Abstract 1105: Development of an in-house methodology to analyze large clinical cancer biomarkers datasets

Abstract Background: BXQ-350 is a novel biologic that modulates sphingolipid metabolism. BXQ-350 is currently being investigated in several clinical studies. Multiple biomarker samples are collected in the course of these studies to support clinical development and illustrate the mechanism of action, support patient selection, and associate response with trends. The resulting large data set associated with these different biomarkers requires an analytical tool to process the data. To meet those ends, Bexion Pharmaceuticals developed an in-house analytical method to perform analysis on these data points. The platform performs data wrangling, processing, calculations, and produces statistical and graphical outputs from multiple different input sources. Methods: The analytical methodology was developed using Python v3.13 and open-source modules. Windows Excel files are used for the input and are formatted in Excel to be properly read. The outputs are statistical Excel files and graphical visualization of the data. Analytical Methodology 1. Import Excel file and create a pandas dataframe object 2. Merge dataframes based on patient identification 3. Perform data wrangling on the merged dataframe Create or update a MySQL table if applicable 4. Process the data and perform calculations, including QC samples incorporated in each analysis 5. Perform statistical analysis of QC samples to determine which changes would be statistically significant 6. Perform a second round of processing and calculations on data that passed QC 7. Group and separate data for output handling 8. Create graphs and tables Results: The analytical methodology was designed to analyze any type of biomarker data set, allowing the comparison of results across biomarkers and supporting analysis of potential correlations. Serial analysis can compare trends in biomarkers results and clinical outcome(s). In addition, statistics are derived for both individual patients and groups to stratify the data. Conclusion: Large biomarker data sets are collected in the course of clinical studies to support clinical development. Furthermore, such analyses can support understanding the mechanism of action and guide patient selection. The method developed allows for flexible and granular analysis of biomarkers and correlative analysis across different groups of biomarkers or individual patients and groups of patients Citation Format: Tariq Arshad, Nikhil Wilkins, Gilles Tapolsky, Mike Gazda. Development of an in-house methodology to analyze large clinical cancer biomarkers datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1105.