A single-cell based precision medicine approach using glioblastoma patient-specific models.
A single-cell based precision medicine approach using glioblastoma patient-specific models.
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DOI:
10.1038/s41698-022-00294-4
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发表时间:
2022-08-08
影响因子:
7.9
通讯作者:
Patel, Anoop P.
中科院分区:
文献类型:
--
作者:
Park, James H.;Feroze, Abdullah H.;Emerson, Samuel N.;Mihalas, Anca B.;Keene, C. Dirk;Cimino, Patrick J.;de Lomana, Adrian Lopez Garcia;Kannan, Kavya;Wu, Wei-Ju;Turkarslan, Serdar;Baliga, Nitin S.;Patel, Anoop P.
Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.
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影响因子:
46.9
作者:
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者:
Newell, Evan W.
影响因子:
5.8
作者:
Liberzon, Arthur;Subramanian, Aravind;Mesirov, Jill P.
通讯作者:
Mesirov, Jill P.
影响因子:
4.6
作者:
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通讯作者:
de Oliveira JRM
影响因子:
9
作者:
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通讯作者:
Schneider-Stock, Regine
DOI:
10.1016/j.bbrc.2014.02.109
发表时间:
2014-03-28
影响因子:
3.1
作者:
Pei, Xin-Hong;Lv, Xin-Quan;Li, Hui-Xiang
通讯作者:
Li, Hui-Xiang