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Data from Personalized Single-Cell Proteogenomics to Distinguish Acute Myeloid Leukemia from Nonmalignant Clonal Hematopoiesis

Authors :
Christopher S. Hourigan
Yuesheng Li
Patrick Burr
J. Philip McCoy
Pradeep K. Dagur
Aik Ooi
Shu Wang
Saurabh Gulati
Aaron Llanso
Robert Durruthy-Durruthy
Adam Sciambi
Catherine Lai
Christin B. DeStefano
Janet Valdez
Julie Thompson
Clifton L. Dalgard
Gauthaman Sukumar
Anthony R. Soltis
Matthew D. Wilkerson
Karolyn A. Oetjen
Katherine E. Lindblad
Katherine R. Calvo
Meghali Goswami
Gege Gui
Chidera Nosiri
Jack Ghannam
Laura W. Dillon
Publication Year :
2023
Publisher :
American Association for Cancer Research (AACR), 2023.

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.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....a4fa206442b59a1c6684b830d92d0762