Böttger, F. | bioRxiv Science

bioRxiv2023

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↗

bioRxiv2018

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

In order to tackle heterogeneity of cancer samples and high data space dimensionality, we propose a method NEAmarker for finding sensitive and robust biomarkers at the pathway level. In this method, scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways, which is then correlated with phenotype variables. The analysis was first done on in vitro anti-cancer drug screen datasets and then on clinical data. In parallel, we tested a panel of state-of-the-art enrichment methods. In this comparison, our method proved superior in terms of 1) universal applicability to different data types with a possibility of cross-platform integration, 2) consistency of the discovered correlates between independent drug screens, and 3) ability to explain differential survival of treated patients. Our new in vitro screen validated performance of the discovered multivariate models. Finally, NEAmarker was the only method to discover predictors of both in vitro response and patient survival given administration of the same drug.

cancer biology↗