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Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer

BACKGROUND: Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gen...

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Autores principales: Kirzinger, Morgan W. B., Vizeacoumar, Frederick S., Haave, Bjorn, Gonzalez-Lopez, Cristina, Bonham, Keith, Kusalik, Anthony, Vizeacoumar, Franco J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660958/
https://www.ncbi.nlm.nih.gov/pubmed/31351478
http://dx.doi.org/10.1186/s12920-019-0554-z
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author Kirzinger, Morgan W. B.
Vizeacoumar, Frederick S.
Haave, Bjorn
Gonzalez-Lopez, Cristina
Bonham, Keith
Kusalik, Anthony
Vizeacoumar, Franco J.
author_facet Kirzinger, Morgan W. B.
Vizeacoumar, Frederick S.
Haave, Bjorn
Gonzalez-Lopez, Cristina
Bonham, Keith
Kusalik, Anthony
Vizeacoumar, Franco J.
author_sort Kirzinger, Morgan W. B.
collection PubMed
description BACKGROUND: Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics. METHODS: We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response. RESULTS: Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib. CONCLUSIONS: This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-66609582019-08-01 Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer Kirzinger, Morgan W. B. Vizeacoumar, Frederick S. Haave, Bjorn Gonzalez-Lopez, Cristina Bonham, Keith Kusalik, Anthony Vizeacoumar, Franco J. BMC Med Genomics Research Article BACKGROUND: Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics. METHODS: We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response. RESULTS: Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib. CONCLUSIONS: This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-27 /pmc/articles/PMC6660958/ /pubmed/31351478 http://dx.doi.org/10.1186/s12920-019-0554-z Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Kirzinger, Morgan W. B.
Vizeacoumar, Frederick S.
Haave, Bjorn
Gonzalez-Lopez, Cristina
Bonham, Keith
Kusalik, Anthony
Vizeacoumar, Franco J.
Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer
title Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer
title_full Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer
title_fullStr Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer
title_full_unstemmed Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer
title_short Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer
title_sort humanized yeast genetic interaction mapping predicts synthetic lethal interactions of fbxw7 in breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6660958/
https://www.ncbi.nlm.nih.gov/pubmed/31351478
http://dx.doi.org/10.1186/s12920-019-0554-z
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