Karkinos. Cancer. Krebs. Why do we use a word that translates to crab in many languages to describe one of the world’s most widespread diseases? A disease that, to this day, we still do not have a one-ring-to-rule-them-all cure for. The approaches for treatment are varied and diverse. Without delving too deep into its history, this diversity is also exhibited in the efficacies of these treatments, also faintly reflecting on how contested the origin of the word “cancer” is. And although the name might be somewhat unremarkable, the disease is anything but.
The story of cancer is long. Likely existing for as long as humans have, only with time have we come to understand this ferocious beast more. The deeper understanding arose from being able to analyse the diseased tissue, these so-called “tumours”, both from when people were alive (as seen early on in the mid-17th century by Scottish surgeon John Hunter) and during autopsies of ill patients, a process that has become much more commonplace since the mid-1700s.
Since then, from the use of microscopic analysis using slices of tumours, to our ability to check out the contents of the diseased cells via sequencing of their DNA, this information has resulted in an explosion of available data and treatments. A recent but undeniably important cornerstone here, was the genesis and improvement of the many computational methods for analysing the vast amounts of data these advancements produced. A lot of this knowledge has, most recently, accumulated in a category of treatments called immunotherapy.
In short, your body has an army of cells ready at your disposal to fight off any intruders. The issue is, what if instead of external intruders, you have a spy in your midst? Most of these spies are obvious, they wear bright neon-coloured clothing, or only a single shoe, which allows your immune cells to recognise them with ease, and rid you of them in a moment’s notice. But there are some which are able to blend in juuuust enough to not be detected, and live long enough to grow and spread. Immunotherapy, as the name might imply, uses this existing army of immune cells within you, by teaching them what these spies (read: tumour cells) look like.
Now the question is, how do we do that? How can we tell our immune cells to find these specific cells or groups of cells? Well, as I alluded to previously, these tumour cells are able to blend in juuuuust enough, but are still different. Similar to how soldiers line up and get assessed based on their form and clothing, cells need to show they are doing the correct things by displaying molecules called “antigens” on their surface to be checked by the immune cells. The antigens that are displayed are small parts of the proteins that the cell is producing. How does this help? Following on with the spy analogy, although they might have the same coloured shoe, they could be a slightly different shade (read: mutation). A jacket that’s slightly too big, or an arm that’s twice the size. To translate these analogies into a bit of scientific jargon: because of mutations in the protein, the display of the antigens, which are parts of the protein, would also be different. What immunotherapy boils down to is finding these new (or modified) antigens, called neo-antigens or neo-epitopes and instructing your immune system to fight them.
Although it sounds great, this isn’t as easy as it might sound. You need to find out and make sure which mutations are normal, and are part of your healthy cells as well, and which ones are unique to the tumour that you want to get rid of. This requires data from different parts of the tumour cells (DNA and protein-level data for mutations, RNA for expression of the genes) to ensure we find and fight the correct neo-epitopes.
This was all a very broad - and rough - introduction to what I am investigating in my research: a look into, and development of, computational methods to both find and prioritise neo-antigen candidates for use in immunotherapy.
But just as we are able to get rid of crabs, so can we be rid of cancer. And hopefully unlike how contested the origin of the word cancer might have been, we will be able to firmly decide its future.
Daniel Giesel
The writer is a Doctoral Researcher with a deep interest in all (well, almost all) things bioinformatics. Specifically, though, he is currently focusing on computational methods for analysis of neo-epitopes.
