Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification …

C Visco, Y Li, ZY Xu-Monette, RN Miranda, TM Green… - Leukemia, 2012 - nature.com
C Visco, Y Li, ZY Xu-Monette, RN Miranda, TM Green, A Tzankov, W Wen, WM Liu, BS Kahl…
Leukemia, 2012nature.com
Gene expression profiling (GEP) has stratified diffuse large B-cell lymphoma (DLBCL) into
molecular subgroups that correspond to different stages of lymphocyte development–
namely germinal center B-cell like and activated B-cell like. This classification has
prognostic significance, but GEP is expensive and not readily applicable into daily practice,
which has lead to immunohistochemical algorithms proposed as a surrogate for GEP
analysis. We assembled tissue microarrays from 475 de novo DLBCL patients who were …
Abstract
Gene expression profiling (GEP) has stratified diffuse large B-cell lymphoma (DLBCL) into molecular subgroups that correspond to different stages of lymphocyte development–namely germinal center B-cell like and activated B-cell like. This classification has prognostic significance, but GEP is expensive and not readily applicable into daily practice, which has lead to immunohistochemical algorithms proposed as a surrogate for GEP analysis. We assembled tissue microarrays from 475 de novo DLBCL patients who were treated with rituximab-CHOP chemotherapy. All cases were successfully profiled by GEP on formalin-fixed, paraffin-embedded tissue samples. Sections were stained with antibodies reactive with CD10, GCET1, FOXP1, MUM1 and BCL6 and cases were classified following a rationale of sequential steps of differentiation of B cells. Cutoffs for each marker were obtained using receiver-operating characteristic curves, obviating the need for any arbitrary method. An algorithm based on the expression of CD10, FOXP1 and BCL6 was developed that had a simpler structure than other recently proposed algorithms and 92.6% concordance with GEP. In multivariate analysis, both the International Prognostic Index and our proposed algorithm were significant independent predictors of progression-free and overall survival. In conclusion, this algorithm effectively predicts prognosis of DLBCL patients matching GEP subgroups in the era of rituximab therapy.
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