Novel Computational Analytics Of Clinical Flow Cytometry Data Identifies Difficult-To-Resolve Leukemia Cells For Precision Diagnosis

Abstract Motivation Diagnosis of leukemia relies on accurate identification of leukemic cell populations. Flow cytometry (FCM) is a primary diagnostic assay routinely used in clinical practice. The assay workflows consist of multiple manual gating steps performed by technicians, followed by interpretation by hematopathologists. Challenges to this process include technical variability in manual gating, difficulty in identification of the atypical leukemic cells, and the growing number of antigens used for diagnosis. Methods Instead of conducting ad hoc analysis of individual samples, our proposed computational approach leverages preexisting clinical FCM data to improve robustness of the computational identification of leukemic cells. Instead of separating cell population identification and sample classification into two steps, our machine learning classification method optimizes them simultaneously, producing gating locations that are recognizable to hematopathologists. Results Our study consists of 10-color FCM data from blood or bone marrow samples of 129 random subjects for chronic lymphocytic leukemia (CLL) diagnosis. Our initial automated gating analysis rendered an accuracy of 89% matched the diagnosis of the hematopathologist. In the remaining cases with discrepant results, the misclassified CLL cells had atypical molecular phenotypes, making them difficult to identify. In our improved pipeline, we demonstrate that these atypical types of CLL cells can be clearly captured using a non-linear embedding dimensionality reduction step. Conclusion The results demonstrate the power of a novel computational analysis pipeline for improving the identification of aberrant leukemia cells for precision diagnosis.