This is my second time at this summer school and I thought it’s important to educate the next generation of scientists and this is a really good venue for doing that. So this is slightly different than other meetings in that you do get to talk about your research but it’s in a much broader context so that you’re able to use bits and pieces from various projects within the lab and just put it into a much broader context to explain concepts to students rather than going in detail on a specific project.
Can you tell us a little about your presentation?
I talked about the importance of using a variety of different molecular profiling platforms to understand functions of proteins that have not previously been described. Specifically my group is interested in understanding non-catalytic functions of a protein group that is known to carry out a catalytic reaction. So these are kinases and a couple of years ago we found that this kinase called Akt, which is a critical player in oncogenesis, is able to promote cancer cell survival through functions that do not require its catalytic activity. But there is no defined path in terms of how you study these novel functions and it’s very difficult because with an enzyme you can simply do a biochemical assay to read out its activity but with some of the functions that we’ve identified now it’s a lot more complicated. But what we’re doing is trying to provide proof of principle using this example to say that you can use a variety of different omics platforms, for example what we’ve done is phosphoproteomics, transcriptomics as well as low resolution metabolomics through magnetic resonance spectroscopy. Then what you do is you do a variety of different models, you bring together the data from the different platforms and you look for what’s common in all the platforms and across all the models. That hopefully reduces the number of hits that you get and then you can take those on and study them further. Because obviously the challenge is that whenever you do these large omics experiments is you end up with a lot of data so how you go through it depends on your approach. The approach here is let’s just do it in tonnes of different cells and then see what the common denominator is.
What’s the main end goal?
Akt is a key player in cancer. It’s a key component of the PI3 kinase signalling pathway which is arguably altered in all human cancers. For that reason it has been pursued as a therapeutic target for a number of years. Now, there is a disconnect between the frequency of PI3 kinase activating lesions in cancer and drugs that target the pathway being effective at killing these cells. So we basically want to understand why that is and part of the explanation is that in terms of specifically targeting the Akt is that a lot of the drugs that have been tested in this context only capture the catalytic functions of Akt. So these are drugs that turn off its ability to phosphorylate protein substrates but, as I said, it seems that Akt can do things other than phosphorylate substrates. So what we’re trying to do here is first more fully understand how Akt promotes tumour growth but at the same time while doing that we will identify novel therapeutic targets that then we can use to develop new drugs that more hopefully effectively target Akt dependent cancers.
What stands this meeting apart from others?
It’s slightly different than most other meetings where you just hear people talk about their latest piece of research. Here you have the opportunity to talk about many things and just put it into a broader context because the purpose of this is to educate scientists in training. A lot of these people are first year students, for example, they may not necessarily have all of the necessary background to fully process the kind of information that you get at more specialised meetings. So this meeting stands out in that it provides you with the opportunity to talk about many things, it attracts some very key players, particularly in Europe, of course there are also people from the US. I myself have already talked to a couple of people about potential collaborations so it’s good, it’s always good.
How have you seen the science community change?
Twenty years ago science had a completely different model and it was every team for itself. The last several years the community has realised that in order to make real progress you need to work collectively and not just on a national scale but on an international scale. So we’re definitely seeing science becoming a global enterprise and the thing that facilitates this is these types of meetings where you start finding people with common interests and talking about specific projects where there might be a synergy between the two groups. So this and other meetings are very good opportunities to do that.
Are we seeing a new trend of openness in sharing data?
That’s the hope and it needs to be incentivised. There are some incentives now because a lot of funding streams are really aimed at funding collaborative work and that’s one step in the right direction. This is particularly challenging for new investigators such as myself because we rely on productivity to be able to go up through the tenure review process and there has to be some institutional support in this sense. What I mean by that is if you have, say, five different collaborators and you’re working on a couple of different projects with these people you may end up being one of many authors on a paper and scientific publications remain our currency in science, therefore, I will be evaluated on that. Now, in the old days it would be seen as, ‘Oh well, you only did a small fraction of the work, therefore you should not be rewarded as much,’ but now people are starting to realise that this is the only way to advance science. So when you get evaluated you have to be evaluated in that context. As I said, a lot of this depends on the institution where you work at because they’re the ones who are going to evaluate you and they have to take into consideration the fact that you’re doing this work because you really want to make progress and this is the best way forward.
Do you have a take-home message?
Science is very slow and it’s very easy to get disappointed. You just have to keep trying. I guess the other thing that I’ve faced many times during my career is I’ve encountered questions in which the answers seem to go against what most people think. So when you actually get that, and if you’re sure that your data is strong, then just try not to think about what other people think, even if it’s everybody else that thinks differently. If you’re sure of your data just push for it. That’s how you bring about change and a lot of times you have to change dogma. So just don’t be afraid of doing that.
