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Cancer cells resistant to immune checkpoint blockade acquire interferon-associated epigenetic memory to sustain T cell dysfunction

Nature Cancer volume 4, pages 43–61 (2023)Cite this article

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Abstract

Prolonged interferon (IFN) signaling in cancer cells can promote resistance to immune checkpoint blockade (ICB). How cancer cells retain effects of prolonged IFN stimulation to coordinate resistance is unclear. We show that, across human and/or mouse tumors, immune dysfunction is associated with cancer cells acquiring epigenetic features of inflammatory memory. Here, inflammatory memory domains, many of which are initiated by chronic IFN-γ, are maintained by signal transducer and activator of transcription (STAT)1 and IFN regulatory factor (IRF)3 and link histone 3 lysine 4 monomethylation (H3K4me1)-marked chromatin accessibility to increased expression of a subset of IFN-stimulated genes (ISGs). These ISGs include the RNA sensor OAS1 that amplifies type I IFN (IFN-I) and immune inhibitory genes. Abrogating cancer cell IFN-I signaling restores anti-programmed cell death protein 1 (PD1) response by increasing IFN-γ in immune cells, promoting dendritic cell and CD8+ T cell interactions, and expanding T cells toward effector-like states rather than exhausted states. Thus, cancer cells acquire inflammatory memory to augment a subset of ISGs that promote and predict IFN-driven immune dysfunction.

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Fig. 1: Distinct ISGs and epigenetic features from cancer cells predict low CD8+ T cell cytolytic activity.
Fig. 2: Widespread remodeling of the relapsed tumor epigenome is characterized by activated enhancers with ISRE-like motifs.
Fig. 3: Tumors resistant to ICB are characterized by H3K4me1-marked inflammatory memory domains regulated by STAT1 and IRF3.
Fig. 4: OAS1 is controlled by inflammatory memory domains and amplifies IFN-I signaling in ICB-resistant cancer cells.
Fig. 5: Blocking OAS1 and IFN-I signaling in tumor cells restores CD8+ T cell-driven response to ICB.
Fig. 6: Inhibiting tumor IFN-I signaling enhances IFN signaling in immune cells to promote DC–CD8+ T cell interactions.
Fig. 7: Inhibiting tumor IFN-I signaling promotes transition of CD8+ T cell clonotypes toward an effector-like state after anti-PD1 therapy.

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Data availability

Raw sequencing reads and processed data for Figs. 2–4 and Extended Data Figs. 3–5 (ATAC-seq, RNA-seq, CUT&RUN) are deposited in the Gene Expression Omnibus under accession number GSE219179. Raw sequencing reads for Figs. 6 and 7 and Extended Data Figs. 7 and 8 (scRNA-seq and scTCR-seq) are deposited in the Sequence Read Archive and available under accession number PRJNA626462. RNA-seq data related to Fig. 5f have been previously deposited in the Gene Expression Omnibus under accession number GSE83850. Human RNA-seq and ATAC-seq tumor data used for Fig. 1 were derived from the TCGA Research Network and downloaded from the UCSC Xena Browser (TCGA PANCAN cohort) and the NCI Genomic Data Commons (https://gdc.cancer.gov/about-data/publications/ATACseq-AWG), respectively. Source data are provided with this paper.

Code availability

Processing and analysis code related to the TCGA and mouse integrated epigenomic analyses is deposited in a GitHub repository at https://github.com/jingyaqiu/ifnar_epigenome.

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Acknowledgements

A.J.M., J.Q., S.W., Y.X., E.J.W. and J.S. were supported by the Mark Foundation for Cancer Research. A.J.M., J.Q., B.X., D.Y., J.L.B., J.C.B. and E.J.W. were supported by the Parker Institute for Cancer Immunotherapy. A.J.M., D.Y., H.I. and E.J.W. were also supported by a program project grant from the NIH (1P01CA210944-01), and J.Q. was supported by the National Human Genome Research Institute (5T32HG000046-18). A.J.M. was additionally supported by the Breast Cancer Research Foundation. We thank the Penn Cytomics and Cell Sorting Resource Laboratory staff, staff at the University Laboratory Animal Resources and the Penn Genomics and Sequencing Core staff.

