Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas

Alessia Vignoli; Gianmaria Miolo; Leonardo Tenori; Angela Buonadonna; Davide Lombardi; Agostino Steffan; Simona Scalone; Claudio Luchinat; Giuseppe Corona

doi:10.1016/j.isci.2023.107678

Novel metabolomics-biohumoral biomarkers model for predicting survival of metastatic soft-tissue sarcomas

iScience. 2023 Aug 19;26(10):107678. doi: 10.1016/j.isci.2023.107678. eCollection 2023 Oct 20.

Authors

Affiliations

¹ Magnetic Resonance Center (CERM) and Department of Chemistry "Ugo Schiff", University of Florence, 50019 Sesto Fiorentino, Italy.
² Medical Oncology and Cancer Prevention Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy.
³ Consorzio Interuniversitario Risonanze Magnetiche MetalloProteine (CIRMMP), 50019 Sesto Fiorentino, Italy.
⁴ Immunopathology and Cancer Biomarkers Unit, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, 33081 Aviano, Italy.
⁵ GiottoBiotech s.r.l, Sesto Fiorentino, Italy.

Abstract

Soft tissue sarcomas (STSs) are rare malignant tumors that are difficult to prognosticate using currently available instruments. Omics sciences could provide more accurate and individualized survival predictions for patients with metastatic STS. In this pilot, hypothesis-generating study, we integrated clinicopathological variables with proton nuclear magnetic resonance (¹H NMR) plasma metabolomic and lipoproteomic profiles, capturing both tumor and host characteristics, to identify novel prognostic biomarkers of 2-year survival. Forty-five metastatic STS (mSTS) patients with prevalent leiomyosarcoma and liposarcoma histotypes receiving trabectedin treatment were enrolled. A score combining acetate, triglycerides low-density lipoprotein (LDL)-2, and red blood cell count was developed, and it predicts 2-year survival with optimal results in the present cohort (84.4% sensitivity, 84.6% specificity). This score is statistically significant and independent of other prognostic factors such as age, sex, tumor grading, tumor histotype, frailty status, and therapy administered. A nomogram based on these 3 biomarkers has been developed to inform the clinical use of the present findings.

Keywords: Cancer; Metabolomics; Systems biology.