Cancer immunotherapy drugs known as immune checkpoint inhibitors (ICIs) can be miracle drugs for cancer patients, curing some and turning deadly disease into a manageable chronic condition in others. But these drugs work for only a subset of patients, with few indications why—a knowledge gap that has detrimental effects on patient prognosis, clinical trial recruitment and research that could lead to new therapies.
A new artificial intelligence model called COMPASS, developed by Harvard Medical School researchers and their colleagues, improves prediction of which patients are most likely to respond to ICIs. Using data from patients treated in the past, the model outperformed the best existing approaches by 8.5%. It makes its predictions based on patients’ tumor gene activity and provides a rationale for its output.
If these results are validated in a future clinical trial, COMPASS could lead to better personalized medicine for cancer patients, more efficient trial enrollment for new therapies and new drug targets for researchers to explore.








