Ontology highlight
ABSTRACT:
PROVIDER: PRJNA252499 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Translational validation of personalized treatment strategy based on genetic characteristics of glioblastoma [xenograft]
| PRJNA252501 | ENA
Project description:Translational validation of personalized treatment strategy based on genetic characteristics of glioblastoma [surgical samples]
| PRJNA252500 | ENA
Project description:Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated. Gene expression profiling experiments were conducted for 25 patient-derived xenograft glioblastoma samples using Affymetrix Human Gene 1.0 ST arrays according to manufacturer's protocol.
2014-06-12 | E-GEOD-58400 | biostudies-arrayexpress
Project description:Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated. Gene expression profiling experiments were conducted for 25 patient-derived xenograft glioblastoma samples using Affymetrix Human Gene 1.0 ST arrays according to manufacturer's protocol.
2014-06-12 | E-GEOD-58399 | biostudies-arrayexpress
Project description:Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated.
2014-06-12 | GSE58399 | GEO
Project description:Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated.
2014-06-12 | GSE58400 | GEO
Project description:Purpose: Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors. Experimental Design: Here, we used 33 single cell–derived subclones generated from five clinical glioblastoma specimens for exploring intra- and interindividual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacologic sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors. Results: Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential antiglioblastoma compounds. A more comprehensive intratumoral analysis revealed that stable genetic and phenotypic characteristics of coexisting subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naïve parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacologic profiles could be shown for establishing rational strategies for individualized secondary lines of treatment. Conclusions: Our data provide a previously unrecognized strategy for revealing functional consequences of intratumor heterogeneity by enabling predictive modeling of treatment-related subclone dynamics in human glioblastoma
2016-12-19 | GSE72732 | GEO
Project description:Purpose: Investigation of clonal heterogeneity may be key to understanding mechanisms of therapeutic failure in human cancer. However, little is known on the consequences of therapeutic intervention on the clonal composition of solid tumors. Experimental Design: Here, we used 33 single cell–derived subclones generated from five clinical glioblastoma specimens for exploring intra- and interindividual spectra of drug resistance profiles in vitro. In a personalized setting, we explored whether differences in pharmacologic sensitivity among subclones could be employed to predict drug-dependent changes to the clonal composition of tumors. Results: Subclones from individual tumors exhibited a remarkable heterogeneity of drug resistance to a library of potential antiglioblastoma compounds. A more comprehensive intratumoral analysis revealed that stable genetic and phenotypic characteristics of coexisting subclones could be correlated with distinct drug sensitivity profiles. The data obtained from differential drug response analysis could be employed to predict clonal population shifts within the naïve parental tumor in vitro and in orthotopic xenografts. Furthermore, the value of pharmacologic profiles could be shown for establishing rational strategies for individualized secondary lines of treatment. Conclusions: Our data provide a previously unrecognized strategy for revealing functional consequences of intratumor heterogeneity by enabling predictive modeling of treatment-related subclone dynamics in human glioblastoma
2016-12-19 | GSE72927 | GEO
Project description:Glioblastoma is the most prevalent and aggressive brain cancer. With a median overall survival of ~15-20 months under standard therapy, novel treatment approaches are desperately needed. A recent phase II clinical trial with a personalized immunotherapy based on tumor lysate-charged dendritic cells (DCs), however, failed to prolong survival. Here, we investigated tumor tissue from patients from this trial to explore glioblastoma (immuno)resistancesurvival-related factors. and strategies to overcome them. We followed an innovative approach of combining mass spectrometry-based quantitative proteomics (n=36) with microRNA sequencing plus RT-qPCR (n=38). Protein quantification identified e.g. huntingtin interacting protein 1 (HIP1), retinol binding protein 1 (RBPP1), ferritin heavy chain (FTH1) and focal adhesion kinase 2 (FAK2) as resistance factor candidates. MicroRNA analysis identified miR-216b, miR-216a, miR-708 and let-7i as molecules potentially associated with overcoming resistancefavourable tissue characteristics as they were enriched in patients with a comparably longer survival. In silico target prediction and subsequent analyses indicated focal adhesion as a pathway of interest. Taken together, we here mapped possible drivers of glioblastoma outcome under immunotherapy’s (immuno)resistance in one of the largest DC vaccination tissue analysis cohorts so far – demonstrating usefulness and feasibility of combined proteomics/miRNomics approaches. Future research should investigate agents that sensitize glioblastoma to (immuno)therapy – potentially building on insights generated here.
2020-01-27 | GSE132554 | GEO
Project description:Mucinous adenocarcinoma (MuC), a subtype of colorectal cancer (CRC), exhibits distinct molecular features and a poorer prognosis compared to non-mucinous CRC (NMuC). Standard CRC treatments often fail to address MuC's unique characteristics, especially in stage II, where the benefit of adjuvant chemotherapy is unclear. Biomarkers that improve risk assessment and guide personalized treatment are needed. Based on RNA-seq data and Cox regression models, we developed a signature reflecting the characteristics of MuC which showed a strong prediction ability and clinical utility in both MuC and NMuC. Our signature represents a valuable tool for predicting recurrence and guiding personalized treatment in CRC.
2025-07-02 | GSE281917 | GEO