Abstract
BackgroundDiagnostic 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.Methods116 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.ResultsThe 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.ConclusionsThis 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.
Highlights
Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management
There is a significant body of research employing gene expression profiling (GEP) in lymphoma, having been used for a number of purposes including distinguishing closely related lymphoma phenotypes such as diffuse large B-Cell lymphoma (DLBCL) versus Burkitt lymphoma [2] and DLBCL from primary mediastinal B-cell lymphoma [3], to trace malignant lymphoma phenotypes to “normal cell of origin” [4] and to identify expression profiles linked to tumour prognosis [5]
Most cases (n = 16) of DLBCL were subclassified into germinal centre B (GCB) and non-GCB cell types (n = 5 and n=11 respectively) by immunohistochemistry according to the Hans algorithm [12]
Summary
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. Genome-wide gene expression profiling (GEP) is a novel approach to disease classification based on the molecular biology of the disease. This ‘genetic fingerprint’ data allows the identification and classification of individual tissue samples according to their distinct gene expression profiles. There is a lack of publications that address the potential value of gene expression microarray in aiding the routine diagnosis and classification of lymphoma in tissue biopsies from individual cases suspected of lymphoma. The feasibility of GEP as a tool to classify tumour tissue has been examined for other cancers, such as breast [6], colon [7], prostate [8] and renal tumours [9]
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