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.

  • Letter
  • Published:

High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade

Nature Medicine volume 26, pages 693–698 (2020)Cite this article

Subjects

An Author Correction to this article was published on 05 February 2021

This article has been updated

Abstract

Although elevated plasma interleukin-8 (pIL-8) has been associated with poor outcome to immune checkpoint blockade1, this has not been comprehensively evaluated in large randomized studies. Here we analyzed circulating pIL-8 and IL8 gene expression in peripheral blood mononuclear cells and tumors of patients treated with atezolizumab (anti-PD-L1 monoclonal antibody) from multiple randomized trials representing 1,445 patients with metastatic urothelial carcinoma (mUC) and metastatic renal cell carcinoma. High levels of IL-8 in plasma, peripheral blood mononuclear cells and tumors were associated with decreased efficacy of atezolizumab in patients with mUC and metastatic renal cell carcinoma, even in tumors that were classically CD8+ T cell inflamed. Low baseline pIL-8 in patients with mUC was associated with increased response to atezolizumab and chemotherapy. Patients with mUC who experienced on-treatment decreases in pIL-8 exhibited improved overall survival when treated with atezolizumab but not with chemotherapy. Single-cell RNA sequencing of the immune compartment showed that IL8 is primarily expressed in circulating and intratumoral myeloid cells and that high IL8 expression is associated with downregulation of the antigen-presentation machinery. Therapies that can reverse the impacts of IL-8-mediated myeloid inflammation will be essential for improving outcomes of patients treated with immune checkpoint inhibitors.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Plasma IL-8 and clinical outcomes in mUC and mRCC.
Fig. 2: On-treatment changes in plasma IL-8 and OS in patients with mUC treated with atezolizumab or chemotherapy.
Fig. 3: Poor clinical outcome and lower expression of antigen-presentation genes associated with IL8-high myeloid subsets in PBMCs.
Fig. 4: scRNA-seq analysis of IL8 gene expression in immune subsets from matched intratumoral and peripheral blood leukocytes from patients with RCC and association of tumor IL8 gene expression with clinical outcomes in mUC and mRCC.

Similar content being viewed by others

Data availability

Qualified researchers may request access to individual patient-level data through the clinical study data request platform (http://www.clinicalstudydatarequest.com). Further details on Roche’s criteria for eligible studies are available here (https://clinicalstudydatarequest.com/Study-Sponsors/Study-Sponsors-Roche.aspx). For further details on Roche’s Global Policy on the Sharing of Clinical Information and how to request access to related clinical study documents, see here (http://www.roche.com/research_and_development/who_we_are_how_we_work/clinical_trials/our_commitment_to_data_sharing.htm). Raw data analyzed in this study have been submitted to the European Genome-Phenome Archive with accession numbers EGAS00001004008, EGAS00001004229, EGAS00001004230, EGAS00001004386 and EGAS00001004387. Raw and processed count matrix of scRNA-seq data have been submitted to Gene Expression Omnibus with accession number GSE145281.

Change history

References

  1. Sanmamed, M. F. et al. Changes in serum interleukin-8 (IL-8) levels reflect and predict response to anti-PD-1 treatment in melanoma and non-small-cell lung cancer patients. Ann. Oncol. 28, 1988–1995 (2017).

    Article  CAS  Google Scholar 

  2. McDermott, D. F. et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat. Med. 24, 749–757 (2018).

    Article  CAS  Google Scholar 

  3. Najjar, Y. G. et al. Myeloid-derived suppressor cell subset accumulation in renal cell carcinoma parenchyma is associated with intratumoral expression of IL1β, IL8, CXCL5 and Mip-1α. Clin. Cancer Res. 23, 2346–2355 (2017).

    Article  CAS  Google Scholar 

  4. Ugel, S., De Sanctis, F., Mandruzzato, S. & Bronte, V. Tumor-induced myeloid deviation: when myeloid-derived suppressor cells meet tumor-associated macrophages. J. Clin. Invest. 125, 3365–3376 (2015).

