Transcription Factor KLLN Inhibits Tumor Growth by AR Suppression, Induces Apoptosis by TP53/TP73 Stimulation in Prostate Carcinomas, and Correlates With Cellular Differentiation
ABSTRACT Context:KLLN is a newly identified gene with unknown function and shares a bidirectional promoter with PTEN.Objective:The objective of the study was to analyze the relationship between KILLIN (KLLN) expression and prostate cancer and the potential tumor suppressive effect.Design:We conducted an in silico analysis to compare KLLN expression in normal prostate and matched primary carcinoma tissues. We subsequently used immunohistochemistry to examine KLLN expression and association with Gleason grade and score in 109 prostatectomy samples. KLLN`s tumor-suppressive effect was studied in androgen-dependent and androgen-independent cell models.Patients:Patients were diagnosed with peripheral zone prostate carcinomas without metastasis at the time of prostatectomy. Each patient's primary tumor comprised at least 2 tumoral regions with different Gleason grades.Results:KLLN expression decreased from normal prostate tissue to primary carcinomas (P < .0001). The loss of epithelial and stromal KLLN expression is associated with poor differentiation and high Gleason scores (P < .0001), consistent with our in vitro observation that KLLN inhibits tumor cell proliferation and invasiveness. KLLN decreases prostate-specific antigen levels and suppresses androgen-mediated cell growth by inhibiting androgen receptor (AR) transcription. As an androgen receptor-regulated target, KLLN also functions as a transcriptional activator, directly promoting the expression of TP53 and TP73, with consequent elevated apoptosis, regardless of AR status.Conclusions:Our observations suggest that KLLN is a transcription factor directly regulating AR, TP53, and TP73 expression, with a role in prostate carcinogenesis. Loss of KLLN associates with high Gleason scores, suggesting that KLLN might be used as a potential prognostic marker for risk management and as a novel therapy target for advanced prostate carcinomas.
- [Show abstract] [Hide abstract]
ABSTRACT: NVP-BEZ235 is a newly developed dual PI3K/mTOR inhibitor, being tested in multiple clinical trials, including for breast cancer. NVP-BEZ235 selectively induces cell growth inhibition in a subset, but not all, breast cancer cell lines. However, it remains a challenge to distinguish between sensitive and resistant tumors, particularly in the pre-treatment setting. Here, we used ten breast cancer cell lines to compare NVP-BEZ235 sensitivity and in the context of androgen receptor (AR) activation during NVP-BEZ235 treatment. We also used female SCID mice bearing breast tumor xenografts to investigate the beneficial effect of dihydrotestosterone/NVP-BEZ235 combination treatment compared to each alone. We found that AR-positive breast cancer cell lines are much more sensitive to NVP-BEZ235 compared to AR-negative cells, regardless of PTEN or PI3KCA status. Re-introducing AR expression in NVP-BEZ235-non-responsive AR-negative cells restored the response. DHT/NVP-BEZ235 combination not only resulted in a more significant growth inhibition than either drug alone, but also achieved tumor regression and complete responses for AR+/ER+ tumors. This beneficial effect was mediated by DHT-induced PTEN and KLLN expression. Furthermore, DHT could also reverse NVP-BEZ235-induced side-effects such as skin rash and weight loss. Our data suggest that AR expression may be an independent predictive biomarker for response to NVP-BEZ235. AR induction could add benefit during NVP-BEZ235 treatment in patients, especially with AR+/ER+ breast carcinomas.Molecular Cancer Therapeutics 12/2013; 13(2). DOI:10.1158/1535-7163.MCT-13-0655 · 6.11 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Men with organ-confined prostate cancer (CaP) are often treated with radical prostatectomy. Despite similar postoperative characteristics, a significant proportion of men with an intermediate risk of progression experience prostate-specific antigen (PSA)-defined failure, while others have relapse-free survival (RFS). Additional prognostic markers are needed to predict the outcome of these patients. KLLN is a transcription factor that regulates the cell cycle and induces apoptosis in cancer cells. We have shown that KLLN is an androgen-regulated gene and that loss of KLLN expression in primary CaP is associated with high Gleason score. In this retrospective study, we evaluated KLLN expression in the high-grade malignancy components from 109 men with intermediate risk CaP. Patients with nuclear KLLN-negative tumors had significantly higher preoperative serum PSA levels (12.24±2.37 ng/ml) and larger tumor volumes (4.61±0.71 cm(3)) compared with nuclear KLLN-positive patients (8.35±2.45 ng/ml, P=0.03, and 2.66±0.51 cm(3), P<0.0001, respectively). None of the nuclear KLLN-positive tumors had capsular penetration, whereas 34% of nuclear KLLN-negative tumors (P=0.004) had capsular penetration. Maintaining KLLN expression in tumor nuclei, but not in cytoplasm or stroma, associated with improved RFS after surgery (P=0.002). Only 7% of patients with nuclear KLLN-positive tumors had tumor recurrence, while 60% of patients in the KLLN-negative group developed PSA-defined failure with median relapse time of 6.6 months (P=0.0003). Our data suggest that KLLN expression may be used as a prognostic marker to predict outcome for intermediate risk patients, which could provide useful information for postoperative management.Endocrine Related Cancer 08/2014; 21(4):579-586. DOI:10.1530/ERC-14-0148 · 4.91 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Module network inference is an established statistical method to reconstruct co-expression modules and their upstream regulatory programs from integrated multi-omics datasets measuring the activity levels of various cellular components across different individuals, experimental conditions or time points of a dynamic process. We have developed Lemon-Tree, an open-source, platform-independent, modular, extensible software package implementing state-of-the-art ensemble methods for module network inference. We benchmarked Lemon-Tree using large-scale tumor datasets and showed that Lemon-Tree algorithms compare favorably with state-of-the-art module network inference software. We also analyzed a large dataset of somatic copy-number alterations and gene expression levels measured in glioblastoma samples from The Cancer Genome Atlas and found that Lemon-Tree correctly identifies known glioblastoma oncogenes and tumor suppressors as master regulators in the inferred module network. Novel candidate driver genes predicted by Lemon-Tree were validated using tumor pathway and survival analyses. Lemon-Tree is available from http://lemon-tree.googlecode.com under the GNU General Public License version 2.0.PLoS Computational Biology 08/2014; 11(2). DOI:10.1371/journal.pcbi.1003983 · 4.87 Impact Factor