[go: up one dir, main page]

WO2018174876A1 - Méthodes et compositions d'évaluation de la pré-éclampsie à l'aide de métabolites - Google Patents

Méthodes et compositions d'évaluation de la pré-éclampsie à l'aide de métabolites Download PDF

Info

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
Authority
WO
WIPO (PCT)
Prior art keywords
carnitine
fatty acid
ceramide
camitine
panel
Prior art date
Application number
PCT/US2017/023680
Other languages
English (en)
Inventor
Bruce Xuefeng Ling
Limin Chen
Shiying Hao
Original Assignee
Mprobe Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mprobe Inc. filed Critical Mprobe Inc.
Priority to PCT/US2017/023680 priority Critical patent/WO2018174876A1/fr
Publication of WO2018174876A1 publication Critical patent/WO2018174876A1/fr

Links

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.

Landscapes

  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Biomedical Technology (AREA)
  • Physics & Mathematics (AREA)
  • Urology & Nephrology (AREA)
  • Immunology (AREA)
  • Hematology (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Pathology (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biotechnology (AREA)
  • Microbiology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Cell Biology (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biophysics (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

L'invention concerne des marqueurs de pré-éclampsie, des panels de marqueurs de pré-éclampsie, et des méthodes d'obtention d'une représentation du niveau des marqueurs de pré-éclampsie pour un échantillon, sur la base d'un profilage métabolique de petites molécules. Ces compositions et méthodes sont utilisables dans un certain nombre d'applications dont, par exemple, le diagnostic de la pré-éclampsie, le pronostic de la pré-éclampsie, la surveillance d'un sujet atteint de pré-éclampsie, et la détermination d'un traitement de la pré-éclampsie. L'invention concerne en outre des systèmes, des dispositifs et des trousses associés, utilisables dans la mise en pratique desdites méthodes.
PCT/US2017/023680 2017-03-22 2017-03-22 Méthodes et compositions d'évaluation de la pré-éclampsie à l'aide de métabolites WO2018174876A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/US2017/023680 WO2018174876A1 (fr) 2017-03-22 2017-03-22 Méthodes et compositions d'évaluation de la pré-éclampsie à l'aide de métabolites

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2017/023680 WO2018174876A1 (fr) 2017-03-22 2017-03-22 Méthodes et compositions d'évaluation de la pré-éclampsie à l'aide de métabolites

Publications (1)

Publication Number Publication Date
WO2018174876A1 true WO2018174876A1 (fr) 2018-09-27

Family

ID=63586483

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2017/023680 WO2018174876A1 (fr) 2017-03-22 2017-03-22 Méthodes et compositions d'évaluation de la pré-éclampsie à l'aide de métabolites

Country Status (1)

Country Link
WO (1) WO2018174876A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020117184A1 (fr) * 2018-12-02 2020-06-11 Binhai Industrial Technology Research Institute Of Zhejiang University Méthodes et compositions permettant une évaluation de la pré-éclampsie par la protéomique
CN114636774A (zh) * 2022-04-22 2022-06-17 苏州市疾病预防控制中心 一种预测青少年高血压患病风险的生物代谢标志物组合物
CN114942292A (zh) * 2022-07-19 2022-08-26 中国医学科学院阜外医院 一种基于液相色谱串联质谱技术检测血液中植物固醇类物质含量的方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070003965A1 (en) * 2005-06-30 2007-01-04 Biocrates Life Sciences Gmbh Device for quantitative analysis of a drug or metabolite profile
US20100113286A1 (en) * 2007-05-05 2010-05-06 Gilles Andre Lajoie Methods for detection of preeclampsia
US20130073212A1 (en) * 2009-12-21 2013-03-21 University College Cork, National University Of Ireland, Cork Detection of risk of pre-eclampsia
US20150056605A1 (en) * 2013-08-21 2015-02-26 Purdue Research Foundation Identification of blood based metabolite biomarkers of pancreatic cancer
US20150087553A1 (en) * 2012-04-13 2015-03-26 Bioscreening and Diagnostics, LLC Early trimester screening for early- and late-onset preeclampsia

