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

Abstract

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.