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research paper

The gene expression signature of relapse in paediatric acute lymphoblastic leukaemia: implications for mechanisms of therapy failure

Alex H. Beesley,1 Aaron J. Cummings,1 Joseph R. Freitas,1 Katrin Hoffmann,1 Martin J. Firth,2 Jette Ford,1 Nicolas H. de Klerk2 and Ursula R. Kees1 1

Division of Children’s Leukaemia and Cancer

Research, and 2Division of Biostatistics and Genetic Epidemiology, Telethon Institute for Child Health Research, and Centre for Child Health Research, University of Western Australia, Perth, WA, Australia

Received 7 June 2005; accepted for publication 30 August 2005 Correspondence: Professor Ursula Kees, Division of Children’s Leukaemia and Cancer Research, Telethon Institute for Child Health

Summary Despite significant improvements in the treatment of childhood acute lymphoblastic leukaemia (ALL), the prognosis for relapsing patients remains poor. The aim of this study was to generate a transcriptional profile of relapsed ALL to increase our understanding of the mechanisms involved in therapy failure. RNA was extracted from 11 pairs of cryopreserved pre-B ALL bone marrow specimens taken from the same patients at diagnosis and relapse, and analysed using HG-U133A microarrays. Relapse specimens overexpressed genes that are involved with cell growth and proliferation, in keeping with their aggressive phenotype. When tested in 72 independent specimens of pre-B ALL and T-ALL, the identified genes could successfully differentiate between diagnosis and relapse in either lineage, indicating the existence of relapse mechanisms common to both. These genes have functions relevant for oncogenesis, drug resistance and metastasis, but are not related to classical multidrug-resistance pathways. Increased expression of the top-ranked gene (BSG) at diagnosis was significantly associated with adverse outcome. Several chromosomal loci, including 19p13, were identified as potential hotspots for aberrant gene expression in relapsed ALL. Our results provide evidence for a link between drug resistance and the microenvironment that has previously only been considered in the context of solid tumour biology.

Research, University of Western Australia, P. O. Box 855, West Perth WA 6872, Australia. E-mail: [email protected]

Keywords: acute leukaemia, gene expression, paediatric haematology, drug resistance, metastasis.

Identification of the biological mechanisms that lead to relapse in children with acute lymphoblastic leukaemia (ALL) is critical for the development of alternative strategies to treat patients refractory to therapy. Molecular abnormalities previously identified in relapsed childhood ALL include p53 mutations, increased cyclin D1 and BAX expression, DHFR gene amplification and deletions at the INK4A/ARF and TEL chromosomal loci (Goker et al, 1995; Maloney et al, 1999; Zhu et al, 1999; Carter et al, 2001; Zuna et al, 2004). Karyotypic changes, such as the development of 6q-, 7p- and 9pstructural abnormalities, have also been documented (Shikano et al, 1990), but aside from these observations little is known about the mechanisms of relapse. In the last few years, microarray technology has been successfully used by a number

of groups to examine the gene expression profile of different diagnostic subtypes in leukaemia but so far only one small study, based on seven individuals, has used this powerful approach to compare diagnosis and relapse gene expression profiles in pre-B ALL (Staal et al, 2003). These researchers found that only a small number of genes, mostly involved in cell proliferation and survival, were altered at relapse, but did not find any increase in drug resistance markers. Several other groups have reported the inclusion of relapsing patients among their cohort but without specific comparison of the expression profile at diagnosis and relapse. The purpose of the present study, therefore, was to use high-density oligonucleotide microarrays to further examine the biology of relapse in childhood ALL.

ª 2005 Blackwell Publishing Ltd, British Journal of Haematology, 131, 447–456

doi:10.1111/j.1365-2141.2005.05785.x

A. H. Beesley et al

Methods Patient specimens Cryopreserved, Ficoll–Hypaque-purified bone marrow (BM) or peripheral blood specimens from children with ALL (72 pre-B ALL and 22 T-ALL specimens, age range 1Æ6–16Æ3 years at diagnosis) were obtained from the Princess Margaret Hospital for Children, Perth, WA, Australia. These children were diagnosed between 1984 and 2000 and treated according to Children’s Cancer Group protocols. Specimens were from either the time of initial diagnosis, the time of relapse or both. Thus 11 of the pre-B ALL patients and four of the T-ALL patients had matched diagnosis and relapse specimens. Normal BM specimens (eight in total) were obtained from healthy donors. Ethical approval for this study was obtained from the Institutional Review Board, and informed consent for the use of tissues for research purposes was obtained for all individuals. Total RNA was extracted from specimens using a combination of TRIZOL Reagent (Invitrogen, Carlsbad, CA, USA), RNeasy Mini kit (QIAGEN, Valencia, CA, USA) and ethanol precipitation as previously described (Hoffmann et al, 2005). Because of the fragility and reduced yields of specimens stored for such extended periods (up to 20 years), thawed cells were used directly for RNA extraction without additional leukaemic blast enrichment [mean blast count (±standard deviation) for diagnosis specimens 93 ± 8% (median 96%); relapse specimens 78 ± 23% (median 90%)]. RNA integrity for all samples was assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA).

Microarrays Biotinylated cRNA was prepared from 2 lg of total RNA as previously described (Hoffmann et al, 2005) and hybridised to Affymetrix HG-U133A oligonucleotide microarrays (Affymetrix, Santa Clara, CA, USA) according to the manufacturer’s protocols. Array images were reduced to intensity values for each probe (CEL files) using Affymetrix MAS 5.0 and subsequently analysed using the statistical software package R1.7.1 (Ihaka & Gentleman, 1996), which is available from http://www.r-project.org. Background correction, quantile normalisation and probe summarisation were performed by robust multiarray analysis (RMA)(Irizarry et al, 2003). All microarrays used in this study passed the quality criteria recommended by Affymetrix for noise, background, absent/ present calls and 3¢/5¢ signal ratios for ACTB and GAPDH.

Gene expression profiling For the identification of genes differentially expressed between diagnosis and relapse specimens (Fig 1), a variance filter was initially applied to eliminate all probe sets with an absolute fold change of 0Æ1 (assessed by permutation test). The remaining probes were ranked for 448

their ability to discriminate between the groups of interest using a supervised decision-tree based algorithm called Random Forest (RF)(Zhang et al, 2001). In contrast to many other supervised analytical methods, the RF has an intrinsic reiterative process that makes it less sensitive to the problem of overfitting of data. For each tree within the RF, a subset of the specimens and probes was randomly chosen (bootstrap sampling with replacement) and analysed, then tested back on the remaining data (Fig 1). This process was repeated 100 000 times for each RF, and the top-ranked probe sets were selected for functional analysis. Prediction algorithms for the classification of independent specimens were obtained by running new RF analyses on the paired pre-B ALL specimens using selected probe sets. Independent specimens were processed using this algorithm and classified accordingly. For class prediction in the T-ALL specimens using the pre-B ALL algorithm, array data were first normalised to account for differences in absolute expression levels between the data sets. Unsupervised analysis of microarray data was performed using complete-linkage hierarchical clustering or principal component analysis. Exact binomial testing was performed to assess P-values for overlaps between gene lists from different analyses.

Functional analysis by gene ontology The top 200 probe sets associated with relapse in the 11 paired pre-B ALL specimens were screened for biological process and cellular component gene ontology (GO) annotations using the NetAffx GO Mining Tool (http://www.affymetrix.com). Exact binomial tests were used to determine either significant enrichment of GO categories in the final probe set list versus the representation on the HG-U133A microarray, or significant bias in the direction of gene regulation in relapse specimens within a particular category (i.e. a proportional skew towards up- or downregulation).

Chromosomal loci associated with relapse The frequency with which particular chromosomal loci were represented by the top-ranked probe sets from each analysis were counted and compared with the representation of the same loci on the microarray as a whole. Significant overrepresentation was assessed by exact binomial test.

In silico analysis of published data sets For the analysis of data published by Yeoh et al (2002), CEL files were downloaded from http://www.stjuderesearch.org/ data/ALL1/index.html, imported into R1.7.1 and processed with RMA as described above. This data set comprised 327 diagnostic samples and 25 relapse samples processed using the HG-U95Av2 microarray as described (Yeoh et al, 2002). Three of the probe sets shown by Yeoh et al (2002) to be T-ALL specific (38319_at, 38242_at and 37988_at) were used in hierarchical clustering of these 352 specimens. This approach


Gene Expression Signature of Relapsed ALL

Patient microarray data (22 283 probe sets) RMA (Background correction, normalisation, probe summation)

Non-stringent variance filter (D vs. R)

Random Forest (RF)

Random selection of training and test specimens Test Specimens (~30%)

Training Specimens (~70%)

x 100 000 Random selection of probe sets Decision tree for optimal discrimination (D vs. R)

Assessment of tree in test specimens

Probe set scoring and ranking

Selection and analysis of top-ranked probe sets

RF prediction algorithm and error rate

Evaluation in independent patient specimens Fig 1. Supervised analysis of paired diagnosis (D) and relapse (R) pre-B acute lymphoblastic leukaemia (ALL) microarray data. Flowchart of analysis and details of the Random Forest (RF).

correctly labelled all 43 diagnostic T-ALL specimens, and further identified four of the relapse specimens as being T-ALL. These 47 specimens were removed from the analysis, as were a further 86 specimens labelled as ‘censored’, ‘NA’ or ‘second AML (acute myeloid leukaemia)’. This left a total of 198 diagnosis and 21 relapse B-lineage specimens for final analysis.

Quantitative reverse transcription polymerase chain reaction Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed on total RNA from BM specimens, using primer and probe sets designed by Applied Biosystems (Foster City, CA, USA) in accordance with their standard protocols (ABI Assays on Demand or Assays by Design). All experiments were run in duplicate on an ABI 7700 sequence detector and data normalised to beta-actin (ACTB) expression levels. Significance differences between diagnosis and relapse expression levels were determined by t-test. P < 0Æ05 was considered significant.

Results Gene expression profile of relapse in pre-B ALL Initial analysis focused on the 11 pairs of pre-B ALL specimens that were taken from the same patients at the times of diagnosis and subsequent relapse. After application of an initial variance filter (Fig 1), the 1865 remaining probe sets were analysed by RF to produce a ranking of importance for their ability to discriminate between diagnosis and relapse specimens. Functional analysis of the genes contained within the top-ranked 200 probe sets was performed by an examination of GO annotations (full list of probe sets available as supplementary data at http://www.ichr.uwa.edu.au/research/ publications/data/BeesleyRelapseSuppl.pdf). Among these genes, the expression of those involved with cell growth, biosynthesis or proliferation was increased in relapse tissues (Fig 2A, D), in keeping with a previous report that such genes are upregulated at relapse (Staal et al, 2003). Categories related to protein handling, including protein transport, the ubiquitin ligase complex and the golgi and endoplasmic reticulum


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A. H. Beesley et al Response to stress

A

∆

Protein transport * Cell cycle ∆ Biosynthesis∆

Response to ext. stim. Protein modification Transcription Signal transduction Growth/maintenance ∆ –30

–20

–10

0

10

20

30

40

50 Mitochondrial Golgi ∆ RNA Pol II complex * Ubiquitin ligase * Cytoskeleton Plasma membrane * ER * ∆

U133A as a whole. A total of seven loci, including 19p13, were significantly over-represented (P < 0Æ05) among this discriminator set (Fig 2B). Accurate classification of the paired pre-B ALL specimens (Figs 3 and 4A) could be achieved using just the 20 highest ranked genes from the RF (RF classification accuracy 95Æ5%). Similar results were achieved using principle component analysis (data not shown). These genes, which represent 21 probe sets, are listed in Table I along with the fold change between diagnosis and relapse, and are ranked according to their ability to distinguish these specimens according to the RF. The two top-ranked probe sets in this list (BSG and GRP58) were perfect separators of the paired pre-B specimens, meaning that the expression level of either of these two genes on their own was sufficient to split the specimens into diagnosis and relapse (probability of observing two perfect separators within this data P < 0Æ005).

