Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients

Gangqin Xi, Wenhui Guo, Deyong Kang, Jianli Ma, Fangmeng Fu, Lida Qiu, Liqin Zheng, Jiajia He, Na Fang, Jianhua Chen, Jingtong Li, Shuangmu Zhuo, Xiaoxia Liao, Haohua Tu, Lianhuang Li, Qingyuan Zhang, Chuan Wang, Stephen A. Boppart, Jianxin Chen

Research output: Contribution to journalArticlepeer-review

Abstract

The notion of personalized medicine demands proper prognostic biomarkers to guide the optimal therapy for an invasive breast cancer patient. However, various risk prediction models based on conventional clinicopathological factors and emergent molecular assays have been frequently limited by either a low strength of prognosis or restricted applicability to specific types of patients. Therefore, there is a critical need to develop a strong and general prognosticator. Methods: We observed five large-scale tumor-associated collagen signatures (TACS4-8) obtained by multiphoton microscopy at the invasion front of the breast primary tumor, which contrasted with the three tumor-associated collagen signatures (TACS1-3) discovered by Keely and coworkers at a smaller scale. Highly concordant TACS1-8 classifications were obtained by three independent observers. Using the ridge regression analysis, we obtained a TACS-score for each patient based on the combined TACS1-8 and established a risk prediction model based on the TACS-score. In a blind fashion, consistent retrospective prognosis was obtained from 995 breast cancer patients in both a training cohort (n= 431) and an internal validation cohort (n = 300) collected from one clinical center, and in an external validation cohort (n = 264) collected from a different clinical center. Results: TACS1-8 model alone competed favorably with all reported models in predicting disease-free survival (AUC: 0.838, [0.800-0.872]; 0.827, [0.779-0.868]; 0.807, [0.754-0.853] in the three cohorts) and stratifying low- and high-risk patients (HR 7.032, [4.869-10.158]; 6.846, [4.370-10.726], 4.423, [2.917-6.708]). The combination of these factors with the TACS-score into a nomogram model further improved the prognosis (AUC: 0.865, [0.829-0.896]; 0.861, [0.816-0.898]; 0.854, [0.805-0.894]; HR 7.882, [5.487-11.323]; 9.176, [5.683-14.816], and 5.548, [3.705-8.307]). The nomogram identified 72 of 357 (~20%) patients with unsuccessful 5-year disease-free survival that might have been undertreated postoperatively. Conclusions: The risk prediction model based on TACS1-8 considerably outperforms the contextual clinical model and may thus convince pathologists to pursue a TACS-based breast cancer prognosis. Our methodology identifies a significant portion of patients susceptible to undertreatment (high-risk patients), in contrast to the multigene assays that often strive to mitigate overtreatment. The compatibility of our methodology with standard histology using traditional (non-tissue-microarray) formalin-fixed paraffin-embedded (FFPE) tissue sections could simplify subsequent clinical translation.

Original languageEnglish (US)
Pages (from-to)3229-3243
Number of pages15
JournalTheranostics
Volume11
Issue number7
DOIs
StatePublished - Jan 1 2021

Keywords

  • Breast cancer
  • Disease-free survival
  • Multiphoton imaging
  • Tumor-associated collagen signatures

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Pharmacology, Toxicology and Pharmaceutics (miscellaneous)

Fingerprint Dive into the research topics of 'Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients'. Together they form a unique fingerprint.

Cite this