%0 Journal Article %1 LAKSHMIKANTH20172238 %A Lakshmikanth, Tadepally %A Olin, Axel %A Chen, Yang %A Mikes, Jaromir %A Fredlund, Erik %A Remberger, Mats %A Omazic, Brigitta %A Brodin, Petter %D 2017 %J Cell Reports %K ASCT Cytometry, Mass %N 9 %P 2238 - 2250 %R https://doi.org/10.1016/j.celrep.2017.08.021 %T Mass Cytometry and Topological Data Analysis Reveal Immune Parameters Associated with Complications after Allogeneic Stem Cell Transplantation %U http://www.sciencedirect.com/science/article/pii/S2211124717311130 %V 20 %X Summary 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 graft-versus-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.

@article{LAKSHMIKANTH20172238, abstract = {Summary 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 graft-versus-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.}, added-at = {2020-04-02T17:06:23.000+0200}, author = {Lakshmikanth, Tadepally and Olin, Axel and Chen, Yang and Mikes, Jaromir and Fredlund, Erik and Remberger, Mats and Omazic, Brigitta and Brodin, Petter}, biburl = {https://www.bibsonomy.org/bibtex/2f55a9e769639f66a1bbb8938da795a3c/lkanth}, description = {Mass Cytometry and Topological Data Analysis Reveal Immune Parameters Associated with Complications after Allogeneic Stem Cell Transplantation - ScienceDirect}, doi = {https://doi.org/10.1016/j.celrep.2017.08.021}, interhash = {e292114733288b46d45500ed7f82a864}, intrahash = {f55a9e769639f66a1bbb8938da795a3c}, issn = {2211-1247}, journal = {Cell Reports}, keywords = {ASCT Cytometry, Mass}, number = 9, pages = {2238 - 2250}, timestamp = {2020-04-02T17:06:23.000+0200}, title = {Mass Cytometry and Topological Data Analysis Reveal Immune Parameters Associated with Complications after Allogeneic Stem Cell Transplantation}, url = {http://www.sciencedirect.com/science/article/pii/S2211124717311130}, volume = 20, year = 2017 }