Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies

Brezina, Stefanie; Soldo, Regina; Kreuzhuber, Roman; Hofer, Philipp; Gsur, Andrea; Weinhaeusel, Andreas

doi:10.3390/microarrays4020162

Cited by 13 publications

(18 citation statements)

References 69 publications

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“…After cultivation, purification and elution of the His-tagged recombinant proteins was performed. Protein estimation was performed by running a gel [70] . Clarified E. coli lysates and plain buffer were used as positive and negative controls, respectively.…”

Section: Methodsmentioning

confidence: 99%

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The Immunome of Colon Cancer: Functional in Silico Analysis of Antigenic Proteins Deduced from IgG Microarray Profiling

Coronell

Sergelen

Hofer

et al. 2018

Genomics, Proteomics &Amp; Bioinformatics

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Characterization of the colon cancer immunome and its autoantibody signature from differentially-reactive antigens (DIRAGs) could provide insights into aberrant cellular mechanisms or enriched networks associated with diseases. The purpose of this study was to characterize the antibody profile of plasma samples from 32 colorectal cancer (CRC) patients and 32 controls using proteins isolated from 15,417 human cDNA expression clones on microarrays. 671 unique DIRAGs were identified and 632 were more highly reactive in CRC samples. Bioinformatics analyses reveal that compared to control samples, the immunoproteomic IgG profiling of CRC samples is mainly associated with cell death, survival, and proliferation pathways, especially proteins involved in EIF2 and mTOR signaling. Ribosomal proteins (e.g., RPL7, RPL22, and RPL27A) and CRC-related genes such as APC, AXIN1, E2F4, MSH2, PMS2, and TP53 were highly enriched. In addition, differential pathways were observed between the CRC and control samples. Furthermore, 103 DIRAGs were reported in the SEREX antigen database, demonstrating our ability to identify known and new reactive antigens. We also found an overlap of 7 antigens with 48 “CRC genes.” These data indicate that immunomics profiling on protein microarrays is able to reveal the complexity of immune responses in cancerous diseases and faithfully reflects the underlying pathology.

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Section: Methodsmentioning

confidence: 99%

“…ARChip Epoxy glass slides [71] were used to spot the protein arrays in duplicate using an Omnigrid arrayer. An illustration of the protein microarray design can be found in [70] .…”

Section: Methodsmentioning

confidence: 99%

The Immunome of Colon Cancer: Functional in Silico Analysis of Antigenic Proteins Deduced from IgG Microarray Profiling

Coronell

Sergelen

Hofer

et al. 2018

Genomics, Proteomics &Amp; Bioinformatics

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“…These UNIPEX clones represent 6369 different human proteins (each protein presented by 2 or more different clones), for which 5449 have been annotated with a gene-symbol, when the cDNA sequence perfectly matched to database entries. Antibody profiling using purified IgG from sera was conducted as described previously [15][16][17].…”

Section: Detection Of Autoantibodiesmentioning

confidence: 99%

“…The clinical activity score was assessed by using the simple clinical colitis activity index (SCCAI) [19]. Immunglobulin G purified from sera of UC and non-UC donors were subjected to high-density protein microarray antibody profiling using AIT's 16k protein-microarray [15]. Much to our surprise, analysis of autoantibody expression revealed 697 autoantigens that were significantly different at the nominal 0�01 significance level (FDR<0�1) of the univariate test between the UC group and the group of non-affected individuals ( Fig 1A, the complete list of autoantibodies is shown in S1 Table).…”

Section: Autoantibody Expression and Immunology Profiles In Uc Patientsmentioning

confidence: 99%

Autoantibodies as diagnostic markers and potential drivers of inflammation in ulcerative colitis

et al. 2020

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To date, no comprehensive analysis of autoantibodies in sera of patients with ulcerative colitis has been conducted. To analyze the spectrum of autoantibodies and to elucidate their role serum-IgG from UC patients (n = 49) and non-UC donors (n = 23) were screened by using a human protein microarray. Screening yielded a remarkable number of 697 differentially-reactive at the nominal 0�01 significance level (FDR<0�1) of the univariate test between the UC and the non-UC group. CD99 emerged as a biomarker to discriminate between both groups (p = 1e-04, AUC = 0�8). In addition, cytokines, chemokines and growth factors were analyzed by Olink's Proseek® Multiplex Inflammation-I 96×96 immuno-qPCR assay and 31 genes were significant at the nominal 0.05 level of the univariate test to discriminate between UC and non-UC donors. MCP-3, HGF and CXCL-9 were identified as the most significant markers to discriminate between UC patients with clinically active and inactive disease. Levels of CXCL10 (cor = 0.3; p = 0.02), CCL25 (cor = 0.25; p = 0.04) and CCL28 (cor = 0.3; p = 0.02) correlated positively with levels of anti CD99. To assess whether autoantibodies are detectable prior to diagnosis with UC, sera from nine donors at two different time points (T-early, median 21 months and T-late, median 6 months) were analyzed. 1201 features were identified with higher reactivity in samples at time points closer to clinical UC presentation. In vitro, additional challenge of peripheral mononuclear cells with CD99 did not activate CD4+ T cells but induced the secretion of IL-10 (-CD99: 20.21±20.25; +CD99: 130.20±89.55; mean ±sd; p = 0.015). To examine the effect of CD99 in vivo, inflammation and autoantibody levels were examined in NOD/ScidIL2Rγ null mice reconstituted with PBMC from UC donors (NSG-UC). Additional challenge with CD99 aggravated disease symptoms and pathological phenotype as indicated by the elevated clinical score (-CD99: 1�85 ± 1�94; +CD99: 4�25 ± 1�48) and histological score (-CD99: 2�16 ± 0�83; +CD99: 3�15 ± 1�16, p = 0�01). Furthermore, levels of anti-CD99 antibodies increased (Control: 398 ± 323; mean MFI ± sd; Ethanol + PBS: 358 ±316; Ethanol + CD99: 1363 ± 1336; Control versus PLOS ONE | https://doi.Ethanol + CD99: p = 0.03). In a highly inflammatory environment, frequencies of pro-inflammatory M1 monocytes (CD14+ CD64+: unchallenged 8.09±4.72; challenged 14.2±8.62; p = 0.07; CD14+ CD1a+: unchallenged 16.29 ±6.97; challenged 43.81±14.4, p = 0.0003) increased and levels of autoantibodies in serum decreased in the NSG-UC mouse model. These results suggest that autoantibodies are potent biomarkers to discriminate between UC and non-UC and indicate risk to develop UC. In an inflammatory environment, auto-antibodies may promote the pathological phenotype by activating M1 monocytes in the NSG-UC animal model and also in patients with UC.

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“…DNA microarray tools are generally unsuitable for this purpose given the different assay setup, objectives and statistical assumptions used. Although several protein microarray-specific tools are available [ 18 – 25 ], none of these include a composite suite of methods that we deemed as essential. Furthermore, no consistency is seen amongst research groups, which impedes collaborative data consolidation and comparison.…”

Section: Introductionmentioning

confidence: 99%

PMA: Protein Microarray Analyser, a user-friendly tool for data processing and normalization

et al. 2018

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ObjectiveProtein microarrays provide a high-throughput platform to measure protein interactions and associated functions, and can aid in the discovery of cancer biomarkers. The resulting protein microarray data can however be subject to systematic bias and noise, thus requiring a robust data processing, normalization and analysis pipeline to ensure high quality and robust results. To date, a comprehensive data processing pipeline is yet to be developed. Furthermore, a lack of analysis consistency is evident amongst different research groups, thereby impeding collaborative data consolidation and comparison. Thus, we sought to develop an accessible data processing tool using methods that are generalizable to the protein microarray field and which can be adapted to individual array layouts with minimal software engineering expertise.ResultsWe developed an improved version of a previously developed pipeline of protein microarray data processing and implemented it as an open source software tool, with particular focus on widening its use and applicability. The Protein Microarray Analyser software presented here includes the following tools: (1) neighbourhood background correction, (2) net intensity correction, (3) user-defined noise threshold, (4) user-defined CV threshold amongst replicates and (5) assay controls, (6) composite ‘pin-to-pin’ normalization amongst sub-arrays, and (7) ‘array-to-array’ normalization amongst whole arrays.Electronic supplementary materialThe online version of this article (10.1186/s13104-018-3266-0) contains supplementary material, which is available to authorized users.

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Immune-Signatures for Lung Cancer Diagnostics: Evaluation of Protein Microarray Data Normalization Strategies

Cited by 13 publications

References 69 publications

The Immunome of Colon Cancer: Functional in Silico Analysis of Antigenic Proteins Deduced from IgG Microarray Profiling

The Immunome of Colon Cancer: Functional in Silico Analysis of Antigenic Proteins Deduced from IgG Microarray Profiling

Autoantibodies as diagnostic markers and potential drivers of inflammation in ulcerative colitis

PMA: Protein Microarray Analyser, a user-friendly tool for data processing and normalization

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