Genomics of childhood leukemias: the virtue of complexity.

In this issue of Journal of Clinical Oncology, Bhojwani et al 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 threeand 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. 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 al 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, 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, “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. 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 JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 26 NUMBER 27 SEPTEMBER 2