Underpinning the vision of precision medicine is the concept that causative mutations in a patient's cancer drive its biology and, by extension, its clinical features and treatment response. However, considerable between-patient heterogeneity in driver mutations complicates evidence-based personalization of cancer care. Here, by reanalyzing data from 1,540 patients with acute myeloid leukemia (AML), we explore how large knowledge banks of matched genomic–clinical data can support clinical decision-making. Inclusive, multistage statistical models accurately predicted likelihoods of remission, relapse and mortality, which were validated using data from independent patients in The Cancer Genome Atlas. Comparison of long-term survival probabilities under different treatments enables therapeutic decision support, which is available in exploratory form online. Personally tailored management decisions could reduce the number of hematopoietic cell transplants in patients with AML by 20–25% while maintaining overall survival rates. Power calculations show that databases require information from thousands of patients for accurate decision support. Knowledge banks facilitate personally tailored therapeutic decisions but require sustainable updating, inclusive cohorts and large sample sizes.

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We thank C. Holmes for stimulating discussions. We gratefully acknowledge D. Weber for clinical data managing, V. Teleanu for assistance in cytogenetics data classification and S. Kayser for assistance in morphological evaluation. This work was supported by grants from the Wellcome Trust (077012/Z/05/Z; P.J.C.), the Bloodwise charity (P.J.C.), the Leukemia and Lymphoma Society (P.J.C.) and the Deutsche Krebshilfe (109675; K.D.), in part by grants from the German Bundesministerium für Bildung und Forschung (BMBF) (01GI9981 (H.D.) and 01KG0605 (R.F.S. and H.D.)), by a Wellcome Trust Senior Clinical Research Fellowship (WT088340MA; P.J.C.), by an EHA early career fellowship (E.P.), and by the Deutsche Forschungsgemeinschaft (project B3, Sonderforschungsbereich (SFB) 1074; K.D. and L.B.); H.D. is coordinating investigator. L.B. is a Heisenberg Professor of the DFG (BU 1339/3-1). We are grateful to all members of the German–Austrian AML Study Group (AMLSG) for their participation in this study and for providing patient samples; a list of participating institutions and investigators appears in the Appendix of Papaemmanuil et al.5. AMLSG treatment trials were in part supported by Amgen and DKH grant 109675.

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Author notes

    • Moritz Gerstung
    •  & Elli Papaemmanuil

    These authors contributed equally to this work.

    • Hartmut Döhner
    •  & Peter J Campbell

    These authors jointly directed this work.


  1. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK.

    • Moritz Gerstung
    • , Elli Papaemmanuil
    • , Inigo Martincorena
    • , Niccolo Bolli
    • , Ultan McDermott
    •  & Peter J Campbell
  2. European Bioinformatics Institute EMBL-EBI, Hinxton, UK.

    • Moritz Gerstung
  3. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.

    • Elli Papaemmanuil
  4. Department of Internal Medicine III, Ulm University, Ulm, Germany.

    • Lars Bullinger
    • , Verena I Gaidzik
    • , Peter Paschka
    • , Konstanze Döhner
    • , Richard F Schlenk
    •  & Hartmut Döhner
  5. Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany.

    • Michael Heuser
    • , Felicitas Thol
    •  & Arnold Ganser
  6. Division of Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, University of Milan, Milan, Italy.

    • Niccolo Bolli
  7. Department of Pathology (UOC), University of Otago, Christchurch, New Zealand.

    • Peter Ganly
  8. Department of Haematology, University of Cambridge, Cambridge, UK.

    • Peter J Campbell


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M.G. developed the statistical methods, analyzed data and wrote the manuscript and supporting information, with input from E.P. and P.J.C. E.P. prepared and curated the genetic and clinical data. I.M. analyzed TCGA data. R.F.S., H.D., K.D., L.B., V.I.G., P.P., M.H., F.T. and A.G., along with all of the institutions contributing to the study group (AMLSG), recruited patients in this study, and collated and contributed clinical data. N.B., P.G. and U.M. provided input into analyses and interpretation of results. E.P., K.D., H.D., R.F.S. and P.J.C. initiated the study. P.J.C. and H.D. wrote the manuscript and are joint corresponding authors.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Hartmut Döhner or Peter J Campbell.

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