An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples

Design and Characterization of a Novel Blood Collection and Transportation Device for Proteomic Applications

A major hurdle for blood-based proteomic diagnostics is efficient transport of specimens from the collection site to the testing laboratory. Dried blood spots have shown utility for diagnostic applications, specifically those where red blood cell hemolysis and contamination of specimens with hemoglobin is not confounding. Conversely, applications that are sensitive to the presence of the hemoglobin subunits require blood separation, which relies on centrifugation to collect plasma/serum, and then cold-chain custody during shipping. All these factors introduce complexities and potentially increased costs. Here we report on a novel whole blood-collection device (BCD) that efficiently separates the liquid from cellular components, minimizes hemolysis in the plasma fraction, and maintains protein integrity during ambient transport. The simplicity of the design makes the device ideal for field use. Whole blood is acquired through venipuncture and applied to the device with an exact volume pipette. The BCD design was based on lateral-flow principles in which whole blood was applied to a defined area, allowing two minutes for blood absorption into the separation membrane, then closed for shipment. The diagnostic utility of the device was further demonstrated with shipments from multiple sites (n = 33) across the U.S. sent to two different centralized laboratories for analyses using liquid chromatography/mass spectrometry (LC/MS/MS) and matrix assisted laser desorption/ionization-time of flight (MALDI-ToF) commercial assays. Specimens showed high levels of result label concordance for the LC/MS/MS assay (Negative Predictive Value = 98%) and MALDI-ToF assay (100% result concordance). The overall goal of the device is to simplify specimen transport to the laboratory and produce clinical test results equivalent to established collection methods.

Extending the information content of the MALDI analysis of biological fluids via multi-million shot analysis

INTRODUCTION

Reliable measurements of the protein content of biological fluids like serum or plasma can provide valuable input for the development of personalized medicine tests. Standard MALDI analysis typically only shows high abundance proteins, which limits its utility for test development. It also exhibits reproducibility issues with respect to quantitative measurements. In this paper we show how the sensitivity of MALDI profiling of intact proteins in unfractionated human serum can be substantially increased by exposing a sample to many more laser shots than are commonly used. Analytical reproducibility is also improved.

METHODS

To assess what is theoretically achievable we utilized spectra from the same samples obtained over many years and combined them to generate MALDI spectral averages of up to 100,000,000 shots for a single sample, and up to 8,000,000 shots for a set of 40 different serum samples. Spectral attributes, such as number of peaks and spectral noise of such averaged spectra were investigated together with analytical reproducibility as a function of the number of shots. We confirmed that results were similar on MALDI instruments from different manufacturers.

RESULTS

We observed an expected decrease of noise, roughly proportional to the square root of the number of shots, over the whole investigated range of the number of shots (5 orders of magnitude), resulting in an increase in the number of reliably detected peaks. The reproducibility of the amplitude of these peaks, measured by CV and concordance analysis also improves with very similar dependence on shot number, reaching median CVs below 2% for shot numbers > 4 million. Measures of analytical information content and association with biological processes increase with increasing number of shots.

CONCLUSIONS

We demonstrate that substantially increasing the number of laser shots in a MALDI-TOF analysis leads to more informative and reliable data on the protein content of unfractionated serum. This approach has already been used in the development of clinical tests in oncology.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

An integrated risk predictor for pulmonary nodules

It is estimated that over 1.5 million lung nodules are detected annually in the United States. Most of these are benign but frequently undergo invasive and costly procedures to rule out malignancy. A risk predictor that can accurately differentiate benign and malignant lung nodules could be used to more efficiently route benign lung nodules to non-invasive observation by CT surveillance and route malignant lung nodules to invasive procedures. The majority of risk predictors developed to date are based exclusively on clinical risk factors, imaging technology or molecular markers. Assessed here are the relative performances of previously reported clinical risk factors and proteomic molecular markers for assessing cancer risk in lung nodules. From this analysis an integrated model incorporating clinical risk factors and proteomic molecular markers is developed and its performance assessed on a subset of 222 lung nodules, between 8mm and 20mm in diameter, collected in a previously reported prospective study. In this analysis it is found that the molecular marker is most predictive. However, the integration of clinical and molecular markers is superior to both clinical and molecular markers separately.

CLINICAL TRIAL REGISTRATION

Registered at ClinicalTrials.gov (NCT01752101).

Consensus paper of the WFSBP Task Force on Genetics: Genetics, epigenetics and gene expression markers of major depressive disorder and antidepressant response

Major depressive disorder (MDD) is a heritable disease with a heavy personal and socio-economic burden. Antidepressants of different classes are prescribed to treat MDD, but reliable and reproducible markers of efficacy are not available for clinical use. Further complicating treatment, the diagnosis of MDD is not guided by objective criteria, resulting in the risk of under- or overtreatment. A number of markers of MDD and antidepressant response have been investigated at the genetic, epigenetic, gene expression and protein levels. Polymorphisms in genes involved in antidepressant metabolism (cytochrome P450 isoenzymes), antidepressant transport (ABCB1), glucocorticoid signalling (FKBP5) and serotonin neurotransmission (SLC6A4 and HTR2A) were among those included in the first pharmacogenetic assays that have been tested for clinical applicability. The results of these investigations were encouraging when examining patient-outcome improvement. Furthermore, a nine-serum biomarker panel (including BDNF, cortisol and soluble TNF-α receptor type II) showed good sensitivity and specificity in differentiating between MDD and healthy controls. These first diagnostic and response-predictive tests for MDD provided a source of optimism for future clinical applications. However, such findings should be considered very carefully because their benefit/cost ratio and clinical indications were not clearly demonstrated. Future tests may include combinations of different types of biomarkers and be specific for MDD subtypes or pathological dimensions.

Plasma degradome affected by variable storage of human blood

Background

The successful application of—omics technologies in the discovery of novel biomarkers and targets of therapeutic interventions is facilitated by large collections of well curated clinical samples stored in bio banks. Mining the plasma proteome holds promise to improve our understanding of disease mechanisms and may represent a source of biomarkers. However, a major confounding factor for defining disease-specific proteomic signatures in plasma is the variation in handling and processing of clinical samples leading to protein degradation. To address this, we defined a plasma proteolytic signature (degradome) reflecting pre-analytical variability in blood samples that remained at ambient temperature for different time periods after collection and prior to processing.

Methods

We obtained EDTA blood samples from five healthy volunteers (n = 5), and blood tubes remained at ambient temperature for 30 min, 8, 24 and 48 h prior to centrifugation and isolation of plasma. Naturally occurred peptides derived from plasma samples were compared by label-free quantitative LC–MS/MS. To profile protein degradation, we analysed pooled plasma samples at T = 30 min and 48 h using PROTOMAP analysis. The proteolytic pattern of selected protein candidates was further validated by immunoblotting.

Results

A total of 820 plasma proteins were surveyed by PROTOMAP, and for 4 % of these, marked degradation was observed. We show distinct proteolysis patterns for talin-1, coagulation factor XI, complement protein C1r, C3, C4 and thrombospondin, and several proteins including S100A8, A9, annexin A1, profiling-1 and platelet glycoprotein V are enriched after 48 h blood storage at ambient temperature. In particular, thrombospondin protein levels increased after 8 h and proteolytic fragments appeared after 24 h storage time.

Conclusions

The overall impact of blood storage at ambient temperature for variable times on the plasma proteome and degradome is relatively minor, but in some cases can cause a potential bias in identifying and assigning relevant proteomic markers. The observed effects on the plasma proteome and degradome are predominantly triggered by limited leucocyte and platelet cell activation due to blood handling and storage. The baseline plasma degradome signature presented here can help filtering candidate protein markers relevant for clinical biomarker studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-016-9126-9) contains supplementary material, which is available to authorized users.

Targeted Quantitative Screening of Chromosome 18 Encoded Proteome in Plasma Samples of Astronaut Candidates

This work was aimed at estimating the concentrations of proteins encoded by human chromosome 18 (Chr 18) in plasma samples of 54 healthy male volunteers (aged 20-47). These young persons have been certified by the medical evaluation board as healthy subjects ready for space flight training. Over 260 stable isotope-labeled peptide standards (SIS) were synthesized to perform the measurements of proteins encoded by Chr 18. Selected reaction monitoring (SRM) with SIS allowed an estimate of the levels of 84 of 276 proteins encoded by Chr 18. These proteins were quantified in whole and depleted plasma samples. Concentration of the proteins detected varied from 10^-6 M (transthyretin, P02766) to 10^-11 M (P4-ATPase, O43861). A minor part of the proteins (mostly representing intracellular proteins) was characterized by extremely high inter individual variations. The results provide a background for studies of a potential biomarker in plasma among proteins encoded by Chr 18. The SRM raw data are available in ProteomeXchange repository (PXD004374).

Mining the Human Proteome: Biomarker Discovery for Human Cancer and Metastases

As our knowledge of the mechanisms underlying cancer development and progression has increased, so too have more effective, less toxic, and targeted therapies begun to reach the clinic. However, the full impact of these clinical advances and the practical success of the emerging field of precision medicine are dependent on the discovery and validation of sensitive and accurate biomarkers that can enable appropriate and rigorous sample type and patient selection, reliable longitudinal monitoring of therapeutic efficacy, and even risk assessment and early detection. Within the context of this review, we examine state-of-the-art approaches to the discovery and validation of noninvasive cancer biomarkers, with a specific emphasis on those that are protein or protein-associated ones. We also review sample selection strategies, currently utilized proteomic approaches for both discovery and validation requirements, and data analysis standards. Finally, we provide examples of these elements of biomarker discovery and validation from our own biomarker research.

Development and validation of a spontaneous preterm delivery predictor in asymptomatic women

BACKGROUND

Preterm delivery remains the leading cause of perinatal mortality. Risk factors and biomarkers have traditionally failed to identify the majority of preterm deliveries.

OBJECTIVE

To develop and validate a mass spectrometry-based serum test to predict spontaneous preterm delivery in asymptomatic pregnant women.

STUDY DESIGN

A total of 5501 pregnant women were enrolled between 17(0/7) and 28(6/7) weeks gestational age in the prospective Proteomic Assessment of Preterm Risk study at 11 sites in the United States between 2011 and 2013. Maternal blood was collected at enrollment and outcomes collected following delivery. Maternal serum was processed by a proteomic workflow, and proteins were quantified by multiple reaction monitoring mass spectrometry. The discovery and verification process identified 2 serum proteins, insulin-like growth factor-binding protein 4 (IBP4) and sex hormone-binding globulin (SHBG), as predictors of spontaneous preterm delivery. We evaluated a predictor using the log ratio of the measures of IBP4 and SHBG (IBP4/SHBG) in a clinical validation study to classify spontaneous preterm delivery cases (<37(0/7) weeks gestational age) in a nested case-control cohort different from subjects used in discovery and verification. Strict blinding and independent statistical analyses were employed.

RESULTS

The predictor had an area under the receiver operating characteristic curve value of 0.75 and sensitivity and specificity of 0.75 and 0.74, respectively. The IBP4/SHBG predictor at this sensitivity and specificity had an odds ratio of 5.04 for spontaneous preterm delivery. Accuracy of the IBP4/SHBG predictor increased using earlier case-vs-control gestational age cutoffs (eg, <35(0/7) vs ≥35(0/7) weeks gestational age). Importantly, higher-risk subjects defined by the IBP4/SHBG predictor score generally gave birth earlier than lower-risk subjects.

CONCLUSION

A serum-based molecular predictor identifies asymptomatic pregnant women at risk of spontaneous preterm delivery, which may provide utility in identifying women at risk at an early stage of pregnancy to allow for clinical intervention. This early detection would guide enhanced levels of care and accelerate development of clinical strategies to prevent preterm delivery.

Clinical Utility of a Plasma Protein Classifier for Indeterminate Lung Nodules

Evaluation of indeterminate pulmonary nodules is a complex challenge. Most are benign but frequently undergo invasive and costly procedures to rule out malignancy. A plasma protein classifier was developed that identifies likely benign nodules that can be triaged to CT surveillance to avoid unnecessary invasive procedures. The clinical utility of this classifier was assessed in a prospective-retrospective analysis of a study enrolling 475 patients with nodules 8-30 mm in diameter who had an invasive procedure to confirm diagnosis at 12 sites. Using this classifier, 32.0 % (CI 19.5-46.7) of surgeries and 31.8 % (CI 20.9-44.4) of invasive procedures (biopsy and/or surgery) on benign nodules could have been avoided. Patients with malignancy triaged to CT surveillance by the classifier would have been 24.0 % (CI 19.2-29.4). This rate is similar to that described in clinical practices (24.5 % CI 16.2-34.4). This study demonstrates the clinical utility of a non-invasive blood test for pulmonary nodules.

Validation of a multiprotein plasma classifier to identify benign lung nodules

INTRODUCTION

Indeterminate pulmonary nodules (IPNs) lack clinical or radiographic features of benign etiologies and often undergo invasive procedures unnecessarily, suggesting potential roles for diagnostic adjuncts using molecular biomarkers. The primary objective was to validate a multivariate classifier that identifies likely benign lung nodules by assaying plasma protein expression levels, yielding a range of probability estimates based on high negative predictive values (NPVs) for patients with 8 to 30 mm IPNs.

METHODS

A retrospective, multicenter, case-control study was performed using multiple reaction monitoring mass spectrometry, a classifier comprising five diagnostic and six normalization proteins, and blinded analysis of an independent validation set of plasma samples.

RESULTS

The classifier achieved validation on 141 lung nodule-associated plasma samples based on predefined statistical goals to optimize sensitivity. Using a population based nonsmall-cell lung cancer prevalence estimate of 23% for 8 to 30 mm IPNs, the classifier identified likely benign lung nodules with 90% negative predictive value and 26% positive predictive value, as shown in our prior work, at 92% sensitivity and 20% specificity, with the lower bound of the classifier's performance at 70% sensitivity and 48% specificity. Classifier scores for the overall cohort were statistically independent of patient age, tobacco use, nodule size, and chronic obstructive pulmonary disease diagnosis. The classifier also demonstrated incremental diagnostic performance in combination with a four-parameter clinical model.

CONCLUSIONS

This proteomic classifier provides a range of probability estimates for the likelihood of a benign etiology that may serve as a noninvasive, diagnostic adjunct for clinical assessments of patients with IPNs.