Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances,...

Area of Science:

• Oncology
• Bioinformatics
• Artificial Intelligence in Medicine

Background:

• Immune checkpoint blockade (ICB) therapies have transformed cancer treatment but exhibit variable patient responses.
• Precise patient stratification is crucial for maximizing ICB efficacy and minimizing toxicity.
• Understanding the biological basis of ICB response is key to developing predictive strategies.

Purpose of the Study:

• To review machine learning and deep learning methodologies for predicting ICB therapy responses.
• To explore the integration of multi-omics data for enhanced predictive accuracy.
• To highlight the role of artificial intelligence in optimizing patient stratification for ICB therapy.

Main Methods:

• Review of machine learning and deep learning models applied to ICB response prediction.
• Integration of diverse data types including clinical, genomic, radiomic, and transcriptomic information.
• Analysis of biological mechanisms underlying ICB response to inform model development.

Main Results:

• Machine learning and deep learning models show promise in predicting ICB responses.
• Multi-omics data integration enhances the precision of predictive models.
• These AI-driven approaches can significantly improve patient stratification for ICB therapy.

Conclusions:

• Artificial intelligence, particularly machine learning and deep learning, offers powerful tools for predicting ICB therapy outcomes.
• Optimizing patient stratification through AI can lead to more effective and personalized cancer treatment.
• Bridging basic research and clinical application via AI is essential for advancing precision oncology.