Innovative AI Tool Enhances Cancer Immunotherapy Predictions

Introduction to the AI Tool for Immunotherapy

Researchers at the National Institutes of Health (NIH) have developed a cutting-edge AI tool that predicts how cancer patients respond to immunotherapy. This breakthrough uses easily accessible clinical data like age, blood tests, and cancer type to make accurate predictions.

Understanding Immune Checkpoint Inhibitors

Immune checkpoint inhibitors are a type of drug that helps the body’s immune system attack cancer cells. While effective, predicting who will benefit from these drugs has been a challenge. Current biomarkers like tumor mutational burden and PD-L1 protein levels are not always reliable.

The newly developed AI model offers a more accessible and potentially more accurate alternative. Instead of relying solely on expensive molecular sequencing data, the model uses routine clinical information, making it easier and cheaper to predict treatment responses.

How the AI Model Works

The AI model, named Logistic Regression-Based Immunotherapy-Response Score (LORIS), considers six key factors: patient’s age, cancer type, history of systemic therapy, blood albumin level, blood neutrophil-to-lymphocyte ratio, and tumor mutational burden. By integrating these data points, the AI tool predicts whether a patient will respond to immune checkpoint inhibitors and their overall survival rate.

What sets LORIS apart is its ability to predict responses even in patients with low tumor mutational burden, extending the benefits of immunotherapy to a broader patient population.

Clinical Validation and Findings

The researchers evaluated the AI model using data from 2,881 patients with 18 different solid tumor types. The results showed that LORIS accurately predicted treatment responses and patient survival rates. This level of accuracy suggests that LORIS could be a valuable tool in clinical settings, guiding treatment decisions more effectively.

Future Steps and Availability

Though promising, the researchers emphasize the need for larger prospective studies to further validate LORIS in real-world clinical environments. To facilitate ongoing research and clinical application, they have made the tool publicly available at https://loris.ccr.cancer.gov.

Key Contributors and Institutions

The study was led by Eytan Ruppin, M.D., Ph.D., from NIH's Center for Cancer Research, and Luc G. T. Morris, M.D., from Memorial Sloan Kettering Cancer Center. Significant contributions were made by Tiangen Chang, Ph.D., and Yingying Cao, Ph.D., of Dr. Ruppin's team.

About the National Cancer Institute

The National Cancer Institute (NCI) is a leader in cancer research and strives to reduce the incidence of cancer and improve the lives of patients. The NCI funds a vast array of research projects and conducts its own studies at the NIH Clinical Center, the world’s largest research hospital. For more information, visit the NCI website at cancer.gov.

About the NIH

The National Institutes of Health (NIH) is the nation’s primary medical research agency. It includes 27 institutes and centers dedicated to conducting and supporting medical research on a vast array of diseases. For more information, visit nih.gov.