    Article  Google Scholar 

  5. Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    Article  CAS  Google Scholar 

  6. Blank, C. U., Haanen, J. B., Ribas, A. & Schumacher, T. N. Cancer immunology. The “cancer immunogram”. Science 352, 658–660 (2016).

    Article  CAS  Google Scholar 

  7. Baggiolini, M., Walz, A. & Kunkel, S. L. Neutrophil-activating peptide-1/interleukin 8, a novel cytokine that activates neutrophils. J. Clin. Invest. 84, 1045–1049 (1989).

    Article  CAS  Google Scholar 

  8. Fernando, R. I., Castillo, M. D., Litzinger, M., Hamilton, D. H. & Palena, C. IL-8 signaling plays a critical role in the epithelial-mesenchymal transition of human carcinoma cells. Cancer Res. 71, 5296–5306 (2011).

    Article  CAS  Google Scholar 

  9. David, J. M., Dominguez, C., Hamilton, D. H. & Palena, C. The IL-8/IL-8R axis: a double agent in tumor immune resistance. Vaccines 4, E22 (2016).

    Article  Google Scholar 

  10. Alfaro, C. et al. Tumor-produced interleukin-8 attracts human myeloid-derived suppressor cells and elicits extrusion of neutrophil extracellular traps (NETs). Clin. Cancer Res. 22, 3924–3936 (2016).

    Article  CAS  Google Scholar 

  11. Sanmamed, M. F. et al. Serum interleukin-8 reflects tumor burden and treatment response across malignancies of multiple tissue origins. Clin. Cancer Res. 20, 5697–5707 (2014).

    Article  CAS  Google Scholar 

  12. Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016).

    Article  CAS  Google Scholar 

  13. Balar, A. V. et al. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial. Lancet 389, 67–76 (2017).

    Article  CAS  Google Scholar 

  14. Powles, T. et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet 391, 748–757 (2018).

    Article  CAS  Google Scholar 

  15. Mariathasan, S. et al. TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).

    Article  CAS  Google Scholar 

  16. Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  18. Powles, T. et al. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature 515, 558–562 (2014).

    Article  CAS  Google Scholar 

  19. Jakubzick, C. V., Randolph, G. J. & Henson, P. M. Monocyte differentiation and antigen-presenting functions. Nat. Rev. Immunol. 17, 349–362 (2017).

    Article  CAS  Google Scholar 

  20. Gabrilovich, D. I., Ostrand-Rosenberg, S. & Bronte, V. Coordinated regulation of myeloid cells by tumours. Nat. Rev. Immunol. 12, 253–268 (2012).

    Article  CAS  Google Scholar 

  21. Mantovani, A. et al. The chemokine system in diverse forms of macrophage activation and polarization. Trends Immunol. 25, 677–686 (2004).

    Article  CAS  Google Scholar 

  22. Bruchard, M. et al. Chemotherapy-triggered cathepsin B release in myeloid-derived suppressor cells activates the Nlrp3 inflammasome and promotes tumor growth. Nat. Med. 19, 57–64 (2013).

    Article  CAS  Google Scholar 

  23. Rini, B. I. et al. IMA901, a multipeptide cancer vaccine, plus sunitinib versus sunitinib alone, as first-line therapy for advanced or metastatic renal cell carcinoma (IMPRINT): a multicentre, open-label, randomised, controlled, phase 3 trial. Lancet Oncol. 17, 1599–1611 (2016).

    Article  CAS  Google Scholar 

  24. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    Article  CAS  Google Scholar 

  25. Lee, Y. S. et al. Interleukin-8 and its receptor CXCR2 in the tumour microenvironment promote colon cancer growth, progression and metastasis. Br. J. Cancer 106, 1833–1841 (2012).

