Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach

Quality Control and Outlier Detection of Targeted Mass Spectrometry Data from Multiplex Protein Panels

Increased throughput as well as increased multiplexing of liquid chromatography coupled to selected reaction monitoring mass spectrometry (LC-SRM-MS) assays for protein quantification challenges routine data analysis. Despite the measurement of multiple transitions from multiple peptides, for clinical applications a single (quantifier) transition from one (quantifier) signature peptide is used to represent the protein quantity with most data used solely to validate the quantifier result. To support the generation of reliable protein results from multiplexed LC-SRM-MS assays with large sample numbers, we developed a data analysis process for quality control and outlier detection using data from an 11-protein multiplex LC-SRM-MS method for dried blood samples (195 492 chromatographic peaks from 1481 samples * 11 proteins * 2 peptides * 3 transitions * 2 isotopologues). The 2-tiered data analysis process detects outliers for ion transition ratio, peptide ratio, and % difference between duplicates, applying less stringent criteria to samples with a small % difference between duplicates (Tier 1) and more stringent criteria to samples with unassessed or a large % difference between duplicates (Tier 2). After manual peak review, 1127 samples (76%) were selected based on the sample quality. The data analysis process thereafter automatically selected quantifier transitions/peptides, removed quality control failures and outliers (8%), averaged duplicates, and generated a comprehensive report listing 6085 quality controlled protein-level results. The proposed data analysis process serves as a starting point toward standardized data analysis of multiplexed LC-SRM-MS protein assays.

HILIC-MRM-MS for Linkage-Specific Separation of Sialylated Glycopeptides to Quantify Prostate-Specific Antigen Proteoforms

Elevated serum prostate-specific antigen (PSA) levels in body fluids may indicate prostate cancer (PCa), but it is noted that the clinical performance is rather poor. Specificity and sensitivity values of 20 and 94% at a cutoff value of 4.1 ng/mL, respectively, result in overdiagnosis and unnecessary interventions. Previous exploratory studies have indicated that the glycosylation of PSA potentially leads to improved PCa diagnosis based on qualitative analyses. However, the applied methods are not suited for a quantitative evaluation or implementation in a medical laboratory. Therefore, in this proof-of-principle study, we have evaluated the use of hydrophilic interaction liquid chromatography (HILIC) in combination with targeted quantitative mass spectrometry for the sialic acid linkage-specific analysis of PSA glyco-proteoforms based on either trypsin or ArgC peptides. The efficiency of PSA proteolysis was optimized as well as the glycopeptide separation conditions (buffer type, strength, and pH). The HILIC-based analysis of PSA glyco-proteoforms presented here has the potential for the clinical validation of patient cohorts. The method shows the feasibility of the use of a HILIC stationary phase for the separation of isomeric glycopeptides to detect specific glyco-proteoforms. This is the first step toward the development and evaluation of PSA glyco-proteoforms for use in a clinical chemistry setting aiming for improved PCa diagnosis or screening.

Peptide Selection for Accurate Targeted Protein Quantification via a Dimethylation High-Resolution Mass Spectrum Strategy with a Peptide Release Kinetic Model

A crucial step in accurate targeted protein quantification using targeted proteomics is to determine optimal proteotypic peptides representing targeted proteins. In this study, a workflow of peptide selection to determine proteotypic peptides using a dimethylation high-resolution mass spectrum strategy with a peptide release kinetic model was investigated and applied in peptide selection of bovine serum albumin. After specificity, digestibility, recovery, and stability evaluation of tryptic peptides in bovine serum albumin, the optimal proteotypic peptide was selected as LVNELTEFAK. The quantification method using LVNELTEFAK gave a linear range of 1-100 ppm with the coefficient greater than 0.9990, and the detection limit of bovine serum albumin in milk was 0.78 mg/kg. Compared with the proteotypic peptides selected by Skyline, the method showed a better performance in method validation. The workflow exhibited high comprehensiveness and efficiency in peptide selection, facilitating accurate targeted protein quantification in the food matrix, which lack protein standards.

Comprehensive proteomics investigation of P. vivax-infected human plasma and parasite isolates

Background

In recent times, Plasmodium vivax (P. vivax) has become a serious threat to public health due to its ability to cause severe infection with fatal outcomes. Its unique biology makes it resilient to control measures that are otherwise effective against P. falciparum. A deeper understanding of P. vivax biology and pathogenesis is, therefore, essential for developing the right control strategies. Proteomics of P. falciparum has been helpful in studying disease biology and elucidating molecular mechanisms involved in the development of disease. However, unlike P. falciparum, proteomics data for P. vivax infection is minimal due to the absence of a continuous culture system. The dependence on clinical samples and animal models has drastically limited P. vivax research, creating critical knowledge gaps in our understanding of the disease. This study describes an in-depth proteomics analysis of P. vivax-infected human plasma and parasite isolates, to understand parasite biology, pathogenesis, and to identify new diagnostic targets for P. vivax malaria.

Methods

A mass-spectrometry- (MS) based proteomics approach (Q Exactive) was applied to analyze human plasma and parasite isolates from vivax malaria patients visiting a primary health centre in India. Additionally, a targeted proteomics assay was standardized for validating unique peptides of most recurring parasite proteins.

Results

Thirty-eight P. vivax proteins were detected in human plasma with high confidence. Several glycolytic enzymes were found along with hypothetical, cytoskeletal, ribosomal, and nuclear proteins. Additionally, 103 highly abundant P. vivax proteins were detected in parasite isolates. This represents the highest number of parasite proteins to be reported from clinical samples so far. Interestingly, five of these; three Plasmodium exported proteins (PVX_003545, PVX_003555 and PVX_121935), a hypothetical protein (PVX_083555) and Pvstp1 (subtelomeric transmembrane protein 1, PVX_094303) were found in both plasma and parasite isolates.

Conclusions

A parasite proteomics investigation is essential to understand disease pathobiology and design novel interventions. Control strategies against P. vivax also depend on early diagnosis. This work provides deeper insights into the biology of P. vivax by identifying proteins expressed by the parasite during its complex life-cycle within the human host. The study also reports antigens that may be explored as diagnostic candidates.

Concentration Determination of >200 Proteins in Dried Blood Spots for Biomarker Discovery and Validation

The use of protein biomarkers as surrogates for clinical endpoints requires extensive multilevel validation including development of robust and sensitive assays for precise measurement of protein concentration. Multiple reaction monitoring (MRM) is a well-established mass-spectrometric method that can be used for reproducible protein-concentration measurements in biological specimens collected via microsampling. The dried blood spot (DBS) microsampling technique can be performed non-invasively without the expertise of a phlebotomist, and can enhance analyte stability which facilitate the application of this technique in retrospective studies while providing lower storage and shipping costs, because cold-chain logistics can be eliminated. Thus, precise, sensitive, and multiplexed methods for measuring protein concentrations in DBSs can be used for de novo biomarker discovery and for biomarker quantification or verification experiments. To achieve this goal, MRM assays were developed for multiplexed concentration measurement of proteins in DBSs.The lower limit of quantification (LLOQ) was found to have a median total coefficient of variation (CV) of 18% for 245 proteins, whereas the median LLOQ was 5 fmol of peptide injected on column, and the median inter-day CV over 4 days for measuring endogenous protein concentration was 8%. The majority (88%) of the assays displayed parallelism, whereas the peptide standards remained stable throughout the assay workflow and after exposure to multiple freeze-thaw cycles. For 190 proteins, the measured protein concentrations remained stable in DBS stored at ambient laboratory temperature for up to 2 months. Finally, the developed assays were used to measure the concentration ranges for 200 proteins in twenty same sex, same race and age matched individuals.

Screening of Processed Foods for Transgenic Proteins from Genetically Engineered Plants Using Targeted Mass Spectrometry

Screening of food products for the presence of material from genetically engineered (GE) plants is typically done using deoxyribonucleic acid (DNA)-based methods to detect the presence of transgenic DNA. In this study, we have demonstrated the feasibility of using targeted mass spectrometry (MS) to detect a protein expressed by transgenic DNA to confirm the presence of GE plant material in processed foods. Scheduled parallel reaction monitoring (sPRM) was used to detect the enzyme, 5-enolpyruvulshikimate-3-phosphate synthase, from Agrobacterium sp. strain CP4 (CP4 EPSPS), which confers glyphosate tolerance in transgenic crops. Five CP4 EPSPS surrogate peptides and their corresponding retention times identified via data-dependent LC/MS/MS analysis of a glyphosate-tolerant soybean certified reference material, GTS 40-3-2, were used to develop the sPRM assay. The assay was used to screen four soy-based infant formulas, four corn-based cereals, corn tortilla chips, and cornmeal for the presence of CP4 EPSPS. At least four of the five selected surrogate peptides were detected in nine of the products analyzed, suggesting that targeted MS can serve as a complementary analytical method to DNA-based methods for the detection of material from GE plants in processed foods.

Delineation of hypoxia-induced proteome shifts in osteosarcoma cells with different metastatic propensities

Osteosarcoma (OS) is the most common bone cancer in children and young adults. Solid tumors are characterized by intratumoral hypoxia, and hypoxic cells are associated with the transformation to aggressive phenotype and metastasis. The proteome needed to support an aggressive osteosarcoma cell phenotype remains largely undefined. To link metastatic propensity to a hypoxia-induced proteotype, we compared the protein profiles of two isogenic canine OS cell lines, POS (low metastatic) and HMPOS (highly metastatic), under normoxia and hypoxia. Label-free shotgun proteomics was applied to comprehensively characterize the hypoxia-responsive proteome profiles in the OS cell phenotypes. Hypothesis-driven parallel reaction monitoring was used to validate the differential proteins observed in the shotgun data and to monitor proteins of which we expected to exhibit hypoxia responsiveness, but which were absent in the label-free shotgun data. We established a "distance" score (|zHMPOS - zPOS|), and "sensitivity" score (|zHypoxia - zNormoxia) to quantitatively evaluate the proteome shifts exhibited by OS cells in response to hypoxia. Evaluation of the sensitivity scores for the proteome shifts observed and principal component analysis of the hypoxia-responsive proteins indicated that both cell types acquire a proteome that supports a Warburg phenotype with enhanced cell migration and proliferation characteristics. Cell migration and glucose uptake assays combined with protein function inhibitor studies provided further support that hypoxia-driven adaption of pathways associated with glycolytic metabolism, collagen biosynthesis and remodeling, redox regulation and immunomodulatory proteins typify a proteotype associated with an aggressive cancer cell phenotype. Our findings further suggest that proteins involved in collagen remodeling and immune editing may warrant further evaluation as potential targets for anti-metastatic treatment strategies in osteosarcoma.

Value of quantifying ABC transporters by mass spectrometry and impact on in vitro-to-in vivo prediction of transporter-mediated drug-drug interactions of rivaroxaban

ABC transporters, such as P-gp and BCRP, are involved in rivaroxaban pharmacokinetics and can lead to drug-drug interactions (DDIs). Investigations of the victim role for rivaroxaban and transporter-mediated DDI are commonly performed using in vitro models. However, interpretation of rivaroxaban efflux transport and DDI studies in cell models may be influenced by P-gp and BCRP transporter abundance. This study aimed to develop an LC-MS/MS quantification method for assessing the relationship between transporter expression and functionality in Caco-2_ATCC, Caco-2_ECACC, MDCK-MDR1, MDCK-BCRP cell models. First, the relative and absolute quantities of the transporters were determined by LC-MS/MS. P-gp and BCRP expression was then confirmed by western blotting and immunofluorescence staining. Finally, P-gp and BCRP functional activities and half-inhibitory concentrations (IC50s) of two specific inhibitors (verapamil and ko143) were determined by bidirectional transport experiments. P-gp and BCRP protein expression was detected at the cell membrane and was greater in the respective transfected models. Efflux ratios were correlated with P-gp and BCRP quantities. The lowest IC50s were obtained in the MDCK-MDR1 and MDCK-BCRP models for verapamil and ko143, respectively. In conclusion, this study demonstrated that LC-MS/MS can accurately quantify P-gp and BCRP efflux transporters and thereby improve the interpretation of transport data and in vitro-in vivo correlations.

MatrisomeDB: the ECM-protein knowledge database

The extracellular matrix (ECM) is a complex and dynamic meshwork of cross-linked proteins that supports cell polarization and functions and tissue organization and homeostasis. Over the past few decades, mass-spectrometry-based proteomics has emerged as the method of choice to characterize the composition of the ECM of normal and diseased tissues. Here, we present a new release of MatrisomeDB, a searchable collection of curated proteomic data from 17 studies on the ECM of 15 different normal tissue types, six cancer types (different grades of breast cancers, colorectal cancer, melanoma, and insulinoma) and other diseases including vascular defects and lung and liver fibroses. MatrisomeDB (http://www.pepchem.org/matrisomedb) was built by retrieving raw mass spectrometry data files and reprocessing them using the same search parameters and criteria to allow for a more direct comparison between the different studies. The present release of MatrisomeDB includes 847 human and 791 mouse ECM proteoforms and over 350 000 human and 600 000 mouse ECM-derived peptide-to-spectrum matches. For each query, a hierarchically-clustered tissue distribution map, a peptide coverage map, and a list of post-translational modifications identified, are generated. MatrisomeDB is the most complete collection of ECM proteomic data to date and allows the building of a comprehensive ECM atlas.

Analysis of ubiquitin signaling and chain topology cross-talk

Protein ubiquitination is a powerful post-translational modification implicated in many cellular processes. Although ubiquitination is associated with protein degradation, depending on the topology of polyubiquitin chains, protein ubiquitination is connected to non-degradative events in DNA damage response, cell cycle control, immune response, trafficking, intracellular localization, and vesicle fusion events. It has been shown that a ubiquitin chain can contain two or more topologies at the same time. These branched chains add another level of complexity to ubiquitin signaling, increasing its versatility and specificity. Mass spectrometry-based proteomics has been playing an important role in the identification of all types of ubiquitin chains and linkages. This review aims to provide an overview of ubiquitin chain topology and associated signaling pathways and discusses the MS-based proteomic methodologies used to determine such topologies. SIGNIFICANCE: Ubiquitination plays important roles in many cellular processes. Proteins can be monoubiquitinated or polyubiquitinated forming non-branched or branched chains in a high number of possible combinations, each associated with different cellular processes. The detection and the topology of ubiquitin chains is thus of extreme importance in order to explain such processes. Advances in mass spectrometry based proteomics allowed for the discovery and topology mapping of many ubiquitin chains. This review revisits the state of the art in ubiquitin chain identification by mass spectrometry and gives an insight on the implication of such chains in many cellular processes.

Determination of Cystatin C in human urine by isotope dilution tandem mass spectrometry

This work presents the development of a methodology for the accurate and precise quantification of the renal biomarker Cystatin C in human urine by Isotope Dilution Mass Spectrometry (IDMS). The procedure is based on the addition of a known quantity of the proteotypic peptide ALDFAVG*EYNK labelled with ¹³C₂-glycine to the urine sample followed by protein hydrolysis using trypsin. Then, preconcentration and purification of the isotope diluted peptide was carried out by a selective monoclonal antibody bound to magnetic beads and final measurement was done after injection of the sample in a HPLC-MS/MS triple quadrupole instrument. The isotopic distribution of the isotope diluted proteotypic peptide was measured by low resolution selected reaction monitoring. Using this aquisition mode, the bandpass of the first quadrupole was widened (FWHM =13 u) so the whole isotopic clusters for both the natural abundance and the labelled peptides entered the collision cell. The proposed acquisition mode provided similar accuracy and precision than the regular SRM mode (FWHM =0.7 u) but a higher sensitivity was observed. The purification of the sample by antibody based enrichment of the target peptide was shown to remove interfering compounds more efficiently in comparison with a sample purification based on semipreparative liquid chromatography. Using 5 ng of the labelled peptide it was possible to quantify Cystatin C in human urine in patients with normal and impaired renal function. Recoveries from 100 to 104% were obtained in samples containing from 90 to 700 μg L^-1 of Cystatin C with relative standard deviations from 0.5 to 6%. The stability of Cystatin C in urine samples was evaluated under different storage conditions showing that only when the urine samples were stored at room temperature during more than 10 days, a significant degradation of Cystatin C was observed.

Quantitative proteomic analysis of aqueous humor after rabbit lensectomy reveals differences in coagulation and immunomodulatory proteins

Proteomics of juvenile rabbit aqueous humor was evaluated before and after surgery to determine proteins responsible for postoperative outcomes.

Identification of a Multiplex Biomarker Panel for Hypertrophic Cardiomyopathy Using Quantitative Proteomics and Machine Learning

Hypertrophic cardiomyopathy (HCM) is defined by pathological left ventricular hypertrophy (LVH). It is the commonest inherited cardiac condition and a significant number of high risk cases still go undetected until a sudden cardiac death (SCD) event. Plasma biomarkers do not currently feature in the assessment of HCM disease progression, which is tracked by serial imaging, or in SCD risk stratification, which is based on imaging parameters and patient/family history. There is a need for new HCM plasma biomarkers to refine disease monitoring and improve patient risk stratification. To identify new plasma biomarkers for patients with HCM, we performed exploratory myocardial and plasma proteomics screens and subsequently developed a multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay to validate the 26 peptide biomarkers that were identified. The association of discovered biomarkers with clinical phenotypes was prospectively tested in plasma from 110 HCM patients with LVH (LVH+ HCM), 97 controls, and 16 HCM sarcomere gene mutation carriers before the development of LVH (subclinical HCM). Six peptides (aldolase fructose-bisphosphate A, complement C3, glutathione S-transferase omega 1, Ras suppressor protein 1, talin 1, and thrombospondin 1) were increased significantly in the plasma of LVH+ HCM compared with controls and correlated with imaging markers of phenotype severity: LV wall thickness, mass, and percentage myocardial scar on cardiovascular magnetic resonance imaging. Using supervised machine learning (ML), this six-biomarker panel differentiated between LVH+ HCM and controls, with an area under the curve of ≥ 0.87. Five of these peptides were also significantly increased in subclinical HCM compared with controls. In LVH+ HCM, the six-marker panel correlated with the presence of nonsustained ventricular tachycardia and the estimated five-year risk of sudden cardiac death. Using quantitative proteomic approaches, we have discovered six potentially useful circulating plasma biomarkers related to myocardial substrate changes in HCM, which correlate with the estimated sudden cardiac death risk.

A Perspective on the Confident Comparison of Glycoprotein Site-Specific Glycosylation in Sample Cohorts

Protein glycosylation, resulting from glycosyl transferase reactions under complex control in the secretory pathway, consists of a distribution of related glycoforms at each glycosylation site. Because the biosynthetic substrate concentration and transport rates depend on architecture and other aspects of cellular phenotypes, site-specific glycosylation cannot be predicted accurately from genomic, transcriptomic, or proteomic information. Rather, it is necessary to quantify glycosylation at each protein site and how this changes among a sample cohort to provide information about disease mechanisms. At present, mature mass spectrometry-based methods allow for qualitative assignment of the glycan composition and glycosylation site of singly glycosylated proteolytic peptides. To make such quantitative comparisons, it is necessary to sample the glycosylation distribution with sufficient coverage and accuracy for confident assessment of the glycosylation changes that occur in the biological cohort. In this Perspective, we discuss the unmet needs for mass spectrometry acquisition methods and bioinformatics for the confident comparison of protein site-specific glycosylation among sample cohorts.

Comparative Proteome Profiling and Mutant Protein Identification in Metastatic Prostate Cancer Cells by Quantitative Mass Spectrometry-based Proteogenomics

BACKGROUND/AIM

Prostate cancer (PCa) is the most frequent cancer found in males worldwide. The aim of this study was to identify new biomarkers using mutated peptides for the prognosis and prediction of advanced PCa, based on proteogenomics.

MATERIALS AND METHODS

The tryptic peptides were analyzed by tandem mass tag-based quantitative proteomics. Proteogenomics were used to identify mutant peptides as novel biomarkers in advanced PCa.

RESULTS

Using a human database, increased levels of INTS7 and decreased levels of SH3BGRL were found to be associated with the aggressiveness of PCa. Using proteogenomics and a cancer mutation database, 70 mutant peptides were identified in PCa cell lines. Using parallel reaction monitoring, the expression of seven mutant peptides was found to be altered in tumors, amongst which CAPN2 D22E was the most significantly up-regulated mutant peptide in PCa tissues.

CONCLUSION

Altered mutant peptides present in PCa tissue could be used as new biomarkers in advanced PCa.

Domain-specific Quantification of Prion Protein in Cerebrospinal Fluid by Targeted Mass Spectrometry

Therapies currently in preclinical development for prion disease seek to lower prion protein (PrP) expression in the brain. Trials of such therapies are likely to rely on quantification of PrP in cerebrospinal fluid (CSF) as a pharmacodynamic biomarker and possibly as a trial endpoint. Studies using PrP ELISA kits have shown that CSF PrP is lowered in the symptomatic phase of disease, a potential confounder for reading out the effect of PrP-lowering drugs in symptomatic patients. Because misfolding or proteolytic cleavage could potentially render PrP invisible to ELISA even if its concentration were constant or increasing in disease, we sought to establish an orthogonal method for CSF PrP quantification. We developed a multi-species targeted mass spectrometry method based on multiple reaction monitoring (MRM) of nine PrP tryptic peptides quantified relative to an isotopically labeled recombinant protein standard for human samples, or isotopically labeled synthetic peptides for nonhuman species. Analytical validation experiments showed process replicate coefficients of variation below 15%, good dilution linearity and recovery, and suitable performance for both CSF and brain homogenate and across humans as well as preclinical species of interest. In n = 55 CSF samples from individuals referred to prion surveillance centers with rapidly progressive dementia, all six human PrP peptides, spanning the N- and C-terminal domains of PrP, were uniformly reduced in prion disease cases compared with individuals with nonprion diagnoses. Thus, lowered CSF PrP concentration in prion disease is a genuine result of the disease process and not an artifact of ELISA-based measurement. As a result, dose-finding studies for PrP lowering drugs may need to be conducted in presymptomatic at-risk individuals rather than in symptomatic patients. We provide a targeted mass spectrometry-based method suitable for preclinical quantification of CSF PrP as a tool for drug development.

Absolute Quantification of Apolipoproteins Following Treatment with Omega-3 Carboxylic Acids and Fenofibrate Using a High Precision Stable Isotope-labeled Recombinant Protein Fragments Based SRM Assay

Stable isotope-labeled standard (SIS) peptides are used as internal standards in targeted proteomics to provide robust protein quantification, which is required in clinical settings. However, SIS peptides are typically added post trypsin digestion and, as the digestion efficiency can vary significantly between peptides within a protein, the accuracy and precision of the assay may be compromised. These drawbacks can be remedied by a new class of internal standards introduced by the Human Protein Atlas project, which are based on SIS recombinant protein fragments called SIS PrESTs. SIS PrESTs are added initially to the sample and SIS peptides are released on trypsin digestion. The SIS PrEST technology is promising for absolute quantification of protein biomarkers but has not previously been evaluated in a clinical setting. An automated and scalable solid phase extraction workflow for desalting and enrichment of plasma digests was established enabling simultaneous preparation of up to 96 samples. Robust high-precision quantification of 13 apolipoproteins was achieved using a novel multiplex SIS PrEST-based LC-SRM/MS Tier 2 assay in non-depleted human plasma. The assay exhibited inter-day coefficients of variation between 1.5% and 14.5% (median = 3.5%) and was subsequently used to investigate the effects of omega-3 carboxylic acids (OM3-CA) and fenofibrate on these 13 apolipoproteins in human plasma samples from a randomized placebo-controlled trial, EFFECT I (NCT02354976). No significant changes were observed in the OM3-CA arm, whereas treatment with fenofibrate significantly increased apoAII and reduced apoB, apoCI, apoE and apoCIV levels. The reduction in apoCIV following fenofibrate treatment is a novel finding. The study demonstrates that SIS PrESTs can facilitate the generation of robust multiplexed biomarker Tier 2 assays for absolute quantification of proteins in clinical studies.

Identification of a Collagen Marker in Urine Improves the Detection of Colorectal Liver Metastases

Previously, we reported a combination of an urine collagen alpha-1(I) natural occurring peptide (NOP) AGPP(-OH)GEAGKP(-OH)GEQGVP(-OH)GDLGAP(-OH)GP (AGP) and serum carcinoembryonic antigen (CEA) to have the potential to detect colorectal liver metastasis (CRLM). The combined method requires further adaption for better sensitivity and specificity prior to clinical implementation. This mass spectrometry study aimed to identify additional collagen NOPs in urine and determine the most discriminating NOP panel. We improved the combined method on the basis of analysis of urine samples from 100 healthy controls and 100 CRLM patients. Two additional NOPs were identified: GPPGEAGK(-OH)P(-OH)GEQGVP(-OH)GDLGAP(-OH)GP (GPP), collagen alpha-1(I), and GNDGARGSDGQPGPP(-OH)GP(-OH)P(-OH)GTAGFP(-OH)GSP(-OH)GAK(-OH)GEVGP (GND), collagen alpha-1(III). A molecular model combining NOPs (AGP, GPP, and GND) and CEA was generated. Molecules that did not contribute significantly were removed, resulting in a model consisting of GND and CEA. With this model, 88% sensitivity and 88% specificity were reached in the discovery set and 75% sensitivity and 100% specificity in the validation set (control, n = 12; CRLM, n = 10). The AUC of the ROC curve is significantly higher than the current model based on AGP and CEA (p = 3.3 × 10^-4). The new model performs better than the currently used techniques in the clinic that have a 57-70% sensitivity and a 90-96% specificity.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Comparing Data-Independent Acquisition and Parallel Reaction Monitoring in Their Abilities To Differentiate High-Density Lipoprotein Subclasses

High-density lipoprotein (HDL) is a diverse group of particles with multiple cardioprotective functions. HDL proteome follows HDL particle complexity. Many proteins were described in HDL, but consistent quantification of HDL protein cargo is still a challenge. To address this issue, the aim of this work was to compare data-independent acquisition (DIA) and parallel reaction monitoring (PRM) methodologies in their abilities to differentiate HDL subclasses through their proteomes. To this end, we first evaluated the analytical performances of DIA and PRM using labeled peptides in pooled digested HDL as a biological matrix. Next, we compared the quantification capabilities of the two methodologies for 24 proteins found in HDL₂ and HDL₃ from 19 apparently healthy subjects. DIA and PRM exhibited comparable linearity, accuracy, and precision. Moreover, both methodologies worked equally well, differentiating HDL subclasses' proteomes with high precision. Our findings may help to understand HDL functional diversity.

Elevated Hexokinase II Expression Confers Acquired Resistance to 4-Hydroxytamoxifen in Breast Cancer Cells

Tamoxifen has been clinically used in treating estrogen receptor (ER)-positive breast cancer for over 30 years. The most challenging aspect associated with tamoxifen therapy is the development of resistance in initially responsive breast tumors. We applied a parallel-reaction monitoring (PRM)-based quantitative proteomic method to examine the differential expression of kinase proteins in MCF-7 and the isogenic tamoxifen-resistant (TamR) cells. We were able to quantify the relative protein expression levels of 315 kinases, among which hexokinase 2 (HK2) and mTOR were up-regulated in TamR MCF-7 cells. We also observed that the TamR MCF-7 cells exhibited elevated rate of glycolysis than the parental MCF-7 cells. In addition, we found that phosphorylation of S6K - a target of mTOR - was much lower in TamR MCF-7 cells, and this phosphorylation level could be restored upon genetic depletion or pharmacological inhibition of HK2. Reciprocally, the level of S6K phosphorylation was diminished upon overexpression of HK2 in MCF-7 cells. Moreover, we observed that HK2 interacts with mTOR, and this interaction inhibits mTOR activity. Lower mTOR activity led to augmented autophagy, which conferred resistance of MCF-7 cells toward tamoxifen. Together, our study demonstrates that elevated expression of HK2 promotes autophagy through inhibiting the mTOR-S6K signaling pathway and results in resistance of MCF-7 breast cancer cells toward tamoxifen; thus, our results uncovered, for the first time, HK2 as a potential therapeutic target for overcoming tamoxifen resistance.

The "Missing" Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes

The Chromosome-centric Human Proteome Project aims at characterizing the expression of proteins encoded in each chromosome at the tissue, cell, and subcellular levels. The proteomic profiling of a particular tissue or cell line commonly results in a substantial portion of proteins that are not observed (the "missing" proteome). The concurrent transcriptome profiling of the analyzed tissue/cells samples may help define the set of untranscribed genes in a given type of tissue or cell, thus narrowing the size of the "missing" proteome and allowing us to focus on defining the reasons behind undetected proteins, namely, whether they are technical (insufficient sensitivity of protein detection) or biological (correspond to not-translated transcripts). We believe that the quantitative polymerase chain reaction (qPCR) can provide an efficient approach to studying low-abundant transcripts related to undetected proteins due to its high sensitivity and the possibility of ensuring the specificity of detection via the simple Sanger sequencing of PCR products. Here we illustrated the feasibility of such an approach on a set of low-abundant transcripts. Although inapplicable to the analysis of whole transcriptome, qPCR can successfully be utilized to profile a limited cohort of transcripts encoded on a particular chromosome, as we previously demonstrated for human chromosome 18.

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

Analysis of Endogenous Peptides Released from Osteoarthritic Cartilage Unravels Novel Pathogenic Markers

Osteoarthritis (OA) is a pathology characterized by the loss of articular cartilage. In this study, we performed a peptidomic strategy to identify endogenous peptides (neopeptides) that are released from human osteoarthritic tissue, which may serve as disease markers. With this aim, secretomes of osteoarthritic and healthy articular cartilages obtained from knee and hip were analyzed by shotgun peptidomics. This discovery step led to the identification of 1175 different peptides, corresponding to 101 proteins, as products of the physiological or pathological turnover of cartilage extracellular matrix. Then, a targeted multiple reaction monitoring-mass spectrometry method was developed to quantify the panel of best marker candidates on a larger set of samples (n = 62). Statistical analyses were performed to evaluate the significance of the observed differences and the ability of the neopeptides to classify the tissue. Eight of them were differentially abundant in the media from wounded zones of OA cartilage compared with the healthy tissue (p < 0.05). Three neopeptides belonging to Clusterin and one from Cartilage Oligomeric Matrix Protein showed a disease-dependent decrease specifically in hip OA, whereas two from Prolargin (PRELP) and one from Cartilage Intermediate Layer Protein 1 were significantly increased in samples from knee OA. The release of one peptide from PRELP showed the best metrics for tissue classification (AUC = 0.834). The present study reveals specific neopeptides that are differentially released from knee or hip human osteoarthritic cartilage compared with healthy tissue. This evidences the intervention of characteristic pathogenic pathways in OA and provides a novel panel of peptidic candidates for biomarker development.

Enhancing cardiac glycolysis causes an increase in PDK4 content in response to short-term high-fat diet

The healthy heart has a dynamic capacity to respond and adapt to changes in nutrient availability. Metabolic inflexibility, such as occurs with diabetes, increases cardiac reliance on fatty acids to meet energetic demands, and this results in deleterious effects, including mitochondrial dysfunction, that contribute to pathophysiology. Enhancing glucose usage may mitigate metabolic inflexibility and be advantageous under such conditions. Here, we sought to identify how mitochondrial function and cardiac metabolism are affected in a transgenic mouse model of enhanced cardiac glycolysis (Glyco^Hi) basally and following a short-term (7-day) high-fat diet (HFD). Glyco^Hi mice constitutively express an active form of phosphofructokinase-2, resulting in elevated levels of the PFK-1 allosteric activator fructose 2,6-bisphosphate. We report that basally Glyco^Hi mitochondria exhibit augmented pyruvate-supported respiration relative to fatty acids. Nevertheless, both WT and Glyco^Hi mitochondria had a similar shift toward increased rates of fatty acid-supported respiration following HFD. Metabolic profiling by GC-MS revealed distinct features based on both genotype and diet, with a unique increase in branched-chain amino acids in the Glyco^Hi HFD group. Targeted quantitative proteomics analysis also supported both genotype- and diet-dependent changes in protein expression and uncovered an enhanced expression of pyruvate dehydrogenase kinase 4 (PDK4) in the Glyco^Hi HFD group. These results support a newly identified mechanism whereby the levels of fructose 2,6-bisphosphate promote mitochondrial PDK4 levels and identify a secondary adaptive response that prevents excessive mitochondrial pyruvate oxidation when glycolysis is sustained after a high-fat dietary challenge.

mTMT: An Alternative, Nonisobaric, Tandem Mass Tag Allowing for Precursor-Based Quantification

Stable isotope labeling of peptides is the basis for numerous mass-spectrometry-based quantification strategies. Isobaric tagging and metabolic labeling, namely, tandem mass tagging (TMT) and SILAC, are among the most widely used techniques for relative protein quantification. Here we report an alternative, precursor-based quantification method using nonisobaric TMT variants: TMTzero (TMT0) and superheavy TMT (shTMT). We term this strategy mass difference tandem mass tagging (mTMT). These TMT variants differ by 11 mass units; however, peptides labeled with these reagents coelute, analogous to SILAC-labeled peptide pairs. As a proof-of-concept, we profiled the proteomes of two cell lines that are frequently used in neuroscience studies, SH-SY5Y and SVGp12, using mTMT and standard SILAC-labeling approaches. We show similar quantified proteins and peptides for each method, with highly correlated fold-changes between workflows. We conclude that mTMT is a suitable alternative for precursor-based protein quantification.

Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome: Where There Is a Will, There Is a Way

There is an exponential increase in biological complexity as initial gene transcripts are spliced, translated into amino acid sequence, and post-translationally modified. Each protein can exist as multiple chemical or sequence-specific proteoforms, and each has the potential to be a critical mediator of a physiological or pathophysiological signaling cascade. Here, we provide an overview of how different proteoforms come about in biological systems and how they are most commonly measured using mass spectrometry-based proteomics and bioinformatics. Our goal is to present this information at a level accessible to every scientist interested in mass spectrometry and its application to proteome profiling. We will specifically discuss recent data linking various protein post-translational modifications to cardiovascular disease and conclude with a discussion for enablement and democratization of proteomics across the cardiovascular and scientific community. The aim is to inform and inspire the readership to explore a larger breadth of proteoform, particularity post-translational modifications, related to their particular areas of expertise in cardiovascular physiology.

Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients

Seminal plasma, because of its proximity to prostate, is a promising fluid for biomarker discovery and noninvasive diagnostics. In this study, we investigated if seminal plasma proteins could increase diagnostic specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 147 most promising biomarker candidates, we combined proteins identified through five independent experimental or data mining approaches: tissue transcriptomics, seminal plasma proteomics, cell line secretomics, tissue specificity, and androgen regulation. A rigorous biomarker development pipeline based on selected reaction monitoring assays was designed to evaluate the most promising candidates. As a result, we qualified 76, and verified 19 proteins in seminal plasma of 67 negative biopsy and 152 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which predicted prostate cancer on biopsy and outperformed age and serum Prostate-Specific Antigen (PSA). A machine-learning approach for data analysis provided improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an in-house immunoassay, TGM4 protein was upregulated 3.7-fold (p = 0.006) and revealed AUC = 0.66 for detecting prostate cancer on biopsy for patients with serum PSA ≥4 ng/ml and age ≥50. Very low levels of TGM4 (120 pg/ml) were detected in blood serum. Collectively, our study demonstrated rigorous evaluation of one of the remaining and not well-explored prostate-specific proteins within the medium-abundance proteome of seminal plasma. Performance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.

Toward a Consensus on Applying Quantitative Liquid Chromatography-Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper

Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug-drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state-of-the-art mass spectrometry, no harmonized guidelines exit for sample analysis and data integration to in vivo translation practices. To address this gap, a workshop was held on September 27 and 28, 2018, in Cambridge, MA, with 100 experts attending from academia, the pharmaceutical industry, and regulators. Various aspects of quantitative proteomics and its applications in translational pharmacology were debated. A summary of discussions and best practices identified by this expert panel are presented in this "White Paper" alongside unresolved issues that were outlined for future debates.

Proteomic studies of bone and skeletal health outcomes

Proteins are an essential part of essentially all biological processes, and there is enormous variation in protein forms and concentrations that is not reflected in DNA or RNA. Recently there have been rapid advances in the ability to measure protein sequence, modification and concentration, particularly with methods based in mass spectrometry. Global measures of proteins in tissues or in the circulation provide a broad assessment of the proteome that can be extremely useful for discovery, and targeted proteomic measures can yield specific and sensitive assessments of specific peptides and proteins. While most proteomic measures are directed at the detection of consensus peptide sequences, mass spectrometry based proteomic methods also allow a detailed examination of the peptide sequence differences that result from genetic variants and that may have important effects on protein function. In evaluating proteomic data, a number of analytical considerations are important, including an understanding of missing data, the challenge of multiple testing and replication, and the use of rapidly evolving methods in systems biology. While proteomics has not yet had a major impact in skeletal research, interesting recent research has used these approaches in the study of bone cell biology and the discovery of biomarkers of skeletal disorders. Proteomics can be expected to have an increasing influence in the study of bone biology and pathophysiology.

Exploring the extracellular matrix in health and disease using proteomics

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins that constitutes the scaffold of multicellular organisms. In addition to providing architectural and mechanical support to the surrounding cells, it conveys biochemical signals that regulate cellular processes including proliferation and survival, fate determination, and cell migration. Defects in ECM protein assembly, decreased ECM protein production or, on the contrary, excessive ECM accumulation, have been linked to many pathologies including cardiovascular and skeletal diseases, cancers, and fibrosis. The ECM thus represents a potential reservoir of prognostic biomarkers and therapeutic targets. However, our understanding of the global protein composition of the ECM and how it changes during pathological processes has remained limited until recently.

In this mini-review, we provide an overview of the latest methodological advances in sample preparation and mass spectrometry-based proteomics that have permitted the profiling of the ECM of now dozens of normal and diseased tissues, including tumors and fibrotic lesions.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

Cancer Proteomics and the Elusive Diagnostic Biomarkers

Despite progress in genomic and proteomic technology and applications, the validation of cancer biomarkers of use as clinical early detection diagnostics has remained elusive. As described in this brief viewpoint, there are now recognized to be many types of clinical biomarkers and proteomic analyses, particularly when combined with other 'omic analyses, have been effective in many such biomarker identifications. However, in the area of early diagnosis of cancers, the problems associated with the conversion from identification to diagnostic have largely not been overcome. Notably, the Clinical Proteomic Tumor Analysis Consortium (CPTAC) of the National Cancer Institute (NCI), has been particularly successful in refining the analytical steps needed to tackle this challenging issue and has provided positive insight into how to solve many of the underlying problems. The potential for developing clinical diagnostics for early detection of highly lethal cancers and possible new therapeutic strategies through proteomic analyses, as seen through these CPTAC successes, is more promising than ever.

Regulated Phosphosignaling Associated with Breast Cancer Subtypes and Druggability

Aberrant phospho-signaling is a hallmark of cancer. We investigated kinase-substrate regulation of 33,239 phosphorylation sites (phosphosites) in 77 breast tumors and 24 breast cancer xenografts. Our search discovered 2134 quantitatively correlated kinase-phosphosite pairs, enriching for and extending experimental or binding-motif predictions. Among the 91 kinases with auto-phosphorylation, elevated EGFR, ERBB2, PRKG1, and WNK1 phosphosignaling were enriched in basal, HER2-E, Luminal A, and Luminal B breast cancers, respectively, revealing subtype-specific regulation. CDKs, MAPKs, and ataxia-telangiectasia proteins were dominant, master regulators of substrate-phosphorylation, whose activities are not captured by genomic evidence. We unveiled phospho-signaling and druggable targets from 113 kinase-substrate pairs and cascades downstream of kinases, including AKT1, BRAF and EGFR. We further identified kinase-substrate-pairs associated with clinical or immune signatures and experimentally validated activated phosphosites of ERBB2, EIF4EBP1, and EGFR. Overall, kinase-substrate regulation revealed by the largest unbiased global phosphorylation data to date connects driver events to their signaling effects.

Addressing Complex Matrix Interference Improves Multiplex Food Allergen Detection by Targeted LC-MS/MS

The frequent use of precautionary food allergen labeling (PAL) such as "may contain" frustrates allergic individuals who rely on such labeling to determine whether a food is safe to consume. One technique to study whether foods contain allergens is targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) employing scheduled multiple reaction monitoring (MRM). However, the applicability of a single MRM method to many commercial foods is unknown as complex and heterogeneous interferences derived from the unique composition of each food matrix can hinder quantification of trace amounts of allergen contamination. We developed a freely available, open source software package MAtrix-Dependent Interference Correction (MADIC) to identify interference and applied it with a method targeting 14 allergens. Among 84 unique food products, we found patterns of allergen contamination such as wheat in grains, milk in chocolate-containing products, and soy in breads and corn flours. We also found additional instances of contamination in products with and without PAL as well as highly variable soy content in foods containing only soybean oil and/or soy lecithin. These results demonstrate the feasibility of applying LC-MS/MS to a variety of food products with sensitive detection of multiple allergens in spite of variable matrix interference.

Pharmacokinetic Analysis of a Novel Human EGFRvIII:CD3 Bispecific Antibody in Plasma and Whole Blood Using a High-Resolution Targeted Mass Spectrometry Approach

Bispecific single chain antibody fragments (bi-scFv) represent an emerging class of biotherapeutics. We recently developed a fully human bi-scFv (EGFRvIII:CD3 bi-scFv) with the goal of redirecting CD3-expressing T cells to recognize and destroy malignant, EGFRvIII-expressing glioma. In mice, we showed that EGFRvIII:CD3 bi-scFv effectively treats orthotopic patient-derived malignant glioma and syngeneic glioblastoma. Here, we developed a targeted assay for pharmacokinetic (PK) analysis of EGFRvIII:CD3 bi-scFv, a necessary step in the drug development process. Using microflow liquid chromatography coupled to a high resolution parallel reaction monitoring mass spectrometry, and data analysis in Skyline, we developed a bottom-up proteomic assay for quantification of EGFRvIII:CD3 bi-scFv in both plasma and whole blood. Importantly, a protein calibrator, along with stable isotope-labeled EGFRvIII:CD3 bi-scFv protein, were used for absolute quantification. A PK analysis in a CD3 humanized mouse revealed that EGFRvIII:CD3 bi-scFv in plasma and whole blood has an initial half-life of ∼8 min and a terminal half-life of ∼2.5 h. Our results establish a sensitive, high-throughput assay for direct quantification of EGFRvIII:CD3 bi-scFv without the need for immunoaffinity enrichment. Moreover, these pharmacokinetic parameters will guide drug optimization and dosing regimens in future IND-enabling and phase I studies of EGFRvIII:CD3 bi-scFv.

Integrated Analysis of Transcriptomic, miRNA and Proteomic Changes of a Novel Hybrid Yellow Catfish Uncovers Key Roles for miRNAs in Heterosis

Heterosis is a complex biological phenomenon in which hybridization produces offspring that exhibit superior phenotypic characteristics compared with the parents. Heterosis is widely utilized in agriculture, for example in fish farming; however, its underlying molecular basis remains elusive. To gain a comprehensive and unbiased molecular understanding of fish heterosis, we analyzed the mRNA, miRNA, and proteomes of the livers of three catfish species, Pelteobagrus fulvidraco, P. vachelli, and their hybrid, the hybrid yellow catfish "Huangyou-1" (P. fulvidraco ♀ × P. vachelli ♂). Using next-generation sequencing and mass spectrometry, we show that the nonadditive, homoeolog expression bias and expression level dominance pattern were readily identified at the transcriptional, post-transcriptional, or protein levels, providing the evidence for the widespread presence of dominant models during hybridization. A number of predicted miRNA-mRNA-protein pairs were found and validated by qRT-PCR and PRM assays. Furthermore, several diverse key pathways were identified, including immune defense, metabolism, digestion and absorption, and cell proliferation and development, suggesting the vital mechanisms involved in the generation of the heterosis phenotype in progenies. We propose that the high parental expression of genes/proteins (growth, nutrition, feeding, and disease resistance) coupled with low parental miRNAs of the offspring, are inherited from the mother or father, thus indicating that the offspring were enriched with the advantages of the father or mother. We provide new and important information about the molecular mechanisms of heterosis, which represents a significant step toward a more complete elucidation of this phenomenon.

Applications of multiple reaction monitoring targeted proteomics assays in human plasma

Introduction: Multiple (or selected) reaction monitoring-mass spectrometry (MRM/SRM) is a targeted proteomic method that can be used for relative and absolute quantification. Multiple reports exist supporting the potential of the approach in proteomic biomarker validation. Areas covered: To get an overview of the applications of MRM in protein quantification in plasma, a search in MedLine/PubMed was performed using the keywords: 'MRM/SRM plasma proteomic/proteomics/proteome'. The retrieved studies were further filtered to focus on disease biomarkers and the main results are summarized. Expert opinion: MRM is increasingly employed for the quantification of both well-established but also newly discovered putative biomarkers and occasionally their post-translationally modified forms in plasma. Fractionation is regularly required for the detection of low abundance proteins. Standardized procedures to facilitate assay establishment and marker quantification have been proposed and, in few cases, implemented. Nevertheless, in most cases, absolute quantification is not performed. To advance, multiple technical issues including the regular use of standard labeled peptides and appropriate quality controls to monitor assay performance should be considered. Additionally, clinical aspects involving careful study design to address biomarker clinical use should also be considered.

Validation of a multiplexed LC-MS/MS clinical assay to quantify insulin-like growth factor-binding proteins in human serum and its application in a clinical study

Circulating insulin-like growth factor-binding proteins (IGFBPs) continue to gain attention as biomarkers of drug activities on insulin like growth factor (IGF)/IGF receptor signaling pathways. A multiplexed LC-MS/MS method was validated for the absolute quantitation of IGFBPs in human serum. The method was used to measure screening concentrations of IGFBPs in spinal and bulbar muscular atrophy (SBMA) patients in a phase 2 clinical trial. Concentrations of IGFBP 1, 2, 3, and 5 were simultaneously determined based on representative signature peptides derived from an optimized trypsin digestion procedure. Signature peptide levels were absolutely quantitated using a sensitive/specific targeted LC-MS/MS method. Corresponding mass-shifted, stable isotope-labeled peptides were employed as internal standards. A true blank matrix for the quantitation of IGFBPs was not available since they are endogenous proteins in human serum. In this method, calibration standards/curves were prepared using authentic synthetic peptides spiked into a surrogate matrix. The surrogate matrix was generated from human serum treated in the same way as the study samples, but using iodoacetic acid instead of iodoacetamide as the alkylation reagent. This surrogate matrix approach allowed for the direct and sensitive/specific quantification of IGFBP 1, 2, 3, and 5 due to the lack of any endogenous background. Equivalent matrix effect and recovery of analytes was achieved for the authentic and surrogate matrices. The fully validated LC-MS/MS assay will allow further evaluation of the utility of IGFBP biomarkers in clinical trials.

Clinical potential of mass spectrometry-based proteogenomics

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry

INTRODUCTION

Numerous diseases are caused by changes in post-translational modifications (PTMs). Therefore, the number of clinical proteomics studies that include the analysis of PTMs is increasing. Combining complementary information-for example changes in protein abundance, PTM levels, with the genome and transcriptome (proteogenomics)-holds great promise for discovering important drivers and markers of disease, as variations in copy number, expression levels, or mutations without spatial/functional/isoform information is often insufficient or even misleading. Areas covered: We discuss general considerations, requirements, pitfalls, and future perspectives in applying PTM-centric proteomics to clinical samples. This includes samples obtained from a human subject, for instance (i) bodily fluids such as plasma, urine, or cerebrospinal fluid, (ii) primary cells such as reproductive cells, blood cells, and (iii) tissue samples/biopsies. Expert commentary: PTM-centric discovery proteomics can substantially contribute to the understanding of disease mechanisms by identifying signatures with potential diagnostic or even therapeutic relevance but may require coordinated efforts of interdisciplinary and eventually multi-national consortia, such as initiated in the cancer moonshot program. Additionally, robust and standardized mass spectrometry (MS) assays-particularly targeted MS, MALDI imaging, and immuno-MALDI-may be transferred to the clinic to improve patient stratification for precision medicine, and guide therapies.