Triple-negative breast cancer survival prediction using artificial intelligence through integrated analysis of tertiary lymphoid structures and tumor budding

被引:7
|
作者
Hou, Xupeng [1 ,2 ,3 ,4 ]
Li, Xueyang [1 ,2 ,3 ]
Han, Yunwei [2 ,3 ,5 ,6 ]
Xu, Hua [2 ,3 ,4 ]
Xie, Yongjie [7 ]
Zhou, Tianxing [7 ]
Xue, Tongyuan [8 ,9 ]
Qian, Xiaolong [2 ,3 ,4 ]
Li, Jiazhen [2 ,3 ,5 ]
Wang, Hayson Chenyu [10 ]
Yan, Jingrui [4 ]
Guo, Xiaojing [2 ,3 ,7 ]
Liu, Ying [10 ]
Liu, Jing [3 ,6 ]
机构
[1] Tianjin Med Univ Canc Inst & Hosp, Dept Breast Canc, Tianjin, Peoples R China
[2] Tianjins Clin Res Ctr Canc, Natl Clin Res Ctr Canc, Key Lab Canc Prevent & Therapy, Tianjin 300060, Peoples R China
[3] Tianjin Med Univ, Key Lab Breast Canc Prevent & Therapy, Minist Educ, Tianjin 300060, Peoples R China
[4] Tianjin Med Univ Canc Inst & Hosp, Natl Clin Res Ctr Canc, Dept Breast Pathol & Lab, Tianjin, Peoples R China
[5] Fudan Univ, Shanghai Canc Ctr, Dept Breast Surg, Shanghai, Peoples R China
[6] Fudan Univ, Canc Inst, Shanghai Canc Ctr, Shanghai, Peoples R China
[7] Tianjin Med Univ Canc Inst & Hosp, Dept Pancreat Canc, Tianjin, Peoples R China
[8] Tianjin Med Univ Canc Inst & Hosp, Natl Clin Res Ctr Canc, Dept Endoscopy Diag & Therapy, Tianjin, Peoples R China
[9] Tianjins Clin Res Ctr Canc, Key Lab Canc Prevent & Therapy, Tianjin 300060, Peoples R China
[10] Shanghai Jiao Tong Univ, Shanghai Peoples Hosp 9, Sch Med, Dept Plast & Reconstruct Surg, Shanghai 200011, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
artificial intelligence; nomogram; tertiary lymphoid structures; triple-negative breast cancer; tumor budding; T-CELL INFILTRATION; DENDRITIC CELLS; B-CELLS; PROGNOSIS; MICROENVIRONMENT; IMMUNOTHERAPY; LYMPHOCYTES; MARKER;
D O I
10.1002/cncr.35261
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BackgroundTriple-negative breast cancer (TNBC) is a highly heterogeneous and clinically aggressive disease. Accumulating evidence indicates that tertiary lymphoid structures (TLSs) and tumor budding (TB) are significantly correlated with the outcomes of patients who have TNBC, but no integrated TLS-TB profile has been established to predict their survival. The objective of this study was to investigate the relationship between the TLS/TB ratio and clinical outcomes of patients with TNBC using artificial intelligence (AI)-based analysis.MethodsThe infiltration levels of TLSs and TB were evaluated using hematoxylin and eosin staining, immunohistochemistry staining, and AI-based analysis. Various cellular subtypes within TLS were determined by multiplex immunofluorescence. Subsequently, the authors established a nomogram model, conducted calibration curve analyses, and performed decision curve analyses using R software.ResultsIn both the training and validation cohorts, the antitumor/protumor model established by the authors demonstrated a positive correlation between the TLS/TB index and the overall survival (OS) and relapse-free survival (RFS) of patients with TNBC. Notably, patients who had a high percentage of CD8-positive T cells, CD45RO-positive T cells, or CD20-positive B cells within the TLSs experienced improved OS and RFS. Furthermore, the authors developed a comprehensive TLS-TB profile nomogram based on the TLS/TB index. This novel model outperformed the classical tumor-lymph node-metastasis staging system in predicting the OS and RFS of patients with TNBC.ConclusionsA novel strategy for predicting the prognosis of patients with TNBC was established through integrated AI-based analysis and a machine-learning workflow. The TLS/TB index was identified as an independent prognostic factor for TNBC. This nomogram-based TLS-TB profile would help improve the accuracy of predicting the prognosis of patients who have TNBC. The tertiary lymphoid structure/tumor budding index is a valuable prognostic tool for patients who have triple-negative breast cancer. The newly developed nomogram based on the tertiary lymphoid structure/tumor budding index offers promise for personalized treatment and prognosis prediction.
引用
收藏
页码:1499 / 1512
页数:14
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