Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Triple-Negative Breast Cancer.
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ABSTRACT:
SUBMITTER: Nair AA
PROVIDER: S-EPMC7322352 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Triple Negative Breast Cancers (TNBC) is a heterogeneous disease at the molecular and clinical level with poor outcome. Molecular subclassification of TNBCs is essential for optimal use of current therapies and for development of new drugs. microRNAs (miRNA) are widely recognized as key players in cancer progression and drug resistance; investigation of their involvement in a TNBC cohort may reveal biomarkers for diagnosis and prognosis of TNBC. Here we stratified a large TNBC cohort into Core Basal (CB, EGFR and/or CK5, 6 positive) and five negative (5NP) if all markers are negative. We determined the complete miRNA expression profile and found a subset of miRNAs specifically deregulated in the two subclasses.We identified a 4-miRNA signature given by miR-155, miR-493, miR-30e and miR-27a expression levels, that allowed subdivision of TNBCs not only into CB and 5NP subgroups (sensitivity 0.75 and specificity 0.56; AUC=0.74) but also into high risk and low risk groups. We tested the diagnostic and prognostic performances of both the 5 IHC marker panel and the 4-miRNA expression signatures, which clearly identify worse outcome patients in the treated and untreated subcohorts. Both signatures have diagnostic and prognostic value, predicting outcomes of patient treatment with the two most commonly used chemotherapy regimens in TNBC: anthracycline or anthracycline plus taxanes. Further investigations of the patients’ overall survival treated with these regimens show that regardless of IHC group subdivision, taxanes addition did not benefit patients, possibly due to miRNA driven taxanes resistance. TNBC subclassification based on the 5 IHC markers and on the miR-155, miR-493, miR-30e, miR-27a expression levels are powerful diagnostic tools. Treatment choice and new drug development should consider this new subtyping and miRNA expression signature in planning low toxicity, maximum efficacy therapies.
| S-EPMC4012726 | biostudies-literature
Project description:Triple negative breast cancer (TNBC) is one of the most aggressive form of breast cancer (BC) with the highest mortality due to high rate of relapse, resistance, and lack of an effective treatment. Various molecular approaches have been used to target TNBC but with little success. Here, using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and have identified two potential genes, TBC1D9 (TBC1 domain family member 9) and MFGE8 (Milk Fat Globule-EGF Factor 8 Protein), that could successfully differentiate TNBC from non-TNBC, irrespective of their heterogeneity. TBC1D9 is under-expressed in TNBC as compared to non-TNBC patients, while MFGE8 is over-expressed. Overexpression of TBC1D9 has a better prognosis whereas overexpression of MFGE8 correlates with a poor prognosis. Protein-protein interaction analysis by affinity purification mass spectrometry (AP-MS) and proximity biotinylation (BioID) experiments identified a role for TBC1D9 in maintaining cellular integrity, whereas MFGE8 would be involved in various tumor survival processes. These promising genes could serve as biomarkers for TNBC and deserve further investigation as they have the potential to be developed as therapeutic targets for TNBC.
| S-EPMC7320018 | biostudies-literature
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by post-transcriptional regulators such as miRNAs. To stratify TNBCs, we profiled 1050 miRNAs in 132 adjuvant TNBC tumors and 40 tumors from other immunophenotypes using an Affymetrix microarray platform. A NMF clustering analysis allowed us to identify 4 TNBC subtypes featuring unique miRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm). Our agglomerative approach was cross-validated by using two other clustering algorithms (k-means and consensus clustering). TNBC miRNAs subgroups were also correlated with the Lehmann intrinsic subtypes, finding a significant enrichment of immmnomodulartory and basal 1 subtypes in our high risk miRNA subgroups. 3 cell line models (MDA MB 468, MDA MB 231 and HS578T) were classified according to our miRNA signature, recapitulating two different miRNA subgroups. The TNBC tumors were compared against other phenotypes identifying differentially expressed miRNAs that together with the altered miRNAs within the subgroups allowed us to define interesting miRNAs for further functional analysis.
2018-08-16 | GSE86277 | GEO
Project description:Triple negative breast cancer (TNBC) represents a challenging tumor type due to their poor prognosis and limited treatment options. It is well recognize that clinical and molecular heterogeneity of TNBC is driven in part by post-transcriptional regulators such as miRNAs. To stratify TNBCs, we profiled 1050 miRNAs in 132 adjuvant TNBC tumors and 40 tumors from other immunophenotypes using an Affymetrix microarray platform. A NMF clustering analysis allowed us to identify 4 TNBC subtypes featuring unique miRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm). Our agglomerative approach was cross-validated by using two other clustering algorithms (k-means and consensus clustering). TNBC miRNAs subgroups were also correlated with the Lehmann intrinsic subtypes, finding a significant enrichment of immmnomodulartory and basal 1 subtypes in our high risk miRNA subgroups. 3 cell line models (MDA MB 468, MDA MB 231 and HS578T) were classified according to our miRNA signature, recapitulating two different miRNA subgroups. The TNBC tumors were compared against other phenotypes identifying differentially expressed miRNAs that together with the altered miRNAs within the subgroups allowed us to define interesting miRNAs for further functional analysis.
2018-08-16 | GSE86281 | GEO
Project description:Current prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple-negative breast cancers (TNBCs) are clinically heterogeneous, and prognostic markers and biology-based therapies are needed to better treat this disease. We assembled Affymetrix gene expression data for 579 TNBCs and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n=394 cases for discovery and n=185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations including immune cells, blood, adipocytes, stroma, angiogenesis, and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed including routine clinical and pathological variables. 73% of TNBCs displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non-basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (HR 0.37, 95% CI 0.22-0.61; P<0.001) and was the only significant predictor in multivariate analysis including routine clincopathological variables. We describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease. Analysis of primary breast cancer biopsies from patients before treatment. No replicates. No control or reference samples are included. The set of 579 TNBCs includes: (1) 67 new GEO Samples (GSM782523-GSM782589), (2) 489 re-analyzed GEO Samples (see 'Relation' links below), and (3) 23 re-analyzed ArrayExpress Samples. Cohorts: HH = University of Hamburg FRA = University of Frankfurt, adjuvant chemotherapy FRA-2 = University of Frankfurt, neoadjuvant chemotherapy FRA-3 = University of Frankfurt, no adjuvant chemotherapy Data processing of the 579 TNBC Samples: MAS5 values were taken from GEO if available. For samples with no MAS5 values, CEL files were downloaded from GEO and the affy package from Bioconductor was used to generate MAS5 values. Next, MAS5 values corresponding only to the 22283 probesets from the U133A array were compiled. Subsequently, normalization of MAS5 data was performed using the command line version of the program CLUSTER 3.0 (Michael Eisen; updated by Michiel de Hoon; http://bonsai.hgc.jp/~mdehoon/software/cluster/command.txt). The following three steps were performed in the following order: 1. log2 transformation of MAS5 values 2. median centering of arrays 3. magnitude normalization of arrays These three steps correspond to the following commands: cluster.com filename -l cluster.com filename -ca m cluster.com filename -na The resulting dataset, which is linked below as a supplementary file, was used for the subsequent analyses.
| S-ECPF-GEOD-31519 | biostudies-other
Project description:Triple-negative breast cancer (TNBC) patients with residual disease after neoadjuvant chemotherapy generally have worse outcome; however, some patients with residual tumor after neoadjuvant chemotherapy do not relapse. We hypothesize that there are subgroups of chemoresistant TNBC patients with different prognosis. In this study, 25 chemoresistant samples from 47 neoadjuvant chemotherapy-treated TNBC (The Methodist Hospital) are chosen for study We used gene expression data of TNBC patients with residual disease and different prognosis to molecularly define the clinically relevant subgroups, and developed a 7-gene prognostic signature for chemoresistant TNBCs
| S-ECPF-GEOD-43502 | biostudies-other
Project description:Triple-negative breast cancer (TNBC) is a specific type of breast cancer with poor overall survival (OS) time. Previous studies revealed that microRNAs (miRNAs/miRs) serve important roles in the pathogenesis, progression and prognosis of TNBC. The present study analyzed the miRNA expression and clinical data of patients with TNBC downloaded from The Cancer Genome Atlas. A total of 194 differentially expressed miRNAs were identified between TNBC and matched normal tissues using the cut-off criteria of P<0.05 and |log2 fold change|>2. Of these miRNAs, 65 were downregulated and 129 were upregulated. Using Kaplan-Meier survival analysis, a total of 77 miRNAs that were closely associated with OS time were identified (P<0.05). The intersection of the 77 miRNAs and 194 differentially expressed miRNAs revealed six miRNAs. Log-rank tests based on survival curves were performed and two miRNAs were eliminated. The prognostic value of the remaining four miRNAs was evaluated with a Cox proportional hazards model using multiple logistic regression with forward stepwise selection of variables. Three miRNAs (miR-21-3p, miR-659-5p and miR-200b-5p) were subsequently identified as independent risk factors associated with OS time in the model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed that the target genes of these three miRNAs were mainly involved in 'cell protein metabolism', 'RNA transcriptional regulation', 'cell migration', 'MAPK signaling pathway', 'ErbB signaling pathway', 'prolactin signaling pathway' and 'adherens junctions'. Taken together, the results obtained in the present study suggested that the three-miRNA signature may serve as a prognostic biomarker for patients with TNBC.
| S-EPMC6923981 | biostudies-literature
Project description:IntroductionCurrent prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple negative breast cancers (TNBC) are clinically heterogeneous and prognostic markers and biology-based therapies are needed to better treat this disease.MethodsWe assembled Affymetrix gene expression data for 579 TNBC and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n = 394 cases for discovery and n = 185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations, including immune cells, blood, adipocytes, stroma, angiogenesis and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed, including routine clinical and pathological variables.ResultsSeventy-three percent of TNBC displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (hazard ratio (HR) 0.37, 95% CI 0.22 to 0.61; P < 0.001) and was the only significant predictor in multivariate analysis including routine clinicopathological variables.ConclusionsWe describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease.
| S-EPMC3262210 | biostudies-literature
Project description:PurposePatients with triple-negative breast cancer (TNBC) and residual disease after neoadjuvant chemotherapy generally have worse outcome; however, some patients with residual tumor after neoadjuvant chemotherapy do not relapse. We hypothesize that there are subgroups of patients with chemoresistant TNBC with different prognosis.Experimental designForty-nine chemoresistant cases from 111 patients with TNBC treated with neoadjuvant chemotherapy (M.D. Anderson Cancer Center, Houston, TX) constituted the discovery cohort, and 25 chemoresistant samples from 47 neoadjuvant chemotherapy-treated TNBC (The Methodist Hospital, Houston, TX) were chosen for validation. Extended validation was carried out in 269 operable TNBC predicted to be chemoresistant by expression pattern from published datasets.ResultsWe established a seven-gene prognostic signature using dChip and gene set enrichment analyses. In the independent validation cohort, the classifier predicted correctly with positive predictive value of 75.0% and negative predictive value (i.e., relapse-free survival; RFS) of 76.9% at 3 years. Those predicted to relapse had a HR of 4.67 [95% confidence interval (CI): 1.27-17.15] for relapse in 3 years. In extended validation, patients predicted not to relapse exhibited 3-year RFS of 78.9%, whereas the 3-year RFS was 48.5% for patients predicted to relapse, with HR of 2.61 (95% CI: 1.52-4.49). The TNBC subgroup that predicted to have relatively favorable prognosis was characterized by high expression of "luminal-like" genes [androgen-receptor (AR) and GATA3], whereas the subgroup with worse prognosis was characterized by expression of cancer stem-cell markers.ConclusionWe developed a clinically relevant signature for patients with chemoresistant TNBC. For these women, new therapeutic strategies like targeting AR activation or cancer stem cells may need to be developed.
| S-EPMC3655097 | biostudies-literature
Project description:Triple Negative Breast Cancers (TNBCs) lack the appropriate targets for currently used breast cancer therapies, conferring an aggressive phenotype, more frequent relapse and poorer survival rates. The biological heterogeneity of TNBC complicates the clinical treatment further. We have explored and compared the biological pathways in TNBC and other subtypes of breast cancers, using an in silico approach and the hypothesis that two opposing effects (Yin and Yang) pathways in cancer cells determine the fate of cancer cells. Identifying breast subgroup specific components of these opposing pathways may aid in selecting potential therapeutic targets as well as further classifying the heterogeneous TNBC subtype.Gene expression and patient clinical data from The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) were used for this study. Gene Set Enrichment Analysis (GSEA) was used to identify the more active pathways in cancer (Yin) than in normal and the more active pathways in normal (Yang) than in cancer. The clustering analysis was performed to compare pathways of TNBC with other types of breast cancers. The association of pathway classified TNBC sub-groups to clinical outcomes was tested using Cox regression model.Among 4729 curated canonical pathways in GSEA database, 133 Yin pathways (FDR?<?0.05) and 71 Yang pathways (p-value <0.05) were discovered in TNBC. The FOXM1 is the top Yin pathway while PPAR? is the top Yang pathway in TNBC. The TNBC and other types of breast cancers showed different pathways enrichment significance profiles. Using top Yin and Yang pathways as classifier, the TNBC can be further subtyped into six sub-groups each having different clinical outcomes.We first reported that the FOMX1 pathway is the most upregulated and the PPAR? pathway is the most downregulated pathway in TNBC. These two pathways could be simultaneously targeted in further studies. Also the pathway classifier we performed in this study provided insight into the TNBC heterogeneity.
| S-EPMC5753474 | biostudies-literature