Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses

Tavassoly, Iman; Hu, Yuan; Zhao, Shan; Mariottini, Chiara; Boran, Aislyn; Chen, Yibang; Li, Lisa; Tolentino, Rosa E.; Jayaraman, Gomathi; Goldfarb, Joseph; Gallo, James M.; Iyengar, Ravi

doi:10.1002/1878-0261.12521

Cited by 29 publications

(28 citation statements)

References 51 publications

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“…XDH is strictly modulated at the transcriptional and post-translational levels and highly expressed in the breast, liver, intestine, kidney and vascular endothelial cells while lowly expressed in the lung and skin [31]. Studies have showed that the expression of XDH may be positively associated with a worse outcome in gastric and lung cancer [32,33]. However, XDH expression decreased in RCC and negatively associated with a high malignity grade and a worse prognosis [14].…”

Section: Discussionmentioning

confidence: 99%

C1QBP regulates apoptosis of renal cell carcinoma via modulating xanthine dehydrogenase (XDH) mediated ROS generation

Wang¹,

Cui²,

Wang³

et al. 2020

Preprint

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Background Complement component 1 Q subcomponent binding protein (C1QBP) is a multifunctional protein and plays a vital role in the progression and metabolism of cancer. Our previous study has revealed that the low expression of C1QBP in renal cell carcinoma (RCC) and knockdown of C1QBP promotes the adhesion and invasion of RCC cells. However, its functions in the metabolism, oxidative stress and apoptosis of RCC cells have not yet been explored. Methods Metabolomics assay was applied to investigate the role of C1QBP in RCC metabolism. C1QBP knockdown and overexpression cells were established via lentiviral infection and subjected to apoptosis and ROS assay in vitro. RNA stability assay and pre-mRNA detection were applied to characterize the mechanism of C1QBP regulating XDH transcription. In vivo, orthotopic tumor xenografts assay was performed to investigate the role of C1QBP progression. Results Metabolomics investigations revealed that C1QBP dramatically diminished the hypoxanthine content in RCC cells. C1QBP promoted the mRNA and protein expression of hypoxanthine catabolic enzyme xanthine dehydrogenase (XDH). Meanwhile, C1QBP regulated the expression of XDH gene at the pre-transcriptional level by regulating XDH transcriptional stimulators IL-6, TNF-α and IFN-γ. Moreover, the expression of C1QBP and XDH was lower in RCC tumors compared with the para-tumor normal tissues, and their down-regulation was associated with higher levels of Fuhrman grade. In RCC cells, C1QBP significantly increased produces reactive oxygen species (ROS) levels, apoptosis, and the expression of apoptotic proteins cleaved caspase-3 and bax/bcl2 via regulating XDH.Conclusions C1QBP promotes the catabolism of hypoxanthine and elevates the apoptosis of RCC cells by modulating XDH-mediated ROS generation.

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Section: Discussionmentioning

confidence: 99%

C1QBP regulates apoptosis of renal cell carcinoma via modulating xanthine dehydrogenase (XDH) mediated ROS generation

Wang¹,

Cui²,

Wang³

et al. 2020

Preprint

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“…The majority of these, including XDH (Xanthine Dehydrogenase) [ 35 , 36 ], ATIC (5-Aminoimidazole-4-Carboxamide Ribonucleotide Formyltransferase/IMP Cyclohydrolase) [ 37 ], CA9 (Carbonic Anhydrase 9) [ 38 ], SLC7A11 (Solute Carrier Family 7 Member 11) [ 39 ], and GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) [ 40 ] are implicated in tumorigenesis. XDH, which encodes for xanthine dehydrogenase, has been reported to be highly expressed in a lung adenocarcinoma (LUAD) subtype associated with poor survival [ 36 ]. In our analysis, XDH was hypomethylated in TSS200 and gene body, and was associated with up-regulated gene expression.…”

Section: Resultsmentioning

confidence: 99%

“…XDH inhibitors may be purine analogs e.g., allopurinol and oxypurinol, or non-purine agents, e.g., topiroxostat. The antitumor effects of allopurinol in NSCLC cell lines have been recently described, as well as a 6-gene signature for allopurinol-sensitive and allopurinol-resistant NSCLC cell lines [ 36 ]. Eniluracil, an orally active dihydropyrimidine dehydrogenase (DPD) inhibitor that enhances activity of chemotaxic agents, also emerged as a drug for XDH.…”

Section: Resultsmentioning

confidence: 99%

Combined Methylome and Transcriptome Analyses Reveals Potential Therapeutic Targets for EGFR Wild Type Lung Cancers with Low PD-L1 Expression

