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Avelumab maintenance in advanced urothelial carcinoma: biomarker analysis of the phase 3 JAVELIN Bladder 100 trial

Nature Medicine volume 27, pages 2200–2211 (2021)Cite this article

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Abstract

In a recent phase 3 randomized trial of 700 patients with advanced urothelial cancer (JAVELIN Bladder 100; NCT02603432), avelumab/best supportive care (BSC) significantly prolonged overall survival relative to BSC alone as maintenance therapy after first-line chemotherapy. Exploratory biomarker analyses were performed to identify biological pathways that might affect survival benefit. Tumor molecular profiling by immunohistochemistry, whole-exome sequencing and whole-transcriptome sequencing revealed that avelumab survival benefit was positively associated with PD-L1 expression by tumor cells, tumor mutational burden, APOBEC mutation signatures, expression of genes underlying innate and adaptive immune activity and the number of alleles encoding high-affinity variants of activating Fcγ receptors. Pathways connected to tissue growth and angiogenesis might have been associated with reduced survival benefit. Individual biomarkers did not comprehensively identify patients who could benefit from therapy; however, multi-parameter models incorporating genomic alteration, immune responses and tumor growth showed promising predictive utility. These results characterize the complex biologic pathways underlying survival benefit from immune checkpoint inhibition in advanced urothelial cancer and suggest that multiple biomarkers might be needed to identify patients who would benefit from treatment.

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Fig. 1: Association between OS and PD-L1 and/or TMB.
Fig. 2: Association between OS and mutations.
Fig. 3: Association between OS and gene expression.
Fig. 4: Elastic net regression analysis to develop a predictive multi-parameter model.
Fig. 5: Cellular dynamics in the tumor.
Fig. 6: Use of elastic net to develop a predictive model using clinical, molecular and derived cellular features.

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Data availability

The analyses in this paper were based on a data cutoff of 21 October 2019. The supplementary figures and tables cited in the manuscript are publicly available (10.25454/pfizer.figshare.14866920).

Upon reasonable request, and subject to certain criteria, conditions and exceptions (see https://www.pfizer.com/science/clinical-trials/trial-data-and-results for more information), Pfizer will provide access to individual de-identified participant data (including the input data tables used for elastic net model building) from Pfizer-sponsored global interventional clinical studies conducted for medicines, vaccines and medical devices (1) for indications that have been approved in the United States and/or European Union or (2) in programs that have been terminated (that is, development for all indications has been discontinued). Pfizer will also consider requests for the protocol, data dictionary and statistical analysis plan. Data may be requested from Pfizer trials 24 months after study completion. The de-identified participant data will be made available to researchers whose proposals meet the research criteria and other conditions, and for which an exception does not apply, via a secure portal. To gain access, data requestors must enter into a data access agreement with Pfizer.

Code availability

The R script used for elastic net analyses is available at 10.25454/pfizer.figshare.14866920. CytoPro is a proprietary cell deconvolution platform from CytoReason. Contact cytoreason@cytoreason.com for details and software requests.

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Acknowledgements

This trial was sponsored by Pfizer as part of an alliance between Pfizer and the healthcare business of Merck KGaA (CrossRef Funder ID: 10.13039/100009945), which provided the study drugs. The investigators worked with Pfizer on the trial design, collection and analysis of data and interpretation of results. Datasets were reviewed by the authors, and all authors participated fully in developing and reviewing the report for publication. Funding for a professional medical writer with access to the data was provided by the sponsors. All authors had full access to all data, and the first author had final responsibility for the decision to submit for publication. The authors vouch for the completeness and accuracy of the data and their analysis and the fidelity of the trial to the protocol and statistical analysis plan. The authors thank the patients and their families, investigators, co-investigators and the study teams at each of the participating centers. The authors also thank A. Donahue and P. Robbins for study setup and supervision of biospecimen acquisition; L. Swaim and S. Dahm for establishing and overseeing performance of the SP263 assay; J. Chelliserry for guiding data acquisition; and H. Campanozzi for overseeing quality control. We thank the scientific team at CytoReason for collaborative work on cell estimates. Medical writing support was provided by M. Holland of ClinicalThinking and was funded by Pfizer and the healthcare business of Merck KGaA.

Author information

Authors and Affiliations

  1. Barts Cancer Institute, Experimental Cancer Medicine Centre, Queen Mary University of London, St. Bartholomew’s Hospital, London, UK

    Thomas Powles

  2. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada

    Srikala S. Sridhar

  3. Gustave Roussy, INSERMU981, Université Paris-Saclay, Villejuif, France

    Yohann Loriot

  4. Department of Medical Oncology, Beth Israel Deaconess Medical Center and IMIM-PSMAR Lab, Harvard Medical School, Boston, MA, USA

    Joaquim Bellmunt

  5. Computational Biology, Oncology Research and Development, Pfizer, La Jolla, CA, USA

    Xinmeng Jasmine Mu & Keith A. Ching

  6. Statistics, Global Biometrics and Data Management, Pfizer, La Jolla, CA, USA

    Jie Pu

  7. Englander Institute for Precision Medicine, Weill Cornell Medicine, Hematology/Oncology, Meyer Cancer Center, New York, NY, USA

