title: OS043. Identification and validation of novel markers for the predictionof pre-eclampsia.
abstract: BACKGROUND
  Currently no test accurately predicts pre-eclampsia (PE) in a healthy nulliparous population. Unbiased protein biomarker discovery has the potential to identify novel markers but multimarker panels are required to achieve clinically relevant prediction of PE. To this purpose, single biomarker performances were obtained and multimarker panels developed in a significant subcohort of the international Screening fOr Pregnancy Endpoints study (SCOPE) study [1].
  
  
  OBJECTIVE
  To identify and validate novel protein markers for PE prediction using chromatographic and mass spectrometric techniques which enable the identification and quantification of plasma proteins present in plasma at sub ng/ml concentration (Pronota, Belgium).
  
  
  METHODS
  Pre-disease plasma samples (22-26 weeks) from women who subsequently developed PE and those with uncomplicated pregnancies [2] were used to generate 30 plasma proteome profiles using the MASStermind™ pipeline. A set of novel protein candidates were validated using an antibody-free mass spectrometry method using multiple reaction monitoring (MASSterclass™) in a subcohort of the SCOPE study (NZ & Aus) [1]. Relative abundance of 40+ proteins was determined in 20week plasma samples from 100 women who developed PE and 200 women who did not develop PE (included women with other pregnancy complications). Multivariate analyses were performed to identify algorithms with predictive performance using combinations confined to a maximum of 6 parameters (protein markers and clinical parameters) to avoid overfitting. Validation of the prediction panels was performed in an independent subcohort of SCOPE (Europe) comprising 50 PE and 150 no PE.
  
  
  RESULTS
  From this large scale biomarker discovery effort a number of key results were obtained: a novel protein, i.e., Insulin-like growth factor binding protein, acid labile subunit (IGFALS), was identified. AUC for this marker for the prediction of all PE was 0.71 (CI 0.68-0.75) which was greater than both PlGF and s-Eng (respective AUCs: 0.64 and 0.61). IGFALS was also found to have predictive value for term (AUC 0.70) as well as preterm disease (AUC 0.75). Using multivariate analysis, marker panels were identified that achieved clinically relevant prediction (exemplary panel prediction of all PE cases AUC=0.79; prediction of preterm PE AUC=0.92). These multivariate models were successfully validated in the European SCOPE subcohort. In addition, predictive algorithms based on mass spectrometric read outs were largely invariant to interchanging the IGFALS mass spectrometry quantitation data with IGFALS ELISA data.
  
  
  CONCLUSIONS
  This study demonstrates the capability of high level LC-MS technologies to discover candidate biomarkers and execute large scale multiplex validation to develop a predictive screening test for preeclampsia.
authors.affiliation: University of Manchester, Manchester, United Kingdom.
authors.forename: J
authors.initials: J
authors.lastname: Myers
authors.affiliation: Pronota, Ghent, Belgium.
authors.forename: R
authors.initials: R
authors.lastname: Tuytten
authors.affiliation: Pronota, Ghent, Belgium.
authors.forename: G
authors.initials: G
authors.lastname: Thomas
authors.affiliation: University of Auckland, Auckland, New Zealand.
authors.forename: L
authors.initials: L
authors.lastname: McCowan
authors.affiliation: University of Adelaide, Adelaide, Australia.
authors.forename: G
authors.initials: G
authors.lastname: Dekker
authors.affiliation: University of Alberta, Alberta, Canada.
authors.forename: P
authors.initials: P
authors.lastname: Baker
authors.affiliation: Kings College London, London, United Kingdom.
authors.forename: L
authors.initials: L
authors.lastname: Poston
authors.affiliation: University College Cork, Cork, Ireland.
authors.forename: L
authors.initials: L
authors.lastname: Kenny
authors.affiliation: Leeds University, Leeds, United Kingdom.
authors.forename: N
authors.initials: N
authors.lastname: Simpson
authors.affiliation: Kings College London, London, United Kingdom.
authors.forename: R
authors.initials: R
authors.lastname: North