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.

Keywords: Breast cancer; Ethiopia; Immune phenotypes; PAM50; Tumour immune microenvironment.

Fig. 1

Heatmap of the expression levels of 54 immune-related genes in 82 BC samples. Unsupervised hierarchical clustering of log-transformed expression levels of immune genes identified the four immunophenotype groups IP1 to IP4. Red marks indicate upregulation, blue marks indicate downregulation of immue relatetd genes. Sample annotations are included at the top of the heatmap. HER2: human epidermal growth factor 2. HR: hormone receptor

Fig. 2

DEG heat map of the distinct immune phenotypes A: IP1 versus IP2, IP3 and IP4; B: IP2 versus IP1, IP3 and IP4; C: IP3 versus IP1, IP2 and IP4; D: IP4 versus IP1, IP2 and IP3. Differentially expressed immune genes between the immune phenotypes were shown. Genes with log2 fold change (FC) ≥ 1.5 and false discovery rate (FDR)-corrected p value < 0.05 were presented. Green: downregulated genes, red: upregulated genes

Fig. 3

Distinct distribution of the immune cell subpopulations in the distinct immune phenotypes A: Log2 abundance score of immune cell subpopulations in IP1-4. Nanostring cell type profiling module was used to assess the immune composition of IPs. B: Frequency of TILs. TILs were counted from H&E stained slides. Data are presented as % of TIL in the different IPs

Fig. 4

Association of the immune phenotypes with the patients’ survival. A: Kaplan-Meier survival curves between IP4 versus IP2 and IP3. Patients were followed for 60 months and survival probabilities were determined by calculating p values from log rank test. B-G: Kaplan-Meier survival curves derived from TCGA data set of 177 African American BC cases. B: Survival probabilities across the four IPS. C: HR + BC survival within the four IPs. D: HR- BC survival within the four IPs. E: survival between the two IP groups F: HR- BC survival within the two IP groups. G: HR + BC survival within the two IP groups. IP1 & IP2 were categorized as “Immune Activated” while IP3 and IP4 were categorized as “Immune Suppressed”

Fig. 5

Immune phenotypes and clinic-pathologic features of BC. A: Proportion of HR+, HER2 + and TNBC tumours. B: Distribution of the intrinsic subtypes within IPs; C: Frequency of high grade tumours; D: Ki-67 proliferation index; E. Proportion of PIK3CA-mutated tumours: Only 9 tumours were grouped in IP1 and the mutation status could not be determined for the majority of samples (proportion not shown in the figure); F: A box and Whisker plot showing log2 expression value of CD8 in the wt and PIK3CA-mutated tumours

Fig. 6

Differentially expressed immune genes in BC subtypes. A: Box and whisker plot showing a differential expression of CD20 and GZMB between early and advanced pathologic stages. B: Volcano plot of DEGs between luminal and non-luminal tumours. Volcano plot is plotted using Graph Pad Prism to show differentially expressed immune genes; FC > 1.5, adjusted P values < 0.05. Each dot represents an immune gene. C: DEG heat map of luminal versus basal like tumours, green shows downregulation and red shows upregulation. D: Box and whisker plot showing a differential expression of CD1A and CTBS between HR + and TNBC. DEG: Differentially expressed genes