Ignite Creation Date:
2025-12-25 @ 2:48 AM
Ignite Modification Date:
2026-02-28 @ 9:04 PM
Study NCT ID:
NCT04846933
Status:
RECRUITING
Last Update Posted:
2025-01-16
First Post:
2021-04-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010051', 'term': 'Ovarian Neoplasms'}], 'ancestors': [{'id': 'D004701', 'term': 'Endocrine Gland Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D010049', 'term': 'Ovarian Diseases'}, {'id': 'D000291', 'term': 'Adnexal Diseases'}, {'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': 'D005833', 'term': 'Genital Neoplasms, Female'}, {'id': 'D014565', 'term': 'Urogenital Neoplasms'}, {'id': 'D000091662', 'term': 'Genital Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D006058', 'term': 'Gonadal Disorders'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D000073336', 'term': 'Whole Genome Sequencing'}, {'id': 'D017423', 'term': 'Sequence Analysis, RNA'}, {'id': 'D000074141', 'term': 'Circulating Tumor DNA'}], 'ancestors': [{'id': 'D017422', 'term': 'Sequence Analysis, DNA'}, {'id': 'D017421', 'term': 'Sequence Analysis'}, {'id': 'D005821', 'term': 'Genetic Techniques'}, {'id': 'D008919', 'term': 'Investigative Techniques'}, {'id': 'D000073888', 'term': 'Cell-Free Nucleic Acids'}, {'id': 'D009696', 'term': 'Nucleic Acids'}, {'id': 'D009706', 'term': 'Nucleic Acids, Nucleotides, and Nucleosides'}, {'id': 'D004273', 'term': 'DNA, Neoplasm'}, {'id': 'D004247', 'term': 'DNA'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NON_RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'BASIC_SCIENCE', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 200}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2012-02-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-01', 'completionDateStruct': {'date': '2029-12', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-01-14', 'studyFirstSubmitDate': '2021-04-12', 'studyFirstSubmitQcDate': '2021-04-12', 'lastUpdatePostDateStruct': {'date': '2025-01-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-04-15', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Successful clinical translation', 'timeFrame': '5 years', 'description': 'The magnitude of successful clinical translation is measured by the number of times project-derived personalized medicine has impacted patients care by application of novel and existing biomarkers and therapies.'}, {'measure': 'Successful prediction of patient outcome with AI methods', 'timeFrame': '5 years', 'description': 'Proportion of patients whose disease outcome (PFS, OS) is predicted correctly with digital histopathology images, genomic data and routine laboratory values'}], 'secondaryOutcomes': [{'measure': 'Successful validation of potentially druggable genetic alterations', 'timeFrame': '5 years', 'description': 'Number of potentially druggable genetic alterations found and validated with in-vitro methods'}, {'measure': 'Successful prediction of genomic features from tumor histology', 'timeFrame': '5 years', 'description': 'Number of genomic features that can be successfully recognized from tumor histology'}, {'measure': 'Prediction of primary treatment response from tumor histology using H&E stained whole slide images and AI-based methods', 'timeFrame': '5 years', 'description': 'Number of patients whose outcome (primary therapy outcome, PFS) is predicted correctly'}, {'measure': 'Establishment of an updated version of Chemoresponse score (CRS) for measuring histological effect in tumor tissue after chemotherapy', 'timeFrame': '5 years', 'description': 'Predictive power of the updated CRS at interval surgery is compared with traditional CRS'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['chemoresistance', 'personalized medicine', 'WGS', 'ctDNA', 'digital pathology', 'FDG PET/CT', 'bioinformatics', 'AI', 'ovarian cancer', 'tumor evolution', 'artificial intelligence', 'organoid', 'DNA methylation', 'radiomics'], 'conditions': ['High Grade Ovarian Serous Adenocarcinoma', 