Noninvasive molecular clock for fetal development predicts gestational age and preterm delivery
Inventors
Moufarrej, Mira N. • Ngo, Thuy T. M. • Camunas-Soler, Joan • Melbye, Mads • Quake, Stephen R.
Assignees
Statens Serum Institut SSI • Leland Stanford Junior University • CZ Biohub SF LLC • Biohub
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Publication Number
US-12444478-B2
Publication Date
2025-10-14
Expiration Date
Abstract
The invention is directed to methods of identifying woman is risk for preterm delivery. In some aspects, the methods include quantitating one or more placental or fetal-tissue specific genes in a biological sample from the woman.
Core Innovation
The disclosure describes a noninvasive method for treating a pregnant subject for elevated risk of having preterm delivery by assaying a maternal sample to determine an expression profile of a panel of genes. The panel includes three or more genes, and maternal sample inputs include expression profiles derived from placental and fetal tissue-specific markers. The gestational-age/time-to-delivery concept is implemented by comparing the patient expression profile to gestational-age reference profiles using computational similarity or gestational functions.
The approach further uses computer processing of the expression profile against reference expression levels of the panel of genes or with a trained machine learning model. It determines that the pregnant subject has an elevated risk of preterm delivery based at least in part on the computer processing. Machine learning is also used to establish reference expression thresholds or values, including reference populations related to term versus preterm delivery and time-matched references.
The disclosure provides specific gene panels for gestational-age and preterm-risk determinations. A nine-placental-gene set is described for gestational age or time to delivery, including CGA, CAPN6, CGB, ALPP, CSHL1, PLAC4, PSG7, PAPPA, and LGALS14. For preterm-risk assessment, genes include CLCN3, DAPP1, POLE2, PPBP, LYPLAL1, MAP3K7CL, MOB1B, RAB27B, RGS18, and TBC1D15, including combinations of subpanels.
Claims Coverage
The independent claim is directed to a complete treatment workflow based on an expression profile from a gene panel of three or more specified genes. The inventive features correspond to gene-panel expression profiling, computer processing against reference levels and/or a trained machine learning model, determining elevated preterm-delivery risk, and selecting and administering a therapeutic intervention from a defined group.
Gene-panel expression profiling from a maternal sample
Assaying a maternal sample obtained or derived from the pregnant subject to determine an expression profile of a panel of genes, wherein the panel of genes comprises three or more genes selected from CLCN3, DAPP1, POLE2, PPBP, LYPLAL1, MAP3K7CL, MOB1B, RAB27B, RGS18, and TBC1D15.
Computational comparison with reference levels or trained machine learning
Computer processing the expression profile against reference expression levels of the panel of genes or with a trained machine learning model.
Determination of elevated risk of preterm delivery
Determining, based at least in part on the computer processing, that the pregnant subject has an elevated risk of having the preterm delivery.
Therapeutic intervention selected from a defined set
Administering to the pregnant subject a therapeutic intervention for the elevated risk of having the preterm delivery, wherein the therapeutic intervention is selected from progesterone, an antibiotic, a cervical cerclage, a cervical pessary, a folate supplement, and an omega-3 fatty acid supplement.
Overall, the claim coverage centers on using a specified multi-gene expression panel from a maternal sample, applying computer processing via reference levels and/or a trained machine learning model, determining elevated preterm-delivery risk, and administering one of a defined set of therapeutic interventions.
Stated Advantages
Not explicitly described in patent.
Documented Applications
Not explicitly described in patent.
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