Author information

Author notes

  1. Bihui Xu

    Present address: Incyte, Wilmington, DE, USA

  2. Joseph L. Benci

    Present address: Bristol Myers Squibb, Princeton, NJ, USA

  3. Yuanming Xu

    Present address: Pfizer, San Diego, CA, USA

  4. These authors contributed equally: Jingya Qiu, Bihui Xu.

Authors and Affiliations

  1. Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA

    Jingya Qiu, Bihui Xu, Darwin Ye, Shangshang Wang, Joseph L. Benci, Yuanming Xu & Andy J. Minn

  2. Abramson Family Cancer Research Institute, University of Pennsylvania, Philadelphia, PA, USA

    Jingya Qiu, Bihui Xu, Darwin Ye, Diqiu Ren, Shangshang Wang, Joseph L. Benci, Yuanming Xu, Junwei Shi & Andy J. Minn

  3. Mark Foundation Center for Immunotherapy, Immune Signaling, and Radiation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Jingya Qiu, Darwin Ye, Shangshang Wang, E. John Wherry, Junwei Shi & Andy J. Minn

  4. Parker Institute for Cancer Immunotherapy, University of Pennsylvania, Philadelphia, PA, USA

    Darwin Ye, Jean-Christophe Beltra, E. John Wherry & Andy J. Minn

  5. Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA, USA

    Diqiu Ren & Junwei Shi

  6. Division of Biostatistics, Department of Epidemiology and Public Health, University of Miami, Miami, FL, USA

    Hemant Ishwaran

  7. Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA

    Jean-Christophe Beltra & E. John Wherry

  8. Institute of Immunology, University of Pennsylvania, Philadelphia, PA, USA

    Jean-Christophe Beltra, E. John Wherry & Andy J. Minn

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Contributions

J.Q., B.X. and A.J.M. designed experiments. B.X. performed the main experiments; D.R., S.W. and J.S. assisted with epigenome profiling; and J.L.B., Y.X. and D.Y. assisted with mouse and immune profiling studies. J.Q. and A.J.M. designed computational methods. J.Q., B.X. and A.J.M. analyzed data, interpreted results and wrote the manuscript. H.I. assisted with computation methods. J.-C.B. provided data for exhausted T cell subsets. J.-C.B. and E.J.W. assisted in the interpretation of T cell exhaustion studies. A.J.M. oversaw the study.

Corresponding author

Correspondence to Andy J. Minn.

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Competing interests

A.J.M. is a project member of the Parker Institute for Cancer Immunotherapy and has received research funding from Merck. He has also received honoraria and travel support from Merck, AstraZeneca and Pfizer. He is a scientific founder for Dispatch Biotherapeutics and a scientific consultant for Takeda, Xilio, H3 Biomedicine and Related Sciences. A.J.M. is an inventor on patents related to the IFN pathway and is on a filed patent related to modified CAR T cells. J.L.B. is currently an employee of Bristol Myers Squibb. Y.X. is a current employee of Pfizer. B.X. is currently an employee of Incyte. E.J.W. is a member of the Parker Institute for Cancer Immunotherapy and has consulting agreements with and/or is on the scientific advisory board for Merck, Roche, Pieris, Elstar and Surface Oncology. E.J.W. has a patent licensing agreement on the PD1 pathway with Roche–Genentech and is a founder of Arsenal Biosciences. The remaining authors declare no relevant competing interests.

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Nature Cancer thanks Zlatko Trajanoski and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Relationship between ISGs and T cell activity in TCGA tumors.

A. Schematic depicting the datasets and strategy used for the integrated analysis of RNA-seq and ATAC-seq data from TCGA tumor samples. B. CD8 T cell cytolytic activity scores (CD8 cytolytic activity) for all TCGA tumors, ordered by increasing median score (red line) for each cancer type. Cancer types with grey labels were excluded from analysis due to known low immune infiltration or low numbers of samples with paired RNA and ATAC data. C. Standardized regression coefficient estimates representing the effect of modified Z-score normalized RNA expression of IFNG.GS, ISG.RS, or 250 random gene sets (n = 38, same size as ISG.RS gene set) on CD8 T cell cytolytic activity. D. Histogram of number of putative cis-regulatory elements linked to each ISG.RS gene.

