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Open Access Research article

Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling

To Ha Loi1, Anna Campain2, Adam Bryant1, Tim J Molloy1, Mark Lutherborrow1, Jennifer Turner3, Yee Hwa Jean Yang2 and David DF Ma1*

Author Affiliations

1 Blood Stem Cell and Cancer Research Unit, Department of Haematology, St Vincent's Hospital, Victoria Street, Darlinghurst, Australia

2 Centre for Mathematical Biology, School of Mathematics and Statistics, University of Sydney, Sydney, Australia

3 Department of Anatomical Pathology, St Vincent's Hospital, Victoria Street, Darlinghurst, Australia

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BMC Medical Genomics 2011, 4:27  doi:10.1186/1755-8794-4-27

Published: 31 March 2011

Abstract

Background

Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies.

Methods

116 lymph node biopsies diagnosed as RL, classical Hodgkin lymphoma (cHL), diffuse large B cell lymphoma (DLBCL) or follicular lymphoma (FL) were assayed by mRNA microarray. Three supervised classification strategies (global multi-class, local binary-class and global binary-class classifications) using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation. The independent error rate was then evaluated by testing the identified gene classifiers on an independent (test) set of array data.

Results

The binary classifications provided prediction accuracies, between a subtype of interest and the remaining samples, of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL, and RL respectively. Identified gene classifiers include LIM domain only-2 (LMO2), Chemokine (C-C motif) ligand 22 (CCL22) and Cyclin-dependent kinase inhibitor-3 (CDK3) specifically for FL, cHL and DLBCL subtypes respectively.

Conclusions

This study highlights the ability of gene expression profiling to distinguish lymphoma from reactive conditions and classify the major subtypes of lymphoma in a diagnostic setting. A cost-effective single platform "mini-chip" assay could, in principle, be developed to aid the quick diagnosis of lymph node biopsies with the potential to incorporate other pathological entities into such an assay.