Abstract
Gene expression analyses were performed on 121 consecutive childhood leukemias (87 B-lineage acute lymphoblastic leukemias (ALLs), 11 T-cell ALLs and 23 acute myeloid leukemias (AMLs)), investigated during an 8-year period at a single center. The supervised learning algorithm k-nearest neighbor was utilized to build gene expression predictors that could classify the ALLs/AMLs according to clinically important subtypes with high accuracy. Validation experiments in an independent data set verified the high prediction accuracies of our classifiers. B-lineage ALLs with uncharacteristic cytogenetic aberrations or with a normal karyotype displayed heterogeneous gene expression profiles, resulting in low prediction accuracies. Minimal residual disease status (MRD) in T-cell ALLs with a high (>0.1%) MRD at day 29 could be classified with 100% accuracy already at the time of diagnosis. In pediatric leukemias with uncharacteristic cytogenetic aberrations or with a normal karyotype, unsupervised analysis identified two novel subgroups: one consisting mainly of cases remaining in complete remission (CR) and one containing a few patients in CR and all but one of the patients who relapsed. This study of a consecutive series of childhood leukemias confirms and extends further previous reports demonstrating that global gene expression profiling provides a valuable tool for genetic and clinical classification of childhood leukemias.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Hall GW . Childhood myeloid leukaemias. Best Pract Res Clin Haematol 2001; 14: 573–591.
Pui CH, Relling MV, Downing JR . Acute lymphoblastic leukemia. N Engl J Med 2004; 350: 1535–1548.
Gustafsson G, Schmiegelow K, Forestier E, Clausen N, Glomstein A, Jonmundsson G et al. Improving outcome through two decades in childhood ALL in the Nordic countries: the impact of high-dose methotrexate in the reduction of CNS irradiation. Nordic Society of Pediatric Haematology and Oncology (NOPHO). Leukemia 2000; 14: 2267–2275.
Forestier E, Heim S, Blennow E, Borgstrom G, Holmgren G, Heinonen K et al. Cytogenetic abnormalities in childhood acute myeloid leukaemia: a Nordic series comprising all children enrolled in the NOPHO-93-AML trial between 1993 and 2001. Br J Haematol 2003; 121: 566–577.
Lie SO, Abrahamsson J, Clausen N, Forestier E, Hasle H, Hovi L et al. Treatment stratification based on initial in vivo response in acute myeloid leukaemia in children without Down's syndrome: results of NOPHO-AML trials. Br J Haematol 2003; 122: 217–225.
Grimwade D . The clinical significance of cytogenetic abnormalities in acute myeloid leukaemia. Best Pract Res Clin Haematol 2001; 14: 497–529.
Johansson B, Mertens F, Mitelman F . Clinical and biological importance of cytogenetic abnormalities in childhood and adult acute lymphoblastic leukemia. Ann Med 2004; 36: 492–503.
Ravindranath Y . Recent advances in pediatric acute lymphoblastic and myeloid leukemia. Curr Opin Oncol 2003; 15: 23–35.
Andersson A, Olofsson T, Lindgren D, Nilsson B, Ritz C, Edén P et al. Molecular signatures in childhood acute leukemia and their correlations to expression patterns in normal hematopoietic subpopulations. Proc Natl Acad Sci USA 2005; 102: 19069–19074.
Yeoh EJ, Ross ME, Shurtleff SA, Williams WK, Patel D, Mahfouz R et al. Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell 2002; 1: 133–143.
Ross ME, Zhou X, Song G, Shurtleff SA, Girtman K, Williams WK et al. Classification of pediatric acute lymphoblastic leukemia by gene expression profiling. Blood 2003; 102: 2951–2959.
Ross ME, Mahfouz R, Onciu M, Liu HC, Zhou X, Song G et al. Gene expression profiling of pediatric acute myelogenous leukemia. Blood 2004; 104: 3679–3687.
van Delft FW, Bellotti T, Luo Z, Jones LK, Patel N, Yiannikouris O et al. Prospective gene expression analysis accurately subtypes acute leukaemia in children and establishes a commonality between hyperdiploidy and t(12;21) in acute lymphoblastic leukaemia. Br J Haematol 2005; 130: 26–35.
Paulsson K, Panagopoulos I, Knuutila S, Jee KJ, Garwicz S, Fioretos T et al. Formation of trisomies and their parental origin in hyperdiploid childhood acute lymphoblastic leukemia. Blood 2003; 102: 3010–3015.
van Dongen JJ, Seriu T, Panzer-Grumayer ER, Biondi A, Pongers-Willemse MJ, Corral L et al. Prognostic value of minimal residual disease in acute lymphoblastic leukaemia in childhood. Lancet 1998; 352: 1731–1738.
Malec M, van der Velden VH, Björklund E, Wijkhuijs JM, Söderhall S, Mazur J et al. Analysis of minimal residual disease in childhood acute lymphoblastic leukemia: comparison between RQ-PCR analysis of Ig/TcR gene rearrangements and multicolor flow cytometric immunophenotyping. Leukemia 2004; 18: 1630–1636.
Björklund E, Mazur J, Söderhäll S, Porwit-MacDonald A . Flow cytometric follow-up of minimal residual disease in bone marrow gives prognostic information in children with acute lymphoblastic leukemia. Leukemia 2003; 17: 138–148.
Neale GA, Coustan-Smith E, Stow P, Pan Q, Chen X, Pui CH et al. Comparative analysis of flow cytometry and polymerase chain reaction for the detection of minimal residual disease in childhood acute lymphoblastic leukemia. Leukemia 2004; 18: 934–938.
Andersson A, Edén P, Lindgren D, Nilsson J, Lassen C, Heldrup J et al. Gene expression profiling of leukemic cell lines reveals conserved molecular signatures among subtypes with specific genetic aberrations. Leukemia 2005; 19: 1042–1050.
Saal LH, Troein C, Vallon-Christersson J, Gruvberger S, Borg Å, Peterson C . BioArray Software Environment (BASE): a platform for comprehensive management and analysis of microarray data. Genome Biol 2002; 3: SOFTWARE0003.
Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N et al. TM4: a free, open-source system for microarray data management and analysis. Biotechniques 2003; 34: 374–378.
Dudoit S, Fridlyand J . Introduction to classification in microarray experiments. In: Berrar DP, Dubitzky W, Granzow M (eds). A Practical Approach to Microarray Data Analysis. Kluwer Academic Publishers: London, 2002, pp 201–215.
Willenbrock H, Juncker AS, Schmiegelow K, Knudsen S, Ryder LP . Prediction of immunophenotype, treatment response, and relapse in childhood acute lymphoblastic leukemia using DNA microarrays. Leukemia 2004; 18: 1270–1277.
Cario G, Stanulla M, Fine BM, Teuffel O, Neuhoff NV, Schrauder A et al. Distinct gene expression profiles determine molecular treatment response in childhood acute lymphoblastic leukemia. Blood 2005; 105: 821–826.
Flotho C, Coustan-Smith E, Pei D, Iwamoto S, Song G, Cheng C et al. Genes contributing to minimal residual disease in childhood acute lymphoblastic leukemia: prognostic significance of CASP8AP2. Blood 2006; 108: 1050–1057.
Nilsson B, Andersson A, Johansson M, Fioretos T . Cross-platform classification in microarray-based leukemia diagnostics. Haematologica 2006; 91: 821–824.
Clark R, Byatt SA, Bennett CF, Brama M, Martineau M, Moorman AV et al. Monosomy 20 as a pointer to dicentric (9;20) in acute lymphoblastic leukemia. Leukemia 2000; 14: 241–246.
Eynon EE, Livak F, Kuida K, Schatz DG, Flavell RA . Distinct effects of Jak3 signaling on alphabeta and gammadelta thymocyte development. J Immunol 1999; 162: 1448–1459.
Ward AC, Touw I, Yoshimura A . The Jak–Stat pathway in normal and perturbed hematopoiesis. Blood 2000; 95: 19–29.
Acknowledgements
Grant support was provided by The Swedish Cancer Society, the Swedish Children's Cancer Foundation, the Medical Faculty of Lund University and the IngaBritt and Arne Lundberg foundation. PE was supported by the Swedish Foundation for Strategic Research through the Lund Center for Stem Cell Biology and Cell Therapy.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supplementary Information accompanies the paper on the Leukemia website (http://www.nature.com/leu)
Supplementary information
Rights and permissions
About this article
Cite this article
Andersson, A., Ritz, C., Lindgren, D. et al. 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. Leukemia 21, 1198–1203 (2007). https://doi.org/10.1038/sj.leu.2404688
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/sj.leu.2404688
Keywords
This article is cited by
-
What is the role of CHCHD2 in adrenal tumourigenesis?
Endocrine (2023)
-
Identification of immune subtypes of Ph-neg B-ALL with ferroptosis related genes and the potential implementation of Sorafenib
BMC Cancer (2021)
-
SCAMP2/5 as diagnostic and prognostic markers for acute myeloid leukemia
Scientific Reports (2021)
-
Deciphering molecular heterogeneity in pediatric AML using a cancer vs. normal transcriptomic approach
Pediatric Research (2021)
-
RET-mediated autophagy suppression as targetable co-dependence in acute myeloid leukemia
Leukemia (2018)