    Article  CAS  Google Scholar 

  26. Steele, C. W. et al. CXCR2 Inhibition profoundly suppresses metastases and augments immunotherapy in pancreatic ductal adenocarcinoma. Cancer Cell 29, 832–845 (2016).

    Article  CAS  Google Scholar 

  27. Ruffell, B., Affara, N. I. & Coussens, L. M. Differential macrophage programming in the tumor microenvironment. Trends Immunol. 33, 119–126 (2012).

    Article  CAS  Google Scholar 

  28. Ruffell, B. & Coussens, L. M. Macrophages and therapeutic resistance in cancer. Cancer Cell 27, 462–472 (2015).

    Article  CAS  Google Scholar 

  29. Ridker, P. M. et al. Effect of interleukin-1β inhibition with canakinumab on incident lung cancer in patients with atherosclerosis: exploratory results from a randomised, double-blind, placebo-controlled trial. Lancet 390, 1833–1842 (2017).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  31. 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  Google Scholar 

  32. Gupta, V. et al. Bioanalytical qualification of clinical biomarker assays in plasma using a novel multi-analyte Simple Plex() platform. Bioanalysis 8, 2415–2428 (2016).

    Article  CAS  Google Scholar 

  33. Wu, T. D., Reeder, J., Lawrence, M., Becker, G. & Brauer, M. J. GMAP and GSNAP for genomic sequence alignment: enhancements to speed, accuracy and functionality. Methods Mol. Biol. 1418, 283–334 (2016).

    Article  Google Scholar 

  34. Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).

    Article  CAS  Google Scholar 

  35. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank L. Goldstein and T. Wu for technical assistance for scRNA-seq analysis. We thank L. Rangell for assistance in IL8 in situ hybridization experiments. Editorial assistance was provided by Anshin Biosolutions (Santa Clara). D.F.M. was supported by a DFHCC KCP SPORE grant (5P50CA101942).

Author information

Author notes

  1. These authors contributed equally: Kobe C. Yuen, Li-Fen Liu.

  2. These authors jointly supervised this work: Mahrukh A. Huseni, Sanjeev Mariathasan.

Authors and Affiliations

  1. Genentech, San Francisco, CA, USA

    Kobe C. Yuen, Li-Fen Liu, Vinita Gupta, Shravan Madireddi, Shilpa Keerthivasan, Congfen Li, Deepali Rishipathak, Patrick Williams, Edward E. Kadel III, Hartmut Koeppen, Ying-Jiun Chen, Zora Modrusan, Jane L. Grogan, Romain Banchereau, Ning Leng, AnnChristine Thastrom, Xiadong Shen, Darren Tayama, Priti S. Hegde, Mahrukh A. Huseni & Sanjeev Mariathasan

  2. Roche Products Ltd., Welwyn Garden City, UK

    Kenji Hashimoto

  3. Netherlands Cancer Institute, Amsterdam, the Netherlands

    Michiel S. van der Heijden

  4. Genitourinary Oncology Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Jonathan E. Rosenberg

  5. Beth Israel Deaconess Medical Center, Boston, MA, USA

    David F. McDermott

  6. Barts Experimental Cancer Medicine Centre, Barts Cancer Institute, Queen Mary University of London, London, UK

    Thomas Powles

Authors
  1. Kobe C. Yuen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li-Fen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vinita Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shravan Madireddi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shilpa Keerthivasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Congfen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Deepali Rishipathak
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Patrick Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Edward E. Kadel III
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hartmut Koeppen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ying-Jiun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Zora Modrusan
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jane L. Grogan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Romain Banchereau
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ning Leng
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. AnnChristine Thastrom
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Xiadong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Kenji Hashimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Darren Tayama
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Michiel S. van der Heijden
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jonathan E. Rosenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. David F. McDermott
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Thomas Powles
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Priti S. Hegde
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Mahrukh A. Huseni
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Sanjeev Mariathasan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.C.Y, L.-F.L., P.S.H., M.A.H. and S.M. contributed to conception, data acquisition, analysis and interpretation and wrote the manuscript; V.G., C.L., D.R., E.E.K., H.K., S.M., S.K., Y.-J.C., Z.M., J.LG., R.M. and N.L. made substantial contributions to the acquisition of data and data analyses; P.W., A.C.T., X.S., K.H. and D.T. supervised analysis of clinical data; M.S.v.d.H., J.E.R., D.F.M. and T.M. contributed to data interpretation, conception of clinical trial design and served as principal investigators for clinical studies.

