A novel Induced-recurrence PDX model recapitulates epi-genomic features of Glioblastoma recurrence [DNA methylation EPIC]
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PROVIDER: PRJNA1132518 | ENA |
REPOSITORIES: ENA
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Project description:A novel Induced-recurrence PDX model recapitulates epi-genomic features of Glioblastoma recurrence [scRNA-seq]
| PRJNA1132516 | ENA
Project description:A novel Induced-recurrence PDX model recapitulates epi-genomic features of Glioblastoma recurrence [RNA-seq]
| PRJNA1132515 | ENA
Project description:Glioblastoma remains incurable and, due to its infiltrative growth, high levels of treatment resistance and population of glioma initiating/stem cells (GICs), recurs in all patients. Here we design and characterize a novel induced-recurrence model in which mice xenografted with primary patient-derived GICs are treated with a therapeutic regimen closely recapitulating patient standard of care, followed by monitoring until tumours recur (IR-PDX). By tracking in vivo tumour growth, we confirm the patient specificity and initial efficacy of treatment prior to recurrence. Availability of longitudinally matched pairs of primary and recurrent GICs enabled patient-specific evaluation of the faithfulness with which the model recapitulated phenotypes associated with the true recurrence. Through comprehensive multi-omic analyses, we showed that the IR-PDX model recapitulated genomic, epigenetic, and transcriptional state heterogeneity changes upon recurrence in a patient-specific manner. The accuracy of the IR-PDX enabled both novel biological insights, including the positive association between glioblastoma recurrence and levels of ciliated neural stem cell-like tumour cells, and the identification of druggable patient-specific therapeutic vulnerabilities. This proof-of-concept study opens the possibility for prospective precision medicine approaches, in which the IR-PDX model is developed between first diagnosis and disease progression to identify target-drug candidates for use as second line of treatment at recurrence.
2025-03-20 | GSE271621 | GEO
Project description:Glioblastoma remains incurable and, due to its infiltrative growth, high levels of treatment resistance and population of glioma initiating/stem cells (GICs), recurs in all patients. Here we design and characterize a novel induced-recurrence model in which mice xenografted with primary patient-derived GICs are treated with a therapeutic regimen closely recapitulating patient standard of care, followed by monitoring until tumours recur (IR-PDX). By tracking in vivo tumour growth, we confirm the patient specificity and initial efficacy of treatment prior to recurrence. Availability of longitudinally matched pairs of primary and recurrent GICs enabled patient-specific evaluation of the faithfulness with which the model recapitulated phenotypes associated with the true recurrence. Through comprehensive multi-omic analyses, we showed that the IR-PDX model recapitulated genomic, epigenetic, and transcriptional state heterogeneity changes upon recurrence in a patient-specific manner. The accuracy of the IR-PDX enabled both novel biological insights, including the positive association between glioblastoma recurrence and levels of ciliated neural stem cell-like tumour cells, and the identification of druggable patient-specific therapeutic vulnerabilities. This proof-of-concept study opens the possibility for prospective precision medicine approaches, in which the IR-PDX model is developed between first diagnosis and disease progression to identify target-drug candidates for use as second line of treatment at recurrence.
2025-03-20 | GSE271620 | GEO
Project description:Glioblastoma remains incurable and, due to its infiltrative growth, high levels of treatment resistance and population of glioma initiating/stem cells (GICs), recurs in all patients. Here we design and characterize a novel induced-recurrence model in which mice xenografted with primary patient-derived GICs are treated with a therapeutic regimen closely recapitulating patient standard of care, followed by monitoring until tumours recur (IR-PDX). By tracking in vivo tumour growth, we confirm the patient specificity and initial efficacy of treatment prior to recurrence. Availability of longitudinally matched pairs of primary and recurrent GICs enabled patient-specific evaluation of the faithfulness with which the model recapitulated phenotypes associated with the true recurrence. Through comprehensive multi-omic analyses, we showed that the IR-PDX model recapitulated genomic, epigenetic, and transcriptional state heterogeneity changes upon recurrence in a patient-specific manner. The accuracy of the IR-PDX enabled both novel biological insights, including the positive association between glioblastoma recurrence and levels of ciliated neural stem cell-like tumour cells, and the identification of druggable patient-specific therapeutic vulnerabilities. This proof-of-concept study opens the possibility for prospective precision medicine approaches, in which the IR-PDX model is developed between first diagnosis and disease progression to identify target-drug candidates for use as second line of treatment at recurrence.
2025-03-20 | GSE271619 | GEO
Project description:Heteromeric protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (CF/MS), but the resulting data can be challenging to process and interpret. Here, we describe EPIC (Elution Profile-based Inference of Complexes), a software toolkit for automated scoring of CF/MS data for large-scale determination of high-confidence physical interaction networks and macromolecular assemblies from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 590 putative worm protein complexes linked to diverse biological processes, including assemblies unique to nematodes. The EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/), and the open source code is available via GitHub (https://github.com/BaderLab/EPIC).
2019-07-16 | PXD011182 | Pride
Project description:This Series contains data from 845 participants (188 men and 657 women) in the EPIC-Italy cohort that was produced at the Human Genetics Foundation (HuGeF) in Turin, Italy. At the last follow-up (2010), 424 participants remained cancer-free, 235 had developed primary breast cancer, 166 had developed primary colorectal cancer, and 20 had developed other primary cancers. Anthropometric measurements, and dietary and lifestyle information obtained by questionnaire are also available. A total of 845 samples from the EPIC-Italy cohort were analyzed.
2013-09-20 | E-GEOD-51032 | biostudies-arrayexpress
Project description:This study will evaluate longitudinal performance of Epi proColon with respect to test positivity, longitudinal adherence to Epi proColon screening, adherence to follow-up colonoscopy and diagnostic yield, as well as assay failure rates.
| 2249389 | ecrin-mdr-crc