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RNA-seq of human ex vivo ovarian cancer models - part IV

ABSTRACT: RNA-seq of human ex vivo ovarian cancer models - part IV

PROVIDER: PRJEB81794 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	ERR13907037_1.fastq.gz	Fastqsanger.gz
	ERR13907037_2.fastq.gz	Fastqsanger.gz
	ERR13907038_1.fastq.gz	Fastqsanger.gz
	ERR13907038_2.fastq.gz	Fastqsanger.gz
	ERR13907039_1.fastq.gz	Fastqsanger.gz

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RNA-seq of human ex vivo ovarian cancer models - part IV

Project description:Ascites or solid tumour samples from patients with ovarian cancer were collected and grown in culture as ex vivo models of purified tumour cells. RNA-seq was performed on these models to establish gene expression profiles, which allows identification of genes that are differentially expressed between models with differing tumour intrinsic properties. These samples have been interrogated (1) for their mRNA levels of the ATP-binding cassette (ABC) transporter protein ABCB1 and (2) for differentially expressed genes associated with sensitivity/resistance to the SAE inhibitor ML-792. These samples are processed in the same manner as previous studies: E-MTAB-7223, E-MTAB-10801 and E-MTAB-11000 with no stromal counterparts included in this current sequencing batch.

2025-04-01 | E-MTAB-14568 | biostudies-arrayexpress

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Project description:Ascites or solid tumour samples from patients with ovarian cancer were collected and grown in culture as ex vivo models of purified tumour cells. RNA-seq was performed on these models to establish gene expression profiles, which allow identification of genes that are differentially expressed between patients with differing tumour intrinsic properties. These samples have been interrogated for the presence of a gene expression signature indicative of sensitivity to an inhibitor of poly(ADP-ribose) glycohydrolase (PARG). These samples are processed in the same manner as previous studies: “E-MTAB-7223 - RNA-seq of human ex vivo ovarian cancer models with matched stromal cells” and “E-MTAB-10801 - RNA-seq of human ex vivo ovarian cancer models with matched stromal cells - part II” with no stromal counterparts included in this current sequencing batch.

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Project description:RNA-seq of human ex vivo ovarian cancer models - part III

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RNA-seq of human ex vivo ovarian cancer models with matched stromal cells - part II

Project description:Ascites or solid tumour from patients with ovarian cancer was collected and grown in culture as ex vivo models. Each sample has a tumour component and some samples have matched stromal cells, which were separated into individual cultures. RNA-seq was performed on these models to establish gene expression profiles, which allow the assessment of the separation protocol and identification of genes that are differentially expressed. The histological subtype from which the models were collected includes majorly high-grade serous, but also low-grade serous, clear cell and mucinous ovarian cancer. The sample subtypes have been assessed using a machine-learning based transcriptional classifier. These samples are processed in the same manner as a previous study, “E-MTAB-7223 - RNA-seq of human ex vivo ovarian cancer models with matched stromal cells”

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RNA-seq of human ex vivo ovarian cancer models with matched stromal cells - part II

Project description:RNA-seq of human ex vivo ovarian cancer models with matched stromal cells - part II

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Project description:Ascites or solid tumour from patients with ovarian cancer was collected and grown in culture as ex vivo models. Each sample has a mixture of tumour and stromal cells which were separated into individual cultures. Therefore each patient has tumour and stromal cultures originating from the same tissue collection. Variant calling (exome-seq) analysis was performed on these matched models to establish tumour specific mutations. Stromal cells were used to rule out germline mutations.

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Project description:Exome sequencing of human ex vivo ovarian cancer models with matched stromal cells

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Project description:RNA-seq of human ex vivo ovarian cancer models with matched stromal cells

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