Aptamer-based proteomics of serum and plasma in acquired aplastic anemia
2018; Elsevier BV; Volume: 68; Linguagem: Inglês
10.1016/j.exphem.2018.09.008
ISSN1873-2399
AutoresValentina Giudice, Angélique Biancotto, Zhijie Wu, Foo Cheung, Julián Candia, Giovanna Fantoni, Sachiko Kajigaya, Olga Rios, Danielle Townsley, Xingmin Feng, Neal S. Young,
Tópico(s)RNA Interference and Gene Delivery
Resumo•SOMAscan is an aptamer-based proteomic technology for large-scale studies.•Nineteen serum proteins are proposed as biomarkers and prognosticators of aplastic anemia (AA).•Serum DKK1, SELL, CCL17, and HGF are validated as novel markers of AA.•Twenty-eight plasma proteins are identified as candidate biomarkers of AA.•More than 600 proteins can be used as biomarkers of AA in both serum and plasma. Single-stranded oligonucleotides containing deoxyuridine are aptamers (SOMAmers) that can bind proteins with high specificity and affinity and slow dissociation rates. SOMAscan, an aptamer-based proteomic technology, allows measurement of more than 1,300 proteins simultaneously for the identification of new disease biomarkers. The aim of the present study was to identify new serum and plasma protein markers for diagnosis of acquired aplastic anemia (AA) and response to immunosuppressive therapies (IST). SOMAscan was used to screen 1,141 serum proteins in 28 AA patients before and after therapy and 1,317 plasma proteins in seven SAA patients treated with standard IST and a thrombopoietin receptor agonist. From our analysis, 19 serum and 28 plasma proteins were identified as possible candidate diagnostic and prognostic markers. A custom immunobead-based multiplex assay with five selected serum proteins (BMP-10, CCL17, DKK1, HGF, and SELL) was used for validation in a verification set (n = 65) of samples obtained before and after IST and in a blinded validation cohort at baseline (n = 16). After technical validation, four biomarkers were employed to predict diagnosis (accuracy, 88%) and long-term response to IST (accuracy, 79%). In conclusion, SOMAscan is a powerful tool for the identification of new biomarkers. We propose further larger studies to validate new candidate serum and plasma diagnostic and prognostic markers of AA. Single-stranded oligonucleotides containing deoxyuridine are aptamers (SOMAmers) that can bind proteins with high specificity and affinity and slow dissociation rates. SOMAscan, an aptamer-based proteomic technology, allows measurement of more than 1,300 proteins simultaneously for the identification of new disease biomarkers. The aim of the present study was to identify new serum and plasma protein markers for diagnosis of acquired aplastic anemia (AA) and response to immunosuppressive therapies (IST). SOMAscan was used to screen 1,141 serum proteins in 28 AA patients before and after therapy and 1,317 plasma proteins in seven SAA patients treated with standard IST and a thrombopoietin receptor agonist. From our analysis, 19 serum and 28 plasma proteins were identified as possible candidate diagnostic and prognostic markers. A custom immunobead-based multiplex assay with five selected serum proteins (BMP-10, CCL17, DKK1, HGF, and SELL) was used for validation in a verification set (n = 65) of samples obtained before and after IST and in a blinded validation cohort at baseline (n = 16). After technical validation, four biomarkers were employed to predict diagnosis (accuracy, 88%) and long-term response to IST (accuracy, 79%). In conclusion, SOMAscan is a powerful tool for the identification of new biomarkers. We propose further larger studies to validate new candidate serum and plasma diagnostic and prognostic markers of AA. Acquired aplastic anemia (AA), a bone marrow (BM) failure syndrome characterized by pancytopenia and BM hypocellularity, is caused by hematopoietic stem and progenitor cell (HSPC) destruction by immune cells [1Young NS Calado RT Scheinberg P Current concepts in the pathophysiology and treatment of aplastic anemia.Blood. 2006; 108: 2509-2519Crossref PubMed Scopus (654) Google Scholar]. BM transplantation remains the first therapeutic choice for young patients with a matched sibling donor. Immunosuppressive therapy (IST), with or without the thrombopoietin (TPO) receptor agonist eltrombopag (EPAG), are considered the standard of care in older patients and a therapeutic option for younger patients without a matched sibling donor [1Young NS Calado RT Scheinberg P Current concepts in the pathophysiology and treatment of aplastic anemia.Blood. 2006; 108: 2509-2519Crossref PubMed Scopus (654) Google Scholar, 2Bacigalupo A How I treat acquired aplastic anemia.Blood. 2017; 129: 1428-1436Crossref PubMed Scopus (187) Google Scholar]. However, the exact mechanisms of action of IST and EPAG are still not well understood [3Cheng H Cheruku PS Alvarado L et al.Interferon-γ perturbs key signaling pathways induced by thrombopoietin, but not eltrombopag, in human hematopoietic stem/progenitor cells.Blood. 2016; 128: 3870Crossref Google Scholar, 4Alvarado LJ Andreoni A Huntsman HD Cheng H Knutson JR Larochelle A Heterodimerization of TPO and IFNγ impairs human hematopoietic stem/progenitor cell signaling and survival in chronic inflammation.Blood. 2017; 130: 4PubMed Google Scholar, 5Qu MM Liu XN Liu XG et al.Cytokine changes in response to TPO receptor agonist treatment in primary immune thrombocytopenia.Cytokine. 2017; 92: 110-117Crossref PubMed Scopus (13) Google Scholar]. Hematologic improvement of blood counts after IST is one of the most supportive indirect evidence of the autoimmunity to HSPCs in BM failure [6Young NS Current concepts in the pathophysiology and treatment of aplastic anemia.Hematology Am Soc Hematol Educ Program. 2013; 2013: 76-81Crossref PubMed Scopus (139) Google Scholar]. Additional lines of indirect evidence for an immune pathophysiology include measurements of activated cytotoxic T cells that inhibit BM proliferation, circulating and exosomal microRNAs (miRNAs), and the presence of pro-inflammatory cytokines in the plasma [1Young NS Calado RT Scheinberg P Current concepts in the pathophysiology and treatment of aplastic anemia.Blood. 2006; 108: 2509-2519Crossref PubMed Scopus (654) Google Scholar, 7Giudice V Feng X Lin Z et al.Deep sequencing and flow cytometric characterization of expanded effector memory CD8+CD57+ T cells frequently reveals T-cell receptor Vβ oligoclonality and CDR3 homology in acquired aplastic anemia.Haematologica. 2018; 103: 759-769Crossref Scopus (34) Google Scholar, 8Hosokawa K Kajigaya S Feng X et al.A plasma microRNA signature as a biomarker for acquired aplastic anemia.Haematologica. 2017; 102: 69-78Crossref PubMed Scopus (29) Google Scholar, 9Giudice V Banaszak LG Gutierrez-Rodrigues F et al.Circulating exosomal microRNAs in acquired aplastic anemia and myelodysplastic syndromes.Haematologica. 2018; 103: 1150-1159Crossref Scopus (23) Google Scholar, 10Feng X Scheinberg P Wu CO et al.Cytokine signature profiles in acquired aplastic anemia and myelodysplastic syndromes.Haematologica. 2011; 96: 602-606Crossref PubMed Scopus (107) Google Scholar, 11Yu W Ge M Lu S et al.Anti-inflammatory effects of interleukin-35 in acquired aplastic anemia.Cytokine. 2015; 76: 409-416Crossref Scopus (8) Google Scholar]. Measurement of proteins on a large scale in biological samples, or proteomics, has improved slowly compared with other "omics" fields [12Chandramouli K Qian PY Proteomics: challenges, techniques and possibilities to overcome biological sample complexity.Hum Genomics Proteomics. 2009; 2009239204Crossref PubMed Google Scholar, 13Gold L Walker JJ Wilcox SK Williams S Advances in human proteomics at high scale with the SOMAscan proteomics platform.N Biotechnol. 2012; 29: 543-549Crossref PubMed Scopus (123) Google Scholar]. Electrophoresis, mass spectrometry, enzyme-linked immunosorbent assay (ELISA), and immunobead-based multiplex assays are the most utilized techniques to detect and quantify proteins, but large-scale studies often are not feasible due to the limited numbers of samples or analytes that can be studied simultaneously or because of technical limitations in the quantification of low-abundance proteins [12Chandramouli K Qian PY Proteomics: challenges, techniques and possibilities to overcome biological sample complexity.Hum Genomics Proteomics. 2009; 2009239204Crossref PubMed Google Scholar, 13Gold L Walker JJ Wilcox SK Williams S Advances in human proteomics at high scale with the SOMAscan proteomics platform.N Biotechnol. 