Bibliographic Details
| Title: |
Definition of a novel breast tumor-specific classifier based on secretome analysis. |
| Authors: |
Sirven, Philémon, Faucheux, Lilith, Grandclaudon, Maximilien, Michea, Paula, Vincent-Salomon, Anne, Mechta-Grigoriou, Fatima, Scholer-Dahirel, Alix, Guillot-Delost, Maude, Soumelis, Vassili |
| Source: |
Breast Cancer Research; 12/20/2022, Vol. 24 Issue 1, p1-15, 15p |
| Subject Terms: |
BREAST tumors, TUMOR microenvironment, CHEMOKINES, SCIENTIFIC knowledge, TRANSFORMING growth factors |
| Abstract: |
Background: During cancer development, the normal tissue microenvironment is shaped by tumorigenic events. Inflammatory mediators and immune cells play a key role during this process. However, which molecular features most specifically characterize the malignant tissue remains poorly explored. Methods: Within our institutional tumor microenvironment global analysis (T-MEGA) program, we set a prospective cohort of 422 untreated breast cancer patients. We established a dedicated pipeline to generate supernatants from tumor and juxta-tumor tissue explants and quantify 55 soluble molecules using Luminex or MSD. Those analytes belonged to five molecular families: chemokines, cytokines, growth factors, metalloproteinases, and adipokines. Results: When looking at tissue specificity, our dataset revealed some breast tumor-specific characteristics, as IL-16, as well as some juxta-tumor-specific secreted molecules, as IL-33. Unsupervised clustering analysis identified groups of molecules that were specific to the breast tumor tissue and displayed a similar secretion behavior. We identified a tumor-specific cluster composed of nine molecules that were secreted fourteen times more in the tumor supernatants than the corresponding juxta-tumor supernatants. This cluster contained, among others, CCL17, CCL22, and CXCL9 and TGF-β1, 2, and 3. The systematic comparison of tumor and juxta-tumor secretome data allowed us to mathematically formalize a novel breast cancer signature composed of 14 molecules that segregated tumors from juxta-tumors, with a sensitivity of 96.8% and a specificity of 96%. Conclusions: Our study provides the first breast tumor-specific classifier computed on breast tissue-derived secretome data. Moreover, our T-MEGA cohort dataset is a freely accessible resource to the biomedical community to help advancing scientific knowledge on breast cancer. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
