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. 2019 Feb 27;8(2):136-151.e7.
doi: 10.1016/j.cels.2019.01.004. Epub 2019 Feb 20.

Convergent Identification and Interrogation of Tumor-Intrinsic Factors That Modulate Cancer Immunity In Vivo

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Free PMC article

Convergent Identification and Interrogation of Tumor-Intrinsic Factors That Modulate Cancer Immunity In Vivo

Adan Codina et al. Cell Syst. .
Free PMC article

Abstract

The genetic makeup of cancer cells directs oncogenesis and influences the tumor microenvironment. In this study, we massively profiled genes that functionally drive tumorigenesis using genome-scale in vivo CRISPR screens in hosts with different levels of immunocompetence. As a convergent hit from these screens, Prkar1a mutant cells are able to robustly outgrow as tumors in fully immunocompetent hosts. Functional interrogation showed that Prkar1a loss greatly altered the transcriptome and proteome involved in inflammatory and immune responses as well as extracellular protein production. Single-cell transcriptomic profiling and flow cytometry analysis mapped the tumor microenvironment of Prkar1a mutant tumors and revealed the transcriptomic alterations in host myeloid cells. Taken together, our data suggest that tumor-intrinsic mutations in Prkar1a lead to drastic alterations in the genetic program of cancer cells, thereby remodeling the tumor microenvironment.

Keywords: Prkar1a; cancer genomics; cancer immunology; in vivo CRISPR screen; transformation.

Conflict of interest statement

Declaration of Interests

The authors declare no competing interests.

Figures

Figure 1.
Figure 1.. Genome scale in vivo CRISPR screens in different mouse hosts identified genes regulating oncogenesis.
(A) Schematics of experiment. Left, p53−/−;Myc mutated non-malignant hepatocyte cell lines are capable of growing as tumors in immunodeficient Rag1−/− mice with deficiency in both innate and adaptive immune system, however they are rejected by Nu/Nu mice. Right, genome-scale CRISPR screen and validation for mutants that can grow as tumors. Genome-scale CRISPR libraries (mBrie or mGeCKO) mutagenized cell pools have robust tumor growth in Nu/Nu host. Validation of individual gene mutant cells with top hits in mouse hosts with different levels of immune competence. (B) Tumor growth curves of mBrie library transduced hepatocytes (n = 21), as compared to various injections using the parental cell line (n = 3 each) over a 30 day period (Kolmogorov-Smirnov (KS) test, mBrie vs all controls, p = 7e-4). Error bars are s.e.m. (C) A color-coded scatterplot showing hit identification for genes whose loss-of-function lead to consistent tumor growth. Screen was performed using a second-generation genome-scale CRISPR knockout library (mBrie) transforming hepatocytes that can grow in fully immunodeficient Rag1−/− mice but not in Nu/Nu mice that have competent innate immunity although lacking mature T cells. Library-transduced cells were capable of escaping the innate immune system and grew as tumors at 100% penetrance. Average sgRNA library representation across all Nu/Nu tumors (n = 21) was plotted against average sgRNA library representation across all cell replicates (n = 3). The 11 top hits that pass all three statistical criteria were plotted as color-coded dots with each dot representing one sgRNA where the same color represents independent sgRNAs for the same gene. Non-targeting control sgRNAs (NTCs) were shown as dark grey dots. Other gene-targeting sgRNAs (GTSs) were shown as light grey dots. (D) A barplot of the number of independent scoring sgRNAs in the mBrie screen. False-discovery rate (FDR) of 0.2% was used as a cutoff for scoring in each tumor. Four sgRNAs per gene were contained in the mBrie library. Highly consistent hits with >=3 scoring sgRNAs (14 genes with 3/4 scoring sgRNAs, 12 genes with 4/4) were shown. (E) A Venn diagram showing the overlap of hits identified using three selection criteria, revealing 11 genes significant regardless of criteria. The first criterion is by abundance (i.e. a gene with one or more sgRNA(s) comprising of >=2% total reads in a tumor); the second criterion is by phenotypic penetrance (i.e. a gene with the frequency of one or more sgRNA(s) passing FDR 0.2% cutoff across 25% of all independent tumor samples); the third criteria is by independent construct (i.e. a gene with >=2 sgRNA(s) passing FDR 0.2% cutoff). (F) RIGER analysis of the genome-scale screen showing top hits. (G) MAGeCK analysis of the genome-scale screen showing top hits. See also: Figures S1 and S2.
Figure 2.
Figure 2.. Convergent screen analysis and validation revealed Prkar1a mutant cancer cell robustly generate tumors in fully immunocompetent host.
(A) Venn diagram showing convergence of two genome-scale in vivo CRISPR screens, with hits identified using the custom 3-Way HitCaller pipeline (Left). The screen was repeated with a genome-scale CRISPR library (mGeCKO) and 5 genes were identified as top hits passing all statistical criteria. The mBrie screen and mGeCKO screen shared 3 hits: Prkar1a, Apc and Tsc2. (Middle) Convergence of two genome-scale in vivo CRISPR screens, with hits identified using RIGER. (Right) Convergence of mBrie in vivo CRISPR screens, showing consistent hits using three different hit calling algorithms (custom 3-Way HitCaller, MAGeCK and RIGER). (B) Quantification of tumor sizes of top screen hit mutants. (Top) tumor volume measurement of mutants in C57BL/6J host 2 weeks post injection. Prkar1a mutant cells grew rapidly in this host, whereas NTC, Apc, Tsc2 or Csnklal mutants did not (n = 10 each); (Bottom) tumor volume measurement of mutants in Nu/Nu host 2.5 weeks post injection. Two-sided unpaired Mann-Whitney test was used to assess statistical significance for Prkar1a, Apc, Tsc2 or Csnklal mutants, relative to NTC (ns, not significant, i.e.*, p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001). (C) Rate of tumor incidence of top screen hit mutants. (Top) tumor incidence rate of mutants in C57BL/6J host. Two-sided Fisher’s exact test was used to assess statistical significance for Prkar1a, Apc, Tsc2 or Csnk1a1 mutants, relative to NTC (ns, not significant, i.e. p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001). (D) Representative H&E images of tumors in C. Scale bar, 250 μm. See also: Figures S2 and S3.
Figure 3.
Figure 3.. Phosphoproteomic characterization of Prkar1a mutant cells identified its downstream signaling targets.
(A) Schematics of Prkar1a phosphoproteomic experiments. Two independent sets of Prkar1a mutant and NTC control cells were compared using a multiplexed shotgun proteomics approach that included stable isotope labeling using amino acids in cell culture (SILAC), followed by protein extraction, trypsin digestion, TiO2 enrichment of phosphopeptides, cation exchange chromatography (CEX) fractionation and LC-MS/MS analysis by LTQ Orbitrap XL. Peptides were subsequently quantified, identified and assembled into groups of proteins, based on shared peptide sequences. (B) Waterfall plots of the top upregulated and downregulated proteins as MS-identified protein groups in Prkar1a mutants. The protein level changes in Prkar1a mutants (Prkar1a /NTC log2 normalized-protein ratio (nr)) are plotted. As expected, the PRKAR1A protein is among the most downregulated. (C) Color-coded scatterplot of differential phosphorylation between independent phosphoproteomic experiments. Phosphorylation changes in each Prkar1a mutant sample (Prkar1a /NTC log2 nr) are plotted for each phosphorylation site (PS). Hypo-PS and hyper-PS (> 1.5-fold change) are shown as respective blue and red dots, and top differential PS (ΔPS, > 3-fold change) are labeled with protein symbols. (D) Western of STAT3 and phospho-STAT3 (pSTAT3) in Prkar1a mutant and NTC cell samples. (E) Waterfall plots of the top hyper-phoshorylation site (PS) and hypo-PS in Prkar1a mutants (Left and right panels, respectively). The PS changes in Prkar1a mutants (Prkar1a /NTC log2 nr) are labeled with the gene symbol (bold), UniProt accession number and phosphorylated residue (in parentheses), as well as the peptide sequence in a 31 residue window, centered on the PS residue. (F) Waterfall plot of the Ingenuity Pathway Analysis of protein groups with PS changes (DPS; > 1.5-fold change) in Prkar1a mutants. The top ten canonical pathways are ranked by significance (−logi0 p value), and the bar colors represent the activation z score, which is presented to the right of each bar. (G) Identification of hyper- and hypo-phosphorylation motifs in Prkar1a mutant cells. (Left) Sequence log-odds diagrams (Logos) are shown for hyper-PS (top) or hypo-PS (bottom) motifs that are over-represented in Prkar1a mutant cells. Each motif is presented above the Logo with an asterisk following the PS and a motif score for the enrichment below the motif. (Right) Logos are shown for the top kinase-interacting motifs that match to the identified hyper-PS (top) or hypo-PS (bottom) motifs in Prkar1a mutant cells. Up to four of the top kinase-substrate motifs (Bonferroni-adjusted p < 1e-3) are presented for each PS motif. All kinase-substrate motifs are shown with the significance of the motif match (upper right of each Logo) and the kinase name with phosphorylated residues in parentheses (upper left of each Logo).
Figure 4.
Figure 4.. Whole transcriptome profiling of Prkar1a mutants identified an upregulated gene expression program of immune response and extracellular protein production.
(A) An overall heatmap of all differentially expressed genes between Prkar1a mutant and control cells. Three independent sgRNAs for Prkar1a and two independent NTCs were subjected to whole transcriptome analysis by mRNA-seq (n = 3 biological replicates for each sgRNA). Differential gene expression (upregulated or downregulated in Prkar1a mutants) is shown by z scores. (B) A color-coded volcano plot of all differentially expressed transcripts between Prkar1a mutant and control cells. The statistical significance (−log10 FDR-corrected p value) was plotted against the log2 fold-change of normalized gene expression levels. Each dot represents one gene, which are color-coded by the most highly enriched gene sets, described in the legend. (C) Waterfall plots of most significantly enriched gene ontologies for genes that are upregulated in Prkar1a mutants. The enrichment analysis was performed by the Database of Annotation, Visualization and Integrated Discovery (DAVID), and the top gene sets are shown, as ranked by FDR-corrected q values. Gene sets including extracellular or secreted proteins emerged on top as most enriched; Gene sets including cAMP catabolic process and response, as well as phosphodiesterase activity indicated the on-target activity of Prkar1a regulated PKA pathway. (D) A waterfall plot of most significantly enriched gene sets of all Prkar1a upregulated genes. Pathway analysis was performed using GSEA with gene expression levels considered during enrichment calculation (methods), and the top gene sets are shown, as ranked by FDR-corrected q values. Gene sets including acute inflammatory response and molecular mediator of immune response emerged on top as the most significantly enriched ones; the gene set of ribonucleotide catabolic process indicated the on-target activity of Prkar1a regulated PKA pathway; Gene sets are not mutually exclusive. (E) A GSEA individual pathway analysis plot showing the enrichment of acute inflammatory response genes in the Prkar1a mutant cells as compared to NTC. (F) A heatmap of all differentially expressed transcripts between Prkar1a mutant and control cells for the genes in the acute inflammatory response category. Relative gene expression levels were shown as z scores. (G) A heatmap of all differentially expressed transcripts between Prkar1a mutant and control cells for the genes in the proteinaceous extracellular matrix category. Relative gene expression levels were shown as z scores. (H) A barplot of IL-6 ELISA between Prkar1a mutant and control cells. See also: Figure S4.
Figure 5.
Figure 5.. Single cell profiling of Prkar1a mutant tumors in hosts of different immune competence
(A) Schematic for single cell RNA-seq (scRNA-seq) experiments. IMC9 tumor cells were transfected with sgPrkar1a or NTC to generate Prkar1a knockout or control cells, respectively. Transfected IMC9 cells were then subcutaneously injected into immunocompromised (Rag1−/−), partially immunocompromised (Nu/Nu), and immunocompetent (C57BL/6) mice as shown, and the resulting tumors were extracted, then sorted to CD45+ tumor-infiltrating immune cells and tumor cells that were mixed in a 1:10 ratio for scRNA-seq. One representative mouse in each group was sequenced. (B) H&E staining of tumors subjected to tumor-immune scRNAseq. Scale bar, 250 μm. (C) Cellular populations within the tumor microenvironment, visualized by t-distributed stochastic neighbor embedding (t-SNE) plot of clustered, dimensionality-reduced single-cell transcriptional profiles. Each dot represents a single cell that is arranged relative to other cells based on transcriptional profiles, such that similar cells are grouped together. See also: Figures S5, S6.
Figure 6.
Figure 6.. Differential single cell expression analysis of the immune microenvironment in the tumors induced by Prkar1a mutant and control cancer cells in Rag1−/− mice.
(A) t-distributed stochastic neighbor embedding (t-SNE) visualization of aligned single-cell transcriptional data generated from Prkar1a−/− or NTC tumors in Rag1−/− mice. Cells of the t-SNE plot are labeled by the tumor dataset (top) and by unsupervised clusters that were identified (bottom). (B) Identification of cell cluster populations based on expression correlation to known immune cell populations. Cluster-specific marker genes were identified as differentially expressed genes (MAST analysis; FDR-adjusted p < 0.001) with the top 95% fold-change between clusters, and the log-normalized mean expression of these marker genes in each cluster was compared by Kendall correlation analysis to ImmGen V1 transcriptional data (85 cell populations of innate immune and stromal cells). The scaled correlation coefficients for the cluster-specific markers are shown for each ImmGen cell population, arranged by unsupervised hierarchical clustering, performed separately within 6 broad cell type categories of the ImmGen data. (C) Classification of cell clusters by cell type-specific characteristic gene expression patterns, as shown by expression-labeled t-SNE plots. Clusters 1 and 2 are identified as myeloid cell populations by the gene expression of canonical myeloid cell surface markers such as Cd14, Fcgr3 (Cd16), Fcgr2b (Cd32) and Itgam (Cd11b). These clusters also express known markers of tumor-associated macrophages (TAMs) and myeloid-derived suppressor cells (MDSCs). Cluster 3 represents the IMC9 cancer cells based on the expression of the previously defined IMC9-specific gene markers including Mgp, Fkbp11, Cdkn2a and Mrpl33. (D) Volcano plots showing differentially expressed genes in the tumor microenvironment between the myeloid-like immune cells in tumors induced by Prkar1a−/− and control cells. Each panel shows a cluster-specific differential expression analysis, for which the FDR-adjusted p values are shown in the y axes and the log-scale expression fold-changes are shown in the x axes. Differentially expressed genes were defined as those with an adjusted p value < 0.01 and a fold-change > 1.8 in the Prkar1a knockout tumor model. Overexpressed and underexpressed genes are depicted by red and blue points, respectively. See also: Figure S7.
Figure 7.
Figure 7.. Flow cytometry analysis of the immune microenvironment in the tumors induced by Prkar1a mutant and control cancer cells.
(A-B) Flow cytometry plots present the differences in the quantities of specific immune cell subsets from tumors that were generated with or without Prkar1a loss. (A) The gating schemes for the detection of dendritic cells (DCs) and tumor-associated macrophages (TAMs) are presented using composite flow cytometry data from five sgPrkar1a-generated tumors or six sgNTC-generated tumors in Rag1−/− mice. (B) Plots show the flow cytometry gating scheme for the quantification of monocytic and polymorphonuclear myeloid-derived suppressor cells (M-MDSCs and PMN-MDSCs, respectively) in tumors that were generated with or without Prkar1a loss. The composite flow cytometry data is shown for eight sgPrkar1a-generated tumors or six sgNTC-generated tumors in Rag1−/− mice. (C) Quantitative analysis of immune cell infiltration differences between Prkar1a−/− and NTC tumors. Immune cells were quantified from the flow cytometry data in (A-B) and normalized to proportion of CD45/CD45.2+ cells for each tumor. The immune cell populations were compared between Prkar1a−/− and NTC tumors by unpaired t-test with Holm-Sidak correction for multiple-testing. Significant population changes are depicted with asterisks (**, adjustedp < 1e-5).

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