Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Dec 1;167(6):1540-1554.e12.
doi: 10.1016/j.cell.2016.11.022.

Tumor Interferon Signaling Regulates a Multigenic Resistance Program to Immune Checkpoint Blockade

Joseph L Benci  1 , Bihui Xu  1 , Yu Qiu  1 , Tony J Wu  1 , Hannah Dada  1 , Christina Twyman-Saint Victor  2 , Lisa Cucolo  1 , David S M Lee  1 , Kristen E Pauken  3 , Alexander C Huang  4 , Tara C Gangadhar  5 , Ravi K Amaravadi  5 , Lynn M Schuchter  5 , Michael D Feldman  6 , Hemant Ishwaran  7 , Robert H Vonderheide  8 , Amit Maity  9 , E John Wherry  10 , Andy J Minn  11
Affiliations

Tumor Interferon Signaling Regulates a Multigenic Resistance Program to Immune Checkpoint Blockade

Joseph L Benci et al. Cell. .

Abstract

Therapeutic blocking of the PD1 pathway results in significant tumor responses, but resistance is common. We demonstrate that prolonged interferon signaling orchestrates PDL1-dependent and PDL1-independent resistance to immune checkpoint blockade (ICB) and to combinations such as radiation plus anti-CTLA4. Persistent type II interferon signaling allows tumors to acquire STAT1-related epigenomic changes and augments expression of interferon-stimulated genes and ligands for multiple T cell inhibitory receptors. Both type I and II interferons maintain this resistance program. Crippling the program genetically or pharmacologically interferes with multiple inhibitory pathways and expands distinct T cell populations with improved function despite expressing markers of severe exhaustion. Consequently, tumors resistant to multi-agent ICB are rendered responsive to ICB monotherapy. Finally, we observe that biomarkers for interferon-driven resistance associate with clinical progression after anti-PD1 therapy. Thus, the duration of tumor interferon signaling augments adaptive resistance and inhibition of the interferon response bypasses requirements for combinatorial ICB therapies.

