Precision Oncology: Multimodal and Multiscale Methods to Promote Mechanistic Understanding

Area of Science:

• Computational biology
• Genomics
• Translational medicine

Background:

• Precision medicine seeks individualized treatments by understanding patient-specific disease mechanisms and drug responses.
• Molecular profiling (genetic, epigenetic, spatial, imaging) across scales provides comprehensive biological insights.
• Integrating diverse data types is crucial for a complete understanding of disease drivers.

Purpose of the Study:

• To review computational strategies for analyzing multimodal and multiscale datasets.
• To distinguish between independent and integrative modeling approaches.
• To highlight emerging methods, particularly in oncology, for revealing mechanisms and guiding therapy.

Main Methods:

• Survey of computational strategies for multimodal and multiscale data analysis.
• Categorization of methods into independent modality analysis versus integrative modeling.
• Focus on applications and emerging trends in oncology.

Main Results:

• Identified distinct computational approaches for integrating diverse biological data.
• Highlighted the benefits of true integrative modeling over independent analyses.
• Showcased the application of these methods in oncology for mechanistic insights and therapeutic guidance.

Conclusions:

• Effective integration of multimodal and multiscale data is essential for advancing precision medicine.
• Emerging computational tools offer powerful ways to uncover actionable biology from complex datasets.
• These integrative approaches hold significant promise for improving cancer treatment and patient outcomes.