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Gene expression profile in childhood ALL: Overlapping with adult ALL?

Pediatric acute lymphoblastic leukemia (ALL) gene expression signatures classify an independent cohort of adult ALL patients

Leukemia volume 18, pages 63–71 (2004)Cite this article

Abstract

Recent reports support a possible future application of gene expression profiling for the diagnosis of leukemias. However, the robustness of subtype-specific gene expression signatures has to be proven on independent patient samples. Here, we present gene expression data of 34 adult acute lymphoblastic leukemia (ALL) patients (Affymetrix U133A microarrays). Support Vector Machines (SVMs) were applied to stratify our samples based on given gene lists reported to predict MLL, BCR-ABL, and T-ALL, as well as MLL and non-MLL gene rearrangement positive pediatric ALL. In addition, seven other B-precursor ALL cases not bearing t(9;22) or t(11q23)/MLL chromosomal aberrations were analyzed. Using top differentially expressed genes, hierarchical cluster and principal component analyses demonstrate that the genetically more heterogeneous B-precursor ALL samples intercalate with BCR-ABL-positive cases, but were clearly distinct from T-ALL and MLL profiles. Similar expression signatures were observed for both heterogeneous B-precursor ALL and for BCR-ABL-positive cases. As an unrelated laboratory, we demonstrate that gene signatures defined for childhood ALL were also capable of stratifying distinct subtypes in our cohort of adult ALL patients. As such, previously reported gene expression patterns identified by microarray technology are validated and confirmed on truly independent leukemia patient samples.

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References

  1. Jaffe ES, Harris NL, Stein H, Vardiman JW . World Health Organization Classification of Tumours. Pathology and Genetics of Tumours of Haematopoietic and Lymphoid Tissues. Lyon: IARC Press, 2001.

    Google Scholar 

  2. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531–537.

    Article  CAS  PubMed  Google Scholar 

  3. Armstrong SA, Staunton JE, Silverman LB, Pieters R, Den Boer ML, Minden MD et al. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat Genet 2002; 30: 41–47.

    Article  CAS  PubMed  Google Scholar 

  4. Kern W, Kohlmann A, Wuchter C, Schnittger S, Schoch C, Mergenthaler S et al. Correlation of protein expression and gene expression in acute leukemia. Cytometry 2003; 55B: 29–36.

    Article  CAS  Google Scholar 

  5. Kohlmann A, Schoch C, Schnittger S, Dugas M, Hiddemann W, Kern W et al. Molecular characterization of acute leukemias by use of microarray technology. Genes Chromosomes Cancer 2003; 37: 396–405.

    Article  CAS  PubMed  Google Scholar 

  6. Schoch C, Kohlmann A, Schnittger S, Brors B, Dugas M, Mergenthaler S et al. Acute myeloid leukemias with reciprocal rearrangements can be distinguished by specific gene expression profiles. Proc Natl Acad Sci USA 2002; 99: 10008–10013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. 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.

    Article  CAS  PubMed  Google Scholar 

  8. Ferrando AA, Neuberg DS, Staunton J, Loh ML, Huard C, Raimondi SC et al. Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell 2002; 1: 75–87.

    Article  CAS  PubMed  Google Scholar 

  9. Dugas M, Schoch C, Schnittger S, Haferlach T, Danhauser-Riedl S, Hiddemann W et al. A comprehensive leukemia database: integration of cytogenetics, molecular genetics and microarray data with clinical information, cytomorphology and immunophenotyping. Leukemia 2001; 15: 1805–1810.

    Article  CAS  PubMed  Google Scholar 

  10. Liu G, Loraine AE, Shigeta R, Cline M, Cheng J, Valmeekam V et al. NetAffx: Affymetrix probesets and annotations. Nucleic Acids Res 2003; 31: 82–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tusher VG, Tibshirani R, Chu G . Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA 2001; 98: 5116–5121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Eisen MB, Spellman PT, Brown PO, Botstein D . Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998; 95: 14863–14868.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hilsenbeck SG, Friedrichs WE, Schiff R, O’Connell P, Hansen RK, Osborne CK et al. Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. J Natl Cancer Inst 1999; 91: 453–459.

