Microarray-Based Classification of a Consecutive Series of 121 Childhood
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Leukemia (2007) 21, 1198–1203 & 2007 Nature Publishing Group All rights reserved 0887-6924/07 $30.00 www.nature.com/leu ORIGINAL ARTICLE Microarray-based classification of a consecutive series of 121 childhood acute leukemias: prediction of leukemic and genetic subtype as well as of minimal residual disease status A Andersson1, C Ritz2, D Lindgren1, P Ede´n2, C Lassen1, J Heldrup3, T Olofsson4,JRa˚de5, M Fontes5, A Porwit-MacDonald6, M Behrendtz7,MHo¨glund1, B Johansson1 and T Fioretos1 1Department of Clinical Genetics, Lund University Hospital, Lund, Sweden; 2Department of Complex System Division, Theoretical Physics, Lund University, Lund, Sweden; 3Department of Pediatrics, Lund University Hospital, Lund, Sweden; 4Department of Hematology, Lund University Hospital, Lund, Sweden; 5Center for Mathematical Sciences, Lund University, Lund, Sweden; 6Department of Pathology, Karolinska Hospital and Institute, Stockholm, Sweden and 7Department of Pediatrics, Linko¨ping University Hospital, Linko¨ping, Sweden Gene expression analyses were performed on 121 consecutive notype, white blood cell (WBC) count, central nervous system childhood leukemias (87 B-lineage acute lymphoblastic leuke- (CNS) involvement, response to therapy and genetic findings.6,7 mias (ALLs), 11 T-cell ALLs and 23 acute myeloid leukemias Despite the quite dramatic progress in treatment, risk- (AMLs)), investigated during an 8-year period at a single center. stratification and biological understanding of childhood leuke- The supervised learning algorithm k-nearest neighbor was 8 utilized to build gene expression predictors that could classify mias over recent decades, further improvements are still the ALLs/AMLs according to clinically important subtypes with needed. Refined risk assessment will hopefully result in the high accuracy. Validation experiments in an independent data identification of individual patients who may benefit from either set verified the high prediction accuracies of our classifiers. more or less intensive treatment regimens. Moreover, increased B-lineage ALLs with uncharacteristic cytogenetic aberrations understanding of the underlying biology of the various ALL and or with a normal karyotype displayed heterogeneous gene expression profiles, resulting in low prediction accuracies. AML subtypes will hopefully result in the development of novel Minimal residual disease status (MRD) in T-cell ALLs with a therapies. In this context, cDNA and oligonucleotide micro- high (40.1%) MRD at day 29 could be classified with 100% arrays have emerged as a promising tool for identifying both accuracy already at the time of diagnosis. In pediatric clinically and biologically important features. So far, only a few leukemias with uncharacteristic cytogenetic aberrations or with large-scale (4100 cases) gene expression studies of childhood a normal karyotype, unsupervised analysis identified two novel leukemias have been reported.9–13 In a previous study, we subgroups: one consisting mainly of cases remaining in complete remission (CR) and one containing a few patients in performed global gene expression profiling of a consecutive CR and all but one of the patients who relapsed. This study of a series of 121 pediatric acute leukemias of different lineages consecutive series of childhood leukemias confirms and (B-lineage ALLs, T-cell ALLs and AMLs) and six normal healthy extends further previous reports demonstrating that global bone marrows, and compared genetic subtype-specific gene gene expression profiling provides a valuable tool for genetic signatures with an extensive set of fractionated normal and clinical classification of childhood leukemias. 9 Leukemia (2007) 21, 1198–1203. doi:10.1038/sj.leu.2404688; hematopoietic cell subpopulations. Herein, we used supervised published online 5 April 2007 learning algorithms to build predictors capable of classifying the Keywords: pediatric leukemia; gene expression profiling; leukemias according to lineages and genetic changes with high supervised classification; ALL; AML accuracy. For the T-cell ALLs, we could classify samples with high tumor load at day 29, already at diagnosis. Among cases with uncharacteristic cytogenetic changes or with a normal karyotype, unsupervised hierarchical clustering analysis (HCA) identified two novel subgroups, one containing mainly patients Introduction in complete remission (CR) and one mainly patients who eventually relapsed. Validation experiments of prediction of Pediatric acute leukemias constitute a heterogeneous disease genetic subtypes in an external data set verified the high entity, comprised of different genetic, morphologic and im- accuracy of our classifier. munophenotypic subgroups with variable clinical