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Abstract
Background: The therapeutic paradigm for breast cancer advanced significantly with programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1) inhibitors. However, adaptive immune resistance, driven by compensatory upregulation of alternative checkpoints—Lymphocyte-activation gene 3 (LAG-3), T-cell immunoreceptor with Ig and ITIM domains (TIGIT), and T-cell immunoglobulin and mucin-domain containing-3 (TIM-3)—limits durable responses. This study aimed to quantitatively synthesize the prognostic significance, tumor microenvironment interactions, and translational surgical implications of these biomarkers in breast cancer.
Methods: A systematic literature search (PubMed, Embase, Cochrane) identified original research evaluating LAG-3, TIGIT, and TIM-3 expression in breast cancer cohorts. Data extraction focused on overall survival (OS) hazard ratios (HR), standardized mean differences (SMD) for tumor-infiltrating lymphocyte (TIL) density, and pathological complete response (pCR) rates. Random-effects meta-analyses generated forest plots, assessed heterogeneity (I-squared), and evaluated publication bias via funnel plots.
Results: Eight major cohorts comprising over 7,200 patients were included. High LAG-3 expression on TILs in triple-negative breast cancer (TNBC) was significantly associated with improved OS (Pooled HR 0.88, 95% CI 0.81-0.95, p=0.002) and higher pCR rates following neoadjuvant chemotherapy. Conversely, elevated TIGIT expression in primary tumors correlated with poorer OS (Pooled HR 1.58, 95% CI 1.18-2.11, p=0.004) and increased locoregional recurrence risk. TIM-3 demonstrated dual prognostic value: favorable in basal-like subtypes but detrimental in luminal subtypes. Funnel plots indicated minimal publication bias.
Conclusion: LAG-3, TIGIT, and TIM-3 function as distinct, non-redundant biomarkers. LAG-3 signifies a primed, actionable immune response, whereas TIGIT and TIM-3 indicate severe immune exhaustion. Mapping these profiles provides critical translational value for optimizing personalized surgical timing, predicting neoadjuvant downstaging, and selecting adjuvant immunotherapies.
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