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.
Keywords: ASCT; CyTOF; bone marrow transplantation; immune system reconstitution; immunotherapy; leukemia; mass cytometry; stem cell transplantation; systems immunology; tumor immunology.
Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.