Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter to the Editor
  • Published:

Gene expression overlap affects karyotype prediction in pediatric acute lymphoblastic leukemia

Leukemia volume 21, pages 1341–1344 (2007)Cite this article

Leukemia is the most common childhood malignancy in the United States.1 Acute lymphoblastic leukemia (ALL) accounts for 75% of new leukemia cases in children. Although the outcome for children with ALL has improved dramatically over the past three decades, 25% of children with ALL still develop recurrent disease. Current risk classification schemes in pediatric ALL use clinical and laboratory parameters such as age and initial white blood cell count, as well as the presence of specific ALL-associated cytogenetic or molecular genetic abnormalities. Stratification based on cytogenetic analysis and molecular genetic detection consider B precursor ALL translocations such as t(12;21)(TEL-AML1), t(1;19)(E2A-PBX1) and t(9;22)(BCR-ABL), as well as numerical imbalances such as hyperdiploidy, specific chromosome trisomies or hypodiploidy. Despite such efforts, current diagnosis and risk classification schemes in pediatric ALL remain imprecise. In particular, it is likely that a significant number of higher-risk children are currently overtreated and could be cured with less intensive regimens, resulting in fewer toxicities and long-term side effects. Conversely, a significant number of children in lower-risk categories still relapse and precise means to prospectively identify them have remained elusive.

The advent of deoxyribonucleic acid based microarray technology raises the possibility of improving our understanding of the pathogenesis and treatment of leukemia. There have been efforts at genome-wide studies of leukemia classification. Both Yeoh et al.2 and Ross et al.3 have reported that gene expression profiling can identify the known prognostic subtypes of ALL. Yeoh et al. have further determined that T-ALL cases can be divided into intrinsic biological clusters, with risk groupings similar to B precursor ALL. We report the results of another gene expression experiment using a different cohort of pediatric ALL cases. Our cohort is more typical of pediatric ALL at presentation in that it includes patients with the above well-defined karyotypes as well as patients with ambiguous cytogenetic abnormalities. Using a variety of machine learning techniques, we have validated the results of Yeoh et al., but have also discovered that expression profiles can overlap when considering cohorts containing a mixture of patients with and without well-defined cytogenetic abnormalities. This overlap results in difficulties predicting karyotype from gene expression and can be seen in both supervised and unsupervised machine learning approaches, revealing complexity and novel biologic clusters not precisely correlated with the known abnormalities.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2

References

  1. Pui CH, Relling MV, Downing JR . Acute lymphoblastic leukemia. New Engl J Med 2004; 350: 1535–1548.

    Article  CAS  PubMed  Google Scholar 

  2. 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 

  3. 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.

    Article  CAS  PubMed  Google Scholar 

  4. Lauer SJ, Shuster JJ, Mahoney Jr DH, Winick N, Toledano S, Munoz L et al. A comparison of early intensive methotrexate/mercaptopurine with early intensive alternating combination chemotherapy for high-risk B-precursor acute lymphoblastic leukemia: a Pediatric Oncology Group phase III randomized trial. Leukemia 2001; 15: 1038–1045.

    Article  CAS  PubMed  Google Scholar 

  5. Mahoney Jr DH, Shuster J, Nitschke R, Lauer SJ, Winick N, Steuber CP et al. Intermediate-dose intravenous methotrexate with intravenous mercaptopurine is superior to repetitive low-dose oral methotrexate with intravenous mercaptopurine for children with lower-risk B-lineage acute lymphoblastic leukemia: a Pediatric Oncology Group phase III trial. J Clin Oncol 1998; 16: 246–254.

    Article  CAS  PubMed  Google Scholar 

  6. Mahoney Jr DH, Shuster JJ, Nitschke R, Lauer S, Steuber CP, Camitta B . Intensification with intermediate-dose intravenous methotrexate is effective therapy for children with lower-risk B-precursor acute lymphoblastic leukemia: A Pediatric Oncology Group study. J Clin Oncol 2000; 18: 1285–1294.

    Article  CAS  PubMed  Google Scholar 

  7. Davidson GS, Hendrickson B, Johnson DK, Meyers CE, Wylie BN . Knowledge mining with VxInsight: discovery through interaction. J Intell Inf Syst 1998; 11: 259–285.

    Article  Google Scholar 

  8. Helman P, Veroff R, Atlas SR, Willman CL . A new bayesian network classification methodology for gene expression data. J Comput Biol 2004; 11: 581–615.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by DHHS. National Institute of Health Grants NCI CA88361 and NCI CA114762, a Specialized Center for Research (SCOR) grant from The Leukemia and Lymphoma Society, and DOE Laboratory Directed Research and Development Funds at Sandia National Laboratories. The work was conducted in part within The University of New Mexico Cancer Center Genomics, Biostatistics, and Computational Biology shared resource facilities and the Children's Oncology Group Statistical Center. Sandia is a multi-program laboratory operated by Sandia Corporation, a Lockheed Martin Company for the United States Department of Energy under contract DE-AC04-94AL85000.

Author information

Authors and Affiliations

  1. Computational Biology, Sandia National Laboratories, Albuquerque, NM, USA

    S B Martin & G S Davidson

  2. Department of Pathology and the Cancer Research and Treatment Center, University of New Mexico School of Medicine, Albuquerque, NM, USA

    M P Mosquera-Caro, J W Potter, E Andries, H Kang, K Ar, Y Xu, I-M Chen, F A Schultz, C S Wilson, R Harvey & C L Willman

  3. Departments of Computer Science, Mathematics and Statistics, Physics and Astronomy, and the Center for High Performance Computing, University of New Mexico, Albuquerque, NM, USA

    E Andries, H Kang, P Helman, R L Veroff, S R Atlas, M Murphy, X Wang & E Bedrick

  4. General Clinical Research Center, The University of Florida, Gainesville, Florida, USA

    J Shuster

  5. Department of Genetics, University of Alabama, Birmingham, AL, USA

    A J Carroll

  6. Department of Pediatrics, Midwest Children's Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA

    B Camitta

Authors
  1. S B Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M P Mosquera-Caro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J W Potter
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G S Davidson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E Andries
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. H Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. P Helman
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. R L Veroff
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. S R Atlas
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. M Murphy
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. X Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. K Ar
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Y Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. I-M Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. F A Schultz
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. C S Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. R Harvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. E Bedrick
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. J Shuster
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. A J Carroll
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. B Camitta
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. C L Willman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C L Willman.

Additional information

Supplementary Information accompanies the paper on the Leukemia website (http://www.nature.com/leu)

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Martin, S., Mosquera-Caro, M., Potter, J. et al. Gene expression overlap affects karyotype prediction in pediatric acute lymphoblastic leukemia. Leukemia 21, 1341–1344 (2007). https://doi.org/10.1038/sj.leu.2404640

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/sj.leu.2404640

Search

Advanced search

Quick links