'High Grade Serous Carcinoma']}, 'referencesModule': {'references': [{'pmid': '40885189', 'type': 'DERIVED', 'citation': 'Afenteva D, Yu R, Rajavuori A, Salvadores M, Launonen IM, Lavikka K, Zhang K, Pirttikoski A, Marchi G, Jamalzadeh S, Isoviita VM, Li Y, Micoli G, Erkan EP, Falco MM, Ungureanu D, Lahtinen A, Oikkonen J, Hietanen S, Vaharautio A, Sur I, Virtanen A, Farkkila A, Hynninen J, Muranen TA, Taipale J, Hautaniemi S. Multi-omics analysis reveals the attenuation of the interferon pathway as a driver of chemo-refractory ovarian cancer. Cell Rep Med. 2025 Sep 16;6(9):102316. doi: 10.1016/j.xcrm.2025.102316. Epub 2025 Aug 29.'}, {'pmid': '37207655', 'type': 'DERIVED', 'citation': 'Lahtinen A, Lavikka K, Virtanen A, Li Y, Jamalzadeh S, Skorda A, Lauridsen AR, Zhang K, Marchi G, Isoviita VM, Ariotta V, Lehtonen O, Muranen TA, Huhtinen K, Carpen O, Hietanen S, Senkowski W, Kallunki T, Hakkinen A, Hynninen J, Oikkonen J, Hautaniemi S. Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma. Cancer Cell. 2023 Jun 12;41(6):1103-1117.e12. doi: 10.1016/j.ccell.2023.04.017. Epub 2023 May 18.'}], 'seeAlsoLinks': [{'url': 'https://www.deciderproject.eu', 'label': 'Project web site'}]}, 'descriptionModule': {'briefSummary': 'Chemotherapy resistance is the greatest contributor to mortality in advanced cancers and severe challenges remain in finding effective treatment modalities to cancer patients with metastasized and relapsed disease. High-grade serous ovarian cancer (HGSOC) is typically diagnosed at a stage where the disease is already widely spread to the abdomen and current standard of practice treatment consists of surgery followed by platinum-taxane based chemotherapy and maintenance therapy. While 90% of HGSOC patients show no clinically detectable signs of cancer after surgery and chemotherapy, only 43% of the patients are alive five years after diagnosis because of chemoresistant cancer.\n\nThis prospective, observational trial focuses on revealing major mechanisms causing chemoresistance in HGSOG patients and derive personalized treatment regimens for chemotherapy resistant HGSOC patients. The investigators recruit newly diagnosed advanced stage HGSOC patients who are then thoroughly followed during their cancer treatment. Longitudinal sampling includes digitalized H\\&E stained histology slides mainly collected during routine diagnostics, fresh tumor \\& ascites samples for next-generation sequencing/proteomics (WGS, RNA-seq, DNA-methylation, ATAC-seq, ChIP-seq, mass cytometry, etc.) and ex vivo experiments, plasma samples for circulating tumor DNA (ctDNA) analyses. Broad range of clinical parameters such as laboratory and radiologic parameters (e.g., FDG PET/CT), given cancer treatments and their outcomes are collected. Radiomic analyses are performed to PET/CT and CT scans. Long-term patient derived organoid lines are established from fresh tumor tissues. Actionable genomic alterations are searched.\n\nThe general objective is to establish a clinically useful precision oncology approach based on multi-level data collected in longitudinal setting, and translate the most potent and validated discoveries into clinical use. DECIDER project will produce AI-powered diagnostic tools, cutting-edge software platforms for clinical decision-making, novel data analysis \\& integration methods, and high-throughput ex vivo drug screening approaches.', 'detailedDescription': 'Specific aims include:\n\n* Develop tools and methods for personalized medicine approaches to cancer patients.\n* Develop open-source visualization and interpretation software that facilitate clinical decision making via data integration and interpretation of multilevel data from cancer patients.\n* Rapidly identify HGSOC patients who are likely to respond poorly to current therapies combining information on digitalized histopathology samples, genomic and clinical data with AI methods.