Cancer Evolution – It’s in the Blood!

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.

Tracking cancer evolution – A major challenge

Researchers now have, at their disposal, powerful genetic sequencing technologies that can decipher entire cancer genomes within only a few days. Applying them to cancer specimens has unequivocally demonstrated the evolutionary nature of cancer. Our own work in kidney cancers, for example, showed that individual tumours harboured many different subclones that were evolving simultaneously but acquired different phenotypes2. These subclones did not intermix, but were spatially demarcated in these tumours3. This is a major problem for scientists who are trying to understand cancer evolution as it is almost impossible to biopsy the same clone on multiple occasions over time, for example to assess how it is changing in the presence of a new selection pressure. Furthermore, efforts to personalise medicine that aim to select the optimal therapy based on the genetic profile of a cancer would appear to be blunt tools, if they only detect one of many subclones in that cancer.

Wouldn’t it be fantastic if there were a technology that could reliably analyse most of the subclones within a cancer? That could also track cancer evolution in real time and without the need to subject patients to repeated biopsies that are suboptimal anyway? If what sounded like science fiction a few years ago had suddenly become reality!

Circulating tumour DNA (ctDNA) was found in the blood of cancer patients several decades ago. This suggested that many tumours shed DNA into the circulation, probably when cancer cells die through apoptosis. The recent development of ultra-sensitive genetic testing technologies such as digital PCR and next generation sequencing, that can detect a single mutated DNA fragment in thousands of normal DNA strands, has unlocked this ctDNA for study by cancer researchers.

What have we learned through circulating tumour DNA testing so far?

This is a rapidly developing field with new results being published almost daily and I can only outline a few of the most remarkable results.

The persistence of cancer specific mutations in the ctDNA after surgical removal of early stage breast cancers identified many of those patients whose cancers eventually relapsed4 and also showed that ctDNA analysis picked up residual cancer cells at much higher sensitivity when compared to radiological scans. Identifying these patients through a simple blood test will now lead to trials giving further drug therapy in order to eradicate the persisting cancer cells.

ctDNA analysis has also been used to track the evolution of drug resistance to anti-Epidermal Growth Factor Receptor therapy in patients with metastatic colorectal cancers. Up to 12 different drug resistant cancer cell subclones, each harbouring a different resistance driver mutation, were detected in individual patients at the time treatment failed5. This polyclonal drug resistance illustrates the enormous evolutionary potential of cancers. This is also a major clinical challenge as complex drug combinations may be required to regain control over the cancer. However, a study that continued to track drug resistant subclones after anti-Epidermal Growth Factor Receptor therapy cessation, found that some of these clones declined spontaneously6. This unexpected result shows that drug resistant clones may have a fitness disadvantage in the absence of the drug and this may be exploitable for therapeutic benefit.

Finally, detection of androgen receptor amplifications or point mutations in their ctDNA before patients with advanced metastatic prostate cancers started treatment with the androgen receptor-targeting drug abiraterone, predicted for early progression7. The ability to discover even tiny drug resistant subclones, which will rapidly outgrow once the drug selection pressure is applied, will help to stratify these patients for alternative therapies.

ctDNA-based tumour predictions and treatment personalisation

Together, these data show that ctDNA analysis is a powerful new tool for detecting subclones that have not yet expanded but will eventually determine treatment outcomes. We can now monitor evolving cancers and that is indeed exciting progress, but the technology is only in its infancy. It remains difficult to detect and track mutations scattered across multiple driver genes or indeed the entire cancer exome or genome, although proof-of-principle studies suggest that this should soon be possible8,9. This will give much clearer insights into clonal complexities of advanced cancers. Multi-timepoint analyses will further enable clonal dynamics analyses. Measuring the speed of clonal expansions and declines may reveal the fitness of individual driver mutations and lead to more accurate predictions about the future behaviour of cancers10. Unravelling the genetic makeup of subclones that respond to therapy and of those that fail to respond, should rapidly identify mechanisms of resistance to new cancer drugs. New ctDNA-based clinical trial designs may also start deploying a second strike that specifically targets genetic aberrations in cancer cells that persisted after first line therapy.

There is no doubt that ctDNA analysis already provides a window into cancer evolution processes and many more questions will be answered by this powerful technology. Eventually, this may even reveal the very molecular processes enabling cancers to evolve rapidly. This could, for the first time, permit us to target evolvability itself in order to reduce cancer mortality.

References

1. Gerlinger M et al. Cancer: evolution within a lifetime. Annual review of genetics 48:215-236, 2014
2. Gerlinger M et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England Journal of Medicine 366:883-892, 2012
3. Gerlinger M et al. Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nature 46:225-233, 2014
4. Garcia I et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Science translational medicine 7:302ra133, 2015
5. Bettegowda C et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Science translational medicine 6:224ra24, 2014
6. Siravegna G et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nature medicine 21:795–801, 2015
7. Romanel A et al. Plasma AR and abiraterone-resistant prostate cancer. Science translational medicine 7:312re10, 2015
8. Murtaza M et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497:108–12, 2013
9. Heitzer E et al. Circulating tumor cells and DNA as liquid biopsies. Genome medicine 5:73, 2013
10. Lipinski, KA et al. Cancer evolution and the limits of predictability in precision cancer medicine Trends in Cancer , Volume 2 , Issue 1 , 49 – 63, 2016

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