Correlation between Candidate Single Nucleotide Variants and Several Clinicopathological Risk Factors Related to Breast Cancer in Jordanian Women: A Genotype-Phenotype Study

Objective: In the present pharmacogenomic work, the genetic, epigenetic and environmental factors associated with BRCA1 induced breast cancer, cancer proneness and its variants across different populations like Indian, Netherland, Belgium, Denmark, Austrian, New Zealand, Sweden, Malaysian and Norwegian and the ‘mutation and methylation-prone’ region of BRCA1 have been computed.Methods: The global variations associated with the disease have been identified from the ‘Leiden open variation database (LOVD 3.0)’ and ‘Indian genome variation database (IGVDB)’. The variants, ‘single nucleotide polymorphisms (SNPs)’ are then characterized. The epigenetic factors associated with breast cancer have been identified from the clinical reports and further scrutinized using EpiGRAPH tool. The various contributing environmental factors responsible for the variations have been considered.Results: All the variants across different populations such as Indian, Netherland, Belgium, Denmark, Austrian, New Zealand, Sweden, Malaysian and Norwegian are found to be in a specific transcript of BRCA1 that ranges within 41,196,312-41,277,500 (81,189 base pairs) of the chromosome 17. Two ‘single nucleotide variations (SNVs)’ (5266dupC: rs397507246 and 68_69delAG: rs386833395) have been identified as risk factors in hereditary breast and ovarian cancer syndrome in the global population and 39 SNPs have been identified as pathogenic and deleterious. ‘Evolutionary history’ seems to be the most significant attribute in the predictability of methylation of BRCA1. Unhealthy dietary habits, obesity, use of unsafe cosmetics, estrogen exposure, ‘hormone replacement therapy (HRT)’, use of oral contraceptives and smoking are the major environmental risk factors associated with breast cancer incidence.Conclusion: This chromosome location (41,196,312-41,277,500 (81,189 base pairs)) can be considered as the population-specific sensitive region corresponding to BRCA1 mutation. This supports the fact that stabilization within the region can be a promising technique to control the epigenetic variants associated with the global position. The global variation in the proneness of the disease may be due to a cumulative effect of genetic, epigenetic and environmental factors subject to further experimentations with identical variations and populations.

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Circulating cell-free DNA (ctDNA) assessments before, during, and after neoadjuvant therapy (NAC) in nonmetastatic inflammatory breast cancer (IBC).

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e12113 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e12113-e12113

Author(s):

Carolyn S. Hall ◽

Salyna Meas ◽

Vanessa Nicole Sarli ◽

Anthony Lucci

Keyword(s):

Breast Cancer ◽

Pathologic Complete Response ◽

Copy Number Variants ◽

Residual Tumor ◽

Complete Response ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Cell Free Dna ◽

Free Dna ◽

Circulating Cell Free Dna

e12113 Background: : IBC is a rare and aggressive subtype of breast cancer. A significant number of IBC patients who achieve pathologic complete response (no residual tumor in breast and axillary nodes, pCR) relapse after NAC. We hypothesized that circulating cell-free DNA (ctDNA) identified in blood before, during, and after NAC would identify novel ctDNA targets. Methods: Plasma ctDNA was extracted from 43 non-metastatic IBC patients (IRB: LAB04-0698) pre, mid, and post-NAC. The Oncomine Pan Cancer ctDNA Assay (ThermoFisher) was used for library preparation, and high throughput next generation sequencing was performed on a GeneStudio S5XL System (ThermoFisher), following manufacturer’s directions. ThermoFisher Ion Reporter 5.10 Software was used to analyze single nucleotide variants (SNVs), and copy number variants (CNVs). Results: Seventeen patients had pre-NAC ctDNA assessments; 7/17 (41%) had PIK3CA SNVs; 5/7 also had MYC or FGFR2 CNVs. Five of 17 (29%) had TP53 SNVs; 2/5 also had FGFR2 CNVs. Ten patients had mid-NAC ctDNA assessments; 9/10 (90%) had PIK3CA SNVs; 5/9 also had FGFR2 CNVs, 2/9 had FGFR2 and FGFR3 CNVs, 2/9 also had TP53 SNVs, 1/9 had FGFR2 and ERB2 CNVs. Thirty-one patients had post-NAC ctDNA assessments; 5/31 (16%) had PIK3CA SNVs; 2/5 had FGFR2 CNVs, 1/5 also had a TP53 mutation and an FGFR2 CNV, 1/5 had FGFR2 CNV, and FGFR3 CNV. Six of 31 (19%) had TP53 SNVs, 1/6 had CCND1 CNVs, no CNVs were detected in 6 patients with TP53 SNVs. Six of 31 (19%) had MAP2K1 SNVs. Three of 31 (10%) had MET SNVs; 1/3 had CCND3 CNVs, no CNVs were detected in 2 patients with MET SNVs. No SNVs or CNVs were detected in 10/31 (32%) of patients post NAC. Conclusions: ctDNA assessments before, during, and after NAC identified novel targets that could be tested in future adjuvant therapies trials in IBC patients who remain at high risk for relapse.

