Ultrasound genomics related mitochondrial gene signature for prognosis and neoadjuvant chemotherapy resistance in triple negative breast cancer.

Abstract

Background: Neoadjuvant chemotherapy (NAC) significantly enhances clinical outcomes in patients with triple-negative breast cancer (TNBC); however, chemoresistance frequently results in treatment failure. Consequently, understanding the mechanisms underlying resistance and accurately predicting this phenomenon are crucial for improving treatment efficacy.

Methods: Ultrasound images from 62 patients, taken before and after neoadjuvant therapy, were collected. Mitochondrial-related genes were extracted from a public database. Ultrasound features associated with NAC resistance were identified and correlated with significant mitochondrial-related genes. Subsequently, a prognostic model was developed and evaluated using the GSE58812 dataset. We also assessed this model alongside clinical factors and its ability to predict immunotherapy response.

Results: A total of 32 significant differentially expressed genes in TNBC across three groups indicated a strong correlation with ultrasound features. Univariate and multivariate Cox regression analyses identified six genes as independent risk factors for TNBC prognosis. Based on these six mitochondrial-related genes, we constructed a TNBC prognostic model. The model's risk scores indicated that high-risk patients generally have a poorer prognosis compared to low-risk patients, with the model demonstrating high predictive performance (p = 0.002, AUC = 0.745). This conclusion was further supported in the test set (p = 0.026, AUC = 0.718). Additionally, we found that high-risk patients exhibited more advanced tumor characteristics, while low-risk patients were more sensitive to common chemotherapy drugs and immunotherapy. The signature-related genes also predicted immunotherapy response with a high accuracy of 0.765.

Conclusion: We identified resistance-related features from ultrasound images and integrated them with genomic data, enabling effective risk stratification of patients and prediction of the efficacy of neoadjuvant chemotherapy and immunotherapy in patients with TNBC.