RESEARCH PROGRAM

The Problem

High-grade serous ovarian cancer (HGSOC) is the most malignant and most frequently encountered type of ovarian cancer. Patients are typically diagnosed at an advanced stage, with large intratumoral heterogeneity and thus exhibit only transient treatment responses.

The standard of care for HGSOC patients consists of debulking surgery combined with platinum-based chemotherapy. Although initially responding to chemotherapy, eventually most patients will develop recurrent disease and resistance to this treatment. In the past 25 years, the overall survival of patients with ovarian cancer has not improved significantly.

Cellular heterogeneity, both genetic and non-genetic, facilitates tumor evolution upon treatment and the subsequent development of resistance. Apart from mutations in DNA repair pathways, HGSOC tumors lack shared targetable genomic drivers and instead, each patient’s tumors present a unique and highly variable combination of copy number aberrations and structural variants from the ancestral dominant cancer clone. This interpatient heterogeneity highly complicates classification and targeting of intratumoral chemoresistance-associated heterogeneity across patients.

Thus, innovative approaches are required to find markers defining the chemoresistant subpopulations, and precision drugs that kill each one of them.

A novel personalized treatment approach

The overall aim of the project is to find markers for HGSOC patient stratification and precision drugs for targeting the cellular subpopulations that do not respond to current treatments.

Figure 1: PARIS project. (A) Phase 1: Identification of chemoresistant subpopulations. (B) Phase 2: Identification and validation of a drug library that targets chemoresistant subpopulations. (C) Personalized treatment approach, enabled by the PARIS drug library.

We use single cell RNA-sequencing (scRNA-seq) data from multiple HGSOC patients before and after treatment to identify cell subpopulations that are resistant to chemotherapy and to infer gene expression signatures that underlie the resistance mechanisms (Fig. 1A). In parallel, we establish and use organoids as an in vitro culture-based approach to identify subpopulations associated with chemoresistance. These organoids are 3D cell cultures grown from the same HGSOC samples to mimic the in vivo tumor. They allow us to perform more detailed monitoring of subpopulation evolution during drug treatment and at the point of emerging resistance.

Once these signatures for resistant subpopulations have been established, we use machine learning tools to predict drugs that kill each subpopulation of chemotherapy resistant cells (Fig. 1B). These drugs are tested and validated in patient derived organoids and xenografts.

The PARIS project will result in a drug library for a new personalized cancer treatment approach for patients with chemoresistant tumours (Fig. 1C).