Nucleus * Cytoplasm * ∆ –30

–20 –10 0 10 20 30 40 Number of genes down (–) or up (+) regulated

Validation in independent patient cohorts

50

Percentage representation

B 7·00 *

6·00 5·00 4·00

2·00 1·00

*

*

3·00

*

*

*

*

0·00 1q22 1q24 3q23 7p15 10q22 12q24 17q21 17q23 19p13 22q13

Chromosomal locus

Fig 2. Characterisation of the top-ranked 200 probe sets associated with relapse in paired pre-B acute lymphoblastic leukaemia (ALL) specimens. (A) Analysis of biological process (upper) or cell component (lower) gene ontology (GO) categories. Data represent the number of genes up- or downregulated in relapse samples compared with diagnosis. *, P < 0Æ05 for category enrichment versus HG-U133A microarray; D, P < 0Æ05 for bias in the direction of gene regulation (i.e. a proportional skew towards up- or downregulation in relapse specimens). (B) Chromosomal loci associated with relapse in ALL. The top 200 probe sets were examined for percentage loci representation (grey bars) and compared with the HG-U133A microarray representation (clear bars); *P < 0Æ05.

compartments, were either significantly over-represented compared with the HG-U133A array (Fig 2A, *) or demonstrated upregulation of expression at relapse (Fig 2A, D). These data indicate that lymphoblasts from patients at the time of relapse have a more active growth pattern than their diagnostic counterparts, which is consistent with their aggressive phenotype. To determine whether a relapse phenotype might be associated with aberrant gene expression from particular chromosomal loci, the representation of loci within the topranked 200 probe sets from the paired pre-B ALL analysis was examined and compared with the representation on the HG450

Importantly, the top 20 genes from the RF were not only capable of accurate diagnosis/relapse discrimination in the original paired pre-B ALL specimens (Figs 3 and 4A), but also in specimens from an independent pre-B ALL cohort (Fig 4B). This cohort comprised 48 diagnosis and two relapse specimens, all from different individuals (i.e. none of the specimens were paired). Although the power of this test-back cohort is limited by the presence of only two relapse patients, the expression profile of these two specimens is sufficiently distinct that they form a separate cluster from the diagnosis specimens (Fig 4B). This finding was confirmed by an independent method, using a prediction algorithm based upon the expression pattern of the 21 probe sets in the original paired pre-B ALL cohort. Using this algorithm on the independent cohort, all specimens were correctly classified and prediction confidence was strong (median voting confidence for diagnosis specimens 90Æ8%, relapse specimens 74Æ5%; overall algorithm accuracy 100%). The gene expression profiles of pre-B and T-lineage leukaemias are known to be distinct (Yeoh et al, 2002). Despite these differences, the 20 genes identified here could once again resolve distinct diagnosis and relapse subgroups when they were tested against an independent cohort of 22 T-ALL specimens (Fig 4C). In this case, however, two of the diagnosis specimens segregated with the relapse specimens. A prediction algorithm based upon the expression of these genes in the original paired pre-B ALL specimens confirmed these two as the only misclassifications (median voting confidence for diagnosis specimens 79Æ8%, relapse specimens 75Æ1%; overall algorithm accuracy 90Æ9%). Upon further investigation, it was found that patient T6 had received a BM transplant 3 months into therapy and patient T17 had been nonresponsive to initial therapy and died during induction. Both of these patients, thus, had clinical features associated with particularly aggressive forms of disease.



Fig 3. Gene expression profiles of paired pre-B acute lymphoblastic leukaemia (ALL) specimens. Two-dimensional hierarchical clustering of the topranked 200 probe sets from the Random Forest (RF) analysis indicating high (red) or low (blue/green) expression.

