Translational Oncogenomics 2008:3 137–149 137

ORIGINAL RESEARCH

Correspondence: Merav Bar, M.D., Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, D1-100, Seattle WA. U.S.A. 98109. Tel: (206)667-4545; Email: mabr@fhcrc.org

Copyright in this article, its metadata, and any supplementary data is held by its author or authors. It is published under the Creative Commons Attribution By licence. For further information go to: http://creativecommons.org/licenses/by/3.0/.

Gene Expression Patterns in Myelodyplasia Underline the Role of Apoptosis and Differentiation in Disease Initiation and ProgressionMerav Bar1, Derek Stirewalt1, Era Pogosova-Agadjanyan1, Vitas Wagner1, Ted Gooley1, Nissa Abbasi1, Ravi Bhatia2, H. Joachim Deeg1 and Jerald Radich11Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington. 2City of Hope National Medical Center, Duarte, California.

Abstract: The myelodysplastic syndromes (MDS) are clonal stem cell disorders, characterized by ineffective and dysplastic hematopoiesis. The genetic and epigenetic pathways that determine disease stage and progression are largely unknown. In the current study we used gene expression microarray methodology to examine the gene expression differences between normal hematopoietic cells and hematopoietic cells from patients with MDS at different disease stages, using both unselected and CD34+ selected cells. Signifi cant differences between normal and MDS hematopoietic cells were observed for several genes and pathways. Several genes promoting or opposing apoptosis were dysregulated in MDS cases, most notably MCL1 and EPOR. Progression from RA to RAEB(T) was associated with increased expression of several histone genes. In addi-tion, the RAR-RXR pathway, critical for maintaining a balance between self-renewal and differentiation of hematopoietic stem cells, was found to be deregulated in hematopoietic cells from patients with advanced MDS compared to patients with refractory anemia. These fi ndings provide new insights into the understanding of the pathophysiology and progression of MDS, and may guide to new targets for therapy. Taken together with previous published data, the present results also underscore the considerable complexity of the regulation of gene expression in MDS.

Keywords: myelodysplasia, gene expression, apoptosis, differentiation

IntroductionThe myelodysplastic syndromes (MDS) are a heterogeneous group of clonal disorders of the hemato-poietic stem cells [1, 2]. The natural history of MDS is that of progressive cytopenia with increasing transfusion needs, infectious and bleeding complications or alternatively, evolution to secondary AML [1, 2]. While relatively simple clinical and pathologic scoring systems with prognostic relevance have been developed, the molecular mechanisms involved in evolution of the disease are largely unknown [3, 4]. Identifying molecular markers of MDS may allow for a more accurate assessment of the prognosis and potentially identify new targets for therapy.

The genetic lesions so far identifi ed in MDS incompletely describe the biology and heterogeneity of the disease. Clonal karyotypic abnormalities are observed in approximately 40–50% of patients with primary MDS, and 90% of therapy-related MDS [2, 5]. Mutations important in denovo AML, for example mutations in RAS proto-oncogenes and FLT3 internal tandem duplications (ITD), have been described in 5%–20% of MDS patients and are variably associated with disease progression [3, 6–9]. Beside overt genetic lesions, epigenetic lesions may also play a roll in the development of MDS. Hyper-methylation has been described in many malignancies including MDS and may be associated with disease development, progression and prognosis [10–14]. For example, p15 promoter hypermethylation has been shown to be associated with MDS progression to AML in some studies [3, 15]. However, none of these alterations are specifi c for MDS and the underlying molecular causes of MDS have remained poorly understood.

The vast number of genetic and epigenetic disturbances in MDS makes investigations to identify potential common pathways that may involved in disease development and progression challenging. Oligonucleotide microarrays have been found to be an excellent tool to study biology and identify potential prognostic factors in many forms of malignancies, including MDS. This platform allows

http://creativecommons.org/licenses/by/3.0/http://creativecommons.org/licenses/by/3.0/

138

Bar et al

Translational Oncogenomics 2008:3

examination of thousands of genes using a single sample. In the current study we used oligonucle-otide microarrays to determine how the genetic expression profi le differs between healthy hema-topoietic cells and hematopoietic cells from patients with MDS, and to identify genes and pathways that might be relevant for MDS evolution and progression.

Materials and Methods

PatientsMononuclear cells (N = 35) or purifi ed CD34+ cells (N = 8) from the marrow of 43 MDS patients were studied. Patients were sub-grouped according to the French-American-British (FAB) classifi ca-tion [16] into refractory anemia (RA, N = 18), refractory anemia with ringed sideroblasts (RARS, N = 11), refractory anemia with excess blasts (RAEB, N = 8), and refractory anemia with excess blasts in transformation (RAEB-t, N = 1). In addi-tion, we included one patient with unclassifi ed MDS, one patient with a mixed MDS/myelopro-liferative picture, and three patients with AML that had evolved from MDS. Three patients had 5q deletion, however not as an isolated lesion, but as part of complex cytogenetic abnormalities. Mono-nuclear (N = 10) or purifi ed CD34+ bone marrow cells (N = 14) from 24 healthy subjects were used as controls. Samples from fi ve normal bone mar-rows, four patients with low grade MDS (RARS or RA), and seven patents with high grade MDS (RAEB-1 or RAEB-2) not used in the microarray studies were used for PCR validation studies of MCL1 expression. Some of the patients had received treatment in the past, including chemo-therapy, erythropoietin or thalidomide, but no treatment was given within 4 weeks of sample acquisition. All patients and healthy donors had given informed consent according to the require-ments of the Institutional Review Board.

Sample preparationHeparinized bone marrow samples were obtained by aspiration from the posterior iliac crest. Mono-nuclear cells were separated by density gradient centrifugation through Ficoll-Hypaque. CD34+ cells were purifi ed by two rounds of high-gradient magnetic cell separation using autoMACS (Milt-enyi Biotec Inc, Auburn, CA) with superparamagnetic

microbead labeling of CD34+ cells. Total RNA was extracted using TRIzol (Invitrogen, Carlsbad, CA.) according to the manufacturer’s protocol. All RNA samples were analyzed on an HP 2100 bio-analizer (Aglient Technologyies, Palo Alto, CA USA) to ensure the integrity of total RNA prior to use in microarray assays [17].

Oligonucleotide microarray gene expressionRNA obtained from mononuclear cells was pre-pared according to the standard Affymetrix proto-col (GeneChip Expression Analyses Technical Manual (http; //www.affymetrix.com/support/tech-nical/manual/expression_manual.affx). For CD34+ cells, RNA was prepared using a single stranded linear amplifi cation protocol (SLAP) prior to RNA labeling and hybridization [17]. Fragmented, bio-tinylated cDNA was hybridized to an Affymetrix HG-U133 microarray according to the manufac-turer’s protocol.

Data analysisDAT fi les for individual samples were generated using Affymetrix MAS 5.0 software. Target signals for probe sets were scaled to 500 for analyses. The detection algorithm was based upon default set-tings per Affymetrix recommendations (https://www.affymetrix.com/support/downloads/manu-als/data_analysis_fundamentals_manual.pdf).

Signals were transformed into log2 intensity. Normalization was performed using the R 2.2.1 software [18]. The expression data was then ana-lyzed using SAM 2.2.1 (Signifi cance Analysis of Microarrays (SAM), Stanford, CA)[19] or Gene-Plus 1.2 (http://www.enodar.com/technology6.htm) for specifi c gene expression analysis and Gene Set Enrichment Analysis software (GSEA v1.0, Broad Institute, MIT, Cambridge, MA) for pathway analysis [20]. We selected a false discov-ery rate (FDR) of 5% to determine statistically signifi cant up or down regulated genes in SAM [21]. Hierarchical clustering was performed using the dChip software available at http://biosun1.harvard.edu/complab/dchip/.

For the analysis of MCL1 levels and transcript ratio in different MDS disease stages, a global p-value was derived using linear regression and tested in the null hypothesis that the mean tran-script ratios were the same across normal, RA, and

139

Gene expression profi les of CD34+ and unselected mononuclear cells in myelodysplasia

Translational Oncogenomics 2008:3

advanced phase MDS. The signifi cant test for trend used linear regression of the mean of the transcript across each group, where each group was assigned values 1, 2, and 3 for normal, low, and advanced MDS, respectively.

PCR validation of MCL1To validate the gene expression of MCL1 in normal and MDS hematopoesis, we developed quantitative RT-PCR assays. Samples from fi ve normal bone marrows, four patients with low grade MDS (RARS or RA), and seven patents with high grade MDS (RAEB-1 or RAEB-2) not used in the micro-array studies were analyzed. For MCL1, the two alternative splice variants were amplifi ed sepa-rately, producing a full-length transcript (T×1) associated with anti-apoptosis, and the alterna-tively spliced, smaller transcript (T×2) that is pro-apoptotic. Quantitative PCR validation for MCL1 splice variants was performed on an ABI 7900 HT Fast Real-Time PCR System. Thermocycler condi-tions were set at: 50 °C for 2 min, 95 °C for 10 min, and 40 cycles at 95 °C for 15 sec and 60 °C for 1 min. A common primer sequence was used for the forward MCL1 primer (E×1): 5 ’ -GAAGGCGCTGGAGACCTTAC-3’ ) . MCL1-T×1 (MCL1-T×1R) and MCL1-T×2 (MCL1-T×1R) reverse primer sequences were 5’-TTTCCGAAGCATGCCTTGG-3’ and 5’-ACTCCACAAACCCATCCTTGG-3’, respec-tively. All probes were FAM-TAMRA; the MCL-1 probe sequence was 5’-ATGGCGTGCAGCG-CAACCAC-3’. Controls genes were obtained from cloned 2.1 TOPO vector plasmids. The size of the MCL1-T×1 and MCL1-T×2 inserts was 517 bp, and 269 bp respectively.

ResultsTo explore the changes in gene expression that occur with the evolution of MDS from normal hematopoietic progenitor cells and during disease progression from RA to RAEB and transformation into secondary AML, we used two general strate-gies. First, we identifi ed expression changes com-mon to all MDS cases compared to normal bone marrow, and secondly, we examined gene expres-sion in MDS cases that correlated with disease subtypes. Since it is not clear to what extent the biology of MDS is determined only by the malig-nant “stem cell” and what the entire cellular envi-ronment contributes, separate analyses were

performed in unselected and CD34+ selected populations of both MDS and normal hematopoi-etic cells.

Gene expression in MDS compared to normal bone marrowWe fi rst compared unselected marrow mononuclear cells from 35 patients with MDS and 10 healthy donors. Unsupervised hierarchical clustering showed complete segregation between normal bone marrow and MDS marrow (Fig. 1A). How-ever, there was no segregation between the differ-ent morphologic subtypes of MDS (Fig. 1A), underscoring the diffi culty of describing MDS biology by morphology alone.

In comparing all MDS cases to normal bone marrow, 516 genes were up-regulated and 2107 were down-regulated in the MDS samples (Supple-mentary Table 1). The top up-regulated genes in MDS included: the HSPA1A (heat shock 70 kDa protein), CEACAM6 (carcinoembryonic antigen-related cell adhesion), DEFA1/4 (defensin alpha 1 and 4), GFI1 (growth factor independent 1) and TCN1 (transcobalamin 1). Among the top down-regulated genes were CREM (a cAMP responsive element modulator), SC5D (sterol-C5-desaturase (ERG3 delta-5-desaturase homolog, fungal)-like), PIK3R1 (phosphoinositide-3-kinase, regulatory subunit 1 (p85 alpha) and IRF4 (interferon regula-tory factor 4). GSEA analysis was then used to discover annotated biological pathways over-represented by genes that were up or down-regulated in MDS compared to normal bone marrow. The pathways most signifi cantly enriched in abnormally regulated genes in MDS were ves-icle transport, glycogen metabolism, chaperone modulated interferon signaling, and the pentose phosphate pathway.

We next compared gene expression in CD34+ selected cells from MDS and normal bone marrow to examine potential changes occurring primarily in the putative MDS “stem cell”. We identifi ed 704 genes that were up-regulated and 826 genes that were down-regulated in the CD34+ cells from MDS patients compared to CD34+ cells from healthy donors (Supplementary Table 2). In com-paring the genes that differed signifi cantly between unsorted samples and purifi ed CD34+ cells, we observed an overlap of 12 genes that were consis-tently up-regulated in both the unselected and CD34+ selected cells, and 95 genes that were

140

Bar et al

Translational Oncogenomics 2008:3

Tabl

e 1.

Top

gen

es d

iffer

entia

lly e

xpre

ssed

in M

DS

com

pare

d to

nor

mal

bon

e m

arro

w.

Gen

e sy

mbo

leD

escr

iptio

nC

D34

+ ce

lls:

Mon

onuc

lear

cel

ls:

SAM

sc

ore1

Fold

ch

ange

q-va

lue(

%)2

SAM

sc

ore

Fold

ch

ange

q-va

lue(

%)

Up

regu

late

d ge

nes:

MA

XM

AX

pro

tein

5.56

1.67

0.00

2.84

3.28

2.61

PM

Lpr

omye

locy

tic le

ukem

ia4.

652.

670.

213.

614.

391.

52P

NU

TL1

sept

in 5

3.57

4.22

1.63

3.94

3.35

0.75

SH

AP

Yca

lciu

m a

ctiv

ated

nuc

leot

idas

e3.

185.

591.

954.

463.

050.

21M

HC

2TA

MH

C c

lass

II tr

ansa

ctiv

ator

2.90

4.03

2.21

3.77

3.09

1.14

ALD

H3B

1al

dehy

de d

ehyd

roge

nase

3 fa

mily

, m

embe

r B1

2.67

2.15

3.03

4.63

1.85

0.21

CC

L18

chem

okin

e (C

-C m

otif)

liga

nd 1

8 (p

ulm

onar

y an

d ac

tivat

ion-

regu

late

d)2.

6212

.50

3.03

3.87

5.61

0.94

SPA

G8

sper

m a

ssoc

iate

d an

tigen

82.

532.

663.

374.

412.

000.

21Q

PR

Tqu

inol

inat

e ph

osph

orib

osyl

trans

fera

se

(nic

otin

ate-

nucl

eotid

e py

roph

osph

oryl

ase

(car

boxy

latin

g))

2.46

3.59

3.37

3.32

3.01

3.11

LAIR

1le

ukoc

yte-

asso

ciat

ed Ig

-like

rece

ptor

12.

211.

844.

764.

161.

630.

34D

own

regu

late

d ge

nes:

VIP

R1

vaso

activ

e in

test

inal

pep

tide

rece

ptor

1−4

.97

0.18

0.00

−2.6

60.

693.

11S

LC2A

14so

lute

car

rier f

amily

2 (f

acili

tate

d gl

ucos

e tra

nspo

rter)

, mem

ber 1

4−4

.12

0.27

0.00

−2.7

10.

653.

11

MC

L1m

yelo

id c

ell l

euke

mia

seq

uenc

e 1

(BC

L2-r

elat

ed)

−4.0

30.

210.

00−3

.32

0.61

1.14

TNFA

IP3

tum

or n

ecro

sis

fact

or, a

lpha

-indu

ced

prot

ein

3−3

.78

0.18

1.31

−4.5

00.

440.

32

DTN

Ady

stro

brev

in, a

lpha

−3.7

70.

241.

31−2

.47

0.78

4.63

NE

LL2

NE

L-lik

e 2

(chi

cken

) ///

NE

L-lik

e 2

(chi

cken

)−3

.72

0.33

1.31

−3.1

70.

631.

43

EP

OR

eryt

hrop

oiet

in re

cept

or−3

.71

0.35

1.31

−3.1

90.

671.

43K

RTH

B6

kera

tin, h

air,

basi

c, 6

(mon

ileth

rix)

−3.6

60.

371.

31−3

.40

0.79

0.94

CX

CR

4ch

emok

ine

(C-X

-C m

otif)

rece

ptor

4−3

.61

0.31

1.31

−3.8

00.

560.

56ZN

F297

Bzi

nc fi

nger

pro

tein

297

B−3

.56

0.31

1.31

−3.8

10.

500.

561 S

core

—Th

e T—

stat

istic

Val

ue.

2 q-v

alue

—Th

e lo

wes

t fal

se d

isco

very

rate

at w

hich

the

gene

is c

alle

d si

gnifi

cant

(Lik

e th

e “p

val

ue” a

dapt

ed to

ana

lysi

s of

a la

rge

num

ber o

f gen

es).

141

Gene expression profi les of CD34+ and unselected mononuclear cells in myelodysplasia

Translational Oncogenomics 2008:3

consistently down-regulated in both the unselected and CD34+ selected cells (Table 1, full list in Supplementary Table 3). These genes are likely to be highly relevant as markers of biological activity, as well as targets for diagnostic and therapeutic tools. The top overlap genes and their expression in all samples are shown in Figure 1B, 1C and Figure 2. The overlap genes fall into several rel-evant biological categories. Interestingly, many genes were deregulated in favor of increased apop-tosis: decreased expression in the anti-apoptotic regulator MCL1, the erythropoetin receptor EPOR, and TNF anti-apoptotic modulator TNFAIP3, and an increased expression in Ca+2 activated nucleo-tidase CANT1, and the inhibitory receptor LAIR1. There was no association of EPOR level and past use of erythropoetin. Dysregulated immune func-tion and cytokine expression have been implicated

in MDS, and in this analysis are highlighted by the increased expression of the CD4/CD8 cytokine CCL18, the decreased expression of the “master” control gene for class II MHC expression CIITA, and the decreased expression of CXCR4, the recep-tor for stroma derived factor 1.

Gene expression in RA compared to advanced MDSThe signals that lead to progression from RA to more advanced disease are poorly understood. We compared unselected mononuclear cells from 16 patients with RA to unselected mononuclear cells from 11 patients with advanced MDS, again using normal bone marrow as reference. Several genes were expressed differently in normal bone marrow, RA and advanced disease (Table 2, Supplementary

NB

M 1

NB

M 3

NB

M 7

NB

M 4

NB

M 9

NB

M 2

NB

M 5

NB

M 6

NB

M 8

NB

M 1

0R

AEB

2R

A 7

RA

11R

A 3

RA

4R

AEB

6R

AEB

7R

AEB

1R

AEB

TR

A 2

AM

L 2

RA

12R

A 10

RA

RS

3R

AR

S 4

RA

EB 5

MD

Sun

clas

sifie

dR

A 15

RA

1R

A 8

RA

5A

ML

3R

A 14

RA

RS

5R

A 13

RA

EB 4

RA

RS

2R

AR

S 6

RA

16R

AR

S 7

RA

6R

A 9

RA

EB 3

RA

RS

1A

ML

1

NBM MDS

d Genes

ea b c d f Genesg a b c ed Genes

A

CB

Figure 1. A. Hierarchical clustering of 1,000 differentially expressed genes in normal marrow and marrow from MDS patients. Each row represents a single probe set and each column a separated normal or MDS marrow sample. Blue coloring represents down-regulated genes in MDS compared to normal, while red represents up-regulated genes in MDS. Unsupervised hierarchical clustering showed complete segregation between normal bone marrow (n = 10) and MDS (n = 35), but no segregation between the different stages of MDS.B, C. Heat map of up- and down-regulated genes in unselected mononuclear and CD34+ selected cells from MDS marrow and normal marrow samples. B) Comparison of gene expression in unselected mononuclear cells in MDS and normal bone marrow: “a”—nor-mal bone marrow samples; “b”—RA samples; “c”—RARS; “d”—RAEB; “e”—RAEB-t; “f”—unclassifi ed MDS; and “g”—AML cases arising from antecedent MDS. C) Comparison of gene expression in CD34+ selected cells: “a”—normal marrow; “b”—RA; “c”—RARS; “d”—RAEB; and “e”—a case of MDS/MPS (a mixed MDS/myeloproliferative picture).

142

Bar et al

Translational Oncogenomics 2008:3

Table 4). Among these genes are MAX (MYC associated factor X), HIST2H2BE (histone 2, H2be), HIST2H2AA (histone 2, H2aa), HIST1H2BG (histone 1, H2bg) and TNFRSF1A (tumor necrosis factor receptor superfamily, mem-ber 1A) which were increasingly expressed with the evolution of RA from normal bone marrow and with progression from RA to more advanced stages of MDS (Fig. 3). ASGR2 (asialoglycoprotein receptor 2), TGFB1 (transforming growth factor, beta 1) IDH3B (isocitrate dehydrogenase 3 NAD+ beta) and EPB41L3 (erythrocyte membrane pro-tein band 4.1-like 3) were down regulated in RA compared to normal bone marrow, and were further down-regulated in advanced MDS (Fig. 3). We next searched for changes in biological pathways asso-ciated with MDS disease progression. Using the GSEA software we identifi ed statistically signifi -cant enrichment in genes involved in the Rac 1 cell

motility signaling pathway and the RAR-RXR pathway with advanced disease.

Validation studies of MCL1In validation studies (Fig. 4), the expression of both the longer anti-apoptotic transcript (T×1) and the shorter pro-apoptotic transcript (T×2) variants of MCL1 decreased signifi cantly from normal bone marrow to low grade, and high grade MDS (global signifi cance levels of T×1 and T×2 when compar-ing normal, low, and high grade MDS were p = 0.03 and p = 0.007, respectively). Moreover, there was a shift of the ratio of the anti-apoptotic/pro-apoptotic mRNA level in these three states, with a T×1/T×2 of 8.4 for normal bone marrow, 4.8 for low grade MDS, and 2.6 for high grade MDS (global signifi cance p = 0.0008, test of trend p = 0.0001). Thus, MDS and progression of MDS

Expr

essi

on –

Log

2

PML

NBM MDSNBM MDS

QPRT

NBM MDSNBM MDSMononuclear cells CD 34+ cells

Expr

essi

on –

Log

2

EPOR

NBM MDSNBM MDSMononuclear cells CD 34+ cells

Expr

essi

on –

Log

2

MCL1

NBM MDSNBM MDSMononuclear cells CD 34+ cells

Expr

essi

on –

Log

2

CD 34+ cellsMononuclear cells

Figure 2. Expression of genes that were up- or down-regulated in MDS compared to normal bone marrow.Log-2 expression level is shown on the Y axis; samples and cell type are indicated on the X axis. For each one of the four genes shown in this fi gure, the expression of the individual samples (for both unselected mononuclear cells and CD34+ selected cells) are shown. NBM: normal bone marrow.

143

Gene expression profi les of CD34+ and unselected mononuclear cells in myelodysplasia

Translational Oncogenomics 2008:3

were associated with not only a global decline in MCL1 level, but a shift in transcript towards a pro-apoptotic bias.

DiscussionMDS comprises a heterogeneous group of clonal disorders that are characterized by aberrant dif-ferentiation in multiple hematopoietic cell lineages and are thought to involve hematopoietic stem cells [1, 2]. However, there is mounting evidence that the disease process is not entirely stem cell-

autonomous and that signals derived from more differentiated cells, in particular monocytes and T lymphocytes, and from the marrow stroma affect the disease process [22–30]. We, therefore, per-formed an analysis of gene expression in both unselected mononuclear cells and in selected CD34+ cells from MDS marrow in comparison to the analogous cell populations from normal mar-row. Our results identifi ed 2623 genes with expres-sion differences between unselected marrow mononuclear cells from healthy donors and MDS patients and 1530 genes with expression differences

Table 2. Top genes differentially expressed in Advanced Disease (RAEB and RAEBT) compared to RA compared to normal bone marrow.

Gene symbole Gene description Linearslope1

P (linearslope)

Up regulated genes:HIST2H2BE histone 2, H2be 1.16 0.00MAX MAX protein 0.47 0.00HIST2H2AA histone 2, H2aa 1.52 0.00TNFRSF1A tumor necrosis factor receptor superfamily,

member 1A0.83 0.00

HIST1H2BG histone 1, H2bg 1.08 0.00ARHGEF2 rho/rac guanine nucleotide exchange factor

(GEF) 20.37 0.00

SNX19 sorting nexin 19 0.29 0.00C1RL complement component 1,

r subcomponent-like0.87 0.00

H2BFS H2B histone family, member S 1.37 0.01HIST1H2BF histone 1, H2bf 0.97 0.01Down regulated genes:ASGR2 asialoglycoprotein receptor 2 −1.75 0.00RNASE4 ribonuclease, RNase A family, 4 −1.11 0.00IDH3B isocitrate dehydrogenase 3 (NAD+) beta −0.36 0.00EPB41L3 erythrocyte membrane protein band

4.1-like 3−1.70 0.00

TGFBI transforming growth factor, beta-induced, 68 kDa

−1.80 0.00

CSPG2 Chondroitin sulfate proteoglycan 2 (versican)

−1.39 0.00

KIAA0399 (ZZEF1) zinc fi nger, ZZ-type with EF-hand domain 1 −1.17 0.00FLJ22222 hypothetical protein FLJ22222 −0.98 0.00IL15 interleukin 15 −0.67 0.01CLIC3 chloride intracellular channel 3 −1.86 0.011Linear Slope—Change of value in the dependent variable (gene expression change) per unit of independent variable (disease stage);The changes in gene expression in the different stages of MDS.

144

Bar et al

Translational Oncogenomics 2008:3

between CD34+ cells from healthy donors and MDS patients. Compared to normal marrow, MDS was associated with an aberrant expression of genes involved in apoptosis, including a decreased expression of MCL1 and EPOR1, and these changes were present both in non-selected and CD34+ selected cell populations. Moreover, the PML gene and genes of the RAR-RXR pathway were found to be associated with the diagnosis of MDS and with advanced disease, respectively, suggesting disruptions of the normal differentiation pathway.

Several genes associated with the promotion of a pro-apoptotic state were identifi ed, including anti-apoptotic regulator MCL1, the erythropoetin receptor EPOR, and TNF anti-apoptotic modulator TNFAIP3. Down regulation of MCL1 is consistent with the increased rate of apoptosis observed in MDS [26, 31–35]. The protein encoded by the MCL1 gene belongs to the Bcl-2 family, known to be regulator of programmed cell death. MCL1 has been shown to be essential in the survival of

hematopoetic stem cells, as inducible deletions of MCL1 in murine models results in a profound loss of bone marrow function, including a loss of hema-topoietic stem cells [36]. MCL1 activity appears to be required for neutrophil, but not for macro-phage survival, [37] suggesting the possibility of lineage specific or differentiation dependent activity. Alternative splicing of the MCL1 gene results in two transcript variants encoding distinct isoforms. The longer gene product (isoform 1; T×1) enhances cell survival by inhibiting apoptosis, while the alternatively spliced shorter gene product (isoform 2; T×2) promotes apoptosis and is death-inducing [38]. These fi ndings are reminiscent of those described for the long and short splice vari-ants of the death signal inhibitory protein FLIP in MDS, [39] suggesting that regulation of splice variants at the transcriptional level is involved in the determination of cell death in MDS. Our data show that not only was MCL1 expression decreased in MDS compared to normal hematopoetic cells, but ratio of long/short transcripts shifted in favor

MAX

NBM RA Advanced Disease

Expr

essi

on –

Log

2

HIST2H2BE

NBM RA Advanced Disease

Expr

essi

on –

Log

2

NBM RA Advanced Disease

CD36 Receptor

Expr

essi

on –

Log

2

TGFB1

NBM RA Advanced Disease

Expr

essi

on –

Log

2

Figure 3. Expression of genes that correlated with advanced disease.Log-2 gene expression of MAX, HIST2H2BE, CD36 Receptor and TGFB1 in normal bone marrow (NBM), RA, and advanced disease (RAEB, RAEB-t, AML).

145

Gene expression profi les of CD34+ and unselected mononuclear cells in myelodysplasia

Translational Oncogenomics 2008:3

of a more pro-apoptotic state. Would such a pattern be compatible with the general observation that apoptosis in marrow cells overall tends to decrease as MDS progressed to more advanced stages? [40] The results appear counterintuitive. However, we have previously shown that the rate of apoptosis differs between clonal and non-clonal hematopoi-etic cells, and the relative proportions of those cell populations change with progression of MDS [41]. Further we observed that expression of the short splice variant of FLIP, characterized anti-apoptotic protein, showed a positive correlation with the extent of apoptosis [39]. Taken together with the lineage specificity of MCL1 as described by Dzhagalove et al. [37] it is conceivable that a pro-apoptotic effect of MCL1 in advanced MDS is expressed only in subset of cells, but does not interfere with increasing proliferation of the mali-gnant clone. Such a model would also be consistent with the observed overall decline in expression of this gene as MDS progresses (see results).

The erythropoietin receptor (EPOR) is a member of the cytokine receptor family. Upon

erythropoietin binding, the erythropoietin receptor activates the Jak2 tyrosine kinase, which in turn activates various intracellular signaling pathways, including, Ras/MAP kinase, phosphatidylinositol 3-kinase and STAT transcription factors [42]. EPOR has an anti-apoptotic function via the Akt-pathway, and signaling via the erythropoietin receptor promotes erythroid cell survival, particu-larly in patients with MDS [43–45]. Thus, the down-regulation of both MCL1 and EPOR may play a role in the dysregulation of apoptosis in hematopoetic cells leading to ineffective hemato-poiesis in MDS. It is clear, however, that the pattern of expression of MCL1 and EPOR by themselves can not explain satisfactorily the extent of apopto-sis and proliferation dysregulations at different stages of MDS. Other factors are involved, and studies of purifi ed cell populations, simultaneously analyzing the impact of various signals will be necessary [46].

Of special biological interest are the MAX (MYC associated factor X) and PML (promyelo-cytic leukemia) genes, which were found to be up-regulated in both unselected mononuclear cells and CD34+ selected cells in MDS compared to normal bone marrow and which showed a correla-tion with progression to advanced disease. The protein encoded by the MAX gene is a transcription factor that interacts with the MYC oncoprotein to form homodimers and heterodimers. Rearrange-ment among these dimer forms provides a complex system of transcriptional regulation [47]. There-fore, alteration in transcription regulation resulted by up regulation of the MAX gene might play a role in the pathophysiology of MDS. In addition, the correlation with progression to advanced dis-ease and the fact that the MAX gene was found to be up regulated in both CD34+ selected cells and unselected marrow cells make this gene a candidate marker for disease progression. The protein encoded by the PML gene is a member of the tri-partite motif (TRIM) family. This phosphoprotein localizes to nuclear bodies where it functions not only as a transcription factor but also as a tumor suppressor. Its expression is cell-cycle related and regulates the p53 response to oncogenic signals [48]. The gene is also involved in the translocation of the retinoic acid receptor alpha gene associated with acute promyelocytic leukemia (APL) [49]. A Further suggestion of the importance of PML and the RARA pathway in MDS disease progression is the deregulation of the RAR-RXR pathway in

0

5

10

15

20

25

MCL1-tx2MCL1-tx1

NBM RA ADVANCED DISEASE

MCL1 Gene Expression

MCL1 tx1/tx2

0

2

4

6

8

10

12

NBM RA ADVANCED DISEASE

MCL1 tx1/tx2

A

B

Figure 4. Quantitative RT-PCR assay of MCL1.A. Expression of MCL1 anti-apoptotic transcript (t×1) and pro-apoptotic transcript (t×2). Values on the y axis represent MCL1 expression relative to endogenous control (beta-2-microglobulin) in normal bone marrow (n = 5), RA patients (n = 4) and MDS patients with advanced disease (n = 7). Both normal and MDS marrows were obtained from individuals who were not used for the gene expression array.B. Relative expression of MCL1 anti-apoptotic transcript (t×1) versus pro-apoptotic transcript (t×2). Values on the y axis repre-sent the relative expression of MCL1 t×1 versus t×2 for each of the individuals shown in panel A.

146

Bar et al

Translational Oncogenomics 2008:3

the advanced MDS noted in our analysis. RXR and RAR are nuclear receptors that bind either all-trans retinoic, 9-cis retinoic acid, or other retinoid ligands [50]. Ligand binding induces a conforma-tional change in the receptors which results in dissociation of the co-repressors and binding of co-activators with histone acetylase activity [50]. The retinoic acid pathway is critical for maintain-ing a balance between self-renewal and differen-tiation of hematopoetic stem cells, [51, 52] and deregulation of the RAR-RXR pathway, as was shown in our study, may affect differentiation and self-renewal, and thereby allow RA to progress to acute leukemia.

Three histone genes (HIST2H2BE, HIST-2H2AA, HIST1H2BG) were found to be up-regulated in MDS compared to normal bone marrow, and moreover found to be correlated with advanced disease. The expression of specifi c his-tone classes dictate changes in chromatin structure and gene expression, and infl uences various path-ways, including cell cycle progression [53–55]. In addition, there is considerable evidence that histone H1 also functions as a non-specifi c repressor of transcription [56]. Moreover, post-translational modifi cations of histones by histone acetylation and deacetylation play a role in tumorgenesis. Histone deacetylases are promising targets in drug development for cancer therapy [57]. Given that our data suggest that aberrations in histone biology are involved in MDS progression, their may be a rationale for using these agents to stem progression of early MDS, especially in combination with agents that block apoptosis, as discussed above.

