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

CSF Proteomics in Patients With Progressive Supranuclear Palsy

BACKGROUND AND OBJECTIVES

Identification of fluid biomarkers for progressive supranuclear palsy (PSP) is critical to enhance therapeutic development. We implemented unbiased DNA aptamer (SOMAmer) proteomics to identify novel CSF PSP biomarkers.

METHODS

This is a cross-sectional study in original (18 clinically diagnosed PSP-Richardson syndrome [PSP-RS], 28 cognitively healthy controls]), validation (23 PSP-RS, 26 healthy controls), and neuropathology-confirmed (21 PSP, 52 non-PSP frontotemporal lobar degeneration) cohorts. Participants were recruited through the University of California, San Francisco, and the 4-Repeat Neuroimaging Initiative. The original and neuropathology cohorts were analyzed with the SomaScan platform version 3.0 (5026-plex) and the validation cohort with version 4.1 (7595-plex). Clinical severity was measured with the PSP Rating Scale (PSPRS). CSF proteomic data were analyzed to identify differentially expressed targets, implicated biological pathways using enrichment and weighted consensus gene coexpression analyses, diagnostic value of top targets with receiver-operating characteristic curves, and associations with disease severity with linear regressions.

RESULTS

A total of 136 participants were included (median age 70.6 ± 8 years, 68 [50%] women). One hundred fifty-five of 5,026 (3.1%), 959 of 7,595 (12.6%), and 321 of 5,026 (6.3%) SOMAmers were differentially expressed in PSP compared with controls in original, validation, and neuropathology-confirmed cohorts, with most of the SOMAmers showing reduced signal (83.1%, 95.1%, and 73.2%, respectively). Three coexpression modules were associated with PSP across cohorts: (1) synaptic function/JAK-STAT (β = -0.044, corrected p = 0.002), (2) vesicle cytoskeletal trafficking (β = 0.039, p = 0.007), and (3) cytokine-cytokine receptor interaction (β = -0.032, p = 0.035) pathways. Axon guidance was the top dysregulated pathway in PSP in original (strength = 1.71, p < 0.001), validation (strength = 0.84, p < 0.001), and neuropathology-confirmed (strength = 0.78, p < 0.001) cohorts. A panel of axon guidance pathway proteins discriminated between PSP and controls in original (area under the curve [AUC] = 0.924), validation (AUC = 0.815), and neuropathology-confirmed (AUC = 0.932) cohorts. Two inflammatory proteins, galectin-10 and cytotoxic T lymphocyte-associated protein-4, correlated with PSPRS scores across cohorts.

DISCUSSION

Axon guidance pathway proteins and several other molecular pathways are downregulated in PSP, compared with controls. Proteins in these pathways may be useful targets for biomarker or therapeutic development.

Description of an activity-based enzyme biosensor for lung cancer detection

BACKGROUND

Lung cancer is associated with the greatest cancer mortality as it typically presents with incurable distributed disease. Biomarkers relevant to risk assessment for the detection of lung cancer continue to be a challenge because they are often not detectable during the asymptomatic curable stage of the disease. A solution to population-scale testing for lung cancer will require a combination of performance, scalability, cost-effectiveness, and simplicity.

METHODS

One solution is to measure the activity of serum available enzymes that contribute to the transformation process rather than counting biomarkers. Protease enzymes modify the environment during tumor growth and present an attractive target for detection. An activity based sensor platform sensitive to active protease enzymes is presented. A panel of 18 sensors was used to measure 750 sera samples from participants at increased risk for lung cancer with or without the disease.

RESULTS

A machine learning approach is applied to generate algorithms that detect 90% of cancer patients overall with a specificity of 82% including 90% sensitivity in Stage I when disease intervention is most effective and detection more challenging.

CONCLUSION

This approach is promising as a scalable, clinically useful platform to help detect patients who have lung cancer using a simple blood sample. The performance and cost profile is being pursued in studies as a platform for population wide screening.

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

Conducting polymer composite-based biosensing materials for the diagnosis of lung cancer: A review

The development of a simple and fast cancer detection method is crucial since early diagnosis is a key factor in increasing survival rates for lung cancer patients. Among several diagnosis methods, the electrochemical sensor is the most promising one due to its outstanding performance, portability, real-time analysis, robustness, amenability, and cost-effectiveness. Conducting polymer (CP) composites have been frequently used to fabricate a robust sensor device, owing to their excellent physical and electrochemical properties as well as biocompatibility with nontoxic effects on the biological system. This review brings up a brief overview of the importance of electrochemical biosensors for the early detection of lung cancer, with a detailed discussion on the design and development of CP composite materials for biosensor applications. The review covers the electrochemical sensing of numerous lung cancer markers employing composite electrodes based on the conducting polyterthiophene, poly(3,4-ethylenedioxythiophene), polyaniline, polypyrrole, molecularly imprinted polymers, and others. In addition, a hybrid of the electrochemical biosensors and other techniques was highlighted. The outlook was also briefly discussed for the development of CP composite-based electrochemical biosensors for POC diagnostic devices.

Systemic Markers of Lung Function and Forced Expiratory Volume in 1 Second Decline across Diverse Cohorts

Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease characterized by airway obstruction and accelerated lung function decline. Our understanding of systemic protein biomarkers associated with COPD remains incomplete. Objectives: To determine what proteins and pathways are associated with impaired pulmonary function in a diverse population. Methods: We studied 6,722 participants across six cohort studies with both aptamer-based proteomic and spirometry data (4,566 predominantly White participants in a discovery analysis and 2,156 African American cohort participants in a validation). In linear regression models, we examined protein associations with baseline forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC). In linear mixed effects models, we investigated the associations of baseline protein levels with rate of FEV1 decline (ml/yr) in 2,777 participants with up to 7 years of follow-up spirometry. Results: We identified 254 proteins associated with FEV1 in our discovery analyses, with 80 proteins validated in the Jackson Heart Study. Novel validated protein associations include kallistatin serine protease inhibitor, growth differentiation factor 2, and tumor necrosis factor-like weak inducer of apoptosis (discovery β = 0.0561, Q = 4.05 × 10-10; β  = 0.0421, Q = 1.12 × 10-3; and β = 0.0358, Q = 1.67 × 10-3, respectively). In longitudinal analyses within cohorts with follow-up spirometry, we identified 15 proteins associated with FEV1 decline (Q < 0.05), including elafin leukocyte elastase inhibitor and mucin-associated TFF2 (trefoil factor 2; β = -4.3 ml/yr, Q = 0.049; β = -6.1 ml/yr, Q = 0.032, respectively). Pathways and processes highlighted by our study include aberrant extracellular matrix remodeling, enhanced innate immune response, dysregulation of angiogenesis, and coagulation. Conclusions: In this study, we identify and validate novel biomarkers and pathways associated with lung function traits in a racially diverse population. In addition, we identify novel protein markers associated with FEV1 decline. Several protein findings are supported by previously reported genetic signals, highlighting the plausibility of certain biologic pathways. These novel proteins might represent markers for risk stratification, as well as novel molecular targets for treatment of COPD.

Identification of serum biomarkers for necrotizing enterocolitis using aptamer-based proteomics

Introduction

Necrotizing enterocolitis (NEC) is a potentially fatal intestinal disease primarily affecting preterm infants. Early diagnosis of neonates with NEC is crucial to improving outcomes; however, traditional diagnostic tools remain inadequate. Biomarkers represent an opportunity to improve the speed and accuracy of diagnosis, but they are not routinely used in clinical practice.

Methods

In this study, we utilized an aptamer-based proteomic discovery assay to identify new serum biomarkers of NEC. We compared levels of serum proteins in neonates with and without NEC and identified ten differentially expressed serum proteins between these groups.

Results

We detected two proteins, C-C motif chemokine ligand 16 (CCL16) and immunoglobulin heavy constant alpha 1 and 2 heterodimer (IGHA1 IGHA2), that were significantly increased during NEC and eight that were significantly decreased. Generation of receiver operating characteristic (ROC) curves revealed that alpha-fetoprotein (AUC = 0.926), glucagon (AUC = 0.860), and IGHA1 IGHA2 (AUC = 0.826) were the proteins that best differentiated patients with and without NEC.

Discussion

These findings indicate that further investigation into these serum proteins as a biomarker for NEC is warranted. In the future, laboratory tests incorporating these differentially expressed proteins may improve the ability of clinicians to diagnose infants with NEC rapidly and accurately.

Comprehensive proteomics and platform validation of urinary biomarkers for bladder cancer diagnosis and staging

BACKGROUND

Bladder cancer (BC) is among the most common cancers diagnosed in men in the USA. The current gold standards for the diagnosis of BC are invasive or lack the sensitivity to correctly identify the disease.

METHODS

An aptamer-based screen analyzed the expression of 1317 proteins in BC compared to urology clinic controls. The top hits were subjected to systems biology analyses. Next, 30 urine proteins were ELISA-validated in an independent cohort of 68 subjects. Three of these proteins were next validated in an independent BC cohort of differing ethnicity.

RESULTS

Systems biology analysis implicated molecular functions related to the extracellular matrix, collagen, integrin, heparin, and transmembrane tyrosine kinase signaling in BC susceptibility, with HNF4A and NFKB1 emerging as key molecular regulators. STEM analysis of the dysregulated pathways implicated a functional role for the immune system, complement, and interleukins in BC disease progression. Of 21 urine proteins that discriminated BC from urology clinic controls (UC), urine D-dimer displayed the highest accuracy (0.96) and sensitivity of 97%. Furthermore, 8 urine proteins significantly discriminated MIBC from NMIBC (AUC = 0.75-0.99), with IL-8 and IgA being the best performers. Urine IgA and fibronectin exhibited the highest specificity of 80% at fixed sensitivity for identifying advanced BC.

CONCLUSIONS

Given the high sensitivity (97%) of urine D-dimer for BC, it may have a role in the initial diagnosis or detection of cancer recurrence. On the other hand, urine IL-8 and IgA may have the potential in identifying disease progression during patient follow-up. The use of these biomarkers for initial triage could have a significant impact as the current cystoscopy-based diagnostic and surveillance approach is costly and invasive when compared to a simple urine test.

High-Multiplex Aptamer-Based Serum Proteomics to Identify Candidate Serum Biomarkers of Oral Squamous Cell Carcinoma

Improved serological biomarkers are needed for the early detection, risk stratification and treatment surveillance of patients with oral squamous cell carcinoma (OSCC). We performed an exploratory study using advanced, highly specific, DNA-aptamer-based serum proteomics (SOMAscan, 1305-plex) to identify distinct proteomic changes in patients with OSCC pre- vs. post-resection and compared to healthy controls. A total of 63 significantly differentially expressed serum proteins (each p < 0.05) were found that could discriminate between OSCC and healthy controls with 100% accuracy. Furthermore, 121 proteins were detected that were significantly altered between pre- and post-resection sera, and 12 OSCC-associated proteins reversed to levels equivalent to healthy controls after resection. Of these, 6 were increased and 6 were decreased relative to healthy controls, highlighting the potential relevance of these proteins as OSCC tumor markers. Pathway analyses revealed potential pathophysiological mechanisms associated with OSCC. Hence, quantitative proteome analysis using SOMAscan technology is promising and may aid in the development of defined serum marker assays to predict tumor occurrence, progression and recurrence in OSCC, and to guide personalized therapies.

Resistin, Elastase, and Lactoferrin as Potential Plasma Biomarkers of Pediatric Inflammatory Bowel Disease Based on Comprehensive Proteomic Screens

Inflammatory bowel disease (IBD) is an immune-mediated chronic inflammation of the intestine, which can present in the form of ulcerative colitis (UC) or as Crohn's disease (CD). Biomarkers are needed for reliable diagnosis and disease monitoring in IBD, especially in pediatric patients. Plasma samples from a pediatric IBD cohort were interrogated using an aptamer-based screen of 1322 proteins. The elevated biomarkers identified using the aptamer screen were further validated by ELISA using an independent cohort of 76 pediatric plasma samples, drawn from 30 CD, 30 UC, and 16 healthy controls. Of the 1322 proteins screened in plasma from IBD patients, 129 proteins were significantly elevated when compared with healthy controls. Of these 15 proteins had a fold change greater than 2 and 28 proteins had a fold change >1.5. Neutrophil and extracellular vesicle signatures were detected among the elevated plasma biomarkers. When seven of these proteins were validated by ELISA, resistin was the only protein that was significantly higher in both UC and CD (p < 0.01), with receiver operating characteristic area under the curve value of 0.82 and 0.77, respectively, and the only protein that exhibited high sensitivity and specificity for both CD and UC. The next most discriminatory plasma proteins were elastase and lactoferrin, particularly for UC, with receiver operating characteristic area under the curve values of 0.74 and 0.69, respectively. We have identified circulating resistin, elastase, and lactoferrin as potential plasma biomarkers of IBD in pediatric patients using two independent diagnostic platforms and two independent patient cohorts.

The Impact of Lung Cancer in Patients with Combined Pulmonary Fibrosis and Emphysema (CPFE)

Given the high risk of lung cancer (LC) in patients with combined pulmonary fibrosis and emphysema (CPFE), and the difficulty of early diagnosis, it is important to understand the impact of LC in these patients. The effect of LC on the development of acute exacerbation (AE) as a natural course of CPFE is still unknown. We retrospectively reviewed medical records of patients at the West China Hospital and enrolled 59 patients with CPFE combined with LC and 68 CPFE patients without LC for initial diagnosis matched in the same period. We compared the clinical characteristics and imaging features of CPFE patients with LC and without LC, and analyzed the associated factors for the prevalence of LC using binary logistic regression. Cox proportional hazards regression analysis was performed to explore risk factors of AE as a natural course of CPFE. Patients with CPFE combined with LC were more common among elderly male smokers. The most common pathological type of tumor was adenocarcinoma (24/59, 40.7%) and squamous cell carcinoma (18/59, 30.5%). Compared with those in the without LC group, the proportions of men, and ex- or current smokers, and the levels of smoking pack-years, serum CRP, IL-6, fibrinogen, complement C3 and C4 in patients with LC were significantly higher (p < 0.05). There was no significant difference in the proportion of natural-course-related AE (10.2% vs. 16.2%, p > 0.05) between the two groups. Logistic regression analysis demonstrated that pack-years ≥ 20 (OR: 3.672, 95% CI: 1.165-11.579), family history of cancer (OR: 8.353, 95% CI: 2.368-10.417), the level of fibrinogen > 4.81 g/L (OR: 3.628, 95% CI: 1.403-9.385) and serum C3 > 1.00 g/L (OR: 5.299, 95% CI: 1.727-16.263) were independently associated with LC in patients with CPFE. Compared to those without AE, CPFE patients with AE had significantly higher levels of PLR and serum CRP, with obviously lower DLCO and VC. The obviously increased PLR (HR: 3.731, 95% CI: 1.288-10.813), and decreased DLCO%pred (HR: 0.919, 95% CI: 0.863-0.979) and VC%pred (HR: 0.577, 95% CI: 0.137-0.918) rather than the presence of LC independently contributed to the development of natural-course-related AE in patients with CPFE. Pack-years, family history of cancer, the levels of fibrinogen and serum C3 were independently associated with LC in patients with CPFE. The presence of LC did not significantly increase the risk of AE as a natural course of CPFE. Clinicians should give high priority to CPFE patients, especially those with more severe fibrosis and systemic inflammation, in order to be alert for the occurrence of AE.

Liquid Biopsy for Lung Cancer: Up-to-Date and Perspectives for Screening Programs

Lung cancer is the deadliest cancer worldwide. Tissue biopsy is currently employed for the diagnosis and molecular stratification of lung cancer. Liquid biopsy is a minimally invasive approach to determine biomarkers from body fluids, such as blood, urine, sputum, and saliva. Tumor cells release cfDNA, ctDNA, exosomes, miRNAs, circRNAs, CTCs, and DNA methylated fragments, among others, which can be successfully used as biomarkers for diagnosis, prognosis, and prediction of treatment response. Predictive biomarkers are well-established for managing lung cancer, and liquid biopsy options have emerged in the last few years. Currently, detecting EGFR p.(Tyr790Met) mutation in plasma samples from lung cancer patients has been used for predicting response and monitoring tyrosine kinase inhibitors (TKi)-treated patients with lung cancer. In addition, many efforts continue to bring more sensitive technologies to improve the detection of clinically relevant biomarkers for lung cancer. Moreover, liquid biopsy can dramatically decrease the turnaround time for laboratory reports, accelerating the beginning of treatment and improving the overall survival of lung cancer patients. Herein, we summarized all available and emerging approaches of liquid biopsy-techniques, molecules, and sample type-for lung cancer.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

Assay Performance of a Label-Free, Solution-Phase CYFRA 21-1 Determination

CYFRA 21.1, a cytokeratin fragment of epithelial origin, has long been a valuable blood-based biomarker. As with most biomarkers, the clinical diagnostic value of CYFRA 21.1 is dependent on the quantitative performance of the assay. Looking toward translation, it is shown here that a free-solution assay (FSA) coupled with a compensated interferometric reader (CIR) can be used to provide excellent analytical performance in quantifying CYFRA 21.1 in patient serum samples. This report focuses on the analytical performance of the high-sensitivity (hs)-CYFRA 21.1 assay in the context of quantifying the biomarker in two indeterminate pulmonary nodule (IPN) patient cohorts totaling 179 patients. Each of the ten assay calibrations consisted of 6 concentrations, each run as 7 replicates (e.g., 10 × 6 × 7 data points) and were performed on two different instruments by two different operators. Coefficients of variation (CVs) for the hs-CYFRA 21.1 analytical figures of merit, limit of quantification (LOQ) of ca. 60 pg/mL, B _max, initial slope, probe-target binding affinity, and reproducibility of quantifying an unknown were found to range from 2.5 to 8.3%. Our results demonstrate the excellent performance of our FSA-CIR hs-CYFRA 21-1 assay and a proof of concept for potentially redefining the performance characteristics of this existing important candidate biomarker.

Comprehensive aptamer-based screen of 1317 proteins uncovers improved stool protein markers of colorectal cancer

BACKGROUND

To screen and validate novel stool protein biomarkers of colorectal cancer (CRC).

METHODS

A novel aptamer-based screen of 1317 proteins was used to uncover elevated proteins in the stool of patients with CRC, as compared to healthy controls (HCs) in a discovery cohort. Selected biomarker candidates from the discovery cohort were ELISA validated in three independent cross-sectional cohorts comprises 76 CRC patients, 15 adenoma patients, and 63 healthy controls, from two different ethnicities. The expression of the potential stool biomarkers within CRC tissue was evaluated using single-cell RNA-seq datasets.

RESULTS

A total of 92 proteins were significantly elevated in CRC samples as compared to HCs in the discovery cohort. Among Caucasians, the 5 most discriminatory proteins among the 16 selected proteins, ordered by their ability to distinguish CRC from adenoma and healthy controls, were MMP9, haptoglobin, myeloperoxidase, fibrinogen, and adiponectin. Except myeloperoxidase, the others were significantly associated with depth of tumor invasion. The 8 stool proteins with the highest AUC values were also discriminatory in a second cohort of Indian CRC patients. Several of the stool biomarkers elevated in CRC were also expressed within CRC tissue, based on the single-cell RNA-seq analysis.

CONCLUSIONS

Stool MMP9, fibrinogen, myeloperoxidase, and haptoglobin emerged as promising CRC stool biomarkers, outperforming stool Hemoglobin. Longitudinal studies are warranted to assess the clinical utility of these novel biomarkers in early diagnosis of CRC.

Comprehensive Proteomics Profiling Reveals Circulating Biomarkers of Hypertrophic Cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is caused by mutations in the genes coding for proteins essential in normal myocardial contraction. However, it remains unclear through which molecular pathways gene mutations mediate the development of HCM. The objectives were to determine plasma protein biomarkers of HCM and to reveal molecular pathways differentially regulated in HCM.

METHODS

We conducted a multicenter case-control study of cases with HCM and controls with hypertensive left ventricular hypertrophy. We performed plasma proteomics profiling of 1681 proteins. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based discrimination model with data from 1 institution (ie, the training set). We tested the discriminative ability in independent samples from the other institution (ie, the test set). As an exploratory analysis, we executed pathway analysis of significantly dysregulated proteins. Pathways with false discovery rate <0.05 were declared positive.

RESULTS

The study included 266 cases and 167 controls (n=308 in the training set; n=125 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.89 (95% CI, 0.83-0.94) in the test set. Pathway analysis revealed that the Ras-MAPK (mitogen-activated protein kinase) pathway, along with its upstream and downstream pathways, was upregulated in HCM. Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

CONCLUSIONS

This study serves as the largest-scale investigation with the most comprehensive proteomics profiling in HCM, revealing circulating biomarkers and exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in HCM.

Predicting disease course in ulcerative colitis using stool proteins identified through an aptamer-based screen

In the search for improved stool biomarkers for inflammatory bowel disease (IBD), an aptamer-based screen of 1129 stool proteins was conducted using stool samples from an IBD cohort. Here we report that of the 20 proteins subsequently validated by ELISA, stool Ferritin, Fibrinogen, Haptoglobin, Hemoglobin, Lipocalin-2, MMP-12, MMP-9, Myeloperoxidase, PGRP-S, Properdin, Resistin, Serpin A4, and TIMP-1 are significantly elevated in both ulcerative colitis (UC) and Crohn's disease (CD) compared to controls. When tested in a longitudinal cohort of 50 UC patients at 4 time-points, fecal Fibrinogen, MMP-8, PGRP-S, and TIMP-2 show the strongest positive correlation with concurrent PUCAI and PGA scores and are superior to fecal calprotectin. Unlike fecal calprotectin, baseline stool Fibrinogen, MMP-12, PGRP-S, TIMP-1, and TIMP-2 can predict clinical remission at Week-4. Here we show that stool proteins identified using the comprehensive aptamer-based screen are superior to fecal calprotectin alone in disease monitoring and prediction in IBD.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990's, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3-5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

Chemical Modification of Aptamers for Increased Binding Affinity in Diagnostic Applications: Current Status and Future Prospects

Aptamers are short single stranded DNA or RNA oligonucleotides that can recognize analytes with extraordinary target selectivity and affinity. Despite their promising properties and diagnostic potential, the number of commercial applications remains scarce. In order to endow them with novel recognition motifs and enhanced properties, chemical modification of aptamers has been pursued. This review focuses on chemical modifications, aimed at increasing the binding affinity for the aptamer's target either in a non-covalent or covalent fashion, hereby improving their application potential in a diagnostic context. An overview of current methodologies will be given, thereby distinguishing between pre- and post-SELEX (Systematic Evolution of Ligands by Exponential Enrichment) modifications.

Comprehensive aptamer-based screening identifies a spectrum of urinary biomarkers of lupus nephritis across ethnicities

Emerging urinary biomarkers continue to show promise in evaluating lupus nephritis (LN). Here, we screen urine from active LN patients for 1129 proteins using an aptamer-based platform, followed by ELISA validation in two independent cohorts comprised of 127 inactive lupus, 107 active LN, 67 active non-renal lupus patients and 74 healthy controls, of three different ethnicities. Urine proteins that best distinguish active LN from inactive disease are ALCAM, PF-4, properdin, and VCAM-1 among African-Americans, sE-selectin, VCAM-1, BFL-1 and Hemopexin among Caucasians, and ALCAM, VCAM-1, TFPI and PF-4 among Asians. Most of these correlate significantly with disease activity indices in the respective ethnic groups, and surpass conventional metrics in identifying active LN, with better sensitivity, and negative/positive predictive values. Several elevated urinary molecules are also expressed within the kidneys in LN, based on single-cell RNAseq analysis. Longitudinal studies are warranted to assess the utility of these biomarkers in tracking lupus nephritis.

Proteomic insights on the metabolism in inflammatory bowel disease

Inflammatory bowel diseases (IBD) are chronic and relapsing inflammatory conditions of the gut that include Crohn's disease and ulcerative colitis. The pathogenesis of IBD is not completely unraveled, IBD are multi-factorial diseases with reported alterations in the gut microbiota, activation of different immune cell types, changes in the vascular endothelium, and alterations in the tight junctions’ structure of the colonic epithelial cells. Proteomics represents a useful tool to enhance our biological understanding and to discover biomarkers in blood and intestinal specimens. It is expected to provide reproducible and quantitative data that can support clinical assessments and help clinicians in the diagnosis and treatment of IBD. Sometimes a differential diagnosis of Crohn's disease and ulcerative colitis and the prediction of treatment response can be deducted by finding meaningful biomarkers. Although some non-invasive biomarkers have been described, none can be considered as the “gold standard” for IBD diagnosis, disease activity and therapy outcome. For these reason new studies have proposed an “IBD signature”, which consists in a panel of biomarkers used to assess IBD. The above described approach characterizes “omics” and in this review we will focus on proteomics.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Airway host-microbiome interactions in chronic obstructive pulmonary disease

Background

Little is known about the interactions between the lung microbiome and host response in chronic obstructive pulmonary disease (COPD).

Methods

We performed a longitudinal 16S ribosomal RNA gene-based microbiome survey on 101 sputum samples from 16 healthy subjects and 43 COPD patients, along with characterization of host sputum transcriptome and proteome in COPD patients.

Results

Dysbiosis of sputum microbiome was observed with significantly increased relative abundance of Moraxella in COPD versus healthy subjects and during COPD exacerbations, and Haemophilus in COPD ex-smokers versus current smokers. Multivariate modeling on sputum microbiome, host transcriptome and proteome profiles revealed that significant associations between Moraxella and Haemophilus, host interferon and pro-inflammatory signaling pathways and neutrophilic inflammation predominated among airway host-microbiome interactions in COPD. While neutrophilia was positively correlated with Haemophilus, interferon signaling was more strongly linked to Moraxella. Moreover, while Haemophilus was significantly associated with host factors both in stable state and during exacerbations, Moraxella-associated host responses were primarily related to exacerbations.

Conclusions

Our study highlights a significant airway host-microbiome interplay associated with COPD inflammation and exacerbations. These findings indicate that Haemophilus and Moraxella influence different components of host immune response in COPD, and that novel therapeutic strategies should consider targeting these bacteria and their associated host pathways in COPD.

Electronic supplementary material

The online version of this article (10.1186/s12931-019-1085-z) contains supplementary material, which is available to authorized users.

The imbalance in the complement system and its possible physiological mechanisms in patients with lung cancer

BACKGROUND

The clinical and experimental evidences for complement-cancer relationships are solid, whereas an epidemiological study reporting the imbalance of complement system in patients is still lacking.

METHODS

Using publicly available databases, we jointly compared the levels of complement components in plasma and lung cancer tissues. With iTRAQ proteomics, quantitative RT-PCR and western blotting, we analysed the differences in complement levels in lung cancer tissues and normal control tissues. Complement components are mainly synthesized by the liver and secreted into the blood. Using paired co-cultures of human normal QSG-7701 hepatocytes with lung cancer cells (A549, LTEP-α-2 or NCI-H1703) or human normal bronchial epithelial (HBE) cells, we examined the effects of lung cancer cells on complement synthesis and secretion in QSG-7701 hepatocytes.

RESULTS

An integrated analysis of transcriptome and proteome datasets from 43 previous studies revealed lower mRNA and protein levels of 25 complement and complement-related components in lung cancer tissues than those in normal control tissues; conversely, higher levels of complement proteins were detected in plasma from patients than those in healthy subjects. Our iTRAQ proteome study identified decreased and increased levels of 31 and 2 complement and complement-related proteins, respectively, in lung cancer tissues, of which the reduced levels of 10 components were further confirmed using quantitative RT-PCR and western blotting. Paired co-cultures of QSG-7701 hepatocytes with A549, LTEP-α-2, NCI-H1703 or HBE cells indicated that lung cancer cells increased complement synthesis and secretion in QSG-7701 cells compared to HBE cells.

CONCLUSIONS

The opposite associations between the levels of complement and complement-related components in lung cancer tissues and plasma from patients that have been repeatedly reported by independent publications may indicate the prevalence of an imbalance in the complement system of lung cancer patients. The possible mechanism of the imbalance may be associated not only with the decreased complement levels in lung cancer tissues but also the concurrent lung cancer tissue-induced increase in hepatocyte complement synthesis and plasma secretion in patients. And the imbalance should be accompanied by a suppression of complement-dependent immunity in lung cancer tissues coupled with a burden of complement immunity in the circulation of patients.

Clinical Validation of a Protein Biomarker Panel for Non-Small Cell Lung Cancer

We validated the diagnostic performance of a previously developed blood-based 7-protein biomarker panel, AptoDetect™-Lung (Aptamer Sciences Inc., Pohang, Korea) using modified aptamer-based proteomic technology for lung cancer detection. Non-small cell lung cancer (NSCLC), 200 patients and benign nodule controls, 200 participants were enrolled. In a high-risk population corresponding to ≥ 55 years of age and ≥ 30 pack-years, the diagnostic performance was improved, showing 73.3% sensitivity and 90.5% specificity with an area under the curve of 0.88. AptoDetect™-Lung (Aptamer Sciences Inc.) offers the best validated performance to discriminate NSCLC from benign nodule controls in a high-risk population and could play a complementary role in lung cancer screening.

Proteomics: Clinical and research applications in respiratory diseases

The proteome is the study of the protein content of a definable component of an organism in biology. However, the tissue-specific expression of proteins and the varied post-translational modifications, splice variants and protein-protein complexes that may form, make the study of protein a challenging yet vital tool in answering many of the unanswered questions in medicine and biology to date. Indeed, the spatial, temporal and functional composition of proteins in the human body has proven difficult to elucidate for many years. Given the effect of microRNA and epigenetic regulation on silencing and enhancing gene transcription, the study of protein arguably provides more accurate information on homeostasis and perturbation in health and disease. There have been significant advances in the field of proteomics in recent years, with new technologies and platforms available to the research community. In this review, we briefly discuss some of these new technologies and developments in the context of respiratory disease. We also discuss the types of data science approaches to analyses and interpretation of the large volumes of data generated in proteomic studies. We discuss the application of these technologies with regard to respiratory disease and highlight the potential for proteomics in generating major advances in the understanding of respiratory pathophysiology into the future.

Aptamer-based search for correlates of plasma and serum water T2: implications for early metabolic dysregulation and metabolic syndrome

Background

Metabolic syndrome is a cluster of abnormalities that increases the risk for type 2 diabetes and atherosclerosis. Plasma and serum water T₂ from benchtop nuclear magnetic resonance relaxometry are early, global and practical biomarkers for metabolic syndrome and its underlying abnormalities. In a prior study, water T₂ was analyzed against ~ 130 strategically selected proteins and metabolites to identify associations with insulin resistance, inflammation and dyslipidemia. In the current study, the analysis was broadened ten-fold using a modified aptamer (SOMAmer) library, enabling an unbiased search for new proteins correlated with water T₂ and thus, metabolic health.

Methods

Water T₂ measurements were recorded using fasting plasma and serum from non-diabetic human subjects. In parallel, plasma samples were analyzed using a SOMAscan assay that employed modified DNA aptamers to determine the relative concentrations of 1310 proteins. A multi-step statistical analysis was performed to identify the biomarkers most predictive of water T₂. The steps included Spearman rank correlation, followed by principal components analysis with variable clustering, random forests for biomarker selection, and regression trees for biomarker ranking.

Results

The multi-step analysis unveiled five new proteins most predictive of water T₂: hepatocyte growth factor, receptor tyrosine kinase FLT3, bone sialoprotein 2, glucokinase regulatory protein and endothelial cell-specific molecule 1. Three of the five strongest predictors of water T₂ have been previously implicated in cardiometabolic diseases. Hepatocyte growth factor has been associated with incident type 2 diabetes, and endothelial cell specific molecule 1, with atherosclerosis in subjects with diabetes. Glucokinase regulatory protein plays a critical role in hepatic glucose uptake and metabolism and is a drug target for type 2 diabetes. By contrast, receptor tyrosine kinase FLT3 and bone sialoprotein 2 have not been previously associated with metabolic conditions. In addition to the five most predictive biomarkers, the analysis unveiled other strong correlates of water T₂ that would not have been identified in a hypothesis-driven biomarker search.

Conclusions

The identification of new proteins associated with water T₂ demonstrates the value of this approach to biomarker discovery. It provides new insights into the metabolic significance of water T₂ and the pathophysiology of metabolic syndrome.

Electronic supplementary material

The online version of this article (10.1186/s40364-018-0143-x) contains supplementary material, which is available to authorized users.

Probing the colorectal cancer proteome for biomarkers: Current status and perspectives

Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide. Biomarkers that can facilitate better clinical management of CRC are in high demand to improve patient outcome and to reduce mortality. In this regard, proteomic analysis holds a promising prospect in the hunt of novel biomarkers for CRC and in understanding the mechanisms underlying tumorigenesis. This review aims to provide an overview of the current progress of proteomic research, focusing on discovery and validation of diagnostic biomarkers for CRC. We will summarize the contributions of proteomic strategies to recent discoveries of protein biomarkers for CRC and also briefly discuss the potential and challenges of different proteomic approaches in biomarker discovery and translational applications.

Ionizing radiation exposure: hazards, prevention, and biomarker screening

Radiation is a form of energy derived from a source that is propagated through material in space. It consists of ionizing radiation or nonionizing radiation. Ionizing radiation is a feature of the environment and an important tool in medical treatment, but it can cause serious damage to organisms. A number of protective measures and standards of protection have been proposed to protect against radiation. There is also a need for biomarkers to rapidly assess individual doses of radiation, which can not only estimate the dose of radiation but also determine its effects on health. Proteomics, genomics, metabolomics, and lipidomics have been widely used in the search for such biomarkers. These topics are discussed in depth in this review.

Slow Off-Rate Modified Aptamer (SOMAmer) as a Novel Reagent in Immunoassay Development for Accurate Soluble Glypican-3 Quantification in Clinical Samples

Accurate quantification of soluble glypican-3 in clinical samples using immunoassays is challenging, because of the lack of appropriate antibody reagents to provide a full spectrum measurement of all potential soluble glypican-3 fragments in vivo. Glypican-3 SOMAmer (slow off-rate modified aptamer) is a novel reagent that binds, with high affinity, to a far distinct epitope of glypican-3, when compared to all available antibody reagents generated in-house. This paper describes an integrated analytical approach to rational selection of key reagents based on molecular characterization by epitope mapping, with the focus on our work using a SOMAmer as a new reagent to address development challenges with traditional antibody reagents for the soluble glypican-3 immunoassay. A qualified SOMAmer-based assay was developed and used for soluble glypican-3 quantification in hepatocellular carcinoma (HCC) patient samples. The assay demonstrated good sensitivity, accuracy, and precision. Data correlated with those obtained using the traditional antibody-based assay were used to confirm the clinically relevant soluble glypican-3 forms in vivo. This result was reinforced by a liquid chromatography tandem mass spectrometry (LC-MS/MS) assay quantifying signature peptides generated from trypsin digestion. The work presented here offers an integrated strategy for qualifying aptamers as an alternative affinity platform for immunoassay reagents that can enable speedy assay development, especially when traditional antibody reagents cannot meet assay requirements.

Trisomy 21 causes changes in the circulating proteome indicative of chronic autoinflammation

Trisomy 21 (T21) causes Down syndrome (DS), but the mechanisms by which T21 produces the different disease spectrum observed in people with DS are unknown. We recently identified an activated interferon response associated with T21 in human cells of different origins, consistent with overexpression of the four interferon receptors encoded on chromosome 21, and proposed that DS could be understood partially as an interferonopathy. However, the impact of T21 on systemic signaling cascades in living individuals with DS is undefined. To address this knowledge gap, we employed proteomics approaches to analyze blood samples from 263 individuals, 165 of them with DS, leading to the identification of dozens of proteins that are consistently deregulated by T21. Most prominent among these proteins are numerous factors involved in immune control, the complement cascade, and growth factor signaling. Importantly, people with DS display higher levels of many pro-inflammatory cytokines (e.g. IL-6, MCP-1, IL-22, TNF-α) and pronounced complement consumption, resembling changes seen in type I interferonopathies and other autoinflammatory conditions. Therefore, these results are consistent with the hypothesis that increased interferon signaling caused by T21 leads to chronic immune dysregulation, and justify investigations to define the therapeutic value of immune-modulatory strategies in DS.

Assessment of Variability in the SOMAscan Assay

SOMAscan is an aptamer-based proteomics assay capable of measuring 1,305 human protein analytes in serum, plasma, and other biological matrices with high sensitivity and specificity. In this work, we present a comprehensive meta-analysis of performance based on multiple serum and plasma runs using the current 1.3 k assay, as well as the previous 1.1 k version. We discuss normalization procedures and examine different strategies to minimize intra- and interplate nuisance effects. We implement a meta-analysis based on calibrator samples to characterize the coefficient of variation and signal-over-background intensity of each protein analyte. By incorporating coefficient of variation estimates into a theoretical model of statistical variability, we also provide a framework to enable rigorous statistical tests of significance in intervention studies and clinical trials, as well as quality control within and across laboratories. Furthermore, we investigate the stability of healthy subject baselines and determine the set of analytes that exhibit biologically stable baselines after technical variability is factored in. This work is accompanied by an interactive web-based tool, an initiative with the potential to become the cornerstone of a regularly updated, high quality repository with data sharing, reproducibility, and reusability as ultimate goals.

The prediction of late-onset preeclampsia: Results from a longitudinal proteomics study

BACKGROUND

Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been reported. We sought to correct this deficiency using a high through-put proteomic platform.

METHODS

A case-control longitudinal study was conducted, including 90 patients with normal pregnancies and 76 patients with late-onset preeclampsia (diagnosed at ≥34 weeks of gestation). Maternal plasma samples were collected throughout gestation (normal pregnancy: 2-6 samples per patient, median of 2; late-onset preeclampsia: 2-6, median of 5). The abundance of 1,125 proteins was measured using an aptamers-based proteomics technique. Protein abundance in normal pregnancies was modeled using linear mixed-effects models to estimate mean abundance as a function of gestational age. Data was then expressed as multiples of-the-mean (MoM) values in normal pregnancies. Multi-marker prediction models were built using data from one of five gestational age intervals (8-16, 16.1-22, 22.1-28, 28.1-32, 32.1-36 weeks of gestation). The predictive performance of the best combination of proteins was compared to placental growth factor (PIGF) using bootstrap.

RESULTS

1) At 8-16 weeks of gestation, the best prediction model included only one protein, matrix metalloproteinase 7 (MMP-7), that had a sensitivity of 69% at a false positive rate (FPR) of 20% (AUC = 0.76); 2) at 16.1-22 weeks of gestation, MMP-7 was the single best predictor of late-onset preeclampsia with a sensitivity of 70% at a FPR of 20% (AUC = 0.82); 3) after 22 weeks of gestation, PlGF was the best predictor of late-onset preeclampsia, identifying 1/3 to 1/2 of the patients destined to develop this syndrome (FPR = 20%); 4) 36 proteins were associated with late-onset preeclampsia in at least one interval of gestation (after adjustment for covariates); 5) several biological processes, such as positive regulation of vascular endothelial growth factor receptor signaling pathway, were perturbed; and 6) from 22.1 weeks of gestation onward, the set of proteins most predictive of severe preeclampsia was different from the set most predictive of the mild form of this syndrome.

CONCLUSIONS

Elevated MMP-7 early in gestation (8-22 weeks) and low PlGF later in gestation (after 22 weeks) are the strongest predictors for the subsequent development of late-onset preeclampsia, suggesting that the optimal identification of patients at risk may involve a two-step diagnostic process.

Clinical validation of a blood-based classifier for diagnostic evaluation of asymptomatic individuals with pulmonary nodules

BACKGROUND

The number of pulmonary nodules detected in the US is expected to increase substantially following recent recommendations for nationwide CT-based lung cancer screening. Given the low specificity of CT screening, non-invasive adjuvant methods are needed to differentiate cancerous lesions from benign nodules to help avoid unnecessary invasive procedures in the asymptomatic population. We have constructed a serum-based multi-biomarker panel and assessed its clinical accuracy in a retrospective analysis of samples collected from participants with suspicious radiographic findings in the Prostate, Lung, Chest and Ovarian (PLCO) cancer screening trial.

METHODS

Starting with a set of 9 candidate biomarkers, we identified 8 that exhibited limited pre-analytical variability with increasing clotting time, a key pre-analytical variable associated with the collection of serum. These 8 biomarkers were evaluated in a training study consisting of 95 stage I NSCLC patients and 186 smoker controls where a 5-biomarker pulmonary nodule classifier (PNC) was selected. The clinical accuracy of the PNC was determined in a blinded study of asymptomatic individuals comprising 119 confirmed malignant nodule cases and 119 benign nodule controls selected from the PLCO screening trial.

RESULTS

A PNC comprising 5 biomarkers: CEA, CYFRA 21-1, OPN, SCC, and TFPI, was selected in the training study. In an independent validation study, the PNC resolved lung cancer cases from benign nodule controls with an AUC of 0.653 (p < 0.0001). CEA and CYFRA 21-1, two of the markers included in the PNC, also accurately distinguished malignant lesions from benign controls.

CONCLUSIONS

A 5-biomarker blood test has been developed for the diagnostic evaluation of asymptomatic individuals with solitary pulmonary nodules.

The maternal plasma proteome changes as a function of gestational age in normal pregnancy: a longitudinal study

OBJECTIVE

Pregnancy is accompanied by dramatic physiological changes in maternal plasma proteins. Characterization of the maternal plasma proteome in normal pregnancy is an essential step for understanding changes to predict pregnancy outcome. The objective of this study was to describe maternal plasma proteins that change in abundance with advancing gestational age and determine biological processes that are perturbed in normal pregnancy.

STUDY DESIGN

A longitudinal study included 43 normal pregnancies that had a term delivery of an infant who was appropriate for gestational age without maternal or neonatal complications. For each pregnancy, 3 to 6 maternal plasma samples (median, 5) were profiled to measure the abundance of 1125 proteins using multiplex assays. Linear mixed-effects models with polynomial splines were used to model protein abundance as a function of gestational age, and the significance of the association was inferred via likelihood ratio tests. Proteins considered to be significantly changed were defined as having the following: (1) >1.5-fold change between 8 and 40 weeks of gestation; and (2) a false discovery rate-adjusted value of P < .1. Gene ontology enrichment analysis was used to identify biological processes overrepresented among the proteins that changed with advancing gestation.

RESULTS

The following results were found: (1) Ten percent (112 of 1125) of the profiled proteins changed in abundance as a function of gestational age; (2) of the 1125 proteins analyzed, glypican-3, sialic acid-binding immunoglobulin-type lectin-6, placental growth factor, C-C motif-28, carbonic anhydrase 6, prolactin, interleukin-1 receptor 4, dual-specificity mitogen-activated protein kinase 4, and pregnancy-associated plasma protein-A had more than a 5-fold change in abundance across gestation (these 9 proteins are known to be involved in a wide range of both physiological and pathological processes, such as growth regulation, embryogenesis, angiogenesis immunoregulation, inflammation etc); and (3) biological processes associated with protein changes in normal pregnancy included defense response, defense response to bacteria, proteolysis, and leukocyte migration (false discovery rate, 10%).

CONCLUSION

The plasma proteome of normal pregnancy demonstrates dramatic changes in both the magnitude of changes and the fraction of the proteins involved. Such information is important to understand the physiology of pregnancy and the development of biomarkers to differentiate normal vs abnormal pregnancy and determine the response to interventions.

Customised nucleic acid libraries for enhanced aptamer selection and performance

Aptamers are short single-stranded oligo(deoxy)nucleotides that are selected to bind to target molecules with high affinity and specificity. Because of their sophisticated characteristics and versatile applicability, aptamers are thought to become universal molecular probes in biotechnological and therapeutic applications. However, the variety of possible interactions with a putative target molecule is limited by the chemical repertoire of the natural nucleobases. Consequently, many desired targets are not addressable by aptamers. This obstacle is overcome by broadening the chemical diversity of aptamers, mainly achieved by nucleobase-modifications and the introduction of novel bases or base pairs. We discuss these achievements and the characteristics of the respective modified aptamers, reflected by SOMAmers (slow off-rate modified aptamers), clickmers, and aptamers bearing an expanded genetic alphabet.

Identification of potential serum biomarkers for breast cancer using a functional proteomics technology

BACKGROUND

Cancer is a genetic disease; its development and metastasis depend on the function of many proteins. Human serum contains thousands of proteins; it is a window for the homeostasis of individual's health. Many of the proteins found in the human serum could be potential biomarkers for cancer early detection and drug efficacy evaluation.

METHODS

In this study, a functional proteomics technology was used to systematically monitor metabolic enzyme and protease activities from resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP. All the experiments were repeated at least twice to ensure the validity of the findings.

RESULTS

For the first time, significant differences were found between breast cancer patient serum and normal serum in two families of enzymes known to be involved in cancer development and metastasis: metabolic enzymes and proteases. Multiple enzyme species were identified in the serum assayed directly or after enrichment. Both qualitative and quantitative differences in the metabolic enzyme and protease activity were detected between breast cancer patient and control group, providing excellent biomarker candidates for breast cancer diagnosis and drug development.

CONCLUSIONS

This study identified several potential functional protein biomarkers from breast cancer patient serum. It also demonstrated that the functional proteomics technology, PEP, can be applied to the analysis of any functional proteins in human serum which contains thousands of proteins. The study indicated that the functional domain of the human serum could be unlocked with the PEP technology, pointing to a novel alternative for the development of diagnosis biomarkers for breast cancer and other diseases.

Molecular Elucidation of Disease Biomarkers at the Interface of Chemistry and Biology

Disease-related biomarkers are objectively measurable molecular signatures of physiological status that can serve as disease indicators or drug targets in clinical diagnosis and therapy, thus acting as a tool in support of personalized medicine. For example, the prostate-specific antigen (PSA) biomarker is now widely used to screen patients for prostate cancer. However, few such biomarkers are currently available, and the process of biomarker identification and validation is prolonged and complicated by inefficient methods of discovery and few reliable analytical platforms. Therefore, in this Perspective, we look at the advanced chemistry of aptamer molecules and their significant role as molecular probes in biomarker studies. As a special class of functional nucleic acids evolved from an iterative technology termed Systematic Evolution of Ligands by Exponential Enrichment (SELEX), these single-stranded oligonucleotides can recognize their respective targets with selectivity and affinity comparable to those of protein antibodies. Because of their fast turnaround time and exceptional chemical properties, aptamer probes can serve as novel molecular tools for biomarker investigations, particularly in assisting identification of new disease-related biomarkers. More importantly, aptamers are able to recognize biomarkers from complex biological environments such as blood serum and cell surfaces, which can provide direct evidence for further clinical applications. This Perspective highlights several major advancements of aptamer-based biomarker discovery strategies and their potential contribution to the practice of precision medicine.

Development of a Protein Biomarker Panel to Detect Non-Small-Cell Lung Cancer in Korea

BACKGROUND

Lung cancer screening using low-dose computed tomography reduces lung cancer mortality. However, the high false-positive rate, cost, and potential harms highlight the need for complementary biomarkers. We compared the diagnostic performance of modified aptamer-based protein biomarkers with Cyfra 21-1.

PATIENTS AND METHODS

Participants included 100 patients diagnosed with lung cancer, and 100 control subjects from Asan Medical Center (Seoul, Korea). We investigated candidate biomarkers with new modified aptamer-based proteomic technology and developed a 7-protein panel that discriminates lung cancer from controls. A naive Bayesian classifier was trained using sera from 75 lung cancers and 75 controls. An independent set of 25 cases and 25 controls was used to verify performance of this classifier. The panel results were compared with Cyfra 21-1 to evaluate the diagnostic accuracy for lung nodules detected by computed tomography.

RESULTS

We derived a 7-protein biomarker classifier from the initial train set comprising: EGFR1, MMP7, CA6, KIT, CRP, C9, and SERPINA3. This classifier distinguished lung cancer cases from controls with an area under the curve (AUC) of 0.82 in the train set and an AUC of 0.77 in the verification set. The 7-marker naive Bayesian classifier resulted in 91.7% specificity with 75.0% sensitivity for the subset of individuals with lung nodules. The AUC of the classifier for lung nodules was 0.88, whereas Cyfra 21-1 had an AUC of 0.72.

CONCLUSION

We have developed a protein biomarker panel to identify lung cancers from controls with a high accuracy. This integrated noninvasive approach to the evaluation of lung nodules deserves further prospective validation among larger cohorts of patients with lung nodules in screening strategy.

Proteomic study of hepatocellular carcinoma using a novel modified aptamer-based array (SOMAscan™) platform

Vascular invasion is a pathological hallmark of hepatocellular carcinoma (HCC), associated with poor prognosis; it is strongly related to the early recurrence and poor survival after curative resection. In order to determine the proteomic backgrounds of HCC carcinogenesis and vascular invasion, we employed a novel modified aptamer-based array (SOMAscan) platform. SOMAscan is based on the Slow Off-rate Modified Aptamers (SOMAmers), which rely on the natural 3D folding of single-stranded DNA-based protein affinity reagents. Currently, the expression level of 1129 proteins can be assessed quantitatively. Correlation matrix analysis showed that the overall proteomic features captured by SOMAscan differ between tumor and non-tumor tissues. Non-tumor tissues were shown to have more homogeneous proteome backgrounds than tumor tissues. A comparative study identified 68 proteins with differential expression between tumor and non-tumor tissues, together with eight proteins associated with vascular invasion. Gene Ontology analysis showed that the extracellular space and extracellular region proteins were predominantly detected. Network analysis revealed the linkage of seven proteins, AKT1, MDM2, PTEN, FGF1, MAPK8, PRKCB, and FN1, which were categorized as the components of "Pathways in cancer" in pathway analysis. The results of SOMAscan analysis were not concordant with those obtained by western blotting; only the determined FN1 levels were concordant between the two platforms. We demonstrated that the proteome captured by SOMAscan includes the proteins relevant to carcinogenesis and vascular invasion in HCC. The identified proteins may serve as candidates for the future studies of disease mechanisms and clinical applications.

Advances in targeted proteomics and applications to biomedical research

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.

Trisomy 21 consistently activates the interferon response

Although it is clear that trisomy 21 causes Down syndrome, the molecular events acting downstream of the trisomy remain ill defined. Using complementary genomics analyses, we identified the interferon pathway as the major signaling cascade consistently activated by trisomy 21 in human cells. Transcriptome analysis revealed that trisomy 21 activates the interferon transcriptional response in fibroblast and lymphoblastoid cell lines, as well as circulating monocytes and T cells. Trisomy 21 cells show increased induction of interferon-stimulated genes and decreased expression of ribosomal proteins and translation factors. An shRNA screen determined that the interferon-activated kinases JAK1 and TYK2 suppress proliferation of trisomy 21 fibroblasts, and this defect is rescued by pharmacological JAK inhibition. Therefore, we propose that interferon activation, likely via increased gene dosage of the four interferon receptors encoded on chromosome 21, contributes to many of the clinical impacts of trisomy 21, and that interferon antagonists could have therapeutic benefits.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Serum mass profile signature as a biomarker of early lung cancer

OBJECTIVES

Circulating molecular biomarkers of lung cancer may allow the pre-selection of candidates for computed tomography screening or increase its efficacy. We aimed to identify features of serum mass profile distinguishing individuals with early lung cancer from healthy participants of the lung cancer screening program.

METHODS

Blood samples were collected during a low-dose computed tomography (LD-CT) screening program performed by one institution (Medical University of Gdansk, Poland). MALDI-ToF mass spectrometry was used to characterize the low-molecular-weight (1000-14,000Da) serum fraction. The analysis comprised 95 patients with early stage lung cancer (including 30 screen-detected cases) and a matched group of 285 healthy controls. The cases were split into two independent cohorts (discovery and validation), analyzed separately 6 months apart.

RESULTS

Several molecular components of serum (putatively components of endogenous peptidome) discriminating patients with early lung cancer from controls were identified in a discovery cohort. This allowed building an effective cancer classifier as a model tuned to maximize negative predictive value, with an area under the curve (AUC) of 0.88, a negative predictive value of 100%, and a positive predictive value of 48%. However, the classifier performed worse in a validation cohort including independent sample sets (AUC 0.73, NPV 88% and PPV 30%).

CONCLUSIONS

We developed a serum mass profile-based signature identifying patients with early lung cancer. Although this marker has insufficient value as a stand-alone preselecting tool for LD-CT screening, its potential clinical usefulness in evaluation of indeterminate pulmonary nodules deserves further investigation.

Identification of functional metabolic biomarkers from lung cancer patient serum using PEP technology

BACKGROUND

Reprogrammed metabolism is a new hallmark of cancer. In many types of cancer, most of the genes in the glycolytic pathway are overexpressed, reflecting an essential shift of metabolism during cancer development. The reprogrammed metabolism contributes to cancer development in multiple ways, from supplying the elevated energy requirement to creating a microenvironment suitable for tumor growth and suppressing the human immune surveillance system.

METHOD

In this study, a functional proteomics top-down approach was used to systematically monitor metabolic enzyme activities in resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP.

RESULTS

We found that the enrichment of low abundance proteins with a bead based product called AlbuVoid™(,) is important to increase the number of observable features and to increase the level of signal achievable from the assay used. From our methods, significant metabolic enzyme activities were detected in both normal and lung cancer patient sera in many fractions after the elution of the 2-D gel separated proteins to the Protein Elution Plate (PEP). Eighteen fractions with the most dramatic metabolic enzyme activity difference between the normal and lung cancer patient sera were submitted for mass spectrometry protein identification. Proteins from the glycolytic metabolic pathway, such as GAPDH along with other proteins not previously annotated to the glycolytic pathway were identified. Further verification with commercially purified GAPDH showed that the addition of purified GAPDH to the metabolic enzyme assay system employed enhanced the enzyme activity, demonstrating that proteins identified from the PEP technology and mass spectrometry could be further verified with biological assay.

CONCLUSION

This study identified several potential functional enzyme biomarkers from lung cancer patient serum, it provides an alternative and complementary approach to sequence annotation for the discovery of biomarkers in human diseases.

A cross sectional study of two independent cohorts identifies serum biomarkers for facioscapulohumeral muscular dystrophy (FSHD)

Measuring the severity and progression of facioscapulohumeral muscular dystrophy (FSHD) is particularly challenging because muscle weakness progresses over long periods of time and can be sporadic. Biomarkers are essential for measuring disease burden and testing treatment strategies. We utilized the sensitive, specific, high-throughput SomaLogic proteomics platform of 1129 proteins to identify proteins with levels that correlate with FSHD severity in a cross-sectional study of two independent cohorts. We discovered biomarkers that correlate with clinical severity and disease burden measured by magnetic resonance imaging. Sixty-eight proteins in the Rochester cohort (n = 48) and 51 proteins in the Seattle cohort (n = 30) had significantly different levels in FSHD-affected individuals when compared with controls (p-value ≤ .005). A subset of these varied by at least 1.5 fold and four biomarkers were significantly elevated in both cohorts. Levels of creatine kinase MM and MB isoforms, carbonic anhydrase III, and troponin I type 2 reliably predicted the disease state and correlated with disease severity. Other novel biomarkers were also discovered that may reveal mechanisms of disease pathology. Assessing the levels of these biomarkers during clinical trials may add significance to other measures of quantifying disease progression or regression.

Proteomic methods for biomarker discovery and validation. Are we there yet?

Noninvasive molecular biomarkers are becoming attractive tools to monitor disease progression, aid drug development programs and use as surrogate outcome measures in clinical trials. Cutting edge proteomic methods to assay biomarkers in body fluids have been developed in the past few years, but transitioning them to clinical practice has been slow and depends on the qualification of both the method and the biomarker.

The challenge of small lung nodules identified in CT screening: can biomarkers assist diagnosis?

Various biomarkers have been developed as noninvasive tests to indicate the presence of lung cancer in asymptomatic persons, and in particular to provide evidence as to whether indeterminate lung nodules detected by screening are malignant. We performed an overview of the range of biomarkers reported in the literature and described those that can complement low-dose computed tomography screening. Several have promising sensitivity and specificity. However to our knowledge, only three techniques have reached the prospective screening phase (phase 4) of the five-phase biomarker development process. Two miRNA signatures (the miR-Test for serum and the miRNA signature classifier test for plasma) are being assessed in prospective screening trials, as is the EarlyCDT-Lung test based on autoantibodies. All will need to undergo prospective studies to determine their ability to improve outcomes before they can become an established adjunct to lung cancer control strategies.