Risk and Survival of Patients with Non-Small Cell Lung Cancer and Pre-Existing Autoimmune Disorders Receiving Immune Checkpoint Blockade Therapy: Survival Analysis with Inverse Probability Weighting from a Nationwide, Multi-Institutional, Retrospective Study (NEJ047)
Lung Cancer(2024)
Abstract
Background The risk and survival of patients with non-small cell lung cancer (NSCLC) with pre-existing autoimmune disorders (AIDs) receiving immune checkpoint blockade (ICB) therapy have not been clearly established. Patients and methods This multi-institutional, retrospective cohort study was conducted in collaboration with 20 centers in Japan. Results In total, 229 patients with advanced or recurrent NSCLC and pre-existing AID, with or without ICB treatment from January 2010–February 2020, were included and analyzed. Among 69 patients who received ICB, 2 received two lines of ICBs with a total of 71 ICB treatments; 57 (80.3 %) and 14 (19.7 %) patients received ICB monotherapy and combination therapy, respectively. AID flares were observed in 18 patients (25.4 %, 95 % confidence interval [CI], 15.8–37.1 %) receiving ICB. AID exacerbations were more likely when NSCLC was diagnosed less than 1 year after the AID diagnosis (odds ratio 5.26 [95 % CI, 1.40–21.61]; P = 0.016). Immune-related adverse events were observed in 32 patients (45.1 %, 95 % CI, 33.2–57.3 %); 17 had grade 3 or higher. The safety profile of combination immunotherapy was not significantly different from that of the monotherapy. After inverse probability weighting, the use of ICB prolonged survival (hazard ratio 0.43 [95 % CI, 0.26–0.70]; P = 0.0006). Conclusions These findings revealed a novel risk factor for AID flares following ICB treatment, that is the diagnosis of NSCLC within 1 year of AID diagnosis, and showed that ICBs may improve survival in this population. These results support the utilization of ICB in patients with NSCLC and pre-existing AID.
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Key words
Immune checkpoint blockade,Non-small cell lung cancer,Pre-existing autoimmune disorders,Inverse probability weighting
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