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Your search keyword '"tumour‐infiltrating immune cells"' showing total 2 results
Start Over Descriptor "tumour‐infiltrating immune cells" Topic triple-negative breast cancer (tnbc)
2 results on '"tumour‐infiltrating immune cells"'

1. 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.

Author

Bao, Xuanwen, Shi, Run, Zhao, Tianyu, Wang, Yanfang, Anastasov, Natasa, Rosemann, Michael, and Fang, Weijia

Subjects
*TRIPLE-negative breast cancer, *HETEROGENEITY, *RNA sequencing, *GENES, *MACROPHAGES
Abstract

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. [ABSTRACT FROM AUTHOR]

Published
2021
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2. 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

Author

Yanfang Wang, Michael Rosemann, Natasa Anastasov, Weijia Fang, Tianyu Zhao, Run Shi, and Xuanwen Bao

Subjects
Cancer Research, Tumour heterogeneity, Angiogenesis, medicine.medical_treatment, Immunology, Cell, RNA-Seq, Triple Negative Breast Neoplasms, Biology, Transcriptome, Genetic Heterogeneity, Lymphocytes, Tumor-Infiltrating, Cell Line, Tumor, Tumor-Associated Macrophages, medicine, Biomarkers, Tumor, Tumor Microenvironment, Immunology and Allergy, Humans, Gene, Neovascularization, Pathologic, Gene Expression Profiling, Immunotherapy, Gene signature, Prognosis, medicine.anatomical_structure, Oncology, Triple-negative Breast Cancer (tnbc), Tumour Heterogeneity, Tumour-infiltrating Immune Cells, M2-like Tumour-associated Macrophages (m2-like Tams), Cancer research, Female
Abstract

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.

Published
2020

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