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Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use ‘omics data to identify gene...

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Detalles Bibliográficos
Autores principales: Benstead-Hume, Graeme, Wooller, Sarah K., Pearl, Frances M.G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042820/
https://www.ncbi.nlm.nih.gov/pubmed/28941356
http://dx.doi.org/10.1515/jib-2017-0027
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author Benstead-Hume, Graeme
Wooller, Sarah K.
Pearl, Frances M.G.
author_facet Benstead-Hume, Graeme
Wooller, Sarah K.
Pearl, Frances M.G.
author_sort Benstead-Hume, Graeme
collection PubMed
description The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use ‘omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.
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spelling pubmed-60428202019-01-28 Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics Benstead-Hume, Graeme Wooller, Sarah K. Pearl, Frances M.G. J Integr Bioinform Review The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use ‘omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development. De Gruyter 2017-09-23 /pmc/articles/PMC6042820/ /pubmed/28941356 http://dx.doi.org/10.1515/jib-2017-0027 Text en ©2017 Graeme Benstead-Hume et al., published by De Gruyter, Berlin/Boston http://creativecommons.org/licenses/by-nc-nd/3.0 This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
spellingShingle Review
Benstead-Hume, Graeme
Wooller, Sarah K.
Pearl, Frances M.G.
Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics
title Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics
title_full Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics
title_fullStr Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics
title_full_unstemmed Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics
title_short Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics
title_sort computational approaches to identify genetic interactions for cancer therapeutics
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6042820/
https://www.ncbi.nlm.nih.gov/pubmed/28941356
http://dx.doi.org/10.1515/jib-2017-0027
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