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

Area of Science:

• Oncology
• Cell Biology
• Bioinformatics

Background:

• Cancer-associated fibroblasts (CAFs) are crucial components of the tumor microenvironment.
• CAFs significantly influence tumor progression and patient outcomes.
• Understanding CAF heterogeneity is essential for targeted cancer therapies.

Purpose of the Study:

• To define and functionally annotate distinct cancer-associated fibroblast (CAF) phenotypes.
• To establish a standardized classification system for CAFs across different cancer types.
• To investigate the spatial distribution of CAF phenotypes within tumors.

Main Methods:

• Analysis of a single-cell RNA sequencing (scRNA-seq) dataset comprising over 16,000 stromal cells from 14 breast cancer patients.
• Functional annotation of identified CAF phenotypes.
• Validation of the CAF classification system in four additional cancer types.
• High-plex imaging mass cytometry for protein-level confirmation and spatial analysis of CAFs in breast cancer.

Main Results:

• Identification and functional annotation of nine distinct CAF phenotypes and one pericyte class.
• Validation of the CAF classification across multiple cancer types.
• Confirmation of CAF phenotypes at the protein level and elucidation of their spatial organization within tumors.

Conclusions:

• A comprehensive CAF classification scheme has been established, enabling cross-study comparisons.
• This classification facilitates the analysis of CAF functional roles in cancer.
• The findings may guide the development of novel therapeutic strategies targeting CAFs.