Korean researchers have developed a technology that predicts immunotherapy response by analyzing tumors cell by cell at a microscopic level. The technology is expected to improve the accuracy of personalized treatment for individual patients.
According to the scientific community Tuesday, a research team led by Professor Park Ji-hwan at the Gwangju Institute of Science and Technology (GIST) recently developed an analytical technique (scMnT) that can precisely predict immunotherapy response at the single-cell level.
The technology overcomes the limitations of conventional "bulk analysis," which examined entire tumors at once and relied only on average values, by quantitatively identifying differences between individual cells.
Immunotherapy activates the body's immune system to induce immune cells to attack cancer. However, the approach has faced limitations, including significant variation in effectiveness among patients and cases of excessive immune response.
These differences are related to "microsatellite instability (MSI)," which arises from accumulated errors during DNA replication. Cancer cells with higher MSI are known to generate more abnormal proteins (neoantigens), making them more easily recognized by immune cells and therefore more responsive to immunotherapy. However, existing assessments simply classified MSI as either positive or negative, making precise prediction difficult.
The research team measured MSI as a numerical value with varying intensity and analyzed it on a cell-by-cell basis. As a result, they confirmed "heterogeneity," meaning cells with high MSI and cells with low MSI coexist within a single tumor. Even within one tumor, each cancer cell shows different degrees of replication errors, which the team was able to precisely distinguish at the cellular level, much like sorting the condition of individual fruits in a box.
Notably, immune cells gathered in large numbers in areas with high MSI and actively attacked cancer, while areas with low MSI showed weaker responses. This demonstrates that differences within a tumor can translate into actual differences in treatment efficacy.
The research team projected that the scMnT technology will be used to establish personalized treatment strategies for individual patients by more accurately predicting immunotherapy response.
"This research is significant in that it provides a foundation for understanding MSI as a 'quantitative indicator' rather than a simple binary metric," Professor Park Ji-hwan said. "It is expected to contribute to establishing personalized treatment strategies for cancer patients and improving the success rate of immunotherapy in the future."
