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Whole genome expression profiling based on paraffin embedded tissue can be used to classify diffuse large B ‐cell lymphoma and predict clinical outcome
Author(s) -
Barrans Sharon L.,
Crouch Simon,
Care Matthew A.,
Worrillow Lisa,
Smith Alex,
Patmore Russell,
Westhead David R.,
Tooze Reuben,
Roman Eve,
Jack Andrew S
Publication year - 2012
Publication title -
british journal of haematology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.907
H-Index - 186
eISSN - 1365-2141
pISSN - 0007-1048
DOI - 10.1111/bjh.12045
Subject(s) - diffuse large b cell lymphoma , lymphoma , immunohistochemistry , gene expression profiling , oncology , medicine , population , pathology , phenotype , biology , gene expression , gene , genetics , environmental health
Summary This study tested the validity of whole‐genome expression profiling ( GEP ) using RNA from formalin‐fixed, paraffin‐embedded ( FFPE ) tissue to sub‐classify D iffuse L arge B ‐cell L ymphoma ( DLBCL ), in a population based cohort of 172 patients. GEP was performed using Illumina Whole Genome cDNA ‐mediated Annealing, Selection, extension & L igation, and tumours were classified into germinal centre ( GCB ), activated B ‐cell ( ABC ) and T ype‐ III subtypes. The method was highly reproducible and reliably classified cell lines of known phenotype. GCB and ABC subtypes were each characterized by unique gene expression signatures consistent with previously published data. A significant relationship between subtype and survival was observed, with ABC having the worst clinical outcome and in a multivariate survival model only age and GEP class remained significant. This effect was not seen when tumours were classified by immunohistochemistry. There was a significant association between age and subtype (mean ages ABC – 72·8 years, GC – 68·4 years, Type‐ III – 64·5 years). Older patients with ABC subtype were also over‐represented in patients who died soon after diagnosis. The relationship between prognosis and subtype improved when only patients assigned to the three categories with the highest level of confidence were analysed. This study demonstrates that GEP ‐based classification of DLBCL can be applied to RNA extracted from routine FFPE samples and has potential for use in stratified medicine trials and clinical practice.