GSTT2 promoter polymorphisms and colorectal cancer risk

Glutathione-S-Transferase Theta 2 (GSTT2) Modulates the Response to Bacillus Calmette-Guérin Immunotherapy in Bladder Cancer Patients

Glutathione-S-transferases (GST) enzymes detoxify xenobiotics and are implicated in response to anticancer therapy. This study evaluated the association of GST theta 1 (GSTT1), GSTT2, and GSTT2B with Mycobacterium bovis Bacillus Calmette-Guérin (BCG) response in non-muscle-invasive bladder cancer treatment. In vitro assessments of GSTT2 knockout (KO) effects were performed using cell lines and dendritic cells (DCs) from GSTT2KO mice. Deletion of GSTT2B, GSTT1, and single-nucleotide polymorphisms in the promoter region of GSTT2 was analysed in patients (n = 205) and healthy controls (n = 150). Silencing GSTT2 expression in MGH cells (GSTT2BFL/FL) resulted in increased BCG survival (p < 0.05) and decreased cellular reactive oxygen species. In our population, there are 24.2% with GSTT2BDel/Del and 24.5% with GSTT2BFL/FL. With ≤ 8 instillations of BCG therapy (n = 51), 12.5% of GSTT2BDel/Del and 53.8% of GSTT2BFL/FL patients had a recurrence (p = 0.041). With ≥9 instillations (n = 153), the disease recurred in 45.5% of GSTT2BDel/Del and 50% of GSTT2BFL/FL. GSTT2FL/FL patients had an increased likelihood of recurrence post-BCG therapy (HR 5.5 [1.87-16.69] p < 0.002). DCs from GSTT2KO mice produced three-fold more IL6 than wild-type DCs, indicating a robust inflammatory response. To summarise, GSTT2BDel/Del patients respond better to less BCG therapy and could be candidates for a reduced surveillance regimen.

Glutathione S-transferase π: a potential role in antitumor therapy

Glutathione S-transferase π (GSTπ) is a Phase II metabolic enzyme that is an important facilitator of cellular detoxification. Traditional dogma asserts that GSTπ functions to catalyze glutathione (GSH)-substrate conjunction to preserve the macromolecule upon exposure to oxidative stress, thus defending cells against various toxic compounds. Over the past 20 years, abnormal GSTπ expression has been linked to the occurrence of tumor resistance to chemotherapy drugs, demonstrating that this enzyme possesses functions beyond metabolism. This revelation reveals exciting possibilities in the realm of drug discovery, as GSTπ inhibitors and its prodrugs offer a feasible strategy in designing anticancer drugs with the primary purpose of reversing tumor resistance. In connection with the authors' current research, we provide a review on the biological function of GSTπ and current developments in GSTπ-targeting drugs, as well as the prospects of future strategies.

Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization

Background

One of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients’ gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients’ clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified.

Methods

To try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L_1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease.

Results

The results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets.

Conclusions

The advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients’ survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi-supervised learning model is one more appropriate tool for survival analysis in clinical cancer research.

The Genetic Architecture of Murine Glutathione Transferases

Glutathione S-transferase (GST) genes play a protective role against oxidative stress and may influence disease risk and drug pharmacokinetics. In this study, massive multiscalar trait profiling across a large population of mice derived from a cross between C57BL/6J (B6) and DBA2/J (D2)—the BXD family—was combined with linkage and bioinformatic analyses to characterize mechanisms controlling GST expression and to identify downstream consequences of this variation. Similar to humans, mice show a wide range in expression of GST family members. Variation in the expression of Gsta4, Gstt2, Gstz1, Gsto1, and Mgst3 is modulated by local expression QTLs (eQTLs) in several tissues. Higher expression of Gsto1 in brain and liver of BXD strains is strongly associated (P < 0.01) with inheritance of the B6 parental allele whereas higher expression of Gsta4 and Mgst3 in brain and liver, and Gstt2 and Gstz1 in brain is strongly associated with inheritance of the D2 parental allele. Allele-specific assays confirmed that expression of Gsto1, Gsta4, and Mgst3 are modulated by sequence variants within or near each gene locus. We exploited this endogenous variation to identify coexpression networks and downstream targets in mouse and human. Through a combined systems genetics approach, we provide new insight into the biological role of naturally occurring variants in GST genes.

Gene-environment interactions in esophageal cancer

Esophageal cancer (EC) is one of the most common malignancies in low- and medium-income countries and represents a disease of public health importance because of its poor prognosis and high mortality rate in these regions. The striking variation in the prevalence of EC among different ethnic groups suggests a significant contribution of population-specific environmental and dietary factors to susceptibility to the disease. Although individuals within a demarcated geographical area are exposed to the same environment and share similar dietary habits, not all of them will develop the disease; thus genetic susceptibility to environmental risk factors may play a key role in the development of EC. A wide range of xenobiotic-metabolizing enzymes are responsible for the metabolism of carcinogens introduced via the diet or inhaled from the environment. Such dietary or environmental carcinogens can bind to DNA, resulting in mutations that may lead to carcinogenesis. Genes involved in the biosynthesis of these enzymes are all subject to genetic polymorphisms that can lead to altered expression or activity of the encoded proteins. Genetic polymorphisms may, therefore, act as molecular biomarkers that can provide important predictive information about carcinogenesis. The aim of this review is to discuss our current knowledge on the genetic risk factors associated with the development of EC in different populations; it addresses mainly the topics of genetic polymorphisms, gene-environment interactions, and carcinogenesis. We have reviewed the published data on genetic polymorphisms of enzymes involved in the metabolism of xenobiotics and discuss some of the potential gene-environment interactions underlying esophageal carcinogenesis. The main enzymes discussed in this review are the glutathione S-transferases (GSTs), N-acetyltransferases (NATs), cytochrome P450s (CYPs), sulfotransferases (SULTs), UDP-glucuronosyltransferases (UGTs), and epoxide hydrolases (EHs), all of which have key roles in the detoxification of environmental and dietary carcinogens. Finally, we discuss recent advances in the study of genetic polymorphisms associated with EC risk, specifically with regard to genome-wide association studies, and examine possible challenges of case-control studies that need to be addressed to better understand the interaction between genetic and environmental factors in esophageal carcinogenesis.

Impact of polyphenol metabolites produced by colonic microbiota on expression of COX-2 and GSTT2 in human colon cells (LT97)

Polyphenols may play an important role in colon cancer prevention. After entering the colon, they are subjected to metabolism by the human gut microbiota. The objective of the present study was to analyze the impact of selected intestinal metabolites on modulation of enzymes involved in detoxification and inflammation in human adenoma cells LT97. LT97 cells were incubated with 3,4-dihydroxyphenylacetic acid (ES) and 3-(3,4-dihydroxyphenyl)-propionic acid (PS), metabolites of quercetin and chlorogenic acid/caffeic acid, respectively. The effect on cell number was analyzed using 4'- 6-diamino-2-phenylindole-dihydrochloride (DAPI)-staining. Modulation of glutathione S-transferase T2 (GSTT2) and cyclooxygenase-2 (COX-2) was measured by real-time PCR and Western blot. Comet assay was performed to assess the impact on DNA damage caused by the GSTT2 substrate cumene hydroperoxide (CumOOH). Polyphenol metabolites did not affect cell number but significantly upregulated GSTT2 expression and decreased COX-2. The latter was confirmed via Western blot. CumOOH-induced DNA damage was significantly reduced compared to the control. An upregulation of GSTT2 and downregulation of COX-2 could possibly contribute to the chemopreventive potential of polyphenols after degradation in the gut. Working with polyphenol metabolites is an important prerequisite to better understand the in vivo effects of pure polyphenols.

