found patterns that held up across many different samples, suggesting there may be underlying rules that can help predict which therapies are most likely to work." The team plans further research using more tumor types and hopes their dataset will assist other scientists working on eliminating persister cells before they cause drug-resistant disease.
