Cancer Genomics & Proteomics[JOURNAL]
Sun
200 papers
QuickProt: A Bioinformatics and Visualization Tool for DIA and PRM Mass Spectrometry-Based Proteomics Datasets.
Arias-Gaguancela O, Palii C, Nissa MU … +2 more , Brand M, Ranish J
Proteomics · 2025 Oct · PMID 40908717 · Full text
Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein predi...
Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various software platforms. However, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results, remains cumbersome due to the lack of integrated tools to facilitate the analyses. To address this challenge, we developed QuickProt, a series of Python-based Google Colab notebooks for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets. These pipelines are designed so that users with no coding expertise can utilize the tool. Furthermore, as open-source code, QuickProt notebooks can be customized and incorporated into existing workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets from a time-course study of human erythropoiesis. The analysis resulted in annotated tables and publication-ready figures revealing a dynamic rearrangement of the proteome during erythroid differentiation, with the abundance of proteins linked to gene regulation, metabolic, and chromatin remodeling pathways increasing early in erythropoiesis. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.
SLB-msSIM: A Spectral Library-Based Multiplex Segmented SIM Platform for Single-Cell Proteomic Analysis.
Senavirathna L, Ma C, Duong VA … +3 more , Tsai HY, Chen R, Pan S
Proteomics · 2025 Oct · PMID 40904101 · Full text
Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional...
Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional level. We report here the development of the spectral library-based multiplex segmented selected ion monitoring (SLB-msSIM) method, a conceptually unique approach with significantly enhanced sensitivity and robustness for single-cell analysis. The single-cell MS data is acquired by a multiplex segmented selected ion monitoring (msSIM) technique, which sequentially applies multiple isolation cycles with the quadrupole using a wide isolation window in each cycle to accumulate and store precursor ions in the C-trap for a single scan in the Orbitrap. Proteomic identification is achieved through spectral matching using a well-defined spectral library. We applied the SLB-msSIM method to interrogate cellular heterogeneity in various pancreatic cancer cell lines, revealing common and distinct functional traits among PANC-1, MIA-PaCa2, AsPc-1, HPAF, and normal HPDE cells. Furthermore, for the first time, our novel data revealed the diverse cell trajectories of individual PANC-1 cells during the induction and reversal of epithelial-mesenchymal transition (EMT). Collectively, our results demonstrate that SLB-msSIM is a highly sensitive and robust platform, applicable to a wide range of instruments for single-cell proteomic studies. SUMMARY: We present the SLB-msSIM method, a conceptually unique approach in mass spectrometry-based single-cell proteomics that significantly enhances sensitivity and robustness. This innovative platform enables detailed analysis of the proteome landscape, capturing cellular heterogeneity, trajectories, and phenotypes at a single-cell resolution. Utilizing the SLB-msSIM technique, we identified both common and distinct functional traits among various pancreatic cancer cell lines and normal cells. Moreover, our study unveiled new insights into the diverse cell trajectories of individual cancer cells during the induction and reversal of epithelial-mesenchymal transition (EMT). In summary, the SLB-msSIM method offers a highly sensitive and robust platform for single-cell proteomic studies, with broad applicability across different instruments.
MRMAssayDB: a comprehensive integrated resource for targeted proteomics assays.
Mohammed Y, Bhowmick P, Borchers CH
Expert Rev Proteomics · 2025 Oct · PMID 40900656 · Publisher ↗
INTRODUCTION: Targeted quantitative proteomics is vital for accurate protein measurement in biological samples. Techniques like Multiple Reaction Monitoring (MRM or SRM) and Parallel Reaction Monitoring (PRM), often used...
INTRODUCTION: Targeted quantitative proteomics is vital for accurate protein measurement in biological samples. Techniques like Multiple Reaction Monitoring (MRM or SRM) and Parallel Reaction Monitoring (PRM), often used with isotopically labeled internal standards, provide absolute quantification, and represent the current gold standard. However, developing and validating assays for individual proteins remains labor-intensive. Several repositories, such as CPTAC, SRMAtlas, PanoramaWeb, and PeptideTracker host targeted assay data with varying levels of detail. MRMAssayDB is an integrated platform that hosts and annotates the curated targeted proteomics assays from these resources. AREAS COVERED: First launched in 2018 and updated in 2021, the latest release of MRMAssayDB includes over 1.1 million assays for 939,000 peptides, enabling quantification of 61,000 proteins from 146 organisms. The database also maps proteins to 19,000 Gene Ontology terms and 4,000 biological pathways. A newly integrated visualization module projects peptide assays onto Alphafold-predicted 3D protein structures, allowing users to examine peptide locations, post-translational modifications, and disease mutations while also supporting mapping to structures in the Protein Data Bank (PDB). EXPERT OPINION: MRMAssayDB significantly improves access to validated proteotypic peptides and transition data, facilitating efficient assay selection and quantitative panel building for researchers in targeted proteomics. Availability: http://mrmassaydb2.proteomicscentre.com.
Serum Proteomics of Ribociclib-Mediated Cardiovascular Toxicity: An Exploratory Case-Control Study.
Fiste O, Samiotaki M, Manios E … +6 more , Trika C, Liacos CI, Dimitrakakis C, Dimopoulos MA, Gavriatopoulou M, Zagouri F
Proteomics Clin Appl · 2025 Sep · PMID 40878930 · Publisher ↗
Cyclin-dependent kinase 4/6 inhibitors have transformed hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (BC) therapeutics. Ribociclib has been associated...
Cyclin-dependent kinase 4/6 inhibitors have transformed hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (BC) therapeutics. Ribociclib has been associated with survival gain, yet its potential cardiovascular toxicities (CVTs) remain an area of uncertainty. Our single-center study prospectively recruited adult patients in order to assess treatment-related CVT incidence and spectrum as well as decipher proteins' differential expression in affected patients by data-independent acquisition liquid chromatography-tandem mass spectrometry (DIA LC-MS/MS). After a median follow-up of 27.2 months, five cases of CVT have occurred among the 62 enrolled participants (8.06%; mean age, 67 years). CVTs were in the form of asymptomatic QTc prolongation, transient ischemic attack, deep vein thrombosis, syncope, and pericardial effusion, which developed within 7.56 months. The in-depth proteomics quantified 144 differentially expressed proteins, of which 109 and 35 were down- and up-regulated, respectively, in these five cases (enrolled participants with CVT) compared to five sex- and age-matched controls (enrolled participants without CVT). Negative regulation of endopeptidase activity, phosphatidylcholine metabolism, and immune response were the most affected signaling pathways in the subsequent functional analysis. Large-scale external validation of our hypothesis-generating findings could potentially support individualized cardiovascular prevention in BC patients under ribociclib combinational therapy. SUMMARY: Ribociclib has unequivocally revolutionized hormone-dependent metastatic breast cancer therapeutics. Its potential cardiotoxicity, however, remain inadequately characterized, whereas the underlying pathophysiological mechanisms are poorly understood so far. Our prospective case-control study revealed that despite cardiovascular toxicity was not very common (<10%), its phenotype was not limited to QTc prolongation. Moreover, utilizing mass spectrometry-based serum proteomics, we highlighted for the very first time a number of distinct proteins, which could be of predictive value to identify patients at high risk. The prospective validation of our preliminary, proof-of-concept study's results in larger cohorts could inform optimized preventive strategies.
Metaproteomic Analysis to Assess the Impact of Storage Media on Human Gut Microbiome in Fecal Samples.
Duong VA, Pan E, Dabral P … +4 more , Utzschneider KM, Lampe JW, Chen R, A J Hullar M
Proteomics · 2025 Oct · PMID 40873297 · Full text
The human gut microbiome is a diverse community of microorganisms residing in the gastrointestinal tract. The storage condition of fecal samples may impact the taxonomic and protein compositions of microbiomes in these s...
The human gut microbiome is a diverse community of microorganisms residing in the gastrointestinal tract. The storage condition of fecal samples may impact the taxonomic and protein compositions of microbiomes in these samples. Here, we performed a mass spectrometry-based metaproteomic study to assess the impact of storage media on human gut microbiome in fecal samples. We evaluated FDA-authorized OMNIgene·GUT (OG), phosphate-buffered saline (PBS), and RNALater (RNAL) buffers and identified 38,185 microbial peptides corresponding to 7348 microbial proteins, which matched 16 phyla, 20 classes, 50 orders, 104 families, 332 genera, and 453 species. We found a high similarity among the fecal microbiomes preserved in OG, PBS, and RNAL in terms of the identification of proteins, taxa, and functional annotations. Both alpha and beta diversity suggested the high similarity among samples stored in the three media. Nonetheless, we also found some notable differences among buffers regarding the abundances of a few taxon groups. A partial human proteome (over 400 proteins) was identified in the fecal samples, with most of these proteins associated with the membrane and extracellular regions. The findings indicate the similarity among microbiomes in the fecal samples stored in OG, PBS, and RNAL regarding proteome profile, taxa, and functional capacity. SUMMARY: This study thoroughly analyzed and compared the metaproteomes of fecal samples preserved at -80°C in PBS, RNALater, and OMNIgene·GUT Dx buffers, offering novel insights into the effectiveness of these buffers in maintaining the stability and composition of the human gut microbiome. We found a high similarity in the identification and quantification of proteins, taxa, and functional annotations across the three buffers, with notable quantitative differences highlighting subtle yet important variations in preservation efficacy. The unique datasets and findings could offer valuable revelations into the impact of fecal sample preservation on translational and clinical analyses of the human gut microbiome.
Proteoformics: Current status and future perspectives.
Fang Z, Zhang Y, Feng X … +3 more , Li N, Chen L, Zhan X
J Proteomics · 2025 Oct · PMID 40834981 · Publisher ↗
Proteoforms represent the ultimate structural/functional forms of a gene product, defined by multiple factors, including amino acid sequences, post-translational modifications, spatial conformations, and interactions wit...
Proteoforms represent the ultimate structural/functional forms of a gene product, defined by multiple factors, including amino acid sequences, post-translational modifications, spatial conformations, and interactions with other molecules. The human proteoform diversity significantly exceeds the number of human genes/transcripts, emphasizing the need for advanced analytical methods to characterize this complexity. Two-dimensional gel electrophoresis-liquid chromatography/mass spectrometry (2DE-LC/MS) and top-down MS (TD-MS) are complementary to detect, identify, and quantify the large-scale proteoforms. The emerging AI tools for structural biology such as AlphaFold 3 and D-I-TASSER will enable proteoformics to be high-throughput and precisely predict spatial conformations and molecular interactions. Integrating the large-scale experimental data derived from 2DE-LC/MS and TD-MS with AI-driven high-throughput structural analysis paves the way to deeply understand proteoform diversity and functionality. The combination of advanced 2DE-LC/MS, TD-MS, and AI-driven structural analysis represents a pivotal advancement in proteoformics. This integrated approach enables the comprehensive profiling of proteoforms, providing critical insights into their roles in health care. Such advancements hold promise for predictive, preventive, and personalized medicine, particularly through biomarker discovery and therapeutic target identification. Future developments in high-throughput capabilities and dynamic modeling are expected to address current challenges and further expand the applicability of proteoformics in biological and clinical research. SIGNIFICANCE: Proteoformics is the future of proteomics, whose two main complementary analytical approaches are 2DE-LC/MS and TD-MS. The AI-driven large-cale structural analysis enables to high-throughput and precisely analyze spatial conformations and molecular interactions of proteoforms, which helps to deeply understand proteoform diversity and functionality. Proteoformics holds transformative potential to uncover biomarkers, guide targeted therapies, and advance predictive diagnosis in the context of personalized medicine.
