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

Maher Albitar, Hong Zhang, Andre Goy, Zijun Y. Xu-Monette, Govind Bhagat, Carlo Visco, Alexandar Tzankov, Xiaosheng Fang, Feng Zhu, Karen Dybkaer, April Chiu, Wayne Tam, Youli Zu, Eric D. Hsi, Fredrick B. Hagemeister, Jooryung Huh, Maurilio Ponzoni, Andrés J.M. Ferreri, Michael B. Møller, Benjamin M. ParsonsJ. Han van Krieken, Miguel A. Piris, Jane N. Winter, Yong Li, Bing Xu, Ken H. Young

Research output: Contribution to journalArticlepeer-review

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 stratifying patients with DLBCL, based on the clinical outcome of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone therapy, can be used to identify patients who may not respond well to these types of therapy, but would otherwise benefit from alternative therapy and clinical trials.

Original languageEnglish (US)
Article number25
JournalBlood Cancer Journal
Volume12
Issue number2
DOIs
StatePublished - Feb 2022

ASJC Scopus subject areas

  • Hematology
  • Oncology

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