PubMed Disclaimer

Figures

Figure 1
Figure 1. PDL1-dependent and PDL-independent resistance to RT and anti-CTLA4
A) Res 499 relapsed 3 weeks after RT + anti-CTLA4. B) Representative contour plot of in vivo PDL1 expression on melanoma cells (blue) and CD45+ immune cells (red) from Res 499 tumors implanted into IFNGKO mice, or C) mice treated with anti-CSF1R. Percentages for boxed populations are indicated. D) Survival of untreated mice with Res 499 or Res 499 PDL1KO tumors (far left) or mice treated with RT + anti-CTLA4 with or without anti-CSF1R or anti-PD1 (n=5–15). E) JB2, which is from a Res 499 PDL1KO tumor, relapsed two months after therapy. F) Tumor growth in mice treated with or without RT + anti-CTLA4 (n=5). *** p < 0.001 vs Res 499 RT + anti-CTLA4. G) Predicted survival of metastatic melanoma patients treated with RT + anti-CTLA4 modeled by random survival forest using the combined IHC PDL1 intensity score on melanoma cells and macrophages. Estimates are based on out-of-bag samples. Error rate is 38.7 +/− 0.01% with n=13. H) Overall survival after starting anti-PD1 for patients initially treated with RT + anti-CTLA4 on a clinical trial. Progression, time of progression, and death after anti-PD1 are indicated. I) Relative expression of IFN and IFN receptor genes from whole tumor lysates. Mean (grey line) and first and third quartiles (dashed lines) of all genes on the microarray are indicated. Error bars are standard deviations. J) IFNG levels in the blood of mice bearing Res 499 tumors after RT + anti-CTLA4 (n=7). * p < 0.05. Unless indicated, error bars are S.E.M. of biological replicates. See also Figure S1.
Figure 2
Figure 2. Prolonged tumor IFNG signaling is sufficient to instigate resistance to RT + anti-CTLA4, while type I and II IFN signaling maintains PDL1-independent resistance
A) Tumor volumes (day 17, split y-axis) and B) survival of mice with indicated tumors treated with RT + anti-CTLA4 (n=5–10). C) Standard (D5) and delayed treatment schedules for anti-CTLA4 + anti-PDL1. Sizes of B16 tumors prior to treatment for each schedule are shown (left). D) Tumor volumes for B16 tumors with or without IFNGRKO treated with anti-CTLA4 + anti-PDL1 according to indicated schedule. E) Tumor volumes relative to the average of untreated controls for B16 tumors with IFNGRKO or IFNA/GRKO. F) Survival of mice with Res 499 tumors with or without indicated KO, or G) after treatment with RT + anti-CTLA4 (n=5–10). H) Gene set analysis examining transcriptomic features associated with resistance to RT + anti-CTLA4, derived from comparing resistant B16 tumors (e.g., Res 499) with parental tumors. Individual gene scores are on top along with an overall gene score and p-value. Heat map shows relative gene expression (columns) for sorted tumor cells with indicated KO (rows). Red is high expression and blue is low. I) Growth of JB2 tumors with or without IFNA/GRKO after RT + anti-CTLA4 (n=5–10). Unless indicated, error bars are S.E.M. of biological replicates. See also Figures S2 and S3.
Figure 3
Figure 3. STAT1 regulates a multigenic resistance program to ICB
A) Protein levels of STAT1 after two weeks of in vitro IFNG treatment of B16 cells followed by one-week washout (denoted B16γ). B) Principle components analysis of differential open chromatin regions (OCRs) from ATAC-seq of melanoma cells sorted from mice with the indicated tumors. C) Differential OCRs (rows) from B16γ vs. B16 (left) or Res 499 vs. B16 (right) are shown for all tumors (columns) color-coded (bottom of heat map) the same as the PCA plot. OCRs with predicted STAT1 binding sites are shown (black lines beside heat maps). D) Normalized coverage from ATAC-seq reads at base pair positions centered on STAT1 motifs. A fitted smoothing spline is shown for Res 499 or B16γ (dark red) or B16 (blue). E) Tumor volumes (day 15, split y-axis) or F) survival of mice bearing Res 499 tumors with STAT1KO and/or PDL1KO after RT + anti-CTLA4 (n=10–15). G) Correlation between STAT1 and the indicated genes from microarray analysis of whole tumor lysates. Blue dots indicate p<0.05. H) Heat map of gene correlation matrix with correlation value color-coded per the legend. I) Undirected ARACNE network graph using TCGA human melanoma expression data. Edges are weighted by mutual information scores and nodes are color-coded by functional groups. J) Correlation between STAT1 and other genes in the network under conditions where PDL1 expression (x-axis) or CD8A expression (y-axis) is restricted to low/intermediate (left) or high (right) expression values. Blue dots indicate p<0.05. K) Gene set analysis of TCIRs, TCIR ligands, and ISGs. Individual gene scores are on top along with an overall gene score and p-value. Heat map shows relative expression of genes (columns) for sorted tumor cells with indicated KO (rows). Red is high expression and blue low. See also Figure S3.
Figure 4
Figure 4. Blocking IFN-driven resistance interferes with multiple TCIR ligands and improves response to ICB
A) Expression of TCIR ligands on Res 499 cells after in vitro treatment with indicated type I or II IFN. B) Expression of TCIR ligands. Shown are representative histograms and MFI values from biological replicates. Isotype controls are shown in histograms on top. C) Gene set analysis of TCIRs, TCIR ligands, and ISGs. Individual gene scores are on top along with an overall gene score and p-value. Heat map shows relative expression of genes (columns) for sorted tumor cells with indicated KO (rows). Red is high expression and blue low. D) Survival of mice bearing Res 499 tumors with indicated KO after RT + anti-CTLA4 (n=20). E) Tumor growth and F) survival of mice with Res 499 tumors treated with anti-CTLA4 + anti-PDL1 along with anti-LAG3 and/or anti-TIM3 (n=5–15). For comparison with anti-CTLA4 + anti-PDL1, * p=0.02 and *** p<0.001. For quadruple ICB vs. anti-CTLA4 + anti-PDL1 + anti-LAG3, p<0.01. G) Survival of mice with Res 237 ICB-resistant breast cancer tumors treated with indicated ICB (n=5–10). H) Tumor growth and H) survival of mice bearing Res 499 tumors with or without IFNA/GRKO after anti-CTLA4 + anti-PDL1 (n=5). See also Figure S4.
