Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge

Tarca, Adi L.; Lauria, Mario; Unger, Michael; Bilal, Erhan; Boué, Stéphanie; Dey, Kushal Kumar; Hoeng, Julia; Koeppl, Heinz; Martin, Florian; Meyer, Pablo; Nandy, Preetam; Norel, Raquel; Peitsch, Manuel C.; Rice, John Jeremy; Romero, Roberto; Stolovitzky, Gustavo; Talikka, Marja; Xiang, Yu‐Tao; Zechner, Christoph

doi:10.1093/bioinformatics/btt492

Cited by 107 publications

(103 citation statements)

References 26 publications

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“…We applied the approach comparable used by the best performing team of the IMPROVER DSC. 38,39 The 5-fold cross-validation performance of this model reached Sp ¼ 0.96 and Se ¼ 0.93 in predicting smokers versus NS; slightly above the performance of models based on the core and extended signatures.…”

Section: Exposure Signature Establishmentmentioning

confidence: 80%

“…However, despite the success of the IMPRO-VER Diagnostic Signature Challenge (DSC), the development of computational methodologies that can be robust and versatile in clinical applications remains challenging. 38,39 The aim of the present study was to identify a whole blood-based gene signature for current smokers (CS) with the potential to distinguish between subjects who smoked and those who had stopped smoking (former smokers (FS)) or never smoked (nonsmokers (NS)). Taking advantage of the lessons learnt from the IMPROVER DSC, we developed a new methodology based on a prediction model that uses high fold-change genes extracted from several publicly available gene expression datasets that profiled samples from CS and NS or FS in the same tissue of interest.…”

Section: Introductionmentioning

confidence: 99%

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Identification of gene expression signature for cigarette smoke exposure response—from man to mouse

et al. 2015

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Gene expression profiling data can be used in toxicology to assess both the level and impact of toxicant exposure, aligned with a vision of 21st century toxicology. Here, we present a whole blood-derived gene signature that can distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Such a signature that can be measured in a surrogate tissue (whole blood) may help in monitoring smoking exposure as well as discontinuation of exposure when the primarily impacted tissue (e.g., lung) is not readily accessible. The signature consisted of LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA1. Several members of this signature have been previously described in the context of smoking. The signature translated well across species and could distinguish mice that were exposed to cigarette smoke from ones exposed to air only or had been withdrawn from cigarette smoke exposure. Finally, the small signature of only 11 genes could be converted into a polymerase chain reaction-based assay that could serve as a marker to monitor compliance with a smoking abstinence protocol.

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Section: Exposure Signature Establishmentmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Identification of gene expression signature for cigarette smoke exposure response—from man to mouse

et al. 2015

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“…Two-sample t test or paired-sample t test according to experimental design was employed as differential expression calling method, followed by the Benjamini-Hochberg (FDR) adjustment. For verifying expression correlation between genes, Pearson correlation analysis was used after CEL files from GSE43580 (27) and GSE31210 (28) were downloaded and normalized by RMA.…”

Section: Flow Cytometric Analysismentioning

confidence: 99%

Long Noncoding RNA PVT1 Promotes Non–Small Cell Lung Cancer Cell Proliferation through Epigenetically Regulating LATS2 Expression

Wan

Sun

Liu

et al. 2016

Molecular Cancer Therapeutics

209

180

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Long noncoding RNAs (lncRNA) are a novel class of transcripts with no protein coding capacity, but with diverse functions in cancer cell proliferation, apoptosis, and metastasis. The lncRNA PVT1 is 1,716 nt in length and located in the chr8q24.21 region, which also contains the myelocytomatosis (MYC) oncogene. Previous studies demonstrated that MYC promotes PVT1 expression in primary human cancers. However, the expression pattern and potential biologic function of PVT1 in non-small cell lung cancer (NSCLC) is still unclear. Here, we found that PVT1 was upregulated in 105 human NSCLC tissues compared with normal samples. High expression of PVT1 was associated with a higher tumor-node-metastasis stage and tumor size, as well as poorer overall survival. Functional analysis revealed that knockdown of PVT1 inhibited NSCLC cell proliferation and induced apoptosis both in vitro and in vivo. RNA immunoprecipitation and chromatin immunoprecipitation assays demonstrated that PVT1 recruits EZH2 to the large tumor suppressor kinase 2 (LATS2) promoter and represses LATS2 transcription. Furthermore, ectopic expression of LATS2 increased apoptosis and repressed lung adenocarcinoma cell proliferation by regulating the Mdm2-p53 pathway. Taken together, our findings indicated that PVT1/EZH2/LATS2 interactions might serve as new target for lung adenocarcinoma diagnosis and therapy.

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“…Brain tissues were collected at 30 month, while brain tissues from 5-month-old mice fed the control diet were also collected and served as young controls. Identification of the transcriptional signature was done using an enhanced version of a rank-based classification method described in the Supplementary Material (Lauria 2013;Tarca et al 2013;Lauria et al 2015).…”

Section: Enhanced Network-based Analysis: Combining Network and Signmentioning

confidence: 99%

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

et al. 2015

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Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multiomics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.

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Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic Signature Challenge

Abstract: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.

Cited by 107 publications

References 26 publications

Identification of gene expression signature for cigarette smoke exposure response—from man to mouse

Identification of gene expression signature for cigarette smoke exposure response—from man to mouse

Long Noncoding RNA PVT1 Promotes Non–Small Cell Lung Cancer Cell Proliferation through Epigenetically Regulating LATS2 Expression

Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

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