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Schelfhorst, T.

Publications and source records attributed to Schelfhorst, T..

2 recordsLinked to original sources

Large pan-cancer cell screen coupled to (phospho-) proteomics underscores high-dose vitamin C as a potent anti-cancer agent

Increasing preclinical and clinical evidence has positioned high-dose vitamin C as a promising anti-cancer treatment that merits more clinical attention. Multiple cytotoxicity mechanisms have been described, including pro-oxidant effects. To contribute to the preclinical understanding of the broad pan-cancer effects of high-dose vitamin C in a global manner, we determined the IC50 of a large panel of cancer cell lines (n=51) representing 7 solid tumour types and generated proteome data. The majority of cell lines were highly sensitive (IC50 range 0.036-10mM, mean 1.7 {+/-} 0.4 mM), well below a clinically achievable dose. The proteome data (>5000 proteins per sample), showed that high sensitivity is associated with proliferation, as indicated by functional enrichment of cell cycle, RNA splicing and chromatin organization, while lower sensitivity is linked to extracellular vesicles, glycolysis, fatty acid metabolism and mitochondria. Moreover, (phospho-)proteome analysis of on-treatment vitamin C effects on four pancreatic ductal adenocarcinoma (PDAC) cells dosed at a range of IC50 values (Hs766 T, 2 mM; Capan-2, 0.6 mM; PANC-1, 0.14 mM and Suit-2, 0.1 mM) revealed, next to cell line specific effects, down-modulation of AKT-MTOR signalling and immune suppressive signalling, while IFN- response was enhanced upon vitamin C. Altogether, our comprehensive pharmacological and (phospho-)proteome analysis is the first to assess cancer vulnerabilities and effects of vitamin C on a large cancer cell line panel and underscores the potential of high-dose vitamin C as an anti-cancer agent.

cancer biology↗

DPHL: A pan-human protein mass spectrometry library for robust biomarker discovery using Data-Independent Acquisition and Parallel Reaction Monitoring

To answer the increasing need for detecting and validating protein biomarkers in clinical specimens, proteomic techniques are required that support the fast, reproducible and quantitative analysis of large clinical sample cohorts. Targeted mass spectrometry techniques, specifically SRM, PRM and the massively parallel SWATH/DIA technique have emerged as a powerful method for biomarker research. For optimal performance, they require prior knowledge about the fragment ion spectra of targeted peptides. In this report, we describe a mass spectrometric (MS) pipeline and spectral resource to support data-independent acquisition (DIA) and parallel reaction monitoring (PRM) based biomarker studies. To build the spectral resource we integrated common open-source MS computational tools to assemble an open source computational workflow based on Docker. It was then applied to generate a comprehensive DIA pan-human library (DPHL) from 1,096 data dependent acquisition (DDA) MS raw files, and it comprises 242,476 unique peptide sequences from 14,782 protein groups and 10,943 SwissProt-annotated proteins expressed in 16 types of cancer samples. In particular, tissue specimens from patients with prostate cancer, cervical cancer, colorectal cancer, hepatocellular carcinoma, gastric cancer, lung adenocarcinoma, squamous cell lung carcinoma, diseased thyroid, glioblastoma multiforme, sarcoma and diffuse large B-cell lymphoma (DLBCL), as well as plasma samples from a range of hematologic malignancies were collected from multiple clinics in China, the Netherlands and Singapore and included in the resource. This extensive spectral resource was then applied to a prostate cancer cohort of 17 patients, consisting of 8 patients with prostate cancer (PCa) and 9 with benign prostate hyperplasia (BPH), respectively. Data analysis of DIA data from these samples identified differential expressions of FASN, TPP1 and SPON2 in prostate tumors. Thereafter, PRM validation was applied to a larger PCa cohort of 57 patients and the differential expressions of FASN, TPP1 and SPON2 in prostate tumors were validated. As a second application, the DPHL spectral resource was applied to a patient cohort consisting of samples from 19 DLBCL patients and 18 healthy individuals. Differential expressions of CRP, CD44 and SAA1 between DLBCL cases and healthy controls were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supported that DIA-PRM MS pipeline enables robust protein biomarker discoveries.

bioinformatics↗