Last week I wrote a post all about how we understand cancer to arise. I explained how mutations in certain genes for certain proteins can drive cancer and I explained some of the important families of proteins such as oncogenes and tumour suppressors.
But even that complicated post was an over-simplification. Sure, a single mutation in a single gene for a single protein might cause cancer, but actually we know that many cancers have several mutations. Sometimes, several hundred mutations.
Several hundred mutations
The above is an image which shows just a few examples of some cancer types and the number of mutations you might expect to find in them. Recognise the word non-synonymous from last week? Non-synonymous mutations are those which cause a change in a protein. Whereas synonymous mutations are effectively “silent” (they don’t change a protein) so we tend to focus on non-synonymous ones when it comes to disease. You can read more about them here.
From the image you can see that some types of cancers can have several hundred mutations per tumour – colorectal cancer for example can have between 500 and 1200 mutations. Lung cancer can have between 100 and 200 (interestingly this is much lower for lung cancer in the “never-smoked” category which has a considerably smaller number of 25) and breast cancer between 25 and 100 mutations per tumour.
What this means is that when it comes to understanding what drives cancer we need to know how many of these mutations are actually relevant to the disease. Do they all contribute to “driving” cancer or are some of them “passenger” mutations? If only a few contribute, then how many?
Often, we treat cancer by designing drugs which target specific proteins. If we suspect a cancer is “driven” by an overactive protein then we can treat patients with an inhibitor of that protein – something that blocks the protein’s function. But for us to do this, we need to know which mutation, potentially hundreds, is actually relevant. It’s like trying to find a needle in a haystack.
The paper published in October 2017 in Cell is an analysis of 7,664 tumours across 29 different tumour types. They looked at all of the mutations each of those tumours had and focussed on the non-synonymous ones, the ones that actually change the proteins inside the cell. But they knew that even with protein changes, not all of the mutations present actually cause cancer. They wanted to establish how many might be responsible for the disease.
“Normal” cells acquire mutations all of the time but this happens at random so sometimes it will affect proteins and sometimes it won’t – we get a reliable mixture of non-synonymous and synonymous mutations that are all mostly harmless even if there is a change in a protein. This is useful information because we can establish an “expected” ratio between the two. In cancer cells, the mutations that change proteins and allow a cell to survive and grow more than it should (i.e. the mutations that drive cancer) are passed on to more cells as the cancer cells replicate. This means you get a skewed ratio in cancer cells – more non-synonymous mutations than synonymous ones. By figuring out how many more mutations there are than we expect from the “normal” cells we know that those are the ones allowing cells to replicate in an uncontrolled way – therefore those are the ones that are driving the cancer.
And that’s exactly what the authors of this study did.
What they found
What they found was that although some cancer cells have hundreds of mutations, only a few of those actually drive the cancer. The average number of cancer drivers per cancer type is just four and the numbers only range from one to ten. So, we can narrow our focus to between one and ten mutations per tumour. We just need to figure out which ones.
This is phenomenally useful information because we can then build further on this knowledge to understand which mutations are the common driver mutations and focus our drug development on those specific mutations. We have a significantly smaller haystack and we can start to really get very specific with our cancer therapies! The better our understanding of how cancer really works, the more complete our understanding of all of the complicated processes involved, the better able we are to design clever therapies that really benefit patients.