Immune Milieu and Genomic Alterations Set the Triple-Negative Breast Cancer Immunomodulatory Subtype Tumor Behavior.
Ontology highlight
ABSTRACT: Methods
Formalin-fixed paraffin-embedded tumor samples from 68 high-risk (stage III-IV) TNBC patients were analyzed through microarrays, immunohistochemistry, and DNA sequencing.Results
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.Conclusion
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 |
REPOSITORIES: biostudies-literature
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.
| S-EPMC7530237 | biostudies-literature
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.
| S-EPMC8484710 | biostudies-literature
Project description:BackgroundPABC, commonly defined as breast cancer diagnosed during or ≤ 1 year after pregnancy, accounts for 7% of all breast cancers in women ≤ 45 years. Compared to age-matched non-PABC patients, PABC is characterized by a particularly aggressive histopathologic profile with poorly differentiated and estrogen- and progesterone receptor negative tumors and associated high mortality rates. This study assessed the genomic background of triple-negative PABC tumors by detection of copy number alterations (CNAs).MethodsMLPA was used to compare CNAs in breast cancer-associated chromosomal loci between triple-negative PABC- and subtype-matched non-PABC patients. Both CNA patterns were evaluated by cluster analysis; associations between individual gene CNAs, pathological characteristics and survival were explored.ResultsTriple-negative PABC tumors exhibited unique CNAs compared to non-PABC tumors, including enrichment for TOP2A copy number loss, an independent predictor of worse overall survival (HR 8.96, p = 0.020). Cluster analysis based on CNA profiles identified a triple-negative PABC-subgroup with a particularly poor prognosis, characterized by chromosome 8p copy number loss. Individual gene CNAs analysis revealed that FGFR1 copy number loss on chromosome 8p11.23 was an independent predictor of poor outcome in multivariate analysis (HR 3.59, p = 0.053) and predicted the development of distant metastases (p = 0.048).ConclusionThis study provides novel insights into the biology of triple-negative PABC tumors suggesting that CNAs, particularly 8p loss and TOP2A loss, are involved in the development of breast cancer during pregnancy. FGFR1 loss and TOP2A loss seem to be promising new biomarkers that independently identify subgroups of PABC patients with poor prognosis. These genomic biomarkers may provide clues for personalized therapy.
| S-EPMC9424154 | biostudies-literature
Project description:In patients with triple-negative breast cancer (TNBC), tumor-infiltrating lymphocytes (TILs) are associated with improved survival. Lehmann et al. identified 4 molecular subtypes of TNBC [basal-like (BL) 1, BL2, mesenchymal (M), and luminal androgen receptor (LAR)], and an immunomodulatory (IM) gene expression signature indicates the presence of TILs and modifies these subtypes. The association between TNBC subtype and TILs is not known. Also, the association between inflammatory breast cancer (IBC) and the presence of TILs is not known. Therefore, we studied the IM subtype distribution among different TNBC subtypes. We retrospectively analyzed patients with TNBC from the World IBC Consortium dataset. The molecular subtype and the IM signature [positive (IM+) or negative (IM-)] were analyzed. Fisher's exact test was used to analyze the distribution of positivity for the IM signature according to the TNBC molecular subtype and IBC status. There were 88 patients with TNBC in the dataset, and among them 39 patients (44%) had IBC and 49 (56%) had non-IBC. The frequency of IM+ cases differed by TNBC subtype (p = 0.001). The frequency of IM+ cases by subtype was as follows: BL1, 48% (14/29); BL2, 30% (3/10); LAR, 18% (3/17); and M, 0% (0/21) (in 11 patients, the subtype could not be determined). The frequency of IM+ cases did not differ between patients with IBC and non-IBC (23% and 33%, respectively; p = 0.35). In conclusion, the IM signature representing the underlying molecular correlate of TILs in the tumor may differ by TNBC subtype but not by IBC status.
| S-EPMC6193579 | biostudies-literature
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.
| S-EPMC5824276 | biostudies-literature
Project description:Triple-negative breast cancer (TNBC) cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptor 2. Currently, apart from poly ADP-ribose polymerase inhibitors, there are few effective therapeutic options for this type of cancer. Here, we present comprehensive characterization of the genetic alterations in TNBC performed by high coverage whole genome sequencing together with transcriptome and whole exome sequencing. Silencing of the BRCA1 gene impaired the homologous recombination pathway in a subset of TNBCs, which exhibited similar phenotypes to tumors with BRCA1 mutations; they harbored many structural variations (SVs) with relative enrichment for tandem duplication. Clonal analysis suggested that TP53 mutations and methylation of CpG dinucleotides in the BRCA1 promoter were early events of carcinogenesis. SVs were associated with driver oncogenic events such as amplification of MYC, NOTCH2, or NOTCH3 and affected tumor suppressor genes including RB1, PTEN, and KMT2C. Furthermore, we identified putative TGFA enhancer regions. Recurrent SVs that affected the TGFA enhancer region led to enhanced expression of the TGFA oncogene that encodes one of the high affinity ligands for epidermal growth factor receptor. We also identified a variety of oncogenes that could transform 3T3 mouse fibroblasts, suggesting that individual TNBC tumors may undergo a unique driver event that can be targetable. Thus, we revealed several features of TNBC with clinically important implications.
| S-EPMC5500377 | biostudies-literature
Project description:BackgroundThere is limited knowledge about the role of cancer-associated fibroblasts (CAF) in the tumor microenvironment of triple-negative breast cancer (TNBC).MethodsThree hundred and thirty-five TNBC samples from four datasets were retrieved and analyzed. In order to determine the CAF subtype by combining gene expression profiles, an unsupervised clustering analysis was adopted. The prognosis, enriched pathways, immune cells, immune scores, and tumor purity were compared between CAF subtypes. The genes with the highest importance were selected by bioinformatics analysis. The machine learning model was built to predict the TNBC CAF subtype by these selected genes.ResultsTNBC samples were classified into two CAF subtypes (CAF+ and CAF-). The CAF- subtype of TNBC was linked to the longer overall survival and more immune cells than the CAF+ subtype. CAF- and CAF+ were enriched in immune-related pathways and extracellular matrix pathways, respectively. Bioinformatics analysis identified 9 CAF subtype-related markers (ADAMTS12, AEBP1, COL10A1, COL11A1, CXCL11, CXCR6, EDNRA, EPPK1, and WNT7B). We constructed a robust random forest model using these 9 genes, and the area under the curve (AUC) value of the model was 0.921.ConclusionThe current study identified CAF subtypes based on gene expression profiles and found that CAF subtypes have significantly different overall survival, immune cells, and immunotherapy response rates.
| S-EPMC9057104 | biostudies-literature
Project description:Background and aimThe spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple-negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m-IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m-IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system.MethodsThe clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD-L1, and Pan-CK markers were stained by m-IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes.ResultsThe components of the TIME score were established by m-IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD-L1+ cell% in the SA, CD3 + cell% in the TC, and PD-L1+ /CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes.ConclusionWe demonstrated that the m-IHC-based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.
| S-EPMC10757132 | biostudies-literature
Project description:BackgroundThe diversity and plasticity behind ER+/PR-/HER2- breast cancer have not been widely explored. It is essential to identify heterogeneous microenvironment phenotypes and investigate specific genomic events driving the formation of these phenotypes.MethodsBased on the immune-related gene expression profiles of 411 ER+/PR-/HER2- breast cancers in the METABRIC cohort, we used consensus clustering to identify heterogeneous immune subtypes and assessed their reproducibility in an independent meta-cohort including 135 patients collected from GEO database. We further analyzed the differences of cellular and molecular characteristics, and potential immune escape mechanism among immune subtypes. In addition, we constructed a transcriptional trajectory to visualize the distribution of individual patient.ResultsOur analysis identified and validated five reproducible immune subtypes with distinct cellular and molecular characteristics, potential immune escape mechanisms, genomic drivers, as well as clinical outcomes. An immune-cold subtype, with the least amount of lymphocyte infiltration, had a poorer prognosis. By contrast, an immune-hot subtype, which demonstrated the highest infiltration of CD8+ T cells, DCs and NK cells, and elevated IFN-γ response, had a comparatively favorable prognosis. Other subtypes showed more diverse gene expression and immune infiltration patterns with distinct clinical outcomes. Finally, our analysis revealed a complex immune landscape consisting of both discrete cluster and continuous spectrum.ConclusionOverall, this study revealed five heterogeneous immune subtypes among ER+/PR-/HER2- breast cancer, also provided important implications for clinical translations.
| S-EPMC8454077 | biostudies-literature
Project description:Different breast cancer (BC) subtypes have unique gene expression patterns, but their regulatory mechanisms have yet to be fully elucidated. We hypothesized that the top upregulated (Yin) and downregulated (Yang) genes determine the fate of cancer cells. To reveal the regulatory determinants of these Yin and Yang genes in different BC subtypes, we developed a lasso regression model integrating DNA methylation (DM), copy number variation (CNV) and microRNA (miRNA) expression of 391 BC patients, coupled with miRNA-target interactions and transcription factor (TF) binding sites. A total of 25, 20, 15 and 24 key regulators were identified for luminal A, luminal B, Her2-enriched, and triple negative (TN) subtypes, respectively. Many of the 24 TN regulators were found to regulate the PPARA and FOXM1 pathways. The Yin Yang gene expression mean ratio (YMR) and combined risk score (CRS) signatures built with either the targets of or the TN regulators were associated with the BC patients' survival. Previously, we identified FOXM1 and PPARA as the top Yin and Yang pathways in TN, respectively. These two pathways and their regulators could be further explored experimentally, which might help to identify potential therapeutic targets for TN.
| S-EPMC6521146 | biostudies-literature