# US 11,441,183 B2 · Methods and systems for determining a pregnancy-related state of a subject

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- Number: US 11,441,183 B2
- Document ID: US11441183B2
- Status: Granted
- Kind: B2
- Application number: 17/522,425
- Filed: 2021-11-09
- Published: 2022-03-10
- Granted: 2022-09-13
- Earliest claimed: 2019-02-14
- Assignees: Mirvie, Inc.
- Applicants: Mirvie, Inc.
- Inventors: Maneesh Jain; Eugeni Namsaraev; Morten Rasmussen; Joan Camunas Soler; Farooq Siddiqui; Mitsu Reddy
- Web page: https://patentgrep.com/patent/US11441183B2

## CPC

- C12Q 1/6876: Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
- C12Q 1/6809: Methods for determination or identification of nucleic acids involving differential detection
- C12Q 1/6874: involving nucleic acid arrays, e.g. sequencing by hybridisation
- C12Q 1/6883: for diseases caused by alterations of genetic material
- G16B 20/40: Population genetics; Linkage disequilibrium
- G16B 25/10: Gene or protein expression profiling; Expression-ratio estimation or normalisation
- G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B 40/20: Supervised data analysis
- G16B 50/00: ICT programming tools or database systems specially adapted for bioinformatics
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof
- G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/30: for calculating health indices; for individual health risk assessment
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- C12Q 2600/112: Disease subtyping, staging or classification
- C12Q 2600/158: Expression markers
- C12Q: MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS (immunoassay G01N33/53); COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA

## Abstract

Methods and systems may perform cell-free identification and/or monitoring of pregnancy-related states. A method for identifying or monitoring a presence or susceptibility of a pregnancy-related state of a subject may comprise assaying a cell-free biological sample derived from the subject to detect a set of biomarkers, and analyzing the set of biomarkers with a trained algorithm to determine the presence or susceptibility of the pregnancy-related state.

## Claims

### Claim 1

A method for treating a pregnant subject for a pregnancy complication, comprising:
(a) assaying nucleic acid molecules obtained from a cell-free blood sample of said pregnant subject to determine at least one ribonucleic acid (RNA) level of at least one pregnancy-associated gene, wherein said at least one pregnancy-associated gene is differentially expressed in a first population of subjects with said pregnancy complication as compared to a second population of subjects without said pregnancy complication, wherein said assaying comprises selectively enriching said nucleic acid molecules for said at least one pregnancy-associated gene with nucleic acid primers or probes having sequence complementarity with a nucleic acid sequence of said at least one pregnancy-associated gene;
(b) computer processing said at least one RNA level of said at least one pregnancy-associated gene determined in (a) (i) against at least one reference RNA level of said at least one pregnancy-associated gene or (ii) with a trained machine learning algorithm;
(c) determining, based at least in part on said computer processing in (b), that said pregnant subject has or is at an elevated risk of having said pregnancy complication; and
(d) providing a therapeutic intervention to said pregnant subject to treat said pregnancy complication, based at least in part on said determining in (c).

### Claim 2 (depends on 1)

The method of claim 1, further comprising reverse transcribing ribonucleic acid (RNA) molecules from said cell-free blood sample to produce complementary deoxyribonucleic acid (cDNA) molecules; and assaying said cDNA molecules or derivatives thereof to determine said at least one RNA level of said at least one pregnancy-associated gene.

### Claim 3 (depends on 1)

The method of claim 1, wherein said assaying comprises nucleic acid sequencing.

### Claim 4 (depends on 1)

The method of claim 1, wherein said pregnancy complication is selected from the group consisting of pre-term birth, preeclampsia, eclampsia, gestational diabetes, a congenital disorder of a fetus, spontaneous abortion, stillbirth, post-partum depression, hemorrhage, hyperemesis gravidarum, premature rupture of membrane, premature rupture of membrane in pre-term birth, placenta previa, placenta accreta, fetal growth restriction, and macrosomia.

### Claim 5 (depends on 1)

The method of claim 1, wherein said cell-free blood sample comprises a plasma sample.

### Claim 6 (depends on 1)

The method of claim 1, wherein said pregnant subject is asymptomatic for said pregnancy complication.

### Claim 7 (depends on 1)

The method of claim 1, wherein said computer processing in (b) comprises said trained machine learning algorithm.

### Claim 8 (depends on 7)

The method of claim 7, wherein said trained machine learning algorithm is selected from the group consisting of a linear regression, a logistic regression, an analysis of variance (ANOVA) model, a deep learning algorithm, a support vector machine (SVM), a neural network, a Random Forest, and a combination thereof.

### Claim 9 (depends on 8)

The method of claim 8, wherein said trained machine learning algorithm comprises said linear regression.

### Claim 10 (depends on 8)

The method of claim 8, wherein said trained machine learning algorithm comprises said ANOVA model.

### Claim 11 (depends on 7)

The method of claim 7, wherein said trained machine learning algorithm is trained with a training dataset comprising: a first set of pregnant subjects with said pregnancy complication, and a second set of pregnant subjects without said pregnancy complication.

### Claim 12 (depends on 1)

The method of claim 1, further comprising monitoring said pregnant subject for said pregnancy complication at least in part by assessing said pregnancy complication of said pregnant subject at a plurality of time points, wherein said assessing is based at least in part on determining whether said pregnant subject has or is at an elevated risk of having said pregnancy complication at each of said plurality of time points.

### Claim 13 (depends on 12)

The method of claim 12, wherein a difference in said assessing of said pregnancy complication of said pregnant subject among said plurality of time points is indicative of one or more clinical indications selected from the group consisting of: (i) a diagnosis of said pregnancy complication of said pregnant subject, (ii) a prognosis of said pregnancy complication of said pregnant subject, and (iii) an efficacy or non-efficacy of a therapeutic intervention for treating said pregnancy complication of said pregnant subject.

### Claim 14 (depends on 1)

The method of claim 1, wherein providing said therapeutic intervention comprises administering a drug.

### Claim 15 (depends on 14)

The method of claim 14, wherein said drug is selected from the group consisting of a corticosteroid, an antibiotic, a tocolytic drug, a calcium channel blocker, a cyclo-oxygenase inhibitor, an oxytocin antagonist, a betamimetic drug, magnesium sulfate, magnesium chloride, and magnesium oxide.

### Claim 16 (depends on 1)

The method of claim 1, wherein said at least one pregnancy-associated gene comprises a member selected from the group consisting of pappalysin 2 (PAPPA2), fatty acid binding protein 1 (FABP1), G protein signaling modulator 2 (GPSM2), corticotropin releasing hormone (CRH), and haptoglobin (HP).

### Claim 17 (depends on 16)

The method of claim 16, wherein said at least one pregnancy-associated gene comprises three or more members selected from the group consisting of pappalysin 2 (PAPPA2), fatty acid binding protein 1 (FABP1), G protein signaling modulator 2 (GPSM2), corticotropin releasing hormone (CRH), and haptoglobin (HP).

### Claim 18 (depends on 1)

The method of claim 1, wherein said assaying comprises array hybridization.

### Claim 19 (depends on 1)

The method of claim 1, wherein said assaying comprises polymerase chain reaction (PCR).

### Claim 20 (depends on 1)

The method of claim 1, wherein said selectively enriching further comprises amplifying at least a portion of said nucleic acid molecules.

### Claim 21 (depends on 1)

The method of claim 1, further comprising determining that said pregnant subject has said pregnancy complication with a clinical sensitivity or clinical specificity of at least about 70%.

### Claim 22 (depends on 1)

The method of claim 1, wherein said selectively enriching comprises nucleic acid amplification with said nucleic acid primers having said sequence complementarity with said nucleic acid sequence of said at least one pregnancy-associated gene.

### Claim 23 (depends on 1)

The method of claim 1, wherein said selectively enriching comprises enrichment with said nucleic acid probes having said sequence complementarity with said nucleic acid sequence of said at least one pregnancy-associated gene.

### Claim 24 (depends on 1)

The method of claim 1, wherein said pregnancy complication is pre-term birth.

### Claim 25 (depends on 1)

The method of claim 1, wherein said pregnancy complication is gestational diabetes.

### Claim 26 (depends on 1)

The method of claim 1, wherein said pregnancy complication is spontaneous abortion.

### Claim 27 (depends on 1)

The method of claim 1, wherein said pregnancy complication is placenta previa or placenta accreta.

### Claim 28 (depends on 1)

The method of claim 1, wherein said pregnancy complication is fetal growth restriction.