Corresponding authors

Correspondence to Mahrukh A. Huseni or Sanjeev Mariathasan.

Ethics declarations

Competing interests

K.C.Y., L.-F.L., V.G., C.L., D.P., P.W., E.E.K., H.K., S.M., S.K., Y.-J.C, Z.M., J.L.G., R.B., N.L., A.C.T., X.S., D.T., P.S.H., M.A.H. and S.M. are employees of Genentech. K.H. is an employee of Roche Products. D.F.M. reports a consulting/advisory role for Bristol-Myers Squibb, Merck, Roche/Genentech, Pfizer, Exelixis, Novartis, Eisai, X4 Pharmaceuticals and Array BioPharma; and reports that his home institution receives research funding from Prometheus Laboratories. T.P. reports honoraria and consulting/advisory roles with Roche/Genentech, Bristol-Myers Squibb and Merck; a consulting/advisory role with AstraZeneca and Novartis; research funding from AstraZeneca/MedImmune and Roche/Genentech; and other relationships with Ipsen and Bristol-Myers Squibb. J.E.R. has received trial funding and consulting fees from Roche Genentech, Bayer, Seattle Genetics, AstraZeneca, Astellas and QED Therapeutics. M.S.v.d.H. has advisory board agreements with Roche Genentech, Astellas and AstraZeneca and has received grants from Astellas.

Additional information

Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Study profile of IMvigor210, IMvigor211 and IMmotion150 trials.

a, Study profile of IMvigor210, IMvigor211 and IMmotion150 trials. Flowchart showing number of intent-to-treat (ITT) patients IMvigor210, IMvigor211 and IMmotion150, as well as the numbers of patients whose plasma, PBMC and RNAseq samples were included for analysis. Tables showing the demographic characteristics of biomarker-evaluable patients in b, IMvigor210 (n = 329) and IMvigor211 (n = 868) cohorts, and c, IMmotion150 (n = 248) cohort. P values are calculated by two-sided Fisher’s exact test. Tables showing univariate and multivariate logistic regression analyses of baseline plasma IL8 with different factors in overall survival in d, IMvigor210 (n = 329) and IMvigor211(n = 868) cohorts and e, IMmotion150 (n = 248) cohort. HR calculated using stratified Cox proportional hazard regression models, and P values calculated using stratified log-rank test (for details, see Methods). P values were adjusted for multiple comparisons. Multivariate analyses adjusted HRs for age, sex, race, ECOG performance status, presence of liver metastasis, and tumor burden (sum of longest diameter, SLD) in mUC; and age, sex, Memorial Sloan Kettering Cancer Risk (MSKCC) prognostic risk score, previous nephrectomy, and SLD in mRCC data sets.

Extended Data Fig. 2 Correlation between pIL8 and other cancer immunotherapy biomarkers.

a, Correlation between pIL8 and neutrophil-to-lymphocyte ratio (NLR) in mUC (IMvigor210) (n = 217). NLR (x-axes) were log10 transformed before Pearson correlations (Corr) with pIL8, which were log 2 transformed (y-axes). The corresponding p values (two tailed t-test) are shown. Correlation between pIL8 and b, T-effector (Teff) (n = 329), c, Tumor mutation burden (TMB) (n = 255), d, Neoantigen load (n = 230), e, PD-1 expression (PDCD1), f, PD-L1 (CD274), g, TGFb-response (F-TBRS), h, Tumor immune dysfunction and exclusion (TIDE) T cell dysfunction signature (n = 329). Pearson correlations (Corr) between labeled biomarkers (x-axes) with pIL8, which were log 2 transformed (y-axes) and corresponding p values (two tailed t-test) are shown. i, Microsatellite instable (MSI) status. MSS, microsatellite stable; MSI-H, microsatellite instable-high (n = 329).

