MRDviz: an integrated platform for interactive visualization and joint simulation of minimal residual disease trajectories and associated survival outcomes

<h4>Motivation</h4>Minimal residual disease (MRD) assessment has become a powerful tool in modern cancer management, offering early, actionable insights across diverse malignancies and often anticipating clinical events traditionally used to gauge therapeutic response. Beyond quantifying residual disease burden, MRD monitoring enables earlier intervention, nuanced treatment decisions, and improved outcome prediction before conventional endpoints appear. MRD negativity is now recognized as a potential surrogate endpoint, informing regulatory frameworks and accelerating drug development, as reflected by the FDA's approval of MRD as an accelerated endpoint in Multiple Myeloma trials. In particular, longitudinal MRD assessments, which track dynamic changes over time rather than single time points, have shown enhanced prognostic value, and more trials are now collecting such data.<h4>Results</h4>Building on this progress, we present MRDviz, an integrated R Shiny platform for exploratory analysis and simulation of longitudinal MRD data alongside survival outcomes. MRDviz offers interactive visualizations of MRD trajectories, real-time data quality checks, and identification of patient subgroups with distinct outcomes. Through coordinated views, customizable filtering, and integrated survival analyses, MRDviz empowers researchers to refine clinical hypotheses, design robust studies, and enhance the interpretation of MRD data, ultimately supporting better clinical decision-making and patient care.<h4>Availability and implementation</h4>MRDviz is freely available at https://github.com/abbvie-external/MRDviz.