Is cancer in your genes?
Over a century ago, the German biologist Theodor Boveri put forward the idea that something inside cells was responsible for causing cancer. Today, we know that the disease is fundamentally driven by faults in the genes that normally act to keep our cells and bodies healthy, only dividing when they should, and dying if they’re damaged. Kat Arnet went to visit The Institute of Cancer Research in Surrey to meet some of the UK’s leading experts on cancer genetics and find out where we’ve come from in terms of understanding the faulty genes that drive the disease. First, she spoke with cancer genetics expert Professor Nazneen Rahman, to find out how studying genetics is useful in cancer.
Nazneen - There are different ways in which we use genetics in cancer. The first fundamental difference is whether we’re actually testing the cancer itself or whether we’re testing the whole person.
If you test the cancer itself, there are all sorts of genetic changes that have made that cell turn into a cancer cell, and by doing that kind of genetic testing we can work out what type of cancer it is and what kind of treatment it might have and that’s one side in which genetics is incredibly useful.
We can also look at the DNA of the whole person to see whether there was some potential reason why they developed cancer in the first place. And also whether or not there might be information there relevant to other members of the family due to inherited predisposition to cancer. So that’s the other way in which genetic information is incredibly helpful in cancer.
Kat - This genetic connection raises the question of whether cancer runs in families. But, as I found out from Professor Richard Houlston, who has spent his career tracking down genetic variations that increase cancer risk, just having several cancer cases in a family doesn’t automatically mean that there’s an inherited gene fault at work.
Richard - As many people know, cancer is actually a relatively common disease so, effectively, if everyone lives to a ripe old age, then you’re going to get lots of cancers in the family. Because, essentially, in most Western countries it’s that and coronary heart disease and then stroke are the major causes of death.
So the next point is some cancers are highly environmental or lifestyle induced. Examples are: if you had a large number of people who are elderly who developed lung cancer and they’re all smokers, you get a conglomeration of cancers in the family simply because of that.
What really alerts you to the fact that there might be an inherited predisposition in families is if cancer is diagnosed at a young age, and secondly when you have more than one cancer diagnosed in a young age of the same type of cancer. So an example is early onset colorectal cancer in a family under the age of 45, so that’s quite unusual. You’ve got, essentially, defined consolations of specific cancers or site-specific cancers that are generally diagnosed at an early age.
Kat - We hear a lot about cancer in adults. Cancer in children is mercifully relatively rare. But is it the same kind of gene faults that are involved in childhood cancers compared to cancers in older people?
Richard - Yes, it is. They’re critically dependent on the early phase of development. So an example is in retinoblastoma, it’s the RB gene that’s important in the early phase of the development of the eye and then it doesn’t have a role. In fact, we know that people with retinoblastoma gene defects that are inherited actually have an increased risk of osteosarcoma in later life. So, effectively, the gene defect is having an effect in different contexts at different ages of the person’s life.
Kat - So, there are relatively rare ‘strong’ hereditary gene faults that significantly increase the risk of one or a few types of cancer in a family. Then there’s a much wider spectrum of genetic variations across the whole population, that have a less severe impact on cancer risk.
On top of this, our DNA is under assault every day - from things in the world around us, such as tobacco smoke, and even the fundamental processes of life at work within our cells. And it’s this damage, combined with our underlying genetic makeup, that can result in cancer developing.
Thanks to advances in DNA-reading technology, known as sequencing, scientists can now delve into the detailed genetic makeup of an individual patient’s cancer, and even use that information to shape treatment.
One example already in the clinic is the breast cancer drug Herceptin, designed to target an overactive protein molecule resulting from a genetic mistake in some types of breast cancer. But it doesn’t work for every patient - because it turns out that not all breast cancers are the same.
To find out how the genetic revolution is changing our understanding of cancer, I spoke to Dr Anguraj Sadanandam, who’s combining lab research with computer modelling to target tumours more effectively.
Anguraj - Conventionally, people were thinking that breast cancer, or colon, or pancreatic cancers were one disease. And that has been tried, and therapies have been given so far and it was not successful because different people respond differently to these drugs. But genomics is improving so much that we can untangle that and try to identify the sub-disease within each cancer type and try to match them to the therapies.
Kat - So, basically, all breast cancers are not the same even though they might all start in the same part of the body?
Anguraj - Yes. So though they start in the same part of the body, the question is are they starting from the same type of the cell - that is one interesting question. If they are not starting from the same type of cell then they will have a different prognosis and therapy responses.
Kat - So by finding the key gene faults that are driving these different groups of cancer cells, how then do you target those with treatments?
Anguraj - These key genes are later translated into proteins, and the proteins are the function units of the cells. These proteins have a particular structure and that structure defines how that cell would move, how that cell would change their shape, and all those things. So we can try to by, again, computational modelling and make certain chemical molecules which would go and bind at that particular structure. So that way the cells cannot get the signal to move or change shape so you know that you can control the cancer cells.
