Development and Validation of a Novel RNA Sequencing–Based Prognostic Score for Acute Myeloid Leukemia. (1st March 2018)

• Record Type: Journal Article
• Authors: Wang, Mei; Lindberg, Johan; Klevebring, Daniel; Nilsson, Christer; Lehmann, Sören; Grönberg, Henrik; Rantalainen, Mattias
• Is Part Of: Journal of the National Cancer Institute. Volume 110:Number 10(2018)
• Page Start: 1094
• Page End: 1101
• Publication Date: 2018-03-01
• Subjects: Cancer -- Periodicals; Cancer -- Research -- Periodicals; 616.994
• DOI: 10.1093/jnci/djy021
• Languages: English
• ISSNs: 0027-8874

Abstract

Abstract: Background: Recent progress in sequencing technologies allows us to explore comprehensive genomic and transcriptomic information to improve the current European LeukemiaNet (ELN) system of acute myeloid leukemia (AML). Methods: We compared the prognostic value of traditional demographic and cytogenetic risk factors, genomic data in the form of somatic aberrations of 25 AML-relevant genes, and whole-transcriptome expression profiling (RNA sequencing) in 267 intensively treated AML patients (Clinseq-AML). Multivariable penalized Cox models (overall survival [OS]) were developed for each data modality (clinical, genomic, transcriptomic), together with an associated prognostic risk score. Results: Of the three data modalities, transcriptomic data provided the best prognostic value, with an integrated area under the curve (iAUC) of a time-dependent receiver operating characteristic (ROC) curve of 0.73. We developed a prognostic risk score (Clinseq-G) from transcriptomic data, which was validated in the independent The Cancer Genome Atlas AML cohort (RNA sequencing, n = 142, iAUC = 0.73, comparing the high-risk group with the low-risk group, hazard ratio [HR]OS = 2.42, 95% confidence interval [CI] = 1.51 to 3.88). Comparison between Clinseq-G and ELN score iAUC estimates indicated strong evidence in favor of the Clinseq-G model (Bayes factor = 26.78). The proposed model remained statistically significant in multivariable analysis including the ELN and other well-known risk factors (HRos = 2.34, 95% CI = 1.30 to 4.22). We further validated the Clinseq-G model in a second independent data set (n = 458, iAUC = 0.66, adjusted HROS = 2.02, 95% CI = 1.33 to 3.08; adjusted HREFS = 2.10, 95% CI = 1.42 to 3.12). Conclusions: Our results indicate that the Clinseq-G prediction model, based on transcriptomic data from RNA sequencing, outperforms traditional clinical parameters and previously reported models based on genomic biomarkers.