Dataset Information

Mouse ovarian cancer models recapitulate the human tumor microenvironment and patient response to treatment

ABSTRACT: Although there are many prospective targets in the tumor microenvironment (TME) of high-grade serous ovarian cancer (HGSOC), pre-clinical testing is challenging especially as there is limited information on the murine TME. Here, we have characterized the TME of six orthotopic, transplantable syngeneic HGSOC lines established from genetic models and compared these to patient biopsies. This analysis identified significant correlations between the transcriptome, host cell infiltrates, immune response, matrisome, vasculature and tissue modulus of mouse and human TMEs, with several stromal and malignant cell targets in common. However, each model showed distinct differences and potential vulnerabilities that enabled us to predict response to chemotherapy and an anti-IL-6 antibody. The transcriptional profiles of the mouse tumors that differed in chemotherapy response were able to classify chemotherapy-sensitive and -refractory patient tumors. These models provide useful pre-clinical tools and may help identify subgroups of HGSOC patients most likely to respond to specific therapies.

ORGANISM(S): Mus musculus Homo sapiens

PROVIDER: GSE132289 | GEO | 2020/01/12

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:High-grade serous ovarian cancer (HGSOC) exhibits significant genomic heterogeneity within a patient at presentation. Recent work has suggested that this heterogeneity is linked to the development of resistance and disease progression in that chemotherapy selects for minor resistant subclones embodied in presentation disease leading to short progression-free survival and resistant relapse. Cell line models support this by showing that relapse is not an immediate descendant of presentation disease, but so far no immediate clinical evidence has been provided that convincingly demonstrates the origin of relapse. It is further still unclear what evolutionary processes shape the mutational landscape of HGSOC in vivo and to what extent the degree of genomic heterogeneity impacts a patient's clinical outcome. We address these questions by inferring evolutionary trees of metastatic disease from structural variations between 138 cancer samples obtained from 17 patients undergoing neoadjuvant chemotherapy for HGSOC. Using novel phylogenetic methods, we quantify genomic changes in the course of chemotherapy and the degree of clonal expansion and show that these indices determine patient outcome with high accuracy. We demonstrate that relapse is indeed a minor subclone of presentation disease by verifying that the focal NF1 deletion of a relapse case was already present at biopsy. By leveraging the information contained in the evolutionary trees, we unveil the etiology of genetic heterogeneity and the tumorigenic potential of HGSOC cell populations. This is the first comprehensive study showing the origin, strength and effect of genetic heterogeneity in HGSOC in a clinical setting. In addition to its immediate clinical significance, it strengthens previous statements that single sample biopsies are seemingly insufficient to predict disease progression and stresses the importance of establishing multiple sampling as a routine approach in the clinic. Clinical data and tissue samples were collected on the prospective CTCR-OV03 and CTCR-OV04 clinical studies designed to identify biomarkers of heterogeneity.

Dataset Information

Mouse ovarian cancer models recapitulate the human tumor microenvironment and patient response to treatment

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure