Peripheral blood cell counts as predictors of immune-related adverse events in cancer patients receiving immune checkpoint inhibitors: a systematic review and meta-analysis.

Abstract

Background: In recent years, immune checkpoint inhibitors (ICIs) have shown significant efficacy in treating various malignancies and have become a key therapeutic approach in cancer treatment. However, while ICIs activate the immune system, they can also induce immune-related adverse events (irAEs). Due to the variability in the frequency and severity of irAEs, clinical management faces a significant challenge in balancing antitumor efficacy with the risk of irAEs. Predicting and preventing irAEs during the early stages of treatment has become a critical research focus in cancer immunotherapy. This study aims to evaluate the predictive value of peripheral blood cell counts for irAEs.

Methods: Studies meeting the inclusion criteria were identified through database searches. The standardized mean difference (SMD) was used to compare continuous blood cell counts. For studies that did not provide adjusted odds ratios (ORs) and 95% confidence intervals (CIs), crude ORs for categorized blood cell counts were calculated. The study protocol was registered on PROSPERO (CRD42024592126).

Results: The meta-analysis included 60 studies involving 16,736 cancer patients treated with ICIs. Compared to patients without irAEs, those experiencing irAEs had significantly higher baseline continuous ALC (SMD = 0.12, 95% CI = 0.01-0.24), while ANC (SMD = -0.18, 95% CI = -0.28 to -0.07) and PLR (SMD = -0.32, 95% CI = -0.60 to -0.04) were significantly lower. Similarly, categorized blood cell counts indicated that higher baseline ALC (OR = 2.46, 95% CI = 1.69-3.57) and AEC (OR = 2.05, 95% CI = 1.09-3.85), along with lower baseline NLR (OR = 0.64, 95% CI = 0.50-0.81) and PLR (OR = 0.63, 95% CI = 0.48-0.82), were associated with an increased risk of irAEs. Subgroup analysis further identified cutoff values for ALC (2×10^9/L), NLR (5 or 3), and PLR (180) as better predictors of irAEs.

Conclusion: Higher baseline ALC and AEC, along with lower baseline ANC, NLR, and PLR, are associated with an increased risk of irAEs. However, further research is needed to determine the optimal cutoff values and to explore the efficacy of blood cell counts in predicting specific types of irAEs.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024592126.