Validation of risk stratification models in acute myeloid leukemia using sequencing-based molecular profiling.
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 >0.5). 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.
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
M
M
Wang
Department of Medical Epidemiology and Biostatistics, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.
J
J
Lindberg
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
D
D
Klevebring
Hematology Centre, Karolinska University Hospital and Department of Medicine, Karolinska Institutet, Huddinge, Stockholm, Sweden.
C
C
Nilsson
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
A S
AS
Mer
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
M
M
Rantalainen
Hematology Centre, Karolinska University Hospital and Department of Medicine, Karolinska Institutet, Huddinge, Stockholm, Sweden.|Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
S
S
Lehmann
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
H
H
Grönberg
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001853:Bone Marrow / Q000737:chemistry; D015331:Cohort Studies; D004252:DNA Mutational Analysis; D004273:DNA, Neoplasm / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D007621:Karyotyping; D015470:Leukemia, Myeloid, Acute / Q000145:classification / Q000235:genetics*; D008297:Male; D046228:Microarray Analysis; D008875:Middle Aged; D008954:Models, Biological; D011379:Prognosis; D012333:RNA, Messenger / Q000235:genetics; D012334:RNA, Neoplasm / Q000235:genetics; D018570:Risk Assessment / Q000379:methods*; D017422:Sequence Analysis, DNA; D059467:Transcriptome; D055815:Young Adult
D004273:DNA, Neoplasm; D012333:RNA, Messenger; D012334:RNA, Neoplasm