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Tumor-derived IFN triggers chronic pathway agonism and sensitivity to ADAR loss

Abstract

Interferons (IFNs) are cytokines that play a critical role in limiting infectious and malignant diseases1,2,3,4. Emerging data suggest that the strength and duration of IFN signaling can differentially impact cancer therapies, including immune checkpoint blockade5,6,7. Here, we characterize the output of IFN signaling, specifically IFN-stimulated gene (ISG) signatures, in primary tumors from The Cancer Genome Atlas. While immune infiltration correlates with the ISG signature in some primary tumors, the existence of ISG signature-positive tumors without evident infiltration of IFN-producing immune cells suggests that cancer cells per se can be a source of IFN production. Consistent with this hypothesis, analysis of patient-derived tumor xenografts propagated in immune-deficient mice shows evidence of ISG-positive tumors that correlates with expression of human type I and III IFNs derived from the cancer cells. Mechanistic studies using cell line models from the Cancer Cell Line Encyclopedia that harbor ISG signatures demonstrate that this is a by-product of a STING-dependent pathway resulting in chronic tumor-derived IFN production. This imposes a transcriptional state on the tumor, poising it to respond to the aberrant accumulation of double-stranded RNA (dsRNA) due to increased sensor levels (MDA5, RIG-I and PKR). By interrogating our functional short-hairpin RNA screen dataset across 398 cancer cell lines, we show that this ISG transcriptional state creates a novel genetic vulnerability. ISG signature-positive cancer cells are sensitive to the loss of ADAR, a dsRNA-editing enzyme that is also an ISG. A genome-wide CRISPR genetic suppressor screen reveals that the entire type I IFN pathway and the dsRNA-activated kinase, PKR, are required for the lethality induced by ADAR depletion. Therefore, tumor-derived IFN resulting in chronic signaling creates a cellular state primed to respond to dsRNA accumulation, rendering ISG-positive tumors susceptible to ADAR loss.

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Fig. 1: ISG signature analysis across TCGA, PDX and CCLE.
Fig. 2: STING-dependent type I IFN signaling drives the chronic ISG signature and primes the cellular response to aberrant dsRNA.
Fig. 3: ADAR dependence in cancer cells displaying the chronic ISG signature.
Fig. 4: Mechanisms of ADAR dependence.

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Data and code availability

The data and code included in this study are available upon request, and correspondence should be addressed to E.R.M. (rob.mcdonald@novartis.com).

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Acknowledgements

We thank J. Engelman and F. Hofmann for critical reading of the manuscript. We thank C. Fiorilla for assistance in obtaining CCLE models. We also thank Y. Feng, G. Yu and J. Reece-Hoyes for kindly providing CRISPR-Cas9 reagents.

Author information

Authors and Affiliations

Authors

Contributions

H.L. and E.R.M. designed the study and wrote the manuscript. H.L. performed most of the experiments with L.K.B., F.S.C. and R.S.D. contributing. J.G. established the ISG core score and conducted the TCGA, PDX and CCLE bioinformatic analyses (with guidance from J.M.K. and K.D.M.) as well as the DRIVE analysis in Fig. 3a. For bulk RNA-seq analyses, D.P.R. performed the library preparation and F.S.C. conducted the analysis. For scRNA-seq, J.T.C. performed the library preparation and conducted the analysis. C.P.B. and J.D.G. designed, executed and analyzed the in vivo study. J.M.K., M.D.J., M.R.S. and J.G. designed the CRISPR library. M.R.S. and L.L. cloned the CRISPR library. R.S.D., S.S. and D.A.R. assisted post screen with PCR and sequencing. G.K. conducted the CRISPR screen analysis. R.A.P., J.M.K. and K.D.M. provided guidance on experimental design/analyses. H.L., E.R.M., J.G., R.A.P., J.M.K. and K.D.M. edited the manuscript.

Corresponding author

Correspondence to E. Robert McDonald III.

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Competing interests

All authors performed the work herein as employees of the Novartis Institutes for Biomedical Research, Inc. NIBR employees are also Novartis stockholders. R.A.P. is an employee and equity holder of Celsius Therapeutics. J.D.G. is a former employee of Novartis and is currently an employee of the Broad Institute of MIT and Harvard.

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Extended data

Extended Data Fig. 1 ISG signatures and their incidence in TCGA primary tumors.

a, Venn diagram showing the overlap of genes among the three ISG signatures. For a global view of IFN-driven responses, these complementary ISG signatures induced by either type I or type II IFNs derived from immune, epithelial and mesenchymal cell lineages were deployed. b, Scatter plots of TCGA primary tumors (n = 11,047) comparing the ISG scores derived from the three signatures. Each dot indicates a TCGA tumor sample. The three ISG signatures are highly correlated in primary tumors, as shown by Pearson’s method (R 2), suggesting that expression levels of the 38 overlapping ISGs among the three signatures reflect the combinatorial output of both type I and type II IFN signaling in primary tumors.

Extended Data Fig. 2 Correlation between ISG core score and tumor purity in TCGA primary tumors.

a, Pearson correlation (R 2) between ISG core score and tumor purity (consensus purity estimate) in different lineages of TCGA primary tumors as indicated. Number of tumors for each lineage: BRCA, n = 1095; UCEC, n = 550; KIRC, n = 536; LUAD, n = 527; HNSC, n = 520; LGG, n = 520; LUSC, n = 501; THCA, n = 501; PRAD, n = 497; SKCM, n = 469; COAD, n = 467; OV, n = 422; BLCA, n = 410; LIHC, n = 372; CESC, n = 304; KIRP, n = 290; READ, n = 166; GBM, n = 163; ACC, n = 79; KICH, n = 66; UCS, n = 57. Each dot indicates a TCGA tumor sample. Dotted lines illustrate the score cutoff for ISG-high tumors. b, Number of TCGA primary tumors with respect to their purity status and ISG core scores.

Extended Data Fig. 3 ISG core score in PDX and CCLE samples.

a,b, ISG core score in PDX (a) and CCLE (b) samples. Dotted lines illustrate score cutoffs for ISG-high, -medium and -low models using k-means clustering (k = 3). Lineages ranked by the median ISG expression as denoted by the black bar.

Extended Data Fig. 4 Genesis of the ISG signature.

a, Sanger sequencing files of genomic DNA from one experiment indicate Cas9-mediated editing at corresponding loci. The sequences complementary to sgRNA sequences are underlined. Arrowheads indicate the conserved position cleaved by Cas9 that is 3 bp upstream of the PAM motif. b, IFNAR1 and IFNLR1 RNA transcript levels in CCLE models (n = 726). Whiskers extend from the lower to the upper adjacent value; the box extends from the 25th to the 75th percentile, with the median in each lineage as indicated by the line in the box. c,d, Expression of multiple ISGs following genetic ablation of IRF3 (c) and signaling adapters including MAVS, MYD88 and STING (d) in ovarian COV318 and pancreatic SW1990 cancer cell lines. Data are mean ± s.e.m., n = 3 biologically independent replicates, except n = 2 for COV318 cell expression of IRF3 sgRNA#2 in c.

Source Data

Extended Data Fig. 5 Expression levels of positive regulators of IFN signaling and negative regulators of immune responses in different ISG classes of TCGA, PDX and CCLE samples.

a,b, Violin plots depict RNA expression levels of IFN pathway components (a) and immunosuppressive factors (b) in different ISG classes of TCGA, PDX and CCLE samples. Number of ISG-high samples: CCLE, n = 107; PDX, n = 104; TCGA, n = 2571. Number of ISG-medium samples: CCLE, n = 326; PDX, n = 251; TCGA, n = 5148. Number of ISG-low samples: CCLE, n = 295; PDX, n = 275; TCGA, n = 3328. Median and interquartile ranges of the expression levels are also indicated.

Extended Data Fig. 6 Additional validation data for ADAR dependence.

a, ADAR depletion by Dox-inducible shRNAs assessed by western blots and growth effects assessed by colony formation assays. Similar results were obtained for MDA-MB-468, HSC4 and SW1990 in two additional experiments. Blot images are cropped to show the relevant bands, and molecular mass markers are indicated (in kD). See Source Data for the uncropped western blots. b, ADAR RNA levels in CAL27 xenograft tumors. Data are mean ± s.e.m., n = 3 mice for each group.

Source Data

Source Data

Extended Data Fig. 7 Genetic suppressor screen to identify genes required for ADAR dependence.

a, Genes identified by the genetic suppressor screen (n = 19,734 genes included in the screen) are grouped based on annotated functions. P value is calculated as described in Methods. b, Diagram showing the functions of identified genes involved in RNA polymerase III transcription and RNAi processes. HATs, histone acetyltransferases; RISC, RNA-induced silencing complex. c, Western blots showing the depletion of ADAR by a Dox-inducible shRNA in control, AGO2-ablated and PKR-ablated HSC4 cells (representative from two repeated experiments). Blot images are cropped to show the relevant bands, and molecular mass markers are indicated (in kD). See Source Data for the uncropped western blots.

Source Data

Extended Data Fig. 8 Additional data for genetic interactions between ADAR and PKR and mechanisms of ADAR dependence.

a, Waterfall plot of ADAR dependence from Project DRIVE, colored by PKR RNA expression levels. b,c, Phosphorylated PKR (p-PKR) levels in b and phosphorylated eIF2α (p-eIF2α) levels in c assessed by western blots in cell lines transduced with Dox-inducible ADAR shRNAs. Similar results were obtained in two additional experiments for SW1990 as in b and for MDA-MB-468 and HSC4 as in c. Blot images are cropped to show the relevant bands, and molecular mass markers are indicated (in kD). See Source Data for the uncropped western blots. d, Volcano plot depicts transcriptional changes following ADAR depletion in SW1990 cells (n = 2 biologically independent replicates for both control and Dox-treated samples). Dotted lines illustrate the cutoff for dysregulated genes with log2 (fold change) >1 and P < .001. P value is calculated as described in Methods. e, Heatmap depicts transcript-level changes of individual ISG and cytokine genes.

Source Data

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Liu, H., Golji, J., Brodeur, L.K. et al. Tumor-derived IFN triggers chronic pathway agonism and sensitivity to ADAR loss. Nat Med 25, 95–102 (2019). https://doi.org/10.1038/s41591-018-0302-5

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