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Comparison of two real-time quantitative polymerase chain reaction strategies for minimal residual disease evaluation in lymphoproliferative disorders: Correlation between immunoglobulin gene mutation load and real-time quantitative polymerase chain reaction performance

  • Irene Della Starza
  • , Marzia Cavalli
  • , Ilaria Del Giudice
  • , Daniela Barbero
  • , Barbara Mantoan
  • , Elisa Genuardi
  • , Marina Urbano
  • , Claudia Mannu
  • , Anna Gazzola
  • , Elena Ciabatti
  • , Anna Guarini
  • , Robin Foà
  • , Sara Galimberti
  • , Pierpaolo Piccaluga
  • , Gianluca Gaidano
  • , Marco Ladetto
  • , Luigia Monitillo

Research output: Contribution to journalArticlepeer-review

Abstract

We compared two strategies for minimal residual disease evaluation of B-cell lymphoproliferative disorders characterized by a variable immunoglobulin heavy chain (IGH) genes mutation load. Twenty-five samples from chronic lymphocytic leukaemia (n=18) or mantle cell lymphoma (n=7) patients were analyzed. Based on IGH variable region genes, 22/25 samples carried >2% mutations, 20/25>5%. In the IGH joining region genes, 23/25 samples carried >2% mutations, 18/25>5%. Real-time quantitative polymerase chain reaction was performed on IGH genes using two strategies: method A utilizes two patient-specific primers, whereas method B employs one patient-specific and one germline primer, with different positions on the variable, diversity and joining regions. Twenty-three samples (92%) resulted evaluable using method A, only six (24%) by method B. Method B poor performance was specifically evident among mutated IGH variable/joining region cases, although no specific mutation load above, which the real-time quantitative polymerase chain reaction failed was found. The molecular strategies for minimal residual disease evaluation should be adapted to the B-cell receptor features of the disease investigated.

Original languageEnglish
Pages (from-to)133-138
Number of pages6
JournalHematological Oncology
Volume32
Issue number3
DOIs
Publication statusPublished - 1 Sept 2014

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Immunoglobulin genes
  • Lymphoproliferative disorders
  • MRD
  • Mutations
  • RQ-PCR

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