CD56, HLA-DR, and CD45 recognize a subtype of childhood AML harboring CBFA2T3-GLIS2 fusion transcript.
Andrea ZangrandoFrancesca CavagneroPamela ScarparoElena VarottoSamuela FrancescatoClaudia TregnagoRosanna CuccurulloFranca FagioliLuca Lo NigroRiccardo MasettiMaria Caterina PuttiCarmelo RizzariNicola SantoroAndrea PessionMartina PigazziFranco LocatelliGiuseppe BassoBarbara BuldiniPublished in: Cytometry. Part A : the journal of the International Society for Analytical Cytology (2021)
The presence of CBFA2T3-GLIS2 fusion gene has been identified in childhood Acute Myeloid Leukemia (AML). In view of the genomic studies indicating a distinct gene expression profile, we evaluated the role of immunophenotyping in characterizing a rare subtype of AML-CBFA2T3-GLIS2 rearranged. Immunophenotypic data were obtained by studying a cohort of 20 pediatric CBFA2T3-GLIS2-AML and 77 AML patients not carrying the fusion transcript. Enrolled cases were included in the Associazione Italiana di Ematologia Oncologia Pediatrica (AIEOP) AML trials and immunophenotypes were compared using different statistical approaches. By multiple computational procedures, we identified two main core antigens responsible for the identification of the CBFA2T3-GLIS2-AML. CD56 showed the highest performance in single marker evaluation (AUC = 0.89) and granted the most accurate prediction when used in combination with HLA-DR (AUC = 0.97) displaying a 93% sensitivity and 99% specificity. We also observed a weak-to-negative CD45 expression, being exceptional in AML. We here provide evidence that the combination of HLA-DR negativity and intense bright CD56 expression detects a rare and aggressive pediatric AML genetic lesion improving the diagnosis performance.
Keyphrases
- acute myeloid leukemia
- allogeneic hematopoietic stem cell transplantation
- copy number
- ejection fraction
- genome wide
- end stage renal disease
- chronic kidney disease
- nk cells
- dna methylation
- newly diagnosed
- transcription factor
- escherichia coli
- editorial comment
- pseudomonas aeruginosa
- binding protein
- mass spectrometry
- machine learning
- early life
- electronic health record
- flow cytometry
- data analysis
- structural basis