Comment[ArrayExpressAccession]	E-GEOD-11877
Public Release Date	2009-06-25
Investigation Title	Children's Oncology Group Study 9906 for High-Risk Pediatric ALL																					
Comment[Submitted Name]	Children's Oncology Group Study 9906 for High-Risk Pediatric ALL
Experiment Description	PAPER 1:&quot;Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study.&quot;  ABSTRACT  We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics.  Four unsupervised clustering methods were utilized to classify patients into similar groups.  The different clustering algorithms showed significant overlap in cluster membership.  Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features.  Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%.  These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years.  Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster.  The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature.  The Sample data have also been used in another study:  PAPER 2: &quot;Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study&quot;.  ABSTRACT  Background. Nearly 25% of children with B-precursor ALL present with   &quot;high-risk&quot; disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting.  Methods.  Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL.  Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001).  Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures.  Results.  A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001).  In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001).  This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006).  Conclusions.   Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens.  NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration. Unsupervised clustering and supervised risk classification analyses of 207 diagnostic samples and associated clinical covariate data. See the Summary for greater details.  The data were analyzed using Microarray Suite version 5.0 (MAS 5.0) in the Affymetrix Gene Chip Operating Software Version 1.4. Probe masking was used (see 9906_TT207_Affymetrix_probe_mask.msk, linked below as a supplementary file). Otherwise all Affymetrix default parameter settings were used. Global scaling as the normalization method, with the default target intensity of 500, was used.																					
Date of Experiment																						
Term Source Name	EFO
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Term Source File	http://bar.ebi.ac.uk:8080/trac/browser/branches/curator/ExperimentalFactorOntology/ExFactorInOWL/currentrelease/eforelease/efo.owl																					
Person Last Name	Murphy	Willman	Ar	Atlas	Bedrick	Bhojwani	Borowitz	Bowman	Camitta	Carroll	Carroll	Chen	Davidson	Devidas	Harvey	Hunger	Kang	Murphy	Pullen	Reaman	Wang	Wilson
Person First Name	Maurice	Cheryl	Kerem	Susan	Edward	Deepa	Michael	W	Bruce	Andrew	William	I-Ming	George	Meenakshi	Richard	Stephen	Huining	Maurice	Jeanette	Gregory	Xuefei	Carla
Person Mid Initials		L		R	J		J	P		J	L		S		C	P				H		S
Person Email	mmurphy@hpc.unm.edu																					
Person Affiliation	UNM Health Sciences Center																					
Person Phone	(505) 925 4001																					
Person Fax																						
Person Address	UNM Cancer Center, UNM Health Sciences Center, 9501 Camino de Salud, Albuquerque, NM, USA																					
Person Roles	submitter																					
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Protocol Name	P-GSE11877-1	P-GSE11877-5	P-GSE11877-4	P-GSE11877-2	P-GSE11877-3	P-GSE11877-6																
Protocol Description	ID_REF = <br>VALUE = Signal<br>ABS_CALL = indicating whether the transcript was present (P), absent (A), or marginal (M)<br>DETECTION P-VALUE = 	GeneChips were scanned using the Affymetrix GeneChip Scanner 3000 7G Plus.	cRNA was fragmented and hybridized overnight to Affymetrix U133 Plus 2.0 Genechips.	Trizol extraction of total RNA was performed according to the manufacturer's instructions.	Biotinylated cRNA was generated from total RNA using the Affymetrix labeling protocol.	The data were analyzed using Microarray Suite version 5.0 (MAS 5.0) in the Affymetrix Gene Chip Operating Software Version 1.4. Probe masking was used; see the supplementary file 9906_TT207_Affymetrix_probe_mask.msk. Otherwise all Affymetrix default parameter settings were used. Global scaling as the normalization method, with the default target intensity of 500, was used.																
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Protocol Type	bioassay_data_transformation	image_aquisition	hybridization	nucleic_acid_extraction	labeling	feature_extraction																
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Experimental Factor Name	MLL 	BLAST COUNT, % OF SAMPLE, -1=UNAVAILABLE 	CNS STATUS 	TRISOMY 4 AND 10 	AMPLIFICATION SET 	HYBRIDIZATION SET 	WBC, 1000/MICROLITER 	RACE 	BCR-ABL, T(9;22) 	TEL-AML,T(12;21) 	SAMPLE VIABILITY, % OF SAMPLE, -1=UNAVAILABLE 	TESTICULAR INVOLVEMENT 	GENDER 	TISSUE TYPE 	AGE IN DAYS AT DIAGNOSIS 	CONGENITAL ABNORMALITY 	E2A-PBX, T(1;19) 	TRAINING OR TEST SET 				
Experimental Factor Type	MLL 	blast count, % of sample, -1=unavailable 	CNS status 	trisomy 4 and 10 	amplification set 	hybridization set 	WBC, 1000/microliter 	race 	BCR-ABL, t(9;22) 	TEL-AML,t(12;21) 	sample viability, % of sample, -1=unavailable 	testicular involvement 	gender 	tissue type 	age in days at diagnosis 	congenital abnormality 	E2A-PBX, t(1;19) 	training or test set 				
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Publication Title	Gene expression classifiers for relapse-free survival and minimal residual disease improve risk classification and outcome prediction in pediatric B-precursor acute lymphoblastic leukemia.	Identification of novel cluster groups in pediatric high-risk B-precursor acute lymphoblastic leukemia with gene expression profiling: correlation with genome-wide DNA copy number alterations, clinical characteristics, and outcome.																				
Publication Author List	Kang H, Chen IM, Wilson CS, Bedrick EJ, Harvey RC, Atlas SR, Devidas M, Mullighan CG, Wang X, Murphy M, Ar K, Wharton W, Borowitz MJ, Bowman WP, Bhojwani D, Carroll WL, Camitta BM, Reaman GH, Smith MA, Downing JR, Hunger SP, Willman CL	Harvey RC, Mullighan CG, Wang X, Dobbin KK, Davidson GS, Bedrick EJ, Chen IM, Atlas SR, Kang H, Ar K, Wilson CS, Wharton W, Murphy M, Devidas M, Carroll AJ, Borowitz MJ, Bowman WP, Downing JR, Relling M, Yang J, Bhojwani D, Carroll WL, Camitta B, Reaman GH, Smith M, Hunger SP, Willman CL																				
PubMed ID	19880498	20699438																				
Publication DOI	10.1182/blood-2009-05-218560	10.1182/blood-2009-08-239681																				
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Comment[SecondaryAccession]	GSE11877																					
Comment[GEOLastUpdateDate]	2011-03-21
Comment[AEExperimentType]	transcription profiling by array																					
Comment[GEOReleaseDate]	2009-06-24
Comment[ArrayExpressSubmissionDate]	2008-06-23
SDRF File	E-GEOD-11877.sdrf.txt
