FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness

Campbell, Thomas M.; Castro, Mauro A. A.; Santiago, Inês de; Fletcher, Michael; Halim, Silvia; Prathalingam, Radhika; Ponder, Bruce A.J.; Meyer, Kerstin B.

doi:10.1093/carcin/bgw065

Cited by 52 publications

(52 citation statements)

References 46 publications

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“…Although, the MAP3K1 mutations were more frequently observed in HER2+ breast cancer and FGFR2 variants are reported relevant in ER+ and PR+ breast cancer (Pham et al 2013;Campbell et al 2016), we found no significant association between genetic polymorphisms and histopathological characteristics of the cancer samples. Further studies of other polymorphisms in these genes and the expression profile could elicit essential information because FGFR2 is a tumour suppressor gene amplified and overexpressed in 10-15% of breast tumours (Rebbeck et al 2009;Ahmad et al 2012;Tiong et al 2013) and enhanced FGFR expression may not only be due to genetic alterations but also to epigenetic deregulation at transcriptional and post-translational levels.…”

Section: Discussioncontrasting

confidence: 79%

“…Diverse FGF10, FGFR2 and MAP3K1 gene polymorphisms have already been identified as breast cancer susceptible in various populations (Rebbeck et al 2009;Ripperger 2009;Harlid et al 2012;Jara et al 2013;Murillo-Zamora et al 2013;Pritchard and Hayward 2013;Siddiqui et al 2014;Zheng et al 2014;Campbell et al 2016), and studies on breast cancer genetic background have also been performed in Slovakia (Franeková et al 2007;Zubor et al 2007Zubor et al , 2008Zubor et al , 2014Kasajová et al 2016). Results from these studies emphasised the relevance of several polymorphisms in breast cancer susceptibility and pointed out the importance of an inter-population genetic variability.…”

Section: Introductionmentioning

confidence: 99%

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Association of single nucleotide polymorphisms in FGF-RAS/MAP signalling cascade with breast cancer susceptibility

Daňková¹,

Žúbor²,

Grendár³

et al. 2017

gpb

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Abstract. The fibroblast growth factor receptors (FGFRs) and Ras/mitogen activated protein (RAS/ MAP) signalling cascades are the main molecular pathways involved in breast carcinogenesis. This study aims to determine the association between FGF10 (rs4415084 C>T), FGFR2 (rs2981582 C>T) and MAP3K1 (rs889312 A>C) gene polymorphisms and breast cancer, to analyse the discriminative ability of each SNP and to test the accuracy of the predictive breast cancer risk model which includes all SNPs. We conducted a case-control study of 170 women (57.06 ± 11.60 years) with histologically confirmed breast cancer and 146 controls (50.24 ± 10.69 years). High resolution melting (HRM) method with Sanger sequencing validation was used in analyses. We have revealed significant association of FGFR2 and MAP3K1 polymorphisms with breast cancer. The odds ratio of FGFR2 T allele was 1.897 (95% CI 1.231-2.936, p = 0.004) and MAP3K1 C allele 1.804 (95% CI 1.151-2.845, p = 0.012). FGFR2 polymorphism achieved the best discriminative ability (41.95%). The Random Forest algorithm selected FGFR2, MAP3K1 and age as important breast cancer predictors. The accuracy of this prediction model approached moderate accuracy (70%), with 35.9% sensitivity and 88.6% specificity.

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Section: Discussioncontrasting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Association of single nucleotide polymorphisms in FGF-RAS/MAP signalling cascade with breast cancer susceptibility

Daňková¹,

Žúbor²,

Grendár³

et al. 2017

gpb

View full text Add to dashboard Cite

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“…The BLCA regulator-target associations are inferred using the R package RTN (Castro et al, 2016b), which is extensively described elsewhere for reconstructing regulatory units for transcriptions factors and upstream regulators (Campbell et al, 2016; Castro et al, 2016a; Fletcher et al, 2013). Briefly, gene expression matrices for a set of samples are used to estimate the associations between a regulator and all potential targets.…”

Section: Star Methodsmentioning

confidence: 99%

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Robertson¹,

Kim²,

Al‐Ahmadie³

et al. 2017

Cell

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1,812

1,624

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Summary We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival ‘neuronal’ subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, lncRNA, and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma-in-situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

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“…FGFR2 is overexpressed in 5%‐10% of breast tumors . System biology approach suggests a link between FGFR2 germline variants could reduce a cell's ability to respond to estrogen activation . In contrast, TOX3 expression is highly up‐regulated in luminal breast cancer compared to normal breast tissues or basal‐like tumors .…”

Section: Germline Variation: Transition From the Candidate Gene Appromentioning

confidence: 99%

Breast cancer: The translation of big genomic data to cancer precision medicine

2017

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Cancer is a complex genetic disease that develops from the accumulation of genomic alterations in which germline variations predispose individuals to cancer and somatic alterations initiate and trigger the progression of cancer. For the past 2 decades, genomic research has advanced remarkably, evolving from single‐gene to whole‐genome screening by using genome‐wide association study and next‐generation sequencing that contributes to big genomic data. International collaborative efforts have contributed to curating these data to identify clinically significant alterations that could be used in clinical settings. Focusing on breast cancer, the present review summarizes the identification of genomic alterations with high‐throughput screening as well as the use of genomic information in clinical trials that match cancer patients to therapies, which further leads to cancer precision medicine. Furthermore, cancer screening and monitoring were enhanced greatly by the use of liquid biopsies. With the growing data complexity and size, there is much anticipation in exploiting deep machine learning and artificial intelligence to curate integrative “−omics” data to refine the current medical practice to be applied in the near future.

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FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness

Cited by 52 publications

References 46 publications

Association of single nucleotide polymorphisms in FGF-RAS/MAP signalling cascade with breast cancer susceptibility

Association of single nucleotide polymorphisms in FGF-RAS/MAP signalling cascade with breast cancer susceptibility

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Breast cancer: The translation of big genomic data to cancer precision medicine

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