2. Academic Validation
  3. A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications

A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications

  • Cancer Cell. 2020 Apr 13;37(4):551-568.e14. doi: 10.1016/j.ccell.2020.03.015.
George W Wright 1,  Da Wei Huang 2,  James D Phelan 2,  Zana A Coulibaly 2,  Sandrine Roulland 2,  Ryan M Young 2,  James Q Wang 2,  Roland Schmitz 2,  Ryan D Morin 3,  Jeffrey Tang 3,  Aixiang Jiang 3,  Aleksander Bagaev 4,  Olga Plotnikova 4,  Nikita Kotlov 4,  Calvin A Johnson 5,  Wyndham H Wilson 2,  David W Scott 6,  Louis M Staudt 7
Affiliations

Affiliations

  • 1 Biometric Research Branch, Division of Cancer Diagnosis and Treatment, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
  • 2 Lymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
  • 3 Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.
  • 4 BostonGene, Waltham, MA 02453, USA.
  • 5 Office of Intramural Research, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892, USA.
  • 6 British Columbia Cancer, Vancouver, BC V5Z 4E6, Canada.
  • 7 Lymphoid Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA. Electronic address: [email protected].
PMID: 32289277 DOI: 10.1016/j.ccell.2020.03.015
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.

Keywords

A53; BN2; DLBCL; EZB; LymphGen; MCD; N1; ST2; genomic classification; lymphoma; naive Bayes.

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