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

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.

Monocytes from children with clinically stable cystic fibrosis show enhanced expression of Toll-like receptor 4.

SUMMARY: Lung disease in patients with cystic fibrosis (CF) is characterized by recurrent bacterial respiratory infections and intense airway inflammation. Pattern recognition receptors such as Toll-like receptor 2 (TLR2) and TLR4 identify bacterial pathogens and activate the innate immune response. We therefore hypothesized that increased expression of these receptors would be found on circulating immune cells from children with CF. A cohort of 66 young children (median age 3 years) with CF was studied and compared to both healthy controls (n = 14) and children without CF who were being investigated for recurrent respiratory infections (non-CF disease controls; n = 17) of a similar age. Surface expression of TLR2 and TLR4 on peripheral blood monocytes was analyzed using flow cytometry. TLR4 expression was significantly higher in patients with CF compared to healthy controls (P = 0.017) and non-CF disease controls (P = 0.025) but did not vary according to the presence or absence of pulmonary infection with Gram-negative or Gram-positive bacteria (P = 0.387) in the CF group. In contrast, TLR2 expression was similar across all three study groups (P = 0.930). The increased surface expression of TLR4 seen in young children with CF appears to be related to having CF per se and not related to current pulmonary infection.