Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy

Abstract

Type I interferons (IFN-Is) are central regulators of anti-tumor immunity and responses to immunotherapy, but they also drive the feedback inhibition underlying therapeutic resistance. In the present study, we developed a mass cytometry approach to quantify IFN-I-stimulated protein expression across immune cells and used multi-omics to uncover pre-therapy cellular states encoding responsiveness to inflammation. Analyzing peripheral blood cells from multiple cancer types revealed that differential responsiveness to IFN-Is before anti-programmed cell death protein 1 (PD1) treatment was highly predictive of long-term survival after therapy. Unexpectedly, IFN-I hyporesponsiveness efficiently predicted long-term survival, whereas high responsiveness to IFN-I was strongly associated with treatment failure and diminished survival time. Peripheral IFN-I responsive states were not associated with tumor inflammation, identifying a disconnect between systemic immune potential and ‘cold’ or ‘hot’ tumor states. Mechanistically, IFN-I responsiveness was epigenetically imprinted before therapy, poising cells for differential inflammatory responses and dysfunctional T cell effector programs. Thus, we identify physiological cell states with clinical importance that can predict success and long-term survival of PD1-blocking immunotherapy.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Divergent ISP connectivity across immune cell subsets.
Fig. 2: Pre-therapy IFN-I response capacity of Teff cell subsets is associated with survival after anti-PD1.
Fig. 3: Pre-therapy IFN-I response is related to changes in peripheral IRC and TME.
Fig. 4: Differential IDO1 regulation in patients treated with anti-PD1.
Fig. 5: Distinct transcriptional programs underlie IFN-I response states.
Fig. 6: Epigenetic encoding of CD4 Teff cell IFN-I responsiveness.

Data availability

ScRNA-seq and scATAC-seq data have been deposited in the Gene Expression Omnibus under accession no. GSE199994. Tumor mRNA-seq is available through the European Genome-phenome Archive, accession no. EGAS00001003280. Blueprint ENCODE data are accessible using the function BlueprintEncodeData() from the R package celldex (v.1.4.0). The JASPAR2020 database used for TF-binding prediction can be accessed through the R package JASPAR2020 (v.0.99.10). The gene sets probed for GSEA analysis are available through the Broad Institute Molecular Signatures Database and can also be accessed through the GSEA application (v.4.2.2). Source data are provided with this paper.

References

  1. Boukhaled, G. M., Harding, S. & Brooks, D. G. Opposing roles of type I interferons in cancer immunity. Annu Rev. Pathol. 16, 167–198 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Cunningham, C. R. et al. Type I and type II interferon coordinately regulate suppressive dendritic cell fate and function during viral persistence. PLoS Pathog. 12, e1005356 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Zhen, A. et al. Targeting type I interferon–mediated activation restores immune function in chronic HIV infection. J. Clin. Invest. 127, 260–268 (2017).

    Article  PubMed  Google Scholar 

  4. Sumida, T.S. et al. Type I interferon transcriptional network regulates expression of coinhibitory receptors in human T cells. Nat. Immunol. 23, 632–642 (2022).

  5. Chen, J. et al. Type I IFN protects cancer cells from CD8+ T cell-mediated cytotoxicity after radiation. J. Clin. Invest. 129, 4224–4238 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Snell, L. M., McGaha, T. L. & Brooks, D. G. Type I interferon in chronic virus infection and cancer. Trends Immunol. 38, 542–557 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wilson, E. B. et al. Blockade of chronic type I interferon signaling to control persistent LCMV infection. Science 340, 202–207 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Gadalla, R. et al. Validation of CyTOF against flow cytometry for immunological studies and monitoring of human cancer clinical trials. Front. Oncol. https://doi.org/10.3389/fonc.2019.00415 (2019).

  9. Clouthier, D. L. et al. An interim report on the investigator-initiated phase 2 study of pembrolizumab immunological response evaluation (INSPIRE). J. Immunother. Cancer 7, 72 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Cristescu, R. et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy. Science 362, eaar3593 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Cindy Yang, S. Y. et al. Pan-cancer analysis of longitudinal metastatic tumors reveals genomic alterations and immune landscape dynamics associated with pembrolizumab sensitivity. Nat. Commun. 12, 5137 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Cano-Gamez, E. et al. Single-cell transcriptomics identifies an effectorness gradient shaping the response of CD4+ T cells to cytokines. Nat. Commun. 11, 1801 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333–337 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Snell, L. M. et al. Dynamic CD4+ T cell heterogeneity defines subset-specific suppression and PD-L1-blockade-driven functional restoration in chronic infection. Nat. Immunol. 22, 1524–1537 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Bengsch, B. et al. Epigenomic-guided mass cytometry profiling reveals disease-specific features of exhausted CD8 T cells. Immunity 48, 1029–1045.e1025 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kroenke, M. A. et al. Bcl6 and Maf cooperate to instruct human follicular helper CD4 T cell differentiation. J. Immunol. 188, 3734–3744 (2012).

    Article  CAS  PubMed  Google Scholar 

  20. Nance, J. P. et al. Bcl6 middle domain repressor function is required for T follicular helper cell differentiation and utilizes the corepressor MTA3. Proc. Natl Acad. Sci. USA 112, 13324–13329 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Shen, E. et al. Chromatin remodeling by the NuRD complex regulates development of follicular helper and regulatory T cells. Proc. Natl Acad. Sci. USA 115, 6780–6785 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Chen, Z. et al. In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262–1280.e1222 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Munz, C. et al. TAF7 (TAFII55) plays a role in the transcription activation by c-Jun. J. Biol. Chem. 278, 21510–21516 (2003).

    Article  CAS  PubMed  Google Scholar 

  24. Martinez, G. J. et al. The transcription factor NFAT promotes exhaustion of activated CD8+ T cells. Immunity 42, 265–278 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Shakiba, M. et al. TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion. J. Exp. Med. 219, e20201966 (2021).

  26. Fahey, L. M. et al. Viral persistence redirects CD4 T cell differentiation toward T follicular helper cells. J. Exp. Med. 208, 987–999 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. York, A. G. et al. Limiting cholesterol biosynthetic flux spontaneously engages type I IFN signaling. Cell 163, 1716–1729 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Xu, S. et al. Uptake of oxidized lipids by the scavenger receptor CD36 promotes lipid peroxidation and dysfunction in CD8+ T cells in tumors. Immunity 54, 1561–1577.e1567 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Franco, F., Jaccard, A., Romero, P., Yu, Y.-R. & Ho, P.-C. Metabolic and epigenetic regulation of T-cell exhaustion. Nat. Metab. 2, 1001–1012 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Wherry, E. J. et al. Molecular signature of CD8+ T cell exhaustion during chronic viral infection. Immunity 27, 670–684 (2007).

    Article  CAS  PubMed  Google Scholar 

  31. Abdel-Hakeem, M. S. et al. Epigenetic scarring of exhausted T cells hinders memory differentiation upon eliminating chronic antigenic stimulation. Nat. Immunol. 22, 1008–1019 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Yates, K. B. et al. Epigenetic scars of CD8+ T cell exhaustion persist after cure of chronic infection in humans. Nat. Immunol. 22, 1020–1029 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Zeng, Z., Wei, F. & Ren, X. Exhausted T cells and epigenetic status. Cancer Biol. Med. 17, 923–936 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Legut, M. et al. A genome-scale screen for synthetic drivers of T cell proliferation. Nature 603, 1–8 (2022).

