Methods and systems for determining a pregnancy-related state of a subject
The present disclosure provides methods and systems directed to 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 said 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.
1. A method for treating a pregnant subject for a pregnancy-related hypertensive disorder, comprising:
(a) obtaining a cell-free blood sample from said pregnant subject;
(b) assaying nucleic acid molecules obtained or derived from said cell-free blood sample to determine at least one ribonucleic acid (RNA) level of at least one pregnancy-associated genomic locus, wherein said at least one pregnancy-associated genomic locus is differentially expressed in a first population of subjects with said pregnancy-related hypertensive disorder as compared to a second population of subjects without said pregnancy-related hypertensive disorder, wherein said assaying comprises selectively enriching said nucleic acid molecules for said at least one pregnancy-associated genomic locus, wherein said selectively enriching comprises use of nucleic acids having sequence complementarity with a nucleic acid sequence of said at least one pregnancy-associated genomic locus;
(c) computer processing said at least one RNA level of said at least one pregnancy-associated genomic locus determined in (b) (i) against at least one reference RNA level of said at least one pregnancy-associated genomic locus or (ii) using a trained machine learning algorithm;
(d) determining, based at least in part on said computer processing in (c), that said pregnant subject has or is at an elevated risk of having said pregnancy-related hypertensive disorder; and
(e) providing a therapeutic intervention to said pregnant subject to treat said pregnancy-related hypertensive disorder, based on said determining in (d).
2. The method of claim 1 , further comprising reverse transcribing ribonucleic acid (RNA) molecules derived 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 genomic locus.
3. The method of claim 1 , wherein said assaying comprises use of nucleic acid sequencing.
4. The method of claim 1 , wherein said pregnancy-related hypertensive disorder is selected from the group consisting of preeclampsia and eclampsia.
5. The method of claim 4 , wherein said pregnancy-related hypertensive disorder comprises said preeclampsia.
6. The method of claim 5 , wherein said at least one pregnancy-associated genomic locus is differentially expressed by subjects with preeclampsia as compared to subjects without preeclampsia, wherein said at least one pregnancy-associated genomic locus comprises a member selected from the group consisting of PAPPA2, FABP1, GPSM2, CRH, and HP.
7. The method of claim 6 , wherein said at least one pregnancy-associated genomic locus comprises three or more members selected from the group consisting of PAPPA2, FABP1, GPSM2, CRH, and HP.
8. The method of claim 4 , wherein said pregnancy-related hypertensive disorder comprises said eclampsia.
9. The method of claim 1 , wherein said cell-free blood sample comprises a plasma sample.
10. The method of claim 1 , wherein said pregnant subject is asymptomatic for said pregnancy-related hypertensive disorder.
11. The method of claim 1 , wherein said computer processing in (c) comprises using said trained machine learning algorithm.
12. The method of claim 11 , 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.
13. The method of claim 12 , wherein said trained machine learning algorithm comprises said linear regression.
14. The method of claim 12 , wherein said trained machine learning algorithm comprises said ANOVA model.
15. The method of claim 11 , wherein said trained machine learning algorithm is trained using a training dataset comprising: a first set of pregnant subjects with said pregnancy-related hypertensive disorder, and a second set of pregnant subjects without said pregnancy-related hypertensive disorder.
16. The method of claim 1 , further comprising monitoring said pregnant subject for said pregnancy-related hypertensive disorder at least in part by assessing said pregnancy-related hypertensive disorder 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 said pregnancy-related hypertensive disorder at each of said plurality of time points.
17. The method of claim 16 , wherein a difference in said assessing of said pregnancy-related hypertensive disorder 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-related hypertensive disorder of said pregnant subject, (ii) a prognosis of said pregnancy-related hypertensive disorder of said pregnant subject, and (iii) an efficacy or non-efficacy of a therapeutic intervention for treating said pregnancy-related hypertensive disorder of said pregnant subject.
18. The method of claim 1 , wherein providing said therapeutic intervention comprises administering a drug.
19. The method of claim 18 , 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.
20. The method of claim 19 , wherein said drug is said corticosteroid.
21. The method of claim 19 , wherein said drug is said tocolytic drug.
22. The method of claim 19 , wherein said drug is said magnesium sulfate.
23. The method of claim 1 , wherein providing said therapeutic intervention comprises performing a delivery of a fetus of said pregnant subject.
24. The method of claim 1 , wherein said assaying comprises use of array hybridization.
25. The method of claim 1 , wherein said assaying comprises use of polymerase chain reaction (PCR).
26. The method of claim 1 , wherein said selectively enriching further comprises amplifying at least a portion of said nucleic acid molecules.
27. The method of claim 1 , further comprising determining that said pregnant subject has said pregnancy-related hypertensive disorder with a clinical sensitivity or clinical specificity of at least about 70%.
28. The method of claim 1 , wherein said nucleic acids are primers or probes having sequence complementarity with said nucleic acid sequence of said at least one pregnancy-associated genomic locus.
29. The method of claim 28 , wherein said nucleic acids are said probes having sequence complementarity with said nucleic acid sequence of said at least one pregnancy-associated genomic locus.