Functional relevance of novel p300-mediated lysine 314 and 315 acetylation of RelA/p65

Nuclear factor kappaB (NF-κB) plays an important role in the transcriptional regulation of genes involved in immunity and cell survival. We show here in vitro and in vivo acetylation of RelA/p65 by p300 on lysine 314 and 315, two novel acetylation sites. Additionally, we confirmed the acetylation on lysine 310 shown previously. Genetic complementation of RelA/p65−/− cells with wild type and non-acetylatable mutants of RelA/p65 (K314R and K315R) revealed that neither shuttling, DNA binding nor the induction of anti-apoptotic genes by tumor necrosis factor α was affected by acetylation on these residues. Microarray analysis of these cells treated with TNFα identified specific sets of genes differently regulated by wild type or acetylation-deficient mutants of RelA/p65. Specific genes were either stimulated or repressed by the acetylation-deficient mutants when compared to RelA/p65 wild type. These results support the hypothesis that site-specific p300-mediated acetylation of RelA/p65 regulates the specificity of NF-κB dependent gene expression

Genomic “Dark Matter” in Prostate Cancer: Exploring the Clinical Utility of ncRNA as Biomarkers

Prostate cancer is the most diagnosed cancer among men in the United States. While the majority of patients who undergo surgery (prostatectomy) will essentially be cured, about 30–40% men remain at risk for disease progression and recurrence. Currently, patients are deemed at risk by evaluation of clinical factors, but these do not resolve whether adjuvant therapy will significantly attenuate or delay disease progression for a patient at risk. Numerous efforts using mRNA-based biomarkers have been described for this purpose, but none have successfully reached widespread clinical practice in helping to make an adjuvant therapy decision. Here, we assess the utility of non-coding RNAs as biomarkers for prostate cancer recurrence based on high-resolution oligonucleotide microarray analysis of surgical tissue specimens from normal adjacent prostate, primary tumors, and metastases. We identify differentially expressed non-coding RNAs that distinguish between the different prostate tissue types and show that these non-coding RNAs can predict clinical outcomes in primary tumors. Together, these results suggest that non-coding RNAs are emerging from the “dark matter” of the genome as a new source of biomarkers for characterizing disease recurrence and progression. While this study shows that non-coding RNA biomarkers can be highly informative, future studies will be needed to further characterize the specific roles of these non-coding RNA biomarkers in the development of aggressive disease

Transcriptome-Wide Detection of Differentially Expressed Coding and Non-Coding Transcripts and Their Clinical Significance in Prostate Cancer

Prostate cancer is a clinically and biologically heterogeneous disease. Deregulation of splice variants has been shown to contribute significantly to this complexity. High-throughput technologies such as oligonucleotide microarrays allow for the detection of transcripts that play a role in disease progression in a transcriptome-wide level. In this study, we use a publicly available dataset of normal adjacent, primary tumor, and metastatic prostate cancer samples (GSE21034) to detect differentially expressed coding and non-coding transcripts between these disease states. To achieve this, we focus on transcript-specific probe selection regions, that is, those probe sets that correspond unambiguously to a single transcript. Based on this, we are able to pinpoint at the transcript-specific level transcripts that are differentially expressed throughout prostate cancer progression. We confirm previously reported cases and find novel transcripts for which no prior implication in prostate cancer progression has been made. Furthermore, we show that transcript-specific differential expression has unique prognostic potential and provides a clinically significant source of biomarker signatures for prostate cancer risk stratification. The results presented here serve as a catalog of differentially expressed transcript-specific markers throughout prostate cancer progression that can be used as basis for further development and translation into the clinic

Individual Patient-Level Meta-Analysis of the Performance of the Decipher Genomic Classifier in High-Risk Men After Prostatectomy to Predict Development of Metastatic Disease.

Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I(2) test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P \u3c .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I(2) = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P \u3c .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients postprostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted

The dual orexin receptor antagonist almorexant induces sleep and decreases orexin-induced locomotion by blocking orexin 2 receptors

Orexin peptides regulate locomotor activity and sleep-wake balance by activating orexin 1 and orexin 2 receptors (OX1R and OX2R). Dual OX1R and OX2R antagonists reduce activity and promote sleep in multiple species, including man. We tested the effects of orexin A and almorexant, a dual OX1R/OX2R antagonist, in C57BL/6J mice and in mice lacking OX1Rs, OX2Rs or both to investigate the roles of the two receptors in orexin-induced locomotion and in sleep/wake regulation. Orexin A induced locomotion primarily by activating OX2Rs as locomotion was increased following intracerebroventricular orexin in OX1R-/- mice but not in OX2R-/- or OX1R-/-/OX2R-/- mice. Almorexant attenuated the orexin A-induced locomotion, demonstrating in vivo that almorexant specifically inhibits the actions of orexin. As in other species, almorexant dose-dependently increased rapid eye movement (REM) and non-REM (NREM) sleep in C57BL/6J mice. Sleep promotion by almorexant was mediated by inhibition of the known OXRs as almorexant was ineffective in OX1R-/-/OX2R-/- mice. Almorexant induced sleep in OX1R-/- mice but not in OX2R-/- mice, demonstrating that antagonism of OX2Rs is sufficient for sleep induction. When almorexant was incubated with the receptors for short periods of time, it behaved as a dual antagonist against orexin A-induced Ca2+ responses. However, with increasing incubation times, almorexant acquired OX2R selectivity confirming the findings from binding assays and further suggesting it may behave as an OX2R antagonist in vivo. Thus, OX2R activation mediates the stimulatory effects of orexin A on locomotion and antagonism of OX2R is sufficient to promote sleep in mice

Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis.

BackgroundCurrently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes.MethodsWe developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individual-level risk of cancer progression, and for genomics-based care versus usual care treatment recommendations.ResultsCancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12% relative increase in the 5-year recurrence-free survival probability, and a 4% relative reduction in the 5-year probability of metastatic disease or death.ConclusionsThe use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making