Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning

Yaoyao Song; Yan Zhang; Songhai Xie; Xiaowei Song

doi:10.3389/fcell.2022.1075810

Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning

Front Cell Dev Biol. 2022 Dec 15:10:1075810. doi: 10.3389/fcell.2022.1075810. eCollection 2022.

Authors

Yaoyao Song^{1

2}, Yan Zhang¹, Songhai Xie³, Xiaowei Song³

Affiliations

¹ Department of General Surgery, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China.
² Department of Burn and Plastic Surgery, The Fourth Medical Center of Chinese PLA General Hospital, Beijing, China.
³ Department of Chemistry, Fudan University, Shanghai, China.

Abstract

We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice.

Keywords: ambient ionization mass spectrometry; biomarkers; clinical screening and diagnosis; metabolomics; triple-negative breast cancer.