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Validation study: Response-predictive gene expression profiling of glioma progenitor cells in vitro

ABSTRACT: Background. In a previous publication we introduced a novel approach to identify genes that hold predictive information about treatment outcome. A linear regression model was fitted by using the least angle regression algorithm (LARS) with the expression profiles of a construction set of 18 glioma progenitor cells enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed predicting therapy-induced impairment of proliferation in vitro. Prediction performance was validated in leave one out cross validation. Methods. In this study, we used an additional validation set of 18 serum-free short-term treated in vitro cell cultures to test the predictive properties of the signature in an independent cohort. We assessed proliferation rates together with transcriptome-wide expression profiles after Sunitinib treatment of each individual cell culture, following the methods of the previous publication. Results. We confirmed treatment-induced expression changes in our validation set, but our signature failed to predict proliferation inhibition. Conclusion. Although the gene signature published from our construction set exhibited good prediction accuracy in cross validation, we were not able to validate the signature in an independent validation data set. Reasons could be regression to the mean, the moderate numbers of samples, or too low differences in the response to proliferation inhibition. At this stage and based on the presented results, we conclude that the signature does not warrant further developmental steps towards clinical application.

ORGANISM(S): Homo sapiens

PROVIDER: GSE76990 | GEO | 2016/02/21

SECONDARY ACCESSION(S): PRJNA309187

REPOSITORIES: GEO

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Validation study: Response-predictive gene expression profiling of glioma progenitor cells in vitro

Project description:Background. In a previous publication we introduced a novel approach to identify genes that hold predictive information about treatment outcome. A linear regression model was fitted by using the least angle regression algorithm (LARS) with the expression profiles of a construction set of 18 glioma progenitor cells enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed predicting therapy-induced impairment of proliferation in vitro. Prediction performance was validated in leave one out cross validation. Methods. In this study, we used an additional validation set of 18 serum-free short-term treated in vitro cell cultures to test the predictive properties of the signature in an independent cohort. We assessed proliferation rates together with transcriptome-wide expression profiles after Sunitinib treatment of each individual cell culture, following the methods of the previous publication. Results. We confirmed treatment-induced expression changes in our validation set, but our signature failed to predict proliferation inhibition. Conclusion. Although the gene signature published from our construction set exhibited good prediction accuracy in cross validation, we were not able to validate the signature in an independent validation data set. Reasons could be regression to the mean, the moderate numbers of samples, or too low differences in the response to proliferation inhibition. At this stage and based on the presented results, we conclude that the signature does not warrant further developmental steps towards clinical application. 40 samples; 18 brain tumor initiating cells (BTICs); 2 treatment conditions: 1 ÂµM Sunitinib or 0.00025% DMSO with supplementation of VEGF and PDGF-AB for 6 hours

2016-02-21 | E-GEOD-76990 | biostudies-arrayexpress

Comprehensive microRNA profiling of Nasopharyngeal Carinoma Specimens

Project description:Publication: Identification of a microRNA signature associated with risk of distant metastasis in nasopharyngeal carcinoma Purpose Despite significant improvement in locoregional control in the contemporary era of nasopharyngeal carcinoma (NPC) treatment, patients still suffer from a significant risk of distant metastasis (DM). Identifying those patients at risk of DM would aid in personalized treatment in the future. MicroRNAs (miRNAs) play many important roles in human cancers; hence, we proceeded to address the primary hypothesis that there is a miRNA expression signature capable of predicting DM for NPC patients. Methods and results The expression of 734 miRNAs was measured in 125 (Training) and 121 (Validation) clinically annotated NPC diagnostic biopsy samples. A 4-miRNA expression signature associated with risk of developing DM was identified by fitting a penalized Cox Proportion Hazard regression model to the Training data set (HR 8.25; p < 0.001), and subsequently validated in an independent Validation set (HR 3.2; p = 0.01). Pathway enrichment analysis indicated that the targets of miRNAs associated with DM appear to be converging on cell-cycle pathways. Conclusions This 4-miRNA signature adds to the prognostic value of the current “gold standard” of TNM staging. In-depth interrogation of these 4-miRNAs will provide important biological insights that could facilitate the discovery and development of novel molecularly targeted therapies to improve outcome for future NPC patients.

2015-07-17 | GSE70970 | GEO

Transgelin contributes to a poor response of metastatic renal cell carcinoma to sunitinib treatment

Project description:Renal cell carcinoma (RCC) represents about 2-3% of all cancers with over 400,000 new cases per year. Sunitinib, a vascular endothelial growth factor tyrosine kinase receptor inhibitor, has been used mainly for first-line treatment of metastatic clear-cell RCC with good or intermediate prognosis. However, about one third of metastatic RCC patients do not respond to sunitinib, leading to disease progression. Here we aim to find and characterize proteins associated with poor sunitinib response in a pilot proteomics study. 16 RCC tumors from patients responding (8) vs. non-responding (8) to sunitinib in 3 months after treatment initiation, together with their adjacent non-cancerous tissues, were analyzed using data independent acquisition mass spectrometry. Proteomics analysis quantified 1996 protein groups (q<0.01) and revealed 27 proteins deregulated between tumors non-responding vs. responding to sunitinib, representing a pattern of deregulated proteins potentially contributing to sunitinib resistance. Gene set enrichment analysis showed up-regulation of epithelial-to-mesenchymal transition with transgelin as one of the most significantly abundant protein. Transgelin expression was silenced by CRISPR/Cas9 and RNA interference, and the cells with reduced transgelin level exhibited significantly slower proliferation. Our data indicate that transgelin is an essential protein supporting RCC cell proliferation which could contribute to intrinsic sunitinib resistance.

2021-11-03 | PXD027065 | Pride

Metabolic signature of idiopathic inflammatory myopathy

Project description:Objective of this study was to find Metabolic signature of idiopathic inflammatory myopathy (IIM). We used the serum samples of healthy control, IIM, ankylosing spondylitis. Metabolites were quantified using biocrates p180 kit. First, we found IIM specific metabolites by using ANOVA with post-hoc analysis. With set of metabolite panel, we made prediction model using logistic regression (LR), support vector machine (SVM), and random forest (RF). We found 7 metabolites as biomarker for classifying IIM from healthy control and ankylosing spondylitis. Also, we validate our model using 5 cross validation method. Out set of metabolites showed the AUC values of 0.955 (LR), 0.908 (RF) and 0.918 (SVM).

2022-06-09 | ST002196 | MetabolomicsWorkbench

Radiomic subtyping improves disease stratification beyond key molecular, clinical and standard imaging characteristics in patients with glioblastoma.

Project description:Background: To analyze the potential of radiomics for disease stratification beyond key molecular, clinical and standard imaging features in patients with glioblastoma. Methods: Quantitative imaging features (n=1043) were extracted from the multiparametric MRI of 181 patients with newly-diagnosed glioblastoma prior to standard-of-care treatment (allocated to a discovery and validation set, 2:1 ratio). A subset of 386/1043 features were identified as reproducible (in an independent MRI-test-retest cohort) and selected for analysis. A penalized Cox-model with 10-fold cross-validation (Coxnet) was fitted on the discovery set to construct a radiomic signature for predicting progression-free and overall survival (PFS, OS). The incremental value of a radiomic signature beyond molecular (MGMT-promoter methylation, DNA-methylation subgroups), clinical (patients age, KPS, extent-of-resection, adjuvant treatment) and standard imaging parameters (tumor volumes) for stratifying PFS and OS was assessed with multivariate Cox-models (performance quantified with prediction error curves). Results: The radiomic signature (constructed from 8/386 features identified through Coxnet) increased the prediction accuracy for PFS and OS (in both discovery and validation set) beyond the assessed molecular, clinical and standard imaging parameters (p≤0.01). Prediction errors decreased by 36% for PFS and 37% for OS when adding the radiomic signature (as compared to 29% and 27% with molecular + clinical features alone). The radiomic signature was - along with MGMT-status - the only parameter with independent significance on multivariate analysis (p≤0.01). Conclusions: Our study stresses the role of integrating radiomics into a multi-layer decision framework with key molecular and clinical features to improve disease stratification and to potentially advance personalized treatment of patients with glioblastoma.

