Diagnostic microRNAs in myelodysplastic syndrome

Experimental Hematology 2011;39:915–926

Diagnostic microRNAs in myelodysplastic syndrome Begum Erdogana, Crystal Faceyb, Julianne Qualtieria, Jason Tedescoa, Elizabeth Rinkera, R. Benjamin Isettc, John Tobiasc, Donald A. Baldwinc, James E. Thompsond,e, Martin Carrolld, and Annette S. Kima,b,f a Department of Pathology, Vanderbilt University Medical Center, Nashville, Tenn., USA; bDepartment of Pathology, Cooper University Hospital, Camden, NJ., USA; cPenn Microarray, Molecular Diagnosis and Genotyping Facilities, University of Pennsylvania, Philadelphia, Pa., USA; dDivision of Hematology and Oncology, University of Pennsylvania, Philadelphia, Pa., USA; eDepartments of Medicine and Immunology, Roswell Park Cancer Institute, Buffalo, NY., USA; fDepartment of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pa., USA

(Received 7 February 2011; revised 7 May 2011; accepted 2 June 2011)

Objective. The myelodysplastic syndromes (MDS) are aging-associated disorders characterized by ineffective maturation of hematopoietic elements, which are often diagnostically challenging. This study identifies microRNAs (miRNA) and miRNA targets that might represent diagnostic markers for MDS. Materials and Methods. This study utilized a total of 42 MDS samples and 45 controls. A discovery set of 20 frozen bone marrow mononuclear cell samples (10 MDS, 10 controls) was profiled on a custom Agilent miRNA microarray. Classifier miRNAs were validated in a separate set of 49 paraffin-embedded particle preparations by real-time polymerase chain reaction (24 MDS, 25 controls). Target prediction analysis was compared to a de novo transcriptional profile of MDS derived from the Microarray Innovations in Leukemia study. c-Myb and Sufu were further investigated by immunohistochemical stains on a set of 26 paraffin-embedded samples. Results. We identified 13 miRNAs of interest from the discovery set, 8 of which proved statistically significant on real-time polymerase chain reaction verification. These eight miRNAs were then examined in an independent real-time polymerase chain reaction validation set. Notably, hsa-miR-378, hsa-miR-632, and hsa-miR-636 demonstrated particularly high discrimination between MDS and normal controls. Target prediction identified potential targets of miRNA regulation that correspond to many of the genes that characterize MDS. Immunohistochemical staining performed on a third validation set confirmed that c-Myb and Sufu are differentially expressed in MDS. Conclusions. Our data utilize both discovery and validation sets and two complementary platforms to identify miRNAs associated with MDS. We have analyzed predicted targets and identified c-Myb and Sufu as potential diagnostic markers of MDS. Ó 2011 ISEH - Society for Hematology and Stem Cells. Published by Elsevier Inc.

The myelodysplastic syndromes (MDS) are a group of aging-associated hematopoietic disorders characterized by ineffective hematopoiesis. Thus, while the bone marrow may be hypercellular with full maturation, the cells are dysplastic and associated with peripheral cytopenias. In 65% of patients the disease is fatal within 3 years, either

Offprint requests to: Annette S. Kim, M.D., Ph.D., Department of Pathology, Vanderbilt University Medical Center, 1301 Medical Center Drive, TVC, Nashville, TN, 37232-5310; E-mail: [email protected] Supplementary data associated with this article can be found in the online version at doi: 10.1016/j.exphem.2011.06.002.

from cytopenias or, in 6% to 33% of cases, from transformation to acute myeloid leukemia [1]. Despite development of specific diagnostic criteria for MDS [2], the morphological findings are often too subtle when a patient first presents with cytopenias to make a clear diagnosis. A clinical biomarker-based assay capable of detecting early MDS would greatly accelerate diagnosis and treatment of these patients. MicroRNAs (miRNAs) are noncoding RNAs that play a key role in development and cellular differentiation by regulating the translation of select target proteins [3]. Given their key roles in cell differentiation and identity, miRNAs are likely to contribute to development of MDS. Indeed,

0301-472X/$ - see front matter. Copyright Ó 2011 ISEH - Society for Hematology and Stem Cells. Published by Elsevier Inc. doi: 10.1016/j.exphem.2011.06.002

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Table 1. Patient characteristics of the discovery, validation, and immunohistochemical sets

Patient

PB Counts Collection ID (Upenn SCC) Age Gender subtype IPSS WBC HCT Plt

Set

Blast

Cytogenetics

BM Cellularity

BM Blasts 0

4.1

24

331

0

46,XX[30]

O90%

RCMD Int-1 RAEB-1 Int-1 RA Int-1

2.7 1.9 3.3

21 21 21

46 189 45

2 0 0

46,XY[15] 46,XY[25] 46,XX[24]

90% 70% 5%

M

RCMD

LR

8.8

40

52

0

46,XY[25]

90%

73

M

RCMD

Int-2

3.5

46

0

84 62 62 67

M M F M

RCMD RCMD RCMD RCMD

Int-1 5 Int-1 8.1 Int-1 24.3 Int-2 12.9

27 26 33 25

213 93 16 55

0 0 5 0

Validation and IHC

72

M

RCMD

Int-1

3

27

!10

0

43-45,XY,-2,-5,add(7) (q22),þ8,-9,-10, 80% -11,-17,add(19)(q13.3),*2-3mar[cp5] 46,XY del(7)(21q36)[25] 65% 46,XY[21] 90% 46,XY[6] 95% 80% 41-44,X,Y del(5) (q11.2q33),-7,-7 add(10)(q22),add[12) (p13),add(16) [q12,1),add(18)(q23),-19,1-3mar[cp25] 45,X-Y[3]/46,XY[17] hypercellular

12 13 14 15 16 17

Validation Validation Validation Validation Validation Validation

IHC IHC IHC IHC

67 57 73 78 68 78

M M M F M F

RCMD RAEB-1 RCMD RCMD RCMD RCMD

LR Int-1 Int-1 Int-1 Int-1 Int-1

1.9 1.7 1.8 5.5 3.6 9.4

33 34 26 32 22 31

129 132 10 138 59 171

0 0 0 1 0 0

46,XY[20] 46,XY,del20(q11.2)[10]/46XY[10] 45,X,-Y[5]/46,XY[15] 47,XY,þ1,der(1,7)(q10p10),þ8 46,XY[20] 47,XX,der(7)t(1,7)(p11,p11),*8[20]

hypercellular hypercellular hypercellular hypercellular 90% 80%

2 5 2.5 4 0 3

18 19 20 21 22 23 24

Validation and IHC Validation and IHC Validation Validation Validation Validation Validation

