Identifying Biomarkers in FFPE Breast TMAs Using DESI-MSI Ólöf Gerdur Ísberg (Presenter) University of Iceland Presenter Bio: I finished a bachelor´s degree in biochemistry in 2012 from University of Iceland. My thesis title was “The role of DNA methylation in epithelial to mesenchymal transition (EMT) in breast epithelial stem cells” and was carried out at a Stem Cell Research Unit at University of Iceland. My thesis work increased my interest for research and molecular biology and therefore, I applied to study M.Sc. in Human biology at University of Copenhagen, Denmark. The M.Sc. in Human Biology is part of the Copenhagen Master of Excellence (COME) program and is one of the Danish elite master´s degrees designed to challenge students at the highest academic level. It is a highly research oriented 2-year interdisciplinary program, offering advanced theoretical and experimental training within the fields of biomedical and natural sciences. I performed my thesis project “Characterization of CD146 as a progenito

Authors: Ólöf Gerdur Ísberg (1-3), Renata Soares (2), James McKenzie (2), Dipa Gurung (2), Hiromi Kudo (2), Rathi Ramakrishnan (2), Jón G Jónasson (4), Sigrídur Klara Bödvarsdóttir (3), Margrét Thorsteinsdóttir (1) and Zoltan Takats (2)
(1) Faculty of Pharmaceutical Sciences, University of Iceland, Reykjavík, Iceland; (2) Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College, London, UK; (3) Biomedical Center, University of Iceland, Reykjavik, Iceland; (4) Department of Pathology, Landspitali-University Hospital, Reykjavík, Iceland.

Breast cancer is one of the most common cancers in the world among females and accounts for 25% of all cancers. DESI-MSI is a powerful tool to investigate the spatial distribution of biomolecules in tissue sections. The distribution of biomolecules such as metabolites can be correlated with clinicopathological information of tissue samples and thus providing essential information for clinical diagnosis. The project aim was to metabolic phenotype FFPE TMA slides of 30 breast cancer samples and 30 normal samples using DESI-MSI. Preliminary results report that DESI-MSI can discriminate between malignant and normal tissue.

Introduction

Breast cancer is one of the most common cancers in the world among females where one out of ten women is assumed to be diagnosed. It is a heterogeneous disease with different patient outcomes dependent on pathological factors including molecular status, tumour size, nodal status and histologic grade. Due to improvement in treatment and early detection the death rate has decreased over the years but despite that, breast cancer remains one of the most common cancers. Different mass spectrometry imaging techniques have been used to investigate cancer including matrix assisted laser desorption ionization mass spectrometry (MALDI-MS), secondary ion mass spectrometry (SIMS) and desorption electrospray ionization mass spectrometry (DESI-MS). By using DESI-MSI the spatial distribution of biomolecules in biological tissue can be investigated, where it is particularly suited for detection of metabolites. Additionally, it has been shown that the reproducibility of DESI-MSI is accurate and suitable for the purpose of clinical research and diagnosis.

Methods

Breast cancer tissue was obtained from 30 breast cancer patients undergoing mastectomy as well as normal control tissue was acquired from 30 individuals. The tissue samples were formalin fixed and paraffin embedded (FFPE). Tissue microarrays (TMAs) cores from regions of interest were collected from the FFPE tissue samples, inserted in a recipient paraffin block and stored at room temperature. Microtome was used to cut 5 µm sections from each block which were then mounted on a microscope slide. Prior to measurements, TMAs were dewaxed by heating the slides for one hour in a 60 °C warming oven and then they were washed for 2x8 minutes with xylene. Dewaxed TMAs slides were placed onto a DESI-MSI instrument, and the area of interest selected. DESI-MSI spectra were collected line-by-line over the area of interest where x and y coordinates were recorded. The scan range was 50-1000 m/z, with one scan/second and 95:5 methanol:water solvent at 0.0015 ml/min flow rate. Following DESI-MSI, the TMAs slides were haematoxylin and eosin (H&E) stained for histopathological validation. An in-house developed DESI-MSI toolbox was used to process raw imaging data and to perform co-registration of the mass spectrometry ion image and the H&E optical image from the TMA. Areas of interest were annotated with help from a qualified histopathologist. Multivariate statistical analysis was conducted on log transformed spectral data including principal component (PCA) and maximum margin criteria (MMC). Additionally, univariate statistical analysis was performed including analysis of variance (ANOVA p<0.05). Furthermore, putative peak identification of m/z values based on accurate mass was carried out using Human Metabolome Database (http://www.hmdb.ca/) and Metlin (https://metlin.scripps.edu).

Results

FFPE clinical tissue samples are the standard for histopathological analysis at hospitals where the paraffin blocks can be stored for many years. Using FFPE TMA allows us to perform high throughput analysis of multiple specimens at the same time. DESI-MSI analysis was performed on FFPE TMAs from 30 breast cancer samples and 30 normal breast tissue samples. From the spectra collected we were able to identify metabolites at the range 50-500 m/z. Using an in-house toolbox, we co-registered and compared ion images generated by DESI-MSI to H&E stained optical images. We were able to distinguish between normal and cancerous breast tissues using multivariate statistical analysis and univariate statistical analysis including PCA, MMC and ANOVA.

Conclusions & Discussion

Our results indicate that DESI-MSI shows a potential to differentiate between breast cancer FFPE TMA and normal breast FFPE TMA based on their metabolic profile.

This project is funded by an Icelandic Research Fund project grant no. 174566051 from the Icelandic Centre for Research.