Circulating RNA markers for diagnostic use

Abstract

Methods are provided for diagnosing and monitoring diseases, in particular pregnancy-related conditions, based on the analysis of biomarkers circulating in the blood of a test subject. Also disclosed herein are reference markers for normalizing diagnostic biomarker levels to improve diagnostic performance. In addition, kits, compositions and devices useful in the methods are described.

Description

[0001] Related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 526,392, filed July 12, 2023, the contents of which are incorporated herein by reference in their entirety for all purposes. Background of the Invention

[0004] Circulating biomarkers in peripheral blood represent a source of non-invasive biomarkers for predicting and monitoring diseases or symptoms. Various methods for measuring biomarkers of this nature have been published in existing publications, see, for example, WO2022 / 192467, WO2019 / 227015, WO2018 / 210275. For example, placental-derived cell-free RNA (cfRNA) is readily detectable in maternal plasma and has been found to have clinical applications in predicting placental or pregnancy-related complications. These complications include preeclampsia, a leading cause of maternal and infant death and long-term health consequences. Recently, a classifier combining measurements of 18 preeclampsia-related cfRNAs in maternal plasma from 5 to 16 weeks of gestation has shown potential to aid in the prediction of preeclampsia (Validation cohort 2: 56% detection rate (DR) at a 31% false positive rate (FPR), or 36% DR at a 10% FPR). To achieve this, the RNA levels of these 18 genes need to be normalized using the RNA levels of an additional 66 reference genes. Therefore, RNA sequencing (RNA-seq) or complex techniques are required for prediction using this 84-gene classifier. Consequently, this method is not easily scalable to widespread screening in the obstetric population.

[0005] This invention provides a method for: (i) normalizing blood levels of a single circulating biomarker (e.g., one of the 18 cfRNAs mentioned above) using the levels of one or two reference genes, and (ii) predicting a disease (e.g., preeclampsia) using the normalized levels of said single circulating biomarker using one or two reference genes. Since only RNA levels of two or three genes are required, this method can be performed using reverse transcription quantitative polymerase chain reaction (RT-qPCR). As shown, the 3-gene classifier of this invention can be used to predict preeclampsia with high performance (20-fold cross-validation: 88% DR at 15% FPR, or 85% DR at 10% FPR). Because RT-qPCR is cheaper and less time-consuming than RNA sequencing, this method can be scaled up to screening large populations and providing test results in a relatively short turnaround time. Women identified as having an elevated risk of preeclampsia may benefit from aspirin prophylaxis to reduce the chance of developing the condition, or, if the condition is confirmed to have developed, to reduce its severity.

[0006] The standardized methods disclosed herein can also be applied to predict or monitor circulating biomarkers for other diseases or conditions, both within and outside the realm of pregnancy (e.g., fetal growth restriction, gestational diabetes, cancer, and transplant rejection). Furthermore, these methods can be applied not only to biomarkers in plasma but also to biomarkers in serum, whole blood, their components, or derivatives. The standardized methods disclosed facilitate rapid and cost-effective identification of diseases, enabling appropriate interventions to be administered to high-risk patients identified by those biomarkers to reduce morbidity or improve outcomes. Summary of the Invention

[0007] This disclosure relates to the use of cell-free RNA biomarkers circulating in an individual's bloodstream for the purpose of assessing the presence or risk of a medical condition in that individual. Because variations may be introduced during sample preparation and processing, it is necessary to standardize the signal of a diagnostically valuable biomarker relative to the signal of at least one reference biomarker to ensure reliable diagnostic readouts, which remain relatively stable across all individuals, whether or not they have the condition to be assessed. The inventors have identified several novel reference biomarkers, each of which can be used alone or in conjunction with only another reference biomarker during the standardization process to achieve high diagnostic performance, thereby significantly reducing the necessary workload in the standardization process.

[0008] Therefore, in a first aspect, the present invention provides a method for analyzing biomarkers present in biological samples taken from an object (e.g., a male or female of any age, of any race or medical background, e.g., a pregnant woman). The method comprises the steps of: (a) quantifying biomarkers in the sample, (b) quantifying one or two reference genes in the sample, and (c) obtaining a standardized amount of biomarker by standardizing the biomarker level obtained in step (a) relative to the level of one or two reference genes 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 genes besides those used in step (b). In some embodiments, only one reference gene, PCBP2, is quantified in step (b). In some embodiments, only one reference gene, STXBP2, is quantified in step (b). In some embodiments, both reference genes, PCBP2 and STXBP2, are quantified in step (b).

[0009] In some embodiments, the biological sample used in this method is a blood sample, such as plasma or serum. In some embodiments, the biomarker is DNA. In some embodiments, the biomarker is RNA. In some embodiments, the biomarker is a protein. In some embodiments, the biomarker is an analyte of alternative nature, such as a metabolite, such as lipids, carbohydrates, or a small molecule that may be organic or inorganic. In some embodiments, when the level of only one reference gene is measured in step (b), the normalization in step (c) includes determining the ratio of the biomarker level obtained in step (a) to the level of one reference gene obtained in step (b). In some embodiments, when the levels of two reference genes are measured in step (b), the normalization in step (c) includes determining the ratio of the biomarker level obtained in step (a) to the geometric mean of the levels of the two reference genes obtained in step (b).

[0010] In some embodiments, the subject of the method is a pregnant woman. In some embodiments, the woman is tested for the presence of pregnancy-related conditions (e.g., preeclampsia) or the risk of developing pregnancy-related conditions. In some implementations, the biomarker is any biomarker associated with pregnancy-related symptoms or an increased risk of developing pregnancy-related symptoms, such as biomarkers associated with the development of preeclampsia (see, for example, WO2022 / 192467; Moufarrej et al. Nature. 2022;602(7898):689-94; Zhou S, Li J, Xue Pet 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 biomarkers associated with the occurrence of preterm birth (see, for example, Ngo TTM, Moufarrej MN, Rasmussen MH et al. Science. 2018;360(6393):1133-6; Liang L, Rasmussen MH, Piening B, Shen JK, Alfirevic A. ExpertRev Mol Med. 2022;24:1-24).Other 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, Röst H, etal. Metabolic Dynamics and Prediction of Gestational Age and Time to Deliveryin 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, RasmussenMH, Camunas-Soler J, Pan W, Okamoto J, et al. Noninvasive blood tests forfetal 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 includes reverse transcription polymerase chain reaction (RT-PCR) to measure the amount of RNA biomarker, for example, using a quantitative RT-PCR (qRT-PCR) method. In some embodiments, after step (c), the method further includes step (d), which includes: first, comparing the amount of the standardized biomarker obtained from step (c) with a standard control value; then determining whether the amount of the standardized biomarker is higher or lower than the standard control value; and finally determining the increased risk of the pregnant woman having pregnancy-related conditions or developing pregnancy-related conditions. When using the biomarker FAM46A RNA or LRRC58 RNA, a standardized amount higher than the standard control value indicates the presence or increased risk of preeclampsia.For example, the amount of a standardized biomarker may be about 2.0%, 2.5%, 3.0%, 3.5%, 4.0%, 4.5%, or 5.0% higher or lower than a standard control value, which can be used as a threshold for determining whether a test subject is positive for the tested symptom. In an implementation of testing pregnant women for the potential presence or risk of preeclampsia, the claimed method further includes preventive or therapeutic treatment steps: (1) administering an effective amount of aspirin, an antihypertensive drug, an anticonvulsant (e.g., magnesium sulfate), or a corticosteroid to a pregnant woman identified in step (d) as having preeclampsia or at an increased risk of developing preeclampsia; or (2) inducing labor in a pregnant woman identified in step (d) as having preeclampsia, particularly when the woman is in late pregnancy (e.g., after at least 30 weeks of gestation, such as 32 or 33 weeks or more).

[0011] In a second aspect, the present invention provides a kit for analyzing biomarkers in biological samples. The kit comprises (1) a first container containing a first reagent for detecting a biomarker; (2) a second container containing a second reagent for detecting a reference gene; and (3) optionally, a third container containing a third reagent for detecting another different reference gene, wherein the one or two reference genes are selected from genes in Table 2, and wherein the kit does not include reagents for detecting any other reference genes besides the reference genes in (2) and optionally (3).

[0012] In some embodiments, the kit includes reagents for detecting the biomarker FAM46A RNA. In some embodiments, the kit includes reagents for detecting the biomarker LRRC58 RNA. In some embodiments, the kit includes one or more reagents for detecting only one reference gene, PCBP2. In some embodiments, the kit includes one or more reagents for detecting only one reference gene, STXBP2. In some embodiments, the kit includes one or more reagents for detecting only two reference genes, PCBP2 and STXBP2. In some embodiments, the first, second, and / or third reagents include reagents for amplification reactions (e.g., PCR, such as RT-PCR, particularly qRT-PCR) to quantify the biomarker or reference gene. In some embodiments, an instruction manual is included in the kit to provide users with information for proper use of the kit.

[0013] In a third aspect, the present invention provides the 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 biomarkers present in biological samples taken from a subject. In this particular use, (1) the amount of biomarkers present in the sample is first determined; (2) the amount of one or two newly identified reference genes present in the sample is then determined; and (3) finally, the standardized amount of the biomarker in the sample is calculated by standardizing the initially determined amount of biomarker relative to the amount of reference genes. In this particular use, there is no further step of quantifying any other reference genes besides the one or two reference genes used in step (2). In some embodiments, only one reference gene, PCBP2, is quantified in step (2). In some embodiments, only one reference gene, STXBP2, is quantified in step (2). In some embodiments, both reference genes, PCBP2 and STXBP2, are quantified in step (2).

[0014] In some embodiments, the biological sample used for this purpose is a blood sample, such as plasma or serum. In some embodiments, the biomarker is DNA. In some embodiments, the biomarker is RNA. In some embodiments, the biomarker is a protein. In some embodiments, the biomarker is an analyte of alternative nature, such as a metabolite, such as lipids, carbohydrates, or a small molecule that may be organic or inorganic. In some embodiments, when the level of only one reference gene is measured in step (2), the standardization in step (3) includes determining the ratio of the biomarker level obtained in step (1) to the level of one reference gene obtained in step (2). In some embodiments, when the levels of two reference genes are measured in step (2), the standardization in step (3) includes determining the ratio of the biomarker level obtained in step (1) to the geometric mean of the levels of the two reference genes obtained in step (2).

[0015] In some embodiments, the purpose of this use is to test pregnant women, for example, to test for the presence of pregnancy-related conditions (e.g., preeclampsia) or the risk of developing pregnancy-related conditions. In some embodiments, the biomarker is FAM46A RNA. In some embodiments, the biomarker is LRRC58 RNA. In some embodiments, step (1) of the testing procedure includes reverse transcription polymerase chain reaction (RT-PCR) to measure the amount of RNA biomarker, for example, using a quantitative RT-PCR (qRT-PCR) method. In some embodiments, after step (3), the testing procedure further includes step (4), which includes: first, comparing the amount of the standardized biomarker obtained from step (3) with a standard control value; then determining whether the amount of the standardized biomarker is higher or lower than the standard control value; and finally determining whether the pregnant woman has a pregnancy-related condition or is at increased risk of developing a pregnancy-related condition. For example, the amount of a standardized biomarker may be approximately 2.0%, 2.5%, 3.0%, 3.5%, 4.0%, 4.5%, or 5.0% higher or lower than a standard control value. These values ​​can be used as thresholds to determine whether a test subject is positive for the tested symptom. In an implementation of testing pregnant women for the potential presence or risk of preeclampsia, the testing process further includes preventative or therapeutic steps: (i) administering an effective amount of aspirin, an antihypertensive drug, an anticonvulsant (e.g., magnesium sulfate), or a corticosteroid to a pregnant woman identified in step (4) as having preeclampsia or at an increased risk of developing preeclampsia; or (ii) inducing labor in a pregnant woman identified in step (4) as having preeclampsia, particularly when the woman is in late pregnancy (e.g., after 33 weeks of gestation). Attached Figure Description

[0016] Figure 1A and Figure 1B The levels of circulating biomarkers were standardized using different methods disclosed in this invention. Two published circulating biomarkers (RNA transcripts of FAM46A and LRRC58) for an 18-gene classifier used for early PE prediction were analyzed by RNA levels of PCBP2 and STXBP2. Figure 1A ) or only through the RNA level of STXBP2 ( Figure 1B Standardization was performed. Maternal plasma samples were collected from women who developed or did not develop PE (preeclampsia) between 11 and 16 weeks of gestation. Mann-Whitney rank-sum test, p < 0.05.

[0017] Figure 2The 3-gene approach was used to predict the performance of preeclampsia in independent maternal plasma samples. RNA levels of two published circulating biomarkers used for PE prediction, LRRC58 (top) and FAM46A (bottom), were standardized by RNA levels of PCBP2 and STXBP2. (i.e., 2-reference gene normalization), as disclosed in this invention. This forms the basis of a 3-gene approach (1 PE-related gene + 2 reference genes) for predicting PE. However, published methods combine RNA levels of all 18 PE-related genes, and these levels need to be normalized by RNA levels of 66 reference genes; therefore, this prior art teaches an 84-gene approach for PE prediction. The performance of the 3-gene approach was evaluated in a test sample (blue curve) that was not involved in model training and was therefore independent of the samples in the training set (20 replicates of cross-validation). The performance of another method for PE prediction is also shown, namely the Fetal Medicine Foundation early pregnancy triple screening (grey dashed line), which requires ultrasound examination. PE, preeclampsia. AUC, area under the ROC curve.

[0018] Figure 3 The 2-gene approach was used to predict the performance of preeclampsia in independent maternal plasma samples. RNA levels of two published circulating biomarkers used for PE prediction, LRRC58 (top) and FAM46A (bottom), were standardized by STXBP2 RNA levels. (i.e., 1-reference gene normalization), as disclosed in this invention. This forms the basis of a 2-gene (1 PE-related gene + 1 reference gene) approach for predicting PE. However, published methods combine RNA levels of all 18 PE-related genes, and these levels need to be normalized by RNA levels of 66 reference genes; therefore, this prior art teaches an 84-gene approach for PE prediction. The performance of the 2-gene approach was evaluated in a test sample (blue curve) that was not involved in model training and was therefore independent of the samples in the training set (20 replicates of cross-validation). The performance of another method for PE prediction is also shown, namely the Fetal Medicine Foundation early pregnancy triple screening (grey dashed line), which requires ultrasound examination. PE, preeclampsia. AUC, area under the ROC curve.

[0019] definition

[0020] As used herein, the term "biological sample" or "sample" includes tissue sections, such as biopsy and autopsy samples, as well as frozen sections prepared for histological purposes, or any form of processing of such samples. Biological samples include blood and blood components or products (e.g., serum, plasma, platelets, erythrocytes, etc.), sputum or saliva, lymph and tongue tissue, cultured cells (e.g., primary cultures, explants, and transformed cells), feces, urine, esophageal biopsy tissue, etc. Biological samples are typically obtained from eukaryotes, which can be mammals, primates, or human subjects.

[0021] In this disclosure, the term "biopsy" refers to the process of removing a tissue sample for diagnostic or prognostic assessment, as well as the tissue sample itself. Any biopsy technique known in the art can be applied to the diagnostic and prognostic methods of this invention. The biopsy technique applied will depend on the type of tissue to be assessed (e.g., tongue, colon, prostate, kidney, bladder, lymph nodes, liver, bone marrow, blood cells, gastric tissue, esophagus, etc.) and other factors. Representative biopsy techniques include, but are not limited to, excisional biopsy, cutting biopsy, needle biopsy, surgical biopsy, and bone marrow biopsy, and may include colonoscopy or endoscopy. Those skilled in the art are familiar with a wide range of biopsy techniques and will select among these techniques and perform them with minimal experimentation.

[0022] As used herein, the term "blood" refers to a blood sample or preparation derived from an individual undergoing testing for the possible presence or risk of a specific medical condition, such as a pregnant woman undergoing testing for pregnancy-related symptoms. The term includes whole blood or any component of blood that contains varying concentrations of hematopoietic cells or any other type of cell or cellular residue (including platelets) of maternal or fetal origin, or even none of the aforementioned cells or cellular residues. Examples of "blood" include plasma and serum. A essentially cell-free blood sample is also referred to as "cell-free," in which a detectable amount of blood cells is absent.

[0023] As used in this application, the term "pregnancy-related condition" refers to any symptom or disease that may affect a pregnant woman, caused by or related to the woman's pregnancy status, the fetus she is carrying, or both the woman and the fetus. Such a symptom or disease may manifest itself for a limited period of time, such as during pregnancy or childbirth, or may persist throughout the lifespan of the fetus after birth. Some examples of pregnancy-related conditions include ectopic pregnancy, preeclampsia, preterm birth, and fetal chromosomal abnormalities such as trisomy 18 or trisomy 21.

[0024] As used in this article, "preeclampsia" refers to a condition that occurs during pregnancy, the main symptoms of which are various forms of high blood pressure, often accompanied by the presence of protein in the urine and edema (swelling). Preeclampsia, sometimes called toxemia of pregnancy, is associated with a more serious condition called "eclampsia," which is preeclampsia with seizures. These conditions usually develop during the second half of pregnancy (after 20 weeks), but they can develop shortly after birth or before 20 weeks of gestation.

[0025] The term "nucleic acid" or "polynucleotide" refers to deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) in single-stranded or double-stranded form and their polymers. Unless specifically defined, the term includes nucleic acids containing known analogs of natural nucleotides, having similar binding properties to reference nucleic acids, and being metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise indicated, a specific nucleic acid sequence also implicitly includes variants of its conserved modifications (e.g., degenerate codon substitutions), alleles, homologous sequences, single nucleotide polymorphisms (SNPs), and complementary sequences, as well as explicitly indicated sequences. Specifically, degenerate codon substitutions can be achieved by producing sequences in which the third position of one or more selected (or all) codons is replaced by a mixture of bases 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.

[0026] The term "gene" refers to a segment of DNA involved in the production of a polypeptide chain; it includes regions before and after the coding region (leader and tail regions), as well as spacer sequences (introns) between the individual coding segments (exons), the regions before and after the coding region being involved in the transcription / translation of the gene product and the regulation of transcription / translation.

[0027] In this application, the terms “polypeptide,” “peptide,” and “protein” are used interchangeably herein and refer to a polymer of amino acid residues. This term applies to amino acid polymers that are artificial chemical mimics of corresponding naturally occurring amino acids, as well as both naturally occurring and non-naturally occurring amino acid polymers. As used herein, the term includes amino acid chains of any length, including full-length proteins (i.e., antigens), wherein the amino acid residues are linked by covalent peptide bonds.

[0028] The term "amino acid" refers to naturally occurring amino acids and synthetic amino acids, as well as amino acid analogs and amino acid mimics that function in a manner similar to naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, and those subsequently modified, such as hydroxyproline, γ-carboxyglutamic acid, and O-phosphoserine. For the purposes of this application, an amino acid analog refers to a compound having the same basic chemical structure as a naturally occurring amino acid, i.e., having a carbon atom bound to a hydrogen group, a carboxyl group, an amino group, and an R group, such as homoserine, ortholeucine, methionine sulfoxide, and methionine methylsulfonium. Such analogs have modified R groups (e.g., ortholeucine) or modified peptide backbones, but retain the same basic chemical structure as naturally occurring amino acids. For the purposes of this application, an amino acid mimic refers to a compound having a structure different from the general chemical structure of an amino acid, but functioning in a manner similar to naturally occurring amino acids.

[0029] Amino acids may include those that have non-naturally occurring D-chirality, as disclosed in WO01 / 12654, which can improve the stability (e.g., half-life), bioavailability, and other characteristics of peptides containing one or more of these D-type amino acids. In some cases, one or more, and possibly all, amino acids of a therapeutic peptide are D-chiral.

[0030] Amino acids can be referred to in this article using either the commonly known three-letter symbols or the single-letter symbols recommended by the IUPAC-IUB Biochemistry Nomenclature Committee. Similarly, nucleotides can be referred to using their generally accepted single-letter codes.

[0031] As used in this application, “increase” or “decrease” refers to a detectable positive or negative change in quantity compared to a comparative control (e.g., an established standard control). An increase is a positive change, which is typically at least 10%, or at least 20%, or 50%, or 100% of the control value, and can be as high as at least 2 times, or at least 5 times, or even 10 times the control value. Similarly, a decrease is a negative change, which is typically at least 10%, or at least 20%, 30%, or 50% of the control value, or even as high as at least 80% or 90% of the control value. Other terms indicating changes or differences in quantity compared to the comparison basis, such as “more,” “less,” “higher,” and “lower,” are used in this application in the same manner as described above. In contrast, the terms “substantially the same” or “substantially no change” indicate that the change in quantity is small or no compared to the standard control value, typically within ±10% of the standard control, or within ±5%, 2%, or even less of the standard control.

[0032] As used herein, a "standard control" refers to a polynucleotide sequence or polypeptide (e.g., DNA, RNA, or protein of a predetermined biomarker gene) present in a predetermined amount or concentration in an established normal, disease-free tissue sample (e.g., a blood sample taken from a healthy pregnant woman carrying a healthy fetus). Standard control values ​​are applicable to the use of the methods of this invention and serve as a benchmark for comparing the amount of biomarker gene DNA, RNA, or protein present in a test sample. The established sample used as a standard control provides an average amount of biomarker DNA, RNA, or protein, which is typical for a specific type of biological sample from an average healthy person who does not suffer from a specified target disease as conventionally defined. Standard control values ​​can be varied depending on the nature of the sample and other factors, such as the sex, age, and ethnicity of the subject establishing such control values.

[0033] In the context of describing healthy individuals who do not suffer from any specific target disease as defined conventionally, the term "average" refers to certain characteristic values, particularly the amounts of biomarkers DNA, RNA, or protein, found in a human biological sample that represent a randomly selected group of healthy individuals who do not suffer from any disease (e.g., pregnancy-related symptoms or conditions). This selected group should include a sufficient number of individuals such that the average amount of the biomarker DNA, RNA, or protein in the sample type among these individuals reflects, with reasonable accuracy, the corresponding amount of the biomarker DNA, RNA, or protein in the general population of healthy individuals. Furthermore, other factors are considered, such as sex, race, and medical history, and preferably, the circumstances of the test subjects are closely matched to the individuals in the selected group from which the "average" value is established.

[0034] As used in this application, the term "quantity" refers to the amount of a target polynucleotide or target polypeptide present in a sample, such as the amount of a pre-selected human biomarker in the form of DNA, RNA, or protein. Such quantities can be expressed in absolute terms, i.e., the total number of polynucleotides or polypeptides in the sample, or in relative terms, i.e., the concentration of polynucleotides or polypeptides in the sample.

[0035] As used in this application, the terms "treat" or "treating" describe actions that eliminate, lessen, alleviate, reverse, or prevent or delay the onset or recurrence of any symptoms that lead to the related condition. In other words, "treating" a condition includes both therapeutic and preventative interventions for that condition.

[0036] As used herein, the term "effective amount" refers to the amount of a given substance that is sufficient in quantity to produce the desired effect. For example, an effective amount of a therapeutic agent for the treatment or prevention of preeclampsia is an amount in which the therapeutic agent achieves a detectable effect on the condition, such that in a patient who has been given a drug for therapeutic purposes, the symptoms of preeclampsia are reduced, reversed, eliminated, prevented, or delayed. An amount sufficient to achieve a specified purpose is defined as a "preventive effective dose" or a "therapeutic effective dose." Dosage ranges vary depending on the nature of the therapeutic agent administered and other factors such as the route of administration and the severity of the patient's condition.

[0037] The term “about” used in this document with a specified value indicates a range of + / - 10% of the value. For example, “about 10” means a range of + / - 10% of 10, i.e., 9 to 11.

[0038] As used herein, the term "subject" or "subject requiring treatment" includes individuals seeking medical attention due to the risk of or actual possession of a pre-determined disease or condition, such as a pregnancy-related condition, eclampsia. Subjects also include individuals currently receiving treatment and seeking adjustments to their treatment regimen. Subjects or individuals requiring treatment include those exhibiting symptoms of a condition or at an increased risk of having the condition or its symptoms. For example, subjects requiring treatment include individuals with a genetic predisposition or family history of any pregnancy-related condition (particularly eclampsia), those who have previously had related symptoms, those who have been exposed to triggering substances or events, and those who have chronic or acute symptoms of the condition. "Subjects requiring treatment" can be of any life age.

[0039] Detailed description

[0040] I. Introduction

[0041] The inventors have discovered that by standardizing procedures based on RNA levels from one or more genes selected from PCBP2, STXBP2, and others named in Table 2, technical variations can be minimized when measuring circulating analytes in biological samples derived from the blood of a test subject. Such samples include plasma, serum, whole blood, or any component or derivative of such samples. Since plasma and serum samples or their derivatives are typically obtained through separation techniques (e.g., centrifugation to remove blood cells), the remaining RNA material in the harvested sample is considered as cell-free RNA (cfRNA). Circulating analytes can be any biomarkers used to predict disease or symptoms, such as RNA, cfRNA, DNA, proteins, or metabolites.

[0042] Using one or more of these reference genes during the standardization process allows for significant improvements in the diagnostic performance of methods utilizing circulating biomarkers. In particular, this invention provides a diagnostic method for detecting or monitoring pregnancy-related conditions (e.g., preeclampsia) by analyzing circulating RNA levels of LRRC58 and / or FAM46A. This invention also provides detection kits, related compositions, and detection devices for such methods.

[0043] II. General Methods

[0044] This invention utilizes conventional techniques from the field of molecular biology. Basic textbooks disclosing the general methods used 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).

[0045] For nucleic acids, sizes are given in kilobases (kb) or base pairs (bp). These sizes are estimates derived from nucleic acids obtained by agarose or acrylamide gel electrophoresis, sequencing, or published DNA sequences. For proteins, sizes are given in kilodaltons (kDa) or the number of amino acid residues. Protein sizes are estimated based on proteins obtained by gel electrophoresis, sequencing, deduced amino acid sequences, or published protein sequences.

[0046] Oligonucleotides that are not commercially available can be chemically synthesized, for example using automated synthesizers (such as Van Devanter). et. al. , Nucleic Acids Res. As described in 12:6159-6168 (1984), according to the solid-phase phosphoramidite method (first developed by Beaucage and Caruthers, Tetrahedron Lett. 22:1859-1862 (1981) Description) Chemical synthesis. Purification of oligonucleotides was performed using any strategy recognized in the art, such as natural acrylamide gel electrophoresis or anion-exchange high-performance liquid chromatography (HPLC), as described by Pearson and Reanier. J. Chrom. As described in 255: 137-149 (1983).

[0047] The target sequence used in this invention, such as a polynucleotide sequence of the human LRRC58 or FAM46A gene, and the synthesized oligonucleotides (e.g., primers) can be verified using, for example, chain termination methods used for sequencing double-stranded templates, such as those employed by Wallace. et al. , GeneAs stated in 16:21-26 (1981).

[0048] III. Sample Acquisition and Biomarker Analysis

[0049] This invention relates to measuring the amount of diagnostically valuable analytes or biomarkers, such as DNA, RNA (e.g., mRNA), proteins, or other chemically significant molecules, found in biological samples, particularly fluid samples, such as bodily fluids (e.g., blood or any component thereof), secretions, sweat, or excretions, as a means of detecting the presence of a pre-selected disease or condition (such as pregnancy-related conditions, e.g., preeclampsia), assessing the risk of developing said disease or condition, and / or monitoring the progression of said disease or condition or the efficacy of treatment. Therefore, the first step in carrying out this invention is to obtain a suitable biological sample from the test subject and extract the analyte, such as mRNA, DNA, or protein, from said sample.

[0050] A. Sample Acquisition and Preparation

[0051] The methods of this invention are used to obtain appropriate biological samples from individuals with a disease or condition to be tested or monitored (e.g., preeclampsia). For example, tissue or blood samples are collected from an individual during a biopsy or blood draw procedure according to standard protocols typically followed in hospitals or clinics. A suitable amount of tissue or blood, or any other fluid, is collected and can be stored according to standard procedures prior to further preparation.

[0052] Analysis of RNA or DNA analytes found in tissue or blood samples of a subject can be performed using, for example, cell-free fractions of whole blood, such as plasma or serum. Methods for preparing biological samples for nucleic acid extraction are well known to those skilled in the art. For example, the tissue or blood sample of the subject is first treated to disrupt cell membranes, thereby releasing the nucleic acids contained within the cells. On the other hand, when using cell-free blood fractions (e.g., plasma or serum), the step of disrupting cell membranes is not required.

[0053] B. RNA extraction and quantification

[0054] There are many methods for extracting RNA from biological samples. General methods for mRNA preparation can be followed (e.g., those developed by Sambrook and Russell). Molecular Cloning: A Laboratory Manual(As described in 3d ed., 2001); RNA can also be obtained from biological samples of the test subject using a variety of commercially available reagents or kits, such as Trizol reagent (Invitrogen, Carlsbad, CA), Oligotex Direct mRNA kit (Qiagen, Valencia, CA), RNeasy Mini kit (Qiagen, Hilden, Germany), and PolyATtract® Series 9600™ (Promega, Madison, WI). Combinations of more than one of these methods can also be used.

[0055] Eliminating contaminating DNA from RNA preparations is essential. Therefore, samples should be carefully processed, thoroughly treated with DNase, and appropriate negative controls should be used in the amplification and quantification steps.

[0056] 1. Quantitative determination of RNA levels based on PCR

[0057] When RNA is extracted from a sample, the amount of biomarker RNA can be quantified. Preferred methods for determining RNA levels are amplification-based methods, such as polymerase chain reaction (PCR), particularly reverse transcription polymerase chain reaction (RT-PCR).

[0058] Prior to the amplification step, a DNA copy (cDNA) of the biomarker RNA is typically synthesized. This is achieved through reverse transcription, which can be performed as a standalone step or within a homogeneous reverse transcription polymerase chain reaction (RT-PCR), a modification of the polymerase chain reaction used to amplify the RNA. Methods suitable for PCR amplification of ribonucleic acid are described in the following literature: Romero and Rotbart in Diagnostic Molecular Biology: Principles and Applications pp.401-406; Persing et al. , eds., MayoFoundation, Rochester, MN, 1993; Egger et al. , J. Clin. Microbiol. 33:1442-1447, 1995; and U.S. Patent No. 5,075,212.

[0059] General PCR methods are well known in the field and therefore will not be described in detail here. For a review of PCR methods, protocols, and the principles of primer design, see, for example, Innis, et al. , PCR Protocols: A Guide to Methods and Applications, Academic Press, Inc. NY, 1990. PCR reagents and protocols are also available from commercial vendors such as Roche Molecular Systems.

[0060] PCR is most commonly performed as an automated process using thermostable enzymes. In this process, the temperature of the reaction mixture is automatically cyclicated through denaturation, primer annealing, and extension reaction zones. Machines specifically designed for this purpose are commercially available.

[0061] Although PCR amplification of target mRNAs is commonly used to carry out this invention, those skilled in the art will recognize that amplification of mRNA species in a sample can be accomplished by any known method, such as ligase chain reaction (LCR), transcription-mediated amplification, and self-sustaining sequence replication-dependent amplification (NASBA), each of which provides sufficient amplification. Recently developed branched DNA techniques can also be used to quantify the amount of mRNA species in a sample. For a review of branched DNA signal amplification for the direct quantification of nucleic acid sequences in clinical samples, see Nolte. Adv. Clin. Chem. 33:201-235, 1998.

[0062] 2. Other quantitative methods

[0063] Biomarker mRNAs can also be detected using other standard techniques well known to those skilled in the art. While the detection step typically precedes the amplification step, amplification is not required in the method of this invention. For example, mRNAs can be identified by size grading (e.g., gel electrophoresis) regardless of whether an amplification step is performed. After running a sample in an agarose or polyacrylamide gel and labeling it with ethidium bromide according to well-known techniques (see, e.g., Sambrook and Russell, ibid.), the presence of a band of the same size as a standard control indicates the presence of the target mRNA, and the amount of said target mRNA can then be compared to the control based on the intensity of the band. Alternatively, oligonucleotide probes specific to biomarker mRNAs can be used to detect the presence of such mRNA species and, based on the signal intensity imparted by the probe, indicate the amount of mRNA compared to a standard comparison.

[0064] Sequence-specific probe hybridization is a well-known method for detecting specific nucleic acids containing other types of nucleic acids. Under sufficiently stringent hybridization conditions, the probe hybridizes only with substantially complementary sequences. The stringency of hybridization conditions can be relaxed to tolerate varying amounts of sequence mismatches.

[0065] Various hybridization methods are well-known in the art, including but not limited to liquid-phase, solid-phase, or mixed-phase hybridization assays. The following article provides an overview of various hybridization assay methods: 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 , IRLPress, 1987.

[0066] Hybridization complexes are detected using well-known techniques. Nucleic acid probes capable of specifically hybridizing with target nucleic acids (i.e., mRNA or amplified DNA) can be labeled using any of several methods commonly used to detect the presence of hybridized nucleic acids. A common detection method is to use probes labeled with... 3 H, 125 I, 35 S, 14 C or 32 Probes such as P are used for autoradiography. The choice of radioisotope depends on research preferences attributed to the ease of synthesis, stability, and half-life of the selected isotope. Other labels include compounds that bind to anti-ligands or antibodies (e.g., biotin and digoxigenin), which are labeled with fluorophores, chemiluminescent agents, and enzymes. Alternatively, the probe may be directly conjugated to a label, such as a fluorophore, chemiluminescent agent, or enzyme. The choice of label depends on the required sensitivity, ease of conjugation to the probe, stability requirements, and available instruments.

[0067] The probes and primers necessary for carrying out this invention can be synthesized and labeled using well-known techniques. The oligonucleotides used as probes and primers can be synthesized using automated synthesizers (such as Needham-VanDevanter). et al. , Nucleic Acids Res. As described in 12:6159-6168, 1984, according to the solid-phase phosphoramidite method (first developed by Beaucage and Caruthers, Tetrahedron Letts. (As described in 22:1859-1862, 1981) Chemical synthesis. Purification of oligonucleotides was performed by natural acrylamide gel electrophoresis or by anion exchange HPLC, as described by Pearson and Regnier. J. Chrom. As described in , 255:137-149, 1983.

[0068] C. DNA extraction and quantification

[0069] The methods for extracting DNA from biological samples are well-known and frequently practiced in the field of molecular biology; see, for example, Sambrook and Russell, ibid. RNA contamination should be eliminated to avoid interfering with DNA analysis.

[0070] After extraction, the target DNA is subjected to sequence-based analysis and quantitative evaluation. An amplification reaction may be performed optionally prior to sequence-based analysis and quantitative evaluation. Various polynucleotide amplification methods are well-established and frequently used in research. For example, the general method of polymerase chain reaction (PCR) for polynucleotide sequence amplification is well known in the art and has been discussed above.

[0071] Although PCR, such as quantitative PCR, is commonly used to carry out the present invention, those skilled in the art will recognize that the amplification of the relevant polynucleotide sequence can be accomplished by any known method, such as ligase chain reaction (LCR), transcription-mediated amplification, loop-mediated isothermal amplification, and self-sustaining sequence replication or nucleic acid sequence-dependent amplification (NASBA), each of which provides sufficient amplification.

[0072] D. Quantification of protein

[0073] Proteins of any specific 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 that has a specific binding affinity for the protein. The protein can then be detected with a labeled antibody that has a specific binding affinity for the protein. Such immunological assays can be performed using microfluidic devices, such as microarray protein chips. Target proteins (e.g., protein biomarkers) can also be detected by gel electrophoresis (e.g., two-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 an appropriate antibody. Both monoclonal and polyclonal antibodies (including antibody fragments with the desired binding specificity) can be used for the specific detection of proteins. Such antibodies and their binding fragments that have a specific binding affinity for a particular protein that is a biomarker can be generated using known techniques.

[0074] In carrying out this invention, other methods can also be used to measure the levels of protein biomarkers. For example, various methods based on mass spectrometry have been developed to rapidly and accurately quantify target proteins, even in large sample volumes. These methods involve highly sophisticated equipment, such as triple quadrupole (triple Q) instruments using multiple reaction monitoring (MRM) technology, matrix-assisted laser desorption / ionization time-of-flight tandem mass spectrometry (MALDI TOF / TOF), ion trap instruments using selective ion monitoring (SIM) mode, and QTOP mass spectrometers based on electrospray ionization (ESI). See, for example, Pan et al. , J Proteome Res. 2009 February; 8(2):787–797.

[0075] IV. Establish Standard Comparison

[0076] To establish a standard control group for implementing the methods of the present invention, a group of healthy individuals who do not have a pre-existing condition (e.g., pregnancy-related condition, such as preeclampsia) as conventionally defined are first selected. Where applicable, these individuals are within appropriate parameters for the purpose of screening and / or monitoring for a pre-existing condition (e.g., preeclampsia) using the methods of the present invention. For example, for the purpose of testing for a pregnancy-related condition, such as preeclampsia, the control group consists of healthy pregnant women each carrying a healthy fetus, without any symptoms or elevated risk of any pregnancy-related condition. Optionally, the individuals are of the same sex, similar age, or similar ethnic background.

[0077] The health status of the selected individuals is confirmed through well-established and routinely employed methods, including but not limited to a general physical examination of the individual and a general review of their medical history.

[0078] Furthermore, the selected group of healthy individuals must be of a reasonable size such that the average amount / concentration of biomarkers in relevant tissue or fluid samples obtained from that group can reasonably be considered representative of the normal or average level of biomarkers in similar samples from a general population of healthy individuals. Preferably, the selected group includes at least 10 human subjects.

[0079] When a biomarker's mean is established based on individual values ​​found in each subject of a selected healthy control group, that mean, median, representative value, or spectrum is considered a standard control. The standard deviation is also determined during the same process. In some cases, separate standard controls may be established for separately defined groups with different characteristics (e.g., age, sex, or ethnic background).

[0080] For applications that include a standardization process that uses one or two reference genes to reduce inter-sample variability in biomarker readout, the same standardization process can also be performed in the generation of the corresponding standard control values.

[0081] V. Standardization

[0082] By demonstrating that certain biomarkers, such as the RNA of PCBP2, STXBP2, and other genes identified in Table 2, are present in relatively stable quantifications in cell-free blood samples obtained from individuals, regardless of the individual's health status, this invention provides the important use of these biomarkers as a benchmark in methods for quantitatively measuring biomarkers (especially diagnostically valuable biomarkers) in samples. This standardization step can significantly improve the comparability of biomarker readings across all samples by minimizing inter-sample variability due to sample processing / manipulation. Therefore, incorporating this standardization step can significantly improve the diagnostic performance of biomarker-based methods.

[0083] In the context of this invention, the standardization step in the 2-gene (1 biomarker with diagnostic value + 1 reference gene) method is performed by calculating the ratio of the biomarker amount to the reference gene amount, while the standardization step in the 3-gene (1 biomarker with diagnostic value + 2 reference genes) method is performed by calculating the ratio of the biomarker amount to the geometric mean of the amounts of the two reference genes. This standardization step produces a standardized amount of biomarker, which allows for meaningful comparisons between the readout of the test sample and the already standardized standard control value.

[0084] When the amount of the standardized biomarker is calculated, the method of the invention is further carried out by comparing the amount of the standardized biomarker with a standard control value to determine whether the amount of the standardized biomarker is greater than the standard control value. This, in turn, allows determination of whether the individual being tested has a condition (e.g., pregnancy-related conditions such as preeclampsia) or is at increased risk of developing such a condition. Subsequently, a healthcare provider can prescribe and administer appropriate medication to the individual for treatment and prevention purposes.

[0085] VI. Treatment Methods

[0086] By illustrating the correlation between the amount of diagnostic biomarkers in relevant samples and the tested disease or symptom (e.g., pregnancy-related conditions), the present invention further provides means for early detection and therefore for treating patients with said symptom or at an increased risk of developing said symptom at a later time, particularly for conditions where early intervention is particularly advantageous. Optionally, the diagnostic method of the present invention is implemented in conjunction with subsequent steps of further testing using conventional methods to confirm the diagnosis of the disease or symptom. The attending physician can then prescribe and administer appropriate treatment to the patient. Treatment of the disease or symptom as used herein includes reducing, reversing, alleviating, or eliminating one or more symptoms of the disease or symptom, and preventing or delaying the onset of one or more related symptoms.

[0087] For example, analyzing biomarkers FAM46A RNA or LRRC58 RNA in cell-free blood components (e.g., plasma or serum) using the method according to the invention can indicate the presence or increased risk of preeclampsia in pregnant women, including during early stages, such as the first 16 weeks of pregnancy, and possibly before the onset of symptoms. As a result, one or more medications, such as aspirin, antihypertensive drugs, anticonvulsants, or corticosteroids, can be administered to the women for prevention.

[0088] VII. Reagent Kits and Devices

[0089] The present invention provides compositions and kits for carrying out the methods described herein to assess the levels of biomarkers (e.g., biomarkers with diagnostic value) in samples taken from subjects. These can be used for a variety of purposes, such as assessing the spectrum of biomarker expression, or for detecting or diagnosing the presence of an associated symptom, determining the risk of developing a symptom, and monitoring the progression of a symptom in an individual, including assessing the likelihood of an effective treatment response resulting from administering treatment to an individual with a symptom.

[0090] Kits used to perform assays to determine biomarker levels typically include at least one reagent for detecting, particularly quantitatively, the biomarker. The kit also includes at least one reagent for detecting, particularly quantitatively, one or more reference genes (e.g., PCBP2, STXBP2, and other genes identified in Table 2). For example, oligonucleotide primers for PCR capable of detecting, particularly quantitatively, DNA or RNA biomarkers and reference genes are included in the kit. Optionally, each oligonucleotide primer is labeled with a detectable portion.

[0091] Kits used to determine protein biomarker levels typically include at least one antibody for specifically binding to the amino acid sequence of the protein biomarker. Optionally, the antibody is labeled with a detectable moiety. The antibody can be a monoclonal or polyclonal antibody. In some cases, the kit may include at least two different antibodies, one for specifically binding to the biomarker protein (i.e., the primary antibody) and another for detection (i.e., the secondary antibody), which is typically linked to the detectable moiety.

[0092] Typically, the kit also includes appropriate standard controls. Standard controls indicate the mean value of biomarkers in tissue or body fluid samples from healthy subjects who do not have the tested condition. In some cases, such standard controls may be provided in the form of set values. Furthermore, the kit of the present invention may provide an instruction manual to guide users in analyzing test samples and assessing the presence, risk, or progression of the condition in the test subjects.

[0093] In a further aspect, the invention may also be embodied in an apparatus or system comprising one or more such apparatuses capable of performing all or some of the method steps described herein. For example, in some cases, after receiving a biological sample, such as a cell-free blood sample taken from a subject being tested to detect a specific condition (e.g., pregnancy-related conditions, such as preeclampsia), assess the risk of developing the condition, or monitor the progression of the condition, the apparatus or system performs the following steps: (a) determining the amount or concentration of a biomarker in the sample; (b) determining the amount or concentration of one or two reference genes selected from PCBP2, STXBP2, and other genes identified in Table 2 in the sample; (c) standardizing the amount of the biomarker relative to the amount of the one or two reference genes; and (d) comparing the standardized amount of the biomarker 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 the subject's condition has changed, i.e., worsened or improved. In other cases, the apparatus or system of the present invention performs the tasks of steps (d) and (e) after steps (a) to (c) have been performed and the amount of standardized biomarker from step (c) has been input into the apparatus. Preferably, the apparatus or system is partially or fully automated.

[0094] Example

[0095] The embodiments described below are provided by way of illustration only and not by way of limitation. Those skilled in the art will readily recognize that various non-critical parameters can be changed or modified to produce substantially the same or similar results.

[0096] introduction

[0097] Currently, there are at least two existing methods, A and B, for early pregnancy prediction of preeclampsia: (A) the Fetal Medicine Foundation (FMF) early pregnancy triple screening, which combines maternal characteristics from 11 to 13 weeks of gestation with MAP, UtA-PI and serum PlGF (1, 2), and (B) a classifier that includes 18 genes associated with preeclampsia measured by maternal plasma cfRNA and 66 reference gene transcripts for standardization (3).

[0098] Several methods have been developed for early pregnancy prediction of preeclampsia, but most of them have not yet undergone external validation or have failed external validation (4). Notably, however, Method A has undergone successful internal and external validation. Method A requires ultrasound examination and therefore necessitates time-consuming training and regular certification of ultrasound personnel. Method B, which does not require ultrasound examination, is considered one of the most recent methods for predicting preeclampsia, but its requirement to measure 84 genes limits its use in large populations for study.

[0099] In this invention, a novel method C is disclosed, which can be used to predict preeclampsia and is based on the normalization of RNA levels of a single circulating biomarker (a member of the aforementioned 18-gene classifier in Method B) using one or two newly identified reference genes. Typically, Method C includes (i) normalizing blood levels of one or more disease-related circulating biomarkers using the levels of one or two reference genes disclosed herein, and (ii) predicting the disease using the normalized levels of the circulating biomarker using one or two reference genes. As shown, Method C is used to normalize the levels of a single preeclampsia-related RNA biomarker in maternal plasma at 12 to 16 weeks of gestation and is used to predict preeclampsia in the late stages of pregnancy.

[0100] The key advantage of this invention (Method C) over these existing methods is its ease of implementation. Existing Method A requires the measurement of three biomarkers using three different types of instruments. Specifically, Method A requires the measurement of UtA-PI via ultrasound, thus necessitating the training, regular evaluation, and certification of ultrasound personnel. Furthermore, Method A requires maternal demographic characteristics and medical history, which are not always accurately available. Existing Method B requires the measurement of a total of 84 (=18+66) cfRNA transcripts via RNA sequencing or a complex technology platform for detection. These methods are not easily scalable for universal screening of all pregnant women and require long turnaround times (several days). However, this invention requires the measurement of RNA levels in only two to three genes via RT-qPCR, which is relatively inexpensive, less labor-intensive, and has a shorter turnaround time (several hours). Therefore, this invention (Method C) is more likely to be scaled up for large-scale or universal screening applications because it requires 28 times fewer genes for RNA measurement than Method B.

[0101] Another advantage of the method of the present invention is its potentially higher performance in predicting preeclampsia. For predicting preeclampsia in preterm birth, existing method A achieved a DR of 64% at 10% FPR, while its performance in predicting term preeclampsia in Asian populations was lower (2). For predicting preeclampsia, existing method B achieved a DR of 36% at 10% FPR, based on results from validation cohort 2 (3). In contrast, method C achieved a DR of 85% at 10% FPR (20-fold cross-validation) for predicting preeclampsia.

[0102] Three previous publications, referred to as Methods D through F, relate to PE prediction based on circulating RNA transcripts and are considered methods that may achieve the DR of Method A, which is the most rigorously validated prediction method currently available. Method D (Zhou...) et al, Am J Obstet Gynecol (2023) requires sequencing of more than 13 genes and RNA, and achieving 67% DR at 10% FPR (5). Method E (Yoffe et al. Sci Rep. 2018;8(1):3401) requires 6 genes and achieves 45% DR at 10% FPR (6). Therefore, similarly, method C has these advantages over methods D and E: method C is easier to operate (3 genes, no RNA sequencing) and achieves a higher DR of 85% at 10% FPR. In addition, method F (Rasmussen et al. NatureMethod C (2022; 601(7893):422-427) was based on blood samples collected between 16 and 27 weeks of gestation (7), which is too late for effective aspirin prophylaxis. In contrast to Method F, Method C was based on blood samples collected before 16 weeks of gestation, which is early enough for effective prophylaxis.

[0103] 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 listed in Table 2 exhibit low coefficients of variation across individuals, even when exposed to drastically changing physiological conditions. Therefore, these genes are well-suited as reference genes for standardizing the measurement of biomarkers, minimizing noise, maximizing biomarker signals, and ultimately improving the discriminative or predictive performance of biomarkers.

[0104] The practical application of this invention is to improve the performance of at least two published circulating biomarkers (3) that are potential for predicting preeclampsia by normalizing the RNA levels of PCBP2 and STXBP2. Originally, this was proposed by Moufarrej... et al. In method (3) described in WO2022 / 192467, a classifier combining all 18 measurements of maternal plasma cfRNA levels of FAM46A, LRRC58, and 16 other genes, for predicting PE, achieved a detection rate (DR) of 56% at a false positive rate (FPR) of 31% (or a DR of 36% at 10% FPR) (validation group 2 in (3)). To achieve this performance, the levels of those 18 cfRNAs need to be normalized to the levels of 66 reference genes; therefore, Moufarrej's method for predicting PE is an 84-gene method.

[0105] In the method designed based on this invention, the maternal plasma RNA level of LRRC58 is normalized using the RNA levels of PCBP2 and STXBP. LRRC58 alone (only Moufarrej) et al.The normalized RNA levels of one of the 18 reported cfRNAs, for predicting preeclampsia, achieved 83% DR at 16% FPR (or 85% DR at 10% FPR) (20 replicate cross-validations; training:test = 1:1). To achieve this performance, maternal plasma RNA levels of FAM46A were normalized using RNA levels of PCBP2 and STXBP, thus making it a 3-gene approach. Therefore, in this embodiment for predicting PE, the method of the present invention reduces the number of genes required for RNA measurement from 84 to 3, a reduction of 28-fold. Therefore, the method of the present invention is easy to implement, requires less complex equipment, and has a shorter turnaround time. This illustrates how the present invention increases the signal-to-noise ratio of the target analyte and improves the performance of circulating biomarkers for disease prediction.

[0106] Because the method of this invention improves the performance of circulatory biomarkers, it more accurately predicts a patient's risk of disease / symptoms. In the above embodiments, the improved biomarkers allow for the daily administration of low-dose prophylactic aspirin to women identified as being at risk of preeclampsia, thereby reducing the incidence and severity of symptoms. Generally, prediction using tests with improved performance will more effectively guide patients toward appropriate interventions, resulting in less overtreatment and undertreatment.

[0107] The method of this invention is based on the systematic identification of circulating RNAs that maintain stable levels in the plasma of subjects undergoing drastic physiological changes. The concept of identifying reference genes for appropriate biological substrates for detecting diseases / symptoms is well-known but often unattainable (10). This disclosure includes a list of reference genes to achieve efficient standardization of circulating biomarkers or analytes. Without this disclosure, significant effort would be required to identify reference genes and design and optimize assays to quantify them.

[0108] Furthermore, it can be inferred that the findings reported in this paper apply not only to biomarkers circulating in plasma but also to biomarkers circulating in serum and whole blood. This inference is based on the fact that hematopoietic cells or blood cells are likely the primary source of nucleic acids (including RNA) in plasma and serum (11,12). Therefore, when measuring any target analyte or biomarker circulating not only in plasma but also in serum, whole blood, its components, or derivatives, genes with stable RNA levels in plasma can be used to minimize noise (through methods based on the RNA levels of those genes).

[0109] background

[0110] Circulating cell-free RNA (cfRNA) in biological fluids represents a valuable source of potential biomarkers for monitoring and diagnosing diseases. Malignant tumors release their RNA into the plasma as cfRNA, thus some cfRNAs can be used to diagnose cancer and monitor treatment. Similarly, the placenta, considered a pseudomalignant organ, also releases its RNA into maternal plasma as cfRNA (13,14). Since the placenta is central to the pathogenesis of many pregnancy-related complications, such as preeclampsia, maternal cfRNAs are a potential source of biomarkers for such diseases.

[0111] Preeclampsia (PE) is a pregnancy-specific hypertension affecting millions of pregnant women (15). Current clinical management strategies involve identifying women at high risk for PE between 11 and 13 weeks of gestation (16), enabling timely aspirin prophylaxis to reduce the incidence and severity of PE (17). Various early pregnancy models for PE prediction have been developed, but most have not undergone external validation or have failed external validation (4). Notably, however, the Fetal Medicine Foundation (FMF) early pregnancy model, the triple screening, has undergone successful internal and external validation. The FMF early pregnancy triple screening, which combines maternal characteristics with mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), and serum placental growth factor (PlGF) measured between 11 and 13 weeks of gestation, achieves a higher detection rate (DR) compared to screening with maternal factors alone (1). The FMF triple screening has been shown to achieve DRs of 75% (18) and 64% (2) for preterm PE prediction in mixed European and Asian populations, with a false positive rate (FPR). However, a significant proportion of women at risk of PE remain unidentified. Furthermore, the implementation of screening is limited by UtA-PI measurement, which requires training for ultrasound personnel and appropriate equipment for ultrasound examinations; and maternal characteristics are often incomplete.

[0112] Since the placenta is central to the pathogenesis of preeclampsia, maternal cell-cfRNA is a potential source of biomarkers for preeclampsia. Maternal plasma cell-cfRNA transcripts of several genes associated with preeclampsia have been reported, including corticotropin-releasing hormone (CRH). CRH mRNA (19-22), but the overlap between the PE and non-PE groups renders them clinically useless. Recently, Moufarrej... et al.A logistic regression model has been developed to predict preeclampsia using 18 allegedly PE-associated cfRNAs measured between 5 and 16 weeks of gestation (3). In external validation (validation cohort 2), the model achieved a 56% DR (36% DR) at 31% FPR for PE prediction. To achieve this performance, in addition to maternal plasma levels of these 18 RNA transcripts, the model requires RNA levels of 66 reference genes for normalization to address unwanted baseline variability between samples, such as gestational age at blood collection. Therefore, this 84-gene approach requires RNA sequencing or sophisticated detection platforms, which are expensive, have long turnaround times, and cannot be easily scaled up for widespread screening.

[0113] Invention Summary

[0114] In this disclosure, the inventors describe that the circulating RNA levels of PCBP2, STXBP, and other genes in Table 2 are relatively stable even when subjects undergo drastic physiological changes. It is further disclosed that the circulating RNA levels of the aforementioned genes can be used to minimize unwanted technical variations (i.e., noise) when measuring the levels of target analytes or circulating biomarkers. Therefore, the method of the present invention increases the signal-to-noise ratio of the measured analyte and improves its discriminative power in tests for predicting or diagnosing diseases or symptoms. As an example, the inventors illustrate that a published 84-gene method for PE prediction can be simplified to a 2- or 3-gene method with improved predictive performance.

[0115] It is understandable that when PE involves multiple etiologies, the definition and diagnostic criteria for PE have evolved over time, and discrepancies exist in existing techniques for PE prediction. To ensure the effectiveness of evaluating the performance of 2- or 3-gene approaches in PE prediction, the inventors followed the updated definition of preeclampsia [Magee] by a representative expert group within the International Society for the Study of Hypertension in Pregnancy. et al. Pregnancy Hypertens [2022;27:148-69] (32). In addition, all diagnoses of PE in this study were confirmed from medical records.

[0116] Example: Predicting preeclampsia using a 2- or 3-gene approach

[0117] A. Recruiting participants and processing samples

[0118] Study population: Participants in this nested case-control study were recruited from existing programs (Early Pregnancy Screening and Preeclampsia Prevention Trial; FORECAST). This study included mothers older than 18 years, singleton pregnancies, and those carrying 11 babies. +0 Up to 13 +6Women with a surviving fetus within a week were excluded. Exclusion criteria included multiple pregnancies, the presence of significant abnormalities identified on ultrasound scans, those unable to provide written informed consent or having learning difficulties, those unable to understand spoken or written Chinese or English, or those who caused termination of pregnancy or miscarriage. This validation study was approved by the Joint Committee on Clinical Research Ethics of the Chinese University of Hong Kong – New Territories East Hospital Cluster (CREC Ref No. 2018.391) and registered on ClinicalTrials.gov (registration number: NCT03941886).

[0119] In the FORECAST study, women were non-selectively screened using the Fetal Medicine Foundation (FMF) early pregnancy triple screening to predict preeclampsia (PE). Each woman screened at high risk for PE was invited to participate in this nested study and contribute a peripheral blood sample before 16 weeks of gestation. Each high-risk woman was matched with at least one low-risk woman based on maternal age (+ / - 3 years), maternal weight at screening (+ / - 5 kg), and other possible confounding factors. The matched women were also invited to participate in the study and contribute blood samples before 16 weeks of gestation. High-risk and low-risk women were followed up until delivery, and pregnancy outcomes, including the presence or absence of preeclampsia, were confirmed in the medical records. The definition of preeclampsia (PE) is provided by the International Society for the Study of Hypertension in Pregnancy (Magee et al.). Pregnancy Hypertens 2022;27:148-69; detailed below) for the diagnosis of PE (23). Therefore, after follow-up, each PE case could be matched with at least one nonPE control based on maternal age (+ / - 3 years), maternal weight at screening (+ / - 5 kg), and other possible confounding factors of this study, including the storage duration of the study blood sample (+ / - 4 weeks).

[0120] In summary, the large and non-selective female cohort was narrowed down to a case-control group of women with and without preeclampsia (PE). Appropriately matched maternal plasma samples from women who eventually developed preeclampsia in late pregnancy (PE women) and women who did not develop preeclampsia (non-PE women) were retrieved for this study. This study collected comprehensive clinical data, including known maternal risk factors for preeclampsia, measurements of biophysical and biochemical markers from the FMF early pregnancy triple screening used to predict PE, pregnancy outcomes, gestational age at delivery, infant birth weight, and pregnancy complications (including preeclampsia).

[0121] During the study, 22 participants were screened for high risk of preterm PE and later developed PE using the FMF early pregnancy triple screening. Using the criteria described above, these 22 participants were matched with participants who were screened for high risk of preterm PE but did not develop PE later, based on three confounding factors: (i) one participant screened for high risk of preterm PE but did not develop PE later, and (ii) one participant screened for low risk of preterm PE but did not develop PE later. Sufficient maternal plasma sample tubes were then retrieved from all participants except for the 12 participants in (i). Therefore, maternal plasma samples were available from 54 participants (PE, n=22; non-PE, n=32) (Table 1). There were no differences in maternal age and weight between the PE and non-PE groups, but the PE group was intentionally selected due to higher mean arterial pressure, uterine artery pulsatility index, and lower placental growth factor, which are biomarkers for the FMF early pregnancy triple screening.

[0122] Definition of preeclampsia: The definition of preeclampsia is based on the definition of the International Society for the Study of Hypertension in Pregnancy (32) [Magee et al.]. Pregnancy Hypertens [2022;27:148-69]. Preeclampsia (new-onset) is gestational hypertension (clinical systolic blood pressure (sBP) ≥ 140 mmHg and / or diastolic blood pressure (dBP) ≥ 90 mmHg at 20 weeks of gestation) accompanied by one or more of the following new-onset symptoms at ≥20 weeks of gestation: 1. Proteinuria 2. Other maternal end-organ dysfunctions, including: Neurological complications (e.g., eclampsia, altered mental status, blindness, stroke, clonic seizures, severe headache, or persistent scotomas) pulmonary edema Hematological complications (e.g., platelet count < 150,000 / μL, disseminated intravascular coagulation (DIC), hemolysis) Acute kidney injury (AKI) (e.g., creatinine ≥ 90) mol / L or 1 mg / dL) Liver involvement (e.g., elevated aminotransferases, such as alanine aminotransferase (ALT) or aspartate aminotransferase (AST) > 40 IU / L), with or without right upper quadrant or epigastric pain. 3. Uteroplacental dysfunction (e.g., placental abruption, angiogenesis imbalance, fetal growth restriction, abnormal umbilical artery Doppler waveform analysis, or intrauterine fetal death).

[0123] Preeclampsia with chronic hypertension is defined as the new onset of proteinuria, other maternal organ dysfunction, or uteroplacental dysfunction (as described above) in women with chronic hypertension.

[0124] Gestational age is determined by measuring the crown-rump length of the fetus at 11 to 13 weeks.

[0125] Research Design Description

[0126] 1. Multiparous women with a history of preeclampsia (PE). The proposed study recruited women with a history of PE as well as other women without such a history, as PE screening via FMF early pregnancy triple screening is performed on a non-selective obstetric population. Women with a history of PE were included using current inclusion / exclusion criteria; women with a history in Asian and mixed European populations accounted for approximately 4.9% and 8.8% of all women currently developing PE during pregnancy, respectively (2,24). The Asian 7-region study included 10,935 non-selective singleton pregnancies, of which 224 developed PE. Of these cases, only 11 women (4.9%) had a prior history of PE. The European 5-country study included 8,775 non-selective singleton pregnancies, of which 239 developed PE. Of these cases, only 21 women (8.8%) had a prior history of PE. The advantage of this study design, which recruits a larger and more complete group of women likely to develop preeclampsia, is that the 2- or 3-gene assessment methods used to predict PE are more likely to be applicable to the general obstetric population.

[0127] 2. The inclusion window for the proposed study was 11 to 16 weeks of gestation, as used in this study. Currently, the FMF triple screening appears to be the only rigorously validated method for effectively predicting PE in a large and non-selective pregnancy group. Therefore, utilizing the current knowledge of these three existing biomarkers is advantageous in developing any new methods for PE prediction. Combining new biomarkers with the triple screening biomarkers could yield tests for more accurate PE prediction. Notably, before 11 weeks, two of the triple screening biomarkers, uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF), were unable to distinguish between women with and without PE. This is because both biomarkers depend on the stage or size of placental development, which is premature and small at <11 weeks. However, the potential new biomarkers for PE prediction presented in this study are expected to be effective even at <11 weeks (i.e., 5 to 16 weeks).

[0128] Blood processing and RNA isolation: Peripheral blood samples from the antecubital fossa of pregnant women were collected in 9 mLEDTA vacuum blood collection tubes (VACUETTE™ K3EDTA Blood Collection Tubes, Greiner Bio-One GmbH, Kremsmunster, Austria) and centrifuged at 1,600 × g for 10 min at 4°C. The plasma was then transferred to a plain polypropylene tube and centrifuged again at 16,000 × g for 10 min at 4°C. The supernatant was collected in a fresh polypropylene tube. Three volumes of TRIzol LS reagent (Thermo Fisher Scientific) were added to one volume of the supernatant and stored at -80°C until RNA extraction. Unless otherwise specified, the remaining steps were performed according to the manufacturer's instructions. Briefly, chloroform was added to the plasma-TRIzol LS mixture. The mixture was then centrifuged at 12,000 × g for 15 min at 4°C. The aqueous layer was transferred to a new tube. One volume of 70% ethanol was added to one volume of the aqueous layer. The mixture was then applied to an RNeasy column (RNeasy Mini Kit, Qiagen). Unless otherwise specified, the remaining steps were performed according to the manufacturer's instructions. Total RNA was eluted with RNase-free water. DNase I (Thermo Fisher Scientific) treatment was performed to remove any contaminating DNA. The methods described above for blood processing and RNA isolation are given by way of illustration only. Those skilled in the art will understand that similar methods are feasible for performing the same task, for example, using RNA later (Thermo Fisher Scientific) and RNA Complete BCT (Streck), with or without modification of the manufacturer's protocols.

[0129] B. Reduce noise in the measurement of cyclic markers

[0130] Systematic data-driven approaches for selecting reference genes to standardize the levels of circulating biomarkers: To accurately quantify the levels of a target analyte in a blood sample, the measured levels of that analyte need to be standardized using an internal control reference gene to eliminate unwanted variations between samples (e.g., slight variations in total RNA concentration). Although the use of reference genes is the most common method for standardizing RT-qPCR data (25), their effectiveness must be experimentally validated according to the Minimum Information (MIQE) guidelines published for real-time quantitative PCR experiments (26), for specific tissue or cell types and specified experimental designs. The reference gene mRNA should be validated to be stably expressed in different samples. Despite improved awareness, the urgent need for careful consideration and empirical validation in selecting reference genes remains widely overlooked (10,27), resulting in inadequately standardized RNA signals, which are often masked by high levels of noise.

[0131] Specifically, for the measurement of circulating biomarkers, to ensure effective standardization, reference genes should be present at relatively constant levels in blood samples collected from different subjects, even if the subjects are undergoing dramatic physiological changes, such as pregnancy. During the 40 weeks of pregnancy, women adapt to a range of hormonal, immune, and hemodynamic changes in response to fetal growth. Therefore, if the circulating RNA level of a gene is stable throughout all three trimesters of a woman's pregnancy, it is reasonable to consider it suitable as a reference gene for standardizing the levels of circulating biomarkers or target analytes. Thus, this study aims to identify reference genes in RNA sequencing datasets of plasma samples from pregnant women before delivery.

[0132] One advantage of the identification strategy of this invention is that, despite undergoing drastic changes, pregnancy is a relatively normal process compared to cancer or other diseases in which the circulatory system is inevitably affected by the disease process and drugs. Therefore, maternal plasma RNA sequencing datasets have relatively low noise levels. Another advantage is that genes with stable RNA levels in maternal plasma may also have stable RNA levels in whole blood, but the reverse is not true; therefore, reference genes identified in maternal plasma may also be useful in whole blood. Compared to whole blood or other biological samples from which typical cellular RNA can be extracted, RNA in maternal plasma is free and characterized by low quantity and low quality. These physical properties of RNA in maternal plasma pose challenges to the construction of RNA sequencing libraries. Unsurprisingly, it is rare to find maternal plasma RNA sequencing datasets for all pre-partum periods of pregnancy, let alone systematically analyze the stability of RNA transcripts in these datasets.

[0133] A systematic analysis was performed on pre-labor plasma RNA sequencing datasets from women across all three gestational periods. High-quality reads were used to estimate RNA levels for each gene based on a trimmed mean of the M value (28). For each gene, the estimated RNA level was expressed as count per million reads (CPM) per sample, along with the coefficient of variation (CV) of the RNA level in samples from different women across all three gestational periods. CV was calculated by dividing the standard deviation by the mean. Genes with RNA levels not exceeding 30 CPM or CV > 50% in the datasets were removed. The retained genes were then sorted based on the stability of their expression levels across all datasets using the NormFinder (29) package (version 5) in the R language (30). NormFinder provides stability values ​​for each pair of genes, where smaller values ​​reflect high stability of RNA levels in the test samples. PCBP2 and STXBP2 had the smallest mean stability value (3.66) and the lowest mean CV. Other genes with relatively low stability values ​​(< or = 4.38, 5th percentile of retained genes) and low mean CVs (< or = 50%) were also documented (Table 2). Therefore, PCBP2, STXBP2 and other genes in Table 2 are well-suited as reference genes for standardizing RNA levels in plasma.

[0134] Therefore, it can be inferred that these reference genes in the above research findings can be applied to reduce technical variability (through standardization or similar techniques) in measuring the levels of other types of analytes circulating in blood besides cfRNA, such as conventional (i.e., non-free) RNA, DNA, proteins, and metabolites. Furthermore, it can be deduced from this finding that the research findings can be applied to reduce technical variability in the levels of circulating analytes, which are present not only in plasma but also in serum, whole blood, and any components or derivatives of such biological samples. This includes plasma, serum, whole blood, peripheral blood mononuclear cells, or components thereof that come into contact with chemicals / objects such as RNA later (ThermoFisher 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), RNAComplete BCT (Streck), or similar objects coated, treated, or loaded with reagents involving anticoagulants, preservatives, protectants, formaldehyde, imidazolidinyl urea, EDTA, or glycine. Two studies form the basis for the inferences observed in data from plasma. First, based on a sex-mismatched bone marrow transplantation model, hematopoietic cells have been shown to be the primary source of cell-free DNA in plasma and serum (11). Second, it has also been observed that among the circulating cfRNA species in maternal plasma, there are relatively more non-placental specific cfRNAs than placental specific cfRNAs (12). This observation can be explained by the proportion of such non-placental specific cfRNAs from maternal tissues (e.g., hematopoietic cells). Since blood cells are likely the primary source of RNA in plasma and serum, it can be inferred that reference genes identified in plasma, as disclosed in this disclosure, can be used to minimize noise when measuring any target analytes or circulating markers in serum, plasma, whole blood, their components, or derivatives.

[0135] Among the vast number of genes with detectable RNA levels in plasma, the genes identified in this paper are well-suited for use as reference genes because their circulating RNA levels remain stable despite drastic physiological changes in the subjects. The results described above from this systematic, data-driven approach allow others to avoid overtesting the thousands of genes detected in blood samples when searching for reference genes for effective standardization.

[0136] Background of the reference genes identified in this article: PCBP2, STXBP2, and other genes listed in Table 2 have not been reported to have stable RNA levels in blood, its components, or derivatives; nor have they been reported as reference genes for standardization. However, the genomic structure and function of PCBP2 and STXBP2 are summarized below.

[0137] The PCBP2 (poly(RC) binding protein 2; HGNC: 8648; NCBI gene: 5094) gene expresses at least seven RNA transcript variants (accession number (fixed) and version number (varies over time): NM_005016.6, NM_031989.5, NM_001098620.3, NM_001128911.2, NM_001128912.2, NM_001128913.2, NM_001128914.2), ranging from 3.0 kb to 3.2 kb, and each containing up to 15 exons. Based on the NCBI gene abstract of the 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 that may be involved in RNA binding (31, 32). This multi-exon mRNA is thought to produce PCBP-1 via retrotransposition, which is an intronless gene with similar function to PCBP2 (33). This gene (PCBP2) and PCBP-1 have paralogous genes (PCBP3 and PCBP4), which are thought to have arisen as a result of the entire gene replication event. This gene (PCBP2) also has two processing pseudogenes (PCBP2P1 and PCBP2P2).

[0138] The STXBP2 (synaptic fusion protein-binding protein 2; HGNC:11445; NCBI gene: 6813) gene is expressed by at least five RNA transcript variants (accession numbers and version numbers: NM_006949.4, NM_001127396.3, NM_001272034.2, NR_073560.2, and NM_001414484.1), ranging from 1.86 kb to 1.99 kb, each containing up to 21 exons. STXBP2 encodes a member of the STXBP / unc-18 / SEC1 family (34-36). The encoded protein is involved in intracellular transport, control of SNARE (soluble NSF attaching protein receptor) complex assembly, and the release of cytotoxic granules by natural killer cells; mutations in this gene are associated with familial phagocytic lymphohistiocytic hyperplasia (37-39).

[0139] Selection of published PE-related cyclic biomarkers: based on Moufarrej et al A univariate analysis (3) reported in the publications created a candidate list of alleged PE-related genes, which were identified by Moufarrej. et al Members of the reported 18-cfRNA classifier were selected. All 18 members were sorted, and the two highest-ranking cfRNAs were chosen for the RT-qPCR assay developed in this study. First, cfRNAs showing progressively higher or lower rankings in the PE and normal blood pressure (NT) groups within the discovery, internal, and external validation cohorts were selected. Then, cfRNAs selected between the PE and NT groups in the internal validation cohort were sorted in ascending order using corrected p-values ​​(one-sided Mann-Whitney rank test, adjusted by Benjamini-Hochberg correction). The two highest-ranking cfRNAs, LRRC58 and FAM46A (also known as TENT5A), were selected for the development of the RT-qPCR assay.

[0140] Design and optimization of RT-qPCR assays, and standardization of circulating biomarker levels: While levels of any transcripts from PCBP2, STXBP2, and other genes in Table 2 can adequately standardize circulating biomarker levels, the following details how PCBP2 and STXBP2 are used as reference genes in specific RT-qPCR assays targeting PCBP2 mRNA and STXBP2 mRNA transcripts. Appropriate details regarding the design and optimization of the RT-qPCR assays also apply to the circulating biomarkers LRRC58 mRNA and FAM46A mRNA transcripts, which will be standardized in this embodiment.

[0141] While many possible RT-qPCR assays for a given gene can be readily obtained using primer design software, selecting and optimizing an RT-qPCR assay with high specificity for the intended transcript is not a straightforward task. Typical examples include PCBP2 and STXBP2, which encode multiple transcript variants, each with multiple exons, and exhibit highly similar nucleotide sequence segments in related gene families, paralogous genes, and pseudogenes. Therefore, primers and hydrolysis probes for RT-qPCR assays can nonspecifically bind to unintended RNA transcripts or to unintended locations on the same transcript, resulting in unwanted technical variations (noise) in the detection signal. In this embodiment, nonspecific amplification signals were examined for possible RT-qPCR assay designs against all known transcripts using Primer-BLAST (40) (NCBI) and computer-simulated PCR (41) on the UCSC Genome Browser (42). To avoid detecting any residual genomic DNA, primers were designed to be transintronic, if possible.

[0142] PCR primers and dual-labeled hydrolysis probes (5' 6FAM and 3' IBFQ, Integrated DNA Technologies) were synthesized and designed to amplify RNA transcripts from the identified reference genes PCBP2 and STXBP2 (reference transcripts) and RNA transcripts from the published PE-related genes LRRC58 and FAM46A (target transcripts) (Table 3). In addition to ordering synthetic RT-qPCR assay kits for PCBP2, STXBP2, LRRC58, and FAM46A RNA transcripts, commercially available pre-designed RT-qPCR assay kits, namely Hs.PT.58.20432738, Hs.PT.58.39066104.gs, Hs.PT.58.78733, and Hs.PT.58.19789006 (Integrated DNA Technologies), were ordered, each targeting the same transcripts. Reactions were assembled according to the reaction conditions (Table 4).

[0143] Thermal cycling and fluorescence detection were performed on a Roche LightCycler 480 (LC480) instrument (Roche Diagnostics, Basel, Switzerland). Each run included a template-free control (NTC). Quantitative cycling (Cq) values ​​were determined using Roche LightCycler 480 software (version 1.5.1.62) (Roche Diagnostics, Basel, Switzerland). The mean Cq values ​​for the replicates were calculated.

[0144] In 2-reference gene normalization, the level of the target transcript is calculated based on the ratio of the mean Cq value of the target transcript to the geometric mean of the two mean Cq values ​​of the reference transcript (43). In 1-reference gene normalization, the level of the target transcript is calculated based on the ratio of the mean Cq value of the target transcript to one mean Cq value of the reference transcript. The detection rate is calculated by dividing the number of samples with a positive amplified signal (Cq value less than 40) by the total number of samples tested in the experiment.

[0145] In the pilot experiments, commercial RT-qPCR assays were initially tested using a thermal profile provided by the manufacturer (Thermo Fisher Scientific). The default thermal profile consisted of: UNG incubation at 50°C for 2 min, polymerase activation at 95°C for 10 min, followed by 40 PCR cycles, each consisting of denaturation at 95°C for 15 s and annealing / extension at 60°C for 1 min. This resulted in a detection rate of <50% in maternal plasma samples in late pregnancy for all four commercial RT-qPCR assays. Changing the annealing / extension temperature did not significantly improve the detection rate. Next, to further optimize the amplification signal, a decremental PCR thermal profile designed by the inventors (Table 5) and four RT-qPCR assays were used, achieving a detection rate of >50% for each assay. This improvement in detection rate is attributed to the optimized annealing / extension temperature in the thermal profile and the shorter PCR amplicon size selected in these RT-qPCR assays. The former improvement may be related to the design of highly specific primers and probes for detecting RNA transcripts with a large number of paralogous genes or pseudogenes within a family. This results in improved specificity in each PCR cycle, with all reagents used to amplify the expected signal, leading to improved detection rates. The latter improvement may be related to the physical properties of the circulating RNA transcripts, which are relatively degraded and at low concentrations compared to RNA transcripts in other biological samples.

[0146] Evaluation of predictive performance: All participants in the relevant study cohort were randomly assigned to training and test sets (training:test ratio = 1:1). Each training and test set contained approximately equal proportions of women with late-stage PE or no-stage PE. A logistic regression model was trained using one or both reference genes normalized to the normalized level for each circulating biomarker (FAM46A RNA or LRRC58 RNA). Predictive performance for PE was evaluated in samples from the test set, which were not involved in model training (i.e., were unaware of the model's training process) and were therefore independent of the training set. The random assignment to training and test sets was repeated 20 times. Thus, a 20-repeated cross-validation method was used in evaluating predictive performance. The advantage of this method is that it minimizes overfitting to the data. Therefore, the estimates of sensitivity (TPR) and specificity (1-FPR) are more generalizable to larger populations. For comparison, another logistic regression model was trained using data from the FMF early pregnancy triple screening; that is, after adjusting for gestational age, maternal size, and past and present obstetric history, the levels of MAP, UtA-PI, and PlGF were reported as multiples of their expected median (MoM).

[0147] C. Results of circulating biomarkers normalized to RNA levels using two reference genes.

[0148] In each sample, the RNA level of circulating FAM46A was normalized using the RNA levels of two reference genes, PCBP2 and STXBP2 (i.e., 2-reference gene normalization). Similarly, the RNA level of circulating LRRC58 was normalized using the RNA levels of PCBP2 and STXBP2. In the non-PE and PE groups, 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), respectively. Figure 1A (Left figure). Compared with the non-PE group, the median log2-normalized level of FAM46A in plasma was 2.3 times higher in the PE group (Mann-Whitney, p <0.03).

[0149] In the non-PE and PE groups, the median (IQR) log2 standardized level of LRRC58 was -11.8 (ranges from -12.9 to -9.4) and -9.7 (ranges from -10.8 to -8.6), respectively. Figure 1A (See right figure). Compared with the non-PE group, the median log2-normalized level of LRRC58 in plasma was 2.1 times higher in the PE group (Mann-Whitney, p < 0.02).

[0150] Based on 20 replicate cross-validation data, using FAM46A 2-reference gene normalized RNA levels, the model for predicting PE achieved a DR of 83% at 16% FPR or 72% at 10% FPR, and an AUC of 0.932, which is greater than the AUC of triple screening (0.834, p < 0.001). (Table 6) Figure 2 (See figure below). Using LRRC58 2-reference gene normalized RNA levels, the model for predicting PE achieved 88% DR at 15% FPR or 85% DR at 10% FPR, and reached an AUC of 0.948, which is greater than the AUC of triple screening (0.834, p < 0.001) (Table 6). Figure 2 (See above image).

[0151] Unlike triple screening, the method using 2-reference gene normalized levels (RNA levels of FAM46A or LRRC58) for each circulating biomarker does not include measurement of UtA-PI, does not require ultrasound examination, and therefore does not require equipment, trained and qualified ultrasound personnel. Notably, this 3-gene approach (2 reference genes and 1 PE-related circulating biomarker) is potentially comparable to or better than triple screening, which is currently the most rigorously validated algorithm for predicting preterm PE (4).

[0152] D. Each Results of circulating biomarkers normalized to RNA levels using a reference gene

[0153] In each sample, the RNA level of circulating FAM46A was normalized only by the RNA level of a single reference gene, STXBP2 (i.e., 1-reference gene normalization). Similarly, the RNA level of circulating LRRC58 was normalized by the RNA level of STXBP2. In the non-PE and PE groups, 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), respectively. Figure 1B (Left figure). Compared with the non-PE group, the median log2 normalized level of FAM46A in plasma was 3.6 times higher in the PE group (Mann-Whitney, p <0.002).

[0154] In the non-PE and PE groups, the median (IQR) log2 standardized level of LRRC58 was -12.9 (-14.6 to -9.5) and 9.9 (-10.7 to -8.8), respectively. Figure 1B(See right figure). Compared with the non-PE group, the median log2-normalized level of LRRC58 in plasma was 3.0 times higher in the PE group (Mann-Whitney, p < 0.003).

[0155] Based on 20 repeated cross-validation data, using FAM46A 1-reference gene normalized RNA levels, the model for predicting PE achieved a DR of 69% at 10% FPR and an AUC of 0.907. (Table 7) Figure 3 (See figure below). Using LRRC58-standardized RNA levels based on a 1-reference gene, the model for predicting PE achieved a DR of 52% at 10% FPR and an AUC of 0.908 (Table 7). Figure 3 (See the figure above). For comparison, in the same cross-validation setting, the FMF triple screening achieved a DR of 51% and an AUC of 0.885 at 10% FPR.

[0156] Unlike triple screening, the method using a 1-reference gene normalized level (RNA level of FAM46A or LRRC58) for each circulating biomarker does not include measurement of UtA-PI, does not require ultrasound examination, and therefore does not require equipment, trained and qualified ultrasound personnel. Notably, this 2-gene method (1 reference gene and 1 PE-related circulating biomarker) is potentially comparable to or better than triple screening, which is currently the most rigorously validated algorithm for predicting preterm PE (4).

[0157] E. discuss

[0158] In this embodiment for predicting preeclampsia (PE), the disclosed method (collectively referred to as Method C) is compared with prior art methods (referred to as Method A, Method B, Method D, Method E, and Method F). Since the prevalence of PE differs in these studies, prevalence-dependent positive predictive value (PPV) and negative predictive value (NPV) should not be compared. Instead, prevalence-independent sensitivity (DR) at a given specificity (1-FPR) is listed below for comparison. For the 3-gene method, the performance of the combination of the reference genes PCBP2 and STXBP2 with the published circulating biomarker PE-associated maternal plasma LRRC58 cfRNA is listed. For the 2-gene method, the performance of the combination of the reference gene STXBP2 with the published PE-associated maternal plasma FAM46A cfRNA is listed.

[0159] Prediction methods before 16 weeks of pregnancy

[0160] Method C: Chim et al. 3-gene approach for all PE; 85% DR at 10% FPR.

[0161] Method C: Chim et al. 2-gene approach for all PE; 69% DR at 10% FPR.

[0162] Method A: Triple screening 3-gene method for preterm PE; at 10% FPR, 64% DR.

[0163] Method D: Zhou et al. 13-marker method for preterm PE; 10% FPR, 51% DR

[0164] Method E: Yoffe et al. 6-marker method for preterm PE; 10% FPR, 45% DR

[0165] Method B: Moufarrej et al. 84-gene^ method used for all PE; 10% FPR, 36% DR

[0166] Prediction methods after 16 weeks of pregnancy

[0167] Method F: Rasmussen et al. 7-marker method; all PE; 10% FPR, 65% DR

[0168] Symbol Explanation

[0169] Methods for predicting all PE or premature PE

[0170] n-markers and PE-related markers

[0171] n-genes include PE-related biomarker genes and reference genes.

[0172] ^ The biomarker levels standardized by methods relying on whole RNA sequencing data or the standardization method for RNA sequencing data is not clearly stated.

[0173] Table 1. Baseline characteristics of the study subjects in this study

[0174] Table 2. Genes with stable RNA levels in prepartum plasma samples collected from women across all three gestation periods.

[0175] Table 2 Explanation: Genes shown above with (i) a coefficient of variation (CV = standard deviation / mean) of RNA level < 50% and (ii) a minimum stability value calculated by NormFinder < or = 4.38 (the 5th percentile of genes retained after filtering as shown in paragraph

[0106] ) are considered reference genes for normalizing the RNA levels of other genes or analytes circulating in blood (including free plasma). Essentially, the minimum stability value is calculated by NormFinder based on pairing the RNA level of the corresponding gene with the RNA levels of any other 515 retained genes after filtering according to the criteria shown in paragraph

[0106] . In the complete results table of NormFinder, the median (5th percentile to 95th percentile) stability value is 5.84 (4.38 to 19.41). Since stability values ​​for 132,870 (= 515^2 / 2 + 515 / 2) possible pairs were calculated, summarized for each gene, minimum values ​​were identified for each gene, and compared in a systematic search against a reference gene, for clarity only genes with stability values ​​less than or equal to the 5th percentile (i.e., 4.38) are shown.

[0176] HGNC, the HUGO Gene Nomenclature Committee. HUGO, the Human Genome Organization.

[0177] Table 3. PCR primer and hydrolysis probe sequences for RNA transcripts of PCBP2, STXBP2, FAM64, and LRRC58 in RT-qPCR assays. All sequences are listed from 5' to 3'. For probes, the 5' end is labeled with 6-carboxyfluorescein (6-FAM), and the 3' end is labeled with Iowa Black Quencher FQ (IBFQ). Efficiency refers to PCR efficiency estimated according to the MIQE guidelines. Exon positions were determined based on RefSeq NM records for each gene in NCBI.

[0178]

[0179] Table 4. Reaction conditions for RT-qPCR assay. Volume is expressed as... L indicates that each reaction was made up to a total volume of 10 mL of RNase-free water. L.

[0180] TaqMan Universal Master Mix II (contains UNG) (Thermo Fisher Scientific, Cat. No. 4440038) Table 5. Thermal cycling program for RT-qPCR assay

[0181] Table 6. Biomarkers normalized by RNA levels of PCBP2 and STXBP2 (i.e., 2-reference gene normalized) for predicting independent # Performance of preeclampsia in maternal plasma samples. Corresponding values ​​of the triple screening markers are also included.

[0182]

[0183] illustrate: # Evaluate performance on test samples that were not used in model training (20 repeated cross-validations) Normalization was performed using the RNA levels of PCBP2 and STXBP2, as disclosed in this invention (see main text). ^ Triple screening based on conventional biomarkers (see main text) PE, preeclampsia AUC, Area under the ROC curve DR, detection rate FPR, False Positive Rate CI, confidence interval Table 7. Markers normalized to STXBP2 RNA levels (i.e., 1-reference gene normalized) for predicting independent # Performance of preeclampsia in maternal plasma samples

[0184] illustrate: # Evaluate performance on test samples that were not used in model training (20 repeated cross-validations) Normalization was performed at the RNA level of STXBP2, as disclosed in this invention (see text). PE, preeclampsia AUC, Area under the ROC curve DR, detection rate FPR, False Positive Rate

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[0239] All patents, patent applications and other publications cited in this application, including GenBank accession numbers or similar serial numbers, are incorporated herein by reference in their entirety for all purposes.

Claims

1. A method for analyzing biomarkers in biological samples taken from a subject, comprising the following steps: (a) Quantify the biomarkers in the sample. (b) Quantify one or two reference genes in the sample, and (c) Obtain the amount of standardized biomarker by standardizing the biomarker level obtained in step (a) relative to the level of one or both reference genes obtained in step (b). The one or two reference genes mentioned herein are selected from the genes in Table 2, and the method mentioned herein does not include quantifying any additional reference genes.

2. The method according to claim 1, wherein the reference gene is PCBP2 or STXBP2.

3. The method according to claim 1, wherein the reference genes are PCBP2 and STXBP2.

4. The method according to any one of claims 1 to 3, wherein the biomarker is DNA, RNA or protein.

5. The method according to any one of claims 1 to 4, wherein the biological sample is a blood sample.

6. The method of claim 5, wherein the blood sample is a plasma or serum sample.

7. The method according to any one of claims 1 to 6, wherein the standardization in step (c) comprises determining the ratio of the biomarker level obtained in step (a) to the reference gene level obtained in step (b).

8. The method according to any one of claims 1 to 6, wherein the standardization in step (c) comprises determining the ratio of the geometric mean of the biomarker level obtained in step (a) to the two reference gene levels obtained in step (b).

9. The method according to any one of claims 1 to 8, wherein the subject is a pregnant woman.

10. The method of claim 9, wherein the biomarker is FAM46A RNA or LRRC58 RNA.

11. The method of claim 10, wherein step (a) comprises reverse transcription polymerase chain reaction (RT-PCR).

12. The method of claim 11, wherein the RT-PCR is quantitative RT-PCR (qRT-PCR).

13. The method according to any one of claims 10 to 12, further comprising step (d): The amount of the standardized biomarker obtained from step (c) is compared with a standard control value to determine whether the amount of the standardized biomarker is higher or lower than the standard control value, and to determine whether the pregnant woman has pregnancy-related conditions or is at increased risk of developing pregnancy-related conditions.

14. The method of claim 13, wherein the pregnancy-related condition is preeclampsia.

15. The method of claim 14, further comprising step (e): Administer an effective amount of aspirin, an antihypertensive drug, an anticonvulsant, or a corticosteroid to the pregnant woman identified in step (d) as having preeclampsia or at increased risk of developing preeclampsia; or Induction of labor is performed on the pregnant woman who was identified as having preeclampsia in step (d).

16. A kit for analyzing biomarkers in biological samples, comprising: (1) A first container containing a first reagent for detecting the biomarker; (2) A second container containing a second reagent for detecting a reference gene; and (3) Optionally, a third container containing a third reagent for detecting another different reference gene. The one or two reference genes mentioned herein are selected from the genes in Table 2, and the kit mentioned herein does not contain any additional reagents for detecting any other reference genes.

17. The kit according to claim 16, wherein the biomarker is FAM46A RNA or LRRC58 RNA.

18. The kit according to claim 16 or 17, wherein one of the reference genes is PCBP2 or STXBP2, or wherein both reference genes are PCBP2 and STXBP2.

19. The kit according to any one of claims 16 to 18, wherein the first reagent, the second reagent, and / or the third reagent are reagents for quantifying the amplification reaction of the biomarker or reference gene.

20. The kit according to any one of claims 16 to 19, further comprising an instruction manual.

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