Relationship of ZNF423 and CTSO with breast cancer risk in two randomised tamoxifen prevention trials

Brentnall, Adam R.; Cuzick, Jack; Byers, Helen; Segal, Corrinne V.; Reuter, Caroline; Detre, Simone; Šestak, Ivana; Howell, Anthony; Powles, Trevor J.; Newman, William G.; Dowsett, Mitchell

doi:10.1007/s10549-016-3885-x

Cited by 5 publications

(3 citation statements)

References 11 publications

(27 reference statements)

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“…They were combined to increase precision and to provide greater power for subgroup analyses, as in previous analyses. 35 Case-control matching was by study, age at baseline (± 2 years), treatment arm, and follow-up time, with two controls per case in IBIS-I and one in Marsden. IBIS-I recruited from 1992 to 2001, and Marsden recruited from 1986 to 1996.…”

Section: Methodsmentioning

confidence: 99%

Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results From Two Randomized Tamoxifen Prevention Trials

Cuzick

Brentnall

Segal

et al. 2017

JCO

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PurposeAt least 94 common single nucleotide polymorphisms (SNPs) are associated with breast cancer. The extent to which an SNP panel can refine risk in women who receive preventive therapy has not been directly assessed previously.Materials and MethodsA risk score on the basis of 88 SNPs (SNP88) was investigated in a nested case-control study of women enrolled in the International Breast Intervention Study (IBIS-I) or the Royal Marsden study. A total of 359 women who developed cancer were matched to 636 controls by age, trial, follow-up time, and treatment arm. Genotyping was done using the OncoArray. Conditional logistic regression and matched concordance indices (mC) were used to measure the performance of SNP88 alone and with other breast cancer risk factors assessed using the Tyrer-Cuzick (TC) model.ResultsSNP88 was predictive of breast cancer risk overall (interquartile range odds ratio [IQ-OR], 1.37; 95% CI, 1.14 to 1.66; mC, 0.55), but mainly for estrogen receptor–positive disease (IQ-OR, 1.44; 95% CI, 1.16 to 1.79; P for heterogeneity = .10) versus estrogen receptor–negative disease. However, the observed risk of SNP88 was only 46% (95% CI, 19% to 74%) of expected. No significant interaction was observed with treatment arm (placebo IQ-OR, 1.46; 95% CI, 1.13 to 1.87; tamoxifen IQ-OR, 1.25; 95% CI, 0.96 to 1.64; P for heterogeneity = .5). The predictive power was similar to the TC model (IQ-OR, 1.45; 95% CI, 1.21 to 1.73; mC, 0.55), but SNP88 was independent of TC (Spearman rank-order correlation, 0.012; P = .7), and when combined multiplicatively, a substantial improvement was seen (IQ-OR, 1.64; 95% CI, 1.36 to 1.97; mC, 0.60).ConclusionA polygenic risk score may be used to refine risk from the TC or similar models in women who are at an elevated risk of breast cancer and considering preventive therapy. Recalibration may be necessary for accurate risk assessment.

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Section: Methodsmentioning

confidence: 99%

Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results From Two Randomized Tamoxifen Prevention Trials

Cuzick

Brentnall

Segal

et al. 2017

JCO

Self Cite

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“…Several studies have presented that SIRT1 can function as a tumor promoter or tumor suppressor depending on its targets in specific cancer and signaling pathways ( Lin and Fang, 2013 ). CTSO is a biomarker that can predict which women will emanate the highest benefit from a selective estrogen receptor modulator (SERM) therapy ( Brentnall et al, 2016 ). With their roles in top 10 scoring GO CC terms, these genes are emphasized in our analysis on the glioma dataset, and these genes are shown with bigger and darker nodes in Figure 7B based on their high betweenness centrality.…”

Section: Discussionmentioning

confidence: 99%

GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning

Ersoz,

Bakir-Gungor,

Yousef

2023

Front. Genet.

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Introduction: Identifying significant sets of genes that are up/downregulated under specific conditions is vital to understand disease development mechanisms at the molecular level. Along this line, in order to analyze transcriptomic data, several computational feature selection (i.e., gene selection) methods have been proposed. On the other hand, uncovering the core functions of the selected genes provides a deep understanding of diseases. In order to address this problem, biological domain knowledge-based feature selection methods have been proposed. Unlike computational gene selection approaches, these domain knowledge-based methods take the underlying biology into account and integrate knowledge from external biological resources. Gene Ontology (GO) is one such biological resource that provides ontology terms for defining the molecular function, cellular component, and biological process of the gene product.Methods: In this study, we developed a tool named GeNetOntology which performs GO-based feature selection for gene expression data analysis. In the proposed approach, the process of Grouping, Scoring, and Modeling (G-S-M) is used to identify significant GO terms. GO information has been used as the grouping information, which has been embedded into a machine learning (ML) algorithm to select informative ontology terms. The genes annotated with the selected ontology terms have been used in the training part to carry out the classification task of the ML model. The output is an important set of ontologies for the two-class classification task applied to gene expression data for a given phenotype.Results: Our approach has been tested on 11 different gene expression datasets, and the results showed that GeNetOntology successfully identified important disease-related ontology terms to be used in the classification model.Discussion: GeNetOntology will assist geneticists and scientists to identify a range of disease-related genes and ontologies in transcriptomic data analysis, and it will also help doctors design diagnosis platforms and improve patient treatment plans.

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“…In this study, the ZNF423 rs8060157 genotype was not associated with survival or prognosis. Additionally, two randomized prevention trials for BC patients treated with SERM therapy were analyzed for ZNF423 and CTSO SNPs significance ( 75 ). The two SNPs for ZNF423 rs8060157 and rs10030044 for CTSO were analyzed together with additional 80 SNPs in the surrounding regions.…”

Section: Snps In the Introns Of The Znf423 And mentioning

confidence: 99%

ZNF423: A New Player in Estrogen Receptor-Positive Breast Cancer

et al. 2018

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Preventive therapy can target hormone-responsive breast cancer (BC) by treatment with selective estrogen receptor modulators (SERMs) and reduce the incidence of BC. Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) with relevant predictive values, SNPs in the ZNF423 gene were associated with decreased risk of BC during SERM therapy, and SNPs in the Cathepsin O gene with an increased risk. ZNF423, which was not previously associated with BC is a multifunctional transcription factor known to have a role in development, neurogenesis, and adipogenesis and is implicated in other types of cancer. ZNF423 is transcriptionally controlled by the homolog ZNF521, early B cell factor transcription factor, epigenetic silencing of the promoter by CpG island hyper-methylation, and also by ZNF423 itself in an auto-regulatory loop. In BC cells, ZNF423 expression is found to be induced by estrogen, dependent on the binding of the estrogen receptor and calmodulin-like 3 to SNPs in ZNP423 intronic sites in proximity to consensus estrogen response elements. ZNF423 has also been shown to play a mechanistic role by trans-activating the tumor suppressor BRCA1 and thus modulating the DNA damage response. Even though recent extensive trial studies did not classify these SNPs with the highest predictive values, for inclusion in polygenic SNP analysis, the mechanism unveiled in these studies has introduced ZNF423 as a factor important in the control of the estrogen response. Here, we aim at providing an overview of ZNF423 expression and functional role in human malignancies, with a specific focus on its implication in hormone-responsive BC.

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Relationship of ZNF423 and CTSO with breast cancer risk in two randomised tamoxifen prevention trials

Cited by 5 publications

References 11 publications

Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results From Two Randomized Tamoxifen Prevention Trials

Impact of a Panel of 88 Single Nucleotide Polymorphisms on the Risk of Breast Cancer in High-Risk Women: Results From Two Randomized Tamoxifen Prevention Trials

GeNetOntology: identifying affected gene ontology terms via grouping, scoring, and modeling of gene expression data utilizing biological knowledge-based machine learning

ZNF423: A New Player in Estrogen Receptor-Positive Breast Cancer

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