Open Access

Evaluation of the NOD/SCID xenograft model for glucocorticoid-regulated gene expression in childhood B-cell precursor acute lymphoblastic leukemia

  • Vivek A Bhadri1, 3,
  • Mark J Cowley2,
  • Warren Kaplan2,
  • Toby N Trahair1, 3 and
  • Richard B Lock1Email author
BMC Genomics201112:565

DOI: 10.1186/1471-2164-12-565

Received: 16 August 2011

Accepted: 17 November 2011

Published: 17 November 2011

Abstract

Background

Glucocorticoids such as prednisolone and dexamethasone are critical drugs used in multi-agent chemotherapy protocols used to treat acute lymphoblastic leukemia (ALL), and response to glucocorticoids is highly predictive of outcome. The NOD/SCID xenograft mouse model of ALL is a clinically relevant model in which the mice develop a systemic leukemia which retains the fundamental biological characteristics of the original disease. Here we report a study evaluating the NOD/SCID xenograft mouse model to investigate glucocorticoid-induced gene expression. Cells from a glucocorticoid-sensitive xenograft derived from a child with B-cell precursor ALL were inoculated into NOD/SCID mice. When highly engrafted the mice were randomized into groups of 4 to receive dexamethasone 15 mg/kg by intraperitoneal injection or vehicle control. Leukemia cells were harvested from mice spleens at 0, 8, 24 or 48 hours thereafter, and gene expression analyzed on Illumina WG-6_V3 chips, comparing all groups to time 0 hours.

Results

The 8 hour dexamethasone-treated timepoint had the highest number of significantly differentially expressed genes, with fewer observed at the 24 and 48 hour timepoints, and with minimal changes seen across the time-matched controls. When compared to publicly available datasets of glucocorticoid-induced gene expression from an in vitro cell line study and from an in vivo study of patients with ALL, at the level of pathways, expression changes in the 8 hour xenograft samples showed a similar response to patients treated with glucocorticoids. Replicate analysis revealed that at the 8 hour timepoint, a dataset with high signal and differential expression, using data from 3 replicates instead of 4 resulted in excellent recovery scores of > 0.9. However at other timepoints with less signal very poor recovery scores were obtained with 3 replicates.

Conclusions

The NOD/SCID xenograft mouse model provides a reproducible experimental system in which to investigate clinically-relevant mechanisms of drug-induced gene regulation in ALL; the 8 hour timepoint provides the highest number of significantly differentially expressed genes; time-matched controls are redundant and excellent recovery scores can be obtained with 3 replicates.

Background

Glucocorticoids such as prednisolone and dexamethasone are critical components of multi-agent chemotherapy protocols used in the treatment of acute lymphoblastic leukemia (ALL) [1] due to their ability to induce apoptosis in lymphoid cells. Despite their use for over 50 years their mechanism of action is not completely understood. Glucocorticoids are steroid hormones that act on target cells through interaction with a specific glucocorticoid receptor (GR) [2]. The GR is held in a cytosolic complex by a number of co-chaperone molecules including HSP-90 and HSP-70 [3], and on ligand binding dissociates from the co-chaperone complex, dimerizes and is transported to the nucleus where it binds to palindromic DNA sequences known as glucocorticoid response elements (GREs) found in the promoter regions of target genes [4]. This leads to the activation of transcription of primary target genes, repression of transcription through interaction with negative GREs [5] or of gene activation through transcription factors such as AP-1 and NF-ΚB [6]. In lymphoid cells, this results in repression of cell cycle progression through cyclin-D3 and C-MYC [7], and cell death through the activation of apoptosis. Glucocorticoids also induce other non-apoptotic mechanisms of programmed cell death including autophagy [8] and mediate a number of pathways involved in the metabolism of carbohydrates, lipids and proteins.

A number of studies have published microarray data of glucocorticoid-induced genes in lymphoid cells, but comparison of the data is complicated by technical differences in platform and chip type. Previous studies of glucocorticoid-induced genes in ALL have been carried out using in vitro cell-line models [915] and patient-derived cells, both in vivo [16] and in vitro [17]. Cell lines are extensively used in the study of ALL but in the process of immortalization acquire multiple genetic defects, particularly in the p53 pathway [18], and mechanisms demonstrated in cell lines are often not replicated in more clinically relevant models. Primary patient cells have a finite supply and rarely survive ex vivo for more than a few days. The non-obese diabetic/severe combined immunodeficient (NOD/SCID) xenograft mouse model is widely used to study ALL. In this model, human leukemia cells obtained from diagnostic bone marrow biopsies are inoculated into NOD/SCID mice, and on engraftment establish a systemic leukemia which retains the fundamental biological characteristics of the original disease [19]. It has also been shown that the in vivo responses to chemotherapeutic agents, including dexamethasone, correlates with patient outcome [20], and thus the NOD/SCID xenograft mouse model provides a stable, reproducible and clinically relevant model with which to study ALL. Here we report the first study investigating glucocorticoid-induced gene expression in ALL using the NOD/SCID xenograft model, the optimal experimental design, and a comparison of our microarray data to publicly available datasets of glucocorticoid-induced genes in other experimental models.

Methods

NOD/SCID xenograft mouse model

All experimental studies were approved by the Human Research Ethics Committee and the Animal Care and Ethics Committee of the University of New South Wales. ALL-3, a glucocorticoid-sensitive xenograft derived from a 12 year old girl with mixed lineage leukemia (MLL)-rearranged BCP-ALL, was chosen for this study. Although MLL-rearranged ALL is associated with a poor prednisolone response and an inferior outcome [21], this patient is currently a long-term survivor. ALL-3 demonstrates in vitro glucocorticoid sensitivity, with an IC50 of 9.4 nM on exposure to dexamethasone. In the in vivo NOD/SCID xenograft mouse model, ALL-3 is highly responsive to 4 weeks of treatment with single agent dexamethasone, with rapid clearance of leukemic blasts from the peripheral blood and recurrence of leukemia delayed by 63.4 days compared to vehicle-treated controls [20].

Cells from ALL-3 were inoculated by tail-vein injection into 28 NOD/SCID mice. The mice were bled weekly and the samples stained with fluorescein isothiocyanate (FITC)-conjugated anti-murine CD45 and allophycocyanin (APC)-conjugated anti-human CD45 (BioLegend, San Diego, CA). Following lysis of erythrocytes with FACS lysing solution (BD Biosciences, San Jose, CA), samples were analyzed by multiparametric flow cytometry on a FACSCanto cytometer (BD Biosciences, San Jose, CA). Engraftment was calculated as the proportion of human versus total CD45+ cells.

When high level (> 70%) engraftment was achieved in the peripheral blood, between 8 and 10 weeks post-transplantation, the mice were randomized and split into groups of 4 to receive either dexamethasone 15 mg/kg (Sigma-Aldrich, St Louis, MO) or vehicle control by intraperitoneal injection. Mice were culled by CO2 asphyxiation at 0 hours (pre-treatment, group 1), 8 hours (groups 2 and 3), 24 hours (groups 4 and 5) or 48 hours (groups 6 and 7) following treatment. The mice in groups 6 and 7 received a second dose of dexamethasone or vehicle control at 24 hours. Two mice succumbed early to thymoma, a well-recognized complication in NOD/SCID mice, resulting in 3 mice in each of groups 6 and 7. Cell suspensions of spleens were prepared and mononuclear cells enriched and purified to > 97% human by density gradient centrifugation using LymphoPrep (Axis-Shield, Norway), and cell viability assessed by trypan blue exclusion. RNA was extracted using the RNeasy mini kit (Qiagen, Hilden, Germany) and the RNA integrity verified (Agilent Bioanalyzer, Santa Clara, CA). The RNA was amplified using the Illumina TotalPrep RNA amplification kit (Ambion, Austin, TX) and hybridized onto Illumina WG-6_V3 chips (Illumina, San Diego, CA). The chips were scanned on the Illumina Bead Array Reader (Illumina, San Diego, CA) and gene expression analyzed. The data have been deposited in NCBI's Gene Expression Omnibus [22] and are accessible through GEO Series accession number GSE30392 http://​www.​ncbi.​nlm.​nih.​gov/​geo/​query/​acc.​cgi?​acc=​GSE30392.

Gene expression and functional analysis

The sample probe profiles with no normalization or background correction were exported from BeadStudio (version 3.0.14, Illumina, San Diego, CA). The data were pre-processed using variance stabilizing transformation [23] and robust spline normalization in lumi [24] which takes advantage of each probe being represented by > 25 beads. Differential gene expression was determined using limma [25] by comparing all treated groups to time 0 hours, with the positive False Discovery Rate correction for multiple testing [26]. Complete linkage hierarchical clustering using Euclidian distance was used to compare groups to each other. Functional analysis was performed using gene set enrichment analysis (GSEA) version 2.04 [27], comparing the limma moderated t-statistic for each probe in a pre-ranked file, against the c2_all collection of gene sets from the Molecular Signatures Database [27] version 2.5 with 1000 permutations. The similarity of the top 100 up- and down-regulated genesets was assessed using meta-GSEA (Cowley et al, manuscript in preparation).

Comparison of models

The molecular response to glucocorticoids in xenografts was compared to publicly available microarray data [13, 16] using parametric analysis of gene set enrichment [28] implemented in the PGSEA package (version 1.20.1, Furge and Dykema) from the Bioconductor project [29], with some modifications to the algorithm to assess significance of the genes that are in the geneset and represented on the microarray, and to allow more control over control sample specification (available upon request). Expression levels of each gene in each sample were converted to expression ratios relative to patient matched controls before glucocorticoid treatment (Schmidt et al), time-matched controls (Rainer et al), or time 0 hours (xenografts). Within each dataset, these gene-level ratios were summarized into geneset-level Z-scores, using PGSEA with genesets from the c2_all collection [27]. The Z-scores from each sample from the 3 studies were combined and then compared by hierarchical clustering of the top 100 gene sets demonstrating the greatest variance across the combined studies.

Replicate analysis

The stability of results when reducing the number of replicates was assessed using the Recovery Score method [30] from the GeneSelector package (version 1.4.0) of the Bioconductor project [29].

Results and Discussion

It has been demonstrated that changes in gene expression can be detected as early as 6 hours after treatment of ALL with glucocorticoids in vivo [16] and in vitro [11], although earlier timepoints show few, if any, significantly differentially expressed genes [17]. In this study the 8 hour dexamethasone-treated timepoint demonstrated the highest number of differentially expressed genes compared to baseline control, with far fewer observed at the 24 and 48 hour dexamethasone-treated timepoints (Tables 1 and 2, and Figure 1). Whilst a similar proportion of up- and down-regulated genes were identified at the 8 hour dexamethasone-treated timepoint (1158 vs 1072 respectively, FDR < 0.05), of those with large fold changes (FC > 2 or FC < 0.5, red dots in Figure 1A), 75% were up regulated (199 vs 65 respectively), consistent with the predominant role of glucocorticoids as transcriptional activators. The large numbers of statistically differentially expressed genes (FDR < 0.05) with small fold changes (0.5 < FC < 2) are indicative of both small measurement error across replicates, and thus the high reproducibility of the xenograft model, and good experimental power resulting from using 4 replicates. There was minimal significant differential gene expression across the time-matched controls (Tables 1 and 2). This demonstrates that in the xenograft mouse model, the 8 hour timepoint provides the greatest information, and that these changes are not sustained over later timepoints. The handling of the mice and intraperitoneal vehicle control injections had minimal effect on gene expression, and thus time-matched controls are redundant.
Table 1

Number of differentially expressed genes by False Discovery Rate (FDR), compared to time 0 hours.

Timepoint (hours)

FDR < 0.25

FDR < 0.1

FDR < 0.05

 

+

-

+

-

+

-

Dex 8

2313

2434

1470

1423

1158

1072

Dex 24

970

1087

273

421

75

195

Dex 48

321

327

41

95

12

44

Con 8

0

0

0

0

0

0

Con 24

0

1

0

1

0

1

Con 48

0

1

0

1

0

1

+ upregulated; - downregulated; Dex, dexamethasone-treated; and Con, control

Table 2

Number of differentially expressed genes by Fold Change (FC), compared to time 0 hours.

Timepoint (hours)

FC > 1.5

FC > 2

FC > 4

 

+

-

+

-

+

-

Dex 8

501

429

201

68

38

0

Dex 24

137

341

15

90

0

0

Dex 48

79

234

5

69

0

3

Con 8

1

37

1

2

0

0

Con 24

1

5

0

0

0

0

Con 48

7

34

0

2

0

0

+ upregulated; - downregulated; Dex, dexamethasone-treated; and Con, control

https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-565/MediaObjects/12864_2011_Article_3723_Fig1_HTML.jpg
Figure 1

Volcano plots of significantly differentially expressed genes following treatment with dexamethasone at 8 hours (A), 24 hours (B), 48 hours (C). Significance was defined as log2 Fold Change > 1 or < -1 with False Discovery Rate (FDR) < 0.05. Each dot represents a single gene, and significant genes indicated by red dots.