Expanding the landscape of lysine acetylation stoichiometry and clinical impact.
Encarnación-Guevara S, Gil J
J Proteomics · 2025 Oct · PMID 40812546 · Publisher ↗
Lysine acetylation, once viewed primarily as a histone mark, is now recognized as a widespread regulator of protein function. Recent breakthroughs in chemical labeling, isotopic tagging workflows, and data-independent ac...
Lysine acetylation, once viewed primarily as a histone mark, is now recognized as a widespread regulator of protein function. Recent breakthroughs in chemical labeling, isotopic tagging workflows, and data-independent acquisition mass spectrometry enable precise, site-specific quantification of acetylation stoichiometry. This quantitative "acetylomics" approach reveals a "rheostat" model, where most acetylation sites exhibit low occupancy, acting as subtle modulators, while a subset of highly acetylated lysines (e.g., p53 C-terminus, AKT1, histones) serve as pivotal regulatory switches in gene expression, metabolism, and cell fate. Site-specific occupancy changes (e.g., p53, PKM2) increasingly serve as robust biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring, often surpassing mRNA or total protein levels. Quantitative acetylation data now guide the development of targeted epigenetic therapies, including HDAC and p300/CBP inhibitors. Beyond oncology, acetylomics can pinpoint metabolic bottlenecks in heart failure, epigenetic deficits in neurodegenerative conditions, and inflammatory signaling nodes. With advances in high-throughput workflows, FFPE and liquid biopsy compatibility, and microfluidic platforms, acetylation stoichiometry is poised for clinical translation. We highlight both the promise and challenges of this emerging dimension of precision medicine, emphasizing the need for integrated multi-omics approaches and robust clinical validation to fully realize the potential of quantitative acetylomics in disease diagnosis and therapy. SIGNIFICANCE: Understanding the extent of acetylation occupancy in proteins, beyond simply determining presence or absence of acetylation, has profound implications for biology and medicine. This review emphasizes the importance of acetylation stoichiometry, connecting advanced proteomic technologies with translational science. We emphasize that quantifying site occupancy reveals which acetylation events truly modulate enzyme function. For instance, it can identify which acetylation events truly modulate enzyme activity or gene expression. Additionally, it can highlight molecular changes in diseases like cancer that are not apparent through qualitative analyses. These quantitative insights pave the way for clinical innovations, including novel biomarkers that stratify patients based on their acetylation profiles and targeted therapies that modulate acetylation levels. In summary, this work highlights the evolving landscape of protein acetylation research over the past two decades and its increasing influence on translational proteomics, celebrating milestones achieved by the global research community.
Proteomic Analysis of Bone Marrow CD138+ Cells to Identify Proteins Associated With the Response of Multiple Myeloma Patients to Commonly Used Therapeutic Regimens.
Paradeisi F, Tserga A, Lygirou V … +12 more , Makridakis M, Stroggilos R, Georgiou G, Spyrou GM, Kostopoulos IV, Liacos CI, Termentzi A, Dimopoulos MA, Tsitsilonis O, Vlahou A, Kastritis E, Zoidakis J
Proteomics · 2025 Aug · PMID 40776449 · Full text
Multiple myeloma (MM) remains incurable; gaps in our understanding of MM molecular pathogenesis and drugs' resistance mechanisms are involved in the failure of therapies. This study aims to identify proteins significantl...
Multiple myeloma (MM) remains incurable; gaps in our understanding of MM molecular pathogenesis and drugs' resistance mechanisms are involved in the failure of therapies. This study aims to identify proteins significantly impacting MM patients' response to commonly used therapeutic regimens. Bone marrow CD138+ selected plasma cells were isolated from patients who had achieved Response (Responders, R) and those who were Non-Responders (NR) to their primary MM therapy. We used LC-MS/MS to investigate the proteomic profile of MM samples, followed by bioinformatics analysis. We identified 1190 proteins, of which 230 showed a statistically significant difference between R and NR, with 27 proteins being upregulated and 203 downregulated in R compared to NR. Pathway enrichment analysis identified pathways related to the immune response and protein synthesis regulation, closely associated with MM progression and response to therapy. The results were validated through individual RNA dataset analysis, corroborating the differential expression of several proteins, including proteins associated with MM (e.g., MIF, ILF3) as well as novel findings (e.g., DCPS and SET). Collectively, proteomics data obtained from R and NR to MM therapy displayed significant changes in the immune system and protein synthesis regulation, supporting their potential role in progression and therapeutic response of MM.
Artificial Intelligence and the Evolving Landscape of Immunopeptidomics.
Vo TH, McNeela E, O'Donovan O … +2 more , Rani S, Mehta JP
Proteomics Clin Appl · 2025 Nov · PMID 40741879 · Full text
BACKGROUND: Immunopeptidomics is the large-scale study of peptides presented by major histocompatibility complex (MHC) molecules and plays a central role in neoantigen discovery and cancer immunotherapy. However, the com...
BACKGROUND: Immunopeptidomics is the large-scale study of peptides presented by major histocompatibility complex (MHC) molecules and plays a central role in neoantigen discovery and cancer immunotherapy. However, the complexity of mass spectrometry data, the diversity of peptide sources, and variability in immune responses present major challenges in this field. REVIEW FOCUS: In recent years, artificial intelligence (AI)-based methods have become central to advancing key steps in immunopeptidomics. It has enabled advances in de novo sequencing, peptide-spectrum matching, spectrum prediction, MHC binding prediction, and T cell recognition modeling. In this review, we examine these applications in detail, highlighting how AI is integrated into each stage of the immunopeptidomics workflow. CASE STUDY: This review presents a focused case study on breast cancer, a heterogeneous and historically less immunogenic tumor type, to examine how AI may help overcome limitations in identifying actionable neoantigens. CHALLENGES AND FUTURE PERSPECTIVES: We discuss current bottlenecks, including challenges in modeling noncanonical peptides, accounting for antigen processing defects, and avoiding on-target off-tumor toxicity. Finally, we outline future directions for improving AI models to support both personalized and off-the-shelf immunotherapy strategies. SUMMARY: Artificial intelligence (AI) is reshaping the immunopeptidomics landscape by overcoming challenges in peptide identification, immunogenicity prediction, and neoantigen prioritization. This review highlights how AI-based tools enhance the detection of MHC-bound peptides-including low-abundance, noncanonical, and post-translationally modified epitopes and improve peptide-spectrum matching and T-cell epitope prediction. By demonstrating a case study on applications in breast cancer, we illustrate the potential of AI to reveal hidden immunogenic features in tumors previously likely considered immunologically "cold." These advancements open new opportunities for expanding neoantigen discovery pipelines and optimizing cancer immunotherapies. Looking ahead, the application of deep learning, transfer learning, and integrated multi-omics models may further elevate the accuracy and scalability of immunopeptidomics, enabling more effective and inclusive vaccine and T-cell therapy development.
Proteomics analysis reveal early event molecular effectors of anti-CD19 CAR-T cell therapy in hematological cancer.
Teibo JO, Silveira RM, Silvestrini VC … +13 more , Archiolli I, Masson AP, de Morais BP, Schmidt D, Dos Santos MH, Ferreira GA, Thomé CH, Helm D, Nirujogi RS, Alessi DR, Picanço-Castro V, de Souza LEB, Faça VM
J Proteomics · 2025 Oct · PMID 40716489 · Publisher ↗
Chimeric antigen receptor T-cell (CAR-T) therapy is at the forefront of the field of cell immunotherapy. In this study, we generated an anti-CD19 CAR-Jurkat T cell line using a locally produced second-generation anti-CD1...
Chimeric antigen receptor T-cell (CAR-T) therapy is at the forefront of the field of cell immunotherapy. In this study, we generated an anti-CD19 CAR-Jurkat T cell line using a locally produced second-generation anti-CD19 CAR construct, which allowed us to analyse early proteomic changes that are crucial for comprehending the signalling pathways and mechanism of action of this CAR-T cell. SILAC-heavy tagged Raji B-cells and anti-CD19 CAR-Jurkat T-cells were co-cultured for ten minutes. The proteomic profiles were acquired via DIA methodology on the Orbitrap Astral LC-MS/MS platform. The proteome was extensively covered, resulting in about 8800 protein identifications at 1 % FDR. The effector CAR-Jurkat cells showed proteomic changes involving antigen presentation by CD74. The target Raji B-cells exhibited more significant alterations. Effector proteins, namely CD247, CD28, DAP, LCK, p38 MAPK, and CASP3, were validated, as they have critical roles in antigen presentation, T-cell activation, and apoptosis. Pharmacological inhibition of LCK using Dasatinib further suggested its pivotal role in early CAR-T signalling. This study led us to identify proteins that function as molecular effectors of anti-CD19 CAR-T cell therapy during the initial phases of CAR-T-target cell engagement, advancing our knowledge of the mechanism and signalling pathways that will support CAR-T cell development. SIGNIFICANCE: Chimeric antigen receptor T-cell (CAR-T cell) therapy is state-of-the-art in cell and immunotherapy. Determining important players in cellular communication and signalling mediated by membranes and intracellular proteins requires understanding the connection between tumours and modified cells. We employed global proteomics in this study to better grasp the functional protein networks using a high-sensitivity mass spectrometric platform for protein identification and quantification. We identified proteins as molecular effectors of anti-CD19 CAR-T cell treatment during the early stages of CAR-T-target cell interaction. Our understanding of the mechanism and signalling pathways will promote the development of new CAR constructs and improve the efficacy and ability to overcome the resistance of this innovative cancer treatment strategy, which will advance the identification of adjuvant molecules for the regulation of CAR-T responses.
A perspective on integrating digital pathology, proteomics, clinical data and AI analytics in cancer research.
Guedes J, Woldmar N, Szasz AM … +8 more , Wieslander E, Pawłowski K, Horvatovich P, Malm J, Szadai L, Németh IB, Marko-Varga G, Gil J
J Proteomics · 2025 Sep · PMID 40680990 · Publisher ↗
Nearly 40 % of individuals will be diagnosed with cancer in their lifetime, translating to an estimated 20 million new cases annually. Despite remarkable therapeutic advances, only 15-20 % of patients achieve durable res...
