4.4 Article

PML risk stratification using anti-JCV antibody index and L-selectin

期刊

MULTIPLE SCLEROSIS JOURNAL
卷 22, 期 8, 页码 1048-1060

出版社

SAGE PUBLICATIONS LTD
DOI: 10.1177/1352458515607651

关键词

Natalizumab; PML; L-selectin; CD62L; JCV index; risk stratification

资金

  1. Deutsche Forschungsgesellschaft (DFG) [CRC128]
  2. Cells-in-Motion Cluster of Excellence [EXC 1003-CiM]
  3. PML consortium
  4. Kompetenznetz Multiple Sklerose (Competence Network for Multiple Sclerosis) - Federal Ministry of Education and Research [FKZ 01GI1308B 01GI0907]
  5. French Ministry of Health [PHRC 2008-005906-38]
  6. French Ministry of Health (ARSEP (French MS Society))
  7. EU (BEST-MS, FP7) [305477]

向作者/读者索取更多资源

Background: Natalizumab treatment is associated with progressive multifocal leukoencephalopathy (PML) development. Treatment duration, prior immunosuppressant use, and JCV serostatus are currently used for risk stratification, but PML incidence stays high. Anti-JCV antibody index and L-selectin (CD62L) have been proposed as additional risk stratification parameters. Objective: This study aimed at verifying and integrating both parameters into one algorithm for risk stratification. Methods: Multicentric, international cohorts of natalizumab-treated MS patients were assessed for JCV index (1921 control patients and nine pre-PML patients) and CD62L (1410 control patients and 17 pre-PML patients). Results: CD62L values correlate with JCV serostatus, as well as JCV index values. Low CD62L in natalizumab-treated patients was confirmed and validated as a biomarker for PML risk with the risk factor CD62L low increasing a patient's relative risk 55-fold (p < 0.0001). Validation efforts established 86% sensitivity/91% specificity for CD62L and 100% sensitivity/59% specificity for JCV index as predictors of PML. Using both parameters identified 1.9% of natalizumab-treated patients in the reference center as the risk group. Conclusions: Both JCV index and CD62L have merit for risk stratification and share a potential biological relationship with implications for general PML etiology. A risk algorithm incorporating both biomarkers could strongly reduce PML incidence.

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