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

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

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, 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.

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.

Study Overview

• Status: Recruiting
• Conditions: High Grade Serous Carcinoma; High Grade Ovarian Serous Adenocarcinoma
• Intervention / Treatment: Genetic: WGS and RNA sequencing; Genetic: circulating tumor DNA (ctDNA); Diagnostic test: FDG PET/CT imaging

Detailed 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 organoid cultures from cancer patients in clinical care.

• Study Type: Interventional
• Enrollment (Estimated): 200
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Johanna Hynninen; Phone Number: +358 50 5383554; Email: johanna.hynninen@utu.fi
• Study Contact Backup: Name: Sampsa Hautaniemi; Phone Number: +358503364765; Email: sampsa.hautaniemi@helsinki.fi

Study Locations

• Finland
  • Turku, Finland, 20520
    • Recruiting
    • Turku University Hospital
    • Contact: Johanna Hynninen; Phone Number: 0505383554; Email: johanna.hynninen@utu.fi
    • Contact: Johanna Hynninen, MD, PhD

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Study Population

High grade serous ovarian cancer patients diagnosed at theTurku University Central Hospital who give their informed consent

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Basic Science
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: HGSOC patients treated with Neoadjuvant chemotherapy (NACT); 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). Digital H&E slides and WGS, RNAseq are obtained from performed surgeries including relapse operations/ascites drainages. Patients are followed with longitudinal ctDNA sampling.	Genetic: WGS and RNA sequencing; Genetic: circulating tumor DNA (ctDNA); Diagnostic test: FDG PET/CT imaging
Other: HGSOC patients treated with primary debulking surgery (PDS); 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.	Genetic: WGS and RNA sequencing; Genetic: circulating tumor DNA (ctDNA); Diagnostic test: FDG PET/CT imaging

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Successful validation of potentially druggable genetic alterations	Number of potentially druggable genetic alterations found and validated with in-vitro methods	5 years
Successful prediction of genomic features from tumor histology	Number of genomic features that can be successfully recognized from tumor histology	5 years
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
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

Collaborators and Investigators

• Sponsor: Turku University Hospital
• Collaborators: University of Helsinki
• Investigators: Study Director: Sampsa Hautaniemi, DTech, Prof, University of Helsinki; Principal Investigator: Johanna Hynninen, MD, PhD, Turku University Hospital

Publications and helpful links

Helpful Links

• Project web site

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2012
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2029

Study Registration Dates

• First Submitted: April 12, 2021
• First Submitted That Met QC Criteria: April 12, 2021
• First Posted (Actual): April 15, 2021

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 14, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Keywords: artificial intelligence; ovarian cancer; ctDNA; DNA methylation; AI; bioinformatics; personalized medicine; FDG PET/CT; radiomics; chemoresistance; digital pathology; organoid; WGS; tumor evolution
• Additional Relevant MeSH Terms: Neoplasms; Neoplasms by Histologic Type; Neoplasms, Glandular and Epithelial; Adenocarcinoma; Carcinoma; Cystadenocarcinoma; Neoplasms, Cystic, Mucinous, and Serous; Cystadenocarcinoma, Serous
• Other Study ID Numbers: TO7/003/21; 965193 (Other Grant/Funding Number: EU HORIZON 2020)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No