Inhibition of signaling downstream of beta-2 adrenoceptor by propranolol in prostate cancer cells.

BACKGROUND: There is accumulating evidence that propranolol, an antagonist of beta-1 and beta-2 adrenoreceptors, extends survival of patients with prostate cancer; yet it is not known whether propranolol inhibits beta-adrenergic signaling in prostate cancer cells, or systemic effects of propranolol play the leading role in slowing down cancer progression. Recently initiated clinical studies offer a possibility to test whether administration of propranolol inhibits signaling pathways in prostate tumors, however, there is limited information on the dynamics of signaling pathways activated downstream of beta-2 adrenoreceptors in prostate cancer cells and on the inactivation of these pathways upon propranolol administration. METHODS: Western blot analysis was used to test the effects of epinephrine and propranolol on activation of protein kinase (PKA) signaling in mouse prostates and PKA, extracellular signal-regulated kinase (ERK), and protein kinase B/AKT (AKT) signaling in prostate cancer cell lines. RESULTS: In prostate cancer cell lines epinephrine induced robust phosphorylation of PKA substrates pS133CREB and pS157VASP that was evident 2 min after treatments and lasted for 3-6 h. Epinephrine induced phosphorylation of AKT in PTEN-positive 22Rv1 cells, whereas changes of constitutive AKT phosphorylation were minimal in PTEN-negative PC3, C42, and LNCaP cells. A modest short-term increase of pERK in response to epinephrine was observed in all tested cell lines. Incubation of prostate cancer cells with 10-fold molar excess of propranolol for 30 min inhibited all downstream pathways activated by epinephrine. Subjecting mice to immobilization stress induced phosphorylation of S133CREB, whereas injection of propranolol at 1.5 mg/kg prevented the stress-induced phosphorylation. CONCLUSIONS: The analysis of pS133CREB and pS157VASP allows measuring activation of PKA signaling downstream of beta-2 adrenoreceptors. Presented results on the ratio of propranolol/epinephrine and the time needed to inhibit signaling downstream of beta-2 adrenoreceptors will help to design clinical studies that examine the effects of propranolol on prostate tumors.

What is the right sequencing approach? Solo VS extended family analysis in consanguineous populations.

BACKGROUND: Testing strategies is crucial for genetics clinics and testing laboratories. In this study, we tried to compare the hit rate between solo and trio and trio plus testing and between trio and sibship testing. Finally, we studied the impact of extended family analysis, mainly in complex and unsolved cases. METHODS: Three cohorts were used for this analysis: one cohort to assess the hit rate between solo, trio and trio plus testing, another cohort to examine the impact of the testing strategy of sibship genome vs trio-based analysis, and a third cohort to test the impact of an extended family analysis of up to eight family members to lower the number of candidate variants. RESULTS: The hit rates in solo, trio and trio plus testing were 39, 40, and 41%, respectively. The total number of candidate variants in the sibship testing strategy was 117 variants compared to 59 variants in the trio-based analysis. We noticed that the average number of coding candidate variants in trio-based analysis was 1192 variants and 26,454 noncoding variants, and this number was lowered by 50-75% after adding additional family members, with up to two coding and 66 noncoding homozygous variants only, in families with eight family members. CONCLUSION: There was no difference in the hit rate between solo and extended family members. Trio-based analysis was a better approach than sibship testing, even in a consanguineous population. Finally, each additional family member helped to narrow down the number of variants by 50-75%. Our findings could help clinicians, researchers and testing laboratories select the most cost-effective and appropriate sequencing approach for their patients. Furthermore, using extended family analysis is a very useful tool for complex cases with novel genes.