An automated semiquantitative B and T cell clonality assay

Background: Rearrangements of the antigen receptor genes in B and T cells generate products of unique length and sequence. Polymerase chain reaction (PCR) assays are routinely used to identify clonal lymphocyte populations by detecting clonal V-J rearrangements or chromosomal translocations within these antigen receptor loci. Multiple primer sets are, however, required to detect the majority of clonal B- and T-cell malignancies. Products from the individual reactions must be analyzed separately to avoid misinterpretation. Moreover, small clonal populations remain difficult to identify. To address these difficulties, we propose that an integrated fluorescence-based approach to clonal B- and T-cell detection would simultaneously identify both B- and T-cell neoplasia; increase amplicon resolution, analytic sensitivity, and assay throughput; produce more comprehensive and semiquantitative data useful for evaluation of hematologic malignancies; and eliminate labor intensive agarose and polyacrylamide gel electrophoresis. Methods and Results: Samples were genomic DNA and cDNA. Differentially labeled primers were used to amplify regions diagnostic for B- and T-cell clonality in a single plate with a single thermocycler program. Combined amplicon products underwent capillary electrophoresis for high resolution fractionation and differential fluorescence detection and quantification. Data were automatically analyzed and archived. In a comparative analysis of a variety of clinical samples, this automated and integrated B- and T-cell assay showed >94% agreement (33 of 35 results) with individual B- and T-cell PCR assays. Furthermore, this assay had an overall monoclonality detection rate of 100%, and as little as 100 ng of sample DNA yielded complete B- and T-cell clonality test results. The limit of detection was approximately 10(-2) cells, and amplicons were sized to within 0.1 basepair. Serial dilutions of clonal B- and T-cell lines comprising a coded proficiency panel were identified and correctly ranked. Specificity was 100% as determined by analysis of 18 control samples that were all negative for B- and T-cell clonality. Conclusions: Our data show that this automated and integrated B- and T-cell clonality assay system is a sensitive and specific tool useful for rapid identification of clonal lymphocyte populations and will likely have broad clinical applications.