Oliver Klein

Background/Objectives: Cytological analysis of pleural and peritoneal effusions is minimally invasive but may show limited sensitivity for malignancy detection because tumor cells can be scarce or morphologically difficult to identify. Proteomics-based mass spectrometry imaging (MSI) enables direct molecular profiling of cytological specimens and may improve the distinction between malignant and benign samples. This study investigated whether MALDI-based MSI profiling of formalin-fixed paraffin-embedded (FFPE) cytology cell blocks could discriminate high-grade serous ovarian carcinoma (HGSOC) from benign effusions and identify discriminatory peptide signatures. Methods: Forty-one FFPE cytological specimens derived from pleural and peritoneal effusions were analyzed, including 18 malignant samples and 23 benign control specimens with reactive or inflammatory cytological backgrounds. MALDI-MSI analyses were performed using proteomic profiling. In a preliminary comparative experiment, two section thicknesses (3 µm and 5 µm) were evaluated to optimize ion peak intensity yield. Classification analyses using linear discriminant analysis (LDA) and support vector machine (SVM) models were performed to identify discriminatory peptide signatures. Results: Comparative analysis of average mass spectra identified multiple differentially expressed ions with significant discriminatory performance (AUROC ≥ 0.7 or ≤0.3; Wilcoxon/Kruskal–Wallis, p < 0.001). Classification analyses achieved accuracy ranged from 91% to 94% for the discrimination of malignant and benign samples. Differential proteomic profiling identified Complement C3, Perilipin-3, arachidonate 5-lipoxygenase, Leukotriene A-4 hydrolase, fibrinogen beta and gamma chains, and serotransferrin as discriminatory proteins associated with immune modulation, lipid metabolism, cytoskeletal and extracellular matrix organization, and metabolic regulation. Notably, Complement C3 was found to be overexpressed in malignant tumor cells, supporting its potential role as a marker of tumor presence and progression. Conclusions: Proteomics-based mass spectrometry imaging enabled reliable discrimination of HGSOC from benign cytological specimens and revealed cancer-associated proteins linked to relevant biological processes. These findings support MALDI-MSI as a complementary molecular approach for the classification of challenging cytological specimens in routine diagnostic pathology.

Post-infectious Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a chronic disease with unresolved pathophysiology and limited diagnostic options. Extracellular vesicles (EVs) carry disease-specific protein and miRNA signatures and may enable improved disease profiling. We aimed to identify novel protein and miRNA markers as potential biomarkers in plasma EVs from female ME/CFS patients, including post-COVID-19 ME/CFS and post-infectious ME/CFS of other origins, compared with healthy controls. EVs were isolated from plasma by size-exclusion chromatography and characterized for number, size, morphology, and surface marker expression. Flow cytometry showed that small EVs strongly expressed tetraspanins, with only minor differences between ME/CFS patients and healthy donors. Proteomic profiling of EVs from ME/CFS patients identified altered cargo proteins, including hemoglobin subunit alpha and insulin-like growth factor-binding protein acid labile subunit compared with healthy controls (n ≤ 10/cohort). Small RNA sequencing followed by qPCR revealed significant downregulation of hsa-let-7b-5p in EVs from post-COVID-19 ME/CFS patients (n = 12) versus healthy controls (n = 15). Reduced hsa-let-7b-5p expression correlated with impaired physical functioning and increased fatigue, pain, and immune activation. These findings indicate that EV cargo differences, particularly hemoglobin subunit alpha and insulin-like growth factor-binding protein acid labile subunit, as well as hsa-let-7b-5p, represent promising candidates for ME/CFS diagnosis and patient stratification.

