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Introduction to Fibroblasts01:09

Introduction to Fibroblasts

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Rudolph Virchow discovered spindle-shaped cells called fibroblasts in 1858. Inactive fibroblasts, called fibrocytes, become activated by various stimuli, such as growth factors and inflammatory cytokines. Activated fibroblasts play a crucial role in wound healing, inflammation, formation of new blood vessels, and cancer progression. Uncontrolled activation of fibroblasts results in fibrosis, the excess deposition of fibrous tissue, which can lead to scarring and affect normal organs. This...
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  9. Predictive And Prognostic Markers
  11. Determination Of Permissive And Restraining Cancer-associated Fibroblast (decaf) Subtypes.

Determination of permissive and restraining cancer-associated fibroblast (DeCAF) subtypes.

Xianlu Laura Peng, Elena V Kharitonova, Yi Xu

    Biorxiv : the Preprint Server for Biology
    |May 27, 2024

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    View abstract on PubMed

    Summary
    This summary is machine-generated.

    A new classifier, DeCAF, distinguishes cancer-associated fibroblast (CAF) subtypes in patients. This tool predicts patient outcomes and treatment responses across multiple cancer types, aiding targeted therapy development.

    Area of Science:

    • Oncology
    • Immunology
    • Bioinformatics

    Background:

    • Cancer-associated fibroblasts (CAFs) are crucial in tumor microenvironments, but their subtypes and clinical relevance are not fully understood.
    • Single-cell RNA sequencing (scRNAseq) has identified diverse CAF subpopulations in pancreatic ductal adenocarcinoma (PDAC), yet translating these findings to clinical application is challenging.

    Purpose of the Study:

    • To develop a robust and clinically applicable classifier for distinguishing between cancer-restraining and cancer-permissive CAF subtypes.
    • To investigate the clinical relevance, prognostic value, and predictive capacity of these CAF subtypes across various cancer types.

    Main Methods:

    • Translating scRNAseq-derived CAF marker genes into bulk RNA sequencing data.
    • Developing and validating a single-sample classifier, DeCAF, using 19 independent transcriptomic datasets from PDAC, mesothelioma, bladder, and renal cell carcinoma.

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  • Analyzing associations between DeCAF-defined CAF subtypes and histological features, immune landscapes, patient prognosis, and treatment response.

    • Main Results:

    • DeCAF successfully classifies CAF subtypes across multiple cancer types, demonstrating clinical replicability and robustness.
    • DeCAF-defined CAF subtypes exhibit distinct histological features and immune microenvironments.
    • CAF subtypes classified by DeCAF are prognostic and predict response to therapy, highlighting their clinical significance.

    Conclusions:

    • DeCAF provides a validated framework for classifying CAF subtypes in patients across diverse cancer types.
    • This classification enables a better understanding of the distinct roles of CAF subtypes in cancer progression and treatment response.
    • DeCAF facilitates the development of targeted therapies aimed at modulating CAF functions, either by targeting permissive CAFs or preserving restraining CAFs.