Application of imaging mass spectrometry for the molecular diagnosis of human breast tumors

• Published in: Scientific reports Vol. 6; no. 1; p. 21043
• Main Authors: Mao, Xinxin, He, Jiuming, Li, Tiegang, Lu, Zhaohui, Sun, Jian, Meng, Yunxiao, Abliz, Zeper, Chen, Jie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.02.2016; Nature Publishing Group
• Subjects: 631/67/1347; 692/4028/546; Adult; Aged; Aged, 80 and over; Air flow; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - metabolism; Breast surgery; Carcinoma, Ductal - diagnosis; Carcinoma, Ductal - metabolism; Fatty acids; Female; Humanities and Social Sciences; Humans; Invasiveness; Lipid Metabolism; Lipids; Lipids - analysis; Mammography; Mass spectrometry; Mass Spectrometry - methods; Mass spectroscopy; Middle Aged; multidisciplinary; Phospholipids; Science; Science (multidisciplinary); Scientific imaging; Surgery; Tumors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/srep21043

Distinguishing breast invasive ductal carcinoma (IDC) and breast ductal carcinoma in situ (DCIS) is a key step in breast surgery, especially to determine whether DCIS is associated with tumor cell micro-invasion. However, there is currently no reliable method to obtain molecular information for breast tumor analysis during surgery. Here, we present a novel air flow-assisted ionization (AFAI) mass spectrometry imaging method that can be used in ambient environments to differentiate breast cancer by analyzing lipids. In this study, we demonstrate that various subtypes and histological grades of IDC and DCIS can be discriminated using AFAI-MSI: phospholipids were more abundant in IDC than in DCIS, whereas fatty acids were more abundant in DCIS than in IDC. The classification of specimens in the subtype and grade validation sets showed 100% and 78.6% agreement with the histopathological diagnosis, respectively. Our work shows the rapid classification of breast cancer utilizing AFAI-MSI. This work suggests that this method could be developed to provide surgeons with nearly real-time information to guide surgical resections.