Identification of grade and origin specific cell populations in serous epithelial ovarian cancer by single cell RNA-seq

Abstract

Here we investigated different cell populations within ovarian cancer using single-cell RNA seq: fourteen samples from nine patients with differing grades (high grade, low grade and benign) as well as different origin sites (primary and metastatic tumor site, ovarian in origin and fallopian in origin). We were able to identify sixteen distinct cell populations with specific cells correlated to high grade tumors, low grade tumors, benign and one population unique to a patient with a breast cancer relapse. Furthermore the proportion of these populations changes from primary to metastatic in a shift from mainly epithelial cells to leukocytes with few cancer epithelial cells in the metastases. Differential gene expression shows myeloid lineage cells are the primary cell group expressing soluble factors in primary samples while fibroblasts do so in metastatic samples. The leukocytes that were captured did not seem to be suppressed through known pro-tumor cytokines from any of the cell populations. Single cell RNA-seq is necessary to de-tangle cellular heterogeneity for better understanding of ovarian cancer progression.

Extensive Intratumor Proteogenomic Heterogeneity Revealed by Multiregion Sampling in High-Grade Serous Ovarian Tumor Specimens

“…One study demonstrated that stable and/or regressing tumors lacked common neoepitopes and mutations compared to progressing tumors in the same patient [3], implicating non-somatic factors within the TME as critical determinants of immune response and overall tumor fate. Multiregion sampling has revealed extensive variation between subpopulations of cells within a single tumor [5][6][7], allowing individual tumor samples to have multiple subtype signatures present with differing levels of activation [8]. Single-cell RNA-Seq of HGSOC samples has revealed grade-specific and cell type-specific transcriptional profiles present within individual tumor specimens [5].…”

“…Multiregion sampling has revealed extensive variation between subpopulations of cells within a single tumor [5][6][7], allowing individual tumor samples to have multiple subtype signatures present with differing levels of activation [8]. Single-cell RNA-Seq of HGSOC samples has revealed grade-specific and cell type-specific transcriptional profiles present within individual tumor specimens [5]. The presence of subclonal cell populations within primary and/or metastatic tumors has been demonstrated to influence the state of immune infiltration and activation [6].…”

Orchestrating Single-Cell Analysis with Bioconductor

“…Data derived from single-cell assays have enabled researchers to unravel tissue heterogeneity at unprecedented levels of detail, enabling the identification of novel cell types, as well as rare cell populations that were previously unidentifiable using bulk assays [92][93][94]. Unsupervised clustering -the process of grouping cells based on a similarity metric without a known reference -is a fundamental step in deconvoluting heterogeneous single-cell data into clusters that relate to biological concepts, such as discrete cell types or cell states.…”

Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

“…Ovarian stromal cells and myofibroblasts were identified based on expression of MUM1L1, ARX, and KLHDC8A, ovary-specific markers known to be expressed in stroma from bulk RNA-seq and immunohistochemistry [18] ( Figure 3D, Supplemental Figure 4A), with myofibroblasts distinguished by higher expression of α-smooth muscle actin and various collagen genes [19] ( Figure 3D, Supplemental Figure 4A). Unlike other non-epithelial cell types, ovarian stromal cells were largely restricted to the left ovary.…”

“…• Endothelial cells: VIM c , EMCN c , CLEC14A [45], CDH5 c , PECAM1 c , VWF c , MCAM [20], SERPINH1 [19] c : canonical marker The marker gene list described above and in Supplemental Table 2 was used for Cel-lAssign [42,18,19]. DCN, TPT1, and RBP1 were selected as markers of ovarian stromal cells based on differential expression results comparing normal fibroblasts (ovarian stromal cells) and malignant fibroblasts from [19] (these were the top 3 genes upregulated in normal fibroblasts by log fold change where Q < 0.05). CellAssign was run with default parameters, the shrinkage prior on δ gc values turned on, and 5 random initializations.…”