Comprehensive Analysis of the Immune Effect of TGFβ1 in Colorectal Adenocarcinoma:A TGFβ1-related Prognostic Model of Tumor Microenvironment

2021 ◽  
Author(s):  
Yucheng Qian ◽  
Lina Zhang ◽  
Jihang Wen ◽  
Yanxia Mei ◽  
Jingsun Wei ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Survival Data ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Immune Checkpoints ◽  
Cox Regression Analysis

Abstract ColorColorectal cancer is one of the most common cancer worldwide. Recently, tumor microenvironment (TME), especially its remoulding , is thought to control the colorectal cancer genesis and progression. In this study, we use ESTIMATEscore to make out the proportion of immune and stromal components in colorectal adenocarcinoma (CRA) samples from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were found by COX regression analysis and protein-protein interaction (PPI) network, among which TGFβ1 was supposed to be a prognosis factor and tumor environment indicator. Continuous analysis showed that TGFβ1 expression is positively correlated with lymph node metastasis (N stage) but negatively correlated with survival. Gene Set Enrichment Analysis (GSEA) revealed that the genes of the high-expression TGFβ1 group were mainly enriched in immune-related activities. Cluster analysis divided the samples into 2 subgroups. 24 HLA-related genes and 8 immune checkpoint genes were found upregulated in the high immunity group as well as TGFβ1, which suggests the possibility of novel therapies targeting immune checkpoints combined with TGFβ1. Tumor-infiltrating immune cell (TIC) profile of CRA patients was described by CIBERSORT analysis. Further analysis showed that the infiltration of Tregs and Neutrophils were positively correlated with TGFβ1 high expression. Then 3 TGFβ1-related genes were picked out to construct a prognostic model, which matches the survival data well.

scholarly journals MYD88 Is a Potential Prognostic Gene and Immune Signature of Tumor Microenvironment for Gliomas

2021 ◽  
Vol 11 ◽  
Author(s):  
Qinglong Guo ◽  
Xing Xiao ◽  
Jinsen Zhang
Keyword(s):  
Tumor Microenvironment ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Who Grade ◽  
Cox Regression Analysis ◽  
Clinical Cases

PurposeTo explore the profiles of immune and stromal components of the tumor microenvironment (TME) and their related key genes in gliomas.MethodsWe applied bioinformatic techniques to identify the core gene that participated in the regulation of the TME of the gliomas. And immunohistochemistry staining was used to calculate the gene expressions in clinical cases.ResultsThe CIBERSORT and ESTIMATE were used to figure out the composition of TME in 698 glioma cases from The Cancer Genome Atlas (TCGA) database. Differential expression analysis identified 2103 genes between the high and the low-score group. Then the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, univariate Cox regression analysis, and protein–protein interaction (PPI) network construction were conducted based on these genes. MYD88 was identified as the key gene by the combination univariate Cox and PPI analysis. Furthermore, MYD88 expression was significantly associated with the overall survival and WHO grade of glioma patients. The genes in the high-expression MYD88 group were mainly in immune-related pathways in the Gene Set Enrichment Analysis (GSEA). We found that macrophage M2 accounted for the largest portion with an average of 27.6% in the glioma TIICs and was associated with high expression of MYD88. The results were verified in CGGA database and clinical cases in our hospital. Furthermore, we also found the MYD88 expression was higher in IDH1 wild types. The methylation rate was lower in high grade gliomas.ConclusionMYD88 had predictive prognostic value in glioma patients by influencing TIICs dysregulation especially the M2-type macrophages.

scholarly journals Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhuomao Mo ◽  
Daiyuan Liu ◽  
Dade Rong ◽  
Shijun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Immune Cell Infiltration ◽  
Immunosuppressive Microenvironment

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

scholarly journals PVT1 is a prognostic marker associated with immune invasion of bladder urothelial carcinoma

2021 ◽  
Vol 19 (1) ◽  
pp. 169-190
Author(s):  
Peiyuan Li ◽  
◽  
Gangjie Qiao ◽  
Jian Lu ◽  
Wenbin Ji ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set ◽  
Bladder Urothelial Carcinoma

<abstract> <p>Plasmacytoma variant translocation 1 (PVT1) is involved in multiple signaling pathways and plays an important regulatory role in a variety of malignant tumors. However, its role in the prognosis and immune invasion of bladder urothelial carcinoma (BLCA) remains unclear. This study investigated the expression of PVT1 in tumor tissue and its relationship with immune invasion, and determined its prognostic role in patients with BLCA. Patients were identified from the cancer genome atlas (TCGA). The enrichment pathway and function of PVT1 were explained by gene ontology (GO) term analysis, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA), and the degree of immune cell infiltration was quantified. Kaplan–Meier analysis and Cox regression were used to analyze the correlation between PVT1 and survival rate. PVT1-high BLCA patients had a lower 10-year disease-specific survival (DSS P &lt; 0.05) and overall survival (OS P &lt; 0.05). Multivariate Cox regression analysis showed that PVT1 (high vs. low) (P = 0.004) was an independent prognostic factor. A nomogram was used to predict the effect of PVT1 on the prognosis. PVT1 plays an important role in the progression and prognosis of BLCA and can be used as a medium biomarker to predict survival after cystectomy.</p> </abstract>

scholarly journals BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Jin Zhou ◽  
Zheming Liu ◽  
Huibo Zhang ◽  
Tianyu Lei ◽  
Jiahui Liu ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Early Stage ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Purpose. Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone. Results. BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t -AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves. Conclusion. Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

scholarly journals FABP4 Might be an Indicator of the Status of Tumor Microenvironment in Colorectal Cancer

2021 ◽  
Author(s):  
Lijun Ning ◽  
Yuqing Yan ◽  
Tianying Tong ◽  
Ziyun Gao ◽  
Zhe Cui ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
T Cells ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Nk Cell ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Fatty Acid Binding ◽  
Cox Regression Analysis ◽  
The Status

Abstract Background: As tumor microenvironment (TME) play an indispensable role in tumorigenesis of colorectal cancer, this study performs a bunch of bioinformatics analysis to identify the indicator of the status of TME in Colorectal cancer (CRC). Results: In the presented study, we applied CIBERSORT and ESTIMATE computational methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 444 COAD-READ cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein–protein interaction (PPI) network construction. Then, fatty acid-binding protein four ( FABP4 ) was determined as a predictive factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that FABP4 expression was positively correlated with the clinical pathologic characteristics (clinical stage, distant metastasis) and negatively correlated with the survival of CRC patients. Gene Set Enrichment Analysis (GSEA) showed that the genes in the high-expression FABP4 group were mainly enriched in immune-related activities. In the low-expression FABP4 group, the genes were enriched in metabolic pathways. CIBERSORT analysis for the proportion of TICs revealed that NK cell, CD4 + T cells and CD8 + T cells were negatively correlated with FABP4 expression, suggesting that FABP4 might be a potential prognostic factor of CRC patients. Conclusion: Our study has developed a new biomarker (FABP4) that can predict the status of tumor microenvironment in Colorectal cancer. Keywords: FABP4, tumor microenvironment, ESTIMATE, CIBERSORT, colorectal cancer

scholarly journals Complement C1q (C1qA, C1qB, and C1qC) May Be a Potential Prognostic Factor and an Index of Tumor Microenvironment Remodeling in Osteosarcoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Long-hao Chen ◽  
Jin-Fu Liu ◽  
Yan- Lu ◽  
Xin-yu He ◽  
Chi- Zhang ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Feature ◽  
Immune Functions ◽  
Complement Components ◽  
Cox Regression Analysis ◽  
Matrix Components

The tumor microenvironment (TME) has important effects on the tumorigenesis and development of osteosarcoma (OS). However, the dynamic mechanism regulating TME immune and matrix components remains unclear. In this study, we collected quantitative data on the gene expression of 88 OS samples from The Cancer Genome Atlas (TCGA) database and downloaded relevant clinical cases of OS from the TARGET database. The proportions of tumor-infiltrating immune cells (TICs) and the numbers of immune and matrix components were determined by CIBERSORT and ESTIMATE calculation methods. Protein-protein interaction (PPI) network construction and Cox regression analysis were conducted to analyze differentially expressed genes (DEGs). The complement components C1qA, C1qB and C1qC were then determined to be predictive factors through univariate Cox analysis and PPI cross analysis. Further analysis found that the levels of C1qA, C1qB and C1qC expression were positively linked to OS patient survival time and negatively correlated with the clinicopathological feature percent necrosis at definitive surgery. The results of gene set enrichment analysis (GSEA) demonstrated that genes related to immune functions were significantly enriched in the high C1qA, C1qB and C1qC expression groups. Proportion analysis of TICs by CIBERSORT showed that the levels of C1qA, C1qB and C1qC expression were positively related to M1 and M2 macrophages and CD8+ cells and negatively correlated with M0 macrophages. These results further support the influence of the levels of C1qA, C1qB and C1qC expression on the immune activity of the TME. Therefore, C1qA, C1qB and C1qC may be potential indicators of remodeling in the OS TME, which is helpful to predict the prognosis of patients with OS and provide new ideas for immunotherapy for OS.