Several studies have examined gene expression in MDS, using either purifi ed CD34+ or unselected mononuclear cells [58–62]. There is little overlap between the genes identifi ed across those studies or our current results. Such discrepancies are not uncommon in gene expression studies, and likely result from differences in cell types studied (CD34+ versus mononuclear cells), analytic tech-niques to defi ne signifi cant genes, and composi-tion of the cohorts of patients studied. For example, the study by Pellagatti et al. [60] exam-ined CD34+ cells from 55 MDS patients obtained from multiple centers, and showed that MDS cells had gene signatures enriched in interferon response genes. Of the 55 patents, 20 had the 5q- chromosomal aberration. Patients with 5q- MDS are unusually sensitive to the drug lenalidomide, and have recently been shown to have a unique

gene expression signature using unselected mononuclear cells [63].

Since it is unknown which cell population, CD34+ selected or unselected mononuclear cells, best describes the pathology of MDS, and given that various studies have used both types of sam-ples, we performed both types of arrays, and focused on genes found to be dysregulated in both cell populations. Thus, we consider our gene selec-tion to be fairly robust. We are comforted by the fact that the most relevant genes identifi ed in the present analysis seem biologically relevant: alter-ations of differentiation and proliferation pathways (PML, RAR-RXR, MAX), involvement in apop-tosis (MCL1, EPOR) and regulation of hemato-poiesis (TNFAIP3, CXCR4, CCL18).

Since the patient population included in this study was relatively small, we were unable to study specific correlations between gene expression, cytogenetics, and clinical presentation. However there did not appear to be an association of previous treatment to the molecular signature, and specifi -cally there was no effect of erythropoietin treatment on EPOR gene expression. These fi ndings strengthen our interpretation that low EPOR levels might be related to the pathophysiology of MDS, and might explain the heterogeneity of response to treatment with erythropoietin among MDS patients.

In conclusion, this study provides new data on gene expression in the different phases of MDS. Although no single gene can likely explain the pathophysiology of the disease, some of the dif-ferences delineated in this study may prove relevant in our efforts to identify prognostic markers and therapeutic targets.

AcknowledgmentsWe thank all the patients and healthy donors who agreed to donate marrows for these investigations.

Author contributionsSupported by NHI/NCI grants: HL36444, HL082941, CA18021. Merav Bar: Conception and design, Collection and/or assembly of data, Data analysis and inter-pretation, Manuscript writing.Derek Stirewalt: Conception and design, Collec-tion and/or assembly of data, Data analysis and interpretation.Era Pogosova-Agadjanyan: Collection and/or assembly of data.

147

Gene expression profi les of CD34+ and unselected mononuclear cells in myelodysplasia

Translational Oncogenomics 2008:3

Vitas Wagner: Collection and/or assembly of data.Ted Gooley: Data analysis and interpretation.Nissa abbasi: Collection and/or assembly of data.Ravi Bhatia: Provision of study material or patients, Data analysis and interpretation.Joachim Deeg: Conception and design, Financial support, Data analysis and interpretation, Manuscript writing.Jeral Radich: Conception and design, Financial support, Data analysis and interpretation, Manuscript writing.

References[1] Aul, C., Bowen, D.T. and Yoshida, Y. 1998. Pathogenesis, etiology

and epidemiology of myelodysplastic syndromes. Haematologica, Jan;83(1):71–86.

[2] Heaney, M.L. and Golde, D.W. 1999. Myelodysplasia. N. Engl. J. Med., May 27;340(21):1649–60.

[3] Fenaux, P. 2001. Chromosome and molecular abnormalities in myelodysplastic syndromes. International journal of hematology, Jun;73(4):429–37.

[4] Steensma, D.P. and List, A.F. 2005. Genetic testing in the myelodys-plastic syndromes: molecular insights into hematologic diversity. Mayo. Clin. Proc., Jan;80(5):681–98.

[5] Olney, H.J. and Le Beau, M.M. 2001. The cytogenetics of myelodysplastic syndromes. Best Pract Res. Clin. Haematol., Sep;14(3):479–95.

[6] Fenaux, P., Morel, P. and Lai, J.L. 1996. Cytogenetics of myelodys-plastic syndromes. Semin. Hematol., Apr;33(2):127–38.

[7] Horiike, S., Misawa, S., Nakai, H., Kaneko, H., Yokota, S., Taniwaki, M. et al. 1994. N-ras mutation and karyotypic evolution are closely associated with leukemic transformation in myelodysplastic syn-drome. Leukemia, Aug;8(8):1331–6.

[8] Neubauer, A., Greenberg, P., Negrin, R., Ginzton, N. and Liu, E. 1994. Mutations in the ras proto-oncogenes in patients with myelo-dysplastic syndromes. Leukemia, Apr;8(4):638–41.

[9] Shih, L.Y., Lin, T.L., Wang, P.N., Wu, J.H., Dunn, P., Kuo, M.C. et al. 2004. Internal tandem duplication of fms-like tyrosine kinase 3 is associated with poor outcome in patients with myelodysplastic syn-drome. Cancer, Sep 1;101(5):989–98.

[10] Aggerholm, A., Holm, M.S., Guldberg, P., Olesen, L.H. and Hokland, P. 2006. Promoter hypermethylation of p15INK4B, HIC1, CDH1, and ER is frequent in myelodysplastic syndrome and predicts poor prognosis in early-stage patients. Eur. J. Haematol., Jan;76(1):23–32.

[11] Guan, X., Sagara, J., Yokoyama, T., Koganehira, Y., Oguchi, M., Saida, T. et al. 2003. ASC/TMS1, a caspase-1 activating adaptor, is downregulated by aberrant methylation in human melanoma. Int. J. Cancer, Nov 1;107(2):202–8.

[12] Herman, J.G. and Baylin, S.B. 2003. Gene silencing in cancer in association with promoter hypermethylation. N. Engl. J. Med., Nov 20;349(21):2042–54.

[13] Jones, P.A. and Baylin, S.B. 2002. The fundamental role of epigen-etic events in cancer. Nat. Rev. Genet., Jun;3(6):415–28.

[14] Machida, E.O., Brock, M.V., Hooker, C.M., Nakayama, J., Ishida, A., Amano, J. et al. 2006. Hypermethylation of ASC/TMS1 is a sputum marker for late-stage lung cancer. Cancer Res., Jun 15;66(12):6210–8.

[15] Quesnel, B., Guillerm, G., Vereecque, R., Wattel, E., Preudhomme, C., Bauters, F. et al. 1998. Methylation of the p15(INK4b) gene in myelodysplastic syndromes is frequent and acquired during disease progression. Blood, Apr 15;91(8):2985–90.

[16] Bennett, J.M., Catovsky, D., Daniel, M.T., Flandrin, G., Galton, D.A., Gralnick, H.R. et al. 1982. Proposals for the classifi cation of the myelodys-plastic syndromes. British journal of haematology, Jun;51(2):189–99.

[17] Stirewalt, D.L., Pogosova-Agadjanyan, E.L., Khalid, N., Hare, D.R., Ladne, P.A., Sala-Torra, O. et al. 2004. Single-stranded linear amplifi -cation protocol results in reproducible and reliable microarray data from nanogram amounts of starting RNA. Genomics, Feb;83(2):321–31.

[18] R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria 2008.

[19] Tusher, V.G., Tibshirani, R. and Chu, G. 2001. Signifi cance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U.S.A., Apr 24;98(9):5116–21.

[20] Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A. et al. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profi les. Proc. Natl. Acad. Sci. U.S.A., Oct 25;102(43):15545–50.

[21] Storey, J.D. and Tibshirani, R. 2003. Statistical signifi cance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A., Aug 5;100(16):9440–5.

[22] Aizawa, S., Nakano, M., Iwase, O., Yaguchi, M., Hiramoto, M., Hoshi, H. et al. 1999. Bone marrow stroma from refractory anemia of myelodysplastic syndrome is defective in its ability to support normal CD34-positive cell proliferation and differentiation in vitro. Leuk. Res., Mar;23(3):239–46.

[23] Copplestone, J.A., Mufti, G.J., Hamblin, T.J. and Oscier, D.G. 1986. Immunological abnormalities in myelodysplastic syndromes. II. Coexistent lymphoid or plasma cell neoplasms: a report of 20 cases unrelated to chemotherapy. British journal of haematology, May;63(1):149–59.

[24] Deeg, H.J., Beckham, C., Loken, M.R., Bryant, E., Lesnikova, M., Shulman, H.M. et al. 2000. Negative regulators of hemopoiesis and stroma function in patients with myelodysplastic syndrome. Leuk. Lymphoma, Apr;37(3–4):405–14.

[25] Flores-Figueroa, E., Arana-Trejo, R.M., Gutierrez-Espindola, G., Perez-Cabrera, A. and Mayani, H. 2005. Mesenchymal stem cells in myelodysplastic syndromes: phenotypic and cytogenetic characteriza-tion. Leuk. Res., Feb;29(2):215–24.

[26] Liesveld, J.L., Jordan, C.T. and Phillips, G.L. 2004. The hematopoi-etic stem cell in myelodysplasia. Stem Cells, 22(4):590–9.

[27] Micheva, I., Thanopoulou, E., Michalopoulou, S., Karakantza, M., Kouraklis-Symeonidis, A., Mouzaki, A. et al. 2004. Defective tumor necrosis factor alpha-induced maturation of monocyte-derived den-dritic cells in patients with myelodysplastic syndromes. Clin. Immu-nol., Dec;113(3):310–7.

[28] Miura, I., Kobayashi, Y., Takahashi, N., Saitoh, K. and Miura, A.B. 2000. Involvement of natural killer cells in patients with myelodys-plastic syndrome carrying monosomy 7 revealed by the application of fl uorescence in situ hybridization to cells collected by means of fl uorescence-activated cell sorting. British journal of haematology, Sep;110(4):876–9.

[29] Tauro, S., Hepburn, M.D., Bowen, D.T. and Pippard, M.J. 2001. Assessment of stromal function, and its potential contribution to deregulation of hematopoiesis in the myelodysplastic syndromes. Haematologica, Oct;86(10):1038–45.

[30] van Lom, K., Hagemeijer, A., Smit, E., Hahlen, K., Groeneveld, K. and Lowenberg, B. 1995. Cytogenetic clonality analysis in myelodysplastic syndrome: monosomy 7 can be demonstrated in the myeloid and in the lymphoid lineage. Leukemia , Nov;9(11):1818–21.

[31] Albitar, M., Manshouri, T., Shen, Y., Liu, D., Beran, M., Kantarjian, H.M. et al. 2002. Myelodysplastic syndrome is not merely “preleukemia”. Blood, Aug;100(3):791–8.

[32] Bouscary, D., Chen, Y.L., Guesnu, M., Picard, F., Viguier, F., Lacombe, C. et al. 2000. Activity of the caspase-3/CPP32 enzyme is increased in “early stage” myelodysplastic syndromes with excessive apoptosis, but caspase inhibition does not enhance colony formation in vitro. Exp. Hematol., Jul;28(7):784–91.

148

Bar et al

Translational Oncogenomics 2008:3

[33] Pecci, A., Travaglino, E., Klersy, C. and Invernizzi, R. 2003. Apoptosis in relation to CD34 antigen expression in normal and myelodysplastic bone marrow. Acta. Haematol., 109(1):29–34.

[34] Shimazaki, K., Ohshima, K., Suzumiya, J., Kawasaki, C. and Kikuchi, M. 2000. Evaluation of apoptosis as a prognostic factor in myelodysplastic syndromes. British journal of haematology, Sep;110(3):584–90.

[35] Washington, L.T., Jilani, I., Estey, E. and Albitar, M. 2002. Less apoptosis in patients with 5q-syndrome than in patients with refrac-tory anemia. Leuk. Res., Oct;26(10):899–902.

[36] Opferman, J.T., Iwasaki, H., Ong, C.C., Suh, H., Mizuno, S., Akashi, K. et al. 2005. Obligate role of anti-apoptotic MCL-1 in the survival of hematopoietic stem cells. Science, (New York, NY), Feb 18;307(5712):1101–4.

[37] Dzhagalov, I., St John, A. and He, Y.W. 2007. The antiapoptotic protein Mcl-1 is essential for the survival of neutrophils but not macrophages. Blood, Feb 15;109(4):1620–6.

[38] Michels, J., Johnson, P.W. and Packham, G. 2005. Mcl-1. Int. J. Biochem. Cell Biol., Feb;37(2):267–71.

[39] Benesch, M., Platzbecker, U., Ward, J., Deeg, H.J. and Leisenring, W. 2003. Expression of FLIP(Long) and FLIP(Short) in bone mar-row mononuclear and CD34+ cells in patients with myelodysplas-t ic syndrome: corre la t ion wi th apoptos is . Leukemia , Dec;17(12):2460–6.

[40] Gersuk, G.M., Beckham, C., Loken, M.R., Kiener, P., Anderson, J.E., Farrand, A. et al. 1998. A role for tumour necrosis factor-alpha, Fas and Fas-Ligand in marrow failure associated with myelodysplastic syndrome. British journal of haematology, Oct;103(1):176–88.

[41] Li, X., Bryant, C.E. and Deeg, H.J. 2004. Simultaneous demonstra-tion of clonal chromosome abnormalities and apoptosis in individual marrow cells in myelodysplastic syndrome. International journal of hematology, Aug;80(2):140–5.

[42] Cheung, J.Y. and Miller, B.A. 2001. Molecular mechanisms of erythropoietin signaling. Nephron, Mar;87(3):215–22.

[43] Hardee, M.E., Rabbani, Z.N., Arcasoy, M.O., Kirkpatrick, J.P., Vujaskovic, Z., Dewhirst, M.W. et al. 2006. Erythropoietin inhibits apoptosis in breast cancer cells via an Akt-dependent pathway with-out modulating in vivo chemosensitivity. Molecular cancer thera-peutics, Feb;5(2):356–61.

[44] Hellstrom-Lindberg, E. 2003. Approach to anemia associated with myelodysplastic syndromes. Curr. Hematol. Rep., Mar;2(2):122–9.

[45] Hellstrom-Lindberg, E., Gulbrandsen, N., Lindberg, G., Ahlgren, T., Dahl, I.M., Dybedal, I. et al. 2003. A validated decision model for treating the anaemia of myelodysplastic syndromes with erythropoi-etin + granulocyte colony-stimulating factor: signifi cant effects on quality of life. British journal of haematology, Mar;120(6):1037–46.

[46] Stirewalt, D.L., Mhyre, A.J., Marcondes, M., Pogosova-Agadjanyan, E., Abbasi, N., Radich, J.P. et al. 2008. Tumour necrosis factor-induced gene expression in human marrow stroma: clues to the pathophysiol-ogy of MDS. British journal of haematology, Feb;140(4):444–53.

[47] Lee, L.A. and Dang, C.V. 2006. Myc target transcriptomes. Curr. Top Microbiol. Immunol., 302:145–67.

[48] Zimber, A., Nguyen, Q.D. and Gespach, C. 2004. Nuclear bodies and compartments: functional roles and cellular signalling in health and disease. Cell Signal, Oct;16(10):1085–104.

[49] Melo, R.A., de Vasconcellos, J.F., Melo, F.C., Machado, C.G., Lacerda, T.M. and Souto, F.R. 2006. PML-RARalpha fusion gene transcripts and biological features in acute promyelocytic leukemia patients. Clin. Lab Haematol., Apr;28(2):126–9.

[50] Bastien, J. and Rochette-Egly, C. 2004. Nuclear retinoid receptors and the transcription of retinoid-target genes. Gene, Mar 17;328:1–16.

[51] Johnson, B.S., Mueller, L., Si, J. and Collins, S.J. 2002. The cytokines IL-3 and GM-CSF regulate the transcriptional activity of retinoic acid receptors in different in vitro models of myeloid differentiation. Blood, Feb 1;99(3):746–53.

[52] Purton, L.E., Dworkin, S., Olsen, G.H., Walkley, C.R., Fabb, S.A., Collins, S.J. et al. 2006. RARgamma is critical for maintaining a balance between hematopoietic stem cell self-renewal and differen-tiation. J. Exp. Med., May 15;203(5):1283–93.

[53] Gunjan, A. and Verreault, A. 2003. A Rad53 kinase-dependent surveil-lance mechanism that regulates histone protein levels in S. cerevisiae. Cell, Nov 26;115(5):537–49.

[54] Quina, A.S., Buschbeck, M. and Di Croce, L. 2006. Chromatin structure and epigenetics. Biochem. Pharmacol. Jul 10.

[55] Quivy, J.P. and Almouzni, G. 2003. Rad53: a controller ensuring the fi ne-tuning of histone levels. Cell, Nov 26;115(5):508–10.

[56] Brown, D.T., Alexander, B.T. and Sittman, D.B. 1996. Differential effect of H1 variant overexpression on cell cycle progression and gene expression. Nucleic Acids Res., Feb 1;24(3):486–93.

[57] Minucci, S. and Pelicci, P.G. 2006. Histone deacetylase inhibitors and the promise of epigenetic (and more) treatments for cancer. Nat. Rev. Cancer, Jan;6(1):38–51.

[58] Hofmann, W.K., de Vos, S., Komor, M., Hoelzer, D., Wachsman, W. and Koeffl er, H.P. 2002. Characterization of gene expression of CD34+ cells from normal and myelodysplastic bone marrow. Blood, Nov 15;100(10):3553–60.

[59] Lee, Y.T., Miller, L.D., Gubin, A.N., Makhlouf, F., Wojda, U., Barrett, A.J. et al. 2001. Transcription patterning of uncoupled proliferation and differentiation in myelodysplastic bone marrow with erythroid-focused arrays. Blood, Sep 15;98(6):1914–21.

[60] Pellagatti, A., Cazzola, M., Giagounidis, A.A., Malcovati, L., Della Porta, M.G., Killick, S. et al. 2006. Gene expression profi les of CD34+ cells in myelodysplastic syndromes: involvement of interferon stimulated genes and correlation to FAB. subtype and karyotype. Blood. Mar 9.

[61] Pellagatti, A., Esoof, N., Watkins, F., Langford, C.F., Vetrie, D., Campbell, L.J. et al. 2004. Gene expression profi ling in the myelo-dysplastic syndromes using cDNA microarray technology. British journal of haematology. Jun;125(5):576–83.

[62] Pellagatti, A., Fidler, C., Wainscoat, J.S. and Boultwood, J. 2005. Gene expression profi ling in the myelodysplastic syndromes. Hema-tology, Aug;10(4):281–7.

[63] Ebert, B.L., Galili, N., Tamayo, P., Bosco, J., Mak, R., Pretz, J. et al. 2008. An Erythroid Differentiation Signature Predicts Response to Lenalidomide in Myelodysplastic Syndrome. PLoS medicine, Feb 12;5(2):e35.

149

Gene expression profi les of CD34+ and unselected mononuclear cells in myelodysplasia

Translational Oncogenomics 2008:3

Gene Expression Patterns in Myelodyplasia Underline the Role of Apoptosis and Differentiation in Disease Initiation and ProgressionMerav Bar, Derek Stirewalt, Era Pogosova-Agadjanyan, Vitas Wagner, Ted Gooley, Nissa Abbasi, Ravi Bhatia, H. Joachim Deeg and Jerald Radich

Supplementary TablesTable S1. MDS (multiple groups) vs NBM mononuclear cells.

Table S2. MDS (multiple groups) vs NBM CD34+ cells.

Table S3. OVELAPPED GENES (up/down regulated) in MDS multiple groups vs NBM (Mononuclear cells and CD 34+ cells).

Table S4. Advanced Disease (RAEB and RAEBT) versus RA versus normal bone marrow.

Sheet1

MDS (multiple groups) vs NBM mononuclear cells:

Positive genes

Gene ID (Affymetrix probe #):Gene NameScore(d)Numerator(r)Denominator(s+s0)Fold Changeq-value(%)

214290_s_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=9.71934019432.55753067620.26313830216.56750663670

218280_x_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=8.83024728792.40993030990.27291764676.21667079490

200799_atHSPA1A8.00331393122.06638547620.2581912314.61933609890

213603_s_atRAC27.97699271460.68065246710.08532695111.624039690

205897_atNFATC47.46332510721.51507518940.20300270562.75347124440

214281_s_atZNF3637.02670409640.94955972910.1351358641.94129222810

211657_atCEACAM67.01955670544.46028849820.635408856335.24434952840

200800_s_atHSPA1A6.83579359962.39413114980.3502345586.44047921190

212749_s_atZNF3636.8056063961.01643368370.14935240512.00717006650

205033_s_atDEFA16.78563794635.22288872850.769697524447.83030966330

206589_atGFI16.76554840132.76478129510.40865590366.51991532070

205513_atTCN16.71355301343.73791844750.556772016220.53436889980

222315_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=6.66372056433.17325404050.476198545510.61217438990

