Application of Advanced Mass Spectrometry-Based Proteomics to Study Hypoxia Driven Cancer Progression

Hypoxia: Intriguing Feature in Cancer Cell Biology

Hypoxia, a key aspect of the tumor microenvironment, plays a vital role in cell proliferation, angiogenesis, metabolism, and the immune response within tumors. These factors collectively promote tumor advancement, aggressiveness, metastasis and result in a poor prognosis. Hypoxia inducible factor 1α (HIF-1α), activated under low oxygen conditions, mediates many of these effects by altering drug target expression, metabolic regulation, and oxygen consumption. These changes promote cancer cell growth and survival. Hypoxic tumor cells develop aggressive traits and resistance to chemotherapy and radiotherapy, leading to increased mortality. Targeting hypoxic tumor offers a potential solution to overcome the challenges posed by tumor heterogeneity and can be used in designing diagnostic and therapeutic nanocarriers for various solid cancers. This concept provides an overview of the intricate relationship between hypoxia and the tumor microenvironment, highlighting its potential as a promising tool for cancer therapies. The article explores the development of hypoxia in cancer cells and its role in cancer progression, along with the latest advancements in hypoxia-triggered cancer treatment.

A Mass Spectrometry Strategy for Protein Quantification Based on the Differential Alkylation of Cysteines Using Iodoacetamide and Acrylamide

Mass spectrometry has become the most prominent yet evolving technology in quantitative proteomics. Today, a number of label-free and label-based approaches are available for the relative and absolute quantification of proteins and peptides. However, the label-based methods rely solely on the employment of stable isotopes, which are expensive and often limited in availability. Here we propose a label-based quantification strategy, where the mass difference is identified by the differential alkylation of cysteines using iodoacetamide and acrylamide. The alkylation reactions were performed under identical experimental conditions; therefore, the method can be easily integrated into standard proteomic workflows. Using high-resolution mass spectrometry, the feasibility of this approach was assessed with a set of tryptic peptides of human serum albumin. Several critical questions, such as the efficiency of labeling and the effect of the differential alkylation on the peptide retention and fragmentation, were addressed. The concentration of the quality control samples calculated against the calibration curves were within the ±20% acceptance range. It was also demonstrated that heavy labeled peptides exhibit a similar extraction recovery and matrix effect to light ones. Consequently, the approach presented here may be a viable and cost-effective alternative of stable isotope labeling strategies for the quantification of cysteine-containing proteins.

Hypoxia induced responses are reflected in the stromal proteome of breast cancer

Cancers are often associated with hypoxia and metabolic reprogramming, resulting in enhanced tumor progression. Here, we aim to study breast cancer hypoxia responses, focusing on secreted proteins from low-grade (luminal-like) and high-grade (basal-like) cell lines before and after hypoxia. We examine the overlap between proteomics data from secretome analysis and laser microdissected human breast cancer stroma, and we identify a 33-protein stromal-based hypoxia profile (33P) capturing differences between luminal-like and basal-like tumors. The 33P signature is associated with metabolic differences and other adaptations following hypoxia. We observe that mRNA values for 33P predict patient survival independently of molecular subtypes and basic prognostic factors, also among low-grade luminal-like tumors. We find a significant prognostic interaction between 33P and radiation therapy.

Tandem mass tagging combined with liquid chromatography-tandem mass spectrometry technique to detect protein markers in gastroesophageal junction adenocarcinoma

BACKGROUND

Gastroesophageal junction adenocarcinoma (GEJA) is a malignant tumor located at the junction of the esophagus and stomach, the incidence of which is increasing year by year, while screening for early biomarkers is limited. Tandem mass tagging (TMT) coupled with liquid chromatography-tandem mass spectrometry (LC/MS/MS) has been used to screen for differential proteins in various cancers.

METHODS

Differential proteins in GEJA and precancerous lesions were screened using TMT-LC/MS/MS, and then proteins that met expectations were selected for trend clustering analysis, combined with GO and KEGG analysis for functional annotation of differential proteins in GEJA. Then, parallel reaction monitoring and immunohistochemistry techniques were used to validate the accuracy of the proteomics data.

