Abstract B72: Single-cell analysis of chemotherapy resistance in ovarian cancer
Authors: Boris J. Winterhoff, Shobhana Talukdar, Zenas Chang, Jason Cepela, Mihir Shetty, Jun Woo, Ying Zhang, Joshua Baller, Constantin Aiferis, Andrew Nelson, Jinhua Wang, Timothy K. Starr
Journal: Clinical Cancer Research (2020)

Abstract A majority of women diagnosed with ovarian cancer will relapse after treatment, and most of these recurrent cancers will become resistant to standard chemotherapies. To better understand the mechanisms of chemotherapy resistance, we have initiated a prospective study to comprehensively analyze the molecular characteristics of tumor samples taken during primary debulking, interval debulking, and at recurrence. We are also generating patient-derived xenograft (PDX) models using the same tissue samples and treating these mice with chemotherapy. Our goal is to define molecular characteristics that are prognostic and predictive for chemotherapy resistance. We have enrolled over 40 women in our study and have completed single-cell RNA sequencing (scRNAseq), bulk tumor exome, germline targeted exome, and NanoString molecular subtyping on a subset of these samples. Five of these patients received neoadjuvant chemotherapy (NACT) and we performed scRNAseq on a sample taken during initial diagnostic procedures (pre-chemo) and a second sample from interval debulking (post-chemo). For two additional patients we have pre-chemo samples from their primary debulking and matching post-chemo samples from PDX mice that were treated with chemotherapy. The majority of samples were harvested from metastatic sites in the omentum. ScRNAseq was performed using the 10X genomics platform, and we obtained data on an average of 3,000 cells per sample. We used multiple bioinformatics methods, including ccFindR, Seurat, SC3, ClusterExperiment, and CIDR, to analyze cell populations present in the samples. Comparison of the pre- and post-chemo scRNAseq datasets on these patients revealed a reduction in the number of distinct cancer epithelial cell types detected in the post-chemo samples compared to the pre-chemo samples, with concomitant changes in gene expression within these identified cell clusters. The immune and stromal composition, including T- and B-cell frequencies and types, also changed significantly when comparing pre- and post-chemo samples, with distinct cell types/clusters found only in the pre- or post-chemo samples. Based on the gene expression patterns in these cell clusters, we have identified specific signaling pathways and cell types that suggest possible mechanisms used within the tumor microenvironment to resist chemotherapy. We conclude that scRNAseq provides valuable information regarding the processes taking place within the tumor microenvironment as the tumor progresses from chemotherapy sensitive to resistant. Our study is ongoing, and we are correlating scRNAseq results with DNA mutations and chromosomal copy number variants detected in the bulk exome and targeted germline data we have collected. We will also compare our scRNAseq datasets to matching bulk RNA sequencing that is currently being performed. We will follow the disease course in these women and determine differences in cell composition between patients who are sensitive, resistant, and refractory to chemotherapy. Citation Format: Boris J. Winterhoff, Shobhana Talukdar, Zenas Chang, Jason Cepela, Mihir Shetty, Jun Woo, Ying Zhang, Joshua Baller, Constantin Aiferis, Andrew Nelson, Jinhua Wang, Timothy K. Starr. Single-cell analysis of chemotherapy resistance in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B72.