AML risk stratification models utilizing ELN-2017 guidelines and additional prognostic factors: a SWOG report

• Published in: Biomarker research Vol. 8; no. 1; p. 29
• Main Authors: Pogosova-Agadjanyan, Era L, Moseley, Anna, Othus, Megan, Appelbaum, Frederick R, Chauncey, Thomas R, Chen, I-Ming L, Erba, Harry P, Godwin, John E, Jenkins, Isaac C, Fang, Min, Huynh, Mike, Kopecky, Kenneth J, List, Alan F, Naru, Jasmine, Radich, Jerald P, Stevens, Emily, Willborg, Brooke E, Willman, Cheryl L, Wood, Brent L, Zhang, Qing, Meshinchi, Soheil, Stirewalt, Derek L
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 12.08.2020; BioMed Central; BMC
• Subjects: Acute myeloid leukemia; AML; Biomarkers; Bone marrow; Cancer; Care and treatment; Chemotherapy; Cytogenetics; Elderly; Gene expression; Gene mutation; Genetic aspects; Genomes; Leukemia; Leukocytes (mononuclear); Mathematical modeling; Medical prognosis; Medical research; Mutation; Myeloid leukemia; p53 Protein; Patients; Prognosis; Prognostic factors; Risk factors; Risk groups; Runx1 protein; Transcription; Tumor proteins; AML; European LeukemiaNet guidelines; ELN; Model development and validation; Biomarkers; Mathematical modeling; Acute myeloid leukemia; Elderly; Prognostic factors
• ISSN: 2050-7771; 2050-7771
• DOI: 10.1186/s40364-020-00208-1

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. 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,  = 185) who received intensive chemotherapy. Models were validated in an independent set of similarly treated patients (validation cohort,  = 166). 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 , , and , demonstrated that these mutations provide a limited overall contribution to risk stratification across the entire population, given the low frequency of mutations and confounding risk factors. 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.