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Cancer genetics refine treatment decisions
Cancer genetics refine treatment decisions
Hinxton, UK, 16 January 2017 – An international research consortium has shown proof of concept that personalised therapy will be possible in the future for people with cancer. Published in Nature Genetics, the study provides details of how a knowledge bank could be used to find the best treatment option for people with acute myeloid leukaemia (AML).
AML is an aggressive blood cancer that develops in bone marrow cells. There are 11 types of AML, each of which has distinct genetic features. According to lead author Moritz Gerstung, now a group leader at EMBL-EBI, today’s study reports how a patient’s individual genetic details can be incorporated into predicting the outcome and treatment choice for that patient.
The AML Knowledge Bank
The AML knowledge bank comprises data from 1540 patients with AML who participated in clinical trials in Germany and Austria. It combines information on the majority of relevant genetic features, treatment schedule and outcome for each person. From this, the team developed a tool that shows how the experience captured in the knowledge bank could be used to provide personalised information about the best treatment options for a new patient.
About acute myeloid leukaemia
Source: Wellcome Trust Sanger Institute
- Acute myeloid leukaemia (AML) is a type of blood cancer that develops when the cells in the bone marrow that produce myeloid cells become cancerous. As blood cells are made in the bone marrow, the cancer will be present in the bone marrow and in the blood.
- Every day we make around 10 billion new blood cells, and the information which controls how these blood cells reproduce is held within our DNA. Every time a cell divides, the entire DNA code has to be copied exactly, and mistakes are made by chance. AML develops when there are a series of errors in the DNA. It usually takes mistakes in several key genes that control blood production to cause AML.
- There were around 2,900 new cases of acute myeloid leukaemia (AML) in the UK in 2013, that’s around 8 cases diagnosed every day (CRUK figures).
- Worldwide, 351,965 people were estimated to have AML in 2012 (GLOBOCAN figures).
More information on AML is available on the Bloodwise website.
The need for risk calculations
There are two major treatment options for young patients with AML – a stem cell transplant or chemotherapy. Stem cell transplants cure more patients overall but up to one in four people die from complications of the transplant and a further one in four experience long-term side effects. Weighing up the benefits of better cure rates with transplant against the risks of worse early mortality is a harrowing decision for patients and their clinicians.
The team showed that these benefits and risks could be accurately calculated for an individual patient, enabling therapeutic choices to become personalised. They estimate that up to one in three patients would be prescribed a different treatment regimen using the tool compared with current practice. In the long term they hope the tool could spare one in ten young AML patients from a transplant while maintaining overall survival rates.
“The underlying knowledge bank is the largest and most comprehensive of its kind,” explains EMBL-EBI Research Group Leader Moritz Gerstung, who led the study. “This allowed us to develop very detailed, accurate statistical models for predicting patient outcomes and linking these predictions with treatment decisions.”
“For any given patient, using the new tool we can compare the likely future outcomes under a transplant route versus a standard chemotherapy route – this means that we can make a treatment choice that is personally tailored to the unique features of that particular patient,” says Dr Peter Campbell of the Wellcome Trust Sanger Institute, senior author on the study.
Towards personalised medicine
The authors believe this work is a step towards validation of genetic techniques as a route to personalised medicine.
“This study provides an important step closer to personalised medicine of AML,” continues Gerstung. “We’ve demonstrated that it’s possible to decode many of the causes underlying differences in outcome, and use this information to make decisions about treatment.”
To extract the required information, the researchers developed complex statistical algorithms and implemented them in an online portal to test predictions for newly diagnosed patients.
The tool they developed is available for scientists to use in research, but it needs further testing before it can be used to prescribe treatments in AML clinics.
“We have done everything to ensure the accuracy of our predictions, but there are clearly additional steps to be taken before our tool can be applied in the clinic,” says Gerstung. “Our study provides methods for implementing such decision-support tools. We released the complete source code so that people can test our analyses and try the online calculator.”
“It has long been recognised that cancer is a complex genetic disease,” says Gerstung. “Our study provides an example of how detailed genetic and clinical information can be rationally incorporated into clinical decisions for individual patients. We tested this philosophy in one type of leukaemia, but the concept could theoretically be applied in other cancers with difficult clinical decisions as well. Our analysis reveals that knowledge banks of up to 10,000 patients would be needed to obtain the precision needed for routine clinical application.”
“Building knowledge banks is not easy,” adds Dr Hartmut Dohner of University of Ulm, a collaborator in the study. “To get accurate treatment predictions you need data from thousands of patients and all tumour types. Furthermore, such knowledge banks will need continuous updating as new therapies become approved and available. As genetic testing enters routine clinical practice, there is an opportunity to learn from patients undergoing care in our health systems. Our paper gives the first real evidence that the approach is worthwhile, how it could be used and what the scale needs to be.”
- Gerstung M, Papaemmanuil E, et al. (2017). Precision oncology in acute myeloid leukemia using a knowledge bank approach. Nature Genetics (in press). Published online 16 January; DOI:
- Papaemmanuil E and Gerstung M (2016) Genomic classification and prognosis in acute myeloid leukemia. New England Journal of Medicine 374:2209-2221; DOI: 10.1056/NEJMoa1516192
This work was supported by the Wellcome Trust, the Bloodwise charity, the Leukemia-Lymphoma Society, Bundesministerium fur Bildung und Forschung, Deutsche Krebshilfe and Deutsche Forschungsgemeinschaft and the European Hematology Association.