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Nat Commun. 2015 Jan 9;6:5901. doi: 10.1038/ncomms6901.

Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes.

Author information

1
Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK.
2
LLR Molecular Haematology Unit, NDCLS, RDM, University of Oxford, Oxford OX3 9DU, UK.
3
1] Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy [2] Departments of Molecular Medicine and Internal Medicine and Medical Therapy, University of Pavia, 27100 Pavia, Italy.
4
Department of Hematology, Oncology, and Palliative Care, Marienhospital Düsseldorf, 40479 Düsseldorf, Germany.
5
1] Department of Hematology Oncology, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy [2] Department of Internal Medicine and Medical Therapy, University of Pavia, 27100 Pavia, Italy.
6
Division of Hematology, Department of Medicine, Karolinska Institutet, SE-171 76 Stockholm, Sweden.
7
Albert Einstein College of Medicine, Bronx, New York 10461, USA.
8
National Genetics Reference Laboratory, Salisbury NHS Foundation Trust, Salisbury SP2 8BJ, UK.
9
MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford OX3 9DS, UK.
10
Department of Haematology, Royal Bournemouth Hospital, Bournemouth BH7 7DW, UK.

Abstract

Cancer is a genetic disease, but two patients rarely have identical genotypes. Similarly, patients differ in their clinicopathological parameters, but how genotypic and phenotypic heterogeneity are interconnected is not well understood. Here we build statistical models to disentangle the effect of 12 recurrently mutated genes and 4 cytogenetic alterations on gene expression, diagnostic clinical variables and outcome in 124 patients with myelodysplastic syndromes. Overall, one or more genetic lesions correlate with expression levels of ~20% of all genes, explaining 20-65% of observed expression variability. Differential expression patterns vary between mutations and reflect the underlying biology, such as aberrant polycomb repression for ASXL1 and EZH2 mutations or perturbed gene dosage for copy-number changes. In predicting survival, genomic, transcriptomic and diagnostic clinical variables all have utility, with the largest contribution from the transcriptome. Similar observations are made on the TCGA acute myeloid leukaemia cohort, confirming the general trends reported here.

PMID:
25574665
PMCID:
PMC4338540
DOI:
10.1038/ncomms6901
[Indexed for MEDLINE]
Free PMC Article

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