Materials and methods: The antiproliferative activity of koenimbin was examined using MTT, and the apoptotic detection was carried out by acridine orange/propidium iodide (AO/PI) double-staining and multiparametric high-content screening (HCS) assays. Caspase bioluminescence assay, reverse transcription polymerase chain reaction (RT-PCR), and immunoblotting were conducted to confirm the expression of apoptotic-associated proteins. Cell cycle analysis was investigated using flow cytometry. Involvement of nuclear factor-kappa B (NF-κB) was analyzed using HCS assay. Aldefluor™ and prostasphere formation examinations were used to evaluate the impact of koenimbin on PC-3 CSCs in vitro.
Results: Koenimbin remarkably inhibited cell proliferation in a dose-dependent manner. Koenimbin induced nuclear condensation, formation of apoptotic bodies, and G0/G1 phase arrest of PC-3 cells. Koenimbin triggered the activation of caspase-3/7 and caspase-9 and the release of cytochrome c, decreased anti-apoptotic Bcl-2 and HSP70 proteins, increased pro-apoptotic Bax proteins, and inhibited NF-κB translocation from the cytoplasm to the nucleus, leading to the activation of the intrinsic apoptotic pathway. Koenimbin significantly (P<0.05) reduced the aldehyde dehydrogenase-positive cell population of PC-3 CSCs and the size and number of PC-3 CSCs in primary, secondary, and tertiary prostaspheres in vitro.
Conclusion: Koenimbin has chemotherapeutic potential that may be employed for future treatment through decreasing the recurrence of cancer, resulting in the improvement of cancer management strategies and patient survival.
SIGNIFICANCE: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.