| Literature DB >> 29280568 |
Alice Santi1, Fernanda G Kugeratski1, Sara Zanivan1,2.
Abstract
Fibroblasts have exceptional phenotypic plasticity and capability to secrete vast amount of soluble factors, extracellular matrix components and extracellular vesicles. While in physiological conditions this makes fibroblasts master regulators of tissue homeostasis and healing of injured tissues, in solid tumors cancer associated fibroblasts (CAFs) co-evolve with the disease, and alter the biochemical and physical structure of the tumor microenvironment, as well as the behavior of the surrounding stromal and cancer cells. Thus CAFs are fundamental regulators of tumor progression and influence response to therapeutic treatments. Increasing efforts are devoted to better understand the biology of CAFs to bring insights to develop complementary strategies to target this cell type in cancer. Here we highlight components of the tumor microenvironment that play key roles in cancer progression and invasion, and provide an extensive overview of past and emerging understanding of CAF biology as well as the contribution that MS-based proteomics has made to this field.Entities:
Keywords: blood vessel; cancer; cancer associated fibroblasts; endothelial cell; extracellular matrix; immune system; invasion; proteome; secretome; tumor microenvironment
Mesh:
Year: 2018 PMID: 29280568 PMCID: PMC5900985 DOI: 10.1002/pmic.201700167
Source DB: PubMed Journal: Proteomics ISSN: 1615-9853 Impact factor: 3.984
Figure 1Tumor progression. Schematic representation of tumor development with highlighted stromal components that contribute to progression and invasion.
Figure 2CAF functions. Schematic representation of key CAF functions and CAF‐derived factors involved.
Figure 3Consensus proteome. Proteins that were found consistently up‐ (red) and downregulated (blue) in the MS‐proteomic comparisons listed in Table 1 between matched CAFs and normal fibroblasts. Protein–protein interactions were define with STRING (version 10.5; all interaction sources were enabled and minimum interaction score of 0.4 was required) and visualized with Cytoscape.
Figure 4Consensus secretome. Secreted proteins that were consistently identified in the MS‐proteomic analysis of CAF‐derived conditioned media listed in Table 2. Protein–protein interactions were define with STRING (version 10.5; the active interaction sources were “experiments”, “databases”, and “co‐expression”, and minimum interaction score of 0.7 was required) and visualized with Cytoscape.
Datasets used for the CAF proteome consensus signature in Figure 3
| Tissue of origin (specie) | Quantification method | Number of proteins identified | Reference |
|---|---|---|---|
| Sporadic colon cancer (mouse) | Fourplex iTRAQ | 1353 |
|
| Gastric cancer (human) | Fourplex iTRAQ | 768 |
|
| Breast cancer (human) | SILAC | 4113 |
|
| Breast cancer (human) | Label‐free quantification | 4094 |
|
Datasets used for the CAF secretome consensus signature in Figure 4
| CAF tissue of origin (specie) | Secretome enrichment strategy | Number of proteins identified | Reference |
|---|---|---|---|
| Colon cancer (human) | Ultrafiltration with Amicon Centriprep tubes YM‐3 | 367 |
|
| Gastric cancer (human) | Ultrafiltration with Amicon Ultra 3 kDa | 1460 |
|
| Colon cancer (human) | Ultrafiltration with Amicon Ultra 3 kDa | 227 |
|
| Oral squamous cell carcinoma (human) | Ultrafiltration with Amicon Ultra 3 kDa | 271 |
|
| Mammary tumor (human) | Affinity based (Strataclean resin) | 1527 |
|
Figure 5MS‐proteomics and CAFs. Scheme showing how MS‐proteomics can contribute to understand CAF biology. TME, tumor microenvironment.