205633_s_atALAS16.6306579372.0330481530.30661333654.95282700570

207269_atDEFA46.5627897335.31987987810.81061257473.35330837680

203757_s_atCEACAM66.37451557614.87243724740.764361964346.18743307970

208864_s_atTXN6.25692507360.8890495510.14209049021.90110391020

219281_atMSRA6.00312627641.13167464890.18851421692.3266603660

209893_s_atFUT45.90227890581.41833173080.24030239062.96071584160

218332_atBEX15.89617302752.72307896530.46183837439.6727083550

203897_atLOC571495.83964392050.55413143070.09489130471.48452696730

207384_atPGLYRP5.79786115856.36694154091.098153503136.90852266430

202708_s_atHIST2H2BE5.78757218471.60325294570.27701649233.55960698950

212408_atLAP1B5.77502977930.85304970570.14771347311.82404921940

203964_atNMI5.77144331260.71030424170.12307220281.66329019230

206207_atCLC5.74417028073.66629606090.638263819120.5782626710

218983_atC1RL5.71881136011.46028454060.25534756243.01031735160

222067_x_atHIST1H2BD5.71529633921.58664449260.27761368763.06144954360

208490_x_atHIST1H2BF5.70247731241.351762530.2370482962.5979267660

202252_atRAB135.65841965091.37991579970.24386946972.85652207160

206772_atPTHR25.6101208121.81181791510.32295524033.59726605350

220005_atGPR865.58506649693.12619834210.55974236729.27665444060

203986_atGENX-34145.56273086632.36162840760.424544795.56223776220

209332_s_atMAX5.56055666370.70472204840.12673588111.67437727210

208496_x_atHIST1H3G5.55935841312.25788357320.40614103386.20642324090

203021_atSLPI5.51490161012.87544067760.521394737610.68975575940

208546_x_atHIST1H2BH5.48791124951.73508284470.31616452343.14140069810

204430_s_atSLC2A55.4804769122.48806827310.45398754766.23959360730

204351_atS100P5.47440964823.56102307110.650485312617.69746118180

202442_atAP3S15.46182354450.90234681920.1652098081.96757102480

212062_atATP9A5.43845318772.18326598710.40144980785.43575724440

210244_atCAMP5.40199488875.0514528350.935108777248.44608084610

207802_atCRISP35.3896274765.76645817271.06991776385.48798256410

205480_s_atUGP25.36963779210.75084709680.13983198231.73026181330

209331_s_atMAX5.34950002230.63757546250.11918412191.55316320440

205041_s_atORM15.33382802124.80247380470.900380324563.47409139980

204804_atSSA15.30732089510.69140987010.13027474381.64561731130

213545_x_atSNX35.30396602270.65866374930.1241832521.61208938470

202875_s_atPBX25.28581835470.70593200510.13355207421.62676486410

200961_atSPS25.25633173530.71309978120.13566491181.68791270050

221523_s_atRRAGD5.24540846971.58548901150.30226225863.4993448940

204220_atGMFG5.21928228080.72611319770.13912127351.70814078920

204185_x_atPPID5.20660513790.63265979250.12151099911.59401402960

220811_atPRG35.1831175913.77464828790.728258277331.4096317280

203041_s_atLAMP25.16864794471.24050930710.24000653952.58736927070

214627_atEPX5.16729368392.71150604960.524743940510.30797400160

211275_s_atGYG5.14983327751.3764379670.26727816082.92201506250

209806_atHIST1H2BK5.14147069941.37807819550.26803190683.01264598680

220615_s_atFLJ104625.08931536552.19471093750.43123893495.9748930180

209357_atCITED25.07390737861.27793583020.25186424092.68961217010

202794_atINPP15.05982024140.79277497760.15668046291.80326587660

221864_atMGC130245.05333754690.88535044550.17520112941.95151469290

207643_s_atTNFRSF1A5.03990350961.0863232540.21554445482.26223113610

217794_atDKFZP564J1575.03836776920.68569699350.13609506591.63839027820

212268_atSERPINB15.0347567321.27983087740.25419914912.69374986010

212082_s_atMYL65.03052087520.55209132310.10974834151.47694320

200734_s_atARF35.0016784320.4199902470.08396986191.34967601170

211743_s_atPRG24.992967653.9768256120.796485355230.13265853020

201315_x_atIFITM24.99031035981.28204083550.25690603252.69391969960

220421_atFLJ214584.98086405592.83254008580.5686844813.15638788120

218643_s_atCRIPT4.94501803751.06957997220.21629445312.10792237810

202529_atPRPSAP14.93940176920.63055605050.12765838451.5865202510

214040_s_atGSN4.92402575932.45787046210.49915873367.13873956310

218870_atARHGAP154.92091416530.51730406230.10512356951.44004155560

219271_atGalNac-T104.919522292.29442635350.46639210446.07280392810

200067_x_atSNX34.91327099740.54918252870.11177533851.48235697460

202349_atDYT14.89850251560.59531527910.12153005481.5410601040

206640_x_atGAGE44.89003954581.0424301760.21317418121.7971110330

200942_s_atHSBP14.8557245050.53778106350.11075197181.45871188250

212203_x_atIFITM34.8501434691.00208992850.20661036832.12566666180

204436_atpp90994.85004013890.84401333150.17402192711.92288932020

208636_atACTN14.82764511461.41694058410.29350553953.08471239660

206157_atPTX34.82695225691.86544686770.38646474394.83780819690

212700_x_atKIAA03564.8249060760.96090874430.19915594821.99566254610

201318_s_atMRCL34.79546800760.60846585920.12688351971.55699437230

202030_atBCKDK4.78697789660.51081784060.10670988081.42444595870

214522_x_atHIST1H3D4.748321342.20817631430.46504357145.55172565790

200011_s_atARF34.73698504720.43716858010.09228835971.37128234840

208579_x_atH2BFS4.73180843651.61700807550.34173151713.74102153510

204636_atCOL17A14.7247852442.74192661630.58032830599.49831039720.212424178

203936_s_atMMP94.72145907213.33413336230.706165893117.59444780510.212424178

219191_s_atBIN24.72050388750.72880004020.15439030611.68311397120.212424178

213102_atACTR34.68222372920.67072219320.14324864251.63707754370.212424178

78047_s_atMMP244.67774499670.37574283180.08032563381.31177235480.212424178

221524_s_atRRAGD4.67412629741.32698919920.28390101482.81085376360.212424178

205040_atORM14.67293723034.86861042371.041873704680.24258583590.212424178

208583_x_atHIST1H2AJ4.65555984880.73093214720.15700198711.72727372120.212424178

217918_atDNCL2A4.64933651850.57781181370.12427833761.53087220180.212424178

206503_x_atPML4.64647828241.37419306010.29574937762.67079699250.212424178

208626_s_atVAT14.6459007840.91607388370.19717895972.03285217770.212424178

208527_x_atHIST1H2BE4.63786908980.94249924160.20321816412.08588464440.212424178

219014_atPLAC84.63518191390.9706082480.20940024922.06991955450.212424178

211004_s_atALDH3B14.63488685450.8416442550.18158895381.85006127060.212424178

212892_atZNF2824.62674219170.79869922250.1726266971.73913358450.212424178

200663_atCD634.61617580230.77309882180.16747603531.80828670390.212424178

214455_atHIST1H2BC4.61533687272.09373152460.4536465234.83857231570.212424178

200696_s_atGSN4.61383583091.46657387350.31786433833.30174288720.212424178

202811_atAMSH4.61270446150.51530961650.111715291.45045799260.212424178

206676_atCEACAM84.61061031074.12443583450.894553119128.62332472110.212424178

210648_x_atSNX34.60942290240.49450542080.1072814171.43653057310.212424178

212768_s_atGW1124.60366824824.70084002541.021107467336.93014597830.212424178

205147_x_atNCF44.60060225841.30221074440.28305223342.76117708350.212424178

205780_atBIK4.5701105182.20958368870.48348583266.75727513230.212424178

203282_atGBE14.55636532111.16479495740.25564125682.53013781830.212424178

200944_s_atHMGN14.53732033310.39400420340.08683632071.32281100530.212424178

206208_atCA44.53723336392.88064052370.634889213912.02071276270.212424178

202100_atRALB4.53518732720.7747510650.17083110561.81282164880.212424178

218145_atC20orf974.53231477950.83600108690.18445344761.91220443730.212424178

51192_atSSH-34.52168085440.79114286270.17496654191.72029263820.212424178

211509_s_atRTN44.52113644130.57795774770.12783461751.54836755280.212424178

207519_atSLC6A44.52067705870.94481611870.20899880851.72137473080.212424178

203765_atGCA4.49274180631.5203744310.33840681183.52053853420.212424178

213572_s_atSERPINB14.47664012831.03334271780.23082997252.2232985660.212424178

203274_atF8A4.47569926640.39708270040.08871970091.33730878210.212424178

215779_s_atHIST1H2BG4.47075465261.7005829930.38037940463.67967606460.212424178

209515_s_atRAB27A4.46927749891.11285325820.24900070732.42984850420.212424178

211883_x_atCEACAM14.4595311022.00785358930.45023872325.22951414070.212424178

221732_atSHAPY4.45859960981.50911193130.33847218043.04664254120.212424178

203042_atLAMP24.45836963281.03355716340.23182401832.17431923950.212424178

203802_x_atWBSCR20A4.45418713450.8326404450.18693432041.76477157240.212424178

205769_atSLC27A24.44956974891.15008444970.25847093422.41867129310.212424178

62212_atMGC9554.4480529450.3893122570.08752419581.31392902870.212424178

210484_s_atMGC319574.44634022162.05273151210.46166766594.05663775440.212424178

215102_atLOC892314.44076747762.34792372370.52872025746.25452634040.212424178

216210_x_atHRIHFB21224.44040353940.7149475240.16100958341.72101588510.212424178

209155_s_atNT5C24.43633579790.80152664110.18067312251.80157233470.212424178

200625_s_atCAP14.42200816460.72041874290.16291664691.74108195980.212424178

201487_atCTSC4.41340481350.76507141590.17335174281.64064591920.212424178

206816_s_atSPAG84.40596622981.16302292970.26396546622.00318581250.212424178

203960_s_atLOC516684.40342230830.62393889780.14169408571.49968000510.212424178

211535_s_atFGFR14.39898015541.18621391390.26965657312.28620752750.212424178

215884_s_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=4.39805143840.66647158020.1515379231.64083785960.212424178

221485_atB4GALT54.39645623841.40340837930.31921354453.11331411190.212424178

209911_x_atHIST1H2BD4.37814042721.07759647540.246131092.42147981140.212424178

215343_atKIAA15094.36591044370.99867849990.22874461422.02449245670.212424178

202990_atPYGL4.3656846131.52023650180.34822407863.53642241940.212424178

209089_atRAB5A4.36353150760.52812271570.12103103071.47814011020.212424178

210789_x_atCEACAM34.35899627850.94709200310.21727295522.0739596160.212424178

213773_x_atWBSCR20A4.33875897160.88445966360.20385084061.94777628560.3384027849

214138_atZNF794.33548931311.35706871450.31301396832.48903088390.3384027849

217837_s_atNEDF4.32808407480.61645096260.142430451.56826777610.3384027849

31845_atELF44.32186448880.59356484470.13733999441.54674858110.3384027849

201301_s_atANXA44.31583418590.75554435870.17506334261.74570688840.3384027849

204039_atCEBPA4.29068865281.09570372630.25536780112.41511935610.3384027849

217780_atPTD0084.28967123320.3053015110.07117130761.24727026070.3384027849

219933_atGLRX24.27192727020.57044513570.13353343811.52130613430.3384027849

211163_s_atTNFRSF10C4.26837883172.57732023090.60381712418.07072280360.3384027849

218773_s_atPILB4.26695027930.56416910060.13221834421.51788408070.3384027849

212230_atPPAP2B4.26588711641.1877898220.27843911232.58812704010.3384027849

205899_atCCNA14.25594105590.86054714930.20219902911.80564309810.3384027849

209215_atTETRAN4.25032407850.99744096010.23467409582.12679723990.3384027849

219295_s_atPCOLCE24.23802711492.30934455770.54491028376.2439988290.3384027849

209944_atAPA14.23706109430.49735742910.11738264281.44360343830.3384027849

209369_atANXA34.23473784652.94128122740.694560403512.76677409640.3384027849

209094_atDDAH14.2270290911.44675917410.34226383193.0412564020.3384027849

207008_atIL8RB4.22555240052.11347874430.50016626095.64121611580.3384027849

208637_x_atACTN14.21275287191.20508272880.28605587972.5859053670.3384027849

214606_atTSPAN-24.20992705941.89902174190.45108186324.77143664930.3384027849

204186_s_atPPID4.19344018410.50236089270.11979684241.43147211540.3384027849

216592_atMAGEC34.18477154161.34578211080.32159034193.1037514210.3384027849

215088_s_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=4.18420175350.3493134690.08348389721.28213771610.3384027849

