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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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High Grade Ovarian Serous Adenocarcinoma, High Grade Serous Carcinoma

Intervention / Treatment
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* Genetic: WGS and RNA sequencing
* Genetic: circulating tumor DNA (ctDNA)
* Diagnostic Test: FDG PET/CT imaging


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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Female

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| HGSOC patients treated with Neoadjuvant chemotherapy (NACT)<br>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<br> <br> Genetic: circulating tumor DNA (ctDNA)<br> <br> Diagnostic Test: FDG PET/CT imaging<br> <br> |
| HGSOC patients treated with primary debulking surgery (PDS)<br>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<br> <br> Genetic: circulating tumor DNA (ctDNA)<br> <br> |

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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chemoresistance, personalized medicine, WGS, ctDNA, digital pathology, FDG PET/CT, bioinformatics, AI, ovarian cancer, tumor evolution