Abstract

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    et al. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine. Nat. Med. 20, 682–688 (2014).

  2. 2.

    Genome sequencing and cancer. Curr. Opin. Genet. Dev. 22, 245–250 (2012).

  3. 3.

    & Lessons from the cancer genome. Cell 153, 17–37 (2013).

  4. 4.

    , & Genomics and the continuum of cancer care. N. Engl. J. Med. 364, 340–350 (2011).

  5. 5.

    et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 122, 3616–3627, quiz 3699 (2013).

  6. 6.

    Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumors. Nature 490, 61–70 (2012).

  7. 7.

    et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 1107–1120 (2012).

  8. 8.

    et al. Global implementation of genomic medicine: we are not alone. Sci. Transl. Med. 7, 290ps13 (2015).

  9. 9.

    & A new initiative on precision medicine. N. Engl. J. Med. 372, 793–795 (2015).

  10. 10.

    et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016).

  11. 11.

    Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  12. 12.

    , & Should persons with acute myeloid leukemia have a transplant in first remission? Leukemia 28, 1949–1952 (2014).

  13. 13.

    et al. Diagnosis and management of acute myeloid leukemia in adults: recommendations from an international expert panel, on behalf of the European LeukemiaNet. Blood 115, 453–474 (2010).

  14. 14.

    et al. Allogeneic stem cell transplantation for acute myeloid leukemia in first complete remission: systematic review and meta-analysis of prospective clinical trials. J. Am. Med. Assoc. 301, 2349–2361 (2009).

  15. 15.

    & How we treat chronic graft-versus-host disease. Blood 125, 606–615 (2015).

  16. 16.

    et al. Curability of patients with acute myeloid leukemia who did not undergo transplantation in first remission. J. Clin. Oncol. 31, 1293–1301 (2013).

  17. 17.

    et al. The value of allogeneic and autologous hematopoietic stem cell transplantation in prognostically favorable acute myeloid leukemia with double-mutant CEBPA. Blood 122, 1576–1582 (2013).

  18. 18.

    , , & Treatment of de novo acute myeloid leukemia in the United States: a report from the Patterns of Care program. Leuk. Lymphoma 55, 2549–2555 (2014).

  19. 19.

    et al. Economic impact of genomic diagnostics for intermediate-risk acute myeloid leukemia. Br. J. Haematol. 174, 526–535 (2016).

  20. 20.

    , & Economics of hematopoietic cell transplantation. Blood 120, 1545–1551 (2012).

  21. 21.

    et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 43, D805–D811 (2015).

  22. 22.

    Sample-size formula for the proportional-hazards regression model. Biometrics 39, 499–503 (1983).

  23. 23.

    , & Sample-size considerations for the evaluation of prognostic factors in survival analysis. Stat. Med. 19, 441–452 (2000).

  24. 24.

    & Precision medicine—personalized, problematic and promising. N. Engl. J. Med. 372, 2229–2234 (2015).

  25. 25.

    et al. The multi-kinase inhibitor midostaurin (M) prolongs survival compared with placebo (P) in combination with daunorubicin (D)-cytarabine (C) induction (ind), high-dose C consolidation (consol) and as maintenance (maint) therapy in newly diagnosed acute myeloid leukemia (AML) patients (pts) age 18–60 with FLT3 mutations (muts): an international prospective randomized (rand) P-controlled double-blind trial. Blood 126, 6 (2015).

  26. 26.

    , , & Random survival forests. Ann. Appl. Stat. 2, 841–860 (2008).

  27. 27.

    et al. DNMT3A mutations in acute myeloid leukemia. N. Engl. J. Med. 363, 2424–2433 (2010).

  28. 28.

    et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).

  29. 29.

    et al. Mutation in TET2 in myeloid cancers. N. Engl. J. Med. 360, 2289–2301 (2009).

  30. 30.

    et al.; German-Austrian Acute Myeloid Leukemia Study Group. All-trans retinoic acid as adjunct to intensive treatment in younger adult patients with acute myeloid leukemia: results of the randomized AMLSG 07-04 study. Ann. Hematol. 95, 1931–1942 (2016).

  31. 31.

    et al. Phase 3 study of all-trans retinoic acid in previously untreated patients 61 years or older with acute myeloid leukemia. Leukemia 18, 1798–1803 (2004).

  32. 32.

    et al. Prospective evaluation of allogeneic hematopoietic stem cell transplantation from matched related and matched unrelated donors in younger adults with high-risk acute myeloid leukemia: German–Austrian trial AMLHD98A. J. Clin. Oncol. 28, 4642–4648 (2010).

  33. 33.

    , & Penalized survival models and frailty. J. Comput. Graph. Stat. 12, 156–175 (2003).

  34. 34.

    & Variable selection with error control: another look at stability selection. J. R. Stat. Soc. Series B Stat. Methodol. 75, 55–80 (2013).

Download references

Acknowledgements

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.

Author information

Author notes

    • Moritz Gerstung
    •  & Elli Papaemmanuil

    These authors contributed equally to this work.

    • Hartmut Döhner
    •  & Peter J Campbell

    These authors jointly directed this work.

Affiliations

  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

Authors

  1. Search for Moritz Gerstung in:

  2. Search for Elli Papaemmanuil in:

  3. Search for Inigo Martincorena in:

  4. Search for Lars Bullinger in:

  5. Search for Verena I Gaidzik in:

  6. Search for Peter Paschka in:

  7. Search for Michael Heuser in:

  8. Search for Felicitas Thol in:

  9. Search for Niccolo Bolli in:

  10. Search for Peter Ganly in:

  11. Search for Arnold Ganser in:

  12. Search for Ultan McDermott in:

  13. Search for Konstanze Döhner in:

  14. Search for Richard F Schlenk in:

  15. Search for Hartmut Döhner in:

  16. Search for Peter J Campbell in:

Contributions

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.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–7 and Supplementary Note

Excel files

  1. 1.

    Supplementary Tables 1–6

    Supplementary Tables 1–6

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ng.3756

Further reading