Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Gene expression profiles of PDX models with acquired resistance to endocrine treatments

ABSTRACT: Acquired resistance to endocrine therapy occurs with high frequency in patients with luminal breast cancer (LBC). We report here the establishment of four patient-derived xenograft models of LBC with acquired resistance in vivo to tamoxifen and estrogen deprivation. CEL files represent expresison data generated from 5 replicates (independent mice) of the following tumor models: HBCx22 (parental), HBCx22 TamR (tamoxifen-resistant), HBCx22 OvaR (ovariectomy-resistant), HBCx34 (parental), HBCx34 TamR (tamoxifen-resistant), HBCx34 OvaR (ovariectomy-resistant)

ORGANISM(S): Homo sapiens

SUBMITTER: Cottu P

PROVIDER: S-ECPF-GEOD-55561 | biostudies-other | 2014 Aug

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Gene expression profiles of PDX models with acquired resistance to endocrine treatments

Project description:Acquired resistance to endocrine therapy occurs with high frequency in patients with luminal breast cancer (LBC). We report here the establishment of four patient-derived xenograft models of LBC with acquired resistance in vivo to tamoxifen and estrogen deprivation. CEL files represent expresison data generated from 5 replicates (independent mice) of the following tumor models: HBCx22 (parental), HBCx22 TamR (tamoxifen-resistant), HBCx22 OvaR (ovariectomy-resistant), HBCx34 (parental), HBCx34 TamR (tamoxifen-resistant), HBCx34 OvaR (ovariectomy-resistant)

2014-07-09 | E-GEOD-55561 | biostudies-arrayexpress

Gene expression profiles of PDX models with acquired resistance to endocrine treatments

2014-07-09 | GSE55561 | GEO

Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.

Project description:BackgroundMachine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn't allow sufficient training of ML classifiers that could be used for improving molecular diagnostics.MethodsWe reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories.ResultsWe identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases.ConclusionsWe collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others - microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

| S-EPMC7499993 | biostudies-literature

Gene Expression Profiles of 4 CRC PDX models treated by RSPO3 antagonist.

Project description:RSPO is a WNT pathway activator and functions as a potent regulator of stem cell growth in colon. RSPO family members were produced by several human tumors representing multiple tumor types including ovarian, pancreatic, colon, breast and lung cancer. Specific monoclonal antibody antagonists of RSPO family members were developed. In human patient-derived tumor xenograft models, anti-RSPO treatment markedly inhibited tumor growth either as single agents or in combination with chemotherapy. Furthermore, blockade of RSPO signaling reduced the tumorigenicity of cancer cells based on serial transplantation studies. In order to assess the impact of RSPO3 inhibition and gain insight in the anti-RSPO3 treatment mechanism of action, the global gene expression profiles of 4 human colorectal cancer patient derived models (PDX) were performed using Affymetrix microarray for the xenografts treated by the anti-RSPO3 antibody.

2016-03-03 | GSE73906 | GEO

Association between plasma metabolites and gene expression profiles in five porcine endocrine tissues.

Project description:BACKGROUND: Endocrine tissues play a fundamental role in maintaining homeostasis of plasma metabolites such as non-esterified fatty acids and glucose, the levels of which reflect the energy balance or the health status of animals. However, the relationship between the transcriptome of endocrine tissues and plasma metabolites has been poorly studied. METHODS: We determined the blood levels of 12 plasma metabolites in 27 pigs belonging to five breeds, each breed consisting of both females and males. The transcriptome of five endocrine tissues i.e. hypothalamus, adenohypophysis, thyroid gland, gonads and backfat tissues from 16 out of the 27 pigs was also determined. Sex and breed effects on the 12 plasma metabolites were investigated and associations between genes expressed in the five endocrine tissues and the 12 plasma metabolites measured were analyzed. A probeset was defined as a quantitative trait transcript (QTT) when its association with a particular metabolic trait achieved a nominal P value < 0.01. RESULTS: A larger than expected number of QTT was found for non-esterified fatty acids and alanine aminotransferase in at least two tissues. The associations were highly tissue-specific. The QTT within the tissues were divided into co-expression network modules enriched for genes in Kyoto Encyclopedia of Genes and Genomes or gene ontology categories that are related to the physiological functions of the corresponding tissues. We also explored a multi-tissue co-expression network using QTT for non-esterified fatty acids from the five tissues and found that a module, enriched in hypothalamus QTT, was positioned at the centre of the entire multi-tissue network. CONCLUSIONS: These results emphasize the relationships between endocrine tissues and plasma metabolites in terms of gene expression. Highly tissue-specific association patterns suggest that candidate genes or gene pathways should be investigated in the context of specific tissues.

| S-EPMC3149573 | biostudies-literature

In vivo PDX CRISPR/Cas9 screens reveal mutual therapeutic targets to overcome heterogeneous acquired chemo-resistance.

Project description:Resistance towards cancer treatment represents a major clinical obstacle, preventing cure of cancer patients. To gain mechanistic insights, we developed a model for acquired resistance to chemotherapy by treating mice carrying patient derived xenografts (PDX) of acute lymphoblastic leukemia with widely-used cytotoxic drugs for 18 consecutive weeks. In two distinct PDX samples, tumors initially responded to treatment, until stable disease and eventually tumor re-growth evolved under therapy, at highly similar kinetics between replicate mice. Notably, replicate tumors developed different mutations in TP53 and individual sets of chromosomal alterations, suggesting independent parallel clonal evolution rather than selection, driven by a combination of stochastic and deterministic processes. Transcriptome and proteome showed shared dysregulations between replicate tumors providing putative targets to overcome resistance. In vivo CRISPR/Cas9 dropout screens in PDX revealed broad dependency on BCL2, BRIP1 and COPS2. Accordingly, venetoclax re-sensitized derivative tumors towards chemotherapy, despite genomic heterogeneity, demonstrating direct translatability of the approach. Hence, despite the presence of multiple resistance-associated genomic alterations, effective rescue treatment for polychemotherapy-resistant tumors can be identified using functional testing in preclinical models.

| S-EPMC9712105 | biostudies-literature

Prediction of antibiotic resistance by gene expression profiles.

Project description:Although many mutations contributing to antibiotic resistance have been identified, the relationship between the mutations and the related phenotypic changes responsible for the resistance has yet to be fully elucidated. To better characterize phenotype-genotype mapping for drug resistance, here we analyse phenotypic and genotypic changes of antibiotic-resistant Escherichia coli strains obtained by laboratory evolution. We demonstrate that the resistances can be quantitatively predicted by the expression changes of a small number of genes. Several candidate mutations contributing to the resistances are identified, while phenotype-genotype mapping is suggested to be complex and includes various mutations that cause similar phenotypic changes. The integration of transcriptome and genome data enables us to extract essential phenotypic changes for drug resistances.

| S-EPMC4351646 | biostudies-literature

Differential Protein and Gene Expression in Endocrine and Neuroendocrine Tumors (NET)

Project description:Interventions: Group 1: Quantitative Expression Analysis of the proteom and gene Expression of Primary Tumor, normal tissue, and metastases Primary outcome(s): Disease associated Proteins and Genes Study Design: Allocation: ; Masking: ; Control: ; Assignment: ; Study design purpose: basic science

| 2431957 | ecrin-mdr-crc

Elacestrant demonstrates strong anti-estrogenic activity in PDX models of estrogen-receptor positive endocrine-resistant and fulvestrant-resistant breast cancer.

Project description:The selective oestrogen receptor (ER) degrader (SERD), fulvestrant, is limited in its use for the treatment of breast cancer (BC) by its poor oral bioavailability. Comparison of the orally bioavailable investigational SERD elacestrant, versus fulvestrant, demonstrates both drugs impact tumour growth of ER+ patient-derived xenograft models harbouring several ESR1 mutations but that elacestrant is active after acquired resistance to fulvestrant. In cell line models of endocrine sensitive and resistant breast cancer both drugs impact the ER-cistrome, ER-interactome and transcription of oestrogen-regulated genes similarly, confirming the anti-oestrogenic activity of elacestrant. The addition of elacestrant to CDK4/6 inhibitors enhances the antiproliferative effect compared to monotherapy. Furthermore, elacestrant inhibits the growth of palbociclib-resistant cells. Lastly, resistance to elacestrant involves Type-I and Type-II receptor tyrosine kinases which are amenable to therapeutic targeting. Our data support the wider clinical testing of elacestrant.

| S-EPMC9709100 | biostudies-literature

Gene expression profiles of MCF-7-derived models of endocrine-resistant breast cancer

Project description:MCF-7:5C and MCF-7:2A are two in vitro models of Estrogen Receptor alpha positive (ER+) estrogen deprivation-resistant breast cancer. Both cell lines grow robustly in the absence of estrogen [PMID:1301400, PMID:7780972]. MCF-7:PF is an in vitro model of antihormone resistant breast cancer that exhibits the characteristics of acquired tamoxifen resistance [PMID:24183378] The goal of this study was to compare basal levels of gene expression during exponential phase of growth in MCF-7-derived models of endocrine resistance, relative to their isogenic parental cells

2014-11-30 | GSE60079 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data