Gene expression profiles in human peripheral blood mononuclear cells as biomarkers for nutritional in vitro and in vivo investigations

Identification of chemopreventive substances may be achieved by measuring biological endpoints in human cells in vitro. Since generally only tumour cells are available for such investigations, our aim was to test the applicability of peripheral blood mononuclear cells (PBMC) as an in vitro primary cell model since they mimic the human in vivo situation and are relatively easily available. Cell culture conditions were refined, and the basal variation of gene expression related to drug metabolism and stress response was determined. Results were compared with profiles of an established human colon cell line (HT29) as standard. For biomarker development of nutritional effects, PBMC and HT29 cells were treated with potentially chemopreventive substances (chrysin and butyrate), and gene expression was determined. Key results were that relevant stress response genes, such as glutathione S-transferase T2 (GSTT2) and GSTM2, were modulated by butyrate in PBMC as in HT29 cells, but the blood cells were less sensitive and responded with high individual differences. We conclude that these cells may serve as a surrogate tissue in dietary investigations and the identified differentially expressed genes have the potential to become marker genes for population studies on biological effects.

Hepatic gene expression in morbidly obese women: implications for disease susceptibility

The objective of this study was to determine the molecular bases of disordered hepatic function and disease susceptibility in obesity. We compared global gene expression in liver biopsies from morbidly obese (MO) women undergoing gastric bypass (GBP) surgery with that of women undergoing ventral hernia repair who had experienced massive weight loss (MWL) following prior GBP. Metabolic and hormonal profiles were examined in MO vs. MWL groups. Additionally, we analyzed individual profiles of hepatic gene expression in liver biopsy specimens obtained from MO and MWL subjects. All patients underwent preoperative metabolic profiling. RNAs were extracted from wedge biopsies of livers from MO and MWL subjects, and analysis of mRNA expression was carried out using Affymetrix HG-U133A microarray gene chips. Genes exhibiting greater than twofold differential expression between MO and MWL subjects were organized according to gene ontology and hierarchical clustering, and expression of key genes exhibiting differential regulation was quantified by real-time-polymerase chain reaction (RT-PCR). We discovered 154 genes to be differentially expressed in livers of MWL and MO subjects. A total of 28 candidate disease susceptibility genes were identified that encoded proteins regulating lipid and energy homeostasis (PLIN, ENO3, ELOVL2, APOF, LEPR, IGFBP1, DDIT4), signal transduction (MAP2K6, SOCS-2), postinflammatory tissue repair (HLA-DQB1, SPP1, P4HA1, LUM), bile acid transport (SULT2A, ABCB11), and metabolism of xenobiotics (GSTT2, CYP1A1). Using gene expression profiling, we have identified novel candidate disease susceptibility genes whose expression is altered in livers of MO subjects. The significance of altered expression of these genes to obesity-related disease is discussed.

Genetic Polymorphism in Glutathione Transferases (GST): Population distribution of GSTM1, T1, and P1 conjugating activity

Glutathione transferases (GST) catalyze the conjugation of glutathione (GSH) with electrophiles, many of which may otherwise interact with protein or DNA. In select cases such as halogenated solvents, GST-mediated conjugation may lead to a more toxic or mutagenic metabolite. Polymorphisms that exert substantial effects on GST function were noted in human populations for several isozymes. This analysis focuses on three well-characterized isozymes, GSTM1, T1, and P1, in which polymorphisms were extensively studied with respect to DNA adducts and cancer in molecular epidemiologic studies. The current review and analysis focused upon how polymorphisms in these GST contributed to population variability in GST function. The first step in developing this review was to characterize the influence of genotype on phenotype (enzyme function) and the frequency of the polymorphisms across major population groups for all three GST. This information was then incorporated into Monte Carlo simulations to develop population distributions of enzyme function. These simulations were run separately for GSTM1, T1, and P1, and also for the combination of these isozymes, to assess the possibility of overlapping substrate specificity. Monte Carlo simulations indicated large interindividual variability for GSTM1 and T1 due to the presence of the null (zero activity) genotype, which is common in all populations studied. Even for GSTM1 or T1 non-null individuals, there was considerable interindividual variability with a bimodal distribution of enzyme activity evident. GSTP1 polymorphisms are associated with somewhat less variability due to the absence of null genotypes. However, in all cases simulated, the estimated variability is sufficiently large to warrant consideration of GST function distributions in assessments involving GST-mediated activation or detoxification of xenobiotics. Ideally, such assessments would involve physiologically based toxicokinetic (PBTK) modeling to assess population variability in internal dose.