Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

Delfani, Payam; Mellby, Linda Dexlin; Nordström, Malin; Holmér, Andreas; Ohlsson, Mattias; Borrebaeck, Carl; Wingren, Christer

doi:10.1371/journal.pone.0159138

Cited by 28 publications

(21 citation statements)

References 42 publications

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“…Thus, studies have been generally restricted to relatively small sample sizes that are inadequate to assess variability on a population scale. New approaches, such as aptamer‐based or antibody‐based affinity proteomics, allow multiplexing and larger sample sizes but are limited to the evaluation of a relatively limited number of candidate proteins (Delfani et al., ). The plasma proteome is particularly complex but offers an important window on individual variation and the relationship of proteins to important phenotypes.…”

Section: Introductionmentioning

confidence: 99%

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

et al. 2018

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SummaryThe biological perturbations associated with incident mortality are not well elucidated, and there are limited biomarkers for the prediction of mortality. We used a novel high‐throughput proteomics approach to identify serum peptides and proteins associated with 5‐year mortality in community‐dwelling men age ≥65 years who participated in a longitudinal observational study of musculoskeletal aging (Osteoporotic Fractures in Men: MrOS). In a discovery phase, serum specimens collected at baseline in 2473 men were analyzed using liquid chromatography–ion mobility–mass spectrometry, and incident mortality in the subsequent 5 years was ascertained by tri‐annual questionnaire. Rigorous statistical methods were utilized to identify 56 peptides (31 proteins) that were associated with 5‐year mortality. In an independent replication phase, selected reaction monitoring was used to examine 21 of those peptides in baseline serum from 750 additional men; 81% of those peptides remained significantly associated with mortality. Mortality‐associated proteins included a variety involved in inflammation or complement activation; several have been previously linked to mortality (e.g., C‐reactive protein, alpha 1‐antichymotrypsin) and others are not previously known to be associated with mortality. Other novel proteins of interest included pregnancy‐associated plasma protein, VE‐cadherin, leucine‐rich α‐2 glycoprotein 1, vinculin, vitronectin, mast/stem cell growth factor receptor, and Saa4. A panel of peptides improved the predictive value of a commonly used clinical predictor of mortality. Overall, these results suggest that complex inflammatory pathways, and proteins in other pathways, are linked to 5‐year mortality risk. This work may serve to identify novel biomarkers for near‐term mortality.

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Section: Introductionmentioning

confidence: 99%

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

et al. 2018

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“…To this end, we have for the first time performed protein expression profiling of breast cancer tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins [25–28]. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics) [29] to reflect histological grade of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Tumor tissue protein signatures reflect histological grade of breast cancer

et al. 2017

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Histological grade is one of the most commonly used prognostic factors for patients diagnosed with breast cancer. However, conventional grading has proven technically challenging, and up to 60% of the tumors are classified as histological grade 2, which represents a heterogeneous cohort less informative for clinical decision making. In an attempt to study and extend the molecular puzzle of histologically graded breast cancer, we have in this pilot project searched for additional protein biomarkers in a new space of the proteome. To this end, we have for the first time performed protein expression profiling of breast cancer tumor tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics). Uniquely, the results showed that several biologically relevant proteins reflecting histological grade could be delineated. In more detail, the tentative biomarker panels could be used to i) build a candidate model classifying grade 1 vs. grade 3 tumors, ii) demonstrate the molecular heterogeneity among grade 2 tumors, and iii) potentially re-classify several of the grade 2 tumors to more like grade 1 or grade 3 tumors. This could, in the long-term run, lead to improved prognosis, by which the patients could benefit from improved tailored care.

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“…Nevertheless, it is important to highlight that this methodology is still being carefully revised. Thus, novel alternatives are continually being proposed in the literature trying to correct bias more efficiently among disease samples, for instance, using co-normalization of control samples [ 29 ] or combining with other normalization approach like LOESS, SVN or QN [ 30 , 31 ].…”

Section: Discussionmentioning

confidence: 99%

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

et al. 2018

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Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

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Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

Cited by 28 publications

References 42 publications

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

High‐throughput serum proteomics for the identification of protein biomarkers of mortality in older men

Tumor tissue protein signatures reflect histological grade of breast cancer

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

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