At the 8 hour timepoint, there were 173 genes upregulated with a t-statistic (the ratio of fold change to standard error) > 10 and 25 genes downregulated with a t-statistic < -10 (corresponding to P < 1.74 × 10-9 and FDR < 2.95 × 10-7, table 3). None of these genes had sustained expression changes at 24 or 48 hours, and although this could potentially reflect the early death of sensitive cells, there was no significant difference in the total number of cells harvested from the spleens at any timepoint compared to the time-matched controls (data not shown), and all harvests had a cell viability of ≥ 96%.
Table 3

Genes regulated 8 hours following dexamethasone treatment.

ProbeSet ID

Gene

t

P

FDR

Definition

Upregulated

     

ILMN_5080450

ZBTB16

83.77

< 2.2E-16

< 2.2E-16

zinc finger and BTB domain containing 16

ILMN_3800088

MMP7

53.22

< 2.2E-16

< 2.2E-16

matrix metallopeptidase 7

ILMN_1770593

CH25H

53.14

< 2.2E-16

< 2.2E-16

cholesterol 25-hydroxylase

ILMN_6560328

C6orf85

44.60

< 2.2E-16

< 2.2E-16

chromosome 6 open reading frame 85

ILMN_7570561

TSC22D3

39.16

< 2.2E-16

< 2.2E-16

TSC22 domain family, member 3

ILMN_580187

PDE8B

33.88

< 2.2E-16

3.90E-16

phosphodiesterase 8B

ILMN_5130066

C8orf61

33.82

< 2.2E-16

3.90E-16

chromosome 8 open reading frame 61

ILMN_4120431

TMEM100

31.38

< 2.2E-16

1.64E-15

transmembrane protein 100

ILMN_650553

BIN1

29.76

< 2.2E-16

4.43E-15

bridging integrator 1

ILMN_1400373

SLA

29.57

< 2.2E-16

4.63E-15

Src-like-adaptor

ILMN_6330593

PTHR1

29.28

< 2.2E-16

5.22E-15

parathyroid hormone receptor 1

ILMN_6110037

LILRA3

29.04

< 2.2E-16

5.75E-15

leukocyte immunoglobulin-like receptor subfamily A, member 3

ILMN_4150477

LOXL4

28.67

< 2.2E-16

6.66E-15

lysyl oxidase-like 4

ILMN_2680079

OGFRL1

28.65

< 2.2E-16

6.66E-15

opioid growth factor receptor-like 1

ILMN_4210411

NDRG2

28.20

< 2.2E-16

8.62E-15

NDRG family member 2

ILMN_3780093

LILRA1

27.86

< 2.2E-16

1.05E-14

leukocyte immunoglobulin-like receptor subfamily A, member 1

ILMN_240441

IL1R2

27.46

< 2.2E-16

1.33E-14

interleukin 1 receptor, type II

ILMN_4730315

MERTK

26.14

< 2.2E-16

3.31E-14

c-mer proto-oncogene tyrosine kinase

ILMN_3800538

ACPL2

25.90

< 2.2E-16

3.72E-14

acid phosphatase-like 2

ILMN_6860392

UGT2B17

25.83

< 2.2E-16

3.72E-14

UDP glucuronosyltransferase 2 family, polypeptide B17

ILMN_4730541

SLC44A1

25.82

< 2.2E-16

3.72E-14

solute carrier family 44, member 1

ILMN_4860546

CTHRC1

25.64

< 2.2E-16

4.10E-14

collagen triple helix repeat containing 1

ILMN_3460270

ZHX3

24.56

< 2.2E-16

8.79E-14

zinc fingers and homeoboxes 3

ILMN_10639

RASSF4

23.21

< 2.2E-16

2.57E-13

Ras association (RalGDS/AF-6) domain family 4

ILMN_1190064

UGT2B7

23.13

< 2.2E-16

2.67E-13

UDP glucuronosyltransferase 2 family, polypeptide B7

ILMN_6400603

MGC2463

23.06

< 2.2E-16

2.71E-13

poliovirus receptor related immunoglobulin domain containing

ILMN_3450187

IRGM

23.04

< 2.2E-16

2.71E-13

immunity-related GTPase family, M

ILMN_6620528

MT1X

22.95

2.40E-16

2.85E-13

metallothionein 1X

ILMN_1260341

IL13RA1

22.47

3.67E-16

4.13E-13

interleukin 13 receptor, alpha 1

ILMN_2650112

SLC16A2

22.25

4.48E-16

4.91E-13

solute carrier family 16, member 2

ILMN_5570170

PNMT

22.01

5.59E-16

5.95E-13

phenylethanolamine N-methyltransferase

ILMN_870376

C9orf152

21.93

6.02E-16

6.25E-13

chromosome 9 open reading frame 152

ILMN_3190379

TGFBR3

21.52

8.78E-16

8.89E-13

transforming growth factor, beta receptor III

ILMN_1780142

DSCR1

21.08

1.33E-15

1.31E-12

Down syndrome critical region gene 1

ILMN_2640341

FKBP5

20.63

2.05E-15

1.89E-12

FK506 binding protein 5

ILMN_7610136

LOC652626

20.43

2.48E-15

2.23E-12

Leukocyte immunoglobulin-like receptor subfamily B member 2

ILMN_1410609

CORO2A

20.34

2.72E-15

2.35E-12

coronin, actin binding protein, 2A

ILMN_1780088

TBXA2R

20.29

2.84E-15

2.40E-12

thromboxane A2 receptor

ILMN_270431

BAALC

20.23

3.02E-15

2.50E-12

brain and acute leukemia, cytoplasmic

ILMN_6280176

GBE1

20.02

3.72E-15

3.01E-12

glucan (1,4-alpha-), branching enzyme 1

ILMN_6060113

TBX15

19.81

4.62E-15

3.67E-12

T-box 15

ILMN_4890743

IQSEC1

19.71

5.09E-15

3.97E-12

IQ motif and Sec7 domain 1

ILMN_150056

DPEP1

19.65

5.41E-15

4.13E-12

dipeptidase 1

ILMN_2060364

BTNL9

19.26

8.04E-15

5.91E-12

butyrophilin-like 9

ILMN_3830735

UPB1

19.23

8.30E-15

5.91E-12

ureidopropionase, beta

ILMN_5670377

STYK1

19.15

9.09E-15

6.35E-12

serine/threonine/tyrosine kinase 1

ILMN_4390630

STAG3

18.72

1.42E-14

9.39E-12

stromal antigen 3

ILMN_4070048

NPHP4

18.44

1.91E-14

