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Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs.

ABSTRACT: 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.

SUBMITTER: Alarcon T

PROVIDER: S-EPMC8248646 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Despite all epidemiological, clinical, and experimental research efforts, therapeutic concepts in sepsis and sepsis-induced multi-organ dysfunction syndrome (MODS) remain limited and unsatisfactory. Currently, gene expression data sets are widely utilized to discover new biomarkers and therapeutic targets in diseases. In the present study, we analyzed MODS expression profiles (comprising 13 sepsis and 8 control samples) retrieved from NCBI-GEO and found 359 differentially expressed genes (DEGs), among which 170 were downregulated and 189 were upregulated. Next, we employed the weighted gene co-expression network analysis (WGCNA) to establish a MODS-associated gene co-expression network (weighted) and identified representative module genes having an elevated correlation with age. Based on the results, a turquoise module was picked as our hub module. Further, we constructed the PPI network comprising 35 hub module DEGs. The DEGs involved in the highest-confidence PPI network were utilized for collecting pathway and gene ontology (GO) terms using various libraries. Nucleotide di- and triphosphate biosynthesis and interconversion was the most significant pathway. Also, 3 DEGs within our PPI network were involved in the top 5 significantly enriched ontology terms, with hypercortisolism being the most significant term. PRKAR1A was the overlapping gene between top 5 significant pathways and GO terms, respectively. PRKAR1A was considered as a therapeutic target in MODS, and 2992 ligands were screened for binding with PRKAR1A. Among these ligands, 3 molecules based on CDOCKER score (molecular dynamics simulated-based score, which allows us to rank the binding poses according to their quality and to identify the best pose for each system) and crucial interaction with human PRKAR1A coding protein and protein kinase-cyclic nucleotide binding domains (PKA RI alpha CNB-B domain) via active site binding residues, viz. Val283, Val302, Gln304, Val315, Ile327, Ala336, Ala337, Val339, Tyr373, and Asn374, were considered as lead molecules.

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Bivalent chromatin as a therapeutic target in cancer: An in silico predictive approach for combining epigenetic drugs.

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