A plasma proteomic signature of cancer-related sarcopenia implicates the IGFBP axis in muscle dysfunction

Cancer-related sarcopenia is associated with poor clinical outcomes but remains difficult to define and quantify in routine oncology practice. Current assessments rely on imaging and functional scales that are time-consuming and provide limited biological insight. We aimed to identify a plasma proteomic signature of cancer-related sarcopenia and to uncover circulating mediators involved in its pathophysiology. Patients were included from two cohorts of the MATCH-R study (NCT02517892): a discovery cohort of advanced cancer patients treated with immunotherapy and an independent validation cohort of metastatic castration-resistant prostate cancer (mCRPC) patients treated with androgen-receptor pathway inhibitors. External validation was performed in the TRACERx cohort of non-small cell lung cancer. Skeletal muscle index at third lumbar vertebra (L3) was quantified using imaging, and ECOG performance status served as a functional proxy. Plasma proteomics was performed using the Olink Explore platform. An extreme gradient boosting (XGBoost) model was trained on a high-contrast subset using a neuromuscular-focused protein panel and validated across cohorts. Functional effects of candidate mediators were assessed in differentiating human myoblasts. The model generated a continuous sarcopenia probability (SP) score that correlated with muscle mass and functional status and consistently stratified overall survival across cohorts. A reduced four-protein model retained comparable performance, supporting translational applicability. Proteins associated with SP included insulin-like growth factor binding protein 1 and 2 (IGFBP1, IGFBP2), and interleukin-6 (IL6). IGFBP1 and IGFBP2 impaired myoblast differentiation, while IL6 induced IGFBP1 expression in liver cells. Plasma proteomics enables scalable and biologically informed assessment of cancer-related sarcopenia, identifies tumor–host mediators of muscle dysfunction, and supports objective patient stratification for therapeutic intervention. We analyzed plasma protein profiles and CT-derived skeletal muscle mass in patients with cancer. Because sarcopenia lacks a single gold-standard definition in oncology, we defined extreme phenotypes based on concordant muscle mass and physical function, using patients with preserved muscle mass and function (ECOG PS 0) and those with low muscle mass and poor function (ECOG PS ≥2). These high-contrast groups were used to identify a muscle-focused plasma protein signature and train a machine-learning model that estimates a continuous probability of sarcopenia. Across multiple independent cancer cohorts, this probability score correlated with muscle mass, functional status, longitudinal functional change, and overall survival, and revealed biologically plausible mediators linking systemic cancer biology to muscle dysfunction. Created in BioRender. (2026) https://BioRender.com/byz3xgi