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CaArray_EXP-520: Gene Expression Profiles Predictive of Outcome and Age in Infant Acute Lymphoblastic Leukemia: a Children's Oncology Group Study

ABSTRACT: Gene expression profiling was performed on 97 cases of infant ALL from Children's Oncology Group Trial P9407. Statistical modeling of an outcome predictor revealed 3 genes highly predictive of event-free survival (EFS), beyond age and MLL status: FLT3, IRX2, and TACC2. Low FLT3 expression was found in a group of infants with excellent outcome (n = 11; 5-year EFS of 100%), whereas differential expression of IRX2 and TACC2 partitioned the remaining infants into 2 groups with significantly different survivals (5-year EFS of 16% vs 64%; P < .001). When infants with MLL-AFF1 were analyzed separately, a 7-gene classifier was developed that split them into 2 distinct groups with significantly different outcomes (5-year EFS of 20% vs 65%; P < .001). In this classifier, elevated expression of NEGR1 was associated with better EFS, whereas IRX2, EPS8, and TPD52 expression were correlated with worse outcome. This classifier also predicted EFS in an independent infant ALL cohort from the Interfant-99 trial. When evaluating expression profiles as a continuous variable relative to patient age, we further identified striking differences in profiles in infants less than or equal to 90 days of age and those more than 90 days of age. These age-related patterns suggest different mechanisms of leukemogenesis and may underlie the differential outcomes historically seen in these age groups. EXP-520 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133_Plus_2 Organism: Homo sapiens (ncbitax)

ORGANISM(S): Homo sapiens

SUBMITTER: Mervi Heiskanen

PROVIDER: E-GEOD-68720 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: a Children's Oncology Group study.

Kang Huining H Wilson Carla S CS Harvey Richard C RC Chen I-Ming IM Murphy Maurice H MH Atlas Susan R SR Bedrick Edward J EJ Devidas Meenakshi M Carroll Andrew J AJ Robinson Blaine W BW Stam Ronald W RW Valsecchi Maria G MG Pieters Rob R Heerema Nyla A NA Hilden Joanne M JM Felix Carolyn A CA Reaman Gregory H GH Camitta Bruce B Winick Naomi N Carroll William L WL Dreyer ZoAnn E ZE Hunger Stephen P SP Willman Cheryl L CL

Blood 20111230 8

Gene expression profiling was performed on 97 cases of infant ALL from Children's Oncology Group Trial P9407. Statistical modeling of an outcome predictor revealed 3 genes highly predictive of event-free survival (EFS), beyond age and MLL status: FLT3, IRX2, and TACC2. Low FLT3 expression was found in a group of infants with excellent outcome (n = 11; 5-year EFS of 100%), whereas differential expression of IRX2 and TACC2 partitioned the remaining infants into 2 groups with significantly differen ...[more]

PMID: 22210879

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Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions. nelso-00262 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A, HG-U133B Organism: Homo sapiens (ncbitax) Material Types: total RNA, synthetic_RNA, organism_part, whole_organism Disease States: Glioma, Glioblastoma, Oligodendroglial Tumor, astrocytomas

Dataset Information

CaArray_EXP-520: Gene Expression Profiles Predictive of Outcome and Age in Infant Acute Lymphoblastic Leukemia: a Children's Oncology Group Study

Publications

Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: a Children's Oncology Group study.

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