Integrative Computational Approaches to Prostate Cancer with Conditional Reprogramming and AI-Driven Precision...

Area of Science:

• Oncology
• Systems Biology
• Genomics

Background:

• Metastatic castration-resistant prostate cancer (mCRPC) poses significant therapeutic challenges due to adaptive lineage plasticity and neuroendocrine transdifferentiation.
• Current genome-based models are insufficient for predicting divergent clinical trajectories in mCRPC, necessitating a dynamic systems approach.

Purpose of the Study:

• To review three key technologies revolutionizing prostate cancer research: conditional reprogramming (CR), multi-omics, and physics-informed machine learning.
• To highlight how these technologies enable a deeper understanding of tumor evolution, treatment response, and resistance in mCRPC.

Main Methods:

• Conditional reprogramming (CR) for generating patient-derived models preserving genomic fidelity and phenotypic plasticity.
• Single-cell and spatial multi-omics to elucidate cellular trajectories and identify key regulatory networks (e.g., FOXA2-NKX2-1-p300/CBP) in transdifferentiation.
• Physics-informed machine learning and digital twin architectures for mechanistic forecasting of tumor dynamics.

Main Results:

• CR facilitates rapid, genetically stable patient-derived models without manipulation.
• Multi-omics identified a therapeutically actionable intermediate state in luminal-to-neuroendocrine transdifferentiation.
• Machine learning models aim for mechanistically sound prediction beyond correlative risk assessment.

Conclusions:

• Integrating CR, multi-omics, and AI represents a paradigm shift towards anticipatory precision oncology for mCRPC.
• Addressing challenges in clinical validation, heterogeneity capture, and causal inference is crucial for translating these advancements.
• A closed-loop experimental-computational approach is essential for proactive and personalized cancer care.