This week we're talking about gene sequencing and how to keep that information safe.
In this episode
00:57 - The Origins of the Moon
The Origins of the Moon
with Alex Halliday, University of Oxford
The moon seems a timeless feature of our night sky, but there was a time before it existed. So how was the moon first formed? New research published this week might change our understanding of our planets largest satellite. Alex Halliday from the Head of 'Mathematical, Physical and Life Sciences Division' at Oxford University spoke to spoke to Ginny Smith about the findings.
Alex - The giant impact theory basically proposes that the Earth was hit when the Earth was about 90% formed. Planets like the Earth grow over a period of time millions of years by accreting other planet by gravity so they get bigger and bigger. And when the Earth was about 90% of its current size, it was hit with another planet about 10% of the size of the Earth which is about the same as planet Mars. And this Mars-size planet hit the Earth with a glancing blow which gave the Earth and the debris all around the Earth from the impact the spin. It also would've heated the planet up. And finally, it would've actually formed a disc around the Earth and that disc of debris and gas would've condensed and coalesced to form the moon that we have today. So the giant impact theory came about in the 1980s as the 'least worst' explanation for the origin of the moon. One thing is, the moon is larger relative to the size of its host planet than any other moon in the solar system, the other thing is that the moon moves around the Earth as the Earth spins- what we actually see in terms of the movement appears to be coming from the moon itself and that's something that's a bit hard to explain. The moon is gradually moving away from the Earth over time. But there is also one other striking thing that was not clear until the Apollo astronauts went to the moon and brought back samples. And that is, that the moon appears to have a slightly lower density to the Earth, suggesting it's not quite the same stuff as the Earth.
Ginny - So effectively, a smaller planet knocked the moon off the Earth. It knocked a chunk of the Earth off and that became the moon. Is that the idea?
Alex - So, that's where the problem lies. This impact would've been so energetic- think about the collision of a 10-kilometre sized object, wiping out the dinosaurs. But this was something actually another planet colliding with the Earth, so it wouldn't have just knocked a bit of the Earth off. It would've actually vaporized parts of the Earth and caused massive damage. If you model this with supercomputing which we can now do, you can track the particles that make up the Earth and the particles that make up the other planet as they collide. What you show in these simulations is that most of the stuff that ends up forming the moon, comes from this other planet that we sometimes call Theia. When we look at those samples that the Apollo astronauts brought back, we find that this doesn't fit at all. If you look at the atoms in those samples, they have a diagnostic fingerprint that suggests they came out of the Earth, not out of this other planet.
Ginny - So, the theory doesn't quite match up with what we actually see when we test it. So, do we need a new theory?
Alex - Over the last decade, there've been a lot of new computer simulations, lots of new measurements of the composition of samples brought back from the moon, providing new constraints. And so, one of the key things was that people had started questioning whether this spin of the moon around the Earth and the spin of the Earth itself, the orbit of the moon around the Earth rather, whether this was actually a result of the giant impact or whether the Earth was already spinning before the giant impact took place.
Ginny - What do we need to do in order to find out how the moon was formed? What research would you love to see happen?
Alex - Well, the main problem we've got at the moment is that this issue about where the atoms come from. If you look at what we call isotopic fingerprints of the atoms in the moon, they look just like the Earth. And so, it looks like a bit of the Earth has formed the moon. If you do the simulations, it suggests that the moon should come out of this other planet which should have a different composition from the Earth. So, there's one possible thing that either those simulations are wrong or alternatively, there's a possibility that maybe this planet Theia actually came from inboard of the Earth rather than outboard of the Earth. It's possible that the compositions of the atoms in that region actually may look more like the Earth in that region. And if we could get samples of Venus or Mercury, we could test that theory.
05:58 - Egyptian Mummies' secrets unraveled
Egyptian Mummies' secrets unraveled
with Stephen Buckley, University of York
New research has shown that the ancient Egyptians were mummifying their dead 1,500 years earlier than we previously believed, meaning the history books may need updating. The Egyptians were also traveling a lot further afield than we had imagined. Georgia Mills spoke to Stephen Buckley, from the University of York, to find out what this research has unraveled.
Stephen - Before this study, we thought that Egyptian mummification started around 2600 BC, but this study actually pushes back the origins of Egyptian mummification to around 4300 BC. So, back by about 1500 years. The big thing we found out is that they were actually using complex recipes, embalming agents on prehistoric mummies, a great dealt earlier than we originally thought.
Georgia - How did you find this out?
Stephen - We found this out using chemistry essentially. The study actually involved looking at funerary textiles that had been treated with embalming agents. Getting a chemical fingerprint of what these materials were, and rather than it just being resin, which is one substance, we actually found it was a mixture of ingredients, and quite complex - and they'd also heated these materials together, so they've gone to quite some effort before they applied them to their dead. So you actually see that the embalming agents in the prehistoric burials are remarkably similar to the embalming agents that we used 3000 years later when Egyptian mummification was at its very best.
Georgia - Why were they making this embalming fluid? What did they want to achieve with it?