Source data

Extended Data Fig. 2 OAS1 cis-regulatory elements in TCGA tumors.

A-C. ATAC tracks at the OAS1 loci for representative LUAD, LUSC, and STAD tumors with high (blue) or low (red) CD8 T cell cytolytic activity. The annotation bar (bottom) demarcates called peaks, with black and grey bars indicating putative cis-REs linked to OAS1 and other peaks in the region, respectively. Highlighted peaks indicate putative cis-REs that negatively correlate with CD8 T cell cytolytic activity. The correlation of average OAS1 cis-RE chromatin accessibility with CD8 T cell cytolytic activity across all paired tumor samples available for each cancer type is depicted in Fig. 1i.

Extended Data Fig. 3 Activated enhancers in ICB-resistant cancer cells.

A. Spearman pairwise correlation of RNA and ATAC libraries. Heatmap is hierarchically clustered. B. PCA of H3K4me3, H3K27ac, and H3K4me1 signals for B16 and Res 499 cells with or without Stat1 knockout (SKO) sorted from in vivo tumors (n = 2 mice per group). C. Strategy for integrated annotation of putative regulatory elements. D. Summary profiles of H3K4me3, H3K27ac, and ATAC signal intensity over activated or deactivated promoters and enhancers (n = 2 mice per condition for H3K4me3 and H3K27ac assays; n = 5 mice per condition for ATAC assay). E. Genomic regions where Res 499 activated or deactivated regulatory elements reside. Only regulatory elements with significant differences in H3K27ac, H3K4me3, or ATAC signal intensity are included (FDR < 0.05). F. Example tracks for activated enhancers in Res 499 that are either pre-existing (left) or de novo activated (right). G. Enriched PANTHER pathways for genes located within a 50 Kb cis-regulatory window of Res 499 activated enhancers. P-values determined by Fisher’s exact test and FDR calculated by the Benjamini-Hochberg procedure. All pathways shown are significant with FDR < 0.01. H. Chromatin accessibility of the subset of Res 499 activated enhancers induced by chronic IFNG signaling (900 out of 3,738 enhancers) in cancer cells sorted from the indicated in vivo tumors (n = 3 mice per condition), where each row represents a chronic IFNG-induced enhancer. Summary enrichment scores for these loci are shown in Fig. 2i. B16 tumors were treated with IFNG in vitro for 6 hours (acute) or 3.5 weeks (chronic) prior to implantation into syngeneic mice. I. Enrichment of archetype motifs in Res 499 activated enhancers that are chronic IFNG-induced or not. P-values are color-coded and larger circle sizes indicate greater significance. TF motifs highlighted in red are associated with IFN signaling, TF motifs highlighted in blue are associated with inflammatory memory. J. Normalized ATAC-seq insertion counts at DNA footprints with ISRE motifs in B16 or Res 499 cells.

Source data

Extended Data Fig. 4 Features of IFN-associated inflammatory memory and resolved domains.

A. Density histogram representing the distribution of H3K27ac, H3K4me1, or ATAC signal intensity at individual Res 499 activated enhancers in cancer cells from B16 or Res 499 WT or Stat1 KO tumors. Dotted line indicates the mean signal intensity of all activated enhancers for the specified condition. P-values determined using the summary enrichment scores for each condition, and a one-way ANOVA with post-hoc Tukey HSD to calculate significance of pairwise comparisons. B. Top GO Biological Processes terms enriched in genes linked to resolved domains. C. Summary enrichment scores of H3K27ac, H3K4me1, or ATAC signal at resolved domains in cancer cells sorted from the indicated in vivo tumors (n = 2 mice per condition for H3K27ac and H3K4me1 assays; n = 5 mice per condition for ATAC assay). D. Representative track for H3K27ac, H3K4me1, and ATAC signal at an IFN-associated inflammatory memory domain (IFN-IMD) (more examples shown in Fig. 3c). Red bars highlight the IFN-IMD domain where persistent memory features specific to Res 499 tumors are revealed by Stat1 KO. E. Summary gene set enrichment scores for the IFN-associated inflammatory memory gene signature from cancer cells sorted from the indicated in vivo tumors. B16 tumors were treated with IFNG in vitro for 6 hours (acute) or 3.5 weeks (chronic) prior to implantation into syngeneic mice (n = 3 mice per condition). P-values determined by two-sided t-test. F. Log2 fold change in RNA expression between Res 499 and B16 tumors for individual genes in the specified gene set. Coral color indicates an inflammatory memory gene that is summarized in Fig. 3f.

