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
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.
Project description:cDNA aCGH study of pure DCIS (breast duct carcinoma in situ) without invasive tumor, DCIS associated with IDC (breast invasive duct carcinoma) and its IDC component 23 patients: 6 pure DCIS without invasive cancer and no history of invasive cancer, 17 DCIS associated with IDC. Out of the latter 1 tumor had only enough DCIS (#16) for aCGH and one - IDC (#23) Keywords: Comparative clinical study
Project description:HER2 gene amplification and protein overexpression (HER2+) define a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. Although gene expression profiling has identified an ERBB2 molecular subtype of breast cancer, it is clear that HER2+ tumors reside in all molecular subtypes and represent a genomically and biologically heterogeneous group. Genome-wide DNA copy number profiling, using BAC array comparative genomic hybridization (aCGH) were performed on 200 tumors with mixed clinical characteristics and amplification of HER2. Genomic Identification of Significant Targets in Cancer (GISTIC) was used to identify significant copy number aberrations (CNAs) in HER2+ tumors. This analysis sheds further light on the genomically complex and heterogeneous nature of HER2+ tumors in relation to other subgroups of breast cancer. Genomic profiling of 200 breast tumors using tiling BAC aCGH (32K, 33K and 38K). A number of cases were hybridized as replicates or dye-swaps.
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.
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.