Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data. (June 2021) - Miller, Hunter A.; Yin, Xinmin; Smith, Susan A.; Hu, Xiaoling; Zhang, Xiang; Yan, Jun; Miller, Donald M.; van Berkel, Victor H.; Frieboes, Hermann B.
Highlights: Designs workflow for personalized prediction of response to first-line chemotherapy. Performs systematic metabolomic analysis of lung cancer fresh tumor core biopsies. Machine learning analyses differentiate between treatment responses and disease stage. Predicts response and identifies metabolites associated with patient classifications. Abstract: Objectives: Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results. This proof-of-concept study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. Materials and methods: Samples ( n = 36) were evaluated with high-resolution 2DLC-MS/MS and 13 C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) in terms of first-line chemotherapy. Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests (RF)) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets. Results: The models predicted patient classifications in the validation subsets with AUC (95 % CI): DC vs. PD (SVM), 0.970(0.961–0.979); CR/PR vs. SD/PD (PLS-DA), 0.880(0.865–0.895); stage I/II/III vs. IV (SVM), 0.902(0.880–0.924). Highest performing model was SVM for DC vs. PD (balanced accuracy = 0.92; kappa = 0.74). Conclusion: This study illustrates a comprehensive evaluation of patient tumor-specific metabolic profiles, with the potential to identify disease stage and predict response to first-line chemotherapy.