Dataset Information

Exploiting the Epigenome to Control Cancer-Promoting Gene-Expression Programs.

ABSTRACT: The epigenome is a key determinant of transcriptional output. Perturbations within the epigenome are thought to be a key feature of many, perhaps all cancers, and it is now clear that epigenetic changes are instrumental in cancer development. The inherent reversibility of these changes makes them attractive targets for therapeutic manipulation, and a number of small molecules targeting chromatin-based mechanisms are currently in clinical trials. In this perspective we discuss how understanding the cancer epigenome is providing insights into disease pathogenesis and informing drug development. We also highlight additional opportunities to further unlock the therapeutic potential within the cancer epigenome.

SUBMITTER: Brien GL

PROVIDER: S-EPMC4889129 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Exploiting the Epigenome to Control Cancer-Promoting Gene-Expression Programs.

Brien Gerard L GL Valerio Daria G DG Armstrong Scott A SA

Cancer cell 20160401 4

The epigenome is a key determinant of transcriptional output. Perturbations within the epigenome are thought to be a key feature of many, perhaps all cancers, and it is now clear that epigenetic changes are instrumental in cancer development. The inherent reversibility of these changes makes them attractive targets for therapeutic manipulation, and a number of small molecules targeting chromatin-based mechanisms are currently in clinical trials. In this perspective we discuss how understanding t ...[more]

PMID: 27070701

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Project description:Background: Selected gene mutations are routinely used to guide the selection of cancer drugs for a given patient tumour. Large pharmacogenomic data sets, such as those by Genomics of Drug Sensitivity in Cancer (GDSC) consortium, were introduced to discover more of these single-gene markers of drug sensitivity. Very recently, machine learning regression has been used to investigate how well cancer cell line sensitivity to drugs is predicted depending on the type of molecular profile. The latter has revealed that gene expression data is the most predictive profile in the pan-cancer setting. However, no study to date has exploited GDSC data to systematically compare the performance of machine learning models based on multi-gene expression data against that of widely-used single-gene markers based on genomics data. Methods: Here we present this systematic comparison using Random Forest (RF) classifiers exploiting the expression levels of 13,321 genes and an average of 501 tested cell lines per drug. To account for time-dependent batch effects in IC 50 measurements, we employ independent test sets generated with more recent GDSC data than that used to train the predictors and show that this is a more realistic validation than standard k-fold cross-validation. Results and Discussion: Across 127 GDSC drugs, our results show that the single-gene markers unveiled by the MANOVA analysis tend to achieve higher precision than these RF-based multi-gene models, at the cost of generally having a poor recall (i.e. correctly detecting only a small part of the cell lines sensitive to the drug). Regarding overall classification performance, about two thirds of the drugs are better predicted by the multi-gene RF classifiers. Among the drugs with the most predictive of these models, we found pyrimethamine, sunitinib and 17-AAG. Conclusions: Thanks to this unbiased validation, we now know that this type of models can predict in vitro tumour response to some of these drugs. These models can thus be further investigated on in vivo tumour models. R code to facilitate the construction of alternative machine learning models and their validation in the presented benchmark is available at http://ballester.marseille.inserm.fr/gdsc.transcriptomicDatav2.tar.gz.

Dataset Information

Exploiting the Epigenome to Control Cancer-Promoting Gene-Expression Programs.

Publications

Exploiting the Epigenome to Control Cancer-Promoting Gene-Expression Programs.

Similar Datasets

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