CD10, scored as positive versus negative

Figure 2e

0% 20% 40% 60% 80% 100%

BNLI

ECAMPO

EORTC

GELA

KIEL

LUBECK

NORDIC

VANCOUVER

WURZBURG

All

Not Scored

Negative

Positive

included/excluded "not scored"

agreement in all scores

agreement in pairs of labs

generalized kappa

first round excluded 45% 87% 0.69included 3% 65% 0.39

second round excluded 82% 95% 0.88included 70% 87% 0.72

allpath 1path 2path 3path 4path 5path 6path 7path 8path 9

CD10 can be reproducibly scored,but is very sensitive to laboratory variations inducing variation of non-scored cases

substantialfair

very goodsubstantial

CD10

same core, distinct staining differences

sparse internal controls

granulocyte stromal fibroblasts

less sensitive technique

more sensitive technique

strong staining / artifacts

BCL-6

GCB versus non-GCB on the basis of CD10, MUM-1 and bcl-6 (25% cut-off

levels)

substantialfair

substantialsubstantial

substantial0.72

moderate/fair0.42-0.56

moderate0.42

included/excluded "not scored"

agreement in all scores

generalized kappa

first round excluded 30% 0.62included 3% 0.36

second round excluded 89% 0.77included 77% 0.62

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

Years

Cu

mu

lativ

e P

rop

ortio

n S

urv

ivin

g

0,0

0,2

0,4

0,6

0,8

1,0

0 5 10 15 20

Molecular quantitative techniques applicableto paraffin embedded tissue specimens

Gene expression analysis in MCLusing qRT-PCR

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

Measurement of Proliferation using Ki-67 immunohistochemistry

Low Proliferation High Proliferation

Survival according to Ki-67 Index (European MCL Study)

Prediction of survival in 73 fresh frozen MCL specimens according to a 5-gene-predictor

Statistical Analysis

Genes % times selected

MYC 44.5

RAN 35.0

CDC14B 32.5

SLC29A2 31.0

HPRT1 28.0

POLE2 26.0

TNFRSF10B 25.0

CDKN3 24.0

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

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

Prediction of survival in FFPE MCL specimen according to the 5-Gene-Predictor

Rosenwald, Campo, et al., unpublished