www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 41), pp: 69408-69421 Research Paper Serum protein fingerprinting by PEA immunoassay coupled with a pattern-recognition algorithms distinguishes MGUS and multiple myeloma Petra Schneiderova1,*, Tomas Pika2,*, Petr Gajdos3, Regina Fillerova1, Pavel Kromer3, Milos Kudelka3, Jiri Minarik2, Tomas Papajik2, Vlastimil Scudla2 and Eva Kriegova1 1Department of Immunology, Faculty of Medicine and Dentistry, Palacky University Olomouc, Olomouc, Czech Republic 2Department of Hemato-Oncology, Faculty of Medicine and Dentistry, Palacky University and University Hospital, Olomouc, Czech Republic 3Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic *These authors have contributed equally to this work Correspondence to: Eva Kriegova, email: [email protected] Keywords: serum pattern, cytokines, growth factors, proximity extension immunoassay, post-transplant serum pattern Received: May 04, 2016 Accepted: July 28, 2016 Published: August 12, 2016 Copyright: Schneiderova et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Serum protein fingerprints associated with MGUS and MM and their changes in MM after autologous stem cell transplantation (MM-ASCT, day 100) remain unexplored. Using highly-sensitive Proximity Extension ImmunoAssay on 92 cancer biomarkers (Proseek Multiplex, Olink), enhanced serum levels of Adrenomedullin (ADM, Pcorr= .0004), Growth differentiation factor 15 (GDF15, Pcorr= .003), and soluble Major histocompatibility complex class I-related chain A (sMICA, Pcorr= .023), all prosurvival and chemoprotective factors for myeloma cells, were detected in MM comparing to MGUS. Comparison of MGUS and healthy subjects revealed elevation of angiogenic and antia-poptotic midkine (Pcorr= .0007) and downregulation of Transforming growth factor beta 1 (TGFB1, Pcorr= .005) in MGUS. Importantly, altered serum pattern was associated with MM-ASCT compared to paired MM at the diagnosis as well as to healthy controls, namely by upregulated B-Cell Activating Factor (sBAFF) (Pcorr< .006) and sustained elevation of other pro-tumorigenic factors. In conclusion, the serum fingerprints of MM and MM-ASCT were characteristic by elevated levels of prosurvival and chemoprotective factors for myeloma cells. INTRODUCTION with the best combination of currently available drugs, a cure is not achieved for most MM patients [2, 3]. Better Monoclonal gammopathy of undetermined characterization of neoplastic cells and microenvironment significance (MGUS) is a precursor lesion to overt in particular myeloma stages is therefore needed as well multiple myeloma (MM), a clonal B-cell malignancy as clarifying of reason(s) for treatment failure in most characterized by excessive multiplication of a plasma MM patients [2, 3, 4]. The neoplastic plasma cells in cell clone(s) in bone marrow, and accumulation of either MGUS and MM share similar genetic abnormalities, a monoclonal immunoglobulin (Ig) (M-protein) or an Ig- probably occurring as early events [5, 6]. The key role free light chain in blood [1]. During the last decade, MM in microenvironment play bone marrow stromal cells and treatment and patient outcomes improved remarkably other microenvironmental cells that secrete a plethora of after the introduction of novel agents and autologous cytokines and growth factors after paracrine stimulation stem cell transplantation (ASCT) [2]. However, even and/or direct interaction with neoplastic cells [7]. www.impactjournals.com/oncotarget 69408 Oncotarget Moreover, also myeloma cells secrete numerous cytokines based on cytogenetic/FISH analysis was not performed and growth factors [8, 9]. The secreted molecules may, in due to the high heterogeneity within the group. turn, promote homing, migration, proliferation, survival of malignant plasma cells as well as contribute to the bone Changes in serum protein pattern in resorption and drug resistance [10]. post-transplant MM Given the key role of cytokines and growth factors in MM pathogenesis, we investigated the complexity of To assess the changes in serum protein pattern serum microenvironment using novel multiplex highly- in MM after ASCT, we compared the post-transplant sensitive PEA immunoassay on 92 cancer-related proteins sera (day 100) with paired samples obtained in MM followed by pattern-recognition analyses. Besides patients at the time of diagnosis and healthy control identification of serum fingerprints distinguishing MGUS subjects. Comparing paired samples from MM- and MM, we for the first time compared paired MM ASCT and MM, the most upregulated protein in post- samples from the time of diagnosis and after autologous transplant sera was sBAFF (Pcorr= .006), followed by stem cell transplantation (MM-ASCT) as well as MGUS CXCL9 (Pcorr= .041). Next twenty-one proteins were to healthy subjects. downregulated (14 proteins after adjustment for multiple comparisons) in MM-ASCT comparing to MM (Figure RESULTS 2D, Supplementary Table S2D). The top-ranked proteins were: elevated sBAFF and downregulated REG4, Serum protein fingerprinting in MGUS and MM sPECAM1, sIL6R, sPDGFB, midkine, sHGF, TGFB1, by PEA immunoassay sAREG, and sMICA in MM-ASCT comparing to MM (Table 1D, Figure 3). Importantly, serum levels of MM- To assess the serum protein fingerprints associated associated pro-tumorigenic factors such as GDF15, with MGUS and MM, we compared serum protein CSF1, suPAR, and HE4 did not change after ASCT pattern obtained by PEA immunoassay in MGUS and comparing to sample at the diagnosis (Supplementary MM and healthy controls. Of ninety-two analyzed Table S2D). biomarkers (Supplementary Table S1), levels of six To exclude the influence of treatment regime biomarkers (sBTC, CA242, sER, GM-CSF, IL2, IL4) on serum pattern, we assessed the protein profile in were below the Proseek limit of detection (LOD) in all subgroups based on ASCT induction regime (IMiD-based/ studied groups and were therefore excluded from further bortezomib-based). We did not detect any differences in analysis. Comparing MGUS and MM, 26 analytes the cytokine levels as a function of the induction regime were deregulated between these groups, whereas 13 as well as the hematological response (CR, VGPR/PR) on analytes reached the significance after the adjustment day 100 (Pcorr> .05). for multiple comparisons (Supplementary Table S2A). Comparing to healthy subjects, the serum of The distribution of serum levels of top-ranked proteins post-transplant MM patients showed permanently (ADM, TRAP, GDF15, suPAR, REG4, TGFB1, sMICA, altered pro-tumorigenic signature characteristic by IL1RA, HE4, sHGFR, sVEGFA; see Table 1A), all found deregulation of 35 proteins (after multiple adjustments: upregulated in MM, is shown in Figure 1. The protein 28 analytes) (Figure 2E, Supplementary Table S2E). serum fingerprints associated with MGUS and MM and Except sTGFA and TGFB1, all deregulated proteins were the changes in protein levels between MGUS and MM for elevated in MM-ASCT. The top-ranked proteins between top-deregulated analytes are shown in Figure 2A. MM-ASCT and healthy controls were as follows: sBAFF, Comparison of protein pattern obtained in MGUS CSF1, sTGFA, TRAP, CXCL10, sTNFR2, sTNFRSF4, and healthy controls revealed deregulation of 33 Flt3L, GDF15, HE4, THPO (Table 1E, Supplementary proteins (Figure 2B), of these 21 reached significance Figure S1C). The serum protein pattern in MM-ASCT and after multiple comparisons (Supplementary Table S2B). its comparison to those of healthy controls, MGUS and The protein levels of top-ranked proteins (midkine, MM, are presented in Figure 4. THPO, sTNFRSF4, sHER4, INFγ, TGFB1, sPECAM1, sIL17RB, KLK6, suPAR) are presented in Table 1B and Pattern-recognition algorithms Supplementary Figure S1A. When comparing MM and controls, we observed To facilitate the selection of the most promising deregulation of 46 serum proteins (Figure 2C), of these 41 circulating proteins distinguishing studied groups analytes reached significance after adjustment for multiple (MGUS, MM, MM-ASCT, healthy subjects), we comparisons (Supplementary Table S2C). The distribution applied advanced binary classification algorithm and of serum levels of top-ranked proteins between MM and analyzed co-occurrence of analytes in classification controls (PGF, GDF15, HE4, sTNFR2, CSF1, midkine, models. The most accurate classification model for sPECAM1, CCL19, sVEGFA, INFγ; see Table 1C) is separation of MGUS and MM utilized in classification shown in Supplementary Figure S1B. The subanalysis rules most frequently sMICA in combination with other www.impactjournals.com/oncotarget 69409 Oncotarget Table 1: Serum levels of top-ranked proteins differentiating between A) MGUS vs MM, B) healthy controls vs MGUS, C) healthy controls vs MM, D) MM vs MM-ASCT and E) healthy controls vs MM-ASCT. Analyte Mean Linear ddCq (95% CI) FC P Pcorr A MGUS MM ADM 46.9 (28.4-65.4) 191 (121-262) 2.80 4.0 × 10-6 3.5 × 10-4 TRAP 28.9 (22.3-35.6) 85.1 (42.8-127) 2.33 3.7 × 10-5 1.6 × 10-3 GDF15 12.6 (9.19-16.0) 53.3 (22.5-84.1) 2.72 1.1 × 10-4 3.0 × 10-3 suPAR 295 (251-338) 486 (368-603) 1.60 2.7 × 10-4 5.9 × 10-3 REG4 5.96 (5.46-6.46) 9.58 (6.78-12.4) 1.30 1.2 × 10-3 .021 TGFB1 42.9 (39.0-46.8) 85.5 (43.7-127) 1.41