Dataset Information

Transcriptomic immunologic signature associated with favorable clinical outcome in basal-like breast tumors.

ABSTRACT:

Background

Most patients with early stage triple negative breast cancer (TNBC) receive adjuvant chemotherapy. Activation of the immune system is associated with tumor response and may help identify TNBC with favorable outcome.

Methods

Gene expression data were obtained from the GEO Dataset GDS2250/GSE3744. Affymetrix CEL files were downloaded and analyzed with Affymetrix Transcriptome Analysis Console 3.0. Functional genomics was implemented with David Bioinformatics Resources 6.8. Data contained at Oncomine were used to identify genes upregulated in basal-like cancer compared to normal breast tissue. Data contained at cBioportal were used to assess for molecular alterations. The KMPlotter online tool, METABRIC and GSE25066 datasets were used to associate gene signatures with clinical outcome.

Results

1564 upregulated genes were identified as differentially expressed between normal and basal-like tumors. Of these, 16 genes associated with immune function were linked with clinical outcome. HLA-C, HLA-F, HLA-G and TIGIT were associated with both improved relapse-free survival (RFS) and overall survival (OS). The combination of HLA-F/TIGIT and HLA-C/HLA-F/TIGIT showed the most favorable outcome (HR for RFS 0.44, p<0.001; HR for OS 0.22, p<0.001; and HR for RFS 0.46, p<0.001; HR for OS 0.15, p<0.001; respectively). The association of HLA-C/HLA-F with outcome was confirmed using the METABRIC and GSE25066 datasets. No copy number alterations of these genes were identified.

Conclusion

We describe a gene signature associated with immune function and favorable outcome in basal-like breast cancer. Incorporation of this signature in prospective studies may help to stratify risk of early stage TNBC.

SUBMITTER: Martinez-Canales S

PROVIDER: S-EPMC5417488 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

Transcriptomic immunologic signature associated with favorable clinical outcome in basal-like breast tumors.

Martínez-Canales Sandra S Cifuentes Francisco F López De Rodas Gregorio Miguel M Serrano-Oviedo Leticia L Galán-Moya Eva María EM Amir Eitan E Pandiella Atanasio A Győrffy Balázs B Ocaña Alberto A

PloS one 20170504 5

<h4>Background</h4>Most patients with early stage triple negative breast cancer (TNBC) receive adjuvant chemotherapy. Activation of the immune system is associated with tumor response and may help identify TNBC with favorable outcome.<h4>Methods</h4>Gene expression data were obtained from the GEO Dataset GDS2250/GSE3744. Affymetrix CEL files were downloaded and analyzed with Affymetrix Transcriptome Analysis Console 3.0. Functional genomics was implemented with David Bioinformatics Resources 6.8 ...[more]

PMID: 28472085

Similar Datasets

Project description:BackgroundBreast cancer heterogeneity is an essential element that plays a role in the therapy response variability and the patient's outcome. This highlights the need for more precise subtyping methods that focus not only on tumor cells but also investigate the profile of stromal cells as well as immune cells.ObjectivesTo mine publicly available transcriptomic breast cancer datasets and reanalyze their transcriptomic profiling using unsupervised clustering in order to identify novel subsets in molecular subtypes of breast cancer, then explore the stromal and immune cells profile in each subset using bioinformatics and systems immunology approaches.Materials and methodsTranscriptomic data from 1,084 breast cancer patients obtained from The Cancer Genome Atlas (TCGA) database were extracted and subjected to unsupervised clustering using a recently described, multi-step algorithm called Iterative Clustering and Guide-gene Selection (ICGS). For each cluster, the stromal and immune profile was investigated using ESTIMATE and CIBERSORT analytical tool. Clinical outcomes and differentially expressed genes of the characterized clusters were identified and validated in silico and in vitro in a cohort of 80 breast cancer samples by immunohistochemistry.ResultsSeven unique sub-clusters showed distinct molecular and clinical profiles between the well-known breast cancer subtypes. Those unsupervised clusters identified more homogenous subgroups in each of the classical subtypes with a different prognostic profile. Immune profiling of the identified clusters showed that while the classically activated macrophages (M1) are correlated with the more aggressive basal-like breast cancer subtype, the alternatively activated macrophages (M2) showed a higher level of infiltration in luminal A and luminal B subtypes. Indeed, patients with higher levels of M1 expression showed less advanced disease and better patient outcomes presented as prolonged overall survival. Moreover, the M1 high basal-like breast cancer group showed a higher expression of interferon-gamma induced chemokines and guanylate-binding proteins (GBPs) involved in immunity against microbes.ConclusionAdding immune profiling using transcriptomic data can add precision for diagnosis and prognosis and can cluster patients according to the available modalities of therapy in a more personalized approach.

Dataset Information

Transcriptomic immunologic signature associated with favorable clinical outcome in basal-like breast tumors.

Background

Methods

Results

Conclusion

Publications

Transcriptomic immunologic signature associated with favorable clinical outcome in basal-like breast tumors.

Similar Datasets

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