Metabolic inhibitors accentuate the anti-tumoral effect of HDAC5 inhibition

Hendrick, Elodie; Peixoto, Paul; Blomme, Arnaud; Polese, Catherine; Mathéus, Nicolas; Cimino, Jonathan; Frère, Antoine; Mouithys‐Mickalad, Ange; Serteyn, Didier; Bettendorff, Lucien; Elmoualij, Benaïssa; Tullio, Pascal De; Eppe, Gauthier; Dequiedt, Samuel; Castronovo, Vincenzo; Mottet, Denis

doi:10.1038/onc.2017.103

Cited by 17 publications

(10 citation statements)

References 52 publications

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“…Previous studies and our data from this study show that HDACIs, including droxinostat, induce cellular apoptosis leading to cancer cell death [ 28 , 34 , 35 ]. A recent report showed that the balance disruption of the anti- and pro-oxidant system might be involved in the cancer cell sensitivity of HDACIs [ 36 – 38 ]. For example, inhibition of HDAC5 increased mitochondrial iron-dependent ROS production in HeLa cells, resulting in apoptosis and autophagy [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…A recent report showed that the balance disruption of the anti- and pro-oxidant system might be involved in the cancer cell sensitivity of HDACIs [ 36 – 38 ]. For example, inhibition of HDAC5 increased mitochondrial iron-dependent ROS production in HeLa cells, resulting in apoptosis and autophagy [ 38 ]. Panobionstat, a pan HDACI, reduced the viability of HeLa cells through induction of ROS [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

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Droxinostat sensitizes human colon cancer cells to apoptotic cell death via induction of oxidative stress

et al. 2018

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Upregulation of histone acetylation plays a critical role in the dysregulation of transcription. It alters the structure of chromatin, which leads to the onset of cancer. Histone deacetylase inhibitors may therefore be a promising way to limit cancer progression. In this study, we examined the effects of droxinostat on the growth of HT-29 colon cancer cells. Our results show that droxinostat effectively inhibited cell growth and colony-forming ability by inducing cellular apoptosis and ROS production in HT-29 cells. Notably, the apoptotic inhibitor Z-VAD-FMK significantly decreased the levels of cellular apoptosis and the antioxidant γ-tocotrienol (GT3) significantly decreased ROS production induced by droxinostat treatment. Z-VAD-FMK and GT3 also partially reversed the negative growth effects of droxinstat on HT-29 cells. GT3 treatment decreased cellular apoptosis and increased colony-forming ability upon droxinostat administration. Z-VAD-FMK treatment also partially decreased droxinostat-induced ROS production. Our findings suggest that the effects of droxinostat on colon cancer cells are mediated by the induction of oxidative stress and apoptotic cell death.Electronic supplementary materialThe online version of this article (10.1186/s11658-018-0101-5) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Droxinostat sensitizes human colon cancer cells to apoptotic cell death via induction of oxidative stress

et al. 2018

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“…Table 3 gives the five highest-weighted target probabilities in the LDA projection, where four of those targets have known links to toxicity. For example, the histone deacetylases 5 and 6 are the highest weighted targets, and each are currently exploited in cancer therapies [52, 53]. Additionally, normal function of the Glucagon-like peptide 1 receptor is required for cell proliferation and hence negative regulation can be linked to apoptosis [54].…”

Section: Resultsmentioning

confidence: 99%

Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity

Allen¹,

Mervin²,

Mahmoud³

et al. 2019

J Cheminform

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Despite the increasing knowledge in both the chemical and biological domains the assimilation and exploration of heterogeneous datasets, encoding information about the chemical, bioactivity and phenotypic properties of compounds, remains a challenge due to requirement for overlap between chemicals assayed across the spaces. Here, we have constructed a novel dataset, larger than we have used in prior work, comprising 579 acute oral toxic compounds and 1427 non-toxic compounds derived from regulatory GHS information, along with their corresponding molecular and protein target descriptors and qHTS in vitro assay readouts from the Tox21 project. We found no clear association between the results of a FAFDrugs4 toxicophore screen and the acute oral toxicity classifications for our compound set; and a screen using a subset of the ToxAlerts toxicophores was also of limited utility, with only slight enrichment toward the toxic set (odds ratio of 1.48). We then investigated to what degree toxic and non-toxic compounds could be separated in each of the spaces, to compare their potential contribution to further analyses. Using an LDA projection, we found the largest degree of separation using chemical descriptors (Cohen’s d of 1.95) and the lowest degree of separation between toxicity classes using qHTS descriptors (Cohen’s d of 0.67). To compare the predictivity of the feature spaces for the toxicity endpoint, we next trained Random Forest (RF) acute oral toxicity classifiers on either molecular, protein target and qHTS descriptors. RFs trained on molecular and protein target descriptors were most predictive, with ROC AUC values of 0.80–0.92 and 0.70–0.85, respectively, across three test sets. RFs trained on both chemical and protein target descriptors combined exhibited similar predictive performance to the single-domain models (ROC AUC of 0.80–0.91). Model interpretability was improved by the inclusion of protein target descriptors, which allow the identification of specific targets (e.g. Retinal dehydrogenase) with literature links to toxic modes of action (e.g. oxidative stress). The dataset compiled in this study has been made available for future application. Electronic supplementary material The online version of this article (10.1186/s13321-019-0356-5) contains supplementary material, which is available to authorized users.

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“…6B). Similar effects in cancer cells include HDAC6 knockdown in pediatric acute myeloid leukemia cells, which enhances cytarabine-induced apoptosis [158-160] and the use of histone deacetylase inhibitors in combination with gemcitabine, which augments killing of pancreatic cancer cell lines [161-165] and HeLa cells [99].…”

Section: Resultsmentioning

confidence: 99%

A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity

Santos

Icyuz

Pound

et al. 2019

Preprint

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10Knowledge about synthetic lethality can be applied to enhance the efficacy of 11 anti-cancer therapies in individual patients harboring genetic alterations in their cancer 12 that specifically render it vulnerable. We investigated the potential for high-resolution 13 phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, 14 comprehensive, and quantitative assessment of drug-gene interaction for gemcitabine 15 and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures 16 yet distinct anti-tumor efficacy. Human deoxycytidine kinase (dCK) was conditionally 17 expressed in the S. cerevisiae genomic library of knockout and knockdown (YKO/KD) 18 strains, to globally and quantitatively characterize differential drug-gene interaction for 19 gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, 20 histone modification, chromatin remodeling, and apoptosis-related processes influence 21 gemcitabine specifically, while drug-gene interaction specific to cytarabine was less 22 enriched in Gene Ontology. Processes having influence over both drugs were DNA 23 repair and integrity checkpoints and vesicle transport and fusion. Non-GO-enriched 24 genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics 25 data were integrated to identify yeast-human homologs with correlated differential gene 26 2 expression and drug-efficacy, thus providing a unique resource to predict whether 27 differential gene expression observed in cancer genetic profiles are causal in tumor-28 specific responses to cytotoxic agents. 29 30 Keywords: 31 yeast phenomics, gene-drug interaction, genetic buffering, quantitative high 32 throughput cell array phenotyping (Q-HTCP), cell proliferation parameters (CPPs), 33 gemcitabine, cytarabine, recursive expectation-maximization clustering (REMc), 34 pharmacogenomics 35 36 Introduction: 37Genomics has enabled targeted therapy aimed at cancer driver genes and 38 oncogenic addiction [1], yet traditional cytotoxic chemotherapeutic agents remain among 39 the most widely used and efficacious anti-cancer therapies [2]. Changes in the genetic 40 network underlying cancer can produce vulnerabilities to cytotoxic chemotherapy that 41 further influence the therapeutic window and provide additional insight into their 42 mechanisms of action [3,4]. A potential advantage of so-called synthetic lethality-based 43 treatment strategies is that they could have efficacy against passenger gene mutation or 44 compensatory gene expression, while classic targeted therapies are directed primarily at 45 driver genes ( Fig. 1A). Quantitative high throughput cell array phenotyping of the yeast 46 knockout and knockdown libraries provides a phenomic means for systems level, high- 47resolution modeling of gene interaction [5][6][7][8][9], which is applied here to predict cancer-48 relevant drug-gene interaction through integration with cancer pharmacogenomics 49 resources ( Fig. 1B). 50Nucleoside analogs include a diverse group of co...

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Metabolic inhibitors accentuate the anti-tumoral effect of HDAC5 inhibition

Cited by 17 publications

References 52 publications

Droxinostat sensitizes human colon cancer cells to apoptotic cell death via induction of oxidative stress

Droxinostat sensitizes human colon cancer cells to apoptotic cell death via induction of oxidative stress

Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity

A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity

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