Edward N. Brody

The use of aptamers in large arrays for molecular diagnostics.

BACKGROUND Aptamers are single-stranded oligonucleotides derived from an in vitro evolution protocol called systematic evolution of ligands by exponential enrichment (SELEX). They bind tightly and specifically to target molecules; most aptamers to proteins bind with Kds (equilibrium dissociation constant) in the range of 1 pM to 1 nM. METHODS AND RESULTS The SELEX protocol has been automated; therefore, hundreds to thousands of aptamers can be made in an economically feasible fashion. Blood and urine can be analyzed on chips that capture and quantitate proteins. SELEX has been adapted to the use of 5-bromo (5-Br) and 5-iodo (5-I) deoxyuridine residues. These halogenated bases can be specifically cross-linked to proteins. Selection pressure during in vitro evolution can be applied for both binding specificity and specific photo-cross-linkability. These are sufficiently independent parameters to allow one reagent, a photo-cross-linkable aptamer, to substitute for two reagents, the capture antibody and the detection antibody, in a typical sandwich array. After a cycle of binding, washing, cross-linking, and detergent washing, proteins will be specifically and covalently linked to their cognate aptamers. CONCLUSIONS Because no other proteins are present on the chips, protein-specific stain will now show a meaningful array of pixels on the chip. Learning algorithms and retrospective studies should lead to a robust, simple, diagnostic chip.

Unlocking Biomarker Discovery: Large Scale Application of Aptamer Proteomic Technology for Early Detection of Lung Cancer

Background Lung cancer is the leading cause of cancer deaths worldwide. New diagnostics are needed to detect early stage lung cancer because it may be cured with surgery. However, most cases are diagnosed too late for curative surgery. Here we present a comprehensive clinical biomarker study of lung cancer and the first large-scale clinical application of a new aptamer-based proteomic technology to discover blood protein biomarkers in disease. Methodology/Principal Findings We conducted a multi-center case-control study in archived serum samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC) in long-term tobacco-exposed populations. Sera were collected and processed under uniform protocols. Case sera were collected from 291 patients within 8 weeks of the first biopsy-proven lung cancer and prior to tumor removal by surgery. Control sera were collected from 1,035 asymptomatic study participants with ≥10 pack-years of cigarette smoking. We measured 813 proteins in each sample with a new aptamer-based proteomic technology, identified 44 candidate biomarkers, and developed a 12-protein panel (cadherin-1, CD30 ligand, endostatin, HSP90α, LRIG3, MIP-4, pleiotrophin, PRKCI, RGM-C, SCF-sR, sL-selectin, and YES) that discriminates NSCLC from controls with 91% sensitivity and 84% specificity in cross-validated training and 89% sensitivity and 83% specificity in a separate verification set, with similar performance for early and late stage NSCLC. Conclusions/Significance This study is a significant advance in clinical proteomics in an area of high unmet clinical need. Our analysis exceeds the breadth and dynamic range of proteome interrogated of previously published clinical studies of broad serum proteome profiling platforms including mass spectrometry, antibody arrays, and autoantibody arrays. The sensitivity and specificity of our 12-biomarker panel improves upon published protein and gene expression panels. Separate verification of classifier performance provides evidence against over-fitting and is encouraging for the next development phase, independent validation. This careful study provides a solid foundation to develop tests sorely needed to identify early stage lung cancer.

Large-scale serum protein biomarker discovery in Duchenne muscular dystrophy

Significance Duchenne muscular dystrophy (DMD) is a rare and devastating muscle disease caused by mutations in the X-linked DMD gene (which encodes the dystrophin protein). Serum biomarkers hold significant potential as objective phenotypic measures of DMD disease state, as well as potential measures of pharmacological effects of and response to therapeutic interventions. Here we describe a proteomics approach to determine serum levels of 1,125 proteins in 93 DMD patients and 45 controls. The study identified 44 biomarkers that differed significantly between patients and controls. These data are being made available to DMD researchers and clinicians to accelerate the search for new diagnostic, prognostic, and therapeutic approaches. Serum biomarkers in Duchenne muscular dystrophy (DMD) may provide deeper insights into disease pathogenesis, suggest new therapeutic approaches, serve as acute read-outs of drug effects, and be useful as surrogate outcome measures to predict later clinical benefit. In this study a large-scale biomarker discovery was performed on serum samples from patients with DMD and age-matched healthy volunteers using a modified aptamer-based proteomics technology. Levels of 1,125 proteins were quantified in serum samples from two independent DMD cohorts: cohort 1 (The Parent Project Muscular Dystrophy–Cincinnati Children’s Hospital Medical Center), 42 patients with DMD and 28 age-matched normal volunteers; and cohort 2 (The Cooperative International Neuromuscular Research Group, Duchenne Natural History Study), 51 patients with DMD and 17 age-matched normal volunteers. Forty-four proteins showed significant differences that were consistent in both cohorts when comparing DMD patients and healthy volunteers at a 1% false-discovery rate, a large number of significant protein changes for such a small study. These biomarkers can be classified by known cellular processes and by age-dependent changes in protein concentration. Our findings demonstrate both the utility of this unbiased biomarker discovery approach and suggest potential new diagnostic and therapeutic avenues for ameliorating the burden of DMD and, we hope, other rare and devastating diseases.

Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool

Background Malignant pleural mesothelioma (MM) is an aggressive, asbestos-related pulmonary cancer that is increasing in incidence. Because diagnosis is difficult and the disease is relatively rare, most patients present at a clinically advanced stage where possibility of cure is minimal. To improve surveillance and detection of MM in the high-risk population, we completed a series of clinical studies to develop a noninvasive test for early detection. Methodology/Principal Findings We conducted multi-center case-control studies in serum from 117 MM cases and 142 asbestos-exposed control individuals. Biomarker discovery, verification, and validation were performed using SOMAmer proteomic technology, which simultaneously measures over 1000 proteins in unfractionated biologic samples. Using univariate and multivariate approaches we discovered 64 candidate protein biomarkers and derived a 13-marker random forest classifier with an AUC of 0.99±0.01 in training, 0.98±0.04 in independent blinded verification and 0.95±0.04 in blinded validation studies. Sensitivity and specificity at our pre-specified decision threshold were 97%/92% in training and 90%/95% in blinded verification. This classifier accuracy was maintained in a second blinded validation set with a sensitivity/specificity of 90%/89% and combined accuracy of 92%. Sensitivity correlated with pathologic stage; 77% of Stage I, 93% of Stage II, 96% of Stage III and 96% of Stage IV cases were detected. An alternative decision threshold in the validation study yielding 98% specificity would still detect 60% of MM cases. In a paired sample set the classifier AUC of 0.99 and 91%/94% sensitivity/specificity was superior to that of mesothelin with an AUC of 0.82 and 66%/88% sensitivity/specificity. The candidate biomarker panel consists of both inflammatory and proliferative proteins, processes strongly associated with asbestos-induced malignancy. Significance The SOMAmer biomarker panel discovered and validated in these studies provides a solid foundation for surveillance and diagnosis of MM in those at highest risk for this disease.

Protein signature of lung cancer tissues.

Lung cancer remains the most common cause of cancer-related mortality. We applied a highly multiplexed proteomic technology (SOMAscan) to compare protein expression signatures of non small-cell lung cancer (NSCLC) tissues with healthy adjacent and distant tissues from surgical resections. In this first report of SOMAscan applied to tissues, we highlight 36 proteins that exhibit the largest expression differences between matched tumor and non-tumor tissues. The concentrations of twenty proteins increased and sixteen decreased in tumor tissue, thirteen of which are novel for NSCLC. NSCLC tissue biomarkers identified here overlap with a core set identified in a large serum-based NSCLC study with SOMAscan. We show that large-scale comparative analysis of protein expression can be used to develop novel histochemical probes. As expected, relative differences in protein expression are greater in tissues than in serum. The combined results from tissue and serum present the most extensive view to date of the complex changes in NSCLC protein expression and provide important implications for diagnosis and treatment.

Validation of a blood protein signature for non-small cell lung cancer

BackgroundCT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations.ResultsBlood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation.ConclusionsSelecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.

High-content affinity-based proteomics: unlocking protein biomarker discovery

Single protein biomarkers measured with antibody-based affinity assays are the basis of molecular diagnostics in clinical practice today. There is great hope in discovering new protein biomarkers and combinations of protein biomarkers for advancing medicine through monitoring health, diagnosing disease, guiding treatment, and developing new therapeutics. The goal of high-content proteomics is to unlock protein biomarker discovery by measuring many (thousands) or all (∼23,000) proteins in the human proteome in an unbiased, data-driven approach. High-content proteomics has proven technically difficult due to the diversity of proteins, the complexity of relevant biological samples, such as blood and tissue, and large concentration ranges (in the order of 1012 in blood). Mass spectrometry and affinity methods based on antibodies have dominated approaches to high-content proteomics. For technical reasons, neither has achieved adequate simultaneous performance and high-content. Here we review antibody-based protein measurement, multiplexed antibody-based protein measurement, and limitations of antibodies for high-content proteomics due to their inherent cross-reactivity. Finally, we review a new affinity-based proteomic technology developed from the ground up to solve the problem of high content with high sensitivity and specificity. Based on a new generation of slow off-rate modified aptamers (SOMAmers), this technology is unlocking biomarker discovery.

Association of growth differentiation factor 11/8, putative anti-ageing factor, with cardiovascular outcomes and overall mortality in humans: analysis of the Heart and Soul and HUNT3 cohorts

AIMS Growth differentiation factor 11 and/or its homologue growth differentiation factor 8 (GDF11/8) reverses age-related cardiac hypertrophy and vascular ageing in mice. We investigated whether GDF11/8 associates with cardiovascular outcomes, left ventricular hypertrophy (LVH), or age in humans. METHODS AND RESULTS We measured plasma GDF11/8 levels in 928 participants with stable ischaemic heart disease in the Heart and Soul study. We adjudicated heart failure hospitalization, stroke, myocardial infarction, death, and their composite endpoint. Left ventricular hypertrophy was evaluated by echocardiography. We used multivariable Cox proportional hazards models to compare rates of cardiovascular events and death across GDF11/8 quartiles and logistic regression models to evaluate the association between GDF11/8 and LVH. Four hundred and fifty participants (48.5%) experienced a cardiovascular event or death during 8.9 years of follow-up. The adjusted risk of the composite endpoint was lower in the highest compared with the lowest GDF11/8 quartile [hazard ratio (HR), 0.45; 95% confidence interval (CI), 0.33-0.60; P < 0.001]. We replicated this relationship of GDF11/8 to adverse events in 971 participants in the HUNT3 cohort (adjusted HR, 0.34; 95% CI, 0.23-0.51; P < 0.001). Left ventricular hypertrophy was present in 368 participants (39.7%) at baseline. Participants in the highest quartile of GDF11/8 were less likely to have LVH than those in the lowest quartile (adjusted OR, 0.55; 95% CI, 0.35-0.86; P = 0.009). GDF11/8 levels were lower in older individuals (P < 0.001). CONCLUSION In patients with stable ischaemic heart disease, higher GDF11/8 levels are associated with lower risk of cardiovascular events and death. Our findings suggest that GDF11/8 has similar cardioprotective properties in humans to those demonstrated in mice.

Life's simple measures: unlocking the proteome.

Using modified nucleotides and selecting for slow off-rates in the SELEX procedure, we have evolved a special class of aptamers, called SOMAmers (slow off-rate modified aptamers), which bind tightly and specifically to proteins in body fluids. We use these in a novel assay that yields 1:1 complexes of the SOMAmers with their cognate proteins in body fluids. Measuring the SOMAmer concentrations of the resultant complexes reflects the concentration of the proteins in the fluids. This is simply done by hybridization to complementary sequences on solid supports, but it can also be done by any other DNA quantification technology (including NexGen sequencing). We use measurements of over 1000 proteins in under 100 μL of serum or plasma to answer important medical questions, two of which are reviewed here. A number of bioinformatics methods have guided our discoveries, including principal component analysis. We use various methods to evaluate sample handling procedures in our clinical samples and can identify many parameters that corrupt proteomics analysis.

Transcription and translation of sheared bacteriophage T4 DNA in vitro

Abstract The synthesis of five specific bacteriophage T4 proteins was measured following transcription and translation in vitro of native and sheared bacteriophage T4 DNA. T4 DNA of decreased molecular weight was found to direct preferentially the synthesis of those proteins whose genes are transcribed earliest by RNA polymerase in vitro . The results support a model for T4 DNA transcription in which the time interval between initiation of RNA synthesis and elongation of a specific gene product reflects the physical distance between a promoter and that gene.