Identification of single-cell blasts in pediatric acute myeloid leukemia using an autoencoder.

ABSTRACT: Pediatric acute myeloid leukemia (AML) is an aggressive blood cancer with a poor prognosis and high relapse rate. Current challenges in the identification of immunotherapy targets arise from patient-specific blast immunophenotypes and their change during disease progression. To overcome this, we present a new computational research tool to rapidly identify malignant cells. We generated single-cell flow cytometry profiles of 21 pediatric AML patients with matched samples at diagnosis, remission, and relapse. We coupled a classifier to an autoencoder for anomaly detection and classified malignant blasts with 90% accuracy. Moreover, our method assigns a developmental stage to blasts at the single-cell level, improving current classification approaches based on differentiation of the dominant

SUBMITTER: Driessen A 

PROVIDER: S-EPMC11358707 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature