Abstract The development of Protein Arginine Methyltransferase 5 (PRMT5) inhibitors, particularly MTA-cooperative agents that selectively target MTAP-deleted cancers, has created an urgent need for robust biomarkers to identify target patient populations and confirm pharmacodynamic (PD) target engagement. The purpose of this study was to develop and apply a highly sensitive, multiplexed liquid chromatography-mass spectrometry (LC-MS/MS) assay to quantify key PRMT5-related metabolites such as methylthioadenosine (MTA), symmetric dimethylarginine (SDMA), S-adenosylmethionine (SAM), and asymmetric dimethylarginine (ADMA), as well as interacting metabolites in the folate cycle, methionine cycle, and polyamine metabolism. The developed multiplexed LC-MS/MS method enables simultaneous quantification of MTA, SDMA, SAM, and SAH, and 12 other related metabolites from cell lysates, tumor tissue, and biofluids. The assay leverages isotopically labeled internal standards and fragment ion monitoring for robust quantitation and resolution of key PRMT biomarkers ADMA (Type I PRMTs, e.g., PRMT1) and SDMA (PRMT5). We then applied this assay to matched adenocarcinoma tumor and normal adjacent tissue (NAT) specimens from n=6 human patients. The results showed statistically significant (p<0.05) increases in SAM and MTA as well as S-adenosylhomocysteine (SAH) levels in the tumor. The elevated MTA levels was driven by a subset of patients, suggesting these individuals have MTAP-deleted tumors. SAM and SAH are reactants and products, respectively, of PRMT5 and Type I PRMTs like PRMT1. The elevation of SAM and SAH in the tumor suggests elevated PRMT activity. In examining SDMA and ADMA levels, we found that ADMA (PRMT1 biomarker) was elevated by 40% in the tumor, while SDMA (PRMT5 biomarker) showed less than 20% elevation. In total, these data demonstrate the utility of the developed metabolite assay for the development of PRMT5 therapies. The differences between tumor and NAT metabolite profiles are consistent with upregulated PRMT1 (or another Type I PRMT) in the tumor, leading to elevated ADMA, SAM, and SAH. PRMT5, on the other hand, is likely inhibited by elevated MTA levels in the MTAP-deleted tumors, making them more susceptible to MTA-cooperative PRMT5 inhibitors, despite not significantly increasing SDMA levels in the tumor. Citation Format: Ethan Stancliffe, Ashima Mehta, Douglas Guzior, Adam Richardson, Tom Cohen, Kevin Cho, Gary Patti. A multiplexed LC-MS/MS metabolite assay to enable patient stratification and pharmacodynamic monitoring for MTA-cooperative PRMT5 inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 536.

Abstract Background: KRAS is among the most common oncogenic drivers in colorectal cancer and is historically “undruggable”; yet direct KRAS inhibitors are now entering clinical trials. The systems-level effects of KRAS modulation remain poorly characterized, limiting therapeutic insights. We applied integrated multiomic profiling to reveal mechanistic vulnerabilities that could enhance translational strategies. Methods: Isogenic HCT-116 colorectal carcinoma cells (homozygous KRAS^G13D mutation vs. partial KRAS loss) were analyzed in biological replicates (n = 3-5) across transcriptomics (15,432 genes), proteomics (6,654 proteins), phosphoproteomics (10,034 phosphopeptides), and metabolomics (3,015 metabolites). Multi-layer data (>30,000 analytes) were integrated via pathway enrichment, correlation networks, and dimensionality reduction to identify core molecular modules downstream of KRAS. Results: KRAS deletion produced broad molecular reprogramming: 640 transcripts, 74 proteins, 744 phosphopeptides, and 519 metabolites showed significant changes (q<0.05). Metabolomics was most perturbed (>17%), revealing increased mitochondrial respiration and branched-chain amino acid catabolism, alongside suppressed glycolysis. Downregulated signaling included collagen biosynthesis, EGFR, IGF transport, and IL-4/IL-13 pathways. Network integration distilled >30,000 features into <100 molecular modules, highlighting metabolic shifts in plasmalogen-associated lipid remodeling and altered phosphorylation of nuclear pore, ribosomal, and DNA repair proteins (ATRX, DAXX). These findings indicate vulnerabilities in oxidative metabolism, lipid metabolism, ribosome biogenesis, and genome stability. Conclusions: This comprehensive multiomic analysis reveals that KRAS-driven colorectal cancer depends on coordinated control of metabolism, extracellular matrix signaling, and DNA repair pathways. KRAS attenuation shifts cells to oxidative metabolism while exposing vulnerabilities in lipid metabolism and DNA repair machinery. These findings suggest combinatorial therapeutic strategies integrating KRAS inhibition with metabolic or DNA damage-targeted therapies. Citation Format: Tom Cohen, Ashima Mehta, Adam Richardson, Monil Gandhi, Douglas Guzior, Kevin Cho, Ethan Stancliffe, Gary Patti. Multiomic dissection of KRAS signaling reveals targetable metabolic and DNA repair vulnerabilities in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4197.

Abstract Introduction: Colorectal cancer (CRC) remains the second leading cause of cancer-related deaths worldwide, underscoring the critical need for improved early detection and risk stratification methods. While polyps are detected in up to 40% of colonoscopies, most are negative for significant lesions, with only about 10% showing advanced adenomas or carcinomas. Although colonoscopy remains the gold standard for CRC screening, it has major limitations in predicting progression from benign neoplasia to advanced adenomas or carcinomas. Precision approaches that integrate molecular insights are necessary to identify biomarkers for risk stratification and better understand which individuals with colon neoplasia are at increased risk to develop advanced adenomas or carcinomas. Methods: This study leverages a multi-modal, integrated analysis of spatial transcriptomics, bulk RNA-sequencing, and metabolomics—an approach that has not been comprehensively applied to risk stratification in CRC. A set of 10 colorectal samples, five from cases that have progressed to CRC and five that have not, were selected for deep multiomics analysis. Starting with spatial transcriptomics from FFPE, single cell analysis across a slice was explored for each sample across the specimen, with a focus on variability between groups across the colonic crypts, tube-like glands in the colon and rectum that produce mucus and renew the intestinal lining. An additional slice of tissue was analyzed for bulk RNA-sequencing. Finally, adjacent tissue from the same individual was analyzed for bulk metabolomics, to identify and quantify the small molecules present within each sample. Results: Integrated bioinformatics analyses were used to compare and combine bulk results, along with clinical and demographic data associated with these samples, for downstream pathway and processes analysis. In addition, these results were further analyzed to cross-compare and validate single cell spatial findings. Conclusion: With this integrated multiomics approach, spatial transcriptomics provides high-resolution insights into gene expression within the structural context of the colonic crypts, while bulk metabolomics captures a systemic overview of metabolic alterations linked to neoplastic progression. By identifying key biomarkers and pathways associated with CRC progression, this study aims to pave the way for personalized screening strategies and targeted interventions to reduce the burden of advanced colorectal cancer. Citation Format: Andrea J. O'Hara, Priya Roy, Dhwani Mulani, Ethan Stancliffe, Tom Cohen, Hemant Roy, Haythem Latif. Integrated analysis for identification of risk stratification biomarkers for colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5381.

Abstract Background: The omics era has greatly expanded the repertoire of approaches available to unravel the complexity underpinning human health, with the ability to rapidly characterize genomes, epigenomes, transcriptomes, proteomes and metabolomes from a wide range of sample types. Urogenital cancers, including prostate cancer, is the most prevalent cancer in men. Current early detection methods rely on blood screening of prostate-specific antigen (PSA), however, it has a high rate of false positives, resulting in the search for alternative biomarkers. Urine is an ultra-non-invasive analyte ideal for urogenital cancer detection, including prostate cancer. Cell free RNA/DNA (cfRNA/DNA), along with metabolomics directly from urine, is an ideal candidate for biomarker identification for use in diagnostics, treatment monitoring and tumor tissue of origin prediction. Methods: Here we describe integrated metabolomics and RNA-Seq analysis from a series of prostate cancer affected and control urine samples. First, cfRNA from affected and control samples were isolated using a specialized method with efficient cfRNA recovery rate from urine. The cfRNA was then subjected to highly sensitive RNA-Seq to evaluate a series of prostate cancer biomarkers. The same urine samples were also subjected to metabolite profiling using multiple complementary LC-MS assays to deliver the highest quality untargeted metabolomics data. Both data types were used for integrated multiomics analysis. Results: Urine proves to be an ideal ultra-non-invasive method for biomarker screening and detection. Integrated analysis across multiple data modalities, including transcriptomics and metabolomics, allows for holistic views of pathways and processes that are highly impacted, with increased statistical significance than any one modality alone. Conclusion: Biomarker detection and multiomics analyses are critical to assess individuals in both pre- and post-treatment during therapeutic development and early-stage clinical trials. Urine offers a truly non-invasive approach that, when combined with omics tools, can provide comprehensive insight across urogenital patient cohorts. ​ Citation Format: Bhaven Mehta, Andrea O’Hara, Ethan Stancliffe, Tom Cohen, David Corney, Haythem Latif. Integrated multiomics for deep molecular exploration of prostate cancer from urine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7843.

