Cancer drugs can shrink fast-growing tumors. But sometimes a few tumor cells survive. These “persister” cells seed new tumors, forcing cancer patients into arduous cycles of testing and treatment. The problem is that persister cells are rare—as few as one in a thousand tumor cells—and they’re genetically identical to the tumor, which makes them hard to find. Plus, their tenacity can be temporary, and by the time a scientist can get them in a petri dish, the qualities that helped them survive may have faded.
To figure out how to beat them, researchers at the University of California, San Francisco (UCSF), built a robotic system that treats thousands of mini tumors at once in the laboratory. Their resulting ResMap platform lets scientists systematically identify, track, and treat surviving cells. The platform revealed shared features among persister cells that could help explain why cancer comes back—features that could be exploited by future drug therapies to beat them. “A few years ago, people were still asking whether persister cells were real,” said Xiaoxiao “Vany” Sun, PhD, an assistant researcher in the UCSF Department of Pharmaceutical Chemistry. “Now we can find them and test ideas for how to eliminate them.”
Sun is first author of the team’s published paper in Science Advances, titled “ResMap: A community resource for systematic mapping of therapy-persistent residual cancer cell dependencies across contexts,” stating, “ResMap establishes a foundation for coordinated community efforts to accelerate rational persister-directed combination strategies toward the clinic.”
Residual disease following targeted therapy remains a key challenge to achieving lasting responses in oncogene-driven cancers, the authors stated. Drug-tolerant persister cells, which the team describes as “subpopulations that survive initial therapy without stable genetic resistance,” can contribute to residual disease and seed tumor relapse. “Targeting drug-tolerant persister cells has emerged as an essential complement to oncogene-directed therapy, yet the field has lacked a unified framework to evaluate and prioritize candidate targets,” they wrote. “Understanding and targeting these cells have emerged as a promising strategy for achieving lasting therapeutic outcomes.”
Cancer cell persistence was first described in 2010, the authors explained, and studies have linked persister survival to different biological processes and resulted in “an expanding list” of candidate therapeutic targets. However, they noted, “… despite over a decade of research, no persister-directed therapy has reached clinical approval.”
For their reported study, the team gathered 94 drug candidates that other laboratories had flagged as potential persister therapies. They wanted to test each drug at different doses, on persisters from two types of lung cancer that had been treated with standard therapies. “As a testbed, we selected four lung cancer models: two with EGFR inhibitor osimertinib (EGFRi)–treated EGFRmut cell lines (PC9 and MGH134) and two with KRAS inhibitor sotorasib (KRASi)–treated KRASG12C cell lines (LU65 and MGH1138-1),” they wrote in summary. Each model was screened under normal oxygen and hypoxic conditions.
It would require 10,000 painstaking, week-long experiments—so they built a robotic platform to eliminate the labor and inconsistency of doing it by hand.
Thousands of miniature tumors sat in stacks of 384-well plates inside controlled incubators. A robotic arm, like those used in pharmaceutical drug screening, moved the plates between experimental stations. One station used sound waves to deposit tiny, precise doses of drug onto each tumor (first, a lung cancer therapy; then, an experimental persister therapy). Other stations stained the tumors with antibodies and took microscopic images of each tumor or group of persisters.
The overall ResMap platform incorporated multiple components, the team explained. “… we developed the ResMap platform incorporating four integrated components: an automated high-throughput workflow, machine learning-based normalization, a persistence-specific metric, and a validated framework.”
Their results showed that of the tested drugs, nine consistently weakened persister cells. The findings suggest that persister cells may share common vulnerabilities, even if they had emerged under different treatment conditions. “Initial screening identified 12 targets with conserved anti-persister activity across genotypes and oxygen environments; follow-up validation reproduced nine of these targets and revealed variable degrees of persister specificity relative to general cytotoxicity.” The investigators suggested that, “Collectively, these findings suggest that although persister biology involves multiple adaptive programs, targeting individual, well-chosen survival pathways may be sufficient to meaningfully reduce residual disease burden.”
Steve Altschuler, PhD, professor of pharmaceutical chemistry at UCSF and co-senior author of the paper, 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 to expand the platform to include more tumor types and treatment conditions. They hope the resulting dataset will be a resource to help researchers eliminate persister cells before they can give rise to drug-resistant disease. “ResMap provides a community resource for coordinated validation efforts and rational combination design aimed at minimizing residual disease following anticancer therapy,” they stated.
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