Extended Data Fig. 3 Elevated baseline pIL8 is associated with poor clinical outcome.

a, Kaplan-Meier curves depict overall survival (OS) of median T-effector (Teff) signature in cohort 2 of IMvigor210. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were calculated using stratified Cox proportional hazards regression models, and p values were calculated using stratified log-rank test. HR and p value are adjusted for age, sex, race, ECOG performance status, presence of liver metastasis, and tumor burden (sum of longest diameter, SLD). b, Kaplan-Meier curves depict overall survival (OS) of baseline plasma IL8 (pIL8) levels in cohort 1 of IMvigor210. Censored data are indicated by vertical tick marks in Kaplan-Meier curves. Number of patients per group and time point are indicated below the graphs. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were calculated using stratified Cox proportional hazards regression models, and p values were calculated using stratified log-rank test. HR and p value are adjusted for age, sex, race, ECOG performance status, presence of liver metastasis, and tumor burden (sum of longest diameter, SLD). c, Association between high vs low pIL8 (median cutoff) and Objective Response Rate (ORR) in cohort 1 of IMvigor210. High baseline pIL8 levels were associated with a higher number of nonresponders (SD and PD) (P = 0.025, two-sided Fisher’s exact test) by Response Evaluation Criteria in Solid Tumors (RECIST) 2.1. [CR: complete response; PR, partial response; SD, stable disease; PD, progressive disease]. d, Association between high vs low Teff (median cutoff) and OS in cohort 2 of IMvigor210 (HR: 0.71, 95% CI: 0.53, 0.95, P = 0.0201). Hazard ratios (HRs) and their 95% confidence intervals (CIs) were calculated using stratified Cox proportional hazards regression models, and p values were calculated using stratified log-rank test. HR and p value are adjusted for sex, age, race, ECOG performance status, presence of liver metastasis, and tumor burden (sum of longest diameter, SLD). e, Association between high vs low baseline plasma IL8 (median cutoff) and Objective Response Rate (ORR) in IMmotion150A trend in low plasma IL8 in Atezo monotherapy associated with a higher number of responders compared to Atezo+Bev and Sunitinib treatment arms (CR and PR) (P = 0.348, 0.409 and 0.271, respectively two-sided Fisher’s exact test) f, Association of high vs low of baseline tumor IL8 expression and Objective Response Rate (ORR) in IMmotion150. A trend observed in low tumor IL8 in atezo monotherapy associated with higher numbers of responders (CR and PR) compared to Atezo+Bev and Sunitinib treatment arms (P = 0.178, 0.05, and 0.773, respectively).

Extended Data Fig. 4 Elevated on-treatment pIL8 is associated with poor clinical outcome.

Association between high vs low ratio of pIL8 levels on treatment cycle 3 day 1 (C3D1) and baseline (C1D1) and Objective Response Rate (ORR) in a, cohort 1, b, cohort 2 of IMvigor210. High ratios were associated with a higher number of nonresponders (SD and PD) in cohort 1 (P = 0.042, two-sided Fisher’s exact test) and cohort 2 (P = 0.027, two-sided Fisher’s exact test) of IMvigor210. c, Kaplan-Meier curves depict OS of C3D1 and C1D1 ratio of pIL8 levels in cohort 1 of IMvigor210 (HR: 4.98, 95% CI: 1.83, 13.5, P = 0.0016). Hazard ratios (HRs) and their 95% confidence intervals (CIs) were calculated using stratified Cox proportional hazards regression models, and p values were calculated using stratified log-rank test. HR and p value are adjusted for sex, age, race, ECOG performance status, presence of liver metastasis, and tumor burden (sum of longest diameter, SLD). d, High ratios were significantly associated with a higher number of nonresponders (SD and PD) in atezolizumb (P = 8.22e-4, two-sided Fisher’s exact test) and but not in chemotherapy (P = 0.060, two-sided Fisher’s exact test) arms of IMvigor211. e, The absolute lymphoid and myeloid counts in patients in atezolizumb (n = 443) and chemotherapy (n = 425) arms IMvigor211. Absolute lymphocyte counts: Atezo, C1D1: minima: 0, maxima: 3.74, Percentile 75%: 1.70, 50%: 1.32, 25%: 1.00. Atezo, C3D1: minima: 0, maxima: 3.5, Percentile 75%: 1.75, 50%: 1.38, 25%: 1.00. Chemo, C1D1: minima: 0, maxima: 4.08, Percentile 75%: 1.70, 50%: 1.20, 25%: 0.90. Chemo, C3D1: minima: 0, maxima: 3.89, Percentile 75%: 1.81, 50%: 1.40, 25%: 1.00. Absolute monocyte counts: Atezo, C1D1: minima: 0, maxima: 2.25, Percentile 75%: 0.80, 50%: 0.61, 25%: 0.49. Atezo, C3D1: minima: 0, maxima: 2.2, Percentile 75%: 0.82, 50%: 0.61, 25%: 0.49. Chemo, C1D1: minima: 0, maxima: 2.2, Percentile 75%: 0.82, 50%: 0.61, 25%: 0.49. Chemo, C3D1: minima: 0, maxima: 3.08, Percentile 75%: 0.90, 50%: 0.70, 25%: 0.50. P values are calculated by two-sided Mann-Whitney U-tests.

Extended Data Fig. 5 Single cell RNAseq profiles of PBMC from bladder patients in IMvigor210 trial.

UMAP plot of the mUC PBMCs, with each cell in the entire single cell RNAseq color coded for (left to right): a, responses (Responders (n = 7903) and nonresponders (n = 6571)); b, the corresponding patient R1 (n = 2761), R2 (n = 1522), R3 (n = 463), R4 (n = 1194), R5 (n = 1963) and NR1 (n = 3189), NR2 (n = 849), NR3 (n = 1018), NR4 (n = 697), NR5 (n = 818) and c, the number of transcripts detected in that cell (log 10 scale). d, the fraction of cells originating from responders and nonresponders; e, the fraction of cells originating from each of the 10 patients; f, box plots of the number of transcripts (log10) across all different cell types. Monocytes (n = 6761), minima: 2.92, maxima: 4.35, and Percentile 75%: 3.59, 50%: 3.39, 25%: 3.20. CD16 Monocytes (n = 623), minima: 2.92, maxima: 3.65, and Percentile 75%: 3.29, 50%: 3.23, 25%: 3.16. DC-like (n = 305), minima: 2.96, maxima: 4.17, and Percentile 75%: 3.55, 50%: 3.35, 25%: 3.19. pDC (n = 391), minima: 2.95, maxima: 3.65, and Percentile 75%: 3.24, 50%: 3.17, 25%: 3.09. Megakaryocyte (n = 294), minima: 2.94, maxima: 3.67, and Percentile 75%: 3.67, 50%: 3.19, 25%: 3.11. CD8 + T cells (n = 565), minima: 2.93, maxima: 4.15, and Percentile 75%: 3.61, 50%: 3,42, 25%: 3.21. CD8 + Tcm (n = 1388), minima: 2.93, maxima: 4.22, and Percentile 75%: 3.51, 50%: 3.34, 25%: 3.19. CD8 + Tem (n = 1194), minima: 2.92, maxima: 4.10, and Percentile 75%: 3.56, 50%: 3,38, 25%: 3.20. CD4 + T cells (n = 443), minima: 2.94, maxima: 4.02, centre: 0.75 and Percentile 75%: 3.58, 50%: 3.39, 25%: 3.21. CD4 + Tcm (n = 451), minima: 2.91, maxima: 4.45, and Percentile 75%: 3.54, 50%: 3.32, 25%: 3.18. CD4 + Tem (n = 335), minima: 2.93, maxima: 4.06, and Percentile 75%: 3.47, 50%: 3.28, 25%: 3.15. Tregs (n = 238), minima: 2.90, maxima: 4.15, and Percentile 75%: 3.49, 50%: 3.30, 25%: 3.14. NK cells (n = 1099), minima: 2.90, maxima: 4.27, and Percentile 75%: 3.56, 50%: 3.36, 25%: 3.16. B cells (n = 387), minima: 2.91, maxima: 3.96, and Percentile 75%: 3.51, 50%: 3.32, 25%: 3.19, g, UMAP plot shows the distribution of different cell types between responders and non responders. Responders (n = 7903) and nonresponders (n = 6571).

