Abstract LB209: Blood serum MRS metabolomics screening of human lung cancer prior to disease diagnosis

Abstract

Abstract Blood serum MRS metabolomics screening of human lung cancer prior to disease diagnosis Lung cancer (LuCa), the leading cause of cancer deaths, is often diagnosed late. Low-dose spiral CT can detect small and early stage LuCa lesions, but cannot be used widely as an annual LuCa screening tool. Metabolomics detects global metabolite variations under physiology and pathology. Metabolomic profiles measured from blood may reveal LuCa at early stages as a screening tool to triage suspicious patients to CT tests. Blood sera obtained from LuCa patients prior to their diagnosis were studied with MRS to establish LuCa screening metabolomic profiles.Samples. Sera from non-small cell LuCa (NSCLC) patients and their age, gender and smoking habit matched healthy controls were grouped according to the design of training-testing-validation cohorts in this study. The training cohort included 25 NSCLC sera from patients at the time of diagnosis (TOD) and controls (Ctrl); the testing cohort consisted 25 sera collected 0.5 to 5 yrs prior to diagnosis (PTD) from the 25 NSCLC patients in the training cohort; and the validation cohort recruited sera collected less than 2 yrs PTD from additional 54 NSCLC patients and controls. MR Spectroscopy. High resolution magic angle spinning (HRMAS) MRS analysis of serum samples are performed at 4°C by a 600MHz Bruker spectrometer at 3,600Hz spinning rate. Spectra are analyzed with a MatLab-based curve fitting program. Data Analysis. 57 spectral regions were selected based on the training and testing cohorts. Following selections of these regions, all data analytical procedures, including principal component and canonical analyses, were performed on the training cohort and followed by the testing and validation cohorts. Analyses of native sera HRMAS MRS of the training cohort selected 57 spectral regions for statistical analyses, and PCA and canonical analysis were conducted to differentiate TOD from Ctrl groups, with the testing and validation cohorts passively followed the calculations to produce scores within each case.Using the mean plus one standard error (M+SE) as the threshold, calculated from the canonical score differences between TOD and PTD for each case, i.e. the difference of the two scores for each patient, a Kaplan-Meier survival analysis indicates better survival rate from their time of NSCLC diagnoses for patients with score differences higher than the threshold (p=0.031). Furthermore, for Stage I and IIA patients, survivals can be predicted (p=0.027) from their PTD samples, if their score values are higher than the M+SE threshold. Since neither testing nor validation cohorts were involved in the determination of values of the canonical score, by collectively examining all Stage I and IIA in both cohorts, the resulting Kaplan-Meier survival predicting capability by the threshold (established by the testing cohort) was enhanced significantly (p=0.0044). For 5-year survival, our data showed Sen=0.63, Spe=1.00, PPV=1.00, NPV=0.21, ACC=0.67, and F1=0.78. Thus, serum metabolomics may be a screening candidate for early detections with the potential to further probe into all the related metabolic pathways for better understanding of disease. Citation Format: Tjada Schult, Mara Lauer, Yannick Berker, Marcella Cardoso, Lindsey Vandergrift, Piet Habbel, Johannes Nowak, Martin Aryee, Mari Mino-Kenudson, David Christiani, Leo Cheng. Blood serum MRS metabolomics screening of human lung cancer prior to disease diagnosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB209.