Introduction: The detection of brain tumor by mass-spectrometry profiling requires identification of biomarkers for each type of tumor. The first step in biomarker mining is collection of tumor samples and organisation of the database for automated analysis of biomarker candidates.

Methods: Tumor tissues were dissected during neurosurgery, and each sample was characterized by trained pathologist. Mass spectra for each sample were measured with continuous flow needle electrospray ionization method [1] on high-resolution ion trap mass spectrometers. Information about tumor type, tumor localization, sample position within the tumor, the medication used prior operation was collected and stored in NoSQL database together with spectra.

Results: A set of over 300 samples of different brain tumors and surrounding brain tissues from over 250 patients were collected in the study. All the samples were provided by the Burdenko Neurosurgical Institute. Collected spectra were filtered and stored in a dedicated column-oriented database MonetDB. User interface allows for visualisation, comparison, transformation (PCA, ICA etc.) of the spectra. The integration with R/Bioconductor makes whole range of statistical analysis and machine learning techniques readily available from API as well as from user interface. To demonstrate the infrastructure performance 3 classifiers were trained on 20 low grade astrocytoma, 28 meningioma, 21 neurinoma, 17 glioblastoma and 9 microscopically intact brain samples. It was shown that the most sensitive classifier among tested was lasso regression. Features, which differ the most between tumor and microscopically intact brain tissue samples, were compared against available literature.

Conclusions: The result of the work creates an infrastructure suitable for identification of biomarkers of different types of the brain tumors.

Patient Care: Creation of mass-spectrometric database will improve further investigations of biomarkers discovery and tissue identification protocols using molecular profiling.

Learning Objectives: Biomarkers detection and molecular profiling using mass-spectrometry allows to create identification methods for fast and reliable brain tumor distinguishing.

References: 1. Kononikhin A., et al. Analytical and Bioanalytical Chemistry. 407 7797 (2015).