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

Xinxin Mao,Yunxiao Meng,Jiuming He,Jian Sun,Zeper Abliz,Tiegang Li,Jie Chen,Zhaohui Lu

doi:10.1038/srep21043

Abstract

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.

Highlights

The spatial distribution of multiple molecules within a given section
The tissue expressions of the two regions were evaluated in the training and validation cohorts using phospholipids and fatty acids. This characteristic profile was observed for all samples harboring coexistent ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC), demonstrating the capability of AFAI-Mass spectrometry imaging (MSI) to distinguish between these cases
Desorption electrospray ionization (DESI) coupled with MSI has already been shown to distinguish cancerous breast tissue from noncancerous tissue[23]; in addition, this approach is useful for tissue-specific metabolomic profiling[24] and for the determination of tumor heterogeneity[25,26]

Summary

Result

A complete description of the samples used in the present study is provided in Supplementary Table S1. From these three samples, the distribution of several detected lipid species [m/z 706.5, PC (30:0); m/z 718.6, PC(32:0) or PE(35:0); m/z 724.5, PE(34:1); m/z 730.5, PC(32:2)] was found to be homogeneous in the ion images, and all of the ions that could distinguish breast cancer tissue from normal breast tissue or benign breast tissue were determined by AFAI-MSI. To further evaluate the predictive ability of high-, intermediate- and low-grade DCIS and high-, intermediate- and low-grade IDC tumor models, an independent test that included a set of 28 breast cancer samples was used. The tissue expressions of the two regions were evaluated in the training and validation cohorts using phospholipids and fatty acids This characteristic profile was observed for all samples harboring coexistent DCIS and IDC, demonstrating the capability of AFAI-MSI to distinguish between these cases. These markers could be beneficial for the selection of patients for clinical therapy at an early stage of aggressive breast cancer

Discussion

Findings

Materials and Methods