Proteomic aging biomarkers predict survival in immunotherapy-treated tumors

Introduction: Plasma proteomics provides a comprehensive view of the biological processes active in an individual. Studies in healthy populations have shown that the plasma proteome undergoes changes with aging, and that proteomics-based models can predict biological age. The difference between biological and chronological age, known as the “age gap,” reflects an individual's risk of disease. Extending this approach, organ-specific aging models have demonstrated that accelerated aging of individual organs is associated with organ-related disorders. Here, we examined age-associated effects on the plasma proteome of cancer patients across multiple tumor types and applied organismal and eleven organ-specific proteomic aging models to identify links between aging, tumor characteristics, and clinical features. Methods: Baseline plasma samples were collected from patients with metastatic solid tumors (non-small cell lung cancer [NSCLC], n=818; small cell lung cancer [SCLC], n=99; renal cell carcinoma [RCC], n=297; melanoma, n=163) and healthy subjects (n=278). Deep plasma proteomic profiling was performed using an aptamer-based assay. Bioinformatic analyses identified age-associated proteomic signatures, and organismal and organ-specific predictors were applied to estimate biological ages for each patient and organ. Results: Proteins involved in multiple signaling pathways, including Wnt, PI3K-Akt, IGF, and Ephrin receptor signaling, were upregulated in older patients (≥65 years) compared with younger patients, along with immune-regulatory proteins, reflecting immune remodeling associated with aging. These trends were consistent across tumor types. The organismal biological age gap was significantly higher in all cancer cohorts versus healthy controls, largest in SCLC and smallest in melanoma, with the most substantial effect in younger patients. Organ-level analyses revealed distinct patterns: lung age gap was highest in NSCLC and SCLC, and kidney age gap was most significant in RCC. Elevated organ-specific gaps correlated with corresponding comorbidities (e.g., cardiac age gap with arrhythmia, ischemic heart disease, and vascular disease) but much less with organ-specific metastases. Focusing on the immune age gap, patients treated with immune checkpoint inhibitor-based therapy who exhibited a high immune age gap had significantly shorter overall survival compared with patients with a low immune age gap (median OS, 16.4 vs. 31.8 months; HR = 0.67, p < 0.0001). The effect varied by indication, being strongest in melanoma (HR = 0.27, p = 0.0007) and absent in SCLC (HR = 0.87, p = 0.65). Conclusions: Proteomic aging predictors capture systemic and organ-specific aging processes in cancer. Distinct age-gap patterns across tumor types, along with their association with survival and comorbidities, highlight the biological and clinical relevance of proteomic aging in oncology.