University of Wisconsin School of Medicine and Public Health Madison, WI
M. Liu1, H. Menon2, Y. Zhu3, S. C. Callahan2, A. R. Brasier4, Y. Ge3, M. E. Kimple4, and A. M. Baschnagel2; 1University of Wisconsin School of Medicine and Public Health, Madison, WI, 2Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 3Department of Cell and Regenerative Biology, University of Wisconsin School of Medicine and Public Health, Madison, WI, 4Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI
Purpose/Objective(s): Current staging of non-small cell lung cancer (NSCLC) involves assessment by conventional imaging; however, standard imaging modalities are not sensitive enough to detect micrometastatic disease. Circulating metastatic cells rely on antioxidant metabolism to survive and colonize distant organs. These oncometabolites in the blood could be used as biomarkers to predict the risk of metastasis formation. The objective of this study was to determine if we could identify a metabolomic signature predictive of early metastasis in patients with NSCLC treated with definitive radiation. Materials/
Methods: Blood plasma samples were collected from patients enrolled on a prospective trial from 2017 to 2019 prior to definitive radiation. Metabolites were extracted and mass peaks were generated using the Flow Injection Ionospray (FIE) Fourier Transform Ion Cyclotron Resonance (FTICR) Mass Spectrometry (MS) workflow. Patients were stratified into two groups based on developing metastasis within one year (early metastasis). Orthogonal signal correction partial least-squares discriminant analysis (OPLS-DA) was used to identify metabolic features contributing to the difference between the groups. Variable importance in projection (VIP) score analysis and independent samples T-tests (p-value < 0.05) were further used to identify metabolites. Univariate receiver operating characteristic (ROC) curves and multivariable partial least-squares discriminant analysis (PLS-DA) models were constructed. Metabolites were identified with the Human Metabolome Database. Results: Of the 28 patient samples analyzed, 5 patients had early metastasis and 23 patients did not. The cohort consisted of 17 males and 11 females with median age of 69, range, 52-84. At diagnosis, 13 patients were stage I (none developed early metastasis) and 15 patients were stage III (5 developed early metastasis). No difference was found between those with or without early metastasis when comparing age, gender, histology, or smoking status (all p >0.05). OPLS-DA analysis yielded 477 metabolites with a VIP score of >1. PLS-DA multivariable analysis constructed with the top 5 and 10 features gave AUC values of 0.808 and 0.800, respectively. 34 metabolites were significantly different between the two groups (p < 0.05). Among these, 4 metabolites were identified and found to be of diagnostic potential. These classes include diacylglycerols (AUC 0.809), phenyl sulfoxides (AUC 0.796), monoacylglycerols (AUC 0.848), and phosphatidylserines (AUC 0.748). Conclusion: We identified several distinct changes in the metabolic profile of patients with NSCLC who developed early metastatic disease after definitive radiation. These findings suggest the feasibility of using metabolomic profiling as a tool for predicting metastatic risk. Further investigations are warranted to elucidate a metabolic phenotype associated with lung cancer metastasis.
