Extending the information content of the MALDI analysis of biological fluids via multi-million shot analysis

Molecular and translational biology of the blood-based VeriStrat® proteomic test used in cancer immunotherapy treatment guidance

INTRODUCTION

The VeriStrat® test (VS) is a blood-based assay that predicts a patient's response to therapy by analyzing eight features in a spectrum obtained from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) analysis of human serum and plasma. In a recent analysis of the INSIGHT clinical trial (NCT03289780), it was found that the VS labels, VS Good and VS Poor, can effectively predict the responsiveness of non-small cell lung cancer (NSCLC) patients to immune checkpoint inhibitor (ICI) therapy. However, while VS measures the intensities of spectral features using MALDI-TOF analysis, the specific proteoforms underlying these features have not been comprehensively identified.

OBJECTIVES

The objective of this study was to identify the proteoforms that are measured by VS.

METHODS

To resolve the features obtained from the low-resolution MALDI-TOF procedure used to acquire mass spectra for VS DeepMALDI® analysis of serum was employed. This technique allowed for the identification of finer peaks within these features. Additionally, a combination of reversed-phase fractionation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was then used to identify the proteoforms associated with these peaks.

RESULTS

The analysis revealed that the primary constituents of the spectrum measured by VS are serum amyloid A1, serum amyloid A2, serum amyloid A4, C-reactive protein, and beta-2 microglobulin.

CONCLUSION

Proteoforms involved in host immunity were identified as significant components of these features. This newly acquired information improves our understanding of how VS can accurately predict patient response to therapy. It opens up additional studies that can expand our understanding even further.

An easy adjustment of instrument settings ('Peak MALDI') improves identification of organisms by MALDI-ToF mass spectrometry

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-ToF MS) is a mature technolaogy with 'auto-execute' instrument settings and peak processing parameters tailored for rapid bacterial identification. Adoption for other organisms has been problematic, with optimisation efforts focusing on sample preparation. Using the Bruker MALDI Biotyper, we demonstrate 'Peak MALDI': easily-applied settings that immediately enhance sensitivity, improve spectrum quality, and increase identification confidence for any target, establishing its potential value for all MALDI-ToF MS systems.

Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2019-2020

This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.

Lymphatic filarial serum proteome profiling for identification and characterization of diagnostic biomarkers

Lymphatic Filariasis (LF) affects more than 863 million people in tropical and subtropical areas of the world, causing high morbidity and long illnesses leading to social exclusion and loss of wages. A combination of drugs Ivermectin, Diethylcarbamazine citrate and Albendazole is recommended by WHO to accelerate the Global Programme to Eliminate Lymphatic Filariasis (GPELF). To assess the outcome of GPELF, to re-evaluate and to formulate further strategies there is an imperative need for high quality diagnostic markers. This study was undertaken to identify Lymphatic Filarial biomarkers which can detect LF infections in asymptomatic cases and would also serve as indicators for differentiating among different clinical stages of the disease. A combination of Fourier-transform infrared spectroscopy (FT-IR), MMP zymography, SDS-PAGE, classical 2DE along with MALDI-TOF/MS was done to identify LF biomarkers from serum samples of different stages of LF patients. FT-IR spectroscopy coupled with univariate and multivariate analysis of LF serum samples, revealed significant differences in peak intensity at 3300, 2950, 1645, 1540 and 1448 cm^-1 (p<0.05). The proteomics analysis results showed that various proteins were differentially expressed (p<0.05), including C-reactive protein, α-1-antitrypsin, heterogeneous nuclear ribonucleoprotein D like, apolipoproteins A-I and A-IV in different LF clinical stages. Functional pathway analysis suggested the involvement of differentially expressed proteins in vital physiological pathways like acute phase response, hemostasis, complement and coagulation cascades. Furthermore, the differentiation between different stages of LF cases and biomarkers identified in this study clearly demonstrates the potential of the human serum profiling approach for LF detection. To our knowledge, this is the first report of comparative human serum profiling in different categories of LF patients.

Semi-Quantitative MALDI Measurements of Blood-Based Samples for Molecular Diagnostics

Accurate and precise measurement of the relative protein content of blood-based samples using mass spectrometry is challenging due to the large number of circulating proteins and the dynamic range of their abundances. Traditional spectral processing methods often struggle with accurately detecting overlapping peaks that are observed in these samples. In this work, we develop a novel spectral processing algorithm that effectively detects over 1650 peaks with over 3.5 orders of magnitude in intensity in the 3 to 30 kD m/z range. The algorithm utilizes a convolution of the peak shape to enhance peak detection, and accurate peak fitting to provide highly reproducible relative abundance estimates for both isolated peaks and overlapping peaks. We demonstrate a substantial increase in the reproducibility of the measurements of relative protein abundance when comparing this processing method to a traditional processing method for sample sets run on multiple matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) instruments. By utilizing protein set enrichment analysis, we find a sizable increase in the number of features associated with biological processes compared to previously reported results. The new processing method could be very beneficial when developing high-performance molecular diagnostic tests in disease indications.

Material-assisted mass spectrometric analysis of low molecular weight compounds for biomedical applications

Low molecular weight compounds play an important role in encoding the current physiological state of an individual. Laser desorption/ionization mass spectrometry (LDI MS) offers high sensitivity with low cost for molecular detection, but it is not able to cover small molecules due to the drawbacks of the conventional matrix. Advanced materials are better alternatives, showing little background interference and high LDI efficiency. Herein, we first classify the current materials with a summary of compositions and structures. Matrix preparation protocols are then reviewed, to enhance the selectivity and reproducibility of MS data better. Finally, we highlight the biomedical applications of material-assisted LDI MS, at the tissue, bio-fluid, and cellular levels. We foresee that the advanced materials will bring far-reaching implications in LDI MS towards real-case applications, especially in clinical settings.

Profiling of intact blood proteins by matrix-assisted laser desorption/ionization mass spectrometry without the need for freezing - Dried serum spots as future clinical tools for patient screening

RATIONALE

To open up new ways for matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS)-based patient screening, blood serum is the most preferred specimen because of its richness in patho-physiological information and due to ease of collection. To overcome deleterious freeze/thaw cycles and to reduce high costs for shipping and storage, we sought to develop a procedure which enables MALDI-MS protein profiling of blood serum proteins without the need for serum freezing.

METHODS

Blood sera from patients/donors were divided into portions which after pre-incubation were fast frozen. Thawed aliquots were deposited on filter paper discs and air-dried at room temperature. Intact serum proteins were eluted with acid-labile detergent-containing solutions and were desalted by employing a reversed-phase bead system. Purified protein solutions were screened by MALDI-MS using standardized instrument settings.

RESULTS

MALDI mass spectra from protein solutions which were eluted from filter paper discs and desalted showed on average 25 strong ion signals (mass range m/z 6000 to 10,000) from intact serum proteins (apolipoproteins, complement proteins, transthyretin and hemoglobin) and from proteolytic processing products. Semi-quantitative analysis of three ion pairs: m/z 6433 and 6631, m/z 8205 and 8916, as well as m/z 9275 and 9422, indicated that the mass spectra from either pre-incubated fast-frozen serum or pre-incubated dried serum spot eluted serum contained the same information on protein composition.

CONCLUSIONS

A workflow that avoids the conventional cold-chain and yet enables the investigation of intact serum proteins and/or serum proteolysis products by MALDI-MS profiling was developed. The presented protocol tremendously broadens the clinical application of MALDI-MS and simultaneously allows a reduction in the costs for storage and shipping of serum samples. This will pave the way for clinical screening of patients also in areas with limited access to health care systems, and/or specialized laboratories.

Detection of Hepatocellular Carcinoma in a High-Risk Population by a Mass Spectrometry-Based Test

Simple Summary

Liver cancer is one of the most common causes of cancer worldwide, but unfortunately, current technology has a limited ability to detect it early in high-risk patients. This study investigates a machine learning algorithm based on protein levels in the blood that can be used to help with diagnosis. The test shows promising results, especially in patients with smaller tumors and compared to current blood detection tests. This research suggests an important role in the future for machine learning algorithm-based blood detection tests.

Abstract

Hepatocellular carcinoma (HCC) is one of the fastest growing causes of cancer-related death. Guidelines recommend obtaining a screening ultrasound with or without alpha-fetoprotein (AFP) every 6 months in at-risk adults. AFP as a screening biomarker is plagued by low sensitivity/specificity, prompting interest in discovering alternatives. Mass spectrometry-based techniques are promising in their ability to identify potential biomarkers. This study aimed to use machine learning utilizing spectral data and AFP to create a model for early detection. Serum samples were collected from three separate cohorts, and data were compiled to make Development, Internal Validation, and Independent Validation sets. AFP levels were measured, and Deep MALDI^® analysis was used to generate mass spectra. Spectral data were input into the VeriStrat^® classification algorithm. Machine learning techniques then classified each sample as “Cancer” or “No Cancer”. Sensitivity and specificity of the test were >80% to detect HCC. High specificity of the test was independent of cause and severity of underlying disease. When compared to AFP, there was improved cancer detection for all tumor sizes, especially small lesions. Overall, a machine learning algorithm incorporating mass spectral data and AFP values from serum samples offers a novel approach to diagnose HCC. Given the small sample size of the Independent Validation set, a further independent, prospective study is warranted.

A proposal for score assignment to characterize biological processes from mass spectral analysis of serum

•

Biological process-associated scores generated from mass spectrometry of serum.

•

Scores demonstrated acceptable levels of reproducibility.

•

Scores associated with biological processes and clinical outcome in cancer patients.

•

Possible application to biomarker studies for treatment or monitoring of disease.

•

Multiple biological processes assessed from 3 µL of serum.

Introduction

Most diseases involve a complex interplay between multiple biological processes at the cellular, tissue, organ, and systemic levels. Clinical tests and biomarkers based on the measurement of a single or few analytes may not be able to capture the complexity of a patient’s disease. Novel approaches for comprehensively assessing biological processes from easily obtained samples could help in the monitoring, treatment, and understanding of many conditions.

Objectives

We propose a method of creating scores associated with specific biological processes from mass spectral analysis of serum samples.

Methods

A score for a process of interest is created by: (i) identifying mass spectral features associated with the process using set enrichment analysis methods, and (ii) combining these features into a score using a principal component analysis-based approach. We investigate the creation of scores using cohorts of patients with non-small cell lung cancer, melanoma, and ovarian cancer. Since the circulating proteome is amenable to the study of immune responses, which play a critical role in cancer development and progression, we focus on functions related to the host response to disease.

Results

We demonstrate the feasibility of generating scores, their reproducibility, and their associations with clinical outcomes. Once the scores are constructed, only 3 µL of serum is required for the assessment of multiple biological functions from the circulating proteome.

Conclusion

These mass spectrometry-based scores could be useful for future multivariate biomarker or test development studies for informing treatment, disease monitoring and improving understanding of the roles of various biological functions in multiple disease settings.

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

Simple Summary

The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient’s systemic status, not only the tumor’s molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning.

Abstract

Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.

Mass Spectrometric (MS) Analysis of Proteins and Peptides

The human genome is sequenced and comprised of ~30,000 genes, making humans just a little bit more complicated than worms or flies. However, complexity of humans is given by proteins that these genes code for because one gene can produce many proteins mostly through alternative splicing and tissue-dependent expression of particular proteins. In addition, post-translational modifications (PTMs) in proteins greatly increase the number of gene products or protein isoforms. Furthermore, stable and transient interactions between proteins, protein isoforms/proteoforms and PTM-ed proteins (protein-protein interactions, PPI) add yet another level of complexity in humans and other organisms. In the past, all of these proteins were analyzed one at the time. Currently, they are analyzed by a less tedious method: mass spectrometry (MS) for two reasons: 1) because of the complexity of proteins, protein PTMs and PPIs and 2) because MS is the only method that can keep up with such a complex array of features. Here, we discuss the applications of mass spectrometry in protein analysis.

A Serum Protein Classifier Identifying Patients with Advanced Non-Small Cell Lung Cancer Who Derive Clinical Benefit from Treatment with Immune Checkpoint Inhibitors

PURPOSE

Pretreatment selection of patients with non-small cell lung cancer (NSCLC) who would derive clinical benefit from treatment with immune checkpoint inhibitors (CPIs) would fulfill an unmet clinical need by reducing unnecessary toxicities from treatment and result in substantial health care savings.

EXPERIMENTAL DESIGN

In a retrospective study, mass spectrometry (MS)-based proteomic analysis was performed on pretreatment sera derived from patients with advanced NSCLC treated with nivolumab as part of routine clinical care (n = 289). Machine learning combined spectral and clinical data to stratify patients into three groups with good ("sensitive"), intermediate, and poor ("resistant") outcomes following treatment in the second-line setting. The test was applied to three independent patient cohorts and its biology was investigated using protein set enrichment analyses (PSEA).

RESULTS

A signature consisting of 274 MS features derived from a development set of 116 patients was associated with progression-free survival (PFS) and overall survival (OS) across two validation cohorts (N = 98 and N = 75). In pooled analysis, significantly better OS was demonstrated for "sensitive" relative to "not sensitive" patients treated with nivolumab; HR, 0.58 (95% confidence interval, 0.38-0-87; P = 0.009). There was no significant association with clinical factors including PD-L1 expression, available from 133 of 289 patients. The test demonstrated no significant association with PFS or OS in a historical cohort (n = 68) of second-line NSCLC patients treated with docetaxel. PSEA revealed proteomic classification to be significantly associated with complement and wound-healing cascades.

CONCLUSIONS

This serum-derived protein signature successfully stratified outcomes in cohorts of patients with advanced NSCLC treated with second-line PD-1 CPIs and deserves further prospective study.

Mass Spectrometry-Based Multivariate Proteomic Tests for Prediction of Outcomes on Immune Checkpoint Blockade Therapy: The Modern Analytical Approach

The remarkable success of immune checkpoint inhibitors (ICIs) has given hope of cure for some patients with advanced cancer; however, the fraction of responding patients is 15-35%, depending on tumor type, and the proportion of durable responses is even smaller. Identification of biomarkers with strong predictive potential remains a priority. Until now most of the efforts were focused on biomarkers associated with the assumed mechanism of action of ICIs, such as levels of expression of programmed death-ligand 1 (PD-L1) and mutation load in tumor tissue, as a proxy of immunogenicity; however, their performance is unsatisfactory. Several assays designed to capture the complexity of the disease by measuring the immune response in tumor microenvironment show promise but still need validation in independent studies. The circulating proteome contains an additional layer of information characterizing tumor-host interactions that can be integrated into multivariate tests using modern machine learning techniques. Here we describe several validated serum-based proteomic tests and their utility in the context of ICIs. We discuss test performances, demonstrate their independence from currently used biomarkers, and discuss various aspects of associated biological mechanisms. We propose that serum-based multivariate proteomic tests add a missing piece to the puzzle of predicting benefit from ICIs.