Leveraging Artificial Intelligence to Uncover Immune Escape Mechanisms and Predict Resistance to Cancer Immunotherapies

Abstract

Emergency care for cancer therapy significantly evolved because of immune checkpoint inhibitors, yet drug resistance remains the main challenge. This assessment explores how Artificial Intelligence (AI) combined with Machine Learning (ML) systems functions to identify immune escape pathways alongside the capability to predict drug resistance patterns in immunotherapy-treated patients with special attention to their influence on medical strategy development.

Methods: This review system searched through PubMed, IEEE Xplore, and Scopus databases for published articles between 2018 January and August 2024. The review investigated how AI and ML were used to understand immune evasion strategies as well as identify cancer therapy resistance predictions.

Results: AI and ML technologies through their technical capabilities analyse complex data patterns between cancer genomic and proteomic information and patient clinical assets which leads to immune escape system identification. Big datasets processed through predictive models succeed in predicting how patients will respond to immunotherapeutic treatments. The scientific utilization of computational platforms enables scientists to identify changes in tumour microenvironments as well as detect immune checkpoint patterns to examine resistance mechanisms.

Conclusion: The understanding of cancer immune evasion has benefited from AI and ML technologies yet challenges persist before practicing these tools clinically. Research must resolve three important problems involving data quality and algorithmic clarity and the diverse nature of cancer variations. Future research needs to adjust predictive models for better clinical use in personalized cancer treatment.