Wang

Yarmus³

et al. 2020

Cancers

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Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 have demonstrated remarkable treatment efficacy in advanced non-small cell lung cancer (NSCLC). However, low expression of programmed death-ligand 1 (PD-L1), epidermal growth factor receptor (EGFR) wild-type NSCLCs are refractory, and only few therapeutic options exist. Currently, combination therapy with ICIs is frequently used in order to enhance the treatment response rates. Yet, this regimen is still associated with poor treatment outcome. Therefore, identification of potential therapeutic targets for this subgroup of NSCLC is strongly desired. Here, we report the distinct methylation signatures of this special subgroup. Moreover, several druggable targets and relevant drugs for targeted therapy were incidentally identified. We found hypermethylated differentially methylated regions (DMRs) in three regions (TSS200, TSS1500, and gene body) are significantly higher than hypomethylated ones. Downregulated methylated genes were found to be involved in negative regulation of immune response and T cell-mediated immunity. Moreover, expression of four methylated genes (PLCXD3 (Phosphatidylinositol-Specific Phospholipase C, X Domain Containing 3), BAIAP2L2 (BAR/IMD Domain Containing Adaptor Protein 2 Like 2), NPR3 (Natriuretic Peptide Receptor 3), SNX10 (Sorting Nexin 10)) can influence patients’ prognosis. Subsequently, based on DrugBank data, NetworkAnalyst 3.0 was used for protein–drug interaction analysis of up-regulated differentially methylated genes. Protein products of nine genes were identified as potential druggable targets, of which the tumorigenic potential of XDH (Xanthine Dehydrogenase), ATIC (5-Aminoimidazole-4-Carboxamide Ribonucleotide Formyltransferase/IMP Cyclohydrolase), CA9 (Carbonic Anhydrase 9), SLC7A11 (Solute Carrier Family 7 Member 11), and GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) have been demonstrated in previous studies. Next, molecular docking and molecular dynamics simulation were performed to verify the structural basis of the therapeutic targets. It is noteworthy that the identified pemetrexed targeting ATIC has been recently approved for first-line use in combination with anti-PD1 inhibitors against lung cancer, irrespective of PD-L1 expression. In future work, a pivotal clinical study will be initiated to further validate our findings.

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“…The integration achieved by connecting dynamical models to network models enables us to identify subcellular processes within the organs of interest. It should be noted that PPI networks allow for the identification of intermediate genes or gene products that could be potentially overlooked [42]. The computational model of the hormone levels and the regression analysis allow for the study of a system from a dynamic perspective and for ranking cell-level information.…”

Section: Discussionmentioning

confidence: 99%

A tissue- and organ-based cell biological atlas of obesity-related human genes and cellular pathways

Tavassoly

Barbieri

Hasselt

et al. 2020

Preprint

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Over the last decades, several features of obesity have been identified at behavioral, physiological, endocrine and genomic levels, and they have revealed the complexity of the disease; obesity results from a combination of genetic predisposition, endocrine disorders, and dysregulation of both food intake and energy expenditure. This complexity makes the development of new therapeutic regimens challenging and bariatric surgery is still the treatment of choice for many obese patients. Given the need for noninvasive therapeutic intervention strategies, we sought to systematically study the biological manifestations of obesity in peripheral organs. We analyzed publicly available datasets of genes, genomic determinants, and levels of obesity-related hormones in the blood, using a combination of methodologies, including graph theory and dynamical modeling, that allow for the integration of different types of datasets. The analysis revealed tissue-and organ-specific metabolic impairments and potential new drug targets. All the data are organized into a tissue/organ-based subcellular-function atlas for human obesity. The data show that the complexity of the obesity arises due to the multiplicity of subcellular processes in different peripheral organs. AMY, CCK, GIP). Plasma levels of some hormones in post-bariatric surgery patients lie between the lean-curves and the obese-curves (GHRL, GCG, LEP, AMY, GIP), while others appear higher than in either lean or obese individuals (INS, CCK, GLP-1, PYY, and OXM) ( Figure 3B). We ran simulated time courses for lean, obese and post-bariatric surgery subjects in a fasting and a postprandial state to estimate reliable initial values for the kinetic parameters such that the simulated curves closely resembled published experimental data [8, 25, 27, 28,[30][31][32][33] ( Figure 3B and Supplementary File 3).The magnitude of the endocrine response to nutrients can be assessed by the difference in plasma levels of the hormones before and after the meal. To quantify it, we chose to calculate the ratio between the 2-hour area under the curve (AUC) of the postprandial plasma concentration and the 2-hour AUC of the fasting plasma concentration for each hormone (Figure 3C and Supplementary Figure S8). To identify which kinetic parameters are more likely to affect the endocrine response represented by the AUC, we performed a multivariable regression analysis (MRA) [34]. The logic of this analysis was to randomly vary the initial values of the set of kinetic parameters used in the model a number of times (Supplementary Figure S9), utilizing each new set of values to run a simulation that by varying each parameter affects the output and expresses it in terms of a numerical Partial Least Square Regression (PLSR) coefficient: the higher the coefficient, the more sensitive is the response to the parameter; the lower the coefficient, the more robust is the response to changes in the parameter. The kinetic parameters for lean, obese, and post-surgery populations were then ranked according to the...

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Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses

Cited by 29 publications

References 51 publications

C1QBP regulates apoptosis of renal cell carcinoma via modulating xanthine dehydrogenase (XDH) mediated ROS generation

C1QBP regulates apoptosis of renal cell carcinoma via modulating xanthine dehydrogenase (XDH) mediated ROS generation

Combined Methylome and Transcriptome Analyses Reveals Potential Therapeutic Targets for EGFR Wild Type Lung Cancers with Low PD-L1 Expression

A tissue- and organ-based cell biological atlas of obesity-related human genes and cellular pathways

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