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.

Preoperative ipilimumab plus nivolumab in locoregionally advanced urothelial cancer: the NABUCCO trial

Nature Medicine volume 26, pages 1839–1844 (2020)Cite this article

Subjects

Abstract

Preoperative immunotherapy with anti-PD1 plus anti-CTLA4 antibodies has shown remarkable pathological responses in melanoma1 and colorectal cancer2. In NABUCCO (ClinicalTrials.gov: NCT03387761), a single-arm feasibility trial, 24 patients with stage III urothelial cancer (UC) received two doses of ipilimumab and two doses of nivolumab, followed by resection. The primary endpoint was feasibility to resect within 12 weeks from treatment start. All patients were evaluable for the study endpoints and underwent resection, 23 (96%) within 12 weeks. Grade 3–4 immune-related adverse events occurred in 55% of patients and in 41% of patients when excluding clinically insignificant laboratory abnormalities. Eleven patients (46%) had a pathological complete response (pCR), meeting the secondary efficacy endpoint. Fourteen patients (58%) had no remaining invasive disease (pCR or pTisN0/pTaN0). In contrast to studies with anti-PD1/PD-L1 monotherapy, complete response to ipilimumab plus nivolumab was independent of baseline CD8+ presence or T-effector signatures. Induction of tertiary lymphoid structures upon treatment was observed in responding patients. Our data indicate that combined CTLA-4 plus PD-1 blockade might provide an effective preoperative treatment strategy in locoregionally advanced UC, irrespective of pre-existing CD8+ T cell activity.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: NABUCCO study design and baseline study population characteristics.
Fig. 2: Pathological tumor regression and outcome to preoperative ipilimumab plus nivolumab.
Fig. 3: pCR to ipilimumab plus nivolumab is independent of baseline CD8+ T cells and inflammatory signatures.
Fig. 4: B cell analysis and assessment of TLS dynamics upon preoperative ipilimumab plus nivolumab.

Data availability

DNA and RNA sequencing data have been deposited in the European Genome-phenome Archive under the accession code EGAS00001004521 and will be made available upon reasonable request for academic use and within the limitations of the provided informed consent by the corresponding author upon acceptance. Every request will be reviewed by the institutional review board of the Netherlands Cancer Institute; the researcher will need to sign a data access agreement with the Netherlands Cancer Institute after approval. Sequencing data correspond with Figs. 3 and 4. Multiplex immunofluorecence raw quantification data corresponding to Figs. 3d and 4 will be made available upon reasonable academic request within the limitations of informed consent by the corresponding author upon acceptance.

References

  1. Rozeman, E. A. et al. Identification of the optimal combination dosing schedule of neoadjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma (OpACIN-neo): a multicentre, phase 2, randomised, controlled trial. Lancet Oncol. 20, 948–960 (2019).

  2. Chalabi, M. et al. Neoadjuvant immunotherapy leads to pathological responses in MMR-proficient and MMR-deficient early-stage colon cancers. Nat. Med. 26, 566–576 (2020).

  3. Stein, J. P. et al. Radical cystectomy in the treatment of invasive bladder cancer: long-term results in 1,054 patients. J. Clin. Oncol. 19, 666–675 (2001).

    Article  CAS  Google Scholar 

  4. Zargar, H. et al. Multicenter assessment of neoadjuvant chemotherapy for muscle-invasive bladder cancer. Eur. Urol. 67, 241–249 (2014).

  5. Advanced Bladder Cancer (ABC) Meta-analysis Collaboration. Neoadjuvant chemotherapy in invasive bladder cancer: update of a systematic review and meta-analysis of individual patient data. Eur. Urol. 48, 202–205 (2005).

    Article  Google Scholar 

  6. Bellmunt, J. et al. Pembrolizumab as second-line therapy for advanced urothelial carcinoma. N. Engl. J. Med. 376, 1015–1026 (2017).

    Article  CAS  Google Scholar 

  7. 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 

  8. Sharma, P. et al. Nivolumab in metastatic urothelial carcinoma after platinum therapy (CheckMate 275): a multicentre, single-arm, phase 2 trial. Lancet Oncol. 18, 312–322 (2017).

