METHODS OF PREPARING AND ANALYZING SAMPLES FOR BIOMARKERS

ASSOCIATED WITH PLACENTA ACCRETA

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. provisional patent application number 63/278,456, filed on November 11, 2021, the contents of which are incorporated by reference herein in their entirety.

BACKGROUND

[0002] Placenta accreta is a serious pregnancy condition that occurs when the placenta grows too deeply into the uterine wall. Typically, the placenta detaches from the uterine wall after childbirth. With placenta accreta, part or all of the placenta remains attached. This can cause severe blood loss after delivery. It is also possible for the placenta to invade the muscles of the uterus (placenta increta) or grow through the uterine wall (placenta percreta).

[0003] Placenta accreta is considered a high-risk pregnancy complication. If the condition is diagnosed during pregnancy, there is increases likelihood that a woman will require cesarian delivery followed by hysterectomy.

[0004] The incidence of placenta accreta spectrum has increased by a factor of approximately 8 since the 1970s, probably owing to increases in cesarean delivery. The incidence of cesarean birth is much higher in women with placenta accreta than those without. For example, in a first pregnancy, the incidence of cesarian birth in women without placenta accreta is 0.03%, while in women with placenta accreta, the incidence is 100-fold higher, or 3%.

[0005] Women with major risk factors, such as placenta previa, previous cesarean delivery, endometrial ablation, or other uterine surgery, should undergo obstetrical sonography in the middle-to-late second trimester to assess for possible placenta accreta spectrum. Patients with suspected placenta accreta spectrum should be referred to a center with multidisciplinary expertise and experience. [0006] Present diagnosis of placenta accreta involves ultrasound, the specificity and sensitivity of which relies on knowledge of patient’s clinical status (e.g., clinical suspicion for accreta, prior knowledge of risk factors). Needed are methods of more sensitive ways of predicting and detecting placenta accreta in a pregnant woman. Provided herein are methods and compositions that address this need.

SUMMARY

[0007] Disclosed herein are circulating microparticle (CMP)-associated proteins, useful for the prediction and detection of plasma accreta. In some embodiments, the CMP-associated proteins are collected from around about 20 weeks to around about 37 weeks of gestation, that can be used to assess risk of placenta accreta. The biomarkers are presented in Tables 1-6, and Table 9, the tables of FIGS. 1A-1D, FIGS. 2A-2C, FIGS. 3A-3K, FIGS. 4A-4J, FIGS. 5A-5C, FIGS.6A- 6F, as well as tables in Example 1. Also provided are surrogates useful for the detection of the biomarkers, presented in FIGS. 10A-10C, FIGS. 11A-11C, FIGS. 12A-12H, FIGS. 13A-13H, FIGS. 14A-14D, and FIGS. 15A-15B. Panels of biomarkers are also presented.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate exemplary embodiments and, together with the description, further serve to enable a person skilled in the pertinent art to make and use these embodiments and others that will be apparent to those skilled in the art. The invention will be more particularly described in conjunction with the following drawings wherein:

[0009] FIGS. 1A-1D depict Table 1 comprising median 24-week samples. The markers are ranked using Lasso regression (alpha = 1). The top 20 markers are displayed. This technique optimizes proteins that may interact and “collaborate” in panels rather than isolated markers. Individual markers are ranked by frequency of utility in multimarker panels.

[0010] FIGS. 2A-2C depict Table 2 comprising median 34-week samples. The markers are ranked using Lasso regression (alpha = 1). The top 20 markers are displayed and ranked by frequency of utility in multimarker panels. [0011] FIGS. 3A-3K. depict Table 3 comprising the top 50 protein markers at median 24 weeks using an ensemble feature selection routine that selects the top individual markers that distinguish case from control. The protein markers that overlap with the top 20 of the Lasso regression of Table 1 are indicated with asterisk. Individual markers are ranked by performance in distinguishing case/control.

[0012] FIGS. 4A-4J depict Table 4 comprising the top 50 protein markers at median 34 weeks using an ensemble feature selection routine that selects the top individual markers that distinguish case from control. The protein markers that overlap with the top 20 of the Lasso regression (Table 2) are indicated with an asterisk. Individual markers are ranked by performance in distinguishing case/control.

[0013] FIGS. 5A-5C depict Table 5 comprising 24-week markers. Individual markers ranked by performance in distinguishing case/control.

[0014] FIGS. 6A-6F depict Table 6 comprising 34-week markers. Individual markers ranked by performance in distinguishing case/control.

[0015] FIG. 7 depicts Table 7 comprising 24-week markers. Top performing multiplex panels are ranked by average AUC of an iterative cross-validation procedure.

[0016] FIG. 8 depicts Table 8 comprising 34-week markers. Top performing multiplex panels are ranked by average AUC of an iterative cross-validation procedure.

[0017] FIG. 9 illustrates a schematic of a protocol for identifying predictive circulating microparticle protein panels for placenta accreta.

[0018] FIGS. 10A-10C provide surrogate peptides, useful for the detection of the biomarkers of Table 1.

[0019] FIGS. 11A-11C provide surrogate peptides, useful for the detection of the biomarkers of Table 2.

[0020] FIGS. 12A-12H provide surrogate peptides, useful for the detection of the biomarkers of Table 3. [0021] FIGS. 13A-13H provide surrogate peptides, useful for the detection of the biomarkers of Table 4.

[0022] FIGS. 14A-14D provide surrogate peptides, useful for the detection of the biomarkers of Table 5.

[0023] FIGS. 15A-15B provide surrogate peptides, useful for the detection of the biomarkers of Table 6.

[0024] FIG. 16A is a density plot of protein versus permuted with a first shaded area represents actual protein AUC and a second shaded area represents AUC from randomly permuting the sample labels (placenta accreta spectrum vs. control) for the second trimester (e.g., 24 weeks).

[0025] FIG. 16B is a density plot of protein versus permuted with a first shaded area represents actual protein AUC and a second shaded area represents AUC from randomly permuting the sample labels (placenta accreta spectrum vs. control) for the third trimester (e.g., 37 weeks).

[0026] FIG. 17 depicts a schematic of exemplary canonical pathways, upstream regulators and molecular and cellular function analyses proposed in the second and third trimester leading to morbid placental adherence.

DETAILED DESCRIPTION

I. Introduction

[0027] Disclosed herein are methods, compositions, systems and articles of manufacture useful in preparing a sample for the detection of biomarkers useful in determining risk of developing, and for treating, placenta accreta. In some embodiments, determination involves detection of placenta accreta biomarkers found in microparticle-enriched fractions from the blood of pregnant women. Exemplary biomarkers useful for the detection of placenta accreta in either or both the second and third trimester 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 present biomarker panels for placenta accreta. Additional marker sets are also presented herein. II. Subjects

[0028] Subjects for providing samples for prediction and treatment of placenta accreta are pregnant human females. The stage of pregnancy can be calculated from the first day of the last normal menstrual period of the pregnant subject.

[0029] In some embodiments, the pregnant women may be about 20 weeks to about 37 weeks of pregnancy. In some embodiments, the pregnant woman may be about 20 weeks of pregnancy, about 21 weeks of pregnancy, about 22 weeks of pregnancy, about 23 weeks of pregnancy, about 24 weeks of pregnancy, about 25 weeks of pregnancy, about 26 weeks of pregnancy, about 27 weeks of pregnancy, about 28 weeks of pregnancy, about 29 weeks of pregnancy, about 30 weeks of pregnancy, about 31 weeks of pregnancy, about 32 weeks of pregnancy, about 33 weeks of pregnancy, about 34 weeks of pregnancy, about 35 weeks of pregnancy, about 36 weeks of pregnancy, or about 37 weeks of pregnancy.

[0030] Pregnant subjects of the methods described herein can belong to one or more classes including primiparous (no previous child brought to delivery, interchangeably referred to herein as nulliparous or parity=0) or multiparous (at least one previous child brought to at least 20 weeks of gestation, referred to interchangeably herein as parity >0, parity >1), primi gravida (first pregnancy) or multigravida (more than one pregnancy).

[0031] In some embodiments, the pregnant human subject is asymptomatic. In some embodiments, the subject may have a risk factor of placenta accreta. The most common risk factor is a previous cesarean delivery, with the incidence of placenta accreta spectrum increasing with the number of prior cesarean deliveries. In a systematic review, the rate of placenta accreta spectrum increased from 0.3% in women with one previous cesarean delivery to 6.74% for women with five or more cesarean deliveries. Additional risk factors include advanced maternal age, multiparity, prior uterine surgeries or curettage, and Asherman syndrome. Placenta previa is another significant risk factor.

III. Sample Preparation

[0032] A sample for use in the methods of the present disclosure is a biological sample obtained from a pregnant subject. In certain embodiments, the sample is collected during a stage of pregnancy described in the preceding section. In some embodiments, the sample is a blood, saliva, tears, sweat, nasal secretions, urine, amniotic fluid or cervicovaginal fluid sample. In some embodiments, the sample is a blood sample. In some embodiments, the sample is a blood plasma sample. In some embodiments, the sample is a blood serum sample. In some embodiments, the sample is a blood product in a different matrix (e.g. Citrate buffer, or Streck tube). In some embodiments, the sample has been stored frozen (e.g., -20°C or -80°C).

[0033] The term “microparticle” refers to an extracellular microvesicle or lipid raft protein aggregate having a hydrodynamic diameter of about 50 to about 5000 nm. As such, 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.

[0034] 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. As such the term “microparticle-associated protein” is not restricted to proteins or fragments thereof that are physically associated with microparticles at the time of detection. The term “microparticle-associated peptide” refers to a protein fragment that is detectable in such a sample.

[0035] The term “polypeptide” as used herein refers to a polymer of amino acids. This includes oligopeptides (which typically have fewer than 10 amino acids), peptides (which typically have between about 10 and about 50 amino acids), and proteins (which include polypeptides assuming secondary, tertiary, or quaternary structures). Depending on context, the term “protein” may refer to a polypeptide lacking secondary structure.

[0036] 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 a mobile phase, refers to non-buffered water, e.g., distilled, deionized, or distilled de-ionized water (“ddHzO”). The high molecular weight fraction can be collected to obtain a microparticle-enriched sample. Proteins within the microparticle- enriched sample are then extracted before digestion with a proteolytic enzyme such as trypsin to obtain a digested sample comprising a plurality of peptides. The digested sample is then subjected to a peptide purification, concentration, and/or a fractionation step before analysis to obtain a proteomic profile of the sample, e.g., by liquid chromatography and mass spectrometry. In some embodiments, the purification/concentration step comprises reverse phase chromatography (e.g., ZipTip® pipette tip with 0.2 pL Cl 8 resin, from Millipore Corporation, Billerica, MA) or ultrafiltration. In some embodiments, the fractionation step may involve the fractionation into 96 fractions with a high pH reverse phase offline HPLC fractionator be with a Mobile phase A is DI H2O with 20 mM Formic Acetate, pH 9.3; mobile phase B is Acetonitrile (Optima™, LC/MS grade, Fisher Chemical™) with 20mM Formic Acetate, pH 9.3.

[0037] In some embodiments, for example, detection of proteins by mass spectrometry, a method of sample preparation can include fragmenting proteins in a sample. Fragmentation can be accomplished using proteases, such as trypsin. Tryptic fragments can usefully serve as surrogate biomarkers because their unique mass can be associated with the parent protein.

[0038] In certain embodiments, the microparticles are placental-derived exosomes or endothelial-derived exosomes. Such exosomes can be isolated using capture agents, such as antibodies, against surface markers for these cells of origin. For example, placental-derived exosomes can be isolated using antibodies directed to PLAP (placental alkaline phosphatase), Klotho, CD34, CD44 or leukemia inhibitory factor (LIF). Endothelial-derived exosomes can be isolated using antibodies directed to ICAM or VCAM.

IV. Methods of Detection

[0039] Biomarkers can be detected and quantified by any method known in the art. This includes, without limitation, immunoassay, chromatography, mass spectrometry, electrophoresis and surface plasmon resonance.

[0040] Detection of a biomarker includes detection of an intact protein, or detection of surrogate for the protein, such as a fragment. Exemplary fragments are provided in FIG. 10A to FIG. 15B. [0041] Immunoassay methods include, for example, radioimmunoassay, enzyme-linked immunosorbent assay (ELISA), sandwich assays and Western blot, immunoprecipitation, immunohistochemistry, immunofluorescence, antibody microarray, dot blotting, and FACS.

[0042] Chromatographic methods include, for example, affinity chromatography, ion exchange chromatography, size exclusion chromatography/gel filtration chromatography, hydrophobic interaction chromatography and reverse phase chromatography, including, e.g., HPLC.

1. Mass Spectrometry

[0043] In some embodiments, detecting the level (e.g., including detecting the presence) of a microparticle-associated protein is accomplished using a liquid chromatography/mass spectrometry (LC/MS)-based proteomic analysis. In an exemplary embodiment, the method involves subjecting a sample to size exclusion chromatography and collecting the high molecular weight fraction (e.g., by size-exclusion chromatography) to obtain a microparticle- enriched sample. In some embodiments, the size exclusion chromatography includes a sizeexclusion column comprising an agarose solid phase and an aqueous liquid phase. The microparticle-enriched sample is then disrupted (using, for example, chaotropic agents, denaturing agents, reducing agents and/or alkylating agents) and the released contents subjected to proteolysis. The disrupted microsome preparation, containing a plurality of 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 necessity of protein concentration/purification, buffer exchange and liquid chromatography steps associated with previous methods.

[0044] Proteins in a sample can be detected by mass spectrometry. Mass spectrometers typically include an ion source to ionize analytes, and one or more mass analyzers to determine mass. Mass analyzers can be used together in tandem mass spectrometers. Ionization methods include, among others, electrospray or laser desorption methods. Mass analyzers include quadrupoles, ion traps, time-of-flight instruments and magnetic or electric sector instruments. In certain embodiments, the mass spectrometer is a tandem mass spectrometer (e.g., “MS-MS”) that uses a first mass analyzer to select ions of a certain mass and a second mass analyzer to analyze the selected ions. One example of a tandem mass spectrometer is a triple quadrupole instrument, the first and third quadrupoles act as mass filters, and an intermediate quadrupole functions as a collision cell. Mass spectrometry also can be coupled with up-stream separation techniques, such as liquid chromatography or gas chromatography. So, for example, liquid chromatography coupled with tandem mass spectrometry can be referred to as “LC-MS-MS”.

[0045] Mass spectrometers useful for the analyses described herein include, without limitation, Altis™ quadrupole, Quantis™ quadrupole, Quantiva™ or Fortis™ triple quadrupole from ThermoFisher Scientific, the 8050 or 8060 triple quadruploes from Shimadzu, the Xevo TQ-XS™ triple quadrupole from Waters, QSight™ Triple Quad LC/MS/MS from Perkin Elmer, Thermo Orbitrap Mass Spectrometer Tribrid Eclipse with a Thermo Fisher Scientific Nanospray Flex™ Ion Source, and others.

[0046] Generally, any mass spectrometric (MS) technique that can provide precise information on the mass of peptides, and preferably also on fragmentation and/or (partial) amino acid sequence of selected peptides (e.g., in tandem mass spectrometry, MS/MS; or in post source decay, TOF MS), can be used in the methods and compositions disclosed herein. In some embodiments, any MS technique can provide process information on the mass of peptides wherein the mass comprises more than 100, more than 1000, more than 10,000, more than 100,000 peptides from a biological sample. Suitable peptide MS and MS/MS techniques and systems are known in the art (see, e.g., Methods in Molecular Biology, vol. 146: “Mass Spectrometry of Proteins and Peptides”, by Chapman, ed., Humana Press 2000; Kassel & Biemann (1990) 4//a/. 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. Accordingly, in some embodiments, the disclosed methods comprise performing quantitative MS to measure one or more peptides. Such quantitative methods can be performed in an automated (Villanueva, et al., Nature Protocols (2006) l(2):880-891) or semi-automated format. In particular embodiments, MS can be operably linked to a liquid chromatography device (LC-MS/MS or LC-MS) or gas chromatography device (GC-MS or GC -MS/MS).

[0047] Selected reaction monitoring is a mass spectrometry method in which a first mass analyzer selects a protein of interest (precursor), a collision cell fragments the protein into product fragments and one or more of the fragments is detected in a second mass analyzer. The precursor and product ion pair is called an SRM "transition”. The method is typically performed in a triple quadrupole instrument. When multiple fragments of a protein are analyzed, the method is referred to as Multiple Reaction Monitoring Mass Spectrometry (“MRM-MS”).

[0048] Typically, protein samples are digested with a proteolytic enzyme, such as trypsin, to produce peptide fragments. Heavy isotope labeled analogues of certain of these peptides are synthesized as standards. These standards are referred to as Stable Isotopic Standards or “SIS”. SIS peptides are mixed with a protease-treated sample. The mixture is subjected to triple quadrupole mass spectrometry. Peptides corresponding to the daughter ions of the SIS standards and the target peptides are detected with high accuracy, in either the time domain or the mass domain. Usually, a plurality of the daughter ions is used to unambiguously identify the presence of a parent ion, and one of the daughter ions, usually the most abundant, is used for quantification. SIS peptides can be synthesized to order or can be available as commercial kits from vendors such as, for example, e.g., Thermo Fisher Scientific (Waltham, MA) or Biognosys AG (Zurich, Switzerland).

[0049] As used herein, the terms “multiple reaction monitoring (MRM)” or “selected reaction monitoring (SRM)” refer to a MS-based quantification method that is particularly useful for quantifying analytes that are in low abundance. In an SRM experiment, a predefined precursor ion and one or more of its fragments are selected by the two mass filters of a triple quadrupole instrument and monitored over time for precise quantification. Multiple SRM precursor and fragment ion pairs can be measured within the same experiment on the chromatographic time scale by rapidly toggling between the different precursor/fragment pairs to perform an MRM experiment. A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted analyte (e.g., peptide or small molecule such as chemical entity, steroid, hormone) can constitute a definitive assay. A large number of analytes can be quantified during a single LC-MS experiment. The term “scheduled,” or “dynamic” in reference to MRM or SRM, refers to a variation of the assay wherein the transitions for a particular analyte are only acquired in a time window around the expected retention time, significantly increasing the number of analytes that can be detected and quantified in a single LC-MS experiment and contributing to the selectivity of the test, as retention time is a property dependent on the physical nature of the analyte. A single analyte can also be monitored with more than one transition. Finally, the assay can include standards that correspond to the analytes of interest (e.g., peptides having the same amino acid sequence as that of analyte peptides), but differ by the inclusion of stable isotopes. Stable isotopic standards (SIS) can be incorporated into the assay at precise levels and used to quantify the corresponding unknown analyte. Additional levels of specificity are contributed by the co-elution of the unknown analyte and its corresponding SIS, and by the properties of their transitions (e.g., the similarity in the ratio of the level of two transitions of the analyte and the ratio of the two transitions of its corresponding SIS).

[0050] Accordingly, detection of a protein target by MRM-MS involves detection of one or more peptide fragments of the protein, typically through detection of a stable isotope standard peptide against which the peptide fragment is compared. Typically, an SIS will, itself, be fragmented in a collision cell as the original digested fragment, and one or more of these fragments is detected by the mass spectrometer.

[0051] Mass spectrometry assays, instruments and systems suitable for biomarker peptide analysis can include, without limitation, 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); inductively coupled plasma mass spectrometry (ICP-MS) atmospheric pressure photoionization mass spectrometry (APPI-MS); APPI-MS/MS; and APPI-(MS)n. Peptide ion fragmentation in tandem MS (MS/MS) arrangements can be achieved using techniques known in the art, such as, e.g., collision induced dissociation (CID). As described herein, detection and quantification of biomarkers by mass spectrometry can involve multiple reaction monitoring (MRM), such as described, inter alia, by Kuhn el 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 quantitation. 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, with the tandem column system described herein.

[0052] In another embodiment, useful in biomarker discovery, fractionated samples are analyzed by nano flow HPLC (e.g., Ultimate 3000, Thermo Fisher Scientific) followed by Thermo Orbitrap Mass Spectrometer (Tribrid Eclipse). The ion source can be a Nanospray Flex™ Ion Source (Thermo Fisher Scientific) equipped with Column Oven (PRSO-V2, Sonation) to heat up the nano column (PicoFrit, 100 pm x 250 mm x 15 pm tip, New Objective) for peptide separation. Peptides can be engaged on a trap column and then were delivered to the separation nano column by the mobile phase.

V. Biomarkers

[0053] As used herein, the term “biomarker” refers to a biological molecule, the presence, form or amount of which exhibits a statistically significant difference between two states. Accordingly, biomarkers are useful, alone or in combination, for classifying a subject into one of a plurality of groups. Biomarkers may be naturally occurring or non-naturally occurring. For example, a biomarker may be a naturally occurring protein or a non-naturally occurring fragment of a protein. Fragments of a protein can function as a proxy or surrogate peptide for the protein or as stand-alone biomarkers.

[0054] Provided herein are compositions of matter comprising one or a plurality of placenta accreta biomarkers in substantially pure form. The biomarkers can be mixed in a container, or can be physically separated, for example, through attachment to solid supports at different addressable locations. As used herein, a chemical entity, such as a polynucleotide or polypeptide, is “substantially pure” if it is the predominant chemical entity of its kind in a composition. This includes the chemical entity representing more than 50%, more than 80%, more than 90% or more than 95% or of the chemical entities of its kind 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 its kind in the composition. Chemical entities which are essentially pure are also substantially pure. 1. Protein Biomarkers

[0055] Provided herein are one or more protein biomarkers associated with increased risk of placenta accreta. Exemplary Biomarkers for inferring placenta accreta in the second trimester are presented in Table 1 (24 weeks +/- 2 weeks), Table 3 (24 weeks +/- 2 weeks), and Table 5 (24 weeks +/- 2 weeks). Biomarkers for inferring placenta accreta in the third trimester are presented in Table 2 (35 weeks +/- 2 weeks), Table 4 (35 weeks +/- 2 weeks), and Table 6 (35 weeks +/- 2 weeks). Although the data collected for the biomarkers of these tables are from pregnant subject at 24 weeks +/- 2 weeks gestation or 35 weeks +/- 2 weeks gestation, the biomarkers may be relevant for assessment at gestational ages that fall outside this range. As discussed below, Table 7 and Table 8 present panels of biomarker for inferring placenta accreta at around 24 and around 35 weeks, respectively. In some embodiments, the one or more protein biomarkers associated with increased risk of placenta accreta include a plurality of protein biomarkers.

[0056] The tables in the drawings provide the UniProt entry number and entry name, protein names, gene names, organism (all homo sapiens), primary gene names and gene name synonyms. The contents of Tables 1, 2, 3, 4, 5, 6, 7, and 8 of FIGS. 1A-1D, FIGS. 2A-2C, FIGS. 3A-3K, FIGS. 4A-4J, FIGS. 5A-5C, FIGS. 6A-6F, and FIGS. 10A-10C, FIGS. 11A-11C, FIGS. 12A-12H, FIGS. 13A-13H, FIGS. 14A-14D, FIGS. 15 A- 15B including their protein name, gene name (primary and synonym), and/or peptide sequence are hereby incorporated in their entirety into the detailed description as if they were provided herein. For the purpose of inclusion into the detailed description. Table 9 provides exemplary biomarkers of the disclosure, useful for the prediction of plasma accreta (protein name and synonyms provided).

Table 9: Exemplary biomarkers

[0057] Also provided for each biomarker is one or more peptide fragments from the protein that function as surrogate markers. A surrogate marker can be used as a measure of the protein for purposes of the models described herein. Accordingly, in some embodiments, the detection of one or more peptide fragments of a protein biomarker serves to detect the protein biomarker. Peptides useful as surrogates for biomarkers are presented in FIGS. 10 A- 10C, FIGS. 11 A-l 1C, FIGS. 12A-12H, FIGS. 13A-13H, FIGS. 14A-14D, and FIGS. 15A-15B.

[0058] The biomarkers can be detected using de novo sequencing of proteins from microparticles isolated from a sample (e.g., blood) taken from a pregnant woman. 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 the panels of Tables 7 and 8) and peptide fragments of the parent proteins (such as those described above in FIGS. 10A- 10C, FIGS. 11A-11C, FIGS. 12A-12H, FIGS. 13A-13H, FIGS. 14A-14D, and FIGS. 15A-15B) are useful as biomarkers of placenta accreta. Accordingly, in some embodiments, detection of a named protein biomarker encompasses detection by a surrogate, e.g., one or more fragments of the protein.

[0059] Proteins, e.g., peptides, detected by mass spectrometry are analyzed to identify those that are up-regulated (increased in amounts) or down-regulated (decreased in amounts) compared with controls. Proteins showing statistically significant differential expression are further analyzed to identify the parent protein. Such proteins can be identified in a protein database such as SwissProt.

[0060] In certain embodiments, biomarkers are in a composition in which the peptide biomarker is paired with a stable isotopic standard of the peptide. In some embodiments, the composition may include one pair of a peptide biomarker and stable isotopic standard of the peptide or a plurality of pairs with each pair comprising a peptide biomarker and a stable isotopic standard of the peptide. The peptide biomarker may include a surrogate biomarker as is described in more detail herein. Such compositions are useful for detection in multiple reaction monitoring mass spectrometry.

[0061] For purposes of mass spectrometry, proteins can be detected intact, or through fragmentation, e.g., LCMS or in multiple reaction monitoring (MRM). In such cases, proteins can be fragmented proteolytically before 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 fragmented forms of them produced in mass spectrometry, can function as surrogates for the full protein.

2. Biomarker Panels

[0062] In certain embodiments, biomarkers are analyzed as a panel. A panel is a plurality of biomarkers used in an algorithm to make a prediction or inference. As used herein, a panel comprising a group of identified biomarkers includes at least those identified biomarkers. A panel consisting of a group of identified biomarkers includes only the identified biomarkers. A panel consisting essentially of a group of identified biomarkers includes the identified biomarkers and no more than one or two other biomarkers. For example, a panel consisting essentially of four identified biomarkers can include up to six total biomarkers. A panel can exist as a conceptual grouping, as a composition of matter (e.g., comprising purified biomarkers, or as an article, such as solid support attached to a capture reagent such as an antibody, further bound to the biomarker. The solid support can be, for example, one or more solid particles, such as beads, or a chip in which biomarkers are attached in an array format.

[0063] Exemplary panels of biomarkers for assessing placenta accreta at around 24 weeks are presented in Table 7.

[0064] Exemplary panels of biomarkers for assessing placenta accreta at around 34 weeks are presented in Table 8.

VI. Data Analysis

[0065] As used herein, the term “analysis” refers to any algorithm that transforms inputs into outputs. Analyses include, without limitation, statistical analyses, machine learning analyses and neural net analyses. 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.

A. Measurements

[0066] A measurement of a variable, such as sequencing reads mapping to a position, can be any combination of numbers and words. A measure can be any scale, including nominal (e.g., name or category), ordinal (e.g., hierarchical order of categories), interval (distance between members of an order), ratio (interval compared to a meaningful “0”), or a cardinal number measurement that counts the number of things in a set. Measurements of a variable on a nominal scale indicate a name or category, e.g., category into which the sequencing read is classified. Measurements of a variable on an ordinal scale produce a ranking, such as “first”, “second”, “third”. Measurements on a ratio scale include, for example, any measure on a pre-defined scale, absolute number of reads, normalized or estimated numbers, as well as statistical measurements such as frequency, mean, median, standard deviation, or quantile. Measurements that involve quantification are typically determined at the ratio scale level. B. Analysis

[0067] In some embodiments, analysis 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, without limitation, correlational, Pearson correlation, Spearman correlation, chi-square, comparison of means (e.g., paired T-test, independent T-test, ANOVA) regression analysis (e.g., simple regression, multiple regression, linear regression, non-linear regression, logistic regression, polynomial regression, stepwise regression, ridge regression, lasso regression, elastic net regression) or non-parametric analysis (e.g., Wilcoxon rank-sum test, Wilcoxon sign-rank test, sign test). Such tools are included in commercially available statistical packages such as MATLAB, JMP Statistical Software and SAS. Such methods produce models or classifiers which one can use to classify a particular biomarker profile into a particular state.

[0068] In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model.

[0069] Statistical analysis can be operator implemented or implemented by machine learning.

[0070] Certain classifiers, such as cut-offs, can be executed by human inspection. Other classifiers, such as multivariate classifiers, can require a computer to execute the classification algorithm.

1. Machine Learning

[0071] In some variations, analysis may involve implementing machine learning techniques including linear and non-linear models, e.g., processes such as CART - classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (e.g., support vector machines). [0072] Classification rules, algorithms, also referred to as models, can be generated by mathematical analysis, including by machine learning techniques that perform analysis of datasets of biomarker measurements derived from subjects classed into one or another group. In some embodiments, one or more classification rules or algorithms may employ one or more of the following: cut-off, linear regression including multiple linear regression, partial least squares regression, principal components regression , binary decision trees including recursive partitioning processes further including classification and regression trees, artificial neural networks including back propagation networks, discriminant analyses further including Bayesian classifier or Fischer analysis, logistic classifiers, and support vector classifiers including support vector machines. In some variations, these datasets of biomarker measurements may comprise more than 100, more than 1000, more than 10,000, or more than 100,000 data entries. In some variations, machine learning techniques that perform analysis of datasets of biomarker measurements derived from subjects classed into one or another group may access test data from the subjects and execute the one or more classification rules on the test data.

[0073] Diagnostic tests are characterized by sensitivity (percentage classified as positive that are true positives) and specificity (percentage classified as negative that are true negatives). The relative sensitivity and specificity of a diagnostic test can involve a trade-off - higher sensitivity can mean lower specificity, while higher specificity can mean lower sensitivity. These relative values can be displayed on a receiver operating characteristic (ROC) curve. The diagnostic power of a set of variables, such as biomarkers, is reflected by the area under the curve (AUC) of an ROC curve.

[0074] In some embodiments, the classifiers of this disclosure have a sensitivity value, a specificity value, a positive predictive value, or a negative predictive value of at least 85%, at least 90%, at least 95%, at least 98%, or at least 99%. Classifiers of this 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.

[0075] Classification can be based on a measurement of a biomarker being above or below a selected cutoff level or value or threshold level or value. In certain embodiments, a cutoff value is obtained by measuring biomarker levels in a plurality of positive and negative reference samples, e.g., at least 10, 20, 50, 100 or 200 samples of each type (e.g., samples from control subjects and test subjects). A cutoff value can be established with respect to a measure of central tendency, such as mean, median or mode in 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, that is, a number of standard deviations above a mean of normal samples, for example one standard deviation, two standard deviations, three standard deviations or four standard deviations. For example, cutoff values can be selected so that the diagnostic test has at least an 80%, a 90%, a 95%, a 98%, a 99%, a 99.5%, or a 99.9% sensitivity value, specificity value and/or positive predictive value.

[0076] Numerically, an increased risk is associated with an odds ratio of over 1.0, preferably over 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 for placenta accreta.

VII. Methods of Assessing Risk of Placenta Accreta

[0077] The phrase “increased risk” of a condition, as used herein, indicates that a subject has a greater likelihood of developing the condition than a general population of subjects. So, for example, a subject who is at “increased risk of placenta accreta” has a greater likelihood of developing placenta accreta than a general population of subjects at the same stage of pregnancy, optionally compared with a population sharing one or more demographic or risk factors. These may include, for example, age, placenta previa, previous cesarean delivery, endometrial ablation, in vitro fertilization, prior uterine infection, or other uterine surgery. For example, a test may indicate that a woman at around 24 weeks or around 34 weeks of pregnancy has a higher risk of developing placenta accreta than a general or control population of woman at around 24 weeks or around 34 weeks pregnancy.

[0078] Classifying can employ a classification rule, algorithm or model determined by statistical analysis and/or machine learning. In some embodiments, the classification rule may be based on one or more values. The one or more values may include one or more demographic or risk factors of a subject compared to the general population of subjects. The one or more values may also include measured values of one or more protein biomarkers. A. Assessing Placenta Accreta

[0079] Provided herein are methods of assessing risk for placenta accreta at anywhere between 20 weeks of pregnancy to about 37 weeks of pregnancy, for example, classifying a pregnant human female as being at an increased risk of placenta accreta. The methods can involve determining a measure of one or a plurality of the biomarkers in Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, or Table 9, and associating the measure to risk of placenta accreta. For example, one can use a panel that includes 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more, or, no more than 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from any one or more of Table 1, Table 2, Table 3, Table 4, Table 5, Table 6, or Table 9. in the determination. In general, an amount of a biomarker that shows a difference compared to a control amount of the biomarker (e.g., a healthy pregnant female or a non-placenta accreta pregnant female), which difference is statistically significant, is associated with increased risk of placenta accreta. The difference can be an up-regulation or a down-regulation, which can be easily determined by the practitioner. Alternatively, determination may be based on a classification algorithm that may employ non-linear and/or hyperdimensional methods.

[0080] In some embodiments, pathways can be interrogated to aid the determination of risk of plasma accreta. For example, in some embodiments, one or more canonical pathways may be over-represented by differentially expressed proteins in second trimester placenta accreta cases. Such pathways may be one or more of the erythropoietin signaling pathway; and the iron homeostasis signaling pathway (making reference to Table 1.7 in Example 1).

[0081] In some embodiments, certain targets may be activated or inhibited, and may be interrogated to aid the determination of risk of plasma accreta. Exemplary targets are provided in Table 1.8 in Example 1.

[0082] In some embodiments, cellular and molecular functions around iron handling and erythrocyte function are over-represented and may be interrogated to aid the determination of risk of plasma accreta. Exemplary targets are provided in Table 1.9 in Example 1.

[0083] In some embodiments, one or more of isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1) are useful to distinguish plasma accreta from controls. In some embodiments, a biomarker panel of the disclosure comprises one, two, three, four, or all five of isthmin-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 to distinguish plasma accreta from controls in the second trimester.

[0084] In some embodiments, one or more of ISM2, ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein, are useful to distinguish plasma accreta from controls. In some embodiments, a biomarker panel of the disclosure comprises one, two, three, or all four of ISM2, ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein. In some embodiments one or more of these markers are useful to distinguish plasma accreta from controls in the third trimester.

[0085] In other embodiments a determination is based on the use of a panel of biomarkers, for example those that are provided in Tables 7 or 8.

B. Assessing Placenta Accreta - Second Trimester

[0086] Provided herein are methods of assessing risk for placenta accreta during the second trimester (e.g. at around 24 weeks of pregnancy), for example, classifying a pregnant human female as being at an increased risk of placenta accreta. The methods can involve determining a measure of one or a plurality of the biomarkers in Table 1, Table 3, Table 5, or Table 9 and associating the measure to risk of placenta accreta. For example, one can use a panel that includes 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more, or, no more than 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from the tables in the determination. In general, an amount of a biomarker that shows a difference compared to a control amount of the biomarker (e.g., a healthy pregnant female or a non-placenta accreta pregnant female), which difference is statistically significant, is associated with increased risk of placenta accreta. The difference can be an up-regulation or a down-regulation, which can be easily determined by the practitioner. Alternatively, determination may be based on a classification algorithm that may employ non-linear and/or hyperdimensional methods.

[0087] A biomarker panel can comprise of any of the biomarker panels presented in Table 7.

A biomarker panel can consist essentially of any of the biomarker panels presented in Table 7. A biomarker panel can consist of any of the biomarker panels presented in Table 7. In some embodiments, one or more of isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1) are useful to distinguish plasma accreta from controls.

C. Assessing Placenta Accreta - Third Trimester

[0088] Provided herein are methods of assessing risk for placenta accreta during the third trimester (e.g. at around 35 weeks of pregnancy), for example, classifying a pregnant human female as being at increased risk of placenta accreta. The methods can involve determining a measure of one or a plurality of the biomarkers in Table 2, Table 4, Table 6, or Table 9 and associating the measure to risk of placenta accreta. For example, one can use a panel that includes 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more, or, no more than 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from the tables in the determination. In general, an amount of a biomarker that shows a difference compared to a control amount of the biomarker (e.g., a healthy pregnant female or a non-placenta accreta pregnant female), which difference is statistically significant, is associated with increased risk of placenta accreta. The difference can be an up-regulation or a down-regulation, which can be easily determined by the practitioner. Alternatively, determination may be based on a classification algorithm that may employ non-linear and/or hyperdimensional methods.

[0089] A biomarker panel can comprise of any of the biomarker panels presented in Table 8. A biomarker panel can consist essentially of any of the biomarker panels presented in Table 8. A biomarker panel can consist of any of the biomarker panels presented in Table 8.

[0090] In some embodiments, one or more of ISM2, ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein, are useful to distinguish plasma accreta from controls.

VIII. Methods of Treating Subjects at Increased Risk of Placenta Accreta

[0091] Methods of treating pregnant subjects suffering from or at increased risk of placenta accreta include assessing the risk of placenta accreta in a pregnant subject and administering one or more therapeutic interventions useful in treating placenta accreta, reducing the risk or placenta accreta and/or reducing neonatal complications of placenta accreta. In some embodiments, administering one or more therapeutic interventions includes administering an effective amount of one or more treatments designed to reduce the risk of placenta accreta. In some embodiments, one or more treatments may include performing a Cesarean hysterectomy, recommending bed rest to the subject to prevent preterm labor, performing a prophylactic embolization, leaving a portion of the placenta left in-situ, interesting a uterine balloon tamponade, administering methotrexate, inserting one or more temporal internal iliac occlusion balloon catheters, inserting one or more ureteral stents, or the like.

[0092] Patients with suspected placenta accreta spectrum should be referred to a center having advanced multidisciplinary surgical expertise and experience in any of the therapeutic interventions listed above or standard therapeutic interventions in the field.

[0093] Surgical planning for uterine conservation would be planned if future fertility was desired or planning for or performing a hysterectomy would be undertaken if fertility was not desired.

[0094] This can allow for appropriate planning to ensure access to special! sts/surgeons and properly equipped facilities (i.e., blood bank support).

IX. Kits and other Articles of Manufacture

[0095] In another embodiment, provided herein are articles of manufacture, e.g., kits of reagents useful in detecting in a sample biomarkers, for increased risk of placenta accreta, in particular, 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 a molecule such as a fluorophore or an enzyme, which can catalyze a detectable reaction to indicate the binding of the reagents to their respective targets.

[0096] In some embodiments, the kits further comprise sample processing materials comprising a high molecular weight gel filtration composition (e.g., agarose such as SEPHAROSE) in a low volume (e.g., 1ml, 3ml, 5ml, 10ml) vertical column for rapid preparation of a microparticle-enriched sample from plasma. For instance, the microparticle- enriched sample can be prepared at the point of care before freezing and shipping to an analytical laboratory for further processing.

[0097] In some embodiments, the kits further comprise instructions for assessing risk of placenta accreta, in particular, placenta accreta. As used herein, the term “instructions” refers to directions for using the reagents contained in the kit for detecting the presence (including determining the expression level) of a protein(s) of interest in a sample from a subject. The proteins of interest may comprise one or more biomarkers of placenta accreta.

[0098] In another embodiment, a kit comprises one or more containers wherein each container containing one or a plurality of stable isotope standard (SIS) peptides corresponding to peptide biomarkers, e.g., peptides produced from protease (e.g., trypsin) digestion of biomarker proteins. In another embodiment, a majority or all of the SIS peptides correspond to the biomarker peptides. In another embodiment, the kit further comprises the biomarker peptides which the SIS peptides correspond.

[0099] In another embodiment, provided is a composition of matter that includes protein biomarkers of placenta accreta and, for a plurality of those biomarkers, a corresponding stable isotope standard peptide. This can be prepared by combining a sample comprising proteins isolated from microparticles, with stable isotope standard peptides.

X. Systems

[0100] Provided herein also are systems comprising a computer comprising a processor and memory. The computer can be configured to receive into memory one or more measurements of one or more biomarkers provided herein that are measured from a sample. The memory can include computer readable instructions which, when executed, classify the sample as at risk of placenta accreta or not at risk of placenta accreta. The computer system can be operatively coupled to a computer network with the aid of a communications interface. The network can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network in some cases is a telecommunication and/or data network. The network can include one or more computer servers, which can enable distributed computing, such as cloud computing. The system can include a first computer connected with a second computer through a communications network, such as, a high-speed transmission network including, without limitation, Digital Subscriber Line (DSL), Cable Modem, Fiber, Wireless, Satellite and, Broadband over Powerlines (BPL). Accordingly, results providing classification of a sample as at increased risk or as not at increased risk of placenta accreta can be transmitted from a transmitting computer to a remote receiving computer, such as located at the office of a healthcare provider or to a mobile device, such as a smart phone.

EXAMPLES

Example 1:

[0101] Maternal EDTA plasma samples were collected from patients who were > 18 years of age, receiving prenatal care and planning on delivery at a hospital in the second and third trimesters at a median of about 26 (+/- 2) and about 35 (+/- 2) weeks’ gestation, respectively. Pregnancy dating was confirmed by ultrasound at < 12 weeks gestation. The samples were aliquoted and stored at - 80 degrees centigrade. 35 Placenta accreta spectrum, referred to herein as “PAS”, cases and 70 controls were analyzed 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 Gynaecology and Obstetrics (“FIGO”) (Table 1.1).

Table 1.1. FIGO clinical and histologic criteria for PAS

[0102] Cases were defined as subjects with clinical or histologic grade 1 to 3 PAS consistent with the 2019 FIGO PAS classification (Table 1.1), delivery >23 weeks gestation and inclusion in the LIFECODES biobank. Prospective cases were first identified in the electronic medical record using the following word searches for records between 2007 and 2020: “adhere-” in operative reports and discharge summaries as well as “-creta,” “hyst-” or “previa” in pathology reports. The medical record for each prospective case was independently reviewed by two obstetricians within the institution’s multidisciplinary PAS team. The higher grade between the clinical and histologic grades was designated as the assigned grade. Disagreement in either inclusion status or assigned PAS grade were adjudicated by a review committee. Subjects identified to have a PAS diagnosis were cross-referenced with the LIFECODES biobank for inclusion. Controls were defined as subjects without a diagnosis of PAS and randomly matched 2: 1. Cases and controls were matched by gestational age of sampling (+1 week) and number of fetuses. Exclusion criteria were defined as current cancer diagnosis, use of immunomodulating medication or documented fetal chromosomal abnormality. Univariate analyses were conducted with chi-square tests and continuous variables were compared with Wilcoxon tests using SAS 9.4. All tests were two-tailed; P<0.05 was used to define statistical significance.

[0103] Compared to controls, cases were more likely to be of older maternal age and have placenta previa at delivery. Twenty (57.1%) of the PAS cases did not have a history of cesarean section nor placenta previa at delivery. Twenty -three (65.7%) of the PAS cases were determined by clinical criteria rather than histologic criteria. Notably, only 11% of grade 1 PAS cases were detected by ultrasound prior to delivery, compared to 57% and 100% of grade 2 and 3 PAS cases, respectively (Table 1.2).

Table 1.2 Characteristics and grading determination of PAS cases. +Data displayed as median (+IQR) or n (%)

[0104] To enrich each sample for circulating microparticles (“CMP”), Size Exclusion Chromatography (SEC) was used for CMP isolation. Anonymized EDTA plasma samples identified only by a study number that was agnostic to case or control status were randomly assorted and shipped on dry ice to NX Prenatal, Inc. (Houston, TX) where CMP protein enrichment was carried out by SEC and isocratically eluted using the NeXosome Elution Reagent. This involved NeXosome Isolation Columns manufactured by AmericanBio, Inc. (Canton, MA). Briefly, these columns were packed by AmericanBio with Sepharose 4B-CL (4% agarose, particle size 45-165 pm) from Cytiva (Marlborough, MA) to a total packed volume of lOmL and delivered 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 isolation, the columns were allowed to equilibrate to room temperature (overnight) and subsequently washed with NeXosome Elution Regent. EDTA plasma samples were thawed and 0.5mL of plasma was applied and allowed to incorporate into the NeXosome Isolation Column. The plasma samples were not filtered, diluted, or pretreated prior to application to the columns. Following the incorporation of the sample into the column, the NeXosome Elution Reagent was added and 0.5mL column fractions were collected. The eluted fractions yielded two peaks. The CMPs were captured in the column void volume and resolved from the high abundant soluble protein peak. Samples were processed according to a randomization scheme. Each CMP-containing fraction (0.5 mL aliquots of each fraction) was pooled within each individual sample and a total protein measurement was performed, using the Pierce BCA Protein Assay Kit (ThermoFisher Scientific). An aliquot containing a total protein of 200 pg from each individual CMP isolate pool was then transferred to 2-mL microcentrifuge tubes (VWR, Radnor, PA) and stored at - 80 °C pending completion of all CMP isolate processing. All CMP isolates were then shipped on dry ice to BGI Americas Corporation (Cambridge, MA) for proteomic analysis.

[0105] A total of 158 Plasma enriched exosome samples were individually processed for LC- MS/MS analysis. 100 pL of enriched exosomes were mixed with 700 pL of lysis buffer that contained 9M urea, at pH 8.5, and 0.5% Rapigest (SKU: 186001861, Waters TM). Samples were water-bath sonicated for 30 minutes followed by spinning at high-speed (14,000 rpm) in a centrifuge for 10 minutes. The protein concentration of samples was measured by the BCA assay (Cat No: A53225, ThermoFisher Scientific) post sample lysis.

[0106] 50 pg of each sample was taken from the lysate and normalized to the same volume with lysis buffer. Samples were reduced in 10 mM DTT for 25 minutes at 60 °C, then the reduced samples were alkylated in 20 mM IAM (iodoacetamide) in a dark environment for 20 minutes at room temperature. Excess IAM in the samples was quenched by adding 100 mM DTT. DI water and HEPE buffer at pH 8.5 were added to each sample so that the final urea concentration was diluted to 1.6 M, and a final pH of 8, for enzymatic digestion. 1 pg of Try/LysC (Cat No: A41007, ThermoFisher Scientific) was added to each sample. The samples were incubated overnight at 37 °C for 12 hours. An additional 1 pg of Tryp/LysC was added to each sample the next day and they were incubated for another 4 hours to complete the enzymatic digestion.

[0107] 10% TFA was added into the digested samples (peptides) to produce a final concentration of 1% TFA — the pH was tested and the samples were acidic. Then, acidified samples were passed through a 10 mg SEK PAK column (Cat No: 60108-302, ThermoFisher Scientific) for desalting. 20% of the desalted peptides of each sample was taken and pooled together to create a composite “library” of peptides. The library samples were then fractionated into 96 fractions with a high pH, reverse-phase, offline HPLC fractionator (VanquishTM, ThermoFisher Scientific). The mobile phase A was made up of DI H2O with 20 mM formic acetate, pH 9.3; the mobile phase B was made up of acetonitrile (OptimaTM, LC/MS grade, Fisher ChemicalTM) with 20mM formic acetate, pH 9.3. The gradient of separation is displayed in Table 1.3. 96 fractions were then combined into 24 fractions and readied for liquid chromatography mass spectrometry (LC/MS) analysis.

Table 1.3 High-pH, Reverse-Phase HPLC Fractionation Gradient Information

[0108] All fractionated samples were analyzed by nanofl ow HPLC (Ultimate 3000, Thermo

Fisher Scientific) followed by Thermo Orbitrap mass spectrometer (Tribrid Eclipse) analysis. A Nanospray FlexTM Ion Source (Thermo Fisher Scientific) was equipped with Column Oven (PRSO-V2, Sonation) to heat up the nanocolumn (PicoFrit, 100 pm x 250 mm x 15 pm tip, New Objective) for peptide separation. The nanoLC method is water acetonitrile based that was 150 minutes long with a 0.300 pL/min flowrate. For each sample injection, all peptides were first engaged on a trap column (Cat. No: 160454, Thermo Fisher) and then were delivered to the separation nanocolumn by the mobile phase. The specifics of the gradient used are provided in Table 1.4. Table 1.4. High pH Reverse Phase HPLC Fractionation Gradient Information

[0109] For the DDA library construction, a DIA library-specific DDA, MS2-based mass spectrometry method on Eclipse was used to sequence fractionated peptides that were eluted from the nanocolumn. For the full MS spectrum, a resolution of 120,000 was used with a scan range of 375 m/z - 1500 m/z. For the dd-MS(MS2), a resolution of 15,000 was used, and the isolation window is 1.6 Da. ‘Standard’ AGC target and ‘Auto’ Max Ion injection times (Max IT) were selected for both MSI and MS2 acquisition. The collision energy (NCE) was set to 35%, and the total cycle time is 1 second. For DIA analytical samples, a high-resolution, full MS scan, followed by two segment DIA methods, was used for the DIA data acquisition. For the full MS scan, a resolution of 120,000 was used for the range of 400 m/z - 1200 m/z with a ‘Standard’ AGC target and 50 ms Max IT. For both DIA segments, the details of the isolation windows (IW) and the precursor mass ranges are shown in Table 1.5 and Table 1.6. For the DIA fragments scan, a resolution of 30,000 was used for the range of 110 m/z - 1,800 m/z with a ‘Standard’ AGC target and ‘Auto’ Max IT.

Table 1.5. DIA segment 1 Precursor Scan Range Information.

Table 1.6. DIA segment 2 Precursor Scan Range Information.

[0110] The initial process is based on the sample data generated from a high-resolution mass spectrometer. The DDA data was identified by the Andromeda search engine within MaxQuant, and Spectronaut™ was used for the identification of results for spectral library construction. MaxQuant was used for the identification of DDA data, which served as a spectrum library for the subsequent DIA analysis. The analysis pipeline used raw data as input files and set corresponding parameters and human databases (UP000005640), then the identification and quantitative analysis was performed. The identified peptides satisfied a FDR of <1% to construct the final spectral library. For this DIA dataset, Spectronaut™ was employed to construct spectral library information to complete deconvolution and extraction, then the mProphet algorithm was used to complete an analytical 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 pipeline described above. MStats, the core algorithm of which is a linear mixed effect model, was used to process the DIA quantification results data according to the predefined comparison group, and then a significance test was performed based on the model. Thereafter, differential protein screening was executed and a fold change of > 2 and an adj P-value of < 0.05 was defined as a significant difference. Based on the quantitative comparison results, the differential proteins between comparison groups were identified; finally a function enrichment analysis, a protein-protein interaction (PPI) examination, and a subcellular localization analysis of the differential proteins were carried out. The sample classification analyses were then implemented as described below.

[OHl] PAS was classified using regularized (LI) regression to define a restricted set of candidate CMP proteins from the superset of all identified proteins. To select the putative panel from the restricted set of candidate CMP proteins, a cross-validation procedure using logistic regression was chosen. The sample was randomly divided into a training and validation set (80% vs. 20%). The proteins in the training set were then ranked by their 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 fivefold cross- validation. To avoid overfitting, given the limited sample size, the model was restricted to no more than 5 predictors. The model with the greatest area under the curve (AUC) and the lowest standard deviation of the AUC was then tested against the set-aside, external validation set. The AUC and standard deviation of the AUC of this external validation set was then recorded and the workflow re-iterated for a total of 1000 iterations (Fig. 9). The models were then ranked by their mean AUC and mean standard deviation of the AUC. The workflow was then repeated with randomly permuted sample labels. Predictive statistics for the observed versus permuted data were then compared. [0112] To establish a panel of CMP proteins that would serve as a classifier for the risk of PAS in the second and third trimesters, a two-step iterative workflow using regularized (LI) regression followed by a cross-validation procedure using logistic regression was used. The two- step workflow was also repeated with randomly permuted sample labels to simulate random chance. In second trimester samples, the mean of all area under the curves (AUCs) for the observed (e.g., first shaded area) versus permuted (e.g., second shaded area) panels was significantly different (0.72 vs. 0.45; p < 2.20e-16; FIG. 16A). The top performing panel of markers distinguished PAS from controls with a mean AUC of 0.83. CMP proteins of this panel included: isthmin-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 mean of all AUCs for the observed (e.g., first shaded area) versus permuted (e.g., second shaded area) panels were also significantly different (0.60 vs 0.52; p=2.79e-5; FIG. 16B). The top performing panel distinguished PAS from controls with a mean AUC of 0.78. CMP proteins in this panel included ISM2, ubiquitin carboxyl -terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein. Additionally, the mean of all AUC in the second trimester was significantly greater than that in the third trimester (p=1.59e-12).

[0113] To determine the biological function of those proteins identified as differentially expressed by PAS status in the differential expression analysis, an Ingenuity Pathway Analysis was also applied. Core analysis with the Ingenuity Knowledge Base reference set was used. Direct and indirect relationships were considered for network and regulatory analyses. IPA is a curated bioinformatic repository of functionally annotated analytes which allows for functional annotation, canonical pathway and network analyses, 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 containing two or more overlapping hits were included, while only upstream regulators with a predicted activation state (activated, inhibited) were included. Overlap ratios were calculated as the percent of overlap between differentially expressed proteins and the target pathway.

[0114] To determine the biological function of those proteins identified as differentially expressed by PAS status in the differential expression analysis, an Ingenuity Pathway Analysis was also applied. This revealed significant over-representation of canonical pathways, upstream regulators, and molecular and cellular functions. In the second trimester, proteomic changes in PAS yielded significant over-representation of several canonical pathways including iron homeostasis signaling and erythropoietin signaling (Table 1.7).

Table 1.7. Canonical pathways significantly over-represented by differentially expressed proteins in second trimester placenta accreta cases.

[0115] Master upstream regulators included seven targets predicted to be significantly activated and six predicted to be significantly inhibited (Table 1.8).

Table 1.8. Select master upstream regulators of target molecules in second trimester placenta accreta dataset.

[0116] IPA molecular and cellular functions analyses revealed 43 select annotated functions which were significantly over-represented based on differentially-expressed molecular hits from second trimester placenta accreta analyses (Table 1.9). Cellular and molecular functions around iron handling and erythrocyte function agreed with canonical iron homeostasis and erythropoietin signaling pathways.

Table 1.9: Select over-represented molecular and cellular functions of proteins altered in second trimester placenta accreta

[0117] As with second trimester data, IPA Core Analysis revealed significant overrepresentation of canonical pathways, upstream regulators, and molecular and cellular functions in the third trimester in PAS. Canonical pathway analysis of third trimester proteomic changes in PAS revealed significant over-representation of pathways including immune and extracellular signaling pathways, specifically involving IL- 15 (Table 1.10).

Table 1.10 Canonical Pathway analysis of Third Trimester Changes

[0118] Master upstream regulators included three predicted to be significantly activated and two significantly inhibited (Table 1.11).

Table 1.11. Select master upstream regulators of target molecules in third trimester placenta accreta dataset.

[0119] Molecular and cellular functional analyses revealed 24 select annotated functions which were significantly over-represented based on differentially expressed molecular hits from third trimester analyses in PAS (Table 1.12).

Table 1.12. Select over-represented molecular and cellular functions of proteins altered in third trimester placenta accreta

[0120] There was some agreement, particularly around immune signaling and cytoskeletal and cell growth functions, between these cellular and molecular functions and the canonical pathways revealed by IPA.

EXEMPLARY EMBODIMENTS

[0121] The following exemplary embodiments are provided herein.

Set I:

[0122] Embodiment 1-1. A method of preparing a peptide sample comprising:

(a) providing a blood, serum or plasma sample from a pregnant subject at around 24 weeks or 34 weeks of pregnancy;

(b) enriching the sample for microparticles by loading the sample on 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) separating the microparticle-associated peptides by mass spectrometry; and

(e) measuring, based on a mass spectrometry signal, one or more peptides corresponding to each of one or more protein biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3 or Table 5, wherein the blood sample is collected at about 24 weeks of pregnancy; and

(ii) a protein biomarker of Table 2, Table 4 or Table 6, wherein the blood sample is collected at about 34 weeks of pregnancy.

[0123] Embodiment 1-2. The method of embodiment 1-1, wherein the one or more protein biomarkers is a plurality of protein biomarkers.

[0124] Embodiment 1-3. The method of embodiment 1-1, wherein the biomarkers comprise a panel of no more than any of 10, 9, 8, 7, 6, 5, 4 or 3 protein biomarkers.

[0125] Embodiment 1-4. The method of embodiment 1-1, wherein the biomarkers comprise, consist essentially of or consist of a panel of biomarkers selected from:

(i) a biomarker panel of Table 7, wherein the blood sample is collected at about 24 weeks of pregnancy; and

(ii) a protein biomarker of Table 8, wherein the blood sample is collected at about 34 weeks of pregnancy.

[0126] Embodiment 1-5. The method of embodiment 1-1, wherein measuring the one or more peptides comprises measuring a surrogate biomarker of any of FIGS. 10A-10C, FIGS. 11 A-l 1C, FIGS. 12A-12H, FIGS. 13A-13H, FIGS. 14A-14D, FIGS.15A-15B.

[0127] Embodiment 1-6. The method of embodiment 1-1, wherein the pregnant subject has one or more risk factors for placenta accreta. [0128] Embodiment 1-7. The method of embodiment 1-1, wherein the pregnant subject is primigravida, multigravida, primiparous or multiparous.

[0129] Embodiment 1-8. The method of embodiment 1-1, wherein the blood sample is plasma or serum.

[0130] Embodiment 1-9. The method of embodiment 1-1, wherein the water is deionized distilled water (“ddH2O”).

[0131] Embodiment 1-10. The method of embodiment 1-1, wherein the size-exclusion chromatography is performed with an agarose solid phase and an aqueous liquid phase.

[0132] Embodiment 1-11. The method of embodiment 1-1, wherein the preparing step further comprises using ultrafiltration or reverse-phase chromatography.

[0133] Embodiment 1-12. The method of embodiment 1-1, wherein the preparing step further comprises denaturation using urea, reduction using dithiothreitol, alkylation using iodoacetamine, and digestion using trypsin after the size exclusion chromatography.

[0134] Embodiment 1-13. The method of embodiment 1-1, wherein the microparticles are further purified to enrich for placental-derived exosomes or vascular endothelial-derived exosomes.

[0135] Embodiment 1-14. The method of embodiment 1-1, wherein mass spectrometry comprises liquid chromatography/mass spectrometry (LC/MS), e.g., liquid chromatography/triple quadrupole mass spectrometry.

[0136] Embodiment 1-15. The method of embodiment 1-1, wherein the mass spectrometry comprises multiple reaction monitoring.

[0137] Embodiment 1-16. The method of embodiment 1-1, wherein the peptide(s) are selected from:

(i) a biomarker panel of Table 7, wherein the blood sample is collected at about 24 weeks of pregnancy; and (ii) a protein biomarker of Table 8, wherein the blood sample is collected at about 34 weeks of pregnancy.

[0138] Embodiment 1-17. A panel comprising a plurality of substantially pure protein biomarkers or surrogate biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3 or Table 5;

(ii) a protein biomarker of Table 2, Table 4 or Table 6;

(iii) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C or FIGS 12A-12H; and

(iv) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D or FIGS. 15A-15B.

[0139] Embodiment 1-18. The panel of embodiment 1-17, further comprising a stable isotope standard peptide paired with each of the surrogate biomarkers.

[0140] Embodiment 1-19. A kit comprising one or a plurality of containers, wherein each container comprises one or more of each of a plurality of Stable Isotopic Standards, each stable isotopic standard corresponding to a surrogate peptide for a biomarker from a panel of biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3, or Table 5;

(ii) a protein biomarker of Table 2, Table 4, or Table 6

(iii) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C or FIGS. 12A-12H; and

(iii) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D or FIGS. 15A-15B.

[0141] Embodiment 1-20. A composition comprising one or a plurality of pairs of polypeptides, each pair comprising a protein biomarkers or surrogate biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3, or Table 5;

(ii) a protein biomarker of Table 2, Table 4, or Table 6

(iii) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C or FIGS. 12A-12H; and (iii) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D or FIGS. 15A-15B.

[0142] Embodiment 1-21. A computer readable medium in tangible, non-transitory form comprising code to implement a classification rule generated by a method as described herein.

[0143] Embodiment 1-22. A system comprising:

(a) a computer comprising:

(i) a processor; and

(ii) a memory, coupled to the processor, the memory storing a module comprising:

(1) test data for a sample from a subject including values indicating a measure of one or more protein biomarkers in the fraction, wherein the protein biomarkers are selected from

(i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the sample is collected at about 20 weeks of pregnancy; and

(ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the sample is collected at about 37 weeks of pregnancy;

(2) a classification rule which, based on values including the measurements, classifies the subject as being at increased risk of placenta accreta, wherein the classification rule is 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 rule on the test data.

[0144] Embodiment 1-23. The system of embodiment 1-22, wherein the protein biomarker is a surrogate biomarker selected from:

(iii) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C or FIGS. 12A-12H; and (iii) a surrogate biomarker of FIGS. 13A-13H, FIG. 14A-14D or FIG 15A-15B.

[0145] Embodiment 1-24. A method comprising:

(a) at a computer system comprising a processor and a memory, coupled to the processor, the memory storing a module comprising:

(1) test data for a sample from a subject including values indicating a measure of one or more protein biomarkers in the fraction, wherein the protein biomarkers are selected from

(i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the sample is collected at about 24 weeks of pregnancy; and

(ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the sample is collected at about 34 weeks of pregnancy;

(2) a classification rule to be executed by the processor, which, based on values including the measurements, classifies the subject as being at increased risk of placenta accreta, wherein the classification rule is configured to have a sensitivity of at least 75%, at least 85% or at least 95%;

(b) accessing the text data; and

(c) executing the classification rule on the test data.

[0146] Embodiment 1-25. A method of assessing risk of placenta accreta in a pregnant subject, the method 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, wherein protein biomarkers are selected from: (i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the blood sample is collected at about 24 weeks of pregnancy; and

(ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the blood sample is collected at about 34 weeks of pregnancy;

(c) assessing risk of placenta accreta based on the one or more measures.

[0147] Embodiment 1-26. The method of embodiment 1-25, wherein the protein biomarker is a surrogate biomarker selected from:

(iii) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C or FIGS. 12A-12H; and

(iii) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D or FIGS 15A-15B.

[0148] Embodiment 1-27. The method of embodiment 1-25, wherein determining a quantitative measure comprises contacting the sample with one or more capture reagents, each capture reagent specifically binding one of the protein biomarkers, and detecting binding between the capture reagent in the protein biomarker.

[0149] Embodiment 1-28. The method of embodiment 1-27, comprising performing an immunoassay.

[0150] Embodiment 1-29. The method of embodiment 1-28, wherein the immunoassay is selected from the group consisting of enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (RIA).

[0151] Embodiment 1-30. The method of embodiment 1-25, wherein the assessing comprises executing a classification rule, which rule classifies the subject at being at risk of placenta accreta, and wherein execution of the classification rule produces a correlation between placenta accreta or term birth with a p value of less than at least 0.05.

[0152] Embodiment 1-31. The method of embodiment 1-25, wherein the assessing comprises executing a classification rule, which rule classifies the subject at being at risk of placenta accreta, and wherein execution of the classification rule produces 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.

[0153] Embodiment 1-32. The method of embodiment 1-25, wherein values on which the classification rule classifies a subject further include at least one of: placenta previa, previous cesarean delivery, endometrial ablation, in vitro fertilization, prior uterine infection, or previous uterine surgery.

[0154] Embodiment 1-33. The method of any of the preceding embodiments, wherein the classification rule employs cut-off, linear regression (e.g., multiple linear regression (MLR), partial least squares (PLS) regression, principal components regression (PCR)), binary decision trees (e.g., recursive partitioning processes such as CART - classification and regression trees), artificial neural networks such as back propagation networks, discriminant analyses (e.g., Bayesian classifier or Fischer analysis), logistic classifiers, and support vector classifiers (e.g., support vector machines).

[0155] Embodiment 1-34. The method of embodiment 1-25, wherein the classification rule is configured to have a sensitivity, specificity, positive predictive value, or negative predictive value of at least 70%, least 80%, at least 90% or at least 95%.

[0156] Embodiment 1-35. The method of embodiment 1-25, wherein assessing an increased risk of placenta accreta comprises determining that the protein biomarker (if upregulated) is above or (if down regulated) is below a threshold level.

[0157] Embodiment 1-36. The method of embodiment 1-35, wherein the threshold level represents a level at least one, at least two or at least three z scores from a measure of central tendency (e.g., mean, median or mode) for the protein determined from at least 50, at least 100 or at least 200 control subjects.

[0158] Embodiment 1-37. The method of embodiment 1-25, wherein the assessing comprises comparing the measure of each protein in the panel to a reference standard.

[0159] Embodiment 1-38. The method of embodiment 1-25, further comprising communicating the risk of placenta accreta for a pregnant subject to a health care provider. [0160] Embodiment 1-39. A method of treating placenta accreta in a pregnant subject, the method comprising:

(a) assessing risk of placenta accreta for a pregnant subject according to the method of any one of embodiments 1-25 to 1-38; and

(b) administering a therapeutic intervention to the subject effective to decrease the risk of placenta accreta and/or reduce neonatal complications of placenta accreta.

[0161] Embodiment 1-40. The method of embodiment 1-39, wherein treating comprises a therapeutic intervention selected from the group consisting of:

(i) referring the subject to a medical center with advanced multidisciplinary surgical expertise and experience;

(ii) surgical uterine conservation; and

(iii) performing a Cesarean hysterectomy, performing a prophylactic embolization, inserting a uterine balloon tamponade, a temporal internal iliac occlusion balloon catheter, a ureteral stents, administering methotrexate, leaving a portion of the placenta in-situ, and referring the subject to bed rest to prevent preterm labor..

[0162] Embodiment 1-41. A method comprising administering to a pregnant subject determined to have an increased risk of placenta accreta by a method as described herein, a therapeutic intervention effective to reduce the risk of placenta accreta.

[0163] Embodiment 1-42. A method of administering to a pregnant subject an effective amount of a treatment designed to reduce the risk of placenta accreta, wherein the subject has an altered quantitative measure as compared to a reference standard of any one of the panels of protein biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3 or Table 5, wherein the blood sample is collected at about 24 weeks of pregnancy; and

(ii) a protein biomarker of Table 2, Table 4 or Table 6, wherein the blood sample is collected at about 34 weeks of pregnancy. [0164] Embodiment 1-43. The method of embodiment 1-42, wherein the protein biomarker is a surrogate biomarker selected from:

(i) a surrogate biomarker of FIGS. 10 A- 10C, FIGS. 11 A-l 1C or FIGS. 12A-12H; and

(ii) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D or FIGS. 15A-15B.

[0165] Embodiment 1-44. A method comprising: a) measuring, via mass spectrometry, 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 proteins, to produce a dataset comprising more than 100, more than 1000, more than 10,000 or more than 100,000 data entries; b) at a computer system comprising one or more processors and memory storing programs foe execution by the one or more processors;

(1) identifying from among the data entries, based on the masses, peptides corresponding to:

(i) a protein biomarker of Table 1, Table 3 or Table 5, wherein the biological sample is collected at about 24 weeks of pregnancy; and

(ii) a protein biomarker of Table 2, Table 4 or Table 6, wherein the biological sample is collected at about 34 weeks of pregnancy;

(2) executing a classification rule on the measures of the identified peptides, which classification rule classifies the sample as being from subject at increased risk of placenta accreta.

Set II:

[0166] Embodiment II- 1. A method of assessing the risk of placenta accreta in a pregnant subject, comprising: (a) providing a sample from a pregnant subject between about 20 weeks of pregnancy to about 37 weeks of pregnancy;

(b) preparing a microparticle-associated peptide fraction from the sample;

(c) measuring a plurality of protein biomarkers in the fraction; and

(d) executing a classification rule on one or more measurement values of (c), wherein the classification rule classifies the sample as being from a subject at increased risk of placenta accreta.

[0167] Embodiment II-2. The method of embodiment II- 1, wherein the protein biomarkers comprise a panel of no more than any of 10, 9, 8, 7, 6, 5, 4 or 3 protein biomarkers.

[0168] Embodiment II-3. The method of embodiment II- 1, wherein the protein biomarkers comprise, consist essentially of or consist of a panel of biomarkers selected from:

(i) a biomarker panel of Table 7; and

(ii) a protein biomarker of Table 8.

[0169] Embodiment II-4. The method of embodiment II- 1, wherein the plurality of protein biomarkers comprise:

(i) a plurality of protein biomarkers from Table 1, Table 3, and Table 5;

(ii) a plurality of protein biomarkers from Table 2, Table 4, and Table 6;

(iii) a plurality of protein biomarkers from Table 9;

(iv) two or more of isthmin-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 isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein. [0170] Embodiment II-5. The method of embodiment II- 1, wherein measuring the plurality of biomarkers comprises measuring the relevant surrogate biomarkers of FIGS. 10A-10C, FIGS.

11A-11C, FIGS. 12A-12H, FIGS. 13A-13H, FIGS. 14A-14D, and FIGS. 15A-15B.

[0171] Embodiment II-6. The method of embodiment II- 1, wherein the pregnant subject has one or more risk factors for placenta accreta.

[0172] Embodiment II-7. The method of embodiment II-l, wherein the pregnant subject is primigravida, multigravida, primiparous or multiparous.

[0173] Embodiment II-8. The method of embodiment II-l, wherein the sample is a blood sample.

[0174] Embodiment II-9. The method of embodiment II-l, wherein the sample is plasma or serum.

[0175] Embodiment II- 10. A panel comprising a plurality of substantially pure protein biomarkers or surrogate biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3, or Table 5;

(ii) a protein biomarker of Table 2, Table 4, or Table 6

(iii) a protein biomarker of Table 9;

(iv) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C, or FIGS. 12A-12H;

(v) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D, or FIGS 15A-15B;

(vi) isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1); and

(vii) isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein. [0176] Embodiment II- 11. The panel of embodiment II- 10, further comprising a stable isotope standard peptide paired with each of the surrogate biomarkers of FIGS.10A- 10C, FIGS. 11A-11C, FIGS. 12A-12H, FIGS. 13A-13H, FIGS. 14A-14D, and FIGS. 15A-15B.

[0177] Embodiment 11-12. A method of preparing a peptide sample, comprising:

(a) providing a sample from a pregnant subject between about 20 weeks of pregnancy to about 37 weeks of pregnancy;

(b) enriching the sample for microparticles by loading the sample on a size-exclusion column and eluting the microparticles from the column using water as a 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) separating the microparticle-associated peptides by mass spectrometry; and

(e) measuring, based on a mass spectrometry signal, one or more peptides corresponding to one or more protein biomarkers.

[0178] Embodiment 11-13. The method of embodiment 11-12, wherein the one or more protein biomarkers includes a plurality of protein biomarkers.

[0179] Embodiment 11-14. The method of embodiment 11-12, wherein the protein biomarkers comprise a panel of no more than any of 10, 9, 8, 7, 6, 5, 4 or 3 protein biomarkers.

[0180] Embodiment 11-15. The method of embodiment 11-12, wherein the protein biomarkers comprise, consist essentially of or consist of a panel of biomarkers selected from:

(i) a biomarker panel of Table 7; and

(ii) a protein biomarker of Table 8.

[0181] Embodiment 11-16. The method of embodiment 11-12, wherein the protein biomarkers comprise: (i) one or more of isthmin-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 isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein.

[0182] Embodiment 11-17. The method of embodiment 11-12, wherein measuring the one or more peptides comprises measuring a surrogate biomarker of any of FIGS. 10A-10C, FIGS. 11A-11C, FIGS. 12A-12H, FIGS. 13A-13H, FIGS. 14A-14D, and FIGS. 15A-15B.

[0183] Embodiment 11-18. The method of embodiment 11-12, wherein the pregnant subject has one or more risk factors for placenta accreta.

[0184] Embodiment 11-19. The method of embodiment 11-12, wherein the pregnant subject is primigravida, multigravida, primiparous or multiparous.

[0185] Embodiment 11-20. The method of embodiment 11-12, wherein the blood sample is plasma or serum.

[0186] Embodiment 11-21. The method of embodiment 11-12, wherein the water is deionized distilled water.

[0187] Embodiment 11-22. The method of embodiment 11-12, wherein the size-exclusion column comprises an agarose solid phase and an aqueous liquid phase.

[0188] Embodiment 11-23. The method of embodiment 11-12, wherein preparing the microparticle-associated peptide fraction further comprises using ultrafiltration or reverse-phase chromatography.

[0189] Embodiment 11-24. The method of embodiment 11-12, wherein preparing the microparticle-associated peptide fraction further comprises denaturation of the microparticle- enriched fraction using urea, reduction of the microparticle-enriched fraction using dithiothreitol, alkylation of the microparticle-enriched fraction using iodoacetamine, and digestion of the microparticle-enriched fraction using trypsin. [0190] Embodiment 11-25. The method of embodiment 11-12, wherein enriching the sample for microparticles includes further purifying the microparticles to enrich for placental-derived exosomes or vascular endothelial-derived exosomes.

[0191] Embodiment 11-26. The method of embodiment 11-12, wherein 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.

[0192] Embodiment 11-27. The method of embodiment 11-12, wherein separating the microparticle-associated peptides by mass spectrometry includes the mass spectrometry comprising multiple reaction monitoring.

[0193] Embodiment 11-28. The method of embodiment 11-12, wherein the one or more peptides are selected from:

(i) a biomarker panel of Table 7, wherein the blood sample is collected at about 20 weeks of pregnancy; and

(ii) a protein biomarker of Table 8, wherein the blood sample is collected at about 37 weeks of pregnancy.

[0194] Embodiment 11-29. A kit comprising one or a plurality of containers, wherein each container comprises one or more of each of a plurality of Stable Isotopic Standards, wherein each stable isotopic standard corresponds to a surrogate peptide for a biomarker from a panel of biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3, or Table 5;

(ii) a protein biomarker of Table 2, Table 4, or Table 6;

(iii) a protein biomarker of Table 9;

(iv) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C, or FIGS. 12A-12H;

(v) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D, or FIGS. 15A-15B; (vi) isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1); and

(vi) isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein.

[0195] Embodiment 11-30. A composition comprising one or a plurality of pairs of polypeptides, wherein each pair of polypeptides comprise one or more protein biomarkers or one or more surrogate biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3, or Table 5;

(ii) a protein biomarker of Table 2, Table 4, or Table 6

(iii) a protein biomarker of Table 9;

(iv) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C, or FIGS. 12A-12H;

(v) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D, or FIGS. 15A-15B;

(vi) isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1); and

(vii) isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and Ig-like domain-containing protein.

[0196] Embodiment II- 31. A computer readable medium in tangible, non-transitory form comprising code 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 of placenta accreta.

[0197] Embodiment 11-32. A system comprising:

(a) a computer comprising:

(i) a processor; and (ii) a memory, coupled to the processor, the memory storing a module comprising:

(1) test data for a sample from a subject including one or more values, wherein each value indicates a measurement of one or more protein biomarkers in a fraction of microparticle-associated peptides, wherein the one or more protein biomarkers are selected from:

(i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the blood sample is collected at about 20 weeks of pregnancy;

(ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the blood sample is collected at about 37 weeks of pregnancy;

(iii) a protein biomarker of Table 9;

(iv) one or more of isthmin-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 isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and/or Ig-like domain-containing protein; and

(2) a classification rule which, based on the one or more values wherein each value indicates the measurement, classifies the subject as being at increased risk of placenta accreta, wherein the classification rule is 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 rule on the test data.

[0198] Embodiment 11-33. The system of embodiment 11-32, wherein the protein biomarker is a surrogate biomarker selected from:

(i) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C, or FIGS. 12A-12H; and (ii) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D, or FIGS. 15A-15B.

[0199] Embodiment 11-34. A method comprising:

(a) using a computer system to compile test data, the computer system comprising a processor and a memory, coupled to the processor, the memory storing a module comprising:

(1) test data for a sample from a subject including values indicating one or more measurement values of one or more protein biomarkers of the disclosure in a fraction of microparticle-associated peptides;

(2) a classification rule to be executed by the processor, which, based on values including the one or more measurement values, classifies the subject as being at increased risk of placenta accreta, wherein the classification rule is configured to have a sensitivity of at least 75%, at least 85% or at least 95%;

(b) accessing the test data; and

(c) executing the classification rule on the test data.

[0200] Embodiment 11-35. A method of assessing risk of placenta accreta in a pregnant subject, the method 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 selected from:

(i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the blood sample is collected at about 20 weeks of pregnancy;

(ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the blood sample is collected at about 37 weeks of pregnancy; (iii) a protein biomarker of Table 9;

(iv) one or more of isthmin-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 isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and/or Ig-like domaincontaining protein; and

(c) assessing risk of placenta accreta based on the one or more quantitative measures.

[0201] Embodiment 11-36. The method of embodiment 11-35, wherein the protein biomarker is a surrogate biomarker selected from:

(i) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C, or FIGS. 12A-12H;

(ii) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D, or FIGS. 15A-15B;

(iii) a surrogate biomarker of isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and/or cartilage acidic protein 1 (CRAC1); and

(iv) a surrogate biomarker of isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and/or Ig-like domain-containing protein.

[0202] Embodiment 11-37. The method of embodiment 11-35, wherein determining the 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 one of the protein biomarkers, and detecting binding between the capture reagent and the protein biomarker.

[0203] Embodiment 11-38. The method of embodiment 11-37, wherein determining the quantitative measure of one or more microparticle-associated protein biomarkers comprises performing an immunoassay. [0204] Embodiment 11-39. The method of embodiment 11-38, wherein the immunoassay is selected from the group consisting of an enzyme immunoassay (EIA), an enzyme-linked immunosorbent assay (ELISA), and a radioimmunoassay (RIA).

[0205] Embodiment 11-40. The method of embodiment 11-35, wherein the assessing risk of placenta accreta comprises executing a classification rule, wherein the classification rule classifies the subject at being at risk of placenta accreta, and wherein execution of the classification rule produces a correlation between placenta accreta or term birth with a p value of less than at least 0.05.

[0206] Embodiment 11-41. The method of embodiment 11-35, wherein the assessing risk of placenta accreta comprises executing a classification rule, wherein the classification rule classifies the subject at being at risk of placenta accreta, and wherein execution of the classification rule produces 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.

[0207] Embodiment 11-42. The method of embodiment 11-35, wherein the classification rule classifies a subject based on one or more values wherein the one or more values further include at least one of: placenta previa, previous cesarean delivery, endometrial ablation, in vitro fertilization, prior uterine infection, or previous uterine surgery.

[0208] Embodiment 11-43. The method of embodiments 11-25 to 11-32, wherein the classification rule employs cut-off, linear regression including multiple linear regression , partial least squares regression, principal components regression , binary decision trees including recursive partitioning processes further including classification and regression trees, artificial neural networks including back propagation networks, discriminant analyses further includingBayesian classifier or Fischer analysis, logistic classifiers, and support vector classifiers including support vector machines.

[0209] Embodiment 11-44. The method of embodiment 11-35, wherein the classification rule is configured to have a sensitivity value, a specificity value, a positive predictive value, or a negative predictive value of at least 70%, least 80%, at least 90% or at least 95%. [0210] Embodiment 11-45. The method of embodiment 11-35, wherein assessing risk of placenta accreta comprises determining that the protein biomarker, if upregulated, is above a threshold level or if down regulated, is below the threshold level.

[0211] Embodiment 11-46. The method of embodiment 11-45, wherein the threshold level represents a level at least one, at least two or at least three z scores from a measure of central tendency including a mean, a median or a mode for the protein biomarker determined from at least 50, at least 100 or at least 200 control subjects.

[0212] Embodiment 11-47. The method of embodiment 11-35, wherein the assessing risk of placenta accreta comprises comparing the one or more quantitative measures of each protein biomarker in the panel to a reference standard.

[0213] Embodiment 11-48. The method of embodiment 11-35, further comprising communicating the risk of placenta accreta for a pregnant subject to a health care provider.

[0214] Embodiment 11-49. A method of treating placenta accreta in a pregnant subject, the method comprising:

(a) assessing risk of placenta accreta for a pregnant subject according to the method of any one of embodiments II- 1 to II-9, and 11-35 to 11-48; and

(b) administering a therapeutic intervention to the subject effective to decrease the risk of placenta accreta and/or reduce neonatal complications of placenta accreta.

[0215] Embodiment 11-50. The method of embodiment 11-49, wherein administering the therapeutic intervention comprises a therapeutic intervention selected from the group consisting of:

(i) referring the subject to a medical center having advanced multidisciplinary surgical expertise and experience;

(ii) planning surgical uterine conservation; and

(iii) performing a Cesarean hysterectomy, performing a prophylactic embolization, inserting a uterine balloon tamponade, a temporal internal iliac occlusion balloon catheter, a ureteral stents, administering methotrexate, leaving a portion of the placenta in-situ, and referring the subject to bed rest to prevent preterm labor.

[0216] Embodiment II- 51. A method comprising administering to a pregnant subject determined to have an increased risk of placenta accreta by a method according to any one of embodiments II- 1 to II-9, and 11-35 to 11-48, a therapeutic intervention effective to reduce the risk of placenta accreta.

[0217] Embodiment 11-52. A method of administering to a pregnant subject an effective amount of a treatment designed to reduce the risk of placenta accreta, wherein the subject has an altered quantitative measure as compared to a reference standard of any one of a panel of protein biomarkers selected from:

(i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the blood sample is collected at about 20 weeks of pregnancy;

(ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the blood sample is collected at about 37 weeks of pregnancy;

(iii) a protein biomarker of Table 9;

(iv) one or more of isthmin-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 isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and/or Ig-like domain-containing protein.

[0218] Embodiment 11-53. The method of embodiment 11-52, wherein the protein biomarker is a surrogate biomarker selected from:

(i) a surrogate biomarker of FIGS. 10A-10C, FIGS. 11A-11C, or FIGS 12A-12H;

(ii) a surrogate biomarker of FIGS. 13A-13H, FIGS. 14A-14D, or FIGS. 15A-15B;

(iii) a surrogate biomarker of isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and/or cartilage acidic protein 1 (CRAC1); and (iv) a surrogate biomarker of isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and/or Ig-like domain-containing protein.

[0219] Embodiment 11-54. A method comprising: a) measuring, via mass spectrometry, 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 proteins, to produce a dataset comprising more than 100, more than 1000, more than 10,000 or more than 100,000 data entries; b) using a computer system comprising one or more processors and memory storing programs for execution by the one or more processors in

(1) identifying from among the data entries, based on the masses, peptides corresponding to:

(i) a protein biomarker of Table 1, Table 3, or Table 5, wherein the biological sample is collected at about 20 weeks of pregnancy; and

(ii) a protein biomarker of Table 2, Table 4, or Table 6, wherein the biological sample is collected at about 37 weeks of pregnancy;

(iii) a protein biomarker of Table 9;

(iv) one or more of isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (144), hemoglobin subunit gamma-2 (HBG2) and/or cartilage acidic protein 1 (CRAC1); and

(v) one or more of isthmin-2 (ISM2), ubiquitin carboxyl-terminal hydrolase 1 (UBP1), immunoglobulin lambda variables 10-54 (LVX54) and/or Ig-like domain-containing protein.

(2) executing a classification rule on one or more measurement values of the identified peptides, wherein the classification rule classifies the sample as being from a subject at increased risk of placenta accreta. [0220] As used herein, the following meanings apply unless otherwise specified. The word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). The words “include”, “including”, and “includes” and the like mean including, but not limited to. 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 use of other terms and phrases for one or more elements, such as “one or more.” The phrase “at least one” includes “one”, “one or more”, “one or a plurality” and “a plurality”. The term “or” is, unless indicated otherwise, non-exclusive, i.e., encompassing both “and” and “or.” The term “any of’ between a modifier and a sequence means that the modifier modifies each member of the sequence. So, 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 recited elements and other elements that do not materially affect the basic and novel characteristics of a claimed combination. Unless otherwise specified, the term “about” in reference to a value refers to 90% to 110% of that value or 95% to 105% of that value.

[0221] It should be understood that the description and the drawings are not intended to limit the invention to the particular form disclosed, but to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present 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. Accordingly, this description and the drawings are to be construed as illustrative only and are for the purpose of teaching those skilled in the art the general manner of carrying out the invention. It is to be understood that the forms of the invention shown and described herein are to be taken 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, all as would be apparent to one skilled in the art after having the benefit of this description of the invention. Changes may be made in the elements described herein without departing from the spirit and scope of the invention as described in the following claims. Headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description.