2012; 29: 543-549Crossref PubMed Scopus (123) Google Scholar, 14Gold L Ayers D Bertino J et al.Aptamer-based multiplexed proteomic technology for biomarker discovery.PLoS One. 2010; 5: e15004Crossref PubMed Scopus (851) Google Scholar, 15Rohloff JC Gelinas AD Jarvis TC et al.Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents.Mol Ther Nucleic Acids. 2014; 3: e201Abstract Full Text Full Text PDF PubMed Scopus (284) Google Scholar]. An aptamer-based multiplexed proteomic technology, the SOMAscan assay, was released in 2010 and currently between 1,300 and 5,000 human proteins can be detected simultaneously [15Rohloff JC Gelinas AD Jarvis TC et al.Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents.Mol Ther Nucleic Acids. 2014; 3: e201Abstract Full Text Full Text PDF PubMed Scopus (284) Google Scholar, 16Ostroff R Foreman T Keeney TR Stratford S Walker JJ Zichi D The stability of the circulating human proteome to variations in sample collection and handling procedures measured with an aptamer-based proteomics array.J Proteomics. 2010; 73: 649-666Crossref PubMed Scopus (50) Google Scholar]. Aptamers are short DNA or RNA molecules that can bind proteins with low affinity. They are sensitive to nuclease degradation [15Rohloff JC Gelinas AD Jarvis TC et al.Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents.Mol Ther Nucleic Acids. 2014; 3: e201Abstract Full Text Full Text PDF PubMed Scopus (284) Google Scholar], but hydrophobic modifications at the 5-position of deoxyuridine nucleotides greatly increase DNase resistance. Modified single-stranded aptamers, or SOMAmers (slow off-rate modified aptamers), are tested against targeted proteins from large libraries of randomized sequences through a technique referred to as SELEX (selected evolution of ligands by exponential enrichment) [13Gold L Walker JJ Wilcox SK Williams S Advances in human proteomics at high scale with the SOMAscan proteomics platform.N Biotechnol. 2012; 29: 543-549Crossref PubMed Scopus (123) Google Scholar, 14Gold L Ayers D Bertino J et al.Aptamer-based multiplexed proteomic technology for biomarker discovery.PLoS One. 2010; 5: e15004Crossref PubMed Scopus (851) Google Scholar, 15Rohloff JC Gelinas AD Jarvis TC et al.Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents.Mol Ther Nucleic Acids. 2014; 3: e201Abstract Full Text Full Text PDF PubMed Scopus (284) Google Scholar]. Only aptamers with slow dissociation rates (>30 min) are further selected in order to minimize nonspecific binding interactions. As a result, SOMAmers are highly specific for epitopes and residues on many human proteins [13Gold L Walker JJ Wilcox SK Williams S Advances in human proteomics at high scale with the SOMAscan proteomics platform.N Biotechnol. 2012; 29: 543-549Crossref PubMed Scopus (123) Google Scholar, 14Gold L Ayers D Bertino J et al.Aptamer-based multiplexed proteomic technology for biomarker discovery.PLoS One. 2010; 5: e15004Crossref PubMed Scopus (851) Google Scholar]. SOMAmer-target protein complexes are captured by biotin–streptavidin beads and nonspecifically bound SOMAmers are removed with a polyanionic-containing buffer. SOMAmers are then released from their specific target protein complexes by denaturation, hybridized to complementary sequences on microarray, and quantified by fluorescence [13Gold L Walker JJ Wilcox SK Williams S Advances in human proteomics at high scale with the SOMAscan proteomics platform.N Biotechnol. 2012; 29: 543-549Crossref PubMed Scopus (123) Google Scholar, 15Rohloff JC Gelinas AD Jarvis TC et al.Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents.Mol Ther Nucleic Acids. 2014; 3: e201Abstract Full Text Full Text PDF PubMed Scopus (284) Google Scholar]. Using this platform, new biomarkers have been discovered in malignant and nonmalignant disorders such as mesothelioma and Alzheimer's disease [17Ostroff RM Mehan MR Stewart A et al.Early detection of malignant pleural mesothelioma in asbestos-exposed individuals with a noninvasive proteomics-based surveillance tool.PLoS One. 2012; 7: e46091Crossref PubMed Scopus (112) Google Scholar, 18Sattlecker M Kiddle SJ Newhouse S et al.Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology.Alzheimers Dement. 2014; 10: 724-734Abstract Full Text Full Text PDF PubMed Scopus (131) Google Scholar]. To broaden current knowledge of proteomics in BM failure, we used SOMAscan to screen serum and plasma proteins from AA patients before and after IST, allowing the identification of new biomarkers of AA and responsiveness to therapy. These proteins may also relate to the overall pathophysiology of BM failure. Sera were collected from 109 AA patients after informed consent was obtained in accordance with the Declaration of Helsinki [19World Medical AssociationWorld Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.JAMA. 2013; 310: 2191-2194Crossref PubMed Scopus (11015) Google Scholar] and protocols approved by the National Heart, Lung, and Blood Institute Institutional Review Board of the National Institutes of Health (NIH) (www.clinicaltrials.gov identifiers: NCT00260689 and NCT01623167). All patients were diagnosed as severe AA (SAA) and hematologic response to IST was defined according to standard criteria [20[No authors listed]Incidence of aplastic anemia: the relevance of diagnostic criteria. By the International Agranulocytosis and Aplastic Anemia Study.Blood. 1987; 70: 1718-1721Crossref Google Scholar, 21Camitta BM Thomas ED Nathan DG et al.Severe aplastic anemia: a prospective study of the effect of early marrow transplantation on acute mortality.Blood. 1976; 48: 63-70Crossref PubMed Google Scholar]. Patients were divided in three cohorts: a discovery set (n = 28) for large-scale proteomics screening using SOMAscan; a verification set (n = 65) including 21 patients from the discovery cohort for technical validation of selected aptamers by Luminex assay; and a validation cohort (n = 16) of SAA patients whose hematologic response to IST was not known at the time of the study. Specimens were collected at the time of diagnosis and after 6, 12, and/or 24 months of initiating IST. Plasma samples were collected in EDTA tubes from seven SAA patients (www.clinicaltrials.gov identifier: NCT01623167) and specimens were collected at the time of diagnosis and after 6 months of initiating IST and EPAG. Healthy controls were recruited from donors at the NIH Clinical Center Department of Transfusion Medicine. Clinical characteristics are summarized in Table 1. After centrifugation at 2000 RPM for 10 min, serum or plasma samples were collected and stored at –80°C until use.Table 1Patient characteristicSerumPlasmaDiscovery set (n = 28)Verification set (n = 65)Validation set (n = 16)Discovery set (n = 7)Median age, years (range)30 (10–65)33 (2–75)47 (9–78)36 (7–65)Sex (M/F)18/1037/287/93/4Treatment ATG+CsA2856 EPAG+ATG+CsA–9167Clinical response NR133133 PR92061 CR61433 Relapse/unknown––4Baseline CBCMedian ANC (cells/µL)392 (0–1, 430)485 (0–1, 881)1, 103 (0–4, 700)500 (20–1, 190)Median ALC (cells/µL)1, 170 (290–2, 691)1358 (137–3, 243)1, 244 (370–2, 580)1, 300 (360–2620)Median AMC (cells/µL)104 (0–250)123 (0–393)226 (0–1, 470)100 (10–240)Median ARC (103 cells/µL)18.275 (2.3–45.8)27.809 (1–130)38.044 (6.6–105.7)36.8 (7.2–65.1)Median Hb (g/dL)7.7 (5–11)8.6 (5.4–13.7)9.4 (7.2–13.2)8.3 (7.6–9.3)Median platelet count (/µL)13, 750 (1, 000–78, 000)26, 497 (1,000–229, 000)57, 375 (12, 000–209, 000)33, 300 (17, 000–59, 000)Post-treatment CBCMedian ANC (cells/µL)1, 339 (30–3, 260)1, 262 (70–3, 260)–1, 300 (320–2, 790)Median ALC (cells/µL)1, 038 (260–1, 910)1, 120 (9–3, 162)–1, 300 (570–2, 580)Median AMC (cells/µL)302 (64–640)307 (10–1310)–200 (50–560)Median ARC (103 cells/µL)42 (1–97)49.55 (2.9–153)–58.2 (9.8–143.6)Median Hb (g/dL)10 (7–15)10.6 (7–16.1)–10.4 (7.2–15)Median platelet count (/µL)62, 536 (6, 000–231, 000)72, 989 (1, 000–346, 000)–70, 400 (6, 000–181, 000)ATG = anti-thymocyte globulin; CsA = cyclosporine; EPAG = eltrombopag; CBC = complete blood count; ANC = absolute neutrophil count; ALC = absolute lymphocyte count; AMC = absolute monocyte count; ARC = absolute reticulocyte count; Hb = hemoglobin Open table in a new tab ATG = anti-thymocyte globulin; CsA = cyclosporine; EPAG = eltrombopag; CBC = complete blood count; ANC = absolute neutrophil count; ALC = absolute lymphocyte count; AMC = absolute monocyte count; ARC = absolute reticulocyte count; Hb = hemoglobin Large-scale proteomic analysis was performed on serum samples in our discovery set of 28 SAA patients: six complete response (CR), nine partial response (PR), and 13 nonresponders (NR) and on plasma samples from seven SAA patients (three CR, one PR, and three NR) before and after IST by SOMAscan (SomaLogic, Boulder, CO, USA) at the Trans-NIH Center for Human Immunology, Autoimmunity, and Inflammation (CHI), as described previously [22Candia J Cheung F Kotliarov Y et al.Assessment of variability in the SOMAscan assay.Sci Rep. 2017; 7: 14248Crossref PubMed Scopus (130) Google Scholar]. Specimens (50 μL) were diluted to three concentrations (0.005%, 1%, and 40%) to separate groups of high, medium, and low abundance proteins, respectively, and then combined with dilution-specific SOMAmers. Quality controls (QC) and calibrators provided by SomaLogic were run together with internal site QC samples. Data generated from these samples were used to assess interassay variability, as described previously [22Candia J Cheung F Kotliarov Y et al.Assessment of variability in the SOMAscan assay.Sci Rep. 2017; 7: 14248Crossref PubMed Scopus (130) Google Scholar]. For independent validation of candidate serum biomarkers, a custom five-plex immunobead-based multiplex assay was designed based on commercial availability for measurement of bone morphogenetic protein 10 (BMP-10), C-C motif chemokine ligand 17 (CCL17), Dickkopf WNT signaling pathway inhibitor 1 (DKK1), hepatocyte growth factor (HGF), and L-selectin (SELL) in serum or plasma (R&D Systems, Minneapolis, MN). The assay was carried out following the manufacturer's instructions, and a standard curve and one internal control were included in each plate to reduce inter-assay variability. Data were analyzed using SomaSuite version 1.0.3 (NEC Corporation, Minato, Tokyo, Japan) and web tools developed by the CHI (https://foocheung.shinyapps.io/adat_v02/ and https://foocheung.shinyapps.io/plotterII/) [23Cheung F Fantoni G Conner M et al.Web tool for navigating and plotting SomaLogic ADAT files.J Open Res Softw. 2017; 5: 20Crossref Google Scholar]. VENNY 2.1, an interactive tool for comparing lists with Venn diagrams, was used to find common or unique proteins between groups [24Oliveros JC. Venny: An interactive tool for comparing lists with Venn's diagrams 2007–2015. Available at: http://bioinfogp.cnb.csic.es/tools/venny/index.html.Google Scholar]. Unpaired or paired t tests for two group comparison, analysis of variance (ANOVA), or Kruskal–Wallis test for three-group comparison were performed with Tukey's test for multiple comparisons and false discovery rate (FDR) for correction. Pearson correlation was performed using an online tool developed by CHI [25Giudice V, Wu Z, Kajigaya S, et al. Circulating S100A8 andS100A9 protein levels in plasma of patients with acquired aplastic anemia and myelodysplastic syndromes. Cytokine. 2018 Jun 26. pii: S1043-4666(18)30274-6. [Epub ahead of print]Google Scholar]. Specificity and sensitivity were calculated by receiver operating characteristic curves using the healthy control group as reference [26Florkowski CM. Sensitivity, specificity, receiver-operating characteristic (ROC) curves and likelihood ratios: communicating the performance of diagnostic tests.Clin Biochem Rev. 2008; 29: S83-S87PubMed Google Scholar]. Logistic regression and generalized linear model analysis were performed to calculate the diagnostic and prognostic power of combined markers. By convention, p < 0.05 was considered statistically significant. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm for visualization of high-throughput data in two or three dimensions were carried out using RStudio software (version 0.99.896, RStudio Inc., Boston, MA, USA). Protein pathway analysis was performed employing open-source pathway databases [27Fabregat A Jupe S Matthews L et al.The Reactome Pathway Knowledgebase.Nucleic Acids Res. 2018; 46: D649-D655Crossref PubMed Scopus (1375) Google Scholar, 28Szklarczyk D Morris JH Cook H et al.The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.Nucleic Acids Res. 2017; 45: D362-D368Crossref PubMed Scopus (3951) Google Scholar]. SOMAscan data from the discovery set and a group of healthy controls (n = 14; M/F, 6/8; mean age, 32.3 years, range 21–62) were combined and normalized as described previously [22Candia J Cheung F Kotliarov Y et al.Assessment of variability in the SOMAscan assay.Sci Rep. 2017; 7: 14248Crossref PubMed Scopus (130) Google Scholar]. t-SNE of all proteins (listed in Supplementary Table E1, online only, available at www.exphem.org) was performed by dividing patients based on hematologic responses to IST at landmark time points (Figure 1A). Although no clear clusters were identified, AA patients, even after treatment, completely separated from healthy subjects and NR sera appeared different from CR and PR sera after treatment. When t-SNE was performed including blood counts, clearer separations were discerned (Figure 1B). Next, serum protein levels were compared between healthy controls and AA patients before or after IST by unpaired t test or compared between patients' groups based on clinical response to therapy (Supplementary Table E2, online only, available at www.exphem.org). AA patients' groups before and after IST were compared by unpaired t test and proteins higher in each respective group were used to build Venn diagrams (Figure 1C and Supplementary Table E3, online only, available at www.exphem.org). Proteins elevated in sera of CR patients before (n = 7) and after (n = 20) therapy compared with NR were selected as candidate biomarkers of responsiveness to IST. Proteins higher in NR before therapy (n = 15) and in PR before and after IST (n = 2) were selected as candidate biomarkers of nonresponsiveness to IST. Among 44 proteins in this group, Wilcoxon Mann–Whitney test with FDR correction was performed to remove proteins showing similar serum levels among groups after treatment and 19 proteins were selected for further investigations (WISP1, DDR2, FRZB, CNTN4, SELL, THBS1, PDGFA, NID2, HGF, BMP10, TEC, CLEC7A, SGTA, TNFRSF4, PPIF, PRKCZ, CCL17, DKK4, and DKK1) (Figure 1D). Fifteen out of these 19 proteins were also different in the plasma of AA patients compared with healthy controls. Because hematological improvements could lead to subsequent increases in serum proteins due to the appearance of adequate cells in the circulation, candidate protein markers were correlated to blood counts, such as hemoglobin level, platelets (PLT), absolute reticulocyte count (ARC), absolute neutrophil count (ANC), and absolute monocyte count (AMC), for each patient before and after therapy (Supplementary Figure E1, online only, available at www.exphem.org). Indeed, CCL17, DKK4, DKK1, PDGFA, and THBS1 were highly correlated with blood counts. Multiple correlations also were described for other proteins. Because transfusions could influence circulating protein levels, transfusion history was documented in our cohort of AA patients: 21 of them (two CR, seven PR, and 12 NR) had received transfusions before starting IST (mean time between last transfusion and starting drug administration, 144 days; range 1 day to 60 months). Protein pathway analysis using the Reactome Pathway Database revealed that proteins appeared related to the Wnt pathway, innate and adaptive immune responses, extracellular matrix or cell-to-cell interactions, and hematopoietic stem cell differentiation (Supplementary Table E4, online only, available at www.exphem.org). The STRING database was also employed for protein pathway analysis using proteins higher in HC compared with AA patients before IST or using the selected 19 markers (Supplementary Tables E5–E7, online only, available at www.exphem.org). Proteins were related to immune response, coagulation, regulation of apoptotic process, regulation of protein phosphorylation, cell adhesion, T-cell receptor, cytokine–cytokine receptor interaction, positive or negative response to cell surface receptor signaling, and Wnt, Ras, HIF-1, NF-κB, and Jak-STAT signaling cascades (Figure 2). To assess generalizability of the preliminary SOMAscan findings, a five-plex immunobead-based multiplex assay was applied to a verification set of 65 SAA patients, with samples obtained before IST, at 6 months of treatment, and/or at 1 year after IST. A validation cohort of 16 patients at diagnosis and a group of age- and sex-matched healthy controls (n = 13; M/F, 7/6; mean age, 34.3 years, range 21–62) also was included. Among the 19 candidate serum markers, proteins linked to the