Array-based pharmacogenomics of molecular-targeted therapies in oncology

Sanoudou, Despina; Mountzios, Giannis; Arvanitis, Demetrios A.; Pectasides, Dimitrios

doi:10.1038/tpj.2011.53

Cited by 13 publications

(9 citation statements)

References 105 publications

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“…So our approach can also be used to study the transcriptomic influence of the pharmacological interventions in cells. And with the increasing concerns on multi-target therapeutics [62, 63], we believe that our method can be further developed and help to design high-efficient combinatorial therapies for complex diseases.…”

Section: Discussionmentioning

confidence: 99%

Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Wang

2014

BMC Bioinformatics

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BackgroundMicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes. Several computational methods have been developed to infer the perturbed miRNA regulatory networks by integrating genome-wide gene expression data and sequence-based miRNA-target predictions. However, most of them only use the expression information of the miRNA direct targets, rarely considering the secondary effects of miRNA perturbation on the global gene regulatory networks.ResultsWe proposed a network propagation based method to infer the perturbed miRNAs and their key target genes by integrating gene expressions and global gene regulatory network information. The method used random walk with restart in gene regulatory networks to model the network effects of the miRNA perturbation. Then, it evaluated the significance of the correlation between the network effects of the miRNA perturbation and the gene differential expression levels with a forward searching strategy. Results show that our method outperformed several compared methods in rediscovering the experimentally perturbed miRNAs in cancer cell lines. Then, we applied it on a gene expression dataset of colorectal cancer clinical patient samples and inferred the perturbed miRNA regulatory networks of colorectal cancer, including several known oncogenic or tumor-suppressive miRNAs, such as miR-17, miR-26 and miR-145.ConclusionsOur network propagation based method takes advantage of the network effect of the miRNA perturbation on its target genes. It is a useful approach to infer the perturbed miRNAs and their key target genes associated with the studied biological processes using gene expression data.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-255) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Wang

2014

BMC Bioinformatics

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“…Even though sharing of data immensely helps in reproducing study results 5 , current sharing practices are inadequate with respect to the size of data and corresponding infrastructure requirements for transfer and storage 2,6 . As computational processing required to process such data is becoming increasingly complex 3 , expertise is now needed for building the tools/workflows genome-wide association approach to understand the effect of drug compounds on the genome, which includes correlating drug response with molecular profiling, such as gene expression 15,16 . This is important for cancer patients, as treatment administration and success does not follow the notion of "one-size-fits-all", resulting in a reduction of patient survival and adverse effects such as toxicity 17 .…”

Section: Introductionmentioning

confidence: 99%

Orchestrating and sharing large multimodal data for transparent and reproducible research

Mammoliti

Смирнов

Nakano

et al. 2020

Preprint

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Reproducibility is essential to Open Science, as there is limited relevance for finding that cannot be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, challenged, and built upon. However, due to recent advances in the biological and computational sciences, it has become difficult to process, analyze, and share data with the community in a manner that is transparent. This has made reproducing research findings more challenging, with some researchers going as far as suggesting that the biomedical sciences are experiencing a “reproducibility crisis”. To overcome these issues, we created a cloud-based platform called ORCESTRA (www.orcestra.ca), which provides a flexible framework for the reproducible processing of multimodal biomedical data. The platform enables processing of genomic and pharmacological profiles of cancer samples through the use of automated processing pipelines that are user-customizable, which are executed through Pachyderm, a data versioning and orchestration tool. ORCESTRA creates an integrated and fully documented data object known as a PharmacoSet (PSet), with a persistent identifier (DOI), that can be used and shared for future analyses using the Bioconductor PharmacoGx package.

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“…In the field of molecular-targeted therapy for cancer, these methodologies have enabled the identification of molecular targets with "key" roles in neoplastic transformation or tumor progression and the subsequent development of targeted agents, which are most likely to be active in a specific molecular setting (Sanoudou et al, 2012). Moreover, the ability to profile each unique cancer for actionable aberrations by using this technologies in a cost-effective way provides unprecedented opportunities for using matched therapies in a selected patient population.…”

Section: Introductionmentioning

confidence: 99%

Clinical Implications of Microarray in Cancer Medicine

Kang¹

2015

International J. of Cancer Research

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Cancers are associated with an array of orchestrated genetic changes and the identification of changes causally related to the carcinogenic process. To elucidate the mechanism of cancer carcinogenesis, it is necessary to reconstruct molecular events at each level. Microarray technology is a versatile platform that allows rapid genetic analysis to take place on a genome-wide scale and has revolutionized to evaluate genetic markers and changes in cancer genetics. Since, their development in the mid-1990s, these technologies have become a key tool in the fight against cancer. Microarray data have led to the identification of molecular subclasses of solid tumors characterized by distinct oncogenic pathways, as well as the development of multigene prognostic or predictive models equivalent or superior to those of established clinical parameters. Currently, several genomic aberrations discovered by these assays are presently being used as predictive markers for cancer treatment with targeted therapeutics. But how do microarrays work and just how have they been used in cancer diagnosis and treatment thus far? Here, we presented a summary of the main applications of microarrays in the field of targeted therapies of cancer and discussed their potential in clinical implementation.

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Array-based pharmacogenomics of molecular-targeted therapies in oncology

Cited by 13 publications

References 105 publications

Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Inferring the perturbed microRNA regulatory networks from gene expression data using a network propagation based method

Orchestrating and sharing large multimodal data for transparent and reproducible research

Clinical Implications of Microarray in Cancer Medicine

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