Abstract 3460: Predicting cessation of immune checkpoint inhibitor therapy due to severe adverse events with an integrated autoimmune disease polygenic risk score

Abstract Background: Immune checkpoint inhibitors (ICIs), including anti-PD1/PD-L1 and anti-CTLA4 agents, have changed the landscape of cancer therapy. However, 6-30% of patients treated with ICIs can develop severe immune-related adverse events (irAEs) depending on the type of therapy used, often leading to cessation of treatment. Hundreds of genetic variants have been associated with autoimmune disorders, and recent genetic studies have developed polygenic risk scores (PRS) for composite risk of autoimmune disease (PRSAID). We used data from an ongoing observational study of patients with non-small cell lung cancer (NSCLC) treated with ICIs to evaluate the association between PRSAID and cessation of ICI therapy due to severe irAEs. Methods: Our cohort comprises 1, 316 advanced NSCLC patients who received ICI therapy from four institutions between 2009 and 2022. Genotyping was performed using the Affymetrix Precision Medicine Array, and imputation was performed using 1000 Genomes Reference. To evaluate cessation of therapy due to severe irAEs, we conducted a thorough review of electronic health records. We identified permanent discontinuation of ICIs due to irAEs as cessation for at least 6 months without any disease progression. We tested the association of a previously published PRSAID (PMID: 35393596) with cessation of ICI due to any severe irAE. PRSAID was converted to a standard normal distribution. We conducted an unconditional logistic regression model to evaluate the association between a standardized PRSAID and discontinuation of ICI therapy due to severe irAEs. All models were adjusted for age at diagnosis, sex, type of ICI therapy, recruitment site, and 5 ancestry-informative principal components. Results: Of the 1, 316 participants with genotype and outcome data, 171 (13. 5%) of patients stopped ICI therapy due to severe irAEs. The most common irAEs leading to discontinuation were colitis, hepatitis, and pneumonitis. The PRSAID predicts the cessation of therapy due to severe irAEs with an odds ratio (OR) per standard deviation (SD) of 1. 20 (95% confidence intervals CI=1. 02-1. 41, p=0. 03). The association was stronger in individuals with high genetic risk (top 25th percentile compared to bottom 75th percentile of the PRSAID) (OR=1. 60, 95% CI = 1. 11-2. 28, p=0. 01). When we stratified the analysis by therapy type, we found an OR of 1. 17 (95% CI-0. 98-1. 39, p = 0. 08) for the anti-PD1/PD-L1 monotherapy groups and an OR of 1. 48 (95% CI=0. 94-2. 40, p=0. 09) for the dual anti-PD1/PD-L1 and anti-CTLA4 therapy group. Conclusions: An integrated PRS for autoimmunity can predict the cessation of ICI therapy due to severe irAEs in patients with advanced cancer. These findings suggest that PRS may help determine which patients may benefit from careful monitoring or even preventative measures for irAEs during ICI therapy. Citation Format: Pooja Middha, Rohit Thummalapalli, Zoe Quandt, Princess Margaret Lung Group, Matthew A. Gubens, Christine M. Lovly, Geoffrey Liu, Melinda C. Aldrich, Adam J. Schoenfeld, Elad Ziv. Predicting cessation of immune checkpoint inhibitor therapy due to severe adverse events with an integrated autoimmune disease polygenic risk score abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 3460.