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Gene expression profile at single cell level of SUM149PT, SUM185PE, SUM229PE, SUM159PT and SUM1315MO2 cell lines

ABSTRACT: SUM149PT, SUM185PE, SUM229PE, SUM159PT and SUM1315MO2 are triple negative breast cancer cell lines. They were sequenced to test the contrastive learning-based algorithm we developed to predict Afatinib sensitivity in TNBC.

ORGANISM(S): Homo sapiens

PROVIDER: GSE228275 | GEO | 2024/03/25

REPOSITORIES: GEO

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Homo sapiens strain:SUM159PT

Project description:SUM159PT cell line transcriptome

| PRJNA224979 | ENA

Isolation of subtype-specific N-Ras responsive genes in basal-like breast cancer cells

Project description:To identify the the N-Ras-controlled genes in basal-like cells, we used shRNA to repress N-RAS in basal-like SUM102PT, SUM149PT and claudin-low SUM159PT cells. We seek the genes regulated only in basal-like, but not in claudin-low cells, to isolate the genes controlled by N-Ras in a breast cancer subtype-specific fashion. We also overexpressed oncogenic form N-Ras(G12D) in these cells in order to compare the genes regulated by wild-type and oncogenic N-Ras. The shRNA silencing and overexpression were conducted by lentiviral infection in the cells, with non-silencing shRNA-infected cells as the controls. The infected cells were then selected in antibiotics for about 5 days before total RNAs were harvested for microarray analysis. N-Ras was silenced by shRNA or oncogenic N-Ras (G12D) was overexpressed in basal-like SUM102PT, SUM149PT and claudin-low SUM159PT cells.

2015-06-06 | E-GEOD-61768 | biostudies-arrayexpress

Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

Project description:We investigate non-genomic mechanisms determining cellular response to EGFR inhibitors in triple negative breast cancer (TNBC). We integrate methods for cellular barcoding and single-cell transcriptomics to enable cell lineage tracing and explore the subclonal evolution of adaptation in an established preclinical model of TNBC in response to incremental concentrations of Afatinib, a second generation EGFR-TKI that irreversibly inhibits both EGFR and HER2. Retrospective lineage tracing data analysis uncovered a pre-existing subpopulation of rare Afatinib-tolerant cells displaying distinct biological features, such as elevated mRNA levels of the IGFBP2 gene. Furthermore, we investigated temporal coordination of transcriptional programs in drug resistant clones with high replication fitness by reordering cells along a pseudotime trajectory. Interestingly, it revealed the activation of biological processes, such as fatty acid metabolism, which have previously been linked to EGFR-TKIs resistance mechanisms.

2024-03-25 | GSE228154 | GEO

Ribosome profiling on SUM159PT cells grown in vitro or in vivo

2016-02-17 | GSE74512 | GEO

Gene expression profile at single cell level of MDAMB468 and CAL51 cell lines

Project description:MDAMB468 and CAL51 are two triple negative breast cancer cell lines. They were sequenced to test the cell mapper algorithm we developed.

2023-03-08 | GSE214827 | GEO

Gene expression profiling of MDA231, BT549, and SUM159PT cells after selumetinib treatment or DUSP4 siRNA knockdown

Project description:MDA231, BT549, and SUM159PT basal-like breast cancer cell lines were transfected with non-targeting siRNA (siCONTROL), siRNA targeting DUSP4 (siDUSP4), or siCONTROL + 4 or 24 hr of 1uM selumetinib. Cells were harvested at 96 hr post-siRNA transfection. Data were Log2 RMA normalized.

2013-09-03 | GSE41816 | GEO

Transcriptome characterization of triple negative breast cancer [Mexico]

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 mRNA and lncRNAs. To stratify TNBCs, we profiled mRNAs and lncRNA in 158 adjuvant TNBC tumors using an Affymetrix microarray platform. Lehmann clustering analysis allowed us to identify TNBC subtypes featuring unique lncRNA expression patterns, disease free and overall survival rates and particular gene ontology enrichments (performed with GSEA algorithm).

2018-08-31 | GSE86946 | GEO

Transcriptome characterization of triple negative breast cancer [Italy]

2018-08-31 | GSE86945 | GEO

Ribosome profiling on SUM159PT cells grown in vitro or in vivo

Project description:SUM159PT cells were grown either in vitro (in culture) or in vivo (mouse), after which RPL10a tagged with GFP was used to perform extraction by immoprecipitation and subsequent ribosome profiling Two batches of in vitro grown cells, each having 2 replicates. Two batches of in vivo grown cells from tumors grown in two individual mice, each having tumors on left (first batch) and right side (second batch). Extraction and ribosome profiling was done independently for the two batches of samples.

2016-02-17 | E-GEOD-74512 | biostudies-arrayexpress

DNA methylation biomarkers for non-invasive detection of triple negative breast cancer using liquid biopsy

Project description:Non-invasive detection of aberrant DNA methylation could provide invaluable biomarkers for earlier detection of triple negative breast cancer (TNBC) which could help clinicians for easier and more efficient treatment options. We evaluated genome-wide DNA methylation data derived from TNBC and normal breast tissues, peripheral blood of TNBC cases and controls, and reference samples of sorted blood and mammary cells. Differentially methylated regions (DMRs) between TNBC and normal breast tissues were stringently selected, verified, and externally validated. A machine learning algorithm was applied to select the top DMRs, which then were evaluated on plasma-derived circulating cell-free DNA (cfDNA) samples of TNBC patients and healthy controls. We identified 23 DMRs accounting for the methylation profile of blood cells and reference mammary cells and then selected six top DMRs for cfDNA analysis. We quantified un-/methylated copies of these DMRs by droplet digital PCR analysis in a plasma test set from TNBC patients and healthy controls and confirmed our findings obtained on tissues. Differential cfDNA methylation was confirmed in an independent validation set of plasma samples. A methylation score combining signatures of the top three DMRs overlapping with the SPAG6, LINC10606, and TBCD/ZNF750 genes had the best capability to discriminate TNBC patients from controls (AUC=0.74 in test set and AUC=0.77 in validation set). Our findings demonstrate the usefulness of cfDNA-based methylation signatures as non-invasive liquid biopsy markers for diagnosis of TNBC.

2022-11-23 | GSE207998 | GEO

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