Abstract 7632: Plasma proteomics for risk prediction of lung cancer

Abstract Background: Current lung cancer screening programs rely heavily on age and smoking history, excluding never-smokers and those with minimal smoking exposure. Such criteria have a low positive predictive value (PPV), limiting molecular prevention strategies. Our previous work identified interleukin-1β (IL-1β) as a mediator of lung cancer initiation through environmental particulate matter (PM) exposure, suggesting potential targets for therapeutic cancer prevention. Here, we sought to identify circulating signals predictive of lung cancer prior to clinical diagnosis and determine if they were useful for clinical trial stratification of IL-1β therapy. Methods: Using human plasma proteomic data from the UK Biobank (n=48,099 individuals; 375 lung cancer cases), we developed a machine-learning framework to identify proteins predictive of lung cancer diagnosis. We validated this model in eight independent human cohorts (2,176 cases, 54,324 controls). We further analysed plasma proteomic murine data from EGFR-mutant mice exposed to PM as well as from baseline samples from the CANTOS trial which previously had demonstrated reduction of lung cancer incidence with IL-1β inhibition. Results: Our machine-learning approach identified a plasma signature of 14 proteins, predictive of lung cancer diagnosis up to 6 years before clinical detection, significantly outperforming current lung cancer risk models (p0.01 by de Long’s test). Validation across eight external human cohorts confirmed consistent associations for all proteins. Mouse experiments demonstrated a sustained increase in circulating signature proteins following PM exposure specifically in EGFR-mutant mice, linking environmental PM exposure directly to the alveolar niche as an early tumour-promoting microenvironment. Retrospective analysis of the CANTOS trial showed the protein signature stratified individuals deriving benefit from IL-1β inhibition, reducing the number needed to treat from 1516 to 55. Discussion: Our findings indicate that a circulating plasma signature derived from alveolar niche remodelling and induced by PM and EGFR-driven oncogenesis can effectively identify individuals at high risk of lung cancer two years before clinical onset. The identified proteins may enable targeted stratification for molecular prevention trials. Future research should focus on extending this approach and developing absolute quantification assays to for clinical translation. Citation Format: Tej Pandya, Maria Zagorulya, Michelle M. Leung, Marcellus Augustine, Lydia Y. Liu, Oleg Blyuss, Jincheng Wu, Marc Pelletier, Vernon Burk, Neil Wright, David Muller, Ka Hung Chan, Ekaterina Pazukhina, Marc Gunter, Elizabeth A. Platz, Karl Smith-Byrne, Nuno Rocha Nene, Eva Camilla Gronroos, Nicholas McGranahan, William Hill, Clare Weeden, Charles Swanton. Plasma proteomics for risk prediction of lung cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7632.