Prof. Dr. med. Viola Heinzelmann

Personalized treatment in oncology requires predictive biomarkers and direct on-target drug response to tailor therapy regimens. However, many efforts to develop truly predictive biomarkers are limited due to only a handful of genetic aberrations demonstrating clear clinical benefit for ‘targeted’ treatments. Together with an emerging trend in using drug combinations rather than single compounds, large prospective clinical trials require long time and enormous investments to demonstrate only marginal benefits. In order to overcome these limitations hampering major breakthroughs, researchers have moved towards ex vivo screening or patient-derived xenograft models envisioning to provide clinically relevant data in a 4-week turnaround. Here we aim to combine single-cell transcriptomics with drug treatment on ex vivo spheroids in a prospectively collected and homogenous cohort of ascites samples derived from high-grade serous ovarian cancer patients to 1) define the cell composition of ascites, an abdominal fluid with malignant cells taken through regular drainage to release patients’ pain; 2) align patients outcome with molecular and cellular signatures and ex vivo drug response, 3) evaluate in silico predicted and cancer cell-specific drug response in patient-derived cultures, and 4) ultimately define novel non-genetic biomarker-drug axes improving personalized treatment. In detail, we will test currently applied and suggested drug regimens in real-time using a straightforward ex vivo spheroid culture system. With an already established single-cell RNA sequencing pipeline we will also identify individual patient tumor heterogeneity and evaluate in silico drug response ex vivo. Our ex vivo spheroid platform will test cell proliferation, apoptosis, and response to single drugs and combinations in ascites-derived samples. Our bimodal approach is novel and the consideration of tumor heterogeneity together with drug response in patients’ material has not been addressed. Thus, we believe that the setup described in this grant can be further developed into a clinical helpful predictive analysis scheme. Moreover, predictors and response patterns delivered by this proposal will be useful for their testing in future ovarian cancer clinical trials.

Several hundred cancer patients in Switzerland carry pathogenic germline variants associated with hereditary breast/ovarian cancer (HBOC) and Lynch syndrome (LS). HBOC and LS cases are at significantly higher risk of primary and secondary cancers and need lifelong cancer surveillance and access to different risk management options. Their close blood relatives have 12.5%-50% probability of inheriting the respective cancer predisposition and need access to genetic evaluation. European-based studies suggest that most cancer patients with hereditary cancer syndromes are not identified and do not receive adequate cancer surveillance. Most evidence comes from cross-sectional studies; there is little available information about changes in adherence to surveillance over time. Little is known about how genetic test results affect subsequent surveillance for HBOC and LS cases and blood relatives, and the overall response of the Swiss healthcare system to mutation carriers' and relatives' needs for long-term surveillance and cancer prevention.

CASCADE II will collect prospective three-year data from confirmed mutation carriers and blood relatives to examine how cancer surveillance practices, uptake of risk management options, and access to genetic services (for untested relatives) change over time. Specific Aim 1: Monitor changes over time in cancer status, surveillance practices, uptake of risk management options, and uptake of genetic testing (for previously untested relatives), and explore whether there are differences in occurrence of these events (or cumulative incidence of events) during the follow-up period among the different participant groups.

Specific Aim 2: Examine the predictive value of individual domain clusters (e.g., cancer status), interpersonal domain clusters (e.g., family environment), and healthcare system domain clusters (e.g., provider specialty) on cancer surveillance practices, uptake of risk management options, and uptake of genetic testing (for previously untested relatives). Specific Aim 3: Explore participants' preferences for the role and involvement of healthcare providers in organization of cancer surveillance and follow-up care.

Longitudinal data from the CASCADE cohort, a prospective, family-based cohort targeting HBOC and LS confirmed cases and blood relatives will address these aims. CASCADE uses surveys to assess cancer status, surveillance, management of hereditary cancer risk, and coordination of care, covering multi-level factors affecting cancer prevention and survivorship. Data from the CASCADE I and CASCADE II studies span a period of over 6 years and 4 data collection points, each approximately 18 months apart, for participants entering the cohort since its initiation. Recruitment takes place in oncology and/or genetic testing centres in three linguistic regions of Switzerland.

Longitudinal survey data will address Aims 1 and 2. We will use Kaplan-Meier analyses and multivariate and/or multi-level Cox Proportional Hazards models to regress "cancer surveillance" event and "use of genetic services" event on predictors. Exploratory factor analyses and hierarchical cluster analyses will generate domain clusters for participants.

Narrative data (focus groups and interviews) from selected participants to present diverse perspectives, triangulated with survey data, will address Aim 3. Data from the CASCADE cohort have considerable potential to enhance the development of high-quality comprehensive support systems to improve cancer surveillance and access to genetic specialists and coordination of cancer care services in Switzerland.