Introduction: Despite achievement of complete remission (CR) following chemotherapy, Acute Myelogenous Leukemia (AML) relapses in the majority of adult patients. While relapsed AML is almost always clonally related to the disease at diagnosis, the actual molecular and cellular contributors to chemotherapy resistance and to AML relapse remain incompletely understood. Some molecular determinants of relapse have been identified in genomic, epigenetic and proteomic aberrations, while cellular relapse reservoirs have been identified in leukemia stem cells as well as in more mature leukemic cell compartments. Here, we set out to determine the cellular composition, gene mutation status and gene expression of paired AML specimens procured at diagnosis and at relapse aiming at a better understanding of the AML relapse process.
Methods: We employed the drop-seq 3' single cell RNA sequencing (scRNA-seq) method (Macosko 2015) with minor modifications to analyze the mRNA expression in single cells derived from 12 paired AML specimens procured at diagnosis and at relapse from prior CR. We obtained scRNA-seq data on 1000-2000 single cells per sample detecting approximately 2000-3000 unique molecular identifiers (UMIs) and 800-1500 genes per cell. Using WES or panel-based sequencing we determined mutations in known driver genes. Subsequently, we optimized novel methods for detection and mapping of mutated driver genes to individual cells using mutation specific PCR conditions and novel bioinformatics approaches. We annotated scRNA-seq expression profiles of the diagnosis and relapsed AML specimens individually using publicly available data for cell type-specific RNA markers derived from sorted normal cell populations and further compared the scRNA-seq data to scRNA-seq data of 5 pooled normal human bone marrows generated for this study.
Results: Through analyses of scRNA-seq data of paired diagnosis and relapse AML specimens via principle components analyses (PCA) or t-distributed stochastic neighbor embedding (t-SNE) we detected varying degrees of separation of cell clusters in all cases analyzed indicative of substantial changes in single cell gene expression between AML diagnosis and relapse. A few of these observed cluster shifts were paralleled by gain or loss of mutated genes (e.g. FLT3-ITD) at relapse while most others lacked obvious clonal genomic markers.
Through subsequent comparison of the expression similarities of single AML cells to sorted normal human bone marrow cells we detected two distinct AML relapse patterns: i) a pattern of relapse suggesting simple leukemia regrowth as evidenced by similar proportions of leukemia cells mapping onto discrete normal bone marrow cells (e.g. monocyte-like or GMPs or CMPs), and, ii) a pattern of relapse whereby the gene expression of relapsed cells (but not diagnosis cells) had similarity to normal hematopoietic cells that are conventionally placed more apical in the classical hematopoiesis differentiation cascade (HSCs, MPPs, CMPs; a phenotypic shift to immaturity). In addition, no leukemia sample mapped to just one classically defined bone marrow cell type but instead to multiple cell types, suggesting that most AML leukemia cells harbor aberrant hybrid cell gene expression patterns. Finally, we detected quantitative shifts in T cells and NK cells in some samples at relapse, which will be analyzed in greater detail.
Conclusions: The comparative analysis of scRNA-seq data of paired AML specimens procured at diagnosis and relapse, identifies frequent and previously unrecognized changes in gene expression in leukemia cells at relapse. Through a comparison of gene mutation and gene expression at single cell resolution we identify two distinct AML relapse patterns in adult AML.
Disclosures
No relevant conflicts of interest to declare.
Memory sequencing reveals heritable single cell gene expression programs associated with distinct cellular behaviors