85 75 75 59 73 70 79

M M F M M M M

RCMD RCMD RCMD RCMD RCMD RCMD RAEB-2

Int-1 LR Int-1 Int-1 LR LR HR

1 8.1 0.7 4.5 3.9 2.1 1.4

27 33 28 20 33 39 29

39 136 51 326 31 0 61

0 0 0 0 0 0 rare

hypercellular hypercellular hypercellular hypercellular hypercellular 15-20% 70%

3 2.5 1.5 0.5 3 4 13

25 26

Validation Validation

68 69

M M

RU Int-1 RAEB-2 HR

5.8 5.8

43 30

0 0

45,X,-Y[5]/46,XY[15] 46,XY[20] 46,XY[20] 47,XY,þ8[19]1/46,XY[1] 46,XY[20] 46,XX[20] 49,XY,þdel(1)(q10),del(5)(q12q32), swl(7)(q22),þ8,del(8)(q24),del(11) (p14),der(12)t(11,12)(q12,p11,2), þ22[13]/50,i dem,*mar[7] 46,XY,-7,þMAR[5] 46,XY,del(7)(q22)[5]/46,XY[15]

27 28

Validation Validation

66 78

F F

RCMD RCMD

LR LR

2.6 23.4

32 36

190 199

0 0

46,XX[20] 46,XX[20]

29

Validation

73

M

RCMD

Int-1

6.7

24

65

0

46,XY[20]

Discovery

249

58

F

RCMD

2 3 4

Discovery Discovery Discovery

234 260 380

61 78 47

M M F

5

Discovery

298

63

6

Discovery

445

7 8 9 10

Discovery Discovery Discovery Discovery

510 584 681 803

11

and and and and

LR

84 9.3

Alive-currently getting chemo, no transformation 2 Dead 30 mo post, of BM failure 6.5 Dead 10 mo post of heart failure na Alive - currently seen with normal count, on vidaza 4 Dead - 13 mo post of cytopenias (intracranial bleed) 1 Dead 3.5 mo post of AML 2 3 0 2

Dead 22 mo post with cytopenias Dead 25 mo post of AML Alive with AML, status post SCT Dead 2 mo post of presumed infection

2

died 3 days after diagnosis of intractanial hemorrhage NA deceased NA deceased alive 22 cycles of Vidaza later deceased of pheumonia secondary to pancytopenia alive 9 months after diagnosis deceased NA deceased from ARDS post SCT deceased from AML/pheumonia stable on Vidaza presumed deceased, went to hospice

normocellular 1.5 lost to followup hypercellular 15 presumed deceased, went to hospice after induction chemo with residual buy stable, 40 2 alive 65% 1 deceased status post cerebrovasclar accident 55% 2 alive, still cytopenic but stable,

B. Erdogan et al./ Experimental Hematology 2011;39:915–926

1

Current status

5.5 on supportive care 0 94 28 3.9 RAEB-1 Int-1 M 60 IHC 42

AML 5 acute myeloid leukemia; HCT 5 hematocrit; HR 5 high risk; IPSS 5 International Prognostic Scoring System; PB 5 peripheral blood; Plt 5 platelet count; RA 5 refractory anemia; RAEB 5 refractory anemia with excess blasts; SCT 5 stem cell transplant; UPenn SCC 5 University of Pennsylvania Stem Cell Core Facility; WBC 5 white blood cell count.

hypercellular 60-70% hypercellular normal 50% hypercellular 70% hypercellular

46,XY[20] 46,XY[20] 46,XY[20] 47,XX,-7,þ8,del11(q21;q25),*mar 46,XY[20] 46,XY[20] 46,XX[20] 43-44,XY,-5,dic(12,22) (p13,p11.2),-18[cp20] 46,XY[20] 0 0 0 0 0 0 0 0 68 96 35 165 83 78 29 25 34 32 30 33 35 26 38 28 LR 2.7 Int-1 1.3 LR 3.9 Int-2 4.6 Int-1 1.5 Int-1 1.9 LR 13.7 Int-1 3.8 RCMD RCMD RA RAEB-1 RCMD RAEB-1 RCMD RCMD F M M F M M F M Validation and IHC IHC IHC IHC IHC IHC IHC IHC 34 35 36 37 38 39 40 41

63 57 66 69 58 81 74 76

60% 70-80% 0 0 373 17 29 27 5.5 1.4 RCMD LR RAEB-2 High M M Validation Validation 32 33

75 76

hypercellular

decreased, septic shock, respiratory failure 1 alive, on vidaza 18.5 presumed deceased, went to hospice after relapsed lost to followup 6 months after diagnosis 1 lost to followup 6 monhts 0 alive 1 alive 8 deceased 3 alive, stable 8 deceased 1 alive 0 deceased 2 50% 0 126 34 3.2 Int-1 RCMD F Validation 31

56

Validation 30

56

M

RAEB-1 Int-2 17.5

32

188

0

73-77!3nO, XY,þadd(1)(q31),þ6,þ6, þ8,addd(8)(q24),der(12)(t(12,17)(p11, 2;q11,2,q11.2),þ13,-16,_17!þ19! þ21!þ3-6,ar[cp13 1/46,XX[2]] 45,XX,dup(3)(p11p2.1),del(5)(q13q33), 6,del(9)(q21.2);-20,þmar[18]/46XX[2] 46,XY[20] 46,XY,t(14,22)(q32;q11.2)[20]

normocellular

8

deceased status post SCT, with subsequent relapse

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several entities have been discovered on 5q that contribute to the disease phenotype in the 5q syndrome subtype of MDS [4], including miR-145 and miR-146a. These miRNAs have recently been identified in the common deleted region in 5q syndrome, and their knockdown appears to recapitulate the thrombocytosis and dysmegakaryopoiesis seen in 5q syndrome [5]. miR-150, an miRNA that influences the lineage determination between erythroid and megakaryocytic progenitors, is also aberrantly increased in 5q syndrome, and is thought to contribute to the megakaryocytic hyperplasia associated with this disease [6,7]. Although miRNAs already have been implicated in the pathogenesis of this subtype of MDS, the role of miRNAs in non-5q syndrome MDS remains the subject of study [8–10]. Taking advantage of the relative stability of miRNAs in a variety of specimen types, we have examined miRNA signatures of MDS in both frozen bone marrow mononuclear cells (BM-MNCs) and paraffin-embedded particle preparations using both a custom Agilent miRNA microarray platform [11] and real-time polymerase chain reaction (RT-PCR). We identified 13 miRNAs that discriminate between MDS patient bone marrow samples and normal controls on an initial discovery set. We then validated the classifying miRNAs on an independent validation set in paraffin-embedded tissues. Target analysis has been performed and compared to genes differentially transcribed in MDS in a de novo analysis of the Microarray Innovations in Leukemia (MILE) data set. Two targets, c-Myb and Sufu, have been identified that are differentially expressed in MDS compared to normal samples by immunohistochemistry on a third validation set, as further support for the importance of these miRNAs in MDS. Thus, we have used a variety of modalities to confirm that the miRNAs identified are consistent and robust using a total of 42 MDS and 45 controls samples.

Materials and methods Patient samples After approval from the University of Pennsylvania and Cooper University Hospital institutional review boards, 10 samples from adult MDS patients were selected as a discovery set. These samples were derived from bone marrow aspirates stored in liquid nitrogen as viable mononuclear cells (BM-MNCs) at the University of Pennsylvania Stem Cell Core Facility between 2003 and 2007. BM-MNCs were utilized rather than purified CD34þ cells in the belief that any miRNA changes related to aberrant maturation should be present in all the aberrantly maturing cells, not just stem cells, and to maximize total RNA yield from the precious patient samples. Associated clinical information, without patient identifiers, was available from the Stem Cell Core Facility database (Table 1). The patient specimens were selected to be nearly homogeneous in MDS subtype, with 7 of 10 cases falling into the category of refractory cytopenia with multilineage dysplasia (RCMD), excluding cases of advanced disease (refractory anemia with excess blasts2) as well as subtypes with well-characterized

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clinical and cytogenetic behavior (refractory anemia with ringed sideroblasts and 5q syndrome). Based on the International Prognostic Scoring System [12], 8 of 10 cases fall within the low risk (LR) or intermediate-1 (Int-1) categories. No patient had been treated previously with 5-azacytidine. Correspondingly, 10 additional bone marrow specimens, processed and frozen in a manner identical to the patient samples, were selected from normal healthy volunteers in a blinded fashion. Average age of the MDS patients was 65.5 years (7 males and 3 females). Dictated by the volunteer popuIation, the controls had an average age of 51.5 years (8 males and 2 females). These samples constituted the discovery set. Paraffin-embedded particle preparations were obtained that corresponded to 2 of 10 patients in the discovery set (collection IDs 260 and 803) to verify the use of the paraffin-embedded samples for miRNA assessment by RT-PCR. In addition, 49 paraffinembedded particle preparations from Vanderbilt University Medical Center (24 MDS samples and 25 normal controls obtained from 20062009) were selected under institutional review board approval as the validation set (Table 1). The selected MDS samples were enriched for the RCMD subtype of MDS, as in the discovery set (19 of 24 cases), with the remaining cases, including 3 cases of refractory anemia with excess blasts2 and 2 cases of refractory anemia. Of the 24 cases, 20 fall within International Prognostic Scoring System LR or Int-1 groups. Twenty of the normal controls samples were obtained from patients in whom a bone marrow biopsy was performed for staging purposes but was negative for malignancy. Five de-identified samples were obtained from patients undergoing joint replacement with no known hematologic disease and processed identically to the other particle preparations. Additional bone marrow biopsy specimens were obtained for immunohistochemical (IHC) studies. For these studies, 16 MDS samples were examined, as well as 10 control samples, including 4 cases where the marrow had been obtained to rule out MDS in a cytopenic patient and was found negative for MDS (Table 1). Of these samples, there are eight new MDS and eight new control samples, with four RCMD samples, one refractory anemia, and three refractory anemia with excess blasts1 (representing 5 Int-1, 2 LR, and 1 Int-2). RNA extraction Cryopreserved BM-MNCs were thawed briefly (w1 minute) at 37 C. Total RNA that includes the miRNA fraction was isolated using the MirVana kit (Ambion/Applied Biosystems, Austin, TX, USA) according to vendor’s instructions. The RNA concentration was measured using Smart Spec 3000 spectrophotometer. The paraffin-embedded particle preparations were extracted using the Recoverall Total RNA Extraction from FFPE kit (Applied Biosystems, Foster City, CA, USA). RNA concentration from the paraffin-embedded samples was measured on a Nanodrop 2000. miRNA microarray After RNA assessment on the Agilent 2100 Bioanalyzer and amplification, a reference pool of all RNAs was generated (excepting only one limited specimen), to ensure that all miRNAs expressed in either the MDS or normal samples were represented in the reference pool. The pool was labeled with Cy3, and each individual sample with Cy5 (labeling reactions were performed with 0.251 mg total RNA). Each sample and an aliquot of the reference pool were cohybridized to custom Agilent miRNA microarrays for dual channel detection [11]. The version of this array

used in these studies (updated from that in the reference) contains probes against 463 known human miRNAs (including 1 exact match and 7 tiled probes per miRNA), as well as against 2583 expressed candidate miRNAs and miRNAs from Caenorhabditis elegans and mouse species, for a total of 44,519 probes. Sample and microarray processing steps were performed by the University of Pennsylvania Microarray Facility. miRNA abundance signals for each sample were normalized to the common reference pool with LOWESS normalization before statistical testing for differences between MDS and normal conditions. miRNA microarray data analysis Statistical analysis, performed at the University of Pennsylvania Bioinformatics Core Facility, was composed of data filtration using GeneSpring v7.31 (miRNAs identified in at least six samples only were assessed) for minimum detection threshold and analysis of variance using Partek Genomics Suite v6.3 (Partek Incorporated, St Louis, MO, USA). Given the small size of the study, a generous false discovery rate of 20% was used to cast a wide net for candidate classifier miRNAs. The analysis focused on known human miRNAs. Initial significance analysis of microarrays was performed, limiting the candidates to those miRNAs for whom the data was of sufficient quality for interpretation in at least 6 of the 20 samples. The full initial significance analysis of microarrays is provided in the Supplementary Materials, including the full list of 53 significant candidate classifier miRNAs (Supplementary Table E1, code to the probe identification numbers provided in Supplementary Table E2; online only, available at www.exphem.org). RT-PCR Total RNA (10 ng) was reverse transcribed using the Taqman MicroRNA Reverse Transcriptase kit. The resultant products were submitted to RT-PCR using the appropriate Taqman MicroRNA Assays (Applied Biosystems, Foster City, CA, USA) on an ABI 7300 RT-PCR instrument or a Bio-Rad CFX instrument. Results were all normalized to U6 expression. In addition, a survey panel composed of equal mixtures of the FirstChoice Human Total RNA Survey Panel (Ambion, Austin, TX, USA) was used in each run to enable plate-to-plate comparisons. The validation set of paraffin-embedded particle preparations, which contained variable levels of peripheral blood contamination were also secondarily normalized to miR23a, which had been proved by both microarray and RT-PCR to not be differentially expressed in the MDS and normal control samples, but expressed at high enough levels to serve as a marker of nonerythroid cells [13]. Analysis of the MILE microarray data GEO repository accession GSE13159 (http://www.ncbi.nlm.nih. gov/geo/query/acc.cgi?acc5GSE13159) contains a total of 2096 samples run on the Affymetrix HG U133 Plus 2.0 platform. The 280. CEL files corresponding to 206 MDS and 74 normal samples were downloaded and imported into Partek Genomics Suite (v6.5), where Robust Multichip Average analysis was applied. MDS vs. normal samples were compared by calculating fold-change and aone-way analysis of variance. The p values resulting from the analysis of variance were corrected for false discovery rate by the step-up method of Benjamini and Hochberg as implemented in Partek. Probe sets on the array were further annotated as to their target status for a list of miRNAs.

B. Erdogan et al./ Experimental Hematology 2011;39:915–926

IHC stains IHC analysis was performed on 4-mm sections from the bone marrow particle preparations and bone marrow biopsies. Staining was performed on the EnVisionþ horseradish peroxidase system (DAKO, Carpinteria, CA, USA), using a rabbit polyclonal antibody against c-Myb (#ab59233; Abcam, Cambridge, MA, USA) and a goat polyclonal antibody against Sufu (#sc-10933; Santa Cruz Biotechnology, Santa Cruz, CA, USA). Expression levels of the proteins were counted in 200 nucleated cells in a blinded fashion by two independent reviewers. A standard 0 to 2-point scale for intensity with a percent of cells staining was used to create a stain index for each sample per hematologic lineage, including documentation of cellular localization pattern (nuclear vs. cytoplasmic), based on the counterstain morphology. Unpaired t-test analysis was used to ascertain significance of differences between MDS and normal control samples.

Results Discovery of a diagnostic signature of MDS Thirteen miRNAs that differentiate MDS samples from normal controls were identified using a class discovery analysis of variance algorithm: hsa-miR-10b, hsa-miR-103, hsa-miR-126, hsa-miR-140, hsa-miR-150, hsa-miR-342, hsa-miR-365, hsa-miR-378, hsa-miR-483, hsa-miR-558, hsa-miR-632, hsa-miR-636, and hsa-miR-639 (Table 2). The miRNA expression profiles of normal controls all clustered tightly by either hierarchical clustering or principle component analysis. Seven of the MDS samples clustered relatively tightly in an exclusive three-dimensional space

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by principle component analysis (Fig. 1A) and supervised hierarchical clustering (Fig. 1B), while the remaining three displayed spacial heterogeneity as one might expect with clinical samples. Of these three cases, one patient (collection ID 380) had an miRNA pattern intermediate between the normal controls and the other MDS patients. This sample is from a patient with time-proven refractory anemia, who experienced complete normalization of her counts with subsequent 5-azacitidine treatment. At this time, there is no clear clinical or cytogenetic explanation for the atypical miRNA expression patterns demonstrated by the remaining two cases (collection IDs 803 and 234). To illustrate the utility of the classifier signature, one of the cases (collection ID 510), which clearly clustered with the MDS samples, was not diagnosed with MDS at the time the sample was obtained because the morphologic changes at that time were insufficient to warrant that diagnosis. However, concurrent cytogenetics, which were not available at the time of diagnosis, demonstrated del(7q), and the patient died from cytopenias within 22 months of collecting this initial specimen. Thus, the initial sample would have been diagnostic by miRNA signature despite displaying nondiagnostic morphology. Microarray results were compared to an alternative common detection method by performing RT-PCR on the 13 miRNAs of interest and 1 miRNA control, miR-23a, chosen because it was not found to be differentially expressed in MDS and normal control BM-MNCs by microarray. Unpaired Student’s t-test analysis was performed to determine the ability of each miRNA to distinguish MDS

Table 2. Summary of RT-PCR results for discovery and validation sets Discovery set (BM-MNCs)

Validation set (PPs)

miRNA

MDS (n 5 10)

Normal controls (n 5 10)

p Value

miR10b miR23a miR103 miR126 miR140 miR150 miR342 miR365 miR378 miR483 miR558 miR632 miR636 miR639

8.30 2.93 2.52 5.32 2.64 2.24 3.45 5.06 4.72 0.39 1.52 1.08 1.75 0.56

9.27 2.64 1.65 5.11 1.69 0.34 1.97 5.64 3.09 0.96 0.99 2.47 1.32 0.73

0.27 0.32 0.042 0.64 0.018 5.3E-04 4.4E-03 0.32 2.9E-05 0.031 0.33 3.8E-03 0.042 0.70

MDS (n 5 24)

Normal controls (n 5 25)

p Value

1.90

1.86

0.94

0.91 2.48 1.78

0.17 2.91 1.86

1.9E-02 0.30 0.75

0.29 4.76

1.83 6.33

4.2E-03 3.0E-02

7.42 6.37

9.17 9.04

2.9E-03 1.8E-03

PPs 5 paraffin-embedded particle preparations. Average normalized log of expression values for the MDS vs. the normal control samples with statistical significance (p values) determined by unpaired Student’s t-test analysis (bold values demonstrate p values !0.05). MDS samples demonstrate decreased expression of miRNAs compared to controls after normalization to a U6 control and comparison to a survey panel control sample to allow for plate-to-plate comparison. Of note, the control miRNA, hsa-miR-23a, chosen because it did not discriminated between MDS and NC samples by microarray on the discovery set, also fails to discriminate by RT-PCR, further verifying the original microarray results. The validation set RT-PCR data was secondarily normalized as well to miR-23a to account for the variable degrees of peripheral blood contamination in the particle preparations. Of the 5 miRNAs with p values !0.05, hsa-miR-378, hsa-miR-632, and hsa-miR-636 are significant after Bonferroni correction.

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Figure 1. (A) Discrimination between the MDS and normal control samples. Principle component analysis of the 20 samples using the 13 miRNA classifier set defined by analysis of variance (Partek Genomics Suite v6.3). Blue shapes are the MDS samples; red shapes are the normal control samples. (B) Supervised hierarchical clustering of the MDS and control samples (x-axis) using the 13 miRNA classifier set (all probes against the 13 miRNAs on the y-axis).

from normal control samples (Table 2, graphical comparison shown in Supplementary Figure E1; online only, available at www.exphem.org). In this analysis, eight miRNAs demonstrated p values of !0.05: hsa-miR-103, hsa-miR-140, hsa-miR-150, hsa-miR-342, hsa-miR-378, hsa-miR-483, hsa-miR-632, and hsa-miR-636. Of these, hsa-miR-150, hsa-miR-342, hsa-miR-378, and hsa-miR-632 demonstrated the highest individual discriminatory power. As expected, miR-23a was not found to be discriminatory. Validation of the eight miRNA diagnostic signature of MDS in paraffin-embedded particle preparations Because BM-MNCs are a rare commodity, we utilized paraffin-embedded tissue particle preparations for our validation set. To do so, we needed to prove that miRNA signatures from the frozen BM-MNCs and the paraffinembedded tissue samples were similar. We examined the correlation of miRNA profiles from paraffin-embedded particle preparations with BM-MNC samples using two pairs of samples, each obtained concurrently (collection IDs 260 and 803). The RT-PCR results across the 13 classifier miRNAs and 1 control from tandem BM-MNCs and paraffin-embedded particle preparations were compared. The relative expression levels spanned O10 detection threshold cycles of RT-PCR and were highly correlated between RNA sources (R2 5 0.94 and 0.85) (Fig. 2). In addition, particle preparations prepared with formalin and B-plus fixative showed a comparable level of miRNA expression (see Supplementary Figure E2; online only, available at www.exphem.org). These results demonstrate that the miRNA trends identified by RT-PCR on BM-MNCs could be replicated in

