Ovarian Cancer Transcriptomic Profiling

Low-quality and non-human RNA-seq reads were identified and removed from the analysis pipeline using the Kraken suite of quality control tools (25, 26 (link)). High-quality, trimmed, human RNA-seq reads were aligned to the human genome (GRCh37; hg19) using TMAP (v5.0.7) and gene counts were calculated using High-throughput sequence analysis in Python (HT-Seq) as described previously (24 (link)). Gene set enrichment analysis (GSEA, RRID:SCR_005724) comparing PDX samples, primary ovarian cancer ascites, and primary ovarian cancer solid tumors was analyzed using the GSEA software (http://www.gsea-msigdb.org/gsea/index.jsp; ref. 27 (link)). A total of 1,742 genes in the ovarian cancer dataset were compared with 7,871 gene sets from the Molecular Signature Database after filtering out for gene set size (minimum 15, maximum 500 genes/set). For comparison of PDX and primary ovarian cancer gene expression, nine solid tumor or ascites PDX samples were compared with 13 primary or omental ovarian tumors, and 10 primary ascites samples. Samples were divided into three groups: PDX, primary ascites, or primary solid tumor. Genes with significantly altered expression between groups were tabulated.
Deconvolution of normalized gene expression data was performed using the publicly available Carcinoma Ecotyper software (https://ecotyper.stanford.edu/carcinoma; ref. 28 (link)). Luca and colleagues analyzed 16 select tumor types, including ovarian serous cystadenocarcinoma, to identify the cellular composition and cell states based on gene expression clusters identified from single-cell RNA-seq datasets. The gene expression clusters can then be used to perform deconvolution on bulk RNA-seq tumor samples. Our ovarian cancer expression data from four PDX ascites samples, five PDX tumors, eight patient ascites samples, and 14 patient tumors were analyzed using this program. Output is given as the abundance by state for 12 cell types including immune cell types, cancer epithelial cells, endothelial cells, and fibroblasts. The average estimated abundance of monocytes/macrophages, CD4⁺ T cells, CD8⁺ T cells, and cancer epithelial cells by state is presented for the four sample types.