Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner

Jianjun Liu; Yang Zhou; Huiying Liu; Mengyan Ma; Fei Wang; Chang Liu; Qihang Yuan; Hongjiang Wang; Xiukun Hou; Peiyuan Yin

doi:10.3389/fonc.2022.939606

Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner

Front Oncol. 2022 Oct 12:12:939606. doi: 10.3389/fonc.2022.939606. eCollection 2022.

Authors

Jianjun Liu^{1

2}, Yang Zhou³, Huiying Liu^{1

2}, Mengyan Ma³, Fei Wang⁴, Chang Liu^{1

2}, Qihang Yuan⁵, Hongjiang Wang⁴, Xiukun Hou³, Peiyuan Yin^{1

2}

Affiliations

¹ Clinical Laboratory of Integrative Medicine, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
² College of Integrative Medicine, Dalian Medical University, Dalian, China.
³ Department of Ultrasound, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
⁴ Breast Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
⁵ Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China.

Abstract

Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30-40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS.

Objective: The objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS.

Methods: Untargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis.

Results: Metabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993).

Conclusions: Our study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy.

Keywords: automated breast volume scanner; biomarker; breast cancer; metabolites; metabolomics.