210788_s_atretSDR44.17903784670.62426625640.14938037881.59200660530.3384027849

220945_x_atFLJ102984.16164524361.69646573070.40764304294.24839511750.3384027849

221766_s_atC6orf374.15881879871.17039455860.28142475432.50801863140.3384027849

221149_atGPR774.15721570251.75782166450.422836294.6760461760.3384027849

210644_s_atLAIR14.15682581350.66169813830.15918351361.63234680220.3384027849

209397_atME24.14615616730.66078273720.15937237061.65937157650.5589193537

212531_atLCN24.14565463143.64743184880.879820480323.02988890750.5589193537

206200_s_atANXA114.14036528580.71969473270.17382397041.73571119590.5589193537

218910_atFLJ103754.13640443061.32071799460.31929131132.77954088410.5589193537

202200_s_atSRPK14.13356158660.74883248420.18115914531.77409639560.5589193537

207677_s_atNCF44.13314005261.57967984960.38219848093.55301378750.5589193537

201605_x_atCNN24.13122236090.73306441580.17744491871.76172612810.5589193537

208749_x_atFLOT14.11758084140.66433848520.1613419411.66313003710.5589193537

65517_atAP1M24.1149612321.12001938010.27218224352.39720231410.5589193537

206851_atRNASE34.1073703412.88068598120.701345567112.63584503950.5589193537

211013_x_atPML4.10476750441.43454660750.34948303553.18221826630.5589193537

214554_atHIST1H2AL4.10207714811.21515831760.2962300012.31924033460.5589193537

205569_atLAMP34.09682261071.60744783440.39236451933.25746149760.5589193537

202286_s_atTACSTD24.09668870352.47946941410.60523744757.10696877180.5589193537

209892_atFUT44.07728206851.41476104540.34698630653.0695167190.5589193537

206039_atRAB33A4.06586580780.68488997930.16844874171.6543179550.5589193537

201023_atTAF74.05605109210.55670919990.13725399091.49288049990.6267294224

209355_s_atPPAP2B4.04626241161.78278971050.44060160444.17731277530.6267294224

205557_atBPI4.04562674573.27334363270.80910668216.99663382090.6267294224

216605_s_atR29124_14.03673422272.03450654330.50399814084.32243393340.6267294224

203725_atGADD45A4.02928203711.10289222380.27371929132.41472995510.6267294224

213589_s_atLOC2842084.02826035961.65600030270.4110956484.9193429310.6267294224

200618_atLASP14.01776868430.69086304080.17195192031.70944092210.6267294224

208052_x_atCEACAM34.01704430850.72869664280.18140119621.76624493830.6267294224

201180_s_atGNAI34.01331664410.33574183270.08365695071.27222178860.6267294224

215071_s_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=4.01059794951.50612982030.37553747333.74077144920.6267294224

204007_atFCGR3A4.00426993422.24713781370.56118539728.13253409830.6267294224

209710_atGATA24.0036510591.0511442910.26254642962.39276860970.6267294224

221408_x_atPCDHB123.99955344881.22206374620.30555004752.32218302380.6267294224

207980_s_atCITED23.98712928641.13238796710.28401084732.54697972870.6267294224

217986_s_atBAZ1A3.97094100110.68026540850.17131088281.66831887190.6267294224

207329_atMMP83.96553952753.58416242770.903827184922.81970021810.7458080126

201302_atANXA43.96134153510.76187771720.19232820761.7985947990.7458080126

201554_x_atGYG3.96067494651.4117430260.35644001213.34049983010.7458080126

205472_s_atDACH3.95738427781.94258446690.49087587424.08805098250.7458080126

209288_s_atCDC42EP33.95328179760.85198421920.21551315161.9372662830.7458080126

204725_s_atNCK13.95303619760.48764883420.1233605791.41092600430.7458080126

214575_s_atAZU13.94922715442.78721774560.705762833211.239053780.7458080126

35254_atFLN293.94884848320.54651601780.1383988321.50135671110.7458080126

217989_atRETSDR23.94858200910.59362480230.15033872941.53534890180.7458080126

209767_s_atPNUTL13.94102109761.78284210170.45238075553.34698147040.7458080126

213101_s_atACTR33.93825016020.4221809990.10720014771.35331811590.7458080126

221484_atB4GALT53.93784823741.07639720320.27334654322.34896024430.7458080126

208660_atCS3.93615936990.43625709290.11083318841.3719590070.7458080126

201176_s_atARCN13.93544167560.56934942630.1446723071.52715942610.7458080126

220416_atATP8B43.93257123531.55138161890.39449549063.62922780160.7458080126

203011_atIMPA13.91920848530.58172002920.14842793681.52898991210.7458080126

217852_s_atFLJ107023.91666202340.51409857480.13125936621.46972351950.7458080126

201061_s_atSTOM3.91308457521.08839640250.27814282612.39270792680.7458080126

201921_atGNG103.90586099730.81496727280.20865240041.83091839360.7458080126

207992_s_atAMPD33.90309857021.12861134870.28915778792.44466680110.7458080126

220291_atFLJ202073.89054159490.63134230810.16227620051.59475218660.7458080126

219146_atFLJ227293.88897443710.56852572860.14618911431.52663890370.7458080126

32837_atAGPAT23.88886757560.49981553170.12852469831.44934992470.7458080126

211773_s_atZNF3063.87700693810.5669455440.14623279071.47481014750.9433144823

201765_s_atHEXA3.869310250.4556337880.11775581651.38741932080.9433144823

209924_atCCL183.86839280641.98827225830.51397889455.61025241580.9433144823

219478_atWFDC13.86698287312.13846728580.55300666076.45672309170.9433144823

210189_atHSPA1L3.85726380541.05367564690.2731666022.00830742570.9433144823

202122_s_atTIP473.85523601210.46834358990.12148246921.41099950170.9433144823

214469_atHIST1H2AE3.84196938931.85641439610.48319343753.70918708770.9433144823

211889_x_atCEACAM13.83426178571.91675664430.49990239364.95656564280.9433144823

203823_atRGS33.83239235550.72709765230.18972422051.60220534180.9433144823

200832_s_atSCD3.8295655031.50361965550.39263453112.96671762640.9433144823

221687_s_atFLJ000013.82293589860.65579050220.17154106681.56998828090.9433144823

203054_s_atTCTA3.82157172350.39980408240.104617711.33628705740.9433144823

206576_s_atCEACAM13.81791210331.42818853430.37407580263.20326682790.9433144823

207949_s_atICA13.81227009030.80693799160.21166863121.65156401160.9433144823

216835_s_atDOK13.81074968950.48425148490.12707512281.41614554770.9433144823

203430_atHEBP23.80634963380.56463869230.14834125781.49523116150.9433144823

200951_s_atCCND23.79862774680.92615083060.2438119481.90602688620.9433144823

200996_atACTR33.79728756070.53471077610.14081387511.47416074710.9433144823

212461_atOAZIN3.79641411220.66359374390.17479487861.68544599320.9433144823

217976_s_atDNCLI13.79566910890.59876119570.1577485231.56714321530.9433144823

207890_s_atMMP253.79354876631.32519570460.34932876482.9087585820.9433144823

203559_s_atABP13.79189950311.43228086010.37772120783.37611667620.9433144823

201500_s_atPPP1R113.78356750770.35994544840.09513387771.30679731731.1415428764

218923_atSPATA13.78248078140.86190214030.22786689221.93601806921.1415428764

209435_s_atARHGEF23.77759462810.50338433080.13325525381.45388307611.1415428764

218963_s_atKRT233.77111354962.38792921820.63321594196.80478856921.1415428764

210925_atMHC2TA3.77001472621.43614749560.38093949223.09187987731.1415428764

221504_s_atATP6V1H3.7683080710.40276537330.1068822841.33870384221.1415428764

210981_s_atGPRK63.76812036320.58118488070.15423734511.54350426821.1415428764

209498_atCEACAM13.76757630222.39650159730.63608574997.21936496781.1415428764

213117_atKIAA13543.76660638391.00349739140.26641950051.95038979451.1415428764

204336_s_atRGS193.76554546980.7163695820.19024324311.7519174351.1415428764

209471_s_atFNTA3.76496364520.31167778270.0827837431.2468255821.1415428764

214151_s_atCPR83.75597983020.87209745970.23218906891.94799495551.1415428764

212358_atCLIPR-593.75491769420.49228434680.13110389811.43244042881.1415428764

203090_atSDF23.75463134860.36305592930.0966954931.29969665371.1415428764

218728_s_atHSPC1633.75117760580.77152564770.20567558481.81724688531.1415428764

218946_atHIRIP53.74607353980.38137388680.10180630011.32139343761.1415428764

58696_atRRP413.74136663220.5446454130.14557392171.4986453951.1415428764

215044_s_atSTAM23.73253887090.47141635570.12629911491.40029655381.1415428764

201553_s_atLAMP13.72660824750.33057561980.08870683421.27263561251.1415428764

201288_atARHGDIB3.7223208270.34147046980.0917359051.28121484081.1415428764

219044_atFLJ109163.72090216730.79947495770.21486051551.51927399681.1415428764

52975_atFLJ000013.71607598830.57271948420.15411942221.53465258261.1415428764

202119_s_atCPNE33.71579422861.43139219530.38521836983.51800392691.1415428764

221819_atRAB353.70806994690.47856156460.12905947611.42237041381.1415428764

208470_s_atHPR3.70616989282.96033312430.798758073711.92641487851.1415428764

211142_x_atHLA-DOA3.69999454110.65619648780.17735066381.59383306751.1415428764

209396_s_atCHI3L13.69935480632.77071829330.74897338559.23407922421.1415428764

219620_x_atFLJ202453.69828469820.55120469130.14904333671.42116731041.1415428764

202907_s_atNBS13.69669739070.53268416740.14409731471.50806683531.1415428764

218213_s_atC11orf103.69088723180.46900008650.12706974151.41188484461.1415428764

203490_atELF43.69040310071.11503387750.30214419591.87316510761.1415428764

218143_s_atSCAMP23.68929994160.38000404210.10300166651.30657662481.1415428764

213851_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=3.68750094540.60270651830.16344579361.48510735591.1415428764

206440_atLIN7A3.68705534541.88627236950.51159318014.21431675671.1415428764

207157_s_atGNG53.674477950.45189691190.12298261631.39727457341.4300662968

210044_s_atLYL13.66705010160.73050716530.19920839511.76996320211.4300662968

209551_atMGC110613.66533058170.66688747550.18194470071.61117711121.4300662968

219296_atHIP14L3.66085485080.71362238540.19493326411.75193176231.4300662968

216881_x_atPRB43.65655224030.74814263050.20460329331.66841969141.4300662968

211728_s_atHYAL33.6505187081.39453211130.38200930412.80366654281.4300662968

35265_atFXR23.64969633450.46968862830.12869252271.42248460211.4300662968

216092_s_atSLC7A83.64072918870.57049336840.15669755671.49564687151.4300662968

206222_atTNFRSF10C3.63315721631.46319649120.40273415223.51248138741.4300662968

218556_atORMDL23.62972026250.64543768840.1778202291.61927899611.4300662968

201968_s_atPGM13.62657271920.73083389170.20152191841.78947749011.4300662968

209238_atSTX3A3.62612368260.87188455250.2404453432.09523085071.4300662968

216460_athttp://genome-www4.stanford.edu/cgi-bin/SMD/source/sourceResult?choice=Gene&option=Name&criteria=3.61295279451.20590512490.3337727322.51818522681.4300662968

