Abstract 7434: Deployment of a visualization tool directly linked to the CellEngine cytometry analysis software to accelerate analysis of complex cytometry data sets in the context of ongoing clinical trials
Authors: Montamat-Sicotte Damien , Bjornson Zach , Franckaert Dean , Dupuis Nicholas , Paramithiotis Eustache 
Published: March 2024

Introduction High-dimension cytometry panels have become a hallmark of clinical phase 1 and 2 trials. These panels can provide hundreds of readouts and need to be combined with subject metadata (e.g., health data, demographics, timepoint, and dose) to provide insights on the identification of biomarkers. Several bioinformatics tools are available to interpret these complex datasets; however, these usually require significant expertise in data manipulation, which sometimes forces the person doing the data interpretation to outsource the data visualization and statistical analysis to a bioinformatics team. As a consequence, the scientist can be one step-removed from the “raw” data, critically preventing the interpretation of biomarker effects in the context of the raw staining profiles and associated quality controls (e.g. in-run controls). Here, we show how a new tool integrated with the CellEngine cytometry analysis software can accelerate data interpretation without removing the scientist from the raw data.

Methods The visualization tool pulls live data from CellEngine, displaying both study- and cohort-level information in summary views as well as the sample-level staining profiles. As a live link, any changes made in CellEngine, such as ones to gating and compensation, or the addition of new samples during an ongoing study, are automatically reflected in the dashboard. This link also gives the ability to consult staining profiles and performance of controls directly within the tool without having to switch between applications. CellEngine is capable of analyzing 10,000s of samples at once, and thus can handle the largest of studies.

Results Here, we show how the visualization tool was used to accelerate the data interpretation of two different flow cytometry assays. In the first example, lymphodepletion and immune system reconstitution in patients undergoing CAR-T cell therapy was monitored using the tool. The live update of analysed data within the tool, coupled with the ability to verify the performance of the two controls ran with each sample, accelerated the data interpretation and improved the data quality. In the second example, we show how the software was used to follow cytokines responses across different timepoints in a vaccination program where samples were analysed using a polyfunctional intra-cellular cytokine staining panel. The tool greatly accelerated the interrogation of this complex data set and allowed the identification of trends within the different T cell responses.

Conclusion This new tool can help maximize the utility of high-dimensional cytometry in clinical trial analysis while allowing to keep the crucial link between graphical representation of large number of samples and the data associated with each individual flow cytometry staining profile.

Citation Format: Damien Montamat-Sicotte, Zach Bjornson, Dean Franckaert, Nicholas Dupuis, Eustache Paramithiotis. Deployment of a visualization tool directly linked to the CellEngine cytometry analysis software to accelerate analysis of complex cytometry data sets in the context of ongoing clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7434.