A significant advancement in the fight against cancer has emerged with the development of a novel artificial intelligence (AI) tool designed to predict patient response to immunotherapy. This innovation, led by researchers at the National Institutes of Health (NIH), holds the potential to revolutionize how doctors approach immunotherapy treatment decisions.
Immunotherapy represents a groundbreaking strategy in cancer treatment by harnessing the body’s immune system to attack cancer cells. However, selecting the right patients for this therapy remains a challenge. Currently, two key biomarkers – tumor mutational burden and the PD-L1 protein – guide treatment decisions. However, these markers have limitations in accurately predicting response. Additionally, other promising methods, like those utilizing molecular sequencing data, are often cost-prohibitive and not routinely available.
Artificial Intelligence Tool Predicts Immunotherapy Response with Routine Data
The newly developed artificial intelligence tool dubbed the Logistic Regression-Based Immunotherapy-Response Score (LORIS), offers a unique approach. Instead of relying on specialized tests, LORIS leverages readily available clinical data points routinely collected during patient assessments. These data points include Patient age, Specific type of cancer, History of systemic therapy, Blood albumin level, Blood neutrophil-to-lymphocyte ratio, and Tumor mutational burden (when available through sequencing panels).
By analyzing these readily accessible data points, LORIS generates a score that predicts a patient’s likelihood of responding favorably to immune checkpoint inhibitor therapy, a specific form of immunotherapy. The researchers evaluated LORIS using data from over 2,800 patients across 18 different solid tumor types who had received immune checkpoint inhibitor treatment. The results were encouraging, demonstrating that LORIS could accurately predict: The likelihood of a patient responding positively to immunotherapy.
Publicly Available Artificial Intelligence Tool Could Revolutionize Immunotherapy Selection
Overall patient survival rates, Progression-free survival, which refers to the time before the cancer worsens. Furthermore, LORIS identified patients with lower tumor mutational burden who might still benefit from immunotherapy – a group often excluded based on traditional methods. This ability to identify potential responders beyond current limitations highlights the potential of LORIS to optimize patient selection for this powerful treatment. While larger clinical trials are necessary to confirm LORIS’ effectiveness in real-world clinical settings, the initial findings suggest a significant leap forward in immunotherapy decision-making.
The researchers have made LORIS publicly available, allowing doctors to estimate a patient’s response to immunotherapy based on the six readily available clinical data points. This accessibility will be crucial for further evaluation and wider adoption. The development of LORIS signifies a significant step toward personalized and data-driven immunotherapy selection. This innovation holds promise for improving patient outcomes by optimizing treatment approaches and potentially expanding access to this life-saving therapy.
Resource: National Institutes of Health, June 03, 2024
This article has been prepared with the assistance of AI and reviewed by an editor. For more details, please refer to our Terms and Conditions. We do not accept any responsibility or liability for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this article. If you have any complaints or copyright issues related to this article, kindly contact the author.
