Identification of genes associated with chemotherapy crossresistance and treatment response in childhood acute lymphoblastic leukemia

Abstract

Acute lymphoblastic leukemia (ALL) can be cured with combination chemotherapy in over 75% of children, but the cause of treatment failure in the remaining patients is unknown. We determined the sensitivity of ALL cells to individual antileukemic agents in 441 patients and used a genome-wide approach to identify 45 genes differentially expressed in ALL exhibiting crossresistance to prednisolone, vincristine, asparaginase, and daunorubicin. We also identified a distinct phenotype of discordant resistance to asparaginase and vincristine and 139 genes whose expression was associated with this novel phenotype. The expression of these genes discriminated treatment outcome in two independent patient populations, identifying a subset of patients with a markedly inferior outcome (37% +/- 13% 5 year DFS).

Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia

“…Whereas gene expression-profiling studies in the acute leukemias have identified gene expression signatures associated with recurrent cytogenetic abnormalities 8,25,26 and in vitro drug responsiveness, [9][10][11]15 fewer studies have reported and validated gene expression classifiers predictive of survival. 13,14 In this study, gene expression classifiers predictive of RFS and end-induction MRD were derived from the gene expression profiles obtained in the pretreatment samples of 207 children with B-precursor high-risk ALL.…”

“…Thus, gene expression profiling and other comprehensive genomic technologies, such as assessment of genome copy number abnormalities or DNA sequencing, have the potential to resolve the underlying genetic heterogeneity of this form of ALL and to capture genetic differences that impact treatment response that can be exploited for improved risk classification and the identification of novel therapeutic targets. [8][9][10][11][12][13][14][15] From the gene expression profiles obtained in the pretreatment leukemic cells of 207 uniformly treated children with high-risk ALL, we used supervised learning algorithms and extensive cross-validation techniques to build a 42-probe-set (38-gene) expression classifier predictive of RFS. In multivariate analysis, the best predictive model for RFS was this gene expression classifier combined with either flow cytometric measures of MRD determined at the end of induction therapy (day 29), or, a 23-probe-set (21-gene) molecular classifier derived from pretreatment samples that could predict levels of end-induction flow MRD at initial diagnosis.…”

A unique complex translocation involving six different chromosomes in a case of childhood acute lymphoblastic leukemia with the Philadelphia chromosome and adverse prognosis

“…This study identified 45 genes differentially expressed between resistant and sensitive ALL samples whose expression pattern was significantly related to treatment response (28). The 45 genes were involved in the regulation of transcription, cellular transport and cell cycle maintenance.…”

A pathway-based gene signature correlates with therapeutic response in adult patients with Philadelphia chromosome-positive acute lymphoblastic leukemia

“…17,18 Briefly, gene expression profiling data from previous studies [7][8][9][10]19,20 comparing therapeutic response with various reagents in ALL patients were pooled and normalized. Relative expression levels were calculated, and associated with outcomes by analysis of variance (ANOVA).…”

“…[6][7][8][9][10] Except for the common finding that most Ph þ ALL cases have deletions on chromosome 7p spanning the IKZF1 gene, 11 the gene expression profiling results have varied considerably in different studies, and have been of limited clinical utility.…”