Multimodal Atlas of Paired Diagnosis and Relapse AML Samples Enables Novel Therapeutic Targeting of Surface Antigens

Note: A.H. and M.U. share co-first authorship. AML is an aggressive clonal malignancy characterized by combinations of chromosomal abnormalities, gene mutations, and cell surface antigen (Ag) expression profiles. This heterogeneity contributes to refractory or relapsed disease that presents a major challenge when treating AML. Previous single cell sequencing studies of primary AML samples have provided remarkable insight into the clonal architecture of AML cell populations and how their mutational, transcriptomic, and surface Ag profiles vary between patients. However, information addressing clonal shifts during progression from diagnosis to relapse in large cohorts is lacking. Here, we adapted single cell RNA sequencing with surface Ag feature barcoding to analyze more than 450,000 cells from 28 paired AML patient bone marrow mononuclear cell samples collected at diagnosis and relapse (56 samples). To our knowledge, it is the largest and most comprehensive single cell AML atlas to date. This atlas contains rich clinical metadata including cytogenetics, mutation status of canonical AML genes, treatment history, and survival information. We leveraged these data to identify potential correlations with clonal heterogeneity during progression to relapse and to propose novel strategies for targeting the surface of AML cells. The feature barcoding panel consists of a comprehensive list of 81 surface Ags reported in clinicaltrials.gov or mined from literature, including large proteomics mass spectrometry datasets of AML patient bone marrow samples (deBoer et. al. Cancer Cell 2018; Jayavelu et al, Cancer Cell 2022). Feature barcoding produces antibody derived tag (ADT) counts which are interpreted as relative values but alone does not indicate absolute number of Ags per cell. We addressed this limitation by measuring absolute Ag density of AML Ags, CD33, CLL1, CD123, and EMR2 in patient samples using the flow cytometric QuantiBRITE assay. Focusing on these four Ags, we modeled the relationship between ADT expression and antibodies per cell (ABC) Ag density from QuantiBRITE. The model was applied to impute absolute Ag density for the remaining 77 Ags in all samples. UMAP visualization after sample transcriptome integration revealed distinct clustering of CD45-dim myeloblasts, T cells, B cells, and erythroid cells. Longitudinal analysis identified 28 surface Ags that were differentially expressed (absolute log 2FC > 1, p < 0.01) between relapse and diagnosis myelobl