| Literature DB >> 32289277 |
George W Wright1, Da Wei Huang2, James D Phelan2, Zana A Coulibaly2, Sandrine Roulland2, Ryan M Young2, James Q Wang2, Roland Schmitz2, Ryan D Morin3, Jeffrey Tang3, Aixiang Jiang3, Aleksander Bagaev4, Olga Plotnikova4, Nikita Kotlov4, Calvin A Johnson5, Wyndham H Wilson2, David W Scott6, Louis M Staudt7.
Abstract
The development of precision medicine approaches for diffuse large B cell lymphoma (DLBCL) is confounded by its pronounced genetic, phenotypic, and clinical heterogeneity. Recent multiplatform genomic studies revealed the existence of genetic subtypes of DLBCL using clustering methodologies. Here, we describe an algorithm that determines the probability that a patient's lymphoma belongs to one of seven genetic subtypes based on its genetic features. This classification reveals genetic similarities between these DLBCL subtypes and various indolent and extranodal lymphoma types, suggesting a shared pathogenesis. These genetic subtypes also have distinct gene expression profiles, immune microenvironments, and outcomes following immunochemotherapy. Functional analysis of genetic subtype models highlights distinct vulnerabilities to targeted therapy, supporting the use of this classification in precision medicine trials. Published by Elsevier Inc.Entities:
Keywords: A53; BN2; DLBCL; EZB; LymphGen; MCD; N1; ST2; genomic classification; lymphoma; naive Bayes
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Year: 2020 PMID： 32289277 DOI： 10.1016/j.ccell.2020.03.015
Source DB: PubMed Journal: Cancer Cell ISSN： 1535-6108 Impact factor: 31.743