The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
Single-cell atlas of tumor cell evolution in response to therapy in hepatocellular carcinoma and intrahepatic cholangiocarcinoma
Open accessAbstract
Background & Aims: Intratumor molecular heterogeneity is a key feature of tumorigenesis and is linked to treatment failure and patient prognosis. Here, we aimed to determine what drives tumor cell evolution by performing single-cell transcriptomic analysis. Methods: We analyzed 46 hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) biopsies from 37 patients enrolled for interventional studies at the NIH Clinical Center, with 16 biopsies collected before and after treatment from 7 patients. We developed a novel machine learning-based consensus clustering approach to track cellular states of 57,000 malignant and non-malignant cells including tumor cell transcriptome-based functional clonality analysis. We determined tumor cell relationships using RNA velocity and reverse graph embedding. We also studied longitudinal samples from 4 patients to determine tumor cellular state and its evolution. We validated our findings in bulk transcriptomic data from 488 patients with HCC and 277 patients with iCCA. Results: Using transcriptomic clusters as a surrogate for functional clonality, we observed an increase in tumor cell state heterogeneity which was tightly linked to patient prognosis. Furthermore, increased functional clonality was accompanied by a polarized immune cell landscape which included an increase in pre-exhausted T-cells. We found that SPP1 expression was tightly associated with tumor cell evolution and microenvironmental reprogramming. Finally, we developed a user-friendly online interface as a knowledge base for a single-cell atlas of liver cancer. Conclusions: Our study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers of tumor evolution in response to therapy.
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Comprehensive Integration of Single-Cell Data
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Integrating single-cell transcriptomic data across different conditions, technologies, and species
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Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
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Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma
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Reversed graph embedding resolves complex single-cell trajectories
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Self-citationDissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
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Liver tumour immune microenvironment subtypes and neutrophil heterogeneity
Xue, R., Zhang, Q., Cao, Q., Kong, R., Xiang, X., Liu, H., Feng, M., Wang, F., Cheng, J., Li, Z., et al. (2022) · Nature · doi:10.1038/s41586-022-05400-x · Cited by 935
Molecular pathogenesis and systemic therapies for hepatocellular carcinoma
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Self-citationZhu, G.-Q., Tang, Z., Huang, R., Qu, W.-F., Fang, Y., Yang, R., Tao, C.-Y., Gao, J., Wu, X.-L., Sun, H.-X., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00529-z · Cited by 336
Biomarkers for immunotherapy of hepatocellular carcinoma
Greten, T. F., Villanueva, A., Korangy, F., Ruf, B., Yarchoan, M., Ma, L., Ruppin, E., & Wang, X. W. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00816-4 · Cited by 227
Self-citationBile acid synthesis impedes tumor-specific T cell responses during liver cancer
Varanasi, S. K., Chen, D., Liu, Y., Johnson, M. A., Miller, C. M., Ganguly, S., Lande, K., LaPorta, M. A., Hoffmann, F. A., Mann, T. H., et al. (2025) · Science · doi:10.1126/science.adl4100 · Cited by 177
Metadata
- Authors
- Ma, L., Wang, L., Khatib, S. A., Chang, C.-W., Heinrich, S., Dominguez, D. A., Forgues, M., Candia, J., Hernandez, M. O., Kelly, M., Zhao, Y., Tran, B., Hernandez, J. M., Davis, J. L., Kleiner, D. E., Wood, B. J., Greten, T. F., & Wang, X. W.
- Venue
- Journal of Hepatology
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 75
- Issue
- 6
- Pages
- 1397-1408
- ISSN
- 0168-8278 (ISSN-L)
- OpenAlex
- W3175283754
- Cited by
- 373
- References
- 63
- FWCI
- 16.37
- Type
- journal article
- Language
- en
- Open access
- green
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- Ma, L., Wang, L., Khatib, S. A., Chang, C.-W., Heinrich, S., Dominguez, D. A., Forgues, M., Candia, J., Hernandez, M. O., Kelly, M., Zhao, Y., Tran, B., Hernandez, J. M., Davis, J. L., Kleiner, D. E., Wood, B. J., Greten, T. F., & Wang, X. W. (2021). Single-cell atlas of tumor cell evolution in response to therapy in hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Journal of Hepatology. https://doi.org/10.1016/j.jhep.2021.06.028
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