Source data

Extended Data Fig. 5 OAS1 regulates IFN-I signaling and IRF3 maintains accessibility of activated enhancers in ICB-resistant cancer cells.

A. Ifnb expression in tumor infiltrating CD45+ immune cells from mice with Res 499 WT or Ifnar1 KO (IFNAR KO) tumors, treated with or without anti-PD1 (n = 29,584 cells from 2 biological replicates per condition). P-values determined by two-sided Wilcoxon test. B. RNA expression of OAS1 in B16 and Res 499 cells in vitro (n = 5 per condition). P-values determined by two-sided t-test. C-D. Protein (C) and RNA (D) expression of OAS1 in TSA and ICB-resistant Res 237 breast cancer cells in vitro (RNA, n = 6 per group). Replicate samples are shown in the protein blot. P-values determined by two-sided t-test. E. Protein expression of OAS1 in Res 499 WT and Oas1 KO cells. Shown is representative of two technical repeats. F. Protein expression of OAS1 following IFNB (1000 U/ml) stimulation in Res 237 control (CTRL) and Res 237 Oas1 KO clones (cl2, 4, 15). Shown is representative of two technical repeats. G. Concentration of IFNA and IFNB after polyI:C transfection of TSA, Res 237 (R237), and Res 237 Oas1 KO clones (cl2, cl4) (TSA, n = 8; Res 237, n = 8; cl2, n = 7; cl4, n = 4 biological replicates). P-values determined by two-sided t-test. H. Oas1 expression in Res 499 control (CTRL) or Res 499 Stat1 KO cells in vitro (left; Res 499 CTRL, n = 10; Res 499 Stat1 KO) or in Res 499 Stat1 KO cells following siRNA knockdown of control RNA (siCTRL) or the indicated IRFs (right; n = 10; siCTRL, n = 10; siIRF1, n = 7; siIRF3, n = 10, siIRF7, n = 7, siIRF9, n = 7). P-values determined by two-sided t-test. I. Chromatin accessibility of individual Res 499 activated enhancers in cancer cells sorted from the indicated in vivo tumors. Boxplots represent the 25th percentile, median, 75th percentile, and 1.5x IQR (whiskers).

Source data

Extended Data Fig. 6 Blocking cancer cell IFN-I signaling increases the frequency of tumor-infiltrating CD8 T cells and improves ICB response.

A. Survival of mice with Res 237 WT or Oas1 KO (KO) tumors either non-treated (NT) or treated with anti-CTLA4 (aCTLA4) (NT, n = 10 mice per condition; aCTLA4, n = 20 mice per condition). Two independent KO clones are shown. P-values determined by two-sided log-rank test. B. Percent of tumor infiltrating CD8 T cells in total CD45+ cells from mice with Res 237 WT or Res 237 Oas1 KO (KO) tumors. P-values determined by two-sided t-test. C. MHC-I surface expression at baseline and after either IFNB or IFNG treatment of the indicated cell lines to assess knockout of B2m and/or Ifnar1 in Res 499 cancer cells. D. Tumor volume growth of mice injected with Res 499 tumors and treated with anti-CTLA4 plus anti-PD1 (dICB), delayed administration of the JAK inhibitor ruxolitinib (JAKi), or both (NT and JAKi, n = 5 mice per condition; dICB and JAKi + dICB, n = 10 mice per condition). P-values determined by a mix-effect regression model. E. Percent of PRF1+ CD8 T cells relative to total CD8 T cells in Res 499 WT or Res 499 Ifnar1 KO (KO) tumors from mice treated with or without anti-PD1 (aPD1) (n = 5 mice per group). P-values determined by two-sided t-test. F-G. Survival of mice with B16 WT or Ifnar1 KO (KO) tumors (F), or CT26 WT or Ifnar1 KO (KO) tumors (G), either non-treated (NT) or treated with anti-PD1 (NT, n = 5 mice per condition; aPD1, n = 10 mice per condition). P-values determined by two-sided log-rank test. H. Representative density plot of peripheral CD45+ cells in control mice or mice treated with anti-CD8 or anti-NK1.1 depleting antibody. I. Data shown in Fig. 5a replotted to emphasize effect of host immune cell status. Boxplots represent the 25th percentile, median, 75th percentile, and 1.5x IQR (whiskers).

