Unknown,Transcriptomics,Genomics,Proteomics

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Predict cytogenetic abnormalities with gene expression profiles

ABSTRACT: Cytogenetic abnormalities (CA) are important clinical parameters in various types of cancer, including multiple myeloma (MM). We developed a model to predict CA in patients with MM using gene expression profiling (GEP) and validated it by different cytogenetic techniques. The model was shown to have an accuracy up to 0.89. These results provide proof of concept for the hypothesis that GEP could serve as a one-stop data source for clinical molecular diagnosis and/or prognosis. 92 paired RNA-DNA samples were hybridized to Affy U133Plus2 and Agilent 244K aCGH arrays and used as training set. Another 23 paired samples as test set.

ORGANISM(S): Homo sapiens

SUBMITTER: Yiming Zhou

PROVIDER: E-GEOD-29023 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Prediction of cytogenetic abnormalities with gene expression profiles.

Zhou Yiming Y Zhang Qing Q Stephens Owen O Heuck Christoph J CJ Tian Erming E Sawyer Jeffrey R JR Cartron-Mizeracki Marie-Astrid MA Qu Pingping P Keller Jason J Epstein Joshua J Barlogie Bart B Shaughnessy John D JD

Blood 20120410 21

Cytogenetic abnormalities are important clinical parameters in various types of cancer, including multiple myeloma. We developed a model to predict cytogenetic abnormalities in patients with multiple myeloma using gene expression profiling and validated it by different cytogenetic techniques. The model has an accuracy rate up to 0.89. These results provide proof of concept for the hypothesis that gene expression profiling is a superior genomic method for clinical molecular diagnosis and/or progn ...[more]

PMID: 22496154

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Project description:The expression level for 15 887 transcripts in lymphoblastoid cell lines from 19 monozygotic twin pairs (10 male, 9 female) were analysed for the effects of genotype and sex. On an average, the effect of twin pairs explained 31% of the variance in normalized gene expression levels, consistent with previous broad sense heritability estimates. The effect of sex on gene expression levels was most noticeable on the X chromosome, which contained 15 of the 20 significantly differentially expressed genes. A high concordance was observed between the sex difference test statistics and surveys of genes escaping X chromosome inactivation. Notably, several autosomal genes showed significant differences in gene expression between the sexes despite much of the cellular environment differences being effectively removed in the cell lines. A publicly available gene expression data set from the CEPH families was used to validate the results. The heritability of gene expression levels as estimated from the two data sets showed a highly significant positive correlation, particularly when both estimates were close to one and thus had the smallest standard error. There was a large concordance between the genes significantly differentially expressed between the sexes in the two data sets. Analysis of the variability of probe binding intensities within a probe set indicated that results are robust to the possible presence of polymorphisms in the target sequences. Keywords: Monozygotic twin pair Expression Profiles Genome-wide gene expression in lymphoblastoid cell lines was determined using microarrays derived from 15 monozygotic (MZ) twin pairs (10 male, 9 female). 10 twin pair are discordant, 4 are concordant for a disease phenotype and their are 5 controls which have no disease phenotype. This data set was analyzed to interrupt replicated effects of sex and genotype, not disease characteristics. Subsets of these samples have been analyzed separately for disease characteristics, (GSE7036 M-bM-^@M-^S 3 MZ twins discordant for bipolar disorder) and (GSE7486 - 5 discordant and 4 concordant MZ twin pairs with idiopathic absence epilepsies and 5 unaffected MZ twin pairs).

Project description:Pediatric acute lymphoblastic leukemia (ALL) contains cytogenetically distinct subtypes that respond differently to cytotoxic drugs. Subtype classification can be also achieved through gene expression profiling. However, how to apply such classifiers to a single patient and correctly diagnose the disease subtype in an independent patient group has not been addressed. Furthermore, the underlying regulatory mechanisms responsible for the subtype-specific gene expression patterns are still largely unknown. Here, by combining three published microarray datasets (PMIDs: 12086872, 12730115, 17002788) on 535 Caucasian samples and generating a new dataset on 100 Chinese children ALL samples, we were able to 1) identify a 62-gene classifier with 97.6% accuracy from the Caucasian samples and validated it on the completely independent set of 100 Chinese samples, 2) to uncover potential regulatory networks of ALL subtypes. The classifier we identified was so far the only one that could be applied directly to a single sample and sustained validation in a large independent patient group. Our results also suggest that the etiology of ALL is largely the same among different ethnic groups, and that the transcription factor hubs in the predicted regulatory network might play important roles in regulating gene expression and development of ALL. A total of 100 Chinese pediatric acute lymphoblastic leukemia bone marrow (BM) samples were analyzed, together with five non-ALL BM samples as negative control (the five samples were combined to generate one control, see details in Li et al. Blood, 2009). The diagnosis of ALL was based on morphology, immunology, cytogenetic and molecular (MICM) classification. Cytogenetic ALL subtypes were identified experimentally by G-banding karyotype and multiplex nested RT-PCR. Among the 100 ALL patients, eleven relapsed within five years. All the samples, including those relapsed afterward, were from patients treated on BCH-2003 protocol and were extracted at their initial diagnosis. The five non-ALL BM samples were taken from the removed bones of patients who had plastic surgery for their bone deformity in Beijing Children’s Hospital. And the informed consent was obtained from parents, guardians, or patients (as appropriate). More details are available in Li et al. Gene expression-based classification and regulatory networks of pediatric acute lymphoblastic leukemia, Blood, 2009. We have generated these ALL and control samples to validate that a high accuracy subtype classifier that we identified could be applied directly to a single sample and sustained validation in a large independent patient group.

Dataset Information

Predict cytogenetic abnormalities with gene expression profiles

Publications

Prediction of cytogenetic abnormalities with gene expression profiles.

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