Bivalent chromatin as a therapeutic target in cancer : An in silico predictive approach for combining epigenetic drugs
Alarcón Cor, Tomás (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Sardanyés, Josep (Centre de Recerca Matemàtica)
Guillamon, Antoni (Centre de Recerca Matemàtica)
Menendez, Javier A. (Institut d'Investigació Biomèdica de Girona)

Data:	2021
Resum:	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.
Ajuts:	Ministerio de Economía y Competitividad MTM2015-71509-C2-1-R Ministerio de Economía y Competitividad MTM2015-71509-C2-2-R Agencia Estatal de Investigación RTI2018-098322-B-I00 Agencia Estatal de Investigación PGC2018-098676-B-I00 Agencia Estatal de Investigación RTI2018-093860-B-C21 Agencia Estatal de Investigación PID2019-10455GB-I00 Agència de Gestió d'Ajuts Universitaris i de Recerca 2014/SGR-229 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-1049 Agència de Gestió d'Ajuts Universitaris i de Recerca 2017/SGR-01735 Ministerio de Ciencia e Innovación RYC-2017-22243
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	PLoS computational biology, Vol. 17, Issue 6 (June 2021) , art e1008408, ISSN 1553-7358

DOI: 10.1371/journal.pcbi.1008408
PMID: 34153035

25 p, 3.5 MB

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