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Precision Oncology: Multimodal and Multiscale Methods to Promote Mechanistic Understanding
Kivilcim Ozturk1, Adam Klie1, Hannah Carter1
1Department of Medicine and Division of Genomics and Precision Medicine, University of California San Diego, La Jolla, California, USA;
Abstract:
Precision medicine aims to tailor treatment to the individual, improving medical outcomes and quality of life. Realizing this vision requires understanding how disease mechanisms and drug responses vary across patients. Advances in molecular profiling have enabled detailed measurement of genetic, epigenetic, spatial, and imaging features at multiple biological scales, from single cells to tissues. These rich and complementary data promise insight into the drivers of human disease where individual data layers have often provided an incomplete picture. Increasingly, studies span multiple measurement modalities and scales, presenting both opportunities and challenges. Central among these is how to combine data types to uncover actionable biology. This review surveys computational strategies for analyzing multimodal and multiscale datasets, distinguishing between approaches that treat each modality independently and those that perform true integrative modeling. We highlight emerging methods, with a focus on oncology, where these tools are helping to reveal mechanisms and guide therapeutic decisions.
Insights
Precision medicine uses multimodal and multiscale data to tailor treatments. Integrative computational strategies are key to uncovering actionable biology and improving patient outcomes, especially in oncology.
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.
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