scholarly journals Screening and validation of serum protein biomarkers for early postmenopausal osteoporosis diagnosis

2017 ◽  
Vol 16 (6) ◽  
pp. 8427-8433 ◽  
Author(s):  
Long Wang ◽  
Ya-Qian Hu ◽  
Zhuo-Jie Zhao ◽  
Hong-Yang Zhang ◽  
Bo Gao ◽  
...  
2010 ◽  
Vol 136 (8) ◽  
pp. 1151-1159 ◽  
Author(s):  
Xiaonan Kang ◽  
Lu Sun ◽  
Kun Guo ◽  
Hong Shu ◽  
Jun Yao ◽  
...  

2020 ◽  
Author(s):  
Angela Mc Ardle ◽  
Anna Kwasnik ◽  
Agnes Szenpetery ◽  
Melissa Jones ◽  
Belinda Hernandez ◽  
...  

AbstractObjectivesTo identify serum protein biomarkers which might separate early inflammatory arthritis (EIA) patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) to provide an accurate diagnosis and support appropriate early intervention.MethodsIn an initial protein discovery phase, the serum proteome of a cohort of patients with PsA and RA was interrogated using unbiased liquid chromatography mass spectrometry (LC-MS/MS) (n=64 patients), a multiplexed antibody assay (Luminex) for 48 proteins (n=64 patients) and an aptamer-based assay (SOMAscan) targeting 1,129 proteins (n=36 patients). Subsequently, analytically validated targeted multiple reaction monitoring (MRM) assays were developed to further evaluate those proteins identified as discriminatory during the discovery. During an initial verification phase, MRM assays were developed to a panel of 150 proteins (by measuring a total of 233 peptides) and used to re-evaluate the discovery cohort (n=60). During a second verification phase, the panel of proteins was expanded to include an additional 23 proteins identified in other proteomic discovery analyses of arthritis patients. The expanded panel was evaluated using a second, independent cohort of PsA and RA patients (n=167).ResultsMultivariate analysis of the protein discovery data revealed that it was possible to discriminate PsA from RA patients with an area under the curve (AUC) of 0.94 for nLC-MS/MS, 0.69 for Luminex based measurements; 0.73 for SOMAscan analysis. During the initial verification phase, random forest models confirmed that proteins measured by MRM could differentiate PsA and RA patients with an AUC of 0.79 and during the second phase of verification the expanded panel could segregate the two disease groups with an AUC of 0.85.ConclusionWe report a serum protein biomarker panel which can separate EIA patients with PsA from those with RA. We suggest that the routine use of such a panel in EIA patients will improve clinical decision making and with continued evaluation and refinement using additional patient cohorts will support the development of a diagnostic test for patients with PsA.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 891-892
Author(s):  
D. Galbraith ◽  
M. Caliskan ◽  
O. Jabado ◽  
S. Hu ◽  
R. Fleischmann ◽  
...  

Background:RA is a systemic autoimmune disease with heterogeneous manifestation. Recent advances in serum proteomics, such as the SomaScan®platform (SomaLogic, Inc., Boulder, USA), allow for a deeper exploration of the protein biomarkers associated with RA and a better understanding of the molecular aetiology of the disease.Objectives:To characterise the differences in baseline serum proteome of patients with RA (enrolled in the Phase IIIb Abatacept vs adaliMumab comParison in bioLogic-naïvERA subjects with background MTX [AMPLE] study)1compared with a healthy population, and to identify serum protein biomarkers associated with disease severity and radiographic progression.Methods:Patients in the AMPLE study had an inadequate response to MTX and were naïve to biologic DMARDs. Protein abundance was assessed in baseline serum samples from 440 AMPLE study patients and 123 healthy individuals with matching demographics using the SomaScan®platform, with 5000+ slow off-rate modified aptamers and up to 8 log of dynamic range.2Differential abundance testing was performed using linear models to identify differences in protein abundance in patients with RA vs healthy individuals. A separate analysis using a linear model was conducted in only the patients with RA to identify the proteins associated with DAS28 (CRP) and TSS. Pathway analyses were performed for proteins significantly (false discovery rate-adjusted p value <0.05) associated with RA and the disease severity measurements to identify over-representation of the molecular pathways.Results:Compared with healthy individuals, >2000 serum proteins were significantly differentially expressed in patients with RA, including many proteins that have been associated with RA (e.g. serum amyloid A [SAA], CRP) and complement. Most of the protein expression differences were of small magnitude (fold change <2). Proteins that were differentially expressed between patients with RA and healthy individuals were enriched in interleukin signalling, neutrophil degranulation, platelet activation/degranulation and extracellular matrix organisation pathways. DAS28 (CRP) was significantly associated with several biomarkers, including SAA, fibrinogen and CRP; in general, proteins associated with DAS28 (CRP) were most strongly enriched in the platelet activation/degranulation pathways (Figure 1), also seen in patients with RA vs healthy individuals. Additionally, many proteins were significantly associated with TSS, including SAA, matrix metalloproteinase-3 and cartilage acidic protein 1. Here, the proteins were most strongly enriched in the extracellular matrix remodelling pathways (Figure 2).Conclusion:Our study revealed that thousands of serum proteins are differentially expressed and several pathways are dysregulated between patients with RA and healthy individuals. Additional pathways were identified that reflect disease severity, including joint damage, distinct from those pathways associated with the disease. The SomaScan®platform provides a unique proteomic tool with a wide dynamic range for the identification of serum protein biomarkers associated with RA and disease severity. Proteomic signatures should be considered in clinical trials to better understand disease pathogenesis and predict risk in response to treatment.References:[1]Schiff M, et al.Ann Rheum Dis2014;73:86–94.[2]Gold L, et al.PLoS One2010;5:e15004.Acknowledgments:Rachel Rankin (medical writing, Caudex; funding: Bristol-Myers Squibb)Disclosure of Interests:David Galbraith Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Minal Caliskan Employee of: Bristol-Myers Squibb, Omar Jabado Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Sarah Hu Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Roy Fleischmann Grant/research support from: AbbVie, Akros, Amgen, AstraZeneca, Bristol-Myers Squibb, Boehringer, IngelhCentrexion, Eli Lilly, EMD Serono, Genentech, Gilead, Janssen, Merck, Nektar, Novartis, Pfizer, Regeneron Pharmaceuticals, Inc., Roche, Samsung, Sandoz, Sanofi Genzyme, Selecta, Taiho, UCB, Consultant of: AbbVie, ACEA, Amgen, Bristol-Myers Squibb, Eli Lilly, Gilead, GlaxoSmithKline, Novartis, Pfizer, Sanofi Genzyme, UCB, Michael Weinblatt Grant/research support from: Amgen, Bristol-Myers Squibb, Crescendo, Lily, Sanofi/Regeneron, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Crescendo, Gilead, Horizon, Lily, Pfizer, Roche, Sean Connolly Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Michael A Maldonado Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Sheng Gao Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb


2020 ◽  
Vol 212 ◽  
pp. 103581 ◽  
Author(s):  
María del Pilar Chantada-Vázquez ◽  
Antonio Castro López ◽  
María García Vence ◽  
Sergio Vázquez-Estévez ◽  
Benigno Acea-Nebril ◽  
...  

2003 ◽  
Vol 13 (Suppl 2) ◽  
pp. 133-139 ◽  
Author(s):  
E. V. Stevens ◽  
L. A. Liotta ◽  
E. C. Kohn

Ovarian cancer is a multifaceted disease wherein most women are diagnosed with advanced stage disease. One of the most imperative issues in ovarian cancer is early detection. Biomarkers that allow cancer detection at stage I, a time when the disease is amenable to surgical and chemotherapeutic cure in over 90% of patients, can dramatically alter the horizon for women with this disease. Recent developments in mass spectroscopy and protein chip technology coupled with bioinformatics have been applied to biomarker discovery. The complexity of the proteome is a rich resource from which the patterns can be gleaned; the pattern rather than its component parts is the diagnostic. Serum is a key source of putative protein biomarkers, and, by its nature, can reflect organ-confined events. Pioneering use of mass spectroscopy coupled with bioinformatics has been demonstrated as being capable of distinguishing serum protein pattern signatures of ovarian cancer in patients with early- and late-stage disease. This is a sensitive, precise, and promising tool for which further validation is needed to confirm that ovarian cancer serum protein signature patterns can be a robust biomarker approach for ovarian cancer diagnosis, yielding improved patient outcome and reducing the death and suffering from ovarian cancer.


2017 ◽  
Vol 55 (10) ◽  
pp. 3057-3071 ◽  
Author(s):  
Mary A. De Groote ◽  
David G. Sterling ◽  
Thomas Hraha ◽  
Theresa M. Russell ◽  
Louis S. Green ◽  
...  

ABSTRACT New non-sputum biomarker tests for active tuberculosis (TB) diagnostics are of the highest priority for global TB control. We performed in-depth proteomic analysis using the 4,000-plex SOMAscan assay on 1,470 serum samples from seven countries where TB is endemic. All samples were from patients with symptoms and signs suggestive of active pulmonary TB that were systematically confirmed or ruled out for TB by culture and clinical follow-up. HIV coinfection was present in 34% of samples, and 25% were sputum smear negative. Serum protein biomarkers were identified by stability selection using L1-regularized logistic regression and by Kolmogorov-Smirnov (KS) statistics. A naive Bayes classifier using six host response markers (HR6 model), including SYWC, kallistatin, complement C9, gelsolin, testican-2, and aldolase C, performed well in a training set (area under the sensitivity-specificity curve [AUC] of 0.94) and in a blinded verification set (AUC of 0.92) to distinguish TB and non-TB samples. Differential expression was also highly significant ( P < 10 −20 ) for previously described TB markers, such as IP-10, LBP, FCG3B, and TSP4, and for many novel proteins not previously associated with TB. Proteins with the largest median fold changes were SAA (serum amyloid protein A), NPS-PLA2 (secreted phospholipase A2), and CA6 (carbonic anhydrase 6). Target product profiles (TPPs) for a non-sputum biomarker test to diagnose active TB for treatment initiation (TPP#1) and for a community-based triage or referral test (TPP#2) have been published by the WHO. With 90% sensitivity and 80% specificity, the HR6 model fell short of TPP#1 but reached TPP#2 performance criteria. In conclusion, we identified and validated a six-marker signature for active TB that warrants diagnostic development on a patient-near platform.


2020 ◽  
Vol 72 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Daniel J. Kass ◽  
Mehdi Nouraie ◽  
Marilyn K. Glassberg ◽  
Nitya Ramreddy ◽  
Karen Fernandez ◽  
...  

Anaerobe ◽  
2020 ◽  
Vol 63 ◽  
pp. 102209 ◽  
Author(s):  
Prabhakar Babele ◽  
Ravi Bhushan Kumar ◽  
Sakshi Rajoria ◽  
Faraz Rashid ◽  
Dipankar Malakar ◽  
...  

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