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Routine measurement of cancer biomarkers is performed for early detection, risk classification, and treatment monitoring, among other applications, and has substantially contributed to better clinical outcomes for patients. However, there remains an unmet need for clinically validated assays of cancer protein biomarkers. Protein tumor markers are of particular interest since proteins carry out the majority of biological processes and thus dynamically reflect changes in cancer pathophysiology. Mass spectrometry-based targeted proteomics is a powerful tool for absolute peptide and protein quantification in biological matrices with numerous advantages that make it attractive for clinical applications in oncology. The use of liquid chromatography-tandem mass spectrometry (LC–MS/MS) based methodologies has allowed laboratories to overcome challenges associated with immunoassays that are more widely used for tumor marker measurements. Yet, clinical implementation of targeted proteomics methodologies has so far been limited to a few cancer markers. This is due to numerous challenges associated with paucity of robust validation studies of new biomarkers and the labor-intensive and operationally complex nature of LC–MS/MS workflows. The purpose of this review is to provide an overview of targeted proteomics applications in cancer, workflows used in targeted proteomics, and requirements for clinical validation and implementation of targeted proteomics assays. We will also discuss advantages and challenges of targeted MS-based proteomics assays for clinical cancer biomarker analysis and highlight some recent developments that will positively contribute to the implementation of this technique into clinical laboratories.

Non‐invasive detection of colorectal cancer with blood‐based markers is a critical clinical need. Here we describe a phased mass spectrometry‐based approach for the discovery, screening, and validation of circulating protein biomarkers with diagnostic value. Initially, we profiled human primary tumor tissue epithelia and characterized about 300 secreted and cell surface candidate glycoproteins. These candidates were then screened in patient systemic circulation to identify detectable candidates in blood plasma. An 88‐plex targeting method was established to systematically monitor these proteins in two large and independent cohorts of plasma samples, which generated quantitative clinical datasets at an unprecedented scale. The data were deployed to develop and evaluate a five‐protein biomarker signature for colorectal cancer detection. Synopsis A five‐protein biomarker signature discovered and validated by mass spectrometry can accurately predict colorectal cancer (CRC) diagnosis non‐invasively from a blood sample. Secreted protein biomarker candidates discovered in tumor tissue were detected in the circulation of healthy and CRC subjects. A five‐protein predictive diagnostic signature was developed in a cohort of 100 healthy and 100 CRC subjects, and independently validated in an external cohort of 67 healthy and 202 CRC subjects. The protein biomarker signature was found to be more effective for CRC subjects with larger tumors. Graphical Abstract A five‐protein biomarker signature discovered and validated by mass spectrometry can accurately predict colorectal cancer (CRC) diagnosis non‐invasively from a blood sample.

Background Meningiomas are the most prevalent primary brain tumors. Due to their increasing burden on healthcare, meningiomas have become a pivot of translational research globally. Despite many studies in the field of discovery proteomics, the identification of grade-specific markers for meningioma is still a paradox and requires thorough investigation. The potential of the reported markers in different studies needs further verification in large and independent sample cohorts to identify the best set of markers with a better clinical perspective. Methods A total of 53 fresh frozen tumor tissue and 51 serum samples were acquired from meningioma patients respectively along with healthy controls, to validate the prospect of reported differentially expressed proteins and claimed markers of Meningioma mined from numerous manuscripts and knowledgebases. A small subset of Glioma/Glioblastoma samples were also included to investigate inter-tumor segregation. Furthermore, a simple Machine Learning (ML) based analysis was performed to evaluate the classification accuracy of the list of proteins. Results A list of 15 proteins from tissue and 12 proteins from serum were found to be the best segregator using a feature selection-based machine learning strategy with an accuracy of around 80% in predicting low grade (WHO grade I) and high grade (WHO grade II and WHO grade III) meningiomas. In addition, the discriminant analysis could also unveil the complexity of meningioma grading from a segregation pattern, which leads to the understanding of transition phases between the grades. Conclusions The identified list of validated markers could play an instrumental role in the classification of meningioma as well as provide novel clinical perspectives in regard to prognosis and therapeutic targets.

Background Dynein axonemal intermediate chain 1 protein (DNAI1) plays an essential role in cilia structure and function, while its mutations lead to primary ciliary dyskinesia (PCD). Accurate quantitation of DNAI1 in lung tissue is crucial for comprehensive understanding of its involvement in PCD, as well as for developing the potential PCD therapies. However, the current protein quantitation method is not sensitive enough to detect the endogenous level of DNAI1 in complex biological matrix such as lung tissue. Methods In this study, a quantitative method combining immunoprecipitation with nanoLC-MS/MS was developed to measure the expression level of human wild-type (WT) DNAI1 protein in lung tissue. To our understanding, it is the first immunoprecipitation (IP)-MS based method for absolute quantitation of DNAI1 protein in lung tissue. The DNAI1 quantitation was achieved through constructing a standard curve with recombinant human WT DNAI1 protein spiked into lung tissue matrix. Results This method was qualified with high sensitivity and accuracy. The lower limit of quantitation of human DNAI1 was 4 pg/mg tissue. This assay was successfully applied to determine the endogenous level of WT DNAI1 in human lung tissue. Conclusions The results clearly demonstrate that the developed assay can accurately quantitate low-abundance WT DNAI1 protein in human lung tissue with high sensitivity, indicating its high potential use in the drug development for DNAI1 mutation-caused PCD therapy.

Intestinal transporter proteins affect the oral bioavailability of many drugs in a significant manner. In order to estimate or predict their impact on oral drug absorption, data on their intestinal expression levels are needed. So far, predominantly mRNA expression data are available which are not necessarily correlated with the respective protein content. All available protein data were assessed by immunoblotting techniques such as Western blotting which both possess a number of limitations for reliable protein quantification. In contrast to this, mass spectrometry-based targeted proteomics may represent a promising alternative method to provide comprehensive protein expression data. In this review, we will summarize so far available intestinal mRNA and protein expression data for relevant human multidrug transporters. Moreover, recently observed mass spectrometry-based targeted proteomic data will be presented and discussed with respect to potential functional consequences. Associated to this, we will provide a short tutorial how to set up these methods and emphasize critical aspects in method development. Finally, potential limitations and pitfalls of this emerging technique will be discussed. From our perspective, LC-MS/MS-based targeted proteomics represents a valuable new method to comprehensively analyse the intestinal expression of transporter proteins. The resulting expression data are expected to improve our understanding about the intestinal processing of drugs.

Background Quantification of drug-target binding is critical for confirming that drugs reach their intended protein targets, understanding the mechanism of action, and interpreting dose-response relationships. For covalent inhibitors, target engagement can be inferred by free target levels before and after treatment. Targeted mass spectrometry assays offer precise protein quantification in complex biological samples and have been routinely applied in pre-clinical studies to quantify target engagement in frozen tumor tissues for oncology drug development. However, frozen tissues are often not available from clinical trials so it is critical that assays are applicable to formalin-fixed, paraffin-embedded (FFPE) tissues in order to extend mass spectrometry-based target engagement studies into clinical settings. Methods Wild-type RAS and RASG12C was quantified in FFPE tissues by a highly optimized targeted mass spectrometry assay that couples high-field asymmetric waveform ion mobility spectrometry (FAIMS) and parallel reaction monitoring (PRM) with internal standards. In a subset of samples, technical reproducibility was evaluated by analyzing consecutive tissue sections from the same tumor block and biological variation was accessed among adjacent tumor regions in the same tissue section. Results Wild-type RAS protein was measured in 32 clinical non-small cell lung cancer tumors (622–2525 amol/µg) as measured by FAIMS-PRM mass spectrometry. Tumors with a known KRASG12C mutation (n = 17) expressed a wide range of RASG12C mutant protein (127–2012 amol/µg). The variation in wild-type RAS and RASG12C measurements ranged 0–18% CV across consecutive tissue sections and 5–20% CV among adjacent tissue regions. Quantitative target engagement was then demonstrated in FFPE tissues from 2 xenograft models (MIA PaCa-2 and NCI-H2122) treated with a RASG12C inhibitor (AZD4625). Conclusions This work illustrates the potential to expand mass spectrometry-based proteomics in preclinical and clinical oncology drug development through analysis of FFPE tumor biopsies.

An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non‐secreted proteins based on parallel reaction monitoring to measure, at steady‐state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene‐specific RNA‐to‐protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics. Synopsis A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced. A targeted proteomics approach based on spike‐in of stable isotope‐labeled protein fragments is developed to measure absolute protein copy numbers across human tissues and cell lines. Transcript and protein levels within a sample do not correlate well, unless a gene‐specific RNA‐to‐protein (RTP) factor is introduced. The RTP‐ratio varies significantly between genes, ranging from thousands to millions of protein copies per mRNA molecule, but does not vary across tissues. Transcriptome analysis can be used as a tool to predict protein copy numbers per cell, thus forming an attractive link between genomics and proteomics. Graphical Abstract A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced.

Although global proteomics has shown promise for discovery of many new proteins, biomarkers, protein modifications, and polymorphisms, targeted proteomics is emerging in the proteomics research field as a complement to untargeted shotgun proteomics, particularly when a determined set of low-abundance functional proteins need to be measured. The function and expression of proteins related to drug absorption, distribution, metabolism, and excretion (ADME) such as cytochrome P450 enzymes and membrane transporters are of great interest in biopharmaceutical research. Since the variation in ADME-related protein expression is known to be a major complicating factor encountered during in vitro–in vivo and in vivo–in vivo extrapolations (IVIVE), the accurate quantification of the ADME proteins in complex biological systems becomes a fundamental element in establishing IVIVE for pharmacokinetic predictions. In this review, we provide an overview of relevant methodologies followed by a summary of recent applications encompassing mass spectrometry-based targeted quantifications of membrane transporters.

[This corrects the article DOI: 10.3389/fimmu.2025.1542034.].

Aims/hypothesis While investigating markers for declining beta cell function in type 1 diabetes, we previously demonstrated 11 statistically significant protein associations with fasting C-peptide/glucose ratios in longitudinal serum samples from newly diagnosed (ND) individuals ( n =86; 228 samples in total) participating in the INNODIA (Innovative approaches to understanding and arresting type 1 diabetes) study. Furthermore, comparison with protein measurements from age- and sex-matched autoantibody-negative unaffected family members (UFMs, n =194) revealed differences in the serum levels of 13 target proteins. To further evaluate these findings, we analysed longitudinal serum drawn during the first year after diagnosis from a new group of ND individuals subsequently enrolled in the study, together with samples from additional UFMs. Methods To validate the previously reported statistically significant protein associations with type 1 diabetes progression, selected reaction monitoring (SRM) MS analyses were carried out. Sera from individuals diagnosed with type 1 diabetes under the age of 18 years ( n =146) were collected within 6 weeks of diagnosis and at 3, 6 and 12 months after diagnosis (560 samples in total). The resulting SRM data were compared with fasting C-peptide/glucose measurements, which were used as a proxy for beta cell function. The protein data were further compared with cross-sectional SRM measurements from age- and sex-matched UFMs ( n =272). Results Our results confirmed the presence of significant ( p <0.05) inverse associations between fasting C-peptide/glucose ratios and peptides from apolipoprotein B-100, apolipoprotein M and glutathione peroxidase 3 (GPX3) in ND individuals. Additionally, we observed consistent differences in the levels of ten of the 13 targeted proteins between individuals with type 1 diabetes and UFMs. These proteins included GPX3, transthyretin, prothrombin, apolipoprotein C1 and afamin. Conclusions/interpretation The validated results reflect the landscape of biological changes accompanying type 1 diabetes. For example, the association of the targeted apolipoproteins with fasting C-peptide/glucose ratios in the first year after diagnosis is likely to relate to lipid abnormalities observed in individuals with type 1 diabetes, and reiterates the connection of apolipoproteins with the underlying changes accompanying the disease. Further research is needed to explore the clinical value and relevance of these targets. Graphical Abstract