features.1,2 The most common subtype is acute lymphoblastic leukemia (ALL) with an incidence of four cases per 100 000 per year; Materials and methods acute myeloid leukemia (AML) is less frequent with only 0.7 3,4 cases per 100 000 per year. The prognosis has improved Patient material significantly over the last few years, with the 7-year event-free Bone marrow (BM; n ¼ 108) or peripheral blood (PB; n ¼ 13) survival for ALL and AML now approaching 80 and 50%, samples from 121 children with ALL (87 B-lineage and 11 T-cell) respectively.3,5 This improved outcome has mainly been or AML (n ¼ 23) were obtained at the time of diagnosis. In achieved through stratifying individual patients to different risk addition, six normal bone marrows (NBMs) were obtained from and treatment groups based on, for example, age, immunophe- healthy donors. Details of the studied cohort have been described elsewhere.9 The leukemias were diagnosed and Correspondence: Dr A Andersson, Department of Clinical Genetics, treated at either Lund University Hospital (n ¼ 89) or Linko¨ping University Hospital, SE-221 85 Lund, Sweden. E-mail: [email protected] University Hospital (n ¼ 32), representing approximately 70% of Received 1 August 2006; revised 6 March 2007; accepted 8 March all childhood ALLs and AMLs diagnosed at these two hospitals 2007; published online 5 April 2007 during the study period (1997–2004). Two different treatment Array-based classification of pediatric AML and ALL A Andersson et al 1199 protocols – NOPHO (Nordic Society of Paediatric Haematology of the analysis is given in Supplementary Information in the and Oncology) ALL-923 and NOPHO ALL-2000 – were used expanded Materials and methods section. during the study period. Protocols were changed on 1 January HCA was performed in TMEV21 and principal component 2001 (time periods (1992–2001 and from 2001 onwards). Risk analysis (PCA) was applied using software developed at the classification during both treatment periods was based on age, Department of Mathematics, Lund, Sweden. Significance WBC count, immunophenotypic and karyotypic features, analysis of microarrays (SAM)21 was used to find genes dividing the ALLs into standard-risk/standard-intensive (SR/SI; associated with the putative novel groups in cases with no age 2–10 years, WBC o10 Â 109/l and no high-risk/intensive specific genetic change. (HR/I) features), intermediate-risk/intermediate-intensive (IR/II; 1–2 or 410 years, WBC 10–50 Â 109/l and no HR/I features), 9 Supervised classification HR/I (WBC 450 Â 10 /l, CNS or testicular leukemia, t(9;22), The k-nearest neighbors (k-NNs) algorithm22 was used for t(4;11), T-cell immunophenotype), very high-risk/extra-intensive supervised classification, where the class of a test sample is (VHR/EI; lymphomatous leukemia, together with other HR/I decided by the majority class among its k-NNs. A cross- criteria). The AMLs were treated according to the NOPHO- 5 validation procedure was used to select the number of neighbors AML93 protocol. The treatment protocols and the risk and genes used for classification. From each ranked list, the stratification for NOPHO ALL-92 have been reported pre- 3 classifier was evaluated in a leave-one-out cross-testing proce- viously. The study was reviewed and approved by the Research dure (for details, see the expanded method section in Ethics Committees of Lund and Linko¨ping Universities, Sweden. Supplementary Information). All cases were analyzed cytogenetically. In addition, the ALLs were screened for MLL rearrangements using Southern blot or fluorescence in situ hybridization analyses (FISH) and for the Validation experiments in an external data set presence of the fusion genes BCR/ABL1 (t(9;22), TCF3/PBX1 To validate the k-NN method to build predictors, the largest (t(1;19)) and ETV6/RUNX1 (t(12;21)) by the use of reverse childhood ALL data set reported to date was used.11 In this transcription-PCR. In addition, FISH investigations were per- analysis, our B-lineage ALL data set was used as a training set formed on cases with either normal karyotypes (NKs) or without and the B-lineage ALLs from the Ross data set were used as a analyzable metaphases, using probe cocktails for the chromo- validation data set. To validate the T-cell ALL classifier for 14 somes commonly gained in high hyperdiploid ALLs. All prediction of MRD, the only data set published reporting both analyses were performed at the Department of Clinical MRD status and gene expression data on pediatric T-cell ALLs Genetics, Lund, Sweden. The genetic features are summarized was used.23 The training and validation data sets were generated in Supplementary Table 1. using different array platforms (cDNA and Affymetrix, respec- tively); therefore, data were matched using the Entrez gene Monitoring of minimal residual disease status numbers, mean-centered