Background: Intrahepatic cholangiocarcinoma (iCC) is a rare malignant liver tumor with limited therapeutic advancements. Despite its increasing global incidence knowledge of treatment options remains stagnant, leading to poor five-year patient survival rates and high recurrence post-surgery. ALDH1A1, a member of the ALDH superfamily, is associated with cancer stem cells and has conflicting reports regarding its prognostic role in iCC. This retrospective study analyzed 69 iCC patient samples from University Hospital Freiburg. Tissue microarrays (TMAs) were constructed, and ALDH1A1 expression was immunohistochemically assessed using machine learning algorithms. Script-based Survival analysis employed Kaplan-Meier curves, log-rank ALDH1A1 overexpression, both in tumor and stromal cells, correlates with favorable overall survival in iCC. Gender-specific analyses indicate a more pronounced effect in females. These findings suggest ALDH1A1 as a potential prognostic biomarker in iCC, warranting further validation in larger cohorts and exploration as a therapeutic target.

We present the proteomic profiling of 79 bladder cancers, including treatment-na & iuml;ve non-muscle-invasive bladder cancer (NMIBC, n = 17), muscle-invasive bladder cancer (MIBC, n = 51), and neoadjuvant-treated MIBC (n = 11). Proteins were extracted from formalin-fixed, paraffin-embedded samples and analyzed using data-independent acquisition, yielding >8,000 quantified proteins. MIBC, compared to NMIBC, shows an extracellular matrix (ECM) and immune response signature as well as alteration of the metabolic proteome together with concomitant depletion of proteins involved in cell-cell adhesion and lipid metabolism. Neoadjuvant treatment did not consistently impact the proteome of the residual tumor mass. NMIBC presents two proteomic subgroups that correlate with histological grade and feature signatures of cell adhesion or lipid/DNA metabolism. Treatment-na & iuml;ve MIBC presents three proteomic subgroups with resemblance to the basal-squamous, stroma-rich, or luminal subtypes and signatures of metabolism, immune functionality, or ECM. The metabolic subgroup presents an immune-depleted microenvironment, whereas the ECM and immune subgroups are enriched for markers of M2-like tumor-associated macrophages and dendritic cells. Markers for natural killer cells are exclusive for the ECM subgroup, and markers for cytotoxic T cells are a hallmark of the immune subgroup. Endogenous proteolysis is increased in MIBC alongside upregulation of matrix metalloproteases, including MMP-14. Genomic panel sequencing yielded the prototypical profile of prevalent FGRF3 alterations in NMIBC and TP53 alterations in MIBC. Tumor-stroma interactions of MIBC were investigated by proteomic analysis of patient-derived xenografts, highlighting specific tumor and stroma contributions to the matrisome and tumor-induced stromal proteome phenotypes. (c) 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Background & Aims Intrahepatic cholangiocarcinoma (ICC) is a poorly understood cancer with dismal survival and high recurrence rates. ICCs are often detected in advanced stages. Surgical resection is the most important first-line treatment but limited to non-advanced cases, whereas chemotherapy provides only a moderate benefit. The proteome biology of ICC has only been scarcely studied and the prognostic value of initial ICC’s proteomic features for the time-to-recurrence (TTR) remains unclear.Methods We dissected formalin-fixed, paraffin-embedded samples from 80 tumor– and 77 matching adjacent non-malignant (TANM) tissues. All samples were measured via liquid-chromatography mass-spectrometry (LC-MS/MS) in data independent acquisition mode (DIA).Results Tumor– and TANM tissue showed strongly different biologies and DNA-repair, translation, and matrisomal processes were upregulated in ICC. In a hierarchical clustering analysis, we determined two proteomic subgroups of ICC, which showed significantly diverging TTRs. Cluster 1, which is associated with a beneficial prognosis, was enriched for matrisomal processes and proteolytic processing, while cluster 2 showed increased RNA and protein turnover. In a second, independent Cox’ proportional hazards model analysis, we identified individual proteins whose expression correlates with TTR distribution. Proteins with a positive hazard ratio were mainly involved in carbon/glucose metabolism and protein turnover. Conversely, proteins associated with a low hazard ratio were mostly linked to the extracellular matrix. Additional proteome profiling of patient-derived xenograft tumor models of ICC successfully distinguished tumor and stromal proteins and provided insights into cell-matrix interactions.Conclusions We successfully determine the proteome biology of ICC and present two proteome clusters in ICC patients with significantly different TTR rates and distinct biological motifs. A xenograft model confirmed the importance of tumor-stroma interactions for this cancer.### Competing Interest StatementThe authors have declared no competing interest.* AAALAC : Association for Assessment and Accreditation of Laboratory Animal Care International AJCC : American Joint Committee on Cancer BCA : Bicinchonic Acid BGN : Biglycan CCA : Cholangiocarcinoma COL3A1 : Collagen alpha-1(III) chain CPHM : Cox’ Proportional Hazards Model CPTAC : Clinical Proteomic Tumor Analysis Consortium DIA : Data Independent Acquisition DNA : Desoxyribonucleic acid ECM : Extracellular Matrix FBN1 : Fibrillin-1 FDR : False Discovery Rate FFPE : Formalin-fixed, Paraffin-embedded FU-ICC : Fudan University Intrahepatic Cholangiocarcinoma Cohort FUS : Oncogene FUS GGT5 : Glutathione Hydrolase 5 GV-SOLAS : Gesellschaft für Versuchstierkunde – Society for Laboratory Animal Science H1-0 : Histone 1.0 HAT : Highly Actionable Targets HCD : Higher-energy Collisional Dissociation HEPES : 4-(2-hydroxyethyl)-1-piperazineethanesulfonic Acid HPLC : High-performance Liquid Chromatography ICC : Intrahepatic Cholangiocarcinoma IDH1/2 : cytoplasmic and mitochondrial isocitrate dehydrogenase 1 and 2 iRTs : Indexed Retention Time Standards ITIH2 : Inter-alpha-trypsin inhibitor heavy chain H2 LC-MS/MS : Liquid-chromatography mass spectrometry LDHA : L-lactate dehydrogenase A chain LFQ : Label-free Quantitation LUM : Lumican MSKCC : Memorial Sloan Kettering Cancer Center New York PARP1 : Poly-[ADP-ribose]-polymerase 1 PCA : Principal Component Analysis PDX : Patient-derived Xenograft PLS : Partial Least Squares PRELP : Prolargin PSMB4 : Proteasome subunit beta type-4 SDS : Sodiumdodecylsulfate SKI : SKI oncoprotein TANM : Tumor-adjacent, non-malignant TEAB : Tetraethylammonium Bromide TNS-1 : Tensin 1 TTR : Time to Recurrence UICC : Union for International Cancer Control VTN : Vitronectin

Mass spectrometry (MS)-based proteomics is rapidly transforming pathology research and diagnostics by enabling comprehensive studies of protein expression and post-translational modifications (PTMs). This article discusses recent advancements in MS-based proteomics, focusing on emerging technologies in sample preparation, MS instrumentation, and data analysis. These developments are scrutinized for their applications in clinical cohort studies and molecular pathology diagnostics. The article reviews innovations in automated sample preparation, chromatography systems, advanced MS technologies, and proteomic data analysis in the context of pathology. Specific applications such as liquid biopsy, spike-in heavy peptide panels, immunopeptidomics, and PTM screening are highlighted alongside opportunities for data integration. Recent technological improvements have significantly increased the throughput, precision, and scope of proteomic studies, enabling the analysis of large clinical cohorts and small specimens with unprecedented sensitivity. Advanced MS techniques have broadened applications, opening new avenues for discovery and diagnosis of marker proteins and therapeutic targets. Advancements in MS-based proteomics have created new opportunities in clinical research and diagnostics. By facilitating more comprehensive and integrated analyses of proteomes, these technologies are set to play a pivotal role in the future of personalized medicine and pathology research.

Background IDH-wildtype glioblastoma (GBM) is the most prevalent primary brain cancer with a 5-year survival rate below 10%. Despite combined treatment through extensive resection and radiochemotherapy, nine out of ten patients develop recurrences. The lack of targeted treatment options and reliable diagnostic markers for recurrent tumors remain major challenges.Methods & Aims In this study, we present the proteomic characterization of tissue and serum from 55 initial GBM tumors and five matching recurrences, which we investigated for proteomic tumor subtypes and proteomic signatures associated with recurrence.Results Primary tumors revealed four distinct subgroups through hierarchical clustering: a neuronal cluster with elevated mature neuron markers, an innate immunity cluster with increased protease expression, a mixed cluster, and a stem-cell cluster. Neurodevelopmental and inflammatory processes were identified as key factors influencing clustering, with proteolytic activity increasing relative to the degree of inflammation. An analysis comprising proteins with lower coverage confirmed and expanded this pattern. Patients in the neuronal cluster exhibited significantly longer survival compared to those in the stem-cell cluster. In a patient-matched differential expression analysis, five recurrent tumors displayed significantly altered protein expression compared to their primary counterparts, emphasizing the proteomic plasticity of recurrent tumors. Investigation of serum proteomes before and after surgery, using a depletion-based protocol, revealed highly patient-specific and stable proteome compositions, despite a notable increase in inflammation markers post-surgery. However, the levels of circulating proteolytic products matched to the proteolytic activity within the tissue and one fragment of proteolysis activated receptor 2 (PAR2) consistently dropped in abundance after removal of inflamed tumors.Conclusion Overall, we describe a large proteomic GBM cohort. We identified distinct tumor subgroups, molecular patterns of recurrence, and matching proteomic patterns in the bloodstream, which may improve risk prediction for recurrent GBM.### Competing Interest StatementThe authors have declared no competing interest.* ABC : Ammonium Bicarbonate AGC : Automatic Gain Control ALB : Albumin BCA : Bicinchonic Acid Assay BLBP : Fatty acid-binding protein, brain CAA : 2-Chloroacetamide CD163 : Scavenger receptor cysteine-rich type 1 protein M130 CPHM : Cox Proportional Hazards Model CT : Computer Tomography CTSG : Cathepsin G DIA : Data Independent Acquisition EDTA : Ethylenediaminetetraacetic acid EGFR : Epidermal growth factor receptor ENO2 : Gamma-enolase FCN1 : Ficolin-1 FDR : False Discovery Rate GBM : Glioblastoma Multiforme GFAP : Glial fibrillary acidic protein HCD : Higher-energy collisional dissociation HEPES : 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HPLC : High Pressure Liquid Chromatography HR : Hazard Ratio HRAS : GTPase HRas IDH : Isocitrate Dehydrogenase IQR : Inter-Quartile Range iRTs : Indexed Retention Time Standards KRAS : GTPase KRas L1CAM : Neural cell adhesion molecule L1 LC-MS/MS : Liquid-Chromatography Tandem Mass-Spectrometry LFQ : Label-Free Quantification LPA : Apolipoprotein (a) LTQ : Linear Quadrupole Ion Trap Mass-Spectrometer LUM : Lumican LysC : Lysyl Endopeptidase C LYZ : Lysozyme C MAP2 : Microtubule-associated protein 2 MARS : Multi Affinity Removal System MNG : Meningioma MRC1 : Macrophage mannose receptor 1 MRI : Magnetic Resonance Imaging mRNA : Messenger Ribonucleic Acid mTOR : Mammalian Target of Rapamycine NCAM1 : Neural cell adhesion molecule 1 NES : Nestin nLC : Nanoflow Liquid Chromatography NOTCH1 : Neurogenic locus notch homolog protein 1 NTRK2 : BDNF/NT-3 growth factors receptor NTRK3 : NT-3 growth factor receptor PAR2 : Proteolysis Activated Receptor 2 PCA : Principal Component Analysis PLS-DA : Partial-Least Squares Discriminant Analysis PMSF : Phenylmethylsulfonyl Fluoride postOP : Collection after Surgery preOP : Collection before Surgery PLA2G7 : Platelet-activating factor acetylhydrolase PTEN : Phosphatidylinositol 3,4,5-trisphosphate 3-phosphatase and dual-specificity protein phosphatase PTEN RTN4 : Reticulon-4 SDS : Sodium Dodecyl Sulfate SOX10 : Transcription factor SOX-10 SOX2 : Transcription factor SOX-2 SP3 : Single-Pot Solid-Phase-Enhanced Sample Preparation SYP : Synaptophysin SYT : Synaptotagmin-1 TCEP : Tris(2-carboxyethyl)phosphine hydrochloride TFA : Trifluoroacetic acid TMZ : Temozolomide UMAP : Uniform Manifold Approximation and Projection for Dimension Reduction VNN1 : Pantetheinase WHO : World Health Organization ZEB1 : Zinc finger E-box-binding homeobox 1

Breast cancer remains the most common cancer in women worldwide. Neoadjuvant chemotherapy (NACT) is often preferred to adjuvant chemotherapy to achieve tumour shrinkage, monitor response to therapy and facilitate surgical removal in the absence of metastases. In addition, there is strong evidence that pathological complete remission (pCR) is associated with prolonged survival. In this study, we sought to identify candidate markers that signal response or resistance to therapy. We present a retrospective longitudinal serum proteomic study of 22 breast cancer patients (11 with pCR and 11 with non-pCR) matched with 21 healthy controls. Serum was analysed by LC-MS/MS after depletion of abundant proteins by immunoaffinity, trypsinisation, isobaric labelling and fractionation by reversed-phase HPLC. We observed an inverse behaviour of the serum proteins c-Met and N-cadherin after the second cycle of chemotherapy with a high predictive value (AUC 0.93). More pronounced changes were observed after the 6th cycle of NACT, with significant changes in the intensity of the proteins contactin-1, centrosomal protein, sex hormone-binding globuline and cholinesterase. Our study highlights the possibility of monitoring response to NACT using serum as a liquid biopsy.

BACKGROUND:There is an urgent need to better understand the mechanisms associated with the development, progression, and onset of recurrence after initial surgery in glioblastoma (GBM). The use of integrative phenotype-focused -omics technologies such as proteomics and lipidomics provides an unbiased approach to explore the molecular evolution of the tumor and its associated environment. METHODS:We assembled a cohort of patient-matched initial (iGBM) and recurrent (rGBM) specimens of resected GBM. Proteome and metabolome composition were determined by mass spectrometry-based techniques. We performed neutrophil-GBM cell coculture experiments to evaluate the behavior of rGBM-enriched proteins in the tumor microenvironment. ELISA-based quantitation of candidate proteins was performed to test the association of their plasma concentrations in iGBM with the onset of recurrence. RESULTS:Proteomic profiles reflect increased immune cell infiltration and extracellular matrix reorganization in rGBM. ASAH1, SYMN, and GPNMB were highly enriched proteins in rGBM. Lipidomics indicates the downregulation of ceramides in rGBM. Cell analyses suggest a role for ASAH1 in neutrophils and its localization in extracellular traps. Plasma concentrations of ASAH1 and SYNM show an association with time to recurrence. CONCLUSIONS:We describe the potential importance of ASAH1 in tumor progression and development of rGBM via metabolic rearrangement and showcase the feedback from the tumor microenvironment to plasma proteome profiles. We report the potential of ASAH1 and SYNM as plasma markers of rGBM progression. The published datasets can be considered as a resource for further functional and biomarker studies involving additional -omics technologies.

Proteomics, the study of proteins and their functions, has greatly evolved due to advances in analytical chemistry and computational biology. Unlike genomics or transcriptomics, proteomics captures the dynamic and diverse nature of proteins, which play crucial roles in cellular processes. This is exemplified in cancer, where genomic and transcriptomic information often falls short in reflecting actual protein expression and interactions. Liquid chromatography–mass spectrometry (LC-MS) is pivotal in proteomic data generation, enabling high-throughput analysis of protein samples. The MS-based workflow involves protein digestion, chromatographic separation, ionization, and fragmentation, leading to peptide identification and quantification. Computational biostatistics, particularly using tools in R (R Foundation for Statistical Computing, Vienna, Austria; www.R-project.org ), aid in data analysis, revealing protein expression patterns and correlations with clinical variables. Proteomic studies can be explorative, aiming to characterize entire proteomes, or targeted, focusing on specific proteins of interest. The integration of proteomics with genomics addresses database limitations and enhances peptide identification. Case studies in intrahepatic cholangiocarcinoma, glioblastoma multiforme, and pancreatic ductal adenocarcinoma highlight proteomics’ clinical applications, from subtyping cancers to identifying diagnostic markers. Moreover, proteomic data augment molecular tumor boards by providing deeper insights into pathway activities and genomic mutations, supporting personalized treatment decisions. Overall, proteomics contributes significantly to advancing our understanding of cellular biology and improving clinical care.