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Use of Single-Nucleotide Polymorphisms and Mammographic Density Plus Classic Risk Factors for Breast Cancer Risk Prediction

JAMA Oncology ◽

10.1001/jamaoncol.2017.4881 ◽

2018 ◽

Vol 4 (4) ◽

pp. 476 ◽

Cited By ~ 46

Author(s):

Elke M. van Veen ◽

Adam R. Brentnall ◽

Helen Byers ◽

Elaine F. Harkness ◽

Susan M. Astley ◽

...

Keyword(s):

Breast Cancer ◽

Risk Factors ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Single Nucleotide Polymorphisms ◽

Risk Prediction ◽

Mammographic Density ◽

Nucleotide Polymorphisms ◽

Single Nucleotide

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Analysis of the TP53 Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach

International Journal of Molecular Sciences ◽

10.3390/ijms20122962 ◽

2019 ◽

Vol 20 (12) ◽

pp. 2962 ◽

Cited By ~ 2

Author(s):

Kumaraswamy Naidu Chitrala ◽

Mitzi Nagarkatti ◽

Prakash Nagarkatti ◽

Suneetha Yeguvapalli

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Risk Factors ◽

Estrogen Receptor ◽

Estrogen Receptor Alpha ◽

Large Deviation ◽

The Other ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

The Impact

Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body mass index, hormone replacement therapy, family history and germline mutations. Of these risk factors, estrogen levels play a crucial role. Among the estrogen receptors, estrogen receptor alpha (ERα) is known to interact with tumor suppressor protein p53 directly thereby repressing its function. Previously, we have studied the impact of deleterious breast cancer-associated non-synonymous single nucleotide polymorphisms (nsnps) rs11540654 (R110P), rs17849781 (P278A) and rs28934874 (P151T) in TP53 gene on the p53 DNA-binding core domain. In the present study, we aimed to analyze the impact of these mutations on p53–ERα interaction. To this end, we, have modelled the full-length structure of human p53 and validated its quality using PROCHECK and subjected it to energy minimization using NOMAD-Ref web server. Three-dimensional structure of ERα activation function-2 (AF-2) domain was downloaded from the protein data bank. Interactions between the modelled native and mutant (R110P, P278A, P151T) p53 with ERα was studied using ZDOCK. Machine learning predictions on the interactions were performed using Weka software. Results from the protein–protein docking showed that the atoms, residues and solvent accessibility surface area (SASA) at the interface was increased in both p53 and ERα for R110P mutation compared to the native complexes indicating that the mutation R110P has more impact on the p53–ERα interaction compared to the other two mutants. Mutations P151T and P278A, on the other hand, showed a large deviation from the native p53-ERα complex in atoms and residues at the surface. Further, results from artificial neural network analysis showed that these structural features are important for predicting the impact of these three mutations on p53–ERα interaction. Overall, these three mutations showed a large deviation in total SASA in both p53 and ERα. In conclusion, results from our study will be crucial in making the decisions for hormone-based therapies against breast cancer.

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