Literature DB >> 26175952

Apoptosis pathway signature for prediction of treatment response and clinical outcome in childhood high risk B-Precursor acute lymphoblastic leukemia.

Ya-Hsuan Chang1, Yung-Li Yang2, Chung-Ming Chen1, Hsuan-Yu Chen3.   

Abstract

The most common cancer in children is acute lymphoblastic leukemia (ALL) and it had high cure rate, especially for B-precursor ALL. However, relapse due to drug resistance and overdose treatment reach the limitations in patient managements. In this study, integration of gene expression microarray data, logistic regression, analysis of microarray (SAM) method, and gene set analysis were performed to discover treatment response associated pathway-based signatures in the original cohort. Results showed that 3772 probes were significantly associated with treatment response. After pathway analysis, only apoptosis pathway had significant association with treatment response. Apoptosis pathway signature (APS) derived from 15 significantly expressed genes had 88% accuracy for treatment response prediction. The APS was further validated in two independent cohorts. Results also showed that APS was significantly associated with induction failure time (adjusted hazard ratio [HR] = 1.60, 95% confidence interval [CI] = [1.13, 2.27]) in the first cohort and significantly associated with event-free survival (adjusted HR = 1.56, 95% CI = [1.13, 2.16]) or overall survival in the second cohort (adjusted HR = 1.74, 95% CI = [1.24, 2.45]). APS not only can predict clinical outcome, but also provide molecular guidance of patient management.

Entities:  

Keywords:  Acute lymphoblastic leukemia; apoptosis; gene signature; prediction and clinical outcome

Year:  2015        PMID: 26175952      PMCID: PMC4497450     

Source DB:  PubMed          Journal:  Am J Cancer Res        ISSN: 2156-6976            Impact factor:   6.166


  33 in total

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Review 2.  Molecular networks as sensors and drivers of common human diseases.

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Journal:  Blood       Date:  2011-09-14       Impact factor: 22.113

4.  Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia.

Authors:  Huining Kang; I-Ming Chen; Carla S Wilson; Edward J Bedrick; Richard C Harvey; Susan R Atlas; Meenakshi Devidas; Charles G Mullighan; Xuefei Wang; Maurice Murphy; Kerem Ar; Walker Wharton; Michael J Borowitz; W Paul Bowman; Deepa Bhojwani; William L Carroll; Bruce M Camitta; Gregory H Reaman; Malcolm A Smith; James R Downing; Stephen P Hunger; Cheryl L Willman
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6.  Risk- and response-based classification of childhood B-precursor acute lymphoblastic leukemia: a combined analysis of prognostic markers from the Pediatric Oncology Group (POG) and Children's Cancer Group (CCG).

Authors:  Kirk R Schultz; D Jeanette Pullen; Harland N Sather; Jonathan J Shuster; Meenakshi Devidas; Michael J Borowitz; Andrew J Carroll; Nyla A Heerema; Jeffrey E Rubnitz; Mignon L Loh; Elizabeth A Raetz; Naomi J Winick; Stephen P Hunger; William L Carroll; Paul S Gaynon; Bruce M Camitta
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9.  mRNA expression profile of multidrug-resistant genes in acute lymphoblastic leukemia of children, a prognostic value for ABCA3 and ABCA2.

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Journal:  Cancer Biol Ther       Date:  2013-10-21       Impact factor: 4.742

10.  Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.

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Journal:  Nucleic Acids Res       Date:  2009-06-05       Impact factor: 16.971

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