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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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).
References
Dunn, G. P., Koebel, C. M. & Schreiber, R. D. Interferons, immunity and cancer immunoediting. Nat. Rev. Immunol. 6, 836–848 (2006).
McNab, F., Mayer-Barber, K., Sher, A., Wack, A. & O'Garra, A. Type I interferons in infectious disease. Nat. Rev. Immunol. 15, 87–103 (2015).
Parker, B. S., Rautela, J. & Hertzog, P. J. Antitumour actions of interferons: implications for cancer therapy. Nat. Rev. Cancer 16, 131–144 (2016).
Zitvogel, L., Galluzzi, L., Kepp, O., Smyth, M. J. & Kroemer, G. Type I interferons in anticancer immunity. Nat. Rev. Immunol. 15, 405–414 (2015).
Gao, J. et al. Loss of IFN-gamma pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 e9 (2016).
Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).
Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554 e12 (2016).
Platanias, L. C. Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat. Rev. Immunol. 5, 375–386 (2005).
Ivashkiv, L. B. & Donlin, L. T. Regulation of type I interferon responses. Nat. Rev. Immunol. 14, 36–49 (2014).
Aran, D., Sirota, M. & Butte, A. J. Systematic pan-cancer analysis of tumour purity. Nat. Commun. 6, 8971 (2015).
Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).
Schroder, K., Hertzog, P. J., Ravasi, T. & Hume, D. A. Interferon-gamma: an overview of signals, mechanisms and functions. J. Leukoc. Biol. 75, 163–189 (2004).
Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).
Chiappinelli, K. B. et al. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 162, 974–986 (2015).
Roulois, D. et al. DNA-demethylating agents target colorectal cancer cells by inducing viral mimicry by endogenous transcripts. Cell 162, 961–973 (2015).
Sistigu, A. et al. Cancer cell-autonomous contribution of type I interferon signaling to the efficacy of chemotherapy. Nat. Med. 20, 1301–1309 (2014).
Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Bamborough, P. et al. 5-(1H-Benzimidazol-1-yl)-3-alkoxy-2-thiophenecarbonitriles as potent, selective, inhibitors of IKK-epsilon kinase. Bioorg. Med. Chem. Lett. 16, 6236–6240 (2006).
McDonald, E. R. et al. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell 170, 577–592 e10 (2017).
Liddicoat, B. J. et al. RNA editing by ADAR1 prevents MDA5 sensing of endogenous dsRNA as nonself. Science 349, 1115–1120 (2015).
Mannion, N. M. et al. The RNA-editing enzyme ADAR1 controls innate immune responses to RNA. Cell Rep. 9, 1482–1494 (2014).
Pestal, K. et al. Isoforms of RNA-editing enzyme ADAR1 independently control nucleic acid sensor MDA5-driven autoimmunity and multi-organ development. Immunity 43, 933–944 (2015).
Hartner, J. C., Walkley, C. R., Lu, J. & Orkin, S. H. ADAR1 is essential for the maintenance of hematopoiesis and suppression of interferon signaling. Nat. Immunol. 10, 109–115 (2009).
Rice, G. I. et al. Mutations in ADAR1 cause Aicardi-Goutieres syndrome associated with a type I interferon signature. Nat. Genet. 44, 1243–1248 (2012).
Sadler, A. J. & Williams, B. R. Interferon-inducible antiviral effectors. Nat. Rev. Immunol. 8, 559–568 (2008).
Pakos-Zebrucka, K. et al. The integrated stress response. EMBO Rep. 17, 1374–1395 (2016).
Pfaller, C. K., Li, Z., George, C. X. & Samuel, C. E. Protein kinase PKR and RNA adenosine deaminase ADAR1: new roles for old players as modulators of the interferon response. Curr. Opin. Immunol. 23, 573–582 (2011).
Dabo, S. et al. Inhibition of the inflammatory response to stress by targeting interaction between PKR and its cellular activator PACT. Sci. Rep. 7, 16129 (2017).
Gray, J. S., Bae, H. K., Li, J. C., Lau, A. S. & Pestka, J. J. Double-stranded RNA-activated protein kinase mediates induction of interleukin-8 expression by deoxynivalenol, Shiga toxin 1, and ricin in monocytes. Toxicol. Sci. 105, 322–330 (2008).
Chung, H. et al. Human ADAR1 prevents endogenous RNA from triggering translational shutdown. Cell 172, 811–824 e14 (2018).
Hartlova, A. et al. DNA damage primes the type I interferon system via the cytosolic DNA sensor STING to promote anti-microbial innate immunity. Immunity 42, 332–343 (2015).
Yu, Q. et al. DNA-damage-induced type I interferon promotes senescence and inhibits stem cell function. Cell Rep. 11, 785–797 (2015).
Harding, S. M. et al. Mitotic progression following DNA damage enables pattern recognition within micronuclei. Nature 548, 466–470 (2017).
Mackenzie, K. J. et al. cGAS surveillance of micronuclei links genome instability to innate immunity. Nature 548, 461–465 (2017).
Rongvaux, A. et al. Apoptotic caspases prevent the induction of type I interferons by mitochondrial DNA. Cell 159, 1563–1577 (2014).
White, M. J. et al. Apoptotic caspases suppress mtDNA-induced STING-mediated type I IFN production. Cell 159, 1549–1562 (2014).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Mostafavi, S. et al. Parsing the interferon transcriptional network and its disease associations. Cell 164, 564–578 (2016).
de Weck, A., Bitter, H. & Kauffmann, A. Fibroblast cell lines misclassified as cancer cell lines. Preprint at bioRxiv, https://doi.org/10.1101/166199 (2017).
Munoz, D. M. et al. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discov. 6, 900–913 (2016).
Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Mavrakis, K. J. et al. Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5. Science 351, 1208–1213 (2016).
Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15, 651–674 (2006).
Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR-Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014).
Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).
Robinson, M. D. & Smyth, G. K. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23, 2881–2887 (2007).
Konig, R. et al. A probability-based approach for the analysis of large-scale RNAi screens. Nat. Meth. 4, 847–849 (2007).
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
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
Ethics declarations
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.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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.
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.
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.
Supplementary information
Supplementary Table
Supplementary Tables 1-3
Source Data
Source Data Fig. 2
Statistics source data
Source Data Fig. 2
Western blots
Source Data Fig. 2
Western blots
Source Data Fig. 4
Western blots
Source Data Extended Data Fig. 4
statistics source data
Source Data Extended Data Fig. 6
statistics source data
Source Data Extended Data Fig. 6
Western blots
Source Data Extended Data Fig. 7
Western blots
Source Data Extended Data Fig. 8
Western blots
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41591-018-0302-5
This article is cited by
-
The role of ADAR1 through and beyond its editing activity in cancer
Cell Communication and Signaling (2024)
-
Suppression of A-to-I RNA-editing enzyme ADAR1 sensitizes hepatocellular carcinoma cells to oxidative stress through regulating Keap1/Nrf2 pathway
Experimental Hematology & Oncology (2024)
-
Caloric restriction leads to druggable LSD1-dependent cancer stem cells expansion
Nature Communications (2024)
-
An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer
Scientific Reports (2024)
-
INPP5A phosphatase is a synthetic lethal target in GNAQ and GNA11-mutant melanomas
Nature Cancer (2024)