| Popis: |
Epidermal Growth Factor Receptor (EGFR) signaling is known to play essential roles in growth and development; nevertheless, overexpression and mutation of EGFR have been reported in several cancers. Non-small cell lung cancer (NSCLC), the most observed type of lung cancer, harbors the highest number of EGFR tyrosine kinase mutations and therefore, EGFR has become an important therapeutic target for treatment of these tumors. Tyrosine Kinase Inhibitors (TKIs) are found to be effective in patients whose tumors contain activating mutations in the tyrosine kinase region of the receptor. This would seem to be beneficial in the treatment of EGFR mutation-positive NSCLC patients but the activating mutations should be sensitive to TKIs. Earlier, a machine learning approach was developed to classify single amino acid polymorphisms (SAPs) in EGFR into driver (cancer-causing) and passenger (neutral) mutations using structural and functional features (Anooshaet al., 2015). This study screened all possible point mutations in EGFR and predicted a list of mutations with high probability of being a driver or a passenger. From this list, we selected 2 mutations (G729E and G719F) with high evolutionary conservation score forin vitrovalidation. If proven to be oncogenic drivers and sensitive to EGFR TKIs, these mutations can aid in the early diagnosis and successful therapy of EGFR mutation-positive NSCLC. |
