Spatially resolved single-cell patterns of drug-resistant ovarian cancers

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Project Summary Ovarian cancers remain one of the deadliest cancers affecting women. Tumors commonly acquire resistance to first-line chemotherapeutics, and only 30% of patients survive beyond 5 years. Novel targeted combination therapies are needed to improve long-term survival outcomes and will depend on an improved understanding of the molecular and genetic mechanisms of drug resistance. Previous work has used next-generation bulk sequencing approaches to globally profile genetic signatures of drug resistance in ovarian cancer tissue. However, rare cells in a highly heterogeneous 3D tissue context may hold the key to understanding these complex processes, and such rare cells are notoriously difficult to identify using these standard methods. We will use an in vivo patient derived xenograft (PDX) model of high grade serous ovarian cancer (HSGSOC) representing multiple different genetic backgrounds. HGSOC PDXs will be allowed to acquire resistance to PARP inhibitor talazoparib. Transcriptional signatures of PARP inhibitor resistance will be assessed using single cell RNA sequencing of single cells isolated from tumor tissue fully resistant to inhibitor as well as from tissue collected at intermediate time points. scRNA seq data will be mined to gain transcriptional signatures of (1) component cell populations and (2) candidate genes driving the resistant phenotype. We will use multiplex single molecule FISH and light sheet microscopy to image target (n ~ 100) mRNA species in thick tissue blocks. We will analyze 3D image datasets to identify resistant cancer cells and chart their 3D position in relation to supporting cell types that express relevant cell signaling ligands and/or receptors and additional tumor features (e.g. stroma, blood vessels). Lastly, we will choose target genes that will be functionally validated in relevant cell culture and additional PDX in vivo models. By examining single cell transcriptional profiles, we will greatly advance our understanding of how the 3D tumor tissue microenvironment allows and encourages rare cells with pre-resistant transcriptional programs to escape PARP inhibitor treatment. Along with an improved understanding of the dynamics of drug resistance, the proposed research has the potential to suggest novel combination treatments that could be exploited in the future to more effectively eliminate HGSOC and prevent recurrence of drug resistant tumors.