Wnt pathway (DKK1 and BMP10), innate and adaptive immune responses (CCL17 and SELL), and hematopoietic stem cell differentiation (HGF and DKK1) were selected for validation. Serum levels of DKK1, SELL, CCL17, and HGF showed significant correlations between the two techniques (all p < 0.01), whereas BMP-10 serum levels differed between the SOMAscan and Luminex assays (r = –0.075, p = 0.675). For this reason, BMP-10 was not included in further analyses. First, SAA patients from the verification cohort were compared with healthy controls, showing that all four selected proteins were significantly higher in healthy controls compared with patients (DKK1, SELL, and CCL17, all p < 0.0001; HGF, p = 0.037) (Figure 3A). All markers displayed a high specificity for AA (DKK1, area under the curve [AUC] = 0.74; SELL, AUC = 0.89; CCL17, AUC = 0.88; and HGF, AUC = 0.80) (Supplementary Figure E2A, online only, available at www.exphem.org). Subsequently, the diagnostic power of combined markers was assessed on verification (AUC = 0.974) (Supplementary Figure E2B, online only, available at www.exphem.org) and validation (AUC = 0.832) sets of SAA patients (Supplementary Figure E2C, online only, available at www.exphem.org). Next, SAA patients were divided based on clinical response at 6-month and/or 1-year time points and groups were compared by one-way ANOVA (Figures 3B and 3C and Supplementary Figure E3A, online only, available at www.exphem.org). In CR cases, DKK1 was significantly higher at baseline compared with NR patients and there were increased serum levels also at both the 6-month and 1-year time points compared with PR and NR sera. Similarly, CCL17 was higher in CR after IST compared with other groups. No significant differences were present for SELL and HGF. By comparing only CR with NR using unpaired t test, DKK1 was significantly increased in CR patients (p = 0.010), whereas SELL and CCL17 levels were only slightly higher than those in NR (p = 0.137 and p = 0.132). No differences were seen for HGF serum levels (p = 0.389). Pearson correlation analysis between selected protein markers and blood counts was performed as described above and multiple correlations were observed (Figure 3D and Supplementary Figure E3B, online only, available at www.exphem.org). Logistic regression and generalized linear model analysis were used to evaluate the prognostic power of combined markers. Data from patients at ≥1-year follow-up were used to generate a model and then functions were applied to patients from the verification and validation sets at baselines or at 6 months of therapy (Supplementary Figures E3C and E3D, online only, available at www.exphem.org). For patients at baseline, sensitivity to predict responsiveness to IST was low (48%), whereas specificity was high (82%); prediction at 6 months of therapy showed higher sensitivity and specificity (76% and 83%, respectively). SOMAscan assay also was employed for screening plasma proteins in a small cohort of AA patients (n = 7) treated with IST and EPAG in order to identify common biomarkers with the serum signature and novel plasma proteins for diagnosis and disease progression. A group of healthy controls (n = 21; mean age, 57 years; range 37–62; M/F, 10/11) also was included. Heatmap (Figure 4A) and PCA were displayed using all 1,317 proteins to visualize a possible signature (Figure 4B). A proteomic profile of CR patients after IST was compared with those of CR and NR patients before IST. Unpaired t test with FDR correction (5%) was employed to compare a proteomic profile of healthy controls with that of AA patients; groups before or after IST were compared by unpaired t test without FDR correction because of the small number of subjects (CR, n = 3; NR, n = 3). In plasma, 600 proteins were different in healthy controls compared with AA and 35% of them were common to the serum proteomic profile. When plasma protein levels were compared between healthy controls and AA, 43 proteins were present in AA patients' plasma and 43% of them (n = 27) were also present in the serum signature (Supplementary Figure E4 and Supplementary Table E8, online only, available at www.exphem.org). In AA patients, 28 proteins
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