The Analysis of Relevant Gene Networks Based on Driver Genes in Breast Cancer

Genetic landscape of breast cancer subtypes following radiation therapy: insights from comprehensive profiling

Background

In breast cancer, in the era of precision cancer therapy, different patterns of genetic mutations dictate different treatments options. However, it is not clear whether the genetic profiling of breast cancer patients undergoing breast-conserving surgery is related to the adverse reactions caused by radiotherapy.

Methods

We collected formalin-fixed paraffin-embedded (FFPE) tumor tissue samples from 54 breast cancer patients treated with radiation after breast-conserving surgery and identified comprehensive molecular information in hundreds of cancer-associated genes by FoundationOne CDx (F1CDx), a next-generation sequencing (NGS)-based assay.

Results

Among our cohort of 54 breast cancer patients, we found high-frequency mutations in cancer-related genes such as TP53 (56%), RAD21 (39%), PIK3CA (35%), ERBB2 (24%), and MYC (22%). Strikingly, we detected that the WNT pathway appears to be a signaling pathway with specific high-frequency mutations in the HER2 subtype. We also compared the mutation frequencies of the two groups of patients with and without cutaneous radiation injury (CRI) after radiotherapy and found that the mutation frequencies of two genes, FGFR1 and KLHL6, were significantly higher in patients with CRI : No subgroup than in those with CRI : Yes.

Conclusion

Different breast cancer subtypes have their own type-specific mutation patterns. FGFR1 and KLHL6 mutations are protective factors for radiation-induced skin toxicity in breast cancer patients.

Advances in computational methods for identifying cancer driver genes

Cancer driver genes (CDGs) are crucial in cancer prevention, diagnosis and treatment. This study employed computational methods for identifying CDGs, categorizing them into four groups. The major frameworks for each of these four categories were summarized. Additionally, we systematically gathered data from public databases and biological networks, and we elaborated on computational methods for identifying CDGs using the aforementioned databases. Further, we summarized the algorithms, mainly involving statistics and machine learning, used for identifying CDGs. Notably, the performances of nine typical identification methods for eight types of cancer were compared to analyze the applicability areas of these methods. Finally, we discussed the challenges and prospects associated with methods for identifying CDGs. The present study revealed that the network-based algorithms and machine learning-based methods demonstrated superior performance.