BACKGROUND AND OBJECTIVES: Childhood acute lymphoblastic leukemia (ALL) is a heterogeneous disease. There are several distinct genetic subtypes, characterized by typical changes in gene expression pattern. In addition to cytogenetic markers, the in vivo response to treatment is an emerging prognostic marker for risk stratification. However, it has not yet been reported whether gene expression profiles can predict risk group stratification already at the time of diagnosis. DESIGN AND METHODS: We analyzed bone marrow samples of 31 ALL patients to identify changes in gene expression that are associated with the current risk assignment, irrespective of the genetic subtype. Gene expression profiles were established using oligonucleotide microarrays. RESULTS: Considering all low- and high-risk patients, no gene was capable of predicting the risk assignment already at time of diagnosis. However, screening for risk group associated genes using more homogeneous subsets of patients revealed 10(6) discriminatory probe sets. The prognostic significance of these probe sets was subsequently determined for the entire series of patients. Using the selected subgroups as the training set and the remaining samples as an independent test set, logistic regression using 3 predictor variables could accurately predict current risk assignment for 10 out of 12 patients. INTERPRETATION AND CONCLUSIONS: Gene expression profiles established from a cytogenetically heterogeneous study group are not, as yet, sufficiently accurate to be used prognostically in a clinical setting. Additional risk-associated gene expression analyses need to be performed in more homogeneous sets of patients.
BACKGROUND AND OBJECTIVES: Childhood acute lymphoblastic leukemia (ALL) is a heterogeneous disease. There are several distinct genetic subtypes, characterized by typical changes in gene expression pattern. In addition to cytogenetic markers, the in vivo response to treatment is an emerging prognostic marker for risk stratification. However, it has not yet been reported whether gene expression profiles can predict risk group stratification already at the time of diagnosis. DESIGN AND METHODS: We analyzed bone marrow samples of 31 ALL patients to identify changes in gene expression that are associated with the current risk assignment, irrespective of the genetic subtype. Gene expression profiles were established using oligonucleotide microarrays. RESULTS: Considering all low- and high-risk patients, no gene was capable of predicting the risk assignment already at time of diagnosis. However, screening for risk group associated genes using more homogeneous subsets of patients revealed 10(6) discriminatory probe sets. The prognostic significance of these probe sets was subsequently determined for the entire series of patients. Using the selected subgroups as the training set and the remaining samples as an independent test set, logistic regression using 3 predictor variables could accurately predict current risk assignment for 10 out of 12 patients. INTERPRETATION AND CONCLUSIONS: Gene expression profiles established from a cytogenetically heterogeneous study group are not, as yet, sufficiently accurate to be used prognostically in a clinical setting. Additional risk-associated gene expression analyses need to be performed in more homogeneous sets of patients.
Authors: Maria Braoudaki; George I Lambrou; Konstantinos Vougas; Kalliopi Karamolegou; George T Tsangaris; Fotini Tzortzatou-Stathopoulou Journal: J Hematol Oncol Date: 2013-07-12 Impact factor: 17.388