paraffin-embedded particle preparations. Thus, an independent validation set of 24 MDS and 25 normal control particle preparations were selected to validate the eight miRNA signatures by RT-PCR. An unpaired Student’s t-test analysis was performed to ascertain their discriminatory power on the validation set. Five miRNAs had individual p values !0.05 and, after Bonferroni correction, hsa-miR-378, hsa-miR-632, and hsa-miR-636 proved highly significant (Table 2). Importantly, two of these miRNAs also ranked among the most highly discriminatory miRNAs in the initial discovery set. MDS-associated miRNAs regulate targets important in hematopoiesis and MDS Target prediction studies using the Miranda, TargetScan, and PicTar-Vert Web-based algorithms were performed to identify potential targets of our MDS-associated miRNAs. The combined list of targets was compared to a list of genes (p ! 0.05) that classified 206 MDS patients from 74 normal controls compiled from the MILE transcriptional profiling studies. Because this gene list was not specifically generated in the original studies [14,15], this analysis was conducted by re-analyzing the publically available MILE data (http://www.ncbi.nlm.nih.gov/geo/query/acc. cgi?acc5GSE13159). The comparison of the miRNA target lists against the MDS classifier genes demonstrated that the eight MDS classifier miRNAs are predicted to regulate 30% of mRNAs identified (see Supplementary Table E3 and Supplementary Figure E3; online only, available at www. exphem.org). Targets identified by at least two of the three prediction algorithms and identified in the transcriptional profiling experiments mentioned here were subsequently

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Figure 2. Comparison of the RT-PCR detection threshold cycles across 13 classifier miRNAs and for 2 different pairs of specimens (RSQ, Pearson correlation coefficient).

examined to enrich for miRNAs most likely to be biologically relevant. The top 50 discriminatory subset of targets included 9 transcription regulators, 22 cell signaling molecules and receptors, 6 RNA processing and translation regulators, 6 proteins involved in cellular structure regulation, 2 proteins involved in ubiquitin regulation, and 5 enzymes and other proteins (transcription factors shown

in Table 3, all targets in Supplementary Table E4; online only, available at www.exphem.org). Targets identified by at least two of the three prediction algorithms were also compared against genes identified in the leukemia diagnostic classification signature identified in the same MILE study [15]. This subset comprised only 19 mRNAs, including 7 transcription regulators (Table 3, all targets in

Table 3. Comparison of target prediction algorithms with transcriptional profiling results MILE Study MDS vs Controls RMA Analysis Gene BACH2 PLAG1 TFDP2 FOXP1 SATB2 ZNF462 PHF20 PHC3 MYB

Transcription Factors

p-value

Fold-Change

2.30E-20 6.71E-06 4.67E-05 4.68E-05 8.91E-05 0.000118 0.000165 0.000371 0.000496

1.54906 1.37772 1.19566 1.27138 1.14018 1.1342 1.24376 1.14357 1.13861

Transcription Factors

p-value

Fold-Change

miRNAs

dachshund homolog 1, transcription regulation, marrow microenvironmer enolase 1, (alpha), transcription regulation (MYC) forkhead box P1, transcription factor, ?tumor suppressor homeobox A3, transcription factor Meis homeobox 1, transcription regulation v-myb myeloblastosis viral oncogene homolog, transcription factor SRY(sex determining region Y)-box 4, transcription factor, DNA damage

0.039274 0.133759 4.68E-05 0.00171 8.99E-05 0.000496 0.450521

1.27652 1.17469 1.27138 1.32686 1.38496 1.13861 1.06625

342 558 150,342 10b 365 103,150,342 140

BTB and CNC homology 1, basic leucine zipper transcription factor 2 pleiomorphic adenoma gene 1 transcription factor Dp-2 (E2F dimerization partner 2) forkhead box PI SATB homeobox 2 zinc finger protein 462 PHD finger protein 20 polyhomeotic homolog 3 (Drosophila) (tumor suppressor) v-myb myeloblastosis viral oncogene homolog (avian)

miRNAs 103 103 342 150 103 342 103 636 103,150

MILE Study, Diagnostic Classification Signature Gene DACH1 ENO1 FOXP1 HOXA3 MEIS1 MYB SOX4

List of transcription factors predicted to be regulated by one of the eight MDS classifier miRNAs by at least two different algorithms (Miranda, TargetScan, or PicTar) that have been identified as differentially expressed in MDS based on the MILE transcriptional profiling study as well as those identified in the leukemia diagnostic classification signature also identified in the MILE study (p values and fold-change values given for the re-analysis of MDS vs. control specimens) [15]. The full set of genes predicted to be regulated by the MDS classifier miRNAs can be found in the Supplementary Material (Supplementary Tables E4 and E5; online only, available at www.exphem.org).

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Supplementary Table E5; online only, available at www. exphem.org). Virtually all of the latter set have been described previously as playing key roles in hematopoiesis or hematopoietic neoplasms. FOXP1 and MYB were identified by both sets of analyses. Expression of c-Myb by IHC stains MYB was selected to be examined in a third validation set, the IHC target validation set, composed of bone marrow biopsy specimens for three reasons. MYB was identified through comparison of miRNA targets in MILE study de novo data analysis. In addition, c-Myb is predicted to be regulated by three different miRNAs (miR-103, miR-150, and miR-342), which were highly discriminatory in the discovery set. Lastly, there is convincing literature to support the association of c-Myb expression with miR-150 [6]. IHC studies on these paraffin-embedded samples demonstrate that c-Myb is overexpressed in MDS bone marrow biopsies (Fig. 3A, B) compared to normal controls, the latter including patients in whom a bone marrow study was performed to rule out a diagnosis of MDS in the clinical context of cytopenias. Analysis of the stain indices calculated by two independent reviewers identified increased c-Myb staining as measured by percent of positive nuclei (Table 4). The myeloid lineage was the main contributing cell lineage to the observed staining pattern. Level of

expression within positive nuclei was not significantly different between the MDS and control samples. This overexpression of c-Myb is compatible with underexpression of its predicted miRNA regulators, miR-103, miR-150, and miR-342. On the other hand, FOXP1, also a predicted target of miR-150 alone, did not show statistically different expression in MDS and control marrows (data not shown). Expression of Sufu by IHC stains Because miR-378 was one of the most highly discriminatory miRNAs after validation and Bonferroni correction, targets of this miRNA were also of great interest. Several potential targets have been studied in the literature, including SUFU, FUS1, TOB2, GaINT7, CYP2E1, ESRRG, and GABPA [16–20]. None of these targets is identified by more than one of the target prediction algorithms, however, and only Sufu has been studied by IHC previously [19]. IHC studies on the IHC target validation set demonstrate that the Hedgehog pathway tumor suppressor Sufu is differentially expressed in MDS bone marrow biopsies compared to normal controls (Fig. 3C, D). Analysis of the stain indices calculated by two independent reviewers identified that, in control marrows, Sufu expression is predominantly limited to the nuclei of neutrophils. However, in MDS marrow biopsy sections, Sufu was found to be significantly overexpressed in the cytoplasm of

Figure 3. Examples of c-Myb staining on (A) normal marrow (200) and (B) MDS marrow (200). Examples of Sufu staining on (C) normal marrow (400), and (D) MDS marrow (400).

