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

Abstract

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.

Breast cancer therapy in China: Introducing the Special Collection

“…It mainly focuses on the evaluation of the personalized breast cancer screening scheme, 1 antihuman epidermal growth factor receptor 2 (HER2) antibody-drug conjugate (ADC) drugs, 2,3 low HER2 expression, 3 real-world research on the cyclin-dependent kinase 4/6 (CDK4/6) inhibitor and its drug-resistance mechanism, 4 breast cancer immunotherapy (antiprogrammed cell death protein 1 [anti-PD-1] antibodies, 5 chimeric antigen receptor T-cell therapy, 6 etc. ), triplenegative breast cancer (TNBC), 5,[7][8][9] and new adjuvant treatments, 8,10 and it includes several articles describing basic and clinical research on TNBC.…”

“…Domestic scholars are enthusiastic about the clinical research and exploration of TNBC. Four studies on TNBC were conducted during this period, namely: Type I PRMT inhibition primes anti-PD-1 immunotherapy in TNBC, 7 ACTL6A as a potential biomarker for predicting prognosis and neoadjuvant therapy response in TNBC, 8 ; Artificial intelligence-based comprehensive analysis of tertiary lymphoid structure-tumor budding profile to predict the survival of patients with TNBC, 9 and CDK inhibitors enhancing the efficacy of PD-1 immune checkpoint blockade in TNBC by affecting the immune microenvironment. 5 The treatment strategy after the failure of first-line CDK4/6 inhibitors has become a hot topic in clinical practice.…”