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A Comprehensive Analysis of Metabolomics and Transcriptomics Reveals Novel Biomarkers and Mechanistic Insights on
Wei Chen1, Chunyu Li1, Yafei Shi1
1National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Abstract:
Of late, lorlatinib has played an increasingly pivotal role in the treatment of brain metastasis from non-small cell lung cancer. However, its pharmacokinetics in the brain and the mechanism of entry are still controversial. The purpose of this study was to explore the mechanisms of brain penetration by lorlatinib and identify potential biomarkers for the prediction of lorlatinib concentration in the brain. Detection of lorlatinib in lorlatinib-administered mice and control mice was performed using liquid chromatography and mass spectrometry. Metabolomics and transcriptomics were combined to investigate the pathway and relationships between metabolites and genes. Multilayer perceptron was applied to construct an artificial neural network model for prediction of the distribution of lorlatinib in the brain. Nine biomarkers related to lorlatinib concentration in the brain were identified. A metabolite-reaction-enzyme-gene interaction network was built to reveal the mechanism of lorlatinib. A multilayer perceptron model based on the identified biomarkers provides a prediction accuracy rate of greater than 85%. The identified biomarkers and the neural network constructed with these metabolites will be valuable for predicting the concentration of drugs in the brain. The model provides a lorlatinib to treat tumor brain metastases in the clinic.
Insights
Researchers identified key biomarkers to predict lorlatinib concentration in the brain for non-small cell lung cancer patients. This advance aids in optimizing lorlatinib treatment for brain metastases.
Area of Science:
- Pharmacology
- Neuroscience
- Oncology
Background:
- Lorlatinib is crucial for treating brain metastases in non-small cell lung cancer (NSCLC).
- The pharmacokinetics and brain entry mechanisms of lorlatinib remain incompletely understood.
- Accurate prediction of brain drug concentration is vital for effective NSCLC brain metastasis therapy.
Purpose of the Study:
- To elucidate the mechanisms of lorlatinib brain penetration.
- To identify predictive biomarkers for lorlatinib brain concentration.
- To develop a model for forecasting lorlatinib distribution in the brain.
Main Methods:
- Liquid chromatography and mass spectrometry for lorlatinib detection in mice.
- Integrated metabolomics and transcriptomics to analyze metabolic and genetic pathways.
- Artificial neural network (multilayer perceptron) for predictive modeling.
- Construction of a metabolite-reaction-enzyme-gene interaction network.
Main Results:
- Nine novel biomarkers correlated with lorlatinib brain concentration were identified.
- A predictive model using these biomarkers achieved over 85% accuracy.
- A detailed interaction network revealed lorlatinib's mechanism of action.
Conclusions:
- Identified biomarkers and the developed neural network model can predict brain drug concentrations.
- This predictive capability is valuable for optimizing lorlatinib therapy in clinical settings.
- The findings enhance the understanding and application of lorlatinib for NSCLC brain metastases.