Comparison with the relapse gene expression profile in T-ALL To identify which of the genes listed in Table I were the most important for a relapse phenotype regardless of disease lineage, an independent RF analysis was performed, this time using the T-ALL specimens. All 22 T-ALL specimens shown in Fig 4C with the exception of T6 and T17 (identified as having highrisk clinical features at diagnosis, see above) were used for this analysis (a total of 15 diagnosis and five relapse specimens). After application of the variance filter, the 4553 probe sets that remained were processed by RF. In contrast to the original preB analysis, 69 probe sets were perfect separators of diagnosis and relapse specimens, with only three predicted by chance alone. The unexpectedly large number of perfect separators in the T-ALL specimens (P < 0Æ0001) suggests that the biology of relapse in this lineage may be more distinct than it is in pre-B ALL. The top-ranked 200 probe sets (RF classification accuracy 100%) were identified and compared with the top 200 from the paired pre-B analysis. In total, there were 28 probe sets shared by both lists, eight of which were in the list shown in Table I (P < 0Æ0001 for degree of overlap, exact binomial test). Of these eight, six were perfect separators of relapse/diagnosis in the T-ALL specimens. Probe sets common to both analyses are highlighted in bold in Table I.

Validation of microarray data by qRT-PCR The differential expression of the top five genes from Table I at relapse and diagnosis was confirmed by qRT-PCR of the 22 paired pre-B ALL specimens (Fig 5). This approach recapitulated the results determined by microarray, with significant increases in expression being observed for all five genes in relapse samples using both techniques. In each case, the mean level of expression in normal BM specimens (obtained from eight healthy donors) was lower than that seen in ALL specimens (Fig 5). The data demonstrate that the increase in expression of these genes at relapse is not a reflection of the lower percentage of leukaemic blasts within these specimens and may in fact be an underestimate of the true effect.

In silico analysis of published data sets To assess whether the identified genes would continue to have relevance for relapse and/or resistance in data sets previously published by others, we examined the data published by Yeoh et al (2002), who used HG-U95Av2 microarrays to perform large-scale gene profiling of childhood ALL for subtype classification and prediction of clinical outcome. The raw data for 198 diagnostic and 21 relapse pre-B ALL specimens from this study were obtained from online depositories and


451

A. H. Beesley et al Table I. The top 20 genes (21 probe sets) associated with relapse in paired pre-B ALL samples. RF rank

Gene symbol

Gene description

HG-U133A probe set

Chromosome locus

D R/D

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

BSG* GRP58*, GTF2F1 RPN1 LAMP1 MFN2 HIPK2 SOX4 FUS PBX2 LRP8 GTF2F1 RoXaN U2AF65 PIP5K1A RAB5C NAKAP95 PI4KII GTBP1 TYMS C14orf139

Basigin (OK blood group), CD147 Glucose regulated protein (58KD) General transcription factor IIF, peptide 1 Ribophorin I Lysosomal-associated membrane protein 1 Mitofusin 2 (mitochondrial fusion protein) Homeodomain interacting protein kinase 2 SRY (sex determining region Y)-box 4 Fusion protein in t(12; 16) liposarcoma Pre-B-cell leukaemia transcription factor 2 Low density lipoprotein receptor protein 8 General transcription factor IIF, peptide 1 Tetratricopeptide-containing protein U2 small nuclear ribonucleoprotein factor Phosphatidylinositol-4-phosphate-5-kinase-Ia RAB5C, member of RAS oncogene family Neighbour of A-kinase anchoring protein 95 Phosphatidylinositol 4-kinase type II GTP binding protein 1 Thymidylate synthetase Chromosome 14 open reading frame 139

208677_s_at 208612_at 202355_s_at 201011_at 201551_s_at 216205_s_at 219028_at 213665_at 217370_x_at 202876_s_at 208433_s_at 202354_s_at 205877_s_at 218381_s_at 211205_x_at 201140_s_at 218064_s_at 209345_s_at 219357_at 217690_at 219563_at

19p13.3 15q15 19p13.3 3q21.3-q25.2 13q34 1p36.21 7q32-q34 6p22.3 16p11.2 6p21.3 1p34 19p13.3 22q13.2 19q13.43 1q22-q24 17q21.2 19p13.13 10q24 22q13.1 18p11.32 14q32.2

2Æ0 2Æ3 1Æ4 1Æ5 2Æ2 1Æ4 0Æ7 0Æ5 1Æ5 0Æ6 0Æ6 1Æ5 0Æ7 1Æ4 1Æ6 0Æ5 1Æ6 1Æ7 1Æ4 1Æ7 0Æ5

Probe sets are ranked by importance according to the RF analysis. Bold entries represent probe sets also identified in the analysis of independent T-ALL relapse specimens. ALL, acute lymphoblastic leukaemia; D R/D, fold change in expression between relapse (R) and diagnosis (D) paired pre-B ALL specimens; RF, Random Forest. *Perfect separators of paired pre-B ALL D/R samples. Perfect separators of T-ALL D/R samples.

processed for analysis (see Methods). Despite difficulties presented by differences in microarray platform, laboratory handling and clinical therapeutic protocols, expression levels of two of the top 20 pre-B ALL genes (BSG and PIP5K1A) were significantly upregulated in the relapse specimens compared with diagnosis (mean expression ± standard error of the mean for BSG: diagnosis 218 ± 4Æ5, relapse 284 ± 18, permutation test P < 0Æ0005; PIP5K1A: diagnosis 105 ± 1Æ1, relapse 114 ± 3Æ3, permutation test P < 0Æ05). To assess whether any of the chromosomal loci identified from the paired pre-B ALL analysis (Fig 2B) might also be associated with relapse in the published data from Yeoh et al (2002), the 198 diagnosis and 21 relapse array data were processed with our standard variance filter. This left 197 probes sets that were directly examined for loci representation compared with the HGU95Av2 array. Of the loci shown in Fig 2B, 19p13 was also found to be significantly over-represented in this independent data (P < 0Æ05, exact binomial test).

Relationship between BSG expression and treatment outcome To assess whether an increased expression of BSG at the time of diagnosis might be associated with a greater probability of 452

relapse, Kaplan–Meier plots were generated to compare the outcome of patients with varying levels of expression of this gene. For this analysis, all available pre-B ALL diagnosis specimens were combined (59 in total, from Fig 4A and B). Patients with high diagnosis levels of BSG expression (equivalent or greater than levels measured in relapse specimens) had significantly decreased relapse-free survival compared with those with lower expression [Fig 6A; Cox proportional hazards ratio (HR) for risk of relapse over the length of the study HR ¼ 3Æ29, 95% confidence interval (CI) 1Æ2–9Æ1, P < 0Æ05]. When the analysis was repeated using the 17 diagnosis specimens from the T-ALL cohort (see Fig 4C), the same relationship between BSG expression and adverse outcome was observed (Fig 6B; HR 7Æ9, 95% CI 1Æ02–60Æ3, P < 0Æ05). The microarray values used to define high and low BSG expression for this analysis are shown in Fig 6C.

Discussion In some patients, relapse phenotypes may be acquired via therapy-induced selection of resistant minor clones present at diagnosis rather than direct adaptation of the original disease; gene profiling cannot distinguish between these possibilities but can still resolve the biological signature of disease



Fig 4. Diagnosis/relapse discrimination in independent acute lymphoblastic leukaemia (ALL) specimens. Hierarchical clustering of (A) original 22 paired pre-B ALL specimens, (B) independent pre-B ALL specimens (48 diagnosis, two relapse) and (C) 22 non-paired T-ALL specimens. Analyses were performed using the top 21 probe sets identified from analysis of paired pre-B specimens. Boxes indicate relapse specimens; ellipses indicate high-risk diagnosis T-ALL specimens that cluster with the relapse group.


453

A. H. Beesley et al *

A

*

Proportion with relapse-free survival

N ormali sed m RNA expr ession

3·50 3·00 2·50 2·00

*

1·50

*

1·00

*

Low BSG expression (n = 47)

High BSG expression (n = 12) pre-B ALL (P = 0.015)

0·50

Time (Years)

0·00 GTF2F1

RPN1

LAMP1

Fig 5. Confirmation of microarray data by qRT-PCR. The mRNA expression of the top five genes identified in the paired pre-B acute lymphoblastic leukaemia (ALL) analysis was measured in eight nonleukaemic (light bars), 11 ALL diagnostic (grey bars) and 11 ALL relapse bone marrow specimens (dark bars), normalised to ACTB. Data represent mean ± standard error; *, P < 0Æ05 relapse versus diagnosis.

recurrence. Because of the limited size of the study, it was not possible to analyse early and late relapsing patients separately, and the identified genes may, therefore, be considered as general hallmarks of re-emerging disease; further studies in independent patient cohorts are required to assess the strength of these findings. Importantly however, the differential expression of these genes was tested not only in independent specimens of B-lineage leukaemia but also in specimens taken from patients with T-ALL, indicating the existence of relapse processes common to both lineages. Clustering of the two high-risk T-ALL diagnosis specimens with relapse tissues (Fig 4C) suggests that these genes may be markers for aggressive disease or primary drug resistance that, in some cases, may be present in dominant clones at the time of diagnosis. Many of the identified genes are multifunctional and have well-documented links with processes known to be important in cancer. For example, FUS produces a ribonuclear protein known to be a downstream target of the BCR/ABL translocation that generates one of the most severe forms of childhood ALL (Perrotti et al, 1998). FUS is itself involved with multiple chromosomal translocations, has been identified as a marker of metastasis (Oue et al, 2004), and appears to be important for both B-lymphocyte development and the maintenance of genomic stability (Hicks et al, 2000). Similarly, thymidylate synthetase (TYMS) is critically involved in folate metabolism, and is the primary target of the successful chemotherapy drug 5-fluorouracil (Rahman et al, 2004). RAB5C is a member of the RAS oncogene family and has recently been associated with cross-resistance to key antileukaemic agents in children with newly diagnosed ALL (Lugthart et al, 2005), while GRP58, the highest-ranked gene in both pre-B and T-ALL analyses, is a member of a stress response protein family that can protect cells from the toxic effects of chemotherapy (Flores-Diaz et al, 2004; Ma & Hendershot, 2004). LAMP1, also highly ranked in 454

B Proportion with relapse-free survival

GRP58

Low BSG expression (n = 9)

High BSG expression (n = 8) T-ALL (P = 0.031)

Time (Years)

C Median BSG expression ± total range (microarray units)

BSG

pre-B ALL

Low

T-ALL

High

Relapse

LowHigh

Relapse

Fig 6. Higher expression of BSG at the time of diagnosis adversely affects outcome. Kaplan–Meier plots comparing relapse-free survival of (A) 59 pre-B acute lymphoblastic leukaemia (ALL) and (B) 17 T-ALL patients with high or low levels of BSG expression at the time of diagnosis. Differences between curves were assessed using the log–rank (Mantel–Haenzel) test. (C) Low and high diagnosis BSG expression levels as defined for survival analysis. High expression indicates levels equivalent or greater than the minimum relapse expression, boxes indicate median values and inter-quartile range and whiskers indicate maximum and minimum values.

both pre-B and T-ALL analyses, codes for a transmembrane protein normally present on the luminal surface of lysosomes, but which is expressed at the surface of metastatic carcinoma cells and contributes to tumour cell adhesion to vascular endothelia (Sawada et al, 1993). Several of the other genes are either candidate tumour suppressors (HIPK2), transcriptional activators (PBX2 and SOX4) or are known to be involved with chromosomal translocations (MFN2 and RPN1). We found neither evidence for consistent differential expression of cyclin D1, p16INK4A, p14ARF, p15INK4B, BAX or DHFR at the time of relapse in either lineage, nor was there altered expression of classical multidrug-resistant proteins such as p-glycoprotein or members of the multidrug resistance-associated protein family. Interestingly, p53 was ranked at #150 in the RF analysis


