Recent advances in machine learning-enhanced extracellular vesicle omics for oncology

Area of Science:

• Biomarkers and diagnostics
• Computational biology and bioinformatics
• Oncology

Background:

• Extracellular vesicles (EVs) are nanoscale particles carrying molecular cargo, reflecting tumor states and serving as potential liquid biopsy biomarkers.
• Machine learning (ML) excels at analyzing complex EV omics data (nucleic acids, proteins, metabolites, lipids) for predictive signatures.
• Clinical translation of EV biomarkers faces hurdles due to vesicle heterogeneity, isolation biases, and co-isolated particles.

Purpose of the Study:

• To review recent advances in ML-enhanced EV omics for oncology applications.
• To evaluate ML applications in early cancer detection, molecular subtyping, prognosis, and treatment response prediction using EV data.
• To critically examine challenges and propose recommendations for EV omics data analysis and clinical translation.

Main Methods:

• Structured review of recent literature on ML applications in EV transcriptomics, proteomics, metabolomics, and lipidomics in cancer.
• Analysis of EV fundamentals, omics profiling, and ML methodologies for biomarker discovery.
• Examination of challenges including data quality, model generalizability, interpretability, ethics, and standardization.

Main Results:

• ML effectively analyzes high-dimensional EV omics data, identifying predictive signatures for various cancer applications.
• Significant progress has been made in using ML-enhanced EV omics for early detection, subtyping, prognosis, and predicting treatment response.
• Key challenges in data quality, standardization, and model interpretability were identified, with practical recommendations provided.

Conclusions:

• ML-enhanced EV multi-omics holds significant potential for advancing liquid biopsy in precision oncology.
• Addressing challenges in standardization, data quality, and model interpretability is crucial for clinical translation.
• Emerging directions like single-vesicle omics and interpretable multimodal fusion promise to enhance EV-based diagnostics.