Methods and kits for assessing and managing clinical risk

Abstract

The present disclosure provides a method of determining whether a pregnant female subject is at the risk of developing preeclampsia, the method comprising (a) determining, in a biological sample of a pregnant female subject, a biomarker profile (the sample biomarker profile), wherein the sample biomarker profile evaluates at least one biomarker that is indicative of the risk of developing preeclampsia, (b) comparing the sample biomarker profile to a corresponding reference biomarker profile, and (c) identifying whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (b).

Description

METHODS AND KITS FOR ASSESSING AND MANAGING CLINICAL RISKFIELD

[0001] The present disclosure relates generally to a method and kit for assessing and managing clinical risk, in particular to methods and kits for identifying a pregnant female subject at risk of developing preeclampsia, including late- stage preeclampsia.BACKGROUND OF THE INVENTION

[0002] Preeclampsia is a pregnancy related disease that remains the leading cause of maternal and foetal morbidity worldwide. It is understood to affect over 4 million women each year and claim the lives of over 70,000 mothers and over 500,000 babies annually. Early-onset preeclampsia (diagnosis < 34 weeks) aetiology is relatively well- characterised with strong links to abnormal placentation. However, the underlying cause of late-onset preeclampsia (diagnosis > 34 weeks) remains poorly defined. As late-onset preeclampsia accounts for over 80% of the disease burden, research into its differential pathogenesis is essential for treatment and prevention developments. However, despite decades of research and advances in the clinical management of patients who develop preeclampsia, treatment and prevention options remain limited. Current preventative methods and management of early- and late-onset preeclampsia typically rely on the knowledge of risk factors, such as primigravity, obesity, chronic cardiovascular disease and family history of preeclampsia. According to the International Society for the Study of Hypertension in Pregnancy (ISSHP), the presence of a major risk factor places women at a 20% great risk of early-onset preeclampsia. High risk women are typically prescribed low dose aspirin from weeks 12 - 36 as a prevention method, which has shown to decrease incidence by about 70%. However, there are currently no available prevention methods for late-onset preeclampsia. To date, the only effective treatment is delivery of the baby and the placenta.

[0003] Notwithstanding the progress has been made in relation to clinical management of the disease, the incidence of the preeclampsia remains largely unchanged. Hence, there remains an urgent need for determining whether a subject has, or is at risk of developing, preeclampsia, including late-onset preeclampsia.SUMMARY OF THE INVENTION

[0004] The present invention is predicated, at least in part, on the inventors’ surprising finding that the level of expression of NCEH11 and / or LRPAP1 in a biological sample of a pregnant female subject, whether at the protein or gene expression (mRNA) level, is indicative of the subject being at greater risk of developing preeclampsia, in particular late-onset preeclampsia. The present inventors have, for the first time, also shown that a biomarker profile in a biological sample of a pregnant female subject can advantageously predict whether that subject is at risk of developing late-onset preeclampsia, thereby allowing for the stratification of those at-risk subjects to an appropriate clinical management protocol aimed at mitigating the risk of complications, including those arising in the fetus and / or the mother as a result of the onset of late-onset preeclampsia.

[0005] Thus, in an aspect disclosed herein, there is provided a method of determining whether a pregnant female subject is at the risk of developing preeclampsia, the method comprising (a) measuring the level of expression of NCEH1 in a biological sample of a pregnant female subject, (b) comparing the measured level of expression of NCEH1 from step (a) to a reference value, and (c) identifying whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (b).

[0006] In an embodiment, the method further comprises (d) measuring the level of expression of LRPAP1 in a biological sample of the pregnant female subject, (e) comparing the measured level of expression of LRPAP1 from step (d) to a reference value, and (f) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (e).

[0007] In yet another aspect disclosed herein, there is provided a method of identifying a pregnant female subject at the risk of developing preeclampsia, the method comprising (a) measuring the level of expression of LRPAP1 in a biological sample of a pregnant female subject, (b) comparing the measured level of expression of LRPAP1 from step (a) to a reference value, and (c) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (b).

[0008] In an embodiment, the method further comprises (d) measuring the level of expression of NCEH1 in a biological sample of the pregnant female subject, (e)comparing the measured level of expression of expression of NCEH1 from step (d) to a reference value, and (f) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (e).

[0009] In an embodiment, the method further comprises (i) measuring at least one other biomarker, physiochemical parameter and / or clinical risk factor in the pregnant female subject, and (ii) comparing the at least one other biomarker, physiochemical parameter and / or clinical risk factor measurement from step (i) to a reference value, and (iii) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (ii).

[0010] In an embodiment, the at least one other biomarker (e.g., the level of expression of the at least one other biomarker) is measured in a biological sample of the pregnant female subject and wherein the at least one other biomarker is selected from the group consisting of FCGR2B, SPINDOC, VWF, ANXA5, LGALS1, VC AN, prothrombin, APOH, CFH, JUND, PRDX2, HBB, NNAT, MCF2L, CYYR1, LSAMP, FSTL1, MFNG, HECW2, SDSL, CRYBG3, RALYL, ARF4, ZHX3, RNASE4, CENPBD1, SELENOS, ZNF124, MEG9, MORC2-AS1, MIR3654, MIR668, MIR487B, MIR512-2, KRT19, CIRBP, SLC9A3R1, MYH10, PPBP, LIMA1, CNPY2, TCERG1, LIN7C, NCEH1, CAV1, PAPP-A, placental growth factor (P1GF), soluble fms-like tyrosine kinase- 1 (sFlt), vascular endothelial growth factor (VEGF) and endoglin.

[0011] In an embodiment, the at least one other biomarker is selected from the group consisting of placental growth factor (P1GF), soluble fms-like tyrosine kinase- 1 (sFlt), vascular endothelial growth factor (VEGF) and endoglin.

[0012] In an embodiment, the physiochemical parameter is measured by ultrasound.

[0013] In an embodiment, the clinical risk factor selected from the group consisting of a history of hypertensive disease during a previous pregnancy, chronic kidney disease, cardiovascular disease, autoimmune disease, diabetes, chronic hypertension, age, a body mass index, arterial pressure (including mean arterial pressure), uterine artery pulsatility index, and a multifetal pregnancy.

[0014] In an embodiment, comparing the measured level of expression of NCEH1 and / or LRPAP1 is performed by an algorithm or by an analytics function or process or other data processing means.

[0015] In an embodiment, the biological sample is selected from the group consisting of placental tissue, amniotic fluid, urine, whole blood, plasma and serum. In an embodiment, the biological sample is placental tissue or whole blood. In an embodiment, the biological sample is whole blood. In an embodiment, the biological sample is plasma. In an embodiment, the biological sample is serum. In an embodiment, the biological sample is placental tissue. In an embodiment, the biological sample is a chorionic villus sample (CVS).

[0016] In an embodiment, the biological sample is obtained from the pregnant female subject at a time point from about 10 weeks to about 40 weeks of gestation. In an embodiment, the biological sample is obtained from the pregnant female subject at a time point from about 10 weeks to about 38 weeks of gestation. In an embodiment, the biological sample is obtained from the pregnant female subject at a time point from about 10 weeks to about 34 weeks of gestation.

[0017] In an embodiment, the biological sample is obtained from the pregnant female subject at a time point from about 11 weeks to about 14 weeks of gestation

[0018] In an embodiment, the measured level of expression of NCEH1 is a measured level of NCEH1 protein. In an embodiment, the measured level of expression of LRPAP1 is a measure level of LRPAP1 protein.

[0019] In an embodiment, the measured level of expression of NCEH1 is a measured level of NCEH1 mRNA. In an embodiment, the measured level of expression of LRPAP1 is a measure level of LRPAP1 mRNA.

[0020] In an embodiment, where the pregnant female subject is determined to be at risk of developing preeclampsia, the method further comprises subjecting the female subject to a clinical management protocol for maximizing the health outcome of the female subject and / or its fetus.

[0021] In an embodiment, the clinical management protocol comprises early delivery of the fetus.

[0022] In an embodiment, the clinical management protocol comprises administration of anti-hypertensive treatment, anti-convulsant treatment and / or the administration of aspirin to the female subject.

[0023] In an embodiment, the preeclampsia is late-onset preeclampsia, early-onset preeclampsia, term preeclampsia or pre-term preeclampsia.

[0024] In an embodiment, the preeclampsia is early-onset preeclampsia.

[0025] In an embodiment, the preeclampsia is late-onset preeclampsia.

[0026] The present disclosure also extends to a clinical management protocol for a pregnant female subject and its fetus, the protocol comprising (i) determining whether a pregnant female subject is at risk of developing preeclampsia in accordance with the methods described herein; and, where the pregnant female subject is determined to be at risk of developing preeclampsia according to step (i), (ii) monitoring the fetus and / or subjecting the fetus to early delivery.

[0027] In yet another aspect disclosed herein, there is provided a panel of biomarkers for determining whether a pregnant female subject is at risk of developing preeclampsia, wherein the panel comprises an agent that specifically binds to:(a) NCEH1 protein or NCEH1 mRNA, and / or(b) LRPAP1 protein or LRPAP1 mRNA,

[0028] The present disclosure also extends to a kit for determining whether a pregnant female subject is at risk of developing preeclampsia in accordance with the methods or protocols described herein. In an embodiment, the preeclampsia is late- onset preeclampsia, early-onset preeclampsia, term preeclampsia or pre-term preeclampsia. In an embodiment, the preeclampsia is early-onset preeclampsia. In an embodiment, the preeclampsia is late-onset preeclampsia.

[0029] Also disclosed herein is a composition comprising (i) a biological sample of a pregnant female subject and (ii) a binding molecule that binds specifically to NCEH1 protein, and / or (iii) a binding molecule that binds specifically to LRPAP1 protein.

[0030] In yet another aspect disclosed herein, there is provided a composition comprising (i) a biological sample of a pregnant female subject and (ii) a nucleic acid molecule comprising a polynucleotide sequence that is complementary to a nucleic acid sequence encoding NCEH1, and / or (iii) a nucleic acid molecule comprising a polynucleotide sequence that is complementary to a nucleic acid sequence encoding LRPAP1.

[0031] In an embodiment, the sample is a blood sample. In an embodiment, the sample is a placental tissue. In an embodiment, the sample is obtained from a pregnant female subject at a time point of from about 11 weeks to about 14 weeks gestation.

[0032] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgement or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 shows immunohistochemistry staining of NCEH1 in a placental explant sample (10 weeks gestation). (A) Strong cytoplasmic staining was seen in syncytiotrophoblast cells.

[0034] Figure 2 shows IHC staining of NCEH1 protein in first and second trimester placental villi tissue. A, B, C) 8 weeks gestation B) Staining of inner cytotrophoblast (CT) and outer syncytiotrophoblast (ST) cell layers. C) IgG control. D, E, F) 12 weeks gestation E) Staining of Hofbauer (HB) cells. F) IgG control. G, H, I) 19 weeks gestation. H) Staining of endothelial cells forming placental endothelium. I) IgG control.

[0035] Figure 3 shows NCEH1 intensity and frequency of cytoplasmic staining in different cell types of the placenta and by gestation week. One-way ANOVA. Error bars represent Mean ± SEM. A) Average stain score in syncytiotrophoblasts by gestation week. ***P = 0.0003; *P = 0.0220. B) Average stain score in cytotrophoblasts. P = 0.0112. C) Average stain score in Hofbauer cells. P = 0.0007. D) Average stain score in placental endothelial cells. P= 0.0259. E) Average stain score in stromal cells. P = 0.0190.

[0036] Figure 4 shows IHC staining of NCEH1 protein in first trimester decidual tissue. A, B, C) 7 weeks gestation. B) Strong staining of the glandular cells (GC) lining the uterine gland. C) IgG control. D, E, F) 10 weeks gestation. E) Strong staining of decidualised stromal (DS) cells. F) IgG control; G, H, I) 11 weeks gestation. H) Staining of cells inside uterine spiral arteries, presumably EVT cells forming a trophoblastic column and trophoblast plug within arteries. I) IgG control.

[0037] Figure 5 shows maternal serum NCEH1 protein concentration and clinical characteristics of each group. A) Circulating levels of NCEH1 protein (y-axis) was measured in serum samples obtained from pregnant female subjects at 11-14 weeks gestation in normotensive pregnancy (n=47) and term preeclamptic pregnancies (n=19). Unpaired t-test. Error bars represent Mean ± SEM. (control 10.87+1.039; PE 5.938+0.9129) P = 0.0061. B) Birth weight of baby (g). P = 0.0089. C) BMI of mother (kg / m2). P = 0.0351. D) Gestational age at birth (weeks). E) Maternal age (years). F). Birth weight (gm) vs NCEH1 concentration in serum (ng / ml). r2 = 0.05749. P = 0.0.0525. G). The level of sensitivity (Y-axis) and specificity (X-axis) for NCEH1 prediction of preeclampsia.

[0038] Figure 6 shows maternal serum LRPAP1 protein concentration and clinical characteristics of each group. Not all clinical data is available for all patients. A) Circulating levels of LRPAP1 protein (y-axis) was measured in serum samples obtained from pregnant female subjects at 11-14 weeks gestation in normotensive pregnancy (n=13) and term preeclamptic pregnancies (n=9). Unpaired t-test. Error bars represent Mean + SEM. (control 5.591+1.755; PE 1.369+0.2496) P = 0.0310. B) Birth weight of baby (g). C) BMI of mother (kg / m2). P = 0.1432. D) Gestational age at birth (weeks). E) Maternal age (years). F) The level of sensitivity (Y-axis) and specificity (X-axis) for NCEH1 prediction of preeclampsia.

[0039] Figure 7 shows the level of NCEH1 gene expression (mRNA) in CVS samples collected from pregnant female subjects at weeks 11 to 14 weeks gestation, as measured by RNA sequencing over two separate runs. The level of NCEH1 gene expression was significantly reduced in CVS samples at week 11 to 14 weeks gestation of those subjects who developed late-onset preeclampsia (34-36 weeks gestation; Preterm) when compared to normotensive subjects (Norm-, (*Run 1: P=0.0474; Run 2: P=0.0074).

[0040] Figure 8 shows the level of LRPAP1 protein in CVS samples collected from pregnant female subjects at 11 to 14 weeks gestation as measured by proteomics. The level of LRPAP1 protein was significantly reduced at weeks 11 to 14 of gestation of those subjects who developed late-onset preeclampsia (PE) when compared to normotensive subjects (Control) (P=0.0064).

[0041] Figure 9 shows immunostaining (immunohistochemistry and immunofluorescence) of LRPAP1 in placental villous (Figure 9A) and decidua (Figure9B and C). A) strong cytoplasmic staining was observed in syncytiotrophoblast and cytotrophoblast. B) strong cytoplasmic staining was observed in the glandular epithelium (GE), cells lining blood vessels (BV) and cells within the decidua. C) colocalization of LRPAP1 with HLAG identified EVTs within the decidua (D) and lining the blood vessels (BV).DETAILED DESCRIPTION OF THE INVENTION

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are described. For the purposes of the present invention, the following terms are defined below.

[0043] The articles "a" and "an" are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, "a biomarker" means one biomarker or more than one biomarker, unless otherwise indicated.

[0044] Throughout this specification, unless the context requires otherwise, the words "comprise”, "comprises” and "comprising” will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. Thus, use of the term "comprising" and the like indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present.

[0045] The present invention is predicated, at least in part, on the inventors’ surprising finding that the level of expression of NCEH11 and / or LRPAP1 in a biological sample of a pregnant female subject, whether at the protein or gene expression (mRNA) level, is indicative of the subject being at greater risk of developing preeclampsia, in particular late-onset preeclampsia. The present inventors have, for the first time, also shown that a biomarker profile in a biological sample of a pregnant female subject can advantageously predict whether that subject is at risk of developing late-onset preeclampsia, thereby allowing for the stratification of those at-risk subjects to an appropriate clinical management protocol aimed at mitigating the risk ofcomplications, including those arising in the fetus and / or the mother as a result of the onset of late-onset preeclampsia.

[0046] Thus, in an aspect disclosed herein, there is provided a method of determining whether a pregnant female subject is at the risk of developing preeclampsia, the method comprising (a) measuring the level of expression of NCEH1 in a biological sample of a pregnant female subject, (b) comparing the measured level of expression of NCEH1 from step (a) to a reference value, and (c) identifying whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (b).Preeclampsia

[0047] Preeclampsia is a pregnancy-specific disease that affects over 4 million women each year and presents as a sudden onset of hypertension and organ dysfunction in late pregnancy (>20 weeks gestation). It is a leading cause of foetal and maternal morbidity and mortality, claiming the lives of 70,000 mothers and over 500,000 babies annually. Despite decades of research and advances in the management of preeclampsia, treatment and prevention options are limited. Early-onset preeclampsia (diagnosis < 34 weeks) aetiology is relatively well-characterised with strong links to abnormal placentation. However, the underlying cause of late-onset preeclampsia (diagnosis > 34 weeks) remains poorly defined. As late-onset preeclampsia accounts for over 80% of the disease burden, research into its differential pathogenesis is essential for treatment and prevention developments.

[0048] Diagnosis for preeclampsia requires new onset hypertension along with one other symptom of organ dysfunction such as proteinuria, impaired liver function and fetal growth restriction (FGR). These physiological mechanisms can manifest in headaches, oedema and develop into seizures in which the disease is then known as eclampsia. The incidence of preeclampsia has been relatively stable, affecting 3-8% of pregnancies and increasing in certain areas such as the United States. Increasing incidence of risk factors such as obesity, may be accounting for this rise.

[0049] Both early- and late-onset preeclampsia can lead to severe disease and adverse obstetric outcomes, although early-onset preeclampsia is associated with a greater risk of foetal morbidity and mortality. Importantly, late-onset preeclampsia is much more common and both subtypes confer long term health consequences for mother and child.

[0050] Current preventative methods and management rely on the knowledge of risk factors, such as primigravity, obesity, chronic cardiovascular disease and family history of preeclampsia. According to the ISSHP, presence of a major risk factor places women at a 20% risk of early-onset preeclampsia. High risk women are prescribed low dose aspirin from weeks 12 - 36 as a prevention method, which has shown to decrease incidence by 70%. There are currently no available prevention methods for late-onset preeclampsia.

[0051] Once diagnosed, treatment is typically delivery of the fetus. To reduce severe symptoms prior to delivery, low doses of pregnancy-safe anti-hypertensives and magnesium sulphate can be used. Induction of labour should be delayed until at least 37 weeks gestation with careful monitoring of the mother and foetus. However, it is not possible to delay delivery in all pregnancies and therefore preeclampsia leads to 15% of all preterm births.

[0052] Adverse outcomes depend on the severity of symptoms and gestation week of onset. Early-onset preeclampsia is associated with higher incidence of adverse foetal outcomes, such as FGR, pre-term birth and late foetal death. It is thought that an exposure to inflammatory cytokines, anti-angiogenic factors and a hypoxic environment in utero results in a cardiovascular adaptation in the child. This increases the incidence of hypertension and major cardiovascular events later in life.

[0053] Women with preeclampsia are at a greater risk of developing chronic cardiovascular and metabolic disease. These are attributed to the systematic inflammation and endothelial dysfunction as a consequence of preeclampsia. High inflammation causes in chronic dysfunction of the immune, metabolic and cardiovascular systems, which results in a higher risk of cardiac events, stroke and diabetes.

[0054] The majority of the research investigating the cause of preeclampsia is focused on the combination of early-onset preeclampsia and FGR, and often does not distinguish mechanisms between early- and late-onset disease. Theories of aetiology of late-onset preeclampsia attribute the disease to placental stress in late pregnancy due the limited space in utero for the placenta. However, the theory that no abnormalities occur in early gestation in late-onset preeclampsia has not yet been verified, largely due to difficulty in sourcing early pregnancy material. Most studies use delivered third-trimester placentas, which is insufficient, as there is differential gene expression and placental responses to external cues in the first compared to third trimester.

[0055] The recently revised two-stage model of preeclampsia connects the abnormalities occurring in the first trimester to the clinical manifestations in the third trimester. The pathogenesis is understood to involve the disruption of angiogenic factors, release of placental toxins and resulting systemic inflammation and oxidative stress. It is also well accepted that the placenta is involved in the pathogenesis of both subtypes; however, the underlying cause driving pathogenesis in late-onset preeclampsia remains ill-defined.NCEH1

[0056] The term "NCEH1" (also known as AADACL1) is an enzyme that hydrolyses 2-acetyl monoalkylglycerol ether as a part of reverse cholesterol transport and is involved in the synthesis of platelet-activating factor (PAF). Reverse cholesterol transport is the process by which cholesterol is removed from tissues and returned to the liver, to prevent accumulation and blockage of vessels.

[0057] PAF is a potent signalling molecule made from lipids that ultimately influences platelet aggregation in response to inflammation. Platelet activation is increased in hypertension via mechanisms such as endothelial dysfunction and decreased NO synthesis. Studies have also shown that PAF is elevated in women with preeclampsia at term. No studies have investigated PAF in early pregnancy as a potential biomarker, however markers of platelet activation including mean platelet volume have been studied.

[0058] NCEH1 function has predominately been characterised in macrophages, specifically foam cells, and cancer cells. However, it is expressed in many cells types within the body, including placental trophoblasts. However, the role of NCEH1 within the placenta is unknown.

[0059] NCEH1 is a key enzyme in cholesterol metabolism and homeostasis of macrophages. A loss of function results in expansion and aggregation of foam cells as a result of an accumulation of cholesterol products. Cholesterol build-up induces ER stress and apoptosis of foam cells. Foam cell aggregation within large blood vessels, leads to adhesion to endothelial walls causing atherosclerosis. A similar disorder, acute atherosis, has been identified in women with preeclampsia. Foam cells aggregate to thespiral artery walls, likely limiting placental perfusion, causing hypoxia and inflammation.

[0060] NCEH1 was first discovered in invasive cancer cells. Since its discovery, it has been identified as a driver of cancer cell proliferation and invasion especially in pancreatic cancer. Over-expression of NCEH1 is present in patients who have more advanced tumour progression and lymph-node metastases.LRPAP1

[0061] Low-density lipoprotein (LDL) receptor-related protein-associated protein 1 (LRPAP1; also known as Receptor Associated Protein (RAP)) is a protein of 357 amino acids in length with a molecular weight of 39 kDa. Its main function is reported to be an antagonist and chaperone of the family of low density lipoprotein (LDL) receptors and to be involved in Megalin / Cubilin endocytosis. LRPAP1 has been shown to interact with the LDL receptor-related protein and facilitates its proper folding and localization by preventing the binding of ligands. Mutations in this gene have been identified in individuals with myopia 23. As noted by Thurner et al. (Blood. 2021; 10;137(23):3251- 3258), LRPAP1 was identified as a suspected common autoantigen, encoded by the LRPAP1 gene located at chromosome 4pl6.3.

[0062] In some embodiments, it may be desirable to measure the level of expression of NCEH1 and / or LRPAP1 at the protein level. However, it will be understood that, in some instances, the biomarker can be a gene expression product, such as a transcript (e.g., mRNA). Methods of measuring expression products such as proteins and transcripts are known to persons skilled in the art, illustrative examples of which are described below. Thus, the level of expression of NCEH1 and / or LRPAP1 can be the level of a gene expression product, including a polynucleotide or polypeptide.

[0063] The term "gene" as used herein refers to any and all discrete coding regions of the cell’s genome, as well as associated non-coding and regulatory regions. The term "gene" is also intended to mean the open reading frame encoding specific polypeptides, introns, and adjacent 5' and 3' non-coding nucleotide sequences involved in the regulation of expression. In this regard, the gene may further comprise control signals such as promoters, enhancers, termination and / or polyadenylation signals that are naturally associated with a given gene, or heterologous control signals. The DNA sequences may be cDNA or genomic DNA or a fragment thereof. The gene may beintroduced into an appropriate vector for extrachromosomal maintenance or for integration into the host.

[0064] The term "nucleic acid" or "polynucleotide" as used herein designates mRNA, RNA, cRNA, cDNA or DNA. The term typically refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide. The term includes single and double stranded forms of DNA or RNA. "Protein," "polypeptide" and "peptide" are also used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same.

[0065] In an embodiment, the measured level of expression of NCEH1 is a measured level of NCEH1 protein. In another embodiment, the measured level of expression of LRPAP1 is a measure level of LRPAP1 protein.

[0066] In another embodiment, the measured level of expression of NCEH1 is a measured level of NCEH1 mRNA. In yet another embodiment, the measured level of expression of LRPAP1 is a measure level of LRPAP1 mRNA.

[0067] As used herein the terms "level" and "amount" are used interchangeably herein to refer to a quantitative amount (e.g., weight or moles or number), a semi- quantitative amount, a relative amount (e.g., weight % or mole % within class or a ratio), a concentration, and the like. Thus, these terms encompasses absolute or relative amounts or concentrations of the biomarker in a sample, including ratios of levels of the biomarker, and odds ratios of levels or ratios of odds ratios. Biomarker levels in cohorts of subjects may be represented as mean levels and standard deviations.

[0068] The level of expression of NCEH1 and / or LRPAP1 may be quantified or detected using any suitable technique, including, but not limited to, nucleic acid- and protein-based assays. In illustrative nucleic acid-based assays, nucleic acid is isolated from cells contained in a biological sample according to standard methodologies (Sambrook, et al., 1989, supra-, and Ausubel et al., 1994, supra). The nucleic acid is typically fractionated (e.g., poly A+RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA. In some embodiments, the nucleic acid is amplified by a template-dependent nucleic acid amplification technique. A number of template dependent processes are available to amplify the biomarker sequences present in a given template sample. Anexemplary nucleic acid amplification technique is the polymerase chain reaction (referred to as PCR), which is described in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159, Ausubel et al. supra), and in Innis et al., ("PCR Protocols", Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. An excess of deoxynucleotide triphosphates are added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated. A reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified. Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 1989, supra. Alternative methods for reverse transcription utilize thermostable, RNA-dependent DNA polymerases. These methods are described in WO 90 / 07641. Polymerase chain reaction methodologies are well known in the art.

[0069] In certain embodiments, the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified using the Real-Time PCR technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. In specific embodiments, multiplexed, tandem PCR (MT- PCR) is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in US Pat. Appl. Pub. No.20070190540. In the first step, RNA is converted into cDNA and amplified using multiplexed gene specific primers. In the second step each individual gene is quantitated by real time PCR.

[0070] In certain embodiments, target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art. Southern blotting involves the use of DNA as a target, whereas Northern blotting involves the use of RNA as a target. Each provides different types of information, although cDNA blotting is analogous, in many aspects, to blotting of RNA species. Briefly, a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by "blotting" on to the filter. Subsequently, the blotted target is incubated with a probe (usually labelled) under conditions that promote denaturation and rehybridisation. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequence under renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection / quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of a biomarker nucleic acid detected with the likelihood that a subject is at risk of developing preeclampsia.

[0071] Also contemplated are biochip-based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analysing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or using fixed probe arrays, one can employ biochip technology to segregate target molecules as high-density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91: 5022-5026); Fodor et al. (1991, Science 251: 767-773). Briefly, nucleic acid probes to biomarker polynucleotides are made and attached to biochips to be used in screening and diagnostic methods, as outlined herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to specific expressed biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or toother probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present invention occur. This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes of the present invention. However, if the number of mismatches is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, with three being desirable, are used to build in a redundancy for a particular target. The probes can be overlapping (i.e. have some sequence in common), or separate.

[0072] In an illustrative biochip analysis, oligonucleotide probes on the biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more biomarker polynucleotides under conditions favouring specific hybridization. Sample extracts of DNA or RNA, either single or double-stranded, may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme. Suitable DNA, which may be used in the method of the invention, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., et al., 1989, supra.

[0073] Suitable RNA, which may be used in the method of the invention, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al. 1994, supra and Sambrook, et al. 1989, supra).

[0074] cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases. Suitably, cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of cDNA may be selected and amplified using asuitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.

[0075] Usually, the target biomarker polynucleotides (e.g., NCEH1 mRNA and LRPAP1 mRNA) are detectably labelled so that their hybridization to individual probes can be determined. The target polynucleotides are typically detectably labelled with a reporter molecule, illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu34), a radioisotope and a direct visual label. In the case of a direct visual label, use may be made of a colloidal metallic or non-metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like. Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin and titanium oxide. In some embodiments, in which an enzyme is used as a direct visual label, biotinylated bases are incorporated into a target polynucleotide.

[0076] The hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA. In this regard, reference may be made, for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In general, whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants. Such variables also influence the time required for hybridization. The preferred conditions will therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experimentation. After the hybrid-forming step, the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide. The hybridization reactions are then detected to determine which of the probes has hybridized to a corresponding target sequence. Depending on the nature of the reporter molecule associated with a target polynucleotide, a signal may be instrumentally detected by irradiating a fluorescentlabel with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a coloured colloidal metallic or non-metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography. Accordingly, a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focused beam or laser light. In such a case, a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the micro-array and record the data directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. For example, with enzymatically generated colour spots associated with nucleic acid array format, visual examination of the array will allow interpretation of the pattern on the array. In the case of a nucleic acid array, the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile. In certain embodiments, oligonucleotide probes specific for different biomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a ‘chip reader’ . A detection system that can be used by a ‘chip reader’ is described for example by Pirrung et al (U.S. Patent No. 5,143,854). The chip reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal. Exemplary chip readers are described for example by Fodor et al (U.S. Patent No., 5,925,525). Alternatively, when the array is made using a mixture of individually addressable kinds of labelled microbeads, the reaction may be detected using flow cytometry.

[0077] In other embodiments, the level of NCEH1 protein and / or LRPAP1 protein is assayed using protein-based assays, suitable examples of which will be known to persons skilled in the art. For example, the protein can be quantified based upon its biological activity or based upon the number of molecules of the protein contained in a sample.

[0078] Antibody-based techniques may suitably be employed to determine the level of a biomarker protein in a sample, non-limiting examples of which includeimmunoassays such as the enzyme-linked immunosorbent assay (ELISA) and the radioimmunoassay (RIA).

[0079] In certain embodiments, protein-capture arrays that permit simultaneous detection and / or quantification of a large number of proteins are employed. For example, low-density protein arrays on filter membranes, such as the universal protein array system (Ge, 2000 Nucleic Acids Res. 28(2):e3) allow imaging of arrayed antigens using standard ELISA techniques and a scanning charge-coupled device (CCD) detector. Immuno-sensor arrays have also been developed that enable the simultaneous detection of clinical analytes. It is now possible using protein arrays to profile protein expression in bodily fluids, such as in sera of healthy subjects or patients, as well as in subjects pre- and post- treatment.

[0080] Exemplary protein capture arrays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of numerous proteins defining a proteome or subproteome. Antibody arrays have been shown to have the required properties of specificity and acceptable background, and some are available commercially (e.g., BD Biosciences, Clontech, BioRad and Sigma). Other illustrative examples of suitable arrays, including the OLink and SOMAscan platforms, as described by Raffield et al. (Proteomics, 2020; 20(12)el900278), the entire contents of which is incorporated herein by reference. Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., 2003 J. Chromatogr. B 787:19-27; Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Pat. App. Pub. 2002 / 0055186; U.S. Pat. App. Pub. 2003 / 0003599; PCT publication WO 03 / 062444; PCT publication WO 03 / 077851; PCT publication WO 02 / 59601; PCT publication WO 02 / 39120; PCT publication WO 01 / 79849; PCT publication WO 99 / 39210). The antigen-binding molecules of such arrays may recognise at least a subset of proteins expressed by a cell or population of cells, illustrative examples of which include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serpins, proteases, kinases, phosphatases, ras-like GTPases, hydrolases, steroid hormone receptors, transcription factors, heat-shock transcription factors, DNA-binding proteins, zinc-finger proteins, leucine-zipper proteins, homeodomain proteins, intracellular signaltransduction modulators and effectors, apoptosis-related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors and cell-surface antigens.

[0081] Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.

[0082] Particles in suspension can also be used as the basis of arrays, providing they are coded for identification; systems include colour coding for microbeads (e.g., available from Quanterix (e.g., Simoa Bead-based assay), Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals e.g., QDots™, available from Quantum Dots), and barcoding for beads (UltraPlex™, available from Smartbeads) and multimetal microrods (Nanobarcodes™ particles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions). Where particles are used, individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array. The particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtitre plate or in separate test tubes.

[0083] In an illustrative example, a protein sample, which is optionally fragmented to form peptide fragments (see, e.g., U.S. Pat. App. Pub. 2002 / 0055186) is delivered to a protein-capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array. Next, the presence or amount of protein or peptide bound to each feature of the array is detected using a suitable detection system. The amount of protein bound to a feature of the array may be determined relative to the amount of a second protein bound to a second feature of the array. In certain embodiments, the amount of the second protein in the sample is already known or known to be invariant.

[0084] In another illustrative example of a protein-capture array is Luminex-based multiplex assay, which is a bead-based multiplexing assay, where beads are internally dyed with fluorescent dyes to produce a specific spectral address. Biomolecules (such as an oligo or antibody) can be conjugated to the surface of beads to capture analytes of interest. Flow cytometric or other suitable imaging technologies known to personsskilled in the art can then be used for characterization of the beads, as well as for detection of analyte presence. The Luminex technology enables are large number of proteins, genes or other gene expression products (e.g., 100 or more, 200 or more, 300 or more, 400 or more) to be detected using very small sample volume (e.g., in a 96 or 384-well plate). In some embodiments, the protein-capture array is Bio-Plex Luminex- 100 Station (Bio-Rad) as described previously.

[0085] Other methods of determining the level of NCEH1 protein and / or the level of LRPAP1 protein in a sample include mass spectrometry (MS). Suitable MS-based methods will be familiar to persons skilled in the art, illustrative examples of which include multiple reaction-monitoring (MRM) mass spectrometry (as described by Lieber and Zimmerman, 2013, Biochem. 52(22):3797-3806 and by Cohen Freue and Zimmerman, 2012, Circ. Cardiovasc. Genet., 5:738), the entire contents of which are incorporated herein by reference), liquid chromatography coupled to tandem mass spectrometry (LC-MS / MS; as described by Aebersold and Mann (2016, Nature, 537: 347 -355; the entire contents of which is incorporated herein by reference), data- independent acquisition mass spectrometry (DIA-MS), trapped ion mobility time of flight mass spectrometry (timsTOF proMS), diaPASEF and sequential window acquisition of all theoretical mass spectra (SWATH-MS; as described by Ludwig et al. (Mol. Syst. Biol, 2018, 14(8)e8126) and Gillet et al. (2012; Mol. Cell Proteomics 11: 0111.016717), the entire contents of which are incorporated herein by reference).

[0086] In some embodiments, the level of expression of NCEH1 and / or LRPAP1 is normalized against a housekeeping biomarker. The term "housekeeping biomarker" refers to a biomarker or group of biomarkers (e.g., polynucleotides and / or polypeptides), which are typically found at a constant level in the cell type(s) being analysed and across the conditions being assessed. In some embodiments, the housekeeping biomarker is a "housekeeping gene." A "housekeeping gene" refers herein to a gene or group of genes which encode proteins whose activities are essential for the maintenance of cell function and which are typically found at a constant level in the cell type(s) being analysed and across the conditions being assessed.

[0087] As herein described, reference to the level of expression of the biomarker (including the level of expression of NCEH1 and / or LRPAP1) includes the concentration of the biomarker (e.g., pg / mL, mg / mL, etc), the absolute amount (abundance) of the biomarker e.g., pg, mg, etc), or a gene expression product thereof(e.g., peptide, pro-peptide, metabolite thereof), illustrative examples of which are described elsewhere herein. Reference to the expression or level of expression of a biomarker may suitably be expressed by the level of activity of the biomarker. For example, where the biomarker is an enzyme, its expression or level of expression may be determined or measured by the level of activity of the enzyme on a suitable substrate.Other biomarkers, physiochemical parameters and clinical risk factor

[0088] In an embodiment, the method further comprises (i) measuring at least one other biomarker, physiochemical parameter and / or clinical risk factor in the pregnant female subject, and (ii) comparing the at least one other biomarker, physiochemical parameter and / or clinical risk factor measurement from step (i) to a reference value, and (iii) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (ii).

[0089] Reference to "at least one other biomarker" typically means a measurable characteristic (e.g., a molecule) that is indicative of a risk of developing preeclampsia, including late-onset preeclampsia, when the level of expression of the at least one other biomarker is evaluated in combination with the level of expression of NCEH1 and / or LRPAP1, as described herein. Suitable other biomarkers may be known to persons skilled in the art, or they may be identifiable from further analyses. For example, the at least one other biomarker may be present in a sample obtained from a subject at a time point before term e.g., from about 10 to about 20 weeks gestation, preferably from about 10 weeks to about 15 weeks gestation), and the level of expression of the at least one other biomarker in the sample improves the predictive power of the methods disclosed herein to identify whether the subject is at increased risk of developing preeclampsia, including late-onset preeclampsia.

[0090] Illustrative examples of suitable other biomarkers include FCGR2B, SPINDOC, VWF, ANXA5, LGALS1, VCAN, prothrombin, APOH, CFH, JUND, PRDX2, HBB, NNAT, MCF2L, CYYR1, LSAMP, FSTL1, MFNG, HECW2, SDSL, CRYBG3, RALYL, ARF4, ZHX3, RNASE4, CENPBD1, SELENOS, ZNF124, MEG9, MORC2-AS1, MIR3654, MIR668, MIR487B, MIR512-2, KRT19, CIRBP, SLC9A3R1, MYH10, PPBP, LIMA1, CNPY2, TCERG1, LIN7C, NCEH1, CAV1, PAPP-A, placental growth factor (P1GF), soluble fms-like tyrosine kinase- 1 (sFlt), vascular endothelial growth factor (VEGF) and endoglin.

[0091] Thus, in an embodiment, the at least one other biomarker is measured in a biological sample of the pregnant female subject and wherein the at least one other biomarker is selected from the group consisting of FCGR2B, SPINDOC, VWF, ANXA5, LGALS1, VCAN, prothrombin, APOH, CFH, JUND, PRDX2, HBB, NNAT, MCF2L, CYYR1, LSAMP, FSTL1, MFNG, HECW2, SDSL, CRYBG3, RALYL, ARF4, ZHX3, RNASE4, CENPBD1, SELENOS, ZNF124, MEG9, MORC2-AS1, MIR3654, MIR668, MIR487B, MIR512-2, KRT19, CIRBP, SLC9A3R1, MYH10, PPBP, LIMA1, CNPY2, TCERG1, LIN7C, NCEH1, CAV1, PAPP-A, placental growth factor (P1GF), soluble fms-like tyrosine kinase- 1 (sFlt), vascular endothelial growth factor (VEGF) and endoglin.

[0092] In an embodiment, the at least one other biomarker is selected from the group consisting of placental growth factor (P1GF), soluble fms-like tyrosine kinase- 1 (sFlt), vascular endothelial growth factor (VEGF) and endoglin.

[0093] Suitable physiochemical parameters will be familiar to persons skilled in the art, illustrative examples of which include ultrasound (ultrasound readings). Thus, in an embodiment, the physiochemical parameter is measured by ultrasound.

[0094] Suitable clinical risk factors will be familiar to persons skilled in the art, illustrative examples of which include a history of hypertensive disease during a previous pregnancy, chronic kidney disease, cardiovascular disease, autoimmune disease, diabetes, chronic hypertension, over 40 years of age, a body mass index above 30, and a multifetal pregnancy. Thus, in an embodiment, the clinical risk factor is selected from the group consisting of a history of hypertensive disease during a previous pregnancy, chronic kidney disease, cardiovascular disease, autoimmune disease, diabetes, chronic hypertension, over 40 years of age, a body mass index above 30, and a multifetal pregnancy.

[0095] In an embodiment, comparing the measured level of NCEH1 or LRPAP1 to the reference value is performed by an algorithm or by an analytics function or process or other data processing means.

[0096] As noted elsewhere herein, the present inventors have unexpectedly found that subjects with, or at risk of, developing preeclampsia, in particular late-onset preeclampsia, have a biomarker profile that distinguishes them from individuals who are normotensive at term, where that biomarker profile evaluates the level of circulatingNCEH1 protein or circulating LRPAP1 protein in blood samples, or where that biomarker profile evaluates the level of NCEH1 gene expression (mRNA) or LRPAP1 gene expression in placental tissue (CVS) samples taken from patients at early gestation (e.g., weeks 11 to 14 gestation), were significantly lower when compared to the profile of corresponding biomarkers of patients who were normotensive at term. Thus, measuring the level of expression of NCEH1 and / or LRPAP1 in a biological sample of a pregnant female subject may comprise determining the level of NCEH1 protein and / or LRPAP1 protein in a whole blood sample from the subject. However, it would be understood by persons skilled in the art that the level of expression of NCEH1 and / or LRPAP1 may be suitably determined (i.e., measured) in a component of whole blood, such as a serum or plasma sample from the subject. In other embodiments, the level of expression of NCEH1 and / or LRPAP1 may be suitably determined (i.e., measured) in other biological samples, illustrative examples of which include placental tissue, amniotic fluid, saliva and urine. Thus, in an embodiment, the biological sample is selected from the group consisting of placental tissue, amniotic fluid, urine, saliva, whole blood, plasma and serum. In an embodiment, the biological sample is placental tissue or whole blood. In an embodiment, the biological sample is whole blood. In an embodiment, the biological sample is a plasma sample. In an embodiment, the biological sample is a serum sample. In an embodiment, the biological sample is a placental tissue sample. In an embodiment, the biological sample is a CVS sample. In an embodiment, the biological sample is saliva. In an embodiment, the biological sample is a urine sample.

[0097] The term "reference value", as used herein, may denote a value (e.g., a numerical value) that is representative of the level of expression of the biomarker (including the level of expression of NCEH1 and / or LRPAP1) in a biological sample of a pregnant female subject obtained pre-term where that subject ultimately develops preeclampsia, including late-onset preeclampsia, or representative of the level of expression of the biomarker (including the level of expression of NCEH1 and / or LRPAP1) in biological samples obtained pre-term from a population of pregnant female subjects, where those subjects ultimately develop preeclampsia, including late-onset preeclampsia. Such reference values may suitably be referred to as "positive control values", "positive controls", "preeclampsia levels", "preeclampsia values", and the like.

[0098] Alternatively, or in addition, the reference value may denote a value (e.g., a numerical value) that is representative of the level of expression of the biomarker (including the level of expression of NCEH1 and / or LRPAP1) in a biological sample of a pregnant female subject obtained pre-term, where that subject is normotensive at term, or representative of the level of expression of the biomarker (including the level of expression of NCEH1 and / or LRPAP1) in biological samples obtained pre-term from a population of pregnant female subjects, where those subjects are normotensive at term. Such reference values may suitably be referred to as "control values", "control levels", "negative control values", "negative control levels", "healthy control values", "healthy control levels", "normotensive levels", "normotensive levels" and the like.

[0099] A reference value will typically provide a compositional value (e.g., concentration, number ratio or mole percentage) of the biomarker in question. Suitable methods of determining a reference value will be familiar to persons skilled in the art.

[0100] In some embodiments, the subject’s risk of developing preeclampsia is determined by comparing the biomarker profile in a sample obtained from the subject (i.e., the sample biomarker profile) with a reference biomarker profile of a subject or of a population of subjects who present normotensive at term. Alternatively, the subject’s risk of developing preeclampsia is determined by comparing the biomarker profile in a sample obtained from the subject (i.e., the sample biomarker profile) with a reference biomarker profile from a subject or a population of subjects who develop preeclampsia.

[0101] It will be understood that, once collected, the reference value(s) can be stored in a database allowing such value(s) to be subsequently retrieved, for example, by a processing system for subsequent use in accordance with the methods described herein. The processing system may also store an indication of the identity of each of the reference biomarkers as a reference biomarker collection or panel where there are two or more reference biomarkers.

[0102] In an embodiment, the reference value that is representative of the level of circulating NCEH1 protein in a biological sample obtained pre-term from a pregnant female subject that is normotensive at term is from about 8 ng / mL to about 20 ng / mL (e.g., about 8, about 9, about 10, about 11, about 12, about 13, about 14, about 15, about 16, about 17, about 18, about 19, or about 20 ng / mL), preferably from about 8 ng / mL to about 15 ng / mL, preferably from about 9 ng / mL to about 15 ng / mL, preferably fromabout 9 ng / mL to about 12 ng / mL, or more preferably from about 10 ng / mL to about 11 ng / mL.

[0103] In an embodiment, the reference value that is representative of the level of circulating NCEH1 protein in a biological sample obtained pre-term from a pregnant female subject that develops preeclampsia at term is from about 1 ng / mL to about 8 ng / mL (e.g., about 1, about 2, about 3, about 4, about 5, about 6, about 7, or about 8 ng / mL), preferably from about 2 ng / mL to about 7 ng / mL, preferably from about 3 ng / mL to about 7 ng / mL, preferably from about 4 ng / mL to about 7 ng / mL, or more preferably from about 5 ng / mL to about 7 ng / mL.Risk or likelihood of developing preeclampsia

[0104] The term "risk" is used to denote a subject's likelihood, based on the sample biomarker profile as determined pre-term for that subject, of developing preeclampsia (or not) on the basis of the sample biomarker profile, as herein described. Accordingly, the terms "risk" and "likelihood" are used interchangeably herein, unless otherwise stated.

[0105] It would be understood by persons skilled in the art that the risk that a subject will develop preeclampsia, including late-onset preeclampsia, will vary, for example, from being at low or decreased risk of developing preeclampsia to being at high or increased risk of developing preeclampsia. By "low or decreased risk" is meant that the subject is less likely to develop preeclampsia as compared to a subject determined to be a "high or increased risk" subject. Conversely, a "high or increased risk" subject is a subject who is more likely to develop preeclampsia as compared to a subject who is not at risk or a "low risk" subject.

[0106] Likelihood is suitably based on mathematical modeling. An increased likelihood, for example, may be relative or absolute and may be expressed qualitatively or quantitatively. For instance, an increased risk may be expressed as simply determining the subject's level of a given biomarker and placing the test subject in an "increased risk" category, based upon the corresponding reference biomarker profile as determined, for example, from previous population studies. Alternatively, a numerical expression of the test subject's increased risk may be determined based upon biomarker level analysis.

[0107] As used herein, the term "probability" refers to the probability of class membership for a sample as determined by a given mathematical model and is construed to be equivalent likelihood in this context.

[0108] In some embodiments, likelihood is assessed by comparing the level or abundance of the measured biomarker to one or more preselected level, also referred to herein as a threshold or reference level or value. Thresholds may be selected that provide an acceptable ability to predict risk, treatment success, etc. In illustrative examples, receiver operating characteristic (ROC) curves are calculated by plotting the value of a variable versus its relative frequency in two populations in which a first population is considered at risk of developing preeclampsia and a second population that is not considered to be at risk, or have a low risk, of developing preeclampsia (called, arbitrarily, for example, "control subjects").

[0109] In some embodiments, the subject is considered at risk of developing preeclampsia where the measured biomarker in the sample biomarker profile (e.g., the level of expression of NCEH1 and / or LRPAP1) for the subject is lower as compared to the threshold or reference level of expression of the corresponding biomarker(s) in a control subject.

[0110] In an embodiment, the threshold level of NCEH1 protein, by concentration, is 10.87+ 1.04 ng / mL. In an embodiment, the threshold level of LRPAP1 protein, by concentration, is about 5.59 + 1.76 pg / mL. In an embodiment, the threshold level of NCEH1 mRNA is about 1634 to about 1663 counts. In an embodiment, the threshold level of LRPAP1 protein, as determined by proteomics analysis, is about 29.65 ± 0.14 LQF (label-free quantification)

[0111] For any particular biomarker, a distribution of biomarker levels for subjects who are at risk or not at risk of developing preeclampsia may overlap. Under such conditions, a test may not absolutely distinguish a subject who is at risk of developing preeclampsia from a subject who is not at risk of developing preeclampsia with absolute (i.e., 100%) accuracy, and the area of overlap indicates where the test cannot distinguish the two subjects. A threshold can be selected above which (or below which, depending on how a biomarker changes with risk) the test is considered to be "positive" and below which the test is considered to be "negative." The area under the ROC curve (AUC) provides the C-statistic, which is a measure of the probability that the perceivedmeasurement will allow correct identification of risk (see, e.g., Hanley et al., Radiology 143: 29-36 (1982)).

[0112] In some embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, and / or AUC or receiver operating characteristic (ROC) values are used as a measure of a method’s ability to predict risk of developing preeclampsia. As used herein, the term "likelihood ratio" is the probability that a given test result would be observed in a subject with a likelihood of such risk, divided by the probability that that same result would be observed in a subject without a likelihood of such risk. Thus, a positive likelihood ratio is the probability of a positive result observed in subjects with the specified risk divided by the probability of a positive results in subjects without the specified risk. A negative likelihood ratio is the probability of a negative result in subjects without the specified risk divided by the probability of a negative result in subjects with specified risk. The term "odds ratio," as used herein, refers to the ratio of the odds of an event occurring in one group (e.g., a control group) to the odds of it occurring in another group e.g., the preeclampsia group), or to a data-based estimate of that ratio. The term "area under the curve" or "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., a control group and a preeclampsia group). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and / or any item of additional biomedical information) in distinguishing or discriminating between two populations (e.g., cases having a condition and controls without the condition). Typically, the feature data across the entire population (e.g., the cases and controls) are sorted in ascending order based on the value of a single feature. Then, for each value for that feature, the true positive and false positive rates for the data are calculated. The sensitivity is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases. The specificity is determined by counting the number of controls below the value for that feature and then dividing by the total number of controls. Although this definition refers to scenarios in which a feature is elevated in cases compared to controls, this definition also applies to scenarios in which a feature is lower in cases compared to the controls (in such a scenario, samples below the value for that feature would be counted). ROC curves can begenerated for a single feature as well as for other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to produce a single value, and this single value can be plotted in a ROC curve. Additionally, any combination of multiple features, in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test. The ROC curve is the plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally presented on the vertical axis and specificity is traditionally presented on the horizontal axis. Thus, "AUC ROC values" are equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. An AUC ROC value may be an alternative to the Mann-Whitney U test, which tests for the median difference between scores obtained in the two groups considered if the groups are of continuous data, or to the Wilcoxon test of ranks.

[0113] In some embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) biomarker or a panel of biomarkers is selected to discriminate between subjects with or without risk of developing preeclampsia with at least about 50%, 55% 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% accuracy or having a C-statistic of at least about 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95.

[0114] In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "preeclampsia" and "control" groups; a value greater than 1 indicates that a positive result is more likely in the preeclampsia group; and a value less than 1 indicates that a positive result is more likely in the control group. In this context, "preeclampsia group" is meant to refer to a population of reference individuals considered to be at risk of developing preeclampsia and a "control group" is meant to refer to a group of subjects considered not to be at risk of developing preeclampsia. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both the "preeclampsia risk" and "control" groups; a value greater than 1 indicates that a negative result is more likely in the " preeclampsia risk" group; and a value less than 1 indicates that a negative result is more likely in the "control" group. In the case of an odds ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "preeclampsia risk" and "control" groups; a value greater than 1 indicates that a positive result is more likely in the "preeclampsia risk" group; and a value less than 1 indicates that a positive resultis more likely in the "control" group. In the case of an AUC ROC value, this is computed by numerical integration of the ROC curve. The range of this value can be 0.5 to 1.0. A value of 0.5 indicates that a classifier (e.g., a biomarker profile) is no better than a 50% chance to classify unknowns correctly between two groups of interest, while 1.0 indicates the relatively best prognostic accuracy. In certain embodiments, biomarkers and / or biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0.1 or less, or at least about 20 or more or about 0.05 or less.

[0115] In certain embodiments, the at least one biomarker is selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, at least about 3 or more or about 0.33 or less, at least about 4 or more or about 0.25 or less, at least about 5 or more or about 0.2 or less, or at least about 10 or more or about 0.1 or less.

[0116] In certain embodiments, the at least one biomarker is selected to exhibit an AUC ROC value of greater than 0.5, preferably at least 0.6, more preferably 0.7, still more preferably at least 0.8, even more preferably at least 0.9, and most preferably at least 0.95.

[0117] In some cases, multiple thresholds may be determined in so-called "tertile," "quartile," or "quintile" analyses. In these methods, the "preeclampsia risk" and "control" groups are considered together as a single population, and are divided into 3, 4, or 5 (or more) "bins" having equal numbers of individuals. The boundary between two of these "bins" may be considered "thresholds." The degree of risk can then be assigned based on which "bin" a test subject falls into.

[0118] In other embodiments, particular thresholds for the reference biomarker(s) measured are not relied upon to determine if the biomarker level(s) obtained from a subject are correlated to risk of developing preeclampsia. For example, a temporal change in the biomarker(s) can be used to rule in or out such risk. Alternatively, biomarker(s) are correlated to such risk by the presence or absence of one or more biomarkers in a particular assay format. In the case of biomarker profiles, the present invention may utilize an evaluation of the entire profile of biomarkers to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk). In such embodiments, an increase, decrease, or other change (e.g., slope over time) in a certain subset of biomarkers may be sufficient to indicate risk ofdeveloping preeclampsia in a subject, while an increase, decrease, or other change in a different subset of biomarkers may be sufficient to indicate the same risk in another subject.

[0119] In certain embodiments, a panel of biomarkers is selected to assist in distinguishing between "preeclampsia risk" and "control" groups with at least about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in combination with at least about 70% 80%, 85%, 90% or 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, 80%, 85%, 90% or 95%.

[0120] The phrases "assessing the likelihood" and "determining the likelihood," as used herein, refer to methods by which the skilled artisan can predict a subject's risk of developing preeclampsia. The probability that an individual identified as being at risk of developing preeclampsia may be expressed as a "positive predictive value" or "PPV." Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. PPV is determined by the characteristics of the predictive methods of the present invention as well as the prevalence of the condition in the population analysed. The statistical algorithms can be selected such that the positive predictive value in a population considered to be at risk of developing preeclampsia is in the range of 70% to 99% and can be, for example, at least 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0121] In other examples, the probability that a subject is identified as not being at risk of developing preeclampsia may be expressed as a "negative predictive value" or "NPV." Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the diagnostic or prognostic method, system, or code as well as the prevalence of risk in the population analysed. The statistical methods and models can be selected such that the negative predictive value in a population considered at risk of developing preeclampsia is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0122] In some embodiments, a subject is determined as being at significant risk of developing preeclampsia. By "significant risk" is meant that the subject has a reasonable probability (e.g., 0.6, 0.7, 0.8, 0.9 or more) of developing preeclampsia.

[0123] The methods broadly described herein also permit the generation of high- density data sets that can be evaluated using informatics approaches. High data density informatics analytical methods are known and software is available to those in the art, e.g., cluster analysis (Pirouette, Informetrix), class prediction (SIMCA-P, Umetrics), principal components analysis of a computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, Improved Outcomes Software), and metabolic pathway analysis (biotech.icmb.utexas.edu). The choice of software packages offers specific tools for questions of interest (Kennedy et al., Solving Data Mining Problems Through Pattern Recognition. Indianapolis: Prentice Hall PTR, 1997; Golub et al., (2999) Science 286:531-7; Eriksson et al., Multi and Megavariate Analysis Principles and Applications: Umetrics, Umea, 2001). In general, any suitable mathematic analyses can be used to evaluate at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc.) biomarker in a biomarker profile with respect to determining the likelihood that a subject is at risk of developing preeclampsia. For example, methods such as multivariate analysis of variance, multivariate regression, and / or multiple regression can be used to determine relationships between dependent variables e.g., clinical measures) and independent variables (e.g., levels of biomarkers). Clustering, including both hierarchical and non-hierarchical methods, as well as nonmetric Dimensional Scaling can be used to determine associations or relationships among variables and among changes in those variables.

[0124] In some embodiments, a biomarker profile is used to assign a risk score which describes a mathematical equation for evaluation or prediction of risk.

[0125] In addition, principal component analysis is a common way of reducing the dimension of studies, and can be used to interpret the variance-covariance structure of a data set. Principal components may be used in such applications as multiple regression and cluster analysis. Factor analysis is used to describe the covariance by constructing "hidden" variables from the observed variables. Factor analysis may be considered an extension of principal component analysis, where principal component analysis is used as parameter estimation along with the maximum likelihood method. Furthermore,simple hypothesis such as equality of two vectors of means can be tested using Hotelling’s T squared statistic.

[0126] In some embodiments, the data sets corresponding to biomarker profiles are used to create a diagnostic or predictive rule or model based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between a biomarker profile and risk of developing preeclampsia observed in control subjects or typically cohorts of control subjects (sometimes referred to as training data), which provides combined control or reference biomarker profiles for comparison with biomarker profiles of a subject. The data are used to infer relationships that are then used to predict the status of a subject and the presence or absence of risk of developing preeclampsia.

[0127] Persons skilled in the art of data analysis will recognize that many different forms of inferring relationships in the training data may be used without materially changing the present invention.Subject

[0128] The terms "subject," "individual" and "patient" are used interchangeably herein to refer to any subject, particularly a vertebrate subject, and even more particularly a mammalian subject. Suitable vertebrate animals that fall within the scope of the invention include, but are not restricted to, any member of the subphylum Chordata including primates, rodents (e.g., mice rats, guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g., cattle), ovines e.g., sheep), caprines (e.g., goats), porcines (e.g., pigs), equines (e.g., horses), canines (e.g., dogs), felines (e.g., cats), avians (e.g., chickens, turkeys, ducks, geese, companion birds such as canaries, budgerigars etc), marine mammals (e.g., dolphins, whales), reptiles (snakes, frogs, lizards, etc.), and fish. A preferred subject is a primate (e.g., a human, ape, monkey, chimpanzee). In an embodiment, the subject is a human subject.Sample

[0129] As noted elsewhere herein, the biological sample may suitably include a sample that is extracted, untreated, treated, diluted or concentrated from a subject. In some embodiments, the biological sample has not been extracted from the subject, such as when the sample biomarker profile can be obtained by evaluating the level of the biomarker in situ. Non-limiting examples of suitable biological samples include tissue,bodily fluid (for example, blood, serum, plasma, saliva, urine, tears, peritoneal fluid, ascitic fluid, vaginal secretion, breast fluid, breast milk, lymph fluid, cerebrospinal fluid or mucosa secretion), umbilical cord blood, chorionic villi, amniotic fluid, an embryo, embryonic tissues, lymph fluid, cerebrospinal fluid, mucosa secretion, or other body exudate, fecal matter, an individual cell or extract of the such sources that contain the protein or nucleic acid of the same, and subcellular structures such as mitochondria, obtained using protocols well established within the art. In certain embodiments, the biological sample contains blood, especially peripheral blood, or a fraction or extract thereof, such as serum or plasma.

[0130] In an embodiment, the biological sample comprises placental tissue. In an embodiment, the biological sample is a CVS sample.

[0131] In some embodiments disclosed herein, the biological sample is a whole blood sample. In some embodiments, the biological sample is a serum sample. In some embodiments, the biological sample is a plasma sample.

[0132] In some embodiments, the sample is a venous blood sample obtained by venous phlebotomy or indwelling cannulation.

[0133] In some embodiments, the sample is a blood sample obtained by microsampling. Microsampling is a term often used to refer to techniques that enable the collection of smaller amounts of blood (typically 50 pL or less) from, for example, a skin prick rather, than by venous phlebotomy or indwelling cannulation. Suitable methods of microsampling of blood will be familiar to persons skilled in the art, illustrative examples of which are described in Guerra Valero et al. (Pediatr. Res. 2012:1-5) and include drawing capillary blood from a lancet finger prick using, for example, a Mitra device. Microsampling may present a simple and convenient solution to the challenges of intravenous blood sample collection. For example, collecting venous blood samples intravenously in children can be difficult, often causing pain and distress to the subject, and the volume of blood that can be collected is much smaller compared to their adult counterparts. Microsampling techniques, in particular those that involve collecting blood from the fingertip using a lancet, at least partly alleviate such difficulties and can minimise the need for patients or study participants to visit a clinic to facilitate sample collection. In summary, microsampling collects a smaller amount of blood, involves a less painful procedure, and is less burdensome and resource intensive.In some embodiments, the sample is a blood sample obtained using microfluidic capillary sampling or capillary microsampling.

[0134] The biological sample may be processed and analyzed for the purpose of determining the sample biomarker profile, in accordance with the present invention, almost immediately following collection (i.e., as a fresh sample), or it may be stored for subsequent analysis. If storage of the biological sample is desired or required, it would be understood by persons skilled in the art that it should ideally be stored under conditions that preserve the integrity of the biomarker of interest within the sample (e.g., at -80°C). In some embodiments, the biological sample is stored at room temperature.

[0135] By " obtained" is meant to come into possession. Biological or reference samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject.Stratifying a subject to a clinical management protocol

[0136] As noted elsewhere herein, the present disclosure extends to methods of stratifying a subject determined to be at risk of developing preeclampsia to a clinical management or treatment protocol. Thus, in an embodiment, where the pregnant female subject is determined to be at risk of developing preeclampsia, the methods described herein further comprise subjecting the pregnant female subject to a clinical management protocol for maximizing the health outcome of the pregnant female subject and / or its fetus. For example, where a pregnant female subject presents with one or more symptoms that places them at risk of developing preeclampsia, such as increased blood pressure, the methods described herein may be used to identify whether that subject is at a higher risk of developing preeclampsia (as evidenced by a change to the subject's biomarker profile, as described herein). The subject identified as being at higher risk to a clinical management protocol may then be stratified to a clinical management protocol that suitably includes closely monitoring the subject (e.g., with increased frequency) and, optionally, subjecting the subject to a treatment regimen for maximizing the health outcome of the pregnant female subject and / or its fetus.

[0137] Suitable clinical management protocols will be familiar to persons skilled in the art, illustrative examples of which include early delivery of the fetus, monitoring thepregnant female subject for aberrant changes in clinical parameters, such as an increase (or further increase) in blood pressure, subjecting the female subject to an antihypertensive treatment, subjecting the female subject to an anti-convulsant treatment and / or administering aspirin to the female subject.

[0138] In an embodiment, the clinical management protocol comprises early delivery of the fetus.

[0139] In an embodiment, the clinical management protocol comprises administration of anti-hypertensive treatment, anti-convulsant treatment and / or the administration of aspirin to the female subject.

[0140] In yet another aspect disclosed herein, there is provided a method of treating a subject determined to be at risk of developing preeclampsia, the method comprising (1) determining whether a subject is at risk of developing preeclampsia according to a method described herein; and (2) exposing the subject determined from step (1) to be at risk of developing preeclampsia to a treatment protocol for preeclampsia.

[0141] The present disclosure also extends to the management of risk of developing preeclampsia in a subject. The management of said risk may suitably include the use of therapeutic agents or protocols for treating preeclampsia, including late-onset preeclampsia. Suitable treatment protocols will be familiar to persons skilled in the art, illustrative examples of which are described elsewhere herein.

[0142] The term "treating" as used herein, unless otherwise indicated, means alleviating, inhibiting the progress of, or preventing, either partially or completely, preeclampsia. The term "treatment" as used herein, unless otherwise indicated, refers to the act of treating.

[0143] Following diagnosis, the treatment regimen to be adopted or prescribed may depend on several factors, including the age, weight and general health of the subject. Another determinative factor may be the degree of risk of developing preeclampsia determined by the sample biomarker profile in accordance with the present invention, as described herein. For instance, where the subject is determined to be at high risk of developing preeclampsia, a more aggressive treatment protocol may be prescribed as compared to a subject who is determined to be at low or lower risk of developing preeclampsia. The treatment protocol may also depend on existing clinical parameters relevant to developing preeclampsia, such as the age and / or sex of the subject.

[0144] Thus, the present disclosure contemplates exposing a subject to a treatment protocol if the subject is determined to be at risk of developing preeclampsia as determined in accordance with the methods described herein. Non-limiting examples of such treatment protocols are described elsewhere herein. In some embodiments, the subject is exposed to a combination of two or more treatment protocols (e.g., 2, 3 or more, 4 or more, 5 or more, 6 or more).

[0145] The present disclosure also extends to a clinical management protocol for a pregnant female subject and its fetus, the protocol comprising (i) determining whether a pregnant female subject is at risk of developing preeclampsia in accordance with the methods described herein; and, where the pregnant female subject is determined to be at risk of developing preeclampsia according to step (i), (ii) monitoring the fetus and / or subjecting the fetus to early delivery.

[0146] In yet another aspect disclosed herein, there is provided a panel of biomarkers for determining whether a pregnant female subject is at risk of developing preeclampsia, wherein the panel comprises an agent that specifically binds to:(a) NCEH1 protein or NCEH1 mRNA, and / or(b) LRPAP1 protein or LRPAP1 mRNA,

[0147] Also disclosed herein is a composition comprising (i) a biological sample of a pregnant female subject and (ii) a binding molecule that binds specifically to NCEH1 protein, and / or (iii) a binding molecule that binds specifically to LRPAP1 protein.

[0148] In yet another aspect disclosed herein, there is provided a composition comprising (i) a biological sample of a pregnant female subject and (ii) a nucleic acid molecule comprising a polynucleotide sequence that is complementary to a nucleic acid sequence encoding NCEH1, and / or (iii) a nucleic acid molecule comprising a polynucleotide sequence that is complementary to a nucleic acid sequence encoding LRPAP1.

[0149] In an embodiment, the sample is a blood sample. In an embodiment, the sample is a placental tissue. In an embodiment, the sample is obtained from a pregnant female subject at a time point of from about 11 weeks to about 14 weeks gestation.Kits

[0150] The present disclosure also extends to a kit for determining whether a pregnant female subject is at risk of developing preeclampsia in accordance with the methods or protocols described herein. In an embodiment, the preeclampsia is late- onset preeclampsia, early-onset preeclampsia, term preeclampsia or pre-term preeclampsia. In an embodiment, the preeclampsia is early-onset preeclampsia. In an embodiment, the preeclampsia is late-onset preeclampsia.

[0151] The kits may suitably contain reagents for obtaining a sample biomarker profile in accordance with the methods as herein described. Kits for carrying out the methods of the present invention may include, in suitable container means, (i) a reagent for detecting the biomarker(s) of interest, (ii) a probe that comprises an antibody or nucleic acid sequence that specifically binds to the biomarker(s), (iii) a label for detecting the presence of the probe and (iv) instructions for how to measure the level of expression of the biomarker(s). The container means of the kits will generally include at least one vial, test tube, flask, bottle, syringe and / or other container into which a first antibody specific for the at least one biomarker or a first nucleic acid specific for the at least one biomarker may be placed and / or suitably aliquoted. Where a second and / or third and / or additional component is provided, the kit will also generally contain a second, third and / or other additional container into which this component may be placed. Alternatively, a container may contain a mixture of more than one reagent, each reagent specifically binding a different biomarker in accordance with the present invention, when required. The kits of the present invention will also typically include means for containing the reagents (e.g., nucleic acids, polypeptides etc.) in close confinement for commercial sale. Such containers may include injection and / or blow- moulded plastic containers into which the desired vials are retained.

[0152] The kits may further comprise positive and negative controls, including a reference biomarker or reference biomarker profile or value, as well as instructions for the use of kit components contained therein, in accordance with the methods described herein.

[0153] All the essential materials and reagents required for detecting and quantifying biomarker expression products may be assembled together in a kit, which is encompassed by the present invention. The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions,blotting membranes, microtiter plate dilution buffers and the like. For example, a nucleic acid-based detection kit may include (i) a biomarker polynucleotide (which may be used as a positive control), (ii) a primer or probe that specifically hybridizes to a biomarker polynucleotide. Also included may be enzymes suitable for amplifying nucleic acids including various polymerases (Reverse Transcriptase, Taq, Sequenase™ DNA ligase etc. depending on the nucleic acid amplification technique employed), deoxynucleotides and buffers to provide the necessary reaction mixture for amplification. Such kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe. Alternatively, a protein-based detection kit may include (i) a biomarker polypeptide (which may be used as a positive control), (ii) an antibody that binds specifically to a biomarker polypeptide. The kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and / or printed instructions for using the kit to quantify the expression of a biomarker gene.

[0154] It will be appreciated that the above-described terms and associated definitions are used for the purpose of explanation only and are not intended to be limiting.

[0155] In order that the invention may be readily understood and put into practical effect, particular preferred embodiments will now be described by way of the following non-limiting examples.EXAMPLESMaterials and Methods

[0156] Human placental and decidual tissue (7-21 weeks gestation) was collected under the Human Research and Ethics Committee approval (Monash Health and the Royal Women’s Hospital, Melbourne #09317B). Written informed consent was obtained from each patient before surgery.

[0157] Early pregnancy maternal serum was collected by Professor Jon Hyett (University of Sydney) and Professor Kaori Koga (University of Tokyo). All patients provided written informed consent before collection under the Royal Prince Alfred Hospital Ethics approval (study No. XI 1-0305 & HREC / ll / RPAH / 472) or Musashion Red Cross Hospital. Serum was taken between 11 and 14 weeks gestation in on-going pregnancies that resulted in live birth and pregnancy outcomes were noted, as well asother clinical details. Groups were split into pregnancies that resulted in term preeclampsia (Term PE; diagnosis > 37 weeks) or control (uncomplicated, normotensive). Table 1 provides clinical information about each sample group.Table 1: Clinical parameters for each group.A. Immunohistochemistry and Immunocytochemistry

[0158] Placental and decidual formalin-fixed and paraffin-embedded tissue from first and second trimester were cut into 4 pm sections and mounted on Superfrost slides (Thermo Scientific, #MENSF41296SP) which was performed by Lanie Santos, Charlotte Fryer, Wei Zhou and Ellen Menkhorst.A.l Preparation of slides

[0159] All incubations were performed at room temperature unless specified otherwise. All slides were dewaxed and rehydrated prior to staining. This was done by placing slides into xylene (3 x 2 minutes), 100% Ethanol (2 x 2 minutes), 70% Ethanol (2 minutes) and distilled H2O (2 minutes). Antigen retrieval was performed using a citrate buffer (0.01 M, Appendix 1) and the slides were heated in the microwave at medium-high power for 5 minutes, then left to cool for 20 minutes. Slides were washed with distilled water for 5 minutes. To block endogenous peroxidase activity, 3% H2O2 in MeOH was applied to the slides in a humidified chamber for 10 minutes. All sections were then washed with TBS (2 x 5 minutes).A.2 Immunohistochemistry (IHC) for NCEH1

[0160] Following preparation of slides, a non-Immune block (10% Normal Goat Serum; 2% Normal Human Serum in TBS) was applied to sections for 1 hour. RabbitMonoclonal NCEH1 antibody (Invitrogen, PA5-55453, 0.1 mg / ml) was applied to primary sections at a final concentration of 0.33 pg / ml in non-Immune block. Negative control sections were applied with Rabbit IgG antibody (Dako, X0903, 20 g / L) at the same final concentration as the primary. These were incubated at 4°C overnight (approx. 18 hours). Following incubation slides were washed in 0.6% Tween in TBS (2 x 5 minutes) and washes in between all following steps are the same. On all sections, Biotinylated Goat Anti-Rabbit IgG (Vector Labs, Vector BA-1000, 1.5 mg / ml) was applied at a dilution of 1:200 in non-immune block for 30 minutes. Slides were then washed. StrepABC / HRP (VECTA stain kit, Vector Labs, PK-6100) was prepared and incubated in the dark for 30 minutes prior to application onto the slides for 30 minutes. Slides were washed prior to DAB (Liquid DAB -Plus substrate chromogen system, DAKO, K3466) application. DAB was applied and slides were watched closely under a microscope until a brown stain developed, then immersed into distilled H2O and this was timed to make sure all slides had the same DAB exposure. Haematoxylin was used to counter stain (40 seconds) followed by wash in water (2 x 2 minutes), acid alcohol (1 second), wash in water (2 x 2 minutes), ammonia water (40 seconds) and wash in water (2 minutes) (Appendix I). Slides were then dehydrated using 100% Ethanol (2 x 2 minutes) and xylene (2 x 2 minutes) then mounted with DPX (BDH Laboratory Supplies, 360294H) and a coverslip.

[0161] Placental IHC slides were scored using a placental cell types scoring sheet. Scoring was based on the intensity of staining in each cellular compartment: syncytiotrophoblast, cytotrophoblasts, stromal cells, Hofbauer cells and endothelial cells. Placenta was scored by two observers blinded to patient details and gestation week. Immunostaining scores ranged from 0-3, with 3 being the most intense. Scores were then averaged for each cell type.B. RNA analysisB.l RNA isolation

[0162] Performed by Leilani Santos, Teresa So and Ellen Menkhorst. Kits used for RNA isolation were QIAshredder (QIAGEN, 79656) and RNeasy Mini Kit (QIAGEN, 74106). Firstly, cells were homogenised using RLT buffer in a TissueLyser machine (QIAGEN, 85600) for 5 minutes at 50 Hz. Lysates were then transferred to QIA shredder columns and centrifuged for 2 minutes at full speed (13000 xg). To isolate the RNA, the eluate was collected from columns and transferred into a fresh tube, and 70%ethanol added. This was then transferred to a RNeasy spin column and centrifuged for 15 seconds at 8000 xg and the flow through was discarded. RW1 was added to spin column and centrifuged for 15 seconds at 8000 xg and flow through discarded. DNase 1 was then added with buffer RDD and left to incubate for 15 minutes. Another volume of RW1 was added, centrifuged and flow through discarded. Buffer RPE was added and first centrifuged for 15 seconds at 8000 xg and flow through discarded, then another volume was added and centrifuged for 2 minutes and flow through discarded. To elute the RNA, the spin column was placed in a new collection tube and RNase free water was added twice to the column membrane and centrifuged for 1 minute at 8000 xg. To determine the concentration of RNA in each sample, spectrophotometry (Nanodrop 2000) was used at an absorbance of A260 / 280 nm.B.2 RNA Sequencing

[0163] The Illumina NovaSeq (total) RNA-seq sequence (RNA-seq) production of a 150bp paired-end run and the primary bioinformatics analysis was performed by the Australian Genome Research Facility (Project codes: CAGRF20062989, CAGRF221213006). After the RNA libraries were prepared using Illumina’s Ribo-zero Gold protocol, all CVS samples were sequenced on an Illumina NovaSeq platform in two flowcells. Following primary bioinformatics analysis (demultiplexing and quality control) the data were processed through RNA-seq expression analysis workflow, including alignment, transcript assembly, quantification and normalization. The cleaned sequence reads were then aligned against the Homo sapien genome (Build version HG38) using STAR aligner (v2.5.3a).B.3 Reverse transcription (RT)

[0164] Performed by Leilani Santos, Teresa So, Ellen Menkhorst and Charlotte Fryer. The kit used for RT was Superscript III First-Strand Synthesis System for RT- PCR (Invitrogen, 18080-051). The total amount of RNA used was 250 ng. The volume of sample to obtain 250 ng was determined and added to the appropriate volume of H2O to make up a final volume of 10 pl, along with 1 pl of each random hexamers and 10 mM dNTP mix. Samples were then incubated at 65 degrees for 5 minutes and then on ice for 1 minute. Superscript III was added to a 10 pl cDNA synthesis mix according the Superscript Kit manufacturer’s instructions and mix was then added to each RNA sample. The samples were then incubated through a series of temperatures; 10 minutes at 25 °C, 50 minutes at 50 °C, 5 minutes at 85 °C, then remained at 4 °C until RNase Hwas added. Samples of cDNA were then incubated at 37 degrees for 20 minutes and stored at -20 °C until PCR was performed.B.4 qPCR

[0165] Performed by Charlotte Fryer and Ellen Menkhorst. cDNA samples were first diluted 1:10 in RNase free water. Each forward and reverse primer (NCEH1; Forward - AGACCTACATTCTGACGTGTGA (SEQ ID NO:1); Reverse CGGCACTCTCCAAACGCTT (SEQ ID NO:2)) was diluted 1:10 to obtain a 10 nM primer working solution. Primers were added to Sybr Green (PowerSYBR Green PCR Master Mix, Applied Biosystems, 4367659) and RNase free H2O to make a primer master mix. Using a 384 well plate, primer master mix was added to wells corresponding to each primer followed by cDNA. The plate was then covered in optical film and centrifuged to bring liquid to the bottom of wells. The qPCR was run at 90 °C for 10 minutes followed by 40 cycles of 95 °C for 15 seconds and 60 °C for 1 minute (Applied Biosystems, Life Technologies, ViiA 7 qPCR).C. Protein analysisC.l Proteomics

[0166] Global proteomics analysis on 15 samples (preterm PE: N = 4; term PE: N = 5; control: N = 6) was performed by using the liquid chromatography-tandem mass spectrometry (LC-MS / MS) method on LTQ Orbitrap Elite (Thermo Scientific) mass spectrometer platform after sample preparation (using the Solid-Phase Protein Preparation protocol (Dagley et al., 2019. Journal of Proteome Research. -, 18:2915- 2924; and Hughes et al. 2019; Nature Protocols', 14:68-85) and stable isotope dimethyl labelling (performed by Wei Zhou, Teresa So and Swati Varshney). Raw mass spectrometric data were processed by Dr Swati Varshney by using Proteome Discoverer 2.1 (PD 2.1) (Thermo Scientific, USA) with Mascot (Matrix Science version 2.4) search algorithm against Human_EST database. The relative expression patterns of identified proteins were determined based on the relative intensities of the reporter ions of the corresponding peptides using the quantitative node in PD 2.1.C.2 ELISA

[0167] Performed by Leilani Santos, Ellen Menkhorst and Charlotte Fryer. Human NCEH1 (AADACL1) ELISA Kit (My Bio Source, MBS9342264) and Human LRPAPELISA Kit (Ray Biotech, ELH-LRPAP-2) was used to measure the concentration of NCEH1 and LRPAP1 protein respectively in maternal serum as per the manufacturer’s instructions. All reagents and samples were left to get to room temperature naturally prior to the experiment. 50 pl of each standards (8, 2, 0.5, 0.25 pg / pl) and samples (diluted 1:2) were placed in wells. Blank wells received no sample. 100 pl of HRP- Conjugate was then added to all wells except blank wells and then the plate was incubated for 1 hour at 37°C. Wells were then washed 4 times with 50 pl of provided washing buffer. Equal amounts of Chromogen Solution A and Solution B were added to every well and mixed gently. The plate was then incubated for 20 minutes, in the dark at 37°C. Following this, the stop solution was added to every well. The ELISA plate was read by an ELISA reader (Biostrategy Spectramax PLUS Plate Reader) at optical density of 450 nm, within 5 minutes of adding the stop solution.E. StatisticsE.l Multiomics

[0168] Performed by Guannan Yang. The RNA counts data were divided according to their types, and five sub-datasets with the most numbers of variables (RNAs) were kept, including mRNA (20918 variables), IncRNA (15044 variables), miRNA (1832 variables), small nucleolar RNA (snoRNA, 541 variables) and transfer RNA (tRNA, 438 variables). RNA datasets were preprocessed separately. Variables with consistently low counts were filtered out, with the inclusion criteria that the counts were higher than a certain cutoff (mRNA and IncRNA: 10; other datasets: 5) in at least three samples, leaving 14543 mRNA, 2528 IncRNA, 197 miRNA, 268 snoRNA and 121 tRNA for further analysis. Trimmed mean of M- values (TMM) and Counts per million (CPM) was used for normalisation to account for differences in library sizes (i.e.total gene counts in a sample) potentially caused by different sequence depth between samples. All above RNA datasets preprocessing was performed by using the R package edgeR.

[0169] Proteins in the proteomic data were filtered by the numbers of missing values. Proteins with more than five missing values in 15 samples were filtered out, leaving 603 variables with 760 (8.4%) missing values in total. The pattern of missing values was checked, and no particular patterns, such as clustering in certain samples or groups were observed. Then, the NIPALS (Non-linear Iterative PArtial Least Squares, implemented in the R package mixOmics) algorithm was used to impute the missing values with the number of components of 14. The filtered and imputed proteomic datawere log transformed and normalised by VSN (Variance Stabilizing Normalization) method implemented in the R package limma.

[0170] The data was statistically evaluated using Data Integration Analysis for Biomarker discovery using Latent components (DIABLO) (Singh et al., 2019; Bioinformatics', 35:3055).E.2 Proteomics

[0171] Performed by Swati Varshney. The data was statistically evaluated using Perseus software (version 1.6.7.0). The protein data was filtered categorically by row for reverse identifications (false positives), contaminants, and proteins “only identified by site”. The fold changes in the protein levels were evaluated by comparing the mean LFQ intensities amid all experimental groups. A protein was considered to be differentially expressed if the difference was statistically significant (p < 0.05), the fold change >1.5 and < 0.66 was identified with a minimum of 2 peptides.

[0172] All other statistical analysis was performed by Charlotte Fryer and Ellen Menkhorst using GraphPad Prism 10. In data sets with only 2 variables, an unpaired t- test was performed. In data sets with more than 2 variables a one-way ANOVA was performed. Other infrequent types of data analysis performed are mentioned in the results section. A two-sided p-value was used for all statistics. P-values of less than 0.05 were considered significant. All graphed data presented is expressed as mean plus / minus standard error of the mean.Example 1 - NCEH1 expression in the placental villi throughout gestation

[0173] NCEH1 was found to be expressed in placental tissue, with IHC staining observed in placental trophoblasts (Figure 1).

[0174] NCEH1 protein expression in the placental villi was assessed by IHC staining. NCEH1 was identified in all cell types of the placenta: cytotrophoblasts, syncytiotrophoblasts, stromal cells, endothelial cells and Hofbauer cells, however, was at low intensity in stromal and endothelial cells. Staining increased with gestation week (Figure 2 A-C). Expression varied in cell types and across gestation (7-21 weeks) (Figure 3 A). In all cell types, there were significant differences in the intensity of NCEH1 staining between 7-10, 11-13 and 14-21 weeks gestation (Figure 3 B-E).Syncytiotrophoblast cells were consistently the highest scoring cell type throughout gestation.Example 2 - NCEH1 expression in EVT cells within the decidua

[0175] Early first trimester decidual tissue (7-13 weeks gestation) was assessed by IHC to identify if NCEH1 is expressed in EVT cells. NCEH1 was identified in uterine glandular cells (Figure 4 A-C), decidualised stromal cells (Figure 8 D-F) and EVT cells within the spiral arteries (Figure 4 G-I). Immunolocalization in EVT cells was confirmed by co-staining of pan-cytokeratin and NCEH1, which identified EVT within the decidual tissue and forming trophoblast plugs.Example 3 - NCEH1 expression in early maternal serum of pregnancies complicated with term preeclampsia and normal pregnancies

[0176] NCEH1 in early pregnancy maternal serum (11-14 weeks gestation) was assessed via ELISA. NCEH1 concentration (ng / ml) was significantly lower (p= 0.0061) in women who then went on to develop term preeclampsia (Mean ± SEM: 5.938 ± 0.9129), compared to uncomplicated pregnancies (10.87 ± 1.039) (Figure 5 A).

[0177] FGR (weight <5th percentile) was associated with term preeclampsia (Z score = 2.316, p = 0.0206) and birth weight (g) was significantly lower in the term preeclampsia group (3245 ± 91.4) than the control group (3539 ± 58.39). Gestation week at birth, maternal age and maternal BMI were not different between groups (Figure 5 C-E). Fetal sex and ethnicity of mother was not significantly different between groups (Tables 2 and 3 below).Table 2: Fetal sex contingency table. Data shown is % (n).

[0178] NCEH1 serum concentration (ng / ml) was positively correlated with birthweight (g) (Figure 5 B and F). This was a significant, strong (r =0.2294) correlation, with an r2 value of 0.05263 (p= 0.0210).Example 4 - LRPAP1 expression in early maternal serum of pregnancies complicated with term preeclampsia and uncomplicated, normotensive pregnancies

[0179] FRPAP1 in early pregnancy maternal serum (11-14 weeks) was assessed via EEISA. LRPAP1 concentration (pg / ml) was significantly lower (p= 0.0310) in women who then went on to develop term preeclampsia (Mean ± SEM: 1.369 ± 1.945), compared to uncomplicated pregnancies (5.591 ± 1.755) (Figure 6A).Example 5 - NCEH1 gene expression (mRNA levels) in chorionic villous samples of pregnancies complicated with preterm preeclampsia and uncomplicated, normotensive pregnancies

[0180] NCEH1 mRNA counts were assessed via RNA sequencing (Illumina NovaSeq RNA-sequencing). Two runs were performed, one in 2020 (sample size: control: 6, preterm preeclampsia: 4, term preeclampsia: 5) and one in 2023 (sample size: control: 17, preterm preeclampsia: 12, term preeclampsia: 12). NCEH1 mRNA counts were significantly lower in both sequencing runs in women who went on to develop preterm preeclampsia (Mean ± SEM: run 1: 1186 ± 116.5; run 2: 1134 ± 71.87) compared to uncomplicated, normotensive pregnancies (run 1: 1634 ± 113.5; run 2: 1663 + 131.1) (Figure 7).Example 6 - LRPAP1 protein expression (label-free quantification) in chorionic villous samples of pregnancies complicated with preterm preeclampsia and uncomplicated, normotensive pregnancies

[0181] LRPAP1 protein expression was assessed via proteomics (LC-MS / MS). LRPAP1 protein (label-free quantification; LFQ) was significantly lower in in women who went on to develop late onset preeclampsia (Mean ± SEM: 29.05 ± 0.11) compared to uncomplicated, normotensive pregnancies (29.63 + 0.14) (Figure 8)Example 7 - LRPAP1 expression in the placental villi throughout gestation

[0182] First and second trimester placental villous (7-18 weeks gestation) was assessed by IHC to identify if LRPAP1 is expressed in placental trophoblast. LRPAP1was found to be expressed in placental tissue, with IHC staining observed in placental trophoblasts (Figure 9).

[0183] LRPAP1 protein expression in the placental villi was assessed by IHC staining. LRPAP1 was identified in cytotrophoblasts and syncytiotrophoblasts (Figure 9).Example 8 - LRPAP1 expression in extravillous trophoblast (EVT) cells within the decidua

[0184] Early first trimester decidual tissue (7-13 weeks gestation) was assessed by IHC to identify if LRPAP1 is expressed in EVT cells. LRPAP1 was identified in uterine glandular cells (Figure 9B), decidualised stromal cells (Figure 9B) and EVT cells within the spiral arteries (Figure 9B&C). Immunolocalization in EVT cells was confirmed by co-staining of HLAG and LRPAP1, which identified EVT within the decidual tissue and lining the wall of blood vessels.Conclusion

[0185] NCEH1 was localised to the first and second trimester placenta and first trimester decidua and found to be strongly expressed in syncytiotrophoblasts and EVT cells. NCEH1 staining intensity significantly increased throughout gestation in the placenta (7-21 weeks). Unexpectedly, the level of expression of NCEH1 and LRPAP1 at the protein and gene product (mRNA) level in early maternal samples of pregnant women who went on to develop term preeclampsia were significantly lower than in women with uncomplicated pregnancies. This study is the first to quantify and explore NCEH1 and LRPAP1 as biomarkers for identifying subjects at risk of developing preeclampsia, including late-onset preeclampsia. The present inventors have also surprisingly found that a biomarker profile in a biological sample of a pregnant female subject can advantageously predict whether that subject is at risk of developing late- onset preeclampsia. These results suggest that the evaluation of biomarkers, such as NCEH1 and LRPAP1 (alone or in combination), can be used to predict preeclampsia, in particular late-onset preeclampsia, thereby allowing for the stratification of those at-risk subjects to an appropriate clinical management protocol aimed at mitigating the risk of complications, including those arising in the fetus and / or the mother as a result of the onset of late-onset preeclampsia.. This study informs new opportunities for predictionand clinical management protocols, allowing more women to have a safe and healthy pregnancy.

Claims

Claims:

1. A method of determining whether a pregnant female subject is at the risk of developing preeclampsia, the method comprising (a) measuring the level of expression of NCEH1 in a biological sample of a pregnant female subject, (b) comparing the measured level of expression of NCEH1 from step (a) to a reference value, and (c) identifying whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (b).

2. The method of claim 1, further comprising (d) measuring the level of expression of LRPAP1 in a biological sample of the pregnant female subject, (e) comparing the measured level of expression of LRPAP1 from step (d) to a reference value, and (f) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (e).

3. A method of identifying a pregnant female subject at the risk of developing preeclampsia, the method comprising (a) measuring the level of expression of LRPAP1 in a biological sample of a pregnant female subject, (b) comparing the measured level of expression of LRPAP1 from step (a) to a reference value, and (c) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (b).

4. The method of claim 3, further comprising (d) measuring the level of expression of NCEH1 in a biological sample of the pregnant female subject, (e) comparing the measured level of expression of expression of NCEH1 from step (d) to a reference value, and (f) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (e).

5. The method of any one of Claims 1 to 4, further comprising (i) measuring at least one other biomarker, physiochemical parameter and / or clinical risk factor in the pregnant female subject, and (ii) comparing the at least one other biomarker, physiochemical parameter and / or clinical risk factor measurement from step (i) to a reference value, and (iii) determining whether the pregnant female subject is at risk of developing preeclampsia based on the comparison of step (ii).

6. The method of Claim 5, wherein the at least one other biomarker is measured in a biological sample of the pregnant female subject and wherein the at least one other biomarker is selected from the group consisting of FCGR2B, SPINDOC, VWF, ANXA5, LGALS1, VCAN, prothrombin, APOH, CFH, JUND, PRDX2, HBB, NNAT, MCF2L, CYYR1, LSAMP, FSTL1, MFNG, HECW2, SDSL, CRYBG3, RALYL, ARF4, ZHX3, RNASE4, CENPBD1, SELENOS, ZNF124, MEG9, M0RC2-AS1, MIR3654, MIR668, MIR487B, MIR512-2, KRT19, CIRBP, SLC9A3R1, MYH10, PPBP, LIMA1, CNPY2, TCERG1, LIN7C, NCEH1, CAV1, PAPP- A, placental growth factor (P1GF), soluble fms-like tyrosine kinase- 1 (sFlt), vascular endothelial growth factor (VEGF) and endoglin.

7. The method of Claim 5 or Claim 6, wherein the at least one other biomarker is selected from the group consisting of placental growth factor (P1GF), soluble fms- like tyrosine kinase- 1 (sFlt), vascular endothelial growth factor (VEGF) and endoglin.

8. The method of Claim 5, wherein the physiochemical parameter is measured by ultrasound.

9. The method of Claim 5, wherein the clinical risk factor selected from the group consisting of a history of hypertensive disease during a previous pregnancy, chronic kidney disease, cardiovascular disease, autoimmune disease, diabetes, chronic hypertension, age, body mass index, arterial pressure, uterine artery pulsatility index, and a multifetal pregnancy.

10. The method of any one of Claims 1 to 9, wherein comparing the measured level of expression of NCEH1 and / or LRPAP1 is performed by an algorithm or by an analytics function or process or other data processing means.

11. The method of any one of claims 1 to 10, wherein the biological sample is selected from the group consisting of placental tissue, amniotic fluid, urine, whole blood, plasma and serum.

12. The method of Claim 11, wherein the biological sample is placental tissue or whole blood.

13. The method of Claim 11, wherein the biological sample is whole blood.

14. The method of any one of Claims 1 to 13, wherein the biological sample is obtained from the pregnant female subject at a time point from about 10 weeks to about 34 weeks of gestation.

15. The method of any one of Claims 1 to 13, wherein the biological sample is obtained from the pregnant female subject at a time point from about 11 weeks to about 14 weeks of gestation16. The method of any one of Claims 1 to 15, wherein the measured level of expression of NCEH1 is a measured level of NCEH1 protein17. The method of any one of Claims 1 to 15, wherein the measured level of expression of LRPAP1 is a measure level of LRPAP1 protein.

18. The method of any one of Claims 1 to 15, wherein the measured level of expression of NCEH1 is a measured level of NCEH1 mRNA19. The method of any one of Claims 1 to 15, wherein the measured level of expression of LRPAP1 is a measure level of LRPAP1 mRNA.

20. The method of any one of Claims 1 to 19, wherein, where the pregnant female subject is determined to be at risk of developing preeclampsia, further subjecting the female subject to a clinical management protocol for maximizing the health outcome of the female subject and / or its fetus.

21. The method of Claim 20, wherein the clinical management protocol comprises early delivery of the fetus.

22. The method of Claim 20, wherein the clinical management protocol comprises administration of anti-hypertensive treatment, anti-convulsant treatment and / or theadministration of aspirin to the female subject.

23. The method of any one of claims 1 to 22, wherein the preeclampsia is late-onset preeclampsia, early-onset preeclampsia, term preeclampsia or pre-term preeclampsia.

24. The method of any one of claims 1 to 23, wherein the preeclampsia is early-onset preeclampsia.

25. The method of any one of claims 1 to 23, wherein the preeclampsia is late-onset preeclampsia.

26. A clinical management protocol for a pregnant female subject and its fetus, the protocol comprising (i) determining whether a pregnant female subject is at risk of developing preeclampsia in accordance with the method of any one of Claims 1 to25, and, where the pregnant female subject is determined to be at risk of developing preeclampsia according to step (i), (ii) monitoring the fetus and / or subjecting the fetus to early delivery.

27. A panel of biomarkers for determining whether a pregnant female subject is at risk of developing preeclampsia, wherein the panel comprises an agent that specifically binds to: a. NCEH1 protein or NCEH1 RNA, and / or b. LRPAP1 protein or LRPAP1 RNA,28. A kit for determining whether a pregnant female subject is at risk of developing preeclampsia in accordance with the method or protocol of any one of Claims 1 to26.

29. The panel biomarkers of claim 27 or kit of claim 28, wherein the preeclampsia is late-onset preeclampsia, early-onset preeclampsia, term preeclampsia or pre-term preeclampsia.

30. The panel biomarkers or kit of any one of claims 27 to 29, wherein the preeclampsia is early-onset preeclampsia.

31. The panel biomarkers or kit of any one of claims 27 to 29, wherein the preeclampsia is late-onset preeclampsia.

32. A composition comprising (i) a biological sample of a pregnant female subject and (ii) a binding molecule that binds specifically to NCEH1 protein, and / or (iii) a binding molecule that binds specifically to LRPAP1 protein.

33. A composition comprising (i) a biological sample of a pregnant female subject and (ii) a nucleic acid molecule comprising a polynucleotide sequence that is complementary to a nucleic acid sequence encoding NCEH1, and / or (iii) a nucleic acid molecule comprising a polynucleotide sequence that is complementary to a nucleic acid sequence encoding LRPAP1.

34. The composition of claim 32 or claim 33, wherein the sample is a blood sample.

35. The composition of claim 32 or claim 33, wherein the sample is a placental tissue.

36. The composition of claim 35, wherein the sample is a CVS sample.

37. The composition of any one of Claims 32 to 36, wherein the sample is obtained from a pregnant female subject at a time point of from about 11 weeks to about 14 weeks gestation.

Images