Gene Expression Signature of Relapsed ALL of the paired pre-B specimens and showed a small increase in expression at relapse, consistent with previous reports (Tsai et al, 1996; Gustafsson et al, 2001). The highest-ranking gene associated with relapse in the preB ALL specimens codes for an extremely interesting multifunctional transmembrane glycoprotein alternatively known as basigin (BSG), CD147 or extracellular matrix metalloproteinase inducer (EMMPRIN) (Yan et al, 2005). Not only was the level of expression of this gene at relapse significantly elevated in several independent data sets, both pre-B ALL and T-ALL cohorts of the present study, as well as the data from Yeoh et al (2002), but high expression at the time of diagnosis was significantly associated with adverse outcome (Fig 6). Aberrant BSG expression is correlated with tumour progression in a range of different cancers (Gabison et al, 2005), and has been shown to increase growth rate and tumourigenic potential both in vitro (Marieb et al, 2004) and in vivo (Zucker et al, 2001). BSG plays a primary role in the process of metastasis by stimulating the secretion of matrix metalloproteinases that degrade basement membranes prior to tumour cell migration (Gabison et al, 2005). Though this is one of the most critical steps in the disease progression of solid tumours, the relevance for ALL is not immediately intuitive. One possibility is that an enhanced ability for leukaemic blasts to invade tissues would enable them to populate so-called sanctuary sites – pockets of disease that can evade chemotherapy or immune surveillance and ultimately result in relapse (Hazlehurst et al, 2003). It is noteworthy that overexpression of BSG in human carcinoma cells has been shown to induce a 10-fold increase in resistance to the drug doxorubicin via stromal activation of the PI3K/Akt signalling pathway (Misra et al, 2003). The interaction between a cancer cell and its microenvironment may, therefore, be a fundamental aspect of both tumour survival and drug sensitivity (Hazlehurst et al, 2003). Chromosomal analysis indicated that particular loci may be potential ‘hot-spots’ for aberrant gene expression in relapsed ALL (Fig 2B). The largest increase in representation for pre-B ALL relapse specimens was for 19p13, which also featured significantly in our analysis of the relapse data from Yeoh et al (2002). Translocations involving 19p13 are common in ALL and are associated with poor prognosis and response to therapy (Khalidi et al, 1999), while other defects at this locus have been linked to drug resistance in vitro (Achuthan et al, 2001; Yanaihara et al, 2003). These observations are consistent with the hypothesis that aberrations at this region are related to an aggressive phenotype in ALL. Upregulation of genes at this locus in ALL presumably reflects changes at the molecular level because there were no consistent cytogenetic abnormalities involving this locus in patient specimens from the present study (karyotypic data available as supplementary material at http://www.ichr.uwa.edu.au/research/publications/ data/BeesleyRelapseSuppl.pdf). In conclusion, the results from this study provide an important insight into the biology of relapsed paediatric ALL.

Lymphoblasts from relapsing patients appear to be in a state of increased proliferation and growth compared with their diagnostic counterparts, which is in keeping with the findings of Staal et al (2003) and is consistent with the aggressive nature of the disease in these individuals. Our results indicate an intriguing link between the expression of metastatic markers and drug resistance that has not previously been explored in the context of blood-borne tumours. Further investigation of these pathways is expected to provide novel therapeutic angles for the treatment of refractory leukaemia.

Acknowledgements The authors would like to thank Dr David Baker, Dr Nick Gottardo and other staff of the Princess Margaret Hospital, Perth for their invaluable support, as well as the patients and parents involved in this study. This research was funded by the NHMRC and NIH, with assistance from the Children’s Leukaemia and Cancer Research Foundation.

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