Stephen - I think in these very early burials, where these ingredients may have started is that they had some symbolic significance connected with the properties of the animals and plants they came from. They noticed that because these recipes contained antibacterial components, the pine resin and an aromatic plant extract are strongly antibacterial, they probably noticed that while they were in direct contact with the body, you actually had preservation of the soft tissue which of course is what a mummy is. And then from that made the connection, if the body survives, perhaps the individual can survive, cheat death if you like. And so, mummification became an integral part of their belief system. They believe later that the body had to be preserved in order for the spirit to get into the afterlife and if there was no body, they were dead forever. So, mummification was extremely important. What's interesting which suggests that recognition started actually in the prehistoric period as early as the late Stone Age.
Georgia - They were mummifying their dead a lot longer ago than we thought. What else does this tell us about Egyptian culture?
Stephen - I think it tells us that the Egyptians had a degree of relative sophistication quite early on. So, their lives were relatively complex even 4000 plus B.C. This is a nice part of the research because it has been true within Egyptology for quite some time, the view that at this time, the Egyptians were barely capable of getting out of the front door. And that they were very localised and exploiting local resources, perhaps making it to the Red Sea, but not going too far. Well, this research shows clearly that they were going up to the north-east and Mediterranean and doing this over a period of centuries. And so, the world is quite big.
Georgia- How do you know that their world was so big?
Stephen - We know their world is big, simply because one or two of the ingredients, the pine resin for example is a temperate pine and the nearest source for that is what is now southeast in Turkey. So, it has to come from at least that far. Why they were able to get around? Certainly, parts of it was the use of waterways, using that rivers that ran in the valleys across the eastern desert. In the late Neolithic period, the environment was warmer and wetter. So, the rainfall meant that you have seasonal rivers and that made it much easier to get around. But the environment did change. So, in the late Neolithic, it was warmer and wetter. But then there was this fairly dramatic change in climate around 3800, 3700 B.C. where it went to cool-dry, seasonal rivers dried to a significant extent. So, it actually became more difficult to get around. You actually see a move away from marine resources in the actual embalming agents to more terrestrial based materials.
10:44 - 'Talking' Plants
with Jim Westwood, Virginia Tech
They may not seem particularly chatty, but plants are constantly communicating with each other and with their surroundings using chemical signals. Researchers from Virginia Tech have found out that parasitic plants have their own special way of communicating with their hosts. Their paper, published in the journal Science, showed that proteins very similar to DNA, can actually move between the two plants, possibly allowing them to send and receive messages. Ginny Smith spoke to Jim Westwood about his research, and started by asking him what we mean when we talk about a parasitic plant.
Jim - So basically, there's one plant that is attacking another plant and physically attaching to it in order to withdraw water and nutrients from this, what we call the host plant. And it gives nothing back. It's just taking its resources, everything it needs to live from that host.
Ginny - What is it you found out that's new?
Jim - The host and the parasite are exchanging messenger RNAs and what is unique about this I guess is that they're exchanging a lot of them and that it's going both ways, that some of the parasite RNA is also going back into the host.
Ginny - What is a messenger RNA?
Jim - I imagine this as like a factory where the executive office, the DNA, writes a memo, that is the messenger RNA, sends to the production floor and where it's turned into a product and directs when something is produced and what exactly is produced.
Ginny - So, is it quite unusual to find these being sent actually out of the plant that produces them into something else?
Jim - Well, we know that plants use these within their system. So, they will be sent around from one cell to another cell in the plant to transmit information say, from a leaf, down to a root or something like that. And so, it can be used as a method of internal communication, but the idea that they are being taken or escaping to another organism is quite new.
Ginny - So, how do they get from one plant to the other?
Jim - The parasite has a structure called a haustorium and that invades the host tissue and forms connections to the vascular system, the tubes where there's water and sugars. Basically, what you get is a cell to cell connection where one cell of the parasite is right up next to the cell of the host and they are exchanging information through the walls basically.
Ginny - Do you think this is just accidental leakage as the parasite is trying to get access to nutrients or do you think the plants have actually evolved to use this for some process?
Jim - Well, that is a great question. We don't know that they are actually used as information, but RNA is an information molecule. It is very possible that it is using them.
Ginny - What kind of things could they be using them for?
Jim - Going back to the factory model, if the parasite is intercepting these internal memos from the host plant in reading them, it could get information on the status of the host and know whether the host cells are under stress or going to keep producing more food. Even more interesting is the idea that the parasite is sending these messages into the host. In that case, you could imagine that the parasite is actually telling a host through these memos basically to well, make more food or stand down security and treat me like a friend.
Ginny - So, if there is this kind of backwards and forwards signalling, is there any way we could sort of hijack this system to get rid of these parasitic plants where they're affecting our crops for example?
Jim - If you could engineer a host plant that is making a specific - we call a silencing RNA molecule that can move into the parasite and shutdown some potentially critical process in the parasite. This could be a very elegant way to get at control of parasitic plants.
Ginny - How big a problem are parasitic plants for agriculture?
Jim - Well, it depends where you're talking. In the developing world, the parasitic plants are a huge problem. In places of sub-Saharan Africa, there is a parasite called witch weed which is devastating to cereal crops and on some of the world's poorest land and affecting the world's poorest farmers.
Ginny - This idea of information being passed from one organism to another, we know that in bacteria, they can actually do this horizontal gene transfer and actually, take part of each other's DNA and start using them. Do you think anything like that could be going on here?
Jim - It is possible. The horizontal gene transfer occurs very rarely between plants, but it seems that it is more likely where there is a physical association, as in with parasitic plants. We don't really have evidence right now that DNA is moving, but there is at least one example that it's been reported by another group where it looks like a horizontal gene transfer event. It could be traced back to RNA intermediate where the RNA would've been converted to DNA in the parasite and inserted into the genome.
Ginny - Could you just talk me through how that mechanism would work?
Jim - There is a process called reverse transcription where you take an RNA molecule and it's translated back into a DNA. And then if that would be inserted into the genome and incorporated, then it could persist. And especially, if it would have a useful function, then it could be selected for and retained.
16:42 - Obesity blamed for 12,000 cancers a year
Obesity blamed for 12,000 cancers a year
with Krishnan Bhaskaran, London School of Hygiene and Tropical Medicine
When you think about things that increase the risk of cancer, you'd probably list things like smoking, UV rays from the sun, or air pollution. But in fact, like the inflation of the nation's waistlines, there's a growing number of cancers that are linked to obesity and being overweight. A new study published in The Lancet this week, from scientists at the London School of Hygiene and Tropical Medicine, has looked at more than 5 million people in the UK, and concluded that around 12,000 cancers every year can be put down to excess weight. Kat Arney spoke to lead researcher Krishnan Bhaskaran to find out more.
Kat - When you think about things that increase the risk of cancer, you probably list things like smoking, UV rays from the sun or air pollution. But in fact, like the inflation of the nation's waistlines, there's a growing number of cancers that are linked to obesity and being overweight. A new study published in The Lancet this week from scientists at London School of Hygiene and Tropical Medicine has looked at more than 5 million people in the UK and concluded that around 12,000 cancers every year can be put down to excess weight. I spoke to lead researcher Krishnan Bhaskaran to find out more.
Krishnan - So, this was a very large study. We were able to include data from over 5 million people. The way that we did that was to access anonymised GP records from across the country. What we did was we put together the height and weight measurements that have just been made in regular GP consultations as part of routine care. We also looked forward in those anonymised records to see later on whether there are any diagnoses of cancer in the same people.
Kat - What sort of timescale are we talking from those measurements being made to then a diagnosis of cancer, 5 years, 10 years, 20 years?
Krishnan - Well, it varied, but the data source that we had access to, the earliest kind of records from that were in the late '80s, early '90s. So for some people, we had about 20 years. So, on average, I think we had I think 6 to 7 years of follow up for the average person in the study.
Kat - So, it sounds like you're taking this information about height and weight and you can convert that to BMI, and then go, "Well, did they get cancer?" How do you actually go about proving whether there is a link and is it for all types of cancer, some types of cancer?
Krishnan - Well so far, that was the simple version. Of course, we have to take into account other the things that might be common causes of having high BMI and later on, getting cancer. So, this is where we do our statistical modelling and tried to use other information we have from the record to discount the effects of other common causes like for example, socio-economic status, smoking habits, drinking. These things could all affect both cancer risk and BMI. So, they could lead to spurious association. So, we tried to collate all the information, on those factors and include them in our modelling so that we really had what we thought was - the real effect of body mass index.
Kat - So, what do you find from the data?
Krishnan - We confirmed that as we thought there were important relationships between body mass index and cancer risk. And what was quite striking, because this was a broad study and I think the first time, a single dataset where we've looked across so many cancers - we looked at 22 of the commonest cancers. It was striking - the variation in effect actually. So for the major of cancers, there was an association and for 10 cancers, there was a very clear positive correlation. So, the higher people's body mass index was the greater their cancer risk seem to be - and for a few cancers though, it's very little effect. For some, there was even apparently lower rates of cancer among those with the higher body mass index. But I was the minority and those are the slightly more surprising results, but again, they are consistent with what has been shown before.
Kat - Which cancers are we talking about here that are most strongly linked to obesity?
Krishnan - In terms of what are the impacts on the population, one has to also think about how common these cancers are. So, it's a slightly different answer if you're saying, which of these cancers are worst affected as a population level. But specific cancer type, they already had the most striking positive relationship with body mass index with cancer of the uterus in women. That's womb cancer, which is actually the fourth most common cancer in women. For cancer of the uterus, we found that increasing the body mass index by 5 units which is for an average height woman would be adding about two stones of weight actually increased the risk of uterus cancer by over 60% so really quite a large effect. Breast cancer has a smaller relative effect or association with body mass index. But I think the actual number of cases of breast cancer that we've estimated that would be attributable to excess weight is larger for womb cancer because it's the function of these two things at how largely the effect is and how common the cancer is.
Kat - How many cases of cancer are we talking about?
Krishnan - That's something we try to estimate in this study. So, by using both the National Cancer Statistics and also the effects that we estimated for body mass index, we estimated for the ten cancers that were really clearly positively associated with body mass index, over 12,000 could be attributable to having excess weight.
Kat - Is that every year in the UK?
Krishnan - That's every year, yeah. So, that's if you like, if we could somehow magically remove all excess weight from the population, we would expect from these results to prevent about 12,000 cancer cases. So, it's really quite a large population impact.
Kat - So, with this study, you kind of feel that this is the strong evidence that we could go out there and say we really know this now.
Krishnan - Yes, I think so. I think this really sort of seals out. So, we already knew there were these important effects, but now, we've already put some more meat on it and worked out exactly what the impacts are. And it really just adds ever more to a case of some ambitious policies to deal with obesity in the population which we know are affecting cardiovascular risk, we know they're affecting diabetes risk, and now, we really know the extent to which they're affecting cancer risk as well.
Kat - So, adding to the weight of evidence.
Krishnan - Exactly.
Kat - Apologies for the bad joke there!
22:20 - How Genes Are Sequenced
How Genes Are Sequenced
with Mike Quail, Sanger Institute
Do you always buy the same clothes as your best friend, or your grandmother? Probably not. But despite your differences,when you, your friend or your grandmother get sick, you're likely be diagnosed and treated the same way. Because we have limited knowledge of how different people respond to sickness as well as to different treatments, we have to make do with a "one size fits all" approach to medicine.
But now that's starting to change.
Just this month, David Cameron said he's backing a research project to sequence 100,000 genomes of NHS patients in England by 2018. Some experts believe this will become a national testbed for sequencing the genomes of the entire UK population.
This means that within a couple of generations, health care could be tailored to a person's unique genetic makeup
Graihagh Jackson met with Dr Mike Quail at the Wellcome Trust's Sanger Institute, where the £200 million project will be hosted, to find out how we sequence genes...
Mike - Sanger was set up around 18 years ago to do pilot work for the human genome project. Since then we've diversified and we have a number of faculty groups essentially who are looking at the function of the genome and what it does. One of the ways we do that is by DNA sequencing. We sequence the genomes from people who have diseases and people that don't have diseases and try and find those bits of the genome that do confer disease. One of the first steps in the process of genome sequencing is getting DNA. And so, we're about to do a very simple experiment to get DNA out of a human being.
Graihagh - Let's get started. So, what do you want me to do first?
Mike - First of all, because this is a lab, we can't put anything in the mouth, so we're going to go outside and we're going to swirl a salt solution around the mouth. You're going to do that because I don't like the taste of salty solution. Just a swirl or two, if it is clear, you can spit into that.
Graihagh - I'll take that. So, salty solution, in it goes. That's horrible. So now, that contains bits of my DNA.
Mike - This contains a suspension of cells from your mouth, both human cells from the inside of your cheek, but also lots of bacterial cells, so you could get quite contaminated results from mouth swirls. So, we have our mouth swillings. We're going to add some common-or-garden washing up liquid. Roughly 3 parts of washing up liquid to one part of mouth swill and we give it a good mix.
Graihagh - So, what you've got now is a very frothy sample.
Mike - And the detergent in the washing up liquid should release the DNA and now, in order to see the DNA, we're going to precipitate it using some ethanol.
Graihagh - What is it that the ethanol does?
Mike - This will mean that the DNA will come of solution and it will form a white milkiness, so white strands within the tube and that is our DNA.
Graihagh - It's my DNA.
Mike - It's your DNA, congratulations!
Graihagh - And now that we've got the DNA, what's the next step?
Mike - So, the first thing we do is we break that DNA down to small bits and we sequence it as the small bits and then put together that jigsaw on the computer after the sequencing.
Graihagh - Can we go and look at the sequencing instruments?
Mike - Everyone wants to do that. So, we'll go in. Knock Knock. Here we are. Here, we have an array of sequencing machines and these can sequence human DNA samples in a small number of days for around $1,000 US per genome.
Graihagh - And these machines actually look a bit like very sophisticated fridges with lights streaming across them. They look very futuristic, very Star Trek if I may say.
Mike - Yeah, a bit like Knight Rider as well.
Graihagh - How much is processed here?
Mike - So, we sequence around 10,000 samples per month. That's a mixture of bacterial samples as well as human and other genomes such as mouse and zebra fish. The DNA itself is double-stranded and by that, it's a bit like a ladder. So, a ladder will have two uprights and some rungs. In DNA, those uprights we call strands are the backbone and the rungs are a number of special bases that are either A, C, G, or, T. And it's the sequence of those bases that gives the DNA its function and is able to code for whether you got blue eyes, or brown eyes, or whether you got a certain disease or not.
Graihagh - What range of information could they glean from this DNA sequence?