    Article  CAS  Google Scholar 

  9. 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 

  10. Massard, C. et al. Safety and efficacy of durvalumab (MEDI4736), an anti-programmed cell death ligand-1 immune checkpoint inhibitor, in patients with advanced urothelial bladder cancer. J. Clin. Oncol. 34, 3119–3125 (2016).

    Article  CAS  Google Scholar 

  11. 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 

  12. Sharma, P. et al. Nivolumab alone and with ipilimumab in previously treated metastatic urothelial carcinoma: CheckMate 032 nivolumab 1 mg/kg plus ipilimumab 3 mg/kg expansion cohort results. J. Clin. Oncol. 37, 1608–1616 (2019).

    Article  CAS  Google Scholar 

  13. Balar, A. V. et al. First-line pembrolizumab in cisplatin-ineligible patients with locally advanced and unresectable or metastatic urothelial cancer (KEYNOTE-052): a multicentre, single-arm, phase 2 study. Lancet Oncol. 18, 1483–1492 (2017).

  14. Carthon, B. C. et al. Preoperative CTLA-4 blockade: tolerability and immune monitoring in the setting of a presurgical clinical trial. Clin. Cancer Res. 16, 2861–2871 (2010).

    Article  CAS  Google Scholar 

  15. Liakou, C. I. et al. CTLA-4 blockade increases IFNγ-producing CD4+ICOShi cells to shift the ratio of effector to regulatory T cells in cancer patients. Proc. Natl Acad. Sci. USA 105, 14987–14992 (2008).

    Article  CAS  Google Scholar 

  16. Necchi, A. et al. Pembrolizumab as neoadjuvant therapy before radical cystectomy in patients with muscle-invasive urothelial bladder carcinoma (PURE-01): an open-label, single-arm, phase II study. J. Clin. Oncol. 36, 3353−3360 (2018).

  17. Necchi, A. et al. Updated results of PURE-01 with preliminary activity of neoadjuvant pembrolizumab in patients with muscle-invasive bladder carcinoma with variant histologies. Eur. Urol. 77, 439−446 (2019).

  18. Powles, T. et al. Clinical efficacy and biomarker analysis of neoadjuvant atezolizumab in operable urothelial carcinoma in the ABACUS trial. Nat. Med. 25, 1706–1714 (2019).

    Article  CAS  Google Scholar 

  19. Cabrita, R. et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577, 561–565 (2020).

    Article  CAS  Google Scholar 

  20. Helmink, B. A. et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 577, 549–555 (2020).

    Article  CAS  Google Scholar 

  21. Meerveld-Eggink et al. Short-term CTLA-4 blockade directly followed by PD-1 blockade in advanced melanoma patients: a single-center experience. Ann. Oncol. 28, 862–867 (2017).

    Article  CAS  Google Scholar 

  22. Teo, M. Y. et al. Alterations in DNA damage response and repair genes as potential marker of clinical benefit from PD-1/PD-L1 blockade in advanced urothelial cancers. J. Clin. Oncol. 36, 1685–1694 (2018).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  24. Hollern, D. P. et al. B cells and T follicular helper cells mediate response to checkpoint inhibitors in high mutation burden mouse models of breast cancer. Cell 179, 1191–1206 (2019).

    Article  CAS  Google Scholar 

  25. Largeot, A., Pagano, G., Gonder, S., Moussay, E. & Paggetti, J. The B-side of cancer immunity: the underrated tune. Cells 8, 449 (2019).

    Article  CAS  Google Scholar 

  26. Griss, J. et al. B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma. Nat. Commun. 10, 4186 (2019).

    Article  Google Scholar 

  27. Silina, K. et al. Germinal centers determine the prognostic relevance of tertiary lymphoid structures and are impaired by corticosteroids in lung squamous cell carcinoma. Cancer Res. 78, 1308–1320 (2018).

    Article  CAS  Google Scholar 

  28. Amaria, R. N. et al. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat. Med. 24, 1649–1654 (2018).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  30. Zargar, H. et al. Multicenter assessment of neoadjuvant chemotherapy for muscle-invasive bladder cancer. Eur. Urol. 67, 241–249 (2015).

    Article  Google Scholar 

  31. Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    Article  CAS  Google Scholar 

  32. Talevich, E., Shain, A. H., Botton, T. & Bastian, B. C. CNVkit: genome-wide copy number detection and visualization from targeted DNA sequencing. PLoS Comput. Biol. 12, e1004873 (2016).

    Article  Google Scholar 

  33. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

    Article  Google Scholar 

  34. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  Google Scholar 

  35. Strimmer, K. fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24, 1461–1462 (2008).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge the clinical trial data managers (C. Hagenaars and J. Kant), clinical trial nurses (A. Lechner, E. van der Laan and S. van der Kolk), the Genomics Core Facility and the Core Facility Molecular Pathology and Biobanking (I. Hofland, S. Cornelissen, L. Braaf and J. Sanders) for support, all at the Netherlands Cancer Institute. We thank B. Stegenga at Bristol-Myers Squibb. M.v.d.B. was financially supported by the Swiss National Science Foundation (CRSII5_177208 and 310030_175565), the University of Zurich Research Priority Program ‘Translational Cancer Research’, the Cancer Research Center Zurich and Worldwide Cancer Research (18−0629).

Author information

Author notes

  1. These authors contributed equally: Nick van Dijk, Alberto Gil-Jimenez.

Authors and Affiliations

  1. Department of Medical Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Nick van Dijk, Jeantine M. de Feijter, Christian U. Blank & Michiel S. van der Heijden

  2. Department of Molecular Carcinogenesis, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Alberto Gil-Jimenez, Yoni Lubeck, Daniel J. Vis, Lodewyk F. A. Wessels & Michiel S. van der Heijden

  3. Oncode Institute, Utrecht, the Netherlands

    Alberto Gil-Jimenez, Daniel J. Vis, Ton N. Schumacher & Lodewyk F. A. Wessels

  4. Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland

    Karina Silina, Henk G. van der Poel & Maries van den Broek

  5. Department of Urology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Kees Hendricksen

  6. Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Laura A. Smit, Maurits L. van Montfoort, Charlotte van Rooijen, Yoni Lubeck & Erik Hooijberg

  7. Core Facility Molecular Pathology & Biobanking, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Dennis Peters & Annegien Broeks

  8. Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Annemarie Bruining & Thierry N. Boellaard

  9. Department of Biometrics, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Karolina Sikorska

  10. Division of Molecular Oncology and Immunology, Netherlands Cancer Institute, Amsterdam, the Netherlands

    Pia Kvistborg, Ton N. Schumacher & Christian U. Blank

  11. Department of Urology, Caritas St. Josef Medical Centre, University of Regensburg, Regensburg, Germany

    Bas W. van Rhijn

Authors
  1. Nick van Dijk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alberto Gil-Jimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karina Silina
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kees Hendricksen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laura A. Smit
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jeantine M. de Feijter
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Maurits L. van Montfoort
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Charlotte van Rooijen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dennis Peters
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Annegien Broeks
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Henk G. van der Poel
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Annemarie Bruining
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yoni Lubeck
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Karolina Sikorska
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Thierry N. Boellaard
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Pia Kvistborg
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Daniel J. Vis
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Erik Hooijberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Ton N. Schumacher
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Maries van den Broek
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Lodewyk F. A. Wessels
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Christian U. Blank
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Bas W. van Rhijn
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Michiel S. van der Heijden
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The trial protocol was designed and written by the authors (N.v.D., K. Sikorska, C.U.B., B.W.v.R. and M.S.v.d.H.). N.v.D. coordinated the trial initiation and study procedures. K. Sikorska analyzed the clinical data. N.v.D., C.U.B., B.W.v.R. and M.S.v.d.H. interpreted clinical data. N.v.D., A.G.-J., K. Silina, M.v.d.B. and M.S.v.d.H. analyzed translational data. A.G.-J., D.J.V., Y.L., L.F.A.W. and K. Silina performed and interpreted bioinformatics analyses. D.P. and C.v.R. performed immunohistochemical staining, multiplex immunofluoresence staining and HALO image analysis. Work related to tertiary lymphoid structures was conducted and supervised by K. Silina and M.v.d.B. RNA and DNA sequencing preparations and multiplex/immunohistochemical assessments were supervised by E.H. and A. Broeks. L.A.S. and M.L.v.M. performed histopathological assessment and assessed tissue availability and immunopathological scoring. K. Sikorska performed statistical analyses. N.v.D., J.M.d.F. and M.S.v.d.H. informed patients and were responsible for patient care. B.W.v.R., K.H. and H.G.v.d.P. performed surgery. T.N.B. and A. Bruining assessed CT and MRI scans and analyzed tumor volumes. P.K. and T.N.S. provided scientific input during protocol writing and design of the study. The manuscript was written by N.v.D., A.G.-J. and M.S.v.d.H., together with all co-authors, who vouch for the accuracy of the data reported and adherence to the protocol. All authors edited and approved the manuscript.

Corresponding author

Correspondence to Michiel S. van der Heijden.

Ethics declarations

Competing interests

T.N.S. is a consultant for Adaptive Biotechnologies, AIMM Therapeutics, Allogene Therapeutics, Amgen, Merus, Neon Therapeutics and Scenic Biotech. T.N.S. has received grant and research support from Merck, Bristol-Myers Squibb and Merck KGaA. T.N.S. is a stockholder in AIMM Therapeutics, Allogene Therapeutics and Neon Therapeutics. L.F.A.W. reports research funding from Genmab. C.U.B. reports personal fees for advisory roles for Merck Sharp & Dohme, Bristol-Myers Squibb, Roche, GlaxoSmithKline, Novartis, Pfizer, Genmab and Eli Lilly and grants from Bristol-Myers Squibb, NanoString and Novartis. M.S.v.d.H. has received research support from Bristol-Myers Squibb, AstraZeneca and Roche and consultancy fees from Bristol-Myers Squibb, Merck, Merck Sharp & Dohme, Roche, AstraZeneca, Seattle Genetics and Janssen (all paid to the Netherlands Cancer Institute). No other authors have disclosures relevant to this work.

Additional information

Peer review information Javier Carmona 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 Assessment of response discrepancies between the responding primary bladder tumor and unresponsive local lymph node micrometastases upon ipilimumab plus nivolumab.

a1-2. Imaging shows a cT4aN1 bladder tumor (enlarged lymph node not shown). Pathological assessment revealed a complete response in the bladder, as shown by multiplex immunofluorescent analysis (a-3) and a non-responding lymph node micrometastasis, annotated by a red arrow (a-4). Experiments and scorings related to the presented micrographs were conducted once. b, Whole-exome sequencing was employed to assess whether lymph node micrometastases are genetically distinct from the primary tumor and could explain unresponsiveness (n=2 patients). The figure shows overlap between somatic variants identified in the baseline tumor (purple) and the post-treatment lymph node metastasis (green). c, Genomic alterations in genes related to interferon gamma signaling, JAK/STAT signaling or antigen presentation machinery for baseline tumor samples (purple, n=2) and matching lymph nodes metastases (green, n=2). Sample labels and genetic alteration type are displayed in figure legends. No specific genetic cause for resistance could be identified in the discrepant mutations.

Extended Data Fig. 2 Analysis of baseline genomic alterations by whole-exome sequencing.

Whole-exome sequencing of pretreatment tumor tissue and germline DNA was employed to identify somatic mutations in baseline tumors (n=24). An oncoprint figure of genetic alterations in significantly mutated bladder cancer genes (by TCGA, Robertson et al, Cell 2018) is shown. Alterations are clustered by response categories, including 14 CR and 10 non-CR tumors. Sample labels and genetic alteration type are displayed in the figure. Abbreviations: CR: complete response, non-CR: non complete response, CNA: copy number alterations.

Extended Data Fig. 3 Assessment of interdependence between baseline B cell presence and CD8 T cell infiltration.

a, Average expression of B-cell-related differentially expressed genes at baseline stratified by intratumoral and stromal CD8+ higher and lower than median groups on multiplex immunofluorescence (mIF). Gene expression levels are represented as transcripts per million (TPM), mean-centered and scaled (Z-scores). b, Intratumoral CD8 density per mm2 by multiplex immunofluorescence, stratified by stromal CD20 higher and lower than median groups on multiplex immunofluorescence (mIF). c, Average expression of B-cell-associated genes at baseline stratified by average TGE8 signature groups (higher or lower than median) showing similar expression of B-cell-related genes in the TGE8 signature groups. Gene expression levels are represented as transcripts per million (TPM), mean-centered and scaled (Z-scores). A Wilcoxon signed tank test was used for all comparisons. The P-value is presented in-between boxplots. All statistical tests were two-sided. Analyses include CR (blue; n=11) and non-CR (orange; n=7) tumors. All boxplots display the median and 25th and 75th percentiles. The whiskers expand from the hinge to largest value not exceeding 1.5× IQR from the hinge. No adjustments were made for multiple comparisons. Abbreviations: CR: complete response, non-CR: non complete response.

Extended Data Fig. 4 Dynamics of tertiary lymphoid structure spectrum upon immunotherapy for response and steroid groups.

Upon multiplex immunofluorescent staining and segregation of tertiary lymphoid structure (TLS) areas, a, Early-TLS, b, Primary follicle-like TLS and c, Secondary follicle-like TLS were quantified as normalized TLS area (square microns per tissue square centimeter). For each TLS maturation stage, four different analysis were performed; 1) Comparison of normalized TLS areas in baseline and post-therapy samples between response groups by Mann Whitney test, 2) Normalized TLS area assessed as fold change (post/pre) upon treatment between response groups, 3) Normalized TLS area comparison in post-therapy samples between patients receiving steroids and no steroids, 4) Normalized TLS area assessed as fold change (post/pre) upon treatment in patients that received steroids and no steroids. Unless otherwise noted, all boxplots display the median and 25th and 75th percentiles. The whiskers expand from the hinge to largest value not exceeding 1.5× IQR from the hinge. A Mann Whitney test was used for comparisons between response and steroid groups. The P-value is presented in-between boxplots. All statistical tests were two-sided. Analyses have 14 CR and 10 non-CR, or 9 steroids (>20mg a day prior to surgery) and 15 no steroid patients. No adjustments were made for multiple comparisons. Abbreviations: TLS: tertiary lymphoid structures, CR: complete response, non-CR: no complete response.

Extended Data Fig. 5 Exploratory assessment of TLS markers and a TLS signature upon immunotherapy for response groups.

a, Average gene expression for TLS-related genes (CCL19, CCL21, CXCL13, CCR7, SELL, LAMP3, CXCR4, CD86, BCL6), as published by Cabrita et al. 19. Pre- and post-treatment samples were compared for CR (n=11 pre-treatment, n=8 post-treatment) and non-CR (n=7 pre-treatment, n=10 post-treatment) tumors using a two-sided t-test. Baseline expression was not significantly different between CR and non-CR (p=0.28). b, TLS gene signature (Cabrita et al., Nature 2020) derived from genes specifically upregulated in CD8+CD20+ metastasized melanoma tumors (CD79B, CD1D, SKAP1, CETP, EIF1AY, RBP5, PTGDS). LAT and CCR6 genes were lowly expressed and thus removed from the analysis. Gene signatures were compared between baseline and post- treatment samples for CR and non-CR tumors. Baseline expression was not significantly different between CR and non-CR (p=0.05). Boxplots in all panels represent the median and 25th and 75th percentiles. The whiskers expand from the hinge to largest value not exceeding 1.5× IQR from the hinge. CR samples are marked in blue, while non-CR samples are displayed in orange. A t-test test was used for comparisons between CR and non-CR. The p-value is presented in-between boxplots. All statistical tests were two-sided. All analyses involved 11 CR and 7 non-CR tumors in pre-treatment samples, and 10 CR and 8 non-CR tumors in post-treatment samples. Abbreviations: TLS: tertiary lymphoid structures, CR: complete response, Non-CR: non complete response.

Extended Data Fig. 6 Assessment of CD27+ B-cells in TLS upon immunotherapy for response groups.

a, Example images of CD20 (yellow) and CD27 (purple) IHC co-staining, revealing CD27+ B-cells (CD20+CD27+) in red, as indicated by the black arrow. Experiments and scorings related to the presented micrographs were conducted once. b/c. Baseline and post-treatment comparison of the mean percentage of CD20+CD27+ cells in germinal center (GC) negative (b) and GC+ (c) TLS per patient between CR (n=9 GC- pre-treatment, n=6 GC+ pre-treatment, n=11 GC- post-treatment, n=8 GC+ post-treatment) and non-CR (n=6 GC- pre-treatment, n=3 GC+ pre-treatment, n=8 GC- post-treatment, n=5 GC+ post-treatment) tumors. The percentage CD20+CD27+ cells in the CD20+ population in TLS was estimated by a pathologist (L.S.). Boxplots represent the median and 25th and 75th percentiles. The whiskers expand from the hinge to largest value not exceeding 1.5× IQR from the hinge. Complete responders are marked in blue, while non-responders are displayed in orange. A Wilcoxon signed tank test was used to compare the percentage of CD20+CD27+ between CR and non-CR tumors. The P-value is presented in-between boxplots. All statistical tests were two-sided. No adjustments were made for multiple comparisons. Abbreviations: CR: complete response, NR: no response, GC: germinal center.

Extended Data Fig. 7 Assessment of CD4+ T-cells and CD4+BCL6+ follicular T helper cells in tumor and TLS regions upon immunotherapy.

a, Example images of CD4 (yellow) and BCL6 (purple) IHC co-stainings, showing CD4+BCL6+ follicular T helper cells pre- and post-treatment, characterized by a purple nucleus and deep orange cytoplasmatic staining. b, Mean absolute CD4+BCL6+ cell counts in co-stains for mature and immature TLS and CD4−BCL6+ cell counts for mature TLS only; for CR tumors (n=7 GC- pre-treatment, n=5 GC+ pre-treatment, n=10 GC- post-treatment, n=8 GC+ post-treatment) and non-CR tumors (n=4 GC- pre-treatment, n=3 GC+ pre-treatment, n=8 GC- post-treatment, n=5 GC+ post-treatment). Co-stainings were assessed and scored (number of cells per TLS) by a pathologist. c, Representative example images of CXCL13 (brown) in TLS for CR and non-CR tumors by immunohistochemistry in pre- and post-treatment specimens. CXCL13 positivity is clearly present in TLS, emphasizing that TLS are characterized by CXCL13 expression. No post-treatment differences were observed between response groups (Quantified data not shown). Unless otherwise noted, all boxplots display the median and 25th and 75th percentiles. The whiskers expand from the hinge to largest value not exceeding 1.5× IQR from the hinge. Complete responders are marked in blue, while non-responders are displayed in orange. A Wilcoxon signed tank test was used to compare the CD4+BCL6+ counts between CR and non-CR tumors. The P-value is presented in-between boxplots. All statistical tests were two-sided. No adjustments were made for multiple comparisons. Experiments and scorings related to the presented micrographs in A and C were conducted once. Abbreviations: CR: complete response, non-CR: non complete response, GC: germinal center.

Extended Data Fig. 8 Exploratory assessment of cellular distribution within TLS regions using a bioinformatic algorithm to analyse multiplex immunofluorescence slides.

a, Data map displaying immune cell subsets by digital multiplex immunofluorescent analysis in baseline tissue in a spatial context. A bioinformatic algorithm (methods) was developed to identify and segment TLS-like structures, as annotated in red in the example map. Tumor is depicted in gray (panCK) b, Distribution of CD3+CD8− and CD20+ cells over the analysed area, between algorithm assigned tumor bed (orange) and TLS (grey) pre- and post-therapy, in CR (n=7 pre, n=5 post) and non-CR (n=7 pre, n=4 post) patients. c, The ratio of FOXP3+/FOXP3- cells was calculated within the TLS CD3+CD8- compartment upon algorithmic TLS segmentation and quantitation of immune cell subsets in multiplex immunofluorescence images (Methods). Pre-treatment and post-therapy samples were compared in the complete cohort (C; n=14 pre, 9 post) p=0.0086) or between CR (n=7 pre, 5 post; p=0.073) and non-CR (n=7 pre, 4 post; p=0.11) d, by Mann Whitney test. Unless otherwise noted, all boxplots display the median and 25th and 75th percentiles. The whiskers expand from the hinge to largest value not exceeding 1.5× IQR from the hinge. Complete responders are marked in blue, while non-responders are displayed in orange. A Wilcoxon signed tank test was used to compare the CD4+BCL6+ counts between CR and non-CR tumors. The P-value is presented in-between boxplots. All statistical tests were two-sided. No adjustments were made for multiple comparisons. Abbreviations: CR: complete response, non-CR: non complete response, GC: germinal center.

Supplementary information

Supplementary Information

Supplementary Table 1, CONSORT diagram, CONSORT checklist and NABUCCO study protocol

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

van Dijk, N., Gil-Jimenez, A., Silina, K. et al. Preoperative ipilimumab plus nivolumab in locoregionally advanced urothelial cancer: the NABUCCO trial. Nat Med 26, 1839–1844 (2020). https://doi.org/10.1038/s41591-020-1085-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-020-1085-z

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