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IH Scientists Develop AI Tool to Predict Immunotherapy Response in Cancer Patients
In a groundbreaking proof-of-concept study, researchers at the National Institutes of Health (NIH) have developed an artificial intelligence (AI) tool to predict how cancer patients will respond to immune checkpoint inhibitors. This innovative machine-learning model utilises routine clinical data, including a simple blood test, to determine the effectiveness of these immunotherapy drugs. The study, published on June 3, 2024, in Nature Cancer, was led by scientists from the National Cancer Institute’s (NCI) Center for Cancer Research and Memorial Sloan Kettering Cancer Center.
AI Model Uses Routine Data for Predictions
Traditional biomarkers like tumour mutational burden and PD-L1 protein have limitations in accurately predicting responses to immune checkpoint inhibitors. The new AI model addresses these limitations by incorporating five routinely collected clinical features: a patient’s age, cancer type, history of systemic therapy, blood albumin level, and blood neutrophil-to-lymphocyte ratio. Additionally, it includes tumour mutational burden data. The model was validated using data from 2,881 patients with 18 different solid tumour types, effectively predicting treatment response and patient survival rates.
Public Availability and Future Prospects
Named the Logistic Regression-Based Immunotherapy-Response Score (LORIS), the AI tool is publicly accessible for further research and clinical application. The researchers emphasise the need for larger prospective studies to validate LORIS in clinical settings. Led by Dr. Eytan Ruppin of NCI and Dr. Luc G. T. Morris of Memorial Sloan Kettering, with significant contributions from Dr. Tiangen Chang and Dr. Yingying Cao, this AI model represents a significant advancement in personalised cancer treatment.
(Visit National Institute of Health for the full story)
*An AI tool was used to add an extra layer to the editing process for this story.