1.25E-11

nephronophthisis 4

ILMN_4220474

C6orf81

18.31

2.16E-14

1.39E-11

chromosome 6 open reading frame 81

ILMN_1470746

PTPN3

18.30

2.23E-14

1.41E-11

protein tyrosine phosphatase, non-receptor type 3

ILMN_5860576

C20orf133

18.25

2.36E-14

1.47E-11

MACRO domain containing 2

ILMN_6020468

PPP1R14A

18.18

2.52E-14

1.55E-11

protein phosphatase 1, regulatory (inhibitor) subunit 14A

ILMN_1400634

MT1M

18.10

2.76E-14

1.64E-11

metallothionein 1M

ILMN_4250315

ITGA9

17.90

3.46E-14

2.03E-11

integrin, alpha 9

ILMN_5080471

MAP3K6

17.40

6.02E-14

3.44E-11

mitogen-activated protein kinase 6

ILMN_5360242

FLJ42461

17.36

6.28E-14

3.53E-11

smoothelin-like 2

ILMN_6620402

NUDT16

17.33

6.50E-14

3.60E-11

nudix (nucleoside diphosphate linked moiety X)-type motif 16

ILMN_3360112

TMEM2

17.26

7.04E-14

3.85E-11

transmembrane protein 2

ILMN_6840743

PER1

17.22

7.41E-14

3.99E-11

period homolog 1

ILMN_4220347

LRRC1

17.12

8.29E-14

4.33E-11

leucine rich repeat containing 1

ILMN_4850592

P2RY14

17.11

8.35E-14

4.33E-11

purinergic receptor P2Y, G-protein coupled, 14

ILMN_6560300

SLC31A2

16.91

1.05E-13

5.39E-11

solute carrier family 31 member 2

ILMN_4060091

DKFZ

16.87

1.11E-13

5.62E-11

DKFZp451A211 protein

ILMN_6770370

LOC92196

16.28

2.23E-13

1.11E-10

death associated protein-like 1

ILMN_580487

IL9R

16.21

2.40E-13

1.18E-10

interleukin 9 receptor

ILMN_1990300

SOCS1

16.18

2.49E-13

1.21E-10

suppressor of cytokine signaling 1

ILMN_5720424

NRP1

16.17

2.54E-13

1.22E-10

neuropilin 1

ILMN_4180427

CIB4

16.11

2.74E-13

1.30E-10

calcium and integrin binding family member 4

ILMN_4180544

ROPN1L

16.08

2.81E-13

1.32E-10

ropporin 1-like

ILMN_4250167

SOX13

16.04

2.95E-13

1.37E-10

SRY (sex determining region Y)-box 13

ILMN_6330170

CHKA

15.81

3.94E-13

1.81E-10

choline kinase alpha, 3

ILMN_4560192

SFXN5

15.62

4.95E-13

2.25E-10

sideroflexin 5

ILMN_2810136

CAPN11

15.56

5.33E-13

2.40E-10

calpain 11

ILMN_2690709

VIPR1

15.38

6.68E-13

2.91E-10

vasoactive intestinal peptide receptor 1

ILMN_630091

NCOA7

15.38

6.69E-13

2.91E-10

nuclear receptor coactivator 7

ILMN_5390730

MGC17330

15.21

8.25E-13

3.55E-10

phosphoinositide-3-kinase interacting protein 1

ILMN_130364

MST150

15.19

8.49E-13

3.62E-10

MSTP150

ILMN_3450241

KIAA0774

14.95

1.16E-12

4.77E-10

KIAA0774

ILMN_2230678

ACACB

14.80

1.41E-12

5.76E-10

acetyl-Coenzyme A carboxylase beta

ILMN_5870307

LOC440359

14.78

1.44E-12

5.83E-10

similar to muscle Y-box protein YB2

ILMN_3840554

SPOCK2

14.76

1.49E-12

5.95E-10

sparc/osteonectin, cwcv and kazal-like domains 2

ILMN_5810600

MAP3K5

14.69

1.63E-12

6.47E-10

mitogen-activated protein kinase 5

ILMN_2360719

IRAK3

14.65

1.71E-12

6.65E-10

interleukin-1 receptor-associated kinase 3

ILMN_1510121

MTSS1

14.64

1.73E-12

6.66E-10

metastasis suppressor 1

ILMN_540671

LILRB2

14.54

1.98E-12

7.41E-10

leukocyte immunoglobulin-like receptor subfamily B, member 2

ILMN_6980377

MTMR15

14.44

2.26E-12

8.39E-10

myotubularin related protein 15

ILMN_6220288

PRDM1

14.43

2.28E-12

8.39E-10

PR domain containing 1, with ZNF domain

ILMN_7330739

NDRG4

14.42

2.30E-12

8.39E-10

NDRG family member 4

ILMN_2600470

WDR60

14.20

3.10E-12

1.12E-09

WD repeat domain 60

ILMN_4050441

SH3MD4

14.16

3.27E-12

1.17E-09

SH3 multiple domains 4

ILMN_6760546

TIPARP

13.89

4.74E-12

1.64E-09

TCDD-inducible poly(ADP-ribose) polymerase

ILMN_2760537

MTE

13.89

4.75E-12

1.64E-09

metallothionein E

ILMN_160019

SORT1

13.79

5.44E-12

1.83E-09

sortilin 1

ILMN_6330132

ISG20

13.60

7.00E-12

2.32E-09

interferon stimulated exonuclease gene 20 kDa

ILMN_1510685

DOK4

13.52

7.86E-12

2.58E-09

docking protein 4

ILMN_1240228

PAG1

13.47

8.50E-12

2.77E-09

phosphoprotein associated glycosphingolipid microdomains 1

ILMN_580592

CPNE8

13.32

1.04E-11

3.31E-09

copine VIII

ILMN_5870301

KIAA0513

13.32

1.05E-11

3.31E-09

KIAA0513

ILMN_20129

CD52

13.32

1.05E-11

3.31E-09

CD52 molecule

ILMN_1820386

PARVB

13.31

1.06E-11

3.31E-09

parvin, beta

ILMN_6200402

MT1A

13.24

1.17E-11

3.64E-09

metallothionein 1A

ILMN_290661

CLN8

13.10

1.43E-11

4.36E-09

ceroid-lipofuscinosis, neuronal 8

ILMN_670082

GNA12

13.08

1.47E-11

4.43E-09

guanine nucleotide binding protein (G protein) alpha 12

ILMN_5570286

TACC2

12.99

1.67E-11

5.00E-09

transforming, acidic coiled-coil containing protein 2

ILMN_3190411

STARD13

12.93

1.81E-11

5.32E-09

START domain containing 13

ILMN_4540138

NGB

12.92

1.85E-11

5.39E-09

neuroglobin

ILMN_2000646

B4GALT4

12.83

2.10E-11

6.07E-09