  36. Feng, D. & Barnes, B. J. Bioinformatics analysis of the factors controlling type I IFN gene expression in autoimmune disease and virus-induced immunity. Front. Immunol. 4, 291 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Sekimata, M. et al. CCCTC-binding factor and the transcription factor T-bet orchestrate T helper 1 cell-specific structure and function at the interferon-gamma locus. Immunity 31, 551–564 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shin, H. et al. A role for the transcriptional repressor Blimp-1 in CD8+ T cell exhaustion during chronic viral infection. Immunity 31, 309–320 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhu, C. et al. An IL-27/NFIL3 signalling axis drives Tim-3 and IL-10 expression and T-cell dysfunction. Nat. Commun. 6, 6072–6072 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Paley, M. A. et al. Progenitor and terminal subsets of CD8+ T cells cooperate to contain chronic viral infection. Science 338, 1220–1225 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Diskin, B. et al. PD-L1 engagement on T cells promotes self-tolerance and suppression of neighboring macrophages and effector T cells in cancer. Nat. Immunol. 21, 442–454 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. Fairfax, B. P. et al. Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat. Med. 26, 193–199 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502.e415 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Miron, M. et al. Human lymph nodes maintain TCF-1hi memory T cells with high functional potential and clonal diversity throughout life. J. Immunol. 201, 2132–2140 (2018).

  45. Wu, T. et al. The TCF1-Bcl6 axis counteracts type I interferon to repress exhaustion and maintain T cell stemness. Sci. Immunol. 1, eaai8593 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Blackburn, S. D., Shin, H., Freeman, G. J. & Wherry, E. J. Selective expansion of a subset of exhausted CD8 T cells by alphaPD-L1 blockade. Proc. Natl Acad. Sci. USA 105, 15016–15021 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Odunsi, K. et al. Metabolic adaptation of ovarian tumors in patients treated with an IDO1 inhibitor constrains antitumor immune responses. Sci. Transl. Med. 14, eabg8402 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Schietinger, A. et al. Tumor-specific T cell dysfunction is a dynamic antigen-driven differentiation program initiated early during tumorigenesis. Immunity 45, 389–401 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Loo Yau, H. et al. DNA hypomethylating agents increase activation and cytolytic activity of CD8+ T cells. Mol. Cell 81, 1469–1483.e1468 (2021).

    Article  CAS  PubMed  Google Scholar 

  51. Sato, T., Issa, J.-P. J. & Kropf, P. DNA hypomethylating drugs in cancer therapy. Cold Spring Harb. Perspect. Med. 7, a026948 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Nixon, A. B. et al. Peripheral immune-based biomarkers in cancer immunotherapy: can we realize their predictive potential? J. Immunother. Cancer 7, 325 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e3529 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Yu, G., Wang, L.-G. & He, Q.-Y. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31, 2382–2383 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. Fornes, O. et al. JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 48, D87–D92 (2019).

    PubMed Central  Google Scholar 

  59. Schep, A. N., Wu, B., Buenrostro, J. D. & Greenleaf, W. J. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat. Methods 14, 975–978 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank past and present members of the Brooks laboratory and Tumor Immunotherapy Program Immune Profiling Core for technical help and discussion. We thank the Biostatistics Department at the Princess Margaret Cancer Center, especially K. Lajkosz and Z. A. Liu for consultation and manuscript review. We thank Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA for providing the anti-PD1 used for the treatment of patients in the INSPIRE trial. This work was supported by the Canadian Institutes of Health Research Foundation (grant no. FDN148386 to D.G.B.), the Canadian Cancer Society (innovation award no. 706230 to D.G.B.), the National Institutes of Health (grant no. AI085043 to D.G.B.), The Terry Fox New Frontiers grant (to D.G.B. and T.L.M.), the Scotiabank Research Chair to D.G.B. and the Princess Margaret Hold’em for Life Cancer Research Fellowship (G.M.B.).

Author information

Authors and Affiliations

Authors

Contributions

G.M.B. and D.G.B. conceived the project and designed research and wrote and edited the manuscript. G.M.B., R.G., H.J.E. and B.N. performed the experiments. M.O.B., D.G., K.T., K.A., A.S., A.R.H., P.S.O. and N.H. designed the trial study, recruited and managed study subjects and collected and interpreted clinical data. M.O.B., S.D.S., D.G., A.G.S. and S.C.M.L. provided additional samples for validation, and collected and interpreted clinical data from the validation studies. G.M.B., D.A-R., R.Q. and S.Y.C.Y. analyzed the data. G.M.B., D.A.-R. and R.Q performed statistical tests and modeling. B.X.W., R.G. and D.G. managed sample processing and storage. R.G., H.J.E, M.G., S.D.S., M.O.B., A.G.S., C.G., T.L.M. and T.J.P. provided technical expertise and scientific feedback.

