Endometriosis is a complex disease that on average, involves a ten-year gap between symptom onset and diagnosis. Moreover, due to the current surgical nature of endometriosis diagnosis (laparoscopy), research studies involving confirmed cases are both difficult to obtain and costly to recruit for. Laparoscopic surgery (recovery times 2-6 weeks) followed by histological confirmation is considered the gold standard for diagnosis. However, even with surgical intervention, 50% of patients have recurrence, underlining the fact that endometriosis shows periodic states of activation, regardless of surgical or therapeutic intervention. Consequently, a non-invasive, reliable biomarker test for endometriosis is a significant unmet medical need. A non-invasive diagnostic for endometriosis would motivate earlier detection of disease and could fundamentally change a patients prognosis by preventing disease progression. Menstrual fluid is a rich source of reproductive tissue that can be utilized for disease diagnosis. Next Gen Jane are developing a menstrual effluent competent collection tool, the Smart Tampon System (STS) which addresses issues of non-invasive sample accessibility and ease of sample transport to a laboratory. Toward development of a reliable biomarker for endometriosis, our preliminary data with the STS device indicates we can detect multiple RNA species in menstrual effluent, some of which have been previously discovered in other related studies. We believe that the STS provides unparalleled ease of access to menstrual effluent and markedly improves sample handling efficiencies. These factors alongside the demonstrated utility of the STS as a sample for RNASeq (amongst other studies) differentiates this proposal from other non-invasive approaches to detection of endometriosis. Our goal is to develop a non-invasive yes/no diagnostic test for endometriosis by examining the genomic signatures of endometrial tissue shed into a tampon during menstruation. In order to further identify and evaluate the performance of genomic markers in menstrual fluid, we will enroll 72 patients with a negative or positive surgical confirmation of endometriosis in order to: Firstly, confirm previously identified biomarkers from pilot data that evaluates diagnosis of disease. Secondly, we will use statistical classification of RNASeq data to examine ~300 pre- and post- laparoscopy matched patient samples to confirm sensitivity and specificity of miRNA and mRNA biomarkers in concert with patient survey data.
Public Health Relevance Statement: A non-invasive, reliable biomarker test for endometriosis is a significant unmet medical need. It would fundamentally change how endometriosis is diagnosed and treated, allowing for earlier interventions and improved prognosis. Toward addressing the issues of sample accessibility and reliable biomarker detection we are developing a genomics collection tool to examine RNA signatures from endometrial tissue shed into a tampon during menstruation. This proposal will identify and evaluate genomic biomarkers for the development of a non-invasive, yes/no diagnostic test for endometriosis.
Project Terms: Address; Affect; Algorithmic Analysis; Algorithms; base; Biological; Biological Markers; biomarker development; biomarker discovery; Blinded; Blood; candidate marker; case control; Classification; classification algorithm; Clinical; Collection; Complex; Confidence Intervals; cost; Data; Databases; Detection; Development; Devices; Diagnosis; diagnosis standard; Diagnostic; Diagnostic tests; Disease; disease classification; disease diagnosis; Disease Progression; Early Diagnosis; Early Intervention; Endometrial; endometriosis; Enrollment; Evaluation; genomic biomarker; genomic signature; Genomics; Goals; Gold; Histologic; Home environment; Hormones; improved; Laboratories; Laparoscopic Surgical Procedures; Laparoscopy; Logistic Regressions; Medical; meetings; Menstrual fluid; Menstruation; Messenger RNA; Methods; MicroRNAs; Modeling; Nature; noninvasive diagnosis; novel; novel marker; Operative Surgical Procedures; outcome forecast; Patients; Performance; Periodicity; Phase; Population; predictive marker; prevent; Procedures; Recovery; recruit; Recurrence; reproductive; research study; RNA; sample collection; Sampling; Sensitivity and Specificity; Signal Transduction; Source; Statistical Data Interpretation; Surveys; Symptoms; System; Tampons; Testing; Therapeutic Intervention; Time; Tissues; tool; transcriptome sequencing; Validation
