Abstract A32: Single-cell RNA-seq analysis of primary tumor and corresponding metastatic lesion in high-grade serous ovarian cancer
Authors: Shih Andrew , Menzin Andrew , Whyte Jill , Lovecchio John , Liew Anthony , Khalili Houman , Onel Kenan , Gregersen Peter , Lee Annette 
Published: August 2018

Abstract
Ovarian cancer is highly curable when diagnosed early as localized disease. Most women come to medical attention, however, with metastatic disease. For these women, cure rates are quite low; only 30% of patients with late-stage high-grade serous ovarian cancer (HGSOC) will live more than 5 years. Although initially sensitive to platinum-based chemotherapy, in most cases, drug resistance develops and a progressive disease course ensues. Therefore, in order to improve prognosis and overall survival, there is an urgent need to understand the basis of drug resistance and to identify new therapeutic targets. Previous studies have stressed the significant role that tumor heterogeneity and microenvironment have in clinical outcome. It is our goal to understand the genomics of metastatic lesions as compared to primary lesions, to identify the genetic drivers of metastasis and drug resistance, which we can then functionally investigate in order to develop novel therapies.
Corresponding primary and metastatic tumor tissue samples from women with HGSOC were analyzed by single-cell RNA-seq. Isolated cells from each paired tissue sample were processed for next-gen sequencing using the BioRad droplet digital SEQ Single Cell Isolator and the Illumina SureCell Whole Transcriptome Analysis 3’ library prep kit, Normalization of expression, clustering of cells and gene expression markers defining each cluster was done by using the Seurat package in R. To identify specific tumor cell subsets in intra- and inter-patient analyses, a graph-based clustering using the principal components of the most variable expressed genes and T-distributed stochastic neighbor embedding (tSNE) analyses was performed.
Overall, we have found that while there is considerable heterogeneity among primary tumor cells from different patients, the expression profiles of metastatic lesions from different patients are remarkably similar, and are distinct from the primary lesions. As one example, by single-cell RNA-seq paired analysis of HGSOC primary tumor and corresponding metastatic lesions from 2 patients (primary fallopian and primary ovarian), we identified several cell clusters based on gene expression of common cellular markers. Further analysis identified significant expression of CD24, EPCAM, and KRT18 in epithelial cells of primary tumors while elevated CD44 expression was found in the T and B cell clusters of the metastatic lesions. Published studies have suggested elevated CD44 as a prognostic marker of poor overall survival. Whether elevated CD44 expression influences survival in our patients remains to be determined since clinical response data are not yet available. Additional analysis of gene expression profiles in other cell clusters is in progress.
Our ability to study patient-derived primary tumor and corresponding metastatic lesions using high-throughput single-cell analysis represents an unprecedented unique opportunity to study ovarian cancer without a priori knowledge of tumor and stromal cell inter-relationships. The single-cell assessment of patient-derived samples can provide critical information needed to understand chemoresistance commonly observed in high-grade serous ovarian cancer.
Citation Format: Andrew Shih, Andrew Menzin, Jill Whyte, John Lovecchio, Anthony Liew, Houman Khalili, Kenan Onel, Peter Gregersen, Annette Lee. Single-cell RNA-seq analysis of primary tumor and corresponding metastatic lesion in high-grade serous ovarian cancer. [abstract]. In: Proceedings of the AACR Conference: Addressing Critical Questions in Ovarian Cancer Research and Treatment; Oct 1-4, 2017; Pittsburgh, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(15_Suppl):Abstract nr A32.