Co-expression modules identified from published immune signatures reveal five distinct immune subtypes in breast cancer.
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.



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.



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.



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.
Department of Surgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA. Dominic.Amara@ucsf.edu.
Dominic
D
Amara
Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
Denise M
DM
Wolf
Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
Laura
L
van 't Veer
Department of Surgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
Laura
L
Esserman
Department of Laboratory Medicine, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
Michael
M
Campbell
Department of Surgery, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
Christina
C
Yau
D015415:Biomarkers; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000276:immunology* / Q000401:mortality; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D007107:Immune System / Q000166:cytology / Q000276:immunology / Q000378:metabolism; D053208:Kaplan-Meier Estimate; D011379:Prognosis; D059467:Transcriptome; D064726:Triple Negative Breast Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000276:immunology / Q000401:mortality; D059016:Tumor Microenvironment / Q000235:genetics / Q000276:immunology
D015415:Biomarkers
Breast cancer; Co-expression modules; Immune expression signatures; Immune subtypes