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Avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma: biomarker analysis of the phase 3 JAVELIN Renal 101 trial

Nature Medicine volume 26, pages 1733–1741 (2020)Cite this article

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Abstract

We report on molecular analyses of baseline tumor samples from the phase 3 JAVELIN Renal 101 trial (n = 886; NCT02684006), which demonstrated significantly prolonged progression-free survival (PFS) with first-line avelumab + axitinib versus sunitinib in advanced renal cell carcinoma (aRCC). We found that neither expression of the commonly assessed biomarker programmed cell death ligand 1 (PD-L1) nor tumor mutational burden differentiated PFS in either study arm. Similarly, the presence of FcɣR single nucleotide polymorphisms was unimpactful. We identified important biological features associated with differential PFS between the treatment arms, including new immunomodulatory and angiogenesis gene expression signatures (GESs), previously undescribed mutational profiles and their corresponding GESs, and several HLA types. These findings provide insight into the determinants of response to combined PD-1/PD-L1 and angiogenic pathway inhibition and may aid in the development of strategies for improved patient care in aRCC.

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Fig. 1: PFS in JAVELIN Renal 101 patients according to PD-L1 expression or the area of the IM as assessed by IHC.
Fig. 2: PFS according to the JAVELIN Renal 101 Immuno and JAVELIN Renal 101 Angio GES.
Fig. 3: PFS according to GES3.
Fig. 4: Impact of protein-altering mutations on clinical outcomes.
Fig. 5: Distribution and biological and clinical significance of select mutations.

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Data availability

All relevant data are available: clinical response data (Supplementary Table 11), CD8+ summary IHC data (Supplementary Table 12), normalized RNA-seq data (Supplementary Table 13), RNA-seq deconvolution data (ImmuneNet; Supplementary Table 14), gene sets analyzed in this study (Supplementary Table 15), pathway scores (Supplementary Table 16), HLA subtypes in JAVELIN Renal 101 patients (Supplementary Table 17), WES data summary statistics (Supplementary Table 18), MATH scores (Supplementary Table 19), FCGR2A and FCGR3A mutation data (Supplementary Table 20) and gene mutation status data (Supplementary Table 21). Databases used in this analysis included TCGA (https://doi.org/10.1038/nature12222) and MSigDB (https://doi.org/10.1093/bioinformatics/btr260). In addition, subject to certain criteria, conditions and exceptions, Pfizer will also provide access to related individual anonymized participant data. See https://www.pfizer.com/science/clinical-trials/trial-data-and-results/ for more information.

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Acknowledgements

The trial was sponsored by Pfizer and is part of an alliance between Pfizer and Merck (Darmstadt, Germany), who 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 its analysis, and the fidelity of the trial to the protocol and statistical analysis plan. The authors thank the patients and their families and the investigators, coinvestigators and study teams at each of the participating centers. The authors also thank R.-Y. Tzeng, D. Eleuteri, E. Conley and J. Imus for their assistance with programming and Y. Waumans, K. Marien and D. Peeters from Histogenex. Patients treated at MSKCC were supported in part by a MSKCC Support Grant/Core Grant (P30 CA008748). Medical writing support was provided by S. Rosebeck of ClinicalThinking and funded by Pfizer and Merck (Darmstadt, Germany).

Author information

Author notes

  1. Xiao Wang

    Present address: Lyell Immunopharma, South San Francisco, CA, USA

  2. Weidong Zhang

    Present address: Jounce Therapeutics, Cambridge, MA, USA

  3. Aleksander Chudnovsky

    Present address: Ipsen, Cambridge, MA, USA

  4. These authors contributed equally: Robert J. Motzer, Paul B. Robbins.

Authors and Affiliations

  1. Department of Medical Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Robert J. Motzer

  2. Translational Oncology, Pfizer, San Diego, CA, USA

    Paul B. Robbins

  3. Department of Genitourinary Oncology, Barts Cancer Institute, Experimental Cancer Medicine Centre, Queen Mary University of London, St Bartholomew’s Hospital, London, UK