217836_s_atYAP3.61254722220.40105423790.1110170231.33156060881.4300662968

213279_atDHRS13.60890764320.40190073370.11136354081.3213332911.4300662968

216028_atDKFZP564C1523.60739983020.5532690380.15337058941.51881004031.4300662968

205715_atBST13.60081823871.16310898450.3230124122.60666146771.6385856601

204429_s_atSLC2A53.59979467761.04954352880.29155649772.38286714441.6385856601

221634_atRPL23AP73.59976187341.64276618480.45635412632.8513720411.6385856601

206209_s_atCA43.59436651531.22745035380.3414928192.83486432271.6385856601

219964_atST7L3.59066944521.19167065360.3318797992.46977329971.6385856601

209250_atDEGS3.58839601280.73418670780.20460024621.73903257651.6385856601

208308_s_atGPI3.58821735160.94764660930.2640995562.17820722721.6385856601

204340_atCXorf123.58373091951.51805149750.42359527872.41423669391.6385856601

203675_atNUCB23.57988724611.01564929270.28370985532.27866988711.6385856601

219356_s_atHSPC1773.57857109510.68006996570.19003952911.65309765731.6385856601

206734_atJRKL3.57193433661.02281645710.2863480571.98875041361.6385856601

209395_atCHI3L13.56524719033.35862033550.942044171514.45465329141.6385856601

207419_s_atRAC23.56363129860.71822846480.20154398831.76193234771.6385856601

215765_atMUF13.56212184050.69923631640.19629769781.53156614161.6385856601

220000_atSIGLEC53.55051756730.99731193520.28089198722.30368862461.6385856601

205847_atPRSS223.54930644640.44057700250.12413044891.3819370461.6385856601

208736_atARPC33.54666808310.4785308120.13492404721.42943655991.6385856601

218451_atCDCP13.53158343751.19883638350.33946143562.97394125571.6385856601

212519_atUBE2E13.52490902610.52256761390.14824995771.48706464341.6385856601

219913_s_atCRNKL13.52454783610.6338036090.17982550911.6552169331.6385856601

209001_s_atDKFZP566D1933.52362473670.26678874640.07571429031.21098973441.6385856601

210101_x_atSH3GLB13.52231720920.44279682770.12571179751.40004731311.6385856601

212971_atCARS3.52201724960.32565781630.09246343591.27233998391.6385856601

207153_s_atGLMN3.51912375340.65177821510.18521037081.52067027261.6385856601

213294_atFLJ383483.51384064860.72531790510.20641741551.8273656821.6385856601

222221_x_atEHD13.51328926950.67742932370.19281911391.70685758952.050953184

219049_atChGn3.51210954851.07514973610.30612648072.36107595992.050953184

201463_s_atTALDO13.50890461450.66720783020.19014704121.71188177292.050953184

209071_s_atRGS53.5078486561.10577192730.31522794622.63098236782.050953184

217007_s_atADAM153.50488620581.45923066850.41634181053.04662410522.050953184

214523_atCEBPE3.49367484251.69645071750.48557773524.36366721742.050953184

222014_x_atMTO13.49362359350.51469450330.14732397171.46538696262.050953184

201594_s_atPPP4R13.49330785260.48802079620.13970162861.4407494812.050953184

208734_x_atRAB23.492250010.40746096640.11667577211.3445814722.050953184

208966_x_atIFI163.49088027570.64861754180.18580343371.62838773642.050953184

212526_atSPG203.49031160590.86763143250.24858280021.97925238732.050953184

209287_s_atCDC42EP33.48848575040.71805332360.20583524631.65718846932.050953184

211961_s_atRAB73.48789632840.3657308990.10485715871.30642160562.050953184

209039_x_atEHD13.48482180940.63188247640.18132418561.66465867252.050953184

218812_s_atC7orf193.48432658530.77304682150.22186405391.82287688352.050953184

214540_atHIST1H2BO3.48331773621.2938109960.37143065723.53680938252.050953184

203013_atHSGT13.48087013260.38509062510.11063056381.28563570642.050953184

204011_atSPRY23.47953863941.6933091320.48664760123.19421441982.050953184

210140_atCST73.47919088271.23661779440.35543258082.76467246432.050953184

214472_atHIST1H3D3.46986510561.24363844090.35841117832.64861249632.050953184

218007_s_atRPS27L3.46529616780.50721159160.1463689011.48341381492.050953184

201319_atMRCL33.45979865120.39736629350.11485243321.31946537682.050953184

214778_atEGFL43.45506706791.45178531070.42019019664.04501347712.050953184

220489_s_atTDE23.45422556551.07380275190.31086642482.50239850242.050953184

213828_x_atH3F3A3.43989143490.38224312950.11112069571.33210067752.050953184

206697_s_atHP3.43854671291.76525165930.51337143474.60736045792.050953184

205479_s_atPLAU3.43833695870.82851149610.24096285681.90514279782.050953184

209628_atNXT23.43567025610.57757012090.16810988191.54775755492.050953184

217977_atSEPX13.43216805961.51733937560.44209355413.84287544172.050953184

202718_atIGFBP23.43123460991.88736987180.55005561742.99223907392.050953184

213214_x_atACTG13.43008007820.30349202510.08847957431.23691235162.050953184

202627_s_atSERPINE13.42974910981.32117347970.3852099492.62042999022.050953184

221856_s_atFLJ112803.42770409680.67222097510.19611406241.63499579772.050953184

200738_s_atPGK13.42700431650.29038678860.08473487681.2369360052.050953184

214381_atna3.42671062711.02994676910.30056426742.08507135022.050953184

209448_atHTATIP23.42084120890.81680241240.23877238451.81973845012.050953184

200654_atP4HB3.4194233420.62102018740.18161547291.61793475252.050953184

210951_x_atRAB27A3.41939439930.79448012390.23234527261.92949111542.050953184

205471_s_atDACH3.41900678421.06421212230.31126353052.41755720192.050953184

214080_x_atPRKCSH3.4187207930.31543262330.09226627221.25793663992.050953184

217835_x_atC20orf243.41785213140.48487077330.14186417511.45737626772.050953184

201602_s_atPPP1R12A3.41755050930.75491100610.22089242111.80288337152.050953184

204093_atCCNH3.41733860850.40222423170.11770101761.33937572572.050953184

221579_s_atNUDT33.41126521520.33581575730.09844316881.27004079162.5040850141

202814_s_atHIS13.41033320410.68809632590.20176806331.75670928682.5040850141

205726_atDIAPH23.41015429630.62145960940.18223797381.63205793582.5040850141

214378_atTFPI3.40610039370.96638730710.28372249651.75823636622.5040850141

219185_atSIRT53.40464848710.39225934770.11521287711.33666237992.5040850141

204121_atGADD45G3.40351651251.7368798860.51031921833.46349910252.5040850141

221505_atANP32E3.40350059410.50874311620.14947642941.45845115182.5040850141

211983_x_atACTG13.39724107120.3109242780.0915225831.24341209352.5040850141

201991_s_atKIF5B3.39703049460.37714099090.11102078461.31530266842.5040850141

219924_s_atZNF2583.39400630450.65715510880.19362224161.55549913072.5040850141

201352_atYME1L13.38680004680.25247612330.07454710051.19491077772.5040850141

204601_atN4BP13.38303145050.71724262820.21201181211.74169896072.5040850141

202266_atTTRAP3.38089747710.54897491480.1623754991.50513108652.5040850141

204949_atICAM33.38060459320.80154003610.23709961161.90876322442.5040850141

212870_atC14orf1383.37745451340.7456738360.22077983081.83653521392.5040850141

205857_atSLC18A23.3710180541.22671696370.36390103642.27417950732.5040850141

32069_atN4BP13.36995391360.71612151060.21250187061.78805008382.5040850141

219253_atFAM11B3.36898897410.67264166260.19965683111.70419469732.5040850141

201673_s_atGYS13.36036585360.72245317880.21499241761.79130583862.5040850141

214465_atORM13.35433349272.14107884080.63830231718.94477573242.5040850141

213526_s_atF259653.3521869230.52386946360.15627692481.44967348482.5040850141

213779_atEMU13.35149967090.60580612050.18075672981.55497888512.5040850141

203399_x_atPSG33.348576520.95990994090.28666208912.05631811492.5040850141

222294_s_atRAB27A3.3450119391.01281527310.30278375432.40734441122.5040850141

213002_atMARCKS3.34205076620.79125737590.23675803611.88026062812.5040850141

213410_atEDRF13.34077419550.57512676990.17215373931.51493319962.5040850141

209188_x_atDR13.33958466050.39647972930.11872126921.3442766242.5040850141

211075_s_atCD473.33198282150.32225112420.09671452151.26282899152.5040850141

202297_s_atRER13.32858062760.3691797380.11091206111.30580086862.5040850141

221554_atLYK53.32617943750.50266538040.15112395161.4134988313.1113177356

219242_atFLJ133863.32111464030.6960529990.2095841531.70045333513.1113177356

204044_atQPRT3.32088774761.30098029470.39175678123.00815508983.1113177356

202275_atG6PD3.31856011671.07002118130.32243537672.50910050123.1113177356

213513_x_atARPC23.31773738560.3318028740.10000878171.28021770933.1113177356

212505_s_atKIAA08923.31456985340.39604168750.11948509311.29583422293.1113177356

200653_s_atCALM13.31307750490.49911126790.15064883551.4713424833.1113177356

201550_x_atACTG13.31115253390.28918428640.08733644361.2249141233.1113177356

219998_atHSPC1593.30966678151.77221632870.53546669375.74853717713.1113177356

216981_x_atSPN3.30386295390.712880780.21577189791.7122471973.1113177356

219770_atFLJ117533.297511460.35615071810.10800590761.30195282423.1113177356

217746_s_atPDCD6IP3.29492588450.25163206040.07636956621.19855019453.1113177356

212988_x_atACTG13.29435290110.29084113670.08828475441.23137793663.1113177356

207094_atIL8RA3.28930665792.22007549550.674937221210.31876075733.1113177356

220911_s_atKIAA13053.28757300061.37236570330.41744037413.11192980763.1113177356

209019_s_atPINK13.28559763870.59097251870.17986758691.57310310183.1113177356

208168_s_atCHIT13.27941397712.01540097630.61456131816.68932038833.1113177356

212305_s_atFLJ392073.27899976550.36541600380.11144130221.28819303143.1113177356

201256_atCOX7A2L3.27591603230.50560075730.15433874141.48301486533.1113177356

218795_atACP63.27464520210.81451413210.24873355182.0102690213.1113177356

210242_x_atLOC2836873.27303012730.39374273650.12029914821.33695033193.1113177356

219079_atNCB5OR3.27210875390.49379301410.15090971951.45423061793.1113177356

202441_atKEO43.26775015050.70576287080.2159782231.73978515533.1113177356

53912_atSNX113.26537574420.59011734470.1807