Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms.

ABSTRACT: Multiple studies have demonstrated that diffuse large B-cell lymphoma (DLBCL) can be divided into subgroups based on their biology; however, these biological subgroups overlap clinically. Using machine learning, we developed an approach to stratify patients with DLBCL into four subgroups based on survival characteristics. This approach uses data from the targeted transcriptome to predict these survival subgroups. Using the expression levels of 180 genes, our model reliably predicted the four survival subgroups and was validated using independent groups of patients. Multivariate analysis showed that this patient stratification strategy encompasses various biological characteristics of DLBCL, and only TP53 mutations remained an independent prognostic biomarker. This novel approach for strati

SUBMITTER: Albitar M 

PROVIDER: S-EPMC8807629 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature