Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data

TL;DR

This paper reviews how integrating longitudinal and multimodal data enhances personalized cancer treatment by capturing disease dynamics and heterogeneity, addressing limitations of traditional cross-sectional analyses.

Contribution

It provides a comprehensive survey of methods for longitudinal and multimodal modeling in oncology, emphasizing their combined potential for improving precision medicine.

Findings

Longitudinal data reveal disease progression patterns not seen in single timepoint data.

Multimodal data integration improves risk assessment and therapy targeting.

Current challenges and future directions are summarized for advancing the field.

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-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.