WO2019055661A1 - PRE-ÉCLAMPSIE BIOMARKERS AND ASSOCIATED SYSTEMS AND METHODS - Google Patents

PRE-ÉCLAMPSIE BIOMARKERS AND ASSOCIATED SYSTEMS AND METHODS Download PDF

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Publication number
WO2019055661A1
WO2019055661A1 PCT/US2018/050893 US2018050893W WO2019055661A1 WO 2019055661 A1 WO2019055661 A1 WO 2019055661A1 US 2018050893 W US2018050893 W US 2018050893W WO 2019055661 A1 WO2019055661 A1 WO 2019055661A1
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WIPO (PCT)
Prior art keywords
proteins
preeclampsia
probes
clec4a
decorin
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
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PCT/US2018/050893
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English (en)
French (fr)
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WO2019055661A8 (en
Inventor
Matthew Cooper
Sharat Singh
Karen A. F. COPELAND
Lyndal HESTERBERG
Amin R. MAZLOOM
Mohammad Abbasi
Richard GIULIO DEL MASTRO
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Biora Therapeutics Inc
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Progenity Inc
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Publication date
Priority to CN201880059588.4A priority Critical patent/CN111094988A/zh
Priority to EP18856306.8A priority patent/EP3682250A4/en
Priority to US16/646,552 priority patent/US20200264188A1/en
Priority to MX2020002788A priority patent/MX2020002788A/es
Priority to SG11202002065VA priority patent/SG11202002065VA/en
Priority to JP2020515010A priority patent/JP2020533595A/ja
Priority to CA3075688A priority patent/CA3075688A1/en
Priority to KR1020207009958A priority patent/KR20200109293A/ko
Priority to AU2018331400A priority patent/AU2018331400A1/en
Application filed by Progenity Inc filed Critical Progenity Inc
Priority to US16/141,881 priority patent/US20190079097A1/en
Publication of WO2019055661A1 publication Critical patent/WO2019055661A1/en
Anticipated expiration legal-status Critical
Publication of WO2019055661A8 publication Critical patent/WO2019055661A8/en
Priority to US17/234,574 priority patent/US11333672B2/en
Priority to US17/691,399 priority patent/US20220268781A1/en
Ceased legal-status Critical Current

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    • 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
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Definitions

  • Figure 16 is a diagram showing exemplary functional roles for various markers in the pathophysiology of preeclampsia.
  • Figure 18 lists various antibodies or other antigen-binding agents for use in some embodiments disclosed herein.
  • the sample may be obtained using any method that can provide a sample suitable for the analytical methods described herein.
  • the sample may be obtained by a non-invasive method such as a throat swab, buccal swab, bronchial lavage, urine collection, scraping of the cervix, cervicovaginal sample secretion collection (e.g. with an ophthalmic sponge such as a Weck-Cel sponge), saliva collection, or feces collection.
  • the sample may be obtained by a minimally-invasive method such as a blood draw.
  • the sample may be obtained by venipuncture.
  • obtaining a sample includes obtaining a sample directly or indirectly.
  • the methods, kits, and systems disclosed herein may comprise data pertaining to one or more samples or uses thereof.
  • the data can be representative of an amount or
  • the methods, compositions, and systems described herein may involve the detection of one or more biomarker belonging to a particular functional class of biomarkers with a connection to one or more pathophysiological features of preeclampsia (see FIG. 16, which shows various pathophysiological features or preeclampsia). While
  • FGF21 Fibroblast growth factor 21, which has been demonstrated to be expressed in placental
  • preeclampsia of a pregnant patient can be detected from no more than six biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from Table 2, Table 3, or Table 5, and any combination thereof.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 2 or Table 5, and any combination thereof ("Group 4").
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 2 or Table 5, and any combination thereof.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PIGF, CD274, CDCPl, FGF- 21, TGFa, UP A, CLEC4A, CLEC4C, ZBTB16, APLPl, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PPY, or SYNDl, and any combination thereof.
  • biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, PIGF, CD274, CDCPl, FGF- 21, TGFa, UP A, CLEC4A, CLEC4C, ZBTB16, APLPl, DPP7, GRAP2, ITGB7, PAG1, TFF2, AMN, CAPG, CLEC1A5, FES, KIM1, PGF, ERBB4, GPNMB, PP
  • preeclampsia of a pregnant patient can be detected from no more than seven biomarkers selected from CCL2, CD134, DCN, HGF, NOS3, P1GF, CD274, CDCP1, FGF-21, TGFa, UP A, CLEC4A, CLEC4C, ZBTB16, APLPl, DPP7, GRAP2, ITGB7, PAGl, TFF2, AMN, CAPG,
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 5.
  • preeclampsia of a pregnant patient can be detected from no more than five biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from no more than four biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from no more than three biomarkers selected from Table 5. In some cases, preeclampsia of a pregnant patient can be detected from 1, 2, 3, 4, 5, 6, 7, 8, or 9 biomarkers selected from Table 5.
  • the methods provided herein can comprise identifying or ruling out a condition (e.g. preeclampsia) from one or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from one or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from two or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from three or more biomarkers selected from Table 2.
  • preeclampsia of a pregnant patient can be detected from four or more biomarkers selected from Table 2.
  • the methods provided herein can comprise detecting a condition (such as preeclampsia) from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 biomarkers selected from (a), (b), or (c).
  • a condition such as preeclampsia
  • kits and compositions for monitoring a condition of a pregnant patient are methods, systems, kits and compositions for monitoring a condition of a pregnant patient.
  • the monitoring is conducted by serial testing, such as serial non-invasive tests, serial minimally-invasive tests (e.g., blood draws), or some combination thereof.
  • the monitoring is conducted by administering serial noninvasive tests or serial minimally-invasive tests (e.g., blood draws).
  • the methods, kits and systems disclosed herein can improve upon the AUP of current methods of monitoring or predicting a status or outcome of a pregnancy (e.g. preeclampsia) or identifying or ruling out a classification of a sample.
  • the AUP of the methods, kits, and systems disclosed herein can be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.1%, 99.2%, 99.3 %, 99.4%, 99.5%, 99.6%, 99.7%, 99.8%, 99.9%, or any range in between these values.
  • disclosure provides for a method, kit, system, or test that has a sensitivity of at least 79% and a specificity of at least 94%. In some embodiments, a method, kit, system, or test has a sensitivity of at least 82% and a specificity of at least 80%. In some embodiments, a method, kit, system of test has a sensitivity of at least 90% and a specificity of at least 80%.
  • a predefined relationship between the index and the threshold value is indicative, with a negative predictive value of at least 80%, at least 85%, at least 90%, at least 91%, at least 92%, at least 93%, at least 94%, at least 95%, at least 96%, at least 97%, at least 97.5%, at least 98%, at least 98.5%, or at least 99%, of the subject not having or developing preeclampsia when used on an unbiased population of pregnant women that have both high blood pressure and proteinuria.
  • a predefined relationship between the index and the threshold value is indicative, with a negative predictive value of at least 90%, of the subject not having or developing preeclampsia.
  • the disclosure provides for a computer-implemented method of assessing the likelihood a pregnant subject has or will develop preeclampsia within a specified period of time, comprising: (a) receiving, at a computer, expression level data derived from a plasma or serum sample from the pregnant subject; (b) applying, by the computer, a classifier algorithm to the expression level data derived from the plasma or serum sample from the pregnant subject using a classification rule or a class probability equation; and (c) using, by the computer, the classification rule or class probability equation to output a classification for the sample, wherein the classification classifies the sample as a having a probability of having preeclampsia with a negative predictive value of greater than 80 percent, wherein the pregnant subject has hypertension or proteinuria.
  • the expression level data comprises levels of proteins selected from the group consisting of Tables 2, 3, 4, and 5.
  • the classifier algorithm is logistic regression.
  • the classifier algorithm is a decision tree, random forest, Bayesian network, support vector machine, neural network, or logistic regression algorithm.
  • PI GF/ sFLT 1 /KXM 1 / CLEC4 A plus all combinations of 5 th markers (CD274 or TFF2 or ADAM12 or DCN or END or HGF or FGF21 or PAPP-A1 or FN or SYND1 or UPA or PAPP-A) were generated and their performance characteristics were compared (Table 11).

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