The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
The Phenotypes of Proliferating Glioblastoma Cells Reside on a Single Axis of Variation
Open accessAbstract
Although tumor-propagating cells can be derived from glioblastomas (GBM) of the proneural and mesenchymal subtypes, a glioma stem-like cell (GSC) of the classic subtype has not been identified. It is unclear whether mesenchymal GSCs (mGSC) and/or proneural GSCs (pGSC) alone are sufficient to generate the heterogeneity observed in GBM. We performed single-cell/single-nucleus RNA sequencing of 28 gliomas, and single-cell ATAC sequencing for 8 cases. We found that GBM GSCs reside on a single axis of variation, ranging from proneural to mesenchymal. In silico lineage tracing using both transcriptomics and genetics supports mGSCs as the progenitors of pGSCs. Dual inhibition of pGSC-enriched and mGSC-enriched growth and survival pathways provides a more complete treatment than combinations targeting one GSC phenotype alone. This study sheds light on a long-standing debate regarding lineage relationships among GSCs and presents a paradigm by which personalized combination therapies can be derived from single-cell RNA signatures, to overcome intratumor heterogeneity. Significance: Tumor-propagating cells can be derived from mesenchymal and proneural glioblastomas. However, a stem cell of the classic subtype has yet to be demonstrated. We show that classic-subtype gliomas are comprised of proneural and mesenchymal cells. This study sheds light on a long-standing debate regarding lineage relationships between glioma cell types. See related commentary by Fine, p. 1650. This article is highlighted in the In This Issue feature, p. 1631
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Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,621
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown
Pertea, M., Kim, D., Pertea, G. M., Leek, J. T., & Salzberg, S. L. (2016) · Nature Protocols · doi:10.1038/nprot.2016.095 · Cited by 6,845
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063
La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., Lidschreiber, K., Kastriti, M. E., Lönnerberg, P., Furlan, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0414-6 · Cited by 4,118
An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma
Neftel, C., Laffy, J., Filbin, M. G., Hara, T., Shore, M. E., Rahme, G. J., Richman, A. R., Silverbush, D., Shaw, M. L., Hebert, C. M., et al. (2019) · Cell · doi:10.1016/j.cell.2019.06.024 · Cited by 2,873
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Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193
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Venkataramani, V., Yang, Y., Schubert, M. C., Reyhan, E., Tetzlaff, S. K., Wißmann, N., Botz, M., Soyka, S. J., Beretta, C. A., Pramatarov, R. L., et al. (2022) · Cell · doi:10.1016/j.cell.2022.06.054 · Cited by 632
Cancer stem cells: advances in knowledge and implications for cancer therapy
Chu, X., Tian, W., Ning, J., Xiao, G., Zhou, Y., Wang, Z., Zhai, Z., Tanzhu, G., Yang, J., & Zhou, R. (2024) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-01851-y · Cited by 553
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Varn, F. S., Johnson, K. C., Martinek, J., Huse, J. T., Nasrallah, M. P., Wesseling, P., Cooper, L. A. D., Malta, T. M., Wade, T. E., Sabedot, T. S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.038 · Cited by 494
Self-citationGangoso, E., Southgate, B., Bradley, L., Rus, S., Galvez-Cancino, F., McGivern, N., Güç, E., Kapourani, C.-A., Byron, A., Ferguson, K. M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.023 · Cited by 359
Metadata
- Authors
- Wang, L., Babikir, H., Müller, S., Yagnik, G., Shamardani, K., Catalan, F., Kohanbash, G., Alvarado, B., Di Lullo, E., Kriegstein, A., Shah, S., Wadhwa, H., Chang, S. M., Phillips, J. J., Aghi, M. K., & Diaz, A. A.
- Venue
- Cancer Discovery
- Published
- 2019-12-01
- Volume
- 9
- Issue
- 12
- Pages
- 1708-1719
- ISSN
- 2159-8274 (ISSN-L)
- 2159-8290
- OpenAlex
- W2975379509
- Cited by
- 363
- References
- 31
- FWCI
- 19.68
- Type
- journal article
- Language
- en
- Open access
- green
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- Wang, L., Babikir, H., Müller, S., Yagnik, G., Shamardani, K., Catalan, F., Kohanbash, G., Alvarado, B., Di Lullo, E., Kriegstein, A., Shah, S., Wadhwa, H., Chang, S. M., Phillips, J. J., Aghi, M. K., & Diaz, A. A. (2019). The Phenotypes of Proliferating Glioblastoma Cells Reside on a Single Axis of Variation. Cancer Discovery. https://doi.org/10.1158/2159-8290.cd-19-0329
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