New Robotic System Accelerates Drug Discovery to Target Hidden Cancer Cells
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The system uses robotic arms to move laboratory dishes containing thousands of miniature tumours through a series of controlled testing stations, enabling scientists to evaluate multiple drug combinations and dosages simultaneously.
University of California, San Francisco researchers have introduced a robotic platform that dramatically accelerates the search for therapies targeting rare cancer cells that evade treatment.
The technology has identified several existing drugs with potential against these persistent cells, paving the way for more effective strategies to prevent cancer relapse.
Cancer treatment has made remarkable progress over the past decade, yet one of its greatest challenges remains the small number of cells that survive therapy.
Often referred to as "persister" cells, these treatment-resistant cells can remain dormant before triggering the return of the disease months or even years later. Because they are extremely rare, sometimes representing just one in every thousand tumour cells, they have proven difficult to study using conventional laboratory methods.
Researchers have now developed an automated robotic system designed to overcome this challenge. Instead of relying on thousands of labour-intensive experiments carried out manually, the platform uses robotic arms to move laboratory dishes containing thousands of miniature tumours through a series of controlled testing stations.
This automation enables scientists to evaluate multiple drug combinations and dosages simultaneously while maintaining highly consistent experimental conditions.
"We expected each tumour to behave as its own special case," senior study author Steve Altschuler of UC San Francisco said in a statement.
"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 focused its work on lung cancer samples and uncovered nearly 10,000 cellular variations that could enable cancer cells to escape the effects of treatment.
Testing these variations through traditional laboratory workflows would have required roughly 10,000 week-long experiments, making the process impractical. The robotic platform reduced this bottleneck by performing large-scale drug screening far more efficiently.
Using the system, researchers evaluated 94 medicines that had previously shown promise against persistent lung cancer cells. Nine drugs consistently demonstrated measurable activity across different tumour samples.
The findings suggest that although persister cells arise through diverse biological mechanisms, they may still share common weaknesses that can be targeted with carefully selected therapies.
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