Ignite Creation Date:
2025-12-24 @ 7:35 PM
Ignite Modification Date:
2026-01-01 @ 2:11 AM
Study NCT ID:
NCT06907303
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2025-04-02
First Post:
2025-03-25
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
RNA Assays for Endometriosis Detection and Diagnosis
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D004715', 'term': 'Endometriosis'}, {'id': 'D004194', 'term': 'Disease'}], 'ancestors': [{'id': 'D005831', 'term': 'Genital Diseases, Female'}, {'id': 'D052776', 'term': 'Female Urogenital Diseases'}, {'id': 'D005261', 'term': 'Female Urogenital Diseases and Pregnancy Complications'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D000091662', 'term': 'Genital Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Tissue, blood, saliva'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 400}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ENROLLING_BY_INVITATION', 'startDateStruct': {'date': '2024-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-03', 'completionDateStruct': {'date': '2026-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-03-25', 'studyFirstSubmitDate': '2025-03-25', 'studyFirstSubmitQcDate': '2025-03-25', 'lastUpdatePostDateStruct': {'date': '2025-04-02', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-04-02', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-08-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Develop a gene signature that detects endometriosis', 'timeFrame': '12-18 months', 'description': 'Gene expression levels in samples from endometriosis subjects and controls'}], 'secondaryOutcomes': [{'measure': 'Assess the diagnostic utility of the gene signature to differentiate between endometriosis and controls', 'timeFrame': '6 months', 'description': 'Algorithmic analysed normalized gene expression levels in samples from endometriosis subjects and controls'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Endometriosis', 'Diagnosis', 'Gene Expression', 'PCR'], 'conditions': ['Endometriosis']}, 'descriptionModule': {'briefSummary': 'Endometriosis is a common disease that affects up to 10% of women of reproductive age. Diagnosis, however, is typically delayed (up to 12 years) and is usually made after surgery. A key unmet need therefore is an accurate biomarker that can be used to detect the disease early. This study is a prospective trial to identify candidate mRNA-markers which can be used to aid in the diagnosis of this disease. It is a discovery/validation study that will identify and confirm a gene expression panel that is specific for endometriosis and provides a non-invasive tool for future use.', 'detailedDescription': 'Endometriosis is a common disease that affects up to 10% of women of reproductive age. Diagnosis, however, is typically delayed (up to 12 years) and is typically made after surgery. A key unmet need therefore is an accurate, non-invasive biomarker that can be used to detect the disease early.\n\nWe hypothesize that endometriosis-related circulating gene expression can be identified using transcriptomic and bioinformatics approaches and used to construct an accurate diagnostic tool for this condition.\n\nThe primary objective is to develop a gene signature that detects endometriosis. The hypothesis is that this disease is characterized by a set of genes that characterize endometriosis tumor biology.\n\nThe aim is to detect over-expressed genes (elevated mRNA expression) in endometriosis tissue. The goal is to identify 10-25 biomarker genes that are highly expressed to form a candidate biomarker panel.\n\nHighly expressed genes will be determined against samples collected from age/menstrual stage matched controls. A bio-informatics approach will be used to identify these over-expressed genes. This form the basis of a potential diagnostic panel.\n\nPer PICOT criteria:\n\n* The target patient population are women aged 20-35 years with a pathological diagnosis of endometriosis.\n* The intervention is sample collection at the time of diagnosis (tissue, blood, saliva)\n* The comparison group are normo-ovulatory subjects (age 20-25 years) undergoing surgery for benign cervical lesions.\n* The outcome is a gene signature that is associated with endometriosis.\n* The follow-up time is one year.\n\nThe secondary objective is to test the diagnostic utility of the 10-25 gene panel. This will be undertaken using the retrospectively collected samples.\n\n* Each of the highly expressed genes will be measured and quantified using an RT-PCR approach.\n* Genes that are statistically over-expressed in the endometriosis samples will be selected for a PCR panel.\n* The expression of genes in the PCR panel will be scored.\n* Low scores will be related to "control" and higher scores to "endometriosis".\n* The scores will be formally evaluated as a diagnostic (area under the curve analysis, accuracy, sensitivity and specificity metrics).\n* A specific comparison will be made between the endometriosis cohort and the control cohort.\n* The metrics for a successful assay are:\n\n * Accuracy \\>80%\n * Sensitivity \\>90%\n * Specificity \\>85%\n * AUC \\>0.8'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT'], 'maximumAge': '35 Years', 'minimumAge': '20 Years', 'genderBased': True, 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Women with high suspicion of endometriosis and women undergoing surgery for benign cervical disease.', 'genderDescription': 'Female', 'eligibilityCriteria': 'Inclusion Criteria:\n\nFor the endometriosis cohort\n\n* a history of infertility more than 1 year\n* age 20-35 years\n* normal liver and kidney function, without gynaecological and other systemic disease\n\nInclusion criteria for controls include normo-ovulatory history, aged between 20-35 years, who exhibit normal liver and kidney function, and do not have any systemic diseases including autoimmune disease.\n\n\\-\n\nExclusion Criteria:\n\nFor the endometriosis cohort\n\n* polycystic ovary syndrome, hyperprolactinemia\n* severe cardiovascular system, liver, kidney, and hematopoietic system disease\n* autoimmune disease\n* uterine fibroids, endometritis, non-vegetative ovarian cysts, ovarian malignancies, and internal genital tuberculosis\n\nExclusion criteria for the controls includes gynaecological malignancies and genital tuberculosis.\n\n\\-'}, 'identificationModule': {'nctId': 'NCT06907303', 'acronym': 'RNA-EndoDx', 'briefTitle': 'RNA Assays for Endometriosis Detection and Diagnosis', 'organization': {'class': 'INDUSTRY', 'fullName': 'Wren Laboratories LLC'}, 'officialTitle': 'Development and Evaluation of RNA-based Markers for Detecting and Diagnosing Endometriosis', 'orgStudyIdInfo': {'id': 'WrenEndoMstudy 01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Endometriosis', 'description': 'Women with suspected endometriosis undergoing laparoscopy to provide tissue for pathological diagnosis of the disease.', 'interventionNames': ['Diagnostic Test: EndoDx']}, {'label': 'Control', 'description': 'Women undergoing surgery for benign cervical disease.', 'interventionNames': ['Diagnostic Test: EndoDx']}], 'interventions': [{'name': 'EndoDx', 'type': 'DIAGNOSTIC_TEST', 'description': 'PCR assay for Endometriosis diagnosis', 'armGroupLabels': ['Control', 'Endometriosis']}]}, 'contactsLocationsModule': {'locations': [{'zip': '06405', 'city': 'Branford', 'state': 'Connecticut', 'country': 'United States', 'facility': 'Wren Laboratories', 'geoPoint': {'lat': 41.27954, 'lon': -72.8151}}, {'city': 'Cape Town', 'country': 'South Africa', 'facility': 'University of Cape Town', 'geoPoint': {'lat': -33.92584, 'lon': 18.42322}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Wren Laboratories LLC', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'University of Cape Town', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}