Source data

Extended Data Fig. 7 Blocking cancer cell IFN-I signaling improves immune cell IFNG signaling and predicted interactions between DCs and CD8 T cells.

A. Distribution of Ifng and IFNG.GS expression in tumor infiltrating CD45+ immune cells from mice with Res 499 WT or Ifnar1 KO (KO) tumors treated with or without anti-PD1 (aPD1) (n = 29,584 cells from 2 biological replicates per condition). P-values determined by two-sided Wilcoxon test. B. Percent CD8 T cells expressing IFNG by flow cytometry (left; n = 13 mice per group) or average expression of Ifng in CD8 T cell subsets by scRNA-seq (right) from the indicated tumors either non-treated (NT) or treated with anti-PD1. P-values determined by two-sided t-test. For heatmap of Ifng expression, boxed values in second column indicate p < 0.05 for comparison between WT vs. Ifnar1 KO, and boxed values in fourth column are for WT + aPD1 vs. Ifnar1 KO + aPD1. C. Average expression of select markers for indicated DC subtype clusters shown in Fig. 6d. D. Average expression of DC3 ligands and CD8 T cell receptors from the receptor-ligands shown in Fig. 6h. The cell types are annotated in the UMAP on the left. E. Cell-cell interaction scores (left) and ligand-receptor interactions (right) between CD8 T cells and DC3 using scTensor. Data are from mice bearing Res 499 WT or Ifnar1 KO tumors treated with or without anti-PD1. Cell-cell interaction scores and the mean (red dot) are from biological replicates. P-value determined by a one-way ANOVA for differences between groups. F. Surface expression of CD86 on MHC-II+ CD11c+ DCs from Res 499 WT or Ifnar1 KO tumors (n = 9 per condition). P-values determined by two-sided t-test. G. Survival of wildtype (WT) or Batf3−/- mice with Res 499 Ifnar1 KO cells treated with or without anti-PD1 (WT mice NT, n = 5; WT mice aPD1, n = 10; Batf3 -/- mice NT, n = 3; Batf -/- mice aPD1, n = 7). P-values determined by two-sided log-rank test. Boxplots represent the 25th percentile, median, 75th percentile, and 1.5x IQR (whiskers).

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Extended Data Fig. 8 Blocking cancer cell IFN-I signaling alters features of tumor-infiltrating CD8 T cells.

A-B. Expression of CD8 T cell gene sets (A) and average expression of select markers (B) used to annotate CD8 T cell states shown in Fig. 7a. C. Percentage CD8 T cells occupying each state for each biological replicate (n = 2 mice per condition). Black dot represents mean. D. Percent of CX3CR1+ effector-like T cells relative to TIM3- CD8 T cells in Res 499 WT or Res 499 Ifnar1 KO (KO) tumors from mice treated with or without anti-PD1 (aPD1) (n = 10 mice per condition). E-F. UMAP of CD8 T cell clonotypes clustered by biophysical features of TCR CDR3 amino acids (E) and the distribution of expanded T cell clonotypes across TCR clusters (F) in Res 499 WT or Res 499 Ifnar1 KO (KO) tumors from mice treated with or without anti-PD1. In the UMAP, TCR clusters are color-coded and circle size indicates clonotype frequency. In the bar plot, each bar is one unique clonotype stratified by TCR cluster, with the height of the bar representing the reciprocal of the rank order by clonotype frequency (higher values indicate greater clonotype expansion). Boxplots represent the 25th percentile, median, 75th percentile, and 1.5x IQR (whiskers).

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Qiu, J., Xu, B., Ye, D. et al. Cancer cells resistant to immune checkpoint blockade acquire interferon-associated epigenetic memory to sustain T cell dysfunction. Nat Cancer 4, 43–61 (2023). https://doi.org/10.1038/s43018-022-00490-y

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