Multi-dimensional genomic analysis in breast cancer patients
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE10099 | GEO | 2009/05/05
SECONDARY ACCESSION(S): PRJNA108245
REPOSITORIES: GEO
Similar Datasets
Project description:Multi-dimensional genomic analysis in breast cancer patients
| PRJNA108245 | ENA
Project description:This study explores the roles of genome copy number abnormalities (CNAs) in breast cancer pathophysiology by identifying associations between recurrent CNAs, gene expression, and clinical outcome in a set of aggressively treated early-stage breast tumors. It shows that the recurrent CNAs differ between tumor subtypes defined by expression pattern and that stratification of patients according to outcome can be improved by measuring both expression and copy number, especially high-level amplification. Sixty-six genes deregulated by the high-level amplifications are potential therapeutic targets. Nine of these (FGFR1, IKBKB, ERBB2, PROCC, ADAM9, FNTA, ACACA, PNMT, and NR1D1) are considered druggable. Low-level CNAs appear to contribute to cancer progression by altering RNA and cellular metabolism.
2015-05-20 | GSE69035 | GEO
Project description:Purpose Despite advances and significant improvement in survival, multiple myeloma (MM) remains incurable and nearly all patients relapse after treatment. Previous studies have shown a complex spectrum of diverse genetic alterations in almost all patients, but evolution of genomic rearrangements throughout myeloma life cycle has not been investigated. Patients and Methods We performed genomic analysis integrating copy number, allele specific copy number, allele ratio calculations, breakpoint sequencing and rearrangement PCR genotyping on matched diagnosis and relapse samples from 24 MM patients either treated with proteasome inhibitor (bortezomib)-based induction regimen or conventional chemotherapeutic agents. Results All relapse samples have a clear relationship to the diagnosis clone with significant increase of copy number abnormalities (CNAs). Despite a wide diversity of CNAs acquired at relapse, regulators of NF-kB activity were targeted in 25% of the patients. Relapse-associated lesions either appeared as new acquisition or were selected from minor subpopulations at diagnosis. In one third of the patients, we found loss of abnormities containing loss of heterozygosity (LOH) providing evidence that the relapse clone derived from an ancestral clone shared with the dominant diagnosis clone. Remarkably, re-emergence of ancestral clones was almost exclusively found in patients treated with bortezomib, attested to a remarkable adaptability of myeloma cells under targeted drug selection pressure. Conclusion These data suggest that genomic instability and clonal selection are the main forces that drive adaptive changes in MM under drug selection pressure, and they support the proposal to combine several anti-myeloma drugs upfront in order to obtain long-term remissions. [SNP genotyping] 24 myeloma patients at diagnosis and relapse examined with high resolution genome-wide chip
2013-02-13 | E-GEOD-25262 | biostudies-arrayexpress
Project description:In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, and digital pathology profiles from pre-treatment biopsies of breast tumors. Leveraging this comprehensive multi-omic dataset, the team developed ensemble machine learning models using different algorithms (Logistic Regression, SVM and Random Forest). These predictive models identifies patients likely to achieve a pathological complete response (pCR) to therapy, showcasing their potential to enhance treatment selection.
Please note that the authors also have an interactive dashboard to apply the fully-integrated NAT response model on new (or any desired) data. The user can find its link in their GitHub repository: https://github.com/micrisor/NAT-ML
For more information and clarification, please refer to the ReadMe_NAT-ML document in the files section.
