Faculty Bibliography

The Children's Cancer Group (CCG) and the Pediatric Oncology Group (POG) joined to form the Children's Oncology Group (COG) in 2000. This merger allowed analysis of clinical, biologic, and early response data predictive of event-free survival (EFS) in acute lymphoblastic leukemia (ALL) to develop a new classification system and treatment algorithm. From 11 779 children (age, 1 to 21.99 years) with newly diagnosed B-precursor ALL consecutively enrolled by the CCG (December 1988 to August 1995, n=4986) and POG (January 1986 to November 1999, n=6793), we retrospectively analyzed 6238 patients (CCG, 1182; POG, 5056) with informative cytogenetic data. Four risk groups were defined as very high risk (VHR; 5-year EFS, 45% or below), lower risk (5-year EFS, at least 85%), and standard and high risk (those remaining in the respective National Cancer Institute [NCI] risk groups). VHR criteria included extreme hypodiploidy (fewer than 44 chromosomes), t(9;22) and/or BCR/ABL, and induction failure. Lower-risk patients were NCI standard risk with either t(12;21) (TEL/AML1) or simultaneous trisomies of chromosomes 4, 10, and 17. Even with treatment differences, there was high concordance between the CCG and POG analyses. The COG risk classification scheme is being used for division of B-precursor ALL into lower- (27%), standard- (32%), high- (37%), and very-high- (4%) risk groups based on age, white blood cell (WBC) count, cytogenetics, day-14 marrow response, and end induction minimal residual disease (MRD) by flow cytometry in COG trials

Childhood acute lymphoblastic leukemia (ALL) is a heterogeneous disease. Current treatment approaches are tailored according to the clinical features of the host, genotypic features of the leukemic blast, and early response to therapy. Although these approaches have been successful in dramatically improving outcomes, approximately 20% of children with ALL still relapse and many of these children do not have an identifiable adverse risk factor at presentation. Further insights into the biologic basis of the disease may contribute to novel, rational treatment strategies.Childhood ALL has served as an example for demonstrating the feasibility and potential of high-throughput technologies such as global gene expression or transcript profiling. In the last decade or so, utilization of these techniques has grown exponentially. As the methodology undergoes refinement and validation, it is plausible that microarrays may be used in the routine management of childhood ALL in the next few years. This article discusses the numerous applications to date of gene expression profiling in childhood ALL.Multiple investigators have made it evident that microarrays can be used as a single platform for the accurate classification of ALL into the various cytogenetic subtypes. Additional promising utilities include prediction of early response to therapy, overall outcome, and adverse effects. Identification of patients who are predicted to have an unfavorable outcome may allow for early intervention such as intensification of therapy or avoidance of drugs that are associated with specific secondary effects such as therapy-related acute myelogenous leukemia. Knowledge has been gained into pathways contributing to leukemogenesis and chemoresistance. Therapeutic targets have been identified, some of which are entering clinical trials following validation in additional preclinical models.These newer methods of genome analyses complemented by studies involving the proteome as well as host polymorphisms will have a profound impact on the diagnosis and management of childhood ALL

BACKGROUND: T-cell acute lymphoblastic leukemia (T-ALL) and T-cell lymphoblastic lymphoma (T-LL) and are often thought to represent a spectrum of a single disease. The malignant cells in T-ALL and T-LL are morphologically indistinguishable, and they share the expression of common cell surface antigens and cytogenetic characteristics. However, despite these similarities, differences in the clinical behavior of T-ALL and T-LL are observed. PROCEDURE: We analyzed the gene expression profiles of T-ALL and T-LL samples obtained from Children's Oncology Group (COG) tumor banks using DNA arrays. Immunohistochemistry was also performed to validate the expression of selected targets. RESULTS: Unsupervised hierarchical clustering of all samples showed complete segregation of T-ALL and T-LL into distinct clusters. Next, we identified the top 201 genes that best differentiated T-ALL from T-LL using significance analysis of microarrays (SAM), a supervised statistical approach. Genes representing several functional groups were differentially expressed in T-LL and T-ALL. Prediction analysis of microarrays (PAM) identified a subset of genes, which accurately classified all 19 T-ALL and T-LL samples with an overall misclassification error rate of 0. Immunohistochemical validation of protein expression of selected genes identified by microarray analysis confirmed overexpression of MLL-1 in T-LL tumor cells compared to T-ALL and CD47 in T-ALL tumors cells when compared to T-LL. CONCLUSIONS: Despite significant similarities between the malignant T-cell precursors, clear differences in the gene expression profiles were observed between T-ALL and T-LL implying underlying differences in the biology of the two entities

Outcome for children with childhood acute lymphoblastic leukemia (ALL) who relapse is poor. To gain insight into the mechanisms of relapse, we analyzed gene-expression profiles in 35 matched diagnosis/relapse pairs as well as 60 uniformly treated children at relapse using the Affymetrix platform. Matched-pair analyses revealed significant differences in the expression of genes involved in cell-cycle regulation, DNA repair, and apoptosis between diagnostic and early-relapse samples. Many of these pathways have been implicated in tumorigenesis previously and are attractive targets for intervention strategies. In contrast, no common pattern of changes was observed among late-relapse pairs. Early-relapse samples were more likely to be similar to their respective diagnostic sample while we noted greater divergence in gene-expression patterns among late-relapse pairs. Comparison of expression profiles of early- versus late-relapse samples indicated that early-relapse clones were characterized by overexpression of biologic pathways associated with cell-cycle regulation. These results suggest that early-relapse results from the emergence of a related clone, characterized by the up-regulation of genes mediating cell proliferation. In contrast, late relapse appears to be mediated by diverse pathways

The best treatment approach for children with B-precursor acute lymphoblastic leukemia (ALL) in second clinical remission (CR) after a marrow relapse is controversial. To address this question, we compared outcomes in 188 patients enrolled in chemotherapy trials and 186 HLA-matched sibling transplants, treated between 1991 and 1997. Groups were similar except that chemotherapy recipients were younger (median age, 5 versus 8 years) and less likely to have combined marrow and extramedullary relapse (19% versus 30%). To adjust for time-to-transplant bias, treatment outcomes were compared using left-truncated Cox regression models. The relative efficacy of chemotherapy and transplantation depended on time from diagnosis to first relapse and the transplant conditioning regimen used. For children with early first relapse (< 36 months), risk of a second relapse was significantly lower after total body irradiation (TBI)-containing transplant regimens (relative risk [RR], 0.49; 95% confidence interval [CI] 0.33-0.71, P < .001) than chemotherapy regimens. In contrast, for children with a late first relapse (> or = 36 months), risks of second relapse were similar after TBI-containing regimens and chemotherapy (RR, 0.92; 95% CI, 0.49-1.70, P = .78). These data support HLA-matched sibling donor transplantation using a TBI-containing regimen in second CR for children with ALL and early relapse

The recent sequencing of the human genome and technical breakthroughs now make it possible to simultaneously determine mRNA expression levels of almost all of the identified genes in the human genome. DNA 'chip' or microarray technology holds great promise for the development of more refined, biologically-based classification systems for childhood ALL, as well as the identification of new targets for novel therapy. To date gene expression profiles have been described that correlate with subtypes of ALL defined by morphology, immunophenotype, cytogenetic alterations, and response to therapy. Mechanistic insights into treatment failure have come from the definition of mRNA signatures that predict in vitro chemoresistance, as well as differences between blasts at relapse and new diagnosis. New bioinformatics tools optimize data mining, but validation of findings is essential since 'over-fitting' the data is a common danger. In the future, genomic analysis will be complemented by evaluation of the cancer proteome