UDP-galactosyltransferase, polypeptide 4

ILMN_7100731

CYGB

12.81

2.17E-11

6.17E-09

cytoglobin

ILMN_7050113

NTRK1

12.71

2.52E-11

7.09E-09

neurotrophic tyrosine kinase receptor, type 1

ILMN_2490670

GNPTAB

12.66

2.71E-11

7.52E-09

N-acetylglucosamine-1-phosphate transferase, alpha and beta

ILMN_20170

ZNF385

12.48

3.55E-11

9.72E-09

zinc finger protein 385

ILMN_2630687

CHPT1

12.43

3.80E-11

1.02E-08

choline phosphotransferase 1

ILMN_4120215

WASF2

12.43

3.81E-11

1.02E-08

WAS protein family, member 2

ILMN_5260494

TMLHE

12.39

4.06E-11

1.08E-08

trimethyllysine hydroxylase, epsilon

ILMN_5220333

C14orf139

12.31

4.54E-11

1.20E-08

chromosome 14 open reading frame 139

ILMN_3850440

FCER1G

12.12

6.07E-11

1.60E-08

Fc fragment of IgE, receptor for; gamma polypeptide

ILMN_1030008

TGFB3

12.11

6.21E-11

1.63E-08

transforming growth factor, beta 3

ILMN_1450468

MYT1

12.02

7.04E-11

1.81E-08

myelin transcription factor 1

ILMN_7560541

SLC2A5

12.01

7.19E-11

1.83E-08

solute carrier family 2 member 5

ILMN_2030438

GBA2

12.01

7.21E-11

1.83E-08

glucosidase, beta (bile acid) 2

ILMN_6840328

SMAD3

12.00

7.35E-11

1.86E-08

SMAD family member 3

ILMN_3930390

SMAP1L

11.91

8.40E-11

2.11E-08

stromal membrane-associated protein 1-like

ILMN_7570196

TSPAN9

11.90

8.54E-11

2.12E-08

tetraspanin 9

ILMN_6980546

CACNA1I

11.90

8.56E-11

2.12E-08

calcium channel, voltage-dependent, T type, alpha 1I subunit

ILMN_1710364

LCN6

11.89

8.72E-11

2.15E-08

lipocalin 6

ILMN_5360424

RPS6KA2

11.77

1.04E-10

2.54E-08

ribosomal protein S6 kinase, 90 kDa, polypeptide 2

ILMN_5890193

MS4A4A

11.72

1.14E-10

2.75E-08

membrane-spanning 4-domains, subfamily A, member 4

ILMN_3390292

KLF9

11.66

1.24E-10

2.98E-08

Kruppel-like factor 9

ILMN_5720059

GFOD1

11.65

1.26E-10

3.02E-08

glucose-fructose oxidoreductase domain containing 1

ILMN_7650523

TMEM46

11.57

1.43E-10

3.39E-08

transmembrane protein 46

ILMN_5700392

LOC654000

11.46

1.70E-10

3.95E-08

ribosome biogenesis protein BMS1 homolog 2

ILMN_4810348

C1orf188

11.40

1.88E-10

4.33E-08

chromosome 1 open reading frame 188

ILMN_4280180

CHRNA3

11.39

1.91E-10

4.37E-08

cholinergic receptor, nicotinic, alpha 3

ILMN_270458

CRISPLD1

11.37

1.96E-10

4.45E-08

cysteine-rich secretory protein LCCL domain containing 1

ILMN_450615

MT2A

11.37

1.97E-10

4.46E-08

metallothionein 2A

ILMN_20470

GRASP

11.35

2.02E-10

4.51E-08

GRP1-associated scaffold protein

ILMN_3370594

LILRA2

11.35

2.03E-10

4.51E-08

leukocyte immunoglobulin-like receptor subfamily A, member 2

ILMN_5220397

RREB1

11.34

2.05E-10

4.53E-08

ras responsive element binding protein 1

ILMN_1410192

TDRD9

11.34

2.07E-10

4.56E-08

tudor domain containing 9

ILMN_4070259

LOC653133

11.27

2.30E-10

4.99E-08

guanine nucleotide binding protein (G protein) alpha 12

ILMN_5960682

RBPMS2

11.24

2.41E-10

5.21E-08

RNA binding protein with multiple splicing 2

ILMN_1440300

SLC27A3

11.22

2.50E-10

5.37E-08

solute carrier family 27, member 3

ILMN_5050768

LONRF1

11.20

2.58E-10

5.53E-08

LON peptidase N-terminal domain and ring finger 1

ILMN_6270273

KHDRBS3

11.18

2.67E-10

5.68E-08

KH domain, RNA binding, signal transduction associated 3

ILMN_7100603

KCNK3

11.17

2.70E-10

5.72E-08

potassium channel, subfamily K, member 3

ILMN_2320129

CSDA

11.03

3.38E-10

7.08E-08

cold shock domain protein A

ILMN_3930022

LOC644739

10.99

3.63E-10

7.54E-08

Wiskott-Aldrich syndrome protein family member 4

ILMN_7400133

CUGBP2

10.90

4.20E-10

8.63E-08

CUG triplet repeat, RNA binding protein 2

ILMN_3290301

FZD8

10.88

4.33E-10

8.76E-08

frizzled homolog 8

ILMN_7320520

MTUS1

10.88

4.33E-10

8.76E-08

mitochondrial tumor suppressor 1

ILMN_3780053

PALLD

10.82

4.79E-10

9.60E-08

palladin, cytoskeletal associated protein

ILMN_6860162

LOC441019

10.74

5.49E-10

1.09E-07

hypothetical LOC441019

ILMN_5810154

ALOX15B

10.74

5.50E-10

1.09E-07

arachidonate 15-lipoxygenase, type B

ILMN_3930736

CHST3

10.73

5.59E-10

1.09E-07

carbohydrate (chondroitin 6) sulfotransferase 3

ILMN_60470

STX11

10.72

5.68E-10

1.10E-07

syntaxin 11

ILMN_3390484

SERINC2

10.69

5.95E-10

1.15E-07

serine incorporator 2

ILMN_1430647

TAX1BP3

10.61

6.82E-10

1.31E-07

Tax1 (human T-cell leukemia virus type I) binding protein 3

ILMN_5960440

VDR

10.60

6.99E-10

1.34E-07

vitamin D (1,25-dihydroxyvitamin D3) receptor

ILMN_6290735

EPHB3

10.51

8.10E-10

1.53E-07

EPH receptor B3

ILMN_2680372

SH2D4A

10.46

8.78E-10

1.64E-07

SH2 domain containing 4A

ILMN_2480050

SOX7

10.44

9.13E-10

1.69E-07

SRY (sex determining region Y)-box 7

ILMN_130128

LOC285016

10.41

9.61E-10

1.76E-07

hypothetical protein LOC285016

ILMN_4890451

GRAMD3

10.39

9.87E-10

1.80E-07

GRAM domain containing 3

ILMN_770161

C10orf73

10.39

9.92E-10

1.81E-07

chromosome 10 open reading frame 73

ILMN_2450202

KIF3C

10.35

1.05E-09

1.88E-07

kinesin family member 3C

ILMN_6840468

HAL

10.35

1.06E-09

1.89E-07

histidine ammonia-lyase

ILMN_2470070

TBL1X

10.30

1.15E-09

2.04E-07

transducin (beta)-like 1X-linked

ILMN_2320114

KLF13

10.27

1.22E-09

2.15E-07

Kruppel-like factor 13

ILMN_6380112

DIP

10.23

1.31E-09

2.27E-07

death-inducing-protein

ILMN_2470358

IFNGR1

10.22

1.32E-09

2.30E-07

interferon gamma receptor 1

ILMN_4250735

IL27RA

10.07

1.70E-09

2.91E-07

interleukin 27 receptor, alpha

ILMN_1470215

MAP3K8

10.07

1.72E-09

2.91E-07

mitogen-activated protein kinase 8

ILMN_2940373

TACC1

10.06

1.74E-09

2.94E-07

transforming, acidic coiled-coil containing protein 1

Downregulated

     

ILMN_770538

LYSMD2

-15.49

5.81E-13

2.58E-10

LysM, putative peptidoglycan-binding, domain containing 2

ILMN_7150059

STAMBPL1

-14.61

1.79E-12

6.84E-10

STAM binding protein-like 1

ILMN_5340692

STRBP

-14.56

1.93E-12

7.31E-10

spermatid perinuclear RNA binding protein

ILMN_4210397

GLDC

-14.05

3.80E-12

1.34E-09

glycine dehydrogenase

ILMN_6980327

DKC1

-13.79

5.44E-12

1.83E-09

dyskeratosis congenita 1, dyskerin

ILMN_50086

TCF12

-13.23

1.19E-11

3.69E-09

transcription factor 12

ILMN_4860356

BYSL

-12.81

2.17E-11

6.17E-09

bystin-like

ILMN_4280228

IVNS1ABP

-12.70

2.55E-11

7.12E-09

influenza virus NS1A binding protein

ILMN_1990379

SLFN11

-11.82

9.63E-11

2.36E-08

schlafen family member 11

ILMN_5220338

MPEG1

-11.64

1.27E-10

3.03E-08

macrophage expressed gene 1

ILMN_450168

SFRS7

-11.50

1.60E-10

3.74E-08

splicing factor, arginine/serine-rich 7, 35 kDa

ILMN_3460687

KIAA0690

-11.42

1.81E-10

4.19E-08

ribosomal RNA processing 12 homolog

ILMN_3400360

MAPRE2

-11.36

1.99E-10

4.48E-08

microtubule-associated protein, RP/EB family, member 2

ILMN_4010414

PPFIBP1

-11.12

2.92E-10

6.16E-08

PTPRF interacting protein, binding protein 1 (liprin beta 1)

ILMN_1190139

UGT3A2

-10.99

3.61E-10

7.54E-08

UDP glycosyltransferase 3 family, polypeptide A2

ILMN_4150201

BCL2

-10.93

3.99E-10

8.24E-08

B-cell CLL/lymphoma 2

ILMN_780240

C12orf24

-10.85

4.53E-10

9.13E-08

chromosome 12 open reading frame 24

ILMN_6760167

MARCH3

-10.73

5.60E-10

1.09E-07

membrane-associated ring finger (C3HC4) 3

ILMN_3940615

PUS7

-10.52

7.99E-10

1.52E-07

pseudouridylate synthase 7 homolog

ILMN_20544

GART

-10.41

9.53E-10

1.76E-07

phosphoribosylglycinamide formyltransferase

ILMN_2480326

HSP90B1

-10.36

1.05E-09

1.88E-07

heat shock protein 90 kDa beta (Grp94), member 1

ILMN_5270367

CTSC

-10.25

1.26E-09

2.20E-07

cathepsin C

ILMN_5420095

MYC

-10.21

1.36E-09

2.34E-07

v-myc myelocytomatosis viral oncogene homolog

ILMN_4610180

PIK3C2B

-10.20

1.38E-09

2.37E-07

phosphoinositide-3-kinase, class 2, beta polypeptide

ILMN_6450300

GEMIN4

-10.00

1.95E-09

3.27E-07

gem (nuclear organelle) associated protein 4

t, t-statistic; and FDR, false discovery rate

The most significantly differentially expressed gene at the 8 hour dexamethasone-treated timepoint was ZBTB16, a known transcriptional repressor and glucocorticoid response gene, which has been shown to modulate glucocorticoid sensitivity in CEM T-ALL cells [31]. Other known glucocorticoid response genes upregulated included TSC22D3 [32] and SOCS1 [15], both downstream targets of the glucocorticoid receptor, FKBP5 [33], a co-chaperone of the glucocorticoid receptor, and MAP kinases 5, 6 and 8 [34]. Downregulated genes at 8 hours included BCL-2 [35] and C-MYC [36], both previously described, but also HSP90B1, a glucocorticoid receptor co-chaperone and regulator of apoptosis. The only pro-apoptotic gene consistently upregulated across multiple microarray analyses is the BH3-only BCL-2 family member BIM, and it has been shown that BIM has a critical role in glucocorticoid sensitivity and resistance [37], although in this current study BIM was only induced 1.3 fold. Thus these genes identified are consistent with previous reports of glucocorticoid-induced genes in ALL. Within these experimental systems however there are significant potential differences in glucocorticoid exposure between in vitro and in vivo models - a crucial one is that cells in vitro are continuously exposed to glucocorticoid whereas in in vivo models the glucocorticoid is subject to pharmacokinetic and pharmacodynamic changes which more accurately reflect changes in patients.

