Dataset Information

Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.

ABSTRACT: Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework that could identify genes associated with TNBC and screen the SNPs reported in these genes using a set of computational predictors. This framework helped identify BRCA1, BRCA2, EGFR, PIK3CA, PTEN, and TP53 as recurrent genes associated with TNBC. We found 2%-29% of reported SNPs across genes to be typed pathogenic by all the predictors in the framework. We demonstrate that our framework prediction on BC samples identifies 99% of alterations as pathogenic by at least one predictor and 32% as pathogenic by all the predictors. Our framework could be an initial step in developing an early diagnosis of TNBC and potentially help improve the understanding of therapeutic resistance and sensitivity.

SUBMITTER: G V

PROVIDER: S-EPMC9763624 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.

G Vigneshwaran V Hasan Qurratulain Annie QA Kumar Rahul R Eranki Avinash A

Frontiers in genetics 20221206

Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework t ...[more]

PMID: 36561320

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Project description:IntroductionStudies of triple-negative breast cancer have recently been extending the inclusion criteria and incorporating additional molecular markers into the selection criteria, opening up scope for targeted therapies. The screening phases required for studies of this type are often prolonged, since the process of determining the molecular subtype and carrying out additional biomarker assessment is time-consuming. Parameters such as germline genotypes capable of predicting the molecular subtype before it becomes available from pathology might be helpful for treatment planning and optimizing the timing and cost of screening phases. This appears to be feasible, as rapid and low-cost genotyping methods are becoming increasingly available. The aim of this study was to identify single nucleotide polymorphisms (SNPs) for breast cancer risk capable of predicting triple negativity, in addition to clinical predictors, in breast cancer patients.MethodsThis cross-sectional observational study included 1271 women with invasive breast cancer who were treated at a university hospital. A total of 76 validated breast cancer risk SNPs were successfully genotyped. Univariate associations between each SNP and triple negativity were explored using logistic regression analyses. Several variable selection and regression techniques were applied to identify a set of SNPs that together improve the prediction of triple negativity in addition to the clinical predictors of age at diagnosis and body mass index (BMI). The most accurate prediction method was determined by cross-validation.ResultsThe SNP rs10069690 (TERT, CLPTM1L) was the only significant SNP (corrected p = 0.02) after correction of p values for multiple testing in the univariate analyses. This SNP and three additional SNPs from the genes RAD51B, CCND1, and FGFR2 were selected for prediction of triple negativity. The addition of these SNPs to clinical predictors increased the cross-validated area under the curve (AUC) from 0.618 to 0.625. Age at diagnosis was the strongest predictor, stronger than any genetic characteristics.ConclusionPrediction of triple-negative breast cancer can be improved if SNPs associated with breast cancer risk are added to a prediction rule based on age at diagnosis and BMI. This finding could be used for prescreening purposes in complex molecular therapy studies for triple-negative breast cancer.

Project description:ObjectiveThe purpose of the present study was to evaluate the associations between seven tagging single nucleotide polymorphisms (tSNPs) and risk of breast cancer assessed by tumor pathological characteristics and body mass index (BMI).MethodsSeven tSNPs of four breast cancer susceptibility genes were analyzed in 734 Chinese women with breast cancer and 672 age-matched healthy controls; then, the association with clinicopathological characteristics, BMI, molecular subtype, TNM (T, tumor; N, lymph node; M, metastasis) staging and lymph node status was determined by unconditional logistic regression.ResultsRs12951053 in TP53 and rs16945628 in BRIP1, displayed increased risk of breast cancer in the BMI ≧ 25 kg/m2 group (OR=1.50, 95% CI: 1.02-2.21, P=0.041 and OR=1.92, 95% CI: 1.13-3.26, P=0.015, respectively). The other five tSNPs (rs1805812, rs2735385 and rs6999227 in NBS1, rs7220719 in BRIP1 and rs2299941 in PTEN) displayed a decreased risk of breast cancer in the 18.5≤BMI<25 kg/m2 group. Rs12951053 in TP53 and rs7220719 in BRIP1 exhibited an increased risk of triple-negative breast cancer (OR=1.50, 95% CI: 1.05-2.15, P=0.026 and OR=2.13, 95% CI: 1.05-4.29, P=0.032, respectively), but three tSNPs in NBS1 (rs1805812, rs2735385 and rs6999227) all displayed a negative association with both luminal B and triple-negative breast cancer. The tSNP rs2299941 in PTEN also exhibited a negative association with each molecular subtype, except triple-negative breast cancer. The majority of tSNPs displayed a negative association with stage II or III breast cancer. Most tSNPs showed a negative association with breast cancer that was lymph node negative or with 1-3 positive nodes. Only rs12951053 in TP53 displayed a positive association with lymph node-negative breast cancer (OR=1.43, 95% CI: 1.08-1.91, P=0.013).ConclusionThe majority of tSNPs displayed a negative association with breast cancer and only a few tSNPs (rs12951053 in TP53, rs16945628 and rs7220719 in BRIP1) showed an increased risk of breast cancer as defined by clinicopathological characteristics.

Dataset Information

Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.

Publications

Analysis of single-nucleotide polymorphisms in genes associated with triple-negative breast cancer.

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