Identifying targets with systems medicine

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Published: 6 Jul 2012
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Dr Leroy Hood – Institute for Systems Biology, USA

Dr Leroy Hood talks to ecancer at the 2012 WIN Symposium, Paris, about systems medicine, a new approach to understanding the complexities of cancer.


By analysing biological circuits at the proteomic and genomic level, observations can be made on when cancer cells form. The ultimate goal of this method is to identify targets efficiently and determine where and why mutations occur.


While many labs do not have access to the costly technologies to implement systems medicine, Dr Hood states that this method is what is needed to solve the current problems with cancer.


Filming supported by Amgen

Identifying targets with systems medicine

Dr Leroy Hood – Institute for Systems Biology, USA


Dr Hood, we’re here at WIN 2012 in Paris and you have just given a nice presentation on systems medicine. Can you give us a brief review of what you spoke about?

Well systems medicine really is a new approach to dealing with the enormous complexity that is present in disease. Probably a simple analogy is suppose you wanted to understand how a radio converted radio waves into sound waves, how would you go about it? Well you would, one, take the radio apart, you would look at the individual parts. Maybe you would study how they worked and that is really what cancer biology is doing right now, they are looking at genes and proteins one at a time. But what you do next for the radio is put the parts back together in their circuits and then come to learn individually how the circuits actually operated to make that conversion. And that is exactly what systems medicine does in living organisms - you have biological circuits and it tries to study the components to put them together in their circuits and then understand exactly how disease arises when those circuits become perturbed, either genetically and/or environmentally, to cause cancer.


So you are looking at the pathways within a cancer cell and how it is disturbed, and trying to identify mutations that can be targeted?


That’s exactly right, but it really goes far beyond that, because in order to get the circuits you have to develop a whole series of new technologies, so I also discussed new technologies at the genomic level, technologies at the proteomic level and especially new technologies at the level of analysing single cells. And the single cell analysis is really important for cancer because one of the fundamental features of cancer is the mutation rate is turned on 400 times as great as a normal cell. So, in a cancer you have many, many different cancers and if you just look at the whole cancer you average all those individual players and what we have to do in the future is try and understand what the individual cancer cells are doing and collectively how they add to the cancer.


I imagine it’s a very complex and very costly technology that you need to do this type of research?


Yes, you need many different types of technologies. You need very powerful computational tools to deal with all the information. And you need just a very different way of thinking about disease.


So for the average research lab, whether in cancer or not, would they have access to do these type of experiments?

The average research lab wouldn’t have access for all the technologies you’d need to do these kinds of experiments. Many labs these days have access to many of the genome technologies, sequencing and arrays and things like that but fewer have access to proteomics, even fewer to metabolomics, and still fewer to single cell analyses. And these are all important components of what we are going to need to study in the future if we are really going to solve the problems of cancer.


How do you see research going ahead today with these limitations?

I see a few academic centres and maybe even some companies realising they need to put together this diverse cross-disciplinary type of infrastructure and they will be the ones that will really forge ahead solving these challenging problems.


So something like WIN could maybe help in this area to bring together these technologies?

WIN has the opportunity, if they choose to do so, to make sure its members have access to these technologies in an integrated and effective form and to put them together with the computational tools you really need to. I mean, what you want to end up with are models for the individual patient that are predictive and actionable, tell you exactly what kind of drugs to use on each individual patient.


Could you give me one example of a success story that you have found? 

One example, it isn’t in the field of cancer, but, for example, we have used the systems approach to identifying effective markers for diagnostics in the blood, and we have used that in post-traumatic stress syndrome in soldiers coming back from Iraq and Afghanistan and we can now definitely distinguish those that have PTSD from those that do not. And that is an example of a systems approach to diagnostics.