cd10, scored as positive versus negative all path 1 path 2 path 3 path 4 path 5 path 6 path 7 path 8...
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CD10, scored as positive versus negative all path 1 path 2 path 3 path 4 path 5 path 6 path 7 path 8 path 9 CD10 can be reproducibly scored, but is very sensitive to laboratory variations inducing variation of non-scored cases substantial fair very good substantial Slide 2 CD10 same core, distinct staining differences sparse internal controls granulocyte stromal fibroblasts Slide 3 less sensitive technique more sensitive technique strong staining / artifacts BCL-6 Slide 4 GCB versus non-GCB on the basis of CD10, MUM-1 and bcl-6 (25% cut-off levels) substantial fair substantial 0.72 moderate/fair 0.42-0.56 moderate 0.42 Slide 5 Conclusions (LLBC, de Jong et al., JCO 2007) Using immunohistochemistry, prognostic biomarkers for DLBCL can be assessed with varying reliability Results in almost all markers are strongly influenced by technical variations (MUM-1, Ki-67, bcl-6) Immunohistochemical amplification techniques may result in unexpected variations (CD5, bcl-6) Results can be reliably interpreted when optimized stains are used and with well-defined scoring guidelines (kappa=0.43- 0.88), including definitions of internal controls at this stage, risk-stratified treatment of DLBCL should be performed in clinical trials with central pathology review Slide 6 Molecular quantitative techniques applicable to paraffin embedded tissue specimens Gene expression analysis in MCL using qRT-PCR Slide 7 The proliferation signature: A gene-expression based predictor of survival in MCL (Cancer Cell 2003) Definition of prognostic subgroups in MCL Gene expression Profiling in MCL using Microarrays Slide 8 Measurement of Proliferation using Ki-67 immunohistochemistry Low ProliferationHigh Proliferation Slide 9 Survival according to Ki-67 Index (European MCL Study) Slide 10 Prediction of survival in 73 fresh frozen MCL specimens according to a 5-gene-predictor Slide 11 Statistical Analysis Application of Cox models and selection of the best 8 genes by stepwise methods, adjusted by bootstrap for a predictor of 5 genes Best model: RAN + MYC - TNFRSF10B + POLE2 + SLC29A2 Slide 12 Application of the 5-Gene-Predictor to Routine Diagnosis Validation of the modified assays in 13 samples with matched frozen and FFPE tissue available (Pearson correlation=0.77, p=0.002) Validation of the 5-Gene-predictor in an independent series of 23 MCL FFPE specimens Slide 13 Prediction of survival in FFPE MCL specimen according to the 5-Gene-Predictor Rosenwald, Campo, et al., unpublished