Biomarker Development Pipeline Research Articles

The mediating effects of public genomic knowledge in precision medicine implementation: A structural equation model approach.

Precision medicine emphasizes predictive, preventive and personalized treatment on the basis of information gleaned from personal genetic and environmental data. Its implementation at health systems level is regarded as multifactorial, involving variables associated with omics technologies, public genomic awareness and adoption tendencies for new medical technologies. However, interrelationships of the various factors and their synergy has not been sufficiently quantified. Based on a survey of 270 participants involved in the use of molecular tests (omics-based biomarkers, OBMs), this study examined how characteristics of omics biomarkers influence precision medicine implementation outcomes (ImO) through an intermediary factor, public genomic awareness (represented by User Response, UsR). A structural equation modelling (SEM) approach was applied to develop and test a 3 latent variable mediation model; each latent variable being measured by a set of indicators ranging between three and six. Mediation analysis results confirmed a partial mediation effect (an indirect effect represented as the product of paths 'a' and 'b' (a*b)) of 0.36 at 90% confidence level, CI = [0.03, 9.94]. Results from the individual mediation paths 'a' and 'b' however, showed that these effects were negative(a = -0.38, b = -0.94). Path 'a' represents the effect of characteristics of OBMs on the mediator, UsR; 'b' represents the effect of the mediator, UsR on implementation outcomes, ImO, holding OBMs constant. The results have both theoretical and practice implications for biomedical genomics research and clinical genomics, respectively. For instance, the results imply better ways have to be devised to more effectively engage the public in addressing extended family support for extended family cascade screening, especially for monogenic hereditary conditions like BRCA-related breast cancer and colorectal cancer in Lynch syndrome families. At basic biomedical research level, results suggest an integrated biomarker development pipeline, with early consideration of factors that may influence biomarker uptake. The results are also relevant at health systems level in indicating which factors should be addressed for successful.

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

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

Standardizing T-Cell Biomarkers in Type 1 Diabetes: Challenges and Recent Advances.

Type 1 diabetes (T1D) results from the progressive destruction of pancreatic β-cells in a process mediated primarily by T lymphocytes. The T1D research community has made dramatic progress in understanding the genetic basis of the disease as well as in the development of standardized autoantibody assays that inform both disease risk and progression. Despite these advances, there remains a paucity of robust and accepted biomarkers that can effectively inform on the activity of T cells during the natural history of the disease or in response to treatment. In this article, we discuss biomarker development and validation efforts for evaluation of T-cell responses in patients with and at risk for T1D as well as emerging technologies. It is expected that with systematic planning and execution of a well-conceived biomarker development pipeline, T-cell-related biomarkers would rapidly accelerate disease progression monitoring efforts and the evaluation of intervention therapies in T1D.

Clinical validation of circulating cytokines as markers of prognosis and response to docetaxel in men with metastatic castration-resistant prostate cancer.

230 Background: Elevated circulating macrophage inhibitory cytokine -1 /growth differentiation factor 15 (MIC-1/GDF15), interleukins 4 (IL4) and 6 (IL6) levels were associated with poor prognosis and resistance to docetaxel chemotherapy in an exploratory cohort of men with metastatic castration resistant prostate cancer (mCRPC). To establish level 2 evidence of biomarker utility, these cytokines were tested in internal and external validation cohorts. Methods: Internal validation cohort: Plasma samples taken at baseline (BL) and preC2 docetaxel (n = 120). MIC-1/GDF15, IL-4 and IL-6 measured by ELISA assay. External validation cohort: Serum samples taken at BL and/or preC3 docetaxel in 430 men with mCRPC on phase III SYNERGY study (docetaxel +/- custirsen as 1st line chemotherapy in mCRPC with no OS benefit in the experimental arm). MIC-1/GDF15 measured by ELISA assay. Associations between cytokine levels, PSA response, time to PSA progression and OS were assessed by non-parametric tests and Cox Regression survival analyses. Results: Internal validation: At a median follow-up of 14 months, higher MIC-1/GDF15 levels at BL and preC2 were associated with shorter OS (BL; HR 1.2 95%CI 1.0-1.4; p = 0.03 and preC2; HR 1.3 95%CI 1.1-1.5; p = 0.004). Increase in MIC-1/GDF15 after chemotherapy correlated with lack of PSA response (p < 0.001). IL4 and IL6 did not correlate with survival or demonstrate additional value. External validation: At a median follow-up of 23 months, higher MIC-1/GDF15 levels at BL and preC3 predicted shorter OS (BL; HR 1.4 95%CI 1.2-1.6; p < 0.0001 and preC3; HR 1.6 95%CI 1.3-1.8; p < 0.0001). Higher pre C3 MIC-1/GDF15 levels were also associated with shorter time to PSA progression (HR 1.2 95% CI 1.0-1.4; p = 0.02). Rise in MIC-1/GDF15 from BL to preC3 correlated with lack of 50% PSA fall at 12 weeks (p < 0.001). Conclusions: Adherence to a biomarker development pipeline provides level 2 evidence of the prognostic value of circulating MIC-1/GDF15 in men with mCRPC receiving docetaxel. A prospective biomarker led study is now necessary to establish clinical utility.

Mass spectrometry: A platform for biomarker discovery and validation for Alzheimer's and Parkinson's diseases.

Accurate, reliable, and objective biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), and related age-associated neurodegenerative disorders are urgently needed to assist in both diagnosis, particularly at early stages, and monitoring of disease progression. Technological advancements in protein detection platforms over the last few decades have resulted in a plethora of reported molecular biomarker candidates for both AD and PD; however, very few of these candidates are developed beyond the discovery phase of the biomarker development pipeline, a reflection of the current bottleneck within the field. In this review, the expanded use of selected reaction monitoring (SRM) targeted mass spectrometry will be discussed in detail as a platform for systematic verification of large panels of protein biomarker candidates prior to costly validation testing. We also advocate for the coupling of discovery-based proteomics with modern targeted MS-based approaches (e.g., SRM) within a single study in future workflows to expedite biomarker development and validation for AD and PD. It is our hope that improving the efficiency within the biomarker development process by use of an SRM pipeline may ultimately hasten the development of biomarkers that both decrease misdiagnosis of AD and PD and ultimately lead to detection at early stages of disease and objective assessment of disease progression. This article is part of the special issue "Proteomics".

Pre- and Post-analytical Factors in Biomarker Discovery.

The translation of promising biomarkers, which were identified in biomarker discovery experiments, to clinical assays is one of the key challenges in present-day proteomics research. Many so-called "biomarker candidates" fail to progress beyond the discovery phase, and much emphasis is placed on pre- and post-analytical variability in an attempt to provide explanations for this bottleneck in the biomarker development pipeline. With respect to such variability, there is a large number of pre- and post-analytical factors which may impact the outcomes of proteomics experiments and thus necessitate tight control. This chapter highlights some of these factors and provides guidance for addressing them on the basis of examples from previously published proteomics studies.

A pilot study to investigate the feasibility of transporting saliva samples at room temperature with MAWI Cell Stabilization buffer

Saliva is considered as the front-line of non-invasive diagnostics as novel biomarkers continue to emerge for an array of systemic diseases. Biomarker development pipeline relies heavily on pre-analytical process such as saliva collection, handling, transport and storage. The aim of this study was to systematically evaluate the applicability of MAWI Cell Stabilization (MCS) buffer to transport and store saliva samples at room temperature for downstream applications. Human and bacterial genomic DNA (gDNA) and total protein in saliva samples with and without MCS buffer were quantified for a week at three time-points at room temperature. Based on our findings, MCS buffer was able to preserve human gDNA and total protein within the testing time-points. While bacterial gDNA was accurately preserved, MCS buffer was unable to halt bacterial growth at room temperature. We have identified a non-alcohol-based, non-lytic buffer that could maintain the integrity of both genomic materials and proteins in saliva samples. MCS buffer offers a method to potentially transport and store saliva samples at room temperature, accelerating the translation of salivary assays in remote/rural and resource limited settings.

Serological Epithelial Component Proteins Identify Intestinal Complications in Crohn's Disease.

Crohn's Disease (CD) is a relapsing inflammation of the gastrointestinal tract that affects a young working age population and is increasing in developing countries. Half of all sufferers will experience stricturing or fistulizing intestinal complications that require extensive surgical interventions and neither genes nor clinical risk factors can predict this debilitating natural history. We applied discovery and verification phase studies as part of an NCI-FDA modeled biomarker pipeline to identify differences in the low-mass (<25kDa) blood-serum proteome between CD behavioral phenotypes. A significant enrichment of epithelial component proteins was identified in CD patients with intestinal complications using quantitative proteomic profiling with label-free Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS). DAVID 6.7 (NIH) was used for functional annotation analysis of detected proteins and immunoblotting and multiple reaction monitoring (MRM) to verify a priori findings in a secondary independent cohort of complicated CD (CCD), uncomplicated inflammatory CD (ICD), Th1/17 pathway inflammation controls (rheumatoid arthritis), inflammatory bowel disease controls (ulcerative colitis), and healthy controls. Seventy-six high-confidence serum proteins were modulated in CCD versus ICD by LC-MS/MS (p < 0.05, FDR q<0.01), annotating to pathways of epithelial barrier homeostasis (p < 0.01). In verification phase, a putative serology panel developed from discovery proteomics data consisting of desmoglein-1, desmoplakin, and fatty acid-binding protein 5 (FABP5) distinguished CCD from all other groups (p = 0.041) and discriminated complication in CD (70% sensitivity and 72.5% specificity at score ≥1.907, AUC = 0.777, p = 0.007). An MRM assay secondarily confirmed increased FABP5 levels in CCD (p < 0.001). In a longitudinal subanalysis-cohort, FABP5 levels were stable over a two-month period with no behavioral changes (p = 0.099). These studies along the biomarker development pipeline provide substantial proof-of-principle that a blood test can be developed specific to transmural intestinal injury. Data are available via the PRIDE proteomics data repository under identifier PXD001821 and PeptideAtlas with identifier PASS00661.

Statistical Approaches to Candidate Biomarker Panel Selection.

The statistical analysis of robust biomarker candidates is a complex process, and is involved in several key steps in the overall biomarker development pipeline (see Fig.22.1, Chap. 19 ). Initially, data visualization (Sect.22.1, below) is important to determine outliers and to get a feel for the nature of the data and whether there appear to be any differences among the groups being examined. From there, the data must be pre-processed (Sect.22.2) so that outliers are handled, missing values are dealt with, and normality is assessed. Once the processed data has been cleaned and is ready for downstream analysis, hypothesis tests (Sect.22.3) are performed, and proteins that are differentially expressed are identified. Since the number of differentially expressed proteins is usually larger than warrants further investigation (50+ proteins versus just a handful that will be considered for a biomarker panel), some sort of feature reduction (Sect.22.4) should be performed to narrow the list of candidate biomarkers down to a more reasonable number. Once the list of proteins has been reduced to those that are likely most useful for downstream classification purposes, unsupervised or supervised learning is performed (Sects.22.5 and 22.6, respectively).

Qualification and Verification of Protein Biomarker Candidates.

The importance of biomarkers has long been recognized by the public, scientific community, and industry. Yet despite extensive efforts and funding investments in biomarker discovery, only 109 protein biomarkers in plasma or serum were approved by the US Food and Drug Administration throughout 2008 (Anderson NL. Clin Chem 56:177-185, 2010), and even fewer protein biomarkers are currently used routinely in the clinic. In recent years, the introduction of new protein biomarkers approved by the US Food and Drug Administration has fallen to an average of 1.5 per year (a median of only 1 per year) (Anderson NL. Clin Chem 56:177-185, 2010). The low efficiency of biomarker development is due to several reasons, including the poor quality of clinical samples, the gap between subjective clinical definition of a disease and objective protein measurements, and high false discovery rate of differentially expressed proteins identified in the initial discovery phase (Rifai N, Gillette MA, Carr SA. Nat Biotechnol 24:971-983, 2006). It has become clear that the vast majority of differentially expressed proteins identified in the discovery phase will ultimately fail as useful clinical biomarkers, and only few true positive candidates can move through the biomarker development pipeline. Isolation of true biomarkers from the large pool of differentially expressed proteins identified in the discovery phase becomes the greatest challenge and the bottleneck in most biomarker pipelines. To succeed, after the initial discovery study (see Chap. 20 ), the authenticity of biomarker candidates need to be tested in a pilot study with high throughput, high accuracy and reasonable cost. This essential process is addressed by qualification and verification phase of the biomarker development pipeline.

Abstract 3967: miRNA reference samples for interlaboratory study

Abstract The biomarkers of interest in this study are microRNA (miRNA), small non-coding RNAs involved in the regulation of gene expression. Currently, over 1000 human miRNAs have been identified. Several laboratories within the Early Detection Research Network are currently profiling miRNA as potential biomarkers. The reference sample paradigm presented and evaluated here is designed to provide measurement assurance in the earliest phases of a cancer biomarker development pipeline. Three commercially available samples of human total RNA (brain, liver, and placenta) were selected for detectable differences in miRNA expression based upon previously reported miRNA microarray experiments. For each round of this study, replicate samples of each tissue were aliquoted, blinded, and distributed to participating laboratories. The samples were then profiled using the standard workflows of each laboratory. This was repeated over multiple rounds of measurement. Raw data were processed in pair-wise comparisons between tissue types. Log2 transformed ratios were calculated for each detectable miRNA. For each of these miRNA ratios, the results among laboratories were compared to the consensus value to provide a measure of concordance and help identify laboratories that might need to make adjustments to their process. Results demonstrated repeatability within labs and reproducibility among labs with respect to direction and magnitude of differential miRNA expression between tissues for a set of miRNAs of interest (MOI). For the first round, 79% of reported results for all log2 ratio combinations and MOI were in agreement with the consensus values. Subsequent rounds demonstrated increased concordance, with 91 and 100% agreement, respectively. The data from the nine “neat” tissue total RNA samples were also used for in silico modeling to predict the values for two complex mixtures based on three components. The two mixture designs provide a pair of reference samples with designed-in reciprocal ratios of 2:1 for brain and placenta, as well as a shared 1:1 liver component. For two brain-selective and two placenta-selective MOI, 91% of reported values were in close agreement with the target values. Overall, measurement results were harmonious for a set of miRNAs of interest measured by all participants, both within rounds and between rounds. The laboratories were also able to demonstrate the ability to resolve the 2-fold differences created with complex mixtures. These results suggest that endogenous miRNAs present within total RNA may be suitable as QA/QC process controls. Citation Format: P. Scott Pine, Lynn R. Sorbara, Sudhir Srivastava, Marc Salit. miRNA reference samples for interlaboratory study. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3967. doi:10.1158/1538-7445.AM2015-3967

Reverse-Polynomial Dilution Calibration Methodology Extends Lower Limit of Quantification and Reduces Relative Residual Error in Targeted Peptide Measurements in Blood Plasma

Matrix effect is the alteration of an analyte's concentration-signal response caused by co-existing ion components. With electrospray ionization (ESI), matrix effects are believed to be a function of the relative concentrations, ionization efficiency, and solvation energies of the analytes within the electrospray ionization droplet. For biological matrices such as plasma, the interactions between droplet components is immensely complex and the effect on analyte signal response not well elucidated. This study comprised of three sequential quantitative analyses: we investigated whether there is a generalizable correlation between the range of unique ions in a sample matrix (complexity); the amount of matrix components (concentration); and matrix effect, by comparing an E. coli digest matrix (∼2600 protein proteome) with phospholipid depleted human blood plasma, and unfractionated, nondepleted human plasma matrices (∼10(7) proteome) for six human plasma peptide multiple reaction monitoring assays. Our data set demonstrated analyte-specific interactions with matrix complexity and concentration properties resulting in significant ion suppression for all peptides (p < 0.01), with nonuniform effects on the ion signals of the analytes and their stable-isotope analogs. These matrix effects were then assessed for translation into relative residual error and precision effects in a low concentration (∼0-250 ng/ml) range across no-matrix, complex matrix, and highly complex matrix, when a standard addition stable isotope dilution calibration method was used. Relative residual error (%) and precision (CV%) by stable isotope dilution were within <20%; however, error in phospholipid-depleted and nondepleted plasma matrices were significantly higher compared with no-matrix (p = 0.006). Finally a novel reverse-polynomial dilution calibration method with and without phospholipid-depletion was compared with stable isotope dilution for relative residual error and precision. Reverse-polynomial dilution techniques extend the Lower Limit of Quantification and reduce error (p = 0.005) in low-concentration plasma peptide assays and is broadly applicable for verification phase Tier 2 multiplexed multiple reaction monitoring assay development within the FDA-National Cancer Institute (NCI) biomarker development pipeline.

Biomarkers of Diastolic and Systolic Heart Failure

s S139 Canadian Cardiovascular Society (CCS) Highlighted Poster HEART FAILURE HIGHLIGHTED POSTER SESSION Thursday, October 17, 2013 126 BIOMARKERS OF DIASTOLIC AND SYSTOLIC HEART FAILURE Z Hollander, KK Lam, V Chen, JE Wilson-McManus, RT Ng, SJ Tebbutt, BM McManus, A Ignaszewski, T Anderson, J Ezekowitz, GY Oudit, JR Dyck Vancouver, British Columbia BACKGROUND: Heart failure (HF) is a modern day epidemic with a rising prevalence and affects over 23 million global citizens. Methods to identify or diagnose a patient with HF include clinical history, physical exam, imaging and blood tests. The most commonly used blood tests are markers of natriuretic peptides, however, there is a large diagnostic “grey zone” in natriuretic peptides that renders them less useful as a stand-alone HF diagnostic test. In addition, they have not been shown to distinguish between diastolic HF (DHF) and systolic HF (SHF). METHODS: A discovery cohort comprised of 38 DHF patients, 54 SHF patients, and 22 healthy controls (HC) were enrolled in the Alberta HEART initiative. Plasma and PAXgene whole blood samples were analyzed at the PROOF Centre of Excellence with iTRAQ and MRM mass spectrometry and Affymetrix GeneST microarrays, respectively. Samples from a separate replication cohort, again from Alberta Heart (35 DHF, 77 SHF, and 36 HC), were analyzed with MRM mass spectrometry. The PROOF Centre biomarker development pipeline was used to identify novel blood test content by comparing DHF with SHF or comparing HC with HF patients. The computational analysis strategy consisted of prefiltering, univariate and multivariate tests, and classification. The performance of the sets of genes and proteins was assessed in the replication cohort. RESULTS: Proteomic biomarkers of HF were identified in the discovery cohort. When these protein panels were tested in the replication cohort, the area under the receiver operating characteristics curve (AUC) was 0.93. The NT-proBNP cut-off between DHF and HC was determined within the discovery cohort. An analysis combining the cohort-specific NT-proBNP cutoff with a set of novel proteins revealed an increase in performance characteristics (Figure), particularly for the specificity (from 34% to 80%) withNRI of 49%. Panels combining genes and proteins were able to distinguish between DHF and SHF patients. Cross-validation results using the discovery cohort data are promising for differentiating DHF from SHF using a combination of blood-based genes and proteins in women (AUC1⁄40.87) and in men (AUC1⁄4 0.83). CONCLUSION: Novel biomarkers of HF that can improve on the performance characteristics of NT-proBNP have been identified and replicated. Biomarker panels containing genes and proteins have been identified for discriminating between DHF and SHF. All results require further replication and validation in larger cohorts from patients outside of Alberta. 127 DECISION-MAKING ABOUT GOALS OF CARE FOR HOSPITALIZED PATIENTS WITH HEART FAILURE (DECIDE-HF): A PILOT STUDY N Aleksova, C Demers, PH Strachan, J Hoefs, M Yous, J MacIver, J Downar, M Wang, R Fowler, DK Heyland, HJ Ross, J You

A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers

BackgroundBiomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble?ResultsThe first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity.ConclusionProteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.

Biomarkers in rheumatology, now and in the future

This review examines the biomarker development process by using rheumatic disorders as the disease model for discussion. We evaluate the current role of biomarkers in the practice of rheumatology and discuss their likely role in the future. We define the essential components of the biomarker development pipeline and discuss the issue of fitness for purpose, i.e. what the biomarker(s) might offer in a clinical setting. As a component of this review we also highlight several emerging technologies that are beginning to provide practical solutions to support biomarker validation. In the process, we highlight some scenarios where additional biomarkers would add considerable value to clinical practice, and we review appropriate methods for each. We also emphasize some important but infrequently discussed considerations, including the need for protein variant verification. Ultimately, the adroit application of the methods of proteomics will transform the practice rheumatology and allow personalized clinical practice to become a reality.

The Journey to Regulation of Protein-Based Multiplex Quantitative Assays

Clinical proteomics presents great promise in biology and medicine because of its potential for improving our understanding of diseases at the molecular level and for detecting disease-related biomarkers for diagnosis, prognosis, and prediction of therapeutic responses. To realize its full potential to improve clinical outcome for patients, proteomic studies have to be well designed, from biosample cohorts to data and statistical analyses. One key component in the biomarker development pipeline is the understanding of the regulatory science that evaluates diagnostic assay performance through rigorous analytical and clinical review criteria. The National Cancer Institute's Clinical Proteomic Technologies for Cancer (CPTC) initiative has proposed an intermediate preclinical "verification" step to close the gap between protein-based biomarker discovery and clinical qualification. In collaboration with the US Food and Drug Administration (FDA), the CPTC network investigators recently published 2 mock submission review documents, first-of-their-kind educational materials that may help the scientific community interested in developing products for the clinic in understanding the likely analytical evaluation requirements for multiplex protein technology-based diagnostic tests. Building on this momentum, the CPTC continues with this report its collaboration with the FDA, as well as its interactions with the AACC and the Centers for Medicare and Medicaid Services, to further the understanding of regulatory requirements for approving multiplex proteomic platform-based tests and analytically validating multiple analytes.

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.

Toward a standardized urine proteome analysis methodology

Urine is an easily accessible bodily fluid particularly suited for the routine clinical analysis of disease biomarkers. Actually, the urinary proteome is more diverse than anticipated a decade ago. Hence, significant analytical and practical issues of urine proteomics such as sample collection and preparation have emerged, in particular for large-scale studies. We have undertaken a systematic study to define standardized and integrated analytical protocols for a biomarker development pipeline, employing two LC-MS analytical platforms, namely accurate mass and time tags and selected reaction monitoring, for the discovery and verification phase, respectively. Urine samples collected from hospital patients were processed using four different protocols, which were evaluated and compared on both analytical platforms. Addition of internal standards at various stages of sample processing allowed the estimation of protein extraction yields and the absolute quantification of selected urinary proteins. Reproducibility of the entire process and dynamic range of quantification were also evaluated. Organic solvent precipitation followed by in-solution digestion provided the best performances and was thus selected as the standard method common to the discovery and verification phases. Finally, we applied this protocol for platforms' cross-validation and obtained excellent consistency between urinary protein concentration estimates by both analytical methods performed in parallel in two laboratories.

On the Development of Plasma Protein Biomarkers

The development of plasma biomarkers has proven to be more challenging than initially anticipated. Many studies have reported lists of candidate proteins rather than validated candidate markers with an assigned performance to a specific clinical objective. Biomarker research necessitates a clear rational framework with requirements on a multitude of levels. On the technological front, the platform needs to be effective to detect low abundant plasma proteins and be able to measure them in a high throughput manner over a large amount of samples reproducibly. At a conceptual level, the choice of the technological platform and available samples should be part of an overall clinical study design that depends on a joint effort between basic and clinical research. Solutions to these needs are likely to facilitate more feasible studies. Targeted proteomic workflows based on SRM mass spectrometry show the potential of fast verification of biomarker candidates in plasma and thereby closing the gap between discovery and validation in the biomarker development pipeline. Biological samples need to be carefully chosen based on well-established guidelines either for candidate discovery in the form of disease models with optimal fidelity to human disease or for candidate evaluation as well-designed and annotated clinical cohort groups. Most importantly, they should be representative of the target population and directly address the investigated clinical question. A conceptual structure of a biomarker study can be provided in the form of several sequential phases, each having clear objectives and predefined goals. Furthermore, guidelines for reporting the outcome of biomarker studies are critical to adequately assess the quality of the research, interpretation and generalization of the results. By being attentive to and applying these considerations, biomarker research should become more efficient and lead to directly translatable biomarker candidates into clinical evaluation.

Restructuring proteomics through verification.

Proteomics technologies have revolutionized cell biology and biochemistry by providing powerful new tools to characterize complex proteomes, multiprotein complexes and post-translational modifications. Although proteomics technologies could address important problems in clinical and translational cancer research, attempts to use proteomics approaches to discover cancer biomarkers in biofluids and tissues have been largely unsuccessful and have given rise to considerable skepticism. The National Cancer Institute has taken a leading role in facilitating the translation of proteomics from research to clinical application, through its Clinical Proteomic Technologies for Cancer. This article highlights the building of a more reliable and efficient protein biomarker development pipeline that incorporates three steps: discovery, verification and qualification. In addition, we discuss the merits of multiple reaction monitoring mass spectrometry, a multiplex targeted proteomics platform, which has emerged as a potentially promising, high-throughput protein biomarker measurements technology for preclinical 'verification'.