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Vergote, I.

Publications and source records attributed to Vergote, I..

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The tumor suppressor protein PTEN undergoes amyloid-like aggregation in tumor cells.

Protein aggregation is an underappreciated mechanism that may contribute to the loss- and oncogenic-gain-of-function of mutant tumor suppressors such as p53 and axin. In the present study, we describe amyloid-like aggregation behaviour of the second most frequently mutated tumor suppressor in human cancer, PTEN. In silico analysis revealed a particularly high aggregation vulnerability for this protein, which was corroborated by in vitro aggregation assays. In cultured tumor cells, we found that under stress conditions, PTEN readily undergoes amyloid-like aggregation as a result of mutation. However, we also show that severe dysregulation of protein homeostasis may lead to aggregation of wild-type PTEN. These observations were supported by a small survey of patient-derived uterine tumor tissues, which found that more than 25% of tumors analyzed displayed wild-type PTEN aggregation. Finally, in an exploratory clinical study we found that PTEN aggregation status was correlated with a decline in clinical outcome. Our findings establish that the tumor suppressor PTEN is highly aggregation-prone and our work suggests that protein aggregation might be an underestimated but prevalent component of cancer cell biology.

cancer biology

A Pan-Cancer Blueprint of the Heterogeneous Tumour Microenvironment Revealed by Single-Cell Profiling

AO_SCPLOWBSTRACTC_SCPLOWThe stromal compartment of the tumour microenvironment consists of a heterogeneous set of tissue-resident and tumour-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer (n=36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumour-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumours treated with checkpoint immunotherapy and identifying a naive CD4+ T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate a first panoramic view on the shared complexity of stromal cells in different cancers.

cancer biology