cytoGPNet: Enhancing Clinical Outcome Prediction Accuracy Using Longitudinal Cytometry Data in Small Cohort Studies

Cytometry data, including flow cytometry and mass cytometry, are now standard in numerous immunological studies, such as cancer immunotherapy and vaccine trials. These data enable the monitoring of an individual's peripheral immune status over time, providing detailed insights into immune cells and their role in clinical outcomes. However, traditional analyses relying on summary statistics, such as cell subset proportions and mean fluorescence intensity, may overlook critical single-cell information. To address this limitation, we introduce cytoGPNet, a novel approach that harnesses extensive cytometry data to predict individual-level outcomes. cytoGPNet is designed to address four key challenges: (1) accommodating varying numbers of cells per sample; (2) analyzing the longitudinal cytometry data to understand temporal relationships; (3) maintaining robustness under the constraints of limited individual samples in immunological studies; and (4) ensuring interpretability to facilitate biomarker identification. We apply cytoGPNet to data from multiple diverse studies, each with unique characteristics. Despite these differences, cytoGPNet consistently outperforms other popular methods in terms of prediction accuracy. Moreover, cytoGPNet provides interpretable results at multiple levels, offering valuable insights. Our findings underscore the effectiveness and versatility of cytoGPNet in enhancing the analysis of cytometry data and its potential to advance immunological research.