MVAC treated tumors
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE70691 | GEO | 2015/08/18
SECONDARY ACCESSION(S): PRJNA289383
REPOSITORIES: GEO
Similar Datasets
Project description:PanCancer Immune panel on treated tumors
| PRJNA495381 | ENA
Project description:Appendiceal cancer patients treated with cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) often demonstrate an unpredictable variability in survival outcomes. Biomarkers predictive of CRS/HIPEC efficacy could better guide treatment decisions. In this study we hypothesized that variation in the transcriptional programming of appendiceal tumors might distinguish molecular subtypes with differential outcomes after CRS/HIPEC. The goal of this study was to investigate the potential of a prognostic gene signature to discriminate patients with favorable and unfavorable outcomes in a “discovery” set of patient (the original tumor series (n=24)), and confirm its prognostic value in a second “validation” series (the validation cohort (n=39)).
2015-12-02 | GSE75535 | GEO
Project description:Bevacizumab induces glioblastoma resistance in two in vivo xenograft models. Two cell lines were developed with acquired resistance to bevacizumab. Gene expression difference were analyzed between treated and untreated tumors. Purpose: Antiangiogenic therapy reduces vascular permeability and delays progression but may ultimately promote an aggressive treatment-resistant phenotype. The aim of the present study was to identify mechanisms responsible for glioblastoma resistance to antiangiogenic therapy. Experimental Design: Glioma stem cell (GSC) NSC11 and U87 cell lines with acquired resistance to bevacizumab were developed from orthotopic xenografts in nude mice treated with bevacizumab. Genome-wide analyses were used to identify changes in tumor subtype and specific factors associated with resistance. Results: Mice with established parental NSC11 and U87 cells responded to bevacizumab, whereas glioma cell lines derived at the time of acquired resistance to anti-VEGF therapy were resistant to bevacizumab and did not have prolongation of survival compared to untreated controls. Gene expression profiling comparing anti-VEGF therapy-resistant cell lines to untreated controls demonstrated an increase in genes associated with a mesenchymal origin, cellular migration/invasion, and inflammation. Gene Set Enrichment Analysis (GSEA) demonstrated that bevacizumab-treated tumors showed a highly significant correlation to published mesenchymal gene signatures. Mice bearing resistant tumors showed significantly greater infiltration of myeloid cells in NSC11 and U87 resistant tumors. Invasion-related genes were also upregulated in both NSC11 and U87 resistant cells, which had higher invasion rates in vitro compared with their respective parental cell lines. Conclusions: Our studies identify multiple pro-inflammatory factors associated with resistance and identify a proneural-to-mesenchymal transition (PMT) in tumors resistant to antiangiogenic therapy. Glioma cell lines were injected into the caudate of nude mice and were allowed to grow untreated (samples labeled control) or were treated with 10 mg/kg IP twice weekly with bevacizumab (samples labeled Avastin). At the time of animal death, tumor tissue from the mouse was removed, and RNA was isolated and analyzed using gene expression. U87R and NSC11R represent cells resistant to bevacizumab (Avastin).
2013-07-01 | E-GEOD-45161 | biostudies-arrayexpress
Project description:Chemokines profiling in tumors treated with UNC1999
| PRJNA872495 | ENA
Project description:Melanoma GEMM Tumors, untreated or vemurafenib treated
| PRJNA844332 | ENA
Project description:Microarrays were used to determine the efficacy of bevacizumab (a monoclonal antibody against the vascular endothelial growth factor and its receptors.) on endometrial cancer cells. Endometrial cancer is the most frequent gynecologic cancer in women. Long term outcomes for patients with advanced stage or recurrent disease are poor. Targeted molecular therapy against the vascular endothelial growth factor (VEGF) and its receptors constitute a new therapeutic option for these patients. The goal of our work was to assess the potential effectiveness of inhibition of VEGF/VEGFR signaling in a xenograft model of endometrial cancer using bevacizumab (Avastin, a humanized antibody against VEGFA). We also aimed to identify molecular markers of sensitivity or resistance to this agent. We show that bevacizumab retards tumor growth in athymic mice by inhibiting molecular components of signaling pathways that sustain cell survival and proliferation. We also demonstrate that resistance to bevacizumab may involve up-regulation of antiapoptotic genes and certain proto-oncogenes. Experiment Overall Design: Human xenografts produced from endometrial cell line, grown in athymic mice, were treated in vivo with bevacizumab.
2009-11-16 | E-GEOD-18195 | biostudies-arrayexpress
Project description:We generated large-scale proteome data for 65 human breast tumors and 53 paired adjacent non-cancerous tissue and performed an integrated proteotranscriptomic characterization. To our best knowledge, the study is one of the largest quantitative proteomic study of human breast tissues, including the analysis of 118 tissue samples from 65 patients with long-term survival outcomes. Our data show that protein expression describes a tumor biology that is only partly captured by the transcriptome, with mRNA abundance incompletely predicting protein abundance in tumors, and even less so in non-cancerous tissue. Furthermore, the tumor proteome described disease pathways and subgroups that were only partially captured by the tumor transcriptome.
2018-12-04 | PXD005692 | Pride
Project description:Melanoma GEMM Tumors, untreated or vemurafenib treated (scRNASeq)
| PRJNA523031 | ENA
Project description:In this comprehensive study, the authors have developed concise models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 Immune Checkpoint Blockade (ICB) treatment in individual tumors. It's important to note that their validation was performed in smaller, independent cohorts, constrained by data availability. The authors have developed two Logistic Regression based models for Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma. The main predictive features for the Ipilimumab treated patients are MHC-II HLA, LDH at treatment initiation and the presence of lymph node metastases (LN met), chosen using forward selection methodology. The main predictive features for the Ipilimumab naive patients are tumor heterogeneity, tumor ploidy and tumor purity, chosen using forward selection methodology.
Please note that in these models, the output ‘1’ means progressive disease (PD) and ‘0’ means non-PD. The original GitHub repository can be accessed at https://github.com/vanallenlab/schadendorf-pd1
2023-11-17 | MODEL2310150001 | BioModels
Project description:To identify a prognostic gene signature accounting for the distinct clinical outcomes in ovarian cancer patients Despite the existence of morphologically indistinguishable disease, patients with advanced ovarian tumors display a broad range of survival end points. We hypothesize that gene expression profiling can identify a prognostic signature accounting for these distinct clinical outcomes. To resolve survival-associated loci, gene expression profiling was completed for an extensive set of 185(90 optimal/95 suboptimal) primary ovarian tumors using the Affymetrix human U133A microarray. Cox regression analysis identified probe sets associated with survival in optimally and suboptimally debulked tumor sets at a P value of <0.01. Leave-one-out cross-validation was applied to each tumor cohort and confirmed by a permutation test. External validation was conducted by applying the gene signature to a publicly available array database of expression profiles of advanced stage suboptimally debulked tumors. The prognostic signature successfully classified the tumors according to survival for suboptimally (P = 0.0179) but not optimally debulked (P = 0.144) patients. The suboptimal gene signature was validated using the independent set of tumors (odds ratio, 8.75; P = 0.0146). To elucidate signaling events amenable to therapeutic intervention in suboptimally debulked patients, pathway analysis was completed for the top 57 survival-associated probe sets. For suboptimally debulked patients, confirmation of the predictive gene signature supports the existence of a clinically relevant predictor, as well as the possibility of novel therapeutic opportunities. Ultimately, the prognostic classifier defined for suboptimally debulked tumors may aid in the classification and enhancement of patient outcome for this high-risk population. Gene expression profiling was completed for an extensive set of 185 primary ovarian tumors and 10 normal ovarian surface epithelium using the Affymetrix human U133A microarray
2011-01-20 | E-GEOD-26712 | biostudies-arrayexpress