Q&A: Stein Aerts on melanoma research

Summary

Professor Stein Aerts of the University of Leuven talks about his recent findings in melanoma research

Targeting aggressive cancer cells

In a melanoma tumour – a type of skin cancer – there are at least two groups of cancer cells, one of which is more invasive, causing the cancer to spread and resist therapy. Professor Stein Aerts (pictured) and his colleagues at the University of Leuven are studying how these invasive cells come into existence within a melanoma tumour and how they can be targeted for treatment.

Could you explain your research and what you have discovered so far?
We found that the cells in a melanoma tumour that are invasive and resistant to therapy have activated different genes to the other cells. We have applied methods based on next-generation sequencing, and with those methods you can assess which regions in the DNA – you could call them switches – are activated. We have found all of the switches, thousands of them, specifically activated in these invasive and drug-resistant cells.

Moreover, we’ve discovered a general code or fingerprint in the sequence that is common to all the genes that are being activated by these cells. With that, we were able to identify the proteins that turn on these genes. We killed those proteins and saw that the resistant and invasive behaviour decreases.

Could your findings be useful to researchers working on other kinds of cancer?
Yes, we think so. There are a couple of cancer types, including breast cancer, with different kinds of cells existing simultaneously within a tumour, which we could term as tumour heterogeneity. Most tumours are heterogeneous. Particularly breast cancer, and prostate cancer as well, have been shown to have similar invasive subpopulations to melanoma. Our publication addresses the issue of invasive stem-cell subpopulations, but it’s still only a piece of the puzzle.

What’s the next step for you?
Now we're looking into the genomics because two-thirds of all of our research and findings has been bio-informatics. We must have generated a terabyte of data. To query the entire genome for the activity, we had to sequence multiple times, different aspects of biochemical purifications of this genome, and of the chromatin, and of gene expression…

Over two years, we’ve had people constantly working with the mathematical models to interpret all this data. I think we will go a bit deeper now, because we’ve found some code and some fingerprints, but there’s more in there.