PB2111 DIGITAL ENUMERATION OF PLASMA CELL BURDEN BY MUM1 IMMUNOPHENOTYPING: IMPLICATIONS FOR MULTIPLE MYELOMA DIAGNOSTICS

Abstract

Background:Accurate plasma cell evaluations are required to diagnose multiple myeloma. Aspirate material is routinely assessed morphologically and by flow cytometry, but its reliability is hindered by peripheral blood haemodilution and patchy disease. Consequently, its sensitivity in diagnosing multiple myeloma is rated at only 75%. Alternatively, whole trephine estimations of plasma cell burden identified by membranous syndecan‐1 (CD138) or nuclear multiple myeloma oncogene‐1 (MUM1) expression may be undertaken. Routine “eye‐ball” estimations of CD138‐expressing plasma cells are grossly inaccurate and subject to extensive visual bias. Automated enumeration technology represents a robust alternative that is precise and overcomes these limitations. Digitally‐derived reference ranges for plasma cells identified by CD138 and MUM1 expression have been recently published. However, no previous studies have utilised enumeration software or these reference ranges to accurately assess plasma cells by MUM1 immunophenotyping in trephine specimens of multiple myeloma.Aims:We utilised an automated platform to digitally quantify plasma cells in trephine specimens obtained from patients with diagnostic, relapsing and remissive multiple myeloma. Variations between the manual and digital quantification of plasma cells immunophenotyped by MUM1 were evaluated, and the overall sensitivity of our automated approach was determined.MethodsWhole bone marrow trephine specimens (n = 91) were obtained from patients with new, relapsing and remissive multiple myeloma. Plasma cells were identified by the immunohistochemical detection of MUM1. Two blinded observers estimated the percent plasma cells per whole trephine specimen by light microscopy. Plasma cell burden per trephine was also quantified digitally with Tissue Image Analysis software (Leica, Ireland). Variations between observers’ manual estimations and digitally derived counts were evaluated using intraclass correlation analyses (ICC). The overall sensitivity of our digital platform in diagnosing multiple myeloma against published digital reference ranges for MUM1 was also determined.Results:We enumerated between 16,484–1,118,868 (mean = 100,084) cells per whole trephine. Interobserver agreement between observers for manual estimations was generally excellent (ICC = 0.94). Overall concordance between manual and digital counts were also high (ICC = 0.89), particularly among cases showing diffuse (ICC = 0.90) and nodular (ICC = 0.82) plasma cell distributions. Plasma cell microaggregates showed the poorest concordance between observers (ICC = 0.51) and when compared to digital counts (ICC = 0.17). Manual estimations typically overestimated MUM1‐expressing plasma cell counts by an average of 8.7% relative to digitally derived values. The overall sensitivity of our digital platform in diagnosing new or relapsing multiple myeloma was 91.7%.Summary/Conclusion:The automated enumeration of plasma cells identified by MUM1 immunoexpression in trephine specimens of multiple myeloma is highly accurate and precise. Concordance rates between manual and digital counts were strong for cases with diffuse and nodular disease distributions. Trephines with microaggregate foci of plasma cells showed poor agreement between manual and digital counts and may be incorrectly classified as “false negatives.” Such cases may require manual verification to ensure accurate diagnoses. Despite this, the overall sensitivity of our digital method was excellent (91.7%). Our data support the incorporation of automated digital enumeration technology as a useful adjunct to diagnose multiple myeloma.