Mass Cytometry and Topological Data Analysis Reveal Immune Parameters Associated with Complications after Allogeneic Stem Cell Transplantation
Lakshmikanth, T., Olin, A., Chen, Y. et al.
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.
Lakshmikanth, T., Olin, A., Chen, Y. et al. "Mass Cytometry and Topological Data Analysis Reveal Immune Parameters Associated with Complications after Allogeneic Stem Cell Transplantation" Cell Reports (2017): 2,238–50