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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
De Gruyter
2017
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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. |
format | Online Article Text |
id | pubmed-6042820 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | De Gruyter |
record_format | MEDLINE/PubMed |
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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