    Thomas Powles

  4. Department of Medical Oncology, Institut Gustave Roussy, Villejuif, France

    Laurence Albiges

  5. Division of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands

    John B. Haanen

  6. Consultant Medical Oncologist at The Royal Marsden NHS Foundation Trust, London, UK

    James Larkin

  7. Computational Biology, Pfizer, San Diego, CA, USA

    Xinmeng Jasmine Mu, Keith A. Ching & Xiao Wang

  8. Graduate School of Medicine, Osaka University Hospital, Osaka, Japan

    Motohide Uemura

  9. Department of Medical Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA

    Sumanta K. Pal

  10. Deputy Director for Science, Moscow Scientific Research Oncology Institute, Moscow, Russian Federation

    Boris Alekseev

  11. Department of Medical Oncology, Institut Paoli Calmettes, Marseille, France

    Gwenaelle Gravis

  12. Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Matthew T. Campbell

  13. Private Medical Institution, Euromedservice, St. Petersburg, Russian Federation

    Konstantin Penkov

  14. Department of Oncology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea

    Jae Lyun Lee

  15. Oncology, Pfizer, New York, NY, USA

    Subramanian Hariharan

  16. Biostatistics, Pfizer, Cambridge, MA, USA

    Weidong Zhang & Jing Wang

  17. Oncology, Pfizer, Cambridge, MA, USA

    Aleksander Chudnovsky

  18. Immuno-Oncology, Pfizer, Milan, Italy

    Alessandra di Pietro

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

    Amber C. Donahue

  20. Department of Medical Oncology, The Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Boston, MA, USA

    Toni K. Choueiri

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Contributions

Conception: R.J.M., P.B.R. and T.K.C. Data acquisition, analysis and interpretation: S.H., A.C.D., R.J.M., P.B.R., X.J.M. and K.A.C. Principal investigator: R.J.M. Biomarker analyses and interpretation: A.C.D., R.J.M., P.B.R., X.J.M. and K.A.C. Supervision of clinical data analysis: S.H., R.J.M. and P.B.R. Supervision of biomarker data analysis: R.J.M. and P.B.R. Acquisition of data and data analysis and interpretation: S.H., A.C.D., R.J.M., J.B.H., P.B.R., X.J.M. and K.A.C. Overall biomarker oversight: R.J.M., P.B.R., X.J.M. and K.A.C. Overall medical oversight: S.H. and R.J.M. R.J.M., P.B.R., T.P., L.A., J.B.H., J.L., X.J.M., K.A.C., M.U., S.K.P., B.A., G.G., M.T.C., K.P., J.L.L., S.H., X.W., W.Z., J.W., A.C., A.d.P., A.C.D. and T.K.C. contributed to the writing of the manuscript and approve its submission.

Corresponding authors

Correspondence to Robert J. Motzer or Toni K. Choueiri.

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Competing interests

R.J.M. reports a consulting or advisory role for Pfizer, Novartis, Eisai, Exelixis, Merck, Genentech, Incyte, Lilly and Roche; research funding (institutional) from Pfizer, Bristol-Myers Squibb, Eisai, Novartis, Genentech and Roche; and reimbursement for travel, accommodations and expenses from Bristol-Myers Squibb. P.B.R. is an employee of Pfizer. T.P. reports personal fees from AstraZeneca, Bristol-Myers Squibb, Eisai, Roche/Genentech, Seattle Genetics, MSD, GSK, Pfizer, Astellas, Merck Serono, Novartis, Ferring and Exelixis and grants from AstraZeneca and Roche outside the submitted work. L.A. reports a consulting/advisory role (institutional) for Novartis, Amgen, Bristol-Myers Squibb, Ipsen, Roche, Pfizer, Astellas Pharma, Merck, MSD, AstraZeneca, Exelixis, Corvus Pharmaceuticals, Peloton therapeutics and research funding from Bristol-Myers Squibb. J.B.H. reports grants and institutional fees from Bristol-Myers Squibb, MSD, Novartis and Neon Therapeutics and institutional fees from Pfizer, Roche/Genentech, Bayer, Amgen, Immunocore, Sanofi, Seattle Genetics, Gadeta BV, GSK, Celsius Therapeutics and AstraZeneca/Medimmune outside the submitted work. J.L. reports personal fees from Eisai, Roche/Genentech, Secarna, Pierre Fabre, EUSA Pharma, MSD, GSK and Kymab and grants and personal fees from Bristol-Myers Squibb, Pfizer and Novartis outside the submitted work. X.J.M. and K.A.C. are employees of Pfizer. M.U. has nothing to disclose. S.P. reports a consulting/advisory role for Genentech, Aveo, Eisai, Roche, Pfizer, Novartis, Exelixis, Ipsen, Bristol-Myers Squibb and Astellas. B.A. reports personal fees from Amgen and Ferring, grants from Ipsen and grants and personal fees from Astellas, AstraZeneca, Bayer, Bristol-Myers Squibb, Janssen, MSD, Pfizer, Roche and Sanofi outside the submitted work. G.G. and K.P. have nothing to disclose. M.T.C. reports personal fees from Eisai Medical Research, EMD Serono, Apricity Health, Pfizer, Genentech and Taiho outside the submitted work. J.L.L. reports grants and personal fees from Pfizer, other fees from Eisai and personal fees from Ipsen, Janssen, Sanofi Aventis, Novartis, Astellas and Bristol-Myers Squibb outside the submitted work. S.H., X.W., W.Z., J.W., A.C., A.d.P. and A.C.D. are employees of Pfizer. T.K.C. reports grants received during the conduct of the study from Pfizer and personal fees received outside the conduct of the study from Agensys, Alexion, Alligent, American Society of Clinical Oncology, Analysis Group, AstraZeneca, Bayer, Bristol-Myers Squibb, Celldex, Cerulean, Clinical Care Options, Corvus, Dana-Farber Cancer Institute, EMD Serono, Eisai, Exelixis, Foundation Medicine, Genentech/Roche, GlaxoSmithKline, Harborside Press, Heron, Ipsen, Kidney Cancer Association, Kidney Cancer Journal, L-path, Lancet Oncology, Lilly, Merck, Michael J. Hennessy Associates, National Comprehensive Cancer Network, Navinata Health, New England Journal of Medicine, Novartis, Peloton Therapeutics, Pfizer, PlatformQ Health, Prometheus Laboratories, Sanofi/Aventis, Seattle Genetics/Astellas, and UpToDate; grants received outside the conduct of the study were from AstraZeneca, Bayer, Bristol-Myers Squibb, Calithera, Cerulean, Corvus, Eisai, Exelixis, Foundation Medicine, Genentech/Roche, GlaxoSmithKline, Ipsen, Merck, Novartis, Peloton Therapeutics, Pfizer, Prometheus Laboratories, Takeda, and Tracon; and medical writing and editorial assistance were provided by ClinicalThinking, Envision Pharma Group, Fishawack Group of Companies, Health Interactions and Parexel and funded by pharmaceutical companies.

