Phenotype in combination with genotype improves outcome prediction in acute myeloid leukemia: a report from Children’s Oncology Group protocol AAML0531
Andrew P. Voigt,
Lisa Eidenschink Brodersen,
Todd A. Alonzo,
Robert B. Gerbing,
Andrew J. Menssen,
Elisabeth R. Wilson,
Samir Kahwash,
Susana C. Raimondi,
Betsy A. Hirsch,
Alan S. Gamis,
Soheil Meshinchi,
Denise A. Wells,
Michael R. Loken
Affiliations
Andrew P. Voigt
Hematologics, Inc, Seattle, WA, USA
Lisa Eidenschink Brodersen
Hematologics, Inc, Seattle, WA, USA
Todd A. Alonzo
Children’s Oncology Group, Monrovia, CA, USA;University of Southern California, Los Angeles, CA, USA
Robert B. Gerbing
Children’s Oncology Group, Monrovia, CA, USA
Andrew J. Menssen
Hematologics, Inc, Seattle, WA, USA
Elisabeth R. Wilson
Hematologics, Inc, Seattle, WA, USA
Samir Kahwash
Nationwide Children’s Hospital, Columbus, OH, USA
Susana C. Raimondi
St. Jude’s Children’s Research Hospital, Memphis, TN, USA
Betsy A. Hirsch
University of Minnesota Medical Center, Minneapolis, MN, USA
Alan S. Gamis
Children’s Mercy Hospitals & Clinics, Kansas City, MO, USA
Soheil Meshinchi
Children’s Oncology Group, Monrovia, CA, USA;Fred Hutchinson Cancer Research Center, Seattle, WA, USA
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