Extended Data Fig. 6 Differential expression analysis of IL8 expression in myeloid cells.

a, Gene set enrichment REACTOME pathways analysis between IL8 high vs IL8 low cells (median cutoff) in all myeloid clusters (n = 8374). Differential expression analysis with the generalized linear models (glm)-based statistical methods of the edgeR package with Benjamini & Hochberg corrections. Normalized enrichment scores, log FDR corrected, are shown in x axis. Top 10 pathways associated with IL8 high myeloid cells were shown in orange and top 10 pathways associated with IL8 low myeloid cells were shown in blue. b, Differential gene expression of IL8 high vs low populations in different myeloid clusters: a, Monocytes (n = 6761), b, CD16 Monocytes (n = 623), c, DC (n = 391), d, DC-like (n = 305) and e, Megakaryocytes (n = 294). Differential expression analysis with the generalized linear models (glm)-based statistical methods of the edgeR package with Benjamini & Hochberg corrections. Genes that are enriched in IL8 high are shown in orange and those that are enriched in IL8 low are shown in blue.

Extended Data Fig. 7 Differential expression analysis of response in myeloid cells.

a, Differential gene expression analysis between responders (n = 3988) and non responders (n = 4386) in all myeloid cells shows a significant enrichment of myeloid inflammatory response genes (red) in non responders whereas a significant enrichment of antigen presentation machinery genes (green) in responders. b, Gene set enrichment REACTOME pathways analysis between responders and non responders in myeloid cells. Differential expression analysis with the generalized linear models (glm)-based statistical methods of the edgeR package with Benjamini & Hochberg corrections. Normalized enrichment scores, log FDR corrected, are shown in x axis. Top 10 pathways associated with responders were shown in green and top 10 pathways associated with non responders were shown in red. Differential gene expression analysis between responders and non responders within c, Monocytes (n = 6761), d, CD16 Monocytes (n = 623), e, DC (n = 391), f, DC-like (n = 305) and g, Megakaryocytes (n = 294) shows a significant enrichment of myeloid inflammatory response genes (red) in non responders whereas a significant enrichment of antigen presentation machinery and T cell activation genes (green) in responders. Differential expression analysis with the generalized linear models (glm)-based statistical methods of the edgeR package with Benjamini & Hochberg corrections.

Extended Data Fig. 8 Differential expression analysis of IL8 expression in tumor associated myeloid cells from single cell RNAseq of RCC patients.

UMAP plot of the mRCC blood and tumor, with each cell in the entire single cell RNAseq color coded for (left to right): a, Blood (n = 13,694) and Tumor (n = 11,765); b, the corresponding patient c, the proportion of cells identified in each cell type in blood and tumor d, the proportion of cells identified in each cell type in each patient. e, Scaled average expression of cell type specific markers in scRNA of mRCC tumor. Tcm, central memory T cell; Tem, effector memory; M1-like, M1-like macrophages; M2-like, M2-like Macrophages. Differential gene expression analysis between IL8-high and IL8-low within each myeloid cell type in the tumor. f, monocytes (n = 2821). g, M1-like macrophages (n = 2452). h, M2-like macrophages (n = 553). i, CD16 monocytes (n = 454) shows a significant enrichment of myeloid inflammatory response genes (orange) in IL8 high whereas a significant enrichment of antigen presentation machinery genes (blue) in IL8 low. Differential expression analysis with the generalized linear models (glm)-based statistical methods of the edgeR package with Benjamini & Hochberg corrections.

Extended Data Fig. 9 Correlation of IL8 gene expression and neutrophil score in bladder and RCC tumors.

IL8 gene expression and histological assessment of neutrophils by H&E stain in a, mRCC (IMmotion 150) (n = 100) tumors. Neutrophil score 0 (n = 37): minima: -2.19, maxima: 4.07, and Percentile 75%: 1.63, 50%: 0.89, 25%: 0.04. Neutrophil score 1 (n = 24): minima: -2.21, maxima: 7.62, and Percentile 75%: 3.8, 50%: 1.5, 25%: 0.10. Neutrophil score 2 (n = 18): minima: -2.54, maxima: 7.72, and Percentile 75%: 6.63, 50%:4.45, 25%:2.6. Neutrophil score 3 (n = 21): minima: -1.96, maxima:12.6, and Percentile 75%: 7.20, 50%: 6.46, 25%: 5.49. P values are calculated by two-sided Mann-Whitney U-tests with Benjamini & Hochberg corrections. b, mUC (IMVigor 210) (n = 339) tumors. Neutrophil score 0 (n = 227): minima: -2.67, maxima: 2.58, and Percentile 75%: 0.25, 50%: -0.34, 25%: -0.89. Neutrophil score 1 (n = 39): minima: -1.06, maxima: 1.95, and Percentile 75%: 0.70, 50%: 0.18, 25%: -0.38. Neutrophil score 2 (n = 33): minima: -0.63, maxima: 2.19, and Percentile 75%: 0.97, 50%: 0.50, 25%: 0.39. Neutrophil score 3 (n = 40): minima: -0.99, maxima: 2.83, and Percentile 75%: 1.60, 50%: 1.27, 25%: 0.92. P values are calculated by two-sided Mann-Whitney U-tests with Benjamini & Hochberg corrections. Neutrophils were identified by trained pathologists based on their unique morphological features. Prevalence of neutrophils was graded on a scale from 0 to 3 as follows: 0 – absence of neutrophils, 1 – rare neutrophils, 2 – moderate number of neutrophils, 3 – numerous neutrophils in the form of large aggregates or sheets. c, Representative images of 22 out of a total of 59 samples examined (37%) showing positive signals of IL8 in situ hybridization (ISH; green signal, left panel) and H&E-stain (right panel) of sections from the same area of the specimen. ISH shows IL8 expression in tumor and myeloid cells; H&E shows neutrophils (yellow arrows) in the vicinity of IL8 expressing cells.

Extended Data Fig. 10 Differential gene expression in high and low pIL8 in CD8 T cell clusters and in PBMC from the entire IMvigor210 cohort.

a, Differential single cell RNAseq gene expression of CD8 T cell clusters from plasma IL8 high (n = 5) vs low patients (n = 5). Differential expression analysis with the generalized linear models (glm)-based statistical methods of the edgeR package with Benjamini & Hochberg corrections. b, Differential NanoString gene expression of plasma IL8 high vs low patients in IMvigor210 (n = 407). Differential expression analysis with the generalized linear models (glm)-based statistical methods of the edgeR package with Benjamini & Hochberg corrections.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuen, K.C., Liu, LF., Gupta, V. et al. High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade. Nat Med 26, 693–698 (2020). https://doi.org/10.1038/s41591-020-0860-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-020-0860-1

This article is cited by

Search

Advanced 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