149
Views
28
CrossRef citations to date
0
Altmetric
Review

Immunoglobulin/T-cell receptor clonality diagnostics

, PhD (Molecular Immunologist) , , PhD (Molecular Pathologist) , , MD PhD (Pathologist) & , MD PhD (Medical Immunologist)
Pages 451-461 | Published online: 29 Nov 2007
 

Abstract

Clonality testing in lymphoid malignancies has become technically relatively easy to perform in routine laboratories using standardized multiplex polymerase chain reaction protocols for Ig/T-cell receptor (TCR) gene analysis. Expertise with clonality diagnostics and knowledge about the biology of Ig/TCR recombination are essential for correct interpretation of the Ig/TCR clonality data. Several immunobiologic and technical pitfalls that should be taken into account to avoid misinterpretation of data are addressed in this review. Furthermore, the need to integrate the molecular data with that from (hemato-)pathology, and preferably also flowcytometric immunophenotyping for appropriate interpretation, is discussed. Such an interactive, multidisciplinary diagnostic model guarantees integration of all available data to reach the most reliable diagnosis.

Acknowledgements

This review is largely based on the results and discussions with many colleagues all over Europe in the BIOMED-2 Concerted Action ‘PCR-based clonality detection in suspect lymphoproliferations’ (BMH4-CT98-3936). The authors thank ILM Wolvers-Tettero, EJ van Gastel-Mol, MECM Oud, B Verhaaf (Erasmus MC, Rotterdam), M van Altena, P Rombout (Radboud University Nijmegen Medical Centre, Nijmegen) for technical assistance.

Notes

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 99.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.