Diagnosis of lung tumor types based on metabolomic profiles in lymph node aspirates

Abstract Background Treatment of lung cancer is evolving from the use of cytotoxic drugs to drugs that interrupt pathways specific to a malignancy. The field of metabolomics has promise with respect to identification of tumor-specific processes and therapeutic targets, but to date has yielded inconsistent data in patients with lung cancer. Lymph nodes are often aspirated in the process of evaluating lung cancer, as malignant cells in lymph nodes are used for diagnosis and staging. We hypothesized that fluids from lymph node aspirates contains tumor-specific metabolites and are a suitable source for defining the metabolomic phenotype of lung cancers. Patients and materials Metabolic profiles were generated from nodal aspirates of ten patients with adenocarcinoma, ten with squamous cell carcinoma, and ten with non-malignant conditions using time-of-flight mass spectrometry. In addition, concentrations of selected metabolites participating in the kynurenine and glutathione pathways were measured in a second set of aspirates using tandem mass spectrometry. Results A list of consensus features that separated these three groups was identified. Two of the consensus features were tentatively identified as kynurenine and as oxidized glutathione. It was shown that metabolite concentrations in these pathways are different for patients with and without malignancy. Conclusion Together the data suggest that metabolomic analysis of lymph node aspirates can identify tumor-specific differences in cancer metabolism and reveal novel therapeutic targets. This proof-of-concept study demonstrates the validity to complement and refine diagnosis of lung cancer based on metabolic signature in lymph node aspirates. Micro abstract Treatment of lung cancer is evolving from the use of cytotoxic drugs to drugs that interrupt metabolic pathways specific to a malignancy. We report here in that the metabolic phenotype of lung cancer can be determined in lymph node aspirates harboring malignant tumor cells. Knowledge about metabolic activity of malignant tumor cells may aide to personalize therapy.