Bringing Mass Spectrometry to Pathologists for Diagnostics of Frozen Surgically Resected Tissue Specimen Tiffany Porta Siegel (Presenter) M4I, Maastricht University

Authors: Tiffany Porta,1,* John F. Cahill,2 Yves J.C. LeBlanc,3 Vilmos Kertesz,2 Gert Eijkel,1 Pierre-Maxence Vaysse,1,4 Heike Grabsch,5 Steven W.M. Olde Damink,4 Gary Van Berkel,2 and Ron Heeren1
(1) Maastricht MultiModal Molecular Imaging institute, Maastricht University, 6229ER Maastricht, The Netherlands; (2) Mass Spectrometry and Laser Spectroscopy Group, Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6131, USA; (3) SCIEX , 71 Four Valley Drive, Concord, Ontario L4K 4V8, Canada; (4) Department of Surgery, Maastricht University Medical Centre, 6229ER Maastricht, The Netherlands; (5) Department of Pathology, Maastricht University Medical Centre, 6229ER Maastricht, The Netherlands

In current clinical practice, accurate tumor diagnosis during surgery remains challenging. To assess the presence of a malignancy, frozen cut tissue sections are evaluated by the pathologist; but this remains challenging because the morphology of the tissue altered. With our approach, we aim for a rapid, on-line solution by combining an optical microscope directly to a mass spectrometer that could help the pathologist with molecular evaluation / diagnostics of fresh tissue based on molecular profiling within minutes. A predictive statistical model based on PCA-LDA analysis of the patient specimens displayed an accuracy of >90% correct classification rate which allowed to differentiate tumor versus non tumor tissues from patients undergoing surgery for colorectal metastasis. This holds great promises for the diagnostics of frozen section for intraoperative tumor diagnostics.

Introduction

Molecular pathology examines the molecular composition of cells, tissues and organs for pathologic diagnoses. Discrimination between cancerous and inflamed tissues is a challenging task. This is especially true for frozen sections where morphology is altered, which can have consequences on patient management. Lately, histology-guided mass spectrometry imaging (MSI) has proven to be complementary to histopathology. Ambient ionization MSI enables characterization of diseased tissues in a time frame compatible with routine diagnostic workflow. Herein, we evaluate the performance of a hybrid optical microscope / laser microdissection-liquid vortex capture (LMD-LVC)/ESI-MS system for molecular histopathology based on tissue-specific molecular signature from well-documented mouse brain and patient’s material.

Methods

Samples from patients undergoing surgery for colorectal liver metastasis (CRLM) were provided for this study by the Department of Pathology of MUMC+. A prototype LMD-LVC system mounted on a Triple TOF MS system employing ESI was used for all experiments. 40x40 µm2 microdissections of tissue were directly collected into the liquid vortex and transported to the sampling interface of the MS. A carrier solvent (chloroform / methanol + 0.1%FA) was used at a flow rate of 125 µL/min. Tissue-specific mass spectra were collected and extracted from raw data files, subjected to processing and peak picking performed with in-house written algorithm. Subsequent multivariate statistical analysis using MATLAB was used to build statistical models.

Results

Data were first collected for mouse brain tissue sections to demonstrate the following: 1) lipids are confidentially detected from 40x40µm2 tissue sections directly infused into the MS system; 2) statistical model based on principal component analysis – linear discriminant analysis (PCA-LDA) allows differentiating tissue-specific molecular profiles. Fifteen data points were collected from the following: granular area, white matter, grey matter and nucleus from the cerebellum. Results demonstrate a carry-over-free and reproducible platform with <20% variation between sampling points. The four groups are clearly differentiated by the two first discriminant functions (DF) of the PCA-LDA tissue-specific classification model. DF1 clearly separates [granular area / grey matter] from [white matter / nucleus] and the loadings highlighted several glycerophospholipids responsible for this differentiation. A cross-validation using 30% of the data as training set displayed a good accuracy of >95% for correct tissue identification. These findings correlated the molecular information generated with previously published results generated by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) (Ogrinc et al, RCM, 2015).

The method was further applied for careful investigation of resected human tissue specimens, from patients undergoing surgery to remove CRLM. In total, more than 1500 tissue microdissections were collected and specific profiles allowed differentiating between cancer cells, inflammatory cells, stroma and fibrotic tissues with high accuracy. Cross-validation of the model was performed using 10% of the data as the Training set and validation on the 90% remaining data. The model displayed an accuracy of >90% correct classification rate

Conclusions & Discussion

In current clinical practice, accurate tumor diagnosis during surgery remains challenging. To assess the presence of a malignancy, frozen cut tissue sections are evaluated by the pathologist; but this remains challenging because the morphology of the tissue altered. With our approach, we for a rapid, on-line approach directly combined with an optical microscope that could help the pathologist with molecular evaluation / diagnostics of fresh tissue based on molecular profiling within minutes.

This holds great promises for the diagnostics of frozen section for intraoperative diagnostics.