Nearly 40 % of individuals will be diagnosed with cancer in their lifetime, translating to an estimated 20 million new cases annually. Despite remarkable therapeutic advances, only 15-20 % of patients achieve durable responses to immunotherapy, and the high cost of treatment (illustrated by immune checkpoint inhibitors like pembrolizumab and nivolumab, totaling roughly $191,000 per year) remains a formidable global challenge. The convergence of digital pathology, high-throughput molecular profiling, and advanced computational strategies has the potential to transform cancer research. By integrating high-resolution morphological data with proteomic, transcriptomic, and spatial molecular insights, we can elucidate the complex interplay between tumor cells and their microenvironment. In this perspective, we review how emerging techniques, from AI-driven image analysis to deep visual proteomics, can accelerate biomarker discovery, refine patient stratification, and ultimately improve clinical outcomes. We illustrate these principles with a case study in melanoma, where the integration of digital pathology and deep proteomic profiling uncovered a molecular signature predictive of recurrence in early-stage disease. As these technologies evolve, we foresee a future of precision oncology characterized by the seamless integration of morphological, clinical, and molecular data enabled by AI-driven analytics. SIGNIFICANCE: This perspective represents a pivotal step toward transforming cancer research by bridging the gap between traditional histopathological evaluation and modern molecular analytics. By integrating digital pathology with spatial proteomics and advanced AI-driven analytics, our approach provides a multidimensional view of tumor biology that captures both morphological nuances and molecular heterogeneity. This comprehensive framework not only enhances our understanding of the tumor microenvironment but also facilitates the discovery of robust biomarkers for disease recurrence and therapeutic response. Ultimately, our findings underscore the potential of precision oncology to tailor treatment strategies to individual patient profiles, thereby improving clinical outcomes and guiding the next generation of personalized cancer care.
Proteomic technologies for profiling cell-membrane/secretome interactions in brain metastatic cancer progression.
Lazar IM
Expert Rev Proteomics · 2025 Aug · PMID 40673921 · Publisher ↗
INTRODUCTION: The ability of cancer cells to disseminate from the primary tumor and form metastatic lesions frequently leads to fatal outcomes. Recently, however, it has been recognized that this process is driven by com...
INTRODUCTION: The ability of cancer cells to disseminate from the primary tumor and form metastatic lesions frequently leads to fatal outcomes. Recently, however, it has been recognized that this process is driven by complex interactions between the cancer and the neighboring cells, and, overall, made possible by a supportive tumor microenvironment. The emergence of high-throughput technologies is expected to bring much-needed clarity to unraveling the players and intricate communication pathways that promote metastatic progression. AREAS COVERED: In this report, the impact of mass spectrometry and proteomic technologies on deciphering the cross-talk between cancer and tumor microenvironment cells is discussed. Focus is placed on the role of cell-membrane and secretome proteins as the main enablers of this cross-talk, and on the challenges presented by metastatic tumors that evolve in the brain. Future prospects are assessed in the context of recent biology, technology, and data analysis breakthroughs. EXPERT OPINION: Advancements in high-throughput proteomic technologies, complemented by the exciting potential of new disease model systems and data processing abilities of artificial intelligence, are expected to bring groundbreaking progress in deciphering the fundamental biological mechanisms that support cancer behavior and metastatic development, revealing novel therapeutic targets, and guiding innovative intervention approaches.
Intercellular transfer of proteins and mitochondria - the contribution of proteomics.
Guzmán-Ortiz AL, Pérez-Figueroa E, Valle-Ríos R … +2 more , Martínez-González KM, Quezada H
J Proteomics · 2025 Sep · PMID 40645288 · Publisher ↗
One of the mechanisms of intercellular communication is the transfer of proteins and organelles among cells. This has been observed in diverse phylogenetic groups, and can be mediated by extracellular vesicles, like exos...
One of the mechanisms of intercellular communication is the transfer of proteins and organelles among cells. This has been observed in diverse phylogenetic groups, and can be mediated by extracellular vesicles, like exosomes or exophores, tunneling nanotubes, pores like plasmodesmata or processes like trogocytosis. The vast majority of studies in this field have used confocal microscopy and flow cytometry to detect proteins from donor cells in recipient cells. Proteomics has not been widely used, despite the fact that efficient tools are available for the labeling, enrichment and unbiased large-scale identification of the transferred proteins. Among these tools are trans-SILAC, affinity capture-MS/MS, BONCAT, TransitID and the use of cells from different species. In this review we describe illustrative examples of the intercellular transfer of proteins and mitochondria indicating the experimental methodologies used, both proteomics and non-proteomics, and emphasizing the capabilities of the mass spectrometry-based strategies.
Plasma Proteomic Profiling of a Group of Anxious Dogs by LC-MS/MS: A Case-Control Study.
Gaither C, Popp R, Borchers CH … +2 more , Beaudry F, Desmarchelier M
Proteomics Clin Appl · 2025 Jul · PMID 40614171 · Full text
PURPOSE: Anxiety is the most common underlying cause of behavioral problems in dogs, which remain a top reason for relinquishment and euthanasia. Despite its high prevalence, anxiety is often underdiagnosed, partly due t...
PURPOSE: Anxiety is the most common underlying cause of behavioral problems in dogs, which remain a top reason for relinquishment and euthanasia. Despite its high prevalence, anxiety is often underdiagnosed, partly due to a limited understanding of biological processes and absence of diagnostic biomarkers. Our study aims to address this knowledge gap. EXPERIMENTAL DESIGN: Plasma from 10 anxious and 10 matched control dogs were analyzed following a label-free quantitation proteomics workflow based on data-dependent acquisition using a Thermo Q Exactive Plus coupled to an EASY-nLC 1200, Vanquish UHPLC, or Evosep One. Data were processed with Proteome Discoverer 2.4 (Thermo), Perseus (Max Planck Institute), Cytoscape and other bioinformatic tools. RESULTS: Between 279 and 350 proteins were identified, and proteins such as fibrinogen, apolipoproteins, and complement system and coagulation cascade proteins were significantly different between groups. Additionally, we identified two putative subgroups of anxious dogs, suggesting potentially different underlying pathophysiological mechanisms for a single anxiety phenotype. CONCLUSIONS AND CLINICAL RELEVANCE: To our knowledge, this is the first comprehensive clinical in-depth proteomic profiling of plasma from anxious dogs. Our findings lay the foundation for elucidating the pathophysiology of canine anxiety and for the future validation and establishment of novel candidate biomarkers for disease diagnosis. Novel biomarkers would allow for a more effective and objective diagnosis of anxiety, even when not phenotypically apparent. SUMMARY: Previous mass spectrometry (MS) studies have found proteomic profile differences in other diseases and other animal species. This is to our knowledge, the first unbiased and comprehensive clinical in-depth proteomic profiling of plasma from dogs suffering from anxiety disorders. These findings have an impact on animal health as they set the foundation to elucidate the pathophysiology of canine anxiety so that in the future novel candidate biomarkers can be established and validated, furthering the potential development of new drugs and guiding patient-specific therapeutic interventions based on biomarker profiles. In the clinic, novel biomarkers could allow for a more effective and objective diagnosis of anxiety disorders, even when not phenotypically apparent. Detection and measurement of early stages of anxiety disorders as well as treatment monitoring in pet dogs would allow patients to be treated quicker, before the potential onset of aggression, and a faster recovery, thus improving the welfare of companion animals.
Covalent Inhibition of the Peptidyl-Prolyl Isomerase Pin1 by Sulfopin Results in a Broad Impact on the Phosphoproteome of Human Osteosarcoma U2-OS Cells.
Roffey SE, Hovey O, Jurcic K … +3 more , Lu KP, Zhou XZ, Litchfield DW
Proteomics · 2025 Jul · PMID 40546013 · Full text
Peptidyl-prolyl isomerase, NIMA-interacting protein 1-(Pin1) catalyses the cis-trans interconversion of the inflexible bond between serine or threonine residues and proline at the +1 position (pSer/pThr-Pro). Although in...
Peptidyl-prolyl isomerase, NIMA-interacting protein 1-(Pin1) catalyses the cis-trans interconversion of the inflexible bond between serine or threonine residues and proline at the +1 position (pSer/pThr-Pro). Although initially discovered as an essential regulator of cell division, Pin1 has since been identified as a regulator of many biological processes and is associated with numerous malignancies and neurodegenerative disorders. Pin1 has been shown to influence phosphorylation by modulating phosphatase accessibility. However, it can also indirectly regulate phosphorylation by isomerizing peptidyl-prolyl bonds on kinases, affecting their subcellular localization and/or substrate specificity. Here, SILAC-based mass spectrometry was employed to identify proteomic and phosphoproteomic changes in human osteosarcoma human osteosarcoma cell line (U2-OS) cells in response to treatment with the selective covalent Pin1 inhibitor Sulfopin. We confirmed that Sulfopin covalently binds Pin1 and profiled Pin1-dependent changes to the proteome and phosphoproteome, identifying 803 phosphosites that underwent significant Sulfopin-dependent changes. The identified phosphosites include substrates for a number of distinct kinases, including protein kinase B (AKT1), aurora kinase A (AURKA), cyclin-dependent kinase (CDK)1 and CK2. Overall, this study reveals the broad impact of Sulfopin on the phosphoproteome, improving our understanding of how Pin1 modulates complex regulatory kinase networks in living cells. SUMMARY: The peptidyl-prolyl isomerase (PPIase) Pin1 has emerged as a potential therapeutic target for numerous malignancies and neurodegenerative disorders based on its altered expression in several diseases. As the activity of Pin1 is phosphorylation-dependent, it is intimately involved with constituents of regulatory kinase networks within cells. To elucidate how Pin1 orchestrates regulatory signalling within cells, we performed quantitative proteomic and phosphoproteomic profiling of SILAC-labelled human osteosarcoma U2-OS cells treated with Sulfopin, a highly selective covalent Pin1 inhibitor. In addition to demonstrating that Pin1 inhibition alters the abundance and phosphorylation of proteins involved in a variety of fundamental cellular processes, these studies revealed that Pin1 inhibition modulates the phosphorylation of 803 phosphorylation sites, ultimately improving our understanding of how a PPIase regulates phosphorylation networks in complex biological systems.
Quantitative proteomics analysis of radiation-induced rectal injury in rectal cancer patients undergoing radiotherapy.
Cai H, Lai Y, Zhu B … +6 more , Zeng J, Wang Y, Wu Y, Zhuang J, Liu X, Guan G
J Proteomics · 2025 Aug · PMID 40541928 · Publisher ↗
Radiation-induced rectal injury (RRI) affects perioperative treatment and the postoperative quality of life in patients with rectal cancer undergoing radiotherapy. This study aimed to clarify the molecular mechanisms of...
