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

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.

Conditions

• High Grade Ovarian Serous Adenocarcinoma
• High Grade Serous Carcinoma

Interventions

GENETIC

WGS and RNA sequencing

GENETIC

circulating tumor DNA (ctDNA)

DIAGNOSTIC_TEST

FDG PET/CT imaging

Sponsors & Collaborators

• University of Helsinki

  collaborator OTHER

• Turku University Hospital

  lead OTHER_GOV

Principal Investigators

• Sampsa Hautaniemi, DTech, Prof · University of Helsinki

• Johanna Hynninen, MD, PhD · Turku University Hospital

Study Design

Allocation

NON_RANDOMIZED

Purpose

BASIC_SCIENCE

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

FEMALE

Healthy Volunteers

No

Timeline & Regulatory

Start

2012-02-01

Primary Completion

2027-12-31

Completion

2029-12-31

Countries

• Finland

Study Locations