RESULTS

Our group screened the differential proteins during GEJA progression using proteomics technology, analyzed the expression trends and functional regions involved in the differential proteins during carcinogenesis, and validated the accuracy of the experimental results.

CONCLUSIONS

The screening of differential proteins in GEJA carcinogenesis based on TMT-LC/MS/MS technology provides detailed information for the elucidation of GEJA progression process, pathogenesis, early screening and screening of candidate markers.

Proteomics for comprehensive characterization of extracellular vesicles in neurodegenerative disease

Extracellular vesicles (EVs) are small lipid bilayer particles ubiquitously released by almost every cell type. A specific and selective constituents of EVs loaded with variety of proteins, lipids, small noncoding RNAs, and long non-coding RNAs are reflective of cellular events, type, and physiologic/pathophysiologic status of the cell of origin. Moreover, these molecular contents carry information from the cell of origin to recipient cells, modulating intercellular communication. Recent studies demonstrated that EVs not only play a neuroprotective role by mediating the removal of toxic proteins, but also emerge as an important player in various neurodegenerative disease onset and progression through facilitating of misfolded proteins propagation. For this reason, neurodegenerative disease-associated differences in EV proteome relative to normal EVs can be used to fulfil diagnostic, prognostic, and therapeutic purposes. Nonetheless, characterizing EV proteome obtained from biological samples (brain tissue and body fluids, including urea, blood, saliva, and CSF) is a challenging task. Herein, we review the status of EV proteome profiling and the updated discovery of potential biomarkers for the diagnosis of neurodegenerative disease with an emphasis on the integration of high-throughput advanced mass spectrometry (MS) technologies for both qualitative and quantitative analysis of EVs in different clinical tissue/body fluid samples in past five years.

Applications of Tandem Mass Spectrometry (MS/MS) in Protein Analysis for Biomedical Research

Mass Spectrometry (MS) allows the analysis of proteins and peptides through a variety of methods, such as Electrospray Ionization-Mass Spectrometry (ESI-MS) or Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry (MALDI-MS). These methods allow identification of the mass of a protein or a peptide as intact molecules or the identification of a protein through peptide-mass fingerprinting generated upon enzymatic digestion. Tandem mass spectrometry (MS/MS) allows the fragmentation of proteins and peptides to determine the amino acid sequence of proteins (top-down and middle-down proteomics) and peptides (bottom-up proteomics). Furthermore, tandem mass spectrometry also allows the identification of post-translational modifications (PTMs) of proteins and peptides. Here, we discuss the application of MS/MS in biomedical research, indicating specific examples for the identification of proteins or peptides and their PTMs as relevant biomarkers for diagnostic and therapy.

Mechanisms and minimization of false discovery of metabolic bio-orthogonal non-canonical amino acid proteomics

Metabolic proteomics has been widely used to characterize dynamic protein networks in many areas of biomedicine, including in the arena of tissue aging and rejuvenation. Bio-orthogonal non-canonical amino acid tagging (BONCAT) is based on mutant methionine-tRNA synthases (MetRS) that incorporates metabolic tags, e.g., azido-nor leucine, ANL, into newly synthesized proteins. BONCAT revolutionizes metabolic proteomics, because mutant MetRS transgene allows one to identify cell type specific proteomes in mixed biological environments. This is not possible with other methods, such as stable isotope labeling with amino acids in cell culture (SILAC), isobaric tags for relative and absolute quantitation (iTRAQ) and tandem mass tags (TMT). At the same time, an inherent weakness of BONCAT is that after click chemistry-based enrichment, all identified proteins are assumed to have been metabolically tagged, but there is no confirmation in Mass Spectrometry data that only tagged proteins are detected. As we show here, such assumption is incorrect and accurate negative controls uncover a surprisingly high degree of false positives in BONCAT proteomics. We show not only how to reveal the false discovery and thus improve the accuracy of the analyses and conclusions but also approaches for avoiding it through minimizing non-specific detection of biotin, biotin-independent direct detection of metabolic tags, and improvement of signal to noise ratio through machine learning algorithms.

Application of Mass Spectrometry in Pancreatic Cancer Translational Research

Pancreatic cancer (PC) is one of the most common malignant tumors in the digestive tract worldwide, with increased morbidity and mortality. In recent years, with the development of surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy, and the change of the medical thinking model, remarkable progress has been made in researching comprehensive diagnosis and treatment of PC. However, the present situation of diagnostic and treatment of PC is still unsatisfactory. There is an urgent need for academia to fully integrate the basic research and clinical data from PC to form a research model conducive to clinical translation and promote the proper treatment of PC. This paper summarized the translation progress of mass spectrometry (MS) in the pathogenesis, diagnosis, prognosis, and PC treatment to promote the basic research results of PC into clinical diagnosis and treatment.

Proteomic Exploration of Plasma Exosomes and Other Small Extracellular Vesicles in Pediatric Hodgkin Lymphoma: A Potential Source of Biomarkers for Relapse Occurrence

Exosomes and other small extracellular vesicles (EVs) are potential sources of cancer biomarkers. Plasma-derived EVs have not yet been studied in pediatric Hodgkin lymphoma (HL), for which predictive biomarkers of relapse are greatly needed. In this two-part proteomic study, we used two-dimensional difference gel electrophoresis (2D-DIGE) followed by liquid chromatography–tandem mass spectrometry (LC–MS/MS) to analyze EV proteins of plasma collected at diagnosis from children with nodular sclerosis HL, relapsed or not. EVs isolated using membrane affinity had radii ranging from 20 to 130 nm and contained the programmed cell death 6-interacting (ALIX) and the tumor susceptibility gene 101 (TSG101) proteins, whereas calnexin (CANX) was not detected. 2D-DIGE identified 16 spots as differentially abundant between non-relapsed and relapsed HL (|fold change| ≥ 1.5, p < 0.05). LC–MS/MS identified these spots as 11 unique proteins, including five more abundant in non-relapsed HL (e.g., complement C4b, C4B; fibrinogen γ chain, FGG) and six more abundant in relapsed HL (e.g., transthyretin, TTR). Shotgun LC–MS/MS on pooled EV proteins from non-relapsed HL identified 161 proteins, including 127 already identified in human exosomes (ExoCarta data). This EV cargo included 89 proteins not yet identified in exosomes from healthy plasma. Functional interrogation by the Database for Annotation, Visualization and Integrated Discovery (DAVID) revealed that the EV proteins participate in platelet degranulation and serine-type endopeptidase activity as the most significant Gene Ontology (GO) biological process and molecular function (p < 0.01).

Quantitative proteomics characterization of cancer biomarkers and treatment

Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.

Quantitative Proteomic Approach Reveals Altered Metabolic Pathways in Response to the Inhibition of Lysine Deacetylases in A549 Cells under Normoxia and Hypoxia

Growing evidence is showing that acetylation plays an essential role in cancer, but studies on the impact of KDAC inhibition (KDACi) on the metabolic profile are still in their infancy. Here, we analyzed, by using an iTRAQ-based quantitative proteomics approach, the changes in the proteome of KRAS-mutated non-small cell lung cancer (NSCLC) A549 cells in response to trichostatin-A (TSA) and nicotinamide (NAM) under normoxia and hypoxia. Part of this response was further validated by molecular and biochemical analyses and correlated with the proliferation rates, apoptotic cell death, and activation of ROS scavenging mechanisms in opposition to the ROS production. Despite the differences among the KDAC inhibitors, up-regulation of glycolysis, TCA cycle, oxidative phosphorylation and fatty acid synthesis emerged as a common metabolic response underlying KDACi. We also observed that some of the KDACi effects at metabolic levels are enhanced under hypoxia. Furthermore, we used a drug repositioning machine learning approach to list candidate metabolic therapeutic agents for KRAS mutated NSCLC. Together, these results allow us to better understand the metabolic regulations underlying KDACi in NSCLC, taking into account the microenvironment of tumors related to hypoxia, and bring new insights for the future rational design of new therapies.