The Role of Mass Spectrometry and Proteogenomics in the Advancement of HLA Epitope Prediction.

A challenge in developing personalized cancer immunotherapies is the prediction of putative cancer-specific antigens. Currently, predictive algorithms are used to infer binding of peptides to human leukocyte antigen (HLA) heterodimers to aid in the selection of putative epitope targets. One drawback of current epitope prediction algorithms is that they are trained on datasets containing biochemical HLA-peptide binding data that may not completely capture the rules associated with endogenous processing and presentation. The field of MS has made great improvements in instrumentation speed and sensitivity, chromatographic resolution, and proteogenomic database search strategies to facilitate the identification of HLA-ligands from a variety of cell types and tumor tissues. As such, these advances have enabled MS profiling of HLA-binding peptides to be a tractable, orthogonal approach to lower throughput biochemical assays for generating comprehensive datasets to train epitope prediction algorithms. In this review, we will highlight the progress made in the field of HLA-ligand profiling enabled by MS and its impact on current and future epitope prediction strategies.

A prognostic molecular signature of hepatic steatosis is spatially heterogeneous and dynamic in human liver.

Metabolic dysfunction-associated steatotic liver disease (MASLD) prevalence is increasing in parallel with an obesity pandemic, calling for novel strategies for prevention and treatment. We defined a circulating proteome of human MASLD across ≈7000 proteins in ≈5000 individuals from diverse, at-risk populations across the metabolic health spectrum, demonstrating reproducible diagnostic performance and specifying both known and novel metabolic pathways relevant to MASLD (central carbon and amino acid metabolism, hepatocyte regeneration, inflammation, fibrosis, insulin sensitivity). A parsimonious proteomic signature of MASLD was associated with a protection from MASLD and its related multi-system metabolic consequences in >26000 free-living individuals, with an additive effect to polygenic risk. The MASLD proteome was encoded by genes that demonstrated transcriptional enrichment in liver, with spatial transcriptional activity in areas of steatosis in human liver biopsy and dynamicity for select targets in human liver across stages of steatosis. We replicated several top relations from proteomics and spatial tissue transcriptomics in a humanized "liver-on-a-chip" model of MASLD, highlighting the power of a full translational approach to discovery in MASLD. Collectively, these results underscore utility of blood-based proteomics as a dynamic "liquid biopsy" of human liver relevant to clinical biomarker and mechanistic applications.

Analysis of endoscopic pancreatic function test (ePFT)-collected pancreatic fluid proteins precipitated via ultracentrifugation.

CONTEXT: We have shown previously that trichloroacetic acid precipitation is an effective method of protein extraction from pancreatic fluid for downstream biomarker discovery, compared to other common extraction methods tested. OBJECTIVE: We aim to assess the utility of ultracentrifugation as an alternative method of protein extraction from pancreatic fluid. DESIGN: Proteins extracted from trichloroacetic acid- and ultracentrifugation-precipitated pancreatic fluid were identified using mass spectrometry techniques (in-gel tryptic digestion followed by liquid chromatography-tandem mass spectrometry; GeLC-MS/MS). Data were analyzed using Proteome Discoverer and Scaffold 3. SETTING: This is a proteomic analysis experiment of endoscopically collected fluid in an academic center. PATIENTS: The study population included adult patients referred to the Center for Pancreatic Disease at Brigham and Women's Hospital, Boston, MA, USA for the evaluation of abdominal pain and gastrointestinal symptoms. INTERVENTIONS: Secretin-stimulated pancreatic fluid was collected as standard of care for the evaluation of abdominal pain and gastrointestinal symptoms. MAIN OUTCOME MEASURES: We compared proteins identified via standard trichloroacetic acid precipitation and this alternative ultracentrifugation strategy. RESULTS: A subset of pancreatic fluid proteins was identified via the ultracentrifugation method. Of these proteins, similar numbers were obtained from fully tryptic or semi-tryptic database searching. Proteins identified in the ultracentrifugation-precipitated samples included previously identified biomarker candidates of chronic pancreatitis. CONCLUSIONS: This alternative ultracentrifugation strategy requires less time and fewer handling procedures than standard trichloroacetic acid precipitation, at the expense of higher sample volume. As such, this method is well suited for targeted assays (i.e., dot blotting or targeted mass spectrometry) if the protein of interest is among those readily identified by ultracentrifugation-promoted precipitation.

Performance assessment and economic analysis of a human Liver-Chip for predictive toxicology.

BACKGROUND: Conventional preclinical models often miss drug toxicities, meaning the harm these drugs pose to humans is only realized in clinical trials or when they make it to market. This has caused the pharmaceutical industry to waste considerable time and resources developing drugs destined to fail. Organ-on-a-Chip technology has the potential improve success in drug development pipelines, as it can recapitulate organ-level pathophysiology and clinical responses; however, systematic and quantitative evaluations of Organ-Chips' predictive value have not yet been reported. METHODS: 870 Liver-Chips were analyzed to determine their ability to predict drug-induced liver injury caused by small molecules identified as benchmarks by the Innovation and Quality consortium, who has published guidelines defining criteria for qualifying preclinical models. An economic analysis was also performed to measure the value Liver-Chips could offer if they were broadly adopted in supporting toxicity-related decisions as part of preclinical development workflows. RESULTS: Here, we show that the Liver-Chip met the qualification guidelines across a blinded set of 27 known hepatotoxic and non-toxic drugs with a sensitivity of 87% and a specificity of 100%. We also show that this level of performance could generate over $3 billion annually for the pharmaceutical industry through increased small-molecule R&D productivity. CONCLUSIONS: The results of this study show how incorporating predictive Organ-Chips into drug development workflows could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs.

Drug development is lengthy and costly, as it relies on laboratory models that fail to predict human reactions to potential drugs. Because of this, toxic drugs sometimes go on to harm humans when they reach clinical trials or once they are in the marketplace. Organ-on-a-Chip technology involves growing cells on small devices to mimic organs of the body, such as the liver. Organ-Chips could potentially help identify toxicities earlier, but there is limited research into how well they predict these effects compared to conventional models. In this study, we analyzed 870 Liver-Chips to determine how well they predict drug-induced liver injury, a common cause of drug failure, and found that Liver-Chips outperformed conventional models. These results suggest that widespread acceptance of Organ-Chips could decrease drug attrition, help minimize harm to patients, and generate billions in revenue for the pharmaceutical industry.