Details

Autor(en) / Beteiligte

• Bainer, Russell O
• Trendowski, Matthew R
• Cheng, Cheng
• Pei, Deqing
• Yang, Wenjian
• Paugh, Steven W
• Goss, Kathleen H
• Skol, Andrew D
• Pavlidis, Paul
• Pui, Ching-Hon
• Gilliam, T Conrad
• Evans, William E
• Onel, Kenan

Titel

A p53-regulated apoptotic gene signature predicts treatment response and outcome in pediatric acute lymphoblastic leukemia

Ist Teil von

• Cancer management and research, 2017-01, Vol.9, p.397-410

Ort / Verlag

New Zealand: Dove Medical Press Limited

Erscheinungsjahr

2017

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Gene signatures have been associated with outcome in pediatric acute lymphoblastic leukemia (ALL) and other malignancies. However, determining the molecular drivers of these expression changes remains challenging. In ALL blasts, the p53 tumor suppressor is the primary regulator of the apoptotic response to genotoxic chemotherapy, which is predictive of outcome. Consequently, we hypothesized that the normal p53-regulated apoptotic response to DNA damage would be altered in ALL and that this alteration would influence drug response and treatment outcome. To test this, we first used global expression profiling in related human B-lineage lymphoblastoid cell lines with either wild type or mutant to characterize the normal p53-mediated transcriptional response to ionizing radiation (IR) and identified 747 p53-regulated apoptotic target genes. We then sorted these genes into six temporal expression clusters (TECs) based upon differences over time in their IR-induced p53-regulated gene expression patterns, and found that one cluster (TEC1) was associated with multidrug resistance in leukemic blasts in one cohort of children with ALL and was an independent predictor of survival in two others. Therefore, by investigating p53-mediated apoptosis in vitro, we identified a gene signature significantly associated with drug resistance and treatment outcome in ALL. These results suggest that intersecting pathway-derived and clinically derived expression data may be a powerful method to discover driver gene signatures with functional and clinical implications in pediatric ALL and perhaps other cancers as well.