HCC spatial transcriptomic profiling reveals significant and potentially targetable cancer-endothelial interactions
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1.
Massachusetts General Hospital
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2.
Massachusetts Institute of Technology
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3.
Dana-Farber Cancer Institute
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4.
Broad Institute
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5.
California Institute of Technology
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6.
Cedars-Sinai Medical Center
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7.
Beth Israel Deaconess Medical Center
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8.
The University of Texas Southwestern Medical Center
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9.
Harvard University
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10.
University of Chicago
Abstract
Background:
HCC is a highly vascular tumor, and many effective drug regimens target the tumor blood vessels. Prior bulk HCC subtyping data used bulk transcriptomes, which contained a mixture of parenchymal and stromal contributions.
Methods:
We utilized computational deconvolution and cell-cell interaction analyses to cell type-specific (tumor-enriched and vessel-enriched) spatial transcriptomic data collected from 41 resected HCC tissue specimens.
Results:
We report that the prior Hoshida bulk transcriptional subtyping schema is driven largely by an endothelial fraction, show an alternative tumor-specific schema has potential prognostic value, and use spatially paired ligand-receptor analyses to identify known and novel (LGALS9 tumor-HAVCR2 vessel) signaling relationships that drive HCC biology in a subtype-specific and potentially targetable manner.
Conclusions:
Our study leverages spatial gene expression profiling technologies to dissect HCC heterogeneity and identify heterogeneous signaling relationships between cancer cells and their endothelial cells. Future validation and expansion of these findings may validate novel cancer-endothelial cell interactions and related drug targets.
Copyright and License
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases.
Funding
David T. Ting received funding support from the NIH (U01CA228963). Yujin Hoshida received funding support from the NIH (CA233794, CA255621), the European Commission (ERC-AdG-2020-101021417), and the Cancer Prevention and Research Institute of Texas (RR180016). Joseph W. Franses received funding support from the NIH (CA263551). Chenyue Lu received fellowship funding support from the Sita Foundation.
Conflict of Interest
Yujin Hoshida advises and owns stock in Alentis and Espervita Therapeutics. He advises Helio Genomics, Roche Diagnostics, and Elevar Therapeutics. David T. Ting consults, advises, and owns stock in Rome Therapeutics and PanTher Therapeutics. He advises and owns stock in TellBio Incorporated. He consults for Moderna, ABRDN, AstraZeneca, Leica Biosystems Imaging, and Sonata Therapeutics and received grants from Sanofi and Incyte. Joseph W. Franses consults for Eisai, Foundation Medicine, Guardant Health, and Genetech. He received grants from Abbvie, Iterion Therapeutics, and Omega Therapeutics. David T. Ting has received an honorarium from NanoString Technologies, whose technology was used in this work. David T. Ting has received consulting fees from ROME Therapeutics and Tekla Capital that are not related to this work. David T. Ting has received honorariums from Moderna, Ikena Oncology, Foundation Medicine, Inc., and Pfizer that are not related to this work. David T. Ting is a founder and has equity in ROME Therapeutics, PanTher Therapeutics, and TellBio, Inc., which is not related to this work. D.T.T. receives research support from ACD-Biotechne, PureTech Health LLC, Ribon Therapeutics, AVA LifeScience GmbH, and Incyte, which was not used in this work. David T. Ting’s interests were reviewed and managed by Massachusetts General Hospital and Mass General Brigham in accordance with their conflict of interest policies. Yujin Hoshida has competing financial interests at Helio Genomics, Alentis Therapeutics, and Espervita Therapeutics, and additionally holds advisory/management and consulting positions at Roche Diagnostics. Joseph W. Franses holds consulting positions at Eisai, Foundation Medicine, Genentech, Guardant Health, and Servier. Martin J. Aryee has financial interests in SeQure Dx, unrelated to this work. The remaining authors have no conflicts to report.
Code Availability
Analyses were performed in R and Python, and all code used for the paper can be found at https://github.com/clu413/HCC_GeoMx_paper. The data are deposited in the Gene Expression Omnibus (GEO), accession number GSE277104.
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Additional details
- National Institutes of Health
- U01CA228963
- National Institutes of Health
- CA233794
- National Institutes of Health
- CA255621
- National Institutes of Health
- CA263551
- European Commission
- ERC-AdG-2020-101021417
- Cancer Prevention and Research Institute of Texas
- RR180016
- Accepted
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2024-07-19Accepted
- Available
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2024-09-27Published online
- Caltech groups
- Division of Biology and Biological Engineering
- Publication Status
- Published