Prognostic significance of TP53 and PIK3CA mutations analyzed by next-generation sequencing in breast cancer

Breast cancer is one of the most prevalent malignant tumors affecting women globally. It is a heterogeneous disease characterized by mutations in several genes. Several gene panels have been applied to assess the risk of breast cancer and determine the appropriate treatment. As a powerful tool, Next-generation sequencing (NGS) has been widely utilized in cancer research due to its advantages, including high speed, high throughput, and high accuracy. In this study, we aim to analyze the correlation between somatic mutations in breast cancer, analyzed using NGS, and the prognosis of patients. Between May 2018 and May 2019, a total of 313 patients with breast cancer underwent surgical treatment, which included total mastectomy and breast-conserving surgery. Among these patients, 265 were diagnosed with invasive ductal carcinoma. In this study, we analyzed the NGS results, clinicopathological characteristics, and their correlation with prognosis. Using a gene panel, we examined 143 somatic mutations in solid cancers. Notably, the study population included patients who had received neoadjuvant chemotherapy. The mean age of the patients was 53.1 (+/- 10.28) years, and the median follow-up time was 48 months (range, 8-54). Among the 265 patients, 68 had received prior systemic therapy. Of these, 203 underwent breast-conserving surgery, and 62 underwent a mastectomy. Various somatic mutations were observed in NGS, with the most frequent mutation being PIK3CA mutations, which accounted for 44% of all mutations. TP53 mutations were the second most frequent, and ERBB2 mutations were the third most frequent. TP53 mutations were associated with poor disease-free survival (P = .027), while PIK3CA mutations were associated with better disease-free survival (P = .035) than PIK3CA wild-type. In our study, we identified various somatic mutations in breast cancer. Particularly, we found that TP53 and PIK3CA mutations are potentially associated with the prognosis of breast cancer. These findings suggest that the presence of specific mutations may have implications for predicting the prognosis of breast cancer. Further research and validation are needed to gain a deeper understanding of the role of these mutations and their mechanisms in prognosis prediction.