However, once again, we can address this question in the light of evolution. If we know the parental relationships between different tumour clones, we can estimate the metastatic migrations that have occurred by comparing the anatomical location of parental clones with that of their descendants. I like the analogy with human migration here – if my parents are in Italy and I am currently in the UK, we can reasonably guess that I must have migrated from Italy to UK.
In this study, we are aiming to develop algorithms to reconstruct metastatic migration patterns by utilising the evolutionary histories of different tumour clones. The hope is to identify hallmarks of tumour clones that tend to disseminate to certain anatomical sites, providing the possibility to predict where certain clones could seed metastases before they actually occur.
When?
Metastases can be seeded by disseminating cancer cells years before they are clinically detectable, or they can be seeded suddenly and grow at a very high pace.
Disentangling these two possibilities is challenging because metastatic cancer cells can seed micro metastases composed of small niches of cancer cells in distant anatomical sites that can remain undetectable using patient clinical imaging.
However, once again, we can address this question in the light of evolution. Cancer cells acquire certain types of genomic mutations at a constant rate through time and during cell divisions. Therefore, counting the number of these mutations in metastatic cancer cells can be used to obtain a “molecular clock” which can be used to estimate the timing of metastatic seeding.
We are developing algorithms to count the number of these “clock” mutations in individual cancer cells and translate these numbers into estimates for the timing of different steps – the which and where – of the metastatic cascade. The hope is to identify metastatic hallmarks to predict whether metastasis has already started at some point during disease progression (for example, at the time of surgery), and thus inform the use of additional, systematic treatments to target potential metastatic cells that have already disseminated.
Incorporating many disciplines
Thanks to the support of CRUK, we are generating a unique dataset of 192,000 cancer cells from primary tumours and matched metastases obtained from patients with non-small cell lung cancer enrolled in the TRACERx study and in the PEACE autopsy programme.
Generating this dataset and answering these three important questions is only achievable by integrating the multi-disciplinary expertise in our group. Computer scientists, evolutionary biologists, cancer genomicists, clinical oncologists and more need to come together to make progress. Not only that, we must also incorporate this study into the multi-disciplinary and collaborative network of the TRACERx and PEACE studies.
For this study, it’s clear that formally integrating the expertise from different disciplines is of fundamental importance. As our project develops, I hope that not only can we shed light on the metastatic process, but also show that integration of a multidisciplinary approach will be important if we are to enhance our understanding of cancer and to identify opportunities for translating this into benefits for patients.