Mike - There are lots of promise around using DNA sequence for personalised medicine for diagnosis of disease, for early risk prediction through to pharmacogenetics where for example, you might be able to work out whether they've got proteins which are susceptible to certain drugs or not. And if we could predict which people those drugs will work in, we will have a much bigger bank of pharmaceutical agents to work from.
Graihagh - Do you think it's going to become the norm in the future and if so, when?
Mike - Very slowly, the human reference genome is being put to use. We can use the presence of the sequence to devise tests to test for the presence of certain diseases, certain genes which cause disease and these can serve as good predictors that can inform clinical decisions. More and more in medicine, DNA sequencing is likely to be used over the coming years, particularly after large scale projects like the 100,000 genomes project. Potentially at some stage, everyone could have their genome sequenced.
28:27 - Personalised Diagnostics
with Simon Goldman, Abcodia
Sequencing your DNA may be one way to provide better diagnostic tools but there are other methods and one such is by monitoring the blood of individuals. Dr Simon Goldman from Abcodia joins us in the studio to tell us about a project he's been working on called ROCA that enables early and reliable detection of ovarian cancer using an algorithm.
Simon - It starts from a group at University College London who were looking at the main marker in the blood for ovarian cancer. It's been used for decades called CA125. This biomarker which indicates the presence of ovarian cancer has been used for a long time and isn't actually particularly very good at doing the job.
Kat - So, this is the molecule produced by the cancer cells and just kind of floats out into the blood around the body.
Simon - Correct. The problem with it is, it's actually produced in a lot of other conditions, not just in cancer. So, it's produced in say, endometriosis or in pregnancy as well.
Kat - Okay, you don't want to confuse ovarian cancer with pregnancy.
Simon - You certainly don't. So what happened was in the later 1990s, a group at UCL had a look at this particular protein and asked the question, "What happens if rather than just looking at this level, we'll look at it at the proteins level over time, because it may be that different people have different baseline levels and maybe that it's the change in level from their baseline that's more important than the absolute level in and of itself?"
Kat - So, who were you looking at to measure these level of change over time?
Simon - So, the trial that was started in the early 2000's had about 200,000 volunteers donated blood to participate in this trial to show that you could look at this over time. It's very important because on the basis of the test as stands just looking at the level, only around about 1 in 27 surgeries for ovarian cancer that was based on this test and on a subsequent ultrasound actually found a cancer. It's very traumatic for the people who get told that they've got ovarian cancer but also, it's a big burden on the health system. So, this trial started in the early 2000's and 200,000 women gave blood and about 50,000 of those gave blood every year for 10 years, and followed through that whole period in order to basically ask the question, what happens if we look at the level of this protein over time? Do changes in the level of that protein actually tell us something about ovarian cancer?
Kat - So, rather than saying, okay, you've got a level of 1, you've got a level of 4, it's about the rate of change over time. so, someone with an accelerated change in it, they may be more at risk than someone who kind of bumbles up and down in their level.
Simon - Correct and similarly, somebody else who might have a baseline level that is say, 4 - let's assume that that's above our cut-off - might actually be someone who doesn't have ovarian cancer. The level of the protein may just be going, sideways over that whole period. Really, what the algorithm that's come out of this whole trial is looking for is, as you say, those inflection points and trying to pick people up. Some of the people might have had baselines that were very, very low and they had a sudden increase, but they never reached the cut-off, so these people weren't even picked up.
Kat - And this is quite important when you think about cancer tests because it suggests particularly for a blood test, you can't just go right - you get your test when you're 50 and that's it, that you have to monitor people over, maybe their entire lifetime. Do you think that would be an idea?
Simon - Correct and I think we're already starting to do that with things like cholesterol testing. But then even something like cholesterol has never been looked at over a long period of time to say, do changes in the level of cholesterol then predict cardiovascular disease for example. More than half of the people who have a heart attack had perfectly normal cholesterol. So you wonder, does that really tell you something or should you be looking at it over time rather than just at a level?
Kat - I guess when you start looking in the general population, look at everyone, you'll find people who have cholesterol levels, like "crikey! That's quite high!" but actually, they'll never have a heart attack.
Simon - Correct and those people may have particular genetic predispositions for example to not have a heart attack. And so, the level of the cholesterol doesn't really give you very much information.
Kat - So, in terms of the algorithm you're using, where are we at the moment with maybe being able to apply this certainly in ovarian cancer?
Simon - The algorithm itself is being commercialised, probably will be available from about next year. At the moment, trial is still ongoing. They've stopped collecting the blood, kinda sad, sitting there, waiting for the last few cases of ovarian cancer to come through. Probably, sometime next year. But the principle can be similarly applied to any other disease. You've got a biobank sitting there with 200,000 women who got all of the diseases that you would have expected 200,000 women to get over that kind of period of time. So for example, you can look at proteins that are related to say, pancreatic cancer and you can see that these proteins are changing 2 to 3 to 4 years out from when it's currently being diagnosed and especially in a disease where from diagnosis, median survival is about 4 months, it's a terrible outcome. If we can pick up these changes much earlier, maybe we can intervene.
Kat - Pancreatic cancer is really critical because it's diagnosed so late and survival is so poor. One of the other cancers we hear about is prostate cancer. There's a blood test that's called PSA test that measures this protein called PSA in the blood, and that again has problems with its reliability. Guys might have a high level and not have cancer. Guys might have cancer and not have a high level. Is this again something similar? Obviously, the study that you've done has women rather than men in it...
Simon - True. We haven't yet found a prostate cancer case in our cohort.
Kat - That's lucky!
Simon - It's interesting if we do, but no, the principle is the same. And the key thing for here is an algorithm that not only compares an individual to their baseline, but once you've got a large cohort like that compares your profile to everybody else. In the case of PSA, I'm not sure that that's being done.
Kat - Yeah and I guess all this kind of work does highlight the fact that we are all unique. You can't just have a one-size-fits-all and even if we could put people into maybe buckets and say, "You're a high risk, you're a low risk, you need more monitoring, you need less monitoring" that's got to be able to save lives in the future.
Simon - And that's basically what the process is. You get a blood test over time, your screened effectively over time. If the level of say, CA125 rises, then a doctor is going to say, "Well look, your level is different from baseline. The algorithm suggests that you're at risk. Maybe you should go and have an ultrasound" and the intervention is accelerated and you can pick up cancer a lot earlier than you would otherwise pick it up.
Kat - Certainly sounds like this is something you're very excited about.
Simon - Indeed.
Kat - I reckon so. In terms of the - you say you've got 200 samples just sitting in a freezer there, are there plans to look at other types of cancer? You mentioned pancreatic there.
Simon - Absolutely. I mean, there's - in total - there's about 5 and a half million samples sitting in a fridge there. And the thing is, as I said before, you can look at a lot of other diseases - cardiovascular disease. Not all biomarkers are going to profile well, but in a lot of cases, no one has ever actually done the study because no one has had these kinds of cohorts over that kind of period of time to be able to do the studies.
35:23 - SWAN UK
with Helen Piper, mother
Diagnosing illnesses isn't always easy especially when it comes to rare diseases. Helen Piper is the mother of 4 year old Alex, who has an undiagnosed development disorder. She told us how difficult it has been, living without a diagnosis, and how SWAN (Syndromes Without A Name) has helped their family...
Helen - We first noticed something wasn't quite right quite early on- probably about 8 to 10 weeks. He wasn't focusing on people. He didn't make eye contact. He wasn't smiling. He was very floppy on his neck. Our first doctor was no help at all. She literally took one look at him and went, "Well, he's probably got Asperger's but that's okay. Here's a leaflet." and off I went out the door and I cried so much because you just think, well, this is not very supportive and I'm not at all sure that's right. So, I took him back again. I saw a different GP who again, almost took one look at him and went, "No, that's neurological." And so, off we ran on a series of various trips too and we started off with a paediatrician. We've seen neurologists, we've had genetics involved, all to try and work out what's going on, and nobody has been able to give us an answer. It was extremely isolating because there are no pathways that are, I suppose, obvious for you when you don't have a diagnosis. If you have a label for your child, there are at least obvious pathways to travel in terms of support groups, in terms of input from social services. We were quite lucky in that, on one of our visits to Great Ormond Street, I cried over them and said, "There must be a support group somewhere." They said, "Have you heard of SWAN?" SWAN stands for a Syndromes Without A Name, and it's an amazing charity that supports all the parents of children who have no diagnosis. We call our children SWANs because it's quite a nice graceful name for these beautiful children of ours.
37:00 - Facial Recognition for Diagnoses
Facial Recognition for Diagnoses
with Christoffer Nellaker, University of Oxford
Diagnosing illnesses isn't always easy, especially when it comes to rarediseases. There may only be a handful of cases across the world meaning even the most experienced of doctors are unlikely to have seen them before. We hear from Helen Piper is the mother of 4 year old Alex, who has an undiagnosed development disorder and how the University of Oxford's Christoffer Nellaker has harnessed the power of facial recognition technology to help diagnose conditions like Alex.
Christopher - So, a rare disease is, as the name implies, rare, but there are a lot of different types of rare diseases. So collectively, they're surprisingly common perhaps. One in 17 people might have a rare disease.
Ginny - That's way more than I would've expected. How were you using facial recognition software to help diagnose these things?
Christopher - During the development of the face and head, very large portion of our genes in our genome are used in the development of the head and face. So, if there is a DNA change in one of these genes involved in this is very likely that it causes a change in the final form of the head or face. So, this is what clinicians had been using for the past 65 years to try and identify and help diagnose these rare diseases is to look if there are commonalities in characteristics between patients and thereby trying to group them into a potential diagnosis.
Ginny - You've trained a computer to do the doctor's job for them so to speak.
Christopher - This research was only possible because we had a great collaboration with Professor Andrew Zisserman at the Department of Engineering Science who's world leading at computer vision. And so together, we set out to try and see if we could use the latest computer vision and facial recognition type approaches to try and aid the job of clinicians in analysing faces and trying to cluster people by commonalities in the head and face.
Ginny - How good is the computer at picking up these differences?
Christopher - So, we used 8 rather well-known syndromes to train the computer to try and analyse these rare diseases. But then we applied this to a further 82 syndromes and the computer could then generalise and try and cluster patients even for these other syndromes that we didn't explicitly use in training.
Ginny - How do you see this being used? Would it be the case of having a computer in every doctor's surgery that could do this?
Christopher - This doesn't require any fancy equipment. The algorithm uses any old photograph. If there's a 2D photograph in like a family album, in theory, if you could show that to a computer then this algorithm could be used to analyse them. Taking a photograph of patients with suspected rare diseases is standard clinical practice anyway. So, this would be seen as an expert tool for clinicians to try and help narrow down to potential diagnosis and sort of suggest which tests might be suitable to be done.
Ginny - So, you mentioned there that it's a narrowing down procedure. It doesn't mean that you could just tell exactly from a photograph what was wrong with the person. You'd still need to do other tests.
Christopher - This has to be used in the hands of an expert. It's not sufficiently accurate to give 100%. Though it does do much better than expected. So, for these untrained syndromes, it narrows the search base for the right syndrome by 27 times better than random chance. This, again it was just a sort of proof of principle study to see if it was possible to do this. So, we're hoping to expand on this.
Ginny - So, I guess that would save the taxpayer quite a lot of money if could run fewer tests rather than having to run all 27.
Christopher - Yes. I mean, this is also - since this algorithm just uses basic equipment, it could also be applicable in parts of the world where this type of expertise is not easily accessible. So, it would help narrow down the tests that might be done in countries and in healthcare systems where money is very much limited to the number of tests you can do.
Ginny - And what kind of tests are we talking about that would need to be done to get a sort of definite diagnosis?
Christopher - So ultimately, you want to identify a genetic cause if there is a genetic cause for a particular rare disease. That's not always the case. But if there is a DNA change then that's what you want to try and identify. And so, there are existing - a lot of specific tests to look for specific changes. As this new genome sequencing, as we heard earlier in the programme, is developing, of course, we're hoping to make this - that this will make it a lot easier to identify the DNA changes in rare diseases.
Ginny - Just briefly to finish up, how long do you think it'll be before something like this is in widespread use?
Christopher - So, we are trying to develop this as quickly as we can of course, but we do have to expand this and workout with clinician collaborations carefully. A number of years still, but we would be hoping to do it as a sort of ongoing research and development as it rolls out in collaboration with clinicians.
42:32 - Keeping Your Genome Safe
Keeping Your Genome Safe
with Anna Middleton and Guy Coates, Sanger Institute
Sequencing your genes may mean better diagnosis and even treatment but what happens to your genome once it has been sequenced? Where does all this data go and how is it stored? And most importantly, is it safe? Back at the Sanger Institute, Graihagh Jackson spoke to Dr Anna Middleton and Dr Guy Coates - one of the people charged with protecting the thousands of genes sequenced at the facility.
Graihagh - So, we're in the middle of the data storage room here. we're surrounded by aircon and all sorts of flashing lights and brightly coloured cables. What's the function of this room?
Guy - The science that we do is all about big data. What we want to do is sequence many, many different genomes of many different people and then analyse them. In order to do that, we need a lot of storage.
Graihagh - How much data are we talking about here?
Guy - We store about 20 Petabytes of data. So, if you think about your hard disk in your laptop which is about a terabyte, a petabyte is a thousand terabytes. So, we're roughly holding about 20,000 times the amount of data you have on your machine at home.
Graihagh - What does that translate into?
Guy - Typical genome is probably of the order of 3 gigabytes, so it's many tens of thousands of genomes.
Graihagh - How do you keep this secure?
Guy - The Sanger Institute actually has a very open data policy. So, most of the sequencing data that is generated here is made available on the internet for other researchers to be able to download.
Graihagh - Does that mean anyone can have access and pinpoint someone's sequence or genome?
Guy - In principle, if the data has been consented for release then yes. Anyone can come along to our website and download as much of the data as they can handle. We do have some data search which involve confidential patient information for researchers who wants to get access to that data have to come and submit proposals to an ethics review board who will decide.
Graihagh - Has there ever been any incidences where data has accidentally been leaked or hacked?
Guy - Not from us. There have been incidences where people have taken what looks like anonymised data then been able to combine that with third party datasets. One of the classic examples has been a study where someone has been able in the US to take genetic information which wasn't even a full genome scan and then link that back in some circumstances to surnames. Now, that's not complete re-identification but it shows how careful that we have to be with genetic data.
Graihagh - So, if your genes can be traced back to your family, what could the outside world learn about you? Dr. Anna Middleton, Senior Scientist at the Sanger Institute and Gene Ethics expert...
