Quantitative Targeted Proteomics of Pancreatic Cancer: Deoxycytidine Kinase Protein Level Correlates to Progression-Free Survival of Patients Receiving Gemcitabine Treatment

Ohmine, Ken; Kawaguchi, Koji; Ohtsuki, Sumio; Motoi, Fuyuhiko; Ohtsuka, Hideo; Kamiie, Junichi; Abe, Takaaki; Unno, Michiaki

doi:10.1021/acs.molpharmaceut.5b00282

Cited by 34 publications

(23 citation statements)

References 32 publications

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“…57 In the present study, several metabolite levels in purine and pyrimidine metabolic pathways changed in the plasma of males with OS infertility; they may reect increased oxidative stress in spermatogenesis, eventually leading to infertility. However, specic molecular mechanisms still require further study.…”

Section: -56mentioning

confidence: 57%

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Zeng

Zhang

et al. 2018

RSC Adv.

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Male infertility has become a global health problem. Currently, the diagnosis of male infertility depends on the results of semen quality or requires invasive surgical intervention. The process is complex and timeconsuming. Metabolomics is an emerging platform with unique advantages in disease diagnosis and pathological mechanism research. In this study, ultra-performance liquid chromatography-electrospray ionization-ion trap-time of flight mass spectrometry (UPLC-ESI-IT-TOFMS) combined with chemometrics methods was used to discover potential biomarkers of male infertility based on nontargeted plasma metabolomics. Plasma samples from healthy controls (HC, n ¼ 43) and various types of infertile patients, i.e., patients having oligozoospermia (OS, n ¼ 36), asthenospermia (AS, n ¼ 56) and erectile dysfunction (ED, n ¼ 45) were analyzed by UPLC-ESI-IT-TOFMS. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. The results of OPLS-DA showed that HCs could be discriminated from infertile patients including OS (R 2 ¼ 0.903,Some potential biomarkers were successfully discovered by variable selection methods and variable important in the projection (VIP) in combination with the T-test. Statistical significance was set at p < 0.05; the Benjamini-Hochberg false discovery rate was used to reduce type 1 errors resulting from multiple comparisons. The identified biomarkers were associated with energy consumption, hormone regulation and antioxidant defenses in spermatogenesis. To elucidate the pathophysiology of male infertility, relative metabolic pathways were studied. It was found that male infertility is closely related to disturbed phospholipid metabolism, amino acid metabolism, steroid hormone biosynthesis metabolism, metabolism of fatty acids and products of carnitine acylation, and purine and pyrimidine metabolisms. Plasma metabolomics provides a novel strategy for the diagnosis of male infertility and offers a new insight to study pathogenesis mechanism.

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Section: -56mentioning

confidence: 57%

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Zeng

Zhang

et al. 2018

RSC Adv.

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“…The k hCNT1 , k hENT1,in , k hENT1,out , and k CDA were considered variable too, in order to account for the different protein expression among subjects. They were supposed log-normally distributed with mean equal to the estimated values and coefficient of variation (CV) taken from Burt et al 38 To our knowledge, no information regarding CDA variability in tissues is present in the literature; thus, we decided to fix the CDA CV equal to the one of the pancreatic cancer, obtained from Ohmine et al 39 Parameters of the distributions are reported in Table 1.…”

Section: Methodsmentioning

confidence: 99%

“…14 In another publication, the concentration of these enzymes in pancreatic tumor samples from 10 different subjects was measured. 39 That information was used for the in vitro to in vivo rescaling.…”

Section: Methodsmentioning

confidence: 99%

Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2’,2’‐difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer

Garcia‐Cremades

Melillo

Trocóniz

et al. 2020

Clinical Translational Sci

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The aim of this work is to build a mechanistic multiscale pharmacokinetic model for the anticancer drug 2', 2'-difluorodeoxycytidine (gemcitabine, dFdC), able to describe the concentrations of dFdC metabolites in the pancreatic tumor tissue in dependence of physiological and genetic patient characteristics, and, more in general, to explore the capabilities and limitations of this kind of modeling strategy. A mechanistic model characterizing dFdC metabolic pathway (metabolic network) was developed using in vitro literature data from two pancreatic cancer cell lines. The network was able to describe the time course of extracellular and intracellular dFdC metabolites concentrations. Moreover, a physiologically-based pharmacokinetic model was developed to describe clinical dFdC profiles by using enzymatic and physiological information available in the literature. This model was then coupled with the metabolic network to describe the dFdC active metabolite profile in the pancreatic tumor tissue. Finally, global sensitivity analysis was performed to identify the parameters that mainly drive the interindividual variability for the area under the curve (AUC) of dFdC in plasma and of its active metabolite (dFdCTP) in tumor tissue. From this analysis, cytidine deaminase (CDA) concentration was identified as the main driver of plasma dFdC AUC interindividual variability, whereas CDA and deoxycytidine kinase concentration mainly explained the tumor dFdCTP AUC variability. However, the lack of in vitro and in vivo information needed to characterize key model parameters hampers the development of this kind of mechanistic approach. Further studies to better characterize pancreatic cell lines and patient enzymes polymorphisms are encouraged to refine and validate the current model. 2′,2′-difluorodeoxycytidine (gemcitabine, dFdC) is a nucleoside antimetabolite prodrug effective against several solid tumors. 1-4 Treatment with dFdC represents the firstline therapy of pancreatic cancer, that constitutes one of the most aggressive and lethal oncology diseases, with an overall 5-year survival rate of < 5%. 5 As a prodrug, dFdC has to be intracellularly metabolized to its active metabolite, dFdC triphosphate (dFdCTP), to Multiscale PK Model for dFdC in Pancreatic Cancer Garcia-Cremades et al. www.cts-journal.comMultiscale PK Model for dFdC in Pancreatic Cancer Garcia-Cremades et al. www.cts-journal.comMultiscale PK Model for dFdC in Pancreatic Cancer Garcia-Cremades et al.

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“…Transcript 34,35 and protein 36,37 levels of human equilibrative nucleoside transporter (hENT1) to predict gemcitabine efficacy against pancreatic cancer are used as a clinical marker of the drug response. While mRNA quantification is simple, poor correlation between protein expression/activity and mRNA expression is often observed because of the poor mRNA stability in tissues or posttranscriptional variability.…”

Section: Quantitative Proteomics Data Can Be Used As a Critical Predimentioning

confidence: 99%

The Promises of Quantitative Proteomics in Precision Medicine

Prasad

Vrana

Mehrotra

et al. 2017

Journal of Pharmaceutical Sciences

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Precision medicine approach has a potential to ensure optimum efficacy and safety of drugs at individual patient level. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models could play a significant role in precision medicine by predicting interindividual variability in drug disposition and response. In order to develop robust PBPK/PD models, it is imperative that the critical physiological parameters affecting drug disposition and response are precisely characterized alongwith with their variability. Currently used PBPK/PD modeling software, e.g., Simcyp and Gastroplus, encompass information such as organ volumes, blood flows to organs, body fat composition, glomerular filtration rate, etc. However, the information on the interindividual variability of the majority of the proteins associated with PK and PD, e.g., drug metabolizing enzymes (DMEs), transporters and receptors, are not fully incorporated into these software. Such information is significant because the population factors (e.g., age, genotype, disease and gender), can affect abundance or activity of these proteins. To fill this critical knowledge gap, mass spectrometry (MS)-based quantitative proteomics has emerged as an important technique to characterize protein abundance of DMEs, transporters and receptors across the population. Integration of these quantitative proteomics data into in silico PBPK/PD modeling tools will be crucial toward precision medicine.

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Quantitative Targeted Proteomics of Pancreatic Cancer: Deoxycytidine Kinase Protein Level Correlates to Progression-Free Survival of Patients Receiving Gemcitabine Treatment

Cited by 34 publications

References 32 publications

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Metabolic profiling putatively identifies plasma biomarkers of male infertility using UPLC-ESI-IT-TOFMS

Mechanistic Multiscale Pharmacokinetic Model for the Anticancer Drug 2’,2’‐difluorodeoxycytidine (Gemcitabine) in Pancreatic Cancer

The Promises of Quantitative Proteomics in Precision Medicine

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