Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy (WGS)

ABSTRACT: Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy (WGS)

PROVIDER: PRJNA668140 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy

Project description:Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy

| PRJNA665372 | ENA

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy (Exome)

Project description:Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy (Exome)

| PRJNA668139 | ENA

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy [scRNA-Seq]

Project description:Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy [scRNA-Seq]

| PRJNA666898 | ENA

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy (RNA-seq)

Project description:Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy (RNA-seq)

| PRJNA665374 | ENA

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy (WGS)

Project description:Purpose: There are three goals of this study of these analysis: 1. To compare the genomic, exome and chromatin accessiblity profiles of the specific engineered fallopian tube cells of high-grade serous tubo-ovarian cancer (HGSC) models (this study) using whole-exome-, whole-genome- and ATAC sequencing. Methods: Genomic DNA was extracted from the cell lines mentioned below. Conclusions: We conclude that whole-exome, whole-genome and ATAC-seq characterization would expedite genetic network analyses and permit the dissection of complex biological functions.

2020-11-17 | GSE159254 | GEO

Development of New Therapeutic Combinations for Ovarian Cancer Using Genetically Defined, Syngeneic Organoid Platform

Project description:Development of New Therapeutic Combinations for Ovarian Cancer Using Genetically Defined, Syngeneic Organoid Platform

| PRJNA613661 | ENA

Genetically Defined Syngeneic Mouse Models of Ovarian Cancer as Tools for the Discovery of Combination Immunotherapy.

Project description:Despite advances in immuno-oncology, the relationship between tumor genotypes and response to immunotherapy remains poorly understood, particularly in high-grade serous tubo-ovarian carcinomas (HGSC). We developed a series of mouse models that carry genotypes of human HGSCs and grow in syngeneic immunocompetent hosts to address this gap. We transformed murine-fallopian tube epithelial cells to phenocopy homologous recombination-deficient tumors through a combined loss of Trp53, Brca1, Pten, and Nf1 and overexpression of Myc and Trp53 R172H, which was contrasted with an identical model carrying wild-type Brca1. For homologous recombination-proficient tumors, we constructed genotypes combining loss of Trp53 and overexpression of Ccne1, Akt2, and Trp53 R172H, and driven by KRAS G12V or Brd4 or Smarca4 overexpression. These lines form tumors recapitulating human disease, including genotype-driven responses to treatment, and enabled us to identify follistatin as a driver of resistance to checkpoint inhibitors. These data provide proof of concept that our models can identify new immunotherapy targets in HGSC. SIGNIFICANCE: We engineered a panel of murine fallopian tube epithelial cells bearing mutations typical of HGSC and capable of forming tumors in syngeneic immunocompetent hosts. These models recapitulate tumor microenvironments and drug responses characteristic of human disease. In a Ccne1-overexpressing model, immune-checkpoint resistance was driven by follistatin.This article is highlighted in the In This Issue feature, p. 211.

| S-EPMC8344888 | biostudies-literature

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy

Project description:This SuperSeries is composed of the SubSeries listed below.

2020-11-17 | GSE158474 | GEO

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy (Exome)

Project description:Purpose: There are three goals of this study: 1. To compare the genomic, exome and chromatin accessiblity profiles of the specific engineered fallopian tube cells of high-grade serous tubo-ovarian cancer (HGSC) models (this study) using whole-exome, whole-genome and ATAC-seq sequencing. Methods: For whole-exome analysis, genomic DNA was extracted from the cell lines mentioned below. Conclusions: We conclude that whole-exome, whole-genome and ATAC-seq characterization would expedite genetic network analyses and permit the dissection of complex biological functions.

2020-11-17 | GSE159253 | GEO

Genetically defined syngeneic mouse models of ovarian cancer as tools for the discovery of combination immunotherapy [scRNA-Seq]

Project description:Purpose: One of the goals of this study is to compare the transcriptome profiles of tumors from mice inoculated with specific engineered fallopian tube cells of high-grade serous tubo-ovarian cancer (HGSC) models (this study) using scRNA sequencing Methods: Tumors from mice inoculated with specific engineered fallopian tube cells of high-grade serous tubo-ovarian cancer (HGSC) models were compared using scRNA sequencing. Conclusions: We conclude that scRNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biological functions.

2020-11-17 | GSE158886 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data