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Updated: Feb 6, 2026
Quantification of Tumor Cell Adhesion in Lymph Node Cryosections
Published on: February 9, 2020
Diagnosis of lung tumor types based on metabolomic profiles in lymph node aspirates
Daniel Sappington1, Scott Helms1, Eric Siegel2
1Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences, United States.
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.
Insights
Metabolomic analysis of lymph node aspirates can identify lung cancer-specific metabolic differences. This approach may help refine diagnosis and discover new therapeutic targets for lung cancer.
Area of Science:
- Oncology
- Metabolomics
- Biochemistry
Background:
- Lung cancer treatment is shifting towards targeted therapies.
- Metabolomics offers potential for identifying tumor-specific targets but has yielded inconsistent data.
- Lymph node aspirates are crucial for lung cancer diagnosis and staging.
Purpose of the Study:
- To investigate if lymph node aspirates contain tumor-specific metabolites.
- To determine if metabolomic analysis of these aspirates can define lung cancer phenotypes.
- To explore novel therapeutic targets for lung cancer.
Main Methods:
- Generated metabolic profiles from lymph node aspirates of lung cancer patients (adenocarcinoma, squamous cell carcinoma) and non-malignant controls using time-of-flight mass spectrometry.
- Quantified specific metabolites in the kynurenine and glutathione pathways using tandem mass spectrometry.
Main Results:
- Identified consensus metabolic features distinguishing the three groups.
- Tentatively identified kynurenine and oxidized glutathione as key differentiating metabolites.
- Demonstrated significant differences in metabolite concentrations between malignant and non-malignant conditions.
Conclusions:
- Metabolomic analysis of lymph node aspirates can reveal tumor-specific metabolic alterations in lung cancer.
- This approach shows potential for identifying novel therapeutic targets.
- The study validates the use of lymph node aspirates for metabolomic profiling to complement lung cancer diagnosis.
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