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Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma

Nature Medicine volume 28, pages 1599–1611 (2022)Cite this article

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Abstract

Atezolizumab (anti-programmed death-ligand 1 (PD-L1)) and bevacizumab (anti-vascular endothelial growth factor (VEGF)) combination therapy has become the new standard of care in patients with unresectable hepatocellular carcinoma. However, potential predictive biomarkers and mechanisms of response and resistance remain less well understood. We report integrated molecular analyses of tumor samples from 358 patients with hepatocellular carcinoma (HCC) enrolled in the GO30140 phase 1b or IMbrave150 phase 3 trial and treated with atezolizumab combined with bevacizumab, atezolizumab alone or sorafenib (multikinase inhibitor). Pre-existing immunity (high expression of CD274, T-effector signature and intratumoral CD8+ T cell density) was associated with better clinical outcomes with the combination. Reduced clinical benefit was associated with high regulatory T cell (Treg) to effector T cell (Teff) ratio and expression of oncofetal genes (GPC3, AFP). Improved outcomes from the combination versus atezolizumab alone were associated with high expression of VEGF Receptor 2 (KDR), Tregs and myeloid inflammation signatures. These findings were further validated by analyses of paired pre- and post-treatment biopsies, in situ analyses and in vivo mouse models. Our study identified key molecular correlates of the combination therapy and highlighted that anti-VEGF might synergize with anti-PD-L1 by targeting angiogenesis, Treg proliferation and myeloid cell inflammation.

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Fig. 1: Study overview.
Fig. 2: Genomic correlates of clinical response to atezolizumab + bevacizumab.
Fig. 3: In situ validation of genomic correlates of clinical response to atezolizumab + bevacizumab.
Fig. 4: Genomic correlates of clinical resistance to atezolizumab + bevacizumab.
Fig. 5: Association between tumor mutations and clinical outcome.
Fig. 6: Molecular correlates of clinical outcome of atezolizumab + bevacizumab versus atezolizumab alone.

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Data availability

All clinical, raw RNA-seq and WES data for the GO30140 and IMbrave150 trials are deposited in the European Genome-Phenome Archive under accession no. EGAS00001005503. Qualified researchers may request access to individual patient-level data through the clinical study data request platform (https://vivli.org/). Further details on Roche’s criteria for eligible studies are available at https://vivli.org/members/ourmembers. 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 https://www.roche.com/research_and_development/who_we_are_how_we_work/clinical_trials/our_commitment_to_data_sharing.htm. MSigDB v.7.5.1 was used for GSEA. GENCODE Human release 40 was used for sequencing pipeline gene modeling. Source Data are provided with this paper.

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Acknowledgements

We are grateful for the participation and commitment of patients, families and doctors in biomarker studies of the GO30140 and IMbrave150 trials. Without their contribution, this study would not have been possible. We thank the following foundations for their financial support. M.R.G. was supported by Fundación Alfonso Martín Escudero Fellowship and a Damon Runyon-Rachleff Innovation Award (no. DR52-18). A.L. was supported by a Damon Runyon-Rachleff Innovation Award (no. DR52-18), an R37 Merit Award (no. R37CA230636) and Icahn School of Medicine at Mount Sinai. The Tisch Cancer Institute and related research facilities are supported by P30 CA196521. We thank J. Munnoz-Rodriguez, X. Wang and X. Wang from Roche Tissue Diagnostics for performing and developing the digital pathology algorithm, and for data analysis of multiplex immunofluorescence assays. We also thank A. Pochiraju, N. Yang, S. Nalle and L. Ma for coordinating sample collection, assay implementation and data transfer. We thank C. V. Wong and J. Cheung for managing and coordinating preclinical studies at Mount Sinai. Figure 1 was created with BioRender.com.

Author information

Author notes

  1. These authors contributed equally: Alexander R. Abbas, Marina Ruiz de Galarreta, Yinghui Guan.

Authors and Affiliations

  1. Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA

    Andrew X. Zhu

  2. Jiahui International Cancer Center, Jiahui Health, Shanghai, China

    Andrew X. Zhu

  3. Department of Oncology Biomarker Development, Genentech, Inc., South San Francisco, CA, USA

    Alexander R. Abbas, Yinghui Guan, Shan Lu & Yulei Wang

  4. Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Marina Ruiz de Galarreta & Amaia Lujambio

  5. Liver Cancer Program, Division of Liver Diseases, Department of Medicine, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Marina Ruiz de Galarreta & Amaia Lujambio

  6. The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Marina Ruiz de Galarreta & Amaia Lujambio

  7. Department of Research Pathology, Genentech, Inc., South San Francisco, CA, USA

    Hartmut Koeppen

  8. Roche Tissue Diagnostics, Tucson, AZ, USA

    Wenjun Zhang

  9. Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan

    Chih-Hung Hsu

  10. Division of Hematology and Oncology, Georgetown University Medical Center, Washington, DC, USA

    Aiwu Ruth He

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

    Baek-Yeol Ryoo

  12. Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China

    Thomas Yau

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

    Ahmed O. Kaseb

  14. Division of Hematology-Oncology, UC San Diego Moores Cancer Center, La Jolla, CA, USA

    Adam M. Burgoyne

  15. Department of Medicine, Division of Hematology-Oncology, UC Irvine Health, Orange, CA, USA

    Farshid Dayyani

  16. Product Development, Genentech, Inc., South San Francisco, CA, USA

    Jessica Spahn & Wendy Verret

  17. The Jonsson Comprehensive Cancer Center, Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Richard S. Finn

  18. Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore

    Han Chong Toh

  19. Graduate School of Biomedical Sciences at Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Amaia Lujambio

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  1. Andrew X. Zhu
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Contributions

A.X.Z. conceived and coordinated the study and interpreted data. Y.W. conceived, designed and supervised the study, interpreted data and wrote the manuscript. A.R.A., M.R.G. and Y.G. made equal contributions to the study. A.R.A. and Y.G. performed data analysis and generated figures. M.R.G. performed in vivo experiments, flow cytometry assays and IHC analyses in the HCC preclinical study. A.L. supervised the preclinical study, analyzed and interpreted data and wrote the manuscript. S.L. performed mutation analysis and serum AFP analysis. H.K. performed PD-L1 IHC analysis. W.Z. developed multiplex IHC panels. C.-H.H., A.R.H., B.-Y.R., T.Y., H.K., A.O.K., A.M.B., F.D., H.C.T. and R.S.F. oversaw patient enrollment and sample collection and reviewed and provided inputs for the manuscript. J.S. and W.V. coordinated the study, interpreted data and reviewed and provided input on the manuscript and study. All authors reviewed and agreed on the final version of the manuscript.

Corresponding author

Correspondence to Yulei Wang.

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Competing interests

A.X.Z. received consulting fees from Bayer, Eisai, Eli Lilly, Exelixis, F. Hoffmann–La Roche, Merck, Gilead, Sanofi Aventis and Sirtex. M.R.G. has received grant support from Genentech. A.R.A., Y.G., S.L., H.K., J.S., W.V. and Y.W. are employees of Genentech and hold stock or other ownership interests in F. Hoffmann–La Roche. W.Z. is an employee of Roche Tissue Diagnostics and holds stock and other ownership interests in Roche. C.-H.H. received consulting fees from AstraZeneca, Eli Lilly and Roche, and received honoraria from Bristol-Meyers Squibb, Eisai, Merck Sharp & Dohme, Ono Pharmaceutical and Roche. A.R.H. has received research funding from Genentech and Merck, and has been on a speakers’ bureau for Eisai, BMS and Exelixis. B.-Y.R. has nothing to declare. T.Y. has received honoraria and consulting fees from Bristol-Myers Squibb. A.O.K. has received honoraria from Bayer Health, Bristol-Myers Squibb, Eisai, Exelixis, Genentech/Roche and Merck; has received consulting fees from Bayer Health, Bristol-Myers Squibb, Eisai, Exelixis, Genentech/Roche and Merck; has received institutional research funding from Adaptimmune, Bayer/Onyx, Bristol-Myers Squibb, Genentech, Hengrui Pharmaceutical and Merck; and travel, accommodation and other expenses support from Bayer/Onyx, Bristol-Myers Squibb, Exelixis and Merck. A.M.B. has received consulting fees from Deciphera, Exelixis and Genentech. F.D. has received consulting fees from Genentech/Roche, Array BioPharma, Exelixis, Eisai, QED Therapeutics and Signatera; has been on a speakers’ bureau for Genentech/Roche, Amgen, Eisai, Ipsen, Exelixis, Sirtex Medical, Deciphera, Natera and Servier; has received institutional research funding from Bristol-Myers Squibb, AstraZeneca, Merck, Genentech, Taiho Pharmaceutical, Exelixis and Ipsen; and has an immediate family member who is an employee of Roche Diagnostics. R.S.F. has received consulting fees from AstraZeneca, Bayer, Bristol-Myers Squibb, Eisai, Exelixis, F. Hoffmann–La Roche/Genentech, Lilly, Merck, Novartis and Pfizer; has received institutional research funding from Bayer, Bristol-Myers Squibb, Eisai, Lilly, Merck, Novartis, Pfizer and F. Hoffmann–La Roche/Genentech; and has provided expert testimony for Novartis. H.C.T. has received honoraria from Roche, MSD Merck, Ipsen and AstraZeneca. A.L. has received consulting fees from AstraZeneca and research funding from Pfizer.

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Nature Medicine thanks David Pinato, Xin Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Saheli Sadanand, in collaboration with the Nature Medicine team.

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Extended data

Extended Data Fig. 1 Clinical outcomes in the biomarker-evaluable population (BEP) are representative of those in the intention-to-treat (ITT) population in GO30140 and IMbrave150 clinical trials.

Kaplan-Meier estimates of progression-free survival (PFS) and overall survival (OS) in patients in the ITT and BEP of a, GO30140 group A & F (A n = 90, F n = 91 biologically independent subjects) or b, IMbrave150 (n = 119 biologically independent subjects). Stratified hazard ratios and two-sided log-likelihood P values for progression or death are reported. 95% confidence intervals are indicated in parentheses. A table comparing independent review facility–assessed objective response rate in the BEP and ITT population in GO30140 group A is included in (a). The variables used for stratification in the Cox model for IMBrave 150 (b) were geographic region (Asia [excluding Japan] vs the rest of the world), α-fetoprotein categorical level at baseline (<400 ng per milliliter vs ≥400 ng per milliliter), and the presence of either macrovascular invasion or extrahepatic spread (vs the d of both). Tick marks indicate censored data. Numbers in parentheses indicate CIs. Atezo, atezolizumab; Bev, bevacizumab; CR, complete response; HR, hazard ratio; PD, progressive disease; PR, partial response; SD, stable disease.

Extended Data Fig. 2 xCell analysis in GO30140 group A cohort.

Deconvolution scores for various cell types and biological programs yielded from performing xCell analysis on expression data from the GO30140 study (n = 90 biologically independent subjects), separated by responder/non-responder status. Responders were defined as patients with complete or partial response as assessed by an independent review facility according to Response Evaluation Criteria in Solid Tumors, version 1.1. Boxes represent median and first/third quartile values. Box whiskers represent the most extreme values within 150% of the interquartile range. P-values are two-sided Student t tests P value comparing scores between responders and non-responders. aDC, activated dendritic cells; DC, dendritic cells; cDC, conventional dendritic cells.

Extended Data Fig. 3 Key biomarker expression by RECIST response and PFS/OS association within treatment groups of IMbrave150.

a, Expression of ABRS, CD274 and Teff signature score by RECIST response in IMbrave150 Atezo-Bev treatment group (n = 119 biologically independent subjects). Box plots are of log2 gene expression scores for response categories, with two-sided P values from Student t tests. Boxes represent median (middle line) and first/third quartile (bottom and upper lines) values. Box whiskers represent the most extreme values within 150% of the interquartile range. b, KM plots of PFS stratified by ABRS, CD274 or Teff signature high vs low expression (median split) within the Aatezo-Bbev (n = 119 biologically independent subjects) or sorafenib (n = 58 biologically independent subjects) treatment group in IMbrave150. c, KM plots of OS stratified by ABRS, CD274 or Teff signature high vs low expression (median split) within the Aatezo-Bbev (n = 119 biologically independent subjects) or sorafenib (n = 58 biologically independent subjects) treatment group in IMbrave150. 95% confidence intervals are indicated in parentheses.