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Extended data

Extended Data Fig. 1 Progression-free survival (PFS) according to (a) CD8+ T cells in the invasive margin, (b) total tumor area and (c) at the tumor center.

Kaplan-Meier analysis was done to evaluate the association of PFS and the presence of CD8+ cells within regions of the tumor (Cox proportional hazards model with one-sided log-rank test). NE, not estimable.

Extended Data Fig. 2 Representative images of CD8 expression by immunohistochemistry in (a, b) CD8+ samples and (c, d) CD8− samples.

a, c, Black boxes denote magnified regions of interest displayed in b and d; scale bars = 2 mm. b, d, Scale bars = 100 μm.

Extended Data Fig. 3 Progression-free survival according to CD8+ T cells following ImmuneNet deconvolution.

Cox proportional hazards model was used and <median group was the reference group. Two-sided Wald test was used for P values.

Extended Data Fig. 4 Heatmap of correlation of WGCNA clusters vs consensus WGCNA clusters.

The heatmap depicts Pearson correlation of signature scores between the 25 original WGCNA clusters and the 23 consensus WGCNA clusters. It demonstrates that 20 of the clusters can be robustly found by both methods. These 20 clusters have been annotated by hypergeometric tests for the top enriched pathways, respectively, and they indeed have the highest correlation to the cluster with a matching annotation by the other method. Three clusters by the consensus method and five clusters by WGCNA (at the bottom right of the heatmap) do not have a matching cluster using the other method, representing contributions from multiple other clusters/biological processes. Importantly, the 26-gene JAVELIN Renal 101 Immuno signature identified by the WGCNA method is highly correlated with the immune response cluster by the consensus method (correlation=0.95, P value <2.2e-16), confirming the robustness of the WGCNA findings.

Extended Data Fig. 5 Volcano plot of association of coexpression signatures with PFS in the combination arm.

Cox proportional hazards model was used and <median (low) group is the reference group. Two-sided Wald test was used for P values. Q values were derived from multiple hypothesis adjustment using FDR.

Extended Data Fig. 6 Progression-free survival according to metabolic pathways: (a) oxygen transport, (b) lipid metabolism, (c) organic acid metabolism and (d) glucocorticoid metabolism.

Cox proportional hazards model was used and <median group was the reference group. Two-sided Wald test was used for P values. No multiple hypothesis adjustment was made.

Extended Data Fig. 7 Correlation between DUX4 signature and expression of HLA-A, -B, and -C.

The triangle symbol in the box represents the mean value. The horizontal line in the box represents the median. Upper and lower box lines represent the third and first quartiles, respectively. Two-sided P value is from Wilcoxon rank-sum test. Analysis value is converted to log2.

Extended Data Fig. 8 HLA types associated with differential progression-free survival.

Cox proportional hazards model was used, and the Other alleles group was the reference group. Two-sided Wald test was used for P values. No multiple hypothesis adjustment was made.

Extended Data Fig. 9 Effect of Fcγ receptor gene polymorphisms on progression-free survival.

*Cox proportional hazards model with wild type as the reference group was used to calculate HR and 95% CI. An HR < 1 indicates better survival in the mutant group, while an HR > 1 indicates better survival in the wild type group. †Two-sided log-rank test was performed to compare between wild type and mutant groups.

Extended Data Fig. 10 Impact of PTEN mutation in combination with other mutations of interest on progression-free survival (PFS) in the (left) avelumab + axitinib and (right) sunitinib arm.

Kaplan-Meier analysis was done to evaluate the association of PFS and the presence of PTEN mutations in combination with other mutations of interest (Cox proportional hazards model).

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Motzer, R.J., Robbins, P.B., Powles, T. et al. Avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma: biomarker analysis of the phase 3 JAVELIN Renal 101 trial. Nat Med 26, 1733–1741 (2020). https://doi.org/10.1038/s41591-020-1044-8

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