Context-Dependent Survival Associations Between Interleukin-13 Expression and Immunotherapy in Advanced Solid Tumors

Background: Interleukin (IL)-13 can modulate tumor immunosurveillance. The interplay between IL-13 and immunotherapy outcomes has not been well elucidated. Methods: IL-13 expression was evaluated by tumor RNA sequencing (514 tumors; advanced/metastatic cancers). Transcripts were normalized to internal housekeeping genes and standardized relative to a reference population (735 tumors; 35 histologies) and ranked as percentile values: IL-13-high (75th-100th percentile) and non-high (0-74th percentile). Results: Overall, 39.7% of 514 patients were men; median age, 61 years; 489 had clinical data annotation (217 immunotherapy treated; 272, immunotherapy naïve). Eighty-three of 514 patients (16.1%) showed high IL-13 expression, which was most common in sarcomas (29.2%) and independently correlated with high expression of IL-4 (odds ratio (OR)=4.20) and IL-2Rα (OR=4.63), non-high expression of TIM3 (OR=0.40), PD-L1 negativity (OR=0.40), and microsatellite instability (OR=4.03). In the immunotherapy-naïve analysis, patients with high versus non-high IL-13 levels had shorter overall survival (OS) from metastatic/advanced disease diagnosis (median, 24.5 versus 43.3 months) (HR 1.70, 95% CI 1.15-2.52, log-rank p=0.007). Interaction analysis between IL-13 levels and immunotherapy demonstrated that, among patients with non-high IL-13, those treated with immunotherapy had significantly shorter OS versus immunotherapy-naïve patients (p<0.001), whereas patients with high IL-13 levels had no significant difference in OS between immunotherapy treatment and naive groups. Conclusion: High IL-13 RNA levels were associated with other important immunoregulatory biomarkers and were most common in sarcomas. High IL-13 expression correlated with poor OS in immunotherapy-naïve patients. The observation that immunotherapy was associated with decreased survival among patients with non-high IL-13 levels is intriguing. These findings are hypothesis-generating, require validation, and have potential implications for biomarker-driven patient stratification to enhance their translational relevance.