Untangling the Metabolic Reprogramming in Brain Cancer: Discovering Key Molecular Players Using Mass Spectrometry

Modern machine-learning applications in ambient ionization mass spectrometry

This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

Mass spectrometry for neurosurgery: Intraoperative support in decision-making

Ambient ionization mass spectrometry was proved to be a powerful tool for oncological surgery. Still, it remains a translational technique on the way from laboratory to clinic. Brain surgery is the most sensitive to resection accuracy field since the balance between completeness of resection and minimization of nerve fiber damage determines patient outcome and quality of life. In this review, we summarize efforts made to develop various intraoperative support techniques for oncological neurosurgery and discuss difficulties arising on the way to clinical implementation of mass spectrometry-guided brain surgery.

Lipid metabolism in malignant tumor brain metastasis: reprogramming and therapeutic potential

INTRODUCTION

Brain metastasis is a highly traumatic event in the progression of malignant tumors, often symbolizing higher mortality. Metabolic alterations are hallmarks of cancer, and the mask of lipid metabolic program rearrangement in cancer progression is gradually being unraveled.

AREAS COVERED

In this work, we reviewed clinical and fundamental studies related to lipid expression and activity changes in brain metastases originating from lung, breast, and cutaneous melanomas, respectively. Novel roles of lipid metabolic reprogramming in the development of brain metastasis from malignant tumors were identified and its potential as a therapeutic target was evaluated. Published literature and clinical studies in databases consisting of PubMed, Embase, Scopus and www.ClinicalTrials.gov from 1990 to 2022 were searched.

EXPERT OPINION

Lipid metabolic reprogramming in brain metastasis is involved in de novo lipid synthesis within low lipid availability environments, regulation of lipid uptake and storage, metabolic interactions between brain tumors and the brain microenvironment, and membrane lipid remodeling, in addition to being a second messenger for signal transduction. Although some lipid metabolism modulators work efficiently in preclinical models, there is still a long way to go from laboratory to clinic. This area of research holds assurance for the organ-targeted treatment of brain metastases through drug-regulated metabolic targets and dietary interventions.

Shapley Value as a Quality Control for Mass Spectra of Human Glioblastoma Tissues

The automatic processing of high-dimensional mass spectrometry data is required for the clinical implementation of ambient ionization molecular profiling methods. However, complex algorithms required for the analysis of peak-rich spectra are sensitive to the quality of the input data. Therefore, an objective and quantitative indicator, insensitive to the conditions of the experiment, is currently in high demand for the automated treatment of mass spectrometric data. In this work, we demonstrate the utility of the Shapley value as an indicator of the quality of the individual mass spectrum in the classification task for human brain tumor tissue discrimination. The Shapley values are calculated on the training set of glioblastoma and nontumor pathological tissues spectra and used as feedback to create a random forest regression model to estimate the contributions for all spectra of each specimen. As a result, it is shown that the implementation of Shapley values significantly accelerates the data analysis of negative mode mass spectrometry data alongside simultaneous improving the regression models’ accuracy.

Incorporation of a Disposable ESI Emitter into Inline Cartridge Extraction Mass Spectrometry Improves Throughput and Spectra Stability

Rapid and reliable methods for detecting tumor margins are crucial for neuro-oncology. Several mass spectrometry-based methods have been recently proposed to address this problem. Inline Cartridge Extraction (ICE) demonstrates the potential for clinical application, based on ex-vivo analysis of dissected tissues, but requires time-consuming steps to avoid cross-contamination. In this work, a method of incorporating a disposable electrospray emitter into the ICE cartridge by PEEK sleeves melting is developed. It reduces total analysis time and improves throughput. The proposed setup also improves the robustness of the ICE molecular profiling as demonstrated with human glial tumor samples in that stability and reproducibility of the spectra were increased.

