WO2025011401A1

WO2025011401A1 - Circulating rna markers for diagnostic use

Info

Publication number: WO2025011401A1
Application number: PCT/CN2024/103266
Authority: WO
Inventors: Stephen Siu-Chung CHIM; Karen Ka-Wing WONG; Elaine Yee-Ling KO; Daljit Singh SAHOTA; Chi-Chiu WANG; Liona Chiu-Yee POON
Original assignee: Chinese University of Hong Kong CUHK
Current assignee: Chinese University of Hong Kong CUHK
Priority date: 2023-07-12
Filing date: 2024-07-03
Publication date: 2025-01-16
Anticipated expiration: 2026-01-12

Abstract

Provided are methods useful for diagnosing and monitoring diseases, especially pregnancy-associated disorders, based on analyzing biomarkers circulating in a test subject's blood. Also disclosed herein are reference markers useful for normalizing diagnostic biomarker levels to improve diagnostic performance. In addition, kits, compositions, and devices useful for the methods are described.

Description

CIRCULATING RNA MARKERS FOR DIAGNOSTIC USE

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 63/526, 392, filed July 12, 2023, the contents of which are hereby incorporated by reference in the entirety for all purposes.

BACKGROUND OF THE INVENTION

Circulating markers in peripheral blood represents a source of noninvasive markers for the prediction and monitoring of diseases or conditions. Various methods useful for measuring markers of this nature have been disclosed in prior publications, see, e.g., WO2022/192467, WO2019/227015, WO2018/210275. For example, cell-free RNAs (cfRNAs) of placental origin is readily detectable in maternal plasma and have found clinical applications in the prediction of placenta-or pregnancy-associated complications. These include preeclampsia, a major cause of mortality and long-term health consequences in both mothers and babies. Recently, a classifier combining the measurements of 18 preeclampsia-associated cfRNAs in maternal plasma at 5-16 weeks' gestation has been shown to be potentially useful for the prediction of preeclampsia (validation-2 cohort: detection rate (DR) of 56%at 31%false positive rate (FPR) or DR 36%at 10%FPR) . To achieve this, the RNA levels of the 18 genes require normalization by the RNA levels of another 66 reference genes. Thus, RNA-sequencing (RNA-seq) or sophisticated technology is needed for prediction by this 84-gene classifier. As such, this method is not easily scalable to the universal screening of the obstetric population.

This invention provides methods for (i) the normalization of blood levels of a single circulating marker (e.g., one of the above 18 cfRNAs) by the levels of 1-2 reference genes, and (ii) the prediction of disease (e.g., preeclampsia) by the 1-or 2-reference gene normalized levels of that single circulating marker. Since only the RNA levels of 2-3 genes are required, this method can be performed using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) . As illustrated, the 3-gene classifier of this invention can be used for predicting preeclampsia with high performance (20-replicate cross-validation: DR 88%at 15%FPR, or DR 85%at 10%FPR) . Because RT-qPCR is inexpensive and less time-consuming than RNA-seq, this method can be scaled up to screen a large population and provide test results within a relatively short turn-around-time. Women identified with a heightened risk for preeclampsia can be benefitted from aspirin prophylaxis to reduce the chance of developing this disorder or the severity of this disorder if it does develop.

The normalization method disclosed herein is also applicable to circulating markers for predicting or monitoring of other diseases or conditions within or beyond the scope of pregnancy, e.g., fetal growth restriction, gestational diabetes, cancer and transplant rejection. Further, this method is also applicable to markers in not only plasma but also serum, whole blood, their fractions or derivatives. With the quick and cost-effective identification of disease facilitated by the disclosed normalization method, appropriate intervention can be administered to the high-risk patients identified by those markers to reduce the morbidity or improve the outcome.

BRIEF SUMMARY OF THE INVENTION

This disclosure relates to the use of cell-free RNA markers circulating in the blood stream of an individual for the purpose of assessing the presence or risk of a medical condition in the individual. Because variations can be introduced during sample preparation and processing, the signal of a biomarker of diagnostic value needs to be normalized over the signal of at least one reference marker, which remains relatively stable among all individuals with or without the condition being assessed, in order to ensure a reliable diagnostic read-out. The present inventors have identified several new reference markers, each of which can be used alone or with just another reference maker in a normalization process to achieve high diagnostic performance, thus significantly reducing the necessary workload in the normalization effort.

Thus, in the first aspect, the present invention provides a method for analyzing a biomarker present in a biological sample taken from a subject, e.g., a man or woman of any age of any ethnic or medical background, for example, a pregnant woman. The method includes these steps: (a) quantifying the biomarker in the sample, (b) quantifying one or two reference genes in the sample, and (c) obtaining a normalized biomarker quantity by normalizing the biomarker level obtain in step (a) over the one or two reference gene level obtained in step (b) , wherein the one or two reference genes are selected from those named in Table 2, and wherein the method does not include any further steps of quantifying any other reference gene in addition to that used in step (b) . In some embodiments, only one reference gene PCBP2 is quantitatively measured in step (b) . In some embodiments, only one reference gene STXBP2 is quantitatively measured in step (b) . In some embodiments, two reference genes PCBP2 and STXBP2 are quantitatively measured in step (b) .

In some embodiments, the biological sample used in this method is a blood sample, e.g., a plasma or serum sample. In some embodiments, the biomarker is a DNA. In some embodiments, the biomarker is an RNA. In some embodiments, the biomarker is a protein. In some embodiments, the biomarker is an analyte of an alternative nature, e.g., a metabolite such as a lipid, a carbohydrate, or a small molecule, which may be organic or inorganic. In some embodiments, where the level of only one reference gene is measured in step (b) , the normalizing in step (c) comprises determining the ratio of the biomarker level obtained in step (a) to the one reference gene level obtained in step (b) . In some embodiments, where the levels of two reference genes are measured in step (b) , the normalizing in step (c) comprises determining the ratio of the biomarker level obtained in step (a) to the geometric mean of the two reference gene levels obtained in step (b) .

In some embodiments, the subject being tested in the method is a pregnant woman. In some embodiments, the woman is being tested for the presence or risk of developing a pregnancy-associated condition, e.g., preeclampsia. In some embodiments, the biomarker is any biomarker that is associated with a pregnancy-associated condition or with an increased risk of developing a pregnancy-associated condition, e.g., biomarker that associated with developing preeclampsia (see, e.g., WO2022/192467; Moufarrej et al. Nature. 2022; 602 (7898) : 689-94; Zhou S, Li J, Xue P et al. Am J Obstet Gynecol 2023. DOI: 10.1016/j. ajog. 2023.05.015; Yoffe L, Gilam A, Yaron O et al. Sci Rep. 2018; 8 (1) : 3401; MacDonald TM, Walker SP, Hannan NJ et al. EBioMedicine. 2022; 75: 103780) or developing preterm birth (see, e.g., Ngo TTM, Moufarrej MN, Rasmussen MH et al. Science. 2018; 360 (6393) : 1133-6; Liang L, Rasmussen MH, Piening B, Shen X, Chen S, H, et al. Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women. Cell. 2020; 181 (7) : 1680-92. e15; Gupta JK, Alfirevic A. Expert Rev Mol Med. 2022; 24: 1-24) . Additional literature includes Gupta JK, Alfirevic A. Systematic review of preterm birth multi-omic biomarker studies. Expert Rev Mol Med. 2022; 24: 1-24; Liang L, Rasmussen MH, Piening B, Shen X, Chen S, H, et al. Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women. Cell. 2020; 181 (7) : 1680-92. e15; MacDonald TM, Walker SP, Hannan NJ, Tong S, Kaitu'u-Lino TJ. Clinical tools and biomarkers to predict preeclampsia. EBioMedicine. 2022; 75: 103780; Ngo TTM, Moufarrej MN, Rasmussen MH, Camunas-Soler J, Pan W, Okamoto J, et al. Noninvasive blood tests for fetal development predict gestational age and preterm delivery. Science. 2018; 360 (6393) : 1133-6. In some embodiments, the biomarker is FAM46A RNA. In some embodiments, the biomarker is LRRC58 RNA. In some embodiments, step (a) of the method comprises a reverse transcription-polymerase chain reaction (RT-PCR) to measure the quantity of an RNA biomarker, e.g., a method of quantitative RT-PCR (qRT-PCR) is used. In some embodiments, the method further comprises a step (d) , following step (c) , which includes: first, comparing the normalized biomarker quantity obtained from step (c) to a standard control value; then determining the normalized biomarker quantity as being either higher or lower than the standard control value; and lastly determining the pregnant woman as suffering from a pregnancy-associated condition or at an increased risk of developing a pregnancy-associated condition. In the case of the biomarker FAM46A RNA or LRRC58 RNA, a normalized quantity higher than the standard control value indicates the presence or a heightened risk of preeclampsia. For example, the normalized biomarker quantity may be about 2.0%, 2.5%, 3.0%, 3.5%, 4.0%, 4.5%, or 5.0%higher or lower than the standard control value may be used as threshold for determining a test subject being positive for the condition being tested for. In the embodiments, where a pregnant woman is being tested for the potential presence or risk of preeclampsia, the claimed method further includes a prophylactic or therapeutic treatment step: (1) administering an effective amount of Aspirin, an antihypertensive medication, an anticonvulsant medication (such as magnesium sulfate) , or a corticosteroid to the pregnant woman determined in step (d) as suffering from preeclampsia or at an increased risk of developing preeclampsia; or (2) inducing labor in the pregnant woman determined in step (d) as suffering from preeclampsia, especially if the woman is in the last trimester of her pregnancy (e.g., after at least 30, such as 32 or 33 or more, weeks of pregnancy) .

In the second aspect, the present invention provides a kit for analyzing a biomarker in a biological sample. The kit includes (1) a first container containing a first agent for detecting the biomarker; (2) a second container containing a second agent for detecting one reference gene; and (3) optionally, a third container containing a third agent for detecting another, different reference gene, wherein the one or two reference genes are selected from the genes in Table 2, and wherein the kit does not include agents for detecting any other reference genes in addition to the reference gene (s) in (2) and optionally (3) .

In some embodiments, the kit contains an agent for detecting biomarker FAM46A RNA. In some embodiments, the kit contains an agent for detecting biomarker is LRRC58 RNA. In some embodiments, the kit contains an agent or agents for detecting only one reference gene, PCBP2. In some embodiments, the kit contains an agent or agents for detecting only one reference gene, STXBP2. In some embodiments, the kit contains an agent or agents for detecting only two reference genes, PCBP2 and STXBP2. In some embodiments, the first, second, and/or third agent comprises an agent for an amplification reaction (e.g., PCR, such as RT-PCR, especially qRT-PCR) for quantifying the biomarker or reference gene (s) . In some embodiments, an instruction manual is included in the kit to provide information for the user in order to properly use the kit.

In the third aspect, the present invention provides a use of newly identified reference gene markers, namely any one or two of PCBP2, STXBP2, or other genes listed in Table 2, for the purpose of analyzing a biomarker present in a biological sample taken from a subject. In this particular use, (1) the quantity of the biomarker present in the sample is first determined; (2) the quantity of one or two of the newly identified reference genes present in the sample is then determined; and (3) lastly, the normalized quantity of the biomarker in the sample is calculated by normalizing the biomarker quantity as initially determined over the quantity of the reference gene (s) . During this particular use, there are no further steps of quantifying any other reference gene in addition to the one or two reference genes used in step (2) . In some embodiments, only one reference gene PCBP2 is quantitatively measured in step (2) . In some embodiments, only one reference gene STXBP2 is quantitatively measured in step (2) . In some embodiments, two reference genes PCBP2 and STXBP2 are quantitatively measured in step (2) .

In some embodiments, the biological sample used for this purpose is a blood sample, e.g., a plasma or serum sample. In some embodiments, the biomarker is a DNA. In some embodiments, the biomarker is an RNA. In some embodiments, the biomarker is a protein. In some embodiments, the biomarker is an analyte of an alternative nature, e.g., a metabolite such as a lipid, a carbohydrate, or a small molecule, which may be organic or inorganic. In some embodiments, where the level of only one reference gene is measured in step (2) , the normalizing in step (3) comprises determining the ratio of the biomarker level obtained in step (1) to the one reference gene level obtained in step (2) . In some embodiments, where the levels of two reference genes are measured in step (2) , the normalizing in step (3) comprises determining the ratio of the biomarker level obtained in step (1) to the geometric mean of the two reference gene levels obtained in step (2) .

In some embodiments, the purpose of this use is testing a pregnant woman, e.g., for the presence or risk of developing a pregnancy-associated condition, such as preeclampsia. In some embodiments, the biomarker is FAM46A RNA. In some embodiments, the biomarker is LRRC58 RNA. In some embodiments, step (1) of the testing process comprises a reverse transcription-polymerase chain reaction (RT-PCR) to measure the quantity of an RNA biomarker, e.g., a method of quantitative RT-PCR (qRT-PCR) is used. In some embodiments, the testing process further comprises a step (4) , following step (3) , which includes: first, comparing the normalized biomarker quantity obtained from step (3) to a standard control value; then determining the normalized biomarker quantity as being either higher or lower than the standard control value; and lastly determining the pregnant woman as suffering from a pregnancy-associated condition or at an increased risk of developing a pregnancy-associated condition. For example, the normalized biomarker quantity being about 2.0%, 2.5%, 3.0%, 3.5%, 4.0%, 4.5%, or 5.0%higher or lower than the standard control value may be used as threshold for determining a test subject being positive for the condition being tested for. In the embodiments, where a pregnant woman is being tested for the potential presence or risk of preeclampsia, the testing process further includes a prophylactic or therapeutic treatment step: (i) administering an effective amount of Aspirin, an antihypertensive medication, an anticonvulsant medication (such as magnesium sulfate) , or a corticosteroid to the pregnant woman determined in step (4) as suffering from preeclampsia or at an increased risk of developing preeclampsia; or (ii) inducing labor in the pregnant woman determined in step (4) as suffering from preeclampsia, especially if the woman is in the last trimester of her pregnancy (e.g., after 33 weeks of pregnancy) .

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1A and Figure 1B: Levels of circulating markers normalized by different methods disclosed in THE INVENTION. Two published circulating markers (RNA transcripts of FAM46A and LRRC58) of the 18-gene classifier for early prediction of PE are normalized by RNA levels of PCBP2 and STXBP2 (Figure 1A) or STXBP2 only (Figure 1B) . Maternal plasma samples were collected at 11 to 16 weeks’ gestation from women who later developed or did not develop PE. PE, preeclampsia. *, Mann-Whitney rank sum test, p < 0.05.

Figure 2: Performance of the 3-gene methods for the prediction of preeclampsia in independent maternal plasma samples. The RNA levels of two published circulating markers namely LRRC58 (top) and FAM46A (bottom) for PE prediction were *normalized by RNA levels of PCBP2 and STXBP2 (i.e., 2-reference gene normalization) , as disclosed in THE INVENTION. This forms the basis of the 3-gene methods for the prediction of PE (1 PE-associated gene + 2 reference genes) . Whereas, the published method combined the RNA levels of all 18 PE-associated genes and these levels required normalization by RNA levels of 66 reference genes; thus, that prior art taught a 84-gene method for PE prediction. Performance of the 3-gene method (blue curve) was assessed in test samples that areto model training, and hence were independent from samples in the training set (20-replicate cross-validation) . Performance of another method for PE prediction, the Fetal Medicine Foundation first trimester triple test (grey-dotted curve) , which required ultrasonography is also shown. PE, preeclampsia. AUC, area under ROC curve.

Figure 3: Performance of the 2-gene methods for the prediction of preeclampsia in independent maternal plasma samples. The RNA levels of two published circulating markers namely LRRC58 (top) and FAM46A (bottom) for PE prediction were *normalized by RNA levels of STXBP2 (i.e., 1-reference gene normalization) , as disclosed in THE INVENTION. This forms the basis of the 2-gene methods for the prediction of PE (1 PE-associated gene + 1 reference genes) . Whereas, the published method combined the RNA levels of all 18 PE-associated genes and these levels required normalization by RNA levels of 66 reference genes; thus, that prior art taught a 84-gene method for PE prediction. Performance of the 2-gene method (blue curve) was assessed in test samples that areto model training, and hence were independent from samples in the training set (20-replicate cross-validation) . Performance of another method for PE prediction, the Fetal Medicine Foundation first trimester triple test (grey-dotted curve) , which required ultrasonography is also shown. PE, preeclampsia. AUC, area under ROC curve.

DEFINITIONS

As used herein, the term "biological sample" or “sample” includes sections of tissues such as biopsy and autopsy samples, and frozen sections taken for histologic purposes, or processed forms of any of such samples. Biological samples include blood and blood fractions or products (e.g., serum, plasma, platelets, red blood cells, and the like) , sputum or saliva, lymph and tongue tissue, cultured cells, e.g., primary cultures, explants, and transformed cells, stool, urine, esophagus biopsy tissue etc. A biological sample is typically obtained from a eukaryotic organism, which may be a mammal, may be a primate and may be a human subject.

In this disclosure the term "biopsy" refers to the process of removing a tissue sample for diagnostic or prognostic evaluation, and to the tissue specimen itself. Any biopsy technique known in the art can be applied to the diagnostic and prognostic methods of the present invention. The biopsy technique applied will depend on the tissue type to be evaluated (e.g., tongue, colon, prostate, kidney, bladder, lymph node, liver, bone marrow, blood cell, stomach tissue, esophagus, etc. ) among other factors. Representative biopsy techniques include, but are not limited to, excisional biopsy, incisional biopsy, needle biopsy, surgical biopsy, and bone marrow biopsy and may comprise colonoscopy or endoscopy. A wide range of biopsy techniques are well known to those skilled in the art who will choose between them and implement them with minimal experimentation.

The term "blood" as used herein refers to a blood sample or preparation from an individual being tested for the possible presence or risk of a particular medical condition, e.g., a pregnant woman being tested for a pregnancy-associated condition. The term encompasses whole blood or any fractions of blood having varying concentrations or even no hematopoietic or any other types of cells or cellular remnants of maternal or fetal origin, including platelets. Examples of "blood" include plasma and serum. A blood sample that is essentially free of cells is also referred to as "acellular, " where no detectable quantity of blood cells are present.

The term "pregnancy-associated disorder, " as used in this application, refers to any condition or disease that may affect a pregnant woman because of or related to her pregnant state, the fetus the woman is carrying, or both the woman and the fetus. Such a condition or disease may manifest its symptoms during a limited time period, e.g., during pregnancy or delivery, or may last the entire life span of the fetus following its birth. Some examples of a pregnancy-associated disorder include ectopic pregnancy, preeclampsia, preterm labor, and fetal chromosomal abnormalities such as trisomy 18 or 21.

The term "preeclampsia" as used herein refers to a condition that occurs during pregnancy, the main symptom of which is various forms of high blood pressure often accompanied by the presence of proteins in the urine and edema (swelling) . Preeclampsia, sometimes called toxemia of pregnancy, is related to a more serious disorder called "eclampsia, " which is preeclampsia together with seizure. These conditions usually develop during the second half of pregnancy (after 20 weeks) , though they may develop shortly after birth or before 20 weeks of pregnancy.

The term “nucleic acid” or “polynucleotide” refers to deoxyribonucleic acids (DNA) or ribonucleic acids (RNA) and polymers thereof in either single-or double-stranded form. Unless specifically limited, the term encompasses nucleic acids containing known analogs of natural nucleotides that have similar binding properties as the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise indicated, a particular nucleic acid sequence also implicitly encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions) , alleles, orthologs, single nucleotide polymorphisms (SNPs) , and complementary sequences as well as the sequence explicitly indicated. Specifically, degenerate codon substitutions may be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and/or deoxyinosine residues (Batzer et al., Nucleic Acid Res. 19: 5081 (1991) ; Ohtsuka et al., J. Biol. Chem. 260: 2605-2608 (1985) ; and Rossolini et al., Mol. Cell. Probes 8: 91-98 (1994) ) . The term nucleic acid is used interchangeably with gene, cDNA, and mRNA encoded by a gene.

The term “gene” means the segment of DNA involved in producing a polypeptide chain; it includes regions preceding and following the coding region (leader and trailer) involved in the transcription/translation of the gene product and the regulation of the transcription/translation, as well as intervening sequences (introns) between individual coding segments (exons) .

In this application, the terms “polypeptide, ” “peptide, ” and “protein” are used interchangeably herein to refer to a polymer of amino acid residues. The terms apply to amino acid polymers in which one or more amino acid residue is an artificial chemical mimetic of a corresponding naturally occurring amino acid, as well as to naturally occurring amino acid polymers and non-naturally occurring amino acid polymers. As used herein, the terms encompass amino acid chains of any length, including full-length proteins (i.e., antigens) , wherein the amino acid residues are linked by covalent peptide bonds.

The term “amino acid” refers to refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and amino acid mimetics that function in a manner similar to the naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those amino acids that are later modified, e.g., hydroxyproline, γ-carboxyglutamate, and O-phosphoserine. For the purposes of this application, amino acid analogs refers to compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., an a carbon that is bound to a hydrogen, a carboxyl group, an amino group, and an R group, e.g., homoserine, norleucine, methionine sulfoxide, methionine methyl sulfonium. Such analogs have modified R groups (e.g., norleucine) or modified peptide backbones, but retain the same basic chemical structure as a naturally occurring amino acid. For the purposes of this application, amino acid mimetics refers to chemical compounds that have a structure that is different from the general chemical structure of an amino acid, but that functions in a manner similar to a naturally occurring amino acid.

Amino acids may include those having non-naturally occurring D-chirality, as disclosed in WO01/12654, which may improve the stability (e.g., half-life) , bioavailability, and other characteristics of a polypeptide comprising one or more of such D-amino acids. In some cases, one or more, and potentially all of the amino acids of a therapeutic polypeptide have D-chirality.

Amino acids may be referred to herein by either the commonly known three letter symbols or by the one-letter symbols recommended by the IUPAC-IUB Biochemical Nomenclature Commission. Nucleotides, likewise, may be referred to by their commonly accepted single-letter codes.

As used in this application, an "increase" or a "decrease" refers to a detectable positive or negative change in quantity from a comparison control, e.g., an established standard control. An increase is a positive change that is typically at least 10%, or at least 20%, or 50%, or 100%, and can be as high as at least 2-fold or at least 5-fold or even 10-fold of the control value. Similarly, a decrease is a negative change that is typically at least 10%, or at least 20%, 30%, or 50%, or even as high as at least 80%or 90%of the control value. Other terms indicating quantitative changes or differences from a comparative basis, such as "more, " "less, " "higher, " and "lower, " are used in this application in the same fashion as described above. In contrast, the term "substantially the same" or "substantially lack of change" indicates little to no change in quantity from the standard control value, typically within ± 10%of the standard control, or within ± 5%, 2%, or even less variation from the standard control.

"Standard control" as used herein refers to a predetermined amount or concentration of a polynucleotide sequence or polypeptide, e.g., the DNA, RNA or protein of a pre-determined marker gene, that is present in an established normal disease-free tissue sample, e.g., a blood sample taken from a healthy pregnant woman with a healthy fetus. The standard control value is suitable for the use of a method of the present invention, to serve as a basis for comparing the amount of DNA, RNA or protein of the marker gene that is present in a test sample. An established sample serving as a standard control provides an average amount of the marker DNA, RNA or protein that is typical for a particular type of biological sample of an average, healthy human without a specific disease of interest as conventionally defined. A standard control value may vary depending on the nature of the sample as well as other factors such as the gender, age, ethnicity of the subjects based on whom such a control value is established.

The term "average, " as used in the context of describing a human who is healthy, free of any particular disease of interest as conventionally defined, refers to certain characteristics, especially the amount of a biomarker DNA, RNA or protein, found in the person's biological sample, that are representative of a randomly selected group of healthy humans who are free of the disease (such as a pregnancy-associated condition or disorder) . This selected group should comprise a sufficient number of humans such that the average amount of the marker DNA, RNA or protein in the type of sample among these individuals reflects, with reasonable accuracy, the corresponding amount of marker DNA, RNA, or protein in the general population of healthy humans. Moreover, other factors such as gender, ethnicity, medical history are also considered and preferably closely matching between the profiles of the test subject and the selected group of individuals establishing the "average" value.

The term "amount" as used in this application refers to the quantity of a polynucleotide of interest or a polypeptide of interest, e.g., a pre-selected human biomarker in its DNA, RNA or protein form, present in a sample. Such quantity may be expressed in the absolute terms, i.e., the total quantity of the polynucleotide or polypeptide in the sample, or in the relative terms, i.e., the concentration of the polynucleotide or polypeptide in the sample.

The term "treat" or "treating, " as used in this application, describes to an act that leads to the elimination, reduction, alleviation, reversal, or prevention or delay of onset or recurrence of any symptom of a relevant condition. In other words, "treating" a condition encompasses both therapeutic and prophylactic intervention against the condition.

The term "effective amount" as used herein refers to an amount of a given substance that is sufficient in quantity to produce a desired effect. For example, an effective amount of an therapeutic agent for treating or preventing preeclampsia is the amount of said therapeutic to achieve a detectable effect on the condition, such that the symptoms of preeclampsia are reduced, reversed, eliminated, prevented, or delayed of the onset in a patient who has been given the agent for therapeutic purposes. An amount adequate to accomplish the specific purpose is defined as the "prophylactically effective dose" or "therapeutically effective dose. " The dosing range varies with the nature of the therapeutic agent being administered and other factors such as the route of administration and the severity of a patient’s condition.

The term "about, " as used herein with a specific value, denotes a range of +/-10%of the value. For example, "about 10" denotes a range of 10 +/-10%, i.e., 9-11.

The term "subject" or "subject in need of treatment, " as used herein, includes individuals who seek medical attention due to risk of, or actual suffering from, a pre-determined disease or condition, such as a pregnancy-associated disorder (e.g., preeclampsia) . Subjects also include individuals currently undergoing therapy that seek manipulation of the therapeutic regimen. Subjects or individuals in need of treatment include those that demonstrate symptoms of the condition or are at an increased risk of suffering from this condition or its symptoms. For example, a subject in need of treatment includes individuals with a genetic predisposition or family history for any one of pregnancy-associated disorder (especially preeclampsia) , those that have suffered relevant symptoms in the past, those that have been exposed to a triggering substance or event, as well as those suffering from chronic or acute symptoms of the condition. A “subject in need of treatment” may be at any age of life.

DETAILED DESCRIPTION OF THE INVENTION

I. Introduction

The present inventors discovered that technical variation in the measurements of a circulating analyte in a biological specimen derived from the blood taken from a test subject can be minimized by a normalization step based on the RNA level of one or more genes selected from PCBP2, STXBP2, and other genes named in Table 2. Such specimen includes samples of plasma, serum, whole blood, or any fraction or derivative of such specimen. As plasma and serum samples or their derivatives are commonly obtained by separation techniques such as centrifugation for removal of blood cells, the RNA species remaining in the harvested sample are regarded as cell-free RNA (cfRNA) . The circulating analyte can be any marker, such as RNA, cfRNA, DNA, protein or metabolite, for prediction of a disease or condition.

The use of one or more of these reference genes in a normalization process allows for significant improvement in the diagnostic performance of a methodology utilizing a circulating biomarker. In particular, the present invention provides a diagnostic method for detecting or monitoring pregnancy-associated conditions such as preeclampsia by analyzing the circulating RNA level of LRRC58 and/or FAM46A. The invention also provides detection kits, related compositions, as well as detection devices useful for such a method.

II. General Methodology

Practicing this invention utilizes routine techniques in the field of molecular biology. Basic texts disclosing the general methods of use in this invention include Sambrook and Russell, Molecular Cloning, A Laboratory Manual (3rd ed. 2001) ; Kriegler, Gene Transfer and Expression: A Laboratory Manual (1990) ; and Current Protocols in Molecular Biology (Ausubel et al., eds., 1994) ) .

For nucleic acids, sizes are given in either kilobases (kb) or base pairs (bp) . These are estimates derived from agarose or acrylamide gel electrophoresis, from sequenced nucleic acids, or from published DNA sequences. For proteins, sizes are given in kilodaltons (kDa) or amino acid residue numbers. Protein sizes are estimated from gel electrophoresis, from sequenced proteins, from derived amino acid sequences, or from published protein sequences.

Oligonucleotides that are not commercially available can be chemically synthesized, e.g., according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Lett. 22: 1859-1862 (1981) , using an automated synthesizer, as described in Van Devanter et. al., Nucleic Acids Res. 12: 6159-6168 (1984) . Purification of oligonucleotides is performed using any art-recognized strategy, e.g., native acrylamide gel electrophoresis or anion-exchange high performance liquid chromatography (HPLC) as described in Pearson and Reanier, J. Chrom. 255: 137-149 (1983) .

The sequence of interest used in this invention, e.g., the polynucleotide sequence of the human LRRC58 or FAM46A gene, and synthetic oligonucleotides (e.g., primers) can be verified using, e.g., the chain termination method for sequencing double-stranded templates of Wallace et al., Gene 16: 21-26 (1981) .

III. Sample Acquisition of and Marker Analysis

The present invention relates to measuring the amount of an analyte, or a biomarker of diagnostic value, such as a DNA, an RNA (e.g., mRNA) , a protein, or a molecule of another chemical nature, found in a biological sample, especially a fluid sample such as a sample of a bodily fluid (e.g., blood or any fraction thereof) , secretion, perspiration, or excretion, as a means to detect the presence, to assess the risk of developing, and/or to monitor the progression or treatment efficacy of a pre-selected disease or condition (such as a pregnancy-associated condition, for example, preeclampsia) . Thus, the first steps of practicing this invention are to obtain an appropriate biological sample from a test subject and extract the analyte, e.g., mRNA or DNA or protein, from the sample.

A. Acquisition and Preparation of Samples

An appropriate biological sample is obtained from a person to be tested or monitored for a pertinent disease or condition (e.g., preeclampsia) using a method of the present invention. Collection of a tissue sample or a blood sample from an individual is performed in accordance with the standard protocol hospitals or clinics generally follow, such as during a biopsy or blood draw process. An appropriate amount of tissue or blood or any other fluid is collected and may be stored according to standard procedures prior to further preparation.

The analysis of an RNA or DNA analyte found in a subject's tissue or blood sample according to the present invention may be performed using, e.g., an acellular fraction of whole blood such as plasma or serum. The methods for preparing biological samples for nucleic acid extraction are well known among those of skill in the art. For example, a subject's tissue or blood sample should be first treated to disrupt cellular membrane so as to release nucleic acids contained within the cells. On the other hand, the step of disruption of cell membrane is not needed in the case of an acellular blood fraction (e.g., plasma or serum) is used.

B. Extraction and Quantitation of RNA

There are numerous methods for extracting RNA from a biological sample. The general methods of mRNA preparation (e.g., described by Sambrook and Russell, Molecular Cloning: A Laboratory Manual 3d ed., 2001) can be followed; various commercially available reagents or kits, such as Trizol reagent (Invitrogen, Carlsbad, CA) , Oligotex Direct mRNA Kits (Qiagen, Valencia, CA) , RNeasy Mini Kits (Qiagen, Hilden, Germany) , and Series 9600^TM (Promega, Madison, WI) , may also be used to obtain RNA from a biological sample from a test subject. Combinations of more than one of these methods may also be used.

It is essential that contaminating DNA be eliminated from the RNA preparations. Thus, careful handling of the samples, thorough treatment with DNase, and proper negative controls in the amplification and quantification steps should be used.

1. PCR-Based Quantitative Determination of RNA Level

Once RNA is extracted from a sample, the amount of a biomarker RNA may be quantified. The preferred method for determining the RNA level is an amplification-based method, e.g., by polymerase chain reaction (PCR) , especially reverse transcription-polymerase chain reaction (RT-PCR) .

Prior to the amplification step, a DNA copy (cDNA) of the biomarker RNA is ususally synthesized. This is achieved by reverse transcription, which can be carried out as a separate step, or in a homogeneous reverse transcription-polymerase chain reaction (RT-PCR) , a modification of the polymerase chain reaction for amplifying RNA. Methods suitable for PCR amplification of ribonucleic acids are described by Romero and Rotbart in Diagnostic Molecular Biology: Principles and Applications pp. 401-406; Persing et al., eds., Mayo Foundation, Rochester, MN, 1993; Egger et al., J. Clin. Microbiol. 33: 1442-1447, 1995; and U.S. Patent No. 5,075,212.

The general methods of PCR are well known in the art and are thus not described in detail herein. For a review of PCR methods, protocols, and principles in designing primers, see, e.g., Innis, et al., PCR Protocols: A Guide to Methods and Applications, Academic Press, Inc. N.Y., 1990. PCR reagents and protocols are also available from commercial vendors, such as Roche Molecular Systems.

PCR is most usually carried out as an automated process with a thermostable enzyme. In this process, the temperature of the reaction mixture is cycled through a denaturing region, a primer annealing region, and an extension reaction region automatically. Machines specifically adapted for this purpose are commercially available.

Although PCR amplification of the target mRNA is typically used in practicing the present invention. One of skill in the art will recognize, however, that amplification of a mRNA species in a sample may be accomplished by any known method, such as ligase chain reaction (LCR) , transcription-mediated amplification, and self-sustained sequence replication or nucleic acid sequence-based amplification (NASBA) , each of which provides sufficient amplification. More recently developed branched-DNA technology may also be used to quantitatively determining the amount of mRNA species in a sample. For a review of branched-DNA signal amplification for direct quantitation of nucleic acid sequences in clinical samples, see Nolte, Adv. Clin. Chem. 33: 201-235, 1998.

2. Other Quantitative Methods

A biomarker mRNA can also be detected using other standard techniques, well known to those of skill in the art. Although the detection step is typically preceded by an amplification step, amplification is not required in the methods of the invention. For instance, the mRNA may be identified by size fractionation (e.g., gel electrophoresis) , whether or not proceeded by an amplification step. After running a sample in an agarose or polyacrylamide gel and labeling with ethidium bromide according to well-known techniques (see, e.g., Sambrook and Russell, supra) , the presence of a band of the same size as the standard comparison is an indication of the presence of a target mRNA, the amount of which may then be compared to the control based on the intensity of the band. Alternatively, oligonucleotide probes specific to a biomarker mRNA can be used to detect the presence of such mRNA species and indicate the amount of mRNA in comparison to the standard comparison, based on the intensity of signal imparted by the probe.

Sequence-specific probe hybridization is a well-known method of detecting a particular nucleic acid comprising other species of nucleic acids. Under sufficiently stringent hybridization conditions, the probes hybridize specifically only to substantially complementary sequences. The stringency of the hybridization conditions can be relaxed to tolerate varying amounts of sequence mismatch.

A number of hybridization formats well known in the art, including but not limited to, solution phase, solid phase, or mixed phase hybridization assays. The following articles provide an overview of the various hybridization assay formats: Singer et al., Biotechniques 4: 230, 1986; Haase et al., Methods in Virology, pp. 189-226, 1984; Wilkinson, In situ Hybridization, Wilkinson ed., IRL Press, Oxford University Press, Oxford; and Hames and Higgins eds., Nucleic Acid Hybridization: A Practical Approach, IRL Press, 1987.

The hybridization complexes are detected according to well-known techniques. Nucleic acid probes capable of specifically hybridizing to a target nucleic acid, i.e., the mRNA or the amplified DNA, can be labeled by any one of several methods typically used to detect the presence of hybridized nucleic acids. One common method of detection is the use of autoradiography using probes labeled with ³H, ¹²⁵I, ³⁵S, ¹⁴C, or ³²P, or the like. The choice of radioactive isotope depends on research preferences due to ease of synthesis, stability, and half lives of the selected isotopes. Other labels include compounds (e.g., biotin and digoxigenin) , which bind to anti-ligands or antibodies labeled with fluorophores, chemiluminescent agents, and enzymes. Alternatively, probes can be conjugated directly with labels such as fluorophores, chemiluminescent agents or enzymes. The choice of label depends on sensitivity required, ease of conjugation with the probe, stability requirements, and available instrumentation.

The probes and primers necessary for practicing the present invention can be synthesized and labeled using well known techniques. Oligonucleotides used as probes and primers may be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Letts., 22: 1859-1862, 1981, using an automated synthesizer, as described in Needham-VanDevanter et al., Nucleic Acids Res. 12: 6159-6168, 1984. Purification of oligonucleotides is by either native acrylamide gel electrophoresis or by anion-exchange HPLC as described in Pearson and Regnier, J. Chrom., 255: 137-149, 1983.

C. Extraction and Quantitation of DNA

Methods for extracting DNA from a biological sample are well known and routinely practiced in the art of molecular biology, see, e.g., Sambrook and Russell, supra. RNA contamination should be eliminated to avoid interference with DNA analysis.

Following its extraction, the target DNA is then subjected to sequence-based analysis and quantitative assessment. An amplification reaction is optional prior to the sequence-based analysis and quantitative assessment. A variety of polynucleotide amplification methods are well established and frequently used in research. For instance, the general methods of polymerase chain reaction (PCR) for polynucleotide sequence amplification are well known in the art and discussed above.

Although PCR such as quantitative PCR is typically used in practicing the present invention, one of skill in the art will recognize that amplification of the relevant polynucleotide sequence may be accomplished by any known method, such as the ligase chain reaction (LCR) , transcription-mediated amplification, loop-mediated isothermal amplification and self-sustained sequence replication or nucleic acid sequence-based amplification (NASBA) , each of which provides sufficient amplification.

D. Quantitation of Proteins

A protein of any particular identity can be detected using a variety of immunological assays. In some embodiments, a sandwich assay can be performed by capturing the protein from a test sample with an antibody having specific binding affinity for the protein. The protein then can be detected with a labeled antibody having specific binding affinity for it. Such immunological assays can be carried out using microfluidic devices such as microarray protein chips. A protein of interest (e.g., a protein biomarker) can also be detected by gel electrophoresis (such as 2-dimensional gel electrophoresis) and western blot analysis using specific antibodies. Alternatively, standard immunohistochemical techniques can be used to detect a given protein (e.g., a protein biomarker) , using the appropriate antibodies. Both monoclonal and polyclonal antibodies (including antibody fragment with desired binding specificity) can be used for specific detection of the protein. Such antibodies and their binding fragments with specific binding affinity to a particular protein as a biomarker can be generated by known techniques.

Other methods may also be employed for measuring the level of a protein biomarker in practicing the present invention. For instance, a variety of methods have been developed based on the mass spectrometry technology to rapidly and accurately quantify target proteins even in a large number of samples. These methods involve highly sophisticated equipment such as the triple quadrupole (triple Q) instrument using the multiple reaction monitoring (MRM) technique, matrix assisted laser desorption/ionization time-of-flight tandem mass spectrometer (MALDI TOF/TOF) , an ion trap instrument using selective ion monitoring SIM) mode, and the electrospray ionization (ESI) based QTOP mass spectrometer. See, e.g., Pan et al., J Proteome Res. 2009 February; 8 (2) : 787–797.

IV. Establishing a Standard Control

In order to establish a standard control for practicing the method of this invention, a group of healthy persons free of a relevant disease being tested for (e.g., a pregnancy-associated condition such as preeclampsia) as conventionally defined is first selected. These individuals are within the appropriate parameters, if applicable, for the purpose of screening for and/or monitoring the pertinent condition (e.g., preeclampsia) using the methods of the present invention. For example, for the purpose of testing for a pregnancy-associated condition such as preeclampsia, the control subjects are healthy pregnant women each carrying a healthy fetus without any symptoms or elevated risks of any pregnancy-associated conditions. Optionally, the individuals are of same gender, similar age, or similar ethnic background.

The healthy status of the selected individuals is confirmed by well established, routinely employed methods including but not limited to general physical examination of the individuals and general review of their medical history.

Furthermore, the selected group of healthy individuals must be of a reasonable size, such that the average amount/concentration of the biomarker in the pertinent tissue or fluid sample obtained from the group can be reasonably regarded as representative of the normal or average level of the biomarker in the same type of samples among the general population of healthy people. Preferably, the selected group comprises at least 10 human subjects.

Once an average value for the biomarker is established based on the individual values found in each subject of the selected healthy control group, this average or median or representative value or profile is considered a standard control. A standard deviation is also determined during the same process. In some cases, separate standard controls may be established for separately defined groups having distinct characteristics such as age, gender, or ethnic background.

For applications where a normalization step using one or two reference genes is included to reduce sample-to-sample variation in a biomarker’s read-out, the same normalization step is also carried out in the generation of the corresponding standard control value.

V. Normalization

By illustrating the relatively stable quantitative presence of certain markers, such as the RNA of PCBP2, STXBP2, and other genes identified in Table 2, in acellular blood samples obtained from individuals regardless of their health status, the present invention offers an important use of these markers as the basis of a normalization step in a method scheme of quantitatively measuring a biomarker (especially one of diagnostic value) in a sample that can significantly improve the comparability among all samples of the read-out of the biomarker quantity by minimizing sample-to-sample variation due to sample handling/processing. Thus, the inclusion of this normalization step can significantly improve the diagnostic performance of a method scheme based on the biomarker quantity.

In the context of the present invention, the normalization step in the 2-gene (1 biomarker of diagnostic value + 1 reference gene) method is carried out by calculating the ratio of the biomarker quantity to the reference gene quantity, whereas the normalization step in the 3-gene (1 biomarker of diagnostic value + 2 reference genes) method is carried out by calculating the ratio of the biomarker quantity to the geometric mean of the quantities of the 2 reference genes. This normalization step yields a normalized biomarker quantity that allows a meaningful comparison between the read-out of a test sample and a standard control value that is already normalized.

Once the normalized biomarker quantity is calculated, the method of this invention is further carried out by comparing the normalized biomarker quantity to a standard control value in order to determine whether or not the normalized biomarker quantity is greater than the standard control value, which in turn allows a determination whether the individual being tested suffers from a condition (e.g., a pregnancy-associated condition such as preeclampsia) or is at an increased risk of developing such condition. Subsequently, appropriate medications can be prescribed and administered to the individual by a healthcare provider for therapeutic and prophylaxis purposes.

VI. Treatment Methods

By illustrating the correlation of the quantity of a diagnostic biomarker in a pertinent sample and a disease or condition being tested for such as a pregnancy-associated disorder, the present invention further provides a means for early detection and therefore for treating patients suffering from the condition or at heightened risk of developing the condition at a later time, especially for a condition that early intervention is particularly beneficial. Optionally, the diagnostic method of this invention is practiced in connection with a subsequent step of further testing utilizing conventional methods to confirm the diagnosis of the disease or condition. An attending physician can then prescribe and administer appropriate treatment to a patient. As used herein, treatment of a disease or condition encompasses reducing, reversing, lessening, or eliminating one or more of the symptoms of disease or condition, as well as preventing or delaying the onset of one or more of the relevant symptoms.

For example, the analysis of biomarker FAM46A or LRRC58 RNA in the acellular blood fraction (such as plasma or serum) in accordance with the method of this invention can indicate the presence or increased risk of preeclampsia in a pregnant woman, including during earlier stages, e.g., during the first 16 weeks of pregnancy, and likely before the onset of the condition. As a result, the woman may be administered one or more medications such as aspirin, antihypertensive drugs, anticonvulsant drugs, or corticosteroids for prophylaxis.

VII. Kits and Devices

The invention provides compositions and kits for practicing the methods described herein to assess the level of a biomarker (e.g., a biomarker of diagnostic value) in a sample taken from subject being tested, which can be used for various purposes such as for assessing the profile of the biomarker expression or for detecting or diagnosing the presence of a pertinent condition, determining the risk of developing the condition, and monitoring the progression of condition in a person, including assessing the likelihood of effective therapeutic response from a therapy given to the person for the condition.

Kits for carrying out assays for determining the biomarker level typically include at least one agent useful for the detection, especially quantitative detection, of the biomarker. The kits also include at least one agent for the detection, especially quantitative detection, of one or more reference genes such as PCBP2, STXBP2, as well as the other genes identified in Table 2. For example, oligonucleotide primers useful for PCR capable of detecting, especially quantitatively detecting, the DNA or RNA biomarker and the reference gene (s) are included in the kits. Optionally, the oligonucleotide primers are each labeled with a detectable moiety.

Kits for carrying out assays for determining a protein biomarker level typically include at least one antibody useful for specific binding to the protein biomarker amino acid sequence. Optionally, this antibody is labeled with a detectable moiety. The antibody can be either a monoclonal antibody or a polyclonal antibody. In some cases, the kits may include at least two different antibodies, one for specific binding to the biomarker protein (i.e., the primary antibody) and the other for detection of the primary antibody (i.e., the secondary antibody) , which is often attached to a detectable moiety.

Typically, the kits also include an appropriate standard control. The standard controls indicate the average value of a biomarker in tissue or bodily fluid sample from healthy subjects not suffering from a condition being tested for. In some cases such standard control may be provided in the form of a set value. In addition, the kits of this invention may provide instruction manuals to guide users in analyzing test samples and assessing the presence, risk, or state of progression of the condition in a test subject.

In a further aspect, the present invention can also be embodied in a device or a system comprising one or more such devices, which is capable of carrying out all or some of the method steps described herein. For instance, in some cases, the device or system performs the following steps upon receiving a biological sample, e.g., an acellular blood sample taken from a subject being tested for detecting a particular condition (e.g., a pregnancy-associated condition such as preeclampsia) , assessing the risk of developing the condition, or monitored for progression of the condition: (a) determining in the sample the amount or concentration of a biomarker; (b) determining in the sample the amount or concentration of one or two reference genes selected from PCBP2, STXBP2, and other genes identified in Table 2; (c) normalizing the quantity of the biomarker over the quantity of the one or two reference genes; and (d) comparing the normalized biomarker quantity with a standard control value; and (e) providing an output indicating whether the condition is present in the subject or whether the subject is at risk of developing the condition, or whether there is a change, i.e., worsening or improvement, in the subject's condition. In other cases, the device or system of the invention performs the task of steps (d) and (e) , after steps (a) - (c) have been performed and the normalized biomarker quantity from step (c) has been entered into the device. Preferably, the device or system is partially or fully automated.

EXAMPLES

The following examples are provided by way of illustration only and not by way of limitation. Those of skill in the art will readily recognize a variety of non-critical parameters that could be changed or modified to yield essentially the same or similar results.

INTRODUCTION

Currently, there are at least two existing Methods A and B for the first-trimester prediction of preeclampsia: (A) the Fetal Medicine Foundation (FMF) first trimester triple test which combines maternal characteristics with MAP, UtA-PI, and serum PlGF at 11-13 weeks’ gestation (1, 2) and (B) a classifier comprising the maternal plasma cfRNA measurements of 18 genes associated with preeclampsia and 66 reference gene transcripts for normalization (3) .

Various methods for the first-trimester prediction of preeclampsia have been developed, but most of them have not undergone or failed external validation (4) . However, it is worthy of note that Method A has undergone successful internal and external validation. Method A requires ultrasonography and hence the time-consuming training and regular certification of ultrasonographers. As for Method B which does not require ultrasonography, it is considered as one of the latest methods for prediction of preeclampsia, but its requirement of measuring 84 genes limits the use of Method B for studying a large population.

In this invention, a new Method C is disclosed, which can be used for the prediction of preeclampsia and is based on the normalization of RNA levels of a single circulating marker (amember of the above 18-gene classifier in Method B) by 1-2 reference genes that are newly identified. In general, Method C comprises (i) the normalization of blood levels of one or more disease-associated circulating marker by the levels of 1-2 reference genes disclosed herein, and (ii) the prediction of disease by the 1-or 2-reference gene normalized levels of the circulating marker. As illustrated, Method C was used for the normalization of the 12-16 week maternal plasma levels of a single preeclampsia-associated RNA marker, and for the prediction of preeclampsia at a later stage of pregnancy.

The key advantage of this invention (Method C) over these existing methods is the ease of implementation. Existing Method A requires measurement of 3 kinds of markers by 3 types of instruments. In particular, Method A requires measurements of UtA-PI by ultrasonography and thus training, regular assessment and certification of ultrasonographers. Further, Method A requires maternal demographics and medical history, which are not always accurately obtained. Existing Method B requires measurements of a total of 84 (=18+66) cfRNA transcripts by RNA-seq or sophisticated technology platform for detection. These methods are not readily scalable for universal screening of all pregnant women and takes a long turn-around time (several days) . This invention, however, requires measurements of the RNA levels of only 2 to 3 genes by RT-qPCR, which is relatively inexpensive, considerably less labor-intensive and takes a shorter turn-around time (several hours) . Thus, this invention (Method C) is more likely to be scaled up to applications in large-scale or universal screening, as it requires RNA measurements of 28-fold smaller number of genes than Method B.

Another advantage of the method of this invention is the potentially higher performance in prediction of preeclampsia. Existing Method A achieves a DR of 64%at 10%FPR for the prediction of preterm-preeclampsia and a less effective performance for the prediction of term-preeclampsia in Asian populations (2) . Existing Method B achieves a DR of 36%at 10%FPR for the prediction of preeclampsia, according to the results in the validation-2 cohort (3) . In contrast, Method C achieved a DR of 85%at 10%FPR for the prediction of preeclampsia (20-fold cross-validation) .

There are three prior publications, referred to as Methods D-F, related to the prediction of PE based on circulating RNA transcripts, which are considered as the methods which may rival the DR of Method A, the most rigorously validated prediction method in existence. Method D (Zhou et al, Am J Obstet Gynecol 2023) requires more than 13 genes and RNA-seq and achieves a DR of 67%at 10%FPR (5) . Method E (Yoffe et al. Sci Rep. 2018; 8 (1) : 3401) requires 6 genes and achieves a DR of 45%at 10%FPR (6) . So, similarly, Method C has these advantages over Methods D and E: Method C is easier to operate (3 genes, no RNA-seq) and achieve a higher DR of 85%at 10%FPR. Further, Method F (Rasmussen et al. Nature. 2022; 601 (7893) : 422-427) is based on blood samples collected at 16-27 weeks’ gestation (7) , which is too late for starting an effective aspirin prophylaxis. Unlike Method F, Method C is based on blood samples collected before 16 weeks’ gestation, which is early enough for the prophylaxis to be effective.

The basis of this invention is that among the thousands of genes detected in blood, the circulating RNA levels of PCBP2, STXBP2 and other genes in Table 2 have a low coefficient of variation across different individuals even when they are exposed to drastically changed physiological states. As such, they are well suited for use as reference genes for normalization of measurements of biomarker for minimizing the noise, maximizing the signal of a biomarker and eventually improving the discriminatory power or prediction performance of the biomarker.

A practical use of this invention is in improving the performance of at least two published circulating markers (3) potentially useful in the prediction of preeclampsia through normalization with the RNA levels of PCBP2 and STXBP2. Originally, in the method described in WO2022/192467 by Moufarrej et al. (3) , a classifier that combined all 18 measurements of maternal plasma cfRNA levels of FAM46A, LRRC58 and 16 other genes achieved a detection rate (DR) of 56%at 31%false positive rate (FPR) (or 36 %DR at 10%FPR) for the prediction of PE (validation-2 cohort in (3) ) . To achieve this performance, the levels of those 18 cfRNAs were required to be normalized by the levels of 66 reference genes, hence Moufarrej’s method for prediction of PE is a 84-gene method.

In the method devised based on the present invention, the maternal plasma RNA levels of LRRC58 were normalized by the RNA levels of PCBP2 and STXBP. Using the normalized RNA levels of LRRC58 alone, which is only one of the 18 cfRNAs reported by Moufarrej et al., a DR of 83%at a FPR of 16% (or DR 85%at 10%FPR) was achieved for the prediction of preeclampsia (20-replicate cross-validation; train: test, 1: 1) . To achieve this performance, the levels of maternal plasma RNA levels of FAM46A were normalized by the RNA levels of PCBP2 and STXBP, hence this is a 3-gene method. Therefore, the method of this invention reduced the number of genes required for RNA measurement by 28-fold from 84 to 3 in this example of prediction of PE. Consequently, the method of this invention is easy to conduct, requires less sophisticated equipment and shorter turn-around time. This illustrates how the present invention increases the signal-to-noise ratio of the target analyte and improves the performance of a circulating marker for the prediction of disease.

As the performance of a circulating marker is improved by the method of the present invention, patients are predicted more accurately to be at risk of the disease/condition. In the above example, low-dose of prophylactic aspirin can be administered daily to the women identified to be at risk for preeclampsia by the improved marker, so that the incidence and severity of the condition can be reduced. In general, a test with improved performance for prediction will more effectively direct patient for the appropriate intervention, leading to less over-and under-treatments.

The method of this invention is based on the systematic identification of circulating RNA with stable levels in plasma of research subjects undergoing drastic physiological changes. This concept of identifying the reference genes for the appropriate biological matrix for detectng a disease/condition is widely known but not commonly accomplished (10) . This disclosure of the present invention includes a list of reference genes to accomplish an effective normalization for circulating markers or analytes. Without this disclosure, tremendous efforts would have to be devoted in the identification of the reference genes, design and optimization of the assays to quantify them.

Further, it can be inferred that the findings reported herein are applicable to markers circulating in not only plasma but also serum and whole blood. The basis for such inference is that haematopoietic cells or blood cells are likely the predominant source of nucleic acids, including RNA, in plasma and serum (11, 12) . Thus, the genes with stable RNA levels in plasma can be used to minimize the noise (via method based on the RNA levels of those genes) when measuring any target analyte or marker circulating in not only plasma but also serum, whole blood, their fractions or derivatives.

BACKGROUND

Circulating cell-free RNA (cfRNA) in biological fluids represents an invaluable source of potential markers for monitoring and diagnosis of diseases. A malignant tumor releases its RNA into plasma as cfRNA, so certain cfRNAs are useful for diagnosing cancer and monitoring its treatment. Likewise, the placenta which is considered to be a pseudomaligant organ, also releases its RNA into maternal plasma as cfRNA (13, 14) . As the placenta is central to the pathogenesis of many pregnancy-associated complications, such as preeclampsia, maternal cfRNA is a source of potential markers for such diseases.

Preeclampsia (PE) is a pregnancy-specific hypertensive disorder that affects millions of pregnancies (15) . Current clinical management strategy is to identify women at high risk of PE at 11 to 13 weeks of gestation (16) , which enables timely aspirin prophylaxis to reduce incidence and severity of PE (17) . Various first trimester models for the prediction of PE have been developed, but most of them have not undergone or failed external validation (4) . However, it is worthy of note that the Fetal Medicine Foundation (FMF) first trimester prediction model, namely the triple test, has undergone successful internal and external validation. The FMF first trimester triple test, which combines maternal characteristics with 11 to 13 weeks’ gestation measurements of mean arterial pressure (MAP) , uterine artery pulsatility index (UtA-PI) , and serum placental growth factor (PlGF) , achieved higher detection rate (DR) than by screening with maternal factors alone (1) . The FMF triple test has been validated to achieve DRs of 75%and 64%at 10%false-positive rate (FPR) for the prediction of preterm-PE in mixed-European (18) and Asian populations (2) , respectively. However, there is still a considerable proportion of women at risk of PE not being identified. Moreover, implementation of the test is constrained by measurement of UtA-PI which requires training in and equipment for ultrasonography; and that maternal characteristics are often incomplete.

As the placenta is central to the pathogenesis of preeclampsia, maternal cfRNA is a source of potential markers for preeclampsia. Maternal plasma cfRNA transcripts of several genes, including corticotropin-releasing hormone (CRH) mRNA, have been reported to be associated with PE (19-22) , but the overlapping measurements between the PE and non-PE groups render them not clinically useful. Recently, Moufarrej et al. have developed a logistic regression model consisting of 18 purportedly PE-associated cfRNAs measured at 5-16 weeks' gestation for predicting preeclampsia (3) . In the external validation (Validation-2 cohort) , this model achieved a DR of 56%at FPR of 31% (DR 36%at 10%FPR) for the prediction of PE. To achieve this performance, in addition to the maternal plasma levels of these 18 RNA transcripts, this model requires the RNA levels of 66 reference genes for normalization to account for the unwanted baseline variations between samples, e.g., gestational age at blood collection. Thus, this 84-gene method requires RNA-seq or sophisticated detection platform, which is expensive, requires a long turn-around-time, and cannot be readily scaled up for universal screening.

SUMMARY OF THE INVENTION

In this disclosure, the present inventors describe that the circulating RNA levels of PCBP2, STXBP and other genes in Table 2 are relatively stable even when the subject undergoes drastic physiological changes. It is further disclosed that the circulating RNA levels of the above genes can be used to minimize the unwanted technical variation (i.e., noise) when measuring the levels of a target analyte or circulating marker. Consequently, the method of this invention increases the signal-to-noise ratio of the analyte being measured and improves its discriminating power in a test for the prediction or diagnosis of a disease or condition. As an example, the inventors illustrated that a published 84-gene method for the prediction of PE can be simplified as a 2-or 3-gene method with improved prediction performance.

Understandably, as PE involves multiple etiologies, the definition and diagnostic criteria of PE are refined over time and vary across the prior arts on PE prediction. To ensure the validity of the assessment of performance of the 2-or 3-gene method in the prediction of PE, the present inventors followed the updated definition of preeclampsia by a well-represented group of experts in the International Society for the Study of Hypertension in Pregnancy [Magee et al. Pregnancy Hypertens. 2022; 27: 148-69] (32) . Also, all diagnoses of PE in this study were confirmed from case notes.

EXAMPLE: Prediction of Preeclampsia by a 2-or 3-Gene Method

A. Recruitment of Participants and Sample Processing

Study Population: Subjects of this nested case-control study were recruited from existing project (Implementation of First-trimester Screening and preventiOn of pREeClAmpSia Trial; FORECAST) . This study included women with maternal age >18 years, singleton pregnancy with viable fetus at 11⁺⁰ to 13⁺⁶ weeks’ gestation. The exclusion criteria were multiple pregnancies, presence of major abnormalities identified at the time of ultrasound scan, those who were unable to provide written informed consent or have learning difficulties, those who were not able to understand spoken and written Chinese or English languages, or pregnancies resulting in termination or miscarriage. Approval for the validation study was obtained from the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (CREC Ref No. 2018.391) and registered with ClinicalTrials. gov (identifier: NCT03941886) .

In the FORECAST study, women were screened unselectively using the Fetal Medicine Foundation (FMF) first trimester triple test for the prediction of PE. Each woman screened high-risk for preterm-PE was invited to participate in this nested study and contribute peripheral blood samples before 16 weeks’ gestation. Each high-risk woman was matched with at least one low-risk woman for maternal age (+/-3 years) , maternal weight at the time of screening (+/-5 kg) and other possible confounding factors. The matched women were also invited to participate the study and contribute blood samples before 16 weeks. The high-risk and low-risk women were followed up until delivery, when the pregnancy outcome, including the presence or absence of preeclampsia, could be confirmed from case notes. The definition of preeclampsia (PE) by the International Society for the Study of Hypertension in Pregnancy (Magee et al. Pregnancy Hypertens. 2022; 27: 148-69; detailed below) was used for the diagnosis of PE (23) . Thus, after follow-up, each PE case could be matched with at least one non-PE control for maternal age (+/-3 years) , maternal weight at the time of screening (+/-5 kg) and other possible confounding factors for this study, including storage duration of the study blood sample (+/-4 weeks) .

In summary, a large and unselective group of women was narrowed down into a case-control cohort of PE and non-PE women. Appropriately matched maternal plasma samples collected at 11-16 weeks were retrieved for this study from women who eventually develop preeclampsia (PE women) or those who do not (non-PE women) later in the pregnancy. For this study, comprehensive clinical data were collected, including known maternal risk factors for preeclampsia, measurements of biophysical and biochemical markers from the FMF first-trimester triple test for prediction of PE, pregnancy outcome, gestational age at delivery, birthweight, pregnancy complications including preeclampsia.

During the study period, 22 participants were screened high risk for preterm-PE by the FMF first trimester triple test and later developed PE. Using the criteria in the last paragraph, each of them was matched for the three confounding factors with (i) one participant screened high risk for preterm-PE and who later did not develop PE, and (ii) one participant screened low risk for preterm-PE and who later did not develop PE. Then, enough tubes of maternal plasma samples were retrieved for all participants, except 12 participants in (i) . Thus, there were maternal plasma samples from 54 participants (PE, n=22; non-PE n=32) for this study (Table 1) . There were no difference in maternal age and weight between PE and non-PE groups, but by design the PE group had higher mean arterial pressure, uterine artery pulsatility index and lower placental growth factor, which were the markers of the FMF first trimester triple test.

Definition of preeclampsia: The definition of preeclampsia is according to that of the International Society for the Study of Hypertension in Pregnancy (32) [Magee et al. Pregnancy Hypertens. 2022; 27: 148-69] . Preeclampsia (de novo) is gestational hypertension (a clinic systolic blood pressure (sBP) ≥ 140 mmHg and/or a diastolic blood pressure (dBP) ≥ 90 mmHg at 20 weeks’ gestation) accompanied by one or more of the following new onset conditions at ≥20 weeks’ gestation:

1. Proteinuria

2. Other maternal end-organ dysfunction, including:

· Neurological complications (e.g., eclampsia, altered mental status, blindness, stroke, clonus, severe headaches, or persistent visual scotomata)

· Pulmonary oedema

· Haematological complications (e.g., platelet count < 150,000/μL, disseminated intravascular coagulation (DIC) , haemolysis)

· Acute kidney injury (AKI) (such as creatinine ≥ 90 μmol/L or 1 mg/dL)

· Liver involvement (e.g., elevated transaminases such as alanine aminotransferase (ALT) or aspartate aminotransferase (AST) > 40 IU/L) with or without right upper quadrant or epigastric abdominal pain)

3. Uteroplacental dysfunction (e.g., placental abruption, angiogenic imbalance, fetal growth restriction, abnormal umbilical artery Doppler waveform analysis, or intrauterine fetal death) .

Preeclampsia superimposed on chronic hypertension is defined among women with chronic hypertension, development of new proteinuria, another maternal organ dysfunction (s) , or evidence of uteroplacental dysfunction (as above) .

Gestational age is determined by the measurement of fetal crown-rump length at 11-13 weeks.

Study Design Notes

1. Parous women with a history of PE. Women who have a history of PE were recruited among other women with no such history for the proposed study, since the screening of PE by the FMF first-trimester triple test were performed on an unselected obstetric population. Using the current inclusion/exclusion criteria, women with a history of PE were enrolled; they account for about 4.9%or 8.8%of all women who developed PE in the current pregnancy in the Asian and mixed-European populations, respectively (2, 24) . The 7-region Asian study comprised 10, 935 unselected singleton pregnancies, of which 224 developed PE. Of these, only 11 women (4.9%) had a previous PE. The 5-country European study comprised 8, 775 unselected singleton pregnancies, of which 239 developed PE. Of these, only 21 women (8.8%) had a previous PE. The advantage of this study design recruiting the larger, more complete group of women who may develop preeclampsia was that the assessed 2-or 3-gene method for prediction of PE are more likely to be applicable to the general obstetric population.

2. Inclusion window of the proposed study is 11-16 weeks’ gestation for this study. Currently, the FMF triple test appears to be the only rigorously validated method for an effective prediction of PE in a large and unselected group of pregnant women. Thus, it is an advantage to leverage on the current knowledge of these 3 existing markers in the development of any new methods for PE prediction. Combining the new and the triple-test markers may result in a test for more accurate prediction of PE. Of note, earlier than 11 weeks, two of the triple-test markers, uterine artery pulsatility index (UtA-PI) , and serum placental growth factor (PlGF) are unlikely to distinguish PE from non-PE women. This is because these two markers are dependent on the developmental stage or size of the placenta, which is considerably premature and small <11 weeks. Nevertheless, potential new markers for PE prediction emerging from this study are expected to also work <11 weeks (i.e., 5-16 weeks) .

Blood processing &RNA isolation: Peripheral blood samples were collected from the antecubital fossa of the pregnant women in EDTA-tubes 9 mL EDTA Vacutainer tubes (VACUETTE^TM K3EDTA Blood Collection Tubes, Greiner Bio-One GmbH, Kremsmunster, Austria) and centrifuged at 1, 600 × g for 10 min at 4℃. Plasma were then be transferred into plain polypropylene tubes and recentrifuged at 16,000 × g for 10 min at 4℃. The supernatants will be collected into fresh polypropylene tubes. Three volumes of TRIzol LS Reagent (Thermo Fisher Scientific) was added to one volume of supernatant and stored at -80℃ until extraction of RNA. The remaining steps were proceeded according to manufacturers’ instructions, unless otherwise stated. Briefly, chloroform was added to the plasma-TRIzol LS mixture. The mixture was then centrifuged at 12,000 × g for 15 min at 4℃. The aqueous layer was transferred into new tubes. One volume of 70%ethanol was added to 1 volume of the aqueous layer. The mixture was then applied to an RNeasy column (RNeasy Mini kit, Qiagen) . The remaining steps were proceeded according to manufacturers’ instructions, unless otherwise stated. Total RNA were eluted with RNase-free water. DNase I (Thermo Fisher Scientific) treatment will be carried out to remove any contaminating DNA. The above methods for blood processing &RNA isolation were given by way of illustration only. Skill artisans appreciate that similar methods are feasible for performing the same tasks, e.g., RNA later (Thermo Fisher Scientific) and RNA Complete BCT (Streck) , without or with modifications to the manufacturers’ protocols.

B. Reduction of Noise in the Measurement of Circulating Markers

A systematic data-driven approach for selection of reference genes for normalization of the levels of circulating markers: To accurately quantify the levels of a target analyte in blood samples, one needs to normalize the measured levels of that analyte with internal control reference genes with a view to accounting for the unwanted variations (e.g., slight variation in the total RNA concentrations) between samples. While the use of reference genes is the most common method for normalization of RT-qPCR data (25) , their utility must be experimentally validated for particular tissues or cell types and specific experimental designs, according to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines (26) . Reference gene mRNAs should be validated to be stably expressed across different samples. Despite increased awareness, the sorely needed careful consideration and empirical validation in the choice of reference genes are still widely disregarded (10, 27) , leading to inadequately normalized RNA signals, which are often masked by high level of noise.

In particular to the measurement of circulating markers, for normalization to be effective, the reference genes should exist at relatively constant levels across the blood samples collected from different subject even when they are undergoing drastic physiological changes, such as pregnancy. During these 40 weeks, a woman adapts to a host of hormonal, immune, hemodynamic changes in response to a growing fetus. Thus, it is reasoned that if the circulating RNA levels of a certain gene are stable across all three trimesters of women, they are well suited as reference genes for normalization of the levels of a circulating maker or target analyte. Therefore, efforts were devoted to the identification of reference genes in RNA-seq datasets of pre-delivery plasma samples of pregnant women.

One advantage of the identification strategy of this invention is that, albeit a drastic change, pregnancy is a relatively normal process compared to cancer or other diseases, where the blood circulation system is inevitably affected by the pathological processes and medications. Thus, maternal plasma RNA-seq dataset has relatively low noise levels. Another advantage is that genes with stable RNA levels in maternal plasma probably also have stable RNA levels in whole blood, but not the other way around; thus, reference genes identified in maternal plasma probably also are useful in whole blood. Compared to whole blood or other biological samples where typical cellular RNA can be extracted, RNA in maternal plasma is cell-free, and characterized by low quantity and low quality. These physical properties of RNA in maternal plasma present challenges in construction of RNA sequencing libraries. Not surprisingly, it is rare to see all-trimester pre-delivery maternal plasma RNA-seq datasets, let alone systematic analysis of stability of RNA transcripts on these datasets.

Pre-delivery plasma RNA-seq datasets from women of all three trimesters were systematically analyzed. High-quality reads were used to estimate the RNA levels of each gene based on trimmed mean of M values (28) . For each gene, the estimated RNA level was expressed as counts per million reads (CPM) in each sample, and a coefficient of variation (CV) of RNA levels across the samples of different women in all trimesters. CV was calculated by dividing the standard deviation by the mean. Genes with RNA levels not exceeding 30 CPM or CV >50%in a dataset were removed. The retained genes were then ranked according to their stability of their expression levels in all datasets using the NormFinder (29) package (version 5) in 'R' (30) . NormFinder provided for each pair of genes a stability value, where a small value reflects high stability of RNA levels across the tested samples. PCBP2 and STXBP2 had the smallest average stability value (3.66) and the lowest average CV. Other genes with relatively low stability value (< or = 4.38, the 5^th percentile of the retained genes) and low average CV (<or = 50%) were also noted (Table 2) . Hence, PCBP2, STXBP2 and other genes in Table 2 are well suited as reference genes for normalization of RNA levels in plasma.

By corollary, these of the above findings may be applied to reduce the technical variations (via normalization or similar techniques) in measuring the levels of other types of analytes circulating in blood other than cfRNA, such as regular (i.e., not cell-free) RNA, DNA, protein and metabolites. Further, it can be inferred from this discovery that the findings may be applied to reduce the technical variations in the levels of circulating analytes in, not only plasma, but also serum, whole blood, as well as any fractions or derivatives of such biological samples. This includes plasma, serum, whole blood, peripheral blood mononuclear cells or their fractions contacted with chemicals/objects such as RNA later (Thermo Fisher Scientific) , TRIzol, TRIzol LS (Thermo Fisher Scientific) , TRI reagent, TRI reagent LS (Sigma-Aldrich) or similar reagents containing phenol or guanidine isothiocyanate, cell-free DNA BCT (Streck) , RNA Complete BCT (Streck) or similar objects coated, treated or loaded with reagents related to anti-coagulants, preservatives, protective agents, formaldehyde, imidazolidinyl urea, EDTA or glycine. Two studies form the basis of this inference from the above findings which were observed in data from plasma. First, based on sex-mismatched bone marrow transplantation model, it has been shown that hematopoietic cells are the predominant origin of cell-free DNA in plasma and serum (11) . Second, it has also been observed that, among the cfRNA species circulating in maternal plasma, there were relatively more non-placental specific cfRNA than placental specific cfRNA (12) . This observation might be explained by the contribution of such non-placental specific cfRNA from maternal tissues such as the haematopoietic cells. Since blood cells are likely the predominant source of RNA in plasma and serum, it could be inferred that the reference genes identified in plasma as disclosed in this disclosure can be used to minimize the noise when measuring any target analyte or circulating marker in serum, plasma, whole blood, their fractions or derivatives.

Among the large number of genes with detectable RNA levels in plasma, the genes identified herein are well suited to serve as reference genes, because their circulating RNA levels remain stable despite the subject undergoes drastic physiological changes. The above results from this systematic data-driven approach enables others to avoid undue testing of the thousands of genes that are detected in a blood sample in their search for reference genes for an effective normalization.

Background on the reference genes identified herein: PCBP2, STXBP2 and other genes in Table 2 have not been reported to have a stable RNA levels in blood, its components or derivatives; neither have they been reported to be useful as reference genes for normalization. However, the genomic structure and function of PCBP2 and STXBP2 are summarized as below.

Expressed from the PCBP2 (Poly (RC) Binding Protein 2; HGNC: 8648; NCBI Gene: 5094) gene are at least 7 RNA transcript variants (Accession no. (stable) with version no. (changeable over time) : NM_005016.6, NM_031989.5, NM_001098620.3, NM_001128911.2, NM_001128912.2, NM_001128913.2, NM_001128914.2) which range from 3.0 kilobases (kb) to 3.2 kb and each contains up to 15 exons. According to NCBI Gene Summary for PCBP2 Gene, the protein encoded by this gene appears to be multifunctional. Along with PCBP-1 and hnRNPK, it is one of the major cellular poly (rC) -binding proteins. The encoded protein contains three K-homologous (KH) domains which may be involved in RNA binding (31, 32) . This multiexon structural mRNA is thought to be retrotransposed to generate PCBP-1, an intronless gene with functions similar to that of PCBP2 (33) . This gene and PCBP-1 have paralogous genes (PCBP3 and PCBP4) which are thought to have arisen as a result of duplication events of entire genes. This gene also has two processed pseudogenes (PCBP2P1 and PCBP2P2) .

Expressed from the STXBP2 (Syntaxin Binding Protein 2; HGNC: 11445 NCBI Gene: 6813) gene are at least 5 RNA transcript variants (Accession Nos. with version Nos.: NM_006949.4, NM_001127396.3, NM_001272034.2, NR_073560.2 and NM_001414484.1) which range from 1.86 kb to 1.99 kb and each contains up to 21 exons. STXBP2 encodes a member of the STXBP/unc-18/SEC1 family (34-36) . The encoded protein is involved in intracellular trafficking, control of SNARE (soluble NSF attachment protein receptor) complex assembly, and the release of cytotoxic granules by natural killer cells; mutations in this gene are associated with familial hemophagocytic lymphohistiocytosis (37-39) .

Selection of published PE-associated circulating markers: A shortlist of the purportedly PE-associated genes which were members of the 18-cfRNA classifier reported by Moufarrej et al. was created, based on the univariate analyses reported in their publication (3) . All 18 members were ranked and the highest two ranked cfRNAs were then selected for the development of RT-qPCR assays in this study. First, the cfRNAs which were either consistently elevated or reduced in the PE and normotensive (NT) groups in the discovery, internal and external validation cohorts were selected. Then, the selected cfRNAs were ranked in ascending order of the adjusted p value (one-sided Mann-Whitney rank test, adjusted by Benjamini-Hochberg correction) between PE and NT groups in the internal validation cohort. The highest two ranked cfRNAs, namely LRRC58 and FAM46A also known as TENT5A, were selected for development of RT-qPCR assays.

Design and optimization of RT-qPCR assays, normalization of circulating marker levels: While the levels of any transcript from PCBP2, STXBP2 and other genes in Table 2 can adequately normalize the levels of circulating markers, it is illustrated below in details with specific RT-qPCR assays for the PCBP2 mRNA and STXBP2 mRNA transcripts how they can be used as reference genes. The appropriate details (on design and optimization) of RT-qPCR assay also apply on the LRRC58 mRNA and FAM46A mRNA transcripts, the circulating markers to be normalized in this example.

Although many possible RT-qPCR assays for a given gene can be readily obtained by primer design software, it is not a trivial task to select the ones with high specificity for the intended transcript and subject them for optimization. As typified by PCBP2 and STXBP2, a gene encodes multiple transcript variants, each with multiple exons, and there are stretches of highly similar nucleotide sequences in other genes of the related gene family, paralogous genes and pseudogenes. Consequently, the primers and hydrolysis probe of a RT-qPCR assay may bind non-specifically to unintended RNA transcripts or unintended locations on the same transcript, leading to unwanted technical variation (noise) in the detection signal. In this example, the possible RT-qPCR assay designs were checked for non-specific amplification signals against all known transcripts using Primer-BLAST (40) (NCBI) and in silico PCR (41) at the UCSC Genome Browser (42) . To avoid detecting any residual genomic DNA, the primers were intron-spanning if possible.

PCR primers and dual-labeled hydrolysis probe (5’ 6FAM &3’ IBFQ, Integrated DNA Technologies) designed to amplify the RNA transcripts from the reference genes PCBP2 and STXBP2 (reference transcripts) identified above and the published PE-associated genes LRRC58 and FAM46A (target transcripts) (Table 3) were synthesized. Besides ordering the synthesis of the RT-qPCR assays for PCBP2, STXBP2, LRRC58 and FAM46A RNA transcripts, the commercial pre-designed RT-qPCR assays Hs. PT. 58.20432738, Hs. PT. 58.39066104. gs, Hs.PT. 58.78733 and Hs. PT. 58.19789006 which targeted the same transcripts, respectively, were also ordered (Integrated DNA Technologies) . Reactions were assembled according to the reaction conditions (Table 4) .

Thermal cycling and fluorescence detection were performed on Roche LightCycler 480 (LC480) instrument (Roche Diagnostics, Basel, Switzerland) . No template controls (NTC) were included in parallel for each run. Quantification cycle (Cq) values were determined using Roche LightCycler 480 software (version 1.5.1.62) (Roche Diagnostics, Basel, Switzerland) . Mean Cq values of the duplicates were calculated.

In 2-reference gene normalization, the level of the target transcript was calculated based on the ratio of mean Cq value of the target transcript to the geometric mean of the two mean Cq values of the reference transcripts (43) . In 1-reference gene normalization, the levels of the target transcript was calculated based on the ratio of mean Cq value of the target transcript to the one mean Cq value of the reference transcript. Detection rate of an assay was calculated as the number of samples with positive amplification signal (aCq value less than 40) divided by the total number of samples being tested in that experiment.

In the pilot experiments, the commercial RT-qPCR assays were initially tested using the thermal profile provided by the manufacturer (Thermo Fisher Scientific) . The default thermal profile was: UNG incubation at 50℃ for 2 minutes, polymerase activation at 95℃ for 10 minutes, then 40 cycles of PCR, each cycle comprising denaturation at 95℃ for 15 seconds and annealing/extension at 60℃ for 1 minute. For all four commercial RT-qPCR assays, this resulted in detection rate <50%in third-trimester maternal plasma samples. The temperature at the annealing/extension step was changed, but the detection rate did not considerably improve. Next, to further optimize the amplification signals, touchdown PCR thermal profile (Table 5) and the four RT-qPCR assays designed by the inventors were used, and a detection rate of >50%was achieved for each assay. This improvement in detection rate is attributed to an optimized annealing/extension temperature in the thermal profile and the shorter PCR amplicon size chosen in these RT-qPCR assays. The former improvement is probably related to the highly-specific primer and probe design of the assay for RNA transcripts with lots of paralogous genes or pseudogenes in the family. With improved specificity in each PCR cycle, all reagents were used to amplify the intended signal and resulted in an improved detection rate. The latter improvement is probably related to the physical property of circulating RNA transcripts, which are relatively degraded and of low concentration compared to RNA transcripts in other biological samples.

Assessment on the performance of prediction: All participants in the concerned study cohort was randomly split into the training and the test sets (train: test ratio = 1: 1) . Each of the training and test sets contained approximately equal ratio of women who later did or did not develop PE. A logistic regression model was trained using the one-or two-reference gene normalized levels of each circulating marker (FAM46A RNA or LRRC58 RNA) . The performance of prediction of PE was assessed in the samples in the test set that were (i.e., blind) to the model training, and hence were independent from samples in the training set. The random splitting into training and test sets were repeated 20 times. Thus, a 20-replicate cross-validation approach was used in assessment of the prediction performance. The advantage of this approach was minimization of data over-fitting. Hence, the estimates of sensitivity (TPR) and specificity (1-FPR) were more generalizable to the larger population. As comparison, another logistic regression model was trained using data from the FMF first-trimester triple test; that is, the levels of MAP, UtA-PI and PlGF which were reported as multiples of their expected median (MoM) after adjusting for gestation, maternal size and past and current obstetric history.

C. Results on Circulating Markers Each Normalized with RNA Levels of Two Reference Genes

In each sample, the RNA level of circulating FAM46A were normalized by the RNA levels of both reference genes, namely PCBP2 and STXBP2 (i.e., the 2-reference gene normalization) . Similarly, the RNA level of circulating LRRC58 was normalized by the RNA levels of both PCBP2 and STXBP2. The median (IQR) log₂ normalized RNA levels of FAM46A in maternal plasma were -7.8 (-12.2 to -5.4) and -5.6 (-7.3 to -4.8) in the no-PE and PE groups, respectively (Fig. 1A, left panel) . The median log₂ normalized level of FAM46A in plasma was 2.3-fold higher in the PE group than that of the no-PE group (Mann-Whitney, p <0.03) .

The median (IQR) log₂ normalized levels of LRRC58 were -11.8 (-12.9 to -9.4) and -9.7 (-10.8 to -8.6) in the no-PE and PE groups, respectively (Fig. 1A, right panel) . The median of log₂ normalized level of LRRC58 in plasma was 2.1-fold higher in the PE group than that of the no-PE group (Mann-Whitney, p <0.02) .

According to the 20-replicate cross-validation data, using the 2-reference gene normalized RNA levels of FAM46A, a model for prediction of PE was achieved at a DR of 83%at a FPR of 16%, or a DR of 72%at a FPR of 10%and an AUC of 0.932, which is greater than that of the triple test (0.834, p < 0.001) . (Table 6, Fig. 2 bottom panel) . Using the 2-reference gene normalized RNA levels of LRRC58, a model for prediction of PE was achieved at a DR of 88%at a FPR of 15%, or a DR of 85%at a FPR of 10%, and an AUC of 0.948, which is greater than that of the triple test (0.834, p < 0.001) (Table 6, Fig. 2 top panel) .

Unlike the triple test, which comprises measurement of UtA-PI, requires ultrasonography and hence equipment, training and certification of ultrasonographers, the method by 2-reference gene normalized levels of each circulating marker (RNA levels of FAM46A or LRRC58) does not. Notably, these 3-gene methods (2 reference genes and 1 PE-associated circulating marker) are potentially performing on par or better than the triple test, which is currently the most rigorously validated algorithm for prediction of preterm-PE (4) .

D. Results on circulating markers each normalized with RNA levels of one reference gene

In each sample, the RNA level of circulating FAM46A was normalized by the RNA level of only one reference gene STXBP2 (i.e., the 1-reference gene normalization) . Similarly, the RNA level of circulating LRRC58 was normalized by the RNA level of STXBP2. The median (IQR) log₂ normalized RNA levels of FAM46A in maternal plasma were -9.3 (-14.4 to -6.0) and -5.7 (-7.7 to -5.0) in the no-PE and PE groups, respectively (Fig. 1B, left panel) . The median log₂ normalized level of FAM46A in plasma was 3.6-fold higher in the PE group than that of the non-PE group (Mann-Whitney, p <0.002) .

The median (IQR) log₂ normalized levels of LRRC58 were -12.9 (-14.6 to -9.5) and -9.9 (-10.7 to -8.8) in the no-PE and PE groups, respectively (Fig. 1B, right panel) . The median log₂ normalized level of LRRC58 in plasma was 3.0-fold higher in the PE group than that of the non-PE group (Mann-Whitney, p <0.003) .

According to the 20-replicate cross-validation data, using the 1-reference gene normalized RNA levels of FAM46A, a model for prediction of PE was achieved at a DR of 69%at a FPR of 10%and an AUC of 0.907. (Table 7, Fig. 3 bottom panel) . Using the 1-reference gene normalized RNA levels of LRRC58, a model for prediction of PE was achieved at a DR of 52%at a FPR of 10%, and an AUC of 0.908 (Table 7, Fig. 3 top panel) . As a comparison, in the same cross-validation setup, the FMF triple test achieved a DR of 51%at a FPR of 10%and an AUC of 0.885.

Unlike the triple test, which comprises measurement of UtA-PI, requires ultrasonography and hence equipment, training and certification of ultrasonographers, the method by 1-reference gene normalized levels of each circulating marker (RNA levels of FAM46A or LRRC58) does not. Notably, these 2-gene methods (1 reference gene and 1 PE-associated circulating marker) are potentially performing on par or better than the triple test, which is currently the most rigorously validated algorithm for prediction of preterm-PE (4) .

E. Discussions

In this example on prediction of preeclampsia (PE) , the disclosed methods (collectively referred to Method C) were compared with prior art methods (referred to as methods A, B, D, E and F) . Since the prevalence of PE is different across these studies, the positive predictive values (PPV) and negative predictive values (NPV) which depends on prevalence should not be compared. Instead, sensitivity (DR) at a given specificity (1-FPR) which are independent of prevalence are listed below for comparison. For the 3-gene method, the performance of the reference genes PCBP2 and STXBP2 combined with the published circulating marker PE-associated maternal plasma LRRC58 cfRNA is listed. For the 2-gene method, the performance of the reference genes STXBP2 combined with the published PE-associated maternal plasma FAM46A cfRNA is listed.

Pre-16 weeks’ gestation prediction methods

Method C: Chim et al. 3-gene meth for all-PE; DR 85%@10%FPR

Method C: Chim et al. 2-gene meth for all-PE; DR 69%@10%FPR

Method A: Triple test. 3-marker meth for preterm-PE; DR 64%@10%FPR

Method D: Zhou et al. 13-marker^ meth for preterm-PE; DR 51%@10%FPR

Method E: Yoffe et al. 6-marker^ meth for preterm-PE; DR 45%@10%FPR

Method B: Moufarrej et al. 84-gene meth for all-PE; DR 36%@10%FPR

Post-16 weeks’ gestation prediction methods

Method F: Rasmussen et al. 7-marker^ meth; All-PE; DR 65%@10%FPR

Key

meth method for prediction of all-or preterm-PE

n-marker PE-associated marker

n-gene include both PE-associated marker genes and reference genes

^ levels of markers normalized by an approach dependent on the entire RNA-seq data or the normalization approach of RNA-seq data was not clearly stated,

Table 1. Baseline characteristics of research subjects in this study.

Table 2. Genes with stable RNA levels in pre-delivery plasma samples collected from women in all three trimesters.

Notes to Table 2: Genes with (i) a coefficient of variation (CV = standard deviation/mean) of RNA levels < 50%and (ii) a minimum stability value calculated by NormFinder < or = 4.38 (5^th percentile of retained genes after filtering criteria as set out in para [0106] ) as shown above are considered as reference genes for normalization of RNA levels of other genes or analytes circulating in blood including cell-free plasma. In essence, the minimum stability value was calculated by NormFinder based on the RNA level of the corresponding gene was paired with the RNA level of any other 515 retained genes after filtering by criteria as set out in para [0106] . In the complete result table of NormFinder, the median (5^th percentile to 95^th percentile) stability value is 5.84 (4.38 to 19.41) . Since stability values of 132, 870 (=515^2/2+515/2) possible pairs were calculated, compiled for each gene with the minimum identified for each gene and compared by us in this systematic search for reference genes, for clarity only the genes with a stability value lower than or equal to the 5^th percentile (i.e. 4.38) are shown. HGNC, HUGO Gene Nomenclature Committee. HUGO, Human Genome Organization.

Table 3. PCR primer and hydrolysis probe sequences in RT-qPCR assays for PCBP2, STXBP2, FAM64 and LRRC58 RNA transcripts. All sequences are listed from the 5' end to the 3' end. For probe, the 5' end was labeled with 6-carboxyfluorescein (6-FAM) and 3' end with Iowa Black Quencher FQ (IBFQ) . Efficiency refers to PCR efficiency estimated by method according to the MIQE guidelines. Exon location are based on the RefSeq NM record of each gene in NCBI.

Table 4. Reaction conditions of RT-qPCR assays. Volumes are in μL with total volume for each reaction made up to 10 μL using RNase-free water.

*TaqMan^TM Universal Master Mix II, with UNG (Thermo Fisher Scientific, Cat. No. 4440038)

Table 5. Thermal profile of RT-qPCR assays

Table 6. Performance of markers normalized by RNA levels of PCBP2 and STXBP2 (i.e. 2-reference gene normalization) for the prediction of preeclampsia in independent^#maternal plasma samples. Corresponding values of the triple test markers are also included.

Notes:

#Performance was assessed in test samples that are naive to model training (20-replicate cross-validation)

*Normalized by RNA levels of PCBP2 and STXBP2, as disclosed in THE INVENTION (see main text)

^Triple test based on conventional markers (see main text)

PE, preeclampsia.

AUC, area under ROC curve.

DR, detecton rate.

FPR, false positive rate.

Cl, confidence interval.

Table 7. Performance of markers normalized by RNA levels of STXBP2 (i.e. 1-reference gene normalization) for the prediction of preeclampsia in independent#maternal plasma samples.

Notes:

#Performance was assessed in test samples that are naive to model training (20-repicat cross-validation)

＊Normaized by RNA levels of STXBP2, as disclosed in THE INVENTION (see main text)

PE, preeclampsia.

AUC, area under ROC curve.

DR, detection rate.

FPR, false positive rate.

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All patents, patent applications, and other publications, including GenBank Accession Numbers or similar sequence identification numbers, cited in this application are incorporated by reference in the entirety of their contents for all purposes.

Claims

A method for analyzing a biomarker in a biological sample taken from a subject, comprising the steps of:

(a) quantifying the biomarker in the sample,

(b) quantifying one or two reference genes in the sample, and

(c) obtaining a normalized biomarker quantity by normalizing the biomarker level obtain in step (a) over the one or two reference gene level obtained in step (b) ,

wherein the one or two reference genes are selected from the genes in Table 2, and wherein the method does not comprise quantifying any additional reference gene.
The method of claim 1, wherein the reference gene is PCBP2 or STXBP2.
The method of claim 1, wherein the reference genes are PCBP2 and STXBP2.
The method of any one of claims 1-3, wherein the biomarker is a DNA, RNA, or protein.
The method of any one of claims 1-4, wherein the biological sample is a blood sample.
The method of claim 5, wherein the blood sample is a plasma or serum sample.
The method of any one of claims 1-6, wherein the normalizing in step (c) comprises determining the ratio of the biomarker level obtained in step (a) to the one reference gene level obtained in step (b) .
The method of any one of claims 1-6, wherein the normalizing in step (c) comprises determining the ratio of the biomarker level obtained in step (a) to the geometric mean of the two reference gene levels obtained in step (b) .
The method of any one of claims 1-8, wherein the subject is a pregnant woman.
The method of claim 9, wherein the biomarker is FAM46A or LRRC58 RNA.
The method of claim 10, wherein step (a) comprises a reverse transcription-polymerase chain reaction (RT-PCR) .
The method of claim 11, wherein the RT-PCR is a quantitative RT-PCR (qRT-PCR) .
The method of any one of claims 10-12, further comprising step (d) :

comparing the normalized biomarker quantity obtained from step (c) to a standard control value, determining the normalized biomarker quantity being higher or lower than the standard control value, and determining the pregnant woman as suffering from a pregnancy-associated condition or at an increased risk of developing a pregnancy-associated condition.
The method of claim 13, wherein the pregnancy-associated condition is preeclampsia.
The method of claim 14, further comprising step (e) :

administering an effective amount of Aspirin, an antihypertensive medication, an anticonvulsant medication, or a corticosteroid to the pregnant woman determined in step (d) as suffering from preeclampsia or at an increased risk of developing preeclampsia; or

inducing labor in the pregnant woman determined in step (d) as suffering from preeclampsia.
A kit for analyzing a biomarker in a biological sample, comprising:

(1) a first container containing a first agent for detecting the biomarker;

(2) a second container containing a second agent for detecting one reference gene; and

(3) optionally a third container containing a third agent for detecting another, different reference gene,

wherein the one or two reference genes are selected from the genes in Table 2, and wherein the kit does not comprise any additional agents for detecting any additional reference genes.
The kit of claim 16, wherein the biomarker is FAM46A or LRRC58 RNA.
The kit of claim 16 or 17, wherein the one reference gene is PCBP2 or STXBP2, or wherein the two reference genes are PCBP2 and STXBP2.
The kit of any one of claims 16-18, wherein the first, second, and/or third agent is an agent for an amplification reaction for quantifying the biomarker or reference gene.
The kit of any one of claims 16-19, further comprising an instruction manual.