ARTICLE ABSTRACT
Leptomeningeal carcinomatosis (LC) is the third most common metastatic complication of the central nervous system. However, the current modalities to reliably diagnose this condition are not satisfactory. Here, we report a preclinical proof of concept for a metabolomics-based diagnostic strategy using a rat LC model incorporating glioma cells that stably express green fluorescent protein. Cytologic diagnoses gave 66.7% sensitivity for the 7-day LC group and 0% for the 3-day LC group. MR imaging could not diagnose LC at these stages. In contrast, nuclear magnetic resonance–based metabolomics on cerebrospinal fluid detected marked differences between the normal and LC groups. Predictions based on the multivariate model provided sensitivity, specificity, and overall accuracy of 88% to 89% in both groups for LC diagnosis. Further statistical analyses identified lactate, acetate, and creatine as specific for the 7-day LC group, with glucose a specific marker of the normal group. Overall, we showed that the metabolomics approach provided both earlier and more accurate diagnostic results than cytology and MR imaging in current use. Cancer Res; 72(20); 5179–87. ©2012 AACR.
