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1.  Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation 
PLoS Computational Biology  2013;9(4):e1002963.
Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.
Author Summary
Novel proteomic technology has led to the generation of vast amounts of biological data and the identification of numerous potential biomarkers. However, computational approaches to translate this information into knowledge capable of impacting clinical care have been lagging. We propose a computational proteomic pipeline for biomarker studies that is founded on the combination of advanced statistical methodologies. We demonstrate our approach through the analysis of data obtained from heart transplant patients. Heart transplantation is the gold standard treatment for patients with end-stage heart failure, but is complicated by episodes of immune rejection that can adversely impact patient outcomes. Current rejection monitoring approaches are highly invasive, requiring a biopsy of the heart. This work aims to reduce the need for biopsies, and demonstrate the power and utility of computational approaches in proteomic biomarker discovery. Our work utilizes novel high-throughput proteomic technology combined with advanced statistical techniques to identify blood markers that guide the decision as to whether a biopsy is warranted, reduce the number of unnecessary biopsies, and ultimately diagnose the presence of rejection in heart transplant patients. Additionally, the proposed computational methodologies can be applied to a range of proteomic biomarker studies of various diseases and conditions.
doi:10.1371/journal.pcbi.1002963
PMCID: PMC3617196  PMID: 23592955
2.  Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma 
Nature biotechnology  2009;27(7):633-641.
Verification of candidate biomarkers relies upon specific, quantitative assays optimized for selective detection of target proteins, and is increasingly viewed as a critical step in the discovery pipeline that bridges unbiased biomarker discovery to preclinical validation. Although individual laboratories have demonstrated that multiple reaction monitoring (MRM) coupled with isotope dilution mass spectrometry can quantify candidate protein biomarkers in plasma, reproducibility and transferability of these assays between laboratories have not been demonstrated. We describe a multilaboratory study to assess reproducibility, recovery, linear dynamic range and limits of detection and quantification of multiplexed, MRM-based assays, conducted by NCI-CPTAC. Using common materials and standardized protocols, we demonstrate that these assays can be highly reproducible within and across laboratories and instrument platforms, and are sensitive to low µg/ml protein concentrations in unfractionated plasma. We provide data and benchmarks against which individual laboratories can compare their performance and evaluate new technologies for biomarker verification in plasma.
doi:10.1038/nbt.1546
PMCID: PMC2855883  PMID: 19561596
3.  A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease 
Nature Biotechnology  2011;29(7):635-643.
We developed a pipeline to integrate the proteomic technologies used from the discovery to the verification stages of plasma biomarker identification and applied it to identify early biomarkers of cardiac injury from the blood of patients undergoing a therapeutic, planned myocardial infarction (PMI) for treatment of hypertrophic cardiomyopathy. Sampling of blood directly from patient hearts before, during and after controlled myocardial injury ensured enrichment for candidate biomarkers and allowed patients to serve as their own biological controls. LC-MS/MS analyses detected 121 highly differentially expressed proteins, including previously credentialed markers of cardiovascular disease and >100 novel candidate biomarkers for myocardial infarction (MI). Accurate inclusion mass screening (AIMS) qualified a subset of the candidates based on highly specific, targeted detection in peripheral plasma, including some markers unlikely to have been identified without this step. Analyses of peripheral plasma from controls and patients with PMI or spontaneous MI by quantitative multiple reaction monitoring mass spectrometry or immunoassays suggest that the candidate biomarkers may be specific to MI. This study demonstrates that modern proteomic technologies, when coherently integrated, can yield novel cardiovascular biomarkers meriting further evaluation in large, heterogeneous cohorts.
doi:10.1038/nbt.1899
PMCID: PMC3366591  PMID: 21685905
4.  Developing Multiplexed Assays for Troponin I and Interleukin-33 in Plasma by Peptide Immunoaffinity Enrichment and Targeted Mass Spectrometry 
Clinical chemistry  2009;55(6):1108-1117.
BACKGROUND
Protein biomarker candidates from discovery proteomics must be quantitatively verified in patient samples before they can progress to clinical validation. Here we demonstrate that peptide immunoaffinity enrichment coupled with stable isotope dilution mass spectrometry (SISCAPA-MRM) can be used to configure assays with performance suitable for candidate biomarker verification. As proof of principle, we configured SISCAPA assays for troponin I (cTnI), an established biomarker of cardiac injury, and interleukin 33 (IL-33), an emerging immunological and cardiovascular marker for which robust immunoassays are currently not available.
METHODS
We configured individual and multiplexed assays in which peptides were enriched from digested human plasma using antipeptide antibodies. Assay performance was established using response curves for peptides and proteins spiked into normal plasma. We quantified proteins using labeled peptides as internal standards, and we measured levels of cTnI in patients who underwent a planned myocardial infarction for hypertrophic obstructive cardiomyopathy.
RESULTS
Measurement of cTnI and IL-33 proteins from trypsin-digested plasma was linear from 1.5 to 5000 μg/L, with imprecision <13% for both proteins, processed individually or multiplexed. Results correlated well (R=0.89) with a commercial immunoassay.
CONCLUSIONS
We used an established biomarker of cardiac injury and an emerging biomarker to demonstrate how SISCAPA can detect and quantify changes in concentration of proteins present at 1–10 μg/L in plasma. Our results demonstrate that these assays can be multiplexed and retain the necessary precision, reproducibility, and sensitivity to be applied to new and uncharacterized candidate biomarkers for verification of low-abundance proteins in blood.
doi:10.1373/clinchem.2009.123935
PMCID: PMC2865473  PMID: 19372185
5.  Quantitative, Multiplexed Assays for Low Abundance Proteins in Plasma by Targeted Mass Spectrometry and Stable Isotope Dilution*S 
Biomarker discovery produces lists of candidate markers whose presence and level must be subsequently verified in serum or plasma. Verification represents a paradigm shift from unbiased discovery approaches to targeted, hypothesis-driven methods and relies upon specific, quantitative assays optimized for the selective detection of target proteins. Many protein biomarkers of clinical currency are present at or below the nanogram/milliliter range in plasma and have been inaccessible to date by MS-based methods. Using multiple reaction monitoring coupled with stable isotope dilution mass spectrometry, we describe here the development of quantitative, multiplexed assays for six proteins in plasma that achieve limits of quantitation in the 1–10 ng/ml range with percent coefficients of variation from 3 to 15% without immunoaffinity enrichment of either proteins or peptides. Sample processing methods with sufficient throughput, recovery, and reproducibility to enable robust detection and quantitation of candidate biomarker proteins were developed and optimized by addition of exogenous proteins to immunoaffinity depleted plasma from a healthy donor. Quantitative multiple reaction monitoring assays were designed and optimized for signature peptides derived from the test proteins. Based upon calibration curves using known concentrations of spiked protein in plasma, we determined that each target protein had at least one signature peptide with a limit of quantitation in the 1–10 ng/ml range and linearity typically over 2 orders of magnitude in the measurement range of interest. Limits of detection were frequently in the high picogram/milliliter range. These levels of assay performance represent up to a 1000-fold improvement compared with direct analysis of proteins in plasma by MS and were achieved by simple, robust sample processing involving abundant protein depletion and minimal fractionation by strong cation exchange chromatography at the peptide level prior to LC-multiple reaction monitoring/MS. The methods presented here provide a solid basis for developing quantitative MS-based assays of low level proteins in blood.
doi:10.1074/mcp.M700354-MCP200
PMCID: PMC2435059  PMID: 17939991
6.  Simultaneous Quantification of Apolipoprotein A-I and Apolipoprotein B by Liquid-Chromatography–Multiple-Reaction–Monitoring Mass Spectrometry 
Clinical chemistry  2010;56(12):1804-1813.
BACKGROUND
If liquid-chromatography–multiple-reaction–monitoring mass spectrometry (LC-MRM/MS) could be used in the large-scale preclinical verification of putative biomarkers, it would obviate the need for the development of expensive immunoassays. In addition, the translation of novel biomarkers to clinical use would be accelerated if the assays used in preclinical studies were the same as those used in the clinical laboratory. To validate this approach, we developed a multiplexed assay for the quantification of 2 clinically well-known biomarkers in human plasma, apolipoprotein A-I and apolipoprotein B (apoA-I and apoB).
METHODS
We used PeptideAtlas to identify candidate peptides. Human samples were denatured with urea or trifluoroethanol, reduced and alkylated, and digested with trypsin. We compared reversed-phase chromatographic separation of peptides with normal flow and microflow, and we normalized endogenous peptide peak areas to internal standard peptides. We evaluated different methods of calibration and compared the final method with a nephelometric immunoassay.
RESULTS
We developed a final method using trifluoroethanol denaturation, 21-h digestion, normal flow chromatography-electrospray ionization, and calibration with a single normal human plasma sample. For samples injected in duplicate, the method had intraassay CVs <6% and interassay CVs <12% for both proteins, and compared well with immunoassay (n = 47; Deming regression, LC-MRM/MS = 1.17 × immunoassay – 36.6; Sx|y = 10.3 for apoA-I and LC-MRM/MS = 1.21 × immunoassay + 7.0; Sx|y = 7.9 for apoB).
CONCLUSIONS
Multiplexed quantification of proteins in human plasma/serum by LC-MRM/MS is possible and compares well with clinically useful immunoassays. The potential application of single-point calibration to large clinical studies could simplify efforts to reduce day-to-day digestion variability.
doi:10.1373/clinchem.2010.152264
PMCID: PMC3103773  PMID: 20923952
7.  Cancer Screening: A Mathematical Model Relating Secreted Blood Biomarker Levels to Tumor Sizes  
PLoS Medicine  2008;5(8):e170.
Background
Increasing efforts and financial resources are being invested in early cancer detection research. Blood assays detecting tumor biomarkers promise noninvasive and financially reasonable screening for early cancer with high potential of positive impact on patients' survival and quality of life. For novel tumor biomarkers, the actual tumor detection limits are usually unknown and there have been no studies exploring the tumor burden detection limits of blood tumor biomarkers using mathematical models. Therefore, the purpose of this study was to develop a mathematical model relating blood biomarker levels to tumor burden.
Methods and Findings
Using a linear one-compartment model, the steady state between tumor biomarker secretion into and removal out of the intravascular space was calculated. Two conditions were assumed: (1) the compartment (plasma) is well-mixed and kinetically homogenous; (2) the tumor biomarker consists of a protein that is secreted by tumor cells into the extracellular fluid compartment, and a certain percentage of the secreted protein enters the intravascular space at a continuous rate. The model was applied to two pathophysiologic conditions: tumor biomarker is secreted (1) exclusively by the tumor cells or (2) by both tumor cells and healthy normal cells. To test the model, a sensitivity analysis was performed assuming variable conditions of the model parameters. The model parameters were primed on the basis of literature data for two established and well-studied tumor biomarkers (CA125 and prostate-specific antigen [PSA]). Assuming biomarker secretion by tumor cells only and 10% of the secreted tumor biomarker reaching the plasma, the calculated minimally detectable tumor sizes ranged between 0.11 mm3 and 3,610.14 mm3 for CA125 and between 0.21 mm3 and 131.51 mm3 for PSA. When biomarker secretion by healthy cells and tumor cells was assumed, the calculated tumor sizes leading to positive test results ranged between 116.7 mm3 and 1.52 × 106 mm3 for CA125 and between 27 mm3 and 3.45 × 105 mm3 for PSA. One of the limitations of the study is the absence of quantitative data available in the literature on the secreted tumor biomarker amount per cancer cell in intact whole body animal tumor models or in cancer patients. Additionally, the fraction of secreted tumor biomarkers actually reaching the plasma is unknown. Therefore, we used data from published cell culture experiments to estimate tumor cell biomarker secretion rates and assumed a wide range of secretion rates to account for their potential changes due to field effects of the tumor environment.
Conclusions
This study introduced a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays. Assuming physiological data on CA125 and PSA from the literature, the model predicted detection limits of tumors that were in qualitative agreement with the actual clinical performance of both biomarkers. The model may be helpful in future estimation of minimal detectable tumor sizes for novel proteomic biomarker assays if sufficient physiologic data for the biomarker are available. The model may address the potential and limitations of tumor biomarkers, help prioritize biomarkers, and guide investments into early cancer detection research efforts.
Sanjiv Gambhir and colleagues describe a linear one-compartment mathematical model that allows estimation of minimal detectable tumor sizes based on blood tumor biomarker assays.
Editors' Summary
Background.
Cancers—disorganized masses of cells that can occur in any tissue—develop when cells acquire genetic changes that allow them to grow uncontrollably and to spread around the body (metastasize). If a cancer (tumor) is detected when it is small, surgery can often provide a cure. Unfortunately, many cancers (particularly those deep inside the body) are not detected until they are large enough to cause pain or other symptoms by pressing against surrounding tissue. By this time, it may be impossible to remove the original tumor surgically and there may be metastases scattered around the body. In such cases, radiotherapy and chemotherapy can sometimes help, but the outlook for patients whose cancers are detected late is often poor. Consequently, researchers are trying to develop early detection tests for different types of cancer. Many tumors release specific proteins—“cancer biomarkers”—into the blood and the hope is that it might be possible to find sets of blood biomarkers that detect cancers when they are still small and thus save many lives.
Why Was This Study Done?
For most biomarkers, it is not known how the amount of protein detected in the blood relates to tumor size or how sensitive the assays for biomarkers must be to improve patient survival. In this study, the researchers develop a “linear one-compartment” mathematical model to predict how large tumors need to be before blood biomarkers can be used to detect them and test this model using published data on two established cancer biomarkers—CA125 and prostate-specific antigen (PSA). CA125 is used to monitor the progress of patients with ovarian cancer after treatment; ovarian cancer is rarely diagnosed in its early stages and only one-fourth of women with advanced disease survive for 5 y after diagnosis. PSA is used to screen for prostate cancer and has increased the detection of this cancer in its early stages when it is curable.
What Did the Researchers Do and Find?
To develop a model that relates secreted blood biomarker levels to tumor sizes, the researchers assumed that biomarkers mix evenly throughout the patient's blood, that cancer cells secrete biomarkers into the fluid that surrounds them, that 0.1%–20% of these secreted proteins enter the blood at a continuous rate, and that biomarkers are continuously removed from the blood. The researchers then used their model to calculate the smallest tumor sizes that might be detectable with these biomarkers by feeding in existing data on CA125 and on PSA, including assay detection limits and the biomarker secretion rates of cancer cells growing in dishes. When only tumor cells secreted the biomarker and 10% of the secreted biomarker reach the blood, the model predicted that ovarian tumors between 0.11 mm3 (smaller than a grain of salt) and nearly 4,000 mm3 (about the size of a cherry) would be detectable by measuring CA125 blood levels (the range was determined by varying the amount of biomarker secreted by the tumor cells and the assay sensitivity); for prostate cancer, the detectable tumor sizes ranged from similar lower size to about 130 mm3 (pea-sized). However, healthy cells often also secrete small quantities of cancer biomarkers. With this condition incorporated into the model, the estimated detectable tumor sizes (or total tumor burden including metastases) ranged between grape-sized and melon-sized for ovarian cancers and between pea-sized to about grapefruit-sized for prostate cancers.
What Do These Findings Mean?
The accuracy of the calculated tumor sizes provided by the researchers' mathematical model is limited by the lack of data on how tumors behave in the human body and by the many assumptions incorporated into the model. Nevertheless, the model predicts detection limits for ovarian and prostate cancer that broadly mirror the clinical performance of both biomarkers. Somewhat worryingly, the model also indicates that a tumor may have to be very large for blood biomarkers to reveal its presence, a result that could limit the clinical usefulness of biomarkers, especially if they are secreted not only by tumor cells but also by healthy cells. Given this finding, as more information about how biomarkers behave in the human body becomes available, this model (and more complex versions of it) should help researchers decide which biomarkers are likely to improve early cancer detection and patient outcomes.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0050170.
The US National Cancer Institute provides a brief description of what cancer is and how it develops and a fact sheet on tumor markers; it also provides information on all aspects of ovarian and prostate cancer for patients and professionals, including information on screening and testing (in English and Spanish)
The UK charity Cancerbackup also provides general information about cancer and more specific information about ovarian and prostate cancer, including the use of CA125 and PSA for screening and follow-up
The American Society of Clinical Oncology offers a wide range of information on various cancer types, including online published articles on the current status of cancer diagnosis and management from the educational book developed by the annual meeting faculty and presenters. Registration is mandatory, but information is free
doi:10.1371/journal.pmed.0050170
PMCID: PMC2517618  PMID: 18715113
8.  Development of a Chip/Chip/SRM platform using digital chip isoelectric focusing and LC-Chip mass spectrometry for enrichment and quantitation of low abundance protein biomarkers in human plasma 
Journal of proteome research  2011;11(2):808-817.
Protein biomarkers are critical for diagnosis, prognosis, and treatment of disease. The transition from protein biomarker discovery to verification can be a rate limiting step in clinical development of new diagnostics. Liquid chromatography-selected reaction monitoring mass spectrometry (LC-SRM MS) is becoming an important tool for biomarker verification studies in highly complex biological samples. Analyte enrichment or sample fractionation is often necessary to reduce sample complexity and improve sensitivity of SRM for quantitation of clinically relevant biomarker candidates present at the low ng/mL range in blood. In this paper, we describe an alternative method for sample preparation for LC-SRM MS, which does not rely on availability of antibodies. This new platform is based on selective enrichment of proteotypic peptides from complex biological peptide mixtures via isoelectric focusing (IEF) on a digital ProteomeChip (dPC™) for SRM quantitation using a triple quadrupole (QQQ) instrument with an LC-Chip (Chip/Chip/SRM). To demonstrate the value of this approach, the optimization of the Chip/Chip/SRM platform was performed using prostate specific antigen (PSA) added to female plasma as a model system. The combination of immunodepletion of albumin and IgG with peptide fractionation on the dPC, followed by SRM analysis, resulted in a limit of quantitation of PSA added to female plasma at the level of ~1–2.5 ng/mL with a CV of ~13%. The optimized platform was applied to measure levels of PSA in plasma of a small cohort of male patients with prostate cancer (PCa) and healthy matched controls with concentrations ranging from 1.5 to 25 ng/mL. A good correlation (r2 = 0.9459) was observed between standard clinical ELISA tests and the SRM-based-assay. Our data demonstrate that the combination of IEF on the dPC and SRM (Chip/Chip/SRM) can be successfully applied for verification of low abundance protein biomarkers in complex samples.
doi:10.1021/pr2006704
PMCID: PMC3656385  PMID: 22098410
Isoelectric focusing; IEF; digital ProteomeChip; dPC; selected reaction monitoring; SRM; prostate specific antigen; PSA; QQQ; LC-Chip
9.  Development of Biomarkers for Screening Hepatocellular Carcinoma Using Global Data Mining and Multiple Reaction Monitoring 
PLoS ONE  2013;8(5):e63468.
Hepatocellular carcinoma (HCC) is one of the most common and aggressive cancers and is associated with a poor survival rate. Clinically, the level of alpha-fetoprotein (AFP) has been used as a biomarker for the diagnosis of HCC. The discovery of useful biomarkers for HCC, focused solely on the proteome, has been difficult; thus, wide-ranging global data mining of genomic and proteomic databases from previous reports would be valuable in screening biomarker candidates. Further, multiple reaction monitoring (MRM), based on triple quadrupole mass spectrometry, has been effective with regard to high-throughput verification, complementing antibody-based verification pipelines. In this study, global data mining was performed using 5 types of HCC data to screen for candidate biomarker proteins: cDNA microarray, copy number variation, somatic mutation, epigenetic, and quantitative proteomics data. Next, we applied MRM to verify HCC candidate biomarkers in individual serum samples from 3 groups: a healthy control group, patients who have been diagnosed with HCC (Before HCC treatment group), and HCC patients who underwent locoregional therapy (After HCC treatment group). After determining the relative quantities of the candidate proteins by MRM, we compared their expression levels between the 3 groups, identifying 4 potential biomarkers: the actin-binding protein anillin (ANLN), filamin-B (FLNB), complementary C4-A (C4A), and AFP. The combination of 2 markers (ANLN, FLNB) improved the discrimination of the before HCC treatment group from the healthy control group compared with AFP. We conclude that the combination of global data mining and MRM verification enhances the screening and verification of potential HCC biomarkers. This efficacious integrative strategy is applicable to the development of markers for cancer and other diseases.
doi:10.1371/journal.pone.0063468
PMCID: PMC3661589  PMID: 23717429
10.  Biomarker Profiling by Nuclear Magnetic Resonance Spectroscopy for the Prediction of All-Cause Mortality: An Observational Study of 17,345 Persons 
PLoS Medicine  2014;11(2):e1001606.
In this study, Würtz and colleagues conducted high-throughput profiling of blood specimens in two large population-based cohorts in order to identify biomarkers for all-cause mortality and enhance risk prediction. The authors found that biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. However, further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers to guide screening and prevention.
Please see later in the article for the Editors' Summary
Background
Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts.
Methods and Findings
106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18–103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1–standard deviation increment, 95% CI 1.53–1.82, p = 5×10−31), albumin (HR 0.70, 95% CI 0.65–0.76, p = 2×10−18), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62–0.77, p = 3×10−12), and citrate (HR 1.33, 95% CI 1.21–1.45, p = 5×10−10). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).
Conclusions
Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.
Please see later in the article for the Editors' Summary
Editors' Summary
Background
A biomarker is a biological molecule found in blood, body fluids, or tissues that may signal an abnormal process, a condition, or a disease. The level of a particular biomarker may indicate a patient's risk of disease, or likely response to a treatment. For example, cholesterol levels are measured to assess the risk of heart disease. Most current biomarkers are used to test an individual's risk of developing a specific condition. There are none that accurately assess whether a person is at risk of ill health generally, or likely to die soon from a disease. Early and accurate identification of people who appear healthy but in fact have an underlying serious illness would provide valuable opportunities for preventative treatment.
While most tests measure the levels of a specific biomarker, there are some technologies that allow blood samples to be screened for a wide range of biomarkers. These include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry. These tools have the potential to be used to screen the general population for a range of different biomarkers.
Why Was This Study Done?
Identifying new biomarkers that provide insight into the risk of death from all causes could be an important step in linking different diseases and assessing patient risk. The authors in this study screened patient samples using NMR spectroscopy for biomarkers that accurately predict the risk of death particularly amongst the general population, rather than amongst people already known to be ill.
What Did the Researchers Do and Find?
The researchers studied two large groups of people, one in Estonia and one in Finland. Both countries have set up health registries that collect and store blood samples and health records over many years. The registries include large numbers of people who are representative of the wider population.
The researchers first tested blood samples from a representative subset of the Estonian group, testing 9,842 samples in total. They looked at 106 different biomarkers in each sample using NMR spectroscopy. They also looked at the health records of this group and found that 508 people died during the follow-up period after the blood sample was taken, the majority from heart disease, cancer, and other diseases. Using statistical analysis, they looked for any links between the levels of different biomarkers in the blood and people's short-term risk of dying. They found that the levels of four biomarkers—plasma albumin, alpha-1-acid glycoprotein, very-low-density lipoprotein (VLDL) particle size, and citrate—appeared to accurately predict short-term risk of death. They repeated this study with the Finnish group, this time with 7,503 individuals (176 of whom died during the five-year follow-up period after giving a blood sample) and found similar results.
The researchers carried out further statistical analyses to take into account other known factors that might have contributed to the risk of life-threatening illness. These included factors such as age, weight, tobacco and alcohol use, cholesterol levels, and pre-existing illness, such as diabetes and cancer. The association between the four biomarkers and short-term risk of death remained the same even when controlling for these other factors.
The analysis also showed that combining the test results for all four biomarkers, to produce a biomarker score, provided a more accurate measure of risk than any of the biomarkers individually. This biomarker score also proved to be the strongest predictor of short-term risk of dying in the Estonian group. Individuals with a biomarker score in the top 20% had a risk of dying within five years that was 19 times greater than that of individuals with a score in the bottom 20% (288 versus 15 deaths).
What Do These Findings Mean?
This study suggests that there are four biomarkers in the blood—alpha-1-acid glycoprotein, albumin, VLDL particle size, and citrate—that can be measured by NMR spectroscopy to assess whether otherwise healthy people are at short-term risk of dying from heart disease, cancer, and other illnesses. However, further validation of these findings is still required, and additional studies should examine the biomarker specificity and associations in settings closer to clinical practice. The combined biomarker score appears to be a more accurate predictor of risk than tests for more commonly known risk factors. Identifying individuals who are at high risk using these biomarkers might help to target preventative medical treatments to those with the greatest need.
However, there are several limitations to this study. As an observational study, it provides evidence of only a correlation between a biomarker score and ill health. It does not identify any underlying causes. Other factors, not detectable by NMR spectroscopy, might be the true cause of serious health problems and would provide a more accurate assessment of risk. Nor does this study identify what kinds of treatment might prove successful in reducing the risks. Therefore, more research is needed to determine whether testing for these biomarkers would provide any clinical benefit.
There were also some technical limitations to the study. NMR spectroscopy does not detect as many biomarkers as mass spectrometry, which might therefore identify further biomarkers for a more accurate risk assessment. In addition, because both study groups were northern European, it is not yet known whether the results would be the same in other ethnic groups or populations with different lifestyles.
In spite of these limitations, the fact that the same four biomarkers are associated with a short-term risk of death from a variety of diseases does suggest that similar underlying mechanisms are taking place. This observation points to some potentially valuable areas of research to understand precisely what's contributing to the increased risk.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001606
The US National Institute of Environmental Health Sciences has information on biomarkers
The US Food and Drug Administration has a Biomarker Qualification Program to help researchers in identifying and evaluating new biomarkers
Further information on the Estonian Biobank is available
The Computational Medicine Research Team of the University of Oulu and the University of Bristol have a webpage that provides further information on high-throughput biomarker profiling by NMR spectroscopy
doi:10.1371/journal.pmed.1001606
PMCID: PMC3934819  PMID: 24586121
11.  Validation Processes of Protein Biomarkers in Serum—A Cross Platform Comparison 
Sensors (Basel, Switzerland)  2012;12(9):12710-12728.
Due to insufficient biomarker validation and poor performances in diagnostic assays, the candidate biomarker verification process has to be improved. Multi-analyte immunoassays are the tool of choice for the identification and detailed validation of protein biomarkers in serum. The process of identification and validation of serum biomarkers, as well as their implementation in diagnostic routine requires an application of independent immunoassay platforms with the possibility of high-throughput. This review will focus on three main multi-analyte immunoassay platforms: planar microarrays, multiplex bead systems and, array-based surface plasmon resonance (SPR) chips. Recent developments of each platform will be discussed for application in clinical proteomics, principles, detection methods, and performance strength. The requirements for specific surface functionalization of assay platforms are continuously increasing. The reasons for this increase is the demand for highly sensitive assays, as well as the reduction of non-specific adsorption from complex samples, and with it high signal-to-noise-ratios. To achieve this, different support materials were adapted to the immobilized biomarker/ligand, allowing a high binding capacity and immobilization efficiency. In the case of immunoassays, the immobilized ligands are proteins, antibodies or peptides, which exhibit a diversity of chemical properties (acidic/alkaline; hydrophobic/hydrophilic; secondary or tertiary structure/linear). Consequently it is more challenging to develop immobilization strategies necessary to ensure a homogenous covered surface and reliable assay in comparison to DNA immobilization. New developments concerning material support for each platform are discussed especially with regard to increase the immobilization efficiency and reducing the non-specific adsorption from complex samples like serum and cell lysates.
doi:10.3390/s120912710
PMCID: PMC3478866  PMID: 23112739
clinical proteomics and diagnostic; multi-analyte immunoassays; serum screening; antibody-antigen interaction
12.  The Path to Clinical Proteomics Research: Integration of Proteomics, Genomics, Clinical Laboratory and Regulatory Science 
Better biomarkers are urgently needed to cancer detection, diagnosis, and prognosis. While the genomics community is making significant advances in understanding the molecular basis of disease, proteomics will delineate the functional units of a cell, proteins and their intricate interaction network and signaling pathways for the underlying disease. Great progress has been made to characterize thousands of proteins qualitatively and quantitatively in complex biological systems by utilizing multi-dimensional sample fractionation strategies, mass spectrometry and protein microarrays. Comparative/quantitative analysis of high-quality clinical biospecimen (e.g., tissue and biofluids) of human cancer proteome landscape has the potential to reveal protein/peptide biomarkers responsible for this disease by means of their altered levels of expression, post-translational modifications as well as different forms of protein variants. Despite technological advances in proteomics, major hurdles still exist in every step of the biomarker development pipeline. The National Cancer Institute's Clinical Proteomic Technologies for Cancer initiative (NCI-CPTC) has taken a critical step to close the gap between biomarker discovery and qualification by introducing a pre-clinical "verification" stage in the pipeline, partnering with clinical laboratory organizations to develop and implement common standards, and developing regulatory science documents with the US Food and Drug Administration to educate the proteomics community on analytical evaluation requirements for multiplex assays in order to ensure the safety and effectiveness of these tests for their intended use.
doi:10.3343/kjlm.2011.31.2.61
PMCID: PMC3116002  PMID: 21474978
Quantitative proteomics; Biomarker; Multiplex protein assays; MRM-MS; Immunoassays
13.  Targeted Proteomic Strategy for Clinical Biomarker Discovery 
Molecular oncology  2008;3(1):33-44.
Summary
The high complexity and large dynamic range of blood plasma proteins currently prohibits the sensitive and high throughput profiling of disease and control plasma proteome sample sets large enough to reliably detect disease indicating differences. To circumvent these technological limitations we describe here a new two stage strategy for the mass spectrometry (MS) assisted discovery, verification and validation of disease biomarkers. In an initial discovery phase N-linked glycoproteins with distinguishable expression patterns in primary normal and diseased tissue are detected and identified. In the second step the proteins identified in the initial phase are subjected to targeted MS analysis in plasma samples, using the highly sensitive and specific selected reaction monitoring (SRM) technology. Since glycosylated proteins, such as those secreted or shed from the cell surface are likely to reside and persist in blood, the two stage strategy is focused on the quantification of tissue derived glycoproteins in plasma. The focus on the N-glycoproteome not only reduces the complexity of the analytes, but also targets an information-rich subproteome which is relevant for remote sensing of diseases in the plasma. The N-glycoprotein based biomarker discovery and validation workflow reviewed here allows for the robust identification of protein candidate panels that can finally be selectively monitored in the blood plasma at high sensitivity in a reliable, non-invasive and quantitative fashion.
doi:10.1016/j.molonc.2008.12.001
PMCID: PMC2753590  PMID: 19383365
14.  Perspectives of targeted mass spectrometry for protein biomarker verification 
Current opinion in chemical biology  2009;13(5-6):518-525.
Summary
The identification of specific biomarkers will improve the early diagnosis of disease, facilitate the development of targeted therapies, and provide an accurate method to monitor treatment response. A major challenge in the process of verifying biomarker candidates in blood plasma is the complexity and high dynamic range of proteins. This article reviews the current, targeted proteomic strategies that are capable of quantifying biomarker candidates at concentration ranges where biomarkers are expected in plasma (i.e. at the ng/ml level). In addition, a workflow is presented that allows the fast and definitive generation of targeted mass spectrometry-based assays for most biomarker candidate proteins. These assays are stored in publicly accessible databases and have the potential to greatly impact the throughput of biomarker verification studies.
doi:10.1016/j.cbpa.2009.09.014
PMCID: PMC2795387  PMID: 19818677
15.  Identification of Multiple Novel Protein Biomarkers Shed by Human Serous Ovarian Tumors into the Blood of Immunocompromised Mice and Verified in Patient Sera 
PLoS ONE  2013;8(3):e60129.
The most cancer-specific biomarkers in blood are likely to be proteins shed directly by the tumor rather than less specific inflammatory or other host responses. The use of xenograft mouse models together with in-depth proteome analysis for identification of human proteins in the mouse blood is an under-utilized strategy that can clearly identify proteins shed by the tumor. In the current study, 268 human proteins shed into mouse blood from human OVCAR-3 serous tumors were identified based upon human vs. mouse species differences using a four-dimensional plasma proteome fractionation strategy. A multi-step prioritization and verification strategy was subsequently developed to efficiently select some of the most promising biomarkers from this large number of candidates. A key step was parallel analysis of human proteins detected in the tumor supernatant, because substantially greater sequence coverage for many of the human proteins initially detected in the xenograft mouse plasma confirmed assignments as tumor-derived human proteins. Verification of candidate biomarkers in patient sera was facilitated by in-depth, label-free quantitative comparisons of serum pools from patients with ovarian cancer and benign ovarian tumors. The only proteins that advanced to multiple reaction monitoring (MRM) assay development were those that exhibited increases in ovarian cancer patients compared with benign tumor controls. MRM assays were facilely developed for all 11 novel biomarker candidates selected by this process and analysis of larger pools of patient sera suggested that all 11 proteins are promising candidate biomarkers that should be further evaluated on individual patient blood samples.
doi:10.1371/journal.pone.0060129
PMCID: PMC3609810  PMID: 23544127
16.  Rapid Verification of Candidate Serological Biomarkers Using Gel-based, Label-free Multiple Reaction Monitoring 
Journal of proteome research  2011;10(9):4005-4017.
Stable isotope dilution-multiple reaction monitoring-mass spectrometry (SID-MRM-MS) has emerged as a promising platform for verification of serological candidate biomarkers. However, cost and time needed to synthesize and evaluate stable isotope peptides, optimize spike-in assays, and generate standard curves, quickly becomes unattractive when testing many candidate biomarkers. In this study, we demonstrate that label-free multiplexed MRM-MS coupled with major protein depletion and 1-D gel separation is a time-efficient, cost-effective initial biomarker verification strategy requiring less than 100 μl serum. Furthermore, SDS gel fractionation can resolve different molecular weight forms of targeted proteins with potential diagnostic value. Because fractionation is at the protein level, consistency of peptide quantitation profiles across fractions permits rapid detection of quantitation problems for specific peptides from a given protein. Despite the lack of internal standards, the entire workflow can be highly reproducible, and long-term reproducibility of relative protein abundance can be obtained using different mass spectrometers and LC methods with external reference standards. Quantitation down to ~200 pg/mL could be achieved using this workflow. Hence, the label-free GeLC-MRM workflow enables rapid, sensitive, and economical initial screening of large numbers of candidate biomarkers prior to setting up SID-MRM assays or immunoassays for the most promising candidate biomarkers.
doi:10.1021/pr2002098
PMCID: PMC3166403  PMID: 21726088
Serum proteomes; serum biomarkers; biomarker verification; biomarker validation; label-free quantitation; multiple reaction monitoring (MRM)
17.  Performance Evaluation of a Multiplex Assay for Future Use in Biomarker Discovery Efforts to Predict Body Composition 
Background
Interest in biomarker patterns and disease has led to the development of immunoassays that evaluate multiple analytes in parallel with little sample. However, there are no current standards for multiplex configuration, validation, and quality, thus, validation by platform, population, and question of interest is recommended. We sought to determine the best blood fraction for multiplex evaluation of circulating biomarkers in postmenopausal women and to explore body composition phenotype discrimination by biomarkers.
Methods
Archived serum and plasma samples from a sample of healthy postmenopausal women with the highest (n=9) and lowest (n=11) percent lean mass, by dual-energy X-ray absorptiometry, were used to measure 90 analytes by bead-based, suspension multiplex assays. Replicates of serum and plasma were analyzed in a random selection of 4 of these individuals.
Results
Ninety-percent of the analytes were detectable for ≥50% of samples; when limited to these well detected analytes, mean replicate correlations for serum and plasma were 0.87and 0.85 respectively. Serum had lower error rates discriminating phenotypes; 7 serum versus 2 plasma analytes discriminated extreme body phenotypes.
Conclusion
Serum and plasma performed similarly for the majority of the analytes. Serum demonstrated a slight advantage in predicting extreme body composition phenotypes in postmenopausal women using parallel evaluation of analytes.
doi:10.1515/CCLM.2011.122
PMCID: PMC3749089  PMID: 21361852
biomarkers; lean mass; multi-analyte; multiplex; phenotype discrimination
18.  The Effect of Pre-Analytical Variability on the Measurement of MRM-MS-Based Mid- to High-Abundance Plasma Protein Biomarkers and a Panel of Cytokines 
PLoS ONE  2012;7(6):e38290.
Blood sample processing and handling can have a significant impact on the stability and levels of proteins measured in biomarker studies. Such pre-analytical variability needs to be well understood in the context of the different proteomics platforms available for biomarker discovery and validation. In the present study we evaluated different types of blood collection tubes including the BD P100 tube containing protease inhibitors as well as CTAD tubes, which prevent platelet activation. We studied the effect of different processing protocols as well as delays in tube processing on the levels of 55 mid and high abundance plasma proteins using novel multiple-reaction monitoring-mass spectrometry (MRM-MS) assays as well as 27 low abundance cytokines using a commercially available multiplexed bead-based immunoassay. The use of P100 tubes containing protease inhibitors only conferred proteolytic protection for 4 cytokines and only one MRM-MS-measured peptide. Mid and high abundance proteins measured by MRM are highly stable in plasma left unprocessed for up to six hours although platelet activation can also impact the levels of these proteins. The levels of cytokines were elevated when tubes were centrifuged at cold temperature, while low levels were detected when samples were collected in CTAD tubes. Delays in centrifugation also had an impact on the levels of cytokines measured depending on the type of collection tube used. Our findings can help in the development of guidelines for blood collection and processing for proteomic biomarker studies.
doi:10.1371/journal.pone.0038290
PMCID: PMC3368926  PMID: 22701622
19.  P26-M Detection of Candidate Protein Biomarkers in Human Serum by Multiple Reaction Monitoring: Improved Limits of Detection and Quantification 
Mass spectrometry–based biomarker discovery in bio-fluids produces a list of candidate proteins that must be verified and quantified in a large number of samples before a candidate becomes a useful diagnostic, prognostic, or pharmacodynamic marker. Because of the high sensitivity and specificity provided by multiple reaction monitoring (MRM), this MS/MS method has recently been used for verification and quantification of potential biomakers. However, the wide dynamic range of protein concentrations in serum prohibits direct detection of many useful biomarkers at the concentration level of low nanogram/mL to picogram/mL range without any sample fractionation and/or enrichment.
In this presentation, we evaluate the utility of two sample enrichment techniques for improving the limit of detection and limit of quantification (LOQ) for MRM analysis of several candidate protein biomarkers.
In the first sample enrichment method, we used immuno-depletion to remove either the six most abundant serum proteins (90% serum depletion) or the twenty most abundant proteins (97% serum depletion) before MRM analysis of low-abundance potential biomarkers. The second sample enrichment method that we evaluated was the glycoprotein affinity enrichment method. Several low-abundance serum proteins were quantified by the MRM method using a triple quadrupole mass spectrometer coupled to a nanoscale liquid chromatograph. The effects of immuno-depletion and affinity enrichment on the LOQ of selected candidate proteins biomarkers in human plasma were compared.
PMCID: PMC2292030
20.  A Bioinformatics Approach for Biomarker Identification in Radiation-Induced Lung Inflammation from Limited Proteomics Data 
Journal of proteome research  2011;10(3):1406-1415.
Many efforts have been made to discover novel biomarkers for early disease detection in oncology. However, the lack of efficient computational strategies impedes the discovery of disease-specific biomarkers for better understanding and management of treatment outcomes. In this study, we propose a novel graph-based scoring function to rank and identify the most robust biomarkers from limited proteomics data. The proposed method measures the proximity between candidate proteins identified by mass spectrometry (MS) analysis utilizing prior reported knowledge in the literature. Recent advances in mass spectrometry provide new opportunities to identify unique biomarkers from peripheral blood samples in complex treatment modalities such as radiation therapy (radiotherapy), which enables early disease detection, disease progression monitoring, and targeted intervention. Specifically, the dose-limiting role of radiation-induced lung injury known as radiation pneumonitis (RP) in lung cancer patients receiving radiotherapy motivates the search for robust predictive biomarkers. In this case study, plasma from 26 locally advanced non-small cell lung cancer (NSCLC) patients treated with radiotherapy in a longitudinal 3×3 matched-control cohort was fractionated using in-line, sequential multi-affinity chromatography. The complex peptide mixtures from endoprotease digestions were analyzed using comparative, high-resolution liquid chromatography (LC)-MS to identify and quantify differential peptide signals. Through analysis of survey mass spectra and annotations of peptides from the tandem spectra, we found candidate proteins that appear to be associated with RP. Based on the proposed methodology, alpha-2-macroglobulin (α2M) was unambiguously ranked as the top candidate protein. As independent validation of this candidate protein, enzyme-linked immunosorbent assay (ELISA) experiments were performed on independent cohort of 20 patients’ samples resulting in early significant discrimination between RP and non-RP patients (p = 0.002). These results suggest that the proposed methodology based on longitudinal proteomics analysis and a novel bioinformatics ranking algorithm is a potentially promising approach for the challenging problem of identifying relevant biomarkers in sample-limited clinical applications.
doi:10.1021/pr101226q
PMCID: PMC3127583  PMID: 21226504
21.  The discovery and identification of a candidate proteomic biomarker of active tuberculosis 
BMC Infectious Diseases  2013;13:506.
Background
Noninvasive and convenient biomarkers for early diagnosis of tuberculosis (TB) remain an urgent need. The aim of this study was to discover and identify potential biomarkers specific for TB.
Methods
The surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) combined with weak cation exchange (WCX) magnetic beads was used to screen serum samples from 180 cases of TB and 211 control subjects. A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by reverse phase-high performance liquid chromatography (RP-HPLC), identified by MALDI-TOF MS, LC-MS/MS and validated using enzyme-linked immunosorbent assay (ELISA).
Results
A total of 35 discriminating m/z peaks were detected that were related to TB (P < 0.01). The model of biomarkers based on the four biomarkers (2554.6, 4824.4, 5325.7, and 8606.8 Da) was established which could distinguish TB from controls with the sensitivity of 83.3% and the specificity of 84.2%. The candidate biomarker with m/z of 2554.6 Da was found to be up-regulated in TB patients, and was identified as a fragment of fibrinogen, alpha polypeptide isoform alpha-E preproprotein. Analysis in 22 patients with TB showed increased fibrinogen degradation product (FDP) (5,005 ± 1,297 vs. 4,010 ± 1,181 ng/mL, P < 0.05) and in 142 patients showed elevated plasma fibrinogen levels.
Conclusions
A diagnostic model for TB with high sensitivity and specificity was developed using mass spectrometry combined with magnetic beads. Fibrinogen was identified as a potential biomarker for TB and showed diagnostic values in clinical application.
doi:10.1186/1471-2334-13-506
PMCID: PMC3870977  PMID: 24168695
Tuberculosis; Biomarker; Proteomics; Mass spectrometry
22.  Mass Spectrometry-Based Multiplexing for the Analysis of Biomarkers in Drug Development and Clinical Diagnostics- How Much is too Much? 
Biomarkers, or more specifically molecular markers, can detect biochemical changes associated with disease processes and drug effects before histopathological and pathophysiological changes occur. Multiplexing technologies such as high-performance liquid chromatography/mass spectrometry (LC-MS) allow for the measurement of molecular marker patterns that confer significantly more information than the measurement of a single parameter alone. The use of multiplexing assays for drug development, and as diagnostic tools, is attractive but will require regulatory review and approval and thus requires validation following regulatory guidances. Multiplexing assays always constitute a compromise. The number of analytes that can reasonably be included in a mass spectrometry-based multiplexing assay depend on the physico-chemical properties of the analytes and their integration into a single assay in terms of extraction, HPLC separation, ionization conditions and mass spectrometry detection. Another aspect includes biomedical considerations such as the differences in physiological concentrations of analytes, the required concentration range, and how much variability is acceptable before the clinical utility of a marker is negatively affected. Regulatory considerations include validation and quality control during sample analysis. Current bioanalytical regulatory guidelines have mostly been developed for single drug compounds and are not always adequate for multiplexing molecular marker assays that often quantify endogenous compounds. Specific guidances for multiplexing assays should be developed. Even if it is possible to integrate a wide variety and large number of analytes into a multiplexing assay, it should always be taken into consideration that a set of shorter, more specialized assays, may offer a more manageable and efficient alternative.
doi:10.1016/j.microc.2012.02.011
PMCID: PMC3640576  PMID: 23645936
liquid chromatography- mass spectrometry; multiplexing; biomarkers; metabolomics; regulatory aspects; validation
23.  Immunoaffinity enrichment and liquid chromatography-selected reaction monitoring mass spectrometry for quantitation of carbonic anhydrase 12 in cultured renal carcinoma cells 
Analytical chemistry  2010;82(21):10.1021/ac101981t.
Liquid chromatography-selected reaction monitoring (LC-SRM) is a highly specific and sensitive mass spectrometry (MS) technique that is widely being applied to selectively qualify and validate candidate markers within complex biological samples. However, in order for LC-SRM methods to take on these attributes, target-specific optimization of sample processing is required, in order to reduce analyte complexity, prior to LC-SRM. In this study, we have developed a targeted platform consisting of protein immunoaffinity enrichment on magnetic beads and LC-SRM for measuring carbonic anhydrase 12 (CA12) protein in a renal cell carcinoma (RCC) cell line (PRC3), a candidate biomarker for RCC whose expression at the protein level has not been previously reported. Sample processing and LC-SRM assay were optimized for signature peptides selected as surrogate markers of CA12 protein. Using LC-SRM coupled with stable isotope dilution, we achieved limits of quantitation in the low fmol range sufficient for measuring clinically relevant biomarkers with good intra- and inter-assay accuracy and precision (≤17%). Our results show that using a quantitative immunoaffinity capture approach provides specific, accurate, and robust assays amenable to high-throughput verification of potential biomarkers.
doi:10.1021/ac101981t
PMCID: PMC3046293  PMID: 20936840
immunoaffinity enrichment; selected reaction monitoring; enhanced signature peptide predictor; carbonic anhydrase 12; renal cell carcinoma; biomarker; Protein G magnetic beads
24.  SOMAmers and SOMAscan – A Protein Biomarker Discovery Platform for Rapid Analysis of Sample Collections From Bench Top to the Clinic 
Biomarkers are fundamental to nearly every step in the drug discovery and development process, from target validation in the laboratory, to patient stratification in the clinic. Recently, genomic discovery tools have had success in this area, but MS-based proteomic methods less so. This is due to difficulties in developing high throughput assays (ELISA, etc.) for triaging biomarker candidates against large clinical sample collections. However, the SomaLogic proteomics platform is ideally suited to this task. At the heart of the detection technology are SOMAmers (Slow Off-rate Modified Aptamers). They are modified DNA aptamers with high affinity (10∧–9 to 10∧–12 M) and high specificity for their cognate analytes. The assay is highly multiplexed, quantifying >1100 proteins simultaneously from a single 65 uL sample. Sensitivity of the array is generally comparable to sandwich ELISA performance (median LLOQ 100 fM, LoD 40 fM). Samples from a wide variety of sources are amenable to analysis – from serum to CSF, cell/tumor extracts, synovial fluid, etc. Biomarker signatures can be defined in as little as 5 weeks and clinically actionable diagnostics in as little as 6 months. We are currently engaged in a number of clinical discovery applications (Phase 0–4) and have a large number of additional studies completed, in design or sample accrual. The technology and these applications will be discussed.
PMCID: PMC3635296
25.  Effect of anticoagulants on multiplexed measurement of cytokine/chemokines in healthy subjects 
Cytokine  2012;60(2):438-446.
Introduction
Cytokines are humoral regulatory molecules that act together in immunologic pathways. Monitoring cytokines and their variations within physiologic ranges is critical for biomarker discovery. Therefore, we evaluated the performance characteristics of 72 analytes measured by multiplex cytokine immunoassay, with an emphasis on the differences of analytes measured in serum compared to plasma, and, in plasma, on the impact of anticoagulants on the cytokine measurement.
Methods
We used fluorescent bead-based (Luminex) immunoassay kits to simultaneously measure 72 analytes. We tested serum and plasma samples from 11 matched donors. Plasma samples were anti-coagulated with sodium heparin, sodium citrate dextrose and ethylene diamine tetra-acetic acid (EDTA), respectively.
Results
Of the 72 cytokines, 12 were undetectable in all types of specimen samples. Nineteen analytes, including PDGF-bb, IL-4, IL-8, IL-9, FGF-b, PAI-1, CXCL-5, CCL-5, CD40L, EGF, VEGF, IL-2ra, IL-3, SDF-1a, PCT, MCP-3, GIP, IL-16 and fibrinogen, showed significant differences between measurements in serum and all types of plasma, regardless of anticoagulant. Among plasma samples, 10 analytes (eotaxin, SCGF-b, MCP-1, SCF, MIP-1b, VEGF, RANTES, PDGF-b, PAI-1 and ITAC) showed significantly higher concentrations in heparinized plasma compared to citrated and EDTA plasma. IP-10, and CTAK were the only 2 cytokines that presented different concentrations in citrate and EDTA plasma.
Conclusions
With their small volume, low cost per test, and multiplex capacity, Luminex-based cytokine assays have enormous potential utility for screening in epidemiologic studies. In our study, we showed that many cytokines’ concentrations differed between serum and plasma samples, and that different anticoagulants used in preparation of plasma samples also affected the measurement of some cytokines. There was no optimal sample preparation that was clearly superior for the measurement of all analytes measured. Ultimately, the utility of cytokine measurement, as biomarker or to monitor the immune system, will depend on attention to detail in the collection and processing of samples in addition to assay precision.
doi:10.1016/j.cyto.2012.05.019
PMCID: PMC3449030  PMID: 22705152
cytokines/chemokines; plasma; serum; anticoagulants

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