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Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression

Abstract

Only a minority of patients derive long-term clinical benefit from anti-programmed cell death protein 1 (anti-PD-1) or anti-programmed death-ligand 1 (anti-PD-L1) monoclonal antibodies. The presence of tertiary lymphoid structures (TLSs) has been associated with improved survival in several tumor types. Here, using a large-scale retrospective analysis of three independent cohorts of patients with cancer who were treated with anti-PD-1 or anti-PD-L1 antibodies, we show that the presence of mature TLSs was associated with improved objective response rates, progression-free survival and overall survival, independent of PD-L1 expression status and CD8+ T cell density. These results pave the way for using TLS detection to select patients who are more likely to benefit from immune checkpoint blockade.

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Fig. 1: Mature TLSs are predictive of outcome in patients with cancer treated with immune checkpoint inhibitors.
Fig. 2: Mature TLSs are predictive of response to immune checkpoint inhibition, independent of PD-L1 expression status.

Data availability

The datasets that support the findings of this study are not publicly available due to information that could compromise research participant consent. According to French/European regulations, any re-use of the data must be approved by the ethics committee. Each request for access to the dataset (including the images) will be granted after a request sent to the corresponding author (A.I.) and approval by the ethics committee. Source data are provided with this paper.

References

  1. 1.

    Vaddepally, R. K., Kharel, P., Pandey, R., Garje, R. & Chandra, A. B. Review of indications of FDA-approved immune checkpoint inhibitors per NCCN guidelines with the level of evidence. Cancers (Basel). 12, 738 (2020).

    CAS  Article  Google Scholar 

  2. 2.

    Davis, A. A. & Patel, V. G. The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors. J. Immunother. Cancer 7, 278 (2019).

    Article  Google Scholar 

  3. 3.

    Cassetta, L. & Kitamura, T. Targeting tumor-associated macrophages as a potential strategy to enhance the response to immune checkpoint inhibitors. Front. Cell Dev. Biol. 6, 38 (2018).

    Article  Google Scholar 

  4. 4.

    Schalper, K. A. et al. Elevated serum interleukin-8 is associated with enhanced intratumor neutrophils and reduced clinical benefit of immune-checkpoint inhibitors. Nat. Med. 26, 688–692 (2020).

    CAS  Article  Google Scholar 

  5. 5.

    Sautès-Fridman, C., Petitprez, F., Calderaro, J. & Fridman, W. H. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat. Rev. Cancer 19, 307–325 (2019).

    Article  Google Scholar 

  6. 6.

    Ladányi, A. et al. Prognostic impact of B-cell density in cutaneous melanoma. Cancer Immunol. Immunother. 60, 1729–1738 (2011).

    Article  Google Scholar 

  7. 7.

    Messina, J. L. et al. 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci. Rep. 2, 765 (2012).

    Article  Google Scholar 

  8. 8.

    Goc, J. et al. Dendritic cells in tumor-associated tertiary lymphoid structures signal a TH1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells. Cancer Res. 74, 705–715 (2014).

    CAS  Article  Google Scholar 

  9. 9.

    Behr, D. S. et al. Prognostic value of immune cell infiltration, tertiary lymphoid structures and PD-L1 expression in Merkel cell carcinomas. Int. J. Clin. Exp. Pathol. 7, 7610–7621 (2014).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Caro, G. D. et al. Occurrence of tertiary lymphoid tissue is associated with T-cell infiltration and predicts better prognosis in early-stage colorectal cancers. Clin. Cancer Res. 20, 2147–2158 (2014).

    Article  Google Scholar 

  11. 11.

    Petitprez, F. et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature 577, 556–560 (2020).

    CAS  Article  Google Scholar 

  12. 12.

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

    CAS  Article  Google Scholar 

  13. 13.

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

    CAS  Article  Google Scholar 

  14. 14.

    Tsou, P., Katayama, H., Ostrin, E. J. & Hanash, S. M. The emerging role of B cells in tumor immunity. Cancer Res. 76, 5597–5601 (2016).

    CAS  Article  Google Scholar 

  15. 15.

    Bruno, T. C. New predictors for immunotherapy responses sharpen our view of the tumour microenvironment. Nature 577, 474–476 (2020).

    CAS  Article  Google Scholar 

  16. 16.

    Kroeger, D. R., Milne, K. & Nelson, B. H. Tumor-Infiltrating plasma cells are associated with tertiary lymphoid structures, cytolytic T-cell responses, and superior prognosis in ovarian cancer. Clin Cancer Res. 22, 3005–3015 (2016).

    CAS  Article  Google Scholar 

  17. 17.

    Germain, C. et al. Presence of B cells in tertiary lymphoid structures is associated with a protective immunity in patients with lung cancer. Am. J. Respir. Crit. Care Med. 189, 832–844 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    Montfort, A. et al. A strong B-cell response is part of the immune landscape in human high-grade serous ovarian metastases. Clin. Cancer Res. 23, 250–262 (2017).

    CAS  Article  Google Scholar 

  19. 19.

    Kalergis, A. M. & Ravetch, J. V. Inducing tumor immunity through the selective engagement of activating Fcγ receptors on dendritic cells. J. Exp. Med. 195, 1653–1659 (2002).

    CAS  Article  Google Scholar 

  20. 20.

    Fridman, W. H. et al. B cells and cancer: To B or not to B? J. Exp. Med. 218, e20200851 (2020).

    Article  Google Scholar 

  21. 21.

    Lu, Y. et al. Complement signals determine opposite effects of B cells in chemotherapy-induced immunity. Cell 180, 1081–1097 (2020).

    CAS  Article  Google Scholar 

  22. 22.

    Schwartz, L. H. et al. RECIST 1.1—update and clarification: from the RECIST committee. Eur. J. Cancer 62, 132–137 (2016).

    Article  Google Scholar 

  23. 23.

    Vigliar, E. et al. PD-L1 expression on routine samples of non-small cell lung cancer: results and critical issues from a 1-year experience of a centralised laboratory. J. Clin. Pathol. 72, 412–417 (2019).

    CAS  Article  Google Scholar 

  24. 24.

    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).

    CAS  Article  Google Scholar 

  25. 25.

    Koeppel, F. et al. Added value of whole-exome and transcriptome sequencing for clinical molecular screenings of advanced cancer patients with solid tumors. Cancer J. 24, 153–162 (2018).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

This study was funded by AstraZeneca.

Author information

Affiliations

Authors

Contributions

A.I., A. Bessede, W.H.F. and F.L.L. conceived of and designed the study. L.V. and F.L.L. performed the histological analyses. S.C., S.L.M., I.S., M.T., G.R., S.P., M.C., F.C., C.L., K.B., M.K., F.C., B.B., Y.L., A.M., J.-C.S. and A.I. provided study materials or treated patients. All authors collected and assembled data. A.I., A. Bessede, L.V. and F.L.L. developed the tables and figures. A.I., A. Bessede, M.B., F.L.L., W.H.F. and C.S.-F. conducted the literature search and wrote the manuscript. All authors were involved in the critical review of the manuscript and approved the final version.

Corresponding author

Correspondence to Antoine Italiano.

Ethics declarations

Competing interests

A. Bessede, J.-P.G. and C.R. are employees of ImmuSmol/Explicyte. E.O. and A.S. are employees of AstraZeneca. A.I. received research grants from AstraZeneca, Bayer, Bristol Myers Squibb, Chugai, Merck, MSD, PharmaMar, Novartis and Roche and received personal fees from Epizyme, Bayer, Lilly, Roche and SpringWorks. B.B. received grants from AstraZeneca, Pfizer, Eli Lilly, Onxeo, Bristol Myers Squibb, Inivata, AbbVie, Amgen, Blueprint Medicines, Celgene, GlaxoSmithKline, Ignyta, Ipsen, Merck KGaA, MSD Oncology, Nektar, PharmaMar, Sanofi, Spectrum Pharmaceuticals, Takeda, Tiziana Therapeutics, Cristal Therapeutics, Daiichi Sankyo, Janssen Oncology, OSE Immunotherapeutics, BeiGene, Boehringer Ingelheim, Genentech, Servier and Tolero Pharmaceuticals. Y.L. received grants from Janssen, Sanofi and MSD, personal fees from Janssen, Sanofi, Astellas, Roche, AstraZeneca, Bristol Myers Squibb, Seattle Genetics, MSD, Clovis, Incyte and Pfizer and non-financial support from Astellas, Roche, AstraZeneca, Bristol Myers Squibb, Seattle Genetics and MSD. A.M. received research grants from Merus, Bristol Myers Squibb, Boehringer Ingelheim, Transgene and MSD and personal fees from Bristol Myers Squibb, AstraZeneca, MedImmune, Oncovir and Merieux. J.-C.S. received consultancy fees from AstraZeneca, Astex, Clovis, GlaxoSmithKline, GamaMabs, Lilly, MSD, Mission Therapeutics, Merus, Pfizer, PharmaMar, Pierre Fabre, Roche/Genentech, Sanofi, Servier, Symphogen and Takeda. L.V., M.B., S.C., S.L.M., M.L., I.S., M.T., G.R., S.P., M.C., F.C., C.L., K.B., M.K., I.G., C.S.-F., V.V., F.C., W.H.F. and F.L.L. declare no competing interests.

Additional information

Peer review information Nature Cancer thanks the anonymous reviewers for their contribution to the peer review of this work.

