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

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.

Graphical abstract

Figure 1

Values of -log₂ fold change (FC) of the 12 parameters that differ between 2-year deceased and survived patients Positive/negative values have higher/lower concentration in plasma samples from deceased patients with respect to survived patients. Parameters that remain statistically significant after FDR correction are marked with asterisks. The accuracy, sensitivity, specificity, and AUROC of each variable are reported. Each optimal cut point was determined by maximizing the Youden-Index.

Figure 2

Overall mSTS patients according to risk score of combined acetate, triglycerides, LDL-2, and red blood cell count biomarkers High-risk (black) group is significantly clustered with respect to the low (gray)-risk group with hazard ratios of 5.15. The number at risk: number of patients stratified according to the combined score at each time point. Cumulative number of events: total number ofdeceased patients at each time point for each risk group based on the combined score. p values are calculated with the log-rank test. LR: predicted by the combined score at low risk of death; HR: predicted by the combined score at high risk.

Figure 3

Overall survival as function of combined score for high and low risk mSTS patients The patients are stratified by (A) trabectedin therapy: low risk for first-line treatment (gray line), high risk for first-line treatment (black line), low risk for second-line treatment (gray dashed line), high risk for second-line treatment (black dashed line) and by (B) Histotype: low risk for patients with L. sarcoma (gray line), high risk for patients with L.sarcoma (black line), low risk for patients with other sarcomas (gray dashed line), high risk for patients with other sarcomas (black dashed line). LR: predicted by the combined score at low risk of death; HR: predicted by the combined score at high risk.

Figure 4

Forest plot for Cox proportional hazards model The figure provides a forest plot reporting the hazard ratio (HR) and the 95% confidence intervals of the HR for each covariate included in the Cox proportional hazards model. Magnitude of significance is denoted with asterisks (∗).

Figure 5

Nomogram predicting 2-year cancer-specific survival of patients with soft tissue sarcomas The nomogram summed the points identified on the scale for each of the three independent prognostic variables (acetate, triglycerides LDL-2, RBC count).