2018-01-05 | GSE103659 | GEO

Gene expression profiles of Sunitinib-treated but not untreated short-term serum-free cultures predict treatment response of human high-grade gliomas in vitro

Project description:High-grade gliomas are amongst the most deadly human tumors. Treatment results are overall disappointing. Nevertheless, in several trials around 20% of patients respond to therapy. Diagnostic strategies to identify those patients that will ultimately profit from a specific targeted therapy are urgently needed. Gene expression profiling of untreated tumors is a well established approach for identifying biomarkers or diagnostic signatures. However, reliable signatures predicting treatment response in gliomas do not exist. Here we suggest a novel strategy for developing diagnostic signatures. We postulate that predictive gene expression patterns emerge only after tumor cells have been treated with the agent in vitro. Moreover, we postulate that enriching specimens for tumor initiating cells sharpens predictive expression patterns. Here, we report on the prediction of treatment response of cancer cells in vitro. As a proof of principle we analyzed gene expression in 18 short-term serum-free cultures of high-grade gliomas enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated but not from untreated glioma cells allowed to predict therapy-induced impairment of proliferation of glioma cells in vitro. Prediction can be achieved with as little as 6 genes allowing for a straightforward translation into the clinic once the predictive power of the signature is shown also in vivo. Our strategy of using expression profiles from in vitro treated BTIC-enriched cultures opens new ways for trial design for patients with malignant gliomas. 72 samples; 18 brain tumor initiating cells (BTICs); 4 treatment conditions: 1 ÂµM Sunitinib or 0.00025% DMSO with and without the combination of recombinant growth factors VEGF and PDGF-AB for 6 hours

2014-10-07 | E-GEOD-51305 | biostudies-arrayexpress

Genetic instability in colon cancer

Project description:Experimental set accompanying Giacomini et al publication "A legacy gene-expression signature of genetic instability in colon cancer". Includes 18 colon cancer cell line training set, 13 colon cancer cell line test set, and 3 cell lines (HCT116, HCT116+ch2, HCT116+ch3) used to evaluate signature after correcting underlying genetic instability. Experiments were performed by comparing mRNA from each colon cancer cell line (Cy5; channel 2) to a "universal" mRNA reference (Cy3; channel 1). A disease state experiment design type is where the state of some disease such as infection, pathology, syndrome, etc is studied. Series type: disease_state_design Series_overall_design: Using regression correlation Keywords: other

2005-12-06 | GSE2591 | GEO

Perturbation-informed signatures from crosswise integration of transcriptome and chromatin accessibility analyses predict susceptibility to candidate anticancer drugs (cisplatin validation dataset) [RNA-seq]

Project description:Transcriptome and chromatin state of cell lines with high (Jurkat) and low (BxPC-3) sensitivity to cisplatin was acquired after 6 h 3 uM treatment with the anticancer drug, in order to validate a drug prediction pipeline based on the construction of a perturbation-informed signature.

2022-08-17 | GSE207611 | GEO

2022-08-17 | GSE207607 | GEO

Construction and Validation of Risk Prediction Model for Gastrointestinal Dysfunction of Patient With Colorectal Cancer

Project description:To understand the current situation of the postoperative gastrointestinal dysfunction in patients with colorectal cancer effect a radical cure, and analyze the risk factors, and build the colorectal cancer radical surgery in patients with gastrointestinal dysfunction risk prediction nomogram model decision tree classification and regression tree model, through internal validation evaluation the performance of the two models in the modeling data set and dividing the postoperative gastrointestinal dysfunction risk level.Two risk prediction models were used to carry out external verification, evaluate the clinical practicability and effectiveness of the model, and provide reference for further promotion of the model.

| 62483 | ecrin-mdr-crc

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