Dataset Information

Tumour-Infiltrating Immune Cell-Based Subtyping and Signature Gene Analysis in Breast Cancer Based on Gene Expression Profiles.

ABSTRACT: Tumour-infiltrating immune cells have been indicated to play an important role in prognosis prediction and therapy sensitivity for breast cancer. In recent years, estimating the abundance of immune cells based on tumour transcriptome data has provided a novel way to analyse the clinical significance of various immune cell subsets. This study integrated breast cancer tissue transcriptome datasets from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. A novel breast cancer immunotyping and a new prognostic model based on tumour-infiltrating immune cell subsets have been established, aiming to provide new clues regarding prognostic prediction and precision therapy for breast cancer. The key differentially expressed gene between different breast cancer immunotypes has also been identified. We performed unsupervised clustering analysis and construct a novel immunotyping which could classify breast cancer cases into immunotype A (B_cellhigh NKhigh CD8+_Thigh CD4+_memory_T_activatedhigh ??Tlow Mast_cell_activatedlow Neutrophillow) and immunotype B (B_celllow NKlow CD8+_Tlow CD4+_memory_T_activatedlow ??Thigh Mast_cell_activatedhigh Neutrophilhigh) in luminal B, HER2-enriched and basal-like subtypes. The 5-year (85.7% vs. 73.4%) and 10-year OS (75.60% vs. 61.73%) of immunotype A population were significantly higher than those of immunotype B. A novel tumour-infiltrating immune cell-based prognostic model had also been established and the result immunorisk score (IRS) could serve as a new prognostic factor for luminal B, HER2-enriched and basal-like breast cancer. The higher IRS was, the worse prognosis was. We further screened the differentially expressed genes between immunotype A and B and identified a novel breast cancer immune-related gene, prostaglandin D2 synthase (PTGDS) and higher PTGDS mRNA expression level was positively correlated with earlier TNM stage. Immune-related signaling pathways analysis and immune cell subsets correlation analysis revealed that PTGDS expression was related with abundance of B cells, CD4+ T cells and CD8+ T cells, which was finally validated by immunohistochemical and immunofluorescence staining. We established a novel immunotyping and a tumour-infiltrating immune cell-based prognostic prediction model in luminal B, HER2-enriched and basal-like breast cancer by analyzing the prognostic significance of multiple immune cell subsets. A novel breast cancer immune signature gene PTDGS was discovered, which might serve as a protective prognostic factor and play an important role in breast cancer development and lymphocyte-related immune response.

SUBMITTER: Jiang J

PROVIDER: S-EPMC6995381 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Tumour-Infiltrating Immune Cell-Based Subtyping and Signature Gene Analysis in Breast Cancer Based on Gene Expression Profiles.

Jiang Jingxin J Pan Weiwei W Xu Yazhang Y Ni Chao C Xue Dan D Chen Zhigang Z Chen Wuzhen W Huang Jian J

Journal of Cancer 20200114 6

Tumour-infiltrating immune cells have been indicated to play an important role in prognosis prediction and therapy sensitivity for breast cancer. In recent years, estimating the abundance of immune cells based on tumour transcriptome data has provided a novel way to analyse the clinical significance of various immune cell subsets. This study integrated breast cancer tissue transcriptome datasets from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and the Mol ...[more]

PMID: 32047563

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Project description:IntroductionTriple-negative breast cancers need to be refined in order to identify therapeutic subgroups of patients.MethodsWe conducted an unsupervised analysis of microarray gene-expression profiles of 107 triple-negative breast cancer patients and undertook robust functional annotation of the molecular entities found by means of numerous approaches including immunohistochemistry and gene-expression signatures. A triple-negative external cohort (n=87) was used for validation.ResultsFuzzy clustering separated triple-negative tumours into three clusters: C1 (22.4%), C2 (44.9%) and C3 (32.7%). C1 patients were older (mean=64.6 years) than C2 (mean=56.8 years; P=0.03) and C3 patients (mean=51.9 years; P=0.0004). Histological grade and Nottingham prognostic index were higher in C2 and C3 than in C1 (P<0.0001 for both comparisons). Significant event-free survival (P=0.03) was found according to cluster membership: patients belonging to C3 had a better outcome than patients in C1 (P=0.01) and C2 (P=0.02). Event-free survival analysis results were confirmed when our cohort was pooled with the external cohort (n=194; P=0.01). Functional annotation showed that 22% of triple-negative patients were not basal-like (C1). C1 was enriched in luminal subtypes and positive androgen receptor (luminal androgen receptor). C2 could be considered as an almost pure basal-like cluster. C3, enriched in basal-like subtypes but to a lesser extent, included 26% of claudin-low subtypes. Dissection of immune response showed that high immune response and low M2-like macrophages were a hallmark of C3, and that these patients had a better event-free survival than C2 patients, characterized by low immune response and high M2-like macrophages: P=0.02 for our cohort, and P=0.03 for pooled cohorts.ConclusionsWe identified three subtypes of triple-negative patients: luminal androgen receptor (22%), basal-like with low immune response and high M2-like macrophages (45%), and basal-enriched with high immune response and low M2-like macrophages (33%). We noted out that macrophages and other immune effectors offer a variety of therapeutic targets in breast cancer, and particularly in triple-negative basal-like tumours. Furthermore, we showed that CK5 antibody was better suited than CK5/6 antibody to subtype triple-negative patients.

Project description:BackgroundThe tumor immune microenvironment has become the focus of research in clear cell renal cell carcinoma (ccRCC) due to its important role in immune surveillance post nephrectomy. This study investigates the correlation of tumor infiltrating immune cell characteristics with rates of recurrence following surgery in localized ccRCC.MethodsWe morphologically identified and scored tumor infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) stained slides of patients with localized ccRCC (stage ≥T1b excluding stage IV). The University of Alabama at Birmingham (UAB) dataset (n = 159) was used to discover and the Fox Chase Cancer Center (FCCC) dataset (n = 198) was used to validate the results of morphologic immune cell analysis. We then performed gene expression analysis using the Immune Profile panel by NanoString in the UAB cohort and identified immune cells and pathways associated with recurrence, followed by validation in the Cancer Genome Atlas (TCGA) ccRCC dataset. Infiltrating immune cell types were identified by gene expression deconvolution.ResultsThe presence of TILs identified by morphology correlated with higher T cell, Th1, CD8+ T and Treg gene signatures. Recurrence was associated with lower T cells and higher neutrophils. Higher Teffector (Teff)/Treg ratio correlated with lower rate of recurrence and was validated in the TCGA dataset. Genes associated with adaptive immune response were downregulated in tumors that recurred. Unsupervised hierarchical clustering identified a subset of patients with over-expression of adaptive response genes including CD8, CD3, GZMA/B, PRF1, IDO1, CTLA4, PDL1, ICOS and TIGIT. These patients had higher morphologic lymphocyte infiltration and T cell gene expression. Higher levels of TILs identified by morphology correlated with higher rates of recurrence in our discovery dataset but not in our validation set.ConclusionsRecurrence of ccRCC following surgery was associated with lower T cell infiltrate, lower adaptive immune response and higher neutrophil gene expression. Presence of higher Teff/Treg ratio correlated with lower recurrence.

Project description:INTRODUCTION: Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance. METHOD: To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures. RESULTS: Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER-/HER2- (basal-like), the HER2+ (HER2-like), and the low- and high-proliferation ER+/HER2- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER- status (basal-like) and ERBB2+ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes. CONCLUSION: This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.

Project description:BackgroundSystemic factors can strongly affect how tumour cells behave, grow, and communicate with other cells in breast cancer. Lipid metabolic reprogramming is a systemic process that tumour cells undergo; however, the formation and dynamics of lipids associated with the tumour immune microenvironment (TIME) remain unclear. The investigation of the sophisticated bidirectional crosstalk of tumour cells with cancer metabolism, gene expression, and TIME could have the potential to identify novel biomarkers for diagnosis, prognosis, and immunotherapy. This study aimed to construct a prognostic signature to detect the bicrosstalk between the lipid metabolic system and the TIME of breast cancer.MethodsTo detect the expression of LRGs and execute GO/KEGG analysis, the R program was chosen. Considering the clinical information and pathological features, a prognostic gene signature was constructed by LASSO Cox regression analysis. TMB, MSI, and immune infiltration analyses were performed, and consensus cluster analysis of LRGs was also performed.ResultsThese 16 lipid metabolism-related genes (LRGs) were mainly involved in the process of lipid metabolism and fatty acid binding in breast cancer. Prognosis analysis identified the prognostic value of FABP7(Fatty acid binding protein 7) and NDUFAB1(NADH:ubiquinone oxidoreductase subunit AB1) in breast cancer patients. The prognostic gene signature constructed with FABP7 and NDUFAB1 was significantly related to immune cell infiltration and could predict the overall survival rate with above average correctness of breast cancer patients. FABP7 and NDUFAB1 were proven to have relevance in immune cell infiltration and tumour mutation burden (TMB). Consensus cluster analysis identified that the upregulated mRNAs were mostly related to the oncogenesis process, while the downregulated mRNAs were associated with immune-related signalling pathways.ConclusionA comprehensive analysis was performed to evaluate the lipid metabolic system and identified a signature constructed by two prognostic genes for immunotherapies in breast cancer. The results also revealed evidence of vulnerabilities in the interplay between the lipid metabolic system and the TIME in breast cancer. Further data with clinical studies and experiments are warranted.

Dataset Information

Tumour-Infiltrating Immune Cell-Based Subtyping and Signature Gene Analysis in Breast Cancer Based on Gene Expression Profiles.

Publications

Tumour-Infiltrating Immune Cell-Based Subtyping and Signature Gene Analysis in Breast Cancer Based on Gene Expression Profiles.

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