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Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs
Tumour cell heterogeneity is a major barrier for efficient design of targeted anti-cancer therapies. A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248646/ https://www.ncbi.nlm.nih.gov/pubmed/34153035 http://dx.doi.org/10.1371/journal.pcbi.1008408 |
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author | Alarcón, Tomás Sardanyés, Josep Guillamon, Antoni Menendez, Javier A. |
author_facet | Alarcón, Tomás Sardanyés, Josep Guillamon, Antoni Menendez, Javier A. |
author_sort | Alarcón, Tomás |
collection | PubMed |
description | Tumour cell heterogeneity is a major barrier for efficient design of targeted anti-cancer therapies. A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. Few strategies have been proposed for quantifying the variability associated to individual cancer-cell heterogeneity and minimizing its undesirable impact on clinical outcomes. Here, we report a computational approach that allows the rational design of combinatorial therapies involving epigenetic drugs against chromatin modifiers. We have formulated a stochastic model of a bivalent transcription factor that allows us to characterise three different qualitative behaviours, namely: bistable, high- and low-gene expression. Comparison between analytical results and experimental data determined that the so-called bistable and high-gene expression behaviours can be identified with undifferentiated and differentiated cell types, respectively. Since undifferentiated cells with an aberrant self-renewing potential might exhibit a cancer/metastasis-initiating phenotype, we analysed the efficiency of combining epigenetic drugs against the background of heterogeneity within the bistable sub-ensemble. Whereas single-targeted approaches mostly failed to circumvent the therapeutic problems represented by tumour heterogeneity, combinatorial strategies fared much better. Specifically, the more successful combinations were predicted to involve modulators of the histone H3K4 and H3K27 demethylases KDM5 and KDM6A/UTX. Those strategies involving the H3K4 and H3K27 methyltransferases MLL2 and EZH2, however, were predicted to be less effective. Our theoretical framework provides a coherent basis for the development of an in silico platform capable of identifying the epigenetic drugs combinations best-suited to therapeutically manage non-uniform responses of heterogenous cancer cell populations. |
format | Online Article Text |
id | pubmed-8248646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82486462021-07-09 Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs Alarcón, Tomás Sardanyés, Josep Guillamon, Antoni Menendez, Javier A. PLoS Comput Biol Research Article Tumour cell heterogeneity is a major barrier for efficient design of targeted anti-cancer therapies. A diverse distribution of phenotypically distinct tumour-cell subpopulations prior to drug treatment predisposes to non-uniform responses, leading to the elimination of sensitive cancer cells whilst leaving resistant subpopulations unharmed. Few strategies have been proposed for quantifying the variability associated to individual cancer-cell heterogeneity and minimizing its undesirable impact on clinical outcomes. Here, we report a computational approach that allows the rational design of combinatorial therapies involving epigenetic drugs against chromatin modifiers. We have formulated a stochastic model of a bivalent transcription factor that allows us to characterise three different qualitative behaviours, namely: bistable, high- and low-gene expression. Comparison between analytical results and experimental data determined that the so-called bistable and high-gene expression behaviours can be identified with undifferentiated and differentiated cell types, respectively. Since undifferentiated cells with an aberrant self-renewing potential might exhibit a cancer/metastasis-initiating phenotype, we analysed the efficiency of combining epigenetic drugs against the background of heterogeneity within the bistable sub-ensemble. Whereas single-targeted approaches mostly failed to circumvent the therapeutic problems represented by tumour heterogeneity, combinatorial strategies fared much better. Specifically, the more successful combinations were predicted to involve modulators of the histone H3K4 and H3K27 demethylases KDM5 and KDM6A/UTX. Those strategies involving the H3K4 and H3K27 methyltransferases MLL2 and EZH2, however, were predicted to be less effective. Our theoretical framework provides a coherent basis for the development of an in silico platform capable of identifying the epigenetic drugs combinations best-suited to therapeutically manage non-uniform responses of heterogenous cancer cell populations. Public Library of Science 2021-06-21 /pmc/articles/PMC8248646/ /pubmed/34153035 http://dx.doi.org/10.1371/journal.pcbi.1008408 Text en © 2021 Alarcón et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Alarcón, Tomás Sardanyés, Josep Guillamon, Antoni Menendez, Javier A. Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs |
title | Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs |
title_full | Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs |
title_fullStr | Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs |
title_full_unstemmed | Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs |
title_short | Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs |
title_sort | bivalent chromatin as a therapeutic target in cancer: an in silico predictive approach for combining epigenetic drugs |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248646/ https://www.ncbi.nlm.nih.gov/pubmed/34153035 http://dx.doi.org/10.1371/journal.pcbi.1008408 |
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