Abstract B59: Single-cell sequencing as a prognostic and predictive tool for ovarian cancer therapy
Authors: Winterhoff Boris J. , Clark Christopher R. , Ramesh Sidharth , Shetty Mihir , Uppendahl Locke , Mitra Amit Kumra , Sebe Attila , Bazzaro Martina , Geller Melissa A. , Abrahante Juan E. , Molly Klein , Hellweg Raffaele , Mullany Sally , Beckman Kenneth , Daniel Jerry , Starr Timothy K. 
Published: August 2018

Abstract
Technological advances allow genomic and transcriptomic analyses to be conducted at the single-cell level. Analysis of gene expression and DNA sequence at this detailed level could lead to prognostic and predictive biomarkers as well as an enhanced understanding of cancer cell and stromal subpopulations including stem cells and chemotherapy-resistant populations. This knowledge will improve our ability to implement a “precision medicine” approach to the treatment of women with ovarian cancer.
We have initiated a project to prospectively study the transcriptome and DNA exome of freshly isolated, high-grade serous ovarian cancer solid tumor samples at the single-cell level. To date we have enrolled 8 patients and have initiated single-cell whole-exome DNA sequencing, using the Fluidigm C1 chip, and single-cell whole-transcriptome RNA sequencing, using the 10X Genomics platform. For comparison of RNA sequencing technologies, we previously performed RNA sequencing of a patient’s sample using the Fluidigm C1 chip. In addition to sequencing the primary tumor samples, we are concurrently attempting to establish patient-derived xenografts (PDXs) and cell lines for each patient. We have successfully generated PDX mice from the first patient and are currently treating a subset of the mice with carboplatin/paclitaxel. Patients will be followed prospectively and information on their clinical response will be incorporated into the analysis.
Analysis of the RNA sequencing results from the first patient using the Fluidigm C1 platform identified several distinct populations of cells characterized by gene expression patterns that correlate with specific signaling pathways and disease states. We could clearly differentiate stromal cells from cancer epithelial cells based on gene expression patterns. Furthermore, using known functional markers, we could define subsets within each population, including three subgroups of cancer cells and four subgroups of stromal cells. Based on their gene expression patterns, we could determine the frequency of cancer epithelial cells compared to cancer cells undergoing epithelial-to-mesenchymal transition, as well as activated and nonactivated fibroblasts and myofibroblasts. We were also able to identify rare cells expressing stem cell markers.
Using the 10X Genomics platform, we have completed RNA sequencing of five patients (~370 million reads/patient). We quantified gene expression on an average of 8,707 cells/patient and 1,723 genes/cell. Using graph-based clustering combined with the t-Distributed Stochastic Neighbor Embedding technique for high dimensionality reduction, we have identified approximately 9 to 15 subsets of cells within these cancer samples based on an unbiased analysis of their gene expression patterns. Using bioinformatic tools, including CellRanger, Seurat, Ingenuity Pathway Analysis, and Gene Set Enrichment Analysis, we can define several of these subsets based on known functional markers, including subsets of immune cells, stromal cells, and cancer epithelial cells. We can estimate the frequency of each subset and can also subdivide the groups using cell-type specific markers, for example, by distinguishing macrophages from dendritic cells within the immune subset.
We will present unpublished data, including RNA sequencing and DNA exome sequencing of single cells on the first 8 enrolled patients. In addition to sequencing the primary tumor samples, we plan on performing single-cell sequencing of matched platinum-resistant tumors generated by treating the PDX mice with carboplatin and paclitaxel. We will also correlate presence and percentage of cell subpopulations with clinical outcomes of the patients. Our long-term goal is to use single-cell data as a prognostic biomarker for chemotherapy resistance as well as a tool for predicting effective therapeutic options, including targeted therapy and immune therapy.
Citation Format: Boris J. Winterhoff, Christopher R. Clark, Sidharth Ramesh, Mihir Shetty, Locke Uppendahl, Amit Kumra Mitra, Attila Sebe, Martina Bazzaro, Melissa A. Geller, Juan E. Abrahante, Klein Molly, Raffaele Hellweg, Sally Mullany, Kenneth Beckman, Jerry Daniel, Timothy K. Starr. Single-cell sequencing as a prognostic and predictive tool for ovarian cancer therapy. [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 B59.