Kat - So these are very, very targeted therapies, targeting the faulty products of these genes in the cancer cells - wipe em out!
Anguraj - Yeah, so that’s the basic idea so that these cells don’t behave that way.
Kat - What do you see as the really big questions that still need to be answered when it comes to using genetics to understand tumours and how to treat them? What are still the big mysteries?
Anguraj - One of the major complications now - we are thinking that one patient do differ from one other patient but the much more complex thing is within a patient there are lot of subtypes and subgroups existing. Because, as I mentioned, if they are starting from the same cell within the tumour they can become into multiple groups. If you take one patient’s tumour and cut them into say 20 or 30 pieces, there are possibilities if they’re not going to twenty different ways but at least four ways, or five ways, or ten ways. That is a more complicated thing and we still are in a really early stage to understand how this change is happening. So if we go and start treating them thinking it’s one tumour, it’s not going to work.
Kat - I guess it’s like looking at large families and saying well, you’ve all got a lot of things in common but, when we really drill down to it, each member of that family is unique and individual.
Anguraj - Between two individuals within a family there may be a significant change to make them behave differently and also do things differently. Similarly the cancer cells can have very tiny changes, but those tiny changes can make them interact differently. At the same time, there are not only cancer cells within a tumour but there are also other cells, immune cells, all those interact and make them even much more complicated, and they behave differently and that’s a major challenge to untangle right now.
Kat - What do you want to find out next - what are you looking at now?
Anguraj - The major challenge right now is to define the key set of genes which are called biomarkers, because these biomarkers would define these subgroups. That’s the challenge and I’ll be thinking bowel cancer, for example, we have defined those genes.
The second thing is which type of technology that would take that to the clinic. The question now how to handle a lot of data given a patient coming into the clinic in a single day or two is very, very challenging. New methods and technologies are coming which will have to deal with thousands and thousands of genes, tens and hundreds of genes, so that we can give the answer to the patient within a day or two.
Kat - Advances in gene sequencing have the potential to target cancer in a more precise and personalised way than ever before, but this new wealth of data brings its own problems, as Nazneen Rahman explains.
Nazneen - So we’re at an extremely exciting time in genetics at the moment. We’re able to generate a vast amount of information about the genetics for an individual but our problem really is about curating that information, understanding its meaning, and there are lots of different ways in which we’re not able to use that information in the best way possible.
So one of the things that we’re currently doing, together with a lot of different people across the UK and indeed the world, is an initiative called the Transforming Genetic Medicine Initiative (the TGMI). Really what that’s trying to do is harness all of this information and use it to bring knowledge and, hopefully, wisdom so that we can make sure that we’re using the information appropriately to bring the maximum benefits. But also to make sure the information is not used inappropriately as well. So I think that’s the key challenge for us in genetic medicine currently.
Kat - How do you go about doing that; how are you bringing all this information together?
Naazneen - At many levels it’s really quite prosaic and a lot of it is logistically trying to pull together lots of different information and then curating it. There are 20,000 genes. At the moment it’s not entirely clear how many of those genes can be associated with human diseases, and how many are not. And, historically, there’s a lot of unclear information that was unclear before because we were having more and more knowledge, but you have back now and then look at the previous things and think oh, now we’ve got this new knowledge, how do we have to reinterpret previous information?
So, at the very highest level, we’d really like to go through all of the genes and say OK, these genes can be associated with human disease and these ones are not and be able to separate those out. For the ones that are associated with human disease: how are they, what diseases, what types of mutations? And then once we’ve sorted that out how does that then translate into how those are going to be tested and what are the best ways of doing that.
So at the heart they are really quite simple questions which, until now, we haven’t had enough data to be able to answer in a comprehensive and careful way.
Kat - It feels like there’s a lot of philosophical changes that need to happen. Instead of saying “you have breast cancer” to saying “you have this particular genetic subtype of breast cancer.” And then there needs to be a philosophical change in the way we develop drugs, the way we test drugs, the way we licence drugs. How can we make that happen?
Nazneen - Well, I think recognising the problem is a key step; debating it. I think the scale of technological change that allowed us to go from taking ten years to do one genome to being able to do ten genomes in one day, is such a massive change that it makes the whole system require a sort of ground up, just let’s think about it again and how we’re going to make it work in this modern world.
So I think the key thing is really recognising the issues and debating them. There are philosophical debates to be had, not least about the sort of predictive information as well. Do we want them, as individuals, in society? Do we want to have this window into the future. When do we want it; how do we want it; how are we going to act upon it? These are not things that we’ve had to think about very much so I think that’s the first thing that has to happen.
But then we have to then overlay that with some really quite pragmatic solutions. And I think we might have to do it in an almost experimental way: try things, get the information iteratively, then improve it. I don’t think we can solve it in isolation and create the perfect system ahead of time, not least because it is still changing very fast. So I would like to see some ways in which we can develop the system and improve it as we’re working through it otherwise we’ll always be chasing our tail, and we may never get there.