Corresponding authors

Correspondence to Giselle M. Boukhaled or David G. Brooks.

Ethics declarations

Competing interests

A.R.H. provides consultation/advice for Merck, GlaxoSmithKline, Bristol-Myers Squibb and Eisai, and receives research funding for Karyopharm Therapeutics, Merck, GlaxoSmithKline, Bristol-Myers Squibb, Roche/Genentech, Boeringher Ingelheim, Janssen, AstraZeneca/MedImmune, Astellas and Macrogenics. A.S. is a consultant for (Advisory Board): Merck (compensated), Bristol-Myers Squibb (compensated), Novartis (compensated), Oncorus (compensated) and Janssen (compensated). A.S. receives grant/research support from (Clinical Trials): Novartis, Bristol-Myers Squibb, Symphogen AstraZeneca/Medimmune, Merck, Bayer, Surface Oncology, Northern Biologics, Janssen Oncology/Johnson & Johnson, Roche, Regeneron, Alkermes, Array Biopharma/Pfizer, GSK, Treadwell. P.S.O. is a scientific advisory board member for Providence, Tessa and holds a sponsored research agreement with EMD Serono. M.O.B. is a consultant and on the advisory board for: Merck, Bristol-Myers Quibb, Novartis, GlaxoSmithKline, Sanofi, LaRoche, Possey, Sun Pharma, Instil Bio, IOVANCE and Pfizer, and receives grant funding from Merck, Takara and the safety review committee for Adaptimmune. G.M.B., H.J.E. and D.G.B. have filed a provisional patent pertaining to this work. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Erwei Song and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard in collaboration with the Nature Immunology team.

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 ISP staining, stable baseline expression and lack of survival correlation in total CD45+ PBMC.

a. Pre-processing gating strategy for CyTOF. b. Plots showing unstimulated or IFNβ-stimulated expression of the indicated ISPs in CD45+ PBMCs from a representative healthy donor measured by CyTOF. c-e. The average arcsinh-transformed ISP expression of the 13-ISPs was used as a single-cell IFN-I signature score (IS) to gauge the intensity of ISP expression between patients and across cell types. c. Paired analysis comparing median IS of total CD45+ cells for each patient in the two pre-therapy time points: Baseline 1 (one week prior to first dose) and Baseline 2 (day 0 of treatment; n = 31). P value determined by the two one-sided test (TOST) for comparison of similarity. A significant result indicates that the compared groups are alike. d. Boxplots comparing the pre-therapy baseline IS of healthy donor (HD, n = 7), patients with no clinical benefit (NCB, n = 18) and patients with clinical benefit (CB, n = 13) following treatment. Boxes show median, upper and lower quartile and whiskers show 1.5x the interquartile range on either side. e. Patient CD45+ cells were stratified based on IS using a cut-off determined by maximally selected log-rank statistics. Kaplan-Meier curves compare overall survival (left panel) and progression-free survival (right panel) in total CD45+ cells in patients with high or low IS. Log-rank P values and the number of patients belonging to each group at the start of therapy are shown on each plot.

Source data

Extended Data Fig. 2 Protein expression levels and IFN-I network connectivity across PBMC subsets.

a. Single-cell heatmap showing the expression of proteins used for clustering in each of the clusters defined by fast-Phenograph. Proteins and clusters were ordered by hierarchical clustering, white spaces divide distinct clusters and expression is scaled by row for each protein and expressed as a percent of maximum expression. b. Kaplan–Meier curves showing overall survival and progression-free survival comparing patients stratified based on their IS in the indicated PBMC population. Sample sizes for each group are shown on the plots. c. Pairwise correlations between median IS across major immune populations. The thickness and color of the lines connecting each population signifies the strength of the correlation. A significant value greater than 0 indicates that higher IS in one subset is accompanied by higher IS in the subset it is compared to, with 1 indicating a perfect correlation. Correlations are overlaid on UMAP coordinates from Fig. 1b. Only correlations with P < 0.05 are shown. P value determined by two-sided Student’s t-test. d. Network map showing the difference in correlation coefficient between patients with no clinical benefit of treatment (NCB, n = 18) and clinical benefit (CB, n = 13) in pre-therapy samples. The thickness and darkness of the line indicates the magnitude of the difference and the color indicates the direction (Red = higher in NCB; Blue= higher in CB). Only differences greater than 0.2 in either direction are shown. e. Same as (c), except mapping the correlations in IS for patients after therapy initiation. f. Same as in (g), except mapping the change in correlation between post-treatment (6 weeks) and pre-treatment for NCB (left) or CB (right) patients.

Extended Data Fig. 3 6-ISP based IRC expression after stimulation culture and across immune subsets in the peripheral blood.

a. Flow cytometry gating strategy for peripheral blood T cells and CD11c+ cells. b. Expression of ISPs measured by flow cytometry comparing IFNβ-stimulated and unstimulated cells within the indicated immune subsets (representative of two healthy donors). Numbers indicate geometric mean values for each histogram. BST2, ISG15 and MX1 were stained in one panel and PKR, IRF7 and IFIT3 were stained in a separate panel together with CD3, CD4, CD8 and CD11c. c. Flow-Match algorithm matching of clusters with similar CyTOF-defined phenotype between the ex vivo and IFNβ stimulated/unstimulated conditions. The number in the table corresponds to the cluster in direct ex vivo and its matched cluster after culture. d. Kaplan–Meier curve comparing overall survival in total CD45+ PBMC from patients with high (n = 16) or low (n = 15) pre-therapy IRC. Log-rank P value = 0.072. e. Pre-therapy IRC of individual immune cell subset for all patients (n = 31). Boxes show median, upper and lower quartile and whiskers show 1.5x the interquartile range on either side. Subsets are ordered from lowest to highest IRC, global P value determined by the Kruskal–Wallis test.

Source data

Extended Data Fig. 4 Pre-therapy CD8 Teff cell IRC correlates to patient survival following anti-PD1 therapy across the cohorts.

a. Median IFNAR1 expression for each patient in the indicated cluster of CD4 T cells from the Discovery cohort direct ex vivo dataset (from Fig. 1b), comparing patients with high (n = 11) versus low (n = 20) CD4 Teff IRC. Boxes show median, upper and lower quartile and whiskers show 1.5x the interquartile range on either side. b. Circulating IFNγ levels were measured in the serum of patients at baseline by Luminex multi-plex ELISA (n = 26; excluding five patients with out of range readings) and stratified into high or low IRC groups based on the individual patients CD4 Teff cell IRC. Boxes show median, upper and lower quartile and whiskers show 1.5x the interquartile range on either side. c. Proteins used to classify T cell subsets. d. Expression of the indicated protein in CD4 T cell clusters (top panels) and CD8 T cell clusters (bottom panels) from the Discovery cohort after overnight culture. e. Kaplan–Meier curves showing overall survival (OS) in the Discovery cohort based on pre-therapy high versus low IRC of the CD8 Teff cell population. t0 is the number of patients in each group at time zero. f. Proteins used to classify T cell subsets in the melanoma and lung validation cohorts. g. Kaplan–Meier curve comparing OS of patients with low or high IRC in the CD8 Teff cells in the melanoma validation cohort (left) and NSCLC validation cohort (right). (e, g) Two-sided log-rank P value is shown.

Source data

Extended Data Fig. 5 Pre-therapy and on-therapy IFN-I responsiveness in CD8 Teff cells.

a. Pre-therapy TuIS within the tumor was stratified based on pre-therapy peripheral CD8 Teff cell IRC (high, n = 13; low, n = 8). Boxes show median, upper and lower quartile and whiskers show 1.5x the interquartile range on either side. b. Paired comparison between matched pre-therapy (Pre) and on-therapy (Post) TuIS in patients with initially high (n = 4) or low (n = 5) peripheral CD8 Teff IRC. P value determined by Wilcoxon’s signed-rank (paired) test.

Source data

Extended Data Fig. 6 Cox’s regression model incorporating multiple IFN-I responsiveness features for better survival prediction.

a. Kaplan–Meier curves comparing overall survival of patients pooled across the three studies and stratified based on the IFN-I-induced induction of PDL1 in myeloid cells. b. Forest plot depicting the hazard ratio (square points) for the three predictors used to establish the combined model using the training set. Error bars indicate the 95% confidence intervals for each hazard ratio. AIC, global P value and concordance index are reported below the plot. c. Kaplan–Meier curves indicating OS of patients in the training set predicted to be low risk compared to patients predicted to be high risk based on the model formula. d. Bootstrapping results for the model in panel (b). e. Kaplan–Meier survival curves indicating OS of patients in the validating test set predicted to be low risk compared to patients predicted to be high risk based on the model formula. (a, c, e) Two-sided log-rank P value is shown.

Extended Data Fig. 7 scRNA-seq from the melanoma validation cohort.

a. Kaplan–Meier curve from Fig. 2f, annotated with the position of each patient selected for multiome sequencing, indicating each patient’s OS time. Two-sided log-rank P value. b. UMAP and Seurat clustering of total PBMCs from the eight patients from the melanoma validation cohort and the two healthy individuals. All 10 samples were integrated together for the UMAP and Seurat clustering. c. Average log2(fold change) of all hallmark IFNA response genes detected in the dataset. Blue (high IRC) and red (low IRC) bars indicate genes with log2(fold change) greater than 0.25 that are significantly differentially expressed. Significance determined by two-sided Wald test and adjusted by the Benjamini & Hochberg method. d. Normalized single-cell gene expression of the 6 ISGs used in the CyTOF IRC score comparing high and low IRC CD4 Teff cells. e. IDO1 normalized gene expression within myeloid cells comparing high and low IRC. f. Bar graphs showing average gene expression in the high IRC CD4 Teff (n = 4) compared to low IRC CD4 Teff (n = 4) for the indicated genes. Data show mean values with error bars representing standard error across the patients. Cytokines (top left), chemokines (top right), S phase (bottom left) and G2M phase genes (bottom left) that were detected in any cells are shown. Using the two-sided Wilcoxon’s rank-sum test, no genes achieved an adjusted P value < 0.05. g. CD8 Teff single cells were assigned a module score based on the expression of genes contained within a CD8 T cell exhaustion signature17. Violin plots depict the single-cell module scores. h. GSEA of PDL1 ligation pathway in IRC high and low CD8 Teff cells. i. Expression of ISGs comprised within the IRF1 regulon (top) and the STAT1 regulon (bottom) related to the SCENIC results from main Fig. 5f. (d, g, i) P value determined by two-sided Wilcoxon’s rank-sum test.

Extended Data Fig. 8 scATAC-seq from the melanoma validation cohort.

a. Plot shows the peak accessibility within myeloid cells in the IRC high and low patient groups for IDO1. Gene track and called peaks are shown below each peak map. b. Differential transcription factor binding motifs enriched in open chromatin regions of CD8 Teff cells from IRC low (top, blue) and IRC high (bottom, red) patients. P value determined by two-sided Wilcoxon’s rank-sum test and adjusted with the Bonferroni correction.

Supplementary information

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Boukhaled, G.M., Gadalla, R., Elsaesser, H.J. et al. Pre-encoded responsiveness to type I interferon in the peripheral immune system defines outcome of PD1 blockade therapy. Nat Immunol 23, 1273–1283 (2022). https://doi.org/10.1038/s41590-022-01262-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-022-01262-7

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer