Australasian Science Magazine

Molecular biologist and science policy leader Professor Keith Yamamoto discusses the current revolution in biological sciences and the emerging field of precision medicine.

SHANE HUNTINGTON
I’m Dr Shane Huntington, thanks for joining us. Scientists have long laboured to understand life and its complex processes. Their work through the centuries has brought us all enormous benefit, from the development of drugs to treat once incurable diseases, to an increasingly sophisticated understanding of the ecological impact of human activities on the planet. As we expand our investigations into data rich fields like genomics and personalised healthcare, biology is becoming a field populated not just by biologists but also by mathematicians, physicists and statisticians. Biology as a result is changing. Naturally these changes bring the promise of better healthcare standards for people with a cancer or other life threatening conditions, as well as preventative measures to keep people well in the first place. But are we really prepared for this revolution in biology? Do we need to adjust their educational models to make sure we equip health researchers with the right skills? How will this affect the type of healthcare systems we'll have in the coming decades? To answer these questions and discuss the broader implications of a new view of biology, we are joined on Up Close by molecular biologist Professor Keith Yamamoto, Vice Chancellor for Research, Executive Vice Dean of the School of Medicine and Professor of Cellular and Molecular Pharmacology at the University of California, San Francisco. Keith is in Melbourne as a guest of the ICT For Life Sciences forum. Welcome to Up Close Keith.

KEITH YAMAMOTO
Thank you, it's nice to be here.

SHANE HUNTINGTON
The field of biology has changed dramatically since you began your research career in the 1970s. Can you give us an idea of the main differences in how biology is practised now compared to back in those days?

KEITH YAMAMOTO
We're at a very interesting time in biology and in science in general. Biological research at the time that I started in the 1970s as you said, was very much a descriptive field. We'd look through microscopes and examined cells, took pictures of them, did experiments in biochemistry where we would break down specific cellular components and look for the presence and absence of a signal. That descriptive period was a wonderful one. Ones where we gained immeasurably in our understanding of the components that are players in biological processes and understanding the framework - an outline - of the way that those biological processes work. Today things have changed a lot because we have realised that if we're going to move forward from collection of information, naming the players that are involved in the play to actually understanding those processes. Understanding them in ways that we can intercept them or modify them then we have to become a quantitative field. We have to understand things in numerical detail. And to do that biology needs to invite into the field - and it's doing this progressively successfully - scientists who practice their work in a different way. People who are doing physics and chemistry and math and computation and engineering, who'd bring a different way of thinking about problems, as well as working on them. So that's the transition; it's a remarkable one that we're just in the midst of right now.

SHANE HUNTINGTON
Now I have to dig a little bit there when you refer to biology as a quantitative field in the current day and in the future, how do you define that relative to what it's done in the past? Certainly I think a lot of biologists would assume they were doing a quantitative version of a research.

KEITH YAMAMOTO
Right. So we were able to infer biological processes and even the ways that those processes and even the ways that those processes worked with descriptive means, looking in a microscope to look at the change and the shape of a cell for example. Or the cells that a particular cell would choose to interact with; maybe even merge with and fuse with. Those kinds of descriptions carried us a long way in making theories about exactly how those processes worked. But they don't actually tell us how the processes work. So now the next step is to understand those processes using quantitative methods of engineering and chemistry and physics that will bring us the real numbers behind those observations. It's those numbers that turn out to give us the mechanistic detail to be able to carry forward. The real test of understanding something in a sense is being able to reproduce it yourself - by putting the pieces together and the steps together - the [imagines] working. So we're getting an outline of the players but don't know how to put them together well enough. We don't know whether when we put together a reaction in a descriptive mode whether when it looks like it's working whether it's working the same way that it works in the cell. But getting the numbers behind it all will tell us that. That level of understanding is crucial for doing some of the things that you talked about in your introductory statement where we have the chance to be able to understand them well enough to be able to intercept disease mechanisms and things of that sort.

SHANE HUNTINGTON
This presumably will mean that we have to look at our education models - especially at university level - for training biologists. Is the current version adequate to deal with this new biology that you speak of? Or do we have to go back to the drawing board and start redescribing the way in which a biologist will go about their day?

KEITH YAMAMOTO
I think we have to go back to the drawing board. But it's going back to the drawing board in I think exciting ways that are going to extend further back from the graduate period of training into undergraduate and even earlier and that is finding a common language for all of these different scientists to speak. The work has gone forward in ways that have taken us to more and more hyper-specialisation. So there are biologists who speak different languages and really can't communicate well with each other. You can imagine what happens when we begin to try to interact with engineers and physicists. So we're at a stage where finding that common language will have a huge payoff; it's going to be very exciting. And we can begin doing that early on. One of the things that we're doing in the University of California, San Francisco UCSF where I work is to begin bringing our first year graduate students together in teams in which the team members - four or five people - come with different backgrounds. Some have been training in physics, some have been training in molecular biology, some have been training in computer science. Bringing them together in teams and then having them to go through a series of so-called boot camp courses - very short intensive courses - intended to bring everyone up to a common level of literacy. And they see immediately the different languages, but somebody on the team understands the language and other people don't and they begin interacting with each other and teaching each other right away. You can see that that can be done any time, it doesn't have to wait until graduate school. So we think that that kind of model can actually get us to where we need to go, not only painlessly but in a way that's fun and interesting.

SHANE HUNTINGTON
In that model you're not just talking about retraining the language skills of the biologists, but the other fields as well - the physicists. So it's a two way process isn't it?

KEITH YAMAMOTO
Absolutely.

SHANE HUNTINGTON
Now Keith you've written about something called new biology, what sets this apart from the old biology?

KEITH YAMAMOTO
So the new biology concept which really part of a title of a report that I was involved in for the National Academy of Sciences in the US. It refers to the fact that we're at a stage in our field where we needed to do two kinds of integration. Now we needed to bring back together the sub-specialty fields in biology, not only medicine but the people that are involved in surgery, the people that are involved in metabolic care and then across that discipline for the basic scientists to the clinicians and beyond. So that level of integration. And then the level of integration that we've been talking about of bringing the different sciences together. What this report says is that this new biology of merging these different practitioners will not only move our knowledge forward in ways that economic scientists really want to be able to do, but they will be able to use the information gained from this new approach to be able to approach and address specific societal issues of great urgency. This kind of new biology could open up approaches to problems that we have not only in health that you referred to at the beginning, but also the environment, energy issues and food and agriculture. In the next 30 or 40 years we need to find a way to double the food production on this planet. This is obviously a global problem and one that right now we have no solution to. There's nobody that has an approach to be able to accomplish that. And the principle of this new biology is that if we can work together and integrate in the ways that I described we've got a shot at it.

SHANE HUNTINGTON
It’s hard to argue against the idea of this new biology. But one of the areas that drives scientists so strongly is the way and the means for them to get grants and to get funding. It would appear that our granting systems across the globe at the moment are totally inept in dealing with this sort of program. Is that your experience as well? How do we deal with that?

KEITH YAMAMOTO
It is indeed. Funding agencies are not international and diverse. Funding agencies within a given country are fractionated. In the US biological research life sciences research is supported by I believe the number's 24, but it's in the 20's different agencies of the US Federal Government. And those agencies actually are not communicating effectively with each other. There are aspects of the budgeting process that actually motivates them to stay apart from each other - basically not to talk to each other. So when it comes to looking at funding mechanisms crossing those boundaries is very difficult. You can imagine that that amplifies further when we begin talking about international co-operation. And so what the new biology report refers to and other things that we've been working on are some mechanisms that will motivate agencies to be able to cross those boundaries and say that here's a pot of money here to do this fantastically important thing, but it's only going to be available if you merge together with another agency and so forth.

SHANE HUNTINGTON
When you've looked at some of these areas of education - especially at University of California - are you concerned that this addition of greater breadth to some of the students will erode the elements of depth? This is always the competing problem that we find when we move into these areas. How are you addressing that?

KEITH YAMAMOTO
You know it's interesting you raise an important point because I think it would be very easy to confuse people - scientists, academicians and the public at large - by saying, oh we're just going to all merge together. An important part of this is that it does not remove the attractiveness or even the obligation to continue to be very specialised in the way that we work. So we still need this very focused expertise but we need to carry out that training in an environment where there is an awareness and a motivation for people to gain a level of literacy that crosses their normal boundaries - that moves outside of those silos. And the way to do that is to show people how exciting things can be when you begin to speak this common language.

SHANE HUNTINGTON
You're listening to Up Close, I'm Shane Huntington. Today we're speaking with molecular biologist Professor Keith Yamamoto about the new biology. Keith there's this idea of convergence that is coming out, a lot of people won't have heard this term. What do you mean by convergence?

KEITH YAMAMOTO
This is exciting. This is sort of one phrase that is able to encompass this concept of scientists coming together, training together, finding a common language, identifying problems that they can use their specialised expertise to approach but only if they do it together. Scientists, as we become more specialised, fall into a little trough where we think, yes this is where I can operate. I know the language, I helped to create it, I know how to make some approaches to problems that are very interesting and we begin to shut out the options to work on things outside of that area of expertise. What convergence does, it says no we don't have to shut out those possibilities if we come together with other people who have different ways - different expertise, different way of looking at problems, different approaches to doing experiments. And working together we can go after problems that none of the scientists individually could go after. That's pretty exciting. It's exciting for the scientists, and it opens the dimensions of the kinds of problems that scientific research can solve.

SHANE HUNTINGTON
How do we open up the information flow to allow this to occur though? Obviously there are particular issues around competition for grants, around intellectual property. All of these things seem to have constrained us into these various silos over the decades.

KEITH YAMAMOTO
Yes. You just referred to a couple of different stakeholder sectors that need to be convinced that there is something really important in it for them. The way to break down these barriers is to create incentives. I don't mean just monetary incentives but intellectual ones, social progress incentives where people think they can actually do something that they just didn't imagine they could do before. So I think that's really the way. Convergence really does this. It establishes this merged field in ways that people can see that they can accomplish things they couldn't do before. Now when you look at information for example, scientists traditionally have been very guarded about sharing that information because the ways that we get credited and reward is all fenced around what I as an individual scientist am able to accomplish. And if we can open that up and say, no actually the reward system's going to spread out here and you have a chance if you accomplish something really bold and exciting and reach beyond what you thought you could do. We're going to set up a system so that you get the kinds of rewards that you deserve. So changing the reward system and changing that culture of individual scientists get credit for what they do and not what they do in groups, is the secret.

SHANE HUNTINGTON
So we're talking about an absolute paradigm shift in activity here because every single aspect of a scientist's life at the moment is based on these various reward mechanisms. Every single aspect of the way universities operate is based on these, and every single aspect of the granting bodies is based on these. So where do we start - what lever do we pull first - or in what order do we go after them?

KEITH YAMAMOTO
You've raised a really important point and it's the kind of concern - the kind of fretting if you will - that stops people from working on problems like this altogether. When you run the list of the ways that things could be stopped, it's so daunting that there are people who will just say, well forget it. That means that this is impossible. And I reject that and the concept of convergence rejects it. The way that I think these big issues can be taken on - well actually like any big social change endeavour - is to start at the top and enunciate the possibilities and the promises, or the threats if we don't respond appropriately in ways that are so clear and compelling across the sector's stakeholders - the government, the funders, the regulators that are trying to keep information separate from each other, the academic promotion policies that say if you're an individual scientists and you've done something as an individual we like you and if you've done it as a team we don't. And make those things go away because people say, oh well it's so important that we take on these issues that we're going to sweep away these little problems. I've said that if universities were organised starting with parking there would be no universities. And so we need to start with the higher issues that look what we could do, or in this case look what we have to do if we're going to be able to move forward.

SHANE HUNTINGTON
It's interesting that we've completely disengaged the public in this process over the years. Is it time the scientific community also reengaged with the public and had that push coming so that this really momentous change you're talking about at all levels actually has the key stakeholders - being the public - advocating for it?

KEITH YAMAMOTO
It's past time. I think that we need to have been doing this all along anyway. It turns out that as much as scientists are wringing their hands right now about the budgetary problems and the world economic situation and the situation in Australia or the US or anywhere else, it turns out that governments support science pretty well. We get a lot of money. In the US the money for bio-medical research alone is about one per cent of the federal budget. That's a lot of money. And so on the one hand we actually have a responsibility that we've abdicated a certain level of communicating to the public of what it is we're doing with their money; and in our case how effectively we're spending it. I think that's really true. But the other reason that your point is really important of bringing the public back in is that they need to feel that they are owners and participants in this process. One of the things that we're working very hard on - a new endeavour called precision medicine - really reaches for a massive participation in data collection and gathering that's going to require the enthusiasm of lots of citizens. In fact not even just patients but people who are well. That's not going to work unless we communicate clearly what the challenges and opportunities are for becoming a part owner of that process.

SHANE HUNTINGTON
Keith, when we think about the immense nature of the problem we're got here, I can't help but look at the problem of climate change. We know in that case exactly what we need to do - everyone knows what we need to do - but every driver economically and otherwise says we shouldn't do it because we don't have the levers. We're failing in that particular area. What makes you think that we can get the kind of groundwork in place to achieve the goal around health that we haven't been able to achieve in something as important as the global climate?

KEITH YAMAMOTO
Yes, a great question. The new biology concept and report took a shot at this kind of a challenge. There are many that are embedded within the kinds of questions that you ask and pointed out that as daunting as each of the problems is - in health, in energy, environment, in food and agriculture - they in fact will not be solved if they're only solved one at a time. So if we take on the food challenge and say, this is really urgent. Let's take away crop controls around the world that are keeping farmers land empty and just say, go for it. Grow as much food as you can, we'll put it all on planes and boats and use petrol to ship it around to places where it's needed. We trash the environment and so forth. Go for it with land use and fresh water use and fertiliser use that gets dumped into our streams because we have to grow this food. Right. If we do that we fail.So it turns out that the problems - as daunting as each of them is individually - won't be solved unless we solve them all together. So as scary as that sounds, the good news is that there's one biology. If we can understand microbes better, we'll be able to understand how they relate to plants so that we can grow crop plants in conditions where we normally wouldn't be able to for example. We can begin to see pathways that can begin to be built to build biofuels that don't depend on non-replaceable resources, and we can begin to put circuits of gene expression into those microbes that help us to generate a whole new generation of drugs and therapeutics. So you can see that yes the problems are integrated but so are the solutions. So a part of taking on these giant issues like climate change is pointing out to people that in fact there's element where we can both contribute other kinds of research to move towards those solutions. The imperative that says that if we don’t solve all of these issues together - so if you're working on health and really focused on that, you better pay attention to climate change too. Because if we don't take that on in a way that works we're going to fail. So in way it engages a broader sector of the community that says, we all have to be in this together in solving these problems.

SHANE HUNTINGTON
When we talk about ownership and some of these areas of data and information, this concept of a knowledge network comes up and the idea that we would all be able to access this and freely provide our data. This is something in fact if you described it to a member of the public they would probably say, don’t you do that already? But we know different. What would be the funding mechanism for something like that, given it takes away things like patent protection and some of the other mechanisms that traditionally control those funding streams?

KEITH YAMAMOTO
Right. Let me back up and just talk a little bit about what a knowledge network is. What the heck are we talking about here. So a knowledge network, the goal of a knowledge network is to be able to bring together with your computational mechanisms massive amounts of information and data - of all sorts actually - and mount it in a computational way that those disparate kinds of information can be compared and analysed and models can be built from them. Well in a way we already know this can be done with information because we look at Google where you can just tap out a few keystrokes and pull together everything known about a given subject that you're asking about. There are ways to do this. Well can we do it with health information for example? That's a big challenge but one that we - and perhaps more importantly the computer companies and so forth - know that can be accomplished. It'll be difficult, and as you point out expensive. So how are we going to get all of that supported? And again I would say the same thing, we enunciate the big goal at a high level and begin to talk with the key stakeholders to motivate them to be involved, to put some of their resources into being able to make this get accomplished. Have government help to enunciate these big challenges and then sit down at the same table with people from the companies that are involved in these processes already, with the academicians that are developing the key concepts to move information around and put it together and so forth. And then create a reward system that says, if we can accomplish this then you're going to get part of the goodies that come out the other end.

SHANE HUNTINGTON
You've written a bit about the idea of these large areas of research - and this directed research - in particular the concept of grand challenges. What do you mean by that? What is a grand challenge? I mean the common view of that term is not necessarily appropriate to what we're doing.

KEITH YAMAMOTO
Well I'm not sure if that's true. But a grand challenge in the way that I think about it at least is an enunciated problem statement that says, here's something that needs to be achieved - and let's say in the next decade. It is something that will have such sweeping importance that we need to put attention and resources in that direction to be able to move our work. We recognise that it's something that can't be done right now, but let us tell you why there's urgency behind it. How are we going to double food production the next 20 or 30 years? Right now we can't do it. How are we going to control healthcare costs; we're on an unsustainable track for that. So a key aspect of the grand challenge idea is that it captures the imagination of people groups and even individual scientists who think, well I'm just one woman but when I look at that grand challenge I can think of a way that my work and the way that I think about a problem can put a little bit of a push in the right direction.We enunciate these challenges in ways that many people can look at that and say, well I could do something that can move in that way. So it puts direction to moving on a problem. So part of it is that it's saying that if we don't work together - if we don't all stand up and say, yeah this is important and find ways that we can contribute - then we're not going to get there in time. The other thing it does is that as it captures people's imagination the technologies that are needed to accomplish the problem that today seems impossible actually begin to emerge. Let me give you two examples from the past. In the 1960s President Kennedy said, we’re going to go to the moon in this decade. There was no way with the technologies in 1961 when he made that statement, no way that we could go to the moon. But when people captured that spirit they began to develop technologies that got us there. In 1990 when the US government said, we're going to sequence the human genome. I can tell you from personal experience that the methods that were used for sequencing DNA had no way to accomplish three billion base periods of sequencing - there was just no way. But people got the little spirit behind it and they began to develop new methods for sequencing DNA that got us there. So just enunciating the problem well can generate the means of solving it.

SHANE HUNTINGTON
So I think when we look at examples like the Apollo program from one example, we have to consider the fact that it also came with the unlimited budget element to it. To some degree the genome project did as well. When we go forward - thinking about these grand challenges - is there a risk that we’re being too prescriptive and perhaps without that budget driver we're not going to get the same outcomes. For example if we were to look at the last hundred years of Nobel prizes, not a single one of them falls into this category. Are we at risk of going in the wrong direction and depleting that Nobel type discovery and heading towards just single ideas?

KEITH YAMAMOTO
You raise an important point, and to me it depends on how we frame those challenges. If they are open enough so that individuals who have their own ways of thinking about problems, their own aspirations for what they want to solve can see how those ways of thinking and those aspirations could fit into or under the umbrella of a grand challenge, then we're okay. That's a challenge in itself, but an important one as you point out.

SHANE HUNTINGTON
I'm Shane Huntington and you're listening to Up Close. We're speaking about a new biology today with Professor Keith Yamamoto, a molecular biologist. Keith, one of the things that's happened with the aerospace industry, in particular the space industry over the last few decades, is we've moved away from a control by NASA - a government organisation - to that of private providers. And this has been very successful. In the healthcare space however I think there'd be a lot more concerns about this. Not just around safety but around safety but around privacy and other areas. How do we draw in those necessary private companies - the Googles, the Microsofts, the big funders - without the sort of concerns being overriding in this discussion.

KEITH YAMAMOTO
Yeah, it's an important point. Again we can go back to the sequencing of the human genome to put a tighter focus on those concerns because they're real. So the human genome sequencing project was enunciated as government funded endeavour. At the end of the day a private company stepped in, said you guys are doing this in the wrong way. We can do it better, faster and cheaper. It became a competition. At the end of the day it worked. We ended up with a sequence of the human genome. But I think we needed to jump ahead and say, what can we learn from that that prevents this from actually generating problems instead of moving toward a solution. And I think what it will take will be to frame the challenges from the outset as things where all stakeholders need to be involved. Where we say that this is not a government project, this is a project where we all need to be playing. There are some very good companies out in the world right now that know pretty well how to handle information and move data around. And so for the government scientists or for the academic scientists in particular to say, oh no we got this. Thank you very much for coming by, but we got it covered. It's just not realistic. So we really need to be able to create mechanisms that say, everybody plays here. I think if we do that then we can avoid these problems.

SHANE HUNTINGTON
Keith, this notion of convergence. What specifically would that mean for the community if we take healthcare for example?

KEITH YAMAMOTO
So this is where we begin talking about this idea of precision medicine. Let me talk about that a little bit because there's a lot of confusing terminology being thrown around - rather loosely I have to say. Many people have heard of the term personalised medicine. Probably many fewer have heard the term precision medicine, although a growing number of scientists have heard it. Are they the same? Can we just use them interchangeably? What's going on here? So let me start by saying, let's compare precision medicine, personalised medicine and what I'll just call today's current practice for medicine because they turn out to be very different. The one common point is that each of the three has as its focus a diagnosis and treatment plan for you an individual. But then they differ.So in current medical practice you go into your doctor's office and say, gee doc I have this pain in my shoulder, deep in my shoulder. I'm a little worried about what it means. Is there a muscle problem or a connective tissue or a neurological problem - that would be very scary. What's going on here? So your doctor hopefully goes to her computer and maybe to a file cabinet and pulls out your file. Gee, the last time you were in was 15 months ago. Where you been? You're supposed to be here every year. What does she look at? She looks at your vital signs that were measured then your blood pressure and heart rate and weight and compares to what she just measured five minutes ago. So now she's got this 15 month window. She looks to see if you were complaining about your shoulder 15 months ago. You weren't. And then she uses what she knows about medicine to make a diagnosis and maybe a treatment plan; maybe writing a prescription for a drug for you to go to a pharmacy for and you begin to take. That's current medical practice in general. Personalised medicine says, gee Shane it's lucky that showed up to me because I'm doing personalised medicine. And we're going to collect much more information about you than just the vital signs and my interview. Maybe if you were going to some kind of a high end academic medical centre they would maybe even sequence your genome. Or maybe they have been doing that. Maybe they have been seeing you every month for the last year and collecting other data about you. Maybe they've interviewed you in detail and found that you grew up in an area that was very smoggy over a certain period of your childhood and so forth. So we begin to collect more and more data about you and from that make a better diagnosis and treatment plan than we would with just those two data points of vital signs over 15 months. So that's personalised medicine.Precision medicine says, no we're not satisfied with just data about you. We’re going to put this on a big database, on a big computer database that has information about lots of other people - maybe really lots of other people, maybe tens of millions of other people - and their experiences. We're not satisfied with that either. We're going to also include in that big computer database all of the other experiments that have been done by scientists that are working with fruit flies and yeast and worms right and mice, and put all of that information in so we understand what they've been learning about biological processes in general. Put all of that information together in ways that can be analysed and come up with a much more - moving toward precise - diagnosis and treatment plan for you, based on all that information. So you can see now that the three are actually very different.

SHANE HUNTINGTON
Let's come back to the earlier discussion we had about the difference between what you've called the qualitative and the quantitative requirements. How does that fit with this move from personalised to precision medicine?

KEITH YAMAMOTO
So the information about your turns out even when it's in quantitative detail is at some level anecdotal because we don't know enough about how to put together the observations about you; the fact that you grew up in a smoggy neighbourhood for a particular six years in your life. What does that mean relative to other things that have happened to you? The fact that you smoked for four years while you were in college, but then you stopped and so forth. What do all these things mean? But if we have the data that's sought from many, many, many people and understood what happens with carcinogen exposure at certain periods in development and in adult life and so forth based on experimental models that have been built by basic scientists. Then we begin to put connection points between those observations. That's what the extra data will do.

SHANE HUNTINGTON
There's a lot of people who would probably be scared to death by the idea of some central repository of information - and even de-identified - the sort of pattern seeking software we can right now put forward would allow for identification if certain organisations chose it. I think that's something that people have to admit. How do we address that particular concern?

KEITH YAMAMOTO
You raise a good point and that is that already the computer programs can probably sniff out who people are if they really want to. Boy you know what's going to happen when people's genomes are sequenced. That is the one absolutely unique identifier. Once you sequence your genome and then begin to tie it to other information you're outed, it's over. So we actually need to be thinking about this as a common problem anyway first of all. Then the second thing, how do we keep that information if we admit that it's not going to be possible to keep a lid on everything? How are we going to keep people from misusing it; using it in irresponsible ways that can be very damaging to you? So we're working on this, but my general answer to that is that I can actually only think of one way and that is to create incentives for good behaviour. For example, what if we were to go to insurance companies and say, we're not going to give you this information but we know you can get it. Or maybe down the road maybe we are going to say, here you have access to it like we have access to it. And we've shown you a model and then hopefully progressively you can see from the data that if you are a responsible steward of that information and use it responsibly then your costs will go down. If you're not then the firewalls will come up, regulatory policies will be built in, we're going to put hard silo walls that fence you off from that information because you've been a bad steward of it and your costs are going to go up. So we're actually working with health economists now to build those models and I'm convinced that we'll be able to get there and we'll be able to convince them that this is not just something that we've made up to convince them to play, but that they really believe in.

SHANE HUNTINGTON
I wonder, Keith, whether this is something of a generational issue, you and I were brought up at a time when protecting our data was absolutely crucial whereas the younger generation are coming through now with the technology that's available to them on their phones and computers and so forth seem to be putting their personal information out ad nauseam. Is this something - a problem - that is just going to go away?

KEITH YAMAMOTO
You know, I think we're actually lucky that we're taking on this issue that we've enunciated the precision medicine idea at this time in our social existence because of that's happened with social networking and all of these things that are distressing about the kinds of things that young people put up on the web. So I think it's probably good that people's sense of what privacy is has changed at least in that sector. So it may help us to be able to move policies that otherwise would be resisted. So I'm hoping that that's the case.

SHANE HUNTINGTON
Now if you were to ask a member from the public what they currently get they'd probably assume their getting all these healthcare benefits already. So the illusion there is somewhat broken in this interview. But what evidence is there that this will actually work? If we take the example of the electronic health record there are parts of the world where it's been introduced at extremely high cost but then not being used. And the argument there is that the money spent on that record could've better been spent on patient care. So in a sense, patient care's gone down as a result. If you think of it in that way and the immense cost that will be involved in putting this into place, what evidence do we have now that this is actually going to be a benefit?

KEITH YAMAMOTO
So the argument that you've just raised - and I've heard it myself - that we have urgent needs right now that need to be filled and spending what turns out to be billions of dollars to build computer networks is misspending the resources that are needed to address urgent problems. Let's take that point back to 1990 when the US government decided to expend billions of dollars to sequence the human genome, and eventually they were able to engage private companies as well. So it wasn't just their money that went in. But there were many scientists at the time - and other people, social commentators - who said we've got big problems in medicine. We can't be just dilly-dallying around asking what the order of the base pairs across three billion base pairs is when we don't even have ways to do it. Now we jump ahead and ask, what have we gotten from sequencing the human genome all right? Have we now understood and cured all disease? No, because disease is complicated. It's not just dependent on the human genome. But we have been able to identify specific mutations in the human genome that lead to specific diseases. There's a form of breast cancer for example in which there's an over-expression of one gene in the human genome. It happens to be called HER2. Women who have breast cancer who overexpress the HER2 gene - not all of them, but the ones who do overexpress the HER2 gene - are specifically helped, 100% guaranteed will be helped by a drug that was made when that mutation was found, that specifically turns down the expression of that gene back down to where it should be. So we've been able to make a drug that says, we see this mutation from sequencing the human genome, we're making a drug to specifically attack that and every single woman who has breast cancer that overexpresses that gene will be helped. Now you can ask, does it cure breast cancer? Does it cure those women? The answer is no. So it extends their life by 12 to 18 months. That's important to them, to their families and that's a legitimate reason to make drugs of this kind but it doesn't cure them. Why not? Well it doesn't cure them because we don't understand enough yet to be able to know the workarounds that the cancer cells have. So that's the way we're working, and so we progressively gain information. And for those women I can assure you that they're glad that we sequenced the human genome.

SHANE HUNTINGTON
Keith, just to finish up it's 2014, we're sitting here, we're talking about the idea of precision medicine. By the end of this decade what sort of things do we need to do to start to bring this to the forefront and to actually make it something that will happen for the average individual across the world?

KEITH YAMAMOTO
I think the most important thing - and it was enunciated in this National Academy of Sciences report in the US - was the imperative for building the enabling tool, building the knowledge network - this big vast computer tool. So we're going to need to be able to work together with common purpose across companies that are involved in this, governments that are involved in supporting that kind of work and supporting the scientists that develop the approaches for being able to accomplish it. I think this something that's doable within this decade. At least at a level where we can begin to see that information really working together.

SHANE HUNTINGTON
Keith, it's been a pleasure. Thank you for talking to us today on Up Close.

KEITH YAMAMOTO
Thank you very much for inviting me.

SHANE HUNTINGTON
Professor Keith Yamamoto is Vice Chancellor for Research, Executive Vice Dean of the School of Medicine and Professor of Cellular and Molecular Pharmacology at the University of California San Francisco.

University of Melbourne