Ignite Creation Date:
2025-12-26 @ 12:15 PM
Ignite Modification Date:
2025-12-26 @ 12:15 PM
Study NCT ID:
NCT06217900
Status:
RECRUITING
Last Update Posted:
2024-01-23
First Post:
2023-12-28
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
a PROspective Case Control Study to Develop and Validate a Blood Test FOr mUlti-caNcers Early Detection（PROFOUND）
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009369', 'term': 'Neoplasms'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'blood'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 16666}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-12-28', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-12', 'completionDateStruct': {'date': '2027-03-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-01-11', 'studyFirstSubmitDate': '2023-12-28', 'studyFirstSubmitQcDate': '2024-01-11', 'lastUpdatePostDateStruct': {'date': '2024-01-23', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-01-23', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-10-31', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'To develop a questionnaire to evaluate the risk factors in the multi-cancer early screening', 'timeFrame': '12 months', 'description': 'To develop a questionnaire to evaluate the high-risk factors in the multi-cancer early screening, including lung cancer, gastrointestinal cancer, gynecological cancer, urogenital neoplasms, etc.'}, {'measure': 'To evaluate the performance of multi-omics early detection models in the population with suspected cancer', 'timeFrame': '12 months', 'description': 'The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in in the population with suspected cancer at 95% confidence interval.'}, {'measure': 'To simulate the positive predictive value and negative predictive value of different multi-cancer early detection models based on the cancer prevalence and staging data of individuals aged 40-75 years in China using multiple models', 'timeFrame': '12 months', 'description': 'To simulate the positive predictive value and negative predictive value of different multi-cancer early detection models（cfDNA methylation-based or multi-omic-based)，based on the sensitivity, specificity and tissue origin accuracy，according to multi cancer prevalence and staging data of individuals aged 40-75 years in China.'}, {'measure': 'To simulate the benefits of clinical utility and health economics using different multi-cancer early detection models', 'timeFrame': '12 months', 'description': 'To simulate the stage-shift and incremental cost-effective ratio (ICER) benefit when compared to usual care (SOC screening) using Markov model based on MCED test performance'}, {'measure': 'To explore biomarkers for cancer screening and construct a multimodal machine learning model based on multi-omics data', 'timeFrame': '12 months', 'description': 'Exploring biomarkers in methylomics and fragmentomics，and constructing multimodal for multi-cancer early detection based on multiomics analysis'}], 'primaryOutcomes': [{'measure': 'The performance of cfDNA methylation-based multiple cancers early detection model in case-control study', 'timeFrame': '12 months', 'description': 'The sensitivity, specificity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval.'}], 'secondaryOutcomes': [{'measure': 'The performance of cfDNA methylation-based multiple cancers early detection model in early stage cancer cases', 'timeFrame': '12 months', 'description': 'The sensitivity and tissue origin accuracy of cfDNA methylation-based multiple cancers early detection model in detecting stage I to II cancer at 95% confidence interval.'}, {'measure': 'The performance of multi-omic-based multiple cancers early detection model in case-control study', 'timeFrame': '12 months', 'description': 'The sensitivity, specificity and tissue origin accuracy of multi-omic-based multiple cancers early detection model in detecting cancer or non-cancer at 95% confidence interval.'}, {'measure': 'The performance of different multi-cancer early detection models in different subgroups', 'timeFrame': '12 months', 'description': 'The sensitivity and specificity of cfDNA methylation-based or multi-omic-based multiple cancers early detection model in different subgroups of the population (such as age, gender, cancer pathological classification, and clinical stage) at 95% confidence interval.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['cell-free DNA', 'methylation', 'cancer early detection'], 'conditions': ['Cancer']}, 'descriptionModule': {'briefSummary': 'This study is a multi-center, case-control study aiming at developing and blinded testing machine learning-based multiple cancers early detection model by prospectively collecting blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis.', 'detailedDescription': 'Blood samples from newly diagnosed cancer patients and individuals without confirmed cancer diagnosis will be prospectively collected to identify cancer-specific circulating signals through integrative multi-omic analysis. Based on the comprehensive molecular profiling, a machine learning-driven model will be trained and blinded validated independent through a two-stage approach in clinically annotated individuals. Approximately 10327 cancer patients will be enrolled in this study and early-stage cancer patients will be enriched to improve the model sensitivity on distinguishing cancers with favorable prognosis. Approximately 6339 age and sex matched controls will be included in model development, which are volunteers without a cancer diagnosis after routine cancer screening tests.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '74 Years', 'minimumAge': '40 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Confirmed cancers or individuals without confirmed cancer will be invited to participate in this case-control study by a designated consenting professional.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria for Case Arm Participants:\n\n* 40-74 years old\n* Clinically and/or pathologically diagnosed cancer\n* No prior or undergoing any systemic or local antitumor therapy, including but not limited to surgical resection, radiochemotherapy, endocrinotherapy, targeted therapy, immunotherapy, interventional therapy, etc.\n* Able to provide a written informed consent and willing to comply with all part of the protocol procedures\n\nExclusion Criteria for Case Arm Participants:\n\n* Pregnancy or lactating women\n* Known prior or current diagnosis of other types of malignancies comorbidities\n* Severe acute infection (e.g. severe or critical COVID-19, sepsis, etc.) or febrile illness (body temperature of ≥ 38.5 °C) within 14 days prior to screen\n* Recipients of organ transplant or prior bone marrow transplant or stem cell transplant\n* Recipients of blood transfusion within 30 days prior to screen\n* Recipients of therapy in past 14 days prior to screen, including oral or IV antibiotics, glucocorticoid, azacitidine, decitabine, procainamide, hydrazine, arsenic trioxide\n* Unsuitable for this trial determined by the researchers\n\nInclusion Criteria for Control Arm Participants:\n\n* 40-74 years old\n* Without confirmed cancer diagnosis\n* Able to provide a written informed consent and willing to comply with all part of the protocol procedures\n\nExclusion Criteria for Control Arm Participants:\n\n* Pregnancy or lactating women\n* Known prior or current diagnosis of other types of malignancies comorbidities\n* Severe acute infection (e.g. severe or critical COVID-19, sepsis, etc.) or febrile illness (body temperature of ≥ 38.5 °C) within 14 days prior to screen\n* Recipients of organ transplant or prior bone marrow transplant or stem cell transplant\n* Recipients of blood transfusion within 30 days prior to screen\n* Recipients of therapy in the past 14 days prior to screen, including oral or IV antibiotics, glucocorticoid, azacitidine, decitabine, procainamide, hydrazine, arsenic trioxide\n* Unsuitable for this trial determined by the researchers'}, 'identificationModule': {'nctId': 'NCT06217900', 'acronym': 'PROFOUND', 'briefTitle': 'a PROspective Case Control Study to Develop and Validate a Blood Test FOr mUlti-caNcers Early Detection（PROFOUND）', 'organization': {'class': 'INDUSTRY', 'fullName': 'Shanghai Weihe Medical Laboratory Co., Ltd.'}, 'officialTitle': 'PROFOUND Study: Development and Validation of a Multi-cancer Early Detection Model Based on Peripheral Blood Multi-omic Analysis and Machine Learning: a Multicenter, Prospective, Observational, Case-control Study', 'orgStudyIdInfo': {'id': 'PROFOUND'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Case arm', 'description': 'Participants with newly diagnosed cancer of lung, breast, digestive tract, urinary tract and etc.'}, {'label': 'Control arm', 'description': 'Participants without a cancer diagnosis after routine cancer screening tests.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '100044', 'city': 'Beijing', 'state': 'Beijing Municipality', 'status': 'RECRUITING', 'country': 'China', 'contacts': [{'name': 'Jun Wang', 'role': 'CONTACT'}], 'facility': "Peking University People's Hospital", 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}, {'zip': '100083', 'city': 'Beijing', 'state': 'Beijing Municipality', 'status': 'NOT_YET_RECRUITING', 'country': 'China', 'contacts': [{'name': 'Ziyu Li', 'role': 'CONTACT'}], 'facility': 'Peking University Cancer Hospital and Institute', 'geoPoint': {'lat': 39.9075, 'lon': 116.39723}}], 'centralContacts': [{'name': 'Yong Qin', 'role': 'CONTACT', 'email': 'qinyong@bytedance.com', 'phone': '+86 186 2629 2273'}], 'overallOfficials': [{'name': 'Jun Wang', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': "Peking University People's Hospital"}, {'name': 'Xiaohui Wu', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Shanghai Weihe Medical Laboratory Co., Ltd.'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Shanghai Weihe Medical Laboratory Co., Ltd.', 'class': 'INDUSTRY'}, 'collaborators': [{'name': "Peking University People's Hospital", 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}