A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions.

Francesco Iorio,Nicolas Levin,Viji M Draviam,Mathew J Garnett,Julio Saez-Rodriguez,Viviane Boilot,Roshan L Shrestha,John D Minna

doi:10.1371/journal.pone.0139446

Francesco Iorio, Nicolas Levin + Show 6 more

Open Access

https://doi.org/10.1371/journal.pone.0139446

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Abstract

We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound). This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent) as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells–consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

Highlights

In the last few years, gene expression signature matching strategies have proven to be effective in identifying unexpected connections between transcriptional profiles of diseases and drug responses, based on genome-wide similarity metrics applied to gene expression data [1]
This response summarizes the consistent effect of an X-treatment on transcription across all the treated cell lines contained in the study, in the form of a genome-wide ranked list of genes (i.e. ‘Prototype Ranked List’ (PRL-signature)—S1 Supplementary Dataset, available at https:// github.com/francescojm/iNRG_cMap/ and at http://www.ebi.ac.uk/~iorio/PLoS_ONE_Submission)
These drugs elicit a transcriptional response similar to that of paclitaxel, according to the distance metric described in the supplementary methods and in [13], they could share a mode of action (MoA) with paclitaxel

Summary

Introduction

In the last few years, gene expression signature matching strategies have proven to be effective in identifying unexpected connections between transcriptional profiles of diseases and drug responses, based on genome-wide similarity metrics applied to gene expression data [1]. The first paradigm is based on the signature reversion principle and aims at identifying drugs inducing a transcriptional response anti-correlated (opposite) to that of a given disease. Drugs identified with this approach are hypothesized to be capable of reverting the disease signature, and the disease phenotype. This idea has been successfully applied in various contexts, including Crohn’s disease [7], skeletal muscle atrophy [8], cancer [9,10,11], and Alzheimer’s disease [12]

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