Identification of a minimum number of genes to predict triple negative breast cancer subgroups from gene expression profiles
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PROVIDER: PRJNA852592 | ENA |
REPOSITORIES: ENA
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Project description:Background: Triple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (Basal-like 1 (BL1); Basal-like 2 (BL2); Mesenchymal (M); Immunomodulatory (IM); Mesenchymal Stem like (MSL) and Luminal androgen receptor (LAR)), to be able to accurately predict them. Methods: Lehman’s TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458) and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA dataset. Results: we defined two signatures with the 120 and 81 top up- and down-regulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88,6% to 89,4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug-target interactions were found among the up-regulated genes in the M, IM and MSL subsets. Conclusions: Our study took full advantage of available TNBC datasets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several datasets, has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.
2022-12-24 | GSE206912 | GEO
Project description:BackgroundTriple-negative breast cancer (TNBC) is a very heterogeneous disease. Several gene expression and mutation profiling approaches were used to classify it, and all converged to the identification of distinct molecular subtypes, with some overlapping across different approaches. However, a standardised tool to routinely classify TNBC in the clinics and guide personalised treatment is lacking. We aimed at defining a specific gene signature for each of the six TNBC subtypes proposed by Lehman et al. in 2011 (basal-like 1 (BL1); basal-like 2 (BL2); mesenchymal (M); immunomodulatory (IM); mesenchymal stem-like (MSL); and luminal androgen receptor (LAR)), to be able to accurately predict them.MethodsLehman's TNBCtype subtyping tool was applied to RNA-sequencing data from 482 TNBC (GSE164458), and a minimal subtype-specific gene signature was defined by combining two class comparison techniques with seven attribute selection methods. Several machine learning algorithms for subtype prediction were used, and the best classifier was applied on microarray data from 72 Italian TNBC and on the TNBC subset of the BRCA-TCGA data set.ResultsWe identified two signatures with the 120 and 81 top up- and downregulated genes that define the six TNBC subtypes, with prediction accuracy ranging from 88.6 to 89.4%, and even improving after removal of the least important genes. Network analysis was used to identify highly interconnected genes within each subgroup. Two druggable matrix metalloproteinases were found in the BL1 and BL2 subsets, and several druggable targets were complementary to androgen receptor or aromatase in the LAR subset. Several secondary drug-target interactions were found among the upregulated genes in the M, IM and MSL subsets.ConclusionsOur study took full advantage of available TNBC data sets to stratify samples and genes into distinct subtypes, according to gene expression profiles. The development of a data mining approach to acquire a large amount of information from several data sets has allowed us to identify a well-determined minimal number of genes that may help in the recognition of TNBC subtypes. These genes, most of which have been previously found to be associated with breast cancer, have the potential to become novel diagnostic markers and/or therapeutic targets for specific TNBC subsets.
| S-EPMC9764480 | biostudies-literature
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
2013-01-15 | E-GEOD-43502 | biostudies-arrayexpress
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
2013-01-15 | GSE43502 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE28571: Gene Copy Number Aberrations are Associated with Survival in Histological Subgroups of Non-Small Cell Lung Cancer (expression data) GSE28572: Gene Copy Number Aberrations are Associated with Survival in Histological Subgroups of Non-Small Cell Lung Cancer (copy number data) Refer to individual Series
2011-11-01 | E-GEOD-28582 | biostudies-arrayexpress
Project description:Triple-Negative Breast Cancer (TNBC) is the most aggressive breast cancer subtype, characterized by extensive intratumoral heterogeneity, high metastasis and chemoresistance, leading to poor clinical outcomes. Despite progress, the mechanistic basis of these aggressive behaviours remains poorly understood. Using single-cell and spatial transcriptome analysis, here we discovered basal epithelial subpopulations located within the stroma that exhibit chemoresistance characteristics. The subpopulations are defined by distinct signature genes that show a frequent gain in copy number and exhibit an activated epithelial-to-mesenchymal transition program. A subset of these genes can accurately predict chemotherapy response and are associated with poor prognosis. Interestingly, among these genes, elevated ITGB1 participates in enhancing intercellular signaling while ACTN1 confers a survival advantage to foster chemoresistance. Furthermore, by subjecting the transcriptional signatures to drug repurposing analysis we find that chemoresistant tumors may benefit from distinct inhibitors in treatment naïve versus post-NAC patients. These findings shed light on the mechanistic basis of chemoresistance while providing the best-in-class biomarker to predict chemotherapy response and alternate therapeutic avenues for improved management of TNBC patients resistant to chemotherapy.
2024-01-05 | GSE252315 | GEO
Project description:Identification of novel molecular subgroups in esophageal adenocarcinoma to predict response to neo-adjuvant therapies
| PRJNA855976 | ENA
Project description:DNA Methylation Markers Predict Recurrence-free Interval in Triple-Negative Breast Cancer.
| PRJNA593505 | ENA