By David Wedge
Cancers are composed of a mixture of different cells, which continue to evolve over time allowing them to evade therapy and to spread to other parts of the body. These mixtures of cells and tumour evolution are described in previous posts. However, identifying these different cells within a tumour is difficult and we have recently launched a public Challenge project to improve the methods used to analyse tumour cells.
The Somatic Mutation Calling – Tumour Evolution and Heterogeneity (SMC-HET) DREAM Challenge is a crowdsourcing competition that will help us optimise the discovery of genetically distinct groups of cells within cancers. These different cells can respond differently to treatment and have different risks of spreading. Better analysis methods for tumour DNA sequences could therefore help improve cancer diagnosis and treatment.
It has been known for at least 40 years that cancers are made up of many cells, each of which carries different mutations (Nowell, 1976). Cells acquire new mutations over time which are passed on to daughter cells as the tumour grows, and different regions of a tumour often contain distinct groups of cells known as ‘subclones’. These subclones share a common set of mutations, and can be pictured as an evolutionary tree, with cells within some branches of the tree having mutations that give them a competitive advantage over other cells. They may give the cells resistance to treatment or allow them to grow and divide faster. Therefore, tumours change over time, with some subclones proliferating while others decline.
A typical cancer carries around 10,000 mutations, from small errors in their DNA, such as one of the 3 billion bases being switched to another base, to much larger changes, such as the loss of a whole chromosome. Working out which of these mutations have occurred in each subclone is a difficult task that requires the use of complex computational programs.
The SMC-HET DREAM Challenge was recently launched with the aim of improving the computational algorithms used by research groups around the world. This Challenge is part of a series of challenges aimed at finding solutions to complex biological questions supported by Sage Bionetworks and known as DREAM challenges. DREAM is an acronym standing for Dialogue for Reverse Engineering Assessments and Methods. It has evolved into the “dream” of open science, combining collaboration and sharing data.
This Challenge tackles three key questions about cancer: how many subclones are within any given tumour, how did these subclones grow and evolve, and which genetic mutations are present in each subclone?
The organisers of the Challenge have created data for a set of 50 simulated tumours, using a method to simulate DNA sequencing data that closely mimics data from real human tumours. Each of these simulated tumours has a distinctive life-history and evolutionary tree, and the simulated sequencing data has been released to the competition entrants.
Participants in the competition have to create computer code that will rebuild each evolutionary tree from the simulated DNA sequencing data. They need to write efficient code that produces accurate results in a sensible time frame, and this submitted code will then be judged using 3 criteria:
- Firstly, it needs to characterise the tumours, finding the number of subclones within each tumour, the number of mutations associated with each subclone and what proportion of the tumour has those mutations.
- Secondly, it must assign each mutation to the correct subclone.
- Thirdly, the code must reconstruct the evolutionary relationship between the subclones, rebuilding the evolutionary tree for each simulated tumour.
In addition to receiving scientific recognition, winners of each sub-challenge will be invited to co-author a Challenge overview paper with Nature Biotechnology. They will also receive a travel award to the next DREAM conference.
The challenge will run until May 2016, but will have a long-term effect on cancer research:
- Participants will upload their computer code to the challenge and this code will be publically available after the challenge, to be used by the wider research community.
- The new simulated data and code can be used to test future new methods, and ensure that future research findings on heterogeneity can be verified.
- Different cells react differently to drugs. Even when a cancer is in remission, a small number of cells with slightly different genetics can survive and be resistant to treatment, leading to a recurrence later. Improved methods will enable scientists to answer important questions about heterogeneity in cancer and how it affects response to treatment.
David Wedge is a Senior Staff Scientist in the Cancer Genome Project at the Wellcome Trust Sanger Institute, working on heterogeneity and evolution within prostate and other cancers.
- L.R. Yates and P.J. Campbell (2012). Evolution of the cancer genome. Nature Reviews Genetics. DOI: 10.1038/nrg3317
- G. Gundem et al. (2015). The evolutionary history of lethal metastatic prostate cancer. Nature. DOI:10.1038/nature14347