Dataset Information

Immune Milieu and Genomic Alterations Set the Triple-Negative Breast Cancer Immunomodulatory Subtype Tumor Behavior.

ABSTRACT: Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease. Seven subtypes have been described based on gene expression patterns. Herein, we characterized the tumor biology and clinical behavior of the immunomodulatory (IM) subtype. Formalin-fixed paraffin-embedded tumor samples from 68 high-risk (stage III-IV) TNBC patients were analyzed through microarrays, immunohistochemistry, and DNA sequencing. The IM subtype was identified in 24% of TNBC tumor samples and characterized by a higher intratumoral (intT) and stromal (strml) infiltration of FOXP3+ TILs (Treg) compared with non-IM subtypes. Further, PD-L1+ (>1%) expression was significantly higher, as well as CTLA-4+ intT and strml expression in the IM subtype. Differential expression and gene set enrichment analysis identified biological processes associated with the immune system. Pathway analysis revealed enrichment of the β-catenin signaling pathway. The non-coding analysis led to seven Long Intergenic Non-Protein Coding RNAs (lincRNAs) (6 up-regulated and 1 down-regulated) that were associated with a favorable prognosis in the TNBC-IM subtype. The DNA sequencing highlighted two genes relevant to immune system responses: CTNNB1 (Catenin β-1) and IDH1. the IM subtype showed a distinct immune microenvironment, as well as subtype-specific genomic alterations. Characterizing TNBC at a molecular and transcriptomic level might guide immune-based therapy in this subgroup of patients.

SUBMITTER: Rodriguez-Bautista R

PROVIDER: S-EPMC8699570 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Immune Milieu and Genomic Alterations Set the Triple-Negative Breast Cancer Immunomodulatory Subtype Tumor Behavior.

Rodríguez-Bautista Rubén R Caro-Sánchez Claudia H CH Cabrera-Galeana Paula P Alanis-Funes Gerardo J GJ Gutierrez-Millán Everardo E Ávila-Ríos Santiago S Matías-Florentino Margarita M Reyes-Terán Gustavo G Díaz-Chávez José J Villarreal-Garza Cynthia C Hernández-Pedro Norma Y NY Ortega-Gómez Alette A Lara-Mejía Luis L Rangel-Escareño Claudia C Arrieta Oscar O

Cancers 20211213 24

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous disease. Seven subtypes have been described based on gene expression patterns. Herein, we characterized the tumor biology and clinical behavior of the immunomodulatory (IM) subtype.<h4>Methods</h4>Formalin-fixed paraffin-embedded tumor samples from 68 high-risk (stage III-IV) TNBC patients were analyzed through microarrays, immunohistochemistry, and DNA sequencing.<h4>Results</h4>The IM subtype was identified in 24% of TNBC ...[more]

PMID: 34944876

Similar Datasets

Project description:Objective: Triple-negative breast cancer (TNBC) is a high heterogeneity cancer. The identification of genomic aberrations that drive each of the TNBC subtypes may predict the prognosis of patients with TNBC and provide novel therapeutic strategies in clinical practice. This study focuses on the transcriptome-based gene expression of TNBC and aims to generate comprehensive gene co-expression networks correlated with the immune-related subtype of TNBC. Methods: The transcriptome profiles of 107 female patients with TNBC were analyzed. Weighted gene co-expression network analysis (WGCNA) was applied to construct related networks and to sort hub-genes associated with the survival of TNBC patients. The data of the transcriptional expression, genomic alteration, survival status, and tumor immune microenvironment, which associated with hub-genes, were extracted, retrieved, and analyzed from Oncomine, UALCAN, TCGA, starBase, Kaplan-Meier Plotter, cBioPortal, and TIMER databases. Results: Immune-related hub-genes, including BIRC3, BTN3A1, CSF2RB, GIMAP7, GZMB, HCLS1, LCP2, and SELL, were found to be associated with clinical features of TNBC evaluated by WGCNA. These hub-genes belonged to the immunomodulatory subtype of TNBC and were upregulated in the TNBC cells. The protein expression of eight immune-related hub-genes was further confirmed to be upregulated in TNBC/CD8+ tissues detected by immunohistochemical staining. Survival analysis revealed that overexpression of eight immune-related hub-genes was in favor of the survival of patients with TNBC. Moreover, a positive correlation between eight immune-related hub-genes and immune cell infiltration was observed in TNBC patients. Furthermore, checkpoint inhibitor genes such as PD-L1, PD-1, and CTLA4 were more enrichment in the immunomodulatory subtype of TNBC and the expression of PD-L1, PD-1, and CTLA4 was positively correlated with eight immune-related hub-genes in the breast cancer dataset of TCGA. Conclusions: Eight immune-related hub-genes were identified to be molecular signatures in the immunomodulatory subtype of TNBC, which may provide therapeutic targets for the treatment of patients with breast cancer.

Project description:BackgroundImmune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB.MethodsOpen datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB.ResultsA total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/.ConclusionTNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.

Project description:ImportanceWhy some triple-negative breast cancers (TNBCs) have high and others have low immune cell infiltration is unknown. Understanding how immune surveillance shapes the cancer genome could help in the selection of patients and the development of more effective immunotherapy strategies.ObjectiveTo examine the association between genomic metrics and the extent of immune infiltration in TNBCs.Design, setting, and participantsThis study, performed from June 1, 2015, through January 31, 2017, used DNA and RNA sequencing data and messenger RNA expression results from The Cancer Genome Atlas (TCGA) breast cancer data set (n = 1215) to calculate previously described immune metagene expression values and histologic lymphocyte counts to quantify immune infiltration and assign prognostic categories to TNBCs. It used the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data set as an independent validation cohort. The study compared clonal heterogeneity, somatic total mutational load, neoantigen load, and somatic copy number alteration levels between immune-rich TNBC cohorts with good prognosis and immune-poor TNBC cohorts with poor prognosis. The study also compared the distribution of mutations in 119 canonical cancer genes.Main outcomes and measuresCorrelation between immune prognostic category and genomic metrics of the cancer.ResultsThis study of 193 TNBC samples with patient survival information found an inverse association between clonal heterogeneity and immune metagene expression (ρ = −0.395, P = 2 × 10−8). The study also found an inverse association between immune metagene expression and somatic copy number alteration levels (ρ = −0.484, P = 2 × 10−10). Lymphocyte-rich TNBCs with good prognosis had significantly lower mutation and neoantigen counts than did lymphocyte-poor TNBCs with poor prognosis. The robustness of the study results was confirmed by using various immune metagenes in the same TCGA data set and in the independent METABRIC data set.Conclusions and relevanceThis study suggests that immune-rich TNBCs may be under an immune surveillance that continuously eliminates many immunogenic clones, resulting in lower clonal heterogeneity. These cancers may also represent the subset of TNBCs that could derive benefit from immune checkpoint inhibitor therapy to tilt the balance in favor of the immune system.

Project description:The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.

Dataset Information

Immune Milieu and Genomic Alterations Set the Triple-Negative Breast Cancer Immunomodulatory Subtype Tumor Behavior.

Publications

Immune Milieu and Genomic Alterations Set the Triple-Negative Breast Cancer Immunomodulatory Subtype Tumor Behavior.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets