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

Author

Listed:
  • Moritz Gerstung

    (Wellcome Trust Sanger Institute)

  • Andrea Pellagatti

    (LLR Molecular Haematology Unit, NDCLS, RDM, University of Oxford)

  • Luca Malcovati

    (Fondazione IRCCS Policlinico San Matteo
    University of Pavia)

  • Aristoteles Giagounidis

    (Oncology, and Palliative Care, Marienhospital Düsseldorf)

  • Matteo G Della Porta

    (Fondazione IRCCS Policlinico San Matteo
    University of Pavia)

  • 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
    University of Pavia)

  • Elli Papaemmanuil

    (Wellcome Trust Sanger Institute)

  • Peter J. Campbell

    (Wellcome Trust Sanger Institute)

  • Jacqueline Boultwood

    (LLR Molecular Haematology Unit, NDCLS, RDM, University of Oxford)

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.

Suggested Citation

  • 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 Kil, 2015. "Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes," Nature Communications, Nature, vol. 6(1), pages 1-11, May.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms6901
    DOI: 10.1038/ncomms6901
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    Cited by:

    1. Nerea Berastegui & Marina Ainciburu & Juan P. Romero & Paula Garcia-Olloqui & Ana Alfonso-Pierola & Céline Philippe & Amaia Vilas-Zornoza & Patxi San Martin-Uriz & Raquel Ruiz-Hernández & Ander Abarra, 2022. "The transcription factor DDIT3 is a potential driver of dyserythropoiesis in myelodysplastic syndromes," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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