\n* Deploy validated personalized medicine treatment options using longitudinal measurement and ex vivo organoid cultures from cancer patients in clinical care.'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Patients with a suspected ovarian cancer diagnosis treated at the Turku University Hospital\n* Ability to understand and the willingness to sign a written informed consent document\n\nExclusion Criteria:\n\n* Age \\<18 years, too poor condition for active treatment (surgery, chemotherapy)\n* FDG PET/CT scan is not performed for patients with diabetes mellitus and poor glucose balance.'}, 'identificationModule': {'nctId': 'NCT04846933', 'acronym': 'DECIDER', 'briefTitle': 'Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Turku University Hospital'}, 'officialTitle': 'Integration of Multiple Data Levels to Improve Diagnosis, Predict Treatment Response and Suggest Targets to Overcome Therapy Resistance in High-grade Serous Ovarian Cancer', 'orgStudyIdInfo': {'id': 'TO7/003/21'}, 'secondaryIdInfos': [{'id': '965193', 'type': 'OTHER_GRANT', 'domain': 'EU HORIZON 2020'}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'OTHER', 'label': 'HGSOC patients treated with Neoadjuvant chemotherapy (NACT)', 'description': 'Diagnostic laparoscopy followed with 3-4 cycles of platinum-taxane NACT and interval debulking surgery (IDS). Treatment response is monitored with FDG PET/CT. IDS is followed by standard adjuvant therapy (ESGO/ESMO + local guidelines).\n\nDigital H\\&E slides and WGS, RNAseq are obtained from performed surgeries including relapse operations/ascites drainages. Patients are followed with longitudinal ctDNA sampling.', 'interventionNames': ['Genetic: WGS and RNA sequencing', 'Genetic: circulating tumor DNA (ctDNA)', 'Diagnostic Test: FDG PET/CT imaging']}, {'type': 'OTHER', 'label': 'HGSOC patients treated with primary debulking surgery (PDS)', 'description': 'PDS is followed by standard adjuvant therapy (ESGO/ESMO + local guidelines). Digital H\\&E slides and WGS, RNAseq obtained from PDS and possible relapse operations/ascites drainages when performed. Patients are followed with longitudinal ctDNA sampling.', 'interventionNames': ['Genetic: WGS and RNA sequencing', 'Genetic: circulating tumor DNA (ctDNA)', 'Diagnostic Test: FDG PET/CT imaging']}], 'interventions': [{'name': 'WGS and RNA sequencing', 'type': 'GENETIC', 'armGroupLabels': ['HGSOC patients treated with Neoadjuvant chemotherapy (NACT)', 'HGSOC patients treated with primary debulking surgery (PDS)']}, {'name': 'circulating tumor DNA (ctDNA)', 'type': 'GENETIC', 'armGroupLabels': ['HGSOC patients treated with Neoadjuvant chemotherapy (NACT)', 'HGSOC patients treated with primary debulking surgery (PDS)']}, {'name': 'FDG PET/CT imaging', 'type': 'DIAGNOSTIC_TEST', 'armGroupLabels': ['HGSOC patients treated with Neoadjuvant chemotherapy (NACT)', 'HGSOC patients treated with primary debulking surgery (PDS)']}]}, 'contactsLocationsModule': {'locations': [{'zip': '20520', 'city': 'Turku', 'status': 'RECRUITING', 'country': 'Finland', 'contacts': [{'name': 'Johanna Hynninen', 'role': 'CONTACT', 'email': 'johanna.hynninen@utu.fi', 'phone': '0505383554'}, {'name': 'Johanna Hynninen, MD, PhD', 'role': 'CONTACT'}], 'facility': 'Turku University Hospital', 'geoPoint': {'lat': 60.45148, 'lon': 22.26869}}], 'centralContacts': [{'name': 'Johanna Hynninen', 'role': 'CONTACT', 'email': 'johanna.hynninen@utu.fi', 'phone': '+358 50 5383554'}, {'name': 'Sampsa Hautaniemi', 'role': 'CONTACT', 'email': 'sampsa.hautaniemi@helsinki.fi', 'phone': '+358503364765'}], 'overallOfficials': [{'name': 'Sampsa Hautaniemi, DTech, Prof', 'role': 'STUDY_DIRECTOR', 'affiliation': 'University of Helsinki'}, {'name': 'Johanna Hynninen, MD, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Turku University Hospital'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Turku University Hospital', 'class': 'OTHER_GOV'}, 'collaborators': [{'name': 'University of Helsinki', 'class': 'OTHER'}], 'responsibleParty': {'type': 'SPONSOR'}}}}