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Table 4. c-Myb and Sufu scoring results from two independent pathologist reviewers by lineage, scored as percent positive c-Myb

Controls average sd MDS average sd p Value

Sufu

Eryth % Positive

Myelo % Positive

Megak % Positive

All % Positive

Eryth % Positive

Myelo % Positive

Megak % Positive

All % Positive

5.5% 4.95

38.6% 15.99

68.0% 39.51

32.5% 14.09

31.1% 20.20

49.4% 18.30

14.8% 33.79

42.0% 14.60

9.1% 10.612 0.257

57.8% 17.24 0.009

80.7% 29.661 0.399

50.1% 16.417 0.008

51.4% 16.684 0.034

9.2% 16.944 1.9E-04

9.4% 21.907 0.681

32.6% 12.251 0.152

All 5 all cells counted together; Eryth 5 erythroid lineage; Myelo 5 myeloid lineage; Megak 5 megakaryocytic lineage; sd 5 standard deviation.

intermediate maturing normoblasts (predominantly orthochromic normoblasts based on counterstain nuclear size and chromatin condensation), while demonstrating decreased expression in the myeloid nuclei (Table 4).

Discussion We have presented the first microarray profiling of miRNA expression in MDS, with the use of both discovery and validation sets that are nearly homogenous in diagnostic subtype of MDS, focusing primarily on the RCMD histologic subtype and the International Prognostic Scoring System Int-1 and LR categories. Three recently published studies have also examined miRNAs in bone marrow cells from patients with MDS and controls by RT-PCR [8–10]. Interestingly, our study and those previous studies were all able to obtain signatures from unsorted bone marrow cells. Indeed, the correlation between our discovery and validation sets in BM-MNCs and paraffin-embedded particle preparation shavings, respectively, certainly demonstrates the robustness of our signature without prior selection based on cell lineage and/or stage of maturation. Two of the studies used RT-PCR without confirmation using a second technology. Other differences include the wide range of MDS clinical subtypes examined in the prior studies. In the case of Hussein et al., this was done intentionally to look at miRNA signatures associated with specific cytogenetic changes, not clinical subtype [8]. Pons et al. compared expression patterns across a wide range of subtypes in both bone marrow and peripheral blood samples, the latter an important specimen in diagnosis due to its accessibility [9]. Sokol et al., although using an miRNA microarray to look for an overall MDS signature, focused their RT-PCR validation not on the MDS diagnostic miRNAs, but on prognostic miRNAs [10]. Given the differences in patient populations, samples, and discovery platforms, it is perhaps not surprising that there are differences among all four studies. Indeed, due to the more numerous miRNAs interrogated with the microarray platform, many of the miRNAs of interest in this current

work were not studied in the previous studies. However, miR-150 was found to be downregulated in MDS by Sokol et al. [10], and Hussein et al. found downregulation of miR-103, miR-378, and miR-140 [8]. Thus, this current work complements those studies in their approach to examining miRNA expression in MDS. During the course of this work, we have demonstrated the first use of paraffin-embedded tissues as a valid source of miRNAs for the study of MDS. We found high correlation between the expression values in tandem frozen BM-MNCs and in paraffin-embedded particle preparations. Although successful extraction of miRNAs from paraffinembedded tissues with sufficient integrity for RT-PCR and microarray experiments has been shown previously, the comparisons demonstrated here support the use of this type of readily available tissue for future diagnostic purposes in MDS, even in institutions without a supply of banked frozen bone marrow aspirate specimens. In addition, it permitted us to mine the pathology archival materials for the larger validation set. Of the 8 miRNAs of interest, many are noteworthy themselves for their roles in hematopoiesis. Notably, miRNAs miR-103 and miR-150 are implicated in the shift from erythroid to megakaryocytic differentiation [6,21–23]. Thus, in all discovery set cases with erythroid hyperplasia and normal or decreased megakaryopoiesis, there was a decreased expression of one or both miRNAsda pattern typical of most subtypes of MDS (data not shown). By contrast, an increase in miR-150 expression is seen in the 5q subtype of MDS, which is often characterized by a megakaryocytic hyperplasia and erythroid hypoplasia [22]. In addition, underexpression of miRNAs miR-103, miR-150, and miR-342 has been noted in myeloid leukemia cell lines with aberrant differentiation, compared to normal or maturing granulocytes [24,25]. Although miR-140 has been predominantly associated with disorders of the cartilage or bone, allelic imbalances of its expression has been seen in plasma cell myeloma, and, through its regulation of Smad3 and Cxcl12, it may have significant roles in hematopoiesis as well [26–28]. Unfortunately, many of

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the high-numbered miRNAs have been far less extensively studied. Of note, studies have demonstrated that miR-378 is regulated in a biphasic manner in erythropoiesis [13] and is implicated in angiogenesis and tumorigenesis through its regulation of the tumor suppressor proteins SUFU and Fus-1 [19,29,30]. In addition, miR-483 has been suggested to play an anti-apoptotic role in certain solid tumors through its regulation of BBC3 [31]. In silico target prediction analyses identified numerous potential targets for regulation by the classifying miRNAs. Because miRNAs regulate protein expression primarily at the translational level, there is not always a clear inverse relationship with transcriptional levels, as shown for c-Myb in Table 4. In addition, there are numerous mechanisms for differential transcriptional expression that do not involve miRNA-associated pathways. Rather, the in silico study highlights the importance of particular genes and pathways in MDS and that miRNAs are predicted to play roles in those pathways as well. In particular, several transcription factors have known roles in hematopoiesis. DACH1 has been shown to regulate the bone marrow microenvironment through its regulation of receptor activator of nuclear factorkB ligand (RANKL) expression [32]. ENO1 is an alternative translational product of the c-MYC promoter binding protein, which is an important suppressor of c-Mycmediated oncogenesis, which plays key roles in many hematopoietic neoplasms [33]. Likewise, FOXP1, HOXA3, BACH2, and PLAG1 have been associated with lymphoid neoplasms [3,34–38]. Most importantly, both MEIS1 and MYB have been shown to play critical roles in the development of the hematopoietic system in various other vertebrate animal models [39–41]. Although miR-103, miR150, and miR-342 failed to meet statistical significance as individual discriminatory markers in the validation set of samples, all three miRNAs have been implicated in hematopoiesis and are individually predicted to regulate c-Myb expression. Their combined identification and strong discriminatory power in the discovery set provided compelling evidence for the potential importance of c-Myb in MDS, which may have failed to reach statistical significance in the RT-PCR validation set because of differences in sample type between the discovery (BM-MNCs) and validation (paraffin-embedded tissue) sets [6]. In corroboration of the underexpression of these miRNAs in MDS, c-Myb was found to be overexpressed in MDS by IHC staining. Hussein et al. also examined the expression of c-Myb in MDS cases with a range of clinical subtypes, including 5q syndrome, refractory anemia with ringed sideroblasts, and refractory anemia with excess blasts [6]. Although they found decreased expression of c-Myb by RT-PCR, corresponding to the overexpression of miR-150 in their 5q specimens, they found increased expression by IHC staining across their clinical subtypes, similar to the results of this study in predominantly RCMD patients. Numerous studies have

