A lot of my work these days is in trying to make sense of a set of cancer cell lines I’m working on, and it’s a hard project. Every time I think I make some headway, I find myself running up against a brick wall – Mostly because I’m finding myself returning back to the same old worn out linear cancer signaling pathway models that biochemists like to toss about.
If anyone remembers the biochemical pathway chart you used to be able to buy at the university chem stores (I had one as a wall hanging all through undergrad), we tend to perceive biochemistry in linear terms. One substrate is acted upon by one enzyme, which then is picked up by another enzyme, which acts on that substrate, ad nauseum. This is the model by which the electron transport cycle works and the synthesis of most common metabolites. It is the default model to which I find myself returning when I think about cellular functions.
Unfortunately, biology rarely picks a method because it’s convenient to the biologist. Once you leave cellular respiration and metabolite synthesis and move on to signaling, nearly all of it, as far as I can tell, works along a network model. Each signaling protein accepts multiple inputs and is likely able to signal to multiple other proteins, propagating signals in many directions. My colleague referred to it as a “hairball diagram” this afternoon, which is pretty accurate. It’s hard to know which connections do what and if you’ve even managed to include all of them into your diagram. (I wont even delve into the question of how many of the ones in the literature are real.)
To me, it rather feels like we’re entering into an era in which systems biology will be the overwhelming force for driving the deep insight. Unfortunately, our knowledge of systems biology in the human cell is pretty poