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WO2018174876A1 - Methods and compositions for providing a preeclampsia assessment with metabolites - Google Patents

Methods and compositions for providing a preeclampsia assessment with metabolites Download PDF

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Publication number
WO2018174876A1
WO2018174876A1 PCT/US2017/023680 US2017023680W WO2018174876A1 WO 2018174876 A1 WO2018174876 A1 WO 2018174876A1 US 2017023680 W US2017023680 W US 2017023680W WO 2018174876 A1 WO2018174876 A1 WO 2018174876A1
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WIPO (PCT)
Prior art keywords
carnitine
fatty acid
ceramide
camitine
panel
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PCT/US2017/023680
Other languages
French (fr)
Inventor
Bruce Xuefeng Ling
Limin Chen
Shiying Hao
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Mprobe Inc.
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Publication date
Application filed by Mprobe Inc. filed Critical Mprobe Inc.
Priority to PCT/US2017/023680 priority Critical patent/WO2018174876A1/en
Publication of WO2018174876A1 publication Critical patent/WO2018174876A1/en

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Classifications

    • 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
    • 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
    • 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
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2570/00—Omics, e.g. proteomics, glycomics or lipidomics; Methods of analysis focusing on the entire complement of classes of biological molecules or subsets thereof, i.e. focusing on proteomes, glycomes or lipidomes
    • 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

Definitions

  • the present disclosure generally relates to small molecule metabolic biomarkers.
  • the present disclosure relates to a panel of metabolite species to diagnose preeclampsia (PE), including methods for identifying such metabolic biomarkers within biological samples.
  • PE preeclampsia
  • This invention pertains to providing a PE assessment with
  • PE is a pregnancy- related vascular disorder, affecting 5-8% of all pregnancies. PE can be remedied by delivery of placenta and fetus, often causing fetal growth restriction and preterm delivery as well as fetal mortality and morbidity.
  • the etiology of PE is unknown.
  • Current diagnosis of PE is based on the signs of hypertension and proteinuria 4 , which lacks sensitivity and specificity, and carries a poor prognosis for adverse maternal and fetal outcomes 5 .
  • PE is a multisystem disorder of pregnancy with the placenta playing a pivotal role.
  • Investigators have used genetic, genomic and proteomic approaches to compare PE and normal placental tissues. Transcriptional profiling of case-control samples has been used to identify disease-specific expression patterns, canonical pathways and gene-gene networks.
  • Proteomics-based biomarker studies have revealed candidate biomarkers for future testing. Placental angiogenic and anti-angiogenic factor imbalance, elevated sFlt-1 and decreased PIGF levels, are suggested in the pathogenesis of PE, and sFlt-1/PIGF ratio has been proposed as a useful index in diagnosis and management of PE.
  • a widely applicable, sensitive and specific molecular PE test in routine clinical practice is unavailable.
  • MS mass spectrometry
  • Metabolites are the downstream products of genes, transcripts and protein functions in biological systems. They are especially sensitive to
  • This invention uses MS to analyze the small molecule metabolites, and uses these metabolites for PE assessment.
  • the present disclosure relates to a panel of metabolite species that is useful for identification of subjects having PE, including methods of identifying such metabolic biomarkers within biological samples.
  • the disclosure includes a method comprising measuring the concentration of 1 to 85 metabolite species in a sample of a serum from a subject, wherein the metabolite species is a component of a panel of a plurality of metabolite species, wherein a change in the concentration of the metabolite species is useful for the identification of subjects having PE.
  • the concentration of the metabolite species is normalized.
  • the method includes the step of comparing the measured concentration of the metabolite species to a
  • predetermined value calculated using a model based on concentrations of a plurality of the metabolic species that are components of the panel.
  • the panel of metabolite species comprises 1 to 85 compounds selected from the group consisting of 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine, Phenylalanine, Proline, Tyrosine, Valine, Free
  • kits for the analysis of a sample of a bio-fluid of a subject comprising aliquots of standards of each compound of a panel of metabolite species; an aliquot of an internal standard; and an aliquot of a control bio-fluid.
  • the control bio-fluid is serum from a control source that is conspecific with the subject.
  • the internal standard consists of 13 C, 15 N-Glycine, 13 C, 2 D4-L-Arginine, 13 Cs, 15 N-L-Proline, 13 C5-Succinylacetone, 13 C6, 15 N4-L-Argininosuccinic Acid, 13 Ce-L- Phenylalanine, 13 Ce-L-Tyrosine, 13 C6-Thyroxine, 13 C6-Triiodothyronine, 15 N2-Urea, 2 D2-L- Citrulline, 2 D2-L-Ornithine, 2 D39-d20:0 Fatty Acid, 2 D3-C12-L-Carnitine, 2 D3-C14-L-
  • Figure 2 Boxplot of selected metabolomics analytes showing the z-score distribution of case and control.
  • the specific selected metabolomics analytes are identifiers for early stage PE (gestational age 24-34 weeks).
  • Figure 3 Boxplot of selected metabolomics analytes showing the z-score distribution of case and control.
  • the specific selected metabolomics analytes are identifiers for late stage PE (gestational age > 34 weeks).
  • Figure 4 Scatterplot of calculated probabilities of having early stage PE.
  • the model was trained with Random Forest algorithm using the metabolomics analytes shown in Figure 2. During the training process, 13/11 case/control were selected out randomly to train the model.
  • Figure 5 Scatterplot of calculated probabilities of having late stage PE.
  • the model was trained with Random Forest algorithm using the metabolomics analytes shown in Figure 3. During training process, 14/12 case control were selected out randomly to train the model.
  • Figure 8 Unsupervised hierarchical clustering analysis with heat map showing the abundance pattern of targeted metabolomics profile of early stage PE versus normal control subjects.
  • Figure 9 Unsupervised hierarchical clustering analysis with heat map showing the abundance pattern of targeted metabolomics profile of late stage PE versus normal control subjects.
  • compositions and reagents are provided for diagnosing and prognosing PE.
  • the methods and compositions find use in a number of applications, including, for example, diagnosing PE, and monitoring an individual with PE.
  • a report may be provided to the patient of the assessment.
  • systems, devices and kits thereof that find use in practicing the subject methods are provided.
  • aspects of the subject invention include compositions, methods, systems and kits that find use in providing a PE assessment, e.g. diagnosing, prognosing, monitoring, and/or treating PE in a subject.
  • PE it is meant a multisystem complication of pregnancy that may be accompanied by one or more of high blood pressure, proteinuria, swelling of the hands and face/eyes (edema), sudden weight gain, higher-than-normal liver enzymes, and thrombocytopenia.
  • compositions useful for providing a PE assessment will be described first, followed by methods, systems and kits for their use.
  • PE biomarkers are provided.
  • a PE marker it is meant a molecular entity whose representation in a sample is associated with a PE phenotype.
  • a PE marker may be differentially represented, i.e.
  • an elevated level of marker is associated with the PE phenotype.
  • the concentration of marker in a sample may be 1.5-fold, 2-fold, 2.5-fold, 3-fold, 4-fold, 5-fold, 7.5-fold, 10- fold, or greater in a sample associated with the PE phenotype than in a sample not associated with the PE phenotype.
  • a reduced level of marker is associated with the PE phenotype.
  • the concentration of marker in a sample may be 10% less, 20% less, 30% less, 40% less, 50% less or more in a sample associated with the PE phenotype than in a sample not associated with the PE phenotype.
  • the inventors have identified the 85 metabolites: 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine,
  • the subject PE biomarkers find use in making a PE assessment for a patient, or "subject".
  • a PE assessment it is generally meant an estimation of a subject's susceptibility to PE, a determination as to whether a subject is presently affected by PE, a prognosis of a subject affected by PE (e.g., identification of PE states, stages of the PE, prediction of responsiveness to a therapy and/or intervention, e.g. sensitivity or resistance a chemotherapy, radiation, or surgery, likelihood that a patient will die from the PE, etc.), and the use of therametrics (e.g., monitoring a subject's condition to provide information as to the effect or efficacy of therapy on the PE).
  • the subject PE biomarkers and biomarker panels may be used to diagnose PE, to provide a prognosis to a patient having PE, to provide a prediction of the
  • a PE biomarker signature for a patient is obtained.
  • PE biomarker signature or more simply, “PE signature”, it is meant a representation of the measured level/activity of a PE biomarker or biomarker panel of interest.
  • a biomarker signature typically comprises the
  • biomarker signatures include collections of measured small molecular metabolites levels.
  • biomarker signature means metabolites signature.
  • biomarker signatures include biomarker profiles and biomarker scores.
  • biomarker profile it is meant the normalized representation of one or more biomarkers of interest, i.e. a panel of biomarkers of interest, in a patient sample.
  • biomarker score it is meant a single metric value that represents the sum of the weighted representations of one or more biomarkers of interest, more usually two or more biomarkers of interest, i.e. a panel of biomarkers of interest, in a patient sample. Biomarker profiles and scores are discussed in greater detail below.
  • the subject methods may be used to obtain a PE signature. That is, the subject methods may be used to obtain a representation of the metabolite, e.g 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine,
  • a representation of the metabolite e.g 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine,
  • the metabolite level of the one or more PE biomarkers of interest is detected in a patient sample. That is, the representation of one or more PE biomarkers, e.g., 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine,
  • one or more PE biomarkers e.g., 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine,
  • sample with respect to a patient encompasses blood and other liquid samples of biological origin, solid tissue samples such as a biopsy specimen or tissue cultures or cells derived or isolated therefrom and the progeny thereof.
  • sample also includes samples that have been manipulated in any way after their procurement, such as by treatment with reagents; washed; or enrichment for certain cell populations.
  • the definition also includes samples that have been enriched for particular types of molecules, e.g., nucleic acids, polypeptides, etc.
  • biological sample encompasses a clinical sample, and also includes tissue obtained by surgical resection, tissue obtained by biopsy, cells in culture, cell supernatants, cell lysates, tissue samples, organs, bone marrow, blood, plasma, serum, and the like.
  • blood sample encompasses a blood sample (e.g., peripheral blood sample) and any derivative thereof (e.g., fractionated blood, plasma, serum, etc.).
  • the biomarker level is typically assessed in a body fluid sample (e.g., a sample of blood, e.g., whole blood, fractionated blood, plasma, serum, etc.) that is obtained from an individual.
  • a body fluid sample e.g., a sample of blood, e.g., whole blood, fractionated blood, plasma, serum, etc.
  • the sample that is collected may be freshly assayed or it may be stored and assayed at a later time. If the latter, the sample may be stored by any convenient means that will preserve the sample so that gene expression may be assayed at a later date.
  • the sample may freshly cryopreserved, that is, cryopreserved without impregnation with fixative, e.g. at 4°C, at - 20°C, at -60°C, at -80°C, or under liquid nitrogen.
  • the sample may be fixed and preserved, e.g. at room temperature, at 4°C, at -20°C, at -60°C, at -80°C, or under liquid nitrogen, using any of a number of fixatives known in the art, e.g. alcohol, methanol, acetone, formalin, paraformaldehyde, etc.
  • fixatives e.g. alcohol, methanol, acetone, formalin, paraformaldehyde, etc.
  • the resultant data provides information regarding activity for each of the PE biomarkers that have been measured, wherein the information is in terms of whether or not the biomarker is present (e.g. expressed and/or active) and, typically, at what level, and wherein the data may be both qualitative and quantitative.
  • the measurement(s) may be analyzed in any of a number of ways to obtain a biomarker signature.
  • the representation of the one or more PE biomarkers may be analyzed individually to develop a biomarker profile.
  • a biomarker profile is the normalized representation of one or more biomarkers in a patient sample, for example, the normalized level of serological metabolite concentrations in a patient sample, the normalized activity of a biomarker in the sample, etc.
  • a profile may be generated by any of a number of methods known in the art. Other methods of calculating a biomarker signature will be readily known to the ordinarily skilled artisan.
  • the measurement of a PE biomarker or biomarker panel may be analyzed collectively to arrive at a PE biomarker score, and the PE biomarker signature is therefore a single score.
  • biomarker assessment score it is meant a single metric value that represents the sum of the weighted representations of each of the biomarkers of interest, more usually two or more biomarkers of interest, in a biomarker panel.
  • the subject method comprises detecting the amount of markers of a PE biomarker panel in the sample, and calculating a PE biomarker score based on the weighted levels of the biomarkers.
  • the biomarker score is based on the weighted levels of the biomarkers.
  • the biomarker score may be a "metabolite biomarker score", or simply “metabolite score", i.e. it comprises the weighted expression level(s) of the one or more biomarkers, e.g. each biomarker in a panel of biomarkers.
  • a PE biomarker score for a patient sample may be calculated by any of a number of methods and algorithms known in the art for calculating biomarker scores. For example, weighted marker levels, e.g. log2 transformed and normalized marker levels that have been weighted by, e.g., multiplying each normalized marker level to a weighting factor, may be totaled and in some cases averaged to arrive at a single value representative of the panel of biomarkers analyzed.
  • weighted marker levels e.g. log2 transformed and normalized marker levels that have been weighted by, e.g., multiplying each normalized marker level to a weighting factor
  • the weighting factor, or simply "weight" for each marker in a panel may be a reflection of the change in analyte level in the sample.
  • the analyte level of each biomarker may be log2 transformed and weighted either as 1 (for those markers that are increased in level in a subgroup of PE of interest, etc.) or -1 (for those markers that are decreased in level in a subgroup of PE of interest, etc.), and the ratio between the sum of increased markers as compared to decreased markers determined to arrive at a PE biomarker signature.
  • the weights may be reflective of the importance of each marker to the specificity, sensitivity and/or accuracy of the marker panel in making the diagnostic, prognostic, or monitoring assessment.
  • weights may be determined by any convenient statistical machine learning methodology, e.g. Principle Component Analysis (PCA), linear regression, support vector machines (SVMs), and/or random forests of the dataset from which the sample was obtained may be used.
  • PCA Principle Component Analysis
  • SVMs support vector machines
  • weights for each marker are defined by the dataset from which the patient sample was obtained.
  • weights for each marker may be defined based on a reference dataset, or "training dataset”.
  • Methods of analysis may be readily performed by one of ordinary skill in the art by employing a computer-based system, e.g. using any hardware, software and data storage medium as is known in the art, and employing any algorithms convenient for such analysis. For example, data mining algorithms can be applied through "cloud computing", smartphone based or client-server based platforms, and the like.
  • a PE biomarker signature may be expressed as a series of values that are each reflective of the level of a different biomarker (e.g., as a biomarker profile, i.e. the normalized expression values for multiple biomarkers), while in other instances, the PE biomarker signature may be expressed as a single value (e.g., a PE biomarker score).
  • the subject methods of obtaining or providing a PE biomarker signature for a subject further comprise providing the PE biomarker signature as a report.
  • the subject methods may further include a step of generating or outputting a report providing the results of a PE biomarker evaluation in the sample, which report can be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium). Any form of report may be provided, e.g. as known in the art or as described in greater detail below.
  • the PE signature that is so obtained may be employed to make an
  • the PE signature is employed by comparing it to a reference or control, and using the results of that comparison (a “comparison result") to make the PE assessment, e.g. diagnosis, prognosis, prediction of responsiveness to treatment, etc.
  • the terms "reference” or “control”, e.t. “reference signature” or “control signature”, “reference profile” or “control profile”, and “reference score” or “control score” as used herein mean a standardized biomarker signature, e.g. biomarker profile or biomarker score, that may be used to interpret the PE biomarker signature of a given patient and assign a diagnostic, prognostic, and/or responsiveness class thereto.
  • the reference or normal control is typically a PE biomarker signature that is obtained from a sample (e.g., a body fluid, e.g. blood) with a known association with a particular phenotype, Typically, the comparison between the PE signature and reference will determine whether the PE signature correlates more closely with the positive reference or the negative reference, and the correlation employed to make the assessment.
  • a PE biomarker signature that is obtained from a sample (e.g., a body fluid, e.g. blood) with a known association with a particular phenotype
  • the comparison between the PE signature and reference will determine whether the PE signature correlates more closely with the positive reference or the negative reference, and the correlation employed to make the assessment.
  • correlates closely it is meant is within about 40% of the reference, e.g. 40%, 35%, or 30%, in some embodiments within 25%, 20%, or 15%, sometimes within 10%, 8%, 5%, or less.
  • the obtained PE signature for a subject is compared to a single reference/control biomarker signature to obtain information regarding the phenotype.
  • the obtained biomarker signature for the subject is compared to two or more different reference/control biomarker signatures to obtain more in-depth information regarding the phenotype of the assayed tissue. For example, a biomarker profile, or a biomarker score to obtain confirmed information regarding whether the tissue has the phenotype of interest.
  • a biomarker profile or score may be compared to multiple biomarker profiles or scores, each correlating with a particular diagnosis, prognosis or therapeutic responsiveness. Reports
  • providing a PE signature or providing a PE assessment e.g., a diagnosis of PE, a prognosis for a patient with PE, a prediction of
  • the subject methods may further include a step of generating or outputting a report providing the results of an analysis of a PE biomarker or biomarker panel, a diagnosis assessment, a prognosis assessment, or a treatment assessment, which report can be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium).
  • an electronic medium e.g., an electronic display on a computer monitor
  • a tangible medium e.g., a report printed on paper or other tangible medium.
  • a "report,” as described herein, is an electronic or tangible document which includes report elements that provide information of interest relating to a diagnosis assessment, a prognosis assessment, a treatment assessment, a monitoring
  • a subject report can be completely or partially electronically generated.
  • a subject report includes at least a PE assessment, e.g., a diagnosis as to whether a subject has a high likelihood of having a PE.
  • a subject report can further include one or more of: 1) information regarding the testing facility; 2) service provider information; 3) patient data; 4) sample data; 5) an assessment report, which can include various information: a) reference values employed, and b) test data, where test data can include: i) the biomarker levels of one or more PE biomarkers, and/or ii) the biomarker signatures for one or more PE biomarkers; 6) other features.
  • the report may include information about the testing facility, which information is relevant to the hospital, clinic, or laboratory in which sample gathering and/or data generation was conducted. This information can include one or more details relating to, for example, the name and location of the testing facility, the identity of the lab technician who conducted and/or analyzed, the location where the sample and/or result data is stored, the lot number of the reagents (e.g., kit, etc.) used in the assay, and the like. Report fields with this information can generally be populated using information provided by the user.
  • the report may include information about the service provider, which may be located outside the healthcare facility at which the user is located, or within the healthcare facility. Examples of such information can include the name and location of the service provider, the name of the reviewer, and where necessary or desired the name of the individual who conducted sample gathering and/or data generation. Report fields with this information can generally be populated using data entered by the user, which can be selected from among pre-scripted selections (e.g., using a drop-down menu). Other service provider information in the report can include contact information for technical information about the result and/or about the interpretive report.
  • the report may include a patient data section, including patient medical history as well as administrative patient data such as information to identify the patient (e.g., name, patient date of birth (DOB), gender, mailing and/or residence address, medical record number (MRN), room and/or bed number in a healthcare facility), insurance information, and the like), the name of the patient's physician or other health
  • patient medical history as well as administrative patient data such as information to identify the patient (e.g., name, patient date of birth (DOB), gender, mailing and/or residence address, medical record number (MRN), room and/or bed number in a healthcare facility), insurance information, and the like), the name of the patient's physician or other health
  • administrative patient data such as information to identify the patient (e.g., name, patient date of birth (DOB), gender, mailing and/or residence address, medical record number (MRN), room and/or bed number in a healthcare facility), insurance information, and the like), the name of the patient's physician or other health
  • a staff physician who is responsible for the patient's care (e.g., primary care physician).
  • the report may include a sample data section, which may provide information about the biological sample analyzed in the monitoring assessment, such as the source of biological sample obtained from the patient (e.g. blood, saliva, or type of tissue, etc.), how the sample was handled (e.g. storage temperature, preparatory protocols) and the date and time collected. Report fields with this information can generally be populated using data entered by the user, some of which may be provided as pre-scripted selections (e.g., using a drop-down menu).
  • the reports can include additional elements or modified elements.
  • the report can contain hyperlinks which point to internal or external databases which provide more detailed information about selected elements of the report.
  • the patient data element of the report can include a hyperlink to an electronic patient record, or a site for accessing such a patient record, which patient record is maintained in a confidential database. This latter embodiment may be of interest in an in-hospital system or in-clinic setting.
  • the report is recorded on a suitable physical medium, such as a computer readable medium, e.g., in a computer memory, zip drive, CD, DVD, etc.
  • the report can include all or some of the elements above, with the proviso that the report generally includes at least the elements sufficient to provide the analysis requested by the user (e.g. a diagnosis, a prognosis).
  • Reagents, systems and kits Also provided are reagents, devices and kite thereof for practicing one or more of the above-described methods.
  • the subject reagents, devices and kits thereof may vary greatly.
  • Reagents and devices of interest include those mentioned above with respect to the methods of assaying metabolites levels, where such reagents may include stable isotope labeled internal standards 13 C, 15 N-Glycine, 13 C, 2 D4-L-Arginine, 13 Cs, 15 N-L- Proline, 13 C5-Succinylacetone, 13 Ce, 15 N4-L-Argininosuccinic Acid, 13 C6-L-Phenylalanine, 13 C6-L-Tyrosine, 13 C6-Thyroxine, 13 C6-Triiodothyronine, 15 N2-Urea, 2 D2-L-Citrulline, 2 D2- L-Ornithine, 2 D39-d20:0 Fatty Acid, 2 D3-C12
  • the subject kits may also comprise one or more biomarker signature references, e.g. a reference for a PE signature, for use in employing the biomarker signature obtained from a patient sample.
  • the reference may be a sample of a known phenotype, e.g. an unaffected individual, or an affected individual, e.g. from a particular risk group that can be assayed alongside the patient sample, or the reference may be a report of disease diagnosis, disease prognosis, or
  • the subject kits may further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit.
  • One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc.
  • Yet another means would be a computer readable medium, e.g., diskette, CD, DVD, etc., on which the information has been recorded.
  • Yet another means that may be present is a website address which may be used via the internet to access the information at a removed site. Any convenient means may be present in the kits.
  • PE and normal control cohorts were constructed to match gestation age, ethnicity, and parity.
  • Serum sample was taken from -80 °C freezer and thawed on ice.10 pL of each serum sample was transferred into a new tube, and 90 ⁇ !_ extraction buffer was added for extraction. The samples were vortexed vigorously for 1 min and subjected to high-speed centrifuge at 12,000 g for 5 min under room
  • Mass spectrometer Machine TSQ Quantiva triple quadrupole mass spectrometer.
  • HESI Heated electrospray ionization
  • Ion transfer tube temperature 350 °C
  • Vaporizer temperature 250 °C
  • SRM Selected-reaction monitoring
  • Receiver-operator characteristic (ROC) analysis was conducted to evaluate the ability of the targeted metabolomics profile in differentiating the subjects in the testing cohort with cancer from those normal control subjects. This process was repeated by 500 times using a bootstrapping algorithm to get extensive evaluation of the model.
  • ROC Receiver-operator characteristic
  • Unsupervised hierarchical clustering analysis was performed to visually depict the association between the PE outcomes and the abundance patterns of these metabolomics profile. This analysis was used to demonstrate the effectiveness of the metabolomics profile in differentiating PE from normal control subjects.
  • PE serum samples and 32 normal controls were purchased from ProMedEx tissue banks. To compare the 85 metabolites between PE and normal control samples, 10 ⁇ of each serum samples were extracted and analyzed by flow injection MS/MS on a TSQ Quantiva (Thermo) triple quadrupole mass spectrometer. Tandem MS data were processed using a meta-calculation software iRC PRO (2Next sri, Prato, Italy). Serum concentration for each analyte was calculated in ⁇ unit and used for further analysis.
  • iRC PRO meta-calculation software
  • p-value 0.01 as the threshold to select metabolomics analytes.
  • Two panels were constructed for early stage PE and late stage PE identification, respectively.
  • the panel for early stage PE identification consisted of the following metabolomics analytes: Argininosuccinic Acid, Aspartate, Methionine, Free Carnitine, C16-Carnitine, C18:1 -Carnitine, C2-Carnitine, C4-Carnitine, C5-Carnitine, C6 DC- Carnitine, Succinylacetone, d 18: 1-16:0 Ceramide, d 18: 1-18:0 Ceramide, Cholesterol, Cortisol, d22:5 Fatty Acid, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 F
  • the panel for late stage PE identification consisted of the following metabolomics analytes: 5-Oxoproline, Aspartate, C14:1 -Carnitine, C2- Carnitine, C6 DC-Carnitine, Succinylacetone, Creatinine, d18:1-16:0 Ceramide, d18:1- 18:0 Ceramide, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid, d18:0 Fatty Acid.
  • the Random Forest based models stratified all subjects into two levels of risk for progression. For the early stage model for which 26 targeted metabolomics profiles were used as predictors, a subject was classified as normal or PE at early stage. For the late stage model for which 19 targeted metabolomics profiles were used as predictors, a subject was classified as normal or PE at late stage.
  • the risk scores of having PE were calculated by the model ( Figure 4 for early stage PE model and Figure 5 for late stage PE model). We use 0.5 as the cutoff threshold for both early stage PE model and late stage PE model.
  • the c statistic of the model was both 1 for differentiating early stage PE subjects ( Figure 6) and late stage PE subjects ( Figure 7) from normal control subjects, in testing cohort.
  • Unsupervised hierarchical clustering analysis was applied to the targeted metaboiomics profiles to visually depict the association of the PE outcomes with the abundance patterns of these metaboiomics profiles ( Figure 8 for early stage PE and Figure 9 for late stage PE). This analysis demonstrated two major clusters reflecting PE (early or late) and normal.
  • the error rate (miss-classification rate) of the unsupervised clustering is 0% for both early state PE and late stage PE, which reinforcing the effectiveness of metaboiomics panels for PE assessment.

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Abstract

Preeclampsia markers, preeclampsia marker panels, and methods for obtaining a preeclampsia marker level representation for a sample are provided, based upon small molecule metabolic profiling. These composition and methods find use in a number of applications, including, for example, diagnosing preeclampsia, prognosing preeclampsia, monitoring a subject with preeclampsia, and determining a treatment for preeclampsia, in addition, systems, devices, and kits thereof that find use in practicing the subject methods are provided.

Description

Field of the invention
The present disclosure generally relates to small molecule metabolic biomarkers. In particular, the present disclosure relates to a panel of metabolite species to diagnose preeclampsia (PE), including methods for identifying such metabolic biomarkers within biological samples. This invention pertains to providing a PE assessment with
metabolites.
Background of the invention
As the leading cause of maternal morbidity and mortality, PE is a pregnancy- related vascular disorder, affecting 5-8% of all pregnancies. PE can be remedied by delivery of placenta and fetus, often causing fetal growth restriction and preterm delivery as well as fetal mortality and morbidity. The etiology of PE is unknown. Current diagnosis of PE is based on the signs of hypertension and proteinuria4, which lacks sensitivity and specificity, and carries a poor prognosis for adverse maternal and fetal outcomes 5. Thus, there is a need to identify PE biomarkers underlying the pathogenetic pathways, and construct a practical and affordable test that can provide a definitive diagnosis for better monitoring, leading to improved outcomes and economic benefits.
Although the pathophysiology remains largely elusive, PE is a multisystem disorder of pregnancy with the placenta playing a pivotal role. Investigators have used genetic, genomic and proteomic approaches to compare PE and normal placental tissues. Transcriptional profiling of case-control samples has been used to identify disease-specific expression patterns, canonical pathways and gene-gene networks. Proteomics-based biomarker studies have revealed candidate biomarkers for future testing. Placental angiogenic and anti-angiogenic factor imbalance, elevated sFlt-1 and decreased PIGF levels, are suggested in the pathogenesis of PE, and sFlt-1/PIGF ratio has been proposed as a useful index in diagnosis and management of PE. However, a widely applicable, sensitive and specific molecular PE test in routine clinical practice is unavailable.
A promising approach is metabolomics, a fast growing area in system biology that uses mass spectrometry (MS) and promises the identification of sensitive metabolite biomarkers associated with disease, drug treatment, toxicity, and
environmental effects. Metabolites are the downstream products of genes, transcripts and protein functions in biological systems. They are especially sensitive to
perturbations in a number of metabolic pathways and varied pathological conditions. This invention uses MS to analyze the small molecule metabolites, and uses these metabolites for PE assessment.
Summary of the invention
The present disclosure relates to a panel of metabolite species that is useful for identification of subjects having PE, including methods of identifying such metabolic biomarkers within biological samples.
In one aspect, the disclosure includes a method comprising measuring the concentration of 1 to 85 metabolite species in a sample of a serum from a subject, wherein the metabolite species is a component of a panel of a plurality of metabolite species, wherein a change in the concentration of the metabolite species is useful for the identification of subjects having PE. In certain embodiments the concentration of the metabolite species is normalized. In preferred embodiments, the method includes the step of comparing the measured concentration of the metabolite species to a
predetermined value calculated using a model based on concentrations of a plurality of the metabolic species that are components of the panel.
In certain embodiments, the panel of metabolite species comprises 1 to 85 compounds selected from the group consisting of 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine, Phenylalanine, Proline, Tyrosine, Valine, Free
Carnitine , C10-Carnitine, C10:1 -Carnitine, C10:2-Camitine, C12-Camitine, C14- Carnitine, C 14:1 -Carnitine, C14:0 OH-Carnitine, C16-Carnitine, C16:1 -Carnitine, C16:0 OH-Carnitine, C18-Carnitine, C18:1 -Carnitine, C18:0 OH-Carnitine, C18:1 OH- Carnitine, C18:2-Carnitine, C2-Carnitine, C3-Carnitine, C3 DC-Camitine, C4-Camitine, C4:0 OH-Carnitine, C5-Carnitine, C5:1 -Carnitine, C5 DC-Camitine, C5:0 OH-Carnitine, C6-Carnitine, C6 DC-Carnitine, C8-Carnitine, C8:0 OH-Carnitine, Succinylacetone, Urea, Creatinine, Dehydroepiandrosterone Sulfate, Triiodothyronine, Thyroxine, d18:1 - 16:0 Ceramide, d18:1-18:1 Ceramide, d18:1-18:0 Ceramide, d 18: 1-20:0 Ceramide, d18:1-22:1 Ceramide, d18:1-22:0 Ceramide, d18:1-24:0 Ceramide, d18:1-24:1
Ceramide, Cholic Acid, Total Deoxycholic Acid, 7-Dehydrocholesterol, Desmosterol, Campesterol, beta-sitosterol, Cholesterol, Lathosterol, Cortisol, 11-Deoxycortisol, 17- Hydoxyprogesterone, Progesterone, d20:4 Fatty Acid, d22:0 Fatty Acid, d22:6 Fatty Acid, d22:5 Fatty Acid, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid and d18:0 Fatty Acid.
Also disclosed is a kit for the analysis of a sample of a bio-fluid of a subject, comprising aliquots of standards of each compound of a panel of metabolite species; an aliquot of an internal standard; and an aliquot of a control bio-fluid. Typically, the control bio-fluid is serum from a control source that is conspecific with the subject. In some embodiments, the internal standard consists of 13C, 15N-Glycine, 13C, 2D4-L-Arginine, 13Cs, 15N-L-Proline, 13C5-Succinylacetone, 13C6, 15N4-L-Argininosuccinic Acid, 13Ce-L- Phenylalanine, 13Ce-L-Tyrosine, 13C6-Thyroxine, 13C6-Triiodothyronine, 15N2-Urea, 2D2-L- Citrulline, 2D2-L-Ornithine, 2D39-d20:0 Fatty Acid, 2D3-C12-L-Carnitine, 2D3-C14-L-
Carnitine, 2D3-C16-L-Camitine, 2D3-C160H-L-Carnitine, 2D3-C18-L-Carnitine, 2D3-C2-L- Carnitine, 2D3-C3-L-Camitine, 2D3-C4-L-Camitine, 2D3-C5DC-L-Camitine, 2D3-C50H-L- Carnitine, 2D3-C8-L-Camitine, 2D3-Creatinine, 2D3-DL-Glutamate, 2D3-L-Aspartate, 2D3- L-Leucine, 2D3-L-Methionine, 2D4-Cholic Acid, 2D4-L-Alanine, 2D7-Cholesterol, 2D7- d18:1-16:0 Ceramide, 2D7-d18:1-18:0 Ceramide, 2D7-d18: 1-24:0 Ceramide, 2Dj- Dehydroepiandrosterone Sulfate, 2D8-L-Valine, 2D9-C5-L-Carnitine, 2D9- Chenodeoxycholic Acid, 2Dg-L-Carnitine, 2Dg-Progesterone. Typically, the kit includes instructions for use.
Brief description of the drawings
The invention will be best understood from the following detailed description when read in conjunction with the accompanying drawings. The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity. Included in the drawings are the following figures.
Figure 1. Outline of the study. Two models were trained specifically for early and late stage PE identification.
Figure 2. Boxplot of selected metabolomics analytes showing the z-score distribution of case and control. The specific selected metabolomics analytes are identifiers for early stage PE (gestational age 24-34 weeks).
Figure 3. Boxplot of selected metabolomics analytes showing the z-score distribution of case and control. The specific selected metabolomics analytes are identifiers for late stage PE (gestational age > 34 weeks).
Figure 4. Scatterplot of calculated probabilities of having early stage PE. The model was trained with Random Forest algorithm using the metabolomics analytes shown in Figure 2. During the training process, 13/11 case/control were selected out randomly to train the model.
Figure 5. Scatterplot of calculated probabilities of having late stage PE. The model was trained with Random Forest algorithm using the metabolomics analytes shown in Figure 3. During training process, 14/12 case control were selected out randomly to train the model. Figure 6. ROC curves for models of early stage PE assessment with targeted metabolomics profile evaluated on testing set (n = 7) consisting of early stage patients and normal control subjects. Average true positive rate was calculated with 500 10-fold CV fits of the model.
Figure 7. ROC curves for models of late stage PE assessment with targeted metabolomics profile evaluated on testing set (n = 7) consisting of late stage patients and normal control subjects. Average true positive rate was calculated with 500 10-fold CV fits of the model.
Figure 8. Unsupervised hierarchical clustering analysis with heat map showing the abundance pattern of targeted metabolomics profile of early stage PE versus normal control subjects.
Figure 9. Unsupervised hierarchical clustering analysis with heat map showing the abundance pattern of targeted metabolomics profile of late stage PE versus normal control subjects.
Detailed description of the invention
Methods, compositions and reagents are provided for diagnosing and prognosing PE. The methods and compositions find use in a number of applications, including, for example, diagnosing PE, and monitoring an individual with PE. A report may be provided to the patient of the assessment. In addition, systems, devices and kits thereof that find use in practicing the subject methods are provided. These and other objects,
advantages, and features of the invention will become apparent to those persons skilled in the art upon reading the details of the compositions and methods as more fully described below.
Before the present methods and compositions are described, it is to be
understood that this invention is not limited to particular method or composition described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.
Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limits of that range is also specifically disclosed. Each smaller range between any stated value or intervening value in a stated range and any other stated or intervening value in that stated range is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range, and each range where either, neither or both limits are included in the smaller ranges is also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or test of the present invention, some potential and preferred methods and materials are now described. All publications mentioned herein are incorporated herein by reference to disclose and describe the methods and/or materials in connection with which the publications are cited. It is understood that the present disclosure supersedes any disclosure of an incorporated publication to the extent there is a contradiction.
As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present invention. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.
It must be noted that as used herein and in the appended claims, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a cell" includes a plurality of such cells and reference to "the peptide" includes reference to one or more peptides and equivalents thereof, e.g. polypeptides, known to those skilled in the art, and so forth.
The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates that may need to be independently confirmed.
As summarized above, aspects of the subject invention include compositions, methods, systems and kits that find use in providing a PE assessment, e.g. diagnosing, prognosing, monitoring, and/or treating PE in a subject. By "PE" it is meant a multisystem complication of pregnancy that may be accompanied by one or more of high blood pressure, proteinuria, swelling of the hands and face/eyes (edema), sudden weight gain, higher-than-normal liver enzymes, and thrombocytopenia.
In describing the subject invention, compositions useful for providing a PE assessment will be described first, followed by methods, systems and kits for their use.
PE markers and panels
In some aspects of the invention, PE biomarkers are provided. By a "PE marker" it is meant a molecular entity whose representation in a sample is associated with a PE phenotype. For example, a PE marker may be differentially represented, i.e.
represented at a different level, in a sample from an individual that will develop or has developed PE as compared to a healthy individual. In some instances, an elevated level of marker is associated with the PE phenotype. For example, the concentration of marker in a sample may be 1.5-fold, 2-fold, 2.5-fold, 3-fold, 4-fold, 5-fold, 7.5-fold, 10- fold, or greater in a sample associated with the PE phenotype than in a sample not associated with the PE phenotype. In other instances, a reduced level of marker is associated with the PE phenotype. For example, the concentration of marker in a sample may be 10% less, 20% less, 30% less, 40% less, 50% less or more in a sample associated with the PE phenotype than in a sample not associated with the PE phenotype.
As demonstrated in the examples of the present disclosure, the inventors have identified the 85 metabolites: 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine,
Ornithine, Phenylalanine, Proline, Tyrosine, Valine, Free Carnitine , C10-Camitine, C10:1 -Carnitine, C10:2-Carnitine, C12-Carnitine, C14-Carnitine, C14:1 -Carnitine, C14:0 OH-Carnitine, C16-Carnitine, C16:1 -Carnitine, C16:0 OH-Carnitine, C18-Carnitine, C18:1 -Carnitine, C18:0 OH-Carnitine, C18:1 OH-Carnitine, C18:2-Camitine, C2- Carnitine, C3-Carnitine, C3 DC-Carnitine, C4-Carnitine, C4:0 OH-Carnitine, C5- Carnitine, C5:1 -Carnitine, C5 DC-Camitine, C5:0 OH-Carnitine, C6-Carnitine, C6 DC- Carnitine, C8-Carnitine, C8:0 OH-Carnitine, Succinylacetone, Urea, Creatinine,
Dehydroepiandrosterone Sulfate, Triiodothyronine, Thyroxine, d18: 1-16:0 Ceramide, d18:1-18:1 Ceramide, d18:1-18:0 Ceramide, d18:1 -20:0 Ceramide, d18:1-22:1
Ceramide, d18: 1-22:0 Ceramide, d18:1 -24:0 Ceramide, d18:1 -24:1 Ceramide, Cholic Acid, Total Deoxycholic Acid, 7-Dehydrocholesterol, Desmosterol, Campesterol, beta- sitosterol, Cholesterol, Lathosterol, Cortisol, 11-Deoxycortisol, 17-Hydoxyprogesterone, Progesterone, d20:4 Fatty Acid, d22:0 Fatty Acid, d22:6 Fatty Acid, d22:5 Fatty Acid, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d 18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid and d18:0 Fatty Acid, that are represented at elevated/lowered levels in blood samples of subtypes of PE, and thus, that find use as biomarkers in providing a PE assessment, e.g.
diagnosing a PE, prognosing a PE, determining a treatment for a subject affected with PE, monitoring a subject with PE, and the like.
Methods The subject PE biomarkers find use in making a PE assessment for a patient, or "subject". By a "PE assessment", it is generally meant an estimation of a subject's susceptibility to PE, a determination as to whether a subject is presently affected by PE, a prognosis of a subject affected by PE (e.g., identification of PE states, stages of the PE, prediction of responsiveness to a therapy and/or intervention, e.g. sensitivity or resistance a chemotherapy, radiation, or surgery, likelihood that a patient will die from the PE, etc.), and the use of therametrics (e.g., monitoring a subject's condition to provide information as to the effect or efficacy of therapy on the PE). Thus, for example, the subject PE biomarkers and biomarker panels may be used to diagnose PE, to provide a prognosis to a patient having PE, to provide a prediction of the
responsiveness of a patient with PE to a medical therapy, to monitor a patient having PE, to treat a patient having PE, etc. In practicing the subject methods, a PE biomarker signature for a patient is obtained. By a "PE biomarker signature" or more simply, "PE signature", it is meant a representation of the measured level/activity of a PE biomarker or biomarker panel of interest. A biomarker signature typically comprises the
quantitative data on the biomarker levels/activity of these one or more biomarkers of interest.
Examples of biomarker signatures include collections of measured small molecular metabolites levels. As used herein, the term "biomarker signature" means metabolites signature. Examples of biomarker signatures include biomarker profiles and biomarker scores. By a "biomarker profile" it is meant the normalized representation of one or more biomarkers of interest, i.e. a panel of biomarkers of interest, in a patient sample. By a "biomarker score" it is meant a single metric value that represents the sum of the weighted representations of one or more biomarkers of interest, more usually two or more biomarkers of interest, i.e. a panel of biomarkers of interest, in a patient sample. Biomarker profiles and scores are discussed in greater detail below.
For example, in some embodiments, the subject methods may be used to obtain a PE signature. That is, the subject methods may be used to obtain a representation of the metabolite, e.g 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine,
Phenylalanine, Proline, Tyrosine, Valine, Free Carnitine , C10-Carnitine, C10:1- Carnitine, C10:2-Carnitine, C12-Camitine, C14-Camitine, C14:1 -Carnitine, C14:0 OHCarnitine, C16-Carnitine, C16:1-Carnitine, C16:0 OH-Camitine, C18-Camitine, C18:1- Carnitine, C18.0 OH-Camitine, C18:1 OH-Carnitine, C18:2-Camitine, C2-Camitine, C3- Carnitine, C3 DC-Carnitine, C4-Carnitine, C4:0 OH-Camitine, C5-Carnitine, C5:1- Carnitine, C5 DC-Carnitine, C5:0 OH-Carnitine, C6-Camitine, C6 DC-Camitine, C8- Carnitine, C8:0 OH-Carnitine, Succinylacetone, Urea, Creatinine,
Dehydroepiandrosterone Sulfate, Triiodothyronine, Thyroxine, d18:1-16:0 Ceramide, d18:1-18:1 Ceramide, d18:1-18:0 Ceramide, d18:1 -20:0 Ceramide, d18:1-22:1
Ceramide, d18: 1-22:0 Ceramide, d18:1 -24:0 Ceramide, d18:1-24:1 Ceramide, Cholic Acid, Total Deoxycholic Acid, 7-Dehydrocholesterol, Desmosterol, Campesterol, beta- sitosterol, Cholesterol, Lathosterol, Cortisol, 11-Deoxycortisol, 17-Hydoxyprogesterone, Progesterone, d20:4 Fatty Acid, d22:0 Fatty Acid, d22:6 Fatty Acid, d22:5 Fatty Acid, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid and d18:0 Fatty Acid, that are up- or down-regulated (i.e., expressed at a higher or lower level, exhibits a higher or lower level of activity, etc.), in patients with PE.
To obtain a PE signature, the metabolite level of the one or more PE biomarkers of interest is detected in a patient sample. That is, the representation of one or more PE biomarkers, e.g., 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate, Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine,
Phenylalanine, Proline, Tyrosine, Valine, Free Carnitine , C10-Carnitine, C10:1- Carnitine, C10:2-Carnitine, C12-Carnitine, C14-Carnitine, C14:1 -Carnitine, C14:0 OH- Carnitine, C16-Carnitine, C16:1 -Carnitine, C16:0 OH-Camitine, C18-Carnitine, C18:1- Carnitine, C18:0 OH-Carnitine, C18:1 OH-Carnitine, C18:2-Carnitine, C2-Camitine, C3- Carnitine, C3 DC-Carnitine, C4-Camitine, C4:0 OH-Carnitine, C5-Carnitine, C5:1- Carnitine, C5 DC-Carnitine, C5:0 OH-Carnitine, C6-Camitine, C6 DC-Carnitine, C8- Carnitine, C8:0 OH-Carnitine, Succinylacetone, Urea, Creatinine,
Dehydroepiandrosterone Sulfate, Triiodothyronine, Thyroxine, d18:1 -16:0 Ceramide, d18:1 -18:1 Ceramide, d18:1 -18:0 Ceramide, d18:1 -20:0 Ceramide, d18:1 -22:1
Ceramide, d18: 1-22:0 Ceramide, d18:1 -24:0 Ceramide, d18:1 -24:1 Ceramide, Cholic Acid, Total Deoxycholic Acid, 7-Dehydrocholesterol, Desmosterol, Campesterol, beta- sitosterol, Cholesterol, Lathosterol, Cortisol, 11-Deoxycortisol, 17-Hydoxyprogesterone, Progesterone, d20:4 Fatty Acid, d22:0 Fatty Acid, d22:6 Fatty Acid, d22:5 Fatty Acid, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid and d18:0 Fatty Acid, and in some instances other PE biomarkers in the art, e.g. a panel of biomarkers, is determined for a patient sample. The term "sample" with respect to a patient encompasses blood and other liquid samples of biological origin, solid tissue samples such as a biopsy specimen or tissue cultures or cells derived or isolated therefrom and the progeny thereof. The definition also includes samples that have been manipulated in any way after their procurement, such as by treatment with reagents; washed; or enrichment for certain cell populations.
The definition also includes samples that have been enriched for particular types of molecules, e.g., nucleic acids, polypeptides, etc. The term "biological sample" encompasses a clinical sample, and also includes tissue obtained by surgical resection, tissue obtained by biopsy, cells in culture, cell supernatants, cell lysates, tissue samples, organs, bone marrow, blood, plasma, serum, and the like. The term "blood sample" encompasses a blood sample (e.g., peripheral blood sample) and any derivative thereof (e.g., fractionated blood, plasma, serum, etc.).
In performing the subject methods, the biomarker level is typically assessed in a body fluid sample (e.g., a sample of blood, e.g., whole blood, fractionated blood, plasma, serum, etc.) that is obtained from an individual. The sample that is collected may be freshly assayed or it may be stored and assayed at a later time. If the latter, the sample may be stored by any convenient means that will preserve the sample so that gene expression may be assayed at a later date. For example, the sample may freshly cryopreserved, that is, cryopreserved without impregnation with fixative, e.g. at 4°C, at - 20°C, at -60°C, at -80°C, or under liquid nitrogen. Alternatively, the sample may be fixed and preserved, e.g. at room temperature, at 4°C, at -20°C, at -60°C, at -80°C, or under liquid nitrogen, using any of a number of fixatives known in the art, e.g. alcohol, methanol, acetone, formalin, paraformaldehyde, etc. The resultant data provides information regarding activity for each of the PE biomarkers that have been measured, wherein the information is in terms of whether or not the biomarker is present (e.g. expressed and/or active) and, typically, at what level, and wherein the data may be both qualitative and quantitative.
Once the representation of the one or more biomarkers has been determined, the measurement(s) may be analyzed in any of a number of ways to obtain a biomarker signature.
For example, the representation of the one or more PE biomarkers may be analyzed individually to develop a biomarker profile. As used herein, a "biomarker profile" is the normalized representation of one or more biomarkers in a patient sample, for example, the normalized level of serological metabolite concentrations in a patient sample, the normalized activity of a biomarker in the sample, etc. A profile may be generated by any of a number of methods known in the art. Other methods of calculating a biomarker signature will be readily known to the ordinarily skilled artisan.
As another example, the measurement of a PE biomarker or biomarker panel may be analyzed collectively to arrive at a PE biomarker score, and the PE biomarker signature is therefore a single score. By "biomarker assessment score" it is meant a single metric value that represents the sum of the weighted representations of each of the biomarkers of interest, more usually two or more biomarkers of interest, in a biomarker panel. As such, in some embodiments, the subject method comprises detecting the amount of markers of a PE biomarker panel in the sample, and calculating a PE biomarker score based on the weighted levels of the biomarkers. In certain embodiments, the biomarker score is based on the weighted levels of the biomarkers. In certain embodiments, the biomarker score may be a "metabolite biomarker score", or simply "metabolite score", i.e. it comprises the weighted expression level(s) of the one or more biomarkers, e.g. each biomarker in a panel of biomarkers.
A PE biomarker score for a patient sample may be calculated by any of a number of methods and algorithms known in the art for calculating biomarker scores. For example, weighted marker levels, e.g. log2 transformed and normalized marker levels that have been weighted by, e.g., multiplying each normalized marker level to a weighting factor, may be totaled and in some cases averaged to arrive at a single value representative of the panel of biomarkers analyzed.
In some instances, the weighting factor, or simply "weight" for each marker in a panel may be a reflection of the change in analyte level in the sample. For example, the analyte level of each biomarker may be log2 transformed and weighted either as 1 (for those markers that are increased in level in a subgroup of PE of interest, etc.) or -1 (for those markers that are decreased in level in a subgroup of PE of interest, etc.), and the ratio between the sum of increased markers as compared to decreased markers determined to arrive at a PE biomarker signature. In other instances, the weights may be reflective of the importance of each marker to the specificity, sensitivity and/or accuracy of the marker panel in making the diagnostic, prognostic, or monitoring assessment. Such weights may be determined by any convenient statistical machine learning methodology, e.g. Principle Component Analysis (PCA), linear regression, support vector machines (SVMs), and/or random forests of the dataset from which the sample was obtained may be used. In some instances, weights for each marker are defined by the dataset from which the patient sample was obtained. In other instances, weights for each marker may be defined based on a reference dataset, or "training dataset". Methods of analysis may be readily performed by one of ordinary skill in the art by employing a computer-based system, e.g. using any hardware, software and data storage medium as is known in the art, and employing any algorithms convenient for such analysis. For example, data mining algorithms can be applied through "cloud computing", smartphone based or client-server based platforms, and the like.
Thus, in some instances, a PE biomarker signature may be expressed as a series of values that are each reflective of the level of a different biomarker (e.g., as a biomarker profile, i.e. the normalized expression values for multiple biomarkers), while in other instances, the PE biomarker signature may be expressed as a single value (e.g., a PE biomarker score).
In some instances, the subject methods of obtaining or providing a PE biomarker signature for a subject further comprise providing the PE biomarker signature as a report. Thus, in some instances, the subject methods may further include a step of generating or outputting a report providing the results of a PE biomarker evaluation in the sample, which report can be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium). Any form of report may be provided, e.g. as known in the art or as described in greater detail below.
The PE signature that is so obtained may be employed to make an
PE assessment. Typically, in making the subject PE assessment, the PE signature is employed by comparing it to a reference or control, and using the results of that comparison (a "comparison result") to make the PE assessment, e.g. diagnosis, prognosis, prediction of responsiveness to treatment, etc. The terms "reference" or "control", e.t. "reference signature" or "control signature", "reference profile" or "control profile", and "reference score" or "control score" as used herein mean a standardized biomarker signature, e.g. biomarker profile or biomarker score, that may be used to interpret the PE biomarker signature of a given patient and assign a diagnostic, prognostic, and/or responsiveness class thereto. The reference or normal control is typically a PE biomarker signature that is obtained from a sample (e.g., a body fluid, e.g. blood) with a known association with a particular phenotype, Typically, the comparison between the PE signature and reference will determine whether the PE signature correlates more closely with the positive reference or the negative reference, and the correlation employed to make the assessment. By "correlates closely", it is meant is within about 40% of the reference, e.g. 40%, 35%, or 30%, in some embodiments within 25%, 20%, or 15%, sometimes within 10%, 8%, 5%, or less.
In certain embodiments, the obtained PE signature for a subject is compared to a single reference/control biomarker signature to obtain information regarding the phenotype. In other embodiments, the obtained biomarker signature for the subject is compared to two or more different reference/control biomarker signatures to obtain more in-depth information regarding the phenotype of the assayed tissue. For example, a biomarker profile, or a biomarker score to obtain confirmed information regarding whether the tissue has the phenotype of interest. As another example, a biomarker profile or score may be compared to multiple biomarker profiles or scores, each correlating with a particular diagnosis, prognosis or therapeutic responsiveness. Reports
In some embodiments, providing a PE signature or providing a PE assessment, e.g., a diagnosis of PE, a prognosis for a patient with PE, a prediction of
responsiveness of a patient with PE to a cancer therapy, includes generating a written report that includes that PE signature and/or the PE assessment e.g. a "diagnosis assessment", a "prognosis assessment", a suggestion of possible treatment regimens (a "treatment assessment") and the like. Thus, the subject methods may further include a step of generating or outputting a report providing the results of an analysis of a PE biomarker or biomarker panel, a diagnosis assessment, a prognosis assessment, or a treatment assessment, which report can be provided in the form of an electronic medium (e.g., an electronic display on a computer monitor), or in the form of a tangible medium (e.g., a report printed on paper or other tangible medium).
A "report," as described herein, is an electronic or tangible document which includes report elements that provide information of interest relating to a diagnosis assessment, a prognosis assessment, a treatment assessment, a monitoring
assessment, etc. and its results. A subject report can be completely or partially electronically generated. A subject report includes at least a PE assessment, e.g., a diagnosis as to whether a subject has a high likelihood of having a PE. A subject report can further include one or more of: 1) information regarding the testing facility; 2) service provider information; 3) patient data; 4) sample data; 5) an assessment report, which can include various information: a) reference values employed, and b) test data, where test data can include: i) the biomarker levels of one or more PE biomarkers, and/or ii) the biomarker signatures for one or more PE biomarkers; 6) other features. The report may include information about the testing facility, which information is relevant to the hospital, clinic, or laboratory in which sample gathering and/or data generation was conducted. This information can include one or more details relating to, for example, the name and location of the testing facility, the identity of the lab technician who conducted and/or analyzed, the location where the sample and/or result data is stored, the lot number of the reagents (e.g., kit, etc.) used in the assay, and the like. Report fields with this information can generally be populated using information provided by the user.
The report may include information about the service provider, which may be located outside the healthcare facility at which the user is located, or within the healthcare facility. Examples of such information can include the name and location of the service provider, the name of the reviewer, and where necessary or desired the name of the individual who conducted sample gathering and/or data generation. Report fields with this information can generally be populated using data entered by the user, which can be selected from among pre-scripted selections (e.g., using a drop-down menu). Other service provider information in the report can include contact information for technical information about the result and/or about the interpretive report.
The report may include a patient data section, including patient medical history as well as administrative patient data such as information to identify the patient (e.g., name, patient date of birth (DOB), gender, mailing and/or residence address, medical record number (MRN), room and/or bed number in a healthcare facility), insurance information, and the like), the name of the patient's physician or other health
professional who ordered the monitoring assessment and, if different from the ordering physician, the name of a staff physician who is responsible for the patient's care (e.g., primary care physician).
The report may include a sample data section, which may provide information about the biological sample analyzed in the monitoring assessment, such as the source of biological sample obtained from the patient (e.g. blood, saliva, or type of tissue, etc.), how the sample was handled (e.g. storage temperature, preparatory protocols) and the date and time collected. Report fields with this information can generally be populated using data entered by the user, some of which may be provided as pre-scripted selections (e.g., using a drop-down menu).
It will also be readily appreciated that the reports can include additional elements or modified elements. For example, where electronic, the report can contain hyperlinks which point to internal or external databases which provide more detailed information about selected elements of the report. For example, the patient data element of the report can include a hyperlink to an electronic patient record, or a site for accessing such a patient record, which patient record is maintained in a confidential database. This latter embodiment may be of interest in an in-hospital system or in-clinic setting. When in electronic format, the report is recorded on a suitable physical medium, such as a computer readable medium, e.g., in a computer memory, zip drive, CD, DVD, etc.
It will be readily appreciated that the report can include all or some of the elements above, with the proviso that the report generally includes at least the elements sufficient to provide the analysis requested by the user (e.g. a diagnosis, a prognosis).
Reagents, systems and kits Also provided are reagents, devices and kite thereof for practicing one or more of the above-described methods. The subject reagents, devices and kits thereof may vary greatly. Reagents and devices of interest include those mentioned above with respect to the methods of assaying metabolites levels, where such reagents may include stable isotope labeled internal standards 13C, 15N-Glycine, 13C, 2D4-L-Arginine, 13Cs, 15N-L- Proline, 13C5-Succinylacetone, 13Ce, 15N4-L-Argininosuccinic Acid, 13C6-L-Phenylalanine, 13C6-L-Tyrosine, 13C6-Thyroxine, 13C6-Triiodothyronine, 15N2-Urea, 2D2-L-Citrulline, 2D2- L-Ornithine, 2D39-d20:0 Fatty Acid, 2D3-C12-L-Carnitine, 2D3-C14-L-Camitine, 2D3-C16- L-Carnitine, 2D3-C160H-L-Carnitine, 2D3-C18-L-Carnitine, 2D3-C2-L-Camitine, 2D3-C3-L- Carnitine, 2D3-C4-L-Carnitine, 2D3-C5DC-L-Camitine, 2D3-C50H-L-Carnitine, 2D3-C8-L- Carnitine, 2D3-Creatinine, 2D3-DL-Glutamate, 2D3-L-Aspartate, 2D3-L-Leucine, 2D3-L- Methionine, 2D4-Cholic Acid, 2D4-L-Alanine, 2D7-Cholesterol, 2D7-d18:1-16:0 Ceramide, 2D7-d18:1-18:0 Ceramide, 2D7-d 18: 1-24:0 Ceramide, 2D7-Dehydroepiandrosterone Sulfate, 2De-L-Valine, 2D9-C5-L-Carnitine, 2D9-Chenodeoxycholic Acid, 2D9-L-Carnitine and 2D9-Progesterone. The subject kits may also comprise one or more biomarker signature references, e.g. a reference for a PE signature, for use in employing the biomarker signature obtained from a patient sample. For example, the reference may be a sample of a known phenotype, e.g. an unaffected individual, or an affected individual, e.g. from a particular risk group that can be assayed alongside the patient sample, or the reference may be a report of disease diagnosis, disease prognosis, or
responsiveness to therapy that is known to correlate with one or more of the subject PE biomarker signatures. In addition to the above components, the subject kits may further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, e.g., a piece or pieces of paper on which the information is printed, in the packaging of the kit, in a package insert, etc. Yet another means would be a computer readable medium, e.g., diskette, CD, DVD, etc., on which the information has been recorded. Yet another means that may be present is a website address which may be used via the internet to access the information at a removed site. Any convenient means may be present in the kits.
The following examples are offered by way of illustration and not by way of limitation.
Examples The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention nor are they intended to represent that the experiments below are all or the only experiments performed. Efforts have been made to ensure accuracy with respect to numbers used (e.g. amounts, temperature, etc.) but some experimental errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, molecular weight is weight average molecular weight, temperature is in degrees Centigrade, and pressure is at or near atmospheric.
Example 1 Study design
The study for PE metabolomics experiment was comprised of the analysis of an independent PE cohort (n=32, containing 16 PE patients at early gestational age and 16 PE patients at late gestational age) and normal control cohort (n=32, containing 15 normal controls at early gestational age and 17 normal controls at late gestational age) (Figure 1). Two specific panels for PE identification at early stage of pregnancy
(gestational age 24-34 weeks) and late stage of pregnancy (gestational age > 34 weeks) were constructed separately. PE and normal control cohorts were constructed to match gestation age, ethnicity, and parity.
Sample purchase
All the serum samples were purchased from ProMedDX Inc. (Norton, MA 02766, http:/ www.promeddx.com). All pregnancy collections were with informed consent and supplied with detailed case report forms. Subjects, who were current smokers, or with substance abuse, or used in vitro fertilization assistance, or with chronic hypertension, or with intrauterine growth restriction complicating pregnancy, were excluded from the study.
Statistical analyses for sample clinical information
Patient demographic data was analyzed using "Epidemiological calculator" (R epicalc package). Student t test was performed to calculate p value for continuous variables, and Fisher exact test was used for comparative analysis of categorical variables. Sample characteristics
As shown in Table 1, the PE and normal control subjects used for serological protein biomarker validation can be divided into early (PE, n=16; control, n=15) and late (PE, n=16; control, n=17) gestation age groups. No significant differences in age (p value, early 0.890, late 0.857, overall 0.600), gestational age (p value, early 0.851 , late 0.895, overall 0.824) at enrollment, or ethnical origin (p value, early 0.570, late 0.123, overall 0.289) were observed. Subjects' concurrent medical conditions and other clinical features were analyzed and shown in Table 2.
Table 1. Ethnicity, age and week of gestation.
Figure imgf000026_0001
Table 2. Concurrent medical conditions and clinical features
Figure imgf000027_0001
Internal standard preparation
Dilute to 200 ml_ with 70%MeOH in a 200-mL volumetric flask to obtain concentrations of different analytes (Table 3). Vortex vigorously and stored at 4 °C prior to use.
Table 3. Concentrations of different internal standard analytes
Figure imgf000027_0002
Figure imgf000028_0001
Sample preparation
Preparation of serum sample: Serum sample was taken from -80 °C freezer and thawed on ice.10 pL of each serum sample was transferred into a new tube, and 90 μ!_ extraction buffer was added for extraction. The samples were vortexed vigorously for 1 min and subjected to high-speed centrifuge at 12,000 g for 5 min under room
temperature. The supernatant from each sample was collected for analysis Preparation of quality control samples (Serum): 1 ml_ of serum samples from 3-5 normal control individuals were pooled and vortexed for 1 min, then centrifuge at 3,000 g for 1 min. The pooled serum sample was divided into 10-uL aliquots and stored at -80 °C before use. 6 of 10-uL aliquots from pooled serum were unambiguously processed as internal quality controls for 90 unknown samples in a 96-well plate during the sample prep.
MS/MS detection
1. Liquid Chromatography
Pump: Thermo Scientific™ Dionex™ UltiMate™ HPG-3200 RS
Autosampler: UltiMate WPS-3000 TRS
Mobile phase 50:50: 1acetonitrile/water/formic acid
LC flow gradient (Table 4)
Table 4. LC flow gradient parameter characteristics
Figure imgf000029_0001
2. Mass spectrometer Machine: TSQ Quantiva triple quadrupole mass spectrometer.
The mass spectrometer conditions were as follows:
Ionization: Heated electrospray ionization (HESI)
Spray voltage: Positive, 3500 V
Sheath gas: 40 Arb
Aux gas: 10 Arb
Sweep gas: 1 Arb
Ion transfer tube temperature: 350 °C
Vaporizer temperature: 250 °C
Data acquisition mode: Selected-reaction monitoring (SRM)
Cycle time: 1 s
Q1 resolution (FWHM): 0.7
Q3 resolution (FWHM): 0.7
CID gas: 1.5 mTorr
Source fragmentation: 0 V
Chrome filter: 3 s
Data analysis and statistics for metabolomics panel construction
Software and packages used in the data analysis included: random forest modeling analysis was performed using R random Forest package (http://www.r- project.org/). Biomarker feature selection and panel optimization was performed using a genetic algorithm (R genalg package). The predictive performance of each biomarker panel analysis was evaluated by ROC curve analysis (Zweig et al. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clinical chemistry 1993;39:561-77; Sing et al. ROCR: visualizing classifier performance in R. Bioinformatics 2005;21:3940-1).
The targeted metabolomics profile was firstly normalized to z-score across all the samples (n = 64). The z-scores of the metabolomics profiles of subjects that were randomized to construct a training cohort (n = 50, 24 for early stage, 26 for late stage) were trained to develop a model by Random Forest analysis using the R package 'randomForest'. All subjects in the training cohort were assigned to one of two possible subgroups (normal control and early stage PE, or normal control and late stage PE) by the model. Two models were developed specifically for early and late stage PE identification, respectively. The models were applied to both training cohort and testing cohort (n=14, 7 for early stage, 7 for late stage), and the probability of having PE for each corresponding subject was calculated. Receiver-operator characteristic (ROC) analysis was conducted to evaluate the ability of the targeted metabolomics profile in differentiating the subjects in the testing cohort with cancer from those normal control subjects. This process was repeated by 500 times using a bootstrapping algorithm to get extensive evaluation of the model.
Unsupervised hierarchical clustering analysis was performed to visually depict the association between the PE outcomes and the abundance patterns of these metabolomics profile. This analysis was used to demonstrate the effectiveness of the metabolomics profile in differentiating PE from normal control subjects.
