Anastrozole Aromatase Inhibitor Plasma Drug Concentration Genome-Wide Association Study: Functional Epistatic Interaction Between SLC38A7 and ALPPL2.
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ABSTRACT:
SUBMITTER: Dudenkov TM
PROVIDER: S-EPMC6612579 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Estrogen-related genes and the fat mass and obesity-associated (FTO) gene play a critical role in estrogen metabolism, and those polymorphisms are associated with a poor prognosis in breast cancer. However, little is known about the association between these polymorphisms and the efficacy of anastrozole. The aim was to investigate the impact of the genetic polymorphisms, CYP19A1, 17-β-HSD-1 and FTO, on the response to anastrozole in metastatic breast carcinoma (MBC) and to evaluate the impact of those polymorphisms on various clinicopathologic features. Two-hundred seventy-two women with hormone receptor-positive MBC treated with anastrozole were identified retrospectively. DNA was extracted from peripheral blood and genotyped for five variants in three candidate genes. Time to progression was improved in patients carrying the variant alleles of rs4646 when compared to patients with the wild-type allele (16.40 months versus 13.52 months; p = 0.049). The rs4646 variant alleles were significantly associated with longer overall survival (37.3 months versus 31.6 months; p = 0.007). This relationship was not observed with the rs10046, rs2830, rs9926298 and rs9939609 polymorphisms. The findings of this study indicate that rs4646 polymorphism in the CYP19A1 gene may serve as a prognostic maker of the response to anastrozole in patients with MBC who are treated with anastrozole.
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Project description:The discovery of several genes that affect the risk for Alzheimer's disease ignited a worldwide search for single-nucleotide polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted because of the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole brain, voxelwise effects of the interaction in the Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.
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Project description:Aromatase inhibitors (AIs), such as anastrozole, are established in the treatment of hormone-dependent breast cancer. However, ?20% of patients with hormone receptor-positive breast tumors treated with anastrozole do not respond and it remains impossible to accurately predict sensitivity. Since polymorphisms in the aromatase gene may influence the response to inhibitory drugs, we evaluated the presence of rs6493497 and rs7176005 polymorphisms (mapping in the 5'-flanking region of the CYP19A1 gene coding for the aromatase protein) in a cohort of 37 patients with postmenopausal breast cancer who received three-month neoadjuvant treatment with anastrozole. We then investigated any association of the polymorphisms with changes in aromatase mRNA expression change and/or response to treatment. We also analyzed five miRNAs computationally predicted to target aromatase, to observe any association between their expression and sensitivity to anastrozole. Three samples carried the two polymorphisms and the remaining samples were wild-type for both, however, no association with response or with aromatase mRNA basal expression level or expression difference after therapy was observed. Polymorphic samples that were resistant to anastrozole showed no change or decrease in aromatase expression following AI treatment, whereas an increase in expression was observed for the polymorphic responsive samples. No statistically significant correlation was observed between miRNA and aromatase mRNA expression, or with response to anastrozole neoadjuvant treatment. These data indicate that the polymorphisms analyzed are not involved in aromatase activity and that other epigenetic mechanisms may regulate aromatase protein expression.
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Project description:In the present study, we employ fluorescence spectroscopy, dynamic light scattering, and molecular docking methods. Binding of anticancer drug anastrozole with human lysozyme (HL) is studied. Binding of anastrozole to HL is moderate but spontaneous. There is anastrozole persuaded hydrodynamic change in HL, leading to molecular compaction. Binding of anastrozole to HL also decreased in vitro lytic activity of HL. Molecular docking results suggest the electrostatic interactions and van der Waals forces played key role in binding interaction of anastrozole near the catalytic site. Binding interaction of anastrozole to proteins other than major transport proteins in blood can significantly affect pharmacokinetics of this molecule. Hence, rationalizing drug dosage is important. This study also points to unrelated effects that small molecules bring in the body that are considerable and need thorough investigation.
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Project description:ObjectiveAromatase inhibitors (AIs) are commonly used to treat hormone receptor positive (HR +) breast cancer. AI-induced musculoskeletal syndrome (AIMSS) is a common toxicity that causes AI treatment discontinuation. The objective of this genome-wide association study (GWAS) was to identify genetic variants associated with discontinuation of AI therapy due to AIMSS and attempt to replicate previously reported associations.MethodsIn the Exemestane and Letrozole Pharmacogenetics (ELPh) study, postmenopausal patients with HR + non-metastatic breast cancer were randomized to letrozole or exemestane. Genome-wide genotyping of germline DNA was conducted followed by imputation. Each imputed variant was tested for association with time-to-treatment discontinuation due to AIMSS using a Cox proportional hazards model assuming additive genetic effects and adjusting for age, baseline pain score, prior taxane treatment, and AI arm. Secondary analyses were conducted within each AI arm and analyses of candidate variants previously reported to be associated with AIMSS risk.ResultsFour hundred ELPh participants were included in the combined analysis. Two variants surpassed the genome-wide significance level in the primary analysis (p value < 5 × 10-8), an intronic variant (rs79048288) within CCDC148 (HR = 4.42, 95% CI: 2.67-7.33) and an intergenic variant (rs912571) upstream of PPP1R14C (HR = 0.30, 95% CI: 0.20-0.47). In the secondary analysis, rs74418677, which is known to be associated with expression of SUPT20H, was significantly associated with discontinuation of letrozole therapy due to AIMSS (HR = 5.91, 95% CI: 3.16-11.06). We were able to replicate associations for candidate variants previously reported to be associated with AIMSS in this cohort, but were not able to replicate associations for any other variants previously reported in other patient cohorts.ConclusionsOur GWAS findings identify several candidate variants that may be associated with AIMSS risk from AI generally or letrozole specifically. Validation of these associations in independent cohorts is needed before translating these findings into clinical practice to improve treatment outcomes in patients with HR + breast cancer.
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Project description:Interactions among genomic loci have often been overlooked in genome-wide association studies, revealing the combinatorial effects of variants on phenotype or disease manifestation. Unexplained genetic variance, interactions among causal genes of small effects, and biological pathways could be identified using a network biology approach. The main objective of this study was to determine the genome-wide epistatic variants affecting feed efficiency traits [feed conversion ratio (FCR) and residual feed intake (RFI)] based on weighted interaction SNP hub (WISH-R) method. Herein, we detected highly interconnected epistatic SNP modules, pathways, and potential biomarkers for the FCR and RFI in Duroc and Landrace purebreds considering the whole population, and separately for low and high feed efficient groups. Highly interacting SNP modules in Duroc (1,247 SNPs) and Landrace (1,215 SNPs) across the population and for low feed efficient (Duroc-80 SNPs, Landrace-146 SNPs) and high feed efficient group (Duroc-198 SNPs, Landrace-232 SNPs) for FCR and RFI were identified. Gene and pathway analyses identified ABL1, MAP3K4, MAP3K5, SEMA6A, KITLG, and KAT2B from chromosomes 1, 2, 5, and 13 underlying ErbB, Ras, Rap1, thyroid hormone, axon guidance pathways in Duroc. GABBR2, GNA12, and PRKCG genes from chromosomes 1, 3, and 6 pointed towards thyroid hormone, cGMP-PKG and cAMP pathways in Landrace. From Duroc low feed efficient group, the TPK1 gene was found involved with thiamine metabolism, whereas PARD6G, DLG2, CRB1 were involved with the hippo signaling pathway in high feed efficient group. PLOD1 and SETD7 genes were involved with lysine degradation in low feed efficient group in Landrace, while high feed efficient group pointed to genes underpinning valine, leucine, isoleucine degradation, and fatty acid elongation. Some SNPs and genes identified are known for their association with feed efficiency, others are novel and potentially provide new avenues for further research. Further validation of epistatic SNPs and genes identified here in a larger cohort would help to establish a framework for modelling epistatic variance in future methods of genomic prediction, increasing the accuracy of estimated genetic merit for FE and helping the pig breeding industry.
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Project description:BackgroundTaking the advantage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger.ResultsIn this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions.ConclusionsExperimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS.
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Project description:Aromatase inhibitors (AIs) reduce breast cancer recurrence and prolong survival, but up to 30% of patients exhibit recurrence. Using a genome-wide association study of patients entered on MA.27, a phase III randomized trial of anastrozole versus exemestane, we identified a single nucleotide polymorphism (SNP) in CUB And Sushi multiple domains 1 (CSMD1) associated with breast cancer-free interval, with the variant allele associated with fewer distant recurrences. Mechanistically, CSMD1 regulates CYP19 expression in an SNP- and drug-dependent fashion, and this regulation is different among 3 AIs: anastrozole, exemestane, and letrozole. Overexpression of CSMD1 sensitized AI-resistant cells to anastrozole but not to the other 2 AIs. The SNP in CSMD1 that was associated with increased CSMD1 and CYP19 expression levels increased anastrozole sensitivity, but not letrozole or exemestane sensitivity. Anastrozole degrades estrogen receptor α (ERα), especially in the presence of estradiol (E2). ER+ breast cancer organoids and AI- or fulvestrant-resistant breast cancer cells were more sensitive to anastrozole plus E2 than to AI alone. Our findings suggest that the CSMD1 SNP might help to predict AI response, and anastrozole plus E2 serves as a potential new therapeutic strategy for patients with AI- or fulvestrant-resistant breast cancers.
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Project description:Bone fractures are a major consequence of osteoporosis. There is a direct relationship between serum estrogen concentrations and osteoporosis risk. Aromatase inhibitors (AIs) greatly decrease serum estrogen levels in postmenopausal women, and increased incidence of fractures is a side effect of AI therapy. We performed a discovery case-cohort genome-wide association study (GWAS) using samples from 1071 patients, 231 cases and 840 controls, enrolled in the MA.27 breast cancer AI trial to identify genetic factors involved in AI-related fractures, followed by functional genomic validation. Association analyses identified 20 GWAS single nucleotide polymorphism (SNP) signals with P < 5E-06. After removal of signals in gene deserts and those composed entirely of imputed SNPs, we applied a functional validation "decision cascade" that resulted in validation of the CTSZ-SLMO2-ATP5E, TRAM2-TMEM14A, and MAP4K4 genes. These genes all displayed estradiol (E2)-dependent induction in human fetal osteoblasts transfected with estrogen receptor-?, and their knockdown altered the expression of known osteoporosis-related genes. These same genes also displayed SNP-dependent variation in E2 induction that paralleled the SNP-dependent induction of known osteoporosis genes, such as osteoprotegerin. In summary, our case-cohort GWAS identified SNPs in or near CTSZ-SLMO2-ATP5E, TRAM2-TMEM14A, and MAP4K4 that were associated with risk for bone fracture in estrogen receptor-positive breast cancer patients treated with AIs. These genes displayed E2-dependent induction, their knockdown altered the expression of genes related to osteoporosis, and they displayed SNP genotype-dependent variation in E2 induction. These observations may lead to the identification of novel mechanisms associated with fracture risk in postmenopausal women treated with AIs.
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