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. 2023 Sep 15;20(1):37.
doi: 10.1186/s12014-023-09425-w.

Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy

Lynn A Beer  1 , Xiangfan Yin  1 , Jianyi Ding  1 , Suneeta Senapati  2 , Mary D Sammel  3 , Kurt T Barnhart  2   4 , Qin Liu  5 , David W Speicher  6 , Aaron R Goldman  7
Affiliations

Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy

Lynn A Beer et al. Clin Proteomics. .

Abstract

Background: Differentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value.

Methods: Plasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP.

Results: A total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors.

Conclusions: Overall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies.

Keywords: Biomarker signatures; Biomarkers; Ectopic pregnancy; Miscarriage; Proteomics; Targeted MS.

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Conflict of interest statement

LAB, DWS, and KTB are inventors on patents for EP diagnosis. No other disclosures were reported.

Figures

Fig. 1
Fig. 1
Scheme for discovery and verification of candidate biomarkers. Candidate biomarkers were identified by LC-MS/MS using label-free quantitation in a discovery cohort of 48 pregnant women. Biomarkers were then verified with targeted PRM-MS in an independent cohort of 74 women
Fig. 2
Fig. 2
Identification of candidate biomarkers in the discovery cohort. (a) Volcano plot for EP vs. non-EP (IUP + EPL) (b) Volcano plot comparing EPL vs. IUP. High priority biomarkers outside the curves (FDR ≤ 0.05) are highlighted in red. Additional proteins having p ≤ 0.05 and fold change ≥ 3 are represented by black circles. Labeled proteins were further investigated in the verification cohort
Fig. 3
Fig. 3
Scatterplots of candidate EP vs. non-EP biomarkers from the discovery cohort. Biomarkers were selected from volcano plots in Fig. 2a for further verification. (a) High-priority (FDR ≤ 0.05) (b) Additional candidate markers (p ≤ 0.05 and fold change ≥ 3). Wilcoxon rank sum test was used to compare EP vs. non-EP (IUP + EPL) and EPL vs. IUP. Statistical significance is shown above brackets (*<0.05, **<0.01, ***<0.001, not significant: p-value reported). For visualization, zero values are plotted on the x-axis
Fig. 4
Fig. 4
Scatterplots of candidate EP vs. non-EP biomarkers from the verification cohort. Wilcoxon rank sum test was used to compare EP vs. non-EP (IUP + EPL) and EPL vs. IUP. Statistical significance is shown above brackets (*<0.05, **<0.01, ***<0.001, not significant: p-value reported). For visualization, zero values are plotted on the x-axis
Fig. 5
Fig. 5
Correlation of predictors of EP vs. non-EP. Cluster dendrogram based on Spearman correlation. Lasso-selected protein markers (NOTUM, PAEP, PAPPA, and PSG2) are noted with red asterisks
Fig. 6
Fig. 6
Prediction ability of multivariable logistics models for EP vs. non-EP. (a) Scatterplot showing the risk score of having EP by each group for the multivariable logistics model with predictors selected from Lasso (Model 1). (b) Prediction ability of Model 2 (using ISM2 instead of NOTUM). (c) Prediction ability of Model 3 (using ISM2 instead of NOTUM, and using ADAM12 instead of PSG2). (d) Prediction ability of Model 4 (using ADAM12 instead of PSG2)
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