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November 25, 2024
Journal Article
Title
Immune landscape of the tumour microenvironment in Ethiopian breast cancer patients
Abstract
Background: The clinical management of breast cancer (BC) is mainly based on the assessment of receptor expression by tumour cells. However, there is still an unmet need for novel biomarkers important for prognosis and therapy. The tumour immune microenvironment (TIME) is thought to play a key role in prognosis and therapy selection, therefore this study aimed to describe the TIME in Ethiopian BC patients.
Methods: RNA was isolated from formalin-fixed paraffin-embedded (FFPE) tissue from 82 women with BC. Expression of PAM50 and 54 immune genes was analysed using the Nanostring platform and differentially expressed genes (DEGs) were determined using ROSALIND®. The abundance of different cell populations was estimated using Nanostring's cell type profiling module, while tumour infiltrating lymphocytes (TILs) were analysed using haematoxylin and eosin (H&E) staining. In addition, the PIK3CA gene was genotyped for three hotspot mutations using qPCR. Kaplan-Meier survival analysis and log-rank test were performed to compare the prognostic relevance of immune subgroups.
Results: Four discrete immune phenotypes (IP1-4) were identified through hierarchical clustering of immune gene expression data. These IPs were characterized by DEGs associated with both immune activation and inhibition as well as variations in the extent of immune infiltration. However, there were no significant differences regarding PIK3CA mutations between the IPs. A downregulation of immune suppressive and activating genes and the lowest number of infiltrating immune cells were found in IP2, which was associated with luminal tumours. In contrast, IP4 displayed an active TME chracterized by an upregulation of cytotoxic genes and the highest density of immune cell infiltrations, independent of the specific intrinsic subtype. IP1 and IP3 exhibited intermediate characteristics. The IPs had a prognostic relevance and patients with an active TME had improved overall survival compared to IPs with a significant downregulation of the majority of immune genes.
Conclusion: Immune gene expression profiling identified four distinct immune contextures of the TME with unique gene expression patterns and immune infiltration. The classification into distinct immune subgroups may provide important information regarding prognosis and the selection of patients undergoing conventional treatments or immunotherapies.
Methods: RNA was isolated from formalin-fixed paraffin-embedded (FFPE) tissue from 82 women with BC. Expression of PAM50 and 54 immune genes was analysed using the Nanostring platform and differentially expressed genes (DEGs) were determined using ROSALIND®. The abundance of different cell populations was estimated using Nanostring's cell type profiling module, while tumour infiltrating lymphocytes (TILs) were analysed using haematoxylin and eosin (H&E) staining. In addition, the PIK3CA gene was genotyped for three hotspot mutations using qPCR. Kaplan-Meier survival analysis and log-rank test were performed to compare the prognostic relevance of immune subgroups.
Results: Four discrete immune phenotypes (IP1-4) were identified through hierarchical clustering of immune gene expression data. These IPs were characterized by DEGs associated with both immune activation and inhibition as well as variations in the extent of immune infiltration. However, there were no significant differences regarding PIK3CA mutations between the IPs. A downregulation of immune suppressive and activating genes and the lowest number of infiltrating immune cells were found in IP2, which was associated with luminal tumours. In contrast, IP4 displayed an active TME chracterized by an upregulation of cytotoxic genes and the highest density of immune cell infiltrations, independent of the specific intrinsic subtype. IP1 and IP3 exhibited intermediate characteristics. The IPs had a prognostic relevance and patients with an active TME had improved overall survival compared to IPs with a significant downregulation of the majority of immune genes.
Conclusion: Immune gene expression profiling identified four distinct immune contextures of the TME with unique gene expression patterns and immune infiltration. The classification into distinct immune subgroups may provide important information regarding prognosis and the selection of patients undergoing conventional treatments or immunotherapies.
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