MSACL 2019 EU Abstract

Towards In vivo Molecular Diagnostics of Esogastric Cancer with Spidermass Real-Time, Mini Invasive Analysis

Nina Ogrinc (1), Pierre-Damien Caux(1), Alexandru Lintis(2,3), Florence Renaud(3,4), Clementine Dejeante(3), Khawla Seddiki (5), Maxence Wisztroski (1), Arnaud Droit (5), Guillaume Piessen (2,3), Michel Salzet (1), Isabelle Fournier(1)
(1) University of Lille, PRISM, Lille, France (2) Department of Digestive and Oncological Surgery, University Lille, Claude Huriez University Hospital, Lille Cedex, France (3) Jean-Pierre Aubert Research Center, Neurosciences and Cancer, University Lille, Lille, France (4) Department of Pathology, Biology Pathology Center, University Hospital, Lille, France (5) Department of molecular medicine, Faculty of medicine, Laval University, Quebec, Canada

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Nina Ogrinc (Presenter) University of Lille, PRISM laboratory

Relevant Financial Disclosures (within past 24 months) No relevant financial relationship(s) to disclose. Discussing Products? NO, presenter will NOT present educational content related to products/services of the ineligible company(ies) listed above. Conflict mitigation NOT REQUIRED.

Introduction: Esogastric cancer is the fourth most commonly diagnosed cancer. Poorly cohesive carcinoma is sometimes difficult to identify via histopathology and presents a challenge for rapid and accurate diagnostics. The extent of the esogastric cancer cannot be deduced from the initial CT-scan or endoscopy exam, thus surgery needs to be performed followed by an intraoperative pathology exam. This exam is long and shows and error rate of about 30%. There is then an urgent need for a novel technology allowing for guided surgery and real-time diagnostic using specific molecular signatures in esogastric cancer while being of minimal invasiveness to the patient’s tissue. The water-assisted laser desorption/ionization mass spectrometry (SpiderMass) has demonstrated the capability to analyse biopsies ex vivo, allowing for correct classification of tumour type and grading (Saudemont et al. Cancer Cell 2018). Herein, we present a pipeline for ex vivo analysis using the SpiderMass system combined with Mass Spectrometry Imaging to asses and classify esogastric cancer.
Objectives: Our aim is to improve intraoperative diagnosis of esogastric cancer.
Methods: Fresh-frozen biopsies of gastric cancer and normal tissue were supplied by the FREGAT (www.fregat-database.org). The small biopsies were sectioned for histopathological examination (5µm), SpiderMass (20µm) and MALDI Imaging (12µm) in 3 rounds. The SpiderMass uses a mini invasive IR-laser microprobe tuned to excite the most intense vibrational band of water. The microprobe is connected to the Xevo instrument via a transfer tube. Three molecular profiles were created of selected regions using 10 s irradiation steps in positive and negative ion mode. The data collected was processed via supervised Abstract Model Builder (Waters) and unsupervised "home-built" software. For MALDI analysis the sections were coated with Norharmane (7mg/mL in CHCl3:MeOH, 2:1, TM-sprayer) and analysed using the RapifleX MALDI Tissue Typer™ at 50 µm.
Results: Acquired spectra were used to build a PCA and LDA-based classification models. The PCA analysis is used to decrease the dimensionality of the data sets and generate a list of features showing the largest variance within the data set. Further on, the features are subjected to supervised LDA through user assigned classes (cancer/normal/necrosis). These classes are usually defined based on tissue phenotypes delineated by a trained pathologist. The LDA is used for a second space transformation, in which it minimizes the intra- (within) class variance and maximizes the inter- (between) class variance. The cross-validation is performed by removing a part of the cohort and building the model again. A comparison of the classification models was made using an unsupervised CNN algorithm. Additional cross-validation was performed by mass spectrometry imaging.
Conclusion: The pipeline allows for rapid molecular detection, guided surgery and real-time analysis of esogastric cancer.