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A Portal Vein Injection Model to Study Liver Metastasis of Breast Cancer
Published on: December 26, 2016
Subtype-specific genetic drivers of immune evasion in breast cancer
O Menyhart1,2,3, B Győrffy1,2,3,4
1Cancer Biomarker Research Group, Institute of Molecular Life Sciences, Hungarian Research Network, Budapest, Hungary.
Background:
Immune evasion is a hallmark of cancer and a driver of therapeutic resistance. Although immunotherapy is effective in highly immunogenic cancers, its efficacy in breast cancer (BC) remains limited. We aimed to determine the prognostic relevance of immune-related gene signatures across distinct BC subtypes.
Materials And Methods:
We used transcriptomic and clinical data from three independent cohorts [Gene Expression Omnibus (GEO), The Cancer Genome Atlas, and GSE96058]. We analyzed 106 genes associated with the evasion of immune destruction (EID) and 182 genes involved in the evasion of killing by cytotoxic T lymphocytes (ECTL). Expression of signatures was stratified by BC subtypes. Cox regression and Kaplan-Meier curves were used to assess survival, with false discovery rate (FDR) correction ensuring statistical robustness.
Results:
High expression of the ECTL signature was significantly associated with improved overall survival (OS) in basal BC patients [hazard ratio (HR) 0.25, 95% confidence interval (CI) 0.16-0.4, P = 8.6e-10, FDR <1%], and similar trends appeared in human epidermal growth factor receptor 2 (HER2)-positive BC. For EID genes, high expression also correlated with favorable OS in basal BC (HR 0.3, 95% CI 0.18-0.49, P = 3.4e-7). Gene-level analyses revealed CXCL10, CXCL9, CXCR4, and JAK3 as robust predictors of OS in basal BC, validated across independent datasets. This four-gene signature demonstrated strong predictive power for response to immune checkpoint inhibitors (ICIs) (area under the curve = 0.722, P = 1.3E-07). In HER2-positive BC, a three-gene signature (IL2RG, CD3E, CD3G) was consistently prognostic (HR 0.41, 95% CI 0.24-0.71, P = 0.0011, GEO) across independent datasets.
Conclusions:
We identified subtype-specific signatures that predict survival and immunotherapy response, providing clinically actionable biomarkers.
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