scholarly journals Significance of m 6A Methyltransferase‑Related lncRNAs in the Prognosis and Immunotherapy of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Lili Li ◽  
Rongrong Xie ◽  
Qichun Wei
Keyword(s):  
Hepatocellular Carcinoma ◽  
Correlation Analysis ◽  
Immune Cells ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Regression Analyses ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) methyltransferase, has been proved to act as an oncogene in several human cancers. However, little is known about its relationship with the long non-coding RNAs (lncRNAs) that remains elusive in HCC.Methods: We comprehensively integrated gene expression data acquired from 371 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression & Kaplan-Meier (K-M) analysis was performed to identify m6A methyltransferase‑related lncRNAs that were related to overall survival (OS). m6A methyltransferase‑related lncRNA signature was constructed using the Least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The signature was validated in an internal validation set. Finally, the correlation analysis between gene signature and immune cells infiltration was also investigated via single-sample Gene Set Enrichment Analysis (ssGSEA) and immunotherapy response was calculated through Tumor Immune Dysfunction and Exclusion (TIDE) algorithm.Results: A total of 21 m6A methyltransferase-related lncRNAs were screened out according to Spearman correlation analysis with the immune score (|R| > 0.3, P < 0.05). We selected 3 prognostic lncRNAs to construct m6A methyltransferase-related lncRNA signature through univariate and LASSO Cox regression analyses. The univariate and multivariate Cox regression analyses demonstrated that the lncRNAs signature was a robust independent prognostic factor in OS prediction with high accuracy. The GSEA also suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes and pathways which were very well-known in the context of HCC tumorigenesis. Besides, we found that the lncRNAs signature was strikingly correlated with the tumor microenvironment (TME) immune cells infiltration and expression of critical immune checkpoints. Finally, results from the TIDE analysis revealed that the m6A methyltransferase-related lncRNAs could efficiently predict the clinical response of immunotherapy in HCC.Conclusion: Together, our study screened potential prognostic m6A methyltransferase related lncRNAs and established a novel m6A methyltransferase-based prognostic model of HCC, which not only provides new potential prognostic biomarkers and therapeutic targets but also deepens our understanding of tumor immune microenvironment status and lays a theoretical foundation for immunotherapy.

scholarly journals CLEC10A is a Potential Prognostic Biomarker and Immune Regulator of Tumor Microenvironment in Breast Cancer

2021 ◽  
Author(s):  
Jincheng He ◽  
Lei Jiang ◽  
Jun Wang ◽  
Guangtao Min ◽  
Xiangwen Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
B Cells ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Cox Regression ◽  
Clinical Stage ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis

Abstract The communication between tumor cells and immune cells influences the ecology of the tumor microenvironment in breast cancer, as well as the disease progression and clinical outcome. The aim of this study was to investigate the prognostic value of the immunomodulatory factor CLEC10A in breast cancer. We applied the CIBERSORT and ESTIMATE calculation methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 1053 BRCA cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein-protein interaction (PPI) network construction. Then, CLEC10A was identified as a prognostic factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that CLEC10A expression was negatively correlated with the clinical pathologic characteristics (age, clinical stage) and positively correlated with survival of BRCA patients. Gene set enrichment analysis (GSEA) showed that genes in the high CLEC10A expression group were mainly enriched in immune-related activities. Genes in the low CLEC10A expression group were enriched in biochemical functions. CIBERSORT analysis of the proportion of TICs revealed that Macrophages M1, B cells memory, B cells naive, T cells CD4+ memory activated, T cells CD8+, and T cells gamma delta were positively correlated with CLEC10A expression, and Macrophages M0, Macrophages M2, Neutrophils, and NK cells resting were positively correlated with CLEC10A expression was negatively correlated, suggesting that CLEC10A may be an important factor in the immune regulation of the tumor microenvironment, especially in mediating the anti-tumor immune response of tumor-infiltrating immune cells at the tumor initiation stage. Therefore, CLEC10A expression may contribute to the prognosis of BRCA patients and provide a new idea for the immunotherapy of BRCA.

scholarly journals Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Chao Ma ◽  
Xin Zhang ◽  
Xudong Zhao ◽  
Nan Zhang ◽  
Sixin Zhou ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Kinase Inhibitor ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Immune Checkpoints ◽  
Cox Regression Analysis

BackgroundAccumulating evidence has demonstrated that immune-related long non-coding ribonucleic acids (irlncRNAs) can be used as prognostic indicators of overall survival (OS) in patients with colorectal cancer (CRC). Our aim in this research, therefore, was to construct a risk model using irlncRNA pairs with no requirement for a specific expression level, in hope of reliably predicting the prognosis and immune landscape of CRC patients.MethodsClinical and transcriptome profiling data of CRC patients downloaded from the Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed (DE) irlncRNAs. The irlncRNA pairs significantly correlated with the prognosis of patients were screened out by univariable Cox regression analysis and a prognostic model was constructed by Lasso and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was then plotted, with the area under the curve calculated to confirm the reliability of the model. Based on the optimal cutoff value, CRC patients in the high- or low-risk groups were distinguished, laying the ground for evaluating the risk model from the following perspectives: survival, clinicopathological traits, tumor-infiltrating immune cells (TIICs), antitumor drug efficacy, kinase inhibitor efficacy, and molecules related to immune checkpoints.ResultsA prognostic model consisting of 15 irlncRNA pairs was constructed, which was found to have a high correlation with patient prognosis in a cohort from the TCGA (p &lt; 0.001, HR = 1.089, 95% CI [1.067–1.112]). According to both univariate and multivariate Cox analyses, this model could be used as an independent prognostic indicator in the TCGA cohort (p &lt; 0.001). Effective differentiation between high- and low-risk patients was also accomplished, on the basis of aggressive clinicopathological characteristics, sensitivity to antitumor drugs, and kinase inhibitors, the tumor immune infiltration status, and the expression levels of specific molecules related to immune checkpoints.ConclusionThe prognostic model established with irlncRNA pairs is a promising indicator for prognosis prediction in CRC patients.

scholarly journals An Immune-Related Gene Prognostic Index for Triple-Negative Breast Cancer Integrates Multiple Aspects of Tumor-Immune Microenvironment

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5342
Author(s):  
Xiaowei Wang ◽  
Wenjia Su ◽  
Dabei Tang ◽  
Jing Jing ◽  
Jing Xiong ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Molecular Taxonomy ◽  
Prognostic Index ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Data Set ◽  
Cox Regression Analysis

Tumor-immune cell compositions and immune checkpoints comprehensively affect TNBC outcomes. With the significantly improved survival rate of TNBC patients treated with ICI therapies, a biomarker integrating multiple aspects of TIME may have prognostic value for improving the efficacy of ICI therapy. Immune-related hub genes were identified with weighted gene co-expression network analysis and differential gene expression assay using The Cancer Genome Atlas TNBC data set (n = 115). IRGPI was constructed with Cox regression analysis. Immune cell compositions and TIL status were analyzed with CIBERSORT and TIDE. The discovery was validated with the Molecular Taxonomy of Breast Cancer International Consortium data set (n = 196) and a patient cohort from our hospital. Tumor expression or serum concentrations of CCL5, CCL25, or PD-L1 were determined with immunohistochemistry or ELISA. The constructed IRGPI was composed of CCL5 and CCL25 genes and was negatively associated with the patient’s survival. IRGPI also predicts the compositions of M0 and M2 macrophages, memory B cells, CD8+ T cells, activated memory CD4 T cells, and the exclusion and dysfunction of TILs, as well as PD-1 and PD-L1 expression of TNBC. IRGPI is a promising biomarker for predicting the prognosis and multiple immune characteristics of TNBC.

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