Gene expression profiles in resistant acute lymphoblastic leukemia

Treatment resistance as indicated by the presence of high levels of minimal residual disease (MRD) after induction therapy and induction consolidation is associated with a poor prognosis in childhood acute lymphoblastic leukemia (ALL). We hypothesized that treatment resistance is an intrinsic feature of ALL cells, which is reflected in the gene expression pattern, and that resistance to chemotherapy can be predicted prior to treatment. To test these hypotheses, gene expression signatures of ALL samples with a high MRD load (MRD-HR) were compared to those of samples without measurable MRD (MRD-SR) during treatment. We identified 54 genes that clearly distinguished resistant from sensitive ALL samples. Genes low expressed in resistant samples were predominantly associated with cell cycle progression and apoptosis, suggesting that impaired cell proliferation and apoptosis are involved in treatment resistance. Prediction analysis using randomly selected samples as a training set and the remaining samples as a test set revealed an accuracy of 84%. We conclude that resistance to chemotherapy seems at least in part to be an intrinsic feature of ALL cells. As treatment response could be predicted with a high accuracy, gene expression profiling could become a clinically relevant tool for treatment stratification in the early course of childhood ALL. A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Keywords: disease_state_design Using regression correlation