Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data

Area of Science:

• Oncology
• Biomedical Data Science
• Computational Biology

Background:

• Cancer's continuous evolution involves genetic, epigenetic, microenvironmental, and phenotypic changes, complicating monitoring and treatment.
• Current oncology research predominantly relies on cross-sectional, single-modality data, hindering a full understanding of cancer's dynamic heterogeneity.
• Therapy resistance, immune evasion, and metastasis are driven by cancer's dynamic nature, necessitating advanced analytical approaches.

Purpose of the Study:

• To review methods for longitudinal and multimodal modeling in cancer research.
• To highlight the synergy of longitudinal and multimodal data for personalized oncology.
• To discuss challenges and future directions in longitudinal multimodal analysis for precision oncology.

Main Methods:

• Surveying existing methods for longitudinal data analysis in cancer.
• Examining techniques for integrating multimodal data sources.
• Focusing on computational approaches for biomarker discovery.

Main Results:

• Longitudinal data reveal disease progression and treatment response patterns missed by single-timepoint analyses.
• Multimodal data integration provides complementary information for precise risk assessment and therapy targeting.
• The synergy of longitudinal and multimodal approaches offers multifaceted insights for personalized cancer care.

Conclusions:

• Longitudinal multimodal analysis is crucial for understanding and managing cancer's dynamic nature.
• Advances in data collection and computational methods enable the discovery of powerful biomarkers for precision oncology.
• Addressing current challenges in longitudinal multimodal analysis will further advance personalized cancer care.