Spatially Resolved Metabolic Phenotyping of Breast Cancer by Desorption Electrospray Ionization Mass Spectrometry

Abstract

Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment. In this prospective single-center study, 126 intraoperative tissue biopsies from tumor and tumor bed from 50 patients undergoing surgical resections were subject to DESI MSI. Global DESI MSI models were able to distinguish adipose, stromal, and glandular tissue based on their metabolomic fingerprint. Tumor tissue and tumor-associated stroma showed evident changes in their fatty acid and phospholipid composition compared with normal glandular and stromal tissue. Diagnosis of breast cancer was achieved with an accuracy of 98.2% based on DESI MSI data (PPV 0.96, NVP 1, specificity 0.96, sensitivity 1). In the tumor group, correlation between metabolomic profile and tumor grade/hormone receptor status was found. Overall classification accuracy was 87.7% (PPV 0.92, NPV 0.9, specificity 0.9, sensitivity 0.92). These results demonstrate that DESI MSI may be a valuable tool in the improved diagnosis of breast cancer in the future. The identified tumor-associated metabolic changes support theories of de novo lipogenesis in tumor tissue and the role of stroma tissue in tumor growth and development and overall disease prognosis. Cancer Res; 75(9); 1828-37. Ó2015 AACR.

Detection of Metastatic Breast and Thyroid Cancer in Lymph Nodes by Desorption Electrospray Ionization Mass Spectrometry Imaging

“…Indeed, many studies have associated fatty acid synthesis and metabolism to breast cancer invasion and aggressiveness. [29, 30] Alterations in the abundance of fatty acids has been previously described in primary human breast cancer and animal models using DESI-MSI [7, 8] and MALDI-MSI [31]. High relative abundances of FA(20:4) and FA(22:6) was consistently observed in all metastatic breast cancer regions of the lymph node samples analyzed.…”

“…[1] In particular, cancer tissue diagnosis using ambient ionization MSI approaches has been explored by several research groups. [2] For instance, DESI-MSI, the most commonly used ambient ionization MS technique, has been employed to analyze several primary human cancer tissues including brain[3], prostate[4], gastric[5], kidney[6] and breast[7, 8]. Other ambient ionization MSI techniques such as laser ablation electrospray ionization mass spectrometry (LAESI-MS)[9], rapid evaporative ionization mass spectrometry (REIMS)[10], and nano-DESI[11] have been used to investigate the molecular signatures of biological samples and for cancer diagnosis.…”

Ambient Mass Spectrometry Imaging Using Direct Liquid Extraction Techniques

“…61 DESI MSI of patient biopsies have indicated the disruption of normal lipid metabolism as the main marker of the altered microenvironment in necrotic tissue. 161,168–171 In a related study, DESI MSI of surgical biopsies has been used for diagnostics of human breast cancer 171 and canine non-Hodgkin’s lymphoma. 172 …”

Ambient mass spectrometry in metabolomics

“…87 Additional examples illustrating the contributions and further potential of DESI-MSI in cancer research exist in the literature. 80, 81, 90, 93 A DESI-MSI-based method based on PCA of lipidomic profiles, for example, allowed identifying specific lymph node tissue types without the need for immunohistochemistry (IHC) experiments. 80 Identification of lymph node micro- and macrometastases was achieved with a sensitivity of 89.5%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 97.2% when compared with standard IHC.…”