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Cancer-Associated Fibroblasts Together with a Decline in CD8+ T Cells Predict a Worse Prognosis for Breast Cancer Patients

Authors
Kim, Hyung SukNoh, Yung-KyunMin, Kyueng-WhanKim, Dong-HoonKwon, Mi JungPyo, Jung SooLee, Jeong-Yeon
Issue Date
Mar-2024
Publisher
Lippincott Williams & Wilkins Ltd.
Keywords
Breast cancer; Cancer-associated fibroblasts; Drug; Machine learning; Prognosis; Tumor-infiltrating lymphocytes
Citation
Annals of Surgical Oncology, v.31, no.3, pp 2114 - 2126
Pages
13
Indexed
SCIE
SCOPUS
Journal Title
Annals of Surgical Oncology
Volume
31
Number
3
Start Page
2114
End Page
2126
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/197131
DOI
10.1245/s10434-023-14715-6
ISSN
1068-9265
1534-4681
Abstract
Background: Cancer-associated fibroblasts (CAFs) play a crucial role in tumor microenvironment regulation and cancer progression. This study assessed the significance and predictive potential of CAFs in breast cancer prognosis. Methods: The study included 1503 breast cancer patients. Cancer-associated fibroblasts were identified using morphologic features from hematoxylin and eosin slides. The study analyzed clinicopathologic parameters, survival rates, immune cells, gene sets, and prognostic models using gene-set enrichment analysis, in silico cytometry, pathway analysis, in vitro drug-screening, and gradient-boosting machine (GBM)-learning. Results: The presence of CAFs correlated significantly with young age, lymphatic invasion, and perineural invasion. In silico cytometry showed altered leukocyte subsets in the presence of CAFs, with decreased CD8+ T cells. Gene-set enrichment analysis showed associations with critical processes such as the epithelial-mesenchymal transition and immune modulation. Drug sensitivity analysis in breast cancer cell lines with varying fibroblast activation protein-α expression suggested that CAF-targeted therapies might enhance the efficacy of certain anticancer drugs including ARRY-520, ispinesib-mesylate, paclitaxel, and docetaxel. Integrating CAF presence with machine-learning improved survival prediction. For breast cancer patients, CAFs were independent prognostic markers for worse disease-specific survival and disease-free survival. Conclusion: This study highlighted the significance of CAFs in breast cancer biology and provided compelling evidence of their impact on patient outcomes and treatment response. The findings offer valuable insights into the potential of CAFs as prognostic and predictive biomarkers and support the development of CAF-targeted therapies to improve breast cancer management.
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