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

NCT04846933RecruitingNAINTERVENTIONAL

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.

Key Facts

Lead Sponsor

Turku University Hospital

Enrollment

200

Start Date

2012-02-01

Completion Date

2027-12-01

Study Type

INTERVENTIONAL

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

Interventions

WGS and RNA sequencingcirculating tumor DNA (ctDNA)FDG PET/CT imaging

Conditions

High Grade Ovarian Serous AdenocarcinomaHigh Grade Serous Carcinoma

Eligibility

Age Range

18 Years+

Sex

FEMALE

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.

Outcome Measures

Primary Outcomes

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.

Time frame: 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

Time frame: 5 years

Secondary Outcomes

Successful validation of potentially druggable genetic alterations

Number of potentially druggable genetic alterations found and validated with in-vitro methods

Time frame: 5 years

Successful prediction of genomic features from tumor histology

Number of genomic features that can be successfully recognized from tumor histology

Time frame: 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

Time frame: 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

Time frame: 5 years

Locations

Turku University Hospital, Turku, Finland

Linked Papers

2025-08-29

Multi-omics analysis reveals the attenuation of the interferon pathway as a driver of chemo-refractory ovarian cancer

Ovarian high-grade serous carcinoma (HGSC) is one of the deadliest gynecological malignancies, with 10%-15% of patients exhibiting primary resistance to first-line chemotherapy. To characterize the molecular drivers of chemo-refractoriness, we perform multi-omics profiling of treatment-naive biopsies from patients with refractory HGSC enrolled in the DECIDER observational trial. We demonstrate that chemo-refractory HGSC is characterized by diminished interferon type I (IFN-I) and enhanced hypoxia pathway activity, and baseline IFN-I activity in chemo-naive cancer is an independent prognostic factor. Single-cell RNA sequencing and spatial protein profiling analyses corroborate the importance of elevated IFN-I activity in response to chemotherapy. Importantly, in vitro experiments demonstrate that high levels of IFN-I signaling increase cell chemosensitivity to platinum in a cell-autonomous manner. Together, these findings indicate that the IFN-I pathway activity in HGSC cancer cells predicts response to first-line chemotherapy in HGSC, proposing the stimulation of the IFN-I response as a therapeutic strategy. The study is registered at ClinicalTrials.gov (NCT04846933).

2023-05-18

Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we integrate clonal composition and topology using whole-genome sequencing data from 510 samples of 148 patients with HGSC in the prospective, longitudinal, multiregion DECIDER study. Our results reveal three evolutionary states, which have distinct features in genomics, pathways, and morphological phenotypes, and significant association with treatment response. Nested pathway analysis suggests two evolutionary trajectories between the states. Experiments with five tumor organoids and three PI3K inhibitors support targeting tumors with enriched PI3K/AKT pathway with alpelisib. Heterogeneity analysis of samples from multiple anatomical sites shows that site-of-origin samples have 70% more unique clones than metastatic tumors or ascites. In conclusion, these analysis and visualization methods enable integrative tumor evolution analysis to identify patient subtypes using data from longitudinal, multiregion cohorts.

Linked Investigators

Alexandra Lahtinen

Daria Afenteva

Sampsa Hautaniemi

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