Biology subjects

Pinter, N.

Publications and source records attributed to Pinter, N..

4 recordsLinked to original sources

bioRxiv2024

Proteomic profiling of IDH-wildtype Glioblastoma Tissue and Serum uncovers prognostic Subtypes and Marker Candidates

BackgroundIDH-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 & AimsIn 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. ResultsPrimary 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. ConclusionOverall, 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.

cancer biology↗

bioRxiv2024

Proteomic Characterization of Intrahepatic Cholangiocarcinoma Identifies Distinct Subgroups and Proteins Associated with Time-To-Recurrence

Background & AimsIntrahepatic 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 ICCs proteomic features for the time-to-recurrence (TTR) remains unclear. MethodsWe 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). ResultsTumor- 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. ConclusionsWe 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.

cancer biology↗

bioRxiv2022

Targeted and Explorative Profiling of Kallikrein Proteases and Global Proteome Biology of Pancreatic Ductal Adenocarcinoma, Chronic Pancreatitis, and Normal Pancreas Highlights Disease-Specific Proteome Remodelling

Pancreatic ductal adenocarcinoma (PDAC) represents one of the most aggressive and lethal malignancies worldwide with an urgent need for new diagnostic and therapeutic strategies. One major risk factor for PDAC is the pre-indication of chronic pancreatitis (CP), which represents highly inflammatory pancreatic tissue. Kallikreins (KLKs) are secreted serine proteases that play an important role in various cancers as components of the tumor microenvironment. Previous studies of KLKs in solid tumors largely relied on either transcriptomics or immunodetection. We present one of the first targeted mass spectrometry profiling of kallikrein proteases in PDAC, CP, and normal pancreas. We show that KLK6 and KLK10 are significantly upregulated in PDAC (n=14) but not in CP (n=7) when compared to normal pancreas (n=21), highlighting their specific intertwining with malignancy. Additional explorative proteome profiling identified 5936 proteins in our pancreatic cohort and observed disease-specific proteome rearrangements in PDAC and CP. As such, PDAC features an enriched proteome motif for extracellular matrix (ECM) and cell adhesion while there is depletion of mitochondrial energy metabolism proteins, reminiscent of the Warburg effect. Although often regarded as a PDAC hallmark, the ECM fingerprint was also observed in CP, alongside with a prototypical inflammatory proteome motif as well as with an increased wound healing process and proteolytic activity, thereby possibly illustrating tissue autolysis. Proteogenomic analysis based on publicly accessible data sources identified 112 PDAC-specific and 32 CP-specific single amino acid variants, which among others affect KRAS and ANKHD1. Our study emphasizes the diagnostic potential of kallikreins and provides novel insights into proteomic characteristics of PDAC and CP.

cancer biology↗

bioRxiv2022

MaxQuant and MSstats in Galaxy enable reproducible cloud-based analysis of quantitative proteomics experiments for everyone

Quantitative mass spectrometry-based proteomics has become a high-throughput technology for the identification and quantification of thousands of proteins in complex biological samples. Two de facto standard tools, MaxQuant and MSstats, allow for the analysis of raw data and finding proteins with differential abundance between conditions of interest. To enable accessible and reproducible quantitative proteomics analyses in a cloud environment, we have integrated MaxQuant (including TMTpro 16/18plex), Proteomics Quality Control (PTXQC), MSstats and MSstatsTMT into the open-source Galaxy framework. This enables the web-based analysis of label-free and isobaric labeling proteomics experiments via Galaxys graphical user interface on public clouds. MaxQuant and MSstats in Galaxy can be applied in conjunction with thousands of existing Galaxy tools and integrated into standardized, sharable workflows. Galaxy tracks all metadata and intermediate results in analysis histories, which can be shared privately for collaborations or publicly, allowing full reproducibility and transparency of published analysis. To further increase accessibility, we provide detailed hands-on training materials. The integration of MaxQuant and MSstats into the Galaxy framework enables their usage in a reproducible way on accessible large computational infrastructures, hence realizing the foundation for high throughput proteomics data science for everyone.

biochemistry↗