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Journal of Clinical Oncology, Vol 26, No 27 (September 20), 2008: pp. 4367-4368 © 2008 American Society of Clinical Oncology. DOI: 10.1200/JCO.2008.16.4285
Genomics of Childhood Leukemias: The Virtue of ComplexityDepartments of Medicine, Pediatrics, and Health Research and Policy, Stanford University School of Medicine, Stanford, CA In this issue of Journal of Clinical Oncology, Bhojwani et al1 report a study of gene expression profiling to predict early response and long-term outcome in children with high risk, pre–B-cell acute lymphoblastic leukemia (ALL). The study used subsets from a group of 99 patients treated on Children's Oncology Group Study 1961. The 82 patients who were considered either rapid or slow early responses (RER and SER) were randomly assigned into training sets (28 and 26 patients) and test sets (14 in each group). Only 59 patients were available for the long-term outcome analysis, 28 patients with continuous remission for at least 4 years and 31 patients with relapse within 3 years. A 24-gene signature was found to predict for early response, and a different 41-gene signature predicted long-term outcome. However, the three- and five-gene models that the authors derived were ultimately not more informative than existing clinical prognostic factors (age, WBC count, and karyotype). The predictive value of these models was better than chance but fell short of a truly useful step forward. The authors suggest that the failure of their expression-based predictors to have independent significance might be related to the possibility that the most important factors have been already identified. However, strong predictors such as certain chromosomal translocations may obscure more subtle prognostic factors, such as expression signatures. One potentially useful approach to this problem would be to stratify or study such subgroups separately for genomic analyses, an admittedly tall order in relatively uncommon diseases such as childhood ALL. This report is one of several recently published genomic studies in childhood ALL.2-14 Because of the relative scarcity of pediatric cancers, all of these studies have the limitation of small numbers of patients and low statistical power. Nonetheless, as with many other types of cancer, genomic studies of childhood ALL have provided new biologic insights into the pathogenesis and classification of the disease and insights into determinants of success or failure of therapies. The major translational goals of such studies are to stratify patients by more precise prognostic risk groups and to eventually select therapies based on more accurate prediction of drug responsiveness. Variations in treatment protocols among various studies and the biologic heterogeneity of childhood leukemias further complicate the analyses of genomic data in this disease. Thus Flotho et al8 recently reported that a 14-gene signature, which included several genes associated with cell proliferation, was predictive of outcome in 286 patients with childhood ALL treated with the Total XIII protocol. The same signature was confirmed to segregate a separate cohort of 127 Australian patients with ALL into two groups by Catchpoole et al,12 but did not have prognostic value in those patients who were treated with other regimens studied in BFM95 and ANZCHOG VIII. A fundamental problem in current genomic translational research is the traditional paradigm of sifting data to identify one or a few markers to use prospectively for prognosis of outcomes or prediction of therapies. If we are to fully exploit the potential of state-of-the-art genetic and genomic technologies for translational research and, ultimately, to individualize cancer therapies, this paradigm needs to fundamentally change. Rather than striving for simplification, the goal of genomic analyses should be to develop analytic approaches that use multidimensional data sets and, as West et al have suggested,15 "embrace the complexity of genomic data for personalized medicine." We offer the following recommendations to meet this challenge. First, sufficient numbers of specimens should be gathered from well-annotated, uniformly treated patients to allow statistical power in the analyses. For relatively uncommon cancers, including childhood leukemias, this is a daunting task, and will require cooperation among various consortia and major centers. Pooling of data from multiple studies, which requires statistical cross-validation of the genomic platforms, is a feasible approach to increasing statistical power.3 Second, as much as possible, cancer genomic studies should stratify patients into homogeneous groups based on known clinical and pathologic prognostic factors, as well as treatment protocols, so that more subtle, underlying factors can be discerned. The number of patients available for study will again be a major issue for adequate statistical power of this approach. Third, translational research should harness the power of high throughput technologies for the acquisition of large-scale databases on each specimen, which would include genome-wide studies of gene copy number, mutations, and polymorphisms, as well as gene expression. The availability of substantial amounts of fresh tumor cells from hematologic malignancies, such as childhood leukemias, makes such studies more feasible than for solid tumors such as lung cancers, where scant diagnostic tissue is available from fine-needle aspirations or bronchoscopies. Fourth, various bioinformatic approaches should be developed and used to integrate these data sets and to develop models that reflect the complex systems biology of individual cancers. These approaches include uncovering the activity of networks of signaling and other regulatory pathways and deriving structural patterns from multidimensional analyses, including mutations, polymorphisms, and variations in expression, as well as clinical and pathologic factors.15,16 Fifth, genetic and genomic studies of cancer should incorporate emerging concepts in cancer biology, such as the role of stromal cells in cancer growth and cancer stem cells. Alterations in cancerous versus stromal cells can be selectively studied via laser capture microdissection, and putative cancer stem cells can be identified and isolated by flow cytometry. Sixth, authors should be required to make available complete software scripts of the statistical analyses used to derive the results presented in published studies. This is in contrast to the verbal descriptions that often appear in such articles. These scripts will enable a more detailed assessment of the results and conclusions of genomic studies by other statisticians and facilitate attempts to improve on the published analyses. Finally, a significant proportion of genomic findings in cancer are not replicated by other researchers, for reasons such as biologic and technical variability, but also in part because authors may be selective in presenting their results. We urge authors to present the most balanced and representative analyses of their data. Web-based public availability of raw data with annotations, as well as complete software scripts as mentioned above, are essential for the expeditious advancement of the science of personalized medicine. For most cancers, the most robust prognostic and predictive models are likely to be complex. This will require adopting a systems biologic approach to integrate various genome scanning technologies with clinical and pathologic data. Simple models are likely not to give us the answers we want and need, and simplicity may not be a desirable approach. In the context of genomics and personalized medicine, we should consider the virtue of complexity. AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The author(s) indicated no potential conflicts of interest. AUTHOR CONTRIBUTIONS Conception and design: Branimir I. Sikic Financial support: Branimir I. Sikic Administrative support: Branimir I. Sikic Provision of study materials or patients: Branimir I. Sikic Collection and assembly of data: Branimir I. Sikic Data analysis and interpretation: Branimir I. Sikic Manuscript writing: Branimir I. Sikic, Robert Tibshirani, Norman J. Lacayo Final approval of manuscript: Branimir I. Sikic, Robert Tibshirani, Norman J. Lacayo REFERENCES
1. Bhojwani D, Kang H, Menezes RX, et al: Gene expression signatures predictive of early response and outcome in high-risk childhood acute lymphoblastic leukemia: A Children's Oncology Group study on behalf of the Dutch Childhood Oncology Group and the German Cooperative Study Group for Childhood Acute Lymphoblastic Leukemia. J Clin Oncol 26:4376-4384, 2008 2. Mullighan CG, Flotho C, Downing JR: Genomic assessment of pediatric acute leukemia. Cancer J 11:268-282, 2005[Medline] 3. Mitchell SA, Brown KM, Henry MM, et al: Inter-platform comparability of microarrays in acute lymphoblastic leukemia. BMC Genomics 5:71, 2004[CrossRef][Medline] 4. Carroll WL, Bhojwani D, Min DJ, et al: Childhood acute lymphoblastic leukemia in the age of genomics. Pediatr Blood Cancer 46:570-578, 2006[CrossRef][Medline] 5. Kager L, Evans WE: Pharmacogenomics of acute lymphoblastic leukemia. Curr Opin Hematol 13:260-265, 2006[Medline] 6. Stegmaier K: Genomic approaches in acute leukemia. Best Pract Res Clin Haematol 19:263-268, 2006[Medline] 7. Catchpoole D, Lail A, Guo D, et al: Gene expression profiles that segregate patients with childhood acute lymphoblastic leukaemia: An independent validation study identifies that endoglin associates with patient outcome. Leuk Res 31:1741-1747, 2007[CrossRef][Medline] 8. Flotho C, Coustan-Smith E, Pei D, et al: A set of genes that regulate cell proliferation predicts treatment outcome in childhood acute lymphoblastic leukemia. Blood 110:1271-1277, 2007 9. Gandemer V, Rio AG, de Tayrac M, et al: Five distinct biological processes and 14 differentially expressed genes characterize TEL/AML1-positive leukemia. BMC Genomics 8:385, 2007[CrossRef][Medline] 10. Kuiper RP, Schoenmakers EF, van Reijmersdal SV, et al: High-resolution genomic profiling of childhood ALL reveals novel recurrent genetic lesions affecting pathways involved in lymphocyte differentiation and cell cycle progression. Leukemia 21:1258-1266, 2007[CrossRef][Medline] 11. Mullighan CG, Goorha S, Radtke I, et al: Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 446:758-764, 2007[CrossRef][Medline] 12. Catchpoole D, Guo D, Jiang H, et al: Predicting outcome in childhood acute lymphoblastic leukemia using gene expression profiling: Prognostication or protocol selection? Blood 111:2486-2487, 2008 13. Ferrando AA, Neuberg DS, Staunton J, et al: Gene expression signatures define novel oncogenic pathways in T cell acute lymphoblastic leukemia. Cancer Cell 1:75-87, 2002[CrossRef][Medline] 14. Golub TR: Genomics: Global views of leukaemia. Nature 446:739-740, 2007 15. West M, Ginsburg GS, Huang AT, et al: Embracing the complexity of genomic data for personalized medicine. Genome Res 16:559-566, 2006 16. Bredel M, Bredel C, Juric D, et al: Functional network analysis reveals extended gliomagenesis pathway maps and three novel MYC-interacting genes in human gliomas. Cancer Res 65:8679-8689, 2005
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Copyright © 2008 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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