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A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer*[S]

  • Ruth Hüttenhain
    Correspondence
    To whom correspondence should be addressed
    Footnotes
    Affiliations
    ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
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  • Meena Choi
    Footnotes
    Affiliations
    §Khoury College of Computer Sciences, Northeastern University, Boston, MA
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  • Laura Martin de la Fuente
    Affiliations
    ¶Department of Surgery and Oncology, Clinical Sciences, Lund University, Lund, Sweden
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  • Kathrin Oehl
    Affiliations
    ‖Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
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  • Ching-Yun Chang
    Affiliations
    **Department of Statistics, Purdue University, West Lafayette, IN
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  • Anne-Kathrin Zimmermann
    Affiliations
    ‖Department of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland
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  • Susanne Malander
    Affiliations
    ¶Department of Surgery and Oncology, Clinical Sciences, Lund University, Lund, Sweden
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  • Håkan Olsson
    Affiliations
    ¶Department of Surgery and Oncology, Clinical Sciences, Lund University, Lund, Sweden
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  • Silvia Surinova
    Footnotes
    Affiliations
    ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland
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  • Timothy Clough
    Affiliations
    **Department of Statistics, Purdue University, West Lafayette, IN
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  • Viola Heinzelmann-Schwarz
    Affiliations
    ‡‡Gynecological Cancer Center, University Hospital Basel, University of Basel, Basel, Switzerland

    §§Ovarian Cancer Research, Department of Biomedicine, University of Basel, Basel, Switzerland
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  • Peter J. Wild
    Affiliations
    ¶¶Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Frankfurt am Main, Germany
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  • Daniela M. Dinulescu
    Affiliations
    ‖‖Department of Pathology, Division of Women's and Perinatal Pathology Brigham and Women's Hospital Harvard Medical School, Boston, MA
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  • Emma Niméus
    Affiliations
    ¶Department of Surgery and Oncology, Clinical Sciences, Lund University, Lund, Sweden

    ‡‡‡Department of Surgery, Skånes University hospital, Lund, Sweden
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  • Olga Vitek
    Affiliations
    §Khoury College of Computer Sciences, Northeastern University, Boston, MA

    **Department of Statistics, Purdue University, West Lafayette, IN
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  • Ruedi Aebersold
    Affiliations
    ‡Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, 8093 Zurich, Switzerland

    §§§Faculty of Science, University of Zurich, 8057 Zurich, Switzerland
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  • Author Footnotes
    * The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Health. E.N. was supported by the Swedish Breast Cancer Association (BRO), Region Skåne, Governmental Funding of Research within the Swedish National Health Service (ALF), Mrs Berta Kamprad Foundation, BioCARE, Marcus and Marianne Wallenberg Foundation. R.A. was supported by the Swiss National Science Foundation (Grant no. 3100A0-688 107679). This work is supported by grants awarded to D.M.D. by the DOD OCRP (W81XWH-15-1-0089), American Cancer Society (RSG-13-083-01-TBG), Ovarian Cancer Research Fund Liz Tilberis award, and the Burroughs-Wellcome Fund Career Award in the Biomedical Sciences 1005320.01. P.J.W. work was funded in part by an Oncosuisse grant.
    [S] This article contains supplemental Figures and Tables.
    ‖‖‖ Current address: Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA.
    ‡‡‡‡ Current address: UCL Cancer Institute, London, United Kingdom.
    §§§§ Authors contributed equally to the work.
Open AccessPublished:July 09, 2019DOI:https://doi.org/10.1074/mcp.RA118.001221
      Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.
      Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

      Graphical Abstract

      Clinical management of aggressive tumors requires sensitive and specific protein biomarkers that can be monitored in a noninvasive way (
      • Ludwig J.A.
      • Weinstein J.N.
      Biomarkers in cancer staging, prognosis and treatment selection.
      ). To establish the clinical value of such biomarkers, it is imperative to reliably quantify proteins of interest in large subject cohorts (
      • Huttenhain R.
      • Malmström J.
      • Picotti P.
      • Aebersold R.
      Perspectives of targeted mass spectrometry for protein biomarker verification.
      ). Blood plasma is the preferred source of protein biomarkers as blood collection is minimally invasive (
      • Surinova S.
      • Schiess R.
      • Huttenhain R.
      • Cerciello F.
      • Wollscheid B.
      • Aebersold R.
      On the development of plasma protein biomarkers.
      ). However, the complex and large dynamic range of protein concentrations in plasma pose a technical challenge for the accurate, sensitive and reproducible quantification of biomarker candidates across hundreds of samples (
      • Anderson N.L.
      • Anderson N.G.
      The human plasma proteome: history, character, and diagnostic prospects.
      ). Although affinity-based assays, such as enzyme-linked immunosorbent assay (ELISA), have traditionally been the method of choice, they are constrained by their limited availability for human proteins and time-consuming development of new and reliable assays.
      Targeted mass spectrometry (MS) based on selected reaction monitoring (SRM)
      The abbreviations used are:
      SRM
      selected reaction monitoring
      ELISA
      enzyme-linked immunosorbent assay
      EOC
      epithelial ovarian cancer
      FDA
      Food and Drug Administration
      GEMM
      genetically engineered mouse model
      LC-MS
      liquid chromatography coupled to mass spectrometry
      TMA
      tissue microarray
      HGSC
      high grade serous ovarian cancer
      DDA
      data-dependent acquisition
      FDR
      false discovery range.
      1The abbreviations used are:SRM
      selected reaction monitoring
      ELISA
      enzyme-linked immunosorbent assay
      EOC
      epithelial ovarian cancer
      FDA
      Food and Drug Administration
      GEMM
      genetically engineered mouse model
      LC-MS
      liquid chromatography coupled to mass spectrometry
      TMA
      tissue microarray
      HGSC
      high grade serous ovarian cancer
      DDA
      data-dependent acquisition
      FDR
      false discovery range.
      is a highly sensitive MS approach for accurate and reproducible protein quantification and for fast and cost-effective development of assays. It has been proposed several years ago as an alternative method to immune reagent-based measurements for developing biomarkers (
      • Huttenhain R.
      • Malmström J.
      • Picotti P.
      • Aebersold R.
      Perspectives of targeted mass spectrometry for protein biomarker verification.
      ,
      • Surinova S.
      • Schiess R.
      • Huttenhain R.
      • Cerciello F.
      • Wollscheid B.
      • Aebersold R.
      On the development of plasma protein biomarkers.
      ). A requirement for successful application of SRM in this area is a rigorous study design, reproducible sample preparation, and appropriate statistical analysis (
      • Oberg A.L.
      • Vitek O.
      Statistical design of quantitative mass spectrometry-based proteomic experiments.
      ). Importantly, the reliable detection of biomarkers requires studying large subject cohorts. Such large-scale studies face substantial challenges on many different levels. First, the investment of substantial resources for SRM-based quantification across large subject cohorts requires a careful selection of protein targets. Second, the collection of clinical samples often spans many years and the duration and condition of sample storage may confound bona fide biomarkers. Third, the concurrent processing of hundreds of samples is typically challenging, requiring the sample set to be processed in batches, thus potentially introducing batch effects. Finally, because the measurements will likely span a considerable time on the mass spectrometer, controls such as heavy labeled internal standards should be included to account for variability in instrument performance.
      Even though SRM is now well established, few studies have successfully applied it to large subject cohorts (
      • Cima I.
      • Schiess R.
      • Wild P.
      • Kaelin M.
      • Schüffler P.
      • Lange V.
      • Picotti P.
      • Ossola R.
      • Templeton A.
      • Schubert O.
      • Fuchs T.
      • Leippold T.
      • Wyler S.
      • Zehetner J.
      • Jochum W.
      • Buhmann J.
      • Cerny T.
      • Moch H.
      • Gillessen S.
      • Aebersold R.
      • Krek W.
      Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer.
      ,
      • Surinova S.
      • Choi M.
      • Tao S.
      • Schüffler P.J.
      • Chang C.-Y.
      • Clough T.
      • Vysloužil K.
      • Khoylou M.
      • Srovnal J.
      • Liu Y.
      • Matondo M.
      • Huttenhain R.
      • Weisser H.
      • Buhmann J.M.
      • Hajdúch M.
      • Brenner H.
      • Vitek O.
      • Aebersold R.
      Prediction of colorectal cancer diagnosis based on circulating plasma proteins.
      ,
      • Drabovich A.P.
      • Dimitromanolakis A.
      • Saraon P.
      • Soosaipillai A.
      • Batruch I.
      • Mullen B.
      • Jarvi K.
      • Diamandis E.P.
      Differential diagnosis of azoospermia with proteomic biomarkers ECM1 and TEX101 quantified in seminal plasma.
      ,
      • Surinova S.
      • Radová L.
      • Choi M.
      • Srovnal J.
      • Brenner H.
      • Vitek O.
      • Hajdúch M.
      • Aebersold R.
      Non-invasive prognostic protein biomarker signatures associated with colorectal cancer.
      ,
      • Duriez E.
      • Masselon C.D.
      • Mesmin C.
      • Court M.
      • Demeure K.
      • Allory Y.
      • Malats N.
      • Matondo M.
      • Radvanyi F.
      • Garin J.
      • Domon B.
      Large-scale SRM screen of urothelial bladder cancer candidate biomarkers in urine.
      ) and there is currently no consistent way to deal with the aforementioned challenges of large-scale SRM-based studies. This manuscript demonstrates the importance of rigorous experimental design, combined with various controls to account for variabilities in SRM measurements, sample preparation and measurements across batches, when using a case study of developing proteomic biomarkers of epithelial ovarian cancer (EOC). We found that these controls were key for achieving optimal predictive performance of the biomarker signature for detecting EOC.
      EOC is the fifth leading cause of cancer death in women and the leading cause of death from gynecological malignancies (
      • Yap T.A.
      • Carden C.P.
      • Kaye S.B.
      Beyond chemotherapy: targeted therapies in ovarian cancer.
      ). The 5-year survival rate for EOC is low as most cases are diagnosed with advanced stage III-IV disease. If diagnosed at an early stage when the cancer remains confined to the ovaries, most patients can be cured by a combination of debulking surgery and platinum- and taxane-based chemotherapy (
      • Yap T.A.
      • Carden C.P.
      • Kaye S.B.
      Beyond chemotherapy: targeted therapies in ovarian cancer.
      ). However, only 20% of the patients with EOC are currently diagnosed with early stage tumors. Therefore, much effort is invested in finding effective strategies for early diagnosis.
      Cancer antigen 125 (CA125) (
      • Bast R.C.
      • Klug T.L.
      • St John E.
      • Jenison E.
      • Niloff J.M.
      • Lazarus H.
      • Berkowitz R.S.
      • Leavitt T.
      • Griffiths C.T.
      • Parker L.
      • Zurawski V.R.
      • Knapp R.C.
      A radioimmunoassay using a monoclonal antibody to monitor the course of epithelial ovarian cancer.
      ) and human epididymis protein 4 (HE4) (
      • Drapkin R.
      • Horsten von, Lin H.H.Y.
      • Mok S.C.
      • Crum C.P.
      • Welch W.R.
      • Hecht J.L.
      Human epididymis protein 4 (HE4) is a secreted glycoprotein that is overexpressed by serous and endometrioid ovarian carcinomas.
      ,
      • Moore R.G.
      • McMeekin D.S.
      • Brown A.K.
      • Disilvestro P.
      • Miller M.C.
      • Allard W.J.
      • Gajewski W.
      • Kurman R.
      • Bast R.C.
      • Skates S.J.
      A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass.
      ) are currently approved by the Food and Drug Administration (FDA) as blood-based biomarkers for monitoring the disease and treatment response. These two markers are also used in the clinic in combination with transvaginal sonography or computer tomography to support the diagnosis of EOC in women with a pelvic mass. More recent in vitro diagnostic multivariate index assays have been cleared by the FDA for assessing the EOC risk in women diagnosed with an ovarian tumor before surgery, OVA1 and ROMA (
      • Moore R.G.
      • McMeekin D.S.
      • Brown A.K.
      • Disilvestro P.
      • Miller M.C.
      • Allard W.J.
      • Gajewski W.
      • Kurman R.
      • Bast R.C.
      • Skates S.J.
      A novel multiple marker bioassay utilizing HE4 and CA125 for the prediction of ovarian cancer in patients with a pelvic mass.
      ,
      • Zhang Z.
      • Chan D.W.
      The road from discovery to clinical diagnostics: lessons learned from the first FDA-cleared in vitro diagnostic multivariate index assay of proteomic biomarkers.
      ). However, these markers lack the sensitivity and the specificity required for stand-alone diagnostic use. Multiple other biomarker panels have been discovered, initially showing promising results for the detection of EOC (
      • Petricoin E.F.
      • Ardekani A.M.
      • Hitt B.A.
      • Levine P.J.
      • Fusaro V.A.
      • Steinberg S.M.
      • Mills G.B.
      • Simone C.
      • Fishman D.A.
      • Kohn E.C.
      • Liotta L.A.
      Use of proteomic patterns in serum to identify ovarian cancer.
      ,
      • Gorelik E.
      • Landsittel D.P.
      • Marrangoni A.M.
      • Modugno F.
      • Velikokhatnaya L.
      • Winans M.T.
      • Bigbee W.L.
      • Herberman R.B.
      • Lokshin A.E.
      Multiplexed immunobead-based cytokine profiling for early detection of ovarian cancer.
      ,
      • Zhang Z.
      • Bast R.C.
      • Yu Y.
      • Li J.
      • Sokoll L.J.
      • Rai A.J.
      • Rosenzweig J.M.
      • Cameron B.
      • Wang Y.Y.
      • Meng X.-Y.
      • Berchuck A.
      • Van Haaften-Day C.
      • Hacker N.F.
      • de Bruijn H.W.A.
      • van der Zee A.G.J.
      • Jacobs I.J.
      • Fung E.T.
      • Chan D.W.
      Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer.
      ,
      • Mor G.
      • Visintin I.
      • Lai Y.
      • Zhao H.
      • Schwartz P.
      • Rutherford T.
      • Yue L.
      • Bray-Ward P.
      • Ward D.C.
      Serum protein markers for early detection of ovarian cancer.
      ,
      • Visintin I.
      • Feng Z.
      • Longton G.
      • Ward D.C.
      • Alvero A.B.
      • Lai Y.
      • Tenthorey J.
      • Leiser A.
      • Flores-Saaib R.
      • Yu H.
      • Azori M.
      • Rutherford T.
      • Schwartz P.E.
      • Mor G.
      Diagnostic markers for early detection of ovarian cancer.
      ). However, follow-up studies in a large prospective cohort collected before clinical diagnosis of EOC demonstrated that the tested biomarkers combined with CA125 showed only little improvement compared with CA125 alone in the early detection of EOC using prediagnostic samples (
      • Zhu C.S.
      • Pinsky P.F.
      • Cramer D.W.
      • Ransohoff D.F.
      • Hartge P.
      • Pfeiffer R.M.
      • Urban N.
      • Mor G.
      • Bast R.C.
      • Moore L.E.
      • Lokshin A.E.
      • McIntosh M.W.
      • Skates S.J.
      • Vitonis A.
      • Zhang Z.
      • Ward D.C.
      • Symanowski J.T.
      • Lomakin A.
      • Fung E.T.
      • Sluss P.M.
      • Scholler N.
      • Lu K.H.
      • Marrangoni A.M.
      • Patriotis C.
      • Srivastava S.
      • Buys S.S.
      • Berg C.D.
      PLCOProject Team A framework for evaluating biomarkers for early detection: validation of biomarker panels for ovarian cancer.
      ). The challenge of detecting reliable novel EOC plasma biomarkers is due in part to the molecular and cellular heterogeneity of EOC. EOC is characterized by disease heterogeneity as it relates to its multiple histotypes (serous, endometrioid, mucinous, and clear cell) and cellular grade (low and high grade for serous and endometrioid EOC) (
      • Kurman R.J.
      • Shih I.-M.
      Pathogenesis of ovarian cancer: lessons from morphology and molecular biology and their clinical implications.
      ,
      • Kurman R.J.
      • Shih I.-M.
      The dualistic model of ovarian carcinogenesis: revisited, revised, and expanded.
      ). The heterogeneity of EOCs indicates that not a single biomarker, but a multivariate protein panel, is required for accurate tumor detection of multiple histotypes.
      This manuscript presents a biomarker development strategy that consists of three phases. The first phase is the generation of a discovery list of EOC biomarker candidates. The second phase is their SRM-based quantification in blood plasma. The third phase is the development and validation of a protein biomarker signature for EOC in patient samples (Fig. 1) (
      • Cima I.
      • Schiess R.
      • Wild P.
      • Kaelin M.
      • Schüffler P.
      • Lange V.
      • Picotti P.
      • Ossola R.
      • Templeton A.
      • Schubert O.
      • Fuchs T.
      • Leippold T.
      • Wyler S.
      • Zehetner J.
      • Jochum W.
      • Buhmann J.
      • Cerny T.
      • Moch H.
      • Gillessen S.
      • Aebersold R.
      • Krek W.
      Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer.
      ,
      • Surinova S.
      • Choi M.
      • Tao S.
      • Schüffler P.J.
      • Chang C.-Y.
      • Clough T.
      • Vysloužil K.
      • Khoylou M.
      • Srovnal J.
      • Liu Y.
      • Matondo M.
      • Huttenhain R.
      • Weisser H.
      • Buhmann J.M.
      • Hajdúch M.
      • Brenner H.
      • Vitek O.
      • Aebersold R.
      Prediction of colorectal cancer diagnosis based on circulating plasma proteins.
      ,
      • Schiess R.
      • Wollscheid B.
      • Aebersold R.
      Targeted proteomic strategy for clinical biomarker discovery.
      ). The discovery list of EOC biomarker candidates was compiled using data from global quantitative MS measurements of tumors collected from a genetically engineered mouse model (GEMM) of endometrioid ovarian cancer (
      • Dinulescu D.M.
      • Ince T.A.
      • Quade B.J.
      • Shafer S.A.
      • Crowley D.
      • Jacks T.
      Role of K-ras and Pten in the development of mouse models of endometriosis and endometrioid ovarian cancer.
      ). The list was further augmented with biomarker candidates, either previously discovered or from ongoing cancer biomarker studies (
      • Bengtsson S.
      • Krogh M.
      • Szigyarto C.A.-K.
      • Uhlen M.
      • Schedvins K.
      • Silfverswärd C.
      • Linder S.
      • Auer G.
      • Alaiya A.
      • James P.
      Large-scale proteomics analysis of human ovarian cancer for biomarkers.
      ,
      • Köbel M.
      • Kalloger S.E.
      • Boyd N.
      • McKinney S.
      • Mehl E.
      • Palmer C.
      • Leung S.
      • Bowen N.J.
      • Ionescu D.N.
      • Rajput A.
      • Prentice L.M.
      • Miller D.
      • Santos J.
      • Swenerton K.
      • Gilks C.B.
      • Huntsman D.
      Ovarian carcinoma subtypes are different diseases: implications for biomarker studies.
      ,
      • Kuk C.
      • Kulasingam V.
      • Gunawardana C.G.
      • Smith C.R.
      • Batruch I.
      • Diamandis E.P.
      Mining the ovarian cancer ascites proteome for potential ovarian cancer biomarkers.
      ,
      • Hudson M.E.
      • Pozdnyakova I.
      • Haines K.
      • Mor G.
      • Snyder M.
      Identification of differentially expressed proteins in ovarian cancer using high-density protein microarrays.
      ,
      • Pitteri S.J.
      • JeBailey L.
      • Faça V.M.
      • Thorpe J.D.
      • Silva M.A.
      • Ireton R.C.
      • Horton M.B.
      • Wang H.
      • Pruitt L.C.
      • Zhang Q.
      • Cheng K.H.
      • Urban N.
      • Hanash S.M.
      • Dinulescu D.M.
      Integrated proteomic analysis of human cancer cells and plasma from tumor bearing mice for ovarian cancer biomarker discovery.
      ). Next, the biomarker candidates were quantified using a multiplexed, targeted MS method in a cohort of more than 200 plasma samples from EOC patients and healthy controls. The SRM data was then used to develop and evaluate the performance of a 5-protein signature, consisting of IGHG2, LGALS3BP, DSG2, L1CAM, and THBS1. The 5-protein signature in combination with CA125 detected EOC with a sensitivity of 94%, which outperformed CA125 alone that had a sensitivity of 87%, albeit at a lower specificity (94% versus 97%). At a specificity of 97% the combined panel showed an improved sensitivity of 94%. Finally, we correlated the abundance differences observed for LGALS3BP in blood plasma samples to protein levels in patient tumors.
      Figure thumbnail gr1
      Fig. 1Study overview. A, In the discovery phase, epithelial ovarian cancer (EOC) biomarker candidates were discovered based on a proteomics-based discovery study using tissue samples from an EOC conditional GEMM. B, Biomarker candidates, i.e. the plasma-detectable, orthologous human proteins detected as differentially abundant in the discovery phase were subsequently quantified in plasma samples derived from a large cohort of EOC patients and healthy controls using selected reaction monitoring (SRM). C, Finally, the most predictive biomarker candidates for the detection of EOC were selected, combined in a protein biomarker signature, and further evaluated in an independent validation set.
      Below we describe the step-by-step experimental design needed for large-scale SRM-based biomarker development studies and we focus specifically on EOC. However, these considerations are broadly applicable and can be used in large-scale studies of other diseases or biological systems.

      DISCUSSION

      In this study, we derived an accurate 5-protein signature for distinguishing individuals with EOC from healthy controls, the sensitivity of the signature in combination with CA125 measurements exceeding that of CA125 ELISA-based measurements alone. Aside from the actual biomarker signature, the clinical importance and novelty of this paper lies in the large-scale application of SRM measurements with a rigorous experimental design and statistical analysis. Our SRM-based strategy is broadly applicable and can be used in any disease entity for the development of diagnostic biomarker assays.
      Even though SRM has shown great promise as a tool for biomarker studies, it has only been applied for a few large-scale studies to date. This is in part because of the requirement of a rigorous study design, reproducible sample preparation, and appropriate statistical analysis for performing SRM measurements across large subject cohorts and developing a biomarker signature. In this study, we implemented a rigorous experimental design and data analysis strategy for protein biomarker development using SRM-based targeted MS. This strategy was applied to discover and validate novel biomarker candidates for EOC in human blood plasma. A high-quality list of biomarker candidates was compiled from a quantitative proteomic study using a GEMM representative of endometrioid OC. The list was augmented with candidates, which were published or from ongoing cancer biomarker studies (Fig. 2). To quantify biomarker candidates in a large subject cohort, we designed an experimental and data analysis strategy, which would account for experimental variability in instrument performance, sample preparation and sample batch effects (Fig. 3). In the case of EOC, we demonstrated the effectiveness of this strategy to identify a signature of selected biomarker candidates, which in combination with CA125 allowed the detection of EOC with a higher sensitivity than CA125 alone (Fig. 4).
      To assemble a biomarker candidate list, we used a conditional GEMM of EOC generated by Adeno-Cre induction of oncogenic K-ras and Pten suppression specifically in the ovarian surface epithelium, which led to widespread, metastatic ovarian endometrioid OC (
      • Dinulescu D.M.
      • Ince T.A.
      • Quade B.J.
      • Shafer S.A.
      • Crowley D.
      • Jacks T.
      Role of K-ras and Pten in the development of mouse models of endometriosis and endometrioid ovarian cancer.
      ). Ideally, biomarker discovery should include multiple animal models or patient samples recapitulating all histological subtypes. However, despite endometrioid OC being only the second most common histological EOC after high-grade serous OC (
      • Rojas V.
      • Hirshfield K.M.
      • Ganesan S.
      • Rodriguez-Rodriguez L.
      Molecular characterization of epithelial ovarian cancer: implications for diagnosis and treatment.
      ,

      . Cancer Genome Atlas Research Network. (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615,

      ), the chosen GEMM is still highly relevant for biomarker discovery as it accurately recapitulates the clinical disease. In addition, the RAS and PI3K/PTEN signaling pathways are altered in various histological subtypes of EOC, such as serous, endometrioid and mucinous OC (
      • Rojas V.
      • Hirshfield K.M.
      • Ganesan S.
      • Rodriguez-Rodriguez L.
      Molecular characterization of epithelial ovarian cancer: implications for diagnosis and treatment.
      ,

      . Cancer Genome Atlas Research Network. (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615,

      ). Further, CA125, the biomarker currently most widely used for OC diagnosis, has shown a better performance for detecting high grade serous OC (HGSC) but revealed a reduced sensitivity in detecting early tumors (
      • Köbel M.
      • Kalloger S.E.
      • Boyd N.
      • McKinney S.
      • Mehl E.
      • Palmer C.
      • Leung S.
      • Bowen N.J.
      • Ionescu D.N.
      • Rajput A.
      • Prentice L.M.
      • Miller D.
      • Santos J.
      • Swenerton K.
      • Gilks C.B.
      • Huntsman D.
      Ovarian carcinoma subtypes are different diseases: implications for biomarker studies.
      ,
      • Buys S.S.
      • Partridge E.
      • Black A.
      • Johnson C.C.
      • Lamerato L.
      • Isaacs C.
      • Reding D.J.
      • Greenlee R.T.
      • Yokochi L.A.
      • Kessel B.
      • Crawford E.D.
      • Church T.R.
      • Andriole G.L.
      • Weissfeld J.L.
      • Fouad M.N.
      • Chia D.
      • O'Brien B.
      • Ragard L.R.
      • Clapp J.D.
      • Rathmell J.M.
      • Riley T.L.
      • Hartge P.
      • Pinsky P.F.
      • Zhu C.S.
      • Izmirlian G.
      • Kramer B.S.
      • Miller A.B.
      • Xu J.-L.
      • Prorok P.C.
      • Gohagan J.K.
      • Berg C.D.
      PLCOProject Team Effect of screening on ovarian cancer mortality: the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial.
      ). Therefore, identifying biomarker candidates that are specific for each histological EOC subtype and combining them with CA125 might increase the sensitivity necessary for early detection of multiple EOC histotypes. We augmented the biomarker candidate list with potential markers for EOC that have been proposed previously, but their performance in detecting EOC was to our knowledge never evaluated systematically in human blood plasma. The relevance of including biomarker candidates from various sources is underscored by the fact that the final biomarker candidate signature combining five novel proteins with CA125 consists of proteins selected from all sources.
      A major limitation for protein biomarker quantification in blood plasma is the complex nature of the body fluid. Its dynamic protein concentration range spans 12 orders of magnitude, with few highly abundant proteins making up 90% of the plasma proteome (
      • Surinova S.
      • Schiess R.
      • Huttenhain R.
      • Cerciello F.
      • Wollscheid B.
      • Aebersold R.
      On the development of plasma protein biomarkers.
      ,
      • Anderson N.L.
      • Anderson N.G.
      The human plasma proteome: history, character, and diagnostic prospects.
      ). Even though we focused on the N-glycosylated proteome, thereby reducing the complexity of the plasma samples, we were able to detect and consistently quantify 65 out of 376 biomarker candidates across hundreds of plasma samples (i.e. the detection rate was 17%). Despite this fact, proteins for which detectability in plasma by SRM was established, could be detected in the sample cohort with a high degree of consistency (only 0.3% missing values across 234 samples). The majority of the undetected biomarker candidates resided in the sub-nanogramm per milliliter concentration range in plasma (Fig. 2A) and were only accessible by MS after extensive sample fractionation and workup. Additionally, biomarker candidates initially discovered in murine tissue had to be translated into their human orthologues, which resulted in some biomarker candidates lacking N-glycosites, and therefore intractable with our strategy. However, the alternative option for systematically quantifying biomarker candidates relies on immunoassays, which require the availability of specific antibodies for each protein, are only available for a small subset of the human proteome and are usually biased for frequently studied proteins (
      • Edwards A.M.
      • Isserlin R.
      • Bader G.D.
      • Frye S.V.
      • Willson T.M.
      • Yu F.H.
      Too many roads not taken.
      ).
      Accurate and reproducible quantification of proteins across a large subject cohort is crucial for biomarker development. We implemented experimental controls in our study and demonstrated a multistep normalization approach, which accounted for variability on all experimental levels: SRM measurements, sample preparation, batch effects and usage of different MS instruments (Fig. 3A). For the enrichment of N-glycosites from plasma we added two N-glycosylated bovine standard proteins in equal amounts to each sample. Because the sample preparation for this study was performed in a 96-well plate format, variability in sample preparation was limited and two standard proteins appeared to be sufficient. However, for future applications it might be beneficial to include a higher number of standard proteins to better estimate the variability in sample preparation. Additionally, unused standard proteins could be used to assess the quantitative accuracy of the SRM assay. Unlike ELISA-based measurements, MS-based quantification is performed on a relative scale. Therefore, the quantitative measurements from different MS batches may not be comparable directly. To remove batch effects between samples profiled on different MS instrument platforms, we included a subset of subjects in all the batches. With the assumption that subjects included in multiple batches have the same protein abundance, batch effects could be removed by equalizing their relative abundance of peptides and transitions. The experimental design and data analysis strategy presented here is broadly applicable to other biomarker studies including large cohorts measured in multiple batches, as well as studies requiring the accurate and reproducible MS-based quantification of proteins across many subjects.
      Finally, to develop a biomarker signature for the detection of EOC, we selected proteins with high predictive ability that fit a logistic regression model on the training set (Fig. 4A). The best performing signature included 5 proteins, namely IGHG2, LGALS3BP, DSG2, L1CAM, and THBS1, which were combined with the ELISA measurement of CA125, the current clinical standard, into a final signature (Fig. 4B). Based on the independent validation set, the 5-protein signature combined with CA125 detected EOC with a higher sensitivity than CA125 alone (Fig. 4C).
      Among the proteins in the signature, LGALS3BP and THBS1 were initially discovered in the endometrioid OC GEMM, DSG2 was derived from previously published OC biomarker studies, and L1CAM and IGHG2 represented candidates from ongoing cancer biomarker studies. LGALS3BP is a member of the scavenger receptor cysteine-rich domain family of proteins (
      • Resnick D.
      • Pearson A.
      • Krieger M.
      The SRCR superfamily: a family reminiscent of the Ig superfamily.
      ), which has not only been previously associated with various malignant tumors but also suggested as a potential biomarker (
      • Qu H.
      • Chen Y.
      • Cao G.
      • Liu C.
      • Xu J.
      • Deng H.
      • Zhang Z.
      Identification and validation of differentially expressed proteins in epithelial ovarian cancers using quantitative proteomics.
      ,
      • Piccolo E.
      • Tinari N.
      • D'Addario D.
      • Rossi C.
      • Iacobelli V.
      • La Sorda R.
      • Lattanzio R.
      • D'Egidio M.
      • Di Risio A.
      • Piantelli M.
      • Natali P.G.
      • Iacobelli S.
      Prognostic relevance of LGALS3BP in human colorectal carcinoma.
      ,
      • Park J.
      • Yun H.S.
      • Lee K.H.
      • Lee K.T.
      • Lee J.K.
      • Lee S.-Y.
      Discovery and validation of biomarkers that distinguish mucinous and nonmucinous pancreatic cysts.
      ). DSG2, a desmosomal cadherin expressed in epithelial derived tissues (
      • Schäfer S.
      • Koch P.J.
      • Franke W.W.
      Identification of the ubiquitous human desmoglein, Dsg2, and the expression catalogue of the desmoglein subfamily of desmosomal cadherins.
      ), is also overexpressed in various malignancies, such as nonsmall cell lung cancer and melanoma (
      • Cai F.
      • Zhu Q.
      • Miao Y.
      • Shen S.
      • Su X.
      • Shi Y.
      Desmoglein-2 is overexpressed in non-small cell lung cancer tissues and its knockdown suppresses NSCLC growth by regulation of p27 and CDK2.
      ,
      • Tan L.Y.
      • Mintoff C.
      • Johan M.Z.
      • Ebert B.W.
      • Fedele C.
      • Zhang Y.F.
      • Szeto P.
      • Sheppard K.E.
      • McArthur G.A.
      • Foster-Smith E.
      • Ruszkiewicz A.
      • Brown M.P.
      • Bonder C.S.
      • Shackleton M.
      • Ebert L.M.
      Desmoglein 2 promotes vasculogenic mimicry in melanoma and is associated with poor clinical outcome.
      ). Knockout of DSG2 was reported to suppress colon and nonsmall cell lung cancer cell proliferation (
      • Cai F.
      • Zhu Q.
      • Miao Y.
      • Shen S.
      • Su X.
      • Shi Y.
      Desmoglein-2 is overexpressed in non-small cell lung cancer tissues and its knockdown suppresses NSCLC growth by regulation of p27 and CDK2.
      ,
      • Kamekura R.
      • Kolegraff K.N.
      • Nava P.
      • Hilgarth R.S.
      • Feng M.
      • Parkos C.A.
      • Nusrat A.
      Loss of the desmosomal cadherin desmoglein-2 suppresses colon cancer cell proliferation through EGFR signaling.
      ). THBS1 is an endogenous angiogenesis inhibitor which has previously been associated with the development of tumor microenvironment and angiogenesis and has been shown to promote migration of cancer cells (
      • Pellatt A.J.
      • Mullany L.E.
      • Herrick J.S.
      • Sakoda L.C.
      • Wolff R.K.
      • Samowitz W.S.
      • Slattery M.L.
      The TGFβ-signaling pathway and colorectal cancer: associations between dysregulated genes and miRNAs.
      ,
      • Roberts D.D.
      Thrombospondins: from structure to therapeutics.
      ,
      • Pal S.K.
      • Nguyen C.T.K.
      • Morita K.-I.
      • Miki Y.
      • Kayamori K.
      • Yamaguchi A.
      • Sakamoto K.
      THBS1 is induced by TGFB1 in the cancer stroma and promotes invasion of oral squamous cell carcinoma.
      ). L1CAM is a cell adhesion molecule, which was originally identified as a neural cell adhesion molecule in the central nervous system (
      • Rathjen F.G.
      • Schachner M.
      Immunocytological and biochemical characterization of a new neuronal cell surface component (L1 antigen) which is involved in cell adhesion.
      ), but L1CAM expression has been identified in a variety of tumor types (
      • Altevogt P.
      • Doberstein K.
      • Fogel M.
      L1CAM in human cancer.
      ) and has been recently reported the be involved in the progression of endometrial cancer (
      • Notaro S.
      • Reimer D.
      • Duggan-Peer M.
      • Fiegl H.
      • Wiedermair A.
      • Rössler J.
      • Altevogt P.
      • Marth C.
      • Zeimet A.G.
      Evaluating L1CAM expression in human endometrial cancer using qRT-PCR.
      ). Lastly, IGHG2 forms the constant region of immunoglobulin heavy chains. It represents a high abundance plasma protein and has to our knowledge not been linked to cancer. Both, IGHG2 and L1CAM, showed a decreased level in plasma of cancer patients, that cannot be biologically explained at this point. Changes in high abundant plasma proteins, such as IGHG2, could result from secondary effects at late stages of EOC and in fact most patients included in our study have late stage EOC. Therefore, we currently do not have evidence that the protein signature reported here could be applied for early stage EOC detection.
      LGALS3BP, DSG2 and L1CAM have been associated with other malignancies indicating that these proteins might not be specific biomarker candidates for EOC. To ensure the specificity of the signature, only a subset of the biomarker candidates in the signature needs to be specifically elevated or downregulated in EOC. The combination with additional general cancer markers can help to increase sensitivity in detecting EOC. For example, a recent study assessed blood-based N-glycoproteins across five solid carcinomas and found significantly different expression levels for THBS1 in four out of five carcinomas compared with controls suggesting THBS1 as a general cancer marker (
      • Sajic T.
      • Liu Y.
      • Arvaniti E.
      • Surinova S.
      • Williams E.G.
      • Schiess R.
      • Huttenhain R.
      • Sethi A.
      • Pan S.
      • Brentnall T.A.
      • Chen R.
      • Blattmann P.
      • Friedrich B.
      • Niméus E.
      • Malander S.
      • Omlin A.
      • Gillessen S.
      • Claassen M.
      • Aebersold R.
      Similarities and differences of blood N-glycoproteins in five solid carcinomas at localized clinical stage analyzed by sWATH-MS.
      ). To evaluate the specificity of the suggested biomarker signature for detection of EOC and its application for early detection of EOC, a follow-up study using an independent patient cohort should be designed to include other malignancies and benign conditions of the ovaries, which are known to result in elevated CA125 levels, as well as pre- and/or early EOC.
      Targeted proteomics is a promising tool for biomarker development and the quantification of biomarker candidates in complex sample matrices without the necessity of a lengthy and expensive development of specific antibodies against the proteins of interest. However, to date only a few large-scale biomarker studies have been conducted using targeted proteomics for systematic quantification, which mostly focused on a few candidates for which antibody-based assays are available (
      • Edwards A.M.
      • Isserlin R.
      • Bader G.D.
      • Frye S.V.
      • Willson T.M.
      • Yu F.H.
      Too many roads not taken.
      ). The experimental design and data analysis considerations detailed in this study as it pertains to EOC will contribute to a broader applicability of targeted proteomics in large-scale studies.

      Data Availability

      For DDA experiments, RAW data and search results have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD005665 (https://www.ebi.ac.uk/pride/archive/projects/PXD005665) (
      • Jones P.
      • Côté R.G.
      • Martens L.
      • Quinn A.F.
      • Taylor C.F.
      • Derache W.
      • Hermjakob H.
      • Apweiler R.
      PRIDE: a public repository of protein and peptide identifications for the proteomics community.
      ,
      • Vizcaíno J.A.
      • Deutsch E.W.
      • Wang R.
      • Csordas A.
      • Reisinger F.
      • Ríos D.
      • Dianes J.A.
      • Sun Z.
      • Farrah T.
      • Bandeira N.
      • Binz P.-A.
      • Xenarios I.
      • Eisenacher M.
      • Mayer G.
      • Gatto L.
      • Campos A.
      • Chalkley R.J.
      • Kraus H.-J.
      • Albar J.P.
      • Martinez-Bartolomé S.
      • Apweiler R.
      • Omenn G.S.
      • Martens L.
      • Jones A.R.
      • Hermjakob H.
      ProteomeXchange provides globally coordinated proteomics data submission and dissemination.
      ). For SRM experiments, SRM data can be accessed, queried, and downloaded via Panorama (https://panoramaweb.org/ovarian_cancer_biomarker.url) (
      • Sharma V.
      • Eckels J.
      • Taylor G.K.
      • Shulman N.J.
      • Stergachis A.B.
      • Joyner S.A.
      • Yan P.
      • Whiteaker J.R.
      • Halusa G.N.
      • Schilling B.
      • Gibson B.W.
      • Colangelo C.M.
      • Paulovich A.G.
      • Carr S.A.
      • Jaffe J.D.
      • MacCoss M.J.
      • MacLean B.
      Panorama: a targeted proteomics knowledge base.
      ).

      Acknowledgments

      We would like to thank Ralph Schiess and Bernd Wollscheid for helpful discussions and enthusiasm throughout the project.

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