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Bao, X.* ; Shi, R.* ; Zhao, T. ; Wang, Y.* ; Anastasov, N. ; Rosemann, M. ; Fang, W.*

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC.

Cancer Immunol. Immunother. 70, 189-202 (2021)
Verlagsversion DOI
Open Access Green: Postprint online verfügbar 09/2021
Triple-negative breast cancer (TNBC) is characterized by a more aggressive clinical course with extensive inter- and intra-tumour heterogeneity. Combination of single-cell and bulk tissue transcriptome profiling allows the characterization of tumour heterogeneity and identifies the association of the immune landscape with clinical outcomes. We identified inter- and intra-tumour heterogeneity at a single-cell resolution. Tumour cells shared a high correlation amongst stemness, angiogenesis, and EMT in TNBC. A subset of cells with concurrent high EMT, stemness and angiogenesis was identified at the single-cell level. Amongst tumour-infiltrating immune cells, M2-like tumour-associated macrophages (TAMs) made up the majority of macrophages and displayed immunosuppressive characteristics. CIBERSORT was applied to estimate the abundance of M2-like TAM in bulk tissue transcriptome file from The Cancer Genome Atlas (TCGA). M2-like TAMs were associated with unfavourable prognosis in TNBC patients. A TAM-related gene signature serves as a promising marker for predicting prognosis and response to immunotherapy. Two commonly used machine learning methods, random forest and SVM, were applied to find the genes that were mostly associated with M2-like TAM densities in the gene signature. A neural network-based deep learning framework based on the TAM-related gene signature exhibits high accuracy in predicting the immunotherapy response.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Triple-negative Breast Cancer (tnbc) ; Tumour Heterogeneity ; Tumour-infiltrating Immune Cells ; M2-like Tumour-associated Macrophages (m2-like Tams) ; Prognosis; Cancer; Survival; Microenvironment; Mechanisms
ISSN (print) / ISBN 0340-7004
e-ISSN 1432-0851
Quellenangaben Band: 70, Heft: 1, Seiten: 189-202 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort One New York Plaza, Suite 4600, New York, Ny, United States
Begutachtungsstatus Peer reviewed