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Single Cell RNA sequence data from a human ovarian cancer sample

ABSTRACT: Single Cell RNA sequence data from a human ovarian cancer sample

PROVIDER: PRJNA508840 | ENA |

REPOSITORIES: ENA

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Single Cell RNA sequence data from a human ovarian cancer sample

Project description:Purpose: Investigate cellular heterogeneity in a fresh human ovarian cancer tissue sample Methods: Enzymatic digestion of fresh tissue sample collected from the operating room to produce single cell suspension. Cells were labelled with fluorescent antibodies to CD3, CD14, CD19, CD20, CD56 and FACS sorted to remove immune cells. The negative population was used for sequencing. Single cells were processed using the Fluidigm C1 Chip to generate barcoded cDNA for each cell. Amplifed cDNA was sequenced using an Illumina HiSeq 2500 machine. Results: Single cell RNA sequence data was obtained for 92 cells and a "bulk" sample of 1000 cells. 26 cells were removed from analysis due to quality control standards. The remaining 66 cells and the bulk sample were analyzed. Conclusion: Single cell RNA sequence analysis reveals heterogeneity in gene expression in cells harvested from a high grade ovarian serous cancer

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Expression data from patients with advanced ovarian cancer

Project description:Integration of several ovarian cancer datasets to identify a reproducible predictors of survival Four microarray datasets from different institutions were reprocessed in a uniform manner, into a single training dataset. Survival analysis was performed and a validation cohort (61 patients from 3 institutions) was profiled using a custom array to confirm the prognostic value of the predictors. The four datasets were obtained from the following reports: Spentzos D, Levine DA, Ramoni MF, et al. Gene expression signature with independent prognostic significance in epithelial ovarian cancer. J Clin Oncol 2004;22:4700-10 Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439:353-7 Marquez RT, Baggerly KA, Patterson AP, et al. Patterns of gene expression in different histotypes of epithelial ovarian cancer correlate with those in normal fallopian tube, endometrium, and colon. Clin Cancer Res 2005;11:6116-26 Zhang L, Volinia S, Bonome T, et al. Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer. Proc Natl Acad Sci U S A 2008;105:7004-9 61 samples

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