Abstract

Patients relapsing with T-cell acute lymphoblastic leukemia (T-ALL) face a dismal outcome. The aim of this study was to identify new markers of drug resistance and clinical response in T-ALL. We measured gene expression and drug sensitivity in 15 pediatric T-ALL cell lines to find signatures predictive of resistance to 10 agents used in therapy. These were used to generate a model for outcome prediction in patient cohorts using microarray data from diagnosis specimens. In three independent T-ALL cohorts, the 10-drug model was able to accurately identify patient outcome, indicating that the in vitro-derived drug-gene profiles were clinically relevant. Importantly, predictions of outcome within each cohort were linked to distinct drugs, suggesting that different mechanisms contribute to relapse. Sulfite oxidase (SUOX) expression and the drug-transporter ABCC1 (MRP1) were linked to thiopurine sensitivity, suggesting novel pathways for targeting resistance. This study advances our understanding of drug resistance in T-ALL and provides new markers for patient stratification. The results suggest potential benefit from the earlier use of 6-mercaptopurine in T-ALL therapy or the development of adjuvants that may sensitize blasts to this drug. The methodology developed in this study could be applied to other cancers to achieve patient stratification at the time of diagnosis.

Highlights

  • Improved survival for pediatric acute lymphoblastic leukemia (ALL) patients is one of the success stories of cancer research

  • Genes and pathways associated with drug resistance in T-cell acute lymphoblastic leukemia (T-ALL) cell lines

  • Our laboratory has developed an authenticated panel of pediatric T-ALL cell lines that have been grown in the absence of drug selection

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Summary

Introduction

Improved survival for pediatric acute lymphoblastic leukemia (ALL) patients is one of the success stories of cancer research. For the significant number of patients that experience relapse, survival rates are dramatically reduced and worsen with each subsequent disease recurrence [3]. This adverse trend is even more pronounced in T-ALL and those with known clinical risk factors [3]. Many researchers have attempted to develop models that can be used to predict clinical outcome based on geneexpression patterns present in patients' leukemia cells at the time of diagnosis [4,5,6,7,8,9,10,11,12].

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