Machine learning-based biomarker profile derived from 4210 serially measured proteins predicts clinical outcome of patients with heart failure

Aims

Risk assessment tools are needed for timely identification of patients with heart failure (HF) with reduced ejection fraction (HFrEF) who are at high risk of adverse events. In this study, we aim to derive a small set out of 4210 repeatedly measured proteins, which, along with clinical characteristics and established biomarkers, carry optimal prognostic capacity for adverse events, in patients with HFrEF.

Methods and results

In 382 patients, we performed repeated blood sampling (median follow-up: 2.1 years) and applied an aptamer-based multiplex proteomic approach. We used machine learning to select the optimal set of predictors for the primary endpoint (PEP: composite of cardiovascular death, heart transplantation, left ventricular assist device implantation, and HF hospitalization). The association between repeated measures of selected proteins and PEP was investigated by multivariable joint models. Internal validation (cross-validated c-index) and external validation (Henry Ford HF PharmacoGenomic Registry cohort) were performed. Nine proteins were selected in addition to the MAGGIC risk score, N-terminal pro-hormone B-type natriuretic peptide, and troponin T: suppression of tumourigenicity 2, tryptophanyl-tRNA synthetase cytoplasmic, histone H2A Type 3, angiotensinogen, deltex-1, thrombospondin-4, ADAMTS-like protein 2, anthrax toxin receptor 1, and cathepsin D. N-terminal pro-hormone B-type natriuretic peptide and angiotensinogen showed the strongest associations [hazard ratio (95% confidence interval): 1.96 (1.17-3.40) and 0.66 (0.49-0.88), respectively]. The multivariable model yielded a c-index of 0.85 upon internal validation and c-indices up to 0.80 upon external validation. The c-index was higher than that of a model containing established risk factors (P = 0.021).

Conclusion

Nine serially measured proteins captured the most essential prognostic information for the occurrence of adverse events in patients with HFrEF, and provided incremental value for HF prognostication beyond established risk factors. These proteins could be used for dynamic, individual risk assessment in a prospective setting. These findings also illustrate the potential value of relatively 'novel' biomarkers for prognostication.

Clinical Trial Registration

https://clinicaltrials.gov/ct2/show/NCT01851538?term=nCT01851538&draw=2&rank=1 24.

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

BACKGROUND AND AIMS

Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD.

APPROACH AND RESULTS

Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82).

CONCLUSIONS

Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.

Abstract 5411: Efficient development of prognostic tests for detecting cancer risk using proteomic technology

Background: Prognostic models for assessing future health outcomes can be developed using time-to-event (also known as “survival”) data. This methodology is ubiquitous in statistical literature and in the analysis of cancer outcomes, but its use in high-dimensional analyses tends to be limited as the methods are difficult to implement in a machine learning environment. Additionally, development of certified prognostic clinical tests using proteomic biomarkers for detecting future cancer risk can be time-consuming, prone to overfitting issues, and difficult to navigate. We demonstrate the utility of combining SomaScan® proteomic data with pipeline machine learning tools and survival analysis methodology to identify powerful and robust LDT-certifiable prognostic tests for assessing future risk of cancer.

Methods: Data pipeline and analysis tools were developed using R. In addition to standard machine learning techniques, statistical methods include elastic net AFT models, subsampling survival techniques, and metrics for assessing predictive survival models. The pipeline takes the analyst from data processing and QC through identification of optimal models for prediction of clinical endpoints, and then through validation on a hold-out test set. The tools include an assessment of model robustness against sample handling issues, longitudinal stability, the impacts of assay noise on model performance, effects of putative interferents, and risk of failure during CLIA validation in the lab. We demonstrate the utility of the tools and methods for development of a lung cancer risk model.

Results: Analysis time for validation of an optimal clinical model was reduced by at least 80%, resulting in the development of 7 LDT-certified tests within 3 years, including a test for lung cancer risk. Inclusion of methods that allow for subsampling and penalized regression using AFT models show improved predictive performance and identification of top features related to clinical endpoints.

Conclusion: Not only are powerful, prognostic tests do-able, but they can be LDT certified in an efficient manner and made to be robust to real-life lab settings. Survival analysis in a machine-learning setting allow us to leverage proteomic technology in new ways, leading to tests that assess future cancer risk, which can be used for precision medicine applications.

Citation Format: Yolanda Hagar, Leigh Alexander, Jessica Chadwick, Gargi Datta, Joe Gogain, Rachel Ostroff, Clare Paterson, Laura Sampson, Caleb Scheidel, Sama Shrestha, Amy Zhang, Michael Hinterberg. Efficient development of prognostic tests for detecting cancer risk using proteomic technology. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5411.

Abstract 4361: The plasma proteome as a cardiovascular disease risk assessment tool in cancer survivors

Cardiovascular disease (CVD) is the most common non-cancer cause of death in cancer survivors and there is an unmet clinical need for easy, accurate, and safe CVD prognostic risk-stratification in adult cancer survivors. This study investigated whether a previously validated 27-plasma protein prognostic model for four-year cardiovascular (CV) events could have such a utility. We used the 27-plasma protein model to predict the four-year risk of a CV event (myocardial infarction, stroke, transient ischemic attack, heart failure hospitalization, death) in 906 participants with a prior history or active malignancy of any type of cancer and compared predictive results to follow-up CV outcome data. The participants were from the BASEL VIII or ARIC (visit 3) studies with a medically adjudicated prior diagnosis of cancer. BASEL VIII is an observational cohort study in patients with suspected coronary artery disease. ARIC is a multi-site cohort study funded by the NHLBI, NCI, and NPCR investigating risk factors for CV health. A subset analysis was conducted to assess model performance in participants with no prior history of CVD and those with stable CVD. The 27-plasma protein model accurately stratified participants into 4 distinct and non-overlapping (95% CI) risk bins. The median time to event for all cancer survivors who had an event in this study was 1.3 years. Observed 4-year event rates across the 4 risk bins (low, medium-low, medium-high, and high) were 11.0%, 17.3%, 31.2% and 60.2%, respectively, which were higher than stratified event rates from our previously published metacohort analyses (5.6%, 11.2%, 20.0% and 43.4%, respectively) in participants with elevated CVD risk factors (e.g., prior events, diabetes, kidney disease and suspected coronary artery disease). The plasma protein model accurately predicted 4-year CVD risk with a C-index of 0.71 (0.68, 0.74) and 4-year AUC of 0.74 (0.69, 0.79). Performance of the protein model was comparable between participants with no prior history of CVD (C-Index: 0.69; AUC: 0.71) and stable CVD (C-Index: 0.69; AUC: 0.72), demonstrating the model accurately predicts CV event risk in cancer survivors regardless of cardiovascular history. Cancer survivors in this cohort can be distinguished with 4-year CV event rates as high as 60.2%, underscoring the urgent need for an easy and accurate risk stratification tool for this population. Prognostic protein testing may provide a novel tool for CVD risk assessment in adult cancer survivors.

Citation Format: Emma V. Troth, Matthew Ayala, Jessica Chadwick, Erin Hales, Michael Hinterberg, Jessica N. Kuzma, Clare Paterson, Rachel Ostroff, Joan E. Walter, Christian Mueller, Josef Coresh. The plasma proteome as a cardiovascular disease risk assessment tool in cancer survivors. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4361.

Defining the serum proteomic signature of hepatic steatosis, inflammation, ballooning and fibrosis in non-alcoholic fatty liver disease

BACKGROUND & AIMS

Despite recent progress, non-invasive tests for the diagnostic assessment and monitoring of non-alcoholic fatty liver disease (NAFLD) remain an unmet need. Herein, we aimed to identify diagnostic signatures of the key histological features of NAFLD.

METHODS

Using modified-aptamer proteomics, we assayed 5,220 proteins in each of 2,852 single serum samples from 636 individuals with histologically confirmed NAFLD. We developed and validated dichotomized protein-phenotype models to identify clinically relevant severities of steatosis (grade 0 vs. 1-3), hepatocellular ballooning (0 vs. 1 or 2), lobular inflammation (0-1 vs. 2-3) and fibrosis (stages 0-1 vs. 2-4).

RESULTS

The AUCs of the four protein models, based on 37 analytes (18 not previously linked to NAFLD), for the diagnosis of their respective components (at a clinically relevant severity) in training/paired validation sets were: fibrosis (AUC 0.92/0.85); steatosis (AUC 0.95/0.79), inflammation (AUC 0.83/0.72), and ballooning (AUC 0.87/0.83). An additional outcome, at-risk NASH, defined as steatohepatitis with NAFLD activity score ≥4 (with a score of at least 1 for each of its components) and fibrosis stage ≥2, was predicted by multiplying the outputs of each individual component model (AUC 0.93/0.85). We further evaluated their ability to detect change in histology following treatment with placebo, pioglitazone, vitamin E or obeticholic acid. Component model scores significantly improved in the active therapies vs. placebo, and differential effects of vitamin E, pioglitazone, and obeticholic acid were identified.

CONCLUSIONS

Serum protein scanning identified signatures corresponding to the key components of liver biopsy in NAFLD. The models developed were sufficiently sensitive to characterize the longitudinal change for three different drug interventions. These data support continued validation of these proteomic models to enable a "liquid biopsy"-based assessment of NAFLD.

CLINICAL TRIAL NUMBER

Not applicable.

IMPACT AND IMPLICATIONS

An aptamer-based protein scan of serum proteins was performed to identify diagnostic signatures of the key histological features of non-alcoholic fatty liver disease (NAFLD), for which no approved non-invasive diagnostic tools are currently available. We also identified specific protein signatures related to the presence and severity of NAFLD and its histological components that were also sensitive to change over time. These are fundamental initial steps in establishing a serum proteome-based diagnostic signature of NASH and provide the rationale for using these signatures to test treatment response and to identify several novel targets for evaluation in the pathogenesis of NAFLD.

Biomarkers for staging fibrosis and non-alcoholic steatohepatitis in non-alcoholic fatty liver disease (the LITMUS project): a comparative diagnostic accuracy study

BACKGROUND

The reference standard for detecting non-alcoholic steatohepatitis (NASH) and staging fibrosis-liver biopsy-is invasive and resource intensive. Non-invasive biomarkers are urgently needed, but few studies have compared these biomarkers in a single cohort. As part of the Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS) project, we aimed to evaluate the diagnostic accuracy of 17 biomarkers and multimarker scores in detecting NASH and clinically significant fibrosis in patients with non-alcoholic fatty liver disease (NAFLD) and identify their optimal cutoffs as screening tests in clinical trial recruitment.

METHODS

This was a comparative diagnostic accuracy study in people with biopsy-confirmed NAFLD from 13 countries across Europe, recruited between Jan 6, 2010, and Dec 29, 2017, from the LITMUS metacohort of the prospective European NAFLD Registry. Adults (aged ≥18 years) with paired liver biopsy and serum samples were eligible; those with excessive alcohol consumption or evidence of other chronic liver diseases were excluded. The diagnostic accuracy of the biomarkers was expressed as the area under the receiver operating characteristic curve (AUC) with liver histology as the reference standard and compared with the Fibrosis-4 index for liver fibrosis (FIB-4) in the same subgroup. Target conditions were the presence of NASH with clinically significant fibrosis (ie, at-risk NASH; NAFLD Activity Score ≥4 and F≥2) or the presence of advanced fibrosis (F≥3), analysed in all participants with complete data. We identified thres holds for each biomarker for reducing the number of biopsy-based screen failures when recruiting people with both NASH and clinically significant fibrosis for future trials.

FINDINGS

Of 1430 participants with NAFLD in the LITMUS metacohort with serum samples, 966 (403 women and 563 men) were included after all exclusion criteria had been applied. 335 (35%) of 966 participants had biopsy-confirmed NASH and clinically significant fibrosis and 271 (28%) had advanced fibrosis. For people with NASH and clinically significant fibrosis, no single biomarker or multimarker score significantly reached the predefined AUC 0·80 acceptability threshold (AUCs ranging from 0·61 [95% CI 0·54-0·67] for FibroScan controlled attenuation parameter to 0·81 [0·75-0·86] for SomaSignal), with accuracy mostly similar to FIB-4. Regarding detection of advanced fibrosis, SomaSignal (AUC 0·90 [95% CI 0·86-0·94]), ADAPT (0·85 [0·81-0·89]), and FibroScan liver stiffness measurement (0·83 [0·80-0·86]) reached acceptable accuracy. With 11 of 17 markers, histological screen failure rates could be reduced to 33% in trials if only people who were marker positive had a biopsy for evaluating eligibility. The best screening performance for NASH and clinically significant fibrosis was observed for SomaSignal (number needed to test [NNT] to find one true positive was four [95% CI 4-5]), then ADAPT (six [5-7]), MACK-3 (seven [6-8]), and PRO-C3 (nine [7-11]).

INTERPRETATION

None of the single markers or multimarker scores achieved the predefined acceptable AUC for replacing biopsy in detecting people with both NASH and clinically significant fibrosis. However, several biomarkers could be applied in a prescreening strategy in clinical trial recruitment. The performance of promising markers will be further evaluated in the ongoing prospective LITMUS study cohort.

FUNDING

The Innovative Medicines Initiative 2 Joint Undertaking.

Proteomic signatures for identification of impaired glucose tolerance

The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79-0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications.

Abstract LB515: Cardiovascular risk assessed by aptamer-based proteomics is increased in early breast cancer

Background: Cancer patients with a high risk for cardiovascular (CV) disease may be susceptible to cardiotoxic injury during cancer therapy. Accordingly, CV risk assessment may help in identifying candidates for preventive cardioprotective treatment. Since existing CV risk scores may lack the sensitivity and granularity to reflect the changes in CV risk that is associated with early cancer, we hypothesized that a CV risk score based on aptamer-based proteomics would permit discrimination of CV risk between patients with early breast cancer and age-, sex- and risk factor-matched healthy subjects. Moreover, we hypothesized that CV risk as assessed by aptamer-based proteomics first increases during and subsequently decreases after anthracycline-containing chemotherapy.

Methods: We included 120 women with early breast cancer participating in the 2x2 factorial, randomized, placebo-controlled Prevention of cardiac dysfunction during adjuvant breast cancer therapy (PRADA) trial who were assigned to candesartan vs placebo and metoprolol vs placebo. Blood samples were obtained serially at: Visit 1 (ie, post-surgery but prior to epirubicin), following the first cycle of epirubicin (Visit 2), after the completion of epirubicin therapy (Visit 3), following adjuvant therapy (Visit 4), and 1-2 years after completion of adjuvant and blinded therapy (Visit 5). Age-, sex-, and risk factor-matched subjects (n = 500) from the Fenland study served as controls. We used highly multiplexed modified aptamer-based proteomics to measure ~5000 plasma proteins. A validated 27-protein CV risk model (CVD) that provides information on absolute risk of myocardial infarction, stroke, heart failure or mortality over a 4-year period was used as the dependent variable.

Results: The CVD risk probability was significantly higher at Visit 1 than in the age-, sex- and risk-factor matched control group (p < 0.001). The CVD risk probability increased significantly from baseline to completion of epirubicin therapy (p < 0.001) and dropped below baseline levels for subsequent timepoints after the completion of epirubicin treatment. The mean CVD risk increased from 15.9% at Visit 2 to 24.6% at Visit 3, resulting in the percentage of subjects in the medium-high risk bin increasing from 9% at Visit 2 to 28% at Visit 3. The CVD risk distribution at the end of study was similar to the initial distribution at baseline and Visit 2.

Conclusion: Using a novel CVD risk proteomics model, we observed that (1) The CVD risk of patients with early breast cancer is increased compared to age-, sex- and risk factor-matched individuals from the general population; (2) CVD risk increases during adjuvant epirubicin-containing chemotherapy, and (3) after completed adjuvant cancer therapy CVD risk returns to pre-chemotherapy level. Use of the CVD risk score may represent a novel tool to identify and monitor cancer patients who may benefit from preventive cardioprotective therapy.

Citation Format: Torbjørn Omland, Jessica Chadwick, Ragnhild Røysland, Geeta Gulati, David Astling, Siri L. Heck, Victoria Vinje, Rachel Ostroff, Peter Ganz, Jürgen Geisler, Steve Williams. Cardiovascular risk assessed by aptamer-based proteomics is increased in early breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB515.

Abstract 2227: Development and validation of a blood-based protein-only predictor of 5-year lung cancer risk in ever smokers

Background: Lung cancer is the second most common cancer type and is the leading cause of cancer death globally, with smoking and advancing age as the leading causal risk factors. The USPSTF guidelines for lung cancer screening recommends annual screening for select current or former smokers over 50 years of age. While annual screening via low dose CT has been demonstrated to decrease lung cancer mortality, compliance with screening guidelines remains low. Additional prognostic tools for future lung cancer risk stratification, particularly those without immutable demographic and health history, may be beneficial in increasing screening compliance and monitoring changing risk across time.

Methods: Using modified-aptamer proteomics technology, SomaScan® assay v4.0, we scanned ~5000 proteins in 6085 EDTA plasma samples from “Ever Smokers” (current or former smokers, aged 50-73) with no known prevalent cancer at visit 3 of the Atherosclerosis Risk in Communities (ARIC) study, for a total of ~30 million protein measurements. A total of 348 incident lung cancer diagnoses occurred in this sample set, with 75 occurring within 5 years of visit 3 blood-draw. Time to lung cancer diagnosis events were modeled with protein measurements using machine learning methods in 70% of ARIC visit 3 ever smokers. A model was selected based on performance in a 15% holdout sample subset and validated in the remaining 15% ARIC visit 3 samples not used for model training or selection.

Results: A 7-feature protein-only accelerated failure time (AFT) Weibull model was successfully developed to predict the probability of a lung cancer diagnosis within 5 years of blood draw. Model performance in training, model selection, and validation datasets was AUC equal to 0.76, 0.72, and 0.83, respectively. Based on predicted probabilities from the model, individuals were stratified into 3 risk bins (low, medium, and high) with a 5-year event rate of 0.49% vs 2.74% in low vs high risk bins. Model performance was additionally assessed in an independent Japanese cohort.

Conclusion: We successfully developed a blood-based protein-only model that predicts risk of developing lung cancer in ever smokers. Performance of the protein model out-performs traditional risk factors for lung cancer and given the lack of immutable factors it has the potential to provide real-time risk which can be repeatedly assessed over time. Proteomics-driven risk stratification may have ability to increase adherence to lung cancer screening guidelines and/or influence a positive behavior change in modifiable risk-related behaviors.

Citation Format: Clare Paterson, Leigh Alexander, Rachel Ostroff, Joseph Gogain, Yolanda Hagar, Hannah Biegel, Stephen Williams. Development and validation of a blood-based protein-only predictor of 5-year lung cancer risk in ever smokers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2227.

A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c-statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a "universal" surrogate end point for cardiovascular risk.

Serum Proteomics and Plasma Fibulin-3 in Differentiation of Mesothelioma From Asbestos-Exposed Controls and Patients With Other Pleural Diseases

INTRODUCTION

Malignant pleural mesothelioma (MPM) is difficult to diagnose. An accurate blood biomarker could prompt specialist referral or be deployed in future screening. In earlier retrospective studies, SOMAscan proteomics (Somalogic, Boulder, CO) and fibulin-3 seemed highly accurate, but SOMAscan has not been validated prospectively and subsequent fibulin-3 data have been contradictory.

METHODS

A multicenter prospective observational study was performed in 22 centers, generating a large intention-to-diagnose cohort. Blood sampling, processing, and diagnostic assessment were standardized, including a 1-year follow-up. Plasma fibulin-3 was measured using two enzyme-linked immunosorbent assays (CloudClone [used in previous studies] and BosterBio, Pleasanton, CA). Serum proteomics was measured using the SOMAscan assay. Diagnostic performance (sensitivity at 95% specificity, area under the curve [AUC]) was benchmarked against serum mesothelin (Mesomark, Fujirebio Diagnostics, Malvern, PA). Biomarkers were correlated against primary tumor volume, inflammatory markers, and asbestos exposure.

RESULTS

A total of 638 patients with suspected pleural malignancy (SPM) and 110 asbestos-exposed controls (AECs) were recruited. SOMAscan reliably differentiated MPM from AECs (75% sensitivity, 88.2% specificity, validation cohort AUC 0.855) but was not useful in patients with differentiating non-MPM SPM. Fibulin-3 (by BosterBio after failed CloudClone validation) revealed 7.4% and 11.9% sensitivity at 95% specificity in MPM versus non-MPM SPM and AECs, respectively (associated AUCs 0.611 [0.557-0.664], p = 0.0015) and 0.516 [0.443-0.589], p = 0.671), both inferior to mesothelin. SOMAscan proteins correlated with inflammatory markers but not with asbestos exposure. Neither biomarker correlated with tumor volume.

CONCLUSIONS

SOMAscan may prove useful as a future screening test for MPM in asbestos-exposed persons. Neither fibulin-3 nor SOMAscan should be used for diagnosis or pathway stratification.

Author Correction: Genetic architecture of host proteins involved in SARS-CoV-2 infection

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21370-6

Genetic architecture of host proteins involved in SARS-CoV-2 infection

Understanding the genetic architecture of host proteins interacting with SARS-CoV-2 or mediating the maladaptive host response to COVID-19 can help to identify new or repurpose existing drugs targeting those proteins. We present a genetic discovery study of 179 such host proteins among 10,708 individuals using an aptamer-based technique. We identify 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links and evidence that putative viral interaction partners such as MARK3 affect immune response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

Impact of Kidney Function on the Blood Proteome and on Protein Cardiovascular Risk Biomarkers in Patients With Stable Coronary Heart Disease

Background

Chronic kidney disease (CKD) confers increased cardiovascular risk, not fully explained by traditional factors. Proteins regulate biological processes and inform the risk of diseases. Thus, in 938 patients with stable coronary heart disease from the Heart and Soul cohort, we quantified 1054 plasma proteins using modified aptamers (SOMAscan) to: (1) discern how reduced glomerular filtration influences the circulating proteome, (2) learn of the importance of kidney function to the prognostic information contained in recently identified protein cardiovascular risk biomarkers, and (3) identify novel and even unique cardiovascular risk biomarkers among individuals with CKD.

Methods and Results

Plasma protein levels were correlated to estimated glomerular filtration rate (eGFR) using Spearman‐rank correlation coefficients. Cox proportional hazard models were used to estimate the association between individual protein levels and the risk of the cardiovascular outcome (first among myocardial infarction, stroke, heart failure hospitalization, or mortality). Seven hundred and nine (67.3%) plasma proteins correlated with eGFR at P<0.05 (ρ 0.06–0.74); 218 (20.7%) proteins correlated with eGFR moderately or strongly (ρ 0.2–0.74). Among the previously identified 196 protein cardiovascular biomarkers, just 87 remained prognostic after correction for eGFR. Among patients with CKD (eGFR <60 mL/min per 1.73 m²), we identified 21 protein cardiovascular risk biomarkers of which 8 are unique to CKD.

Conclusions

CKD broadly alters the composition of the circulating proteome. We describe protein biomarkers capable of predicting cardiovascular risk independently of glomerular filtration, and those that are prognostic of cardiovascular risk specifically in patients with CKD and even unique to patients with CKD.

Genetic architecture of host proteins interacting with SARS-CoV-2

Strategies to develop therapeutics for SARS-CoV-2 infection may be informed by experimental identification of viral-host protein interactions in cellular assays and measurement of host response proteins in COVID-19 patients. Identification of genetic variants that influence the level or activity of these proteins in the host could enable rapid 'in silico' assessment in human genetic studies of their causal relevance as molecular targets for new or repurposed drugs to treat COVID-19. We integrated large-scale genomic and aptamer-based plasma proteomic data from 10,708 individuals to characterize the genetic architecture of 179 host proteins reported to interact with SARS-CoV-2 proteins or to participate in the host response to COVID-19. We identified 220 host DNA sequence variants acting in cis (MAF 0.01-49.9%) and explaining 0.3-70.9% of the variance of 97 of these proteins, including 45 with no previously known protein quantitative trait loci (pQTL) and 38 encoding current drug targets. Systematic characterization of pQTLs across the phenome identified protein-drug-disease links, evidence that putative viral interaction partners such as MARK3 affect immune response, and establish the first link between a recently reported variant for respiratory failure of COVID-19 patients at the ABO locus and hypercoagulation, i.e. maladaptive host response. Our results accelerate the evaluation and prioritization of new drug development programmes and repurposing of trials to prevent, treat or reduce adverse outcomes. Rapid sharing and dynamic and detailed interrogation of results is facilitated through an interactive webserver ( https://omicscience.org/apps/covidpgwas/ ).

Plasma protein patterns as comprehensive indicators of health

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Improving Assessment of Drug Safety Through Proteomics: Early Detection and Mechanistic Characterization of the Unforeseen Harmful Effects of Torcetrapib

Supplemental Digital Content is available in the text.

Background:

Early detection of adverse effects of novel therapies and understanding of their mechanisms could improve the safety and efficiency of drug development. We have retrospectively applied large-scale proteomics to blood samples from ILLUMINATE (Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events), a trial of torcetrapib (a cholesterol ester transfer protein inhibitor), that involved 15 067 participants at high cardiovascular risk. ILLUMINATE was terminated at a median of 550 days because of significant absolute increases of 1.2% in cardiovascular events and 0.4% in mortality with torcetrapib. The aims of our analysis were to determine whether a proteomic analysis might reveal biological mechanisms responsible for these harmful effects and whether harmful effects of torcetrapib could have been detected early in the ILLUMINATE trial with proteomics.

Methods:

A nested case-control analysis of paired plasma samples at baseline and at 3 months was performed in 249 participants assigned to torcetrapib plus atorvastatin and 223 participants assigned to atorvastatin only. Within each treatment arm, cases with events were matched to controls 1:1. Main outcomes were a survey of 1129 proteins for discovery of biological pathways altered by torcetrapib and a 9-protein risk score validated to predict myocardial infarction, stroke, heart failure, or death.

Results:

Plasma concentrations of 200 proteins changed significantly with torcetrapib. Their pathway analysis revealed unexpected and widespread changes in immune and inflammatory functions, as well as changes in endocrine systems, including in aldosterone function and glycemic control. At baseline, 9-protein risk scores were similar in the 2 treatment arms and higher in participants with subsequent events. At 3 months, the absolute 9-protein derived risk increased in the torcetrapib plus atorvastatin arm compared with the atorvastatin-only arm by 1.08% (P=0.0004). Thirty-seven proteins changed in the direction of increased risk of 49 proteins previously associated with cardiovascular and mortality risk.

Conclusions:

Heretofore unknown effects of torcetrapib were revealed in immune and inflammatory functions. A protein-based risk score predicted harm from torcetrapib within just 3 months. A protein-based risk assessment embedded within a large proteomic survey may prove to be useful in the evaluation of therapies to prevent harm to patients.

Clinical Trial Registration:

URL: https://www.clinicaltrials.gov. Unique identifier: NCT00134264.

Abstract 450: Serum biomarkers in patients treated with stereotactic body radiation therapy (SBRT) for prostate cancer

Purpose: The goal of this study was to determine the feasibility for developing prognostic protein biomarkers in serum samples from patients undergoing Stereotactic Body Radiation Therapy (SBRT) for organ confined prostate cancers.

Methods: 130 patients presenting with organ confined prostate cancers were treated with SBRT to doses of 35-36.25 Gy in 5 fractions. Peripheral blood samples were collected prospectively from patients at time 0 prior to radiation and serially after the first treatment (24 hours), follow-up months 1, 3, 6, 9, and 12 during the first year, and every 6 months thereafter, for up to 3 years. Processed study samples were analyzed by SomaLogic, Inc., using the SOMAscan Version 3 proteomic assay. Statistical analysis was performed on log-transformed data, with statistically significant differences identified by evaluating false discovery rate corrected p-values. Protein correlations were discovered with the Jonckheere-Terpstra (JT) test. Ingenuity Pathway Analysis (IPA) software was used to analyze cellular signaling pathways. PSA levels and clinical recurrence information were prospectively obtained at each follow-up visit and maintained in an institutional database.

Results:

Patient stratification by risk assessment identified 27 low-, 71 intermediate- and 32 high-risk patients in the study cohort. Proteins that function in cell proliferation, angiogenesis, protein signaling, gonad development, and cell migration correlated with Gleason's grade. CGA.LHB, KLK3, and CNTFR correlated both with tumor stage and Gleason's grade. With a median follow up of 3 years, ten patients experienced biochemical failures. While no proteins identified as differentially expressed in recurrent patients achieved significance, IPA pathway analysis of the top differentially expressed proteins converged on the IL-6 canonical pathway.

183 proteins were attributed to radiation effects on differential expression in patients by comparing pre-treatment to the 3 months post-treatment specimens. IPA network pathway analyses of these paired samples revealed changes in cell activation and signaling pathways, with the primary regulatory networks associated with ERK1/2, NF-κB, IFNG, ADIPOQ, histone 3, and histone 4. Of particular interest, high adiponectin levels in patients presenting with higher tumor stage decreased after radiation exposure, underscoring the potential for this molecule as a signal for determining response to radiation treatment.

Conclusion:

These hypothesis-generating experiments show differential serum protein levels in prostate cancer patient cohorts that have been stratified by risk factors and in longitudinal studies of patients following treatment with SBRT. Candidate biomarker proteins and molecular pathways have been identified for validation as potential predictors of prostate cancer response to radiation treatment.

Citation Format: Einsley Janowski, Simeng Suy, Rency S. Varghese, Olga Timofeeva, Stuart G. Field, Rachel Ostroff, Steve Williams, Anatoly Dritschilo, Sean P. Collins. Serum biomarkers in patients treated with stereotactic body radiation therapy (SBRT) for prostate cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 450.

Biomarkers of pulmonary tuberculosis identified in multiplexed proteomic assay (SOMAscan) of human serum (MPF2P.808)

Background: A rapid, accurate, and inexpensive tuberculosis (TB) diagnostic test in the developing world would allow earlier treatment and reduce transmission. Methods: We used slow off-rate modified aptamers (SOMAmers) in a highly multiplexed proteomic assay (SOMAscan) to measure 1129 human proteins and 16 M. tuberculosis proteins in serum samples collected in S. Africa, Vietnam and Peru, provided by the Foundation for Innovative New Diagnostics. Results: Among the top host serum biomarkers distinguishing TB from non-TB were factors involved in cell-matrix interactions such tissue remodeling and fibrosis (Kallistatin, TSP4, gelsolin, CDON) which were lower in TB compared to non-TB, and factors of innate immunity, acute phase reactants, and inflammation (LBP, ITI heavy chain H4, NPS-PLA2, IP-10), which were higher in TB, regardless of the HIV status. A 9-marker model performed well in a training set of 173 TB vs. 160 non-TB samples (sensitivity 90% / specificity 85%, AUC=0.94), which was confirmed in a blinded verification set of 132 TB vs. 118 non-TB samples (sensitivity 80% / specificity 84%, AUC=0.88). Conclusions: The discovery of robust, quantitative, non-culture based diagnostic biomarkers of active pulmonary TB has great potential to facilitate the rapid and accurate diagnosis of TB disease.

An integrated approach to blood-based cancer diagnosis and biomarker discovery

Disrupted or abnormal biological processes responsible for cancers often quantitatively manifest as disrupted additive and multiplicative interactions of gene/protein expressions correlating with cancer progression. However, the examination of all possible combinatorial interactions between gene features in most case-control studies with limited training data is computationally infeasible. In this paper, we propose a practically feasible data integration approach, QUIRE (QUadratic Interactions among infoRmative fEatures), to identify discriminative complex interactions among informative gene features for cancer diagnosis and biomarker discovery directly based on patient blood samples. QUIRE works in two stages, where it first identifies functionally relevant gene groups for the disease with the help of gene functional annotations and available physical protein interactions, then it explores the combinatorial relationships among the genes from the selected informative groups. Based on our private experimentally generated data from patient blood samples using a novel SOMAmer (Slow Off-rate Modified Aptamer) technology, we apply QUIRE to cancer diagnosis and biomarker discovery for Renal Cell Carcinoma (RCC) and Ovarian Cancer (OVC). To further demonstrate the general applicability of our approach, we also apply QUIRE to a publicly available Colorectal Cancer (CRC) dataset that can be used to prioritize our SOMAmer design. Our experimental results show that QUIRE identifies gene-gene interactions that can better identify the different cancer stages of samples, as compared to other state-of-the-art feature selection methods. A literature survey shows that many of the interactions identified by QUIRE play important roles in the development of cancer.

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.

Preoteomic insights in oncology: What have we learned from measuring millions of proteins?

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Background: We have assayed over 8400 blood samples from 10 different malignancies, generating over 8 million quantitative protein measurements using SOMAscan, a new proteomic assay based on modified aptamers. It is capable of simultaneously measuring more than a thousand proteins from small volumes of biological samples such as plasma, tissues or cells. Methods: Bioinformatic analysis has revealed biomarker panels for early detection, differential diagnosis or prognosis in 6 of these cancers: lung, mesothelioma, ovarian, pancreatic, bladder, and renal cell carcinoma. Individual classifiers have been built with 7-13 biomarkers and AUC ranges of 0.84-0.99. These analyses have revealed unique biomarkers for each cancer, as well as common markers. Results: Several themes emerge from our extensive survey of oncology proteomic profiles. Many of the proteins correlate with disease burden or histopathological grade, and thus have utility for diagnosis, prognosis and recurrence monitoring. Parallels between serum and lung tumor tissue protein measurements demonstrate that many of the blood biomarkers arise from the tumor environment. Inflammatory response proteins are identified in many cancers and reveal important connections between tumor development and host response. Patterns can be found for biological processes in tumor biology, including angiogenesis, apoptosis, inflammation, metabolism and tumor invasion. Conclusions: With this new proteomics technology – which is fast, economical, highly scalable and flexible – we now have a powerful tool that enables proteome-wide proteomics, biomarker discovery and advancing the next generation of evidence-based, “personalized” diagnostics and therapeutics for oncology.

Exposing the criminal record of every blood sample: Use of SOMAmer technology and sample mapping vectors to mitigate false biomarker discoveries in lung cancer

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Background: Biomarker discovery studies may fail to translate to the clinic because the study population does not match the intended clinical use or because hidden preanalytic variability in the discovery samples contaminates the apparent disease specific information in the biomarkers. This can arise from differences in blood sample processing between study sites or in samples collected differently at the same study site. Methods: To better understand the effect of different blood sample processing procedures, we evaluated protein measurement bias in a large multi-center lung cancer study using the >1000 protein SOMAscan™ assay. These analyses revealed that perturbations in serum collection and processing result in changes to families of proteins from known biological pathways. We subsequently developed protein biomarker signatures of cell lysis, platelet activation and complement activation and assembled these preanalytic signatures into quantitative multi-dimensional Sample Mapping Vector (SMV) scores. Results: The SMV score provides critical evaluation of the quality of every blood-based sample used in discovery and also enables the evaluation of candidate protein biomarkers for resistance to preanalytic variability. Despite uniform processing protocols for each clinic, the SMV analysis revealed unexpected case/control bias arising from collecting case and control serum from different clinics at the same academic centers, an effect that created false or bias-contaminated disease markers. We therefore used the SMV score to remove bias-susceptible analytes and to define a well-collected, unbiased training set. An improved classifier was developed, resistant to common artifacts in serum processing. Conclusions: . The performance of this classifier to detect lung cancer in a high-risk population is more likely to represent real-world diagnostic results. We believe this approach is generally applicable to clinical investigations in all fields of biomarker discovery and translational medicine.

Abstract B24: Untangling the interplay of lung cancer biomarkers and preanalytic variability with SOMAmer proteomic technology

Biomarker discovery studies may fail to validate because the clinical population does not represent the intended clinical use or because hidden preanalytic variability in the discovery samples contaminates the apparent disease specific information in the biomarkers. This preanalytic variability can arise from differences in blood sample processing between study sites, or worse, introduce case/control bias in samples collected differently at the same study site. To better understand the effect of different blood sample processing procedures, we evaluated protein measurement bias in a large multi-center lung cancer study. These analyses revealed that perturbations in serum protocols result in changes to many proteins in a coordinated fashion. We subsequently developed protein biomarker signatures of processes such as cell lysis, platelet activation and complement activation and assembled these preanalytic signatures into quantitative multidimensional Sample Mapping Vectors (SMV) scores. The SMV score provides critical evaluation of both the quality of every blood sample used in discovery, and also enables the evaluation of candidate protein biomarkers for resistance to preanalytic variability.

The underlying platform technology uses SOMAmers (Slow Off-rate Modified Aptamers) as affinity reagents to quantify proteins. Following analytic validation of the assay, which simultaneously measured approximately 850 proteins with sub-pM limits of detection and intra and inter-assay CV of &lt;5%, we initiated a lung cancer discovery program. The intended use was defined as early detection of lung cancer in individuals with indeterminate pulmonary nodules and in high-risk smokers. This case/control study included almost 1000 samples from 4 different centers, with blinded verification in ∼400 samples. Although the AUC of ∼0.9 in both training and blinded verification was promising, we had to eliminate several markers due to preanalytic bias leading to site-to-site differences. Even worse, when the SMV scores of preanalytic effects were applied retrospectively, we found there were substantial case-control biases within centers, and that a number of cancer markers were influenced by these biases. As a result, our initial diagnostic performance was partly dependent on markers that not only related to cancer biology, but that were also contaminated by preanalytic case/control bias.

To eliminate this effect, we used the SMV score to define an unbiased fraction of the original sample set, including samples from the Pittsburgh Lung Screening Study (PLuSS, supported by NCI SPORE in Lung Cancer grant P50 090440), and re-assayed the unaffected samples using a new version of the assay with 1033 analytes. The new 7-marker classifier had an AUC of 0.86 and contained several new markers that were not available in the prior version of the assay, which in turn enabled the elimination of some markers that were partly contaminated with case/control bias. The modest loss in performance was an acceptable price for avoiding dependence on hidden case/control information.

Applying quantitative measures of preanalytic variability, we identified preanalytic sample bias across 4 large study centers, revealing unintentional differences inherent in how biological samples are obtained, processed and stored for different study populations. This study highlights the consistency of cohort studies and provides a tool for evaluating bias in case/control studies before proceeding to biomarker discovery. Choosing biomarkers with not just the best case/control discrimination but that are also resistant to sample processing bias increases the likelihood that a robust biomarker panel will perform well in the clinic.

Detection of resectable pancreatic cancer with SOMAmer proteomic technology

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Background: To improve the current dismal 5-year survival rates for pancreatic adenocarcinoma (PC) it is essential to develop strategies focused on detection in high-risk populations. This study employed a unique SOMAmer proteomics platform to identify a biomarker panel that discriminates between pancreatic cancer and control patients. A secondary goal was to compare the panel with CA 19-9.

Methods: Plasma samples (total < 20 ul) were analyzed for 825-proteins using a SOMAmer proteomics platform on training set of 100 cases and 69 controls from a single institution. The control group contained individuals with GI symptoms, including acute and chronic pancreatitis, and normal controls. An independent blinded validation set from another center consisted of 43 PC patients and 47 controls. About two thirds (96/143) of the study cases were in resectable stages I-IIb. Biomarkers were identified by Random Forest backwards selection, using a false-discovery-rate corrected value of p<0.001. CA19-9 levels were available on a subset of the patients from the training set (87 PC and 29 controls). A CA 19-9 level of > 40 U/ml was defined as positive.

Results: In the training set, 37 markers were significantly different between case and controls. A 10 marker Random Forests classifier had an AUC of 0.91 for training and 0.90 for the independent validation set. Comparison of the SOMAmer results with CA 19-9 levels are shown in the table. There was 78% agreement between the two tests. One could maximize sensitivity with some sacrifice of specificity by combining either test positive (95% and 66%, respectively) or conversely maximize specificity (97%) with lower sensitivity of 77% if both tests must be positive.

Conclusions: 1) The 10 marker SOMAmer biomarker panel is promising for the detection of resectable pancreatic cancer with an AUC of 0.91 on a training set, which was confirmed on an independent validation set. 2) Preliminary data suggests that this test is complementary to CA 19-9 with improved combined sensitivity of up to 95%. 3) Studies to assess further applications of these panels, including for risk assessment in individuals with a family history of pancreatic cancer, are underway.

[Table: see text]

New SOMAmer-based assay to discover biomarkers relevant to malignant melanoma

Malignant melanoma is a serious form of skin cancer with approximately 138,000 new cases predicted worldwide in 2010 and an estimated 48,000 deaths. The number is expected to double in less than a decade. There is an unmet need for new molecular technologies that provide for cost-effective, reliable and accurate detection of malignant melanoma at tumor burden levels below those currently observable with standard imaging or histopathological techniques. It would be desirable if a new test could predict a) sentinel lymph node (SLN) positivity in order to avoid SLN biopsy, an invasive and expensive procedure, b) recurrence to help monitor disease and make treatment decisions and c) survival time in metastatic melanoma.

Secreted proteins and those released during apoptosis from tumor cells and surrounding tissues undoubtedly contain important biologic information that would theoretically enable early diagnosis and prognostic and therapeutic decisions in oncology. However, there is great difficulty in finding and quantifying such signals for large numbers of low abundance proteins. SomaLogic therefore created a highly multiplexed proteomic assay which is continuously expanding in breadth. It currently measures 825 proteins simultaneously from ~15ul blood, with throughput of 300 samples /day. The average dynamic range of each protein in the assay is &gt;3 logs — with nearly seven logs of dynamic range achieved through multiple dilutions — and the median lower limit of quantification is below 1 pM. The median coefficient of variation for each protein is &lt;5%. This assay performance arises from the selection of high affinity aptamers (SOMAmers) that bind selectively to their target proteins with slow off-rates.

Retrospective analysis of serum, collected from patients before or after wide local excision / sentinel lymph node removal was done using this aptamer proteomics platform. In patients with metastatic stage IV disease at the time of diagnosis follow-up data was collected for several years to monitor time to survival. A naïve Bayesian statistical program was used for analysis of each comparison, negative to positive SLN cases, recurrence and non recurred cases and in case of metastatic melanoma alive vs. dead of disease within a year of blood draw.

Of the 39 patients who had SLN biopsy, 25 were negative (stage II) and 14 were positive (stage III) by pathological analysis. 13 proteins were significantly differentially expressed (p&lt;0.01). A three protein marker panel generated a Receiver Operating Characteristic (ROC) curve area under the curve (AUC) of 0.9 and could identify SLN positive patients with a specificity and sensitivity of 0.92 and 0.93 respectively.

Comparison of patients alive with stage IV disease (N=22) verses those dead of disease in 1 year after blood draw (N=15) to yielded 37 proteins significantly different (p=&lt;0.01). A signature of 2 proteins had an area under the ROC curve of 0.89 with a sensitivity and specificity of 0.87 and 0.91 respectively.

Thus in a preliminary study we have discovered new proteins associated with a regional verses local disease at the time of diagnosis in patients with melanoma lesions &gt;1mm. In metastatic melanoma, we were able to build classifier to predict survival time within a year. These results will be further validated on a larger sample set.

Detection of rare cancers with aptamer proteomic technology

The need for sensitive, early detection of aggressive, rare malignancies such as pancreatic cancer and mesothelioma is high. Just as importantly, the stringent specificity required of diagnostic tests for these low prevalence diseases creates unique challenges. A diagnostic test which identified these rare diseases early in a significant number of patients without creating a large number of false positive results would be clinically important and would deliver health-economic benefits.

However, there is great difficulty in precisely quantifying such signals for large numbers of low abundance proteins. Our group therefore created a highly multiplexed proteomic assay which is continuously expanding in breadth. It currently measures 825 proteins simultaneously from ~15ul blood, with throughput of 300 samples/day. The average dynamic range of each protein in the assay is &gt;3 logs — with nearly seven logs of dynamic range achieved through multiple dilutions — and the median lower limit of quantification is below 1 pM. The median coefficient of variation for each protein is &lt;5%. This assay performance arises from the selection of high affinity aptamers that bind selectively to their target proteins with slow offrates. We have applied this proteomics technology to the discovery of biomarkers and diagnostic algorithms for two rare malignancies, pancreatic cancer and mesothelioma.

Pancreatic cancer is the fourth leading cause of cancer-related death in the USA. While the 5-year survival is only 5%, this has shown to be increased by early surgical intervention. Plasma samples were analyzed in a prospectively designed case:control study from 143 cases of pancreatic cancer and 116 controls of a similar age and gender distribution. 25% of each group was retained as a blinded verification set. In the training set, 47 markers were significantly different at a false-discovery-rate corrected value of p&lt;0.001. An 11 marker panel had a Receiver Operating Characteristic (ROC) curve area under the curve (AUC) of 0.91. A specificity-driven decision threshold relevant to early detection in asymptomatic patients of 97.5% had a sensitivity of ~60% in the training set. This test performance was effectively maintained in the blinded verification set, with a specificity of 96.5% and a sensitivity of 65%.

Other decision thresholds relevant to symptomatic patients enable a sensitivity-driven approach of 90% sensitivity and 75% specificity. The results of this test using the high specificity decision threshold will deliver a positive predictive value of greater than 10% in a population with a disease prevalence of 0.4% or more. Additionally, when the test is used in symptomatic subjects as a differential diagnostic, non-invasive, rapid and sensitive detection of pancreatic cancer enables swift clinical decisions for treatment of this aggressive disease.

The second rare cancer analyzed in this clinical series was malignant pleural mesothelioma, which is an aggressive, asbestos-related pulmonary cancer. This disease causes an estimated 15,000 to 20,000 deaths per year worldwide. Between 1940 and 1979, approximately 27.5 million people were occupationally exposed to asbestos in the United States. The incidence of pleural mesothelioma in the US is 3,000 new cases/year and will not peak for another 20 years. Mesothelioma has a latency period of 20-40 years from asbestos exposure, but once diagnosed this aggressive disease is often fatal within 14 months. Because diagnosis is difficult, most patients present at a clinically advanced stage where possibility of cure is minimal. Therefore, we have conducted a broad search for new serum biomarkers with our aptamer-based proteomic platform and defined a classifier for the detection of mesothelioma in asbestos exposed individuals.

Serum samples were analyzed with the aptamer proteomics platform in a prospectively designed case:control study of 357 serum samples obtained from patients diagnosed with mesothelioma or lung cancer compared to asbestos exposed controls, high risk smokers and benign lung disease. These samples were divided into a training and test set for classifier development and verification. The objective of the study was to discover proteins which are involved in mesothelioma and to develop algorithms and classifiers for the disease. The initial results are promising. Nineteen significant biomarkers were discovered. Classifiers were built with subsets of these biomarkers resulting in an AUC of 0.95 or better with an overall accuracy of 93%. Applying a 13-plex Random Forest classifier to the blinded test set resulted in a specificity of 100% and sensitivity of 80% for distinction of asbestos exposed controls from mesothelioma. Refinement and confirmation of classifier performance will be established through ongoing validation studies.

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine.

The stability of the circulating human proteome to variations in sample collection and handling procedures measured with an aptamer-based proteomics array

Blood-based protein biomarkers hold great promise to advance medicine with applications that detect and diagnose diseases and aid in their treatment. We are developing such applications with our proteomics technology that combines high-content with low limits of detection. Biomarker discovery relies heavily on archived blood sample collections. Blood is dynamic and changes with different sampling procedures potentially confounding biomarker studies. In order to better understand the effects of sampling procedures on the circulating proteome, we studied three sample collection variables commonly encountered in archived sample sets. These variables included (1) three different sample tube types, PPT plasma, SST serum, and Red Top serum, (2) the time from venipuncture to centrifugation, and (3) the time from centrifugation to freezing. We profiled 498 proteins for each of 240 samples and compared the results by ANOVA. The results found no significant variation in the measurements for most proteins (approximately 99%) when the two sample processing times tested were 2h or less, regardless of sample tube type. Even at the longest timepoints, 20 h, approximately 82% of the proteins, on average for the three collection tube types, showed no significant change. These results are encouraging for proteomic biomarker discovery.

Photoaptamer arrays applied to multiplexed proteomic analysis

Multiplexed photoaptamer-based arrays that allow for the simultaneous measurement of multiple proteins of interest in serum samples are described. Since photoaptamers covalently bind to their target analytes before fluorescent signal detection, the arrays can be vigorously washed to remove background proteins, providing the potential for superior signal-to-noise ratios and lower limits of quantification in biological matrices. Data are presented here for a 17-plex photoaptamer array exhibiting limits of detection below 10 fM for several analytes including interleukin-16, vascular endothelial growth factor, and endostatin and able to measure proteins in 10% serum samples. The assays are simple, scalable, and reproducible. Affinity of the capture reagent is shown to be directly correlated to the limit of detection for the analyte on the array.

Processing of photoaptamer microarrays

Photoaptamers are single-stranded nucleic acids selected for their high affinity to specific proteins of interest. Photoaptamer microarrays capture and quantify proteins from complex samples using a unique protocol that leverages both high-affinity capture with covalent retention of analytes. The initial capture of proteins from solution is similar to the well-known antibody capture, but the "secondary binding event" affected by photoaptamers is a covalent crosslink between the photoaptamer capture agent and the protein analyte. The nature of this specific covalent reaction allows a unique microarray processing that is described in detail in this chapter.

An optical thin film assay incorporating rhinovirus protease inhibitors as detector reagents

Human rhinoviruses (HRV) are the main cause of the common cold. Viral replication utilizes the activity of the HRV3C protease (3CP) enzyme [Antimicrob. Agents Chemother. 43 (1999) 2444; Antimicrob. Agents Chemother. 44 (2000) 1236]. Therefore, 3CP is an attractive target for antiviral drug development, and a new class of orally bioavailable irreversible 3CP inhibitors has been designed [J. Med. Chem. 45 (2002) 1607]. We have used related inhibitors to develop a rapid test for rhinovirus. The optical immuno assay (OIA) thin film detection technology utilizes an optically coated silicon surface to convert specific molecular binding events into visual color changes by altering the reflective properties of light through molecular thin films. The purpose of this study was to develop a rapid assay for the determination of 3CP combining the Thermo Electron Bio Star OIA technology and the newly designed inhibitor compounds. The advantage of this assay was in its approach, in which therapeutic and diagnostic targets are the same thus allowing patients with detected rhinoviruses to receive optimal treatment. Three different biotinylated inhibitor compounds were synthesized. The length of the spacer between the inhibitor and biotin core was 5, 10, and 15 atoms. These compounds were incorporated into the OIA format for the HRV assay development. A rapid (20 min) OIA test was developed using a 15 atom spacer biotinylated inhibitor (4). Forty different HRV serotypes were studied and thirty three serotypes of these 40 were detected (80%).

In-process monitoring of protein purification with thin film silicon sensor technology

We have developed a rapid and sensitive thin film assay for in-process monitoring of target protein purification. This novel biosensor method provides rapid (5-min) visual evaluation of column purification fractions. The method can be used to monitor the efficiency of purification and potential loss of protein if the column binding capacity is exceeded. The eluted fractions containing the highest yield of target protein can be quickly identified, pooled, and processed. This convenient platform, known as the SILAS product, is a thin-film detection technology in which specific molecular interactions are transduced into visible color changes based on changes in the optical thickness of layers on a silicon surface. The results are interpreted without instrumentation. Proteins eluted from a purification column are adsorbed to the assay surface, and the ligand of interest (target) can be identified with specific binding reagents. Here we demonstrate two protein purification applications for the SILAS technology product: monitoring antibody elution from a Protein G column and evaluating the efficiency of purification of a glutathione-S-transferase (GST)-tagged recombinant protein through each step of the purification process.

Silicon-based biosensors for rapid detection of protein or nucleic acid targets

BACKGROUND

We developed a silicon-based biosensor that generates visual, qualitative results or quantitative results for the detection of protein or nucleic acid targets in a multiplex format.

METHODS

Capture probes were immobilized either passively or covalently on the optically coated surface of the biosensor. Intermolecular interactions of the immobilized capture probe with specific target molecules were transduced into a molecular thin film. Thin films were generated by enzyme-catalyzed deposition in the vicinity of the surface-bound target. The increased thickness on the surface changed the apparent color of the biosensor by altering the interference pattern of reflected light.

RESULTS

Cytokine detection was achieved in a 40-min multiplex assay. Detection limits were 4 ng/L for interleukin (IL)-6, 31 ng/L for IL1-beta, and 437 ng/L for interferon-gamma. In multianalyte experiments, cytokines were specifically detected with signal-to-noise ratios ranging from 15 to 80. With a modified optical surface, specificity was also demonstrated in a nucleic acid array with unambiguous discrimination of single-base changes in a 15-min assay. For homozygous wild-type and homozygous mutant samples, signal-to-noise ratios of approximately 100 were observed. Heterozygous samples yielded approximately equivalent signals for wild-type and mutant capture probes.

CONCLUSIONS

The thin-film biosensor allows rapid, sensitive, and specific detection of protein or nucleic acid targets in an array format with results read visually or quantified with a charge-coupled device camera. This biosensor is suited for multianalyte detection in clinical diagnostic assays.

Silicon-based Biosensors for Rapid Detection of Protein or Nucleic Acid Targets

Background: We developed a silicon-based biosensor that generates visual, qualitative results or quantitative results for the detection of protein or nucleic acid targets in a multiplex format.

Methods: Capture probes were immobilized either passively or covalently on the optically coated surface of the biosensor. Intermolecular interactions of the immobilized capture probe with specific target molecules were transduced into a molecular thin film. Thin films were generated by enzyme-catalyzed deposition in the vicinity of the surface-bound target. The increased thickness on the surface changed the apparent color of the biosensor by altering the interference pattern of reflected light.

Results: Cytokine detection was achieved in a 40-min multiplex assay. Detection limits were 4 ng/L for interleukin (IL)-6, 31 ng/L for IL1-β, and 437 ng/L for interferon-γ. In multianalyte experiments, cytokines were specifically detected with signal-to-noise ratios ranging from 15 to 80. With a modified optical surface, specificity was also demonstrated in a nucleic acid array with unambiguous discrimination of single-base changes in a 15-min assay. For homozygous wild-type and homozygous mutant samples, signal-to-noise ratios of ∼100 were observed. Heterozygous samples yielded approximately equivalent signals for wild-type and mutant capture probes.

Conclusions: The thin-film biosensor allows rapid, sensitive, and specific detection of protein or nucleic acid targets in an array format with results read visually or quantified with a charge-coupled device camera. This biosensor is suited for multianalyte detection in clinical diagnostic assays.

Rapid multiserotype detection of human rhinoviruses on optically coated silicon surfaces

BACKGROUND

More than 100 immunologically distinct serotypes of human rhinoviruses (HRV) have been discovered, making detection of surface exposed capsid antigens impractical. However, the non-structural protein 3C protease (3Cpro) is essential for viral replication and is relatively highly conserved among serotypes, making it a potential target for diagnostic testing. The thin film biosensor is an assay platform that can be formatted into a sensitive immunoassay for viral proteins in clinical specimens. The technology utilizes an optically coated silicon surface to convert specific molecular binding events into visual color changes by altering the reflective properties of light through molecular thin films.

OBJECTIVE

To develop a rapid test for detection of HRV by developing broadly serotype reactive antibodies to 3Cpro and utilizing them in the thin film biosensor format.

STUDY DESIGN

Polyclonal antibodies to 3Cpro were purified and incorporated into the thin film assay. The in vitro sensitivity, specificity and multiserotype cross-reactivity of the 3Cpro assay were tested. Nasal washes from naturally infected individuals were also tested to verify that 3Cpro was detectable in clinical specimens.

RESULTS

The 3Cpro assay is a 28-min, non-instrumented room temperature test with a visual limit of detection of 12 pM (picomolar) 3Cpro. In terms of viral titer, as few as 1000 TCID(50) equivalents of HRV2 were detectable. The assay detected 45/52 (87%) of the HRV serotypes tested but showed no cross-reactivity to common respiratory viruses or bacteria. The thin film assay detected 3Cpro in HRV-infected cell culture supernatants coincident with first appearance of cytopathic effect. Data are also presented demonstrating 3Cpro detection from clinical samples collected from HRV-infected individuals. The assay detected 3Cpro in expelled nasal secretions from a symptomatic individual on the first day of illness. In addition, 9/11 (82%) concentrated nasal wash specimens from HRV infected children were positive in the 3Cpro test.

CONCLUSION

We have described a novel, sensitive thin film biosensor for rapid detection of HRV 3Cpro. This test may be suitable for the point of care setting, where rapid HRV diagnostic test results could contribute to clinical decisions regarding appropriate antibiotic or antiviral therapy.

Genetic and physical characterization of proBA genes of the marine bacterium Vibrio parahaemolyticus

Intracellular proline pools have been implicated in the halotolerance of many organisms. To examine this relationship in a moderately halotolerant marine bacterium, Vibrio parahaemolyticus, proline biosynthesis genes were cloned in various plasmids. Some genetic and structural properties of those genes were examined. Subcloning showed that about 3.1 kilobases of V. parahaemolyticus DNA could complement proA and proB but not proC mutations of Escherichia coli. The same fragment would also complement some Pro- mutants of V. parahaemolyticus. Gamma-delta insertion mutagenesis of this subcloned fragment indicated that proB and proA genes of V. parahaemolyticus might be transcribed from different promoters. Two other genes, phoE and gpt, which map closely to the proBA genes in E. coli, were also found to be in close proximity to the proBA genes of V. parahaemolyticus.

Imagery and posttraumatic stress disorder: an overview

A review of theories of traumatic neurosis or posttraumatic stress disorder reveals a relative neglect of the role of posttraumatic imagery. The broad range of imagery has not been recognized, nor its role in the disorder adequately formulated. A two-dimensional framework for understanding posttraumatic stress disorder based on 1) repetitions of trauma-related images, affects, somatic states, and actions and 2) defensive functioning puts into perspective the centrality of traumatic imagery, implies a reorganization of DSM-III criteria, points to new directions for research, and clarifies diagnostic and clinical confusion.