Abstract
Purpose
There is a growing body of literature demonstrating that immune-related expression signatures predict breast cancer prognosis and chemo-/targeted-therapy responsiveness. However, it is unclear whether these signatures correlate with each other or represent distinct immune-related signals.
Methods
We evaluated 57 published immune-related expression signatures in four public breast cancer datasets totaling 3295 samples. For each dataset, we used consensus clustering to group signatures together based on their co-expression pattern. Signatures that were in the same consensus cluster across all four datasets were used to define immune modules. Tumors were then classified into immune subtypes based on their module scores using consensus clustering. Survival analysis was conducted using Cox proportional hazards modeling.
Results
Consensus clustering consistently yields four distinct co-expression modules across the datasets. These modules appear to represent distinct immune components and signals, where constituent signatures relate to (1) T-cells and/or B-cells (T/B-cell), (2) interferon (IFN), (3) transforming growth factor beta (TGFB), and (4) core–serum response, dendritic cells, and/or macrophages (CSR). Subtyping of tumors based on these co-expression modules consistently yields subsets that fall into five major immune subtypes: T/B-cell/IFN High, IFN/CSR High, CSR High, TGFB High, and Immune Low. Basal and/or triple-negative breast cancer patients with CSR High tumors have significantly worse outcome relative to those within the T/B-cell/IFN High subtype.
Conclusion
Our exploratory study identified four distinct immune co-expression modules (T/B-cell, IFN, TGFB, or CSR) from published immune signatures. Using these modules, we identified five immune subtypes with significant outcome differences in basal breast cancers.
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Acknowledgements
This study was funded in part by NCI grant U01 CA196406 and the Breast Cancer Research Foundation (BCRF).
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10549_2016_4041_MOESM3_ESM.pdf
Supplementary Figure 1. Association between individual signatures and recurrence-free survival within each intrinsic subtypes of the METABRIC cohort. Subtypes are arranged along the columns; and signatures are arranged along the row. Heatmap color indicates whether a given signature is significantly associated with RFS after adjustment for multiple testing using the BH FDR correction (red: associated with increased hazard; blue: associated with decreased hazard, gray: BH FDR corrected p > 0.05) (PDF 118 kb)
10549_2016_4041_MOESM4_ESM.pdf
Supplementary Figure 2. Heatmaps showing patient immune subtypes for the remaining three datasets considered in our study: 2a-TCGA; 2b-pooled triple negative; 2c-EMC344 (PDF 530 kb)
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Supplementary Figure 3. Bar plots showing the percentage of each immune subtype in each intrinsic or receptor subtype for each dataset: 3a-METABRIC; 3b-TCGA; 3c-EMC344 (PDF 69 kb)
10549_2016_4041_MOESM6_ESM.pdf
Supplementary Figure 4. Bar plots showing the percentage of each intrinsic or receptor subtype in each immune subtype for each dataset: 4a-METABRIC; 4b-TCGA; 4c-EMC344 (PDF 116 kb)
10549_2016_4041_MOESM7_ESM.pdf
Supplementary Figure 5. Gene overlap between signatures. Signatures are arranged in order by their co-expression module assignment along both x- and y- axis; and the heatmap color intensity reflects the fraction of genes in the y-axis signature that is also in the x-axis signature. The row annotation reflects module assignment (TGFB: hotpink, a lymphocyte: T/B-cell: red, IFN: black and CSR: cyan) (PDF 295 kb)
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Amara, D., Wolf, D.M., van ’t Veer, L. et al. Co-expression modules identified from published immune signatures reveal five distinct immune subtypes in breast cancer. Breast Cancer Res Treat 161, 41–50 (2017). https://doi.org/10.1007/s10549-016-4041-3
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DOI: https://doi.org/10.1007/s10549-016-4041-3