Radiation-induced rectal injury (RRI) affects perioperative treatment and the postoperative quality of life in patients with rectal cancer undergoing radiotherapy. This study aimed to clarify the molecular mechanisms of RRI and identify potential therapeutic targets. Hematoxylin-eosin and Masson's staining were utilized to evaluate RRI. Initially, 16 rectal samples were examined using data-independent acquisition proteomics to identify the differentially abundant proteins (DAPs). Subsequently, parallel reaction monitoring (PRM) was employed to validate DAP expression. Furthermore, DAP levels were assessed using enzyme-linked immunosorbent assay (ELISA) in 118 patients with rectal cancer. Pathologic examination revealed manifestations of RRI in rectal samples. A total of 1391 DAPs were identified, with 619 upregulated and 772 downregulated. Functional annotation revealed that these proteins are primarily involved in regulating actin cytoskeleton, metabolic pathways, and immune pathways. Enrichment analysis indicated significant enrichment of DAPs in pathways, including macrophage chemotaxis, neutrophil extracellular traps, and lipid metabolism. The expression of significantly upregulated DAPs (LBP, ITIH4, SERPINA3, FN1, PLG, HRG, FGA, and SAA1) in the relevant pathway was validated using PRM. ELISA revealed that the SAA1 level in the RRI group was significantly high. In conclusion, our study provides a proteomic profile of RRI, identifying biological markers and potential molecular regulatory mechanisms. SIGNIFICANCE: Radiation-induced intestinal injury (RII) significantly impacts the postoperative quality of life in patients undergoing pelvic radiotherapy. However, the current understanding of the mechanism behind RII remains unclear. In this study, Hematoxylin-eosin and Masson's staining were used to assess RRI. We employed data-independent acquisition proteomics analysis to characterize the proteomic profile associated with RII. Through this analysis, we identified differentially expressed proteins(DEPs) and potential molecular pathways implicated in RII. Parallel reaction monitoring and enzyme-linked immunosorbent assay further employed to validate the expression of DEPs. Our findings offer novel insights into the prevention and treatment strategies for RII, thereby potentially improving the clinical outcomes and quality of life for affected patients.
The melanoma MEGA-study: Integrating proteogenomics, digital pathology, and AI-analytics for precision oncology.
Guedes J, Szadai L, Woldmar N … +19 more , Jánosi ÁJ, Koroncziová K, Lengyel BM, Kelemen B, Boltas E, Gyulai R, Wieslander E, Pawłowski K, Horvatovich P, Betancourt L, Szasz AM, Vereb Z, Horvath P, Oskolás H, Appelqvist R, Malm J, Marko-Varga G, Németh IB, Gil J
J Proteomics · 2025 Aug · PMID 40532957 · Publisher ↗
Melanoma remains the most aggressive form of skin cancer, characterized by high metastatic potential, genetic heterogeneity, and resistance to conventional therapies. The Melanoma MEGA-Study is a multi-center initiative...
Melanoma remains the most aggressive form of skin cancer, characterized by high metastatic potential, genetic heterogeneity, and resistance to conventional therapies. The Melanoma MEGA-Study is a multi-center initiative designed to address these clinical challenges by integrating advanced proteogenomic profiling, clinical metadata, with AI-driven digital pathology and machine learning analytics, aiming to enhance personalized treatment strategies and improve patient outcomes. Between 2013 and 2022, a cohort of 1653 melanoma patients each contributed a primary tumor sample, with 361 providing 819 metastatic tumor samples. Clinical data collection for this cohort continued until May 2023. Comprehensive analyses using high-resolution mass spectrometry, optimized workflows for formalin-fixed paraffin-embedded tissues, and advanced digital pathology platforms enabled precise mapping of the tumor microenvironment, identification of metabolic reprogramming, and characterization of immune evasion signatures. The European Cancer Moonshot Lund Center's MEGA-Study, under the academic umbrella of Lund and Szeged universities, marks a significant advancement in its collaborative efforts with the National Institutes of Health (NIH) under the Cancer Moonshot partnership. This initiative exemplifies the center's dedication to pioneering cancer research and underscores the strength of its international collaborations. SIGNIFICANCE: The significance of this study lies in its pioneering integration of high-resolution proteomics, AI-driven digital pathology, and comprehensive clinical annotation to unravel the complex molecular landscape of melanoma. By leveraging a robust, population-based cohort of 1653 patients, including extensive analyses of both primary and metastatic tumor specimens, our approach provides unprecedented insights into the proteogenomic alterations that underpin tumor progression, immune evasion, and therapeutic resistance. The preliminary application of advanced mass spectrometry techniques to formalin-fixed paraffin-embedded tissues, combined with state-of-the-art digital pathology and machine learning, has enabled the identification of novel protein biomarkers and metabolic signatures that hold promise for refining patient stratification and informing personalized treatment strategies. This integrative framework not only deepens our understanding of melanoma biology but also establishes a scalable model for precision oncology that can be extended to other complex malignancies. Ultimately, our findings have the potential to transform clinical practice by facilitating earlier risk stratification, improving prognostication, and guiding the development of targeted therapeutic interventions for this highly aggressive cancer.
Multi-omics reveals miR-181a-5p regulates PPAR-driven lipid metabolism in Oral squamous cell carcinoma: Insights from CRISPR/Cas9 knockout models.
Wang T, Liu Y, Wu X … +8 more , Wang X, Shi S, Song X, Ma Y, Zhang Z, Gao J, Sun R, Song G
J Proteomics · 2025 Aug · PMID 40490244 · Publisher ↗
Oral squamous cell carcinoma (OSCC) remains a therapeutic challenge due to its complex molecular landscape and metabolic adaptability. This study integrates proteomic and transcriptomic analyses to investigate the role o...
