Computer-assisted quantification of tumor-associated collagen signatures to improve the prognosis prediction of breast cancer

• Published in: BMC medicine Vol. 19; no. 1; pp. 1 - 273
• Main Authors: Xi, Gangqin, Qiu, Lida, Xu, Shuoyu, Guo, Wenhui, Fu, Fangmeng, Kang, Deyong, Zheng, Liqin, He, Jiajia, Zhang, Qingyuan, Li, Lianhuang, Wang, Chuan, Chen, Jianxin
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 18.11.2021; BioMed Central; BMC
• Subjects: Biomarkers; Breast cancer; Cancer therapies; Chemotherapy; Collagen; Computer-aided medical diagnosis; Feature extraction; Fibers; Health aspects; Health hazards; Hospitals; Invasiveness; Medical prognosis; Metastasis; Methods; Microscopy, Medical; Multiphoton imaging; Pathology; Patients; Prognosis; Regression analysis; Risk; Risk groups; Robustness (mathematics); Statistical analysis; Subgroups; Survival analysis; TACS corresponding microscopic features; Tumors; China
• ISSN: 1741-7015; 1741-7015
• DOI: 10.1186/s12916-021-02146-7

Background Collagen fibers play an important role in tumor initiation, progression, and invasion. Our previous research has already shown that large-scale tumor-associated collagen signatures (TACS) are powerful prognostic biomarkers independent of clinicopathological factors in invasive breast cancer. However, they are observed on a macroscale and are more suitable for identifying high-risk patients. It is necessary to investigate the effect of the corresponding microscopic features of TACS so as to more accurately and comprehensively predict the prognosis of breast cancer patients. Methods In this retrospective and multicenter study, we included 942 invasive breast cancer patients in both a training cohort (n = 355) and an internal validation cohort (n = 334) from one clinical center and in an external validation cohort (n = 253) from a different clinical center. TACS corresponding microscopic features (TCMFs) were firstly extracted from multiphoton images for each patient, and then least absolute shrinkage and selection operator (LASSO) regression was applied to select the most robust features to build a TCMF-score. Finally, the Cox proportional hazard regression analysis was used to evaluate the association of TCMF-score with disease-free survival (DFS). Results TCMF-score is significantly associated with DFS in univariate Cox proportional hazard regression analysis. After adjusting for clinical variables by multivariate Cox regression analysis, the TCMF-score remains an independent prognostic indicator. Remarkably, the TCMF model performs better than the clinical (CLI) model in the three cohorts and is particularly outstanding in the ER-positive and lower-risk subgroups. By contrast, the TACS model is more suitable for the ER-negative and higher-risk subgroups. When the TACS and TCMF are combined, they could complement each other and perform well in all patients. As expected, the full model (CLI+TCMF+TACS) achieves the best performance (AUC 0.905, [0.873-0.938]; 0.896, [0.860-0.931]; 0.882, [0.840-0.925] in the three cohorts). Conclusion These results demonstrate that the TCMF-score is an independent prognostic factor for breast cancer, and the increased prognostic performance (TCMF+TACS-score) may help us develop more appropriate treatment protocols. Keywords: Breast cancer, Multiphoton imaging, TACS corresponding microscopic features, Prognosis