Example 2 Sample collection
32 PE serum samples and 32 normal controls were purchased from ProMedEx tissue banks. To compare the 85 metabolites between PE and normal control samples, 10 μΙ of each serum samples were extracted and analyzed by flow injection MS/MS on a TSQ Quantiva (Thermo) triple quadrupole mass spectrometer. Tandem MS data were processed using a meta-calculation software iRC PRO (2Next sri, Prato, Italy). Serum concentration for each analyte was calculated in μΜ unit and used for further analysis.
Statistical analysis for 85 metabolomics analytes and panel construction
A t-test was performed for metabolomics profile between early stage PE and corresponding normal control subjects (Table 5), and between late stage PE and corresponding normal control subjects (Table 6). P-values, odds ratios, and fold change for each metabolomics analyte were listed.
Table 5. Statistical summary of selected analytes in identifying early stage PE
Figure imgf000032_0001
Figure imgf000033_0001
Table 6. Statistical summary of selected analytes in identifying late stage PE
Figure imgf000034_0001
We used p-value = 0.01 as the threshold to select metabolomics analytes. Two panels were constructed for early stage PE and late stage PE identification, respectively. The panel for early stage PE identification consisted of the following metabolomics analytes: Argininosuccinic Acid, Aspartate, Methionine, Free Carnitine, C16-Carnitine, C18:1 -Carnitine, C2-Carnitine, C4-Carnitine, C5-Carnitine, C6 DC- Carnitine, Succinylacetone, d 18: 1-16:0 Ceramide, d 18: 1-18:0 Ceramide, Cholesterol, Cortisol, d22:5 Fatty Acid, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid, d18:0 Fatty Acid. The panel for late stage PE identification consisted of the following metabolomics analytes: 5-Oxoproline, Aspartate, C14:1 -Carnitine, C2- Carnitine, C6 DC-Carnitine, Succinylacetone, Creatinine, d18:1-16:0 Ceramide, d18:1- 18:0 Ceramide, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid, d18:0 Fatty Acid.
Performance of metabolomics profile-based prognostic algorithm
The Random Forest based models stratified all subjects into two levels of risk for progression. For the early stage model for which 26 targeted metabolomics profiles were used as predictors, a subject was classified as normal or PE at early stage. For the late stage model for which 19 targeted metabolomics profiles were used as predictors, a subject was classified as normal or PE at late stage. The risk scores of having PE were calculated by the model (Figure 4 for early stage PE model and Figure 5 for late stage PE model). We use 0.5 as the cutoff threshold for both early stage PE model and late stage PE model. The c statistic of the model was both 1 for differentiating early stage PE subjects (Figure 6) and late stage PE subjects (Figure 7) from normal control subjects, in testing cohort.
Unsupervised hierarchical clustering with metaboiomics profiles
Unsupervised hierarchical clustering analysis was applied to the targeted metaboiomics profiles to visually depict the association of the PE outcomes with the abundance patterns of these metaboiomics profiles (Figure 8 for early stage PE and Figure 9 for late stage PE). This analysis demonstrated two major clusters reflecting PE (early or late) and normal. The error rate (miss-classification rate) of the unsupervised clustering is 0% for both early state PE and late stage PE, which reinforcing the effectiveness of metaboiomics panels for PE assessment.

Claims

Claims What is claimed is:
1. A method comprising:
measuring the concentration of 1 to 85 metabolite species in a sample of a bio-fluid from a subject to be tested for PE, wherein the 1 to 85 metabolite species is a component of a panel of a plurality of metabolite species, wherein a change in the concentration of the metabolite species is a characteristic that is associated with PE.
2. The method of claim 1 wherein the concentrations of the metabolite species are normalized.
3. The method of claim 1 , further comprising the step of comparing the measured concentration of the 1 to 85 metabolite species to a predetermined value calculated using a model based on concentrations of a plurality of the metabolite species that are components of the panel.
4. The method of claim 1 , wherein the panel for early stage PE identification comprises 1 to 26 metabolite species selected from the group consisting of Argininosuccinic Acid, Aspartate, Methionine, Free Carnitine , C16-Carnitine, C18:1 -Carnitine, C2-Carnitine, C4-Carnitine, C5-Carnitine, C6 DC-Carnitine, Succinylacetone, d18:1 -16:0 Ceramide, d 18:1 -18:0 Ceramide, Cholesterol, Cortisol, d22:5 Fatty Acid, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid, d18:0 Fatty Acid.
5. The method of claim 1 , wherein the panel for late stage PE identification comprises 1 to 19 metabolite species selected from the group consisting of 5-Oxoproline, Aspartate, C14:1 -Carnitine, C2-Carnitine, C6 DC-Carnitine, Succinylacetone, Creatinine, d18:1- 16:0 Ceramide, d18:1-18:0 Ceramide, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid, d18:0 Fatty Acid.
6. The method of claim 1 wherein the panel comprises metabolite species that have been identified by liquid chromatography-mass spectrometry (LC-MS).
7. The method of claim 1 , wherein the bio-fluid is selected from the group consisting of blood, plasma, serum, sweat, saliva, sputum, and urine.
8. The method of claim 1 , wherein the bio-fluid is serum.
9. A panel of metabolite species, the metabolite species are selected from a group consisting of 5-Oxoproline, Alanine, Arginine, Argininosuccinic Acid, Aspartate,
Citrulline, Glutamate, Glycine, Homocitrulline, Leucine, Methionine, Ornithine,
Phenylalanine, Proline, Tyrosine, Valine, Free Carnitine , C10-Carnitine, C10:1- Carnitine, C10:2-Carnitine, C12-Camitine, C14-Camitine, C14:1 -Carnitine, C14:0 OH- Carnitine, C16-Carnitine, C16:1 -Carnitine, C16:0 OH-Camitine, C18-Camitine, C18:1- Carnitine, C18:0 OH-Carnitine, C18:1 OH-Carnitine, C18:2-Camitine, C2-Camitine, C3- Carnitine, C3 DC-Carnitine, C4-Camitine, C4:0 OH-Camitine, C5-Carnitine, C5:1- Carnitine, C5 DC-Carnitine, C5:0 OH-Carnitine, C6-Camitine, C6 DC-Camitine, C8- Carnitine, C8:0 OH-Carnitine, Succinylacetone, Urea, Creatinine,
Dehydroepiandrosterone Sulfate, Triiodothyronine, Thyroxine, d18: 1-16:0 Ceramide, d18:1 -18:1 Ceramide, d18:1 -18:0 Ceramide, d18:1 -20:0 Ceramide, d18:1 -22:1
Ceramide, d18: 1-22:0 Ceramide, d18:1 -24:0 Ceramide, d18:1-24:1 Ceramide, Cholic Acid, Total Deoxycholic Acid, 7-Dehydrocholesterol, Desmosterol, Campesterol, beta- sitosterol, Cholesterol, Lathosterol, Cortisol, 11-Deoxycortisol, 17-Hydoxyprogesterone, Progesterone, d20:4 Fatty Acid, d22:0 Fatty Acid, d22:6 Fatty Acid, d22:5 Fatty Acid, d20:5 Fatty Acid, d20:3 Fatty Acid, d24:0 Fatty Acid, d18:2 Fatty Acid, d18:3 Fatty Acid, d14:0 Fatty Acid, d18:1 Fatty Acid, d16:0 Fatty Acid, d16:1 Fatty Acid, d18:0 Fatty Acid.
10. The panel of claim 9, wherein the panel is provided in a diagnostic cassette.
11. The diagnostic cassette of claim 10, further comprising reagents for the detection of the metabolite species of the panel.
12. A kit for the analysis of a sample of a biofluid of a subject, comprising: a. Aliquots of standards of each compound of a panel of metabolite species; b. An aliquot of an internal standard; and c. An aliquot of a control biofluid.
13. The kit of claim 12, wherein the control biofluid is serum from a control source that is conspecific with the subject.
14. The kit of claim 12, wherein the panel consists of 13C, 15N-Glycine, 13C, 2D4-L- Arginine, 13Cs, 15N-L-Proline, 13C5-Succinylacetone, 13Ce, 15N4-L-Argininosuccinic Acid ,
13Ce-L-Phenylalanine, 13C6-L-Tyrosine, 13C6-Thyroxine, 13C6-Triiodothyronine, 15N2-Urea, 2D2-L-Citrulline, 2D2-L-Ornithine, 2D39-d20:0 Fatty Acid, 2D3-C12-L-Carnitine, 2D3-C14-L- Carnitine, 2D3-C16-L-Camitine, 2D3-C160H-L-Carnitine, 2D3-C18-L-Camitine, 2D3-C2-L- Carnitine, 2D3-C3-L-Camitine, 2D3-C4-L-Carnitine, 2D3-C5DC-L-Camitine, 2D3-C50H-L- Carnitine, 2D3-C8-L-Camitine, 2D3-Creatinine, 2D3-DL-Glutamate, 2D3-L-Aspartate, 2D3- L-Leucine, 2D3-L-Methionine, 2D4-Cholic Acid, 2D4-LrAlanine, 2D7-Cholesterol, 2D7- d18:1-16:0 Ceramide, 2D7-d18:1-18:0 Ceramide, 2D7-d 18: 1-24:0 Ceramide, 2D7- Dehydroepiandrosterone Sulfate, 2De-L-Valine, 2D9-C5-L-Carnitine, 2D9- Chenodeoxycholic Acid, 2Dg-L-Carnitine, 2Dg-Progesterone.
15. The kit of claim 12, further comprising instructions for use.
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