Multi-Cohort Transcriptomic Subtyping of B-Cell Acute Lymphoblastic Leukemia.

ABSTRACT: RNA sequencing provides a snapshot of the functional consequences of genomic lesions that drive acute lymphoblastic leukemia (ALL). The aims of this study were to elucidate diagnostic associations (via machine learning) between mRNA-seq profiles, independently verify ALL lesions and develop easy-to-interpret transcriptome-wide biomarkers for ALL subtyping in the clinical setting. A training dataset of 1279 ALL patients from six North American cohorts was used for developing machine learning models. Results were validated in 767 patients from Australia with a quality control dataset across 31 tissues from 1160 non-ALL donors. A novel batch correction method was introduced and applied to adjust for cohort differences. Out of 18,503 genes with usable expression, 11,830 (64%) were confounded b

SUBMITTER: Makinen VP 

PROVIDER: S-EPMC9099612 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature