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PRKCI promotes immune suppression in ovarian tumors through YAP1

ABSTRACT: A key feature of ovarian high-grade serous carcinoma is frequent amplification of the 3q26 locus. Here we show that PRKCI, located on the 3q26 locus is not only amplified and overexpressed in 78% of HGSC patient samples but is also expressed in early fallopian tube lesions, called Serous Tubal Intraepithelial Carcinoma. In vivo studies in a transgenic mouse model establish PRKCI as an ovarian cancer oncogene and identify YAP1 as a downstream regulator. Together, PRKCI and YAP1 regulate TNFα to promote an immune suppressive microenvironment and inhibit cytotoxic T-cell infiltration in tumors. High PRKCI expressing human ovarian tumors show decreased cytotoxic T-cell infiltration. Taken together, we identify PRKCI and YAP1 as key mediators of a tumor-promoting immune microenvironment in ovarian cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE57262 | GEO | 2018/06/01

REPOSITORIES: GEO

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PRKCI promotes immune suppression in ovarian cancer through YAP1

Project description:A key feature of high-grade serous ovarian carcinoma (HGSOC) is frequent amplification of the 3q26 locus harboring PRKC-iota (PRKCI). Here, we show that PRKCI is also expressed in early fallopian tube lesions, called Serous Tubal Intraepithelial Carcinoma. Transgenic mouse studies establish PRKCI as an ovarian cancer specific oncogene and system level and functional analyses identify YAP1 as a downstream effector in tumor progression. Mechanistically, the oncogenic activity of the PRKCI-YAP1 axis relates in part to the upregulation of TNFα to promote an immune suppressive tumor microenvironment characterized by an abundance of myeloid-derived suppressor cells and inhibition of cytotoxic T cell infiltration. In human ovarian cancers, high PRKCI expression also correlates with high expression of YAP1 and low infiltration of cytotoxic T-cell. The PRKCI-YAP1 regulation of the tumor immunity provides a therapeutic strategy for highly lethal ovarian cancer. Robust multi-array average (RMA) method was used with default options (with background correction, quantile normalization, and log transformation) to normalize raw data from batches using R/Bioconductor‘s affy package

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