Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA123251 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE13914: Molecular profiling of breast cancer cell lines defines relevant tumor models (aCGH) GSE15361: Molecular profiling of breast cancer cell lines defines relevant tumor models (gene expression) Refer to individual Series
2010-06-25 | E-GEOD-15376 | biostudies-arrayexpress
Project description:Molecular profiling of breast cancer cell lines defines relevant tumor models (aCGH)
| PRJNA123169 | ENA
Project description:Three-dimensional (3D) cancer spheroid models provide physiologically relevant platforms for studying tumor biology and therapeutic response. Using the liquid overlay method, we optimized conditions for reproducible spheroid generation from MDA-MB-231 breast cancer cells and extended this approach to 10 human cancer cell lines. Growth outcomes varied, yielding compact spheroids, loose aggregates, or no spheroids, with compactness strongly associated with high breast cancer stem cell (BCSC) content (≥20%). Transcriptomic profiling revealed distinct gene expression programs between spheroid-forming and non-forming cells, highlighting pathways in extracellular matrix remodeling, differentiation, and developmental lineage specification. Notably, PROM1, HOXB4, BMP5, and TENM4 emerged as key regulators, with TENM4 consistently upregulated across compact spheroid models. Drug response assays demonstrated increased chemoresistance in 3D spheroids compared to 2D cultures, with triple-negative breast cancer (TNBC) spheroids exhibiting reduced sensitivity to bortezomib+nedaplatin despite context-dependent synergy. Collectively, these findings establish optimized experimental parameters for spheroid culture, define molecular features linked to spheroid formation and stemness, and underscore the importance of 3D models for evaluating therapeutic efficacy.
2025-11-18 | GSE306569 | GEO
Project description:Integrated molecular profiling of patient-derived ovarian cancer models identifies clinically relevant signatures and tumor vulnerabilities.
| PRJNA648042 | ENA
Project description:Metastasis remains the major cause of mortality in triple-negative breast cancer (TNBC), yet the metastatic potential of primary tumors is difficult to predict at diagnosis. Here, we leveraged patient-derived xenograft (PDX) models as a functional readout of primary tumor metastatic propensity. We first performed integrated genomic and transcriptomic profiling of primary breast tumors and their matched PDX models and found that key driver mutations, recurrent copy number alterations, and molecular subtype features were largely conserved between patient tumors and xenografts. These findings support the utility of matched PDX models as clinically relevant platforms for studying tumor-intrinsic features associated with breast cancer progression. We next focused on TNBC PDX models with reproducible metastatic phenotypes across biological replicates and classified primary tumors into metastatic and non-metastatic groups based on their matched PDX behavior. Single-cell transcriptomic profiling of these tumors revealed that metastatic samples were characterized by hypoxia-associated metabolic reprogramming, with coordinated activation of hypoxia and glycolysis pathways. This metabolic phenotype was associated with poor clinical outcome in independent breast cancer cohorts. Network-based prioritization identified adenylate kinase 1 (AK1) as a candidate regulator of this metastatic program. Functionally, AK1 promoted metastatic potential in primary tumor by supporting glycolytic ATP production, resistance to hypoxic and oxidative stress, and promoting trans-endothelial migration. Together, our findings establish primary tumor–matched PDX models as a clinically relevant discovery platform for metastatic potential, overcoming the challenge that future metastatic events are not readily predictable from primary tumors at diagnosis. Using reproducible PDX metastasis as a functional readout, we identify AK1-mediated metabolic stress adaptation as a key mechanism underlying metastatic potential in primary TNBC.
2026-06-30 | GSE334033 | GEO
Project description:Metastasis remains the major cause of mortality in triple-negative breast cancer (TNBC), yet the metastatic potential of primary tumors is difficult to predict at diagnosis. Here, we leveraged patient-derived xenograft (PDX) models as a functional readout of primary tumor metastatic propensity. We first performed integrated genomic and transcriptomic profiling of primary breast tumors and their matched PDX models and found that key driver mutations, recurrent copy number alterations, and molecular subtype features were largely conserved between patient tumors and xenografts. These findings support the utility of matched PDX models as clinically relevant platforms for studying tumor-intrinsic features associated with breast cancer progression. We next focused on TNBC PDX models with reproducible metastatic phenotypes across biological replicates and classified primary tumors into metastatic and non-metastatic groups based on their matched PDX behavior. Single-cell transcriptomic profiling of these tumors revealed that metastatic samples were characterized by hypoxia-associated metabolic reprogramming, with coordinated activation of hypoxia and glycolysis pathways. This metabolic phenotype was associated with poor clinical outcome in independent breast cancer cohorts. Network-based prioritization identified adenylate kinase 1 (AK1) as a candidate regulator of this metastatic program. Functionally, AK1 promoted metastatic potential in primary tumor by supporting glycolytic ATP production, resistance to hypoxic and oxidative stress, and promoting trans-endothelial migration. Together, our findings establish primary tumor–matched PDX models as a clinically relevant discovery platform for metastatic potential, overcoming the challenge that future metastatic events are not readily predictable from primary tumors at diagnosis. Using reproducible PDX metastasis as a functional readout, we identify AK1-mediated metabolic stress adaptation as a key mechanism underlying metastatic potential in primary TNBC.
2026-06-30 | GSE336789 | GEO
Project description:Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets predicted genomic instability in our samples. This remarkable congruence suggests a biological dependency of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome. Keywords: disease state analysis 44 samples
2009-11-30 | E-GEOD-11901 | biostudies-arrayexpress
Project description:Summary: Breast cancer cell lines have been used widely to investigate breast cancer pathobiology and new therapies. Breast cancer is a molecularly heterogeneous disease, and it is important to understand how well and which cell lines best model that diversity. In particular, microarray studies have identified molecular subtypes (luminal A, luminal B, ERBB2-associated, basal-like and normal-like) with characteristic gene-expression patterns and underlying DNA copy number alterations (CNAs). Here, we studied a collection of breast cancer cell lines to catalog molecular profiles and to assess their relation to breast cancer subtypes. Whole-genome DNA microarrays were used to profile gene expression and CNAs in a collection of 52 widely-used breast cancer cell lines, and comparisons were made to existing profiles of primary breast tumors. Hierarchical clustering was used to identify gene-expression subtypes, and Gene Set Enrichment Analysis (GSEA) to discover biological features of those subtypes. Genomic and transcriptional profiles were integrated to discover within high-amplitude CNAs candidate cancer genes with coordinately altered gene copy number and expression. Transcriptional profiling of breast cancer cell lines identified one luminal and two basal-like (A and B) subtypes. Luminal lines displayed an estrogen receptor (ER) signature and resembled luminal-A/B tumors, basal-A lines were associated with ETS-pathway and BRCA1 signatures and resembled basal-like tumors, and basal-B lines displayed mesenchymal and stem-cell characteristics. Compared to tumors, cell lines exhibited similar patterns of CNA, but an overall higher complexity of CNA (genetically simple luminal-A tumors were not represented), and only partial conservation of subtype-specific CNAs. We identified 80 high-level DNA amplifications and 13 presumptive homozygous deletions, and the resident genes with concomitantly altered gene-expression, highlighting known and novel candidate breast cancer genes. Overall, breast cancer cell lines were genetically more complex than tumors, but retained expression patterns with relevance to the luminal-basal subtype distinction. The compendium of molecular profiles defines cell lines suitable for investigations of subtype-specific pathobiology, biomarkers and therapies, and provides a resource for discovery of new breast cancer genes. HEEBO oligonucleotide microarrays from the Stanford Functional Genomics Facility were used to perform gene expression profiling of 50 human breast epithelial cell lines, in comparison to a universal RNA reference. Expression data were analyzed by hierarchical clustering to identify subgroups, and gene set enrichment analysis to identify subgroup-specific gene pathways.
2010-06-24 | E-GEOD-15361 | biostudies-arrayexpress
Project description:The study of breast cancer pathogenesis relies heavily on the use of established cell lines often derived from metastatic lesions, which while having significantly contributed to the knowledge of breast cancer biology may inadvertently limit the understanding of the mechanisms governing the metastatic process. Our goal was to establish primary cultures from dissociation of breast tumors in order to provide cellular models that may better recapitulate breast cancer pathogenesis and the metastatic process. These cellular models differ from recently developed patient derived xenograft models (PDX) in that they can be used for both in vitro and in vivo studies. Here we report the characterization of six cellular models derived from the dissociation of primary breast tumor specimens, referred to as “dissociated tumor (DT) cells”. Among the DT cells are those that are tumorigenic and metastatic in immunosuppressed mice, and a group of cancer-associated fibroblasts (CAFs). In vitro, DT cells were characterized by proliferation assays, colony formation assays, protein and gene expression profiling, including PAM50 predictor analysis. The latter showed DT cultures similar to their paired primary tumor and as belonging to the basal and Her2-enriched subtypes, offering novel cellular models of these ER-negative breast cancer subtypes. In vivo, three DT cultures are tumorigenic in NOD/SCID and NSG mice, and one of these is metastatic to lymph nodes and lung after orthotopic inoculation into the mammary fat pad, without excision of the primary tumor. DT cultures comprised of CAFs were isolated from luminal-A, Her2-enriched and basal primary tumors, providing subtype-specific components of the tumor microenvironment. Altogether, these DT cultures provide closer-to-primary cellular models for the study of breast cancer pathogenesis, metastasis and tumor microenvironment. reference x sample
2014-08-22 | E-GEOD-55070 | biostudies-arrayexpress