Oral squamous cell carcinoma (OSCC) remains a therapeutic challenge due to its complex molecular landscape and metabolic adaptability. This study integrates proteomic and transcriptomic analyses to investigate the role of miR-181a-5p in OSCC pathogenesis using CRISPR/Cas9-generated whole-body knockout (KO) mice. By inducing OSCC with the chemical carcinogen 4-nitroquinoline 1-oxide (4NQO), we identified significant dysregulation of lipid metabolism-associated proteins and tumor regulators in miR-181a-5p-KO tumors compared to wild-type controls. Quantitative proteomics revealed enrichment of the PPAR signaling pathway, with 12 key genes upregulated in KO mice, mechanistically linking miR-181a-5p deficiency to enhanced lipid droplet biogenesis and immunosuppressive microenvironments. Serum biomarker validation demonstrated elevated Cyfra21-1, SCC-Ag, and ISG20 levels in KO mice, correlating with tumor aggressiveness and radioresistance. Multi-omics integration further identified a diagnostic-prognostic protein signature with 89 % specificity for miR-181a-5p-deficient OSCC subtypes. These findings establish miR-181a-5p as a master regulator of PPAR-mediated metabolic reprogramming and immune evasion, offering novel proteome-driven insights into therapeutic targeting of lipid metabolism and biomarker discovery in OSCC. SIGNIFICANCE: This study integrates transcriptomic and proteomic analyses to elucidate the critical role of miR-181a-5p in regulating lipid metabolism via the PPAR signaling pathway during oral squamous cell carcinoma (OSCC) pathogenesis. Loss of miR-181a-5p enhances lipid metabolism, promoting membrane biosynthesis and metastasis. Multi-omics profiling identified a specific diagnostic-prognostic protein signature, highlighting CES3 and ISG20 as potential biomarkers for early diagnosis and therapeutic targeting in miR-181a-5p-deficient OSCC. The research establishes a foundation for miRNA-based liquid biopsy and PPAR-targeted nanotherapy. Mouse knockout models recapitulating human OSCC spatial biology validated miR-181a-5p's role in tumor initiation.
Extracellular vesicle proteomics in brain solid tumors: future strategy for personalized medicine.
Spinelli S, Garbarino A, Pavanello M … +3 more , Candiano G, Panfoli I, Bruschi M
Expert Rev Proteomics · 2025 Jun · PMID 40445167 · Publisher ↗
INTRODUCTION: Brain solid tumors are a leading cause of cancer-related mortality at all ages. The updated 2021WHO Central Nervous System tumor classification is based on genomic diagnostics. Nonetheless, extracellular ve...
INTRODUCTION: Brain solid tumors are a leading cause of cancer-related mortality at all ages. The updated 2021WHO Central Nervous System tumor classification is based on genomic diagnostics. Nonetheless, extracellular vesicles (EVs) proteomics is a platform for protein biomarker discovery in brain tumor patient management. Tumor metabolic reprogramming, also through epigenetic mechanisms, is central to cancer. EVs transfer proteins and nucleic acids that relay the metabolic and redox state of the parent cells. AREAS COVERED: This review addresses the biomarker discovery and translation focusing on recent (from 2022 to 2024 PubMed) proteomic studies of EVs in brain tumor patients, selected for their focus on metabolic aspects. The most treatable adult brain tumor is isocitrate dehydrogenase-mutated glioma. Minimally invasive collection of EVs from cerebrospinal fluid or prospectively blood allows proteomic and metabolic analysis of the parent tumor cells. EXPERT OPINION: The CSF EVs express key CNS tumor biomarkers and therapy targets undetectable in whole CSF and relay the brain tumor metabolic adaptations. Metabolic dependencies can represent potential therapy targets. The clinical implications of biomarker EV proteomic discovery represent a promising platform for diagnostic and prognostic purposes in brain solid tumors, a leading cause of cancer-related mortality at all ages.
Optimization of bottom-up proteomic sample preparation by design of experiments for absolute quantification of 257 proteins in human plasma using UPLC-MRM-MS.
Arnarson K, Y Holm K, Gunnarsdottir Thormar V … +5 more , Eiríksson FF, Mohammed Y, Borchers CH, Bodvarsdottir SK, Thorsteinsdottir M
Expert Rev Proteomics · 2025 May · PMID 40396504 · Publisher ↗
INTRODUCTION: Targeted protein absolute quantification using mass spectrometry holds promise for identifying biomarkers for diagnosis, prognosis, and personalized medicine. However, complex and time-consuming workflows,...
INTRODUCTION: Targeted protein absolute quantification using mass spectrometry holds promise for identifying biomarkers for diagnosis, prognosis, and personalized medicine. However, complex and time-consuming workflows, particularly during sample preparation, present significant bottlenecks. Addressing these challenges is critical for the applicability of absolute quantification of proteins in clinical research settings. AREAS COVERED: We explore optimization strategies for protein digestion in bottom-up proteomics sample preparation. Design of experiments (DoE), a statistical approach for systematically evaluating multiple experimental factors, was used for simultaneous optimization of digestion time, temperature, enzyme-to-protein substrate ratio, and denaturing agent. Furthermore, the lower limit of quantification (LLOQ) for our platform was improved by using the Waters Xevo TQ-XS UPLC-MRM-MS. The integration of automated sample preparation into the workflow enabled reproducible absolute quantification of 257 proteins in human plasma. EXPERT OPINION: We successfully reduced protein digestion time from 18 hours (overnight) to 4 hours while maintaining relative digestion efficiency. We improved the sensitivity of the assay via the optimized workflow and were able to quantify proteins that previously fell below the LLOQ. These advancements, combined with automation, provide a practical, efficient, and reproducible workflow suitable for clinical research.
About
- Frequency
- Sun
- Papers found
- 200
- RSS feed
- Subscribe