Comparison and meta-analysis of microarray data: from the bench to the computer desk

Moreau, Yves; Aerts, Stein; Moor, Bart De; Strooper, Bart De; Dabrowski, Michael

doi:10.1016/j.tig.2003.08.006

Cited by 158 publications

(123 citation statements)

References 59 publications

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“…Despite the fact that this is the most widely used antigen for enrichment of GSCs, there are several arguments to suggest the existence of CD133-negative GSCs: CD133 is not detectable in many fresh GBM specimens [14,31,51], and some studies revealed CD133-negative GBM cultures with the ability to self-renew and to form tumours in xenotransplantation assays [14,51,52]. Microarray analysis of gene expression profiling have been source of criticisms [18,53,54]; it should be noted, however, that we used data from similar DNA chip microarray platforms allowing comparison of data obtained in the same way. This approach reveals eight genes that are strongly overexpressed in cultures enriched in GSCs compared to serum-cultured GBM cells.…”

Section: Discussionmentioning

confidence: 99%

Identification of OLIG2 as the most specific glioblastoma stem cell marker starting from comparative analysis of data from similar DNA chip microarray platforms

et al. 2014

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Despite advances in surgical and adjuvant treatments, overall survival of glioblastoma (GBM) patients remains poor. The cancer stem cell concept suggests that a rare stem cell population, called glioma stem cells (GSCs), has high ability to self-renewal leading to recurrence in GBM. The identification of specific markers of GSCs would provide a powerful tool to detect and to characterise them in order to develop targeted therapies. We carried out a comparative analysis based on the identification of inter-study concordances to identify the genes that exhibit at best differential levels of expression between GSC-enriched cell cultures and differentiated tumour cell cultures from independent studies using DNA chip microarray technologies. We finally studied the protein expression of the marker we considered the most specific by immunohistochemistry and semi-quantitative analysis on a retrospective series of 18 GBMs. Of the selected studies, 32 genes were retained. Among them, eight genes were identified to be overexpressed in GSC-enriched cultures compared to differentiated tumour cell cultures. Finally, among the eight genes, oligodendrocyte lineage transcription factor 2 (OLIG2) was characterised by the most different expression level in the "GSC model" compared to the "differentiated tumour cells model". Our approach suggests that OLIG2 is the most specific GSC marker; additional investigations with careful considerations about methodology and strategies of validation are, however, mandatory.

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Section: Discussionmentioning

confidence: 99%

Identification of OLIG2 as the most specific glioblastoma stem cell marker starting from comparative analysis of data from similar DNA chip microarray platforms

et al. 2014

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“…First, the use of a number of heterogeneous enabling platforms (e.g., cDNA gridding vs. oligonucleotide arraying) has limited the potential and the effectiveness of meta-analysis, since results obtained with different platforms, in different laboratories, frequently using not standardized protocols/ statistical tools are difficult to compare. [3][4][5] Second, several published expression profiling studies are not compliant with the MIAME (minimum information about a microarray experiments) guidelines; 6,7 thereby further limiting, in some cases, the comparability of microarray experiments. Finally, the high intrinsic "genetic noise", embedded in the system, considerably diminishes the applicative potential of cancer signatures (this issue will be analyzed in detail later).…”

Section: Introductionmentioning

confidence: 99%

Unbiased vs. biased approaches to the identification of cancer signatures: the case of lung cancer

2008

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Expression profiling analysis of human cancers is a promising approach to obtain precise molecular classification of cancers, to develop stratification tools for therapeutic regimens, and to predict the biological behavior of neoplasia. Direct profiling of human cancers (herein defined as "the unbiased approach") presents, however, intrinsic problems connected with the high genetic noise embedded in the system. This, in turn, leads to fitting of the noise in the data (the so-called "overtraining") with consequent instability of the identified signatures, when applied on different cohorts of patients. To circumvent these problems, "biased approaches"-which exploit the molecular knowledge of cancer obtained in model systems-are being developed. Biased approaches, however, are not problem-free, in that they provide information limited to single oncogenic events, thereby failing, at least in principle, to capture the complex repertoire of alterations of human cancers. In this review, we compare the two approaches and provide a test case, from our studies, of how "integrated" strategies, which combine biased and unbiased approaches, might lead to the identification of stable and reliable predictive signatures in cancer.

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“…Using two publicly available data sets (both dealing with acute leukaemia), we show that this quality measure can be used to compare different microarray data sets with respect to their ability to discriminate between genes whose expression is and is not affected by the different conditions. Since, in the near future, microarray data sets that address similar hypotheses will become increasingly available (even to such an extent that meta-analysis techniques will be necessary to analyse them simultaneously; see Rhodes et al (2002) and Moreau et al (2003)), assessing their quality could become an important issue.…”

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confidence: 99%

Balancing false positives and false negatives for the detection of differential expression in malignancies

et al. 2004

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A basic problem of microarray data analysis is to identify genes whose expression is affected by the distinction between malignancies with different properties. These genes are said to be differentially expressed. Differential expression can be detected by selecting the genes with P-values (derived using an appropriate hypothesis test) below a certain rejection level. This selection, however, is not possible without accepting some false positives and negatives since the two sets of P-values, associated with the genes whose expression is and is not affected by the distinction between the different malignancies, overlap. We describe a procedure for the study of differential expression in microarray data based on receiver-operating characteristic curves. This approach can be useful to select a rejection level that balances the number of false positives and negatives and to assess the degree of overlap between the two sets of P-values. Since this degree of overlap characterises the balance that can be reached between the number of false positives and negatives, this quantity can be seen as a quality measure of microarray data with respect to the detection of differential expression. As an example, we apply our method to data sets studying acute leukaemia.

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Comparison and meta-analysis of microarray data: from the bench to the computer desk

Cited by 158 publications

References 59 publications

Identification of OLIG2 as the most specific glioblastoma stem cell marker starting from comparative analysis of data from similar DNA chip microarray platforms

Identification of OLIG2 as the most specific glioblastoma stem cell marker starting from comparative analysis of data from similar DNA chip microarray platforms

Unbiased vs. biased approaches to the identification of cancer signatures: the case of lung cancer

Balancing false positives and false negatives for the detection of differential expression in malignancies

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