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Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling
Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17177 Stockholm, Sweden.;Uppsala Univ, Dept Med Sci, Uppsala, Sweden..
Karolinska Inst, Sci Life Lab, Dept Med Epidemiol & Biostat, Stockholm, Sweden.;Uppsala Univ, Dept Med Sci, Uppsala, Sweden..
Karolinska Inst, Dept Med Epidemiol & Biostat, Nobels Vag 12A, S-17177 Stockholm, Sweden..
Karolinska Univ Hosp, Hematol Ctr, Stockholm, Sweden..
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2017 (English)In: Leukemia, ISSN 0887-6924, E-ISSN 1476-5551, Vol. 31, no 10, p. 2029-2036Article in journal (Refereed) Published
Abstract [en]

Risk stratification of acute myeloid leukemia (AML) patients needs improvement. Several AML risk classification models based on somatic mutations or gene-expression profiling have been proposed. However, systematic and independent validation of these models is required for future clinical implementation. We performed whole-transcriptome RNA-sequencing and panel-based deep DNA sequencing of 23 genes in 274 intensively treated AML patients (Clinseq-AML). We also utilized the The Cancer Genome Atlas (TCGA)-AML study (N = 142) as a second validation cohort. We evaluated six previously proposed molecular-based models for AML risk stratification and two revised risk classification systems combining molecular-and clinical data. Risk groups stratified by five out of six models showed different overall survival in cytogenetic normal-AML patients in the Clinseq-AML cohort (P-value < 0.05; concordance index > 40.5). Risk classification systems integrating mutational or gene-expression data were found to add prognostic value to the current European Leukemia Net (ELN) risk classification. The prognostic value varied between models and across cohorts, highlighting the importance of independent validation to establish evidence of efficacy and general applicability. All but one model replicated in the Clinseq-AML cohort, indicating the potential for molecular-based AML risk models. Risk classification based on a combination of molecular and clinical data holds promise for improved AML patient stratification in the future.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2017. Vol. 31, no 10, p. 2029-2036
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Cancer and Oncology
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URN: urn:nbn:se:uu:diva-337072DOI: 10.1038/leu.2017.48ISI: 000412179600003PubMedID: 28167833OAI: oai:DiVA.org:uu-337072DiVA, id: diva2:1179751
Funder
Swedish Cancer SocietySwedish Research CouncilStockholm County CouncilAvailable from: 2018-02-02 Created: 2018-02-02 Last updated: 2018-02-02Bibliographically approved

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Lehmann, Sören

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