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Development and Validation of a Novel RNA Sequencing-Based Prognostic Score for Acute Myeloid Leukemia
Karolinska Inst, Dept Med Epidemiol & Biostat, POB 281, SE-17177 Stockholm, Sweden.
Karolinska Inst, Dept Med Epidemiol & Biostat, Sci Life Lab, Stockholm, Sweden.
Karolinska Inst, Dept Med Epidemiol & Biostat, POB 281, SE-17177 Stockholm, Sweden.
Karolinska Univ Hosp, Hematol Ctr, Stockholm, Sweden;Karolinska Inst, Dept Med, Huddinge, Sweden.ORCID iD: 0000-0003-0695-0050
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2018 (English)In: Journal of the National Cancer Institute, ISSN 0027-8874, E-ISSN 1460-2105, Vol. 110, no 10, p. 1094-1101Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
OXFORD UNIV PRESS INC , 2018. Vol. 110, no 10, p. 1094-1101
National Category
Cancer and Oncology
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URN: urn:nbn:se:uu:diva-375583DOI: 10.1093/jnci/djy021ISI: 000455206400007PubMedID: 29506270OAI: oai:DiVA.org:uu-375583DiVA, id: diva2:1284482
Funder
Swedish Research CouncilSwedish Cancer SocietySwedish e‐Science Research CenterStockholm County CouncilAvailable from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-01-31Bibliographically approved

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