Phenotype in combination with genotype improves outcome prediction in acute myeloid leukemia: a report from Children’s Oncology Group protocol AAML0531

Abstract

Diagnostic biomarkers can be used to determine relapse risk in acute myeloid leukemia, and certain genetic aberrancies have prognostic relevance. A diagnostic immunophenotypic expression profile, which quantifies the amounts of distinct gene products, not just their presence or absence, was established in order to improve outcome prediction for patients with acute myeloid leukemia. The immunophenotypic expression profile, which defines each patient’s leukemia as a location in 15-dimensional space, was generated for 769 patients enrolled in the Children’s Oncology Group AAML0531 protocol. Unsupervised hierarchical clustering grouped patients with similar immunophenotypic expression profiles into eleven patient cohorts, demonstrating high associations among phenotype, genotype, morphology, and outcome. Of 95 patients with inv(16), 79% segregated in Cluster A. Of 109 patients with t(8;21), 92% segregated in Clusters A and B. Of 152 patients with 11q23 alterations, 78% segregated in Clusters D, E, F, G, or H. For both inv(16) and 11q23 abnormalities, differential phenotypic expression identified patient groups with different survival characteristics (P<0.05). Clinical outcome analysis revealed that Cluster B (predominantly t(8;21)) was associated with favorable outcome (P<0.001) and Clusters E, G, H, and K were associated with adverse outcomes (P<0.05). Multivariable regression analysis revealed that Clusters E, G, H, and K were independently associated with worse survival (P range <0.001 to 0.008). The Children’s Oncology Group AAML0531 trial: clinicaltrials.gov Identifier: 00372593.

Utility of CD36 as a novel addition to the immunophenotypic signature of RAM‐phenotype acute myeloid leukemia and study of its clinicopathological characteristics

“…The CBFA2T3-GLIS2 fusion gene was initially reported to occur in a significant number of pediatric AMKL patients. In COG data, 63% of RAM patients were found to have the CBFA2T3-GLIS2 chimeric fusion gene (Voigt et al, 2017).…”

“…The recent WHO classification of AML is mainly based on associated chromosomal aberrancies or genetic mutations which also defines the risk‐stratification of these groups (Arber et al, 2016; Merino, Boldu, & Ermens, 2018). Immunophenotyping is widely used for the diagnosis, sub‐classification, prognostication, and residual disease monitoring in hematological malignancies (Calvo, McCoy, & Stetler‐Stevenson, 2011; Craig & Foon, 2008; Johansson et al, 2014; Peters & Ansari, 2011; Voigt et al, 2017). In AML, the immunophenotypic features are commonly used for the lineage‐wise differentiation of leukemic blasts like monocytic, megakaryoblastic, and so on (Arber et al, 2016; Chen & Cherian, 2017; Craig & Foon, 2008; Merino et al, 2018).…”

Clinical Implication of Multi-Parameter Flow Cytometry in Myelodysplastic Syndromes

“…Regarding risk-stratification, the development of a prognostic scoring system combining clinical parameters and morphology with results from flow cytometry and NGS, similar as was recently published for AML, is of interest [51].…”

On Its Way to Primetime: Artificial Intelligence in Flow Cytometry Diagnostics

“…in 4). Furthermore, metadata extracted from raw FCM data may also be clustered, for example, in order to define diagnostic or prognostic subgroups (8).…”