• 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br Figure Transcriptomic and Epigenetic Map of Pancreatic


    Figure 1. Transcriptomic and Epigenetic Map of Pancreatic Cancer Cells Reveals a Unique Stem Cell State
    (A) Tumor organoid formation from primary Msi2+ and Msi2 REM2-KPf/fC tumor cells. Representative images, scale bars represent 100 mm.
    (C) Principal-component analysis of KPf/fC stem (purple) and non-stem (gray) cells.
    (D) Transcripts enriched in stem (red and pink) and non-stem N-octanoyl-L-Homoserine lactone (dark blue and light blue). Pink, light blue, local false discovery rate (lfdr) < 0.3; red, dark blue, lfdr < 0.1.
    (EÐJ) GSEA cell states and corresponding heatmaps associated with development (E and F), metabolism (G and H), and cancer relapse (I and J). (E, G, and I) Red denotes overlapping gene signatures; blue denotes non-overlapping gene signatures.
    (F, H, and J) Red, over-represented gene expression; blue, under-represented gene expression; shades denote fold change.
    (K) Single-cell sequencing of KPR172H/+C tumors (left) and map of Msi2 expression in ETC and EMT clusters (right); CAF, cancer-associated Þbroblasts (red); EMT, mesenchymal tumor cells (olive green); Endo, endothelial cells (green); ETC, epithelial tumor cells (blue); TAM, tumor-associated macrophages (magenta).
    (L) Hockey stick plots of H3K27ac occupancy ranked by signal density. Stem cell super-enhancers (left) or shared super-enhancers (right) are demarcated by highest ranking and intensity signals.
    (MÐO) H3K27ac ChIP-seq reads across genes marked by stem cell super-enhancers (M), shared super-enhancers (N), or non-stem super-enhancers (O). See also Figure S1.
    pancreatic cancer have a more specialized super-enhancer landscape than non-stem cells and raise the possibility that su-per-enhancer linked genes and their regulators may serve to control stem cell identity in pancreatic cancer. In support of this, key transcription factors and programs that underlie devel-opmental and stem cell states, such as Tead4, Wnt7b, and Msi2 (Figure 1L) and Foxp, Klf7, and Hmga1 (Table S2), were associated with super-enhancers in KPf/fC stem cells. 
    Genome-wide CRISPR Screen Identifies Core Functional Programs in Pancreatic Cancer
    To deÞne which of the programs uncovered by the transcrip-tional and epigenetic analyses represented true functional de-pendencies of stem cells, we carried out a genome-wide CRISPR screen. Thus, primary cell cultures enriched for stem cells (Figure S2A) were derived from REM-KPf/fC mice and trans-duced with the mouse GeCKO CRISPRv2 single guide RNA
    Figure 2. Genome-Scale CRISPR Screen Identifies Core Stem Cell Programs in Pancreatic Cancer
    (A) Schematic of CRISPR screen.
    (B) Number of guides in each replicate following lentiviral infection (gray bars), puromycin selection (red bars), and sphere formation (blue bars). (C and D) Volcano plots of guides depleted in 2D (C) and 3D (D). Genes indicated on plots, p < 0.005.
    (legend continued on next page)
    (sgRNA) library (Sanjana et al., 2014; Figure 2A). The screen was multiplexed in order to identify genes required in conventional 2D cultures, as well as in 3D stem cell sphere cultures (Rovira et al., 2010) that selectively allow stem cell growth (Fox et al., 2016; Figure 2A). The screens showed clear evidence of selection, with 807 genes depleted in 2D (Figures 2B and 2C) and an additional 178 in 3D stem cell cultures (Figures 2B and 2D). Importantly, the screens showed a loss of oncogenes and an enrichment of tumor suppressors in conventional cultures (Fig-ures 2C and S2B) and a loss of stem cell signals and gain of negative regulators of stem signals in stem cell conditions (Fig-ures 2D and S2C).
    Computational integration of the transcriptomic and CRISPR-based functional genomic data was carried out using a network propagation method similar to one developed previously (Va-nunu et al., 2010). First, the network was seeded with genes that were preferentially enriched in stem cells and also identiÞed as essential for stem cell growth (Figure 2E). The genes most proximal to the seeds were then determined using the mouse search tool for the retrieval of interacting genes/proteins (STRING) interactome (Szklarczyk et al., 2015) based on known and predicted protein-protein interactions using network propa-gation. Fold-change in RNA expression from the RNA-seq was overlaid onto the resulting subnetwork. The network was subse-quently clustered into functional communities based on high interconnectivity between genes, and gene set over-representa-tion analysis was performed on each community; this analysis identiÞed seven subnetworks built around distinct biological pathways, thus providing a systems-level view of core programs that may be involved in driving pancreatic cancer growth. These programs identiÞed stem and pluripotency pathways, develop-mental and proteasome signals, lipid metabolism and nuclear receptors, cell adhesion, cell-matrix, and cell migration, and im-muno-regulatory signaling as pathways integral to the stem cell state (Figures 2E and S2D).