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The TNF family member receptor activator for NF-κB ligand (RANKL) and its receptors RANK and osteoprotegerin are key regulators of bone remodeling but also influence cellular functions of tumor and immune effector cells. In this work, we studied the involvement of RANK-RANKL interaction in NK cell-mediated immunosurveillance of acute myeloid leukemia (AML). Substantial levels of RANKL were found to be expressed on leukemia cells in 53 of 78 (68%) investigated patients. Signaling via RANKL into the leukemia cells stimulated their metabolic activity and induced the release of cytokines involved in AML pathophysiology. In addition, the immunomodulatory factors released by AML cells upon RANKL signaling impaired the anti-leukemia reactivity of NK cells and induced RANK expression, and NK cells of AML patients displayed significantly upregulated RANK expression compared with healthy controls. Treatment of AML cells with the clinically available RANKL Ab Denosumab resulted in enhanced NK cell anti-leukemia reactivity. This was due to both blockade of the release of NK-inhibitory factors by AML cells and prevention of RANK signaling into NK cells. The latter was found to directly impair NK anti-leukemia reactivity with a more pronounced effect on IFN-γ production compared with cytotoxicity. Together, our data unravel a previously unknown function of the RANK-RANKL molecule system in AML pathophysiology as well as NK cell function and suggest that neutralization of RANKL with therapeutic Abs may serve to reinforce NK cell reactivity in leukemia patients.

Aim: To investigate whether diagnostic data from implanted cardiac resynchronization therapy defibrillators (CRT-Ds) retrieved automatically at 24 h intervals via a Home Monitoring function can enable dynamic prediction of cardiovascular hospitalization and death., Methods and Results: Three hundred and seventy-seven heart failure patients received CRT-Ds with Home Monitoring option. Data on all deaths and hospitalizations due to cardiovascular reasons and Home Monitoring data were collected prospectively during 1-year follow-up to develop a predictive algorithm with a predefined specificity of 99.5%. Seven parameters were included in the algorithm: mean heart rate over 24 h, heart rate at rest, patient activity, frequency of ventricular extrasystoles, atrial-atrial intervals (heart rate variability), right ventricular pacing impedance, and painless shock impedance. The algorithm was developed using a 25-day monitoring window ending 3 days before hospitalization or death. While the retrospective sensitivities of the individual parameters ranged from 23.6 to 50.0%, the combination of all parameters was 65.4% sensitive in detecting cardiovascular hospitalizations and deaths with 99.5% specificity (corresponding to 1.83 false-positive detections per patient-year of follow-up). The estimated relative risk of an event was 7.15-fold higher after a positive predictor finding than after a negative predictor finding., Conclusion: We developed an automated algorithm for dynamic prediction of cardiovascular events in patients treated with CRT-D devices capable of daily transmission of their diagnostic data via Home Monitoring. This tool may increase patients' quality of life and reduce morbidity, mortality, and health economic burden, it now warrants prospective studies. ClinicalTrials.gov  NCT00376116.