Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data.

Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.

Multiplex genome editing eliminates the Warburg Effect without impacting growth rate in mammalian cells.

The Warburg effect is ubiquitous in proliferative mammalian cells, including cancer cells, but poses challenges for biopharmaceutical production, as lactate accumulation inhibits cell growth and protein production. Previous efforts to eliminate lactate production via knockout have failed in mammalian bioprocessing since lactate dehydrogenase has proven essential. However, here we eliminated the Warburg effect in Chinese hamster ovary (CHO) and HEK293 cells by simultaneously knocking out lactate dehydrogenase and regulators involved in a negative feedback loop that typically inhibits pyruvate conversion to acetyl-CoA. In contrast to long-standing assumptions about the role of aerobic glycolysis, Warburg-null cells maintain wildtype growth rate while producing negligible lactate. Further characterization of Warburg-null CHO cells showed a compensatory increase in oxygen consumption, a near total reliance on oxidative metabolism, and higher cell densities in fed-batch cell culture. These cells remained amenable for production of diverse biotherapeutic proteins, reaching industrially relevant titers and maintaining product glycosylation. Thus, the ability to eliminate the Warburg effect is an important development for biotherapeutic production and provides a tool for investigating a near-universal metabolic phenomenon.

Proteomic profiling reveals that ESR1 mutations enhance cyclin-dependent kinase signaling.

Three quarters of all breast cancers express the estrogen receptor (ER, ESR1 gene), which promotes tumor growth and constitutes a direct target for endocrine therapies. ESR1 mutations have been implicated in therapy resistance in metastatic breast cancer, in particular to aromatase inhibitors. ESR1 mutations promote constitutive ER activity and affect other signaling pathways, allowing cancer cells to proliferate by employing mechanisms within and without direct regulation by the ER. Although subjected to extensive genetic and transcriptomic analyses, understanding of protein alterations remains poorly investigated. Towards this, we employed an integrated mass spectrometry based proteomic approach to profile the protein and phosphoprotein differences in breast cancer cell lines expressing the frequent Y537N and Y537S ER mutations. Global proteome analysis revealed enrichment of mitotic and immune signaling pathways in ER mutant cells, while phosphoprotein analysis evidenced enriched activity of proliferation associated kinases, in particular CDKs and mTOR. Integration of protein expression and phosphorylation data revealed pathway-dependent discrepancies (motility vs proliferation) that were observed at varying degrees across mutant and wt ER cells. Additionally, protein expression and phosphorylation patterns, while under different regulation, still recapitulated the estrogen-independent phenotype of ER mutant cells. Our study is the first proteome-centric characterization of ESR1 mutant models, out of which we confirm estrogen independence of ER mutants and reveal the enrichment of immune signaling pathways at the proteomic level.

Decoding of the surfaceome and endocytome in primary glioblastoma cells identifies potential target antigens in the hypoxic tumor niche.

Immunotherapies with antibody-drug-conjugates (ADC) and CAR-T cells, targeted at tumor surface antigens (surfaceome), currently revolutionize clinical oncology. However, target identification warrants a better understanding of the surfaceome and how it is modulated by the tumor microenvironment. Here, we decode the surfaceome and endocytome and its remodeling by hypoxic stress in glioblastoma (GBM), the most common and aggressive brain tumor in adults. We employed a comprehensive approach for global and dynamic profiling of the surfaceome and endocytosed (endocytome) proteins and their regulation by hypoxia in patient-derived GBM cultures. We found a heterogeneous surface-endocytome profile and a divergent response to hypoxia across GBM cultures. We provide a quantitative ranking of more than 600 surface resident and endocytosed proteins, and their regulation by hypoxia, serving as a resource to the cancer research community. As proof-of-concept, the established target antigen CD44 was identified as a commonly and abundantly expressed surface protein with high endocytic activity. Among hypoxia induced proteins, we reveal CXADR, CD47, CD81, BSG, and FXYD6 as potential targets of the stressed GBM niche. We could validate these findings by immunofluorescence analyses in patient tumors and by increased expression in the hypoxic core of GBM spheroids. Selected candidates were finally confronted by treatment studies, showing their high capacity for internalization and ADC delivery. Importantly, we highlight the limited correlation between transcriptomics and proteomics, emphasizing the critical role of membrane protein enrichment strategies and quantitative mass spectrometry. Our findings provide a comprehensive understanding of the surface-endocytome and its remodeling by hypoxia in GBM as a resource for exploration of targets for immunotherapeutic approaches in GBM.

