Functional and molecular characterization of avatar model systems to elucidate cancer cell – adipocyte communication
Problem and rational
Precision oncology has revolutionized cancer therapy and opened a new path to patient-tailored treatment options. Still, only few patients benefit from comprehensive analysis of targetable transcriptional or genomic alterations. Functional precision oncology can complement the information on targetable vulnerabilities, particularly in heterogenous diseases where standard therapy is not equally effective (1). Standard therapy of ovarian cancer is an example of a “one-size-fits-all” strategy. Patients with all subtypes of ovarian cancer receive a combination of cytoreductive surgery and platinum-based chemotherapy regardless of the underlying molecular drivers or individual pathobiology (2). Under this paradigm most patients suffer from relapse within two years and for certain disease subtypes, such as low-grade serous ovarian cancer, objective patient benefit from chemotherapy is extremely low. Understanding the underlying pathobiology is critical to identify personalized treatment options and to improve patient outcome. Ovarian cancer presents a unique dissemination pattern preferentially colonizing to the omentum (3). The omentum is rich in adipose tissue and is composed of adipocytes, immune cells, fibroblasts and endothelial cells and thereby creating a unique supportive niche milieu (4). Beyond their function as a depot of lipids, adipocytes are capable of secreting bioactive factors, so-called adipokines, that may stimulate the metastasis process (5), maintain the supportive tumor microenvironment (6) and modulate response to cancer treatment (7). However, the underlying molecular mechanisms are by far not fully understood and studying the cell-cell communication between cancer cells and components of the microenvironment, such as adipocytes, can provide new insights into the pathobiology and reveal novel tumor vulnerabilities.
Figure1. Workflow and possible application of patient-derived avatar models of complex cellular systems. (Created with BioRender.com)
In this study, we will apply functional precision oncology tools and expand them to investigate complex models of cells present in the microenvironment of metastasized ovarian cancer. We will collect ascites fluid, tumor tissue and adipose tissue from a cohort of women with epithelial ovarian cancers and establish 3D avatar culture models of cancer cells and mature adipocytes reflecting patient-individual cellular and molecular characteristics that are not represented in conventional cell lines. To map the cell-cell communication within the metastatic niche, patient-derived cancer cells and adipocytes will be co-cultured short-term. Collecting conditioned media from adipose tissue and adipocyte cultures will allow us to further decipher molecular factors that convey information between the cells. Our avatar models will be comprehensively characterized to understand individual patient profiles using state-of-the-art techniques. Cellular features, such as cell viability, lipid accumulation and tumor markers, will be studied using high-content imaging. Transcriptomics will be applied to analyze genome-wide gene expression, and proteomics and metabolomics will be applied to measure intracellular and secreted levels of proteins and metabolites. Using a high-throughput drug testing platform, we will profile the patient-derived avatar models for drug sensitivity and resistance against a comprehensive library of annotated drugs. We aim to measure differential drug response of cancer models in the presence of adipocytes or adipocyte-conditioned media compared to mono-culture models. Moreover, we will be able to identify cell type-specific drug profiles, either affecting cancer cells or adipocytes alone. Based on the obtained data, we will test potentially effective drug combinations to target the adipocyte – cancer cell communication across multiple drug concentrations, as well as drug treatment sequence and timing. By measuring genomic alterations in the cancer avatar models, we will be able to map patient-individual drug sensitivity profiles to existing genomic determinants of drug response.
Impact on the broader field
The proposed study represents a translational workflow extending current precision oncology approaches. It will provide a major opportunity to uncover new patient-specific targetable cancer vulnerabilities arising from specific interactions between ovarian cancer cells and surrounding adipocytes. We suggest modifying our workflow of patient-derived avatar models for future studies to apply to more complex cellular systems considering the multilayered composition of the tumor microenvironment including immune cells, fibroblasts and endothelial cells. It will also be applicable to other cancer types where patient material is available. Our approach has the potential to improve treatment decision by not only focusing on cancer cells but mimicking the surrounding complex cellular environment in each individual patient.
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