Abstract Introduction: Formalin-fixed paraffin-embedded (FFPE) tissues are invaluable for retrospective clinical studies due to superior morphological preservation and easy storage, but their use in metabolomics is limited by a lack of established sample preparation protocols and concerns over metabolite stability. Here we optimized a novel FFPE metabolomic workflow and validated findings with matched fresh-frozen (FF) samples to identify metabolic signatures altered in human colorectal cancer (CRC). Methods: We analyzed 12 FFPE human tissue samples, comprising 6 paired tumor and Nearby Adjacent Tissue (NAT) specimens from adenocarcinoma patients, alongside matched FF samples for validation. Global metabolomics assays were performed on both polar and lipid fractions using a next-generation mass spectrometry platform. Results: Metabolic profiling detected 2,564 unique metabolites across 200+ classes from FFPE tissues with low technical variation (median CV < 5%). Unsupervised analyses showed clear tumor-NAT distinctions. Statistical analysis identified 200 differential metabolites (|log2(fc)| > 1, p < 0.05). Pathway analysis revealed 39 altered pathways (p<0.05), with upregulated diacylglycerophosphoinositols and downregulated triacylglycerols in tumors being the most significant findings. Consistent upregulation of central carbon metabolites and amino acids indicated metabolic reprogramming for biosynthesis and oxidative stress buffering. FF sample validation showed concordance across the most significant hits and pathways identified, confirming the robustness and biological relevance of FFPE-derived signatures. Conclusion: Our study confirms FFPE metabolomic profiling reliably identifies significant metabolic perturbations in human cancer, consistent with FF findings. The identified shifts in lipid, central carbon, and amino acid metabolism highlight extensive metabolic reprogramming in CRC. FFPE archives are a valuable resource for large-scale retrospective clinical metabolomics studies, offering a powerful avenue for discovery research in human genetics and disease, and for identifying novel biomarkers. Citation Format: Tom Cohen, Ashima Mehta, Adam Richardson, Monil Gandhi, Douglas Guzior, Kevin Cho, Ethan Stancliffe, Gary Patti. Comprehensive metabolomic profiling of FFPE human tissues reveals key metabolic reprogramming in colorectal cancer and associated pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4710.

Influenza A virus (IAV) infection remodels cellular processes to support viral replication. The modulation of host factors by the virus drives pathogenesis during infection, and these factors may serve as therapeutic targets. Here, we infect mice with two IAV strains, H1N1 and H5N1, and analyze lung tissue with multi-omics. Using network propagation analysis, we identify twenty-four distinct host modules altered by infection, encompassing 2920 genes/proteins. Independently, we develop a computational pipeline, MidTOD, which integrates metabolomic data with other OMICs data-types, linking metabolites to gene/protein alterations. Combining datasets from both approaches reveals alterations in mitochondrial and peroxisomal metabolism in IAV-infected cells and identifies arginine:glycine amidinotransferase (GATM) as a host dependency factor in both human cells and mice. Knockdown of this enzyme reduces IAV-mediated pathology and host inflammatory responses after infection. Collectively, this work provides an integrated systems-level view of host changes during infection and identifies an abundance of IAV-host factors.

KRAS mutations cause significant metabolic and protein dysregulation in cancer, leading to alterations in cellular signaling pathways, promoting uncontrolled cell growth and proliferation. The metabolic reprogramming of altered KRAS function includes increased glucose, amino acid, and lipid metabolism. The changes in the cellular proteome stem from modifications in protein expression and phosphorylation. Understanding the interplay between these metabolic and protein-level alterations is crucial for developing targeted therapeutic strategies against KRAS-driven cancers. Accordingly, we performed an integrated metabolomic and phosphoproteomic analysis of HCT116 cells with and without a KRAS mutation. HCT-116 cells containing wildtype (WT, n=5) and mutant KRAS (MUT, n=5) were profiled to assess metabolite, protein, and phosphoprotein levels between WT and MUT cells. Metabolomic profiling was completed with LC/MS to capture polar and lipid metabolites. Data was processed through an in-house untargeted metabolomic analysis pipeline. For phosphoteomic analysis, enriched phosphopeptides and flow-through peptides were analyzed with DIA LC/MS/MS. The resulting data was processed with DIA-NN and combined into aggregated protein and phosphosite profiles. The multi-omic analysis profiled 2,025 metabolites, 6,654 proteins, and 10,034 phosphosites. When considering each data type individually, all three analyte types seperated the WT and MUT cells through PCA analysis, underscoring the magnitude of dysregulation from KRAS mutation. When considering the metabolites, proteins, and phosphosites that reached statistical significance, 85 metabolites and 145 proteins were differentially abundant. Strikingly, 1,036 phosphosites were unique to either KRAS and WT samples, with ∼800 of these being unique to KRAS samples. The results of a joint pathway analysis identified >70 pathways that reached significant enrichment (p<0.05) and had support at both the protein, metabolite, and phosphosite level. The most enriched pathway was RAS signaling. Other enriched pathways include selenocysteine synthesis, sialic acid metabolism, ROS detoxification, and respiration. This study showed extensive differential phosphorylation in KRAS mutant cells, suggesting a widespread rewiring of signaling networks thatimpacts diverse cellular functions. The multi-omic approach taken enabled the identification of previously known and novel pathways affected by KRAS signaling, offering potential new targets for therapeutic intervention in KRAS-driven cancers. Ethan Stancliffe, Tom Cohen, Ashima Mehta, Cassandra Kempf, Adam Richardson, Monil Gandhi, Douglas V. Guzior, Kevin Cho, Gary J. Patti. Unbiased phosphoproteomic and metabolic profiling in WT and KRAS mutant colorectal cancer cells reveals molecular interplay between protein regulation and metabolic rewiring [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5761.

e15716 Background: Although fresh-frozen samples are currently the gold standard when performing mass spectrometry-based metabolomics, clinical workflows commonly produce formalin-fixed paraffin-embedded (FFPE) tissues. Fixation in formalin and embeddement in paraffin offers a number of advantages, such as mitigating the risks of infectious agents and preserving the architectural components of the tissue. The latter is important for pathological assesement of cancer, where changes in tissue architecture can be used for diagnosis and to guide treatment decisions. Methods: A concern of using FFPE for metabolomics is that it chemically modifies metabolites and lipids. Thus, while metabolomics data can be generated from FFPE specimens, there remains a major question about its reliability. In this study, we optimized a sample preparation method for use in FFPE metabolomics, then validated the approach on 12 tissue samples from colon cancer patients comparing the results to healthy nearby adjacent tissue (NAT). The analysis was performed on matched fresh frozen and FFPE tissues. By comparing the data, we establish a panel of metabolites and lipids that can be reliably profiled from FFPE tissues by using our workflow. Results: A total of 946 unique metabolites and lipids were measured from FFPE samples using our next-generation metabolomics platform. Across all assays, the coefficient of variation (CV) values were less than 10%. More than 50% of the unique metabolites and lipids from FFPE samples were also identified in matched fresh-frozen tissues. Molecules measured from both tissue types spanned multiple chemical classes ranging from fatty acids to central carbon metabolites. Next, changes in metabolite abundance across cancer and NAT tissues were used to assess the reliability of our FFPE metabolomics workflow in producing biologically relevant findings. Among the metabolites and lipids that were measured in both platforms, a large fraction showed consistent fold changes between fresh frozen and FFPE specimens. As an example, metabolites in glycolysis and the TCA cycle were found to be altered with a similar statistical magnitude in both fresh frozen and FFPE samples. These results are consistent with expected changes associated with the Warburg effect. Conclusions: This study reveals that metabolite profiling in FFPE tissues can effectively identify biologically significant compounds and pathways, offering a new tool for discovery research in cancer.

e15714 Background: KRAS mutations cause significant metabolic and protein dysregulation in cancer, leading to alterations in cellular signaling pathways, promoting uncontrolled cell growth and proliferation. The metabolic reprogramming of altered KRAS function includes increased glucose, amino acid, and lipid metabolism. The changes in the cellular proteome stem from modifications in protein expression and phosphorylation. Understanding the interplay between these metabolic and protein-level alterations is crucial for developing targeted therapeutic strategies against KRAS-driven cancers. Accordingly, we performed an integrated metabolomic and phosphoproteomic analysis of HCT116 cells with and without a KRAS mutation. Methods: HCT-116 cells containing wildtype (WT, n = 5) and mutant KRAS (MUT, n = 5) were profiled to assess metabolite, protein, and phosphoprotein levels between WT and MUT cells. Metabolomic profiling was completed with LC/MS to capture polar and lipid metabolites. Data was processed through an in-house untargeted metabolomic analysis pipeline. For phosphoteomic analysis, enriched phosphopeptides and flow-through peptides were analyzed with DIA LC/MS/MS. The resulting data was processed with DIA-NN and combined into aggregated protein and phosphosite profiles. Results: The multi-omic analysis profiled 2,025 metabolites, 6,654 proteins, and 10,034 phosphosites. When considering each data type individually, all three analyte types seperated the WT and MUT cells through PCA analysis, underscoring the magnitude of dysregulation from KRAS mutation. When considering the metabolites, proteins, and phosphosites that reached statistical significance, 85 metabolites and 145 proteins were differentially abundant. Strikingly, 1,036 phosphosites were unique to either KRAS and WT samples, with ~800 of these being unique to KRAS samples. The results of a joint pathway analysis identified > 70 pathways that reached significant enrichment (p < 0.05) and had support at both the protein, metabolite, and phosphosite level. The most enriched pathway was RAS signaling. Other enriched pathways include selenocysteine synthesis, sialic acid metabolism, ROS detoxification, and respiration. Conclusions: This study showed extensive differential phosphorylation in KRAS mutant cells, suggesting a widespread rewiring of signaling networks that impacts diverse cellular functions. The multi-omic approach taken enabled the identification of previously known and novel pathways affected by KRAS signaling, offering potential new targets for therapeutic intervention in KRAS-driven cancers.

The omics era has greatly expanded the repertoire of approaches available for researchers and clinicians to unravel the complexity underpinning human health: Next Generation Sequencing (NGS) approaches can characterize genomes, epigenomes, transcriptomes, and proteomes. The analyses are critical to assess in individuals both pre- and post-treatment during therapeutic development and early-stage clinical trials. Peripheral blood mononuclear cells (PBMCs) offer a non-invasive approach that, when combined with omics tools, can provide a near holistic view of immune processes across patient cohorts. For fresh blood draws, this starts with automated sample handling and processing to ensure high viability and yield of PBMCs, along with simultaneous plasma separation and collection, which is then aliquoted for downstream analysis. These PBMC and plasma aliquots were then processed such that whole exome sequencing, whole genome methylation sequencing, single cell and bulk RNA sequencing, Olink Explore HT and Seer proteomic analysis, and metabolomic analysis can all be collected from patient blood draw. Integrated analysis across multiple data modalities allows for holistic views of pathways and processes that are highly impacted, with increased statistical significance than any one modality alone. Inclusion of dual proteomics assays provided a greater breadth of downstream functional results, revealing complementarity between assays. While genomics, transcriptomics, and proteomics provide information about genetic and functional potential, inclusion of metabolomics grants deeper phenotypic insights within and across individuals within a patient cohort. Ultimately, this approach reduces the need for repeated patient collections and lowers bio-storage requirements, making it a more patient-friendly and cost-effective solution, while rapidly producing a diverse set of multiomics results and allows for deep exploration of the molecular underpinnings of cancerous tissue. Andrea J. O'Hara, Tom Cohen, Ethan Stancliffe, Adam Richardson, David Corney, Laure Turner, Haythem Latif, Ginger Zhou. Integrated multiomics unlocks holistic phenotypic insights allowing for deep molecular exploration [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3691.

Background Aneurysmal subarachnoid hemorrhage (aSAH) is a severe type of stroke that is associated with poor outcome. A subset of patients with aSAH will develop secondary complications, most notably delayed cerebral ischemia (DCI), which potentiates neurological injury. In this study, we investigate the relationship between cerebrospinal fluid (CSF) iron accumulation, brain metabolism, and neuronal injury in patients with aSAH with or without DCI. Methods We collected longitudinal CSF samples of patients immediately after hospitalization and 5 to 8 days after onset of ictus. CSF was analyzed with electron paramagnetic resonance spectroscopy and metabolomics to determine the presence of redox‐active iron species and metabolic alterations associated with aSAH and DCI. Neuronal pathology induced by iron overload was characterized in neuronal and meningeal cell models. Results Electron paramagnetic resonance spectroscopy identified higher levels of an Fe(III) protoporphyrin IX (hemin)‐like molecule in the CSF of patients who developed DCI compared with patients who did not show secondary ischemic injury after aSAH or controls without neurological disease. Treatment of a human neuronal cell line with Fe(III)‐containing hemin resulted in the disruption of the axonal mitochondrial network and loss of viability. This was cell‐type dependent as a meningeal cell line was resistant to hemin treatment, despite both cell types upregulating the iron ferroxidase ceruloplasmin. Metabolomic profiling of the same CSF samples uncovered significant dysregulation of metabolic pathways associated with energy generation and amino acid utilization, consistent with mitochondrial dysfunction. Using machine learning, we identified a set of metabolites that predicted intensive care unit length of stay. Conclusion aSAH leads to the accumulation of an Fe(III)‐containing heme species in the CSF of a subset of patients who subsequently develop DCI. The accumulation of an Fe(III) protoporphyrin induces axonal mitochondrial dysfunction, leading to cell death. aSAH alters the CSF metabolome involved in mitochondrial function and a subset of these metabolites are predictive of intensive care unit stay. These results identify potential biomarkers for mitochondrial pathology and provide insight into alterations in brain iron metabolism triggered by aSAH.

In dogs, brain aging may lead to cognitive decline and cognitive dysfunction syndrome (CDS) [...]

Abstract Despite technological advances in molecular medicine over the last 30 years, no single approach has proven to be sufficient to meet the diagnostic needs in cancer care. Here we use a multiomic approach leveraging proteomic, metabolomic, and transcriptomic technologies to study the metabolic shifts that occur during colorectal cancer (CRC) progression, Our integrated analysis identified systemic changes to beta oxidation pathways and localized changes to tyrosine metabolism within the tumor, creating a mechanistic, actionable description of the core CRC metabolic program. We conducted untargeted proteomics and metabolomics profiling on serum samples from CRC patients (n=10) and healthy controls (n=10). Our serum proteomics data is generated through sample enrichment on the Seer Proteograph system followed by LC/MS analysis with the Thermo Orbitrap Astral. Our metabolomics approach employs LC/MS assays for unbiased profiling of the serum metabolome, exposome, and lipidome. We supplemented this discovery work with both targeted serum and tumor-specific approaches including targeted proteomics to quantify select targets, inflammatory profiling with Alamar proteomics, public transcriptomics data from TCGA, and in vitro metabolic flux data. These large and diverse datasets were then integrated through joint pathway analysis and network integration. The global serum proteomics and metabolomics profiles generated show distinct separation between healthy and diseased individuals. Joint pathway analysis of these discovery datasets highlighted enrichment in beta oxidation pathways and tyrosine metabolism, among others. Interestingly, the Alamar inflammatory panel revealed that many of the analytes in the tyrosine metabolism pathway were well correlated with inflammatory status, while the beta oxidation signature had a lower correlation. To determine the relationship of these systemic findings from serum to tumor metabolism itself, we integrated tumor-specific transcriptomics data and found alterations to tyrosine metabolism with differences in tyrosine aminotransferase expression. The beta oxidation related genes, on the other hand, were not concordant with the serum findings. However, our in vitro metabolic flux studies have shown beta oxidation in CRC cells is upregulated to provide additional fuel for oxidative phosphorylation. This result suggests that beta oxidation may not be transcriptionally regulated in the tumor but rather a consequence of organismal metabolic rewiring. The results of this work, which is currently expanding into a larger and more diverse patient population, underscores the power of multiomic profiling for enhancing our understanding of metabolic dysregulation in CRC. Ultimately, a comprehensive model of the molecular alterations in CRC will yield a better understanding of tumor phenotypes and inform better diagnostics and therapies. Citation Format: Gary Patti, Ethan Stancliffe, Adam Richardson, Ashima Mehta, Monil Gandhi, Kevin Cho. Integrated multi-omics analysis reveals systemic and localized metabolic disruptions in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4428.

Metabolites that mark aging are not fully known. We analyze 408 plasma metabolites in Long Life Family Study participants to characterize markers of age, aging, extreme longevity, and mortality. We identify 308 metabolites associated with age, 258 metabolites that change over time, 230 metabolites associated with extreme longevity, and 152 metabolites associated with mortality risk. We replicate many associations in independent studies. By summarizing the results into 19 signatures, we differentiate between metabolites that may mark aging-associated compensatory mechanisms from metabolites that mark cumulative damage of aging and from metabolites that characterize extreme longevity. We generate and validate a metabolomic clock that predicts biological age. Network analysis of the age-associated metabolites reveals a critical role of essential fatty acids to connect lipids with other metabolic processes. These results characterize many metabolites involved in aging and point to nutrition as a source of intervention for healthy aging therapeutics.

Tumors are comprised of a multitude of cell types spanning different microenvironments. Mass spectrometry imaging (MSI) has the potential to identify metabolic patterns within the tumor ecosystem and surrounding tissues, but conventional workflows have not yet fully integrated the breadth of experimental techniques in metabolomics. Here, we combine MSI, stable isotope labeling, and a spatial variant of Isotopologue Spectral Analysis to map distributions of metabolite abundances, nutrient contributions, and metabolic turnover fluxes across the brains of mice harboring GL261 glioma, a widely used model for glioblastoma. When integrated with MSI, the combination of ion mobility, desorption electrospray ionization, and matrix assisted laser desorption ionization reveals alterations in multiple anabolic pathways. De novo fatty acid synthesis flux is increased by approximately 3-fold in glioma relative to surrounding healthy tissue. Fatty acid elongation flux is elevated even higher at 8-fold relative to surrounding healthy tissue and highlights the importance of elongase activity in glioma.

Peak-detection algorithms currently used to process untargeted metabolomics data were designed to maximize sensitivity at the sacrifice of selectively. Peak lists returned by conventional software tools therefore contain a high density of artifacts that do not represent real chemical analytes, which, in turn, hinder downstream analyses. Although some innovative approaches to remove artifacts have recently been introduced, they involve extensive user intervention due to the diversity of peak shapes present within and across metabolomics data sets. To address this bottleneck in metabolomics data processing, we developed a semisupervised deep learning-based approach, PeakDetective, for classification of detected peaks as artifacts or true peaks. Our approach utilizes two techniques for artifact removal. First, an unsupervised autoencoder is used to extract a low-dimensional, latent representation of each peak. Second, a classifier is trained with active learning to discriminate between artifacts and true peaks. Through active learning, the classifier is trained with less than 100 user-labeled peaks in a matter of minutes. Given the speed of its training, PeakDetective can be rapidly tailored to specific LC/MS methods and sample types to maximize performance on each type of data set. In addition to curation, the trained models can also be utilized for peak detection to immediately detect peaks with both high sensitivity and selectivity. We validated PeakDetective on five diverse LC/MS data sets, where PeakDetective showed greater accuracy compared to current approaches. When applied to a SARS-CoV-2 data set, PeakDetective enabled more statistically significant metabolites to be detected. PeakDetective is open source and available as a Python package at https://github.com/pattilab/PeakDetective.

Although nicotinamide adenine dinucleotide phosphate (NADPH) is produced and consumed in both the cytosol and mitochondria, the relationship between NADPH fluxes in each compartment has been difficult to assess due to technological limitations. Here we introduce an approach to resolve cytosolic and mitochondrial NADPH fluxes that relies on tracing deuterium from glucose to metabolites of proline biosynthesis localized to either the cytosol or mitochondria. We introduced NADPH challenges in either the cytosol or mitochondria of cells by using isocitrate dehydrogenase mutations, administering chemotherapeutics or with genetically encoded NADPH oxidase. We found that cytosolic challenges influenced NADPH fluxes in the cytosol but not NADPH fluxes in mitochondria, and vice versa. This work highlights the value of using proline labeling as a reporter system to study compartmentalized metabolism and reveals that NADPH homeostasis in the cytosolic and mitochondrial locations of a cell are independently regulated, with no evidence for NADPH shuttle activity.