Automated TRCBC1 and TRBC2 BaseScope™ as a Novel, Quicker and Cheaper Method for the Diagnosis of T-Cell Lymphoma
While assessment of B-cell populations for monotypia (kappa or lambda light chain restriction) as a surrogate for monoclonality, and thus likely malignancy (lymphoma or leukaemia), is well understood, an analogous approach for suspected T-cell lymphoma does not exist in routine clinical practice.
At present, cases of suspected T-cell lymphoma undergo T-cell receptor clonality studies, which introduce clinical delay, are costly and require highly trained staff for interpretation, as well as losing any morphological or immunophenotypic context of clonal populations of cells.
The University of Cambridge: ·
- Undertook a pilot study in T-cell lymphomas and corresponding benign samples on the BOND RX research stainer, using BaseScope™ probes located in the highly divergent 3’UTRs of TRBC1 and TRBC2. ·
- Showed that accurate diagnosis of T-cell lymphomas could be achieved in the majority of cases with this method. ·
- Obtained similar results for the companion animals, dog and cat, in which the clinical question of T-cell lymphoma arises more frequently than in humans.
Data will be presented indicating the potential utility of this approach in clinical practice.
For Research Use Only. Not for use in diagnostic procedures.
About the presenter
Having undertaken her training and subsequently worked in both Oxford and Cambridge, Liz Soilleux is an Associate Professor, Honorary Consultant Pathologist and Research Group Leader in the Department of Pathology, University of Cambridge. She spends half of her time contributing to Cambridge’s large regional Haematopathology and Oncology Diagnostic Service, diagnosing lymphomas and leukaemias. She also undertakes molecular reporting (including of T and B-cell clonality studies) in the East Genomic Laboratory Hub, the largest of the UK’s genomic laboratory hubs. Her research interests lie in the use of harnessing aspects of T-cell receptor biology in diagnostics and in using machine learning approaches to advance diagnostic practice.
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