Gene-based outcome prediction in multiple cohorts of pediatric T-cell acute lymphoblastic leukemia: a Children's Oncology Group study

Amanda L Cleaver, Alex H Beesley, Martin J Firth, Nina C Sturges, Rebecca A O'Leary, Stephen P Hunger, David L Baker, Ursula R Kees, Amanda L Cleaver, Alex H Beesley, Martin J Firth, Nina C Sturges, Rebecca A O'Leary, Stephen P Hunger, David L Baker, Ursula R Kees

Abstract

Background: Continuous complete clinical remission in T-cell acute lymphoblastic leukemia (T-ALL) is now approaching 80% due to the implementation of aggressive chemotherapy protocols but patients that relapse continue to have a poor prognosis. Such patients could benefit from augmented therapy if their clinical outcome could be more accurately predicted at the time of diagnosis. Gene expression profiling offers the potential to identify additional prognostic markers but has had limited success in generating robust signatures that predict outcome across multiple patient cohorts. This study aimed to identify robust gene classifiers that could be used for the accurate prediction of relapse in independent cohorts and across different experimental platforms.

Results: Using HG-U133Plus2 microarrays we modeled a five-gene classifier (5-GC) that accurately predicted clinical outcome in a cohort of 50 T-ALL patients. The 5-GC was further tested against three independent cohorts of T-ALL patients, using either qRT-PCR or microarray gene expression, and could predict patients with significantly adverse clinical outcome in each. The 5-GC featured the interleukin-7 receptor (IL-7R), low-expression of which was independently predictive of relapse in T-ALL patients. In T-ALL cell lines, low IL-7R expression was correlated with diminished growth response to IL-7 and enhanced glucocorticoid resistance. Analysis of biological pathways identified the NF-kappaB and Wnt pathways, and the cell adhesion receptor family (particularly integrins) as being predictive of relapse. Outcome modeling using genes from these pathways identified patients with significantly worse relapse-free survival in each T-ALL cohort.

Conclusions: We have used two different approaches to identify, for the first time, robust gene signatures that can successfully discriminate relapse and CCR patients at the time of diagnosis across multiple patient cohorts and platforms. Such genes and pathways represent markers for improved patient risk stratification and potential targets for novel T-ALL therapies.

Figures

Figure 1
Figure 1
Kaplan-Meier survival curves for patients predicted as CCR or relapse using the 5-GC model in (A) Training Cohort (n = 50); (B) Validation Cohort (n = 34); (C) COG 9404 (Winter et al, n = 44); (D) POG 8704 (Winter et al, n = 41).
Figure 2
Figure 2
Functional relevance of IL-7R as a prognostic marker. (A) Kaplan-Meier survival curves based on levels of IL-7R (qRT-PCR mRNA expression tertiles) in the Training Cohort; (B) IL-7R mRNA expression in a panel of T-ALL cell lines measured by qRT-PCR; (C) Cell surface IL-7R (CD127) protein expression in T-ALL cell lines measured by flow cytometry; (D) Growth response of T-ALL cell lines over 4 days to exogenous IL-7 (10 ng/ml) as measured by MTT (% proliferation compared to medium control).
Figure 3
Figure 3
Kaplan-Meier survival curves for patients predicted as CCR or relapse using the 14 gene 'Cell Adhesion Receptor' biological model in (A) Training Cohort (n = 50); (B) COG 9404 (Winter et al, n = 44); (C) POG 8704 (Winter et al, n = 41).

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