Anna - So, you can tell somebody's past, present, future from their genes varying from the age that they would've been predisposed about their first tooth through to whether they're predisposed to high cholesterol, through to whether their higher chance and average of getting cancer. So, a whole collection of different things.
Graihagh - It sounds like a never ending list of possibilities.
Anna - Yeah. Actually, virtually, everything about us has some pathway in us that links to our genes. So, together with the environment, genes make us who we are.
Graihagh - You can potentially glean quite a lot of information about someone. So, how is that kept anonymous?
Anna - As far as identifying an individual goes, that would actually be very difficult. If you manage to get somebody sequenced and you had their raw A, Cs, Ts, and Gs, the bits that make up the sequence. You couldn't look at that and go, "Oh, I've just identified that person from that." You can't do that. You need a way of interpreting it. If that individual had other things online that identified them such as their photographs, such as links to a particular condition, you could in theory, match them altogether, but it would be quite a complex exercise. So, what we do is to try and get the data we have on campus as safe as possible. We use the same sort of systems that banks do to protect identifiable data and we're just as cautious as we can be.
Graihagh - Your genome may just about be safe from prying eyes for now, but what happens if you consent for someone to look at a specific gene and they accidentally see another, one that's life threatening but curable?
Anna - In a research setting, if incedental findings are discovered there isn't a duty to be sharing those. Now, that's actually quite different from a clinical setting. So, if you had an x-ray of your lung and then picked up an unexpected rib fracture then you would expect the doctor to explore that and share that. The same in genomics is expected. If you genuinely saw something accidental that you weren't expecting then there would be an expectation to share that, particularly if it was very serious and potentially life threatening, and also, actionable. So, if we pick up genes relating to even serious conditions, if you can do nothing about it then really, what's the point in knowing?
Graihagh - So, if it's incurable, that's not something that would be communicated to a patient.
Anna - So for example, if you were looking at say, a 2-year-old with a severe developmental disorder say, you would be looking to try and find the genetics behind that developmental disorder. So, how helpful is it to then be looking at the Alzheimer genes that wouldn't even be relevant until that child was an adult.
Graihagh - If you're looking at sequencing the gene, is it a case of just the gene being, this means you have Alzheimer's?
Anna - So, the vast majority of genes that we have, give some level of prediction about the future, but it's very difficult to be precise. So, when you're discovering things, incidental findings say, in a research setting or even in a clinical setting, it's very hard to actually interpret them without clinical data attached to them.
Graihagh - Is there a standard policy across the world that people can look to that can follow, should they find themselves in these circumstances?
Anna - Generically, if we were to look across the world, what are people doing, I'd say in the UK and in Europe more generally, we're being more conservative about incidental findings and we're saying, "Okay, the information that you can get from a whole sequence is vast, you know, what to look at and share." Is it just information relating to serious actionable conditions? What about information relating to pregnancy or carrier testing? What about response to medications? All these different genes play a part in these different things. At the moment, we're saying it's actually very hard to manage all of that. What we'll do is really just focus on answering the clinical question. So, if somebody comes in with breast cancer, we'll really only just look at the breast cancer genes and to find out what chemotherapies are going to be most helpful for them and look at trying to support them with their cancer. Let's not think about all these other pieces of information perhaps until another date. That's very different from what's going on in the states at the moment. There, they're recommending every time a sequence is done to automatically look for 24 cancer and cardiac conditions at the same time every time a sequence is done. So, they're using that opportunity to do a screen of other things at the same time and that gives a very strong public health message. So, it's just a different kind of approach. So, we're more conservative here. it may well be we go to the American position at some point, but we just don't quite feel ready yet.
Graihagh - When are we likely to be able to see this sort of wide scale access to your genomic sequence?
Anna - The 100,000 genomes project has started. So, this is this massive government initiative to sequence 100,000 people in the NHS, so it wouldn't be available for everybody. But the government has really made a massive commitment to create the infrastructure to support sequencing on a large scale.
50:13 - Do emotional or pain-induced tears differ?
Do emotional or pain-induced tears differ?
Professor Vingerhoets - Crying predominantly expresses powerlessness or the strong desire to be reunited with a lost valued person, object or location. The advantage of crying aloud is that it is emitted in all directions. It is then very likely to be heard by parents, who can provide care, but this means it may also be heard by predators. Most mammal offspring will make distress calls if separated from their carers, however humans are the only species who shed emotional tears. The advantage of crying tears, a visual signal, is that it cannot be detected in the same way by predators but may easily be seen by the parents or caregivers.Physical pain tears and emotional tears are both produced by the same glands in the eyes. Like other glands (such as salivary glands or sweat glands) they are connected to our blood stream. Some ingredients of the tears originate from the blood, and the composition of blood can be effected by your hormones, which in turn are affected by stresses and your emotional state. This could lead us to think that tears produced in different emotional states could differ in their composition. However, a great deal of mystery still surrounds this idea. Over 30 years ago, American researchers compared the biochemical composition of emotional and irritant (onion) tears and found that the emotional tears contained more proteins. However, that has never been replicated and we do not know what this would mean.