In the first of our guest blog posts, Dr. Marco Gerlinger highlights some of the remarkable developments being made in ctDNA analysis, a powerful new technology with the potential to transform tumour predictions and treatment outcomes.
Photo credit: Csutkaa via Foter.com / CC BY-NC-SA
Cancer cells are masters in adapting to changing environments. This allows them to colonise other organs, to form metastases and also to acquire drug resistance. Darwinian evolution is thought to be a key driver of this adaptability. Randomly acquired mutations encode for novel phenotypes and some of these phenotypes may allow individual cells to survive changes in the environment1.
This adaptability is a key reason for the high rates of mortality from metastatic cancers. Treating a cancer that cannot evolve would probably be an easy task – maybe as straightforward as eradicating a bacterial infection with antibiotics. Thus, there is great need to understand how and why cancers readily evolve and to use this information to design more effective treatment approaches for ever-changing cancers.
Photo credit: Living in Monrovia / Foter.com / CC BY-SA
One of the striking achievements of cancer genomics and its allied bioinformatics has been to construct phylogenetic trees depicting the trajectories of sub-clones in cancers and their ancestral relationships. It’s like taking a peek back in time at the origin and prior evolutionary history of the malignancy.
But what about the converse? Is it possible to infer, from features of cancer cells, what their future potential or ability to evolve into more malignant, metastatic or drug-resistant phenotypes may be? There’s no doubt this would be extremely useful, particularly in the context of early diagnosis and intervention.
Genome sequencing has revealed that a plethora of gene mutations can co-exist in individual cancers: thousands in some cases 1,2. Based on Darwinian theory, we assume that whilst most are irrelevant, buried in the background is a modest number of mutations (perhaps counted in single figures) that are functionally active in a way that contributes to cancer clonal development. The ‘offspring’ of the cells with these mutations will be more successful than the cells that surround them. Continue reading
For some time before we had the benefit of cancer genomics, it was generally believed that for a cancer to disseminate and become potentially lethal, it would have had to accrue several mutations that, collectively, would provide a kind of ‘full house’ for malignancy.
It was further assumed that, in the absence of rampant genetic instability, the critical set of mutations would arise one at a time and that it would, therefore, take time to assemble a ‘full house’ set. The linear relationship of cancer incidence to age (in log-log plots) was taken to indicate that four to six rate-limiting mutational events might be involved 1,2. However this inference rested on some questionable biological assumptions 3. Continue reading