Mutational Landscape and In-Silico Analysis of , , and in Patients with Breast Cancer from Khyber Pakhtunkhwa.

Herein, we report the mutational spectrum of three breast cancer candidate genes (, , and ) using WES for identifying potential biomarkers. The WES data were thoroughly analyzed using SAMtools for variant calling and identification of the mutations. Various bioinformatic tools (SIFT, PolyPhen-2, Mutation Taster, ISPRED-SEQ, SAAFEQ-SEQ, ConSurf, PROCHECK etc.) were used to determine the pathogenicity and nature of the SNVs. Selected interaction site (IS) mutations were visualized in PyMOL after building 3D structures in Swiss-Model. Ramachandran plots were generated by using the PROCHECK server. The selected IS mutations were subjected to molecular dynamic simulation (MDS) studies using Gromacs 4.5. STRING and GeneMANIA were used for the prediction of gene-gene interactions and pathways. Our results revealed that the luminal A molecular subtype of the breast cancer was most common, whereas a high percentage of was Her2 negatives. Moreover, the somatic mutations were more common as compared to the germline mutations in , , and . 20% of the identified mutations are reported for the first time from Khyber Pakhtunkhwa. In the enrolled cohort, 23 mutations were nonsynonymous SNVs. The frequency of mutations was the highest in , followed by and . A total of 13 mutations were found to be highly pathogenic. Four novel mutations were identified on and one each on and . SAAFEQ-SEQ predicted the destabilizing effect for all mutations. ISPRED-SEQ predicted 9 IS mutations (6 on and 3 on ), whereas no IS mutation was predicted on . The IS mutations were , , , , , and ; whereas for , the IS mutations were , , and . Analysis from the ConSurf Web server revealed five SNVs with a highly conserved status (conservation score 9) across and . was found predominant in the grade 3 tumors, whereas was distributed on ER+, ER-, PR+, PR-, Her2+, and Her2- patients. mutation was found to be recurring in the 14/19 (73.6%) patients and, therefore, can be considered as a potential biomarker. Finally, these mutations were studied in the context of their potential association with different hormonal and social factors.