Abstract

2562 Background: Immune checkpoint inhibitors (ICIs) have been established as the standard of care for numerous advanced malignancies. Emerging evidence suggests that the time-of-day of administering these immunotherapies may impact patient outcomes. We sought to determine the consistency in the impact of ICI time-of-day infusion on survival of patients with metastatic cancer. Methods: We performed a meta-analysis of published studies that have reported on ICI time-of-day infusion and efficacy in patients with advanced solid tumors. Overall survival (OS) and progression free survival (PFS) were compared between patients who had received ICIs before and after the daily time cut-off of each study. Hazard ratios (HRs) with their 95% confidence interval (CI) were collected from the studies and pooled. Results: This meta-analysis identified 7 studies of 1019 patients (PS 0, 43%; PS 1, 39%; males, 65%; females, 35%) who had stage IV non-small cell lung cancer (NSCLC), urothelial cancer, renal cell carcinoma, or melanoma, and were treated with mostly anti-PD-1 (nivolumab or pembrolizumab), anti-PD-L1 (atezolizumab), and anti-CTLA-4 (ipilimumab) agents. The time cut-offs selected to stratify patterns of ICI infusion were 16:00 or 16:30 in 5 studies (based on differences in adaptive immune response reported in previous vaccination studies) and 13:00 (based on median time of ICI infusions) in 2 studies. The pooled analysis of OS results showed a statistically significant benefit in favor of patients receiving infusions in the morning or in the early part of the day with a HR: 0.49, [95% CI: 0.36-0.69] (p < 0.0001). A similar benefit was observed for PFS with a HR: 0.56, [0.44-0.72] (p < 0.00001). Conclusions: Patients with advanced cancers may benefit from earlier time-of-day ICI infusions. Our findings are consistent with established circadian mechanisms that control immune cell functions and trafficking over a 24-h period. Prospective randomized trials are warranted to establish ICI timing recommendations for optimizing treatment efficacy in patients with different cancer types and at minimal cost.

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