Remaining challenges in predicting patient outcomes for diffuse large B-cell lymphoma

• Type of Material: Article
• Authors: R. Andrew Harkins, Emory University; Andres Chang, Emory University; Sharvil P. Patel, Emory University; Michelle J. Lee, Emory University; Jordan S. Goldstein, Weill Cornell Medical College; Selin Merdan, Emory University; Christopher Flowers, Emory University; Jean Koff, Emory University
• Language: English
• Date: 2019-09-14
• Publisher: Taylor & Francis Ltd.
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2019 Informa UK Limited, trading as Taylor & Francis Group
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1080/17474086.2019.1660159
• Title of Journal or Parent Work: Expert Review of Hematology
• Volume: 12
• Issue: 11
• Start Page: 959
• End Page: 973
• Grant/Funding Information: This work was supported in part by the National Center for Advancing Translational Sciences of the National Institutes of Health under RA Harkin’s Award Numbers UL1TR002378 and TL1TR002382;; CR Flowers’ NCI Award K24CA208132.; Winship Cancer Institute of Emory University Nell W. and William S. Elkin Fellowship to A Chang; A Chang’s National Institutes of Health National Cancer Institute(NCI) grant T32CA160040
• Keywords: Elderly patients; outcomes prediction; Circulating tumor DNA; Science & Technology; To-monocyte; B-cell lymphoma; Life Sciences & Biomedicine; Parafin embedded tissue; diffuse large B-cell lymphoma; Vitamin D deficiency; prognosis; Of-origin; Prognostic index; Comprehensive geriatric assessment; Hematology; non-Hodgkin lymphoma; Digital gene expression; DLBCL
• Research Categories: Health Sciences, Immunology; Health Sciences, Oncology; Biology, Cell

Abstract

Introduction Diffuse large B-cell lymphoma (DLBCL) is the most common non-Hodgkin lymphoma and is an aggressive malignancy with heterogeneous outcomes. Diverse methods for DLBCL outcomes assessment ranging from clinical to genomic have been developed with variable predictive and prognostic success. Areas covered The authors provide an overview of the various methods currently used to estimate prognosis in DLBCL patients. Models incorporating cell of origin, genomic features, sociodemographic factors, treatment effectiveness measures, and machine learning are described. Expert opinion The clinical and genetic heterogeneity of DLBCL presents distinct challenges in predicting response to therapy and overall prognosis. Successful integration of predictive and prognostic tools in clinical trials and in a standard clinical workflow for DLBCL will likely require a combination of methods incorporating clinical, sociodemographic, and molecular factors with the aid of machine learning and high-dimensional data analysis.