Cancer-associated fibroblast classification in single-cell and spatial proteomics data

Lena Cords, Sandra Tietscher, Tobias Anzeneder, Claus Langwieder, Martin Rees, Natalie Souza and Bernd Bodenmiller ()
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Lena Cords: University of Zurich
Sandra Tietscher: University of Zurich
Tobias Anzeneder: Patients’ Tumor Bank of Hope (PATH)
Claus Langwieder: Pathology at Josefshaus
Martin Rees: Pathology at Josefshaus
Natalie Souza: University of Zurich
Bernd Bodenmiller: University of Zurich

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract Cancer-associated fibroblasts (CAFs) are a diverse cell population within the tumour microenvironment, where they have critical effects on tumour evolution and patient prognosis. To define CAF phenotypes, we analyse a single-cell RNA sequencing (scRNA-seq) dataset of over 16,000 stromal cells from tumours of 14 breast cancer patients, based on which we define and functionally annotate nine CAF phenotypes and one class of pericytes. We validate this classification system in four additional cancer types and use highly multiplexed imaging mass cytometry on matched breast cancer samples to confirm our defined CAF phenotypes at the protein level and to analyse their spatial distribution within tumours. This general CAF classification scheme will allow comparison of CAF phenotypes across studies, facilitate analysis of their functional roles, and potentially guide development of new treatment strategies in the future.

Date: 2023
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DOI: 10.1038/s41467-023-39762-1

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