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Proteomic discovery of plasma protein biomarkers and development of models predicting prognosis of high-grade serous ovarian carcinoma

  • Se Ik Kim
    Affiliations
    Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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  • Suhyun Hwangbo
    Affiliations
    Department of Genomic Medicine, Seoul National University Hospital, Seoul 03080, Republic of Korea
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  • Kisoon Dan
    Affiliations
    Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul 03082, Republic of Korea
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  • Hee Seung Kim
    Affiliations
    Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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  • Hyun Hoon Chung
    Affiliations
    Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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  • Jae-Weon Kim
    Affiliations
    Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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  • Noh Hyun Park
    Affiliations
    Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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  • Yong-Sang Song
    Affiliations
    Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
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  • Dohyun Han
    Correspondence
    Correspondence to: Dohyun Han, PhD, Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, 71 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea. Tel: 82-2-2072-1719 Fax: 82-2-2072-4406,
    Affiliations
    Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul 03082, Republic of Korea

    Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul 03080, Republic of Korea
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  • Maria Lee
    Correspondence
    Correspondence to: Maria Lee, MD, PhD, Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea. Tel: 82-2-2072-2842, Fax: 82-2-762-3599,
    Affiliations
    Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea

    Department of Obstetrics and Gynecology, Seoul National University Hospital, Seoul 03080, Republic of Korea
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Open AccessPublished:January 16, 2023DOI:https://doi.org/10.1016/j.mcpro.2023.100502

      Highlights

      • We aimed to investigate novel, blood-based prognostic biomarkers in HGSOC.
      • MS-based label-free quantification was conducted using fresh-frozen plasma samples.
      • Candidate biomarkers were validated with an independent set of plasma samples via ELISA.
      • Plasma GSN was identified as an independent poor prognostic biomarker for progression-free survival.
      • We successfully developed models and nomograms predicting the 18-month PFS rate for clinical use.

      ABSTRACT

      Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry-based proteomics methods. We conducted label-free liquid chromatography–tandem mass spectrometry using frozen plasma samples (n=20) obtained from patients with newly diagnosed HGSOC. Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve HGSOC patients, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n=202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with five-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073–2.256; P=0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

      Graphical abstract

      Keywords

      ABBREVIATIONS:

      aHR (adjusted hazard ratio), AUC (area under the receiver operating characteristic curve), BP (biological process), CI (confidence interval), CT (computed tomography), ELISA (enzyme-linked immunosorbent assay), FDR (false discovery rate), FIGO (Federation of Gynecology and Obstetrics), GO (gene ontology), GSN (gelsolin), HGSOC (high-grade serous ovarian carcinoma), HRD (homologous recombination deficiency), iBAQ (intensity-based absolute quantification), IDS (interval debulking surgery), IHC (immunohistochemstry), IQR (interquartile range), LC (liquid chromatography), MS (mass spectrometry), NAC (neoadjuvant chemotherapy), PARP (poly-(ADP-ribose) polymerase), PDS (primary debulking surgery), PFS (progression-free survival), PRMT1 (protein arginine methyltransferase 1), OS (overall survival), RCT (randomized controlled trial), SIGLEC14 (sialic acid-binding Ig-like lectin 14), SND1 (staphylococcal nuclease, tudor domain containing 1), SNUH (Seoul National University Hospital), TFA (trifluoroacetic acid), VCAN (versican)

      INTRODUCTION

      Ovarian cancer is one of the most lethal cancers among women. Annually, 313,959 new ovarian cancer cases and 207,252 related deaths are expected worldwide (
      • Sung H.
      • Ferlay J.
      • Siegel R.L.
      • Laversanne M.
      • Soerjomataram I.
      • Jemal A.
      • et al.
      Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
      ). The absence of disease-specific early symptoms and effective screening methods leads to ovarian cancer being diagnosed at an advanced stage and having high recurrence and mortality rates despite treatment, consisting of extensive cytoreductive surgery followed by taxane- and platinum-based chemotherapy (
      • Bristow R.E.
      • Tomacruz R.S.
      • Armstrong D.K.
      • Trimble E.L.
      • Montz F.J.
      Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis.
      ,
      • Chi D.S.
      • Eisenhauer E.L.
      • Zivanovic O.
      • Sonoda Y.
      • Abu-Rustum N.R.
      • Levine D.A.
      • et al.
      Improved progression-free and overall survival in advanced ovarian cancer as a result of a change in surgical paradigm.
      ,
      • Matulonis U.A.
      • Sood A.K.
      • Fallowfield L.
      • Howitt B.E.
      • Sehouli J.
      • Karlan B.Y.
      Ovarian cancer.
      ). Meanwhile, ovarian cancer is not a single disease, but a heterogeneous disease comprising various histologic subtypes with different carcinogenic routes and clinical features. Among the subtypes of ovarian cancer, high-grade serous ovarian carcinoma (HGSOC) is the most common and responds very well to chemotherapy; however, it frequently relapses, with acquisition of chemoresistance (
      • Matulonis U.A.
      • Sood A.K.
      • Fallowfield L.
      • Howitt B.E.
      • Sehouli J.
      • Karlan B.Y.
      Ovarian cancer.
      ).
      Since The Cancer Genomic Atlas reported results from integrated genomic analyses of HGSOC (
      Integrated genomic analyses of ovarian carcinoma.
      ), the management of HGSOC rapidly evolved. Maintenance therapy with poly-(ADP-ribose) polymerase (PARP) inhibitors, such as olaparib and niraparib, was incorporated into the primary treatment of HGSOC based on landmark phase III randomized controlled trials (RCTs) (
      • Moore K.
      • Colombo N.
      • Scambia G.
      • Kim B.G.
      • Oaknin A.
      • Friedlander M.
      • et al.
      Maintenance Olaparib in Patients with Newly Diagnosed Advanced Ovarian Cancer.
      ,
      • González-Martín A.
      • Pothuri B.
      • Vergote I.
      • DePont Christensen R.
      • Graybill W.
      • Mirza M.R.
      • et al.
      Niraparib in Patients with Newly Diagnosed Advanced Ovarian Cancer.
      ,
      • Ray-Coquard I.
      • Pautier P.
      • Pignata S.
      • Pérol D.
      • González-Martín A.
      • Berger R.
      • et al.
      Olaparib plus Bevacizumab as First-Line Maintenance in Ovarian Cancer.
      ). After a complete or partial response to first-line platinum-based chemotherapy, olaparib maintenance therapy can be offered to patients with BRCA1/2 mutated, advanced HGSOC to improve survival outcomes, while niraparib maintenance therapy confers survival benefits in advanced HGSOC, regardless of BRCA1/2 mutational status or homologous recombination deficiency (HRD). Accurate prediction of prognosis is necessary to facilitate molecular profiling-based HGSOC treatment.
      In this regard, our research team has focused on discovering prognostic protein biomarkers in HGSOC using mass spectrometry (MS) proteomics. This emerging technology allows high-throughput and individualized characterization and quantification of proteins in biospecimens (
      • Macklin A.
      • Khan S.
      • Kislinger T.
      Recent advances in mass spectrometry based clinical proteomics: applications to cancer research.
      ). Previously, we identified six protein biomarkers associated with progression-free survival (PFS) through the label-free quantitative proteomic analysis of frozen primary HGSOC tissues and validated them using immunohistochemical (IHC) staining in an independent sample set (
      • Kim S.I.
      • Jung M.
      • Dan K.
      • Lee S.
      • Lee C.
      • Kim H.S.
      • et al.
      Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma.
      ).
      However, liquid biopsy has many advantages, such as non-invasiveness, swiftness, real-time monitoring, and the possibility of overcoming tumor heterogeneity (
      • Mattox A.K.
      • Bettegowda C.
      • Zhou S.
      • Papadopoulos N.
      • Kinzler K.W.
      • Vogelstein B.
      Applications of liquid biopsies for cancer.
      ,
      • Perakis S.
      • Speicher M.R.
      Emerging concepts in liquid biopsies.
      ). Thus, we aimed to investigate whether we could identify novel, prognostic protein biomarkers for HGSOC from blood samples using MS-based proteomics. Biomarker candidates were validated using an enzyme-linked immunosorbent assay (ELISA) in an independent dataset. We also developed models to predict 18-month PFS rates in HGSOC patients.

      EXPERIMENTAL PROCEDURES

      Ethics statement

      This study was approved by the Institutional Review Board of Seoul National University Hospital (SNUH; No. H-2010-152-1167) and was conducted in accordance with the Declaration of Helsinki. At our institution, we routinely asked patients with newly diagnosed ovarian cancer who were scheduled to undergo primary treatment to donate their biospecimens (e.g., blood, urine, and cancer tissues) for research purposes with written informed consent since June 2012.

      Sample collection

      In this study, we used plasma samples from HGSOC patients that were obtained one day before primary debulking surgery (PDS) or initiation of neoadjuvant chemotherapy (NAC) and stored at the SNUH Hunan Biobank. The process for the collection of plasma from whole blood was as follows: Collect 6 mL of blood sample into the EDTA tube, and centrifuge for 10 minutes at 1,551 g at 4°C. After centrifugation, carefully collect the plasma layer with a transfer pipette without disturbing the buffy coat layer. Pipette the 700 μL of plasma into a 1.8 mL labeled cryovial, up to 4 vials. Place all aliquots upright in a labeled rack in a -196°C LN2 tank. All the plasma samples used in this study had never been thawed before.

      Experimental design and statistical rationale

      This study included three phases: (
      • Sung H.
      • Ferlay J.
      • Siegel R.L.
      • Laversanne M.
      • Soerjomataram I.
      • Jemal A.
      • et al.
      Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
      ) biomarker discovery through proteomic and bioinformatic analyses, (
      • Bristow R.E.
      • Tomacruz R.S.
      • Armstrong D.K.
      • Trimble E.L.
      • Montz F.J.
      Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis.
      ) prognostic validation of candidate biomarkers using ELISA, and (
      • Chi D.S.
      • Eisenhauer E.L.
      • Zivanovic O.
      • Sonoda Y.
      • Abu-Rustum N.R.
      • Levine D.A.
      • et al.
      Improved progression-free and overall survival in advanced ovarian cancer as a result of a change in surgical paradigm.
      ) construction of models predicting the 18-month PFS rate in patients with HGSOC (Supplementary Figure 1).
      For the first phase (discovery), we retrieved the frozen plasma obtained from patients who met the following criteria: (
      • Sung H.
      • Ferlay J.
      • Siegel R.L.
      • Laversanne M.
      • Soerjomataram I.
      • Jemal A.
      • et al.
      Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
      ) newly diagnosed with HGSOC between June 2012 and December 2016, without any history or evidence of other malignancies; (
      • Bristow R.E.
      • Tomacruz R.S.
      • Armstrong D.K.
      • Trimble E.L.
      • Montz F.J.
      Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis.
      ) completed primary treatment, consisting of primary debulking surgery (PDS; not NAC-IDS) and taxane- and platinum-based adjuvant chemotherapy; and (
      • Chi D.S.
      • Eisenhauer E.L.
      • Zivanovic O.
      • Sonoda Y.
      • Abu-Rustum N.R.
      • Levine D.A.
      • et al.
      Improved progression-free and overall survival in advanced ovarian cancer as a result of a change in surgical paradigm.
      ) patients whose disease relapsed within 18 months after PDS, that is, PFS <18 months (poor prognosis group) or those whose disease did not relapse for at least 18 months after PDS, that is, ≥18 months of PFS (good prognosis group). Twenty patients from the two groups (10 in each group) were selected for further proteomic analyses. The order of sample preparation was randomized and independent of the patient list. The proteomic profiles of the two groups were compared.
      In the second phase (validation), we retrieved pre-treatment frozen plasma of patients who met the following conditions: (
      • Sung H.
      • Ferlay J.
      • Siegel R.L.
      • Laversanne M.
      • Soerjomataram I.
      • Jemal A.
      • et al.
      Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
      ) newly administered HGSOC between June 2012 and December 2019, without any history or evidence of other malignancies; (
      • Bristow R.E.
      • Tomacruz R.S.
      • Armstrong D.K.
      • Trimble E.L.
      • Montz F.J.
      Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis.
      ) completed primary treatment, consisting of either PDS or NAC-IDS, followed by postoperative taxane- and platinum-based adjuvant chemotherapy. We excluded patients if they had enrolled in clinical trials for primary treatment; did not provide written informed consent; or were lost to follow-up during primary treatment or within 18 months after initiation of primary treatment, without relapse or disease progression. A total of 202 consecutive patients with HGSOC were included in this phase, and the sample size was adequate for multivariate survival analysis and further dvelopment of predictive models. The order of sample preparation was also randomized and independent of the patient list. ELISA was conducted with technical triplicates on pooling samples for the standard curve and batch control.
      In the medical record review, we collected patients’ clinicopathologic data. Disease progression was ascertained based on computed tomography (CT) scans by applying the Response Evaluation Criteria in Solid Tumors version 1.1 (
      • Eisenhauer E.A.
      • Therasse P.
      • Bogaerts J.
      • Schwartz L.H.
      • Sargent D.
      • Ford R.
      • et al.
      New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1).
      ). PFS and overall survival (OS) were defined as the time intervals from the date of initial diagnosis to the date of disease progression and to the date of cancer-related death or last follow-up, respectively.

      4. Proteomic and bioinformatic analyses

      The overall workflow of proteomic and bioinformatic analyses are depicted in Figure 1A.
      Figure thumbnail gr1
      Figure 1Proteomic analysis of ovarian cancer blood samples with respect to survival outcome. (A) Overall workflow of proteomic analysis; (B) Total number of proteins identified in each sample; (C) Dynamic range of proteins quantified in our study. Well-known ovarian cancer marker candidates are color coded.

      4.1 Sample preparation

      Protein digestion was performed using 2 μL of each plasma sample as previously described, with some modifications (
      • Park J.
      • Kim H.
      • Kim S.Y.
      • Kim Y.
      • Lee J.S.
      • Dan K.
      • et al.
      In-depth blood proteome profiling analysis revealed distinct functional characteristics of plasma proteins between severe and non-severe COVID-19 patients.
      ,
      • Rhee S.J.
      • Han D.
      • Lee Y.
      • Kim H.
      • Lee J.
      • Lee K.
      • et al.
      Comparison of serum protein profiles between major depressive disorder and bipolar disorder.
      ). Briefly, 23 μL of protein digestion buffer, including reduction and alkylation reagents, was added to 2 μL plasma samples in 96 well-plates. The mixture was boiled for 25 min at 60°C to denature and alkylate the proteins. After cooling samples to room temperature, protein digestion was performed at 37°C overnight using a trypsin/LysC mixture (Promega, Madison, WI, USA) at a 100:1 protein-to-protease ratio. The second digestion was performed at 37°C for 2 h using trypsin (enzyme-to-substrate ratio [w/w], 1:1000). All resulting peptides were acidified with 10% trifluoroacetic acid (TFA). The acidified peptides were loaded onto custom-made styrene divinylbenzene reversed-phase sulfonate-StageTips according to previously described procedures (
      • Rhee S.J.
      • Han D.
      • Lee Y.
      • Kim H.
      • Lee J.
      • Lee K.
      • et al.
      Comparison of serum protein profiles between major depressive disorder and bipolar disorder.
      ,
      • Kim H.
      • Dan K.
      • Shin H.
      • Lee J.
      • Wang J.I.
      • Han D.
      An efficient method for high-pH peptide fractionation based on C18 StageTips for in-depth proteome profiling.
      ). The StageTip was washed three times with 100 μL 0.2% TFA. Three fractionations were performed using elution buffers with a step gradient of increasing acetonitrile (40%, 60%, and 80%) in 1% ammonium hydroxide. 1, 2, and 3. All the eluted peptides were dried using a SpeedVac centrifuge (Thermo Fisher Scientific, Waltham, MA, USA).

      4.2 LC-MS/MS analysis

      All liquid chromatography with tandem MS (LC-MS/MS) analyses were conducted using an Ultimate 3000 UHPLC system (Dionex, Sunnyvale, CA, USA) coupled with a Q-Exactive HF-X mass spectrometer (Thermo Fisher Scientific), as previously described, with some modifications (
      • Meier F.
      • Geyer P.E.
      • Virreira Winter S.
      • Cox J.
      • Mann M.
      BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes.
      ). Peptides were separated on a two-column system equipped with a trap column (Thermo Fisher Scientific, Acclaim PepMap, C18 5 μm, 100 Å, 300 μm I.D. × 5 mm) and an analytical column (Thermo Fisher Scientific, EASY-Spray column, C18 1.9 μm, 100 Å, 75 μm I.D. × 50 cm) using 90-min gradients from 7% to 30% ACN at a flow rate of 300 nl/min. Column temperature was maintained at 60°C using a column heater. MaxQuant.Live version 1.2 was used for BoxCar acquisition (
      • Wichmann C.
      • Meier F.
      • Virreira Winter S.
      • Brunner A.D.
      • Cox J.
      • Mann M.
      MaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides.
      ). The MS1 resolution was set to 120,000 at m/z 200 for BoxCar, and the acquisition cycle comprised two BoxCar scans at 12 boxes (scaled width, 1 Th overlap) with a maximum ion injection time of 20.8 per box, with the individual AGC target set to 250,000. MS/MS spectra were acquired at a higher-energy collisional dissociation-normalized collision energy of 30, with a resolution of 17,500 at m/z 200. The maximum ion injection durations for the full and MS/MS scans were 20 ms and 100 ms, respectively.

      4.3 Data processing

      All raw MS files were processed using MaxQuant (version 1.6.1.0) (
      • Tyanova S.
      • Temu T.
      • Cox J.
      The MaxQuant computational platform for mass spectrometry-based shotgun proteomics.
      ). MS/MS spectra were searched against the Human UniprotKB protein sequence database (December 2014, with 88,657 entries of 20,459 human genes) using the Andromeda search engine (
      • Cox J.
      • Neuhauser N.
      • Michalski A.
      • Scheltema R.A.
      • Olsen J.V.
      • Mann M.
      Andromeda: a peptide search engine integrated into the MaxQuant environment.
      ). Primary searches were performed using 6 ppm precursor ion tolerance for total protein-level analysis. MS/MS ion tolerance was set at 20 ppm. Cysteine carbamidomethylation was used as a fixed modification. Protein N-acetylation and methionine oxidation are considered variable modifications. Enzyme specificity was set to full tryptic digestion. Peptides with a minimum length of six amino acids and up to two missed cleavages were considered. The required false discovery rate (FDR) was set to 1% at peptide, protein, and modification levels. To maximize the number of quantification events across samples, we enabled the “Match between Runs’ options on the MaxQuant platform. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (
      • Perez-Riverol Y.
      • Bai J.
      • Bandla C.
      • García-Seisdedos D.
      • Hewapathirana S.
      • Kamatchinathan S.
      • et al.
      The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences.
      ) partner repository with the dataset identifier PXD034646. Annotated MS/MS spectra can be accessed through MS-Viewer (
      • Baker P.R.
      • Chalkley R.J.
      MS-viewer: a web-based spectral viewer for proteomics results.
      ) (https://msviewer.ucsf.edu/cgi-bin/mssearch.cgi?report_title=MS-Viewer&search_key=bzgazjrsgb&search_name=msviewer) with the following search keys: bzgazjrsgb.

      4.4 Label-free quantification and statistical analysis

      For label-free quantification, the intensity-based absolute quantification (iBAQ) algorithm (
      • Schwanhäusser B.
      • Busse D.
      • Li N.
      • Dittmar G.
      • Schuchhardt J.
      • Wolf J.
      • et al.
      Global quantification of mammalian gene expression control.
      ) was used on the MaxQuant platform. Briefly, iBAQ values, determined using MaxQuant, are the raw intensities divided by the number of theoretical peptides (
      • Schwanhäusser B.
      • Busse D.
      • Li N.
      • Dittmar G.
      • Schuchhardt J.
      • Wolf J.
      • et al.
      Global quantification of mammalian gene expression control.
      ). Thus, the iBAQ values were proportional to the molar quantities of the proteins. Perseus software was used for statistical analysis (
      • Tyanova S.
      • Temu T.
      • Sinitcyn P.
      • Carlson A.
      • Hein M.Y.
      • Geiger T.
      • et al.
      The Perseus computational platform for comprehensive analysis of (prote)omics data.
      ). First, we eliminated proteins identified as “reverse” and “only identified by site.” After filtering values of at least 70% in each group, missing values were imputed by random numbers drawn from a normal distribution with a width of 0.3 and a down-shift of 1.8. Finally, data were normalized using a width-adjustment function that subtracts the medians and scales all values in a sample to yield equal interquartile ranges (IQRs) (
      • Deeb S.J.
      • Tyanova S.
      • Hummel M.
      • Schmidt-Supprian M.
      • Cox J.
      • Mann M.
      Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles.
      ). For pairwise proteome comparisons, we performed a two-sided t-test with a significance level (P value) of <0.05, and a fold-change of >1.5. Support vector machine analysis was performed using the R/Bioconductor package “GNC” (
      • Aibar S.
      • Fontanillo C.
      • Droste C.
      • Roson-Burgo B.
      • Campos-Laborie F.J.
      • Hernandez-Rivas J.M.
      • et al.
      Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles.
      ).

      4.5 Bioinformatic analysis

      Principal component analysis was performed using Perseus software with proteomic expression profiles (
      • Tyanova S.
      • Temu T.
      • Sinitcyn P.
      • Carlson A.
      • Hein M.Y.
      • Geiger T.
      • et al.
      The Perseus computational platform for comprehensive analysis of (prote)omics data.
      ). Gene ontology (GO) enrichment analysis was performed using the EnrichR analysis tool (https://maayanlab.cloud/Enrichr/), according to the biological process (BP) in the GO analysis (
      • Kuleshov M.V.
      • Jones M.R.
      • Rouillard A.D.
      • Fernandez N.F.
      • Duan Q.
      • Wang Z.
      • et al.
      Enrichr: a comprehensive gene set enrichment analysis web server 2016 update.
      ). EnrichR uses the Fisher exact test to calculate P values. Statistical significance was set at P value <0.05, and GO analysis was used to identify significant GOBP terms.

      Enzyme-linked immunosorbent assay

      ELISA kits for human proteins were used to quantify the plasma levels of endogenous proteins, according to the manufacturer’s instructions. ELISA kits for gelsolin (GSN; abx253831), versican (VCAN; abx153474), staphylococcal nuclease, tudor domain containing 1 (SND1; abx383338), sialic acid-binding Ig-like lectin 14 (SIGLEC14; abx545882), and protein arginine methyltransferase 1 (PRMT1; abx258982) were purchased from Abbexa (Cambridge, UK), whereas a kit for CD163 (DC1630) was purchased from R&D Systems (Minneapolis, MN, USA).
      After determining the optimal dilution factor for each protein, the concentrations of GSN, VCAN, SND1, CD163, SIGLEC14, and PRMT1 were measured and quantified in the pre-treatment and frozen plasma samples (n=202). Optical density at 450 nm was measured using a SPARK multimode microplate reader (Tecan Systems, Inc., San Jose, CA, USA).

      Model construction

      We constructed regression-based models to predict 18-month PFS rates using clinical variables and the ELISA results for protein biomarkers in patients with HGSOC (n=202). The 18-month PFS rate was defined by binarizing the PFS for 18 months. Each of the six identified protein biomarkers was binarized based on the optimal cutoff obtained from maximally selected log-rank statistics (maxstat) (
      • Hothorn T.
      • Lausen B.
      Maximally selected rank statistics in R.
      ). To select important predictors for the 18-month PFS rate, stepwise selection was performed based on the area under the receiver operating characteristic curve (AUC). During stepwise selection, predictors contributing to AUC improvement were selected in a stepwise fashion (
      • Hwangbo S.
      • Kim S.I.
      • Kim J.H.
      • Eoh K.J.
      • Lee C.
      • Kim Y.T.
      • et al.
      Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma.
      ). From variable selection to model evaluation, five-fold cross-validation was used, considering the two-class proportions of the 18-month PFS rate. The AUC, sensitivity, and specificity were used as evaluation measures. The optimal cutoff for calculating sensitivity and specificity was determined as a value corresponding to the maximum value of balanced accuracy, defined as the average of the sensitivity and specificity. Based on the logistic regression model including the selected predictors, we developed a nomogram for clinical use.
      R statistical software (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria) was used to construct predictive models and plot nomograms.

      Statistical analysis

      Clinicopathologic characteristics were compared between the good and poor prognosis groups by using Student’s t and Mann–Whitney U tests for continuous variables and Pearson’s chi-squared and Fisher’s exact tests for categorical variables. The Pearson’s correlation coefficient test was used to measure the relationship between continuous variables. For survival analysis, we used the Kaplan–Meier method with the log-rank test. In the multivariate analysis, a Cox proportional hazards model was constructed, and adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were calculated.
      Statistical analyses were performed using SPSS Statistics (version 25.0; IBM Corp., Armonk, NY, USA) and GraphPad Prism 5 (GraphPad Inc., La Jolla, CA, USA). All statistical tests were two-sided, and a P value <0.05 was considered statistically significant.

      RESULTS

      Characteristics of patients in the discovery phase

      The clinicopathologic characteristics of 20 patients with HGSOC for whom proteomic analysis was performed are presented in Supplementary Table 1. The mean patient age was 54.9 years, which was similar between the good and poor prognosis groups (P=0.609). Between the two groups, there was no differences in parity, menopausal status, initial serum CA-125 levels, International Federation of Gynecology and Obstetrics (FIGO) stage, residual tumor after PDS, and total number of cycles of post-operative adjuvant chemotherapy (Supplementary Table 2). In relation to germline BRCA mutational status, seven and two patients had BRCA1 and BRCA2 mutations, respectively, while the other 11 patients harbored wild-type BRCA1/2. None of the patients received first-line PARP inhibitor maintenance therapy. The median length of observations was 34.0 months, during which 15 patients experienced disease recurrence. Patients in the good prognosis group had a significantly better PFS than those in the poor prognosis group (median, 48.4 vs. 12.4 months; P<0.001).

      2. Results of proteomic and bioinformatic analyses

      2.1 Global proteomic analysis of plasma samples

      To identify prognostic biomarkers for HGSOC, we performed MS-based label-free quantification using frozen plasma samples from chemotherapy-naïve patients (n=20). To increase the proteome depth, we applied BoxCar acquisition using a small amount (2 μL) of plasma sample, without depletion of highly abundant proteins. In total, 1912 proteins were identified at the protein FDR 1% level. An average of 1082 protein groups were quantified per sample (Figure 1B). Signal intensities for the quantified proteins overall spanned approximately seven orders of magnitude (Figure 1C), and included several previously reported ovarian cancer marker candidates, such as HE4, MSLN, VCAM-1, CEA, CRP, PROZ, LCAT, and M-CSF. Details of the identified and quantified proteins are presented in Supplementary Table 3.
      To identify the differences within and between groups, the protein profiles were plotted as multi-scatter plots. Pearson correlation coefficient (PCC) values for proteome pairs were calculated (Supplementary Figure 2). The intra-group correlation displayed average PCCs of 0.84 and 0.83 in the good and poor response groups, respectively. The average intergroup PCC value, between the good and poor response group, was 0.82.

      2.2 Label-free quantification

      Next, we assessed significant quantitative differences between samples from patients with good and poor prognosis, based on pairwise comparisons. First, we compared the good and poor prognosis groups via principal component analysis of a filtered list with approximately 1028 proteins (with 70% valid iBAQ values in at least one group). Although tumor proteomes were correlated regardless of prognosis (Supplementary Figure 2), the two good and poor response groups were separated independently (Figure 2A).
      Figure thumbnail gr2
      Figure 2Statistical and functional differences between good and poor prognosis groups. (A) Principal component analysis; (B) Volcano plot; (C) Gene ontology biological process (GOBP) enrichment tree-map of upregulated proteins in the good prognosis group; (D) GOBP enrichment tree-map of upregulated proteins in the poor prognosis group.
      Pairwise comparisons via t-test and filtering (P<0.05; fold-change, >1.5) revealed significant alterations in 96 proteins, of which 35 proteins had higher expression in the good prognosis group than the poor prognosis group. The other 61 proteins had higher expression in the poor prognosis group than the good prognosis group (Figure 2B and Supplementary Table 4). Using the stringent filtering criterion of FDR <0.05, PLXND1, SIGLEC14, SND1, and PRMT1 were found to be upregulated in patients with a poor prognosis. GO enrichment analysis based on biological processes revealed that proteins upregulated in the good prognosis group were significantly enriched for terms such as “actin filament organization,” “regulation of lipase activity,” “cellular response to chemical stress,” “glucose 6-phosphate metabolic process,” and “regulation of cell death” (Figure 2C and Supplementary Table 5). In contrast, proteins upregulated in the poor prognosis group were significantly enriched in “neutrophil degranulation,” “neutrophil-mediated immunity,” “aspartate metabolic process,” and “negative regulation of lipoprotein particle clearance” GO-BPs (Figure 2D and Supplementary Table 5).

      2.3 Selection of candidate prognostic biomarkers

      Potential plasma biomarker candidates for sequential validation experiments were first selected among differentially expression proteins (DEPs) that met one or more of the following criteria: (
      • Sung H.
      • Ferlay J.
      • Siegel R.L.
      • Laversanne M.
      • Soerjomataram I.
      • Jemal A.
      • et al.
      Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
      ) identified as DEPs (PLXND1, SND1, SIGLEC14, and PRMT1) with FDR-adjusted P value <0.05; and (
      • Bristow R.E.
      • Tomacruz R.S.
      • Armstrong D.K.
      • Trimble E.L.
      • Montz F.J.
      Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis.
      ) previously found to be differentially expressed in frozen tissues between the poor prognosis and good prognosis group of HGSOC patients (
      • Kim S.I.
      • Jung M.
      • Dan K.
      • Lee S.
      • Lee C.
      • Kim H.S.
      • et al.
      Proteomic Discovery of Biomarkers to Predict Prognosis of High-Grade Serous Ovarian Carcinoma.
      ), considering that increased levels of cancer tissue-specific proteins can be released into the blood (
      • Landegren U.
      • Hammond M.
      Cancer diagnostics based on plasma protein biomarkers: hard times but great expectations.
      ). Consequently we first selected 18 potential biomarkers (GSN, VCAN, SND1, SIGLEC14, CD163, PRMT1, PLXND1, F12, HPR, HSPA5, ACY1, CD248, C5, GRHPR, MCAM, PPP1R7, STAB1, and UGGT1). Among the 14 proteins that overlapped with our previous tissue data, six proteins (GSN, VCAN, CD163, F12, HPR, and HSPA5) were selected according to concordant expression patterns between tissue and plasma. We further selected prognostic biomarker candidates on the basis of the following parameters: (
      • Sung H.
      • Ferlay J.
      • Siegel R.L.
      • Laversanne M.
      • Soerjomataram I.
      • Jemal A.
      • et al.
      Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
      ) the targeted proteins were upregulated in patients with a poor prognosis (upregulated proteins are more suitable as biomarkers than downregulated proteins); (
      • Bristow R.E.
      • Tomacruz R.S.
      • Armstrong D.K.
      • Trimble E.L.
      • Montz F.J.
      Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis.
      ) a commercial ELISA kit was available for the protein; and (
      • Chi D.S.
      • Eisenhauer E.L.
      • Zivanovic O.
      • Sonoda Y.
      • Abu-Rustum N.R.
      • Levine D.A.
      • et al.
      Improved progression-free and overall survival in advanced ovarian cancer as a result of a change in surgical paradigm.
      ) proteins could be detected in our validation cohort using the selected ELISA kits. Finally, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers for the validation stage (Supplementary Figure 3).

      2.4 Validation of protein biomarkers through ELISA

      Protein biomarkers underwent prognostic validation by using independent plasma samples obtained from patients with HGSOC (n=202). Clinicopathologic characteristics of the patients are presented in Table 1. Of all patients, 88.6% had advanced-stage (FIGO stage III–IV) disease and 92.1% underwent PDS, rather than NAC followed by IDS. Optimal debulking (with no gross residual tumor) was achieved in 71.8% of cases. Germline and/or somatic BRCA1/2 testing was conducted in 158 patients (78.2%), and 36.1% (57/158) had mutations in BRCA1 or BRCA2. Three patients received first-line PARP inhibitor maintenance therapy (olaparib). The median length of observation was 43.8 months, during which 134 patients (66.3%) experienced relapse and 30 (14.9%) died of the disease. The median PFS was 24.6 months, and the 18-month PFS rate was 62.9% (127/202) (Supplementary Figure 4).
      Table 1Clinicopathologic characteristics of the patients in validation phase
      CharacteristicsAll (n=202, %)Good prognosis (n=127, %)Poor prognosis (n=75, %)P
      Age, years
       Mean ± SD57.0 ± 10.855.8 ± 10.459.2 ± 11.20.029
      Parity
       Median (range)2 (0–8)2 (0–6)2 (0–8)0.008
      Menopausal status
       Menopause142 (70.3)85 (66.9)57 (76.0)0.173
      Serum CA-125, IU/mL
       Median (range)800.5 (5.1–10000)654.0 (5.1–10000)1459.0 (19.5–10000)0.028
      FIGO stage0.001
       I–II23 (11.4)20 (15.7)3 (4.0)
       III131 (64.9)86 (67.7)45 (60.0)
       IV48 (23.8)21 (16.5)27 (36.0)
      Primary treatment strategy0.267
       PDS186 (92.1)119 (93.7)67 (89.3)
       NAC-IDS16 (7.9)8 (6.3)8 (10.7)
      Residual tumor after surgery<0.001
       No gross145 (71.8)106 (83.5)39 (52.0)
       <1 cm33 (16.3)17 (13.4)16 (21.3)
       ≥1 cm24 (11.9)4 (3.1)20 (26.7)
      Chemotherapy regimen0.027
       Paclitaxel-Carboplatin184 (91.1)120 (94.5)64 (85.3)
       Paclitaxel-Carboplatin-BEV18 (8.9)7 (5.5)11 (14.7)
      Total cycles of chemotherapy0.089
       4–6168 (83.2)110 (86.6)58 (77.3)
       7–934 (16.8)17 (13.4)17 (22.7)
      Recurrence
       No68 (33.7)68 (53.5)0<0.001
       Yes134 (66.3)59 (46.5)75 (100.0)
      PSR
      PSR was defined as relapse ≥6 months after completion of taxane- and platinum-based chemotherapy, whereas PRR as relapse <6 months.
      104 (77.6)59 (46.5)45 (60.0)<0.001
      PRR30 (22.4)030 (40.0)
      Platinum sensitivity<0.001
       Platinum-sensitive
      In addition to PSR, the patients who completed taxane- and platinum-based chemotherapy and did not experience disease recurrence during at least six months of follow-up period were considered platinum-sensitive.
      172 (85.1)127 (100.0)45 (60.0)
       Platinum-resistant30 (14.9)030 (40.0)
      g/tBRCA mutational status
      Germline and/or somatic BRCA1/2 mutational status.
       Not tested (unknown)44 (21.8)25 (19.7)19 (25.3)0.347
       Tested158 (78.2)102 (80.3)56 (74.7)
      Both wild-type101 (50.0)57 (44.9)44 (58.7)0.017
      BRCA1 mutation40 (19.8)31 (24.4)9 (12.0)
      BRCA2 mutation17 (8.4)14 (11.0)3 (4.0)
      Abbreviations: BEV, bevacizumab; CA-125, cancer antigen 125; FIGO, International Federation of Gynecology and Obstetrics; NAC, neoadjuvant chemotherapy; IDS, interval debulking surgery; PDS, primary debulking surgery; PRR, platinum-resistant recurrence; PSR, platinum-sensitive recurrence; SD, standard deviation.
      a PSR was defined as relapse ≥6 months after completion of taxane- and platinum-based chemotherapy, whereas PRR as relapse <6 months.
      b In addition to PSR, the patients who completed taxane- and platinum-based chemotherapy and did not experience disease recurrence during at least six months of follow-up period were considered platinum-sensitive.
      c Germline and/or somatic BRCA1/2 mutational status.
      Table 1 also compares clinicopathologic characteristics between the good and poor prognosis groups. Patients in the poor prognosis group (n=75) were significantly older (P=0.029) and had more advanced disease (P=0.001), compared to those in the good prognosis group (n=127). While the two groups had similar proportion of PDS (P=0.267), optimal debulking was less frequently achieved in the poor prognosis group (52.0% vs. 83.5%; P<0.001). Among the patients who received germline and/or somatic BRCA1/2 testing, BRCA1/2 mutations were less frequently observed in the poor prognosis group (21.4% vs. 44.1%; P=0.004). Comparing the survival outcomes, the poor prognosis group showed worse PFS (median, 12.5 vs. 54.1 months; P<0.001) and OS (5-year OS rate, 57.1% vs. 94.0%; P<0.001), compared to the good prognosis group (Supplementary Figure 4).
      Six protein biomarkers, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1, were subjected to further prognostic validation using ELISA (Supplementary Table 6). The ELISA results are summarized in Table 2 and Supplementary Figure 5. Table 2 also compares ELISA results between the good and poor prognosis groups. Plasma GSN levels were significantly higher in the poor prognosis group than those in the good prognosis group (median, 23.150 vs. 19.300 ng/mL; P=0.001). However, plasma levels of VCAN, SND1, SIGLEC14, CD163, and PRMT1 were similar between the two groups.
      Table 2ELISA results of the six plasma protein biomarkers
      ProteinAll (n=202, %)Good prognosis (n=127, %)Poor prognosis (n=75, %)P
      GSN, ng/mL0.001
      Median (range)20.525 (8.300–96.200)19.300 (8.300–96.200)23.150 (11.000–62.950)
      Mean ± SD22.462 ± 10.09521.197 ± 10.34424.603 ± 9.341
      Cut-off24.350
      VCAN, ng/mL0.679
      Median (range)4.339 (1.676–15.041)4.218 (1.687–10.513)4.408 (1.676–15.041)
      Mean ± SD4.571 ± 1.7544.537 ± 1.6764.629 ±1.888
      Cut-off5.832
      SND1, ng/mL0.193
      Median (range)17.670 (1.800–45.040)17.700 (1.800–45.040)17.120 (6.160–38.020)
      Mean ± SD18.440 ± 7.27619.089 ± 7.73317.342 ± 6.326
      Cut-off25.900
      SIGLEC14a, ng/mL0.315
      Median (range)5.250 (2.700–29.070)5.125 (2.720–29.070)5.440 (2.700–20.010)
      Mean ± SD5.941 ± 3.0825.828 ± 3.0366.129 ± 3.169
      Cut-off6.300
      CD163, ug/uL0.339
      Median (range)0.583 (0.000–1.608)0.576 (0.240–1.608)0.608 (0.000–1.511)
      Mean ± SD0.643 ± 0.2630.635 ± 0.2550.655 ± 0.277
      Cut-off0.394
      PRMT1, ng/mL0.947
      Median (range)2.496 (0.816–12.520)2.456 (0.816–12.520)2.640 (1.056–6.016)
      Mean ± SD3.043 ± 1.8003.184 ± 2.0902.804 ± 1.126
      Cut-off4.840
      Abbreviations: SD, standard deviation.
      Missing data: a1.
      No correlation was observed between serum CA-125 levels and the plasma levels of each protein biomarker (Supplementary Table 7). Plasma GSN levels were significantly correlated with plasma VCAN (Pearson’s correlation coefficient r=0.224; P=0.001), SND1 (r=0.177; P=0.012), and CD163 levels (r=0.351; P<0.001), but the correlations were weak. A weak positive correlation was also observed between plasma VCAN and SND1 levels (r=0.167; P=0.017). Plasma VCAN levels were significantly moderately correlated with plasma SIGLEC14 levels (r=0.501; P<0.001), and weakly correlated with plasma CD163 levels (r=0.341; P<0.001). Using the cut-off values determined by maxstat (
      • Hothorn T.
      • Lausen B.
      Maximally selected rank statistics in R.
      ), the validation set was divided into high (≥ cut-off value) and low (< cut-off value) plasma level groups for each protein.
      We then compared the clinicopathologic characteristics of the patients with high and low plasma levels of the six protein biomarkers (Supplementary Table 8). Patients with high GSN levels (n=62) were significantly older (P=0.001), had higher initial serum CA-125 levels (P=0.043), more advanced disease (P=0.012), less commonly achieved optimal debulking (P=0.011), and more commonly showed platinum resistance (P=0.040) than did those with low GSN levels (n=140). For VCAN, high plasma levels were associated with old age at the initial diagnosis (P<0.001). For SND1, high plasma levels were associated with advanced disease (P=0.032) and suboptimal debulking (P=0.027). However, for SIGLEC14, CD163, and PRMT1, no significant differences in patient age, FIGO stage, or residual tumor after surgery were observed between the high and low expression groups.
      In assessing the platinum sensitivity of patients with respect to the plasma levels of each protein biomarker, we observed a significant difference only for GSN. Patients with high GSN levels were less sensitive to platinum-based chemotherapy than those with low GSN levels (77.4% vs. 88.6%; P=0.040).
      In survival analysis, the high GSN group showed significantly worse PFS than did the low GSN group (median, 15.6 vs. 29.4 months; P=0.001). In contrast, the high VCAN group showed significantly better PFS than did the low VCAN group (median, not reached vs. 23.2 months; P=0.042). PFS was also better in the high than in the low SND1 group, but the difference was not statistically significant (median, 40.2 vs. 22.6 months; P=0.066). No differences in PFS were observed between groups with high and with low plasma levels of SIGLEC14, CD163, and PRMT1 (Figure 3).
      Figure thumbnail gr3
      Figure 3Comparison of progression-free survival based on the plasma levels of proteins. (A) GSN; (B) VCAN; (C) SND1; (D) SIGLEC14; (E) CD163; (F) PRMT1.
      Figure thumbnail gr4
      Figure 4Predictive performance of the developed models. ROC curves with the AUCs for 18-month PFS rate. The regression-based models underwent 5-fold cross-validation (presented as line). (A) A model using the cut-off values for plasma GSN and VCAN; (B) A model using the continuous values for plasma GSN and VCAN.
      In the multivariate analysis adjusted for patient age, FIGO stage, and residual tumor after surgery, a high plasma GSN level was identified as an independent poor prognostic biomarker for PFS (aHR, 1.556; 95% CI, 1.073–2.256; P=0.020). However, subsequent multivariate analyses revealed no influence of VCAN (aHR, 0.617; 95% CI 0.370–1.030; P=0.065) and SND1 (aHR, 0.789; 95% CI 0.454–1.372; P=0.401) plasma levels on PFS (Table 3).
      Table 3Factors associated with progression-free survival.
      CharacteristicsUuivariate analysisMultivariate Analysis
      HR95% CIPaHR95% CIPaHR95% CIPaHR95% CIP
      Age, years
       <55Ref.Ref.Ref.Ref.
       ≥551.2530.892–1.7610.1941.0640.754–1.5030.7231.1810.829–1.6820.3571.0870.770–1.5340.637
      FIGO stage
       I-IIRef.Ref.Ref.Ref.
       III2.6441.281–5.4570.0092.1511.027–4.5060.0422.0220.961–4.2530.0632.1121.006–4.4330.048
       IV4.4402.064–9.551<0.0013.1771.442–7.0000.0043.2081.454–7.0760.0043.2831.484–7.2640.003
      Residual tumor after surgery
       No grossRef.Ref.Ref.Ref.
       Gross2.3131.617–3.308<0.0011.8981.311–2.7470.0011.9541.355–2.817<0.0011.9491.349–2.816<0.001
      GSN
       LowRef.Ref.
       High1.8501.292–2.6480.0011.5561.073–2.2560.020
      VCAN
       LowRef.Ref.
       High0.6010.365–0.9880.0450.6170.370–1.0300.065
      SND1
       LowRef.Ref.
       High0.6070.355–1.0400.0690.7890.454–1.3720.401
      Abbreviations: aHR, adjusted hazard ratio; CI, confidence interval; CA-125, cancer antigen 125; FIGO, International Federation of Gynecology and Obstetrics; HR, hazard ratio.

      Development of models predicting 18-month PFS rate

      Next, we constructed regression-based models predicting the 18-month PFS rate using clinical variables and plasma levels of five plasma protein biomarkers in HGSOC patients (n=202). Herein, SND1 was excluded as the high SND1 group showed better PFS than did the low SND1 group in the validation phase, which was contrary to the results in the development phase. Through stepwise selection methods, four predictors were selected: FIGO stage, residual tumor after surgery, GSN, and VCAN. Various models were developed using these predictors. Each predictive model underwent five-fold cross-validation to compute the AUC. Among them, the model using cutoff plasma values for GSN (24.350 ng/mL) and VCAN (5.832 ng/mL) showed the best predictive performance, with an AUC of 0.779 (Figure 5 and Supplementary Table 9). This model also showed better predictive performance than did the model using continuous values for plasma GSN and VCAN levels, and those replacing the two protein biomarkers, GSN and VCAN, with serum CA-125 levels (Supplementary Table 9).
      Figure thumbnail gr5
      Figure 5Regression-based nomograms predicting 18-month PFS rate. (A) A nomogram using the cut-off values for plasma GSN and VCAN; (B) A nomogram using the continuous values for plasma GSN and VCAN.
      Using regression-based models, nomograms were then developed for clinical use. Finally, we fitted a user-friendly interface onto the developed nomograms and posted them on a website (http://asiansgo.org/software/nomogram_ovarian).

      DISCUSSION

      Our proteomic analysis study identified plasma protein biomarkers that might be associated with the prognosis of HGSOC. In validation with ELISA, high plasma levels of GSN were associated with worse PFS, while VCAN, SND1, SIGLEC14, CD163, and PRMT1 did not affect the survival outcomes of patients with HGSOC. We also developed models and nomograms to predict the 18-month PFS rate for clinical purposes.
      GSN, a calcium-dependent multi-functional actin-binding protein, has cytoplasmic and plasma isoforms, which are encoded by the same gene (
      • Kwiatkowski D.J.
      • Stossel T.P.
      • Orkin S.H.
      • Mole J.E.
      • Colten H.R.
      • Yin H.L.
      Plasma and cytoplasmic gelsolins are encoded by a single gene and contain a duplicated actin-binding domain.
      ). Plasma GSN is a well-known poor prognostic biomarker for PFS and OS in patients with ovarian cancer. In addition, the expression and secretion of GSN were higher in chemoresistant ovarian cancer cells than in chemosensitive ovarian cancer cells (
      • Asare-Werehene M.
      • Nakka K.
      • Reunov A.
      • Chiu C.T.
      • Lee W.T.
      • Abedini M.R.
      • et al.
      The exosome-mediated autocrine and paracrine actions of plasma gelsolin in ovarian cancer chemoresistance.
      ). Consistently, the current study showed that high plasma GSN levels were associated with poor prognostic factors, such as advanced-stage disease and residual tumor after surgery, loss of platinum sensitivity, and reduced PFS. Recently, Asare–Werehene et al. demonstrated that plasma GSN confers chemoresistance in ovarian cancer by inhibiting the antitumor functions of macrophages through apoptosis and modulating the tumor microenvironment (
      • Asare-Werehene M.
      • Tsuyoshi H.
      • Zhang H.
      • Salehi R.
      • Chang C.Y.
      • Carmona E.
      • et al.
      Plasma Gelsolin Confers Chemoresistance in Ovarian Cancer by Resetting the Relative Abundance and Function of Macrophage Subtypes.
      ).
      VCAN, a large extracellular matrix proteoglycan, is known to play role in promoting tumorigenesis and enhancing tumor progression and metastasis (
      • Du W.W.
      • Yang W.
      • Yee A.J.
      Roles of versican in cancer biology--tumorigenesis, progression and metastasis.
      ). Researchers have reported positive associations between high tissue expression of VCAN and poor survival outcomes in various malignancies including breast cancer (
      • Suwiwat S.
      • Ricciardelli C.
      • Tammi R.
      • Tammi M.
      • Auvinen P.
      • Kosma V.M.
      • et al.
      Expression of extracellular matrix components versican, chondroitin sulfate, tenascin, and hyaluronan, and their association with disease outcome in node-negative breast cancer.
      ) and renal cell carcinoma (
      • Mitsui Y.
      • Shiina H.
      • Kato T.
      • Maekawa S.
      • Hashimoto Y.
      • Shiina M.
      • et al.
      Versican Promotes Tumor Progression, Metastasis and Predicts Poor Prognosis in Renal Carcinoma.
      ). In advanced-stage serous ovarian cancer, Ghosh et al. reported that high VCAN expression in the tumor stroma was associated with increased angiogenesis and significantly worse PFS and OS than low VCAN expression (
      • Ghosh S.
      • Albitar L.
      • LeBaron R.
      • Welch W.R.
      • Samimi G.
      • Birrer M.J.
      • et al.
      Up-regulation of stromal versican expression in advanced stage serous ovarian cancer.
      ). However, such an association seems to differ depending on the specimen type. In contrast to this study, we measured plasma VCAN levels instead of tissue expression and observed that VCAN did not affect PFS in patients with HGSOC.
      SND1, a component of the RNA-induced silencing complex, is an oncogene involved in tumorigenesis, tumor progression, and metastasis in multiple malignancies, including breast cancer (
      • Yu L.
      • Liu X.
      • Cui K.
      • Di Y.
      • Xin L.
      • Sun X.
      • et al.
      SND1 Acts Downstream of TGFβ1 and Upstream of Smurf1 to Promote Breast Cancer Metastasis.
      ) and colorectal cancer (
      • Tsuchiya N.
      • Ochiai M.
      • Nakashima K.
      • Ubagai T.
      • Sugimura T.
      • Nakagama H.
      SND1, a component of RNA-induced silencing complex, is up-regulated in human colon cancers and implicated in early stage colon carcinogenesis.
      ). In ovarian cancer, SND1 promotes epithelial-to-mesenchymal transition (EMT), which facilitates metastasis of ovarian cancer (
      • Xin L.
      • Zhao R.
      • Lei J.
      • Song J.
      • Yu L.
      • Gao R.
      • et al.
      SND1 acts upstream of SLUG to regulate the epithelial-mesenchymal transition (EMT) in SKOV3 cells.
      ). Furthermore, Wang et al. reported that miR-1224-5p inhibits the proliferation and invasion of ovarian cancer by targeting SND1 (
      • Wang J.
      • Hu Y.
      • Ye C.
      • Liu J.
      miR-1224-5p inhibits the proliferation and invasion of ovarian cancer via targeting SND1.
      ). Recently, Cui et al. suggested a potential correlation between the tissue expression of SND1 and tumor mutational burden or microsatellite instability across all The Cancer Genome Atlas tumors (
      • Cui X.
      • Zhang X.
      • Liu M.
      • Zhao C.
      • Zhang N.
      • Ren Y.
      • et al.
      A pan-cancer analysis of the oncogenic role of staphylococcal nuclease domain-containing protein 1 (SND1) in human tumors.
      ). In contrast, our study showed that high or low plasma SND1 levels did not affect PFS in HGSOC patients. Such inconsistent results between our study and previous studies might originate from differences in specimen types, histological subtypes, and sample sizes. To the best of our knowledge, no previous study has investigated the relationship between plasma SND1 levels and survival outcomes in ovarian cancer. Therefore, further prospective studies are warranted to investigate the relationship between plasma SND1 levels and survival outcomes.
      CD163, a multifunctional receptor containing a scavenger receptor cysteine-rich domain, is specifically expressed in monocytes and macrophages and can be cleaved from the cell membrane of monocytes and macrophages (
      • Moestrup S.K.
      • Møller H.J.
      CD163: a regulated hemoglobin scavenger receptor with a role in the anti-inflammatory response.
      ). Besides its multiple functions, such as immune modulation, high serum CD163 levels have been associated with poor survival outcomes in various malignancies (
      • Ding D.
      • Yao Y.
      • Yang C.
      • Zhang S.
      Identification of mannose receptor and CD163 as novel biomarkers for colorectal cancer.
      ,
      • Kazankov K.
      • Rode A.
      • Simonsen K.
      • Villadsen G.E.
      • Nicoll A.
      • Møller H.J.
      • et al.
      Macrophage activation marker soluble CD163 may predict disease progression in hepatocellular carcinoma.
      ,
      • Jensen T.O.
      • Schmidt H.
      • Møller H.J.
      • Høyer M.
      • Maniecki M.B.
      • Sjoegren P.
      • et al.
      Macrophage markers in serum and tumor have prognostic impact in American Joint Committee on Cancer stage I/II melanoma.
      ), including ovarian cancer. No et al. reported that high serum CD163 levels were an independent poor prognostic factor for PFS in patients with epithelial ovarian cancer (n=55) (
      • No J.H.
      • Moon J.M.
      • Kim K.
      • Kim Y.B.
      Prognostic significance of serum soluble CD163 level in patients with epithelial ovarian cancer.
      ). In contrast, no reduction in PFS due to high plasma CD163 levels was observed in our study. While a previous study examined serum samples of patients with all histological subtypes and grades of epithelial ovarian cancer, the current study examined plasma samples of patients with HGSOC. Such differences may underlie the inconsistent results.
      PRMT1 mediates epigenetic modifications. Aberrant expression of PRMT1 has been reported to be involved in tumorigenesis (
      • Yoshimatsu M.
      • Toyokawa G.
      • Hayami S.
      • Unoki M.
      • Tsunoda T.
      • Field H.I.
      • et al.
      Dysregulation of PRMT1 and PRMT6, Type I arginine methyltransferases, is involved in various types of human cancers.
      ) and is an unfavorable prognostic biomarker in breast cancer (
      • Mathioudaki K.
      • Scorilas A.
      • Ardavanis A.
      • Lymberi P.
      • Tsiambas E.
      • Devetzi M.
      • et al.
      Clinical evaluation of PRMT1 gene expression in breast cancer.
      ) and colorectal cancer (
      • Papadokostopoulou A.
      • Mathioudaki K.
      • Scorilas A.
      • Xynopoulos D.
      • Ardavanis A.
      • Kouroumalis E.
      • et al.
      Colon cancer and protein arginine methyltransferase 1 gene expression.
      ). In non-small cell lung cancer, PRMT1 has been suggested to be a regulator of EMT (
      • Avasarala S.
      • Van Scoyk M.
      • Karuppusamy Rathinam M.K.
      • Zerayesus S.
      • Zhao X.
      • Zhang W.
      • et al.
      PRMT1 Is a Novel Regulator of Epithelial-Mesenchymal-Transition in Non-small Cell Lung Cancer.
      ). Recently, Matsubara et al. investigated the prognostic role of PRMT1 tissue expression in patients with ovarian serous carcinoma (n=51) (
      • Matsubara H.
      • Fukuda T.
      • Awazu Y.
      • Nanno S.
      • Shimomura M.
      • Inoue Y.
      • et al.
      PRMT1 expression predicts sensitivity to platinum-based chemotherapy in patients with ovarian serous carcinoma.
      ). They found that high PRMT1 expression was associated with platinum resistance and reduced OS. In contrast, we could not identify any association between plasma PRMT1 levels and response to platinum-based chemotherapy or PFS.
      SIGLEC family proteins play diverse immune and non-immune regulatory roles in the tumor microenvironment and participate in tumor progression. Facilitating tumor immune escape is one of the mechanisms by which tumors progress (
      • Jiang K.Y.
      • Qi L.L.
      • Kang F.B.
      • Wang L.
      The intriguing roles of Siglec family members in the tumor microenvironment.
      ). Compared with other SGILEC family proteins, the prognostic role of SIGLEC14 in ovarian cancer is not fully understood. We observed no association between plasma SIGLEC14 levels and PFS in HGSOC patients.
      In the current study, we developed two regression-based models and nomograms to predict the 18-month PFS rate in patients with newly diagnosed HGSOC. In both models, only two (GSN and VCAN) of the six protein biomarkers were selected and incorporated. Although independent multivariate analyses indicated GSN as the solitary independent prognostic factor for PFS, the addition of VCAN to GSN seems to confer further improvement in performance in predicting the 18-month PFS rate. In the validation phase, we could not conduct external validation due to the scarcity of resources and time to conduct a prospective multicenter study that could collect plasma samples from the enrolled subjects. Instead, we implemented five-fold cross-validation, a well-established statistical method, to prevent overfitting and increase the robustness and prediction accuracy of the developed model. A further increase in the predictive performance is expected if the multi-omics data of HGSOC patients are integrated into the developed models.
      Throughout the study, patients’ BRCA1/2 mutational status and the use of first-line PARP inhibitor maintenance treatment were not considered, because of their low frequency in our study. In particular, only a few patients were eligible (n=3 in the second phase). Such a low frequency might have originated from the socio-medical environment in Korea. In October 2019 and August 2020, the Korean Ministry of Food and Drug Safety approved olaparib and niraparib as first-line maintenance therapies based on the SOLO1 (
      • Moore K.
      • Colombo N.
      • Scambia G.
      • Kim B.G.
      • Oaknin A.
      • Friedlander M.
      • et al.
      Maintenance Olaparib in Patients with Newly Diagnosed Advanced Ovarian Cancer.
      ) and PRIMA trials (
      • González-Martín A.
      • Pothuri B.
      • Vergote I.
      • DePont Christensen R.
      • Graybill W.
      • Mirza M.R.
      • et al.
      Niraparib in Patients with Newly Diagnosed Advanced Ovarian Cancer.
      ), respectively. Furthermore, it was not until October 2021 that the National Health Insurance System started to cover both olaparib and niraparib in patients with BRCA1/2 mutated HGSOC. Despite the approval of PARP inhibitors, HGSOC patients find these difficult to use beyond insurance coverage because of their high cost.
      In the era of precision cancer medicine, it is critical to predict prognosis or survival outcomes precisely. Our results indicated that adding plasma levels of GSN and VCAN to the clinical factors in predictive models improved the models’ performance. Applying the developed models, if a patient with HGSOC was predicted to be at high risk of disease progression within 18 months from the initiation of primary treatment, physicians might consider incorporating bevacizumab into conventional taxane- and platinum-based chemotherapy. In particular, based on the germline/somatic BRCA1/2 mutational status and homologous recombination deficiency, first-line PARP inhibitor maintenance therapy may be recommended more strongly (
      • Moore K.
      • Mirza M.
      • Gourley C.
      • Pignata S.
      • Ali T.
      • Lechpammer S.
      • et al.
      Evolution of the Ovarian Cancer Treatment Paradigm, Including Maintenance Treatment, in the US and Europe: A Real-World Chart Review Analysis (2017-2020) (028).
      ). After completion of chemotherapy, a high-risk patient might undergo more intensive surveillance.
      Our study had several limitations. First, as this study had a retrospective design, inevitable issues, such as selection bias, might exist. Second, the sample size might have been insufficient for discovering and validating plasma protein biomarkers. In particular, in the validation phase, we failed to observe a relationship of high plasma SND1 levels with poor prognosis, which was marked in the development phase. Third, external validation of the developed models is needed. Fourth, we only investigated statistical correlations, but did not evaluate the biological interactions between protein biomarkers. Lastly, we did not investigate longitudinal changes in each plasma protein biomarker over the course of the primary treatment. Such information might enable us to calculate the kinetics of each biomarker during a specific period and predict the primary treatment success more accurately.
      In conclusion, we successfully generated proteomic profiles of plasma samples from HGSOC patients. A subsequent ELISA study assessed the prognostic value of the six protein biomarkers. Plasma GSN was identified as a poor prognostic biomarker for PFS in HGSOC, but plasma VCAN, SND1, SIGLEC14, CD163, and PRMT1 levels were not. Combined with clinical factors, we developed models and nomograms to predict the 18-month PFS rate for clinical purposes. Our study results provided insights into the protein biomarkers that might potentially develop HGSOC and offered clues for developing therapeutic targets. Further translational and prospective validation studies are needed.

      AUTHOR CONTRIBUTIONS

      DH, and ML conceived the project. SIK, SH, KD, DH, and ML provided methodology. DH and ML provided resources. DH and ML provided software. SIK, SH, KD, and DH conducted formal analysis. SIK, HHC, and ML performed funding acquisition. HSK, HHC, JWK, NHP, and YSS performed validations. SIK, SH, and DH wrote the manuscript, and all authors contributed to the final version.

      DATA AVAILABILITY

      The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentralproteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD034636.

      CONFLICT OF INTEREST

      The authors declare that they have no conflict of interest

      ACKNOWLEDGEMENTS

      The biospecimens and data used in this study were provided by the Biobank of Seoul National University Hospital, a member of Korea Biobank Network. This work was supported by the Korea Medical Device Development Fund grant funded by the Korean government: the Ministry of Trade, Industry and Energy (No. RS-2020-KD000028). This work was also supported by the National Research Foundation (NRF) of Korea (No. NRF-2020R1G1A1005711) and the Cooperative Research Program of Basic Medical Science and Clinical Science from the Seoul National University College of Medicine (No. 800-20210297).

      Supplementary data

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