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Neoadjuvant cabozantinib and nivolumab convert locally advanced hepatocellular carcinoma into resectable disease with enhanced antitumor immunity

Nature Cancer volume 2, pages 891–903 (2021)Cite this article

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Abstract

A potentially curative hepatic resection is the optimal treatment for hepatocellular carcinoma (HCC) but most patients are not candidates for resection and most resected HCCs eventually recur. Until recently, neoadjuvant systemic therapy for HCC has been limited by a lack of effective systemic agents. Here, in a single-arm phase 1b study, we evaluated the feasibility of neoadjuvant cabozantinib and nivolumab in patients with HCC, including patients outside of traditional resection criteria (ClinicalTrials.gov ID NCT03299946). Of 15 patients enrolled, 12 (80%) underwent successful margin-negative resection and 5 out of 12 (42%) had major pathological responses. In-depth biospecimen profiling demonstrated an enrichment in effector T cells, as well as tertiary lymphoid structures, CD138+ plasma cells, and a distinct spatial arrangement of B cells in responders compared to nonresponders, indicating an orchestrated B cell contribution to antitumor immunity in HCC.

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Fig. 1: Clinical responses to neoadjuvant cabozantinib and nivolumab.
Fig. 2: Cabozantinib enhances systemic and local antitumor T cell responses.
Fig. 3: Response to cabozantinib and nivolumab is characterized by an immune-rich TME.
Fig. 4: Spatial relationships among cell types are distinct with respect to response.
Fig. 5: Proximity between lymphoid and macrophage subtypes is key determinant of response to cabozantinib and nivolumab.

Data availability

The CyTOF datasets analyzed in this study have been deposited in FlowRepository (FR-FCM-Z34P). The imaging mass cytometry dataset has been deposited as part of the published Code Ocean capsule (https://doi.org/10.24433/CO.3769407.v2). Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code developed for all the spatial analyses performed in this study has been published as an executable version on Code Ocean (https://doi.org/10.24433/CO.3769407.v2). The code is distributed under the Massachusetts Institute of Technology license (https://opensource.org/licenses/MIT).

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Acknowledgements

We thank the patients and their families who participated in this research and the clinical and laboratory research teams. We also thank E. Wang, A. Cooper and S. Isibor for helpful discussions and technical assistance. We thank the University of Maryland School of Medicine Center for Innovative Biomedical Resources Flow Cytometry and Mass Cytometry Core Facility for their help with the CyTOF data collection and acknowledge the support of Therapeutics Insights Services at Fluidigm for the IMC data collection. Finally, we acknowledge the support from the Emerson Collective Cancer Research Fund (grant no. 640183 to E.M.J.), the National Cancer Institute (NCI) Specialized Program of Research Excellence in Gastrointestinal Cancers (grant no. P50CA062924 to E.M.J.), the NCI Informatics Technologies for Cancer Research (grant no. U01CA212007 to E.J.F.), the National Institutes of Health (NIH) Multiscale Modeling Consortium (grant no. U01CA212007 to E.J.F.), Exelixis (M.Y.), Bristol Myers Squibb (M.Y.), the NIH Center Core Grant P30CA006973 and Johns Hopkins Bloomberg–Kimmel Institute for Cancer Immunotherapy.

Author information

Author notes

  1. These authors contributed equally: Won Jin Ho, Qingfeng Zhu.

Authors and Affiliations

  1. Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Won Jin Ho, Jennifer Durham, Aleksandra Popovic, Stephanie Xavier, James Leatherman, Aditya Mohan, Guanglan Mo, Shu Zhang, Nicole Gross, Soren Charmsaz, Brad Wilt, Elizabeth A. Sugar, Daniel A. Laheru, Elizabeth M. Jaffee & Mark Yarchoan

  2. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Qingfeng Zhu & Robert A. Anders

  3. Fluidigm Corporation, San Francisco, CA, USA

    Dongxia Lin & Derek Quong

  4. Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Ihab R. Kamel

  5. Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Matthew Weiss, Benjamin Philosophe, Richard Burkhart, William R. Burns, Chris Shubert, Aslam Ejaz & Jin He

  6. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Atul Deshpande, Genevieve Stein-O’Brien & Elana J. Fertig

  7. Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Atul Deshpande, Ludmila Danilova, Genevieve Stein-O’Brien & Elana J. Fertig

  8. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Elana J. Fertig

  9. Department of Applied Mathematics and Statistics, Johns Hopkins University Whiting School of Engineering, Baltimore, MD, USA

    Elana J. Fertig

Authors
  1. Won Jin Ho
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  2. Qingfeng Zhu
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  3. Jennifer Durham
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Contributions

W.J.H., Q.Z. and M.Y. designed and led all correlative analyses. M.Y. conceived and designed the clinical trial. J.D., A.P., B.W., I.R.K., M.W., B.P., R.B., W.R.B., C.S., A.E., J.H., D.A.L. and M.Y. consented patients and/or conducted the clinical trial. Q.Z. and R.A.A. performed the histopathological review and immunohistochemical analyses. W.J.H. led the development of the mass cytometry workflows. S.X., J.L., A.M., G.M., N.G. and S.C. provided sample and data processing support. S.Z., D.L., D.Q., A.D., L.D., G.S. and E.J.F. provided bioinformatics support. E.A.S. and E.M.J. were involved in study design and data interpretation. W.J.H. and M.Y. wrote the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Mark Yarchoan.

Ethics declarations

Competing interests

W.J.H. is a coinventor of patents with potential for receiving royalties from Rodeo Therapeutics, is a consultant for Exelixis and receives research funding from Sanofi. R.A.A. reports receiving a commercial research support from Bristol Myers Squibb and is a consultant/advisory board member for Bristol Myers Squibb, Merck, AstraZeneca, Incyte and RAPT Therapeutics. E.M.J. reports receiving a commercial research grant from Bristol Myers Squibb and Aduro Biotech and is a consultant/advisory board member for the Lustgarten Foundation, Parker Institute for Cancer Immunotherapy, CStone Pharmaceuticals, Dragonfly, Genocea, Achilles Therapeutics and Adaptive Biotechnologies; she is cofounder of Abmeta Biotech. M.Y. reports receiving research grants from Incyte, Bristol Myers Squibb and Exelixis and is a consultant for AstraZeneca, Eisai, Exelixis and Genentech. The other authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Tim Greten, Song Liuand the other, anonymous, reviewer(s) 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 Cabozantinib induces changes in plasma correlates.

Concentrations (pg/ml) of VEGF-R2, VEGF-C, VEGF-A, c-MET, Angiopoietin-1, Angiopoietin-2, Tie-2, and AXL in longitudinally obtained plasma samples are shown as line graphs. AXL and c-MET are measured by ELISA assays. All other correlates were measured by Luminex multiplex assays. Data representative of two technical replicates. Each line represents an individual patient. Red and blue lines reflect pathologic non-responders and responders, respectively. Indicated are significant FDR-adjusted P values (<0.05, paired two-tailed t-tests).

Source data

Extended Data Fig. 2 T cell activation is observed in both Nanostring and CyTOF analysis.

T cell activation is observed in both Nanostring and CyTOF analysis. Pre- vs. post-cabozantinib changes in lymphoid cells, granzyme expression, and expression of IL2, IFNγ, PD1 in T cells are shown for two patients for whom the data was available. Y-axis represents a non-universal unit scale (expression levels for Nanostring and abundance levels or metal intensities for CyTOF) log2-transformed for ease of visualization. Abbreviation: N, naïve; NK, natural killer cells; ns, Nanostring; PB, peripheral blood; TIL, tumor infiltrating leukocytes.

Source data

Extended Data Fig. 3 Immunohistochemistry (IHC) analysis of immune cells.

Immunohistochemistry (IHC) analysis of immune cells. a, Each tissue section was manually annotated in image analysis software HALO™ into non-tumor and tumor regions (live, necrosis). b, Density of CD3, CD8, and CD20 in post-treatment surgical samples in the non-tumor regions by IHC (nonresponder, n = 4 patients; responder, n = 3 patients) (mean ± s.d., all P > 0.05, unpaired two-tailed t-test). c, Representative staining results of baseline core biopsies (PRE) and post-treatment surgical samples (POST) for CD3, CD8, and CD20 IHC (one patient selected from each group, representative of n = 3 nonresponder, n = 2 responder paired patient samples; quantitative data shown in panel b).

Source data

Extended Data Fig. 4 Workflow for imaging mass cytometry (IMC).

A tissue microarray of 37 cores was constructed from 12 total patients who underwent surgery after neoadjuvant cabozantinib and nivolumab. 5 patients were pathologic responders and 7 patients were nonresponders. This tissue microarray was then stained with a cocktail of metal-conjugated antibodies against 27 markers. Metal intensities from the stained slide are acquired by Hyperion™. The resulting images were evaluated using MCD Viewer™ and then segmented into single cell data using Ilastik and CellProfiler.

