Serum biomarkers predictive of pre-eclampsia

Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression

Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased 'omics' analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and accuracy than any individual markers. Individual biomarkers and the more complex multi-marker models were then tested in a blinded validation study. The multi-marker models retained their predictive power in the validation study, the best of which attained an AUC of 0.84, with a sensitivity of 43% and a specificity of 88%. One of the markers with m/z 761.38, which was downregulated, was identified as a fibrinogen alpha chain. Machine learning-based analysis yielded a classifier that correctly categorizes every subject in the study and demonstrates parameter constraints required for high confidence in classifier output. These results suggest that serum peptide biomarker models could be optimized to assess breast cancer stage in a clinical setting.

Maternal whole blood mRNA signatures identify women at risk of early preeclampsia: a longitudinal study

PURPOSE

To determine whether previously established mRNA signatures are predictive of early preeclampsia when evaluated by maternal cellular transcriptome analysis in samples collected before clinical manifestation.

MATERIALS AND METHODS

We profiled gene expression at exon-level resolution in whole blood samples collected longitudinally from 49 women with normal pregnancy (controls) and 13 with early preeclampsia (delivery <34 weeks of gestation). After preprocessing and removal of gestational age-related trends in gene expression, data were converted into Z-scores based on the mean and standard deviation among controls for six gestational-age intervals. The average Z-scores of mRNAs in each previously established signature considered herein were compared between cases and controls at 9-11, 11-17, 17-22, 22-28, 28-32, and 32-34 weeks of gestation.Results: (1) Average expression of the 16-gene untargeted cellular mRNA signature was higher in women diagnosed with early preeclampsia at 32-34 weeks of gestation, yet more importantly, also prior to diagnosis at 28-32 weeks and 22-28 weeks of gestation, compared to controls (all, p < .05). (2) A combination of four genes from this signature, including a long non-protein coding RNA [H19 imprinted maternally expressed transcript (H19)], fibronectin 1 (FN1), tubulin beta-6 class V (TUBB6), and formyl peptide receptor 3 (FPR3) had a sensitivity of 0.85 (0.55-0.98) and a specificity of 0.92 (0.8-0.98) for prediction of early preeclampsia at 22-28 weeks of gestation. (3) H19, FN1, and TUBB6 were increased in women with early preeclampsia as early as 11-17 weeks of gestation (all, p < .05). (4) After diagnosis at 32-34 weeks, but also prior to diagnosis at 11-17 weeks, women destined to have early preeclampsia showed a coordinated increase in whole blood expression of several single-cell placental signatures, including the 20-gene signature of extravillous trophoblast (all, p < .05). (5) A combination of three mRNAs from the extravillous trophoblast signature (MMP11, SLC6A2, and IL18BP) predicted early preeclampsia at 11-17 weeks of gestation with a sensitivity of 0.83 (0.52-0.98) and specificity of 0.94 (0.79-0.99).

CONCLUSIONS

Circulating early transcriptomic markers for preeclampsia can be found either by untargeted profiling of the cellular transcriptome or by focusing on placental cell-specific mRNAs. The untargeted cellular mRNA signature was consistently increased in early preeclampsia after 22 weeks of gestation, and individual mRNAs of this signature were significantly increased as early as 11-17 weeks of gestation. Several single-cell placental signatures predicted future development of the disease at 11-17 weeks and were also increased in women already diagnosed at 32-34 weeks of gestation.

The prediction of early preeclampsia: Results from a longitudinal proteomics study

OBJECTIVES

To identify maternal plasma protein markers for early preeclampsia (delivery <34 weeks of gestation) and to determine whether the prediction performance is affected by disease severity and presence of placental lesions consistent with maternal vascular malperfusion (MVM) among cases.

STUDY DESIGN

This longitudinal case-control study included 90 patients with a normal pregnancy and 33 patients with early preeclampsia. Two to six maternal plasma samples were collected throughout gestation from each woman. The abundance of 1,125 proteins was measured using high-affinity aptamer-based proteomic assays, and data were modeled using linear mixed-effects models. After data transformation into multiples of the mean values for gestational age, parsimonious linear discriminant analysis risk models were fit for each gestational-age interval (8-16, 16.1-22, 22.1-28, 28.1-32 weeks). Proteomic profiles of early preeclampsia cases were also compared to those of a combined set of controls and late preeclampsia cases (n = 76) reported previously. Prediction performance was estimated via bootstrap.

RESULTS

We found that 1) multi-protein models at 16.1-22 weeks of gestation predicted early preeclampsia with a sensitivity of 71% at a false-positive rate (FPR) of 10%. High abundance of matrix metalloproteinase-7 and glycoprotein IIbIIIa complex were the most reliable predictors at this gestational age; 2) at 22.1-28 weeks of gestation, lower abundance of placental growth factor (PlGF) and vascular endothelial growth factor A, isoform 121 (VEGF-121), as well as elevated sialic acid binding immunoglobulin-like lectin 6 (siglec-6) and activin-A, were the best predictors of the subsequent development of early preeclampsia (81% sensitivity, FPR = 10%); 3) at 28.1-32 weeks of gestation, the sensitivity of multi-protein models was 85% (FPR = 10%) with the best predictors being activated leukocyte cell adhesion molecule, siglec-6, and VEGF-121; 4) the increase in siglec-6, activin-A, and VEGF-121 at 22.1-28 weeks of gestation differentiated women who subsequently developed early preeclampsia from those who had a normal pregnancy or developed late preeclampsia (sensitivity 77%, FPR = 10%); 5) the sensitivity of risk models was higher for early preeclampsia with placental MVM lesions than for the entire early preeclampsia group (90% versus 71% at 16.1-22 weeks; 87% versus 81% at 22.1-28 weeks; and 90% versus 85% at 28.1-32 weeks, all FPR = 10%); and 6) the sensitivity of prediction models was higher for severe early preeclampsia than for the entire early preeclampsia group (84% versus 71% at 16.1-22 weeks).

CONCLUSION

We have presented herein a catalogue of proteome changes in maternal plasma proteome that precede the diagnosis of preeclampsia and can distinguish among early and late phenotypes. The sensitivity of maternal plasma protein models for early preeclampsia is higher in women with underlying vascular placental disease and in those with a severe phenotype.

Potential urine biomarkers for gestational hypertension and preeclampsia

Differential proteomic technology was used to identify urine proteomic profile of gestational hypertension and preeclampsia. Urine samples were collected from 10 patients with gestational hypertension, 10 patients with mild preeclampsia, 10 patients with severe preeclampsia and 10 normal pregnancies and analyzed by 2‑D difference gel electrophoresis, then matrix assisted laser desorption ionization mass spectrometry was used to identify differential proteins. Subsequently, ELISA was used to verify the content variation of the identified proteins in 200 urine samples. In total, 30 differential proteins were identified. For prostaglandin‑H2 D‑isomerase (L‑PGDS), perlecan and other 15 proteins, the contents in patients with gestational hypertension were higher than that of normal pregnancies, but lower in mild and severe preeclampsia. By contrast, serum albumin and α‑1‑antitrypsin was lower in samples from patients with gestational hypertension and higher in patients with mild and severe preeclampsia compared with normal pregnancies. ELISA verified that the urinary concentration of L‑PGDS and perlecan were significantly lower in patients with preeclampsia than in normal pregnancies (P<0.05). Urine proteomics is a useful tool to identify potential biomarkers to distinguish between different types of hypertensive disorders in pregnancy. L‑PGDS and perlecan could potentially be used as markers to reflect the state of renal function, and may participate in the genesis and development of renal injury during preeclampsia.

Plasma biomarkers for the identification of women at risk for early-onset preeclampsia

BACKGROUND

To identify potential biomarkers in the 1st trimester of pregnancy for the identification of women destined to develop early onset preeclampsia (EOPE).

METHODS

Blood samples were obtained from pregnant women at 11-13 weeks of gestation. Women were followed up until delivery. Five samples from EOPE complicated pregnancies and 5 from unaffected ones were analysed using 2-DE and MALDI-TOF-TOF MS/MS. The altered expression of selected proteins was verified by ELISA in an extended sample cohort.

RESULTS

Twelve proteins were differentially expressed in the plasma of women who subsequently developed EOPE as compared to controls. Alpha-1-antitrypsin (A1AT), CD5 antigen-like molecule (CD5L) Keratin, type I cytoskeletal 9 (K1C9), Myeloid cell nuclear differentiation antigen (MNDA), Transferrin (TRFE) and Vitamin D-binding protein (VTDB) were up-regulated with fold changes 3.14, 2.18, 1.53, 1.53, 4.26 3.38 respectively, whereas Alpha-2-HS-glycoprotein (FETUA), Beta-2-glycoprotein 1 (APOH), Complement factor B (CFAB), Haptoglobin (HPT), Vitronectin (VTNC) and Zinc-alpha-2-glycoprotein (ZA2G) were down-regulated with fold changes -0.38, -0.76, -0.24, -0.47, -0.23, and -0.50 respectively. The down-regulation of APOH, VTNC and HPT was verified using ELISA.

CONCLUSIONS

The differentially expressed proteins represent potential biomarkers for the early screening for EOPE. Follow-up experiments however are necessary for evaluation.

Global "omics" evaluation of human placental responses to preeclamptic conditions

BACKGROUND

Preeclampsia (PE) is a leading cause of maternal death. Its cause is still debated but there is general agreement that the placenta plays a central role. Perhaps the most commonly proposed contributors to PE include placental hypoxia, oxidative stress, and increased proinflammatory cytokines. How the placenta responds to these abnormalities has been considered but not as part of a comprehensive analysis of low-molecular-weight biomolecules and their responses to these accepted PE conditions.

OBJECTIVE

Using a peptidomic approach, we sought to identify a set of molecules exhibiting differential expression in consequence of provocative agents/chemical mediators of PE applied to healthy human placental tissue.

STUDY DESIGN

Known PE conditions were imposed on normal placental tissue from 13 uncomplicated pregnancies and changes in the low-molecular-weight peptidome were evaluated. A t test was used to identify potential markers for each imposed stress. These markers were then submitted to a least absolute shrinkage and selection operator multinomial logistic regression model to identify signatures specific to each stressor. Estimates of model performance on external data were obtained through internal validation.

RESULTS

A total of 146 markers were increased/decreased as a consequence of exposure to proposed mediators of PE. Of these 75 changed with hypoxia; 23 with hypoxia-reoxygenation/oxidative stress and 48 from exposure to tumor necrosis factor-α. These markers were chemically characterized using tandem mass spectrometry. Identification rates were: hypoxia, 34%; hypoxia-reoxygenation, 60%; and tumor necrosis factor-α, 50%. Least absolute shrinkage and selection operator modeling specified 16 markers that effectively distinguished all groups, ie, the 3 abnormal conditions and control. Bootstrap estimates of misclassification rates, multiclass area under the curve, and Brier score were 0.108, 0.944, and 0.160, respectively.

CONCLUSION

Using this approach we found previously unknown molecular changes in response to individual PE conditions that allowed development biomolecular signatures for exposure to each accepted pathogenic condition.