Extended Data Fig. 4 Association of PD-L1 protein expression with clinical outcome.

a, Images of PD-L1 staining on tumor cells and immune cells by PD-L1 immunohistochemistry (SP263) (scale bars in each main image and its magnified inset are 90 µm and 20 µm, respectively) are representing. PD-L1 IHC done with the same assay conditions in 180 GO30140 group A and F samples and 199 IMbrave150 tissue samples. b, Prevalence and proportions of PD-L1 staining in either tumor cells (TC) or immune cells (IC). c, Discrete PD-L1 staining at canonical thresholds are tested with a Cox proportional hazards model for association with either overall survival (OS) or progression-free survival (PFS). Points and whiskers in the plot are hazard ratio and confidence interval, respectively. Atezo, atezolizumab; Bev, bevacizumab.

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Extended Data Fig. 5 Association of frequently mutated genes with clinical outcome in IMbrave 150.

Forest plots of progression-free survival (PFS) (a) or overall survival (OS) (b) in IMbrave150 patients (n = 130) either possessing (Mut) or not possessing (WT) mutations in the indicated gene, for the six genes observed to be mutated in > 10% of patients in the study. Mutations are defined as somatic short variants that are either recurrent in cancer (known) or disrupt tumor suppressor genes or are in known hotspot regions (likely) by Foundation Medicine criteria. Statistics are calculated by a two-sided Cox model stratified with variables as stated elsewhere. Points and whiskers in the plot are hazard ratio and confidence interval, respectively. Atezo, atezolizumab; Bev, bevacizumab; NA, not achieved.

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Extended Data Fig. 6 Efficacy and mechanism of action study of atezolizumab-bevacizumab in an immunogenic hepatocellular carcinoma mouse model.

a, Survival curves in C57BL/6 WT females. Number of mice per group is shown as well as median survival in days. Log-rank Mantel-Cox test. Treatment groups are compared with the control group (IgG1 + IgG2) and two-sided P value is adjusted. b-i, Quantification of (b) CD8 T cells, (c) proliferating CD8 T cells, (d) proliferating SIINFELKL-specific CD8 T cells, (e) granzyme B-expressing SIINFELKL-specific CD8 T cells, (f) proliferating Tregs, (g) ratio of proliferating Tregs, (h) conventional dendritic cells (cDCs), and (i) monocyte-derived macrophages (MoMa) in the livers of MYC-lucOS;CTNNB1 2 weeks after starting treatments (n = 5 biologically independent animals). Results of an analysis of variance test with multiple comparisons is shown. Mean and standard deviation (SD) are shown. j, Representative pictures of the stainings for CD8 (pink) and endomucin (brown) in tumor area quantified in (k). k & l, Number of CD8 + T cells and endomucin-positive vessels per mm2 of (k) tumor and (l) peritumor areas in MYC-lucOS; CTNNB1 mice treated with the corresponding treatments for 2 weeks (n = 2-5 biologically independent animals). An analysis of variance test with multiple comparisons is shown. Mean and standard deviation (SD) are shown. C, control (IgG1 + IgG2), P, anti–PD-L1; PV, anti–PD-L1 + anti-VEGF; Undef, undefined; V, anti-VEGF.

Extended Data Table 1 Summary of clinical biomarker datasets and key biomarker analyses carried out in this study
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Extended Data Table 2 GO30140 demographic summary in BEP and ITT populations
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Extended Data Table 3 IMbrave150 demographic summary in the BEP versus ITT population
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Extended Data Table 4 Curated signatures representing potential clinically relevant pathways and immune subsets identified from genome-wide DEG, GESA and xCell analyses
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Zhu, A.X., Abbas, A.R., de Galarreta, M.R. et al. Molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma. Nat Med 28, 1599–1611 (2022). https://doi.org/10.1038/s41591-022-01868-2

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