identified c-Myb as vital to hematopoiesis, required for self-renewal of hematopoietic stem cells in mice and zebrafish [39]. One hypothesis is that c-Myb overexpression in MDS bone marrows may contribute to the characteristic marrow hypercellularity in the face of aberrant or ineffective hematopoiesis. Our study suggests that it might have utility as a diagnostic marker of MDS, in addition to the miRNA signature. The miRNA, miR-378, was identified as one of the most highly significant discriminators of MDS in both the discovery and validation set. Therefore, it was of interest to examine targets of miR-378. Suppressor of fused, Sufu, was chosen to be studied further due to the demonstration of its repression by miR-378 expression by IHC stains [19], the requisite methodology for examination of the IHC target validation set. Sufu is a tumor suppressor gene that plays a key role in the Hedgehog signaling pathway. Both cytoplasmic and nuclear localizations of Sufu are known, with Sufu negatively regulating Gli transcription factors by sequestering them in the cytoplasm and by recruiting SAP18 of the histone deacetylase complex to repress Gli-mediated transcription in the nucleus [42,43]. Sufu has not been extensively studied in hematopoietic cells, so the discovery of the nuclear localization of this protein in the neutrophils of control marrows may suggest interesting physiologic roles of the Hedgehog pathway in hematopoiesis, which is differentially regulated and compartmentalized within the cells in MDS. Why the nuclear expression of Sufu decreases in the neutrophils of MDS patients is unclear. However, the association between the increased cytoplasmic expression of Sufu in the erythroid lineage may speak to the decreased levels of miR-378 in those cells, which may predominant in the miRNA signature due to erythroid hyperplasia in MDS.

Conclusions In summary, miRNA profiling by RT-PCR on frozen BM-MNCs and paraffin-embedded clot sections has been demonstrated to be a feasible method for identifying patients with MDS using a classifier miRNA signature. These miRNAs are predicted to regulate many mRNAs associated with MDS and their expression levels correspond to what might be predicted given the known biologic behavior of these select miRNAs. The expression pattern of these miRNAs in MDS has been corroborated by the demonstration of the increased expression of c-Myb, the predicted regulatory target of miR-103 miR-150, and miR-342, as well as the differential expression levels and localization of Sufu, the predicted target of miR-378. Ultimately, it is hoped that miRNA profiling of MDS will provide practicing pathologists with new markers for the identification of MDS, and provide clinicians with additional prognostic information to guide the use of appropriate therapeutic interventions.

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Acknowledgments The authors would like to acknowledge Michael R. Wood and Christine Eischen for their thoughtful reading of versions of the manuscript, William Wu and William DuPont for their helpful suggestions on statistical analysis, and Sandra Olson for her assistance with immunohistochemistry. Work supported by a Cooper Cancer Center (Camden, NJ, USA) small research grant; University of Pennsylvania Resident Research Funding, the Clinical and Translational Research Enhancement Award (Department of Pathology, Vanderbilt University Medical Center, Nashville, TN, USA), American Cancer Society Institutional Research Grant (#IRG-58-009-51), and the Vanderbilt CTSA grant UL1 RR024975.

Conflict of interest disclosure No financial interest/relationships with financial interest relating to the topic of this article have been declared.

References 1. Ma X, Does M, Raza A, Mayne ST. Myelodysplastic syndromes: incidence and survival in the United States. Cancer. 2007;109:1536–1542. 2. Swerdlow SH, Campo E, Harris NL, et al., eds. World Health Organization Classification of Tumours of Haematopoietic and Lymphoid Tissues. 4th ed. Lyon, France: IARC Press; 2008. 3. Garzon R, Croce CM. MicroRNAs in normal and malignant hematopoiesis. Curr Opin Hematol. 2008;15:352–358. 4. Ebert BL, Pretz J, Bosco J, et al. Identification of RPS14 as a 5q- syndrome gene by RNA interference screen. Nature. 2008;451: 335–339. 5. Starczynowski DT, Kuchenbauer F, Argiropoulos B, et al. Identification of miR-145 and miR-146a as mediators of the 5q- syndrome phenotype. Nat Med. 2010;16:49–58. 6. Hussein K, Theophile K, Busche G, et al. Significant inverse correlation of microRNA-150/MYB and microRNA-222/p27 in myelodysplastic syndrome. Leuk Res. 2010;34:328–334. 7. Barroga CF, Pham H, Kaushansky K. Thrombopoietin regulates c-Myb expression by modulating micro RNA 150 expression. Exp Hematol. 2008;36:1585–1592. 8. Hussein K, Theophile K, Busche G, et al. Aberrant microRNA expression pattern in myelodysplastic bone marrow cells. Leuk Res. 2010; 34:1169–1174. 9. Pons A, Nomdedeu B, Navarro A, et al. Hematopoiesis-related microRNA expression in myelodysplastic syndromes. Leuk Lymphoma. 2009;50:1854–1859. 10. Sokol L, Caceres G, Volinia S, et al. Identification of a risk dependent microRNA expression signature in myelodysplastic syndromes. Br J Haematol. 2011;153:24–32. 11. Nelson PT, Baldwin DA, Scearce LM, Oberholtzer JC, Tobias JW, Mourelatos Z. Microarray-based, high-throughput gene expression profiling of microRNAs. Nat Methods. 2004;1:155–161. 12. Greenberg P, Cox C, LeBeau MM, et al. International scoring system for evaluating prognosis in myelodysplastic syndromes. Blood. 1997; 89:2079–2088. 13. Bruchova H, Yoon D, Agarwal AM, Mendell J, Prchal JT. Regulated expression of microRNAs in normal and polycythemia vera erythropoiesis. Exp Hematol. 2007;35:1657–1667. 14. Haferlach T, Kohlmann A, Wieczorek L, et al. Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group. J Clin Oncol. 2010;28: 2529–2537.

925

15. Mills KI, Kohlmann A, Williams PM, et al. Microarray-based classifiers and prognosis models identify subgroups with distinct clinical outcomes and high risk of AML transformation of myelodysplastic syndrome. Blood. 2009;114:1063–1072. 16. Eichner LJ, Perry MC, Dufour CR, et al. miR-378(*) mediates metabolic shift in breast cancer cells via the PGC-1b/ERRg transcriptional pathway. Cell Metab. 2010;12:352–361. 17. Feng M, Li Z, Aau M, Wong CH, Yang X, Yu Q. Myc/miR-378/ TOB2/cyclin D1 functional module regulates oncogenic transformation. Oncogene. 2011;30:2242–2251. 18. Kahai S, Lee SC, Lee DY, et al. MicroRNA miR-378 regulates nephronectin expression modulating osteoblast differentiation by targeting GalNT-7. PloS One. 2009;4:e7535. 19. Lee DY, Deng Z, Wang CH, Yang BB. MicroRNA-378 promotes cell survival, tumor growth, and angiogenesis by targeting SuFu and Fus-1 expression. Proc Natl Acad Sci U S A. 2007;104:20350–20355. 20. Mohri T, Nakajima M, Fukami T, Takamiya M, Aoki Y, Yokoi T. Human CYP2E1 is regulated by miR-378. Biochem Pharmacol. 2010;79:1045–1052. 21. Yang GH, Wang F, Yu J, Wang XS, Yuan JY, Zhang JW. MicroRNAs are involved in erythroid differentiation control. J Cell Biochem. 2009; 107:548–556. 22. Hussein K, Dralle W, Theophile K, Kreipe H, Bock O. Megakaryocytic expression of miRNA 10a, 17-5p, 20a and 126 in Philadelphia chromosome-negative myeloproliferative neoplasm. Ann Hematol. 2009;88:325–332. 23. Garzon R, Pichiorri F, Palumbo T, et al. MicroRNA fingerprints during human megakaryocytopoiesis. Proc Natl Acad Sci U S A. 2006;103: 5078–5083. 24. Fatica A, Rosa A, Ballarino M, De Marchis ML, Rasmussen KD, Bozzoni I. Role of microRNAs in myeloid differentiation. Biochem Soc Trans. 2008;36:1201–1205. 25. Merkerova M, Belickova M, Bruchova H. Differential expression of microRNAs in hematopoietic cell lineages. Eur J Haematol. 2008; 81:304–310. 26. Lionetti M, Biasiolo M, Agnelli L, et al. Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma. Blood. 2009;114:e20–e26. 27. Nicolas FE, Pais H, Schwach F, et al. Experimental identification of microRNA-140 targets by silencing and overexpressing miR-140. RNA. 2008;14:2513–2520. 28. Pais H, Nicolas FE, Soond SM, et al. Analyzing mRNA expression identifies Smad3 as a microRNA-140 target regulated only at protein level. RNA. 2010;16:489–494. 29. Pizzimenti S, Ferracin M, Sabbioni S, et al. MicroRNA expression changes during human leukemic HL-60 cell differentiation induced by 4-hydroxynonenal, a product of lipid peroxidation. Free Radic Biol Med. 2009;46:282–288. 30. Wang S, Olson EN. AngiomiRsdkey regulators of angiogenesis. Curr Opin Genet Dev. 2009;19:205–211. 31. Veronese A, Lupini L, Consiglio J, et al. Oncogenic role of miR-483-3p at the IGF2/483 locus. Cancer Res. 2010;70:3140–3149. 32. Sundaram K, Mani SK, Kitatani K, Wu K, Pestell RG, Reddy SV. DACH1 negatively regulates the human RANK ligand gene expression in stromal/preosteoblast cells. J Cell Biochem. 2008;103: 1747–1759. 33. Lung J, Liu KJ, Chang JY, Leu SJ, Shih NY. MBP-1 is efficiently encoded by an alternative transcript of the ENO1 gene but posttranslationally regulated by proteasome-dependent protein turnover. FEBS J. 2010;277:4308–4321. 34. Green M, Gandhi MK, Camilleri E, Marlton P, Lea R, Griffiths L. High levels of BACH2 associated with lower levels of BCL2 transcript abundance in t(14;18)(q21;q34) translocation positive non-Hodgkin’s lymphoma. Leuk Res. 2009;33:731–734.

926

B. Erdogan et al./ Experimental Hematology 2011;39:915–926

35. Pallasch CP, Patz M, Park YJ, et al. miRNA deregulation by epigenetic silencing disrupts suppression of the oncogene PLAG1 in chronic lymphocytic leukemia. Blood. 2009;114:3255–3264. 36. Patz M, Pallasch CP, Wendtner CM. Critical role of microRNAs in chronic lymphocytic leukemia: overexpression of the oncogene PLAG1 by deregulated miRNAs. Leuk Lymphoma. 2010;51: 1379–1381. 37. Starkova J, Zamostna B, Mejstrikova E, Krejci R, Drabkin HA, Trka J. HOX gene expression in phenotypic and genotypic subgroups and low HOXA gene expression as an adverse prognostic factor in pediatric ALL. Pediatr Blood Cancer. 2010;55:1072–1082. 38. Takakuwa T, Luo WJ, Ham MF, Sakane-Ishikawa F, Wada N, Aozasa K. Integration of Epstein-Barr virus into chromosome 6q15 of Burkitt lymphoma cell line (Raji) induces loss of BACH2 expression. Am J Pathol. 2004;164:967–974.

39. Soza-Ried C, Hess I, Netuschil N, Schorpp M, Boehm T. Essential role of c-myb in definitive hematopoiesis is evolutionarily conserved. Proc Natl Acad Sci U S A. 2010;107:17304–17308. 40. Wheadon H, Ramsey JM, Dobbin E, et al. Differential Hox expression in murine embryonic stem cell models of normal and malignant hematopoiesis. Stem Cells Dev; 2011 Jan 5; [Epub ahead of print]. 41. Ramsay RG, Gonda TJ. MYB function in normal and cancer cells. Nat Rev. 2008;8:523–534. 42. Cheng SY, Bishop JM. Suppressor of fused represses Gli-mediated transcription by recruiting the SAP18-mSin3 corepressor complex. Proc Natl Acad Sci U S A. 2002;99:5442–5447. 43. Paces-Fessy M, Boucher D, Petit E, Paute-Briand S, BlanchetTournier MF. The negative regulator of Gli, Suppressor of fused (Sufu), interacts with SAP18, Galectin3 and other nuclear proteins. Biochem J. 2004;378:353–362.

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Supplementary Figure E1. Comparison of the average fold-change values for MDS and control samples by microarray and RT-PCR. As expected, miR-23a shows no significant change by either microarray or RT-PCR.

Supplementary Figure E2. Comparison of the expression of selected miRNAs in RNA extracted from particle preparations fixed in formalin and B-Plus fixative. All expression is normalized to a U6 control RNA as well as to the FirstChoice Human Total RNA Survey Panel (SP) (Ambion).

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Supplementary Figure E3. Venn diagram (http://bioinfogp.cnb.csic.es/tools/venny/index.html) of genes that discriminate MDS from controls in the MILE study with predicted targets of miRNAs identified in 2 of 3 different target prediction algorithms.