Endometrial Gene Panel for Early Ectopic Pregnancy Detection
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Solution Overview
Problem
Current methods for diagnosing ectopic pregnancy are unreliable, particularly in early stages, leading to delayed diagnosis and increased morbidity and mortality due to the inability to accurately distinguish between intrauterine and ectopic pregnancies using existing biomarkers like hCG and ultrasound.
Innovation Solution
A method involving the detection of differential expression of cilia-associated genes such as ARMC3, C20orf85, CFAP47, CFAP126, DNAH12, LRRC46, LPAR3, RSPH4A, STOML3, TPPP3, WDR49, and ZBBX in endometrial biopsies to classify pregnancy location, providing a more accurate and efficient diagnostic tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods (hCG and ultrasound) are used for diagnosing ectopic pregnancy, then the diagnostic process is simple and widely available, but the accuracy is insufficient particularly in early stages leading to delayed diagnosis
Solution Approach 1:
The invention changes the diagnostic parameter from traditional hCG levels and ultrasound findings to a panel of specific gene expression markers (including cilia-associated genes and other differentially expressed genes). This parameter change enables accurate detection of ectopic pregnancy in early stages by measuring molecular signatures in endometrial tissue that are distinct between ectopic and intrauterine pregnancies, thereby resolving the contradiction between diagnostic accuracy and method complexity
Solution Approach 2:
The invention replaces the mechanical/physical diagnostic methods (ultrasound imaging and serum hCG measurement) with a molecular biology-based detection system. By substituting the mechanical ultrasound approach with gene expression analysis of endometrial biopsies, the system achieves superior diagnostic accuracy particularly for early ectopic pregnancy detection, while the complexity is managed through standardized molecular assays
2Reliability
If serial assessments are performed to confirm pregnancy location, then diagnostic confidence increases, but time is lost and morbidity increases
Solution Approach 1:
The invention performs preliminary molecular characterization of the endometrium early in pregnancy when tissue is available, detecting ectopic-specific gene expression patterns before clinical symptoms worsen. By conducting this molecular assessment upfront rather than waiting for serial follow-up, the system achieves both high diagnostic confidence and rapid diagnosis, resolving the time-reliability contradiction
Solution Approach 2:
The invention provides immediate feedback through quantitative gene expression measurements that directly indicate ectopic pregnancy status. The differential expression patterns of specific gene panels provide clear diagnostic feedback in a single assessment, eliminating the need for serial follow-up visits and reducing both time loss and diagnostic uncertainty
3Measurement precision
If traditional biomarkers (hCG) are used, then the test is simple to perform, but the ability to distinguish between intrauterine and ectopic pregnancy is insufficient
Solution Approach 1:
The invention segments the diagnostic approach by analyzing multiple individual gene expressions rather than relying on a single biomarker. The gene panel is divided into functional categories (cilia-associated genes, differentiation markers, proliferation markers) that collectively provide superior discrimination between ectopic and intrauterine pregnancy, while the segmented analysis is integrated into a unified diagnostic interpretation
Data Source
AI summary
The disclosure generally provides methods for early detection of ectopic pregnancy, and utilization of endometrial molecular signatures. This disclosure more specifically relates to gene sets that, in aggregate, reliably classify the location of a nonviable pregnancy as intrauterine or ectopic and can identify a patient for therapeutic intervention. The disclosure also illustrates cilia-associated genes as a classifier for ectopic pregnancy with high accuracy for delineating ectopic from intrauterine pregnancies.