Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC

High Grade Ovarian Serous Adenocarcinoma Clinical Trial

— DECIDER  

Official title:

Integration of Multiple Data Levels to Improve Diagnosis, Predict Treatment Response and Suggest Targets to Overcome Therapy Resistance in High-grade Serous Ovarian Cancer

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04846933
• Other study ID #: TO7/003/21
• Secondary ID: 965193
• Status: Recruiting
• Start date: February 1, 2012
• Est. completion date: December 2029

Study information

• Verified date: June 2023
• Source: Turku University Hospital
• Contact: Johanna Hynninen
• Phone: +358 50 5383554
• Email: johanna.hynninen[@]utu.fi
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

This 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, 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.

The 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.

Description:

Specific aims include:

• Develop tools and methods for personalized medicine approaches to cancer patients.

• Develop open-source visualization and interpretation software that facilitate clinical decision making via data integration and interpretation of multilevel data from cancer patients.

• 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.

• Deploy validated personalized medicine treatment options using longitudinal measurement and ex vivo cultures from cancer patients in clinical care.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200
• Est. completion date: December 2029
• Est. primary completion date: December 2027
• Accepts healthy volunteers: No
• Gender: Female
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients with a suspected ovarian cancer diagnosis treated at the Turku University Hospital

• Ability to understand and the willingness to sign a written informed consent document

Exclusion Criteria:

• Age <18 years, too poor condition for active treatment (surgery, chemotherapy)

• FDG PET/CT scan is not performed for patients with diabetes mellitus and poor glucose balance.

Related Conditions & MeSH terms

• High Grade Ovarian Serous Adenocarcinoma
• High Grade Serous Carcinoma

Intervention

Genetic:
• WGS and RNA sequencing

• circulating tumor DNA (ctDNA)

Diagnostic Test:
• FDG PET/CT imaging

Locations

Country	Name	City	State
Finland	Turku University Hospital	Turku

Sponsors (2)

Lead Sponsor	Collaborator
Turku University Hospital	University of Helsinki

Country where clinical trial is conducted

Finland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Successful clinical translation	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.	5 years
Primary	Successful prediction of patient outcome with AI methods	Proportion of patients whose disease outcome (PFS, OS) is predicted correctly with digital histopathology images, genomic data and routine laboratory values	5 years
Secondary	Successful validation of potentially druggable genetic alterations	Number of potentially druggable genetic alterations found and validated with in-vitro methods	5 years
Secondary	Successful prediction of genomic features from tumor histology	Number of genomic features that can be successfully recognized from tumor histology	5 years
Secondary	Prediction of primary treatment response from tumor histology using H&E stained whole slide images and AI-based methods	Number of patients whose outcome (primary therapy outcome, PFS) is predicted correctly	5 years
Secondary	Establishment of an updated version of Chemoresponse score (CRS) for measuring histological effect in tumor tissue after chemotherapy	Predictive power of the updated CRS at interval surgery is compared with traditional CRS	5 years

External Links

•   Project web site