Prediction of cancer survival rate with metabolomics

by | Dec 17, 2020 | Literature, Data analysis, Oncology

Integration of serum metabolomics into clinical assessment to improve outcome prediction of metastatic soft tissue sarcoma patients treated with trabectedin

Soft tissue sarcomas (STS) are a heterogenous group of cancers that affect tissues such as adipose tissue, muscle, cartilage, vessels, and nerves. In this publication, the authors combined metabolomic data with more classical biomarkers of cancer progression and survival to train models to predict the outcome of antitumor therapy with trabectedin in patients with metastatic forms of STS.


Profiling of the patients’ amino acids, amino acid related metabolites and bile acids levels in serum enabled the identification of a metabolic pattern in weak responders to the antitumor cancer treatment. Three patients with a particularly poor outcome clearly clustered separately from the other patients in a principal component analysis (PCA). The separation of these two clusters was driven by lower levels of amino acids in the patients with poor outcomes, while bile acids were not involved. Using univariate Cox proportional hazards regression analysis, the authors identified two amino acids, citrulline and histidine, that were associated with the overall survival of the patients.


Next, a risk prediction model was developed that combined metabolomic data with endpoints such as ECOG performance status (a therapy tolerance indicator), hemoglobin levels and tumor grading. The model was able to identify two groups of patients with either a high risk or a low to medium risk for overall survival after treatment.


This model focused on a small number of patients (n = 24), however, and it would require to be validated using a larger independent cohort of patients. Nevertheless, this promising study is a good example of the approach that can be taken to integrate metabolomic data with clinical variables to improve current diagnostic and prognostic biomarkers for cancer and other diseases.

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Miolo G, Di Gregorio E, Saorin A, Lombardi D, Scalone S, Buonadonna A, Steffan A, Corona G: Integration of serum metabolomics into clinical assessment to improve outcome prediction of metastatic soft tissue sarcoma patients treated with trabectedin (2020) Cancers (Basel) | doi: 10.3390/cancers12071983