期刊
CELL REPORTS
卷 20, 期 9, 页码 2238-2250出版社
CELL PRESS
DOI: 10.1016/j.celrep.2017.08.021
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资金
- Swedish Society for Medical Research [S14-0173]
- Swedish Society of Medicine [SLS505921]
- Karolinska Institutet
- Swedish Childhood Cancer Foundation [PR2013-0141]
- Swedish Cancer Foundation [160385]
- Ake Olsson Foundation
- Felix Mindus Foundation
- Magnus Bergvall Foundation
- Tore Nilsson Foundation
Human immune systems are variable, and immune responses are often unpredictable. Systems-level analyses offer increased power to sort patients on the basis of coordinated changes across immune cells and proteins. Allogeneic stem cell transplantation is a well-established form of immunotherapy whereby a donor immune system induces a graftversus- leukemia response. This fails when the donor immune system regenerates improperly, leaving the patient susceptible to infections and leukemia relapse. We present a systems-level analysis by mass cytometry and serum profiling in 26 patients sampled 1, 2, 3, 6, and 12 months after transplantation. Using a combination of machine learning and topological data analyses, we show that global immune signatures associated with clinical outcome can be revealed, even when patients are few and heterogeneous. This high-resolution systems immune monitoring approach holds the potential for improving the development and evaluation of immunotherapies in the future.
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