Determination of Brain Tissue Samples Storage Conditions for Reproducible Intraoperative Lipid Profiling

Ex-vivo molecular profiling has recently emerged as a promising method for intraoperative tissue identification, especially in neurosurgery. The short-term storage of resected samples at room temperature is proposed to have negligible influence on the lipid molecular profiles. However, a detailed investigation of short-term molecular profile stability is required to implement molecular profiling in a clinic. This study evaluates the effect of storage media, temperature, and washing solution to determine conditions that provide stable and reproducible molecular profiles, with the help of ambient ionization mass spectrometry using rat cerebral cortex as model brain tissue samples. Utilizing normal saline for sample storage and washing media shows a positive effect on the reproducibility of the spectra; however, the refrigeration shows a negligible effect on the spectral similarity. Thus, it was demonstrated that up to hour-long storage in normal saline, even at room temperature, ensures the acquisition of representative molecular profiles using ambient ionization mass spectrometry.

Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a dataset of 124 high-resolution negative ionization mode lipid profiles of human brain tumors resected during neurosurgery. The dataset is supplemented with 38 non-tumor pathological brain tissue samples resected during elective surgery. The change in lipid composition alterations of brain tumors enables the possibility of discriminating between malignant and healthy tissues with the implementation of ambient mass spectrometry. On the other hand, the collection of clinical samples allows the comparison of the metabolism alteration patterns in animal models or in vitro models with natural tumor samples ex vivo. The presented dataset is intended to be a data sample for bioinformaticians to test various data analysis techniques with ambient mass spectrometry profiles, or to be a source of clinically relevant data for lipidomic research in oncology.

Assessment of variation of inline cartridge extraction mass spectra

Recently, mass-spectrometry methods show its utility in tumor boundary location. The effect of differences between research and clinical protocols such as low- and high-resolution measurements and sample storage have to be understood and taken into account to transfer methods from bench to bedside. In this study, we demonstrate a simple way to compare mass spectra obtained by different experimental protocols, assess its quality, and check for the presence of outliers and batch effect in the dataset. We compare the mass spectra of both fresh and frozen-thawed astrocytic brain tumor samples obtained with the inline cartridge extraction prior to electrospray ionization. Our results reveal the importance of both positive and negative ion mode mass spectrometry for getting reliable information about sample diversity. We show that positive mode highlights the difference between protocols of mass spectra measurement, such as fresh and frozen-thawed samples, whereas negative mode better characterizes the histological difference between samples. We also show how the use of similarity spectrum matrix helps to identify the proper choice of the measurement parameters, so data collection would be kept reliable, and analysis would be correct and meaningful.

Rapid estimation of tumor cell percentage in brain tissue biopsy samples using inline cartridge extraction mass spectrometry

Tumor cell percentage (TCP) is an essential characteristic of biopsy samples that directly affects the sensitivity of molecular testing in clinical practice. Apart from clarifying diagnoses, rapid evaluation of TCP combined with various neuronavigation systems can be used to support decision making in neurosurgery. It is known that ambient mass spectrometry makes it possible to rapidly distinguish healthy from malignant tissues. In connection with this, here we demonstrate the possibility of using non-imaging ambient mass spectrometry to evaluate TCP in glial tumor tissues with a high degree of confidence. Molecular profiles of histologically annotated human glioblastoma tissue samples were obtained using the inline cartridge extraction ambient mass spectrometry approach. XGBoost regressors were trained to evaluate tumor cell percentage. Using cross-validation, it was estimated that the TCP was determined by the regressors with a precision of approximately 90% using only low-resolution data. This result demonstrates that ambient mass spectrometry provides an accurate method todetermine TCP in dissected tissues even without implementing mass spectrometry imaging. The application of such techniques offers the possibility to automate routine tissue screening and TCP evaluation to boost the throughput of pathology laboratories. Rapid estimation of tumor cell percentage during neurosurgery.

Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues

Recently developed methods of ambient ionization allow the collection of mass spectrometric datasets for biological and medical applications at an unprecedented pace. One of the areas that could employ such analysis is neurosurgery. The fast in situ identification of dissected tissues could assist the neurosurgery procedure. In this paper tumor tissues of astrocytoma and glioblastoma are compared. The vast majority of the data representation methods are hard to use, as the number of features is high and the amount of samples is limited. Furthermore, the ratio of features and samples number restricts the use of many machine learning methods. The number of features could be reduced through feature selection algorithms or dimensionality reduction methods. Different algorithms of dimensionality reduction are considered along with the traditional noise thresholding for the mass spectra. From our analysis, the Isomap algorithm appears to be the most effective dimensionality reduction algorithm for negative mode, whereas the positive mode could be processed with a simple noise reduction by a threshold. Also, negative and positive mode correspond to different sample properties: negative mode is responsible for the inner variability and the details of the sample, whereas positive mode describes measurement in general.

[Analysis of phosphatidylcholines alterations in human glioblastoma multiform tissues ex vivo]

Significant metabolism alteration is accompanying the cell malignization process. Energy metabolism disturbance leads to the activation of de novo synthesis and beta-oxidation processes of lipids and fatty acids in a cancer cell, which becomes an indicator of pathological processes inside the cell. The majority of studies dealing with lipid metabolism alterations in glial tumors are performed using the cell lines in vitro or animal models. However, such conditions do not entirely represent the physiological conditions of cell growth or possible cells natural variability. This work presents the results of the data obtained by applying ambient mass spectrometry to human glioblastoma multiform tissues. By analyzing a relatively large cohort of primary and secondary glioblastoma samples, we identify the alterations in cells lipid composition, which accompanied the development of grade IV brain tumors. We demonstrate that primary glioblastomas, as well as ones developed from astrocytomas, are enriched with mono- and diunsaturated phosphatidylcholines (PC 26:1, 30:2, 32:1, 32:2, 34:1, 34:2). Simultaneously, the saturated and polyunsaturated phosphatidylcholines and phosphatidylethanolamines decrease. These alterations are obviously linked to the availability of the polyunsaturated fatty acids and activation of the de novo lipid synthesis and beta-oxidation pathways under the anaerobic conditions in the tumor core.

The software for interactive evaluation of mass spectra stability and reproducibility

SUMMARY

Mass spectrometry methods are widely used for the analysis of biological and medical samples. Recently developed methods such as DESI, REIMS, NESI allow fast analyses without sample preparation at the cost of higher variability of spectra. In biology and medicine, MS profiles are often used with machine learning (classification, regression, etc.) algorithms and statistical analysis, which are sensitive to outliers and intraclass variability. Here we present SSM Display software, a tool for fast visual outlier detection and variance estimation in mass spectrometric profiles. The tool speeds up the process of manual spectra inspection, improves accuracy and explainability of outlier detection, and decreases the requirements to the operator experience. It was shown that the batch effect could be revealed through SSM analysis and that the SSM calculation can also be used for tuning novel ion sources concerning the quality of obtained mass spectra.

AVAILABILITY

Source code, example datasets, binaries, and other information are available at https://github.com/EvgenyZhvansky/R_matrix.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[The role of lipids in the classification of astrocytoma and glioblastoma using MS tumor profiling]

Express MS identification of biological tissues has become a much more accessible research method due to the application of direct specimen ionization at atmospheric pressure. In contrast to traditional methods of analysis employing GC-MS methods for determining the molecular composition of the analyzed objects it eliminates the influence of mutual ion suppression. Despite significant progress in the field of direct MS of biological tissues, the question of mass spectrometric profile attribution to a certain type of tissue still remains open. The use of modern machine learning methods and protocols (e.g., "random forests") enables us to trace possible relationships between the components of the sample MS profile and the result of brain tumor tissue classification (astrocytoma or glioblastoma). It has been shown that the most pronounced differences in the mass spectrometric profiles of these tumors are due to their lipid composition. Detection of statistically significant differences in lipid profiles of astrocytoma and glioblastoma may be used to perform an express test during surgery and inform the neurosurgeon what type of malignant tissue he is working with. The ability to accurately determine the boundaries of the neoplastic growth significantly improves the quality of both surgical intervention and postoperative rehabilitation, as well as the duration and quality of life of patients.

Inline cartridge extraction for rapid brain tumor tissue identification by molecular profiling

The development of perspective diagnostic techniques in medicine requires efficient high-throughput biological sample analysis methods. Here, we present an inline cartridge extraction that facilitates the screening rate of mass spectrometry shotgun lipidomic analysis of tissue samples. We illustrate the method by its application to tumor tissue identification in neurosurgery. In perspective, this high-performance method provides new possibilities for the investigation of cancer pathogenesis and metabolic disorders.