4.3 Article

Molecular Subtypes and Genomic Profile of Primary Central Nervous System Lymphoma

期刊

出版社

OXFORD UNIV PRESS INC
DOI: 10.1093/jnen/nlz125

关键词

Brain lymphoma; Molecular subtype; Mutation profiling; Primary central nervous system lymphomas (PCNSL)

资金

  1. Hungarian Science Foundation [OTKA-PD115792]
  2. Hungarian National Research, Development and Innovation Office (NKFIH) [KH17-126718, NVKP_16-1-2016-0004, 119950]
  3. Momentum grant [LP-95021]
  4. Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences [BO/00320/18/5]
  5. New National Excellence Program of the Ministry for Innovation and Technology [UNKP-19-4-SE-77, UNKP-19-2-I-SE-47]
  6. Complementary Research Excellence Program of Semmelweis University [EFOP-3.6.3-VEKOP-16-2017-00009]
  7. Higher Education Institutional Excellence Programme of the Ministry of Human Capacities in Hungary
  8. Hungarian Brain Research Program [2017-1.2.1-NKP-2017-00002]
  9. University of Pecs [KA-2019-32]
  10. European Union [754432]
  11. Polish Ministry of Science and Higher Education
  12. MRC [G1100578, G0701018, MR/N004272/1] Funding Source: UKRI

向作者/读者索取更多资源

Primary central nervous system lymphomas (PCNSL) are aggressive non-Hodgkin lymphomas affecting the central nervous system (CNS). Although immunophenotyping studies suggested an uniform activated B-cell (ABC) origin, more recently a spectrum of ABC and germinal center B-cell (GC) cases has been proposed, with the molecular subtypes of PCNSL still being a matter of debate. With the emergence of novel therapies demonstrating different efficacy between the ABC and GC patient groups, precise assignment of molecular subtype is becoming indispensable. To determine the molecular subtype of 77 PCNSL and 17 secondary CNS lymphoma patients, we used the NanoString Lymphoma Subtyping Test (LST), a gene expression-based assay representing a more accurate technique of subtyping compared with standard immunohistochemical (IHC) algorithms. Mutational landscapes of 14 target genes were determined using ultra-deep next-generation sequencing. Using the LST-assay, a significantly lower proportion (80% vs 95%) of PCNSL cases displayed ABC phenotype compared with the IHC-based characterization. The most frequently mutated genes included MYD88, PIM1, and KMT2D. In summary, we successfully applied the LST-assay for molecular classification of PCNSL, reporting higher proportion of cases with GC phenotype compared with IHC analyses, leading to a more precise patient stratification potentially applicable in the diagnostic algorithm of PCNSL.

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