Data analysis
death
DISEASE PROGRESSION
Data normality and homoscedasticity were confirmed using the Shapiro–Wilks test and the Breush–Pagan test, respectively. Group comparisons over time were performed using a two‐way repeated measures ANOVA using IBM SPSS Statistics (version 27, International Business Machines Corporation). Progression‐free survival (PFS) was calculated from the time of treatment initiation until radiographic criteria of disease progression were observed. Overall survival (OS) was calculated from the time of initiation of treatment until patient death. Survival curves were estimated using the Kaplan–Meier method, then compared using the log‐rank test using GraphPad Prism (version 7.0, GraphPad Software Inc).Mass spectra were processed, quantified, and analyzed using Metabolite Detector software (Version 2.06, Technische Universität Carolo‐Wilhelmina zu Braunschweig). Sample peaks were normalized utilizing the spiked‐in internal controls. That is, intensities of the internal standard metabolites were integrally normalized against the sum of the metabolite intensities for each sample, adjusting for inter‐sample variations in concentrations. Metabolites were identified referencing two in‐house feature libraries (GOLM metabolite data base and NIST), with reference to retention indices, retention times and individual ions. A second normalization step was performed using median fold‐change methods. Missing values were imputed with the minimum quantitative value in the data set. Batch‐dependent noise was removed, and inter‐batch variation was corrected using the ComBat algorithm (through the Bioconductor R package “sva”) in R environment (version 3.3).Changes in metabolite abundance levels from baseline were calculated for each post‐treatment time point. Treatment‐related perturbations at each time point were compared. Data analysis was performed using SIMCA‐P+ software (version 15.0, Umetrics AB). Data structure was explored by principal component analysis (PCA) and outliers were removed. Subsequently, a supervised analysis was performed using orthogonal partial least squares discriminant analysis (O‐PLS‐DA). Candidate metabolite selection was based on variable importance on projection (VIP) thresholds set to maximize R