Trajectory-informed graph-based clustering for longitudinal cancer subtyping

TL;DR

This paper introduces a trajectory-informed graph clustering method that integrates multi-modal longitudinal data to identify cancer subtypes with distinct progression patterns, improving personalized treatment strategies.

Contribution

It presents a novel approach combining temporal modeling and graph-based analysis for cancer subtyping, capturing disease evolution more effectively than static methods.

Findings

Identified clinically relevant subtypes with distinct prognostic trajectories

Demonstrated improved subtyping accuracy on real-world liver metastases data

Showed advantages over traditional static clustering methods

Abstract

Cancer subtyping plays a crucial role in informing prognosis and guiding personalized treatment strategies. However, conventional subtyping approaches often rely on static, biopsy-derived scores that hardly capture the biological heterogeneity and temporal evolution of the disease. In this study, we propose a novel trajectory-informed clustering method for cancer subtyping that integrates multi-modal clinical data and longitudinal patient trajectories. Our method constructs a patient similarity graph using time-varying imaging-derived features, clinical covariates, and transitions among key clinical states such as therapy, surveillance, relapse, and death. This graph structure enables the identification of patient subgroups that are not only phenotypically and genotypically distinct but also aligned with patterns of disease progression. We position our approach within the landscape of existing subtyping methods and highlight its advantages in terms of temporal modeling and graph-based interpretability. Through simulation studies and application to a real world dataset of liver metastases, we demonstrate the ability of our framework to uncover clinically relevant subtypes with distinct prognostic trajectories. Our results underscore the potential of trajectory-informed clustering to enhance personalized oncology by bridging cross-sectional biomarkers with dynamic disease evolution.