Use of NQO1 status as a selective biomarker for oesophageal squamous cell carcinomas with greater sensitivity to 17-AAG

Hadley, Katie; Hendricks, Denver T.

doi:10.1186/1471-2407-14-334

Cited by 17 publications

(12 citation statements)

References 16 publications

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“…Thus, the mechanisms of acquired resistance to these drugs may be similar. NQO1 is a homodimeric metabolic enzyme that catalyzes the conversion of quinones to hydroquinones and has an important role in sensitivity to 17-AAG [ 29 , 21 ]. Loss or low activity of NQO1, by the reduction of its mRNA or the emergence of inactivating polymorphisms in the NQO1 gene, leads to resistance to 17-AAG in pancreatic cancer cells and glioblastoma cell lines [ 30 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

P-glycoprotein confers acquired resistance to 17-DMAG in lung cancers with an ALK rearrangement

et al. 2015

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BackgroundBecause anaplastic lymphoma kinase (ALK) is dependent on Hsp90 for protein stability, Hsp90 inhibitors are effective in controlling growth of lung cancer cells with ALK rearrangement. We investigated the mechanism of acquired resistance to 17-(Dimethylaminoethylamino)-17-demethoxygeldanamycin (17-DMAG), a geldanamycin analogue Hsp90 inhibitor, in H3122 and H2228 non-small cell lung cancer cell lines with ALK rearrangement.MethodsResistant cell lines (H3122/DR-1, H3122/DR-2 and H2228/DR) were established by repeated exposure to increasing concentrations of 17-DMAG. Mechanisms for resistance by either NAD(P)H/quinone oxidoreductase 1 (NQO1), previously known as a factor related to 17-DMAG resistance, or P-glycoprotein (P-gp; ABCB1/MDR1) were queried using RT-PCR, western blot analysis, chemical inhibitors, the MTT cell proliferation/survival assay, and cellular efflux of rhodamine 123.ResultsThe resistant cells showed no cross-resistance to AUY922 or ALK inhibitors, suggesting that ALK dependency persists in cells with acquired resistance to 17-DMAG. Although expression of NQO1 was decreased in H3122/DR-1 and H3122/DR-2, NQO1 inhibition by dicumarol did not affect the response of parental cells (H2228 and H3122) to 17-DMAG. Interestingly, all resistant cells showed the induction of P-gp at the protein and RNA levels, which was associated with an increased efflux of the P-gp substrate rhodamine 123 (Rho123). Transfection with siRNA directed against P-gp or treatment with verapamil, an inhibitor of P-gp, restored the sensitivity to the drug in all cells with acquired resistance to 17-DMAG. Furthermore, we also observed that the growth-inhibitory effect of 17-DMAG was decreased in A549/PR and H460/PR cells generated to over-express P-gp by long-term exposure to paclitaxel, and these cells recovered their sensitivity to 17-DMAG through the inhibition of P-gp.ConclusionP-gp over-expression is a possible mechanism of acquired resistance to 17-DMAG in cells with ALK rearrangement.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1543-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

P-glycoprotein confers acquired resistance to 17-DMAG in lung cancers with an ALK rearrangement

et al. 2015

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“…In S4 Table , many genes used by QRFs are pointed out to be related with cancers in literatures. For example, the inhibition activity of 17-AAG can be increased by the expression of NQO1 [ 23 ]. Also, the mutation of BRAF is predicted as a drug efficacy marker for some MEK inhibitors, including AZD6244, PD-0325901 and PLX4720 [ 24 ].…”

Section: Resultsmentioning

confidence: 99%

A quantile regression forest based method to predict drug response and assess prediction reliability

Fang

Yang

et al. 2018

PLoS ONE

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Drug response prediction is a critical step for personalized treatment of cancer patients and ultimately leads to precision medicine. A lot of machine-learning based methods have been proposed to predict drug response from different types of genomic data. However, currently available methods could only give a “point” prediction of drug response value but fail to provide the reliability and distribution of the prediction, which are of equal interest in clinical practice. In this paper, we proposed a method based on quantile regression forest and applied it to the CCLE dataset. Through the out-of-bag validation, our method achieved much higher prediction accuracy of drug response than other available tools. The assessment of prediction reliability by prediction intervals and its significance in personalized medicine were illustrated by several examples. Functional analysis of selected drug response associated genes showed that the proposed method achieves more biologically plausible results.

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“…Kung et al demonstrated that β-Lapachone-induced cytotoxicity of three different lung cancer cell lines was positively correlated with NQO1 expression and enzyme activity [ 37 ]. Hadley et al suggested that stratification of patients on the basis of NQO1 protein levels could identify a subset of esophageal squamous cell carcinomas patients that may potentially benefit from administration of low doses of 17-AAG, possibly in combination with other chemotherapeutics [ 38 ]. Huang et al reported that the potency and NQO1-dependent therapeutic window of deoxynyboquinone and its apparent reduced metabolism by one-electron oxidoreductases make this drug (or derivatives) very promising [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

High expression of NQO1 is associated with poor prognosis in serous ovarian carcinoma

Cui

Yan

et al. 2015

BMC Cancer

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BackgroundNAD(P)H:quinone oxidoreductase (NQO1) is a flavoprotein that catalyzes two-electron reduction and detoxification of quinones and its derivatives. NQO1 catalyzes reactions that have a protective effect against redox cycling, oxidative stress and neoplasia. High expression of NQO1 is associated with many solid tumors including those affecting the colon, breast and pancreas; however, its role in the progression of ovarian carcinoma is largely undefined. This study aimed to investigate the clinicopathological significance of high NQO1 expression in serous ovarian carcinoma.MethodsNQO1 protein expression was assessed using immunohistochemical (IHC) staining in 160 patients with serous ovarian carcinoma, 62 patients with ovarian borderline tumors and 53 patients with benign ovarian tumors. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect NQO1 mRNA expression levels. The correlation between high NQO1 expression and clinicopathological features of ovarian carcinoma was evaluated by Chi-square and Fisher’s exact test. Overall survival (OS) rates of all of ovarian carcinoma patients were calculated using the Kaplan-Meier method, and univariate and multivariate analyses were performed using the Cox proportional hazards regression model.ResultsNQO1 protein expression in ovarian carcinoma cells was predominantly cytoplasmic. Strong, positive expression of NQO1 protein was observed in 63.8% (102/160) of ovarian carcinomas, which was significantly higher than in borderline serous tumors (32.3%, 20/62) or benign serous tumors (11.3%, 6/53). Importantly, the rate of strong, positive NQO1 expression in borderline serous tumors was also higher than in benign serous tumors. High expression of NQO1 protein was closely associated with higher histological grade, advanced clinical stage and lower OS rates in ovarian carcinomas. Moreover, multivariate analysis indicated that NQO1 was a significant independent prognostic factor, in addition to clinical stage, in patients with ovarian carcinoma.ConclusionsNQO1 is frequently upregulated in ovarian carcinoma. High expressin of NQO1 protein may be an effective biomarker for poor prognostic evaluation of patients with serous ovarian carcinomas.

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Use of NQO1 status as a selective biomarker for oesophageal squamous cell carcinomas with greater sensitivity to 17-AAG

Cited by 17 publications

References 16 publications

P-glycoprotein confers acquired resistance to 17-DMAG in lung cancers with an ALK rearrangement

P-glycoprotein confers acquired resistance to 17-DMAG in lung cancers with an ALK rearrangement

A quantile regression forest based method to predict drug response and assess prediction reliability

High expression of NQO1 is associated with poor prognosis in serous ovarian carcinoma

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