Figure 5
Figure 5. Inhibiting IFN-driven resistance preferentially expands distinct populations of TEX
A) Feature summary of nine populations (clusters) of CD44high CD8 peripheral T cells identified using co-expression of six TCIRs. Heat map shows the scaled MFI (rows) characterizing each cluster (columns). Clusters are additionally categorized (bottom boxes) by TCIR and PD1 status (see legend). Baseline frequency of T cells in each cluster compared to total splenic T cells (box plot) and frequency of Ki67high T cells is also shown (black box indicates too few events). B) Co-expression of the six TCIRs on T cells belonging to the PD1highTCIRhigh clusters Cl.5.2 and Cl.5.3. C) Percentage of CD8 TILs that are Ki67+GzmB+ from Res 499 tumors with or without IFNA/GRKO grouped by anti-CTLA4 + anti-PDL1 treatment (ICB). D) Distribution of Ki67+GzmB+ CD8 TILs in each TCIR cluster, and E) percentage of Ki67+GzmB+ T cells in each TCIR cluster. F) Representative contour plots of PD1 and Eomes expression (red), and Ki67 and GzmB expression (blue), from CD8 TILs belonging to the PD1highTCIRhigh Cl.5.2 cluster. G) Percentage of indicated peripheral PD1+ T cells over time. ICB was given at day 13. H) Pie chart summarizing the average frequency of PD1+ CD8 peripheral T cells in each TCIR cluster. I) Day 20 percentage of Eomes+ Ki67+GzmB+ T cells in each TCIR cluster after ICB. J) Representative contour plots and K) summary of Eomes and Ki67/GzmB status in T cells from the indicated TCIR clusters at day 20. Ki67/GzmB analysis is restricted to the Eomes+ population from each cluster. Unless indicated, error bars are S.E.M. of biological replicates. See also Figure S5.
Figure 6
Figure 6. Disrupting IFN-driven resistance renders highly multi-ICB resistant tumors sensitive to ICB monotherapy
A) Tumor growth of Res 499 tumors with the indicated KO after anti-PD1 (left) or anti-CTLA (right) (n=5–10). *** p<0.001 for comparisons with Res 499. For anti-CTLA4 (right), p=0.037 for IFNA/GRKO vs. IFNGRKO. B) Survival of mice with Res 499 tumors or C) Res 237 ICB-resistant breast cancer tumors treated with anti-CTLA4 (n=5–10). D) Contour plot of indicated TCIR ligands in Res 499 tumors from mice after treatment with a JAK inhibitor (JAKi). Red contours represent melanoma cells and blue indicate CD45+ immune cells. Statistical summary from biological replicates is shown on right. E) Treatment schedules for anti-CTLA4 and JAKi. F) Tumor growth curves of Res 499 tumors from each treatment schedule (D8, n=10; D5, n=7). G) Tumor growth of Res 237 breast cancer tumors treated with anti-CTLA4 and/or JAKi for five days starting on day 10 (n=6). Unless indicated, error bars are S.E.M. of biological replicates. See also Figure S6.
Figure 7
Figure 7. ISGs associated with IFN-driven resistance can predict clinical response to anti-PD1
A random forest model for melanoma response/progression after anti-PD1 was developed using the number (Log10) of nsSNVs and the average mRNA expression of IFIT1 and MX1 (IFIT1/MX1) for a cohort of 27 patients. Shown are A) overall error rates, error rates for progression or response, and B) variable importance scores (greater than 0 is deemed significant) determined from out-of-bag (OOB) samples. Error bars represent Monte Carlo standard deviations. C) Partial plots showing the adjusted effects of the indicated variables on the probability of response. Red dashed lines are standard errors. D) Predicted probabilities of response from OOB samples as a function of IFIT1/MX1 and nsSNVs. Larger circle sizes represent higher probability (legend). Actual response (blue) and progression (red) are denoted by circle color. Quadrants are divided by values from partial plots approximating 50% probability of response. E) Association between IFIT1/MX1 and nsSNVs with clinical response to anti-PD1 compared to other genes. Gene expression correlation is shown in the heat map (left). The frequency of bootstrap samples that each variable was selected as significant for predicting response and its variable importance are plotted (right). Grey dotted line for each axis is the upper 5% quantile. Top variables are highlighted in blue and IDILS TCIR ligands in red. F) Partial plot representing the adjusted effects of IFN-I on the probability of response. Red dashed lines are standard errors. G) Model for IFN-driven resistance.
See this image and copyright information in PMC

Comment in

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Blackburn SD, Shin H, Freeman GJ, Wherry EJ. Selective expansion of a subset of exhausted CD8 T cells by PD-L1 blockade. Proc Natl Acad Sci USa. 2008a;105:15016–15021. - PMC - PubMed
    1. Blackburn SD, Shin H, Haining WN, Zou T, Workman CJ, Polley A, Betts MR, Freeman GJ, Vignali DAA, Wherry EJ. Coregulation of CD8+ T cell exhaustion by multiple inhibitory receptors during chronic viral infection. Nat Immunol. 2008b;10:29–37. - PMC - PubMed
    1. Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10:1–8. - PMC - PubMed
    1. Chen X, Ishwaran H. Random forests for genomic data analysis. Genomics. 2012;99:323–329. - PMC - PubMed
    1. Cheon H, Stark GR. Unphosphorylated STAT1 prolongs the expression of interferon-induced immune regulatory genes. Proc Natl Acad Sci USa. 2009;106:9373–9378. - PMC - PubMed