Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data.

ABSTRACT: Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoi

SUBMITTER: Zhuang L 

PROVIDER: S-EPMC11844620 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature