WO2023087004A2 - Methods of preparing and analyzing samples for biomarkers associated with placenta accreta - Google Patents

Methods of preparing and analyzing samples for biomarkers associated with placenta accreta Download PDF

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WO2023087004A2
WO2023087004A2 PCT/US2022/079835 US2022079835W WO2023087004A2 WO 2023087004 A2 WO2023087004 A2 WO 2023087004A2 US 2022079835 W US2022079835 W US 2022079835W WO 2023087004 A2 WO2023087004 A2 WO 2023087004A2
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protein
biomarker
biomarkers
sample
microparticle
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French (fr)
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WO2023087004A3 (en
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Thomas F. MCELRATH
Kevin P. Rosenblatt
Brian D. Brohman
Prem P. Gurnani
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Brigham and Womens Hospital Inc
NX Prenatal Inc
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Brigham and Womens Hospital Inc
NX Prenatal Inc
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Priority to US18/709,475 priority Critical patent/US20250102482A1/en
Priority to EP22893920.3A priority patent/EP4430406A4/en
Priority to JP2024528448A priority patent/JP2024546438A/ja
Priority to CN202280075559.3A priority patent/CN118451327A/zh
Publication of WO2023087004A2 publication Critical patent/WO2023087004A2/en
Publication of WO2023087004A3 publication Critical patent/WO2023087004A3/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/573—Immunoassay; Biospecific binding assay; Materials therefor for enzymes or isoenzymes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/689—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to pregnancy or the gonads
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
    • G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
    • G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
    • G01N2030/8831—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials involving peptides or proteins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/91—Transferases (2.)
    • G01N2333/912—Transferases (2.) transferring phosphorus containing groups, e.g. kinases (2.7)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/90—Enzymes; Proenzymes
    • G01N2333/914—Hydrolases (3)
    • G01N2333/948—Hydrolases (3) acting on peptide bonds (3.4)
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/36—Gynecology or obstetrics
    • G01N2800/368—Pregnancy complicated by disease or abnormalities of pregnancy, e.g. preeclampsia, preterm labour
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
    • G01N30/02—Column chromatography
    • G01N30/62—Detectors specially adapted therefor
    • G01N30/72—Mass spectrometers
    • G01N30/7233—Mass spectrometers interfaced to liquid or supercritical fluid chromatograph
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry

Definitions

  • 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).
  • 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.
  • CMP-associated proteins useful for the prediction and detection of plasma accreta.
  • 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.
  • 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.
  • FIGS. 1A-1D depict Table 1 comprising median 24-week samples.
  • 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.
  • 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.
  • FIGS. 5A-5C depict Table 5 comprising 24-week markers. Individual markers ranked by performance in distinguishing case/control.
  • FIGS. 6A-6F depict Table 6 comprising 34-week markers. Individual markers ranked by performance in distinguishing case/control.
  • FIG. 7 depicts Table 7 comprising 24-week markers. Top performing multiplex panels are ranked by average AUC of an iterative cross-validation procedure.
  • FIG. 8 depicts Table 8 comprising 34-week markers. Top performing multiplex panels are ranked by average AUC of an iterative cross-validation procedure.
  • FIG. 9 illustrates a schematic of a protocol for identifying predictive circulating microparticle protein panels for placenta accreta.
  • FIGS. 10A-10C provide surrogate peptides, useful for the detection of the biomarkers of Table 1.
  • FIGS. 11A-11C provide surrogate peptides, useful for the detection of the biomarkers of Table 2.
  • FIGS. 12A-12H provide surrogate peptides, useful for the detection of the biomarkers of Table 3.
  • FIGS. 13A-13H provide surrogate peptides, useful for the detection of the biomarkers of Table 4.
  • FIGS. 14A-14D provide surrogate peptides, useful for the detection of the biomarkers of Table 5.
  • FIGS. 15A-15B provide surrogate peptides, useful for the detection of the biomarkers of Table 6.
  • 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).
  • 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).
  • 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.
  • 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.
  • Table 7 and Table 8 present biomarker panels for placenta accreta. Additional marker sets are also presented herein. II.
  • 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.
  • the pregnant women may be about 20 weeks to about 37 weeks of pregnancy.
  • 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.
  • the pregnant human subject is asymptomatic.
  • 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.
  • 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.
  • a sample for use in the methods of the present disclosure is a biological sample obtained from a pregnant subject.
  • the sample is collected during a stage of pregnancy described in the preceding section.
  • the sample is a blood, saliva, tears, sweat, nasal secretions, urine, amniotic fluid or cervicovaginal fluid sample.
  • the sample is a blood sample.
  • the sample is a blood plasma sample.
  • the sample is a blood serum sample.
  • the sample is a blood product in a different matrix (e.g. Citrate buffer, or Streck tube).
  • the sample has been stored frozen (e.g., -20°C or -80°C).
  • microparticle refers to an extracellular microvesicle or lipid raft protein aggregate having a hydrodynamic diameter of about 50 to about 5000 nm.
  • 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.
  • 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.
  • microparticle-associated peptide refers to a protein fragment that is detectable in such a sample.
  • polypeptide 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.
  • 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”).
  • ddHzO distilled, deionized water
  • 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.
  • 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.
  • 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 (OptimaTM, LC/MS grade, Fisher ChemicalTM) with 20mM Formic Acetate, pH 9.3.
  • Mobile phase A is DI H2O with 20 mM Formic Acetate, pH 9.3
  • mobile phase B is Acetonitrile (OptimaTM, LC/MS grade, Fisher ChemicalTM) with 20mM Formic Acetate, pH 9.3.
  • 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.
  • the microparticles are placental-derived exosomes or endothelial-derived exosomes.
  • exosomes can be isolated using capture agents, such as antibodies, against surface markers for these cells of origin.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the size exclusion chromatography includes a sizeexclusion column comprising an agarose solid phase and an aqueous liquid phase.
  • 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.
  • 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.
  • MS-MS tandem mass spectrometer
  • 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”.
  • Mass spectrometers useful for the analyses described herein include, without limitation, AltisTM quadrupole, QuantisTM quadrupole, QuantivaTM or FortisTM triple quadrupole from ThermoFisher Scientific, the 8050 or 8060 triple quadruploes from Shimadzu, the Xevo TQ-XSTM triple quadrupole from Waters, QSightTM Triple Quad LC/MS/MS from Perkin Elmer, Thermo Orbitrap Mass Spectrometer Tribrid Eclipse with a Thermo Fisher Scientific Nanospray FlexTM Ion Source, and others.
  • 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.
  • MS/MS tandem mass spectrometry
  • TOF MS post source decay
  • 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.
  • the disclosed methods comprise performing quantitative MS to measure one or more peptides.
  • 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).
  • 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”).
  • MRM-MS Multiple Reaction Monitoring Mass Spectrometry
  • SIS Stable Isotopic Standards
  • 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).
  • MRM multiple reaction monitoring
  • SRM selected reaction monitoring
  • a series of transitions 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.
  • 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 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).
  • 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.
  • 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.
  • 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 (MS);
  • 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).
  • CID collision induced dissociation
  • 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.
  • MRM multiple reaction monitoring
  • 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.
  • 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 FlexTM 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.
  • 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.
  • 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.
  • 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.
  • 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
  • Biomarkers associated with increased risk of placenta accreta 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).
  • 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.
  • Table 7 and Table 8 present panels of biomarker for inferring placenta accreta at around 24 and around 35 weeks, respectively.
  • the one or more protein biomarkers associated with increased risk of placenta accreta include a plurality of protein biomarkers.
  • 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.
  • 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.
  • 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.
  • a surrogate e.g., one or more fragments of the protein.
  • 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.
  • biomarkers are in a composition in which the peptide biomarker is paired with a stable isotopic standard of the peptide.
  • 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.
  • 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.
  • biomarkers are analyzed as a panel.
  • a panel is a plurality of biomarkers used in an algorithm to make a prediction or inference.
  • 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.
  • analysis refers to any algorithm that transforms inputs into outputs. Analyses include, without limitation, statistical analyses, machine learning analyses and neural net analyses.
  • data may include data received from various data sources, metadata associated with the data, and/or a combination of both data and metadata.
  • a measurement of a variable 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.
  • 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.
  • 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.
  • Statistical analysis can be operator implemented or implemented by machine learning.
  • classifiers such as cut-offs
  • Other classifiers such as multivariate classifiers, can require a computer to execute the classification algorithm.
  • 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).
  • 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).
  • 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.
  • 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.
  • these datasets of biomarker measurements may comprise more than 100, more than 1000, more than 10,000, or more than 100,000 data entries.
  • 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.
  • 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.
  • ROC receiver operating characteristic
  • the diagnostic power of a set of variables, such as biomarkers, is reflected by the area under the curve (AUC) of an ROC curve.
  • 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.
  • 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.
  • 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.
  • the cutoff can be set based on variance or standard deviation.
  • 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.
  • 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.
  • 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.
  • the phrase “increased risk” of a condition 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.
  • Classifying can employ a classification rule, algorithm or model determined by statistical analysis and/or machine learning.
  • 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.
  • 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.
  • an amount of a biomarker that shows a difference compared to a control amount of the biomarker 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.
  • determination may be based on a classification algorithm that may employ non-linear and/or hyperdimensional methods.
  • pathways can be interrogated to aid the determination of risk of plasma accreta.
  • 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).
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • a determination is based on the use of a panel of biomarkers, for example those that are provided in Tables 7 or 8.
  • 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.
  • an amount of a biomarker that shows a difference compared to a control amount of the biomarker 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.
  • determination may be based on a classification algorithm that may employ non-linear and/or hyperdimensional methods.
  • 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.
  • 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.
  • 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.
  • an amount of a biomarker that shows a difference compared to a control amount of the biomarker 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.
  • determination may be based on a classification algorithm that may employ non-linear and/or hyperdimensional methods.
  • 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.
  • 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.
  • 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.
  • administering one or more therapeutic interventions includes administering an effective amount of one or more treatments designed to reduce the risk of placenta accreta.
  • 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.
  • 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.
  • 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.
  • 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.
  • a high molecular weight gel filtration composition e.g., agarose such as SEPHAROSE
  • a low volume e.g., 1ml, 3ml, 5ml, 10ml
  • the microparticle- enriched sample can be prepared at the point of care before freezing and shipping to an analytical laboratory for further processing.
  • kits further comprise instructions for assessing risk of placenta accreta, in particular, placenta accreta.
  • 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.
  • 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.
  • SIS stable isotope standard
  • a majority or all of the SIS peptides correspond to the biomarker peptides.
  • the kit further comprises the biomarker peptides which the SIS peptides correspond.
  • 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.
  • 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).
  • DSL Digital Subscriber Line
  • BPL Broadband over Powerlines
  • 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).
  • 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.
  • 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.
  • 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.
  • BCA assay Cat No: A53225, 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.
  • OptimaTM, LC/MS grade, Fisher ChemicalTM acetonitrile
  • 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.
  • DIA analytical samples a high-resolution, full MS scan, followed by two segment DIA methods, was used for the DIA data acquisition.
  • 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.
  • IW isolation windows
  • precursor mass ranges are shown in Table 1.5 and Table 1.6.
  • 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.
  • 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 SpectronautTM 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.
  • SpectronautTM 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.
  • 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.
  • LI regularized
  • 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.
  • AIC Akaike information criterion
  • 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.
  • 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.
  • 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).
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • HBG2 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • 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.
  • 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.
  • 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.
  • 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).
  • Master upstream regulators included three predicted to be significantly activated and two significantly inhibited (Table 1.11).
  • Embodiment 1-1 A method of preparing a peptide sample comprising:
  • Embodiment 1-2 The method of embodiment 1-1, wherein the one or more protein biomarkers is a plurality of protein biomarkers.
  • 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.
  • 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:
  • 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.
  • Embodiment 1-6 The method of embodiment 1-1, wherein the pregnant subject has one or more risk factors for placenta accreta.
  • Embodiment 1-7 The method of embodiment 1-1, wherein the pregnant subject is primigravida, multigravida, primiparous or multiparous.
  • Embodiment 1-8 The method of embodiment 1-1, wherein the blood sample is plasma or serum.
  • Embodiment 1-9 The method of embodiment 1-1, wherein the water is deionized distilled water (“ddH2O”).
  • 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.
  • Embodiment 1-11 The method of embodiment 1-1, wherein the preparing step further comprises using ultrafiltration or reverse-phase chromatography.
  • 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.
  • 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.
  • 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.
  • LC/MS liquid chromatography/mass spectrometry
  • Embodiment 1-15 The method of embodiment 1-1, wherein the mass spectrometry comprises multiple reaction monitoring.
  • Embodiment 1-16 The method of embodiment 1-1, wherein the peptide(s) are selected from:
  • Embodiment 1-17 A panel comprising a plurality of substantially pure protein biomarkers or surrogate biomarkers selected from:
  • Embodiment 1-18 The panel of embodiment 1-17, further comprising a stable isotope standard peptide paired with each of the surrogate biomarkers.
  • kits 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:
  • 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:
  • 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.
  • Embodiment 1-22 A system comprising:
  • a memory coupled to the processor, the memory storing a module comprising:
  • 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
  • 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%;
  • Embodiment 1-2 The system of embodiment 1-22, wherein the protein biomarker is a surrogate biomarker selected from:
  • Embodiment 1-24 A method comprising:
  • 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
  • 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%;
  • Embodiment 1-25 A method of assessing risk of placenta accreta in a pregnant subject, the method comprising:
  • 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
  • Embodiment 1-26 The method of embodiment 1-25, wherein the protein biomarker is a surrogate biomarker selected from:
  • 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.
  • Embodiment 1-28 The method of embodiment 1-27, comprising performing an immunoassay.
  • 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).
  • EIA enzyme immunoassay
  • ELISA enzyme-linked immunosorbent assay
  • RIA radioimmunoassay
  • 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.
  • Embodiment 1-3 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.
  • a classification rule which rule classifies the subject at being at risk of placenta accreta
  • AUC area under the curve
  • 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.
  • 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).
  • 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 e.g., support vector machines
  • 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%.
  • 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.
  • 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.
  • a measure of central tendency e.g., mean, median or mode
  • 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.
  • 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.
  • Embodiment 1-39 A method of treating placenta accreta in a pregnant subject, the method comprising:
  • Embodiment 1-40 The method of embodiment 1-39, wherein treating comprises a therapeutic intervention selected from the group consisting of:
  • Embodiment 1-4 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.
  • 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:
  • Embodiment 1-43 The method of embodiment 1-42, wherein the protein biomarker is a surrogate biomarker selected from:
  • 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;
  • 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;
  • 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.
  • 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:
  • Embodiment II-4 The method of embodiment II- 1, wherein the plurality of protein biomarkers comprise:
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • HBG2 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • 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.
  • FIGS. 12A-12H FIGS. 13A-13H, FIGS. 14A-14D, and FIGS. 15A-15B.
  • Embodiment II-6 The method of embodiment II- 1, wherein the pregnant subject has one or more risk factors for placenta accreta.
  • Embodiment II-7 The method of embodiment II-l, wherein the pregnant subject is primigravida, multigravida, primiparous or multiparous.
  • Embodiment II-8 The method of embodiment II-l, wherein the sample is a blood sample.
  • Embodiment II-9 The method of embodiment II-l, wherein the sample is plasma or serum.
  • Embodiment II- 10 A panel comprising a plurality of substantially pure protein biomarkers or surrogate biomarkers selected from:
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • H4 histone H4
  • H4 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • 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.
  • Embodiment 11-12 A method of preparing a peptide sample, comprising:
  • Embodiment 11-13 The method of embodiment 11-12, wherein the one or more protein biomarkers includes a plurality of protein biomarkers.
  • 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.
  • 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:
  • 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
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • HBG2 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • ISM2 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domain-containing protein Ig-like domain-containing protein
  • 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.
  • Embodiment 11-18 The method of embodiment 11-12, wherein the pregnant subject has one or more risk factors for placenta accreta.
  • Embodiment 11-19 The method of embodiment 11-12, wherein the pregnant subject is primigravida, multigravida, primiparous or multiparous.
  • Embodiment 11-20 The method of embodiment 11-12, wherein the blood sample is plasma or serum.
  • Embodiment 11-21 The method of embodiment 11-12, wherein the water is deionized distilled water.
  • Embodiment 11-22 The method of embodiment 11-12, wherein the size-exclusion column comprises an agarose solid phase and an aqueous liquid phase.
  • Embodiment 11-23 The method of embodiment 11-12, wherein preparing the microparticle-associated peptide fraction further comprises using ultrafiltration or reverse-phase chromatography.
  • 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.
  • enriching the sample for microparticles includes further purifying the microparticles to enrich for placental-derived exosomes or vascular endothelial-derived exosomes.
  • 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.
  • LC/MS liquid chromatography/mass spectrometry
  • 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.
  • Embodiment 11-28 The method of embodiment 11-12, wherein the one or more peptides are selected from:
  • kits 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:
  • FIGS. 13A-13H, FIGS. 14A-14D, or FIGS. 15A-15B isthmin-2 (ISM2), sulfhydryl oxidase 1 (QSOX1), histone H4 (H4), hemoglobin subunit gamma-2 (HBG2) and cartilage acidic protein 1 (CRAC1); and
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • H4 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • ISM2 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domain-containing protein Ig-like domain-containing protein
  • 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:
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • H4 histone H4
  • H4 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • ISM2 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domain-containing protein Ig-like domain-containing protein
  • 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.
  • Embodiment 11-32 A system comprising:
  • 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:
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • HBG2 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • ISM2 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domain-containing protein Ig-like domain-containing protein
  • 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%;
  • Embodiment 11-33 The system of embodiment 11-32, wherein the protein biomarker is a surrogate biomarker selected from:
  • Embodiment 11-34 A method comprising:
  • 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;
  • 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%;
  • Embodiment 11-35 A method of assessing risk of placenta accreta in a pregnant subject, the method comprising:
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • HBG2 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • ISM2 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domaincontaining protein ISM2
  • ISM1 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domaincontaining protein Ig-like domaincontaining protein
  • Embodiment 11-36 The method of embodiment 11-35, wherein the protein biomarker is a surrogate biomarker selected from:
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • HBG2 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • ISM2 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domain-containing protein 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.
  • 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.
  • 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.
  • the immunoassay is selected from the group consisting of an enzyme immunoassay (EIA), an enzyme-linked immunosorbent assay (ELISA), and a radioimmunoassay (RIA).
  • 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.
  • 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.
  • ROC receiver operating characteristic
  • 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.
  • 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.
  • 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%.
  • 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.
  • 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.
  • 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.
  • 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.
  • Embodiment 11-49 A method of treating placenta accreta in a pregnant subject, the method comprising:
  • Embodiment 11-50 The method of embodiment 11-49, wherein administering the therapeutic intervention comprises a therapeutic intervention selected from the group consisting of:
  • 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.
  • 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:
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • HBG2 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • ISM2 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domain-containing protein 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.
  • Embodiment 11-53 The method of embodiment 11-52, wherein the protein biomarker is a surrogate biomarker selected from:
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • H4 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • 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 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domain-containing protein Ig-like domain-containing protein
  • 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
  • ISM2 isthmin-2
  • QSOX1 sulfhydryl oxidase 1
  • H4 histone H4
  • HBG2 hemoglobin subunit gamma-2
  • CRAC1 cartilage acidic protein 1
  • ISM2 isthmin-2
  • UBP1 ubiquitin carboxyl-terminal hydrolase 1
  • LVX54 immunoglobulin lambda variables 10-54
  • Ig-like domain-containing protein 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.
  • 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”.

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