Era L. Pogosova-Agadjanyan, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA
Anna Moseley, SWOG Statistical Center, Fred Hutch, Seattle, WA USA
Megan Othus, SWOG Statistical Center, Fred Hutch, Seattle, WA USA
Frederick R. Appelbaum, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA Departments of Oncology and Hematology, University of Washington, Seattle, WA USA
Thomas R. Chauncey, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA Departments of Oncology and Hematology, University of Washington, Seattle, WA USA VA Puget Sound Health Care System, Seattle, WA USA
I-Ming L. Chen, Department of Pathology, University of New Mexico, UNM Comprehensive Cancer Center, Albuquerque, NM USA
Harry P. Erba, Duke Cancer Institute, Durham, NC USA
John E. Godwin, Providence Cancer Center, Earle A. Chiles Research Institute, Portland, OR USA
Isaac C. Jenkins, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USAClinical Biostatistics, Fred Hutch, Seattle, WA USA
Min Fang, Departments of Laboratory Medicine and Pathology, University of Washington, Seattle, WA USA
Mike Huynh, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA
Kenneth J. Kopecky, SWOG Statistical Center, Fred Hutch, Seattle, WA USA
Alan F. List, Malignant Hematology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL USA
Jasmine Naru, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA
Jerald P. Radich, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USADepartments of Oncology and Hematology, University of Washington, Seattle, WA USA
Emily Stevens, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA
Brooke E. Willborg, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USA
Cheryl L. Willman, Department of Pathology, University of New Mexico, UNM Comprehensive Cancer Center, Albuquerque, NM USA
Brent L. Wood, Departments of Laboratory Medicine and Pathology, University of Washington, Seattle, WA USA
Qing Zhang, Bioinformatics Shared Resource, Fred Hutch, Seattle, WA USA
Soheil Meshinchi, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USADepartment of Pediatrics, University of Washington, Seattle, WA USA
Derek L. Stirewalt, Clinical Research Division, Fred Hutch, 1100 Fairview Ave N, D5-112, Seattle, WA 98109 USADepartments of Oncology and Hematology, University of Washington, Seattle, WA USA

Document Type

Article

Publication Date

1-1-2020

Abstract

BACKGROUND: The recently updated European LeukemiaNet risk stratification guidelines combine cytogenetic abnormalities and genetic mutations to provide the means to triage patients with acute myeloid leukemia for optimal therapies. Despite the identification of many prognostic factors, relatively few have made their way into clinical practice.

METHODS: In order to assess and improve the performance of the European LeukemiaNet guidelines, we developed novel prognostic models using the biomarkers from the guidelines, age, performance status and select transcript biomarkers. The models were developed separately for mononuclear cells and viable leukemic blasts from previously untreated acute myeloid leukemia patients (discovery cohort,

RESULTS: Models using European LeukemiaNet guidelines were significantly associated with clinical outcomes and, therefore, utilized as a baseline for comparisons. Models incorporating age and expression of select transcripts with biomarkers from European LeukemiaNet guidelines demonstrated higher area under the curve and C-statistics but did not show a substantial improvement in performance in the validation cohort. Subset analyses demonstrated that models using only the European LeukemiaNet guidelines were a better fit for younger patients (age < 55) than for older patients. Models integrating age and European LeukemiaNet guidelines visually showed more separation between risk groups in older patients. Models excluding results for

CONCLUSIONS: While European LeukemiaNet guidelines remain a critical tool for triaging patients with acute myeloid leukemia, the findings illustrate the need for additional prognostic factors, including age, to improve risk stratification.

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