Utilizing Artificial Intelligence to Identify Immune Escape Mechanisms and Forecast Resistance to Cancer Immunotherapies

Abstract

Objective: The approach to emergency care in cancer therapy has greatly advanced due to the introduction of immune checkpoint inhibitors; however, drug resistance continues to be a significant obstacle. This evaluation investigates the role of Artificial Intelligence (AI) and Machine Learning (ML) technologies in discovering immune escape mechanisms and their ability to forecast drug resistance trends in patients undergoing immunotherapy, focusing on their impact on the formulation of medical strategies.

Methods: This review framework explored articles published from January 2018 to August 2024 across the PubMed, IEEE Xplore, and Scopus databases. The investigation focused on the application of AI and ML in comprehending immune evasion tactics and in predicting resistance to cancer therapies.

Results: AI and ML technologies leverage their analytical abilities to assess intricate data correlations between cancer genomic and proteomic details along with clinical patient information, facilitating the identification of immune evasion mechanisms. By processing large datasets with predictive models, these technologies effectively anticipate patients’ responses to immunotherapy treatments. The scientific application of computational platforms allows researchers to pinpoint alterations in tumor microenvironments and to identify immune checkpoint trends to investigate resistance mechanisms.

Conclusion: The insights into how cancer evades the immune system have improved due to AI and machine learning technologies; however, significant challenges remain before these tools can be applied in clinical settings. Research needs to address three key issues related to the quality of data, the transparency of algorithms, and the varied nature of cancer types. Upcoming studies should refine predictive models to enhance their application in personalized cancer therapies.