Abstract 2605: Improving data quality in oncology immunotherapy clinical research by big data analytics and data visualization

• Published in: Cancer research (Chicago, Ill.) Vol. 77; no. 13_Supplement; p. 2605
• Main Authors: Xue, Chengsen, Cuomo, Joanne, Meyers, Walter, Closkey, Thomas W. Mc
• Format: Journal Article
• Language: English
• Published: 01-07-2017
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2017-2605

Abstract
Flow cytometric assessment of cellular and intracellular biomarkers facilitates the development of new classes of cancer immunotherapy therapeutics. This powerful tool has multiple applications including 1) detecting PD1, PDL-1 and caspase-3 for apoptosis, 2) isolating, defining, and quantifying dendritic cells and their intracellular cytokine profile, 3) immunophenotyping subpopulations of B, T, NK, regulatory T, and helper T cells, and, 4) using tetramer analysis to detect and monitor the frequency of antigen-specific cytotoxic T lymphocytes (CTL). However, lack of knowledge of normal distributions and standard ranges for the esoteric reportables generated by flow cytometry is one of the most challenging problems in this promising field.
Here we describe a new method to improve data quality in clinical research by big data analytics and data visualization. Each data point was tagged. By applying a simple statistical analysis, a self-defined range allowed all of the outlier numbers to be identified. By repeating this process, clear patterns emerged, and all questionable data points were marked. In addition, the system can differentiate biodiversity in subjects from a more homogenous population, which can be exploited further for the purpose of personal and precision medicine. Other benefits of the new system are to predict the outcome of the clinical study and to generate new clues in the understanding of the underlying biological mechanisms.
Big data analytics and data visualization show unparalleled advantages in oncology studies through simultaneously monitoring unlimited data points related to surface and intracellular biomarkers. It opens a new avenue in cost saving for long, time-consuming clinical trials by using all available data to detect a status change of the immune system. With the improvement of data quality, big data analytics has become an essential tool and an integrated part of cancer immunotherapy and the development of cancer vaccines.
Citation Format: Chengsen Xue, Joanne Cuomo, Walter Meyers, Thomas W. Mc Closkey. Improving data quality in oncology immunotherapy clinical research by big data analytics and data visualization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2605. doi:10.1158/1538-7445.AM2017-2605