Extended Data Fig. 5 Representative IMC data.

a, Representative multicolored images for every core in the tissue microarray constructed from post-treatment surgical samples for aSMA, CD4, CD20, CD8a, and DNA, stratified by response in one set, and aSMA, Vimentin, CD16, E-Cadherin, CD68, and Ki-67, stratified by response in another. b, Abundances of major immune cell types assessed by IMC across the three representative cores from non-responder (NR) and responder (R) patient samples are shown.

Source data

Extended Data Fig. 6 Combination of cabozantinib and nivolumab promotes local T cell responses.

a, Six post-treatment surgical samples were enzymatically dissociated into single-cell tumor infiltrating leukocytes and were assayed by a 28-marker CyTOF panel dedicated to phenotyping T cells. FlowSOM algorithm was employed to generate 40 metaclusters which were annotated into 21 final clusters. Scaled expression profile for each of the clusters are shown in the heatmap and UMAP. b, Stacked bar plots show immune cell subtype distribution at the single cell level for non-responder (NR, n = 79939 cells) and responder (R, n = 25086 cells) samples. ***P < 2.2e-16 (Chi-squared). c, Abundance of each subtype as a percentage of CD45 cells for each patient sample. Abbreviations: DNT, double-negative T; DPT, double-positive T; EFF, effector; EM, effector memory; EX, exhaustion marker positive; N, naïve; NK, natural killer; UA, unassigned. d, Comparison of CD8 T cell quantification by CyTOF, IMC, and IHC.

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Extended Data Fig. 7 Combination of cabozantinib and nivolumab promotes systemic T cell responses.

a, PBMCs from eight paired pre- and post-treatment samples were assayed by a 28-marker CyTOF panel dedicated to phenotyping T cells. FlowSOM algorithm was employed to generate 30 metaclusters which were then annotated into 23 final clusters. Scaled expression profile for each of the clusters are shown in the heatmap. b, Radar plot showing average fold changes for the abundance of every T cell subtype. Color legends apply to both panels A and B. P-values (edgeR) are annotated. Abbreviations: CM, central memory, DNT, double-negative T; DPT, double-positive T; Eff, effector; EM, effector memory; EX, exhaustion marker positive; N, naïve; NK, natural killer; UA, unassigned.

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Extended Data Fig. 8 Treatment alters levels of immunomodulatory chemokines in plasma.

Concentrations (pg/ml) of MIG (CXCL9), IP-10 (CXCL10), I-TAC (CXCL11), MCP-1 (CCL2), Eotaxin-3 (CCL26), Rantes (CCL5), MCP-2 (CCL8), and MIP-1a (CCL3) in longitudinally obtained plasma samples are shown as line graphs. All correlates were measured by Luminex multiplex assays. Data representative of two technical replicates. Each line represents an individual patient. Red and blue lines reflect pathologic non-responders and responders, respectively. All comparisons not statistically significant by FDR-adjusted P values (<0.05 considered significant, paired two-tailed t-tests).

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Extended Data Fig. 9 B cells indirectly contribute to antitumor immune response.

a, Results from immunohistochemistry of CD138 in nonresponders (NR) and responders (R) for tumor regions (left; NR, n = 7; R, n = 5 patients) and non-tumor regions (right; NR, n = 4; R, n = 5). Data represented as mean±s.d.; P values based on unpaired two-tailed t-test. b, Representative dual CD20-IgA staining result of a responder patient (left) and a positive control tonsil tissue (right). Image selected from one of five responder patient. c, Violin plots of per-cell metal intensities for CCR7, TNFα, IL2, and IFNγ in NR vs. R samples measured by CyTOF (NR, n = 2789 cells; R, n = 1593 cells). Indicated are FDR-adjusted P values (linear modeling). d, Violin plot (left) of per-cell granzyme B (GZMB) metal intensity in NR (n = 2789 cells) vs. R (n = 1593 cells). FDR-adjusted P value (linear modeling). Representative multicolored image of IMC in a responder core exhibiting a tertiary lymphoid aggregate with a prominent focus of B cells along with CD3, HLADR, and granzyme B expression. Image selected from one of 15 responder cores.

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Ho, W.J., Zhu, Q., Durham, J. et al. Neoadjuvant cabozantinib and nivolumab convert locally advanced hepatocellular carcinoma into resectable disease with enhanced antitumor immunity. Nat Cancer 2, 891–903 (2021). https://doi.org/10.1038/s43018-021-00234-4

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