Proteomic Characterization of Undifferentiated Small Round Cell Sarcomas with EWSR1- and CIC::DUX4-Translocations Reveals Diverging Tumor Biology and Distinct Diagnostic Markers.

Undifferentiated small round cell sarcomas of bone and soft tissue (USRS) are a group of tumors with heterogenic genomic alterations sharing similar morphology. In the present study, we performed a comparative large-scale proteomic analysis of USRS (n=42) with diverse genomic translocations including classic Ewing sarcomas with EWSR1::FLI1 fusions (n=24) or EWSR1::ERG - fusions (n=4), sarcomas with an EWSR1 - rearrangement (n=2), CIC::DUX4 fusion (n=8), as well as tumors classified as USRS with no genetic data available (n=4). Proteins extracted from formalin-fixed, paraffin-embedded (FFPE) pretherapeutic biopsies were analyzed qualitatively and quantitatively using shot gun mass spectrometry (MS). More than 8000 protein groups could be quantified using data-independent acquisition. Unsupervised hierarchical cluster analysis based on proteomic data allowed stratification of the 42 cases into distinct groups reflecting the different molecular genotypes. Protein signatures that significantly correlated with the respective genomic translocations were identified and used to generate a heatmap of all 42 sarcomas with assignment of cases with unknown molecular genetic data to either the EWSR1- or CIC-rearranged groups. MS-based prediction of sarcoma subtypes was molecularly confirmed in two cases where next-generation sequencing was technically feasible. MS also detected proteins routinely used in the immunohistochemical approach for the differential diagnosis of USRS. BCL11B highly expressed in Ewing sarcomas and Bach2 as well as ETS-1 highly expressed in CIC::DUX4-associated sarcomas, were among proteins identified by the present proteomic study and were chosen for immunohistochemical confirmation of MS data in our study cohort. Differential expression of these 3 markers in the two genetic groups were further validated in an independent cohort of n= 34 USRS. Finally, our proteomic results point towards diverging signaling pathways in the different USRS subgroups.

Proteomic Profiling of Colorectal Adenomas Identifies a Predictive Risk Signature for Development of Metachronous Advanced Colorectal Neoplasia.

BACKGROUND & AIMS: Colonic adenomatous polyps, or adenomas, are frequent precancerous lesions and the origin of most cases of colorectal adenocarcinoma. However, we know from epidemiologic studies that although most colorectal cancers (CRCs) originate from adenomas, only a small fraction of adenomas (3%-5%) ever progress to cancer. At present, there are no molecular markers to guide follow-up surveillance programs. METHODS: We profiled, by mass spectrometry-based proteomics combined with machine learning analysis, a selected cohort of formalin-fixed, paraffin-embedded high-grade (HG) adenomas with long clinical follow-up, collected as part of the Danish national screening program. We grouped subjects in the cohort according to their subsequent history of findings: a nonmetachronous advanced neoplasia group (G0), with no new HG adenomas or CRCs up to 10 years after polypectomy, and a metachronous advanced neoplasia group (G1) where individuals developed a new HG adenoma or CRC within 5 years of diagnosis. RESULTS: We generated a proteome dataset from 98 selected HG adenoma samples, including 20 technical replicates, of which 45 samples belonged to the nonmetachronous advanced neoplasia group and 53 to the metachronous advanced neoplasia group. The clear distinction of these 2 groups seen in a uniform manifold approximation and projection plot indicated that the information contained within the abundance of the ∼5000 proteins was sufficient to predict the future occurrence of HG adenomas or development of CRC. CONCLUSIONS: We performed an in-depth analysis of quantitative proteomic data from 98 resected adenoma samples using various novel algorithms and statistical packages and found that their proteome can predict development of metachronous advanced lesions and progression several years in advance.

Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data.

Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires specialized knowledge. To enable easy access to ML for biomarker discovery without any programming or bioinformatics skills, we developed "OmicLearn" (http://OmicLearn.org), an open-source browser-based ML tool using the latest advances in the Python ML ecosystem. Data matrices from omics experiments are easily uploaded to an online or a locally installed web server. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental data sets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.

Dynamic human liver proteome atlas reveals functional insights into disease pathways.

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence-unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non-alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web-based dashboard application for the interactive exploration of our resource (www.liverproteome.org).

Sequential Defects in Cardiac Lineage Commitment and Maturation Cause Hypoplastic Left Heart Syndrome.

BACKGROUND: Complex molecular programs in specific cell lineages govern human heart development. Hypoplastic left heart syndrome (HLHS) is the most common and severe manifestation within the spectrum of left ventricular outflow tract obstruction defects occurring in association with ventricular hypoplasia. The pathogenesis of HLHS is unknown, but hemodynamic disturbances are assumed to play a prominent role. METHODS: To identify perturbations in gene programs controlling ventricular muscle lineage development in HLHS, we performed whole-exome sequencing of 87 HLHS parent-offspring trios, nuclear transcriptomics of cardiomyocytes from ventricles of 4 patients with HLHS and 15 controls at different stages of heart development, single cell RNA sequencing, and 3D modeling in induced pluripotent stem cells from 3 patients with HLHS and 3 controls. RESULTS: Gene set enrichment and protein network analyses of damaging de novo mutations and dysregulated genes from ventricles of patients with HLHS suggested alterations in specific gene programs and cellular processes critical during fetal ventricular cardiogenesis, including cell cycle and cardiomyocyte maturation. Single-cell and 3D modeling with induced pluripotent stem cells demonstrated intrinsic defects in the cell cycle/unfolded protein response/autophagy hub resulting in disrupted differentiation of early cardiac progenitor lineages leading to defective cardiomyocyte subtype differentiation/maturation in HLHS. Premature cell cycle exit of ventricular cardiomyocytes from patients with HLHS prevented normal tissue responses to developmental signals for growth, leading to multinucleation/polyploidy, accumulation of DNA damage, and exacerbated apoptosis, all potential drivers of left ventricular hypoplasia in absence of hemodynamic cues. CONCLUSIONS: Our results highlight that despite genetic heterogeneity in HLHS, many mutations converge on sequential cellular processes primarily driving cardiac myogenesis, suggesting novel therapeutic approaches.

High-resolution serum proteome trajectories in COVID-19 reveal patient-specific seroconversion.

A deeper understanding of COVID-19 on human molecular pathophysiology is urgently needed as a foundation for the discovery of new biomarkers and therapeutic targets. Here we applied mass spectrometry (MS)-based proteomics to measure serum proteomes of COVID-19 patients and symptomatic, but PCR-negative controls, in a time-resolved manner. In 262 controls and 458 longitudinal samples of 31 patients, hospitalized for COVID-19, a remarkable 26% of proteins changed significantly. Bioinformatics analyses revealed co-regulated groups and shared biological functions. Proteins of the innate immune system such as CRP, SAA1, CD14, LBP, and LGALS3BP decreased early in the time course. Regulators of coagulation (APOH, FN1, HRG, KNG1, PLG) and lipid homeostasis (APOA1, APOC1, APOC2, APOC3, PON1) increased over the course of the disease. A global correlation map provides a system-wide functional association between proteins, biological processes, and clinical chemistry parameters. Importantly, five SARS-CoV-2 immunoassays against antibodies revealed excellent correlations with an extensive range of immunoglobulin regions, which were quantified by MS-based proteomics. The high-resolution profile of all immunoglobulin regions showed individual-specific differences and commonalities of potential pathophysiological relevance.

Aggrecan: a new biomarker for acute type A aortic dissection.

Acute type A aortic dissection (ATAAD) constitutes a life-threatening aortic pathology with significant morbidity and mortality. Without surgical intervention the usual mortality rate averages between 1 and 2% per hour. Thus, an early diagnosis of ATAAD is of pivotal importance to direct the affected patients to the appropriate treatment. Preceding tests to find an appropriate biomarker showed among others an increased aggrecan (ACAN) mRNA expression in aortic tissue of ATAAD patients. As a consequence, we investigated whether ACAN is a potential biomarker for diagnosing ATAAD. Mean ACAN protein concentration showed a significantly higher plasma concentration in ATAAD patients (38.59 ng/mL, n = 33) compared to plasma of patients with thoracic aortic aneurysms (4.45 ng/mL, n = 13), patients with myocardial infarction (11.77 ng/mL, n = 18) and healthy volunteers (8.05 ng/mL, n = 12). Cardiac enzymes like creatine kinase MB and cardiac troponin T showed no correlation with ACAN levels in ATAAD patients. Receiver-operator characteristics (ROC) curve analysis for ATAAD patients versus control subjects an optimum discrimination limit of ACAN plasma levels at 14.3 ng/mL with a corresponding sensitivity of 97% and specificity of 81%. According to our findings ACAN is a reliable potential biomarker in plasma samples to detect ATAAD with high sensitivity and specificity.

Spatially and cell-type resolved quantitative proteomic atlas of healthy human skin.

Human skin provides both physical integrity and immunological protection from the external environment using functionally distinct layers, cell types and extracellular matrix. Despite its central role in human health and disease, the constituent proteins of skin have not been systematically characterized. Here, we combine advanced tissue dissection methods, flow cytometry and state-of-the-art proteomics to describe a spatially-resolved quantitative proteomic atlas of human skin. We quantify 10,701 proteins as a function of their spatial location and cellular origin. The resulting protein atlas and our initial data analyses demonstrate the value of proteomics for understanding cell-type diversity within the skin. We describe the quantitative distribution of structural proteins, known and previously undescribed proteins specific to cellular subsets and those with specialized immunological functions such as cytokines and chemokines. We anticipate that this proteomic atlas of human skin will become an essential community resource for basic and translational research ( https://skin.science/ ).

The proteome landscape of the kingdoms of life.

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported1, advances in mass-spectrometry-based proteomics2 have enabled increasingly comprehensive identification and quantification of the human proteome3-6. However, there have been few comparisons across species7,8, in stark contrast with genomics initiatives9. Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.

Limited Environmental Serine and Glycine Confer Brain Metastasis Sensitivity to PHGDH Inhibition.

A hallmark of metastasis is the adaptation of tumor cells to new environments. Metabolic constraints imposed by the serine and glycine-limited brain environment restrict metastatic tumor growth. How brain metastases overcome these growth-prohibitive conditions is poorly understood. Here, we demonstrate that 3-phosphoglycerate dehydrogenase (PHGDH), which catalyzes the rate-limiting step of glucose-derived serine synthesis, is a major determinant of brain metastasis in multiple human cancer types and preclinical models. Enhanced serine synthesis proved important for nucleotide production and cell proliferation in highly aggressive brain metastatic cells. In vivo, genetic suppression and pharmacologic inhibition of PHGDH attenuated brain metastasis, but not extracranial tumor growth, and improved overall survival in mice. These results reveal that extracellular amino acid availability determines serine synthesis pathway dependence, and suggest that PHGDH inhibitors may be useful in the treatment of brain metastasis. SIGNIFICANCE: Using proteomics, metabolomics, and multiple brain metastasis models, we demonstrate that the nutrient-limited environment of the brain potentiates brain metastasis susceptibility to serine synthesis inhibition. These findings underscore the importance of studying cancer metabolism in physiologically relevant contexts, and provide a rationale for using PHGDH inhibitors to treat brain metastasis.This article is highlighted in the In This Issue feature, p. 1241.

A streamlined mass spectrometry-based proteomics workflow for large-scale FFPE tissue analysis.

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis, and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Herein we describe a MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from histopathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including low-input cancer tissue isolated by laser microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent acquisition (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total workup took less than a day. We observed a moderate trend towards lower protein identification in long-term stored samples (>15 years), but clustering into distinct proteomic subtypes was independent of archival time. Our results underscore the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and they prove the feasibility of analyzing large tissue cohorts in a robust, timely, and streamlined manner. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Proteomics of Cytochrome c Oxidase-Negative versus -Positive Muscle Fiber Sections in Mitochondrial Myopathy.

The mosaic distribution of cytochrome c oxidase+ (COX+) and COX- muscle fibers in mitochondrial disorders allows the sampling of fibers with compensated and decompensated mitochondrial function from the same individual. We apply laser capture microdissection to excise individual COX+ and COX- fibers from the biopsies of mitochondrial myopathy patients. Using mass spectrometry-based proteomics, we quantify >4,000 proteins per patient. While COX+ fibers show a higher expression of respiratory chain components, COX- fibers display protean adaptive responses, including upregulation of mitochondrial ribosomes, translation proteins, and chaperones. Upregulated proteins include C1QBP, required for mitoribosome formation and protein synthesis, and STOML2, which organizes cardiolipin-enriched microdomains and the assembly of respiratory supercomplexes. Factoring in fast/slow fiber type, COX- slow fibers show a compensatory upregulation of beta-oxidation, the AAA+ protease AFG3L1, and the OPA1-dependent cristae remodeling program. These findings reveal compensatory mechanisms in muscle fibers struggling with energy shortage and metabolic stress.

Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies.

Plasma and serum are rich sources of information regarding an individual's health state, and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative readout of the plasma proteome. Here, we employ Plasma Proteome Profiling to define quality marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma, and whole blood of 20 individuals (> 6,000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments, we determine sample quality-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource ( www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates.

Metallopeptidase inhibitor 1 (TIMP-1) promotes receptor tyrosine kinase c-Kit signaling in colorectal cancer.

Colorectal cancer (CRC) is the third most prevalent cancer worldwide causing an estimated 700 000 deaths annually. Different types of treatment are available for patients with advanced metastatic colorectal cancer, including targeted biological agents, such as cetuximab, a monoclonal antibody that targets EGFR. We have previously reported a study indicating multiple levels of interaction between metallopeptidase inhibitor 1 (TIMP-1) and the epidermal growth factor (EGF) signaling axis, which could explain how TIMP-1 levels can affect the antitumor effects of EGFR inhibitors. We also reported an association between TIMP-1-mediated cell invasive behavior and KRAS status. To gain insight into the molecular mechanisms underlying the effects of TIMP-1 in CRC, we examined by transcriptomics, proteomics, and kinase activity profiling a matched pair of isogenic human CRC isogenic DLD-1 CRC cell clones, bearing either an hemizygous KRAS wild-type allele or KRAS G13D mutant allele, exposed, or not, to TIMP-1. Omics analysis of the two cell lines identified the receptor tyrosine kinase c-Kit, a proto-oncogene that can modulate cell proliferation and invasion in CRC, as a target for TIMP-1. We found that exposure of DLD-1 CRC cells to exogenously added TIMP-1 promoted phosphorylation of c-Kit, indicative of a stimulatory effect of TIMP-1 on the c-Kit signaling axis. In addition, TIMP-1 inhibited c-Kit shedding in CRC cells grown in the presence of exogenous TIMP-1. Given the regulatory roles that c-Kit plays in cell proliferation and migration, and the realization that c-Kit is an important oncogene in CRC, it is likely that some of the biological effects of TIMP-1 overexpression in CRC may be exerted through its effect on c-Kit signaling.

Myosin binding protein H-like (MYBPHL): a promising biomarker to predict atrial damage.

Myosin binding protein H-like (MYBPHL) is a protein associated with myofilament structures in atrial tissue. The protein exists in two isoforms that share an identical amino acid sequence except for a deletion of 23 amino acids in isoform 2. In this study, MYBPHL was found to be expressed preferentially in atrial tissue. The expression of isoform 2 was almost exclusively restricted to the atria and barely detectable in the ventricle, arteria mammaria interna, and skeletal muscle. After atrial damage induced by cryo- or radiofrequency ablation, MYBPHL was rapidly and specifically released into the peripheral circulation in a time-dependent manner. The plasma MYBPHL concentration remained substantially elevated up to 24 hours after the arrival of patients at the intensive care unit. In addition, the recorded MYBPHL values were strongly correlated with those of the established biomarker CK-MB. In contrast, an increase in MYBPHL levels was not evident in patients undergoing aortic valve replacement or transcatheter aortic valve implantation. In these patients, the values remained virtually constant and never exceeded the concentration in the plasma of healthy controls. Our findings suggest that MYBPHL can be used as a precise and reliable biomarker to specifically predict atrial myocardial damage.

Plasma proteome profiling discovers novel proteins associated with non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population and can progress to cirrhosis with limited treatment options. As the liver secretes most of the blood plasma proteins, liver disease may affect the plasma proteome. Plasma proteome profiling of 48 patients with and without cirrhosis or NAFLD revealed six statistically significantly changing proteins (ALDOB, APOM, LGALS3BP, PIGR, VTN, and AFM), two of which are already linked to liver disease. Polymeric immunoglobulin receptor (PIGR) was significantly elevated in both cohorts by 170% in NAFLD and 298% in cirrhosis and was further validated in mouse models. Furthermore, a global correlation map of clinical and proteomic data strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis. The prominent diabetic drug target DPP4 is an aminopeptidase like ANPEP, ENPEP, and LAP3, all of which are up-regulated in the human or mouse data. Furthermore, ANPEP and TGFBI have potential roles in extracellular matrix remodeling in fibrosis. Thus, plasma proteome profiling can identify potential biomarkers and drug targets in liver disease.

The Case for Proteomics and Phospho-Proteomics in Personalized Cancer Medicine.

The concept of personalized medicine is predominantly been pursued through genomic and transcriptomic technologies, leading to the identification of multiple mutations in a large variety of cancers. However, it has proven challenging to distinguish driver and passenger mutations and to deal with tumor heterogeneity and resistant clonal populations. More generally, these heterogeneous mutation patterns do not in themselves predict the tumor phenotype. Analysis of the expressed proteins in a tumor and their modification states reveals if and how these mutations are translated to the functional level. It is already known that proteomic changes including posttranslational modifications are crucial drivers of oncogenesis, but proteomics technology has only recently become comparable in depth and accuracy to RNAseq. These advances also allow the rapid and highly sensitive analysis of formalin-fixed and paraffin-embedded biobank tissues, on both the proteome and phosphoproteome levels. In this perspective, pioneering mass spectrometry-based proteomic studies are highlighted that pave the way toward clinical implementation. It is argued that proteomics and phosphoproteomics could provide the missing link to make omics analysis actionable in the clinic.

Mechanisms Preserving Insulin Action during High Dietary Fat Intake.

Prolonged intervention studies investigating molecular metabolism are necessary for a deeper understanding of dietary effects on health. Here we provide mechanistic information about metabolic adaptation to fat-rich diets. Healthy, slightly overweight men ingested saturated or polyunsaturated fat-rich diets for 6 weeks during weight maintenance. Hyperinsulinemic clamps combined with leg balance technique revealed unchanged peripheral insulin sensitivity, independent of fatty acid type. Both diets increased fat oxidation potential in muscle. Hepatic insulin clearance increased, while glucose production, de novo lipogenesis, and plasma triacylglycerol decreased. High fat intake changed the plasma proteome in the immune-supporting direction and the gut microbiome displayed changes at taxonomical and functional level with polyunsaturated fatty acid (PUFA). In mice, eucaloric feeding of human PUFA and saturated fatty acid diets lowered hepatic triacylglycerol content compared with low-fat-fed control mice, and induced adaptations in the liver supportive of decreased gluconeogenesis and lipogenesis. Intake of fat-rich diets thus induces extensive metabolic adaptations enabling disposition of dietary fat without metabolic complications.

Plasma Proteome Profiling Reveals Dynamics of Inflammatory and Lipid Homeostasis Markers after Roux-En-Y Gastric Bypass Surgery.

Obesity-related diseases affect half of the global population, and bariatric surgery is one of the few interventions with long-lasting weight loss and cardio-metabolic effects. Here, we investigated the effect of Roux-en-Y gastric bypass surgery on the plasma proteome, hypothesizing that specific proteins or protein patterns may serve as key mediators and markers of the metabolic response. We performed mass spectrometry (MS)-based proteomics on two longitudinal studies encompassing 47 morbidly obese patients, generating quantitative information on more than 1,700 proteins. A global correlation matrix incorporating about 200,000 relationships revealed functional connections between proteins and assigned them to physiological processes. The main classes of significantly altered proteins were markers of systemic inflammation and those involved in lipid metabolism. Our data highlight robust correlative and anti-correlative behaviors of circulating proteins to each other and to clinical parameters. A group of inflammation-related proteins showed distinct inverse relationships to proteins consistently associated with insulin sensitivity.