At the later timepoints, significant differential gene expression was much less marked and predominantly downregulated. At 24 hours 5 genes were upregulated (t-statistic > 6) and 10 genes downregulated (t-statistic < -6, table 4), and at 48 hours 1 gene was upregulated (t-statistic > 6) and 15 genes downregulated (t-statistic < -6, table 5). At 24 hours, upregulated genes included NFKBIA, an inhibitor of NF-ΚB, and TRIM74, which was sustained at 48 hours, the significance of which is uncertain. Downregulated genes were those involved in cell cycle progression, including CCNF at 24 hours, and CCNF, CDC20 and AURKA at 48 hours, consistent with growth arrest.
Table 4

Genes regulated 24 hours following dexamethasone treatment.

ProbeSet ID

Gene

t

P

FDR

Definition

Upregulated

     

ILMN_3930687

FAM112A

6.67

1.32E-06

0.0091

family with sequence similarity 112, member A

ILMN_6620255

TRIM74

6.29

3.06E-06

0.0132

tripartite motif-containing 74

ILMN_4280113

NFKBIA

6.23

3.48E-06

0.0138

nuclear factor kappa B inhibitor, alpha

ILMN_2140136

EMR2

6.10

4.65E-06

0.0149

egf-like containing, mucin-like, hormone receptor-like 2

ILMN_7000397

ANKRD15

6.08

4.91E-06

0.0149

ankyrin repeat domain 15

Downregulated

     

ILMN_870524

HOXB8

-8.60

2.53E-08

0.0011

homeo box B8

ILMN_4830520

LOC144501

-6.72

1.19E-06

0.0091

hypothetical protein LOC144501

ILMN_6110332

ARHGAP19

-6.70

1.24E-06

0.0091

Rho GTPase activating protein 19

ILMN_2970619

ESPL1

-6.65

1.38E-06

0.0091

extra spindle pole bodies homolog 1

ILMN_3130541

CCNF

-6.64

1.43E-06

0.0091

cyclin F

ILMN_4760577

CENPA

-6.62

1.46E-06

0.0091

centromere protein A

ILMN_4810646

PIF1

-6.54

1.76E-06

0.0095

PIF1 5'-to-3' DNA helicase homolog

ILMN_1070762

PSRC1

-6.40

2.38E-06

0.0114

proline/serine-rich coiled-coil 1

ILMN_4860703

LOC648695

-6.19

3.82E-06

0.0138

retinoblastoma binding protein 4

ILMN_1110538

INCENP

-6.05

5.19E-06

0.0149

inner centromere protein antigens 135/155 kDa

t, t-statistic; and FDR, false discovery rate

Table 5

Genes regulated 48 hours following dexamethasone treatment.

ProbeSet ID

Gene

t

P

FDR

Definition

Upregulated

     

ILMN_6620255

TRIM74

6.30

3.01E-06

0.0089

tripartite motif-containing 74

Downregulated

     

ILMN_4810646

PIF1

-8.85

1.58E-08

0.0004

PIF1 5'-to-3' DNA helicase homolog

ILMN_870524

HOXB8

-8.66

2.26E-08

0.0004

homeo box B8

ILMN_1450193

LGALS1

-8.57

2.66E-08

0.0004

lectin, galactoside-binding, soluble, 1 (galectin 1)

ILMN_4760577

CENPA

-7.64

1.71E-07

0.0018

centromere protein A

ILMN_4730605

AURKA

-7.47

2.42E-07

0.0021

aurora kinase A

ILMN_1500010

CDC20

-6.84

9.09E-07

0.0053

CDC20 cell division cycle 20 homolog

ILMN_4060064

HMMR

-6.82

9.61E-07

0.0053

hyaluronan-mediated motility receptor

ILMN_2070408

MID1

-6.80

9.97E-07

0.0053

midline 1 (Opitz/BBB syndrome)

ILMN_2070288

MT1E

-6.66

1.36E-06

0.0065

metallothionein 1E

ILMN_1070762

PSRC1

-6.60

1.55E-06

0.0067

proline/serine-rich coiled-coil 1

ILMN_150543

C20orf129

-6.46

2.12E-06

0.0077

chromosome 20 open reading frame 129

ILMN_5870193

FAM64A

-6.45

2.14E-06

0.0077

family with sequence similarity 64, member A

ILMN_2810201

KIF14

-6.34

2.77E-06

0.0089

kinesin family member 14

ILMN_1050195

KIF20A

-6.28

3.11E-06

0.0089

kinesin family member 20A

ILMN_3130541

CCNF

-6.05

5.21E-06

0.0131

cyclin F

t, t-statistic; and FDR, false discovery rate

Functional analysis using GSEA and meta-GSEA on the expression profiles obtained at 8 hours and 24 hours after dexamethasone treatment (additional files 1 and 2), revealed a significant upregulation of metabolic pathways, particularly adipogenesis at 8 hours, and a marked effect on pathways associated with cell cycling and proliferation, particularly downregulation of C-MYC at 8 hours and NF-ΚB at 24 hours, and upregulation of apoptotic pathways at 24 hours. Glucocorticoids are known to have effects on multiple cellular metabolic pathways, including glucose and carbohydrate metabolism, and have pro-adipogenic effects [38]. Suppression of C-MYC is a critical step prior to the initiation of apoptosis by dexamethasone in ALL [39] and suppression of NF-ΚB has been described [40].

To determine whether the molecular response to glucocorticoids in this xenograft model of ALL mimicked the effects seen in either glucocorticoid-treated patients with ALL [16] or cell-line models of ALL [13], we applied parametric gene set enrichment analysis (PGSEA) [28]. Comparing gene expression profiles from multiple experiments is notoriously difficult and typically any true similarities are swamped by technical differences in microarray vendor, normalization strategies and analytical approach. By summarizing genes at the gene set level (such as genes in the same pathway), these technical differences are mitigated, allowing comparison of samples from multiple studies.

We performed PGSEA on the 6-8 hour samples from the 3 studies, and then two-dimensional hierarchical clustering to identify the relationships between the different ALL models (Figure 2 and annotated in additional file 3). This revealed considerable heterogeneity in the molecular response to glucocorticoids in patients into at least 2, and possibly 4 different groups (green bars, Figure 2), which may represent different modes of response to glucocorticoids in patients. Relative to this inter-patient heterogeneity, both cell lines (purple bars, Figure 2) and xenografts (black bars, Figure 2) are remarkably reproducible; we anticipate that adding additional xenograft models of ALL from distinct patients will mirror the heterogeneity of the patient from whom they were derived. It is also evident that overall, glucocorticoid-treated xenografts co-cluster with a group of 3 patients (B-ALL-37, -38, and -40), all of whom had BCP-ALL and a good early prednisolone response, with varied cytogenetics (hyperploidy, t(12;21), and normal respectively). At more relaxed clustering thresholds, the glucocorticoid-treated xenografts cluster with 4 other patients with BCP-ALL (B-ALL-24, -31, -33, and 43) and the cell lines.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-565/MediaObjects/12864_2011_Article_3723_Fig2_HTML.jpg
Figure 2

Parametric GSEA of combined top 100 glucocorticoid-induced gene sets with greatest variance from xenograft, patient and cell line models. Hierarchical clustering with gene sets in rows, samples in columns (xenografts - black, patient - green, cell line - purple). Each colour of each cell represents the Z-score (see legend). Boxes 1-5 represent defined clusters.

We identified 5 clusters of gene sets with distinct expression profiles, each behaving differently in the 3 models of ALL. Cluster 1 demonstrated the markedly heterogeneous patterns seen in patient samples, with the xenograft samples showing a pattern similar to 8 of the patients; cluster 2 showed genesets that showed strong enrichment in the cell line study, and included a number of genesets associated with cell proliferation; cluster 3 did not show any striking differences across the three ALL models; cluster 4 showed genesets downregulated in both xenografts and cell lines compared to the patient samples, and included a number genesets associated with cell cycle progression, DNA/RNA replication and MYC; cluster 5 showed genesets strongly downregulated in the xenograft and cell line models, and included genesets associated with MYC and metabolic processes, particularly catabolism and energy production. In this limited comparison, it is clear that glucocorticoid-induced gene expression patterns seen in ALL are dependent on the experimental model, and that the patterns derived from the xenograft model show a greater similarity to patient-derived data than to cell lines.

A search of the TRANSFAC database v8.3 [41] of CoMoDis [42] identified GRE motifs (within 100 kb either side of the gene) in only 25 (14.5%) of the top 173 upregulated genes at the 8 hour timepoint in this study, and no GRE motifs were identified in the upregulated genes at 24 or 48 hours. This supports accumulating evidence that glucocorticoids exert long-range effects through very distal steroid receptor binding sites [43]. Analysis of significantly differentially expressed glucocorticoid-induced genes in an in vitro cell line study [13] revealed a similar number of early response genes after 6 hours of exposure (60 upregulated (t-stat > 10) and 27 downregulated (t-stat < -10)) but a significantly greater number of genes after 24 hours (593 upregulated (t-stat > 10) and 782 downregulated (t-stat < -10)). Interestingly, all but 2 of the genes upregulated at 6 hours remained significantly upregulated at 24 hours, and 17 of the downregulated genes at 6 hours remained downregulated at 24 hours. GRE motifs were identified in 15 (25.0%) of the top 60 upregulated genes at 6 hours, and 87 (14.6%) of the top 593 genes at 24 hours. The observed difference between the studies in gene expression at later timepoints is consistent with continuous rather than physiological glucocorticoid exposure. In addition, in the cell line study, the GR (NR3C1) undergoes highly significant early and sustained autoupregulation, which in the continuous presence of ligand drives downstream gene expression. In contrast, in the xenograft model minimal GR upregulation is seen at the early timepoint but no significant change in GR expression is seen at either of the later timepoints.

Given the good statistical power observed in Figure 1A, we proceeded to determine whether we could use fewer replicates and still identify a majority of the differentially expressed genes. Replicate analysis (Figure 3) revealed that at the 8 hour dexamethasone-treated timepoint, a dataset with high signal and differential expression, using data from any 3 randomly chosen biological replicates instead of 4 resulted in excellent recovery scores of > 0.9. That is, on average, 90% of the differentially expressed genes identified from all 4 samples were also identified in any combination of 3 arrays. At 24 hours, a timepoint with less signal, the average recovery score was 0.85 with 3 replicates, but was more variable than at 8 hours. Using just 2 biological replicates recovered 88% of the list of differentially expressed genes at 8 hours, which dropped to 14% at 24 hours. This confirms that the 8 hour time point has the strongest signal, which is reproducible across different subsets of biological replicates. We recommend using a minimum of 3 biological replicates, since fewer replicates destabilized our ability to identify differentially expressed genes. This has important considerations for experimental design, and has significant implications on cost and animal numbers.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2164-12-565/MediaObjects/12864_2011_Article_3723_Fig3_HTML.jpg
Figure 3

Recovery scores at 8 hours and 24 hours when randomly selecting all combinations of 3 replicates (3rep) or 2 replicates (2rep) from the set of 4 biological replicates. The Recovery Score represents the proportion of differentially expressed genes from all 4 replicates recovered when using fewer replicates.

Conclusions

We conclude that the NOD/SCID ALL xenograft mouse model provides biologically relevant insights into glucocorticoid-induced gene expression, in a consistent, reproducible and clinically relevant model system. We have demonstrated that the 8 hour timepoint provides the highest number of significantly differentially expressed genes, that time-matched controls are redundant and excellent recovery scores can be obtained with 3 replicates. We have thus established the optimal experimental design, with subsequent important implications for costs and animal numbers.

Declarations

Acknowledgements of Research Support

This research was supported by Children's Cancer Institute Australia for Medical Research (CCIA) and by a grant from the National Health and Medical Research Council (NHMRC). VAB was supported by fellowships from the Leukaemia Foundation and the Steven Walter Foundation. TNT was supported by fellowships from the Cancer Institute NSW and the NHMRC. RBL was supported by a fellowship from the NHMRC. MJC and WK were supported by the Cancer Institute NSW. CCIA is affiliated to Sydney Children's Hospital and the University of New South Wales.

Authors’ Affiliations

(1)
Children's Cancer Institute Australia for Medical Research, Lowy Cancer Research Centre, University of New South Wales
(2)
Peter Wills Bioinformatics Centre, Garvan Institute of Medical Research
(3)
Centre for Children's Cancer and Blood Disorders, Sydney Children's Hospital

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