Endometrial Gene Panel for Implantation Failure Detection
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Solution Overview
Problem
Current methods for diagnosing endometrial infertility, particularly recurrent implantation failure (RIF), are unreliable due to the masking of endometrial pathology by menstrual cycle progression variations, leading to misclassification and ineffective treatment approaches.
Innovation Solution
A method involving the measurement of specific gene expression profiles in endometrial samples, using a panel of genes and fragments, to identify pathological states independent of menstrual cycle effects, thereby accurately predicting the risk of implantation failure and distinguishing between healthy and pathological endometria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transcriptomics is used to assess endometrial receptivity, then measurement precision is improved, but menstrual cycle progression variations mask endometrial pathology leading to misclassification
Solution Approach 1:
The patent extracts and removes the menstrual cycle progression effect from the transcriptomic data through computational correction methods. This allows the underlying endometrial pathology signal to be isolated and detected without the confounding variation introduced by normal cycle progression, thereby resolving the masking problem while preserving measurement precision.
Solution Approach 2:
The patent applies parameter changes by transforming the transcriptomic data through mathematical models that adjust for menstrual cycle phase variations. This computational parameter adjustment separates the pathology-related gene expression changes from those naturally occurring due to cycle progression, enabling reliable pathology detection despite the dynamic nature of the endometrium.
2Measurement precision
If histological methods are used for endometrial dating, then measurement precision is improved, but subjectivity reduces reproducibility
Solution Approach 1:
The patent replaces the manual, subjective histological assessment process with an automated computational analysis system. Transcriptomic data are processed through algorithmic models that objectively identify endometrial phase and pathology without human interpretation, thereby eliminating subjectivity while maintaining or improving dating accuracy through consistent, reproducible computational methods.
3Ease of operation
If clinical definitions of RIF are used, then diagnosis is simplified, but patient misclassification occurs due to heterogeneous etiology
Solution Approach 1:
The patent applies local quality by moving from a uniform clinical classification approach to a personalized, molecular-level assessment. Each patient's endometrial transcriptome is analyzed individually to identify specific pathology patterns and etiologies, allowing tailored diagnosis and treatment strategies that account for the heterogeneous nature of implantation failure rather than applying a one-size-fits-all clinical definition.
Data Source
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AI summary
The present invention is based, in part, on the definition of a plurality of panels of genes and/or fragments thereof (target sequences within the genes) as defined in the claims, which allow the accurate classifying of an endometrium being at risk of having IF, including RIF. The combinations of genes and/or fragments thereof used according to the present invention result in a surprising improvement in the ability to classify an endometrium being in a pathological state and therefore being at risk of having an IF irrespective of progression of endometrial dating. The invention discloses an in vitro method for prognosing the risk of implantation failure of an embryo in a subject, wherein said implantation failure is due to molecular disruption of endometrium, (i.e., pathological endometrium) thereby excluding implantation failure due to menstrual cycle progression variations in the timing of the window of implantation (WOI) . This has been achieved by defining specific gene panels from an endometrial pathology gene panel where gene expression data corresponding to variations in the timing of the WOI were removed.