    Article  CAS  PubMed  Google Scholar 

  14. Hubbell E, Liu WM, Mei R . Robust estimators for expression analysis. Bioinformatics 2002; 18: 1585–1592.

    Article  CAS  PubMed  Google Scholar 

  15. Liu WM, Mei R, Di X, Ryder TB, Hubbell E, Dee S et al. Analysis of high density expression microarrays with signed-rank call algorithms. Bioinformatics 2002; 18: 1593–1599.

    Article  CAS  PubMed  Google Scholar 

  16. Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS et al. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc Natl Acad Sci USA 2000; 97: 262–267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D . Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000; 16: 906–914.

    Article  CAS  PubMed  Google Scholar 

  18. Vapnik V . Statistical Learning Theory. New York: Wiley, 1998.

    Google Scholar 

  19. Bene MC, Castoldi G, Knapp W, Ludwig WD, Matutes E, Orfao A et al. Proposals for the immunological classification of acute leukemias. European Group for the Immunological Characterization of Leukemias (EGIL). Leukemia 1995; 9: 1783–1786.

    CAS  PubMed  Google Scholar 

  20. Leo A, Wienands J, Baier G, Horejsi V, Schraven B . Adapters in lymphocyte signaling. J Clin Invest 2002; 109: 301–309.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Rozovskaia T, Ravid-Amir O, Tillib S, Getz G, Feinstein E, Agrawal H et al. Expression profiles of acute lymphoblastic and myeloblastic leukemias with ALL-1 rearrangements. Proc Natl Acad Sci USA 2003; 100: 7853–7858.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Kawagoe H, Humphries RK, Blair A, Sutherland HJ, Hogge DE . Expression of HOX genes, HOX cofactors, and MLL in phenotypically and functionally defined subpopulations of leukemic and normal human hematopoietic cells. Leukemia 1999; 13: 687–698.

    Article  CAS  PubMed  Google Scholar 

  23. Masternak K, Muhlethaler-Mottet A, Villard J, Zufferey M, Steimle V, Reith W . CIITA is a transcriptional coactivator that is recruited to MHC class II promoters by multiple synergistic interactions with an enhanceosome complex. Genes Dev 2000; 14: 1156–1166.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Villadangos JA, Ploegh HL . Proteolysis in MHC class II antigen presentation: who's in charge? Immunity 2000; 12: 233–239.

    Article  CAS  PubMed  Google Scholar 

  25. Schwarz G, Brandenburg J, Reich M, Burster T, Driessen C, Kalbacher H . Characterization of legumain. Biol Chem 2002; 383: 1813–1816.

    Article  CAS  PubMed  Google Scholar 

  26. Liu C, Sun C, Huang H, Janda K, Edgington T . Overexpression of legumain in tumors is significant for invasion/metastasis and a candidate enzymatic target for prodrug therapy. Cancer Res 2003; 63: 2957–2964.

    CAS  PubMed  Google Scholar 

  27. Weiner HL, Huang H, Zagzag D, Boyce H, Lichtenbaum R, Ziff EB . Consistent and selective expression of the discoidin domain receptor-1 tyrosine kinase in human brain tumors. Neurosurgery 2000; 47: 1400–1409.

    Article  CAS  PubMed  Google Scholar 

  28. Dyer MJ . The role of CAMPATH-1 antibodies in the treatment of lymphoid malignancies. Semin Oncol 1999; 26: 52–57.

    CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by a grant from the Deutsche José Carreras Leukämie-Stiftung e.V. (DJCLS-R00/13).

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Authors and Affiliations

  1. Department of Internal Medicine III, Laboratory for Leukemia Diagnostics, Ludwig-Maximilians-University, Munich, Germany

    A Kohlmann, C Schoch, S Schnittger, W Hiddemann, W Kern & T Haferlach

  2. Department of Medical Informatics, Biometrics and Epidemiology, Ludwig-Maximilians-University, Munich, Germany

    M Dugas

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  1. A Kohlmann
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  2. C Schoch
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  7. T Haferlach
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Supplementary information accompanies this paper on the Cell Death and Differentiation website: http://www.nature.com/cdd

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Kohlmann, A., Schoch, C., Schnittger, S. et al. Pediatric acute lymphoblastic leukemia (ALL) gene expression signatures classify an independent cohort of adult ALL patients. Leukemia 18, 63–71 (2004). https://doi.org/10.1038/sj.leu.2403167

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