Germline mutation analysis and postoperative recurrence risk prediction in breast cancer patients from western China

• At present, there is no reliable multi-gene risk prediction model for postoperative recurrence risk of breast cancer in the Chinese population. • In the breast cancer cohort from western China, 20.1 % of patients carried deleterious germline variants. • In patients with early and locally advanced breast cancer, single-nucleotide polymorphisms (SNPs), rather than deleterious germline mutations, impact 3-year disease-free survival (DFS). • By integrating SNP status with clinical phenotypes, a risk prediction model was established to effectively identify patients with a high risk of postoperative recurrence. Much of our understanding of the germline mutation spectrum derives from hereditary breast cancer data in white populations. Additionally, the influence of genetic variants on breast cancer prognosis remains a topic of debate. Identifying patients at high risk of postoperative recurrence is crucial for guiding clinical decision-making; however, there is currently no reliable multigene risk prediction model tailored to the Chinese population. A single-center retrospective study involving 1067 breast cancer patients was conducted. Survival analyses were performed using the Kaplan‒Meier method and Cox proportional hazards regression. Postoperative recurrence risk prediction models were developed utilizing the Cox regression methodology. In this cohort, 229 germline pathogenic/likely pathogenic (P/LP) mutations were identified in 215 patients (20.1 %). No significant differences in disease-free survival (DFS) were observed between germline P/LP mutation carriers and non-carriers. However, 10 single-nucleotide polymorphisms (SNPs) were significantly associated with DFS outcomes. By integrating the SNP status and clinical phenotype, a postoperative recurrence risk prediction model was established. The area under the curve values for 1- and 3-year DFS in the training set were 0.840 and 0.754. This model can accurately predict the DFS of patients in both the training set (hazard ratio [HR] 5.23, 95 % confidence interval [CI] 2.96–9.34; p < 0.0001) and the validation set (HR 2.88, 95 % CI 1.41–6.06; p = 0.003) In patients with early and locally advanced breast cancer, SNPs, rather than germline P/LP mutations, impact DFS. Using a genetic-clinical model, we successfully identified patients at high risk of postoperative recurrence.