    Cora N. Sternberg

  8. Yale Cancer Center, New Haven, CT, USA

    Daniel P. Petrylak

  9. Istituto Nazionale per lo Studio e la Cura dei Tumori, IRCCS Fondazione Giovanni Pascale, Naples, Italy

    Rosa Tambaro

  10. Service d’Oncologie Médicale, Clinique St Anne, Strasbourg, France

    Louis M. Dourthe

  11. Department of Medical Oncology, Hospital Universitario Central de Asturias. Instituto de Investigación Sanitaria del Principado de Asturias, Oviedo, Spain

    Carlos Alvarez-Fernandez

  12. Department of Medical Oncology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, Netherlands

    Maureen Aarts

  13. Pfizer Italia srl, Milano, Italy

    Alessandra di Pietro

  14. Department of Medicine, Division of Medical Oncology, University of Washington; Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle Cancer Care Alliance, Seattle, WA, USA

    Petros Grivas

  15. Translational Oncology, Pfizer, La Jolla, CA, USA

    Craig B. Davis

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Contributions

Conceptualization: T.P., X.J.M., K.A.C., J.P., A.d.P. and C.B.D. Data acquisition: T.P., S.S.S., Y.L., J.B., C.N.S., D.P.P., R.T., L.M.D., C.A.-F., M.A. and P.G. Data analysis and interpretation: X.J.M., K.A.C., J.P., A.d.P. and C.B.D. Validation: K.A.C., J.P. and A.d.D. Supervision of data analysis: T.P., A.d.P. and C.B.D. Manuscript drafting: T.P., X.J.M., K.A.C., J.P. and C.B.D. Manuscript review and approval for submission: all authors.

Corresponding authors

Correspondence to Thomas Powles or Craig B. Davis.

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Competing interests

T.P.: grant support, paid to Barts Cancer Institute, and consulting fees from Astellas, Bristol Myers Squibb, Eisai, Exelixis, Johnson & Johnson Healthcare Systems, the healthcare business of Merck KGaA, Darmstadt, Germany, Merck & Co., Kenilworth, NJ, Novartis and Seattle Genetics; grant support, paid to Barts Cancer Institute, and consulting fees and travel support from AstraZeneca; grant support, paid to Barts Cancer Institute, consulting fees and travel support from F. Hoffmann-La Roche, Ipsen Biopharmaceuticals and Pfizer; and consulting fees from Incyte. S.S.S.: advisory board fees from AstraZeneca, Bristol Myers Squibb, F. Hoffmann-La Roche and the healthcare business of Merck KGaA, Darmstadt, Germany, Merck & Co., Kenilworth, NJ; and grant support, paid to Princess Margaret Cancer Centre, advisory board fees and lecture fees from Pfizer. Y.L.: advisory board fees and travel support from Astellas, Bristol Myers Squibb, Janssen Biotech and Seattle Genetics; advisory board fees, lecture fees and travel support from AstraZeneca and Merck & Co., Kenilworth, NJ; advisory board fees and lecture fees from Pfizer; and advisory board fees from Sanofi. J.B.: advisory board fees and lecture fees from AstraZeneca and Merck & Co., Kenilworth, NJ; lecture fees from Bristol Myers Squibb; advisory board fees from Genentech, Janssen Global Services and Pierre Fabre; advisory board fees and travel support from Pfizer; and grant support from Takeda Oncology. X.M.: employee and stockholder of Pfizer. K.A.C.: employee and stockholder of Pfizer. J.P.: employee and stockholder of Pfizer. C.N.S.: consulting fees from Astellas, AstraZeneca, Genentech, Incyte, the healthcare business of Merck KGaA, Darmstadt, Germany, Merck & Co., Kenilworth, NJ, Pfizer, Sanofi, Genzyme, Immunomedics, Bristol Myers Squibb, Foundation Medicine, Medscape and UroToday. D.P.: grant support, paid to Yale University, from Advanced Accelerator Applications, Agensys, Astellas, AstraZeneca, Bayer, BioXcel Therapeutics, Bristol Myers Squibb, Clovis Oncology, Eisai, Eli Lilly, Endocyte, Genentech, Gilead Sciences, Innocrin, MedImmune, Medivation, the healthcare business of Merck KGaA, Darmstadt, Germany, Mirati, Novartis, Pfizer, Progenics, Replimune, Roche, Sanofi Aventis and Seattle Genetics; and consulting fees from Advanced Accelerator Applications, Amgen, Astellas, AstraZeneca, Bayer, Bicycle Therapeutics, Boehringer Ingelheim, Bristol Myers Squibb, Clovis Oncology, Eli Lilly, Exelixis, Gilead Sciences, Incyte, Ipsen, Janssen, Mirati, Monopteros, Pfizer, Pharmacyclics, Regeneron, Roche, Seattle Genetics and Urogen. R.T.: nothing to disclose. L.M.D.: consulting fees from Janssen, Astellas, Bayer and Pfizer; and travel support from Janssen and Pfizer. C.A.-F.: consulting fees from AstraZenenca, Pfizer, Ipsen and Boehringer Ingelheim; and travel fees from Roche, Pfizer and Astellas. M.A.: consulting fees from Astellas, Bristol Myers Squibb, the healthcare business of Merck KGaA, Darmstadt, Germany, Merck & Co., Kenilworth, NJ, Novartis, Pierre Fabre, Pfizer and Sanofi; grant support, paid to Maastricht University Medical Centre, from Pfizer; and travel support from Sanofi. A.d.P.: employee and stockholder of Pfizer. P.G.: consulting fees from AstraZeneca, Astellas, Bayer, Bristol Myers Squibb, Clovis Oncology, Dyania Health, Driver, Exelixis, Foundation Medicine, Genentech/Roche, Genzyme, Gilead/Immunomedics, GlaxoSmithKline, Guardant Health, Heron Therapeutics, Infinity Pharmaceuticals, Janssen, the healthcare business of Merck KGaA, Darmstadt, Germany, Merck & Co., Kenilworth, NJ, Mirati Therapeutics, Pfizer, Regeneron, Seattle Genetics, 4D Pharma PLC, UroGen, and QED Therapeutics; research funding, paid to his institution, from the healthcare business of Merck KGaA, Darmstadt, Germany, Merck & Co., Kenilworth, NJ, Pfizer, Clovis Oncology, Bavarian Nordic, Immunomedics, Debiopharm, Bristol Myers Squibb, QED Therapeutics, GlaxoSmithKline, Mirati Therapeutics and Kure It Cancer Research. C.B.D.: employee and stockholder of Pfizer.

Additional information

Peer review information Nature Medicine thanks Brooke Fridley, Tian Zhang, Guillermo de Velasco and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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.

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Extended data

Extended Data Fig. 1 COSMIC v3 mutational signatures.

(a) Scatter plot of the number of somatic mutations per tumor sample according to the Catalogue of Somatic Mutations in Cancer version 3 (COSMIC v3) DNA single-base substitution (SBS) signatures. Only samples with mutation counts > 0 are shown. (b) Co-occurrence of SBS signatures in individual patients with the highest overall tumor mutational burden.

Extended Data Fig. 2 Weighted Gene Correlation Network Analysis clustering.

Cluster plot of identified modules. Rows and columns correspond to individual genes.

Extended Data Fig. 3 Association of overall survival (OS) benefit with immune gene signatures.

Forest plot showing hazard ratio (HR) and 95% CI for OS by treatment and immune gene signatures. HRs per 1-unit increase of signature score were calculated within each signatures treatment arm; P values are shown for the interaction term. Cox proportional hazards models with no adjustment of baseline covariates were used. A two-sided Wald test was used for P values. TAM, tumor-associated macrophage.

Extended Data Fig. 4 Generation of the gene-mutation/expression model.

(a) Selection of alpha to minimize mean cross-validation error (cross-validation mean [cvm]). (b) Local maximum concordance index (C-index) used to select 22 features.

Extended Data Fig. 5 Validation of the gene-expression/mutation elastic net model on immune checkpoint inhibitor cohorts.

(a) Holdout from avelumab/best supportive care (BSC) arm of JAVELIN Bladder 100. (b) IMvigor 210. (a-b) Cox proportional hazards models were used. Two-sided Wald test was used for P values.

Extended Data Fig. 6 Additional analyses of OS based on CD8+ cell infiltration.

Kaplan-Meier plot of overall survival (OS) by treatment arm in subgroups defined by (a) CD8+ cells in the total area of tumor; interaction term P = 0.0185. (b) CD8+ cells in the tumor center; P = 0.00185. (c) CD8+ cells in the invasive margin; P = 0.0881. (a-c) Cox proportional hazards models with no adjustment of baseline covariates were used. A two-sided Wald test was used to determine P values.

Extended Data Fig. 7 Generation and validation of the CMC model.

(a) Selection of alpha to minimize mean cross-validation error (cross-validation mean [cvm]). (b) Local maximum concordance index (C-index) used to select 19 features.

Extended Data Fig. 8 Performance of CMC model in independent sets.

(a) Holdout from avelumab/best supportive care (BSC) arm of JAVELIN Bladder 100. (b) IMvigor210. (a-b) Cox proportional hazards models were used. A two-sided Wald test was used to determine P values.

Extended Data Table 1 Fcγ receptor allele classification scheme
Full size table
Extended Data Table 2 Comparison of GEM and CMC elastic net models
Full size table

Supplementary information

Supplementary Information

Supplementary References for Table 13 and Supplementary Figs. 1–6

Reporting Summary

Supplementary Tables 1–13

Excel file containing combined tables for the supplement

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Powles, T., Sridhar, S.S., Loriot, Y. et al. Avelumab maintenance in advanced urothelial carcinoma: biomarker analysis of the phase 3 JAVELIN Bladder 100 trial. Nat Med 27, 2200–2211 (2021). https://doi.org/10.1038/s41591-021-01579-0

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