Genetic Analysis of Oligo-Metastatic Breast Cancer: Correlation with Clinicopathological Features [post]

Kuikui Jiang, Danyang Zhou, Fei Xu, Wen Xia, Qiufan Zheng, Qianyi Lu, Liye Wang, Kaping Lee, Hanjia Luo, Ping Zhang, Rongzhen Luo, Ruoxi Hong (+1 others)
2021 unpublished
Purpose: We aimed to identify the relationship between the genomic characteristics and clinicopathological features of oligo-metastatic breast cancer.Methods: Oligo-metastatic breast cancer diagnosed by pathology from January 2001 and August 2019 were identified and we matched the poly-metastatic patients based on the clinicopathological features of the oligo-metastatic patients included. The database of all genomic alterations was shown according to the FoundationOne CDx reports.
more » ... gical characteristics were collected and the results of next-generation sequencing were analyzed.Results: A total of 26 breast cancer patients were enrolled in our study, including 14 patients with oligo-metastatic disease and 12 patients with poly-metastatic disease. There was no significant difference in number of gene alteration, tumor mutational burden, variants of unknown significance (VUS), and actional mutation in oligo- and poly-metastasis. PIK3CA, TP53 and ERBB2 were the most common shared alterations identified in patients included. Based on the median time of oligo-progression disease (oligo-PD), we divided the patients with oligo-metastasis into longer oligo-PD group (oligo-PD > 31.04 months) and shorter oligo-PD group (oligo-PD ≤ 31.04 months). The analysis of PIK3CA mutation sites showed that H1047R was associated with a good prognosis in patients with metastatic breast cancer. HER2 positive patients with oligo-metastasis was more likely to have a good prognosis. In addition, VUS might also be a potential prognostic biomarker in metastatic breast cancer. Conclusion: Through the genetic analysis of oligo-metastasis, we found PIK3CA H1047R, HER2 and VUS might predict the different clinical outcomes of breast cancer patients with oligo-metastasis for the individualized treatment.
doi:10.21203/ fatcat:sn4vq3c4ybgzziv5cz65k3jqhy