High-dimensional Immune Profiles and Machine Learning May Predict Acute Myeloid Leukemia Relapse Early following Transplant.

Identification of early immune signatures associated with acute myeloid leukemia (AML) relapse following hematopoietic stem cell transplant (HSCT) is critical for patient outcomes. We analyzed PBMCs from 58 patients with AML undergoing HSCT, focusing on T cell subsets and functional profiles. High-dimensional flow cytometry coupled with Uniform Manifold Approximation and Projection dimensionality reduction and PhenoGraph clustering revealed distinct changes in CD4+ and CD8+ T cell populations in 16 patients who relapsed within 1 y of HSCT. We observed increased IL-2, IL-10, and IL-17-producing CD4+ T cells, alongside decreased CD8+ T cell function early in relapsing patients. Notably, relapsing patients exhibited increased TCF-1intermediate cells, which lacked granzyme B or IFN-γ production in the CD4+ T cell compartment. We then developed a supervised machine learning algorithm that predicted AML relapse with 90% accuracy within 30 d after HSCT using high-throughput assays. The algorithm leverages condensed immune phenotypic data, alongside the ADASYN algorithm, for data balancing and 100 rounds of XGBoost supervised learning. This approach holds potential for detecting relapse-associated immune signatures months before clinical manifestation. Our findings demonstrate a distinct immunological signature potentially capable of predicting AML relapse as early as 30 d after HSCT.

T cell exhaustion is associated with cognitive status and amyloid accumulation in Alzheimer's disease.

Studies over the last 100 years have suggested a link between inflammation, infectious disease, and Alzheimer's Disease (AD). Understanding how the immune system changes during the development of AD may facilitate new treatments. Here, we studied an aging cohort who had been assessed for AD pathology with amyloid positron emission tomography and cognitive testing, and conducted high dimensional flow cytometry on peripheral blood mononuclear and cerebrospinal fluid cells. Participants were assigned a classification of being amyloid negative cognitively normal, amyloid positive cognitively normal (APCN), or amyloid positive mild cognitive impairment (APMCI), an early stage of AD. We observed major alterations in the peripheral innate immune system including increased myeloid and plasmacytoid dendritic cells in the blood of APMCI participants. When the adaptive immune system was examined, amyloid positive participants, regardless of cognitive status, had increased CD3+ T cells. Further analyses of CD4+ and CD8+ T cells revealed that APMCI participants had an increase in more differentiated phenotype T cells, such as effector memory and effector memory CD45RA expressing (TEMRA), compared to those with normal cognition. When T cell function was measured, we observed that T cells from APCN participants had increased IFNγ+GzB- producing cells compared to the other participants. In contrast, we demonstrate that APMCI participants had a major increase in T cells that lacked cytokine production following restimulation and expressed increased levels of PD-1 and Tox, suggesting these are exhausted cells. Rejuvenation of these cells may provide a potential treatment for AD.