Ignite Creation Date:
2025-12-24 @ 3:57 PM
Ignite Modification Date:
2026-01-01 @ 7:36 AM
Study NCT ID:
NCT06824792
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-02-13
First Post:
2025-02-07
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Optimal Standard Treatment Selection for Solid Tumor Patients by Biologically-informed Multi-agent System
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['PHASE4'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'TREATMENT', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 3000}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-03-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-02', 'completionDateStruct': {'date': '2030-02-28', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-02-08', 'studyFirstSubmitDate': '2025-02-07', 'studyFirstSubmitQcDate': '2025-02-08', 'lastUpdatePostDateStruct': {'date': '2025-02-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-02-13', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2028-02-29', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Progression-free survival (PFS)', 'timeFrame': 'Every 6 weeks, up to 2 years since enrollment', 'description': 'Defined as the time from enrollment to documented disease progression per RECIST 1.1 or death due to any cause, whichever occurs first.'}], 'secondaryOutcomes': [{'measure': 'Overall response rate (ORR)', 'timeFrame': 'Every 6 weeks, up to 2 years since enrollment', 'description': 'Defined as the proportion of cases showing the best response of complete response (CR) or partial response (PR) (i.e., CR+PR) per RECIST 1.1 (based on CT, MRI or PET-CT), during the period from the start of the investigational drug to withdrawal from the trial.'}, {'measure': 'Duration of response (DoR)', 'timeFrame': 'Every 6 weeks, up to 2 years since enrollment', 'description': 'Defined as the time from the first documented response, i.e. CR or PR, per RECIST 1.1, to disease progression or death from any cause, whichever occurs first.'}, {'measure': 'Time to treatment failure (TTF)', 'timeFrame': 'Every 6 weeks, up to 2 years since enrollment', 'description': 'Defined as the time from the start of enrollment to the termination of treatment for any reason, including disease progression per RECIST 1.1, treatment toxicity, or death.'}, {'measure': 'Time to progression (TTP)', 'timeFrame': 'Every 6 weeks, up to 2 years since enrollment', 'description': 'Defined as the time from enrollment to the occurrence of objective tumor progression per RECIST 1.1, excluding death.'}, {'measure': 'Best of response (BoR)', 'timeFrame': 'Every 6 weeks, up to 2 years since enrollment', 'description': 'Defined as the best therapeutic effect recorded from the start of treatment until disease progression or recurrence, per RECIST 1.1.'}, {'measure': 'Treatment-emergent adverse events (TEAE)', 'timeFrame': 'Every 6 weeks, up to 2 years since enrollment', 'description': 'Defined as adverse events that emerge or worsen in severity following the initiation of intervention, per CTCAE 5.0.'}]}, 'oversightModule': {'isUsExport': True, 'oversightHasDmc': True, 'isFdaRegulatedDrug': True, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence', 'Real World Study', 'Advanced Solid Tumors', 'Standard Treatment', 'Multi-omics'], 'conditions': ['Advanced Solid Tumors']}, 'descriptionModule': {'briefSummary': 'This study is an exploratory cohort study conducted under real-world conditions, aiming to evaluate the feasibility of an artificial intelligence (AI)-guided standard treatment selection model for advanced solid tumors, as well as its superiority compared to clinician-selected treatment plans. A multi-agent system based on multimodal AI models will rank the priority of standard treatment options based on the personalized information of the patients, including including demographics, clinical information, and multi-omics data. The final treatment plan will be jointly selected by the patient and the clinician from the AI-recommended options, thereby delivering a personalized treatment.', 'detailedDescription': 'This study is an exploratory cohort study conducted under real-world conditions, aiming to evaluate the feasibility of an artificial intelligence (AI)-guided standard treatment selection model for advanced solid tumors, as well as its superiority compared to clinician-selected treatment plans. The study will prospectively collect patient data of multiple dimensions, including demographics, clinical information (pathological classification, tumor staging, imaging findings, previous treatment regimens and their effectiveness, performance status scores), and multi-omics data (DNA gene panel testing, whole-exome sequencing, transcriptome sequencing, etc.). A multi-agent system based on multimodal AI models will rank the priority of standard treatment options based on the personalized information of the patients. The final treatment plan will be jointly selected by the patient and the clinician from the AI-recommended options, thereby delivering a personalized treatment.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Voluntarily participate in the clinical study, fully understand and be informed about the study, sign the informed consent form, and be willing and able to comply with and complete all trial procedures.\n* Aged ≥18 years, no gender restrictions.\n* Patients with advanced or metastatic malignant tumors confirmed by histology or cytology.\n* Able to provide tumor tissue and peripheral blood samples for multi-omics testing, or able to provide qualified whole-exome sequencing and transcriptomics data.\n\nExclusion Criteria:\n\n* As assessed by the investigator, no standard treatment is available, or the patient is unsuitable for guideline-recommended anti-tumor therapies.\n* Other conditions deemed unsuitable for participation in this study by the investigator.'}, 'identificationModule': {'nctId': 'NCT06824792', 'acronym': 'SINGULARITY', 'briefTitle': 'Optimal Standard Treatment Selection for Solid Tumor Patients by Biologically-informed Multi-agent System', 'organization': {'class': 'OTHER', 'fullName': 'Cancer Institute and Hospital, Chinese Academy of Medical Sciences'}, 'officialTitle': 'Real-world Study to Investigate Optimal Standard Treatment Selection for Solid Tumor Patients by Guided by Biologically-informed Multi-agent System', 'orgStudyIdInfo': {'id': 'SINGULARITY-001'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Quasar', 'description': 'This arm involves the prospective collection of individual patient data, including demographic information, clinical details (such as pathological classification, tumor staging, imaging findings, prior treatments and their efficacy, and performance status scores), and multi-omics data (DNA gene panel testing, whole-exome sequencing, and transcriptome sequencing). An artificial intelligence model (namely, Quasar) integrates this multidimensional information to prioritize standard treatment options and identify the optimal personalized treatment plan for each patient. Based on the AI-recommended treatment list, the final treatment plan is jointly selected by the patient and the physician. If treatment adjustments are required due to tumor progression, intolerance, or other reasons, the AI model will generate a new optimal treatment plan based on updated patient characteristics. This iterative process continues until the patient withdraws from the study.', 'interventionNames': ['Drug: Biologically-informed multi-agent system (Quasar) including targeted drugs Osimertinib, chemotherapy pemetrexed, immunotherapy pembrolizumab et al. approved by China CDE.']}], 'interventions': [{'name': 'Biologically-informed multi-agent system (Quasar) including targeted drugs Osimertinib, chemotherapy pemetrexed, immunotherapy pembrolizumab et al. approved by China CDE.', 'type': 'DRUG', 'otherNames': ['KEYTRUDA et al.'], 'description': "Quasar is a biologically-informed multi-agent system developed based on multi-omics and multi-modal data. By integrating multidimensional information such as patients' demographic, clinical, and omics data (including DNA genotyping, whole-exome sequencing, transcriptome sequencing, etc.), it prioritizes standard treatment plans and recommends the optimal personalized treatment plan. Including targeted drugs, chemotherapy, immunotherapy approved by China CDE.", 'armGroupLabels': ['Quasar']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Langfang', 'state': 'Hebei', 'country': 'China', 'contacts': [{'name': 'Ning LI, M.D.', 'role': 'CONTACT', 'email': 'lining@cicams.ac.cn', 'phone': '+86 (010) 8778 8165'}, {'name': 'Yale JIANG, M.D.', 'role': 'CONTACT', 'email': 'yalejiang@cicams.ac.cn', 'phone': '+86 (010) 8778 8165'}, {'name': 'Ning LI, M.D.', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Yale JIANG, M.D.', 'role': 'SUB_INVESTIGATOR'}, {'name': 'Shuhang Wang, PhD', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Cancer Institute and Hospital, Chinese Academy of Medical Sciences (Langfang Branch)', 'geoPoint': {'lat': 39.52079, 'lon': 116.71471}}], 'centralContacts': [{'name': 'Ning LI, M.D.', 'role': 'CONTACT', 'email': 'lining@cicams.ac.cn', 'phone': '+86 (010) 8778-8165'}, {'name': 'Yale JIANG, M.D.', 'role': 'CONTACT', 'email': 'yalejiang@cicams.ac.cn', 'phone': '+86 (010) 8778-8713'}], 'overallOfficials': [{'name': 'Shuhang Wang, PhD', 'role': 'STUDY_DIRECTOR', 'affiliation': 'National Cancer Center of China'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'NING LI', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR_INVESTIGATOR', 'investigatorTitle': 'Vice Director of Cancer Institute and Hospital, Chinese Academy of Medical Sciences', 'investigatorFullName': 'NING LI', 'investigatorAffiliation': 'Cancer Institute and Hospital, Chinese Academy of Medical Sciences'}}}}