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

Overview of attention for article published in Cancer Management and Research, September 2017
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Title
A p53-regulated apoptotic gene signature predicts treatment response and outcome in pediatric acute lymphoblastic leukemia
Published in
Cancer Management and Research, September 2017
DOI 10.2147/cmar.s139864
Pubmed ID
Authors

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

Abstract

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 TP53 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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 29%
Student > Bachelor 2 12%
Student > Postgraduate 2 12%
Student > Ph. D. Student 2 12%
Professor 1 6%
Other 0 0%
Unknown 5 29%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 35%
Medicine and Dentistry 4 24%
Social Sciences 1 6%
Nursing and Health Professions 1 6%
Unknown 5 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 September 2017.
All research outputs
#18,572,036
of 23,002,898 outputs
Outputs from Cancer Management and Research
#1,054
of 2,014 outputs
Outputs of similar age
#242,414
of 316,299 outputs
Outputs of similar age from Cancer Management and Research
#12
of 17 outputs
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So far Altmetric has tracked 2,014 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.