2023-08-28 | BIOMD0000001075 | BioModels
Project description:Treating unselected cancer patients with new drugs dilutes proof of efficacy when only a fraction of patients respond to therapy. We conducted a meta-analysis on eight primary breast cancer microarray datasets representing diverse breast cancer phenotypes. We present a high-throughput protocol which incorporates drug sensitivity signatures to guide preclinical testing for effective therapeutic agents. Specifically, we focus on drug classes currently undergoing early phase clinical testing. Our genomic and experimental results suggest that the majority of basal-like breast cancers should respond to inhibitors of the phosphatidylinositol-3-kinase pathway, and that a relatively low toxicity histone deacetylase inhibitor, valproic acid, may target aggressive breast cancers. For a subset of drugs, prediction of sensitivity associates with tumor recurrence, suggesting clinical relevance. Preclinical studies using both cell lines and patient tumors grown in 3-dimensional in vitro and orthotopic in vivo preclinical models provide an efficient and highly relevant assessment of drug sensitivity in tumor phenotypes, and validate our genomic analyses. Together, our results show that high-throughput transcriptional profiling can significantly impact drug selection for breast cancer patients. Pre-identification of patient response may not only improve therapeutic response rates, it can also assist in quickly identifying the optimal inclusion criteria for clinical trials. Our model facilitates personalized drug therapy for cancer patients and may be generalized for study of drug efficacy in other diseases. Breast cancer pleural effusion samples from triple negative patients. Compared samples that are computationally predicted to be sensitive to valproic acid and those that are not predicted to be sensitive.
2011-06-01 | E-GEOD-18331 | biostudies-arrayexpress
Project description:Purpose Despite advances and significant improvement in survival, multiple myeloma (MM) remains incurable and nearly all patients relapse after treatment. Previous studies have shown a complex spectrum of diverse genetic alterations in almost all patients, but evolution of genomic rearrangements throughout myeloma life cycle has not been investigated. Patients and Methods We performed genomic analysis integrating copy number, allele specific copy number, allele ratio calculations, breakpoint sequencing and rearrangement PCR genotyping on matched diagnosis and relapse samples from 24 MM patients either treated with proteasome inhibitor (bortezomib)-based induction regimen or conventional chemotherapeutic agents. Results All relapse samples have a clear relationship to the diagnosis clone with significant increase of copy number abnormalities (CNAs). Despite a wide diversity of CNAs acquired at relapse, regulators of NF-kB activity were targeted in 25% of the patients. Relapse-associated lesions either appeared as new acquisition or were selected from minor subpopulations at diagnosis. In one third of the patients, we found loss of abnormities containing loss of heterozygosity (LOH) providing evidence that the relapse clone derived from an ancestral clone shared with the dominant diagnosis clone. Remarkably, re-emergence of ancestral clones was almost exclusively found in patients treated with bortezomib, attested to a remarkable adaptability of myeloma cells under targeted drug selection pressure. Conclusion These data suggest that genomic instability and clonal selection are the main forces that drive adaptive changes in MM under drug selection pressure, and they support the proposal to combine several anti-myeloma drugs upfront in order to obtain long-term remissions.
2013-02-13 | GSE25262 | GEO
Project description:The traditional method for studying cancer in vitro is to grow immortalized cancer cells in two-dimensional (2D) monolayers on plastic. However, many cellular features are impaired in these unnatural conditions and big alterations in gene expression in comparison to tumors have been reported. Three-dimensional (3D) cell culture models have become increasingly popular and are suggested to be better models than 2D monolayers due to improved cell-to-cell contacts and structures that resemble in vivo architecture. The aim of this study was to develop a simple high-throughput 3D drug screening method and to compare drug responses in JIMT1 breast cancer cells when grown in 2D, in polyHEMA coated anchorage independent 3D models and in Matrigel on-top 3D cell culture models. We screened 102 compounds with multiple concentrations and biological replicates for their effects on cell proliferation. The cells were either treated immediately upon plating or they were allowed to grow in 3D for four days prior to the drug treatment. Big variations in drug responses were observed between the models indicating that comparisons of culture model influenced drug sensitivities cannot be made based on effects of a single drug. However, we show with the 63 most prominent drugs that, in general, JIMT1 cells grown on Matrigel were significantly more sensitive to drugs than cells grown in 2D cultures, while responses of cells grown in polyHEMA resembled those of 2D. Furthermore, comparison of gene expression profiles of the cell culture models to xenograft tumors indicated that cells cultured in Matrigel and as xenografts most closely resembled each other. In this study we also suggest that 3D cultures can provide a platform for systematic experimentation of larger compound collections in a high-throughput mode and be used as alternatives for traditional 2D screens towards better comparability to in vivo state. Gene expression analysis of JIMT1 breast cancer cells cultured as xenografts for 43 days, in two dimensional cultures for seven days (2D7d), in polyHEMA three dimensional cell culture models for four and seven days (PH7d and PH7d), and in Matrigel three dimensional cultures for four and seven days (MG4d and MG7d). Two biological replicates was included for each sample.
2013-10-30 | E-GEOD-42529 | biostudies-arrayexpress
Project description:Complex three-dimensional (3D) in vitro model systems that recapitulate human tumor biology are essential to better understand the pathophysiology of the disease and to aid in the discovery of novel anti-cancer therapies. 3D organotypic cultures exhibit intercellula communication, nutrient and oxygen gradients, and cell polarity that is lacking in traditional two-dimensional (2D) monolayer cultures. In the present study, we could demonstrate that 2D and 3D cancer models exhibit different drug sensitivities towards both targeted inhibitors of EGFR signaling and broad acting cytotoxic agents. Changes in the kinase activities of Erb family members and differential expression of apoptosis- and survival-associated genes before and after drug treatment may account for the differential drug sensitivities. Importantly, EGFR oncoprotein addiction was evident only in the 3D cultures mirroring the effect of EGFR inhibition in the clinic. Furthermore, targeted drug efficacy was strongly increased when incorporating cancer-associated fibroblasts into the 3D cultures. Taken together, we could provide conclusive evidence that complex 3D cultures are more predictive of the clinical outcome than their 2D counterparts. In the future, 3D cultures will be instrumental for understanding the mode of action of drugs, identifying genotype-drug response relationships and developing patient-specific and personalized cancer treatments.
2017-11-30 | GSE102722 | GEO
Project description:Treating unselected cancer patients with new drugs dilutes proof of efficacy when only a fraction of patients respond to therapy. We conducted a meta-analysis on eight primary breast cancer microarray datasets representing diverse breast cancer phenotypes. We present a high-throughput protocol which incorporates drug sensitivity signatures to guide preclinical testing for effective therapeutic agents. Specifically, we focus on drug classes currently undergoing early phase clinical testing. Our genomic and experimental results suggest that the majority of basal-like breast cancers should respond to inhibitors of the phosphatidylinositol-3-kinase pathway, and that a relatively low toxicity histone deacetylase inhibitor, valproic acid, may target aggressive breast cancers. For a subset of drugs, prediction of sensitivity associates with tumor recurrence, suggesting clinical relevance. Preclinical studies using both cell lines and patient tumors grown in 3-dimensional in vitro and orthotopic in vivo preclinical models provide an efficient and highly relevant assessment of drug sensitivity in tumor phenotypes, and validate our genomic analyses. Together, our results show that high-throughput transcriptional profiling can significantly impact drug selection for breast cancer patients. Pre-identification of patient response may not only improve therapeutic response rates, it can also assist in quickly identifying the optimal inclusion criteria for clinical trials. Our model facilitates personalized drug therapy for cancer patients and may be generalized for study of drug efficacy in other diseases.
2011-06-01 | GSE18331 | GEO
Project description:Genomic copy number alterations (CNAs) in diffuse large B-cell lymphoma (DLBCL) have roles in disease pathogenesis but overall clinical relevance remains unclear. Herein, an unbiased algorithm was uniformly applied across three genome profiling datasets comprising 392 newly-diagnosed DLBCL specimens that defined 32 overlapping CNAs, involving 36 minimal common regions (MCRs). Scoring criteria were established for 50 aberrations within the MCRs while considering peak gains/losses. Application of these criteria to independent datasets revealed novel candidate genes with coordinated expression, such as CNOT2, potentially with pathogenic roles. No one single aberration significantly associated with patient outcome across datasets, but genomic complexity, defined by imbalance in more than one MCR, significantly portended adverse outcome in two of three independent datasets. Thus, the standardized scoring of CNAs currently developed can be uniformly applied across platforms, affording robust validation of genomic imbalance and complexity in DLBCL and overall clinical utility as biomarkers of patient outcome.
2015-12-01 | GSE74025 | GEO