Personalized Single-Cell Proteogenomics to Distinguish Acute Myeloid Leukemia from Nonmalignant Clonal Hematopoiesis
Laura W. Dillon, Jack Ghannam, Chidera Nosiri, Gege Gui, Meghali Goswami, Katherine R. Calvo, Katherine E. Lindblad, Karolyn A. Oetjen, Matthew D. Wilkerson, Anthony R. Soltis, Gauthaman Sukumar, Clifton L. Dalgard, Julie Thompson, Janet Valdez, Christin B. DeStefano, Catherine Lai, Adam Sciambi, Robert Durruthy-Durruthy, Aaron Llanso, Saurabh Gulati, Shu Wang, Aik T. Ooi, Pradeep K. Dagur, J. Philip McCoy, Patrick Burr, Yuesheng Li, Christopher S. Hourigan
Abstract
Abstract Genetic mutations associated with acute myeloid leukemia (AML) also occur in age-related clonal hematopoiesis, often in the same individual. This makes confident assignment of detected variants to malignancy challenging. The issue is particularly crucial for AML posttreatment measurable residual disease monitoring, where results can be discordant between genetic sequencing and flow cytometry. We show here that it is possible to distinguish AML from clonal hematopoiesis and to resolve the immunophenotypic identity of clonal architecture. To achieve this, we first design patient-specific DNA probes based on patient's whole-genome sequencing and then use them for patient-personalized single-cell DNA sequencing with simultaneous single-cell antibody–oligonucleotide sequencing. Examples illustrate AML arising from DNMT3A- and TET2-mutated clones as well as independently. The ability to personalize single-cell proteogenomic assessment for individual patients based on leukemia-specific genomic features has implications for ongoing AML precision medicine efforts. Significance: This study offers a proof of principle of patient-personalized customized single-cell proteogenomics in AML including whole-genome sequencing–defined structural variants, currently unmeasurable by commercial “off-the-shelf” panels. This approach allows for the definition of genetic and immunophenotype features for an individual patient that would be best suited for measurable residual disease tracking.