Methods for preparing and analyzing samples for biomarkers associated with placenta accreta

Abstract

Disclosed herein are methods of preparing and analyzing samples for biomarkers associated with placenta accreta in a pregnant subject. Also disclosed are methods of assessing the risk of placenta accreta in a pregnant subject. TIFF2024546438000022.tif98127

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 63 / 278,456, filed November 11, 2021, the contents of which are incorporated by reference in their entirety herein. [Background technology]

[0002] background Placenta accreta is a serious pregnancy condition that occurs when the placenta grows too deeply into the uterine wall. Normally, the placenta detaches from the uterine wall after birth. In placenta accreta, part or all of the placenta remains attached. This can lead to severe blood loss after delivery. The placenta can also invade the uterine muscle (placenta incarcerated) or grow through the uterine wall (placenta perforated).

[0003] Placenta accreta is considered a high-risk pregnancy complication. If the condition is diagnosed during pregnancy, women are more likely to require a hysterectomy after a C-section delivery.

[0004] The incidence of placenta accreta spectrum disorders has increased approximately eight-fold since the 1970s, possibly due to an increase in cesarean section deliveries. The incidence of cesarean section delivery is much higher in women with placenta accreta than in women without placenta accreta. For example, in first pregnancies, the incidence of cesarean section delivery in women without placenta accreta is 0.03%, whereas in women with placenta accreta, the incidence is 100 times higher, or 3%.

[0005] Women with major risk factors, such as placenta previa, previous cesarean delivery, endometrial resection, or other uterine surgery, should undergo obstetric ultrasound in the mid-to-late second trimester to evaluate for possible placenta accreta spectrum. Patients with suspected placenta accreta spectrum should be referred to a center with multidisciplinary expertise and experience.

[0006] Current diagnosis of placenta accreta includes ultrasound examination, the specificity and sensitivity of which depend on knowledge of the patient's clinical condition (e.g., clinical suspicion of placenta accreta, prior knowledge of risk factors).There is a need for a more sensitive method of predicting and detecting placenta accreta in pregnant women.Provided herein are methods and compositions that address this need. Summary of the Invention

[0007] overview Disclosed herein are circulating microparticle (CMP)-associated proteins useful for predicting and detecting placenta accreta. In some embodiments, CMP-associated proteins can be collected from about 20 weeks to about 37 weeks of gestation and used to assess the risk of placenta accreta. Biomarkers are shown in Tables 1-6 and Table 9, the tables in Figures 1A-1D, 2A-2C, 3A-3K, 4A-4J, 5A-5C, 6A-6F, and the tables in Example 1. Also provided are alternatives useful for detecting biomarkers shown in Figures 10A-10C, 11A-11C, 12A-12H, 13A-13H, 14A-14D, and 15A-15B. Panels of biomarkers are also presented. [Brief description of the drawings]

[0008] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate exemplary embodiments and, together with the description, further serve to enable one of ordinary skill in the relevant art to make and use these and other embodiments that will be apparent to one of ordinary skill in the art. The present invention is described in more detail in conjunction with the following drawings:

[0009] [Figure 1A]Figures 1A-1D show Table 1 with median values ​​for 24-week samples. Markers are ranked using Lasso regression (α = 1). The top 20 markers are displayed. This technique optimizes proteins that may interact and "cooperate" within a panel rather than isolated markers. Individual markers are ranked by frequency of usefulness in multimarker panels. [Figure 1B] See legend to Figure 1A. [Figure 1C] See legend to Figure 1A. [Figure 1D] See legend to Figure 1A. [Figure 2A] Figures 2A-2C show Table 2 with median values ​​for the 34-week samples. Markers are ranked using Lasso regression (α = 1). The top 20 markers are displayed and ranked by frequency of utility in a multimarker panel. [Figure 2B] See legend to Figure 2A. [Figure 2C] See legend to Figure 2A. [Figure 3A] Figures 3A-3K show Table 3 with the top 50 protein markers at median 24 weeks using an ensemble feature selection routine to select the top individual markers that distinguish between cases and controls. Protein markers that overlap with the top 20 from the Lasso regression in Table 1 are indicated with an asterisk. Individual markers are ranked by their performance in distinguishing between cases / controls. [Figure 3B] See legend to Figure 3A. [Figure 3C] See legend to Figure 3A. [Figure 3D] See legend to Figure 3A. [Figure 3E] See legend to Figure 3A. [Figure 3F] See legend to Figure 3A. [Figure 3G] See legend to Figure 3A. [Figure 3H] See legend to Figure 3A. [Figure 3I]See legend to Figure 3A. [Figure 3J] See legend to Figure 3A. [Figure 3K] See legend to Figure 3A. [Figure 4A] Figures 4A-4J show Table 4 with the top 50 protein markers at median week 34 using an ensemble feature selection routine that selects the top individual markers that distinguish between cases and controls. Protein markers that overlap with the top 20 from the Lasso regression (Table 2) are indicated with an asterisk. Individual markers are ranked by their performance in distinguishing between cases / controls. [Figure 4B] See legend to Figure 4A. [Figure 4C] See legend to Figure 4A. [Figure 4D] See legend to Figure 4A. [Figure 4E] See legend to Figure 4A. [Figure 4F] See legend to Figure 4A. [Figure 4G] See legend to Figure 4A. [Figure 4H] See legend to Figure 4A. [Figure 4I] See legend to Figure 4A. [Figure 4J] See legend to Figure 4A. [Figure 5A] 5A-5C show Table 5 containing the 24-week markers. Individual markers are ranked by their performance in distinguishing cases / controls. [Figure 5B] See legend to Figure 5A. [Figure 5C] See legend to Figure 5A. [Figure 6A] 6A-6F show Table 6 containing the 34-week markers. Individual markers are ranked by their performance in distinguishing cases / controls. [Figure 6B] See legend to Figure 6A. [Figure 6C] See legend to Figure 6A. [Figure 6D] See legend to Figure 6A. [Figure 6E] See legend to Figure 6A. [Figure 6F] See legend to Figure 6A. [Figure 7] FIG. 7 shows Table 7 containing the 24-week markers. The best performing multiplex panels are ranked by the average AUC of the iterative cross-validation procedure. [Figure 8] FIG. 8 shows Table 8 containing the 34-week markers. The best performing multiplex panels are ranked by the average AUC of the iterative cross-validation procedure. [Figure 9] FIG. 9 shows a schematic of the protocol for identifying a predictive circulating particulate protein panel for placenta accreta. [Figure 10A] 10A-10C provide alternative peptides useful for detecting the biomarkers of Table 1. [Figure 10B] See legend to Figure 10A. [Figure 10C] See legend to Figure 10A. [Figure 11A] 11A-11C provide alternative peptides useful for detecting the biomarkers in Table 2. [Figure 11B] See legend to Figure 11A. [Figure 11C] See legend to Figure 11A. [Figure 12A] 12A-12H provide alternative peptides useful for detecting the biomarkers in Table 3. [Figure 12B] See legend to Figure 12A. [Figure 12C] See legend to Figure 12A. [Figure 12D] See legend to Figure 12A. [Figure 12E] See legend to Figure 12A. [Figure 12F] See legend to Figure 12A. [Figure 12G] See legend to Figure 12A. [Figure 12H] See legend to Figure 12A. [Figure 13A]13A-13H provide alternative peptides useful for detecting the biomarkers in Table 4. [Figure 13B] See legend to Figure 13A. [Figure 13C] See legend to Figure 13A. [Figure 13D] See legend to Figure 13A. [Figure 13E] See legend to Figure 13A. [Figure 13F] See legend to Figure 13A. [Figure 13G] See legend to Figure 13A. [Figure 13H] See legend to Figure 13A. [Figure 14A] 14A-14D provide alternative peptides useful for detecting the biomarkers in Table 5. [Figure 14B] See legend to Figure 14A. [Figure 14C] See legend to Figure 14A. [Figure 14D] See legend to Figure 14A. [Figure 15A] 15A-15B provide alternative peptides useful for detecting the biomarkers in Table 6. [Figure 15B] See legend to Figure 15A. [Figure 16] Figure 16A is a density plot of protein vs. permutation where the first shaded area represents actual protein AUC and the second shaded area represents AUC from randomly permuting sample labels (placenta accreta spectrum vs. control) from the second trimester (e.g., 24 weeks). Figure 16B is a density plot of protein vs. permutation where the first shaded area represents actual protein AUC and the second shaded area represents AUC from randomly permuting sample labels (placenta accreta spectrum vs. control) from the third trimester (e.g., 37 weeks). [Figure 17] FIG. 17 shows a schematic diagram of exemplary canonical pathways, upstream regulators, and molecular and cellular function analyses proposed in the second and third trimesters leading to pathologic placenta accreta. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Detailed Description I. Introduction Disclosed herein are methods, compositions, systems and articles of manufacture that are useful for preparing samples for determining the risk of developing placenta accreta and detecting biomarkers useful for treating placenta accreta.In some embodiments, determining involves detecting placenta accreta biomarkers found in microparticle-enriched fractions from the blood of pregnant women.Exemplary biomarkers useful for detecting placenta accreta in either or both of the second and third trimesters are presented in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6 and Table 9.As discussed below, Table 7 and Table 8 show biomarker panels for placenta accreta.Additional marker sets are also presented herein.

[0011] II. Subject The subject to provide the sample for prediction and treatment of placenta accreta is a pregnant human female. The stage of pregnancy can be calculated from the first day of the pregnant subject's last normal menstrual period.

[0012] In some embodiments, the pregnant female may be about 20 weeks to about 37 weeks pregnant. In some embodiments, the pregnant female may be about 20 weeks, about 21 weeks, about 22 weeks, about 23 weeks, about 24 weeks, about 25 weeks, about 26 weeks, about 27 weeks, about 28 weeks, about 29 weeks, about 30 weeks, about 31 weeks, about 32 weeks, about 33 weeks, about 34 weeks, about 35 weeks, about 36 weeks, or about 37 weeks pregnant.

[0013] Pregnant subjects for the methods described herein can belong to one or more classes including primiparas (no previous births, referred to herein interchangeably as nulliparous or parity=0) or multiparous (at least one previous birth that has reached at least 20 weeks of gestation, referred to herein interchangeably as parity greater than 0 (>0) and parity ≥ 1 (≧1)), primiparas (first pregnancy) or multiparous (two or more pregnancies).

[0014] In some embodiments, the pregnant human subject is asymptomatic. In some embodiments, the subject may have risk factors for placenta accreta. The most common risk factor is previous cesarean section delivery, and the incidence of placenta accreta spectrum increases with the number of previous cesarean section deliveries. In a systematic review, placenta accreta spectrum rates increased from 0.3% in women with one previous cesarean section delivery to 6.74% in women with five or more previous cesarean section deliveries. Additional risk factors include advanced maternal age, multiparity, previous uterine surgery or curettage, and Asherman's syndrome. Placenta previa is another significant risk factor.

[0015] III. Sample preparation The sample for use in the method of the present disclosure is a biological sample obtained from a pregnant subject. In certain embodiments, the sample is collected during the stages of pregnancy described in the previous section. In some embodiments, the sample is a blood, saliva, tear, sweat, nasal secretion, urine, amniotic fluid or cervicovaginal fluid sample. In some embodiments, the sample is a blood sample. In some embodiments, the sample is a plasma sample. In some embodiments, the sample is a serum sample. In some embodiments, the sample is a blood product in different matrices (e.g., citrate buffer, or Streck tube). In some embodiments, the sample is stored frozen (e.g., at -20°C or -80°C).

[0016] The term "microparticle" refers to an extracellular microvesicle or lipid raft protein aggregate having a hydrodynamic diameter of about 50 to about 5000 nm. Thus, the term microparticle encompasses exosomes (about 50 to about 100 nm), microvesicles (about 100 to about 300 nm), ectosomes (about 50 to about 1000 nm), apoptotic bodies (about 50 to about 5000 nm) and lipid-protein aggregates of the same dimensions.

[0017] The term "microparticle-associated protein" refers to a protein or fragment thereof that is detectable in a microparticle-enriched sample from a mammalian (e.g., human) subject. Thus, the term "microparticle-associated protein" is not limited to a protein or fragment thereof that is physically associated with a microparticle upon detection. The term "microparticle-associated peptide" refers to a protein fragment that is detectable in such a sample.

[0018] As used herein, the term "polypeptide" refers to a polymer of amino acids. It includes oligopeptides (typically having fewer than 10 amino acids), peptides (typically having from about 10 to about 50 amino acids), and proteins (including polypeptides that have secondary, tertiary, or quaternary structure). Depending on the context, the term "protein" can refer to a polypeptide that lacks secondary structure.

[0019] Biomarkers for placenta accreta can be derived from microparticles. Microparticles can be isolated from blood (e.g., serum or plasma) or other biological samples by size exclusion chromatography. The mobile phase / elution buffer can be, for example, a buffered solution such as PBS, or a non-buffered solution. Water as the mobile phase refers to non-buffered water, for example, distilled water, deionized water, or distilled deionized water ("ddH2O"). The high molecular weight fraction can be collected to obtain a microparticle-enriched sample. Proteins in the microparticle-enriched sample are then extracted before digestion with a proteolytic enzyme such as trypsin to obtain a digested sample containing multiple peptides. The digested sample is then subjected to a peptide purification, enrichment and / or fractionation step before analysis to obtain a proteomic profile of the sample, for example, by liquid chromatography and mass spectrometry. In some embodiments, the purification / enrichment step comprises reverse phase chromatography (e.g., ZipTip® pipette tip with 0.2 μL C18 resin from Millipore Corporation, Billerica, MA) or ultrafiltration. In some embodiments, the fractionation step may involve fractionation into 96 fractions using a high pH reverse-phase offline HPLC fractionator where mobile phase A is DI HO with 20 mM Formic Acetate, pH 9.3; mobile phase B is acetonitrile with 20 mM Formic Acetate, pH 9.3 (Optima™, LC / MS grade, Fisher Chemical™).

[0020] In some embodiments, for example, detection of proteins by mass spectrometry, the method of sample preparation may include fragmenting the proteins in the sample. Fragmentation may be performed using a protease such as trypsin. Tryptic fragments may serve as useful surrogate biomarkers since their unique mass can be related to the parent protein.

[0021] In some embodiments, the microparticle is placenta-derived exosome or endothelium-derived exosome.Such exosome can be isolated by using capture agent, such as antibody, against the surface marker of these derived cells.For example, placenta-derived exosome can be isolated by using antibody against PLAP (placental alkaline phosphatase), Klotho, CD34, CD44 or leukemia inhibitory factor (LIF).Endothelium-derived exosome can be isolated by using antibody against ICAM or VCAM.

[0022] IV. Detection Method Biomarkers can be detected and quantified by any method known in the art, including, but not limited to, immunoassays, chromatography, mass spectrometry, electrophoresis, and surface plasmon resonance.

[0023] Detection of the biomarkers includes detection of the intact protein or detection of surrogates of the protein, such as fragments. Exemplary fragments are shown in Figures 10A through 15B.

[0024] Immunoassay methods include, for example, radioimmunoassays, enzyme-linked immunosorbent assays (ELISAs), sandwich assays and Western blots, immunoprecipitation, immunohistochemistry, immunofluorescence, antibody microarrays, dot blotting, and FACS.

[0025] Chromatographic methods include, for example, affinity chromatography, ion exchange chromatography, size exclusion / gel filtration chromatography, hydrophobic interaction chromatography, and reverse phase chromatography, including, for example, HPLC.

[0026] 1. Mass spectrometry In some embodiments, detection of the level of (e.g., including detection of the presence of) microparticle-associated proteins is performed using liquid chromatography / mass spectrometry (LC / MS)-based proteomic analysis. In exemplary embodiments, the method involves subjecting a sample to size-exclusion chromatography and collecting (e.g., by size-exclusion chromatography) a high molecular weight fraction to obtain a microparticle-enriched sample. In some embodiments, the size-exclusion chromatography comprises a size-exclusion column comprising an agarose solid phase and an aqueous liquid phase. The microparticle-enriched sample is then disrupted (e.g., using a chaotropic agent, a denaturant, a reducing agent, and / or an alkylating agent) and the released contents are subjected to proteolysis. The disrupted microsomal preparation containing multiple peptides is then processed using the tandem column system described herein prior to peptide analysis by mass spectrometry to provide a proteomic profile of the sample. The methods disclosed herein avoid the need for protein concentration / purification, buffer exchange, and liquid chromatography steps associated with previous methods.

[0027] Proteins in a sample can be detected by mass spectrometry. A mass spectrometer typically includes an ion source to ionize the analyte, and one or more mass spectrometers to determine the mass. Mass spectrometers can also be used together in a tandem mass spectrometer. Ionization methods include electrospray or laser desorption, among others. Mass spectrometers include quadrupoles, ion traps, time-of-flight devices, and magnetic or electric sector devices. In certain embodiments, the mass spectrometer is a tandem mass spectrometer (e.g., "MS-MS") that uses a first mass spectrometer to select ions of a certain mass and a second mass spectrometer to analyze the selected ions. One example of a tandem mass spectrometer is a triple quadrupole device, where the first and third quadrupoles act as mass filters and the middle quadrupole acts as a collision cell. Mass spectrometry can also be combined with upstream separation techniques, such as liquid or gas chromatography. Thus, for example, a combination of liquid chromatography and tandem mass spectrometry can be referred to as "LC-MS-MS".

[0028] Mass spectrometers useful for the analyses described herein include, but are not limited to, ThermoFisher Scientific's Altis™ quadrupole, Quantis™ quadrupole, Quantiva™ or Fortis™ triple quadrupole, Shimadzu's 8050 or 8060 triple quadrupole, Waters' Xevo TQ-XS™ triple quadrupole, Perkin Elmer's QSight™ Triple Quad LC / MS / MS, Thermo Fisher Scientific's Thermo Orbitrap Mass Spectrometer Tribrid Eclipse equipped with a Nanospray Flex™ Ion Source, and the like.

[0029] In general, any mass spectrometry (MS) technique that can provide accurate information about the mass of peptides (e.g., in tandem mass spectrometry, MS / MS; or in post-source decay, TOF MS), and preferably about the fragmentation and / or (partial) amino acid sequence of selected peptides, can be used in the methods and compositions disclosed herein. In some embodiments, any MS technique can provide process information about the mass of peptides, including more than 100, more than 1000, more than 10,000, more than 100,000 peptides from biological samples. Suitable peptide MS and MS / MS techniques and systems are known in the art (see, for example, Methods in Molecular Biology, vol. 146: "Mass Spectrometry of Proteins and Peptides" by Chapman, ed., Humana Press 2000; Kassel & Biemann (1990) Anal. Chem. 62:1691-1695; Methods Enzymol 193: 455-79; or Methods in Enzymology, vol. 402: "Biological Mass Spectrometry" by Burlingame, ed., Academic Press 2005) and can be used in practicing the methods disclosed herein. Thus, in some embodiments, the disclosed methods include performing quantitative MS to measure one or more peptides. Such quantitative methods can be performed in an automated format (Villanueva, et al., Nature Protocols (2006) 1(2):880-891) or semi-automated format. In certain embodiments, the MS can be operatively linked to a liquid chromatography device (LC-MS / MS or LC-MS) or a gas chromatography device (GC-MS or GC-MS / MS).

[0030] Selected Reaction Monitoring is a mass spectrometry technique in which a protein of interest (precursor) is selected in a first mass spectrometer, the protein is fragmented into product fragments in a collision cell, and one or more fragments are detected in a second mass spectrometer. The precursor and product ion pairs are called SRM "transitions." This method is typically performed on triple quadrupole instruments. When multiple fragments of a protein are analyzed, the method is referred to as Multiple Reaction Monitoring Mass Spectrometry ("MRM-MS").

[0031] Typically, protein samples are digested with proteolytic enzymes such as trypsin to produce peptide fragments. Heavy isotope-labeled analogs of some of these peptides are synthesized as standards. These standards are called stable isotope standards or "SIS". The SIS peptides are mixed with the protease-treated sample. This mixture is subjected to triple quadrupole mass spectrometry. Peptides corresponding to the SIS standards and daughter ions of the target peptides are detected with high precision in either the time domain or mass domain. Typically, multiple daughter ions are used to unambiguously identify the presence of the parent ion, and one of the daughter ions, usually the most abundant, is used for quantification. SIS peptides can be synthesized to order or are available as commercial kits from suppliers, for example, Thermo Fisher Scientific (Waltham, MA) or Biognosys AG (Zurich, Switzerland).

[0032] As used herein, the terms "multiple reaction monitoring (MRM)" or "selected reaction monitoring (SRM)" refer to MS-based quantification methods that are particularly useful for quantifying low abundance analytes. In an SRM experiment, a predefined precursor ion and one or more of its fragments are selected by two mass filters of a triple quadrupole instrument and monitored over time for accurate quantification. Multiple SRM precursor and fragment ion pairs can be measured within the same experiment on the chromatographic time scale by performing an MRM experiment by rapidly switching between different precursor / fragment pairs. A series of transitions (precursor / fragment ion pairs) in combination with the retention time of the target analyte (e.g., peptides or small molecules, e.g., chemical entities, steroids, hormones) can constitute a definitive assay. A large number of analytes can be quantified during a single LC-MS experiment. The terms "scheduled" or "dynamic" in reference to MRM or SRM refer to a variant of the assay in which transitions of a particular analyte are acquired only in a time window around the expected retention time, which contributes to the selectivity of the test, since retention time is a property that depends on the physical properties of the analyte, greatly increasing the number of analytes that can be detected and quantified in a single LC-MS experiment. A single analyte can also be monitored at more than one transition. Finally, the assay can include a standard that corresponds to the analyte of interest (e.g., a peptide with the same amino acid sequence as that of the analyte peptide), but differs by the inclusion of a stable isotope. Stable isotope standards (SIS) can be incorporated into the assay at precise levels and used to quantify the corresponding unknown analyte. An additional level of specificity is contributed by the co-elution of the unknown analyte with its corresponding SIS, and by the properties of their transitions (e.g., the similarity of the ratio of the levels of the two transitions of the analyte and the ratio of the two transitions of the corresponding SIS).

[0033] Thus, detection of a protein target by MRM-MS typically involves detection of one or more peptide fragments of the protein, typically by detection of stable isotope standard peptides to which the peptide fragments are compared. Typically, the SIS itself is fragmented in a collision cell as the original digested fragments, and one or more of these fragments are detected by the mass spectrometer.

[0034] Mass spectrometry assays, devices and systems suitable for biomarker peptide analysis include, but are not limited to, matrix-assisted laser desorption / ionization time-of-flight (MALDI-TOF) MS; MALDI-TOF post-source decay (PSD); MALDI-TOF / TOF; surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF) MS; electrospray ionization mass spectrometry (ESI-MS); ESI-MS / MS; ESI-MS / (MS)n (n is an integer greater than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI quadrupole orthogonal TOF (Q-TOF); ESI Fourier transform MS systems; desorption / ionization on silicon (DIOS); secondary ion mass spectrometry (SIMS); atmospheric pressure chemical ionization mass spectrometry (APCI-MS); APCI-MS / MS; APCI-(MS)n; ion mobility spectrometry (IMS); These include inductively coupled plasma mass spectrometry (ICP-MS) atmospheric pressure photoionization mass spectrometry (APPI-MS); APPI-MS / MS; and APPI-(MS)n. Fragmentation of peptide ions in tandem MS (MS / MS) configurations can be achieved using techniques known in the art, such as collision-induced dissociation (CID). As described herein, detection and quantification of biomarkers by mass spectrometry can involve multiple reaction monitoring (MRM), particularly as described by Kuhn et al. (2004) Proteomics 4:1175-1186. Scheduled multiple reaction monitoring (scheduled MRM) mode acquisition during LC-MS / MS analysis enhances the sensitivity and accuracy of peptide quantification. Anderson and Hunter (2006) Mol. Cell. Proteomics 5(4):573-588. Mass spectrometry-based assays can be advantageously combined with upstream peptide or protein separation or fractionation methods, such as, for example, the tandem column systems described herein.

[0035] In another embodiment, useful for biomarker discovery, the fractionated samples are analyzed by nanoflow HPLC (e.g., Ultimate 3000, Thermo Fisher Scientific) followed by a Thermo Orbitrap mass spectrometer (Tribrid Eclipse). The ion source can be a Nanospray Flex™ ion source (Thermo Fisher Scientific) equipped with a column oven (PRSO-V2, Sonation) to heat a nanocolumn (PicoFrit, 100 μm×250 mm×15 μm tip, New Objective) for peptide separation. Peptides can be captured in the trap column and then transported by the mobile phase to the separation nanocolumn.

[0036] V. Biomarkers As used herein, the term "biomarker" refers to a biological molecule whose presence, form or amount shows a statistically significant difference between two states.Therefore, biomarkers are useful alone or in combination to classify subjects into one of several groups.Biomarkers can be naturally occurring or non-naturally occurring.For example, biomarkers can be naturally occurring proteins or non-naturally occurring protein fragments.Fragments of proteins can function as surrogate or surrogate peptides of the protein or as independent biomarkers.

[0037] Provided herein is a composition of matter that comprises one or more placenta accreta biomarkers in substantially pure form. The biomarkers can be mixed in a container or can be physically separated, for example, by attaching to a solid support at different addressable locations. As used herein, a chemical entity, such as a polynucleotide or polypeptide, is "substantially pure" if it is the main chemical entity of that type in the composition. This includes chemical entities that represent more than 50%, more than 80%, more than 90%, or more than 95% of the chemical entities of that type in the composition. A chemical entity is "essentially pure" if it represents more than 98%, more than 99%, more than 99.5%, more than 99.9%, or more than 99.99% of the chemical entities of that type in the composition. An essentially pure chemical entity is also substantially pure.

[0038] 1. Protein Biomarkers Provided herein is one or more protein biomarkers associated with increased risk of placenta accreta.Exemplary biomarkers for predicting placenta accreta in the second trimester are shown in Table 1 (24 weeks + / - 2 weeks), Table 3 (24 weeks + / - 2 weeks) and Table 5 (24 weeks + / - 2 weeks).Biomarkers for predicting placenta accreta in the third trimester are shown in Table 2 (35 weeks + / - 2 weeks), Table 4 (35 weeks + / - 2 weeks) and Table 6 (35 weeks + / - 2 weeks).The data collected for the biomarkers in these tables are from pregnant subjects at 24 weeks + / - 2 weeks or 35 weeks + / - 2 weeks of gestation, but the biomarkers may be relevant for evaluation at gestational ages outside this range.As discussed below, Tables 7 and 8 provide panels of biomarkers for predicting placenta accreta around about 24 weeks and around about 35 weeks, respectively. In some embodiments, the one or more protein biomarkers associated with an increased risk of placenta accreta comprise a plurality of protein biomarkers.

[0039] The tables in the figures provide the UniProt entry number and entry name, protein name, gene name, organism (all homo sapiens), primary gene name and gene synonym name. The contents of Tables 1, 2, 3, 4, 5, 6, 7 and 8 of Figures 1A-1D, 2A-2C, 3A-3K, 4A-4J, 5A-5C, 6A-6F, and 10A-10C, 11A-11C, 12A-12H, 13A-13H, 14A-14D, 15A-15B, including their protein names, gene names (primary and synonymous) and / or peptide sequences, are incorporated in their entirety into the detailed description herein as if they were provided herein. For purposes of inclusion in the detailed description, Table 9 provides exemplary biomarkers of the present disclosure useful for predicting placenta accreta (protein names and synonyms are provided).

[0040] Table 9. Exemplary biomarkers TIFF2024546438000002.tif46165TIFF2024546438000003.tif231165TIFF2024546438000004.tif230165TIFF20245464380 00005.tif211165TIFF2024546438000006.tif220165TIFF2024546438000007.tif229165TIFF2024546438000008.tif116165

[0041] For each biomarker, one or more peptide fragments from the protein are also provided that serve as surrogate markers. The surrogate markers can be used as a measure of the protein for purposes of the models described herein. Thus, in some embodiments, detection of one or more peptide fragments of a protein biomarker serves to detect the protein biomarker. Peptides useful as surrogates for the biomarkers are shown in Figures 10A-10C, 11A-11C, 12A-12H, 13A-13H, 14A-14D, and 15A-15B.

[0042] Biomarkers can be detected using de novo sequencing of proteins from microparticles isolated from samples (e.g., blood) taken from pregnant women. Proteins can be sequenced by mass spectrometry, e.g., single or double (MS / MS) mass spectrometry. Both parent proteins (such as those provided in Tables 1-6 and Table 9; or panels of Tables 7 and 8) as well as peptide fragments of parent proteins (such as those described above in Figures 10A-10C, 11A-11C, 12A-12H, 13A-13H, 14A-14D, and 15A-15B) are useful as biomarkers for placenta accreta. Thus, in some embodiments, detection of the named protein biomarkers encompasses detection by surrogates, e.g., one or more fragments of the protein.

[0043] Proteins, e.g., peptides, detected by mass spectrometry are analyzed to identify those that are upregulated (increased in amount) or downregulated (decreased in amount) compared to a control. Proteins that show statistically significant differential expression are further analyzed to identify parent proteins. Such proteins can be identified in protein databases such as SwissProt.

[0044] In certain embodiments, the biomarker is in a composition in which the peptide biomarker is paired with a peptide stable isotope standard.In some embodiments, the composition can comprise one pair of peptide biomarker and peptide stable isotope standard, or multiple pairs, each pair comprising a peptide biomarker and a peptide stable isotope standard.The peptide biomarker can comprise a surrogate biomarker, as described in more detail herein.Such a composition is useful for detection in multiple reaction monitoring mass spectrometry.

[0045] For the purpose of mass spectrometry, proteins can be detected as they are or fragmented, for example, through LCMS or in multiple reaction monitoring (MRM). In such cases, proteins can be proteolytically fragmented prior to analysis. Proteolytic fragmentation includes both chemical and enzymatic fragmentation. Chemical fragmentation includes, for example, treatment with cyanogen bromide. Enzymatic fragmentation includes, for example, digestion with proteases such as trypsin, chymotrypsin, LysC, ArgC, GluC, LysN and AspN. Detection of these protein fragments, or their fragmented forms generated in mass spectrometry, can serve as a surrogate for the intact protein.

[0046] 2. Biomarker Panel In certain embodiments, biomarkers are analyzed as a panel. A panel is a plurality of biomarkers used in an algorithm to make predictions or inferences. As used herein, a panel that includes a group of identified biomarkers includes at least the identified biomarkers. A panel that consists of a group of identified biomarkers includes only the identified biomarkers. A panel that essentially consists of a group of identified biomarkers includes the identified biomarkers and only one or two other biomarkers. For example, a panel that essentially consists of four identified biomarkers can include up to six biomarkers in total. A panel can exist as a conceptual group, as a composition of matter (e.g., including purified biomarkers), or as an article, such as a solid support attached to a capture reagent, such as an antibody, and further bound to a biomarker. A solid support can be, for example, one or more solid particles, such as beads, or a chip to which biomarkers are attached in an array format.

[0047] An exemplary panel of biomarkers for assessing placenta accreta before and after 24 weeks is shown in Table 7.

[0048] An exemplary panel of biomarkers for assessing placenta accreta before and after 34 weeks is shown in Table 8.

[0049] VI. Data Analysis As used herein, the term "analysis" refers to any algorithm that transforms input into output. Analysis includes, but is not limited to, statistical analysis, machine learning analysis, and neural net analysis. The term "data" may include data received from various data sources, metadata associated with the data, and / or a combination of both data and metadata.

[0050] A. Measurement The variables of a measurement, such as the mapping of a sequencing read to a position, can be any combination of numbers and words. The measures can be of any scale, including nominal (e.g., names or categories), ordinal (e.g., hierarchical order of categories), interval (distance between members of an order), ratio (interval compared to a meaningful "0"), or cardinal measurements that count the number of things in a set. A nominal measurement variable indicates a name or category, e.g., a category into which a sequencing read falls. An ordinal measurement variable gives a ranking, such as "1st place", "2nd place", "3rd place", etc. Ratio-scale measurements include any measure on a predefined scale, absolute number of reads, normalized or estimated number, as well as statistical measurements such as frequency, mean, median, standard deviation, or quantiles. Measurements with quantification are usually determined at the ratio-scale level.

[0051] B. Analysis In some embodiments, the analysis is a statistical analysis of a sufficiently large number of samples to provide statistically meaningful results. Any statistical method known in the art can be used for this purpose. Exemplary methods or tools include, but are not limited to, correlation, Pearson correlation, Spearman correlation, chi-square, mean comparison (e.g., paired T-test, independent T-test, ANOVA), regression analysis (e.g., simple regression, multiple regression, linear regression, nonlinear regression, logistic regression, polynomial regression, stepwise regression, ridge regression, lasso regression, elastic net regression) or nonparametric analysis (e.g., Wilcoxon rank sum test, Wilcoxon signed rank test, sign test). Such tools are included in commercially available statistical packages such as MATLAB, JMP Statistical Software and SAS. Such methods result in models or classifiers that can be used to classify specific biomarker profiles into specific conditions.

[0052] In statistics and machine learning, lasso (also known as least absolute shrinkage and selection operator, Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to increase the predictive accuracy and interpretability of the resulting statistical model.

[0053] The statistical analysis can be performed by an operator or by machine learning.

[0054] Certain classifiers, such as cutoffs, can be implemented by human inspection, while others, such as multivariate classifiers, may require a computer to run the classification algorithm.

[0055] 1. Machine Learning In some variations, the analysis may involve the implementation of machine learning techniques including linear and non-linear models, e.g., processes such as CART (Classification And Regression Trees), artificial neural networks such as backpropagation networks, discriminant analysis (e.g., Bayesian classifiers or Fisher analysis), logistic classifiers, and support vector classifiers (e.g., support vector machines).

[0056] Classification rules, algorithms, also referred to as models, can be generated by mathematical analysis, including machine learning techniques that perform analysis of a dataset of biomarker measurements from subjects classified into one or another group. In some embodiments, the one or more classification rules or algorithms may employ one or more of the following: cutoffs, linear regression including multiple linear regression, partial least squares regression, principal component regression, binary decision trees including recursive partitioning processes further including classification trees and regression trees, artificial neural networks including backpropagation networks, discriminant analysis further including Bayesian classifiers or Fisher analysis, logistic classifiers, and support vector classifiers including support vector machines. In some variations, these datasets of biomarker measurements may include more than 100, more than 1000, more than 10,000, or more than 100,000 data entries. In some variations, the machine learning techniques that perform analysis of a dataset of biomarker measurements from subjects classified into one or another group may access test data from the subjects and perform one or more classification rules on the test data.

[0057] Diagnostic tests are characterized by sensitivity (proportion classified as positive that is true positive) and specificity (proportion classified as negative that is true negative). The relative sensitivity and specificity of diagnostic tests may involve trade-offs, where high sensitivity may mean low specificity, but high specificity may mean low sensitivity. These relative values ​​can be displayed in receiver operating characteristic (ROC) curves. The diagnostic power of a set of variables, such as biomarkers, is reflected by the area under the curve (AUC) of the ROC curve.

[0058] In some embodiments, the classifiers of the present disclosure have a sensitivity value, specificity value, positive predictive value, or negative predictive value of at least 85%, at least 90%, at least 95%, at least 98%, or at least 99%. The classifiers of the present disclosure have an AUC of at least 0.6, at least 0.7, at least 0.8, at least 0.9, or at least 0.95.

[0059] The classification can be based on the measurement of the biomarker being above or below a selected cutoff level or value or threshold level or value. In certain embodiments, the cutoff value is obtained by measuring the biomarker level in multiple positive and negative reference samples, for example, at least 10, 20, 50, 100 or 200 samples of each type (e.g., samples from control and test subjects). The cutoff value can be established with respect to a measure of central tendency, such as the mean, median or mode of the negative samples. A measure of deviation from this measure of central tendency can be used to set the cutoff. For example, the cutoff can be set based on variance or standard deviation. For example, the cutoff can be based on Z-score, i.e., the number of standard deviations above the mean of normal samples, for example, 1 standard deviation, 2 standard deviations, 3 standard deviations or 4 standard deviations. For example, the cutoff value can be selected so that the diagnostic test has a sensitivity value, specificity value and / or positive predictive value of at least 80%, 90%, 95%, 98%, 99%, 99.5% or 99.9%.

[0060] Numerically, the increased risk is associated with an odds ratio of greater than 1.0 for placenta accreta, preferably greater than 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9 or 3.0.

[0061] VII. How to assess the risk of placenta accreta The phrase "increased risk" of a condition used herein indicates that the subject is more likely to develop the condition than the general population of subjects.Thus, for example, a subject with "increased risk of placenta accreta" is more likely to develop placenta accreta than the general population of subjects at the same stage of pregnancy, optionally compared with a population that shares one or more demographic or risk factors.These can include, for example, age, placenta previa, previous cesarean delivery, endometrial resection, in vitro fertilization, previous uterine infection or other uterine surgery.For example, a test can show that a woman around 24 weeks or around 34 weeks of pregnancy has a higher risk of developing placenta accreta than the general population or control population women around 24 weeks or around 34 weeks of pregnancy.

[0062] The classification can employ classification rules, algorithms or models determined by statistical analysis and / or machine learning.In some embodiments, the classification rule can be based on one or more values.The one or more values ​​can include one or more demographic or risk factors of the subject compared to the general population of subjects.The one or more values ​​can also include the measured value of one or more protein biomarkers.

[0063] A. Evaluation of placenta accreta Provided herein is a method for assessing the risk of placenta accreta anywhere between 20 weeks and about 37 weeks of gestation, for example, classifying a pregnant human female as having an increased risk of placenta accreta.The method can involve determining the measure of one or more biomarkers in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, or Table 9, and correlating the measure with the risk of placenta accreta.For example, the determination can use a panel that includes 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more biomarkers, or 2, 3, 4, 5, 6, 7, 8, 9, or 10 or less biomarkers from any one or more of Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, or Table 9.In general, the amount of biomarker that shows a difference compared to the control amount of biomarker (e.g., healthy pregnant women or pregnant women without placenta accreta), and the difference is statistically significant, is associated with an increased risk of placenta accreta.The difference can be up-regulation or down-regulation, which can be easily determined by practitioners. Alternatively, the determination may be based on a classification algorithm, which may employ non-linear and / or hyper-dimensional methods.

[0064] In some embodiments, pathways can be examined to help determine the risk of placenta accreta.For example, in some embodiments, one or more canonical pathways can be over-represented by the differentially expressed proteins in second trimester placenta accreta cases.Such pathways can be one or more of the following: erythropoietin signaling pathway; and iron homeostasis signaling pathway (see Table 1.7 in Example 1).

[0065] In some embodiments, certain targets can be activated or inhibited and examined to help determine the risk of placenta accreta. Exemplary targets are shown in Table 1.8 in Example 1.

[0066] In some embodiments, cellular and molecular functions related to iron handling and red blood cell function are prominent and may be examined to help determine the risk of placenta accreta. Exemplary targets are shown in Table 1.9 in Example 1.

[0067] In some embodiments, one or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1) are useful for distinguishing placenta accreta from controls. In some embodiments, the biomarker panel of the present disclosure includes one, two, three, four or all five of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1). In some embodiments, one or more of these markers are useful for distinguishing placenta accreta from controls in the second trimester.

[0068] In some embodiments, one or more of ISM2, ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54) and Ig-like domain-containing protein are useful for distinguishing placenta accreta from controls. In some embodiments, the biomarker panel of the present disclosure includes one, two, three or all four of ISM2, ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54) and Ig-like domain-containing protein. In some embodiments, one or more of these markers are useful for distinguishing placenta accreta from controls in the third trimester.

[0069] In other embodiments, the determination is based on the use of a panel of biomarkers, such as those provided in Tables 7 or 8.

[0070] B. Placenta accreta – Second Trimester Evaluation Provided herein is a method for assessing the risk of placenta accreta during the second trimester (e.g., around 24 weeks of pregnancy), for example, classifying a pregnant human female as having an increased risk of placenta accreta. The method can involve determining the measure of one or more biomarkers in Table 1, Table 3, Table 5, or Table 9, and relating the measure to the risk of placenta accreta. For example, a panel comprising 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more biomarkers from the table, or 2, 3, 4, 5, 6, 7, 8, 9, or 10 or less biomarkers, can be used in the determination. In general, the amount of biomarker that shows a difference compared to the control amount of biomarker (e.g., healthy pregnant women or pregnant women without placenta accreta), and the difference is statistically significant, is associated with an increased risk of placenta accreta. The difference can be up-regulation or down-regulation, which can be easily determined by practitioners. Alternatively, the determination can be based on a classification algorithm that can employ non-linear and / or hyper-dimensional methods.

[0071] The biomarker panel can be composed of any of the biomarker panels shown in Table 7. The biomarker panel can consist essentially of any of the biomarker panels shown in Table 7. The biomarker panel can consist of any of the biomarker panels shown in Table 7. In some embodiments, one or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1) are useful to distinguish placenta accreta from controls.

[0072] C. Placenta accreta – Third Trimester Evaluation Provided herein is a method for assessing the risk of placenta accreta during the third trimester (e.g., around 35 weeks of pregnancy), for example, classifying a pregnant human female as having an increased risk of placenta accreta. The method can involve determining the measure of one or more biomarkers in Table 2, Table 4, Table 6, or Table 9, and relating the measure to the risk of placenta accreta. For example, a panel comprising 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more biomarkers from the table, or 2, 3, 4, 5, 6, 7, 8, 9, or 10 or less biomarkers, can be used in the determination. In general, the amount of biomarker that shows a difference compared to the control amount of biomarker (e.g., healthy pregnant women or pregnant women without placenta accreta), and the difference is statistically significant, is associated with an increased risk of placenta accreta. The difference can be up-regulation or down-regulation, which can be easily determined by practitioners. Alternatively, the determination can be based on a classification algorithm that can employ non-linear and / or hyper-dimensional methods.

[0073] The biomarker panel can be composed of any of the biomarker panels shown in Table 8. The biomarker panel can consist essentially of any of the biomarker panels shown in Table 8. The biomarker panel can consist of any of the biomarker panels shown in Table 8.

[0074] In some embodiments, one or more of ISM2, ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54) and Ig-like domain-containing protein are useful in distinguishing placenta accreta from controls.

[0075] VIII. Methods of Treating Subjects at Increased Risk for Placenta Accreta The method of treating a pregnant subject suffering from placenta accreta or at increased risk of placenta accreta comprises assessing the risk of placenta accreta in the pregnant subject, and administering one or more therapeutic interventions useful for treating placenta accreta, reducing the risk of placenta accreta and / or reducing the neonatal complications of placenta accreta.In some embodiments, administering one or more therapeutic interventions comprises administering an effective amount of one or more treatments designed to reduce the risk of placenta accreta.In some embodiments, the one or more treatments can include performing hysterectomy cesarean section, recommending bed rest to the subject to prevent premature birth, performing prophylactic embolization, leaving part of the placenta in situ, taking interest in uterine balloon tamponade, administering methotrexate, inserting one or more temporary internal iliac occlusion balloon catheters, inserting one or more ureteral stents, etc.

[0076] Patients suspected of having placenta accreta spectrum disorders should be referred to centers with a high degree of multidisciplinary surgical expertise and experience in either the therapeutic interventions listed above or those standard in the field.

[0077] A surgical plan for uterine preservation is planned if future fertility is desired, or a hysterectomy is planned or undertaken if fertility is not desired.

[0078] This may allow for appropriate planning to ensure access to specialists / surgeons and appropriately equipped facilities (i.e., blood bank support).

[0079] IX. KITS AND OTHER MANUFACTURES In another aspect, provided herein is an article of manufacture, such as a reagent kit, useful for detecting sample biomarkers related to placenta accreta, particularly increased risk of placenta accreta.Reagents capable of detecting protein biomarkers include, but are not limited to, antibodies.Antibodies capable of detecting protein biomarkers are also typically directly or indirectly linked to molecules such as fluorophores or enzymes that can catalyze detectable reactions that indicate the binding of the reagent to its respective target.

[0080] In some embodiments, the kit further comprises sample processing materials including a high molecular weight gel filtration composition (e.g., agarose such as SEPHAROSE) in a low volume (e.g., 1 ml, 3 ml, 5 ml, 10 ml) vertical column for rapid preparation of microparticle-enriched samples from plasma. For example, microparticle-enriched samples can be prepared at the clinical site prior to freezing and transport to an analytical laboratory for further processing.

[0081] In some embodiments, the kit further comprises instructions for assessing placenta accreta, particularly the risk of placenta accreta.As used herein, the term "instructions" refers to instructions for using the reagents contained in the kit to detect the presence (including determining expression level) of protein of interest in a sample from a subject.The protein of interest can comprise one or more biomarkers of placenta accreta.

[0082] In another embodiment, the kit comprises one or more containers, each containing one or more stable isotope standard (SIS) peptides corresponding to peptide biomarkers, e.g., peptides generated from protease (e.g., trypsin) digestion of a biomarker protein. In another embodiment, most or all of the SIS peptides correspond to the biomarker peptides. In another embodiment, the kit further comprises a biomarker peptide to which the SIS peptide corresponds.

[0083] In another aspect, a composition of matter is provided that includes protein biomarkers for placenta accreta and corresponding stable isotope standard peptides for a plurality of those biomarkers, which can be prepared by combining a sample that includes the proteins isolated from the microparticles with the stable isotope standard peptides.

[0084] X. System Also provided herein is a system including a computer including a processor and a memory. The computer can be configured to receive one or more measurements of one or more biomarkers provided herein measured from a sample into the memory. The memory can include computer-readable instructions that, when executed, classify the sample as at risk for placenta accreta or not at risk for placenta accreta. The computer system can be operatively connected to a computer network using a communication interface. The network can be the Internet, the Internet and / or an extranet, or an intranet and / or an extranet that communicates with the Internet. In some cases, the network is a telecommunications network and / or a data network. The network can include one or more computer servers that can enable distributed computing, such as cloud computing. The system can include a first computer connected to a second computer through a communication network, such as a high-speed transmission network, including, but not limited to, digital subscriber line (DSL), cable modem, fiber, wireless, satellite, and broadband over power line (BPL). Thus, results providing a classification of the sample as having an increased risk of placenta accreta or as not having an increased risk of placenta accreta can be transmitted from the sending computer to a remote receiving computer, such as in a health care provider's office, or to a mobile device, such as a smartphone. EXAMPLES

[0085] Example 1: Maternal EDTA plasma samples were collected from patients over 18 years of age who attended prenatal care and were scheduled for delivery in a hospital at a median of approximately 26 (+ / - 2) weeks and approximately 35 (+ / - 2) weeks gestation in the second and third trimesters, respectively. Pregnancy dates were confirmed by ultrasound at 12 weeks gestation or less. Samples were aliquoted and stored at -80 degrees Celsius. Thirty-five cases of placenta accreta spectrum, referred to herein as "PAS," including 27 cases of grade 1 PAS, 7 cases of grade 2 PAS, and 1 case of grade 3 PAS as defined by the International Federation of Gynecology and Obstetrics ("FIGO"), and 70 controls were analyzed (Table 1.1).

[0086] Table 1.1 FIGO Clinical and Histological Criteria for PAS TIFF2024546438000009.tif70160

[0087] Cases were defined as subjects with clinical or histologic grade 1–3 PAS consistent with the 2019 FIGO PAS classification (Table 1.1), delivered at >23 weeks' gestation, and enrolled in the LIFECODES biobank. Prospective cases were initially identified within the electronic medical record using the following word searches for records from 2007 to 2020: "adhere-" in operative reports and discharge summaries, and "-creta," "hyst-," or "previa" in pathology reports. The medical records of each prospective case were independently reviewed by two obstetricians within the institution's multidisciplinary PAS team. The higher of the clinical and histologic grades was designated as the assigned grade. Discrepancies in either inclusion status or assigned PAS grade were adjudicated by a review committee. Subjects identified with a diagnosis of PAS were cross-referenced with the LIFECODES biobank for inclusion. Controls were defined as subjects without a diagnosis of PAS and were randomly matched 2:1. Cases and controls were matched by gestational age at sampling (+1 week) and number of fetuses. Exclusion criteria were defined as a current cancer diagnosis, use of immunomodulatory medications, or documented fetal chromosomal abnormalities. Univariate analyses were performed with chi-square tests, and continuous variables were compared with Wilcoxon tests using SAS 9.4. All tests were two-sided, with P<0.05 used to define statistical significance.

[0088] Compared to controls, cases were older maternal age and more likely to have placenta previa at delivery. Twenty of the PAS cases (57.1%) did not have placenta previa at delivery or a history of cesarean section. Twenty-three of the PAS cases (65.7%) were adjudicated by clinical rather than histological criteria. Of note, only 11% of grade 1 PAS cases were detected by ultrasound before delivery, compared with 57% and 100% of grade 2 and 3 PAS cases, respectively (Table 1.2).

[0089] Table 1.2. Characteristics and staging of PAS cases. Data presented as median (+IQR) or n (%). TIFF2024546438000010.tif175160

[0090] To enrich each sample for circulating microparticles ("CMPs"), size-exclusion chromatography (SEC) was used to separate CMPs. De-identified EDTA plasma samples, identified only by study number independent of case or control status, were randomly sorted and shipped on dry ice to NX Prenatal, Inc. (Houston, TX), where CMP protein was enriched by SEC and eluted isocratically with NeXosome elution reagent. This required NeXosome separation columns manufactured by AmericanBio, Inc. (Canton, MA). Briefly, these columns were packed with Sepharose 4B-CL (4% agarose, 45-165 μm particle size) from Cytiva (Marlborough, MA) to a total loading volume of 10 mL by AmericanBio and shipped to NX Prenatal. Once received by NX Prenatal, the columns were stored at 2-8°C until use. Prior to using the columns for CMP separation, the columns were equilibrated to room temperature (overnight) and then washed with NeXosome elution reagent. EDTA plasma samples were thawed and 0.5 mL of plasma was applied and loaded onto a NeXosome isolation column. Plasma samples were not filtered, diluted, or pretreated prior to application to the column. After loading of the samples onto the column, NeXosome elution reagent was added and 0.5 mL column fractions were collected. The elution fractions produced two peaks. CMPs were trapped in the column void volume and separated from the abundant soluble protein peak. Samples were processed according to a randomization scheme. Each CMP-containing fraction (0.5 mL aliquot of each fraction) was pooled within each individual sample and total protein measurements were performed using the Pierce BCA Protein Assay Kit (ThermoFisher Scientific). Aliquots containing 200 μg of total protein from each individual CMP isolate pool were then transferred to 2 mL microcentrifuge tubes (VWR, Radnor, PA) and stored at -80 °C until processing of all CMP isolates was completed.All CMP isolates were then shipped on dry ice to BGI Americas Corporation (Cambridge, MA) for proteomic analysis.

[0091] A total of 158 plasma-enriched exosome samples were processed individually for LC-MS / MS analysis. 100 μL of enriched exosomes were mixed with 700 μL of lysis buffer containing 9 M urea at pH 8.5 and 0.5% Rapigest (SKU: 186001861, Waters™). Samples were sonicated in a water bath for 30 minutes, followed by spinning at high speed (14,000 rpm) in a centrifuge for 10 minutes. After sample lysis, the protein concentration of the samples was measured by BCA assay (catalog number: A53225, ThermoFisher Scientific).

[0092] 50 μg of each sample was taken from the lysate and normalized to the same volume with lysis buffer. Samples were reduced in 10 mM DTT at 60°C for 25 min, and then the reduced samples were alkylated in 20 mM IAM (iodoacetamide) at room temperature for 20 min in a dark environment. Excess IAM in the samples was quenched by adding 100 mM DTT. For enzymatic digestion, DI water and pH 8.5 HEPE buffer were added to each sample to dilute to a final urea concentration of 1.6 M and a final pH of 8. 1 μg of Tryp / LysC (Cat. No.: A41007, ThermoFisher Scientific) was added to each sample. Samples were incubated overnight at 37°C for 12 h. The next day, an additional 1 μg of Tryp / LysC was added to each sample and they were incubated for another 4 h to complete the enzymatic digestion.

[0093] 10% TFA was added to the digested samples (peptides) resulting in a final concentration of 1% TFA - pH was tested and the samples were acidic. The acidified samples were then passed through a 10 mg SEK PAK column (catalog number: 60108-302, ThermoFisher Scientific) for desalting. 20% of the desalted peptides from each sample were removed and pooled together to create a composite "library" of peptides. The library samples were then fractionated into 96 fractions using a high pH, ​​reversed-phase, offline HPLC fractionator (Vanquish™, ThermoFisher Scientific). Mobile phase A consisted of DI H2O with 20 mM formic acetate, pH 9.3; mobile phase B consisted of acetonitrile (Optima™, LC / MS grade, Fisher Chemical™) with 20 mM formic acetate, pH 9.3. The separation gradient is shown in Table 1.3. The 96 fractions were then combined into 24 fractions and prepared for liquid chromatography mass spectrometry (LC / MS) analysis.

[0094] Table 1.3: High pH reversed-phase HPLC fractionation gradient information TIFF2024546438000011.tif76160

[0095] All fractionated samples were analyzed by nanoflow HPLC (Ultimate 3000, Thermo Fisher Scientific) followed by Thermo Orbitrap mass spectrometer (Tribrid Eclipse). The nanospray Flex™ ion source (Thermo Fisher Scientific) was equipped with a column oven (PRSO-V2, Sonation) to heat the nanocolumn (PicoFrit, 100 μm × 250 mm × 15 μm tip, New Objective) for peptide separation. The nanoLC method was water-acetonitrile based and 150 min long with a flow rate of 0.300 μL / min. For each sample injection, all peptides were first bound to a trap column (catalog number: 160454, Thermo Fisher) and then delivered to the separation nanocolumn by the mobile phase. The gradients used are detailed in Table 1.4.

[0096] Table 1.4: High pH reversed-phase HPLC fractionation gradient information TIFF2024546438000012.tif76128

[0097] For the construction of the DDA library, fractionated peptides eluted from the nanocolumn were sequenced using DDA specific to the DIA library, MS2-based mass spectrometry on Eclipse. For the full MS spectrum, a resolution of 120,000 was used with a scan range of 375 m / z to 1500 m / z. For dd-MS (MS2), a resolution of 15,000 was used with an isolation window of 1.6 Da. The "standard" AGC target and "auto" maximum ion injection time (max IT) were selected for both MS1 and MS2 acquisitions. The collision energy (NCE) was set to 35% with a total cycle time of 1 s. For the DIA analysis samples, a high-resolution full MS scan followed by two segmented DIA methods was used for DIA data acquisition. For the full MS scan, a resolution of 120,000 was used with a range of 400 m / z to 1200 m / z with a "standard" AGC target and 50 ms max IT. Details of the isolation window (IW) and precursor mass range for both DIA segments are given in Table 1.5 and Table 1.6. For the DIA fragment scan, a resolution of 30,000 was used for the range 110 m / z to 1,800 m / z, with a "standard" AGC target and "auto" maximum IT.

[0098] (Table 1.5) DIA Segment 1 Precursor Scan Range Information TIFF2024546438000013.tif120128

[0099] (Table 1.6) DIA Segment 2 Precursor Scan Range Information TIFF2024546438000014.tif75128

[0100] The first process is based on the sample data generated from the high-resolution mass spectrometer. The DDA data was identified by the Andromeda search engine in MaxQuant, and Spectronaut™ was used to identify the results for spectral library construction. MaxQuant was used to identify the DDA data, which served as the spectral library for the subsequent DIA analysis. The analysis pipeline used the raw data as the input file, and after setting the corresponding parameters and human database (UP000005640), identification and quantitative analysis were performed. The identified peptides met an FDR of 1% or less to construct the final spectral library. In this DIA dataset, Spectronaut™ was employed to construct the spectral library information, and after completing deconvolution and extraction, the mProphet algorithm was used to complete the analysis quality control (1% FDR) to obtain reliable quantitative results. GO, COG and Pathway functional annotation analysis and time series analysis were also performed in the above pipeline. MStats, with a linear mixed-effect model as the core algorithm, was used to process the DIA quantitative result data according to predefined comparison groups, and then significance tests were performed based on the model. Then, differential protein screening was performed, and a fold change of 2 or more and an adjusted P value of less than 0.05 were defined as significant differences. Based on the quantitative comparison results, differential proteins between the comparison groups were identified; finally, functional enhancement analysis, protein-protein interaction (PPI) testing and subcellular localization analysis of the differential proteins were performed. Then, sample classification analysis was performed as described below.

[0101] Regularized (L1) regression was used to classify PAS and define a limited set of CMP protein candidates from a superset of all identified proteins. A cross-validation procedure using logistic regression was chosen to select a putative panel from the limited set of CMP protein candidates. Samples were randomly split into a training set and a validation set (80% vs. 20%). Proteins in the training set were then ranked by Akaike Information Criterion (AIC) using an ensemble feature selection procedure. The top 10 proteins were then passed to the glmulti package in R version 3.6.3, where the training set was subjected to 5-fold cross-validation. Due to the limited sample size, the models were restricted to no more than 5 predictors to avoid overfitting. The model with the largest area under the curve (AUC) and smallest AUC standard deviation was then tested against a set-side external validation set. The AUC and AUC standard deviation of this external validation set were then recorded, and the workflow was repeated a total of 1000 times (Figure 9). We then ranked the models by the mean AUC and the mean standard deviation of the AUC. We then repeated the workflow with randomly permuted sample labels. We then compared the prediction statistics of the observed and permuted data.

[0102] To establish a panel of CMP proteins that would serve as classifiers of PAS risk in the second and third trimesters, we used a two-step iterative workflow with regularized (L1) regression followed by a cross-validation procedure with logistic regression. The two-step workflow was also repeated with randomly permuted sample labels to simulate random chance. For second trimester samples, the means of all areas under the curve (AUC) of the observed (e.g., first shaded area) and permuted (e.g., second shaded area) panels were significantly different (0.72 vs. 0.45; p < 2.20e-16; Figure 16A). The best-performing marker panel differentiated PAS from controls with a mean AUC of 0.83. The CMP proteins in this panel included ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and cartilage acidic protein 1 (CRAC1). Within third trimester samples, the average of all AUCs for observed (e.g., first shaded area) and permuted (e.g., second shaded area) panels were also significantly different (0.60 vs. 0.52; p=2.79e-5; Figure 16B). The best performing panel differentiated PAS from controls with an average AUC of 0.78. The CMP proteins in this panel included ISM2, ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and Ig-like domain-containing protein. Furthermore, the mean of all AUCs in the second trimester was significantly higher than that in the third trimester (p=1.59e-12).

[0103] Ingenuity Pathway Analysis was also applied to determine the biological functions of proteins identified as differentially expressed by PAS status in the differential expression analysis. A core analysis using the reference set from the Ingenuity Knowledge Base was used. Direct and indirect relationships were considered for network and regulatory analysis. IPA is a curated bioinformatics repository of functionally annotated analytes that allows for functional annotation, canonical pathway and network analysis, and upstream regulator analysis. Significantly (P<0.05) over-represented canonical pathways, upstream regulators, and molecular and cellular functions were identified. Only relevant pathways and biological functions with two or more overlapping hits were included, and only upstream regulators with predicted activation states (activated, inhibited) were included. Overlap was calculated as the percentage of overlap between the differentially expressed proteins and the target pathway.

[0104] Ingenuity Pathway Analysis was also applied to determine the biological functions of proteins identified as differentially expressed with PAS status in the differential expression analysis. This revealed a significant over-representation of canonical pathways, upstream regulators, and molecular and cellular functions. In the second trimester, proteomic changes in PAS led to a significant over-representation of several canonical pathways, including iron homeostasis signaling and erythropoietin signaling (Table 1.7).

[0105] Table 1.7. Canonical pathways significantly over-represented by differentially expressed proteins in cases of second trimester placenta accreta TIFF2024546438000015.tif35160

[0106] The master upstream regulator contained seven targets predicted to be significantly activated and six targets predicted to be significantly inhibited (Table 1.8).

[0107] Table 1.8. Selection of master upstream regulators of target molecules within the second trimester placenta accreta dataset TIFF2024546438000016.tif134165

[0108] IPA molecular and cellular function analysis revealed 43 select annotated functions that were significantly over-represented based on differentially expressed molecular hits from second trimester placenta accreta analysis (Table 1.9). Cellular and molecular functions related to iron handling and red blood cell function were consistent with canonical iron homeostasis and erythropoietin signaling pathways.

[0109] Table 1.9. Selection of over-represented molecular and cellular functions among proteins altered in second trimester placenta accreta TIFF2024546438000017.tif242158TIFF2024546438000018.tif137158

[0110] Similar to the second trimester data, IPA core analysis revealed significant over-representation of canonical pathways, upstream regulators, and molecular and cellular functions in the third trimester at PAS. Canonical pathway analysis of third trimester proteomic changes in PAS revealed significant over-representation of pathways, including immune and extracellular signaling pathways, particularly involving IL-15 (Table 1.10).

[0111] Table 1.10. Standardized Path Analysis of Change in Third Trimester TIFF2024546438000019.tif60166

[0112] Master upstream regulators included three predicted to be significantly activated and two predicted to be significantly inhibited (Table 1.11).

[0113] Table 1.11. Selection of master upstream regulators of target molecules within the third trimester placenta accreta dataset TIFF2024546438000020.tif55166

[0114] Molecular and cellular function analysis revealed 24 select annotated functions that were significantly over-represented based on differentially expressed molecular hits from the third trimester analysis in PAS (Table 1.12).

[0115] Table 1.12. Selection of over-represented molecular and cellular functions among proteins altered in third trimester placenta accreta TIFF2024546438000021.tif137167

[0116] There was some concordance between these cellular and molecular functions and the canonical pathways revealed by IPA, particularly for immune signaling and cytoskeleton and cell proliferation functions.

[0117] Exemplary Aspects The following exemplary embodiments are provided herein.

[0118] Set I : Aspect I-1. 1. A method for preparing a peptide sample, comprising: (a) providing a blood, serum or plasma sample from a pregnant subject at or near 24 or 34 weeks' gestation; (b) enriching the sample for microparticles by loading the sample onto a size exclusion column and eluting the microparticles from the column using water as the mobile phase to produce a microparticle-enriched fraction; (c) preparing a microparticle-associated peptide fraction from the microparticle-enriched fraction by contacting with a protease; (d) isolating the microparticle-associated peptides by mass spectrometry; and (e) based on the mass spectrometry signal, (i) a protein biomarker of Table 1, Table 3 or Table 5, wherein the blood sample is taken at about 24 weeks of gestation; and (ii) a protein biomarker of Table 2, Table 4 or Table 6, wherein the blood sample is taken at about 34 weeks of gestation. measuring one or more peptides corresponding to each of one or more protein biomarkers selected from The method comprising:

[0119] Aspect I-2. The method of embodiment I-1, wherein said one or more protein biomarkers is a plurality of protein biomarkers.

[0120] Aspect I-3. The method of embodiment I-1, wherein said biomarkers comprise a panel of no more than 10, 9, 8, 7, 6, 5, 4, or 3 protein biomarkers.

[0121] Aspect I-4. The biomarker is (i) a biomarker panel of Table 7, wherein the blood sample is taken at about 24 weeks' gestation; and (ii) The protein biomarkers of Table 8, wherein the blood sample is taken at about 34 weeks of gestation. The method of embodiment I-1, comprising, consisting essentially of, or consisting of a panel of biomarkers selected from:

[0122] Aspect I-5. The method of embodiment I-1, wherein the step of measuring the one or more peptides comprises measuring any of the surrogate biomarkers in Figures 10A-10C, Figures 11A-11C, Figures 12A-12H, Figures 13A-13H, Figures 14A-14D, and Figures 15A-15B.

[0123] Aspect I-6. The method of embodiment I-1, wherein said pregnant subject has one or more risk factors for placenta accreta.

[0124] Aspect I-7. The method of embodiment I-1, wherein said pregnant subject is a primigravid, multigravid, primipara or multipara.

[0125] Aspect I-8. The method of embodiment I-1, wherein said blood sample is plasma or serum.

[0126] Aspect I-9. The method of embodiment I-1, wherein the water is deionized distilled water ("ddH2O").

[0127] Aspect I-10. The method of embodiment I-1, wherein size exclusion chromatography is performed using an agarose solid phase and an aqueous liquid phase.

[0128] Aspect I-11. The method of embodiment I-1, wherein said preparing step further comprises using ultrafiltration or reverse phase chromatography.

[0129] Aspect I-12. The method of embodiment I-1, wherein said preparing step further comprises denaturation with urea, reduction with dithiothreitol, alkylation with iodoacetamine, and digestion with trypsin after size exclusion chromatography.

[0130] Aspect I-13. The method of embodiment I-1, wherein the microparticles are further purified to enrich for placenta-derived exosomes or vascular endothelium-derived exosomes.

[0131] Aspect I-14. The method of embodiment I-1, wherein the mass spectrometry comprises liquid chromatography / mass spectrometry (LC / MS), eg, liquid chromatography / triple quadrupole mass spectrometry.

[0132] Aspect I-15. The method of embodiment I-1, wherein the mass spectrometry comprises multiple reaction monitoring.

[0133] Aspect I-16. The peptide is (i) a biomarker panel of Table 7, wherein the blood sample is taken at about 24 weeks' gestation; and (ii) The protein biomarkers of Table 8, wherein the blood sample is taken at about 34 weeks of gestation. The method of embodiment I-1, wherein the

[0134] Aspect I-17. (i) a protein biomarker from Table 1, Table 3 or Table 5; (ii) a protein biomarker in Table 2, Table 4 or Table 6; (iii) a surrogate biomarker of Figure 10A-10C, Figure 11A-11C, or Figure 12A-12H; and (iv) a surrogate biomarker of Figures 13A-13H, 14A-14D or 15A-15B A panel comprising a plurality of substantially pure protein biomarkers or surrogate biomarkers selected from:

[0135] Aspect I-18. The panel of embodiment I-17, further comprising stable isotope standard peptides paired with each of said surrogate biomarkers.

[0136] Aspect I-19. A kit comprising one or more containers, each container containing one or more of each of a plurality of stable isotope standards, each stable isotope standard comprising: (i) a protein biomarker from Table 1, Table 3 or Table 5; (ii) a protein biomarker in Table 2, Table 4 or Table 6 (iii) a surrogate biomarker of Figure 10A-10C, Figure 11A-11C, or Figure 12A-12H; and (iii) a surrogate biomarker of Figures 13A-13H, 14A-14D or 15A-15B a surrogate peptide for a biomarker from a panel of biomarkers selected from the one or more containers corresponding to the surrogate peptide for a biomarker from a panel of biomarkers selected from the one or more containers corresponding to the surrogate peptide for a biomarker from a panel of biomarkers selected from the one or more containers corresponding to the surrogate peptide for a biomarker from the ...

[0137] Aspect I-20. A composition comprising one or more pairs of polypeptides, each pair comprising: (i) a protein biomarker from Table 1, Table 3 or Table 5; (ii) a protein biomarker in Table 2, Table 4 or Table 6 (iii) a surrogate biomarker of Figure 10A-10C, Figure 11A-11C, or Figure 12A-12H; and (iii) a surrogate biomarker of Figures 13A-13H, 14A-14D or 15A-15B The composition comprising a protein biomarker or a surrogate biomarker selected from:

[0138] Aspect I-21. A computer-readable medium in a tangible, non-transitory form that includes code for implementing the classification rules generated by the methods described herein.

[0139] Aspect I-22. Systems including: (a) A computer comprising: (i) Processor and (ii) a memory coupled to the processor, (1) Test data for a sample from a subject, the test data including values ​​representing measures of one or more protein biomarkers in a fraction, the protein biomarkers being: (i) a protein biomarker of Table 1, Table 3 or Table 5, wherein the sample is taken at about 20 weeks' gestation; and (ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the sample is taken at about 37 weeks' gestation. Selected from: Test data for a sample from said subject; (2) a classification rule for classifying a subject as being at increased risk for placenta accreta based on a value, including a measurement, the classification rule being configured to have a sensitivity of at least 75%, at least 85%, or at least 95%; and (3) computer-executable instructions for implementing the classification rules on the test data. the memory storing a module including The computer comprising:

[0140] Aspect I-23. The protein biomarker is (iii) a surrogate biomarker of Figure 10A-10C, Figure 11A-11C, or Figure 12A-12H; and (iii) a surrogate biomarker of Figures 13A-13H, 14A-14D or 15A-15B The system of embodiment I-22, wherein the surrogate biomarker is selected from the group consisting of:

[0141] Aspect I-24. (a) a computer system including a processor and a memory coupled to the processor, the memory comprising: (1) Test data for a sample from a subject, the test data including values ​​representing measures of one or more protein biomarkers in a fraction, the protein biomarkers being: (i) a protein biomarker in Table 1, Table 3, or Table 5, where the sample is taken at about 24 weeks' gestation; and (ii) a protein biomarker in Table 2, Table 4, or Table 6, where the sample is taken at about 34 weeks' gestation. Selected from: Test data for a sample from said subject; (2) classification rules implemented by a processor that classify a subject as being at increased risk for placenta accreta based on a value including a measurement, the classification rules being configured to have a sensitivity of at least 75%, at least 85%, or at least 95%; Remembering modules that contain In the computer system (b) accessing the text data; and (c) executing said classification rules against said test data. A method comprising:

[0142] Aspect I-25. 1. A method for assessing the risk of placenta accreta in a pregnant subject, comprising: (a) preparing a microparticle-enriched fraction from a blood sample from a pregnant subject; (b) determining a measure of one or more microparticle-associated protein biomarkers in the fraction, the protein biomarkers being: (i) a protein biomarker of Table 1, Table 3 or Table 5, wherein the blood sample is taken at about 24 weeks of gestation; and (ii) a protein biomarker of Table 2, Table 4 or Table 6, wherein the blood sample is taken at about 34 weeks of gestation. a step selected from the group consisting of: (c) assessing the risk of placenta accreta based on one or more measures. The method comprising:

[0143] Aspect I-26. The protein biomarker is (iii) a surrogate biomarker of Figure 10A-10C, Figure 11A-11C, or Figure 12A-12H; and (iii) a surrogate biomarker of Figures 13A-13H, 14A-14D or 15A-15B The method of embodiment I-25, wherein the surrogate biomarker is selected from the group consisting of:

[0144] Aspect I-27. The method of embodiment I-25, wherein the step of determining a quantitative measure comprises contacting said sample with one or more capture reagents, each capture reagent specifically binding to one of said protein biomarkers, and detecting binding between capture reagents at said protein biomarkers.

[0145] Aspect I-28. The method of embodiment I-27, comprising performing an immunoassay.

[0146] Aspect I-29. The method of embodiment I-28, wherein the immunoassay is selected from the group consisting of an enzyme immunoassay (EIA), an enzyme linked immunosorbent assay (ELISA), and a radioimmunoassay (RIA).

[0147] Aspect I-30. The method of embodiment I-25, wherein said evaluating step comprises executing a classification rule that classifies said subject as at risk for placenta accreta, and execution of said classification rule results in a correlation between placenta accreta or full-term birth with a p-value of at least less than 0.05.

[0148] Aspect I-31. The method of embodiment I-25, wherein said evaluating step comprises executing a classification rule, which classifies said subject as at risk for placenta accreta, and wherein executing said classification rule generates a Receiver Operating Characteristic (ROC) curve, and the ROC curve has an area under the curve (AUC) of at least 0.6, at least 0.7, at least 0.8, or at least 0.9.

[0149] Aspect I-32. The method of embodiment I-25, wherein the values ​​by which the classification rules classify the subject further include at least one of placenta previa, previous cesarean delivery, endometrial ablation, in vitro fertilization, previous uterine infection, or previous uterine surgery.

[0150] Aspect I-33. The method of any of the preceding aspects, wherein the classification rules employ cutoffs, linear regression (e.g., multiple linear regression (MLR), partial least squares (PLS) regression, principal component regression (PCR)), binary decision trees (e.g., a recursive partitioning process such as CART - classification and regression trees), artificial neural networks such as backpropagation networks, discriminant analysis (e.g., a Bayesian classifier or Fisher analysis), logistic classifiers, and support vector classifiers (e.g., support vector machines).

[0151] Aspect I-34. The method of embodiment I-25, wherein the classification rules are configured to have a sensitivity, specificity, positive predictive value, or negative predictive value of at least 70%, at least 80%, at least 90%, or at least 95%.

[0152] Aspect I-35. The method of embodiment I-25, wherein the step of assessing an increased risk of placenta accreta comprises determining whether said protein biomarker is above a threshold level (if upregulated) or below a threshold level (if downregulated).

[0153] Aspect I-36. The method of embodiment I-35, wherein the threshold level represents the level of at least one, at least two, or at least three z-scores from a measure of central tendency (e.g., mean, median, or mode) of the protein determined from at least 50, at least 100, or at least 200 control subjects.

[0154] Aspect I-37. The method of embodiment I-25, wherein said evaluating step comprises comparing the measure of each protein in the panel to a reference standard.

[0155] Aspect I-38. The method of embodiment I-25, further comprising informing a health care provider of the pregnant subject's risk of placenta accreta.

[0156] Aspect I-39. 1. A method of treating placenta accreta in a pregnant subject, comprising: (a) assessing the risk of placenta accreta in a pregnant subject by the method of any one of embodiments I-25 to I-38; and (b) administering to the subject a therapeutic intervention effective to reduce the risk of placenta accreta and / or reduce the neonatal complications of placenta accreta. The method comprising:

[0157] Aspect I-40. To treat, (i) referring the subject to a medical center with a high degree of multidisciplinary surgical expertise and experience; (ii) Surgical uterine preservation; and (iii) performing a cesarean section with a hysterectomy, performing prophylactic embolization, inserting a uterine balloon tamponade, a temporary internal iliac occlusion balloon catheter, a ureteral stent, administering methotrexate, leaving a portion of the placenta in situ, and instructing the subject on bed rest to prevent premature birth; The method of embodiment I-39, comprising a therapeutic intervention selected from the group consisting of:

[0158] Aspect I-41. A method comprising administering to a pregnant subject determined to have an increased risk of placenta accreta by the methods described herein a therapeutic intervention effective in reducing the risk of placenta accreta.

[0159] Aspect I-42. 1. A method of administering to a pregnant subject an effective amount of a treatment designed to reduce the risk of placenta accreta, comprising administering to the subject: (i) a protein biomarker in Table 1, Table 3 or Table 5, where a blood sample is taken at approximately 24 weeks' gestation; and (ii) a protein biomarker in Table 2, Table 4, or Table 6, where the blood sample is taken at approximately 34 weeks' gestation. having an altered quantitative measure compared to a reference standard of any one of a panel of protein biomarkers selected from The method.

[0160] Aspect I-43. The protein biomarker is (i) a surrogate biomarker of Figures 10A-10C, 11A-11C, or 12A-12H; and (ii) a surrogate biomarker of Figures 13A-13H, 14A-14D or 15A-15B The method of embodiment I-42, wherein the surrogate biomarker is selected from the group consisting of:

[0161] Aspect I-44. (a) measuring by mass spectrometry the masses of more than 100, more than 1000, more than 10,000 or more than 100,000 peptides from a biological sample comprising peptide fragments of a protein to generate a dataset comprising more than 100, more than 1000, more than 10,000 or more than 100,000 data entries; (b) a computer system including one or more processors and a memory storing a program for execution by the one or more processors, (1) from among the data entries, based on the mass, (i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the biological sample is taken at about 24 weeks' gestation; and (ii) a protein biomarker of Table 2, Table 4 or Table 6, wherein the biological sample is taken at about 34 weeks' gestation; identifying a peptide for (2) executing a classification rule against the measures of the identified peptides, the classification rule classifying the sample as being from a subject at increased risk for placenta accreta. A method comprising:

[0162] Set II :

[0163] Aspect II-1. 1. A method for assessing the risk of placenta accreta in a pregnant subject, comprising: (a) providing a sample from a pregnant subject between about 20 weeks gestation and about 37 weeks gestation; (b) preparing a microparticle-associated peptide fraction from the sample; (c) measuring a plurality of protein biomarkers in the fraction; and (d) implementing a classification rule on the one or more measures of (c) that classifies the sample as being from a subject at increased risk of placenta accreta. The method comprising:

[0164] Aspect II-2. The method of embodiment II-1, wherein said protein biomarkers comprise a panel of no more than 10, 9, 8, 7, 6, 5, 4, or 3 protein biomarkers.

[0165] Aspect II-3. The protein biomarker is (i) the biomarker panel in Table 7; and (ii) Protein biomarkers in Table 8 The method of embodiment II-1, comprising, consisting essentially of, or consisting of a panel of biomarkers selected from:

[0166] Aspect II-4. said plurality of protein biomarkers comprising: (i) multiple protein biomarkers from Tables 1, 3 and 5; (ii) multiple protein biomarkers from Tables 2 , 4 , and 6 ; (iii) multiple protein biomarkers from Table 9 ; (iv) two or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and cartilage acidic protein 1 (CRAC1); or (v) two or more of ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and Ig-like domain-containing protein. The method of embodiment II-1, comprising:

[0167] Aspect II-5. The method of embodiment II-1, wherein the step of measuring the plurality of biomarkers comprises measuring relevant surrogate biomarkers of Figures 10A-10C, 11A-11C, 12A-12H, 13A-13H, 14A-14D and 15A-15B.

[0168] Aspect II-6. The method of embodiment II-1, wherein said pregnant subject has one or more risk factors for placenta accreta.

[0169] Aspect II-7. The method of embodiment II-1, wherein said pregnant subject is a primigravid, multigravid, primipara or multipara.

[0170] Aspect II-8. The method of embodiment II-1, wherein the sample is a blood sample.

[0171] Aspect II-9. The method of embodiment II-1, wherein the sample is plasma or serum.

[0172] Aspect II-10. (i) a protein biomarker from Table 1, Table 3 or Table 5; (ii) a protein biomarker in Table 2, Table 4 or Table 6; (iii) protein biomarkers in Table 9 ; (iv) a surrogate biomarker in Figures 10A-10C, 11A-11C, or 12A-12H; (v) a surrogate biomarker in Figures 13A-13H, 14A-14D or 15A-15B; (vi) ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and cartilage acidic protein 1 (CRAC1); and (vii) ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54) and Ig-like domain-containing protein A panel comprising a plurality of substantially pure protein biomarkers or surrogate biomarkers selected from:

[0173] Aspect II-11. Stable isotope standard peptides paired with each of the surrogate biomarkers of Figures 10A-10C, 11A-11C, 12A-12H, 13A-13H, 14A-14D and 15A-15B. The panel of embodiment II-10, further comprising:

[0174] Aspect II-12. 1. A method for preparing a peptide sample, comprising: (a) providing a sample from a pregnant subject between about 20 weeks gestation and about 37 weeks gestation; (b) enriching the sample for microparticles by loading the sample onto a size exclusion column and eluting the microparticles from the column using water as the mobile phase to produce a microparticle-enriched fraction; (c) preparing a microparticle-associated peptide fraction from the microparticle-enriched fraction by contacting the microparticle-enriched fraction with a protease; (d) isolating the microparticle-associated peptides by mass spectrometry; and (e) measuring one or more peptides corresponding to the one or more protein biomarkers based on the mass spectrometry signals. The method comprising:

[0175] Aspect II-13. The method of embodiment II-12, wherein the one or more protein biomarkers comprises a plurality of protein biomarkers.

[0176] Aspect II-14. The method of embodiment II-12, wherein the protein biomarkers comprise a panel of no more than 10, 9, 8, 7, 6, 5, 4, or 3 protein biomarkers.

[0177] Aspect II-15. Protein biomarkers (i) the biomarker panel in Table 7; and (ii) Protein biomarkers in Table 8 The method of embodiment II-12, comprising, consisting essentially of, or consisting of a panel of biomarkers selected from:

[0178] Aspect II-16. Protein biomarkers (i) one or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and cartilage acidic protein 1 (CRAC1); or (ii) one or more of ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and Ig-like domain-containing protein. The method of embodiment II-12, comprising:

[0179] Aspect II-17. The method of embodiment II-12, wherein the step of measuring the one or more peptides comprises measuring a surrogate biomarker of any of Figures 10A-10C, Figures 11A-11C, Figures 12A-12H, Figures 13A-13H, Figures 14A-14D, and Figures 15A-15B.

[0180] Aspect II-18. The method of embodiment II-12, wherein said pregnant subject has one or more risk factors for placenta accreta.

[0181] Aspect II-19. The method of embodiment II-12, wherein said pregnant subject is a primigravid, multigravid, primipara, or multipara.

[0182] Aspect II-20. The method of embodiment II-12, wherein the blood sample is plasma or serum.

[0183] Aspect II-21. The method of embodiment II-12, wherein the water is deionized distilled water.

[0184] Aspect II-22. The method of embodiment II-12, wherein the size exclusion column comprises an agarose solid phase and an aqueous liquid phase.

[0185] Aspect II-23. The method of embodiment II-12, wherein the step of preparing the microparticle-associated peptide fraction further comprises using ultrafiltration or reverse phase chromatography.

[0186] Aspect II-24. The method of embodiment II-12, wherein the step of preparing the microparticle-associated peptide fraction further comprises denaturing the microparticle-enriched fraction with urea, reducing the microparticle-enriched fraction with dithiothreitol, alkylating the microparticle-enriched fraction with iodoacetamine, and digesting the microparticle-enriched fraction with trypsin.

[0187] Aspect II-25. The method of embodiment II-12, wherein the step of enriching said sample for microparticles comprises further purifying the microparticles to enrich for placenta-derived exosomes or vascular endothelium-derived exosomes.

[0188] Aspect II-26. The method of embodiment II-12, wherein the step of separating the microparticle-associated peptides by mass spectrometry comprises separating the microparticle-associated peptides by liquid chromatography / mass spectrometry (LC / MS), including liquid chromatography / triple quadrupole mass spectrometry.

[0189] Aspect II-27. The method of embodiment II-12, wherein the step of separating the microparticle-associated peptides by mass spectrometry comprises mass spectrometry comprising multiple reaction monitoring.

[0190] Aspect II-28. the one or more peptides being (i) a biomarker panel in Table 7, with a blood sample taken at approximately 20 weeks of gestation; and (ii) A blood sample is taken at approximately 37 weeks of gestation, and the protein biomarkers in Table 8 The method of embodiment II-12, wherein the

[0191] Aspect II-29. A kit comprising one or more containers, each container containing one or more of each of a plurality of stable isotope standards, each stable isotope standard comprising: (i) a protein biomarker from Table 1, Table 3 or Table 5; (ii) a protein biomarker in Table 2, Table 4 or Table 6; (iii) protein biomarkers in Table 9 ; (iv) a surrogate biomarker in Figures 10A-10C, 11A-11C, or 12A-12H; (v) a surrogate biomarker in Figures 13A-13H, 14A-14D or 15A-15B; (vi) ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and cartilage acidic protein 1 (CRAC1); and (vi) ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54) and Ig-like domain-containing protein a surrogate peptide for a biomarker from a panel of biomarkers selected from the one or more containers corresponding to the surrogate peptide for a biomarker from a panel of biomarkers selected from the one or more containers corresponding to the surrogate peptide for a biomarker from a panel of biomarkers selected from the one or more containers corresponding to the surrogate peptide for a biomarker from the ...

[0192] Aspect II-30. A composition comprising one or more pairs of polypeptides, each pair of polypeptides comprising: (i) a protein biomarker from Table 1, Table 3 or Table 5; (ii) a protein biomarker in Table 2, Table 4 or Table 6; (iii) protein biomarkers in Table 9 ; (iv) a surrogate biomarker in Figures 10A-10C, 11A-11C, or 12A-12H; (v) a surrogate biomarker in Figures 13A-13H, 14A-14D or 15A-15B; (vi) ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and cartilage acidic protein 1 (CRAC1); and (vii) ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54) and Ig-like domain-containing protein The composition comprising one or more protein biomarkers or one or more surrogate biomarkers selected from:

[0193] Aspect II-31. A computer readable medium in a tangible, non-transitory form comprising code for implementing one or more classification rules generated by analysis of one or more datasets of biomarker measurements derived from one or more pregnant subjects classified into a first group at risk for placenta accreta or a second group not at risk for placenta accreta.

[0194] Aspect II-32. Systems including: (a) A computer that is (i) The processor and (ii) a memory coupled to the processor, (1) test data for a sample from a subject, the test data including one or more values, each value representing a measurement of one or more protein biomarkers in a fraction of microparticle-associated peptides, the one or more protein biomarkers being: (i) protein biomarkers in Table 1 , Table 3 or Table 5 , with a blood sample taken at approximately 20 weeks' gestation; (ii) a protein biomarker in Table 2, Table 4, or Table 6, with a blood sample taken at approximately 37 weeks' gestation; (iii) protein biomarkers in Table 9 ; (iv) one or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and / or cartilage acidic protein 1 (CRAC1); and (v) one or more of ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and / or Ig-like domain-containing protein. Selected from: Test data for a sample from said subject; (2) a classification rule for classifying a subject as being at increased risk of placenta accreta based on one or more values, each value representing said measurement, the classification rule being configured to have a sensitivity value of at least 75%, at least 85%, or at least 95%; and (3) computer-executable instructions for implementing the classification rules on the test data. the memory storing a module including The computer comprising:

[0195] Aspect II-33. The protein biomarker is (i) a surrogate biomarker of Figures 10A-10C, 11A-11C, or 12A-12H; and (ii) a surrogate biomarker of Figures 13A-13H, 14A-14D or 15A-15B The system of embodiment II-32, wherein the surrogate biomarker is selected from:

[0196] Aspect II-34. (a) using a computer system to compile test data, the computer system including a processor and a memory coupled to the processor, the memory comprising: (1) test data for a sample from a subject, the test data including a value indicative of one or more measurements of one or more protein biomarkers of the present disclosure in a fraction of microparticle-associated peptides; (2) a classification rule implemented by a processor that classifies the subject as being at increased risk for placenta accreta based on values ​​that include the one or more measurements, the classification rule being configured to have a sensitivity of at least 75%, at least 85%, or at least 95%. storing a module including: (b) accessing said test data; and (c) executing the classification rules against the test data. A method comprising:

[0197] Aspect II-35. 1. A method for assessing the risk of placenta accreta in a pregnant subject, comprising: (a) preparing a microparticle-enriched fraction from a blood sample from a pregnant subject; (b) determining a quantitative measure of one or more microparticle-associated protein biomarkers in the microparticle-enriched fraction, wherein the one or more microparticle-associated protein biomarkers are (i) a protein biomarker of Table 1, Table 3 or Table 5, wherein the blood sample is taken at about 20 weeks' gestation; (ii) a protein biomarker of Table 2, Table 4 or Table 6, wherein the blood sample is taken at about 37 weeks' gestation; (iii) protein biomarkers in Table 9 ; (iv) one or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and / or cartilage acidic protein 1 (CRAC1); and (v) one or more of ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and / or Ig-like domain-containing protein. and (c) assessing the risk of placenta accreta based on one or more quantitative measures. The method comprising:

[0198] Aspect II-36. The protein biomarker is (i) a surrogate biomarker in Figures 10A-10C, 11A-11C, or 12A-12H; (ii) a surrogate biomarker in Figures 13A-13H, 14A-14D or 15A-15B; (iii) surrogate biomarkers for ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and / or cartilage acidic protein 1 (CRAC1); and (iv) surrogate biomarkers for ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and / or Ig-like domain-containing proteins. The method of embodiment II-35, wherein the surrogate biomarker is selected from the group consisting of:

[0199] Aspect II-37. Determining said quantitative measure of one or more microparticle associated protein biomarkers comprises: contacting the sample with one or more capture reagents, each capture reagent specifically binding to one of the protein biomarkers; and detecting binding of the capture reagent to the protein biomarker. The method of embodiment II-35, comprising:

[0200] Aspect II-38. The method of embodiment II-37, wherein said determining said quantitative measure of one or more microparticle-associated protein biomarkers comprises performing an immunoassay.

[0201] Aspect II-39. The method of embodiment II-38, wherein the immunoassay is selected from the group consisting of an enzyme immunoassay (EIA), an enzyme linked immunosorbent assay (ELISA), and a radioimmunoassay (RIA).

[0202] Aspect II-40. The method of embodiment II-35, wherein said step of assessing the risk of placenta accreta comprises executing a classification rule, wherein said classification rule classifies said subject as at risk for placenta accreta, and wherein execution of said classification rule results in a correlation between placenta accreta or full-term birth with a p-value of at least less than 0.05.

[0203] Aspect II-41. The method of embodiment II-35, wherein said step of assessing the risk of placenta accreta comprises executing a classification rule, wherein said classification rule classifies said subject as at risk for placenta accreta, and wherein executing said classification rule generates a receiver operating characteristic (ROC) curve, wherein said ROC curve has an area under the curve (AUC) of at least 0.6, at least 0.7, at least 0.8, or at least 0.9.

[0204] Aspect II-42. The method of embodiment II-35, wherein the classification rules classify the subject based on one or more values, the one or more values ​​further comprising at least one of placenta previa, previous cesarean delivery, endometrial resection, in vitro fertilization, previous uterine infection, or previous uterine surgery.

[0205] Aspect II-43. The method of embodiment II-25 to 32, wherein the classification rules employ cutoffs, linear regression including multiple linear regression, partial least squares regression, principal component regression, binary decision trees including recursive partitioning processes further including classification and regression trees, artificial neural networks including backpropagation networks, discriminant analysis further including Bayesian classifiers or Fisher analysis, logistic classifiers, and support vector classifiers including support vector machines.

[0206] Aspect II-44. The method of embodiment II-35, wherein the classification rules are configured to have a sensitivity value, specificity value, positive predictive value, or negative predictive value of at least 70%, at least 80%, at least 90%, or at least 95%.

[0207] Aspect II-45. The method of embodiment II-35, wherein the step of assessing the risk of placenta accreta comprises determining whether said protein biomarker, if upregulated, is above a threshold level, or, if downregulated, is below a threshold level.

[0208] Aspect II-46. The method of embodiment II-45, wherein said threshold level represents at least one, at least two, or at least three z-score levels from a measure of central tendency comprising the mean, median, or mode of said protein biomarker determined from at least 50, at least 100, or at least 200 control subjects.

[0209] Aspect II-47. The method of embodiment II-35, wherein said assessing the risk of placenta accreta comprises comparing said one or more quantitative measures of each protein biomarker in a panel to a reference standard.

[0210] Aspect II-48. The method of embodiment II-35, further comprising the step of informing a health care provider of the pregnant subject's risk of placenta accreta.

[0211] Aspect II-49. 1. A method of treating placenta accreta in a pregnant subject, comprising: (a) assessing the risk of placenta accreta in a pregnant subject by any one of the methods of embodiments II-1-9 and 35-48; and (b) administering to the subject a therapeutic intervention effective to reduce the risk of placenta accreta and / or reduce the neonatal complications of placenta accreta. The method comprising:

[0212] Aspect II-50. administering the therapeutic intervention (i) referring the subject to a medical center with a high degree of multidisciplinary surgical expertise and experience; (ii) planning for surgical uterine preservation; and (iii) performing a cesarean section with a hysterectomy, performing prophylactic embolization, inserting a uterine balloon tamponade, a temporary internal iliac occlusion balloon catheter, a ureteral stent, administering methotrexate, leaving a portion of the placenta in situ, and instructing the subject on bed rest to prevent premature birth; The method of embodiment II-49, comprising a therapeutic intervention selected from the group consisting of:

[0213] Aspect II-51. A method comprising administering a therapeutic intervention effective in reducing the risk of placenta accreta to a pregnant subject determined to have an increased risk of placenta accreta by any one of the methods of aspects II-1 to 9 and 35 to 48.

[0214] Aspect II-52. 1. A method of administering to a pregnant subject an effective amount of a treatment designed to reduce the risk of placenta accreta, comprising administering to the subject: (i) protein biomarkers in Table 1 , Table 3 or Table 5 , with a blood sample taken at approximately 20 weeks' gestation; (ii) a protein biomarker in Table 2, Table 4, or Table 6, with a blood sample taken at approximately 37 weeks' gestation; (iii) protein biomarkers in Table 9 ; (iv) one or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and / or cartilage acidic protein 1 (CRAC1); and (v) one or more of ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and / or Ig-like domain-containing protein. The method of any one of the following, wherein the protein biomarkers have an altered quantitative measure compared to a reference standard:

[0215] Aspect II-53. The protein biomarker is (i) a surrogate biomarker in Figures 10A-10C, 11A-11C, or 12A-12H; (ii) a surrogate biomarker in Figures 13A-13H, 14A-14D or 15A-15B; (iii) surrogate biomarkers for ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and / or cartilage acidic protein 1 (CRAC1); and (iv) surrogate biomarkers for ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and / or Ig-like domain-containing proteins. The method of embodiment II-52, wherein the surrogate biomarker is selected from the group consisting of:

[0216] Aspect II-54. (a) measuring by mass spectrometry the masses of more than 100, more than 1000, more than 10,000 or more than 100,000 peptides from a biological sample comprising peptide fragments of a protein to generate a dataset comprising more than 100, more than 1000, more than 10,000 or more than 100,000 data entries; (b) a computer system including one or more processors and a memory storing a program for execution by the one or more processors; (1) from among the data entries, based on the mass, (i) a protein biomarker of Table 1, Table 3 or Table 5, wherein the biological sample is taken at about 20 weeks' gestation; and (ii) a protein biomarker of Table 2, Table 4 or Table 6, wherein the biological sample is taken at about 37 weeks' gestation; (iii) protein biomarkers in Table 9 ; (iv) one or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and / or cartilage acidic protein 1 (CRAC1); and (v) one or more of ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and / or Ig-like domain-containing protein. identifying a peptide corresponding to (2) implementing a classification rule against the measurements of one or more of the identified peptides that classifies the sample as being from a subject at increased risk for placenta accreta. The stage of using A method comprising:

[0217] As used herein, the following meanings apply unless otherwise indicated: The word "may" is used in a permissive sense (i.e., meaning having the possibility) rather than a mandatory sense (i.e., meaning must). The words "include," "including," and "includes," etc., mean including without limitation. The singular forms "a," "an," and "the" include plural referents. Thus, for example, reference to "an element" includes a combination of two or more elements, notwithstanding the use of other terms and phrases for one or more elements, such as "one or more." The phrase "at least one" includes "one or more," "one or a plurality," and "a plurality." The term "or," unless otherwise indicated, is non-exclusive, i.e., encompasses both "and" and "or." The term "any of" between a modifier and a series means that the modifier modifies every member of the series. Thus, for example, the phrase "at least any of 1, 2, or 3" means "at least 1, at least 2, or at least 3." The term "consisting essentially of" refers to the inclusion of the recited elements and other elements that do not materially affect the basic and novel characteristics of the claimed combination. Unless otherwise specified, the term "about" in reference to a value refers to 90%-110% of the value or 95%-105% of that value.

[0218] It should be understood that the description and drawings are not intended to limit the invention to the particular forms disclosed; on the contrary, the intent is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims. Further modifications and alternative embodiments of various aspects of the invention will be apparent to those skilled in the art in view of this description. Thus, this description and drawings should be interpreted as illustrative only and are for the purpose of teaching those skilled in the art the general manner of carrying out the invention. It should be understood that the forms of the invention shown and described herein should be interpreted as examples of embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed or omitted, and certain features of the invention may be utilized independently, as would be apparent to any person skilled in the art after having the benefit of this description of the invention. Changes may be made to the elements described herein without departing from the spirit and scope of the invention as described in the following claims. The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description.

Claims

1. 1. A method for assessing the risk of placenta accreta in a pregnant subject, comprising: (a) providing a sample from a pregnant subject between about 20 weeks and about 37 weeks of gestation; (b) preparing a microparticle-associated peptide fraction from the sample; (c) measuring a plurality of protein biomarkers in said fraction, said plurality of protein biomarkers comprising: (i) multiple protein biomarkers from Table 1, Table 3, and Table 5; (ii) multiple protein biomarkers from Table 2, Table 4, and Table 6; (iii) multiple protein biomarkers from Table 9; (iv) two or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and cartilage acidic protein 1 (CRAC1); or (v) two or more of ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and Ig-like domain-containing protein and (d) implementing a classification rule on the one or more measurements of (c) that classifies the sample as being from a subject at increased risk of placenta accreta. The method comprising:

2. 2. The method of claim 1, wherein said protein biomarkers comprise a panel of no more than 10, 9, 8, 7, 6, 5, 4, or 3 protein biomarkers.

3. the protein biomarker is (i) the biomarker panel in Table 7; and (ii) Protein biomarkers in Table 8 2. The method of claim 1, comprising, consisting essentially of, or consisting of a panel of biomarkers selected from:

4. 10. The method of claim 1, wherein measuring said plurality of biomarkers comprises measuring relevant surrogate biomarkers in Figures 10A-10C, 11A-11C, 12A-12H, 13A-13H, 14A-14D and 15A-15B.

5. 10. The method of claim 1, wherein the pregnant subject has one or more risk factors for placenta accreta.

6. 2. The method of claim 1, wherein the pregnant subject is a primigravid woman, a multigravid woman, a primipara, or a multipara.

7. The method of claim 1, wherein the sample is a blood sample.

8. 10. The method of claim 1, wherein the sample is plasma or serum.

9. 1. A kit comprising one or more containers, each container containing one or more of each of a plurality of stable isotope standards, each stable isotope standard comprising: (i) protein biomarkers in Table 1, Table 3, or Table 5; (ii) protein biomarkers in Table 2, Table 4, or Table 6; (iii) protein biomarkers in Table 9; (iv) surrogate biomarkers in Figures 10A-10C, Figures 11A-11C, or Figures 12A-12H; (v) a surrogate biomarker in Figures 13A-13H, Figures 14A-14D, or Figures 15A-15B; (vi) ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and cartilage acidic protein 1 (CRAC1); and (vii) ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and Ig-like domain-containing protein a kit comprising one or more containers corresponding to surrogate peptides for a biomarker from a panel of biomarkers selected from:

10. 1. A method for assessing the risk of placenta accreta in a pregnant subject, comprising: (a) preparing a microparticle-enriched fraction from a blood sample from a pregnant subject; (b) determining a quantitative measure of one or more microparticle-associated protein biomarkers in the microparticle-enriched fraction, wherein the one or more microparticle-associated protein biomarkers are: (i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the blood sample is taken at about 20 weeks' gestation; (ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the blood sample is taken at about 37 weeks' gestation; (iii) protein biomarkers in Table 9; (iv) one or more of ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and / or cartilage acidic protein 1 (CRAC1); and (v) one or more of ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and / or Ig-like domain-containing protein a step selected from the group consisting of (c) assessing the risk of placenta accreta based on said one or more quantitative measures. The method comprising:

11. the protein biomarker is (i) surrogate biomarkers in Figures 10A-10C, Figures 11A-11C, or Figures 12A-12H; (ii) a surrogate biomarker in Figures 13A-13H, Figures 14A-14D, or Figures 15A-15B; (iii) surrogate biomarkers for ismin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2), and / or cartilage acidic protein 1 (CRAC1); and (iv) surrogate biomarkers for ismin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variable 10-54 (LVX54), and / or Ig-like domain-containing proteins.

11. The method of claim 10, wherein the surrogate biomarker is selected from the group consisting of:

12. determining said quantitative measure of one or more microparticle-associated protein biomarkers comprises: contacting the sample with one or more capture reagents, each capture reagent specifically binding to one of the protein biomarkers; and detecting binding of said capture reagent to said protein biomarker.

11. The method of claim 10, comprising:

13. 13. The method of claim 12, wherein determining the quantitative measure of one or more microparticle-associated protein biomarkers comprises performing an immunoassay.

14. The method of claim 13, wherein the immunoassay is selected from the group consisting of an enzyme immunoassay (EIA), an enzyme-linked immunosorbent assay (ELISA), and a radioimmunoassay (RIA).

15. 11. The method of claim 10, wherein the step of assessing the risk of placenta accreta comprises executing a classification rule, wherein the classification rule classifies the subject as at risk for placenta accreta, and wherein execution of the classification rule results in a correlation between placenta accreta and full-term birth with a p-value of at least less than 0.

05.

16. 11. The method of claim 10, wherein assessing the risk of placenta accreta comprises executing a classification rule, wherein the classification rule classifies the subject as at risk for placenta accreta, and wherein executing the classification rule generates a receiver operating characteristic (ROC) curve, wherein the ROC curve has an area under the curve (AUC) of at least 0.6, at least 0.7, at least 0.8, or at least 0.

9.

17. The method of claim 10, wherein the classification rules classify the subject based on one or more values, the one or more values ​​further including at least one of placenta previa, previous cesarean delivery, endometrial resection, in vitro fertilization, previous uterine infection or previous uterine surgery.

18. 11. The method of claim 10, wherein the classification rules employ cutoffs, linear regression including multiple linear regression, partial least squares regression, principal component regression, binary decision trees including recursive partitioning processes further including classification and regression trees, artificial neural networks including backpropagation networks, discriminant analysis further including Bayesian classifiers or Fisher analysis, logistic classifiers, and support vector classifiers including support vector machines.

19. The method described in claim 10, wherein the classification rules are configured to have a sensitivity value, specificity value, positive predictive value or negative predictive value of at least 70%, at least 80%, at least 90% or at least 95%.

20. The step of assessing the risk of placenta accreta, comprising: determining that said protein biomarker is above a threshold level if upregulated, or below a threshold level if downregulated.

11. The method of claim 10, comprising:

21. 21. The method of claim 20, wherein said threshold level represents the level of at least one, at least two, or at least three z-scores from a measure of central tendency comprising the mean, median, or mode of said protein biomarker determined from at least 50, at least 100, or at least 200 control subjects.

22. 11. The method of claim 10, wherein said assessing the risk of placenta accreta comprises comparing said one or more quantitative measures of each protein biomarker in a panel to a reference standard.

23. A machine learning method for generating a classifier for classifying risk of placenta accreta, comprising: (a) providing a dataset comprising biomarker measurements derived from biological samples from subjects in the second trimester of pregnancy, and classifying the subjects into (i) subjects at increased risk of placenta accreta and (ii) subjects not at increased risk of placenta accreta; (b) analyzing the dataset using a machine learning algorithm to generate a classifier that classifies the samples as having an increased risk of placenta accreta or as not having an increased risk of placenta accreta. The method comprising:

Images