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High-Resolution Twin-Ion Metabolite Extraction (HiTIME) Mass Spectrometry: Nontargeted Detection of Unknown Drug Metabolites by Isotope Labeling, Liquid Chromatography Mass Spectrometry, and Automated High-Performance Computing

The metabolic fate of a compound can often determine the success of a new drug lead. Thus, significant effort is directed toward identifying the metabolites formed from a given molecule. Here, an automated and nontargeted procedure is introduced for detecting drug metabolites without authentic metabolite standards via the use of stable isotope labeling, liquid chromatography mass spectrometry (LC/MS), and high-performance computing. LC/MS of blood plasma extracts from rats that were administered a 1:1 mixture of acetaminophen (APAP) and ¹³C₆-APAP resulted in mass spectra that contained “twin” ions for drug metabolites that were not detected in control spectra (i.e., no APAP administered). Because of the development of a program (high-resolution twin-ion metabolite extraction; HiTIME) that can identify twin-ions in high-resolution mass spectra without centroiding (i.e., reduction of mass spectral peaks to single data points), 9 doublets corresponding to APAP metabolites were identified. This is nearly twice that obtained by use of existing programs that make use of centroiding to reduce computational cost under these conditions with a quadrupole time-of-flight mass spectrometer. By a manual search for all reported APAP metabolite ions, no additional twin-ion signals were assigned. These data indicate that all the major metabolites of APAP and multiple low-abundance metabolites (e.g., acetaminophen hydroxy- and methoxysulfate) that are rarely reported were detected. This methodology can be used to detect drug metabolites without prior knowledge of their identity. HiTIME is freely available from https://github.com/bjpop/HiTIME

Additional file 1 of A tumor focused approach to resolving the etiology of DNA mismatch repair deficient tumors classified as suspected Lynch syndrome

Additional file 1: Table S1. Table displaying optimal cut-offs for the six tumor features determined previously (Walker et al. 2023) in the additive feature combination approach. Table S2. SLS tumors (n=13) that showed discordant MMR IHC findings between clinical diagnostic testing before study entry and testing completed internally during this study and the change in their MMR status and/or pattern of MMR protein loss. Table S3. The concordance between the final MMR IHC result and the predicted dMMR status from the additive feature combination approach overall and by tumor type. Table S4. The tumor MLH1 methylation testing completed for SLS tumors prior to entering the study showing either negative, inconclusive, or not tested results and the subsequent MLH1 methylation testing results from internal testing using MethyLight and MS-HRM assays highlighting the positive MLH1 methylation results found by this study. Table S5. Presentation of germline pathogenic variants and variants of uncertain clinical significance (VUS) identified in the MMR, MUTYH and POLE genes. Table S6. Summary of the clinicopathological features for the double somatic MMR mutation (dMMR-DS) tumors overall and by tumor type. Figure S1. Bar plots presenting the results from the additive tumor feature combination approach to assess the MMR status in the double somatic mutation cohort for A) all tumors combined and separated by B) CRC, C) EC and D) SST tissue types. Figure S2. Bar plot presenting the prevalence of pathogenic/likely pathogenic somatic mutations (including loss of heterozygosity, LOH) by subtype for the study cohort. Figure S3. Pie graphs displaying the frequency of the mutation combination type (two single somatic mutations versus a single somatic mutation with loss of heterozygosity (LOH)) as well as the type of mutation A) overall and B) separated by tissue type. Figure S4. Bar graphs presenting the site distribution in the double somatic mutation cohort across all CRCs and SSTs. Figure S5. Boxplots presenting the site distribution in the double somatic mutation cohort across all A) CRCs and B) SSTs. Significant (< 0.05) p-values are indicated for pairwise (t-test) and multigroup comparisons (Anova). Figure S6. Scatter plots presenting the PREMM5 score distribution in the test cohort for A) all tumors combined and separated by B) CRC, C) EC and D) SST tissue types. Figure S7. The distribution of tumor values for each of the six features that are included in the additive feature combination approach for determining tumor dMMR status grouped by molecular subtype and by combining sporadic dMMR groups dMMR-DS and dMMR-MLH1me into a “sporadic combined” group