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. 2006 Jul 15;108(2):711-7.
doi: 10.1182/blood-2006-02-002824.

Biologic pathways associated with relapse in childhood acute lymphoblastic leukemia: a Children's Oncology Group study

Deepa Bhojwani  1 , Huining Kang, Naomi P Moskowitz, Dong-Joon Min, Hokyung Lee, Jeffrey W Potter, George Davidson, Cheryl L Willman, Michael J Borowitz, Ilana Belitskaya-Levy, Stephen P Hunger, Elizabeth A Raetz, William L Carroll
Affiliations

Biologic pathways associated with relapse in childhood acute lymphoblastic leukemia: a Children's Oncology Group study

Deepa Bhojwani et al. Blood. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
Unsupervised analysis of paired diagnostic and relapse samples. (A) Samples (70; 35 patients at diagnosis and relapse) were clustered by hierarchic clustering using the Pearson correlation coefficient as the similarity measure. The diagnostic and relapse samples from multiple patients clustered closely together. D indicates sample from initial diagnosis; R, sample from relapse. (B) The Pearson correlation coefficient (CC) of the paired sample for each patient (32 pre-B) is represented as a gray circle; the higher the CC, the more similar the paired samples. Patients have been arranged from left to right according to the length of time to relapse. There is a clear trend toward decreasing CC as the time to relapse increases, as indicated by the regression line (P value of F test = .002).
Figure 2.
Figure 2.
Genes differentially expressed at diagnosis and relapse in B-precursor ALL. (A) Heatmap of top 126 probe sets (48 high at diagnosis, 78 high at relapse; FDR < 10%). (B) Quantitative real time–PCR (qRT-PCR) validation of selected targets on independent samples (29 initial diagnosis, 19 relapse). The y-axis represents normalized ΔCT values (CT of gene of interest – CT of housekeeping gene). A high ΔCT signifies low expression, and vice versa. CT indicates threshold cycles for amplification. *SCGF was not expressed in 2 of the diagnosis samples; thus, an arbitrary CT value of 40 was used. Median expression is indicated by the horizontal bars.
Figure 3.
Figure 3.
VxInsight analysis for 60 patients at relapse. (A) The VxInsight program located and positioned clusters and used a mountainous terrain metaphor to display the results. Mountains are shown over the clusters such that the height of each mountain represents the number of elements in that cluster. VxInsight defined 3 clusters from the entire cohort of 60 patients. (B) A detailed view of the middle-right cluster shows that all the early-relapse T-cell patients are closely clustered together. Blue lines indicate the strongest similarity links.
Figure 4.
Figure 4.
Genes differentially expressed between early and late relapse in patients (n = 54) with precursor B-ALL. (A) Patients were classified into 2 groups—early relapse (< 36 months from diagnosis) and late relapse (≥ 36 months)—using binary classification: 114 probe sets were chosen by SAM, with an FDR of 2.5% or less. (B). A linear regression model of survival analysis was used to show the possible association of the gene expression values with time to relapse. A number of probe sets (118) were chosen, with an FDR of 1.5% or less. (C) Functional categorization: 263 probe sets that were differentially expressed between early and late relapse with an FDR less than 5% were grouped according to function using the GeneOntology Biological Process classification. Yellow bars represent the number of genes relatively overexpressed, and blue bars represent genes relatively underexpressed at relapse.
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