scholarly journals Detecting Variants in the NBN Gene While Testing for Hereditary Breast Cancer: What to Do Next?

2021 ◽  
Vol 22 (11) ◽  
pp. 5832
Author(s):  
Roberta Zuntini ◽  
Elena Bonora ◽  
Laura Maria Pradella ◽  
Laura Benedetta Amato ◽  
Michele Vidone ◽  
...  

The NBN gene has been included in breast cancer (BC) multigene panels based on early studies suggesting an increased BC risk for carriers, though not confirmed by recent research. To evaluate the impact of NBN analysis, we assessed the results of NBN sequencing in 116 BRCA-negative BC patients and reviewed the literature. Three patients (2.6%) carried potentially relevant variants: two, apparently unrelated, carried the frameshift variant c.156_157delTT and another one the c.628G>T variant. The latter was subsequently found in 4/1390 (0.3%) BC cases and 8/1580 (0.5%) controls in an independent sample, which, together with in silico predictions, provided evidence against its pathogenicity. Conversely, the rare c.156_157delTT variant was absent in the case-control set; moreover, a 50% reduction of NBN expression was demonstrated in one carrier. However, in one family it failed to co-segregate with BC, while the other carrier was found to harbor also a probably pathogenic TP53 variant that may explain her phenotype. Therefore, the c.156_157delTT, although functionally deleterious, was not supported as a cancer-predisposing defect. Pathogenic/likely pathogenic NBN variants were detected by multigene panels in 31/12314 (0.25%) patients included in 15 studies. The risk of misinterpretation of such findings is substantial and supports the exclusion of NBN from multigene panels.

2019 ◽  
Vol 19 (5) ◽  
pp. e563-e577 ◽  
Author(s):  
Elham Vahednia ◽  
Fatemeh Homaei Shandiz ◽  
Matineh Barati Bagherabad ◽  
Atefeh Moezzi ◽  
Fahimeh Afzaljavan ◽  
...  

2019 ◽  
Vol 20 (12) ◽  
pp. 2962 ◽  
Author(s):  
Kumaraswamy Naidu Chitrala ◽  
Mitzi Nagarkatti ◽  
Prakash Nagarkatti ◽  
Suneetha Yeguvapalli

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.


2020 ◽  
Vol 31 ◽  
pp. S327
Author(s):  
W. Muñoz-Montaño ◽  
C. De la Garza-Ramos ◽  
A. Tabares ◽  
P. Cabrera-Galeana ◽  
V. Perez ◽  
...  

2004 ◽  
Vol 22 (14_suppl) ◽  
pp. 9534-9534
Author(s):  
N. Kauff ◽  
T. Cigler ◽  
K. Hurley ◽  
H. Huang ◽  
H. Rapaport ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document