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

Moritz Gerstung, Andrea Pellagatti, Luca Malcovati, Aristoteles Giagounidis, Matteo G Della Porta, Martin Jädersten, Hamid Dolatshad, Amit Verma, Nicholas C. P. Cross, Paresh Vyas, Sally Killick, Eva Hellström-Lindberg, Mario Cazzola, Elli Papaemmanuil, Peter J. Campbell () and Jacqueline Boultwood ()
Additional contact information
Moritz Gerstung: Wellcome Trust Sanger Institute
Andrea Pellagatti: LLR Molecular Haematology Unit, NDCLS, RDM, University of Oxford
Luca Malcovati: Fondazione IRCCS Policlinico San Matteo
Aristoteles Giagounidis: Oncology, and Palliative Care, Marienhospital Düsseldorf
Matteo G Della Porta: Fondazione IRCCS Policlinico San Matteo
Martin Jädersten: Karolinska Institutet
Hamid Dolatshad: LLR Molecular Haematology Unit, NDCLS, RDM, University of Oxford
Amit Verma: Albert Einstein College of Medicine
Nicholas C. P. Cross: National Genetics Reference Laboratory, Salisbury NHS Foundation Trust
Paresh Vyas: MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford
Sally Killick: Royal Bournemouth Hospital
Eva Hellström-Lindberg: Karolinska Institutet
Mario Cazzola: Fondazione IRCCS Policlinico San Matteo
Elli Papaemmanuil: Wellcome Trust Sanger Institute
Peter J. Campbell: Wellcome Trust Sanger Institute
Jacqueline Boultwood: LLR Molecular Haematology Unit, NDCLS, RDM, University of Oxford

Nature Communications, 2015, vol. 6, issue 1, 1-11

Abstract: 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.

Date: 2015
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DOI: 10.1038/ncomms6901

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