Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression Project for Oncology (expO)

ABSTRACT: The mission of expO is to build on the technologies and outcomes of the Human Genome Project to accelerate improved clinical management of cancer patients. IGC's Expression Project for Oncology (expO) seeks to integrate longitudinal clinical annotation with gene expression data for a unique and powerful portrait of human malignancies, providing critical perspective on diagnostic markers, prognostic indicators, and therapeutic targets. The goal of expO and its consortium supporters is to procure tissue samples under standard conditions and perform gene expression analyses on a clinically annotated set of deidentified tumor samples. The tumor data is updated with clinical outcomes and is released into the public domain without intellectual property restriction. Series-matrices are available at ftp://ftp.ncbi.nlm.nih.gov/pub/geo/DATA/SeriesMatrix/GSE2109/. For more information, see http://www.intgen.org/ Keywords: cancer portraits

ORGANISM(S): Homo sapiens

SUBMITTER: Erin Curley

PROVIDER: E-GEOD-2109 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Small cell lung cancer (SCLC) is a subtype of lung cancer with poor prognosis due to early dissemination and rapid growth. We here analyze gene expression profile of 23 clinical SCLC samples. EZH2 was found to be highly expressed in SCLC samples compared to 42 normal tissues including the normal lung, and other PRC2 members, SUZ12 and EED, were also highly expressed in SCLC. To obtain target genes of PRC2 in SCLC, H3K27me3 mark was mapped in three SCLC cell lines, Lu130, H209 and DMS53, and compared to normal small airway epithelial cells (SAEC). Whereas H3K27me3(+) genes in SAEC were significantly overlapped with PRC-target genes in ES cells (P=1.7x10-31), genes with H3K27me3 in SCLC cell lines but not in SAEC were not significantly overlapped with PRC-target genes in ES cells (P=0.64). These genes with H3K27me3 specifically in SCLC cell lines but not in SAEC showed decreased expression, not only in SCLC cell lines but also in clinical SCLCs, and showed enrichment of GO-terms such as plasma membrane (P=8.1x10-21) and cell adhesion (P=1.7x10-8). Introduction of JUB, a gene showing specific H3K27me3 modification and the strongest repression in the three SCLC cell lines, resulted in repression of cellular growth in DMS53. In clinical SCLC cases, lower JUB level correlated to shorter survival (P=0.002), or a set of PRC target genes (JUB, EPHB4) and marker genes of classic type SCLC (GRP, ASCL1) correlated to shorter survival (P=0.0001) and classified SCLC into two groups with distinct prognosis. Growth of SCLC cell lines was repressed when treated with 3-Deazaneplanocin A, an inhibitor against PRC2. It is suggested that high expression of PRC2 in SCLC contributed to repression of genes including non-PRC-target genes in ES cells, and that the gene repression may play a role in genesis of SCLC. Gene expression in 23 clinical SCLC samples, 42 normal tissue samples, 3 small cell lung cancer (SCLC) cell lines, and normal small airway epithelial cell (SAEC) was analyzed by Affymetrix arrays. This dataset is part of the TransQST collection.

Project description:PAPER 1:"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." 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: "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". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" 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.

Dataset Information

Expression Project for Oncology (expO)

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets