Up-regulation of the kinase gene SGK1 by progesterone activates the AP-1-NDRG1 axis in both PR-positive and -negative breast cancer cells.

Preoperative progesterone intervention has been shown to confer a survival benefit to breast cancer patients independently of their progesterone receptor (PR) status. This observation raises the question how progesterone affects the outcome of PR-negative cancer. Here, using microarray and RNA-Seq-based gene expression profiling and ChIP-Seq analyses of breast cancer cells, we observed that the serum- and glucocorticoid-regulated kinase gene (SGK1) and the tumor metastasis-suppressor gene N-Myc downstream regulated gene 1 (NDRG1) are up-regulated and that the microRNAs miR-29a and miR-101-1 targeting the 3'-UTR of SGK1 are down-regulated in response to progesterone. We further demonstrate a dual-phase transcriptional and post-transcriptional regulation of SGK1 in response to progesterone, leading to an up-regulation of NDRG1 that is mediated by a set of genes regulated by the transcription factor AP-1. We found that NDRG1, in turn, inactivates a set of kinases, impeding the invasion and migration of breast cancer cells. In summary, we propose a model for the mode of action of progesterone in breast cancer. This model helps decipher the molecular basis of observations in a randomized clinical trial of the effect of progesterone on breast cancer and has therefore the potential to improve the prognosis of breast cancer patients receiving preoperative progesterone treatment.

Identifying cancer driver genes from functional genomics screens.

With the emerging advances made in genomics and functional genomics approaches, there is a critical and growing unmet need to integrate plural datasets in order to identify driver genes in cancer. An integrative approach, with the convergence of multiple types of genetic evidence, can limit false positives through a posterior filtering strategy and reduce the need for multiple hypothesis testing to identify true cancer vulnerabilities. We performed a pooled shRNA screen against 906 human genes in the oral cancer cell line AW13516 in triplicate. The genes that were depleted in the screen were integrated with copy number alteration and gene expression data and ranked based on ROAST analysis, using an integrative scoring system, DepRanker, to compute a Rank Impact Score (RIS) for each gene. The RIS-based ranking of candidate driver genes was used to identify the putative oncogenes AURKB and TK1 as essential for oral cancer cell proliferation. We validated the findings, showing that shRNA mediated genetic knockdown of TK1 or pharmacological inhibition of AURKB by AZD-1152 HQPA in AW13516 cells could significantly impede their proliferation. Next we analysed alterations in AURKB and TK1 genes in head and neck cancer and their association with prognosis using data on 528 patients obtained from TCGA. Patients harbouring alterations in AURKB and TK1 genes were associated with poor survival. To summarise, we present DepRanker as a simple yet robust package with no third-party dependencies for the identification of potential driver genes from a pooled shRNA functional genomic screen by integrating results from RNAi screens with gene expression and copy number data. Using DepRanker, we identify AURKB and TK1 as potential therapeutic targets in oral cancer. DepRanker is in the public domain and available for download at http://www.actrec.gov.in/pi-webpages/AmitDutt/DepRanker/DepRanker.html.