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070003965A1 (en) * 2005-06-30 2007-01-04 Biocrates Life Sciences Gmbh Device for quantitative analysis of a drug or metabolite profile
US20100113286A1 (en) * 2007-05-05 2010-05-06 Gilles Andre Lajoie Methods for detection of preeclampsia
US20130073212A1 (en) * 2009-12-21 2013-03-21 University College Cork, National University Of Ireland, Cork Detection of risk of pre-eclampsia
US20150087553A1 (en) * 2012-04-13 2015-03-26 Bioscreening and Diagnostics, LLC Early trimester screening for early- and late-onset preeclampsia
US20150056605A1 (en) * 2013-08-21 2015-02-26 Purdue Research Foundation Identification of blood based metabolite biomarkers of pancreatic cancer

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
CHEN ET AL.: "Biomarker identification and pathway analysis of preeclampsia based on serum metabolomics", BIOCHEM BIOPHYS RES COMMUN, vol. 485, 7 February 2017 (2017-02-07), pages 119 - 125, XP055555918 *

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020117184A1 (fr) * 2018-12-02 2020-06-11 Binhai Industrial Technology Research Institute Of Zhejiang University Méthodes et compositions permettant une évaluation de la pré-éclampsie par la protéomique
CN114636774A (zh) * 2022-04-22 2022-06-17 苏州市疾病预防控制中心 一种预测青少年高血压患病风险的生物代谢标志物组合物
CN114636774B (zh) * 2022-04-22 2024-05-17 苏州市疾病预防控制中心 一种预测青少年高血压患病风险的生物代谢标志物组合物
CN114942292A (zh) * 2022-07-19 2022-08-26 中国医学科学院阜外医院 一种基于液相色谱串联质谱技术检测血液中植物固醇类物质含量的方法
CN114942292B (zh) * 2022-07-19 2023-09-12 中国医学科学院阜外医院 一种基于液相色谱串联质谱技术检测血液中植物固醇类物质含量的方法

Similar Documents

Publication Publication Date Title
Dayon et al. Proteomics of human biological fluids for biomarker discoveries: technical advances and recent applications
Menon et al. Amniotic fluid metabolomic analysis in spontaneous preterm birth
CN113396332B (zh) 评价妊娠进展和早产流产以进行临床干预的方法及其应用
Tomás et al. A general method to derive robust organ-specific gene expression-based differentiation indices: application to thyroid cancer diagnostic
CN105229471B (zh) 用于基于生物化学标记分析确定先兆子痫风险的系统和方法
US20130073212A1 (en) Detection of risk of pre-eclampsia
CN105209909A (zh) 与肾功能相关的生物标记及其使用方法
US20150087553A1 (en) Early trimester screening for early- and late-onset preeclampsia
CN103229052A (zh) 用于前列腺癌的生物标记及其使用方法
CN111989090A (zh) 循环微粒的分层自发性早产风险的用途
SA516380503B1 (ar) مركبات سيراميد واستخدامها في تشخيص cvd
WO2018136085A1 (fr) Procédés et compositions permettant de fournir une évaluation d'un cancer de l'ovaire à un stade précoce avec des métabolites
Bahado-Singh et al. Metabolomic analysis for first-trimester trisomy 18 detection
WO2018174876A1 (fr) Méthodes et compositions d'évaluation de la pré-éclampsie à l'aide de métabolites
US20230298758A1 (en) Systems and Methods for Evaluating Gestational Progress and Applications Thereof
EP3158341A2 (fr) Marqueurs biochimiques pour une utilisation dans la détermination du risque de diabète
US20110136241A1 (en) Type ii diabetes molecular bioprofile and method and system of using the same
US20160018413A1 (en) Methods of Prognosing Preeclampsia
US20150310169A1 (en) Metabolic profiling in tissue and serum is indicative of tumor differentiation in prostate cancer
US20240393337A1 (en) Lung Cancer Prediction and Uses Thereof
JP2025526257A (ja) 認知症リスクを評価する方法
US20150080263A1 (en) Prenatal screening for down syndrome and trisomy 18
WO2023278502A1 (fr) Détermination de la santé rénale et utilisations associées
WO2020117184A1 (fr) Méthodes et compositions permettant une évaluation de la pré-éclampsie par la protéomique
TWI858977B (zh) 生物標記物組、為個體提供老化標記物水平表示的方法、用於提供對個體的生物年齡預測的方法及套組

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17901810

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17901810

Country of ref document: EP

Kind code of ref document: A1