A plasma proteomic signature of cancer-related sarcopenia implicates the IGFBP axis in muscle dysfunction
Year: 2026
Type: article
Abstract: Background 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. Methods 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 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. Results 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 IGFBP1, IGFBP2, and IL6. IGFBP1 and IGFBP2 impaired myoblast differentiation, while IL6 induced IGFBP1 expression in liver cells. Conclusions 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.
Source: Molecular Cancer
Authors: Filippo Gustavo Dall’Olio, Wael Salem Zrafi, Xinran Song, Littisha Lawrance, Ekaterina Shalimanova, Anna Schwager, Nadia Myszka, Fei Chen, Rebecca Ibrahim, Marie Guinhut, Pierre Busson, Catherine Brenner, Karim Benihoud, Fabrice Barlesi, Caroline Even, Dimitria Brempou, Nathalie Lassau, Claudio Nicotra, Maud Ngo Camus, Marine Aglave, Yohann Loriot, Diana Cardenas, Mariam Jamal-Hanjani, Carla M Prado, Antoine Italiano, Yegor Vassetzky, Benjamin Besse
Institutions: Centre National de la Recherche Scientifique, Université Paris-Saclay, Institut Gustave Roussy, Fondation Gustave Roussy, Guerbet (France), Inserm, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, Dynamique de l'information génétique : bases fondamentales et cancer, Institut Curie, University College London, University of Alberta, Koltzov Institute of Developmental Biology, Prédicteurs moléculaires et nouvelles cibles en oncologie
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $5,090
Funders: Amgen, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, AstraZeneca, GlaxoSmithKline, Sanofi, Gilead Sciences, Regeneron Pharmaceuticals, Merck KGaA, Taiho Pharmaceutical, Exelixis, Nestlé Health Science, Jazz Pharmaceuticals, Astex Pharmaceuticals, BeiGene, Incyte, F-star Therapeutics, Novo Nordisk, Astellas Pharma, Institut Gustave-Roussy, PharmaMar, Fondation Gustave Roussy, Genentech, Chugai Pharmaceutical, Daiichi Sankyo Europe
OpenAlex ID: W7166563782