Clinical trial NCT04846933: Multi-layer Data to Improve Diagnosis, Predict Therapy Resistance and Suggest Targeted Therapies in HGSOC
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
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: High Grade Ovarian Serous Adenocarcinoma
High Grade Serous Carcinoma
Keywords: chemoresistance
personalized medicine
WGS
ctDNA
digital pathology
FDG PET/CT
bioinformatics
AI
ovarian cancer
tumor evolution
artificial intelligence
organoid
DNA methylation
radiomics
Lead sponsor: Turku University Hospital
Sponsor class: OTHER_GOV
Collaborators: name: University of Helsinki
First posted: 2021-04-15
Enrollment: 200
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: BASIC_SCIENCE

Brief 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, 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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Successful clinical translation
timeFrame: 5 years
measure: Successful prediction of patient outcome with AI methods
timeFrame: 5 years
Secondary outcomes:
measure: Successful validation of potentially druggable genetic alterations
timeFrame: 5 years
measure: Successful prediction of genomic features from tumor histology
timeFrame: 5 years
measure: Prediction of primary treatment response from tumor histology using H&E stained whole slide images and AI-based methods
timeFrame: 5 years
measure: Establishment of an updated version of Chemoresponse score (CRS) for measuring histological effect in tumor tissue after chemotherapy
timeFrame: 5 years
Other outcomes:
Locations:
city: Turku
country: Finland
facility: Turku University Hospital
Study officials:
affiliation: University of Helsinki
name: Sampsa Hautaniemi, DTech, Prof
role: STUDY_DIRECTOR
affiliation: Turku University Hospital
name: Johanna Hynninen, MD, PhD
role: PRINCIPAL_INVESTIGATOR
References: