The AUC for Ceritinib separation along LV1 was 0.71, with moderate sensitivity (0.74) but poor specificity (0.60). The loading plot (Supplemental Figure S1) indicates

a number of peaks contribute to the separation. The score plot from the OSC-PLS analysis of the NOESY spectra shown in Figure 1 (d), shows an even better separation between the two patient cohorts, and the loading plot (Supplemental Figure S2) shows Inhibitors,research,lifescience,medical mostly lipid peaks. These results show promise for the future study of lipids. However, a major challenge in using NOESY to study lipids is that it cannot fully distinguish lipids with different fatty acid chains as they overlap. As a result, the following analysis will focus on CPMG spectra since they contain a larger number of peaks from identifiable and quantifiable metabolites. Figure 1 (a) The averaged Carr-Purcell-Meiboom-Gill (CPMG) spectra (bottom) for the HCC patients (blue dashed line, n=40) and HCV patients (red solid line n=22), along with the difference spectrum Inhibitors,research,lifescience,medical (top, black solid line). Major differences in metabolites are indicated … Considering the contribution to the loading plots from Inhibitors,research,lifescience,medical many low-lying and unidentified metabolite peaks, as well as noise, a more

targeted approach was also pursued. Individual peaks from 19 known metabolites (See Supplemental Information Table S1) were integrated and analyzed to reduce the contribution Inhibitors,research,lifescience,medical from chemical noise and to focus the analysis on known metabolite species so as to provide more mechanistic information. Initially, PCA analysis was performed on the 19 metabolites to see the data clustering.

The results are shown in Figure S3; as anticipated, clear separation of the two groups was not observed in the PCA results. A PLS-DA model was built based on these metabolite signals to investigate classification and discrimination. The cross-validated prediction result and ROC curve are shown in Figure S4. The two sample classes are somewhat separated by this model, but a number of Inhibitors,research,lifescience,medical misclassifications still exist. The area under curve (AUC) is 0.71. The model was further tested by MCCV, and the results of the classification confusion matrix are shown in Supplementary Table S2. The low sensitivity (54%) and specificity (58%) that result from the MCCV procedure indicate that the model is not very strong. However, this model is still better than the permutation result (these data are provided in Table S2 as the values GSK-3 in parentheses). The sensitivity and specificity of the permutation test are only 50% and 48%, respectively, which is essentially a random result, as anticipated. The sensitivity and specificity results for both the true model and permutation test from 200 iterations are also plotted (see Supplemental Information Figure S5). Although not very impressive there is still some separation, which indicates that the predictive model is better than a random one.