Multi-omics analysis to uncover the molecular basis of tumor budding in head and neck squamous cell carcinoma.

Tumor budding (TB) is a prognostic biomarker in HPV-negative and HPV-positive head and neck squamous cell carcinoma (HNSCC). Analyzing TCGA and CPTAC mutation, RNA, and RPPA data and performing proteomics and IHC in two independent in-house cohorts, we uncovered molecular correlates of TB in an unprecedentedly comprehensive manner. NSD1 mutations were associated with lower TB in HPV-negative HNSCC.

Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics.

Proteomics is making important contributions to drug discovery, from target deconvolution to mechanism of action (MoA) elucidation and the identification of biomarkers of drug response. Here we introduce decryptE, a proteome-wide approach that measures the full dose-response characteristics of drug-induced protein expression changes that informs cellular drug MoA. Assaying 144 clinical drugs and research compounds against 8,000 proteins resulted in more than 1 million dose-response curves that can be interactively explored online in ProteomicsDB and a custom-built Shiny App.

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens.

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements.

Decrypting drug actions and protein modifications by dose- and time-resolved proteomics.

Although most cancer drugs modulate the activities of cellular pathways by changing posttranslational modifications (PTMs), little is known regarding the extent and the time- and dose-response characteristics of drug-regulated PTMs. In this work, we introduce a proteomic assay called decryptM that quantifies drug-PTM modulation for thousands of PTMs in cells to shed light on target engagement and drug mechanism of action. Examples range from detecting DNA damage by chemotherapeutics, to identifying drug-specific PTM signatures of kinase inhibitors, to demonstrating that rituximab kills CD20-positive B cells by overactivating B cell receptor signaling.

Getting Ready for Large-Scale Proteomics in Crop Plants.

Plants are an indispensable cornerstone of sustainable global food supply. While immense progress has been made in decoding the genomes of crops in recent decades, the composition of their proteomes, the entirety of all expressed proteins of a species, is virtually unknown. In contrast to the model plant , proteomic analyses of crop plants have often been hindered by the presence of extreme concentrations of secondary metabolites such as pigments, phenolic compounds, lipids, carbohydrates or terpenes.

Evaluation of Disposable Trap Column nanoLC-FAIMS-MS/MS for the Proteomic Analysis of FFPE Tissue.

Proteomic biomarker discovery using formalin-fixed paraffin-embedded (FFPE) tissue requires robust workflows to support the analysis of large cohorts of patient samples. It also requires finding a reasonable balance between achieving a high proteomic depth and limiting the overall analysis time. To this end, we evaluated the merits of online coupling of single-use disposable trap column nanoflow liquid chromatography, high-field asymmetric-waveform ion-mobility spectrometry (FAIMS), and tandem mass spectrometry (nLC-FAIMS-MS/MS).

Impact of ASA-score, age and learning curve on early outcome in the initiation phase of an oncological robotic colorectal program.

The ASA score is known to be an independent predictor of complications and mortality following colorectal surgery. We evaluated early outcome in the initiation phase of a robotic oncological colorectal resection program in dependence of comorbidity and learning curve. 43 consecutive colorectal cancer patients (median age: 74 years) who underwent robotic surgery were firstly analysed defined by physical status (group A = ASA1 + 2; group B = ASA3).