Head and neck squamous cell carcinoma (HNSCC) is often diagnosed at advanced stages. Due to pronounced intratumoral heterogeneity (ITH), reliable risk stratification and prediction of treatment response remain challenging. This study aimed to identify peptide signatures in HNSCC tissue that are associated with treatment outcomes in HPV-negative, advanced-stage HNSCC patients undergoing 5-fluorouracil/platinum-based chemoradiotherapy (CDDP-CRT). We integrated matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) of tryptic peptides with univariate statistics and machine learning approaches to uncover potential prognostic patterns. Formalin-fixed, paraffin-embedded whole tumor sections from 31 treatment-naive, HPV-negative HNSCC patients were digested in situ with trypsin, and the generated peptides were analyzed using MALDI-MSI. Clinical follow-up revealed recurrence or progression (RecPro) in 20 patients, while 11 patients showed no evidence of disease (NED). Classification models were developed based on the recorded peptide profiles using both unrestricted and feature-restricted approaches, employing either the full set of m/z features or a subset of the most discriminatory m/z features, respectively. The unrestricted model achieved a balanced accuracy of 71% at the patient level (75% sensitivity, 66% specificity), whereas the feature-restricted model reached a balanced accuracy of 72%, showing increased specificity (92%) but reduced sensitivity (52%) in the CDDP-CRT cohort. In order to assess treatment specificity, models trained on the CDDP-CRT cohort were tested on an independent patient cohort treated with mitomycin C-based CRT (MMC-CRT). Neither model demonstrated prognostic performance in the MMC-CRT patient cohort, suggesting specificity for platinum-based therapy. Presented findings highlight the potential of MALDI-MSI–based proteomic profiling to identify patients at elevated risk of recurrence following CDDP-CRT. This approach may support more personalized risk assessment and treatment planning, ultimately contributing to improved therapeutic outcomes in HPV-negative HNSCC.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, accentuated by its heterogeneity and complex tumour microenvironment (TME). The role of TME on tumour pathophysiology is pivotal, especially the influence of components of the extracellular matrix (ECM), such as collagen. We introduce a novel multimodal imaging strategy to unravel the complex spatial heterogeneity of CRC by integrating the imaging features from two-photon laser scanning microscopy (2PLSM) and histology with proteomics signatures from matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI MSI). Our study is the first to correlate the structural coherence of collagen fibres and the nuclei distribution profile of tumour tissue with the peptide signatures, offering insights into the proteomic landscape of CRC within regions of high nuclei distribution (HND), as well as chaotic and organised regions of collagen. We use this approach to distinguish the patient tissues originating from left-sided colorectal cancer (LSCC) and from right-sided colorectal cancer (RSCC). This discriminative signature highlights the role of high nuclei distribution and collagen architecture in tumour progression. Complementary m/z values of several proteins associated to components of ECM, such as plectin, vinculin, vimentin, and myosin, have shown differentially intensity distributions between LSCC and RSCC. Our findings demonstrate the potential of combining structural information with peptide features to identify molecular signatures in different tumour regions and retrieve new insights into CRC pathophysiology.

Despite numerous diagnostic and therapeutic advances, pancreatic ductal adenocarcinoma (PDAC) has a high mortality rate, and is the fourth leading cause of cancer death in developing countries. Besides its increasing prevalence, pancreatic malignancies are characterized by poor prognosis. Omics technologies have potential relevance for PDAC assessment but are time-intensive and relatively cost-intensive and limited by tissue heterogeneity. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can obtain spatially distinct peptide-signatures and enables tumor classification within a feasible time with relatively low cost. While MALDI-MSI data sets are inherently large, machine learning methods have the potential to greatly decrease processing time. We present a pilot study investigating the potential of MALDI-MSI in combination with neural networks, for classification of pancreatic ductal adenocarcinoma. Neural-network models were trained to distinguish between pancreatic ductal adenocarcinoma and other pancreatic cancer types. The proposed methods are able to correctly classify the PDAC types with an accuracy of up to 86% and a sensitivity of 82%. This study demonstrates that machine learning tools are able to identify different pancreatic carcinoma from complex MALDI data, enabling fast prediction of large data sets. Our results encourage a more frequent use of MALDI-MSI and machine learning in histopathological studies in the future.