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 the presence of TLS and their maturation stage in tumors.

a: This is a TLS-positive primary pancreatic adenocarcinoma associated with a T CD8 + lymphocyte density of 154/mm² and negative for PD-L1. The TLS are delineated with the black lines on the HES slide, highlighting their vicinity to tumor cells. Scale bar indicates 300 µm in size. Representative of 540 tumors analyzed (Discovery cohort n = 328, validation cohort A n = 131, validation cohort B n = 81). b: This panel shows representative examples of immature and mature TLS observed in tumor samples. Upper panel: This mature TLS is detected in a primary pancreatic adenocarcinoma associated with a T CD8 + lymphocyte density of 154/mm² and negative for PD-L1. Mature TLS are defined by the presence of a network of CD23-positive dendritic cells on immunofluorescence (note that in this case, the presence of a germinal center visible on the HES was already diagnostic of a mature TLS). Lower panel: The picture shows an immature TLS detected in a primary adenocarcinoma of the lung concomitantly showing a high T CD8 + lymphocyte infiltrate of 372/mm² and a PD-L1 TPS score of 1%. The tumor was only associated with immature TLS displaying no germinal center and no network of CD23-positive dendritic cells on immunofluorescence. Representative of 540 tumors analyzed (Discovery cohort n = 328, validation cohort A n = 131, validation cohort B n = 81). The pictures from the left to right column correspond to 1) Hematoxylin Eosin Saffron (HES) staining, 2) Double immunohistochemistry staining of CD3/CD20 (CD3 in brown, CD20 in purple), 3) Double immunohistochemistry staining of CD8/PD-L1 (CD8 in brown, PD-L1 in purple), 4) Multiplex immunofluorescence assay of CD4 (blue), CD8 (yellow), CD20 (orange), CD21 (green) and CD23 (pink). The scale bars on the HES images indicate 50 µm and 100 µm for the upper and lower panels, respectively. Black cropped arrows highlight the tumor cells in the samples.

Extended Data Fig. 2 Predictive value of TLS status according to CPS PD-L1 scores.

Objective response rates (OR: objective response, SD: stable disease; PD: progressive disease; chi squared (χ²) test) and Kaplan-Meier curves of progression-free survival (log-rank test) and overall survival (log-rank test) of 328 cancer patients treated with anti-PD1/PD-L1 antagonists according to CPS PD-L1 scores (a: CPS PD-L1 < 1, N = 223 patients; b: CPS PD-L1 ≥ 1, N = 105 patients) and TLS status (red curve: mature-TLS positive tumors; blue line: mature-TLS negative tumors).

Source data

Extended Data Fig. 3 Predictive value of TLS status according to T CD8 cell density.

Objective Response rates (OR: objective response, SD: stable disease, PD: progressive disease; chi squared (χ²) test) and Kaplan-Meier curves of progression-free (log-rank test) and overall survival (log-rank test) of 328 cancer patients treated with anti-PD1/PD-L1 antagonists according to T CD8 + cell density (a: low density, N = 165 patients; b: high density, N = 163 patients) and TLS status (red curve: mature-TLS positive tumors; blue line: mature-TLS negative tumors).

Source data

Extended Data Fig. 4 Outcome of cancer patients (non-small cell lung cancer excluded) according to TLS status.

a) Objective Response rates (OR: objective response, SD: stable disease, PD: progressive disease; chi squared (χ²) test) and Kaplan-Meier curves (log-rank test) of progression-free (b) and overall survival (c) of 201 cancer patients (all tumor types except non-small cell lung cancer) treated with anti-PD1/PD-L1 antagonists according to TLS status (red curve: mature-TLS positive tumors; blue line: mature-TLS negative tumors).

Source data

Extended Data Fig. 5 Comparison of the TLS screening with pathology and immunohistochemistry pathology versus the screening with immunofluorescence (method by Petitprez et al).

First line: conspicuous mature TLS with CD23 + follicular dendritic cells network. Second line: mature TLS defined by isolated CD23 + cell displaying dendritic morphology. Pictures from the left to right column correspond to 1) Hematoxylin Eosin Saffron (HES) staining, 2) Multiplex immunofluorescence assay of CD23 (pink) and CD20 (orange), 3) Double immunohistochemistry staining of CD23/CD20 (CD23 in brown, CD20 in red). The scale bars on the HES images indicate 50 µm in size. Representative of 540 tumors analyzed (Discovery cohort n = 328, validation cohort A n = 131, validation cohort B n = 81).

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Tables

Supplementary Tables 3–5.

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Source Data Fig. 1

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Source Data Fig. 2

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Source Data Extended Data Fig. 2

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Source Data Extended Data Fig. 3

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Source Data Extended Data Fig. 4

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Vanhersecke, L., Brunet, M., Guégan, JP. et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer 2, 794–802 (2021). https://doi.org/10.1038/s43018-021-00232-6

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