Alveolar epithelial cells are competent producers of interstitial extracellular matrix with disease relevant plasticity in a human in vitro 3D model.

Alveolar epithelial cells (AEC) have been implicated in pathological remodelling. We examined the capacity of AEC to produce extracellular matrix (ECM) and thereby directly contribute towards remodelling in chronic lung diseases. Cryopreserved type 2 AEC (AEC2) from healthy lungs and chronic obstructive pulmonary disease (COPD) afflicted lungs were cultured in decellularized healthy human lung slices for 13 days. Healthy-derived AEC2 were treated with transforming growth factor ß1 (TGF-ß1) to evaluate the plasticity of their ECM production. Evaluation of phenotypic markers and expression of matrisome genes and proteins were evaluated by RNA-sequencing, mass spectrometry and immunohistochemistry. The AEC2 displayed an AEC marker profile similar to freshly isolated AEC2 throughout the 13-day culture period. COPD-derived AECs proliferated as healthy AECs with few differences in gene and protein expression while retaining increased expression of disease marker HLA-A. The AEC2 expressed basement membrane components and a complex set of interstitial ECM proteins. TGF-ß1 stimuli induced a significant change in interstitial ECM production from AEC2 without loss of specific AEC marker expression. This study reveals a previously unexplored potential of AEC to directly contribute to ECM turnover by producing interstitial ECM proteins, motivating a re-evaluation of the role of AEC2 in pathological lung remodelling.

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics.

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

Long-Read Sequencing Reveals Genetic Adaptation of Bartonella Adhesin A Among Different Bartonella henselae Isolates.

Bartonella henselae is the causative agent of cat scratch disease and other clinical entities such as endocarditis and bacillary angiomatosis. The life cycle of this pathogen, with alternating host conditions, drives evolutionary and host-specific adaptations. Human, feline, and laboratory adapted B. henselae isolates often display genomic and phenotypic differences that are related to the expression of outer membrane proteins, for example the Bartonella adhesin A (BadA). This modularly-structured trimeric autotransporter adhesin is a major virulence factor of B. henselae and is crucial for the initial binding to the host via the extracellular matrix proteins fibronectin and collagen. By using next-generation long-read sequencing we demonstrate a conserved genome among eight B. henselae isolates and identify a variable genomic badA island with a diversified and highly repetitive badA gene flanked by badA pseudogenes. Two of the eight tested B. henselae strains lack BadA expression because of frameshift mutations. We suggest that active recombination mechanisms, possibly via phase variation (i.e., slipped-strand mispairing and site-specific recombination) within the repetitive badA island facilitate reshuffling of homologous domain arrays. The resulting variations among the different BadA proteins might contribute to host immune evasion and enhance long-term and efficient colonisation in the differing host environments. Considering the role of BadA as a key virulence factor, it remains important to check consistently and regularly for BadA surface expression during experimental infection procedures.

Landscape of surfaceome and endocytome in human glioma is divergent and depends on cellular spatial organization.

Therapeutic strategies directed at the tumor surfaceome (TS), including checkpoint inhibitor blocking antibodies, antibody drug conjugates (ADCs), and chimeric antigen receptor T (CAR-T) cells, provide a new armament to fight cancer. However, a remaining bottleneck is the lack of strategies to comprehensively interrogate patient tumors for potential TS targets. Here, we have developed a platform (tumor surfaceome mapping [TS-MAP]) integrated with a newly curated TS classifier (SURFME) that allows profiling of primary 3D cultures and intact patient glioma tumors with preserved tissue architecture. Moreover, TS-MAP specifically identifies proteins capable of endocytosis as tractable targets for ADCs and other modalities requiring toxic payload internalization. In high-grade gliomas that remain among the most aggressive forms of cancer, we show that cellular spatial organization (2D vs. 3D) fundamentally transforms the surfaceome and endocytome (e.g., integrins, proteoglycans, semaphorins, and cancer stem cell markers) with general implications for target screening approaches, as exemplified by an ADC targeting EGFR. The TS-MAP platform was further applied to profile the surfaceome and endocytome landscape in a cohort of freshly resected gliomas. We found a highly diverse TS repertoire between patient tumors, not directly associated with grade and histology, which highlights the need for individualized approaches. Our data provide additional layers of understanding fundamental to the future development of immunotherapy strategies, as well as procedures for proteomics-based target identification and selection. The TS-MAP platform should be widely applicable in efforts aiming at a better understanding of how to harness the TS for personalized immunotherapy.

Targeted Selected Reaction Monitoring Verifies Histology Specific Peptide Signatures in Epithelial Ovarian Cancer.

Epithelial ovarian cancer (OC) is a disease with high mortality due to vague early clinical symptoms. Benign ovarian cysts are common and accurate diagnosis remains a challenge because of the molecular heterogeneity of OC. We set out to investigate whether the disease diversity seen in ovarian cyst fluids and tumor tissue could be detected in plasma. Using existing mass spectrometry (MS)-based proteomics data, we constructed a selected reaction monitoring (SRM) assay targeting peptides from 177 cancer-related and classical proteins associated with OC. Plasma from benign, borderline, and malignant ovarian tumors were used to verify expression (n = 74). Unsupervised and supervised multivariate analyses were used for comparisons. The peptide signatures revealed by the supervised multivariate analysis contained 55 to 77 peptides each. The predictive (Q2) values were higher for benign vs. low-grade serous Q2 = 0.615, mucinous Q2 = 0.611, endometrioid Q2 = 0.428 and high-grade serous Q2 = 0.375 (stage I-II Q2 = 0.515; stage III Q2 = 0.43) OC compared to benign vs. all malignant Q2 = 0.226. With targeted SRM MS we constructed a multiplexed assay for simultaneous detection and relative quantification of 185 peptides from 177 proteins in only 20 µL of plasma. With the approach of histology-specific peptide patterns, derived from pre-selected proteins, we may be able to detect not only high-grade serous OC but also the less common OC subtypes.

Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance.

Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.

Citrullination of extracellular histone H3.1 reduces antibacterial activity and exacerbates its proteolytic degradation.

BACKGROUND: Cystic fibrosis (CF), involves excessive airway accumulation of neutrophils, often in parallel with severe infection caused by Pseudomonas aeruginosa. Free histones are known to possess bactericidal properties, but the degree of antibacterial activity exerted on specific lung-based pathogens is largely unknown. Neutrophils have a high content of peptidyl deiminase 4 (PADI4), which citrullinate cationic peptidyl-arginines. In histone H3.1, several positions in the NH2-terminal tail are subject to citrullination. METHODS: Full-length and segmented histone subunit H3.1 was investigated for bactericidal activity towards P. aeruginosa (strain PAO1). PADI4-induced citrullination of histone H3.1 was assessed for antibacterial activity towards P. aeruginosa. Next, the effect of neutrophil elastase (NE)-mediated proteolysis of histone H3.1 was investigated. Finally, PADI4, H3.1, and citrullinated H3.1 were examined in healthy control and CF patient lung tissues. RESULTS: Full-length histone H3.1 and sections of the histone H3.1 tail, displayed bactericidal activity towards P. aeruginosa. These antibacterial effects were reduced following citrullination by PADI4 or proteolysis by NE. Interestingly, citrullination of histone H3.1 exacerbated NE-mediated degradation. In CF lung tissue, citrullinated histone H3.1 and PADI4 immunoreactivity was abundant. Degraded histone H3.1 was detected in the sputum of CF patients but was absent in the sputum of healthy controls. CONCLUSIONS: Citrullination impairs the antibacterial activity of histone H3.1 and exacerbates its proteolytic degradation by NE. Citrullination is likely to play an important role during resolution of acute inflammation. However, in chronic inflammation akin to CF, citrullination may dampen host defense and promote pathogen survival, as exemplified by P. aeruginosa.

Matrisome Properties of Scaffolds Direct Fibroblasts in Idiopathic Pulmonary Fibrosis.

In idiopathic pulmonary fibrosis (IPF) structural properties of the extracellular matrix (ECM) are altered and influence cellular responses through cell-matrix interactions. Scaffolds (decellularized tissue) derived from subpleural healthy and IPF lungs were examined regarding biomechanical properties and ECM composition of proteins (the matrisome). Scaffolds were repopulated with healthy fibroblasts cultured under static stretch with heavy isotope amino acids (SILAC), to examine newly synthesized proteins over time. IPF scaffolds were characterized by increased tissue density, stiffness, ultimate force, and differential expressions of matrisome proteins compared to healthy scaffolds. Collagens, proteoglycans, and ECM glycoproteins were increased in IPF scaffolds, however while specific basement membrane (BM) proteins such as laminins and collagen IV were decreased, nidogen-2 was also increased. Findings were confirmed with histology, clearly showing a disorganized BM. Fibroblasts produced scaffold-specific proteins mimicking preexisting scaffold composition, where 11 out of 20 BM proteins were differentially expressed, along with increased periostin and proteoglycans production. We demonstrate how matrisome changes affect fibroblast activity using novel approaches to study temporal differences, where IPF scaffolds support a disorganized BM and upregulation of disease-associated proteins. These matrix-directed cellular responses emphasize the IPF matrisome and specifically the BM components as important factors for disease progression.

Mandible reconstruction with patient-specific implants: Case report of five consecutive patients.

The purpose of this case report was to assess whether patient-specific implants (PSI: Xillocs) and soft tissue flaps contribute in reconstructing large mandibular defects. Five patients with a medical situation and history not suitable for free microvascular bone flaps were operated with PSI and evaluated. The mean follow-up time was 12 months. The excellent fit, convenient surgery, and esthetic outcome were seen as the major advantages. The PSI can, in the authors' experience, be considered as a useful alternative provided they are well embedded by viable tissue and attached to vital resection margins of the recipient bone.

The Human Salivary Antimicrobial Peptide Profile according to the Oral Microbiota in Health, Periodontitis and Smoking.

Antimicrobial peptides (AMPs) are a diverse family of peptides that defend the mucosal surfaces of the oral cavity and other locations. Many AMPs have multiple functions and properties that influence aspects of innate defense and colonization by microorganisms. The human oral cavity is home to the second-most diverse microbiome, and the health of the mouth is influenced by the presence of these bacteria as well as by extrinsic factors such as periodontitis and smoking. This study hypothesized that the AMP profile is different in the presence of extrinsic factors and that this would also be reflected in the bacteria present. The AMP profile was analyzed by quantitative selected-reaction-monitoring mass spectrometry analysis and 40 bacterial species were quantified by DNA-DNA hybridization in saliva donated by 41 individuals. Periodontal status was assessed through dental examination and smoking status through medical charting. Periodontal health (in nonsmokers) was associated with a higher abundance of ribonuclease 7, protachykinin 1, ß-defensin 128, lipocalin 1, bactericidal permeability-increasing protein fold-containing family B member 3, and bone-marrow proteoglycan. Nonsmoking periodontal disease was associated with an abundance of neutrophil defensin 1 and cathelicidin. However, 7 AMPs were overabundant in periodontal disease in smokers: adrenomedullin, eosinophil peroxidase, 3 different histones, myeloperoxidase, and neutrophil defensin 1. There were no differentially abundant AMPs in smokers versus nonsmokers with periodontal health. Correlation network inference of healthy nonsmokers, healthy smokers, nonsmoking periodontitis, or smoking periodontitis donors demonstrated very different networks growing in complexity with increasing numbers of stressors. The study highlights the importance of the interaction between the oral cavity and its resident microbiota and how this may be influenced by periodontal disease and smoking.

Quantitative proteomic characterization of the lung extracellular matrix in chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis.

Remodeling of the extracellular matrix (ECM) is a common feature in lung diseases such as chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF). Here, we applied a sequential tissue extraction strategy to describe disease-specific remodeling of human lung tissue in disease, using end-stages of COPD and IPF. Our strategy was based on quantitative comparison of the disease proteomes, with specific focus on the matrisome, using data-independent acquisition and targeted data analysis (SWATH-MS). Our work provides an in-depth proteomic characterization of human lung tissue during impaired tissue remodeling. In addition, we show important quantitative and qualitative effects of the solubility of matrisome proteins. COPD was characterized by a disease-specific increase in ECM regulators, metalloproteinase inhibitor 3 (TIMP3) and matrix metalloproteinase 28 (MMP-28), whereas for IPF, impairment in cell adhesion proteins, such as collagen VI and laminins, was most prominent. For both diseases, we identified increased levels of proteins involved in the regulation of endopeptidase activity, with several proteins belonging to the serpin family. The established human lung quantitative proteome inventory and the construction of a tissue-specific protein assay library provides a resource for future quantitative proteomic analyses of human lung tissues. SIGNIFICANCE: We present a sequential tissue extraction strategy to determine changes in extractability of matrisome proteins in end-stage COPD and IPF compared to healthy control tissue. Extensive quantitative analysis of the proteome changes of the disease states revealed altered solubility of matrisome proteins involved in ECM regulators and cell-ECM communication. The results highlight disease-specific remodeling mechanisms associated with COPD and IPF.

Development of Large-scale Cross-linking Mass Spectrometry.

Cross-linking mass spectrometry (CLMS) provides distance constraints to study the structure of proteins, multiprotein complexes and protein-protein interactions which are critical for the understanding of protein function. CLMS is an attractive technology to bridge the gap between high-resolution structural biology techniques and proteomic-based interactome studies. However, as outlined in this review there are still several bottlenecks associated with CLMS which limit its application on a proteome-wide level. Specifically, there is an unmet need for comprehensive software that can reliably identify cross-linked peptides from large data sets. In this review we provide supporting information to reason that targeted proteomics of cross-links may provide the required sensitivity to reliably detect and quantify cross-linked peptides and that a reporter ion signature for cross-linked peptides may become a useful approach to increase confidence in the identification process of cross-linked peptides. In addition, the review summarizes the recent advances in CLMS workflows using the analysis of condensin complex in intact chromosomes as a model complex.

Comprehensive Proteomic Characterization of Ontogenic Changes in Hematopoietic Stem and Progenitor Cells.

Hematopoietic stem and progenitor cells (HSPCs) in the fetus and adult possess distinct molecular landscapes that regulate cell fate and change their susceptibility to initiation and progression of hematopoietic malignancies. Here, we applied in-depth quantitative proteomics to comprehensively describe and compare the proteome of fetal and adult HSPCs. Our data uncover a striking difference in complexity of the cellular proteomes, with more diverse adult-specific HSPC proteomic signatures. The differential protein content in fetal and adult HSPCs indicate distinct metabolic profiles and protein complex stoichiometries. Additionally, adult characteristics include an arsenal of proteins linked to viral and bacterial defense, as well as protection against ROS-induced protein oxidation. Further analyses show that interferon α, as well as Neutrophil elastase, has distinct functional effects in fetal and adult HSPCs. This study provides a rich resource aimed toward an enhanced mechanistic understanding of normal and malignant hematopoiesis during fetal and adult life.

Quantitative proteomic characterization of lung-MSC and bone marrow-MSC using DIA-mass spectrometry.

Mesenchymal stromal cells (MSC) are ideal candidates for cell therapies, due to their immune-regulatory and regenerative properties. We have previously reported that lung-derived MSC are tissue-resident cells with lung-specific properties compared to bone marrow-derived MSC. Assessing relevant molecular differences between lung-MSC and bone marrow-MSC is important, given that such differences may impact their behavior and potential therapeutic use. Here, we present an in-depth mass spectrometry (MS) based strategy to investigate the proteomes of lung-MSC and bone marrow-MSC. The MS-strategy relies on label free quantitative data-independent acquisition (DIA) analysis and targeted data analysis using a MSC specific spectral library. We identified several significantly differentially expressed proteins between lung-MSC and bone marrow-MSC within the cell layer (352 proteins) and in the conditioned medium (49 proteins). Bioinformatics analysis revealed differences in regulation of cell proliferation, which was functionally confirmed by decreasing proliferation rate through Cytochrome P450 stimulation. Our study reveals important differences within proteome and matrisome profiles between lung- and bone marrow-derived MSC that may influence their behavior and affect the clinical outcome when used for cell-therapy.

Dinosaur: A Refined Open-Source Peptide MS Feature Detector.

In bottom-up mass spectrometry (MS)-based proteomics, peptide isotopic and chromatographic traces (features) are frequently used for label-free quantification in data-dependent acquisition MS but can also be used for the improved identification of chimeric spectra or sample complexity characterization. Feature detection is difficult because of the high complexity of MS proteomics data from biological samples, which frequently causes features to intermingle. In addition, existing feature detection algorithms commonly suffer from compatibility issues, long computation times, or poor performance on high-resolution data. Because of these limitations, we developed a new tool, Dinosaur, with increased speed and versatility. Dinosaur has the functionality to sample algorithm computations through quality-control plots, which we call a plot trail. From the evaluation of this plot trail, we introduce several algorithmic improvements to further improve the robustness and performance of Dinosaur, with the detection of features for 98% of MS/MS identifications in a benchmark data set, and no other algorithm tested in this study passed 96% feature detection. We finally used Dinosaur to reimplement a published workflow for peptide identification in chimeric spectra, increasing chimeric identification from 26% to 32% over the standard workflow. Dinosaur is operating-system-independent and is freely available as open source on https://github.com/fickludd/dinosaur .

Cancer associated proteins in blood plasma: Determining normal variation.

Protein biomarkers have the potential to improve diagnosis, stratification of patients into treatment cohorts, follow disease progression and treatment response. One distinct group of potential biomarkers comprises proteins which have been linked to cancer, known as cancer associated proteins (CAPs). We determined the normal variation of 86 CAPs in 72 individual plasma samples collected from ten individuals using SRM mass spectrometry. Samples were collected weekly during 5 weeks from ten volunteers and over one day at nine fixed time points from three volunteers. We determined the degree of the normal variation depending on interpersonal variation, variation due to time of day, and variation over weeks and observed that the variation dependent on the time of day appeared to be the most important. Subdivision of the proteins resulted in two predominant protein groups containing 21 proteins with relatively high variation in all three factors (day, week and individual), and 22 proteins with relatively low variation in all factors. We present a strategy for prioritizing biomarker candidates for future studies based on stratification over their normal variation and have made all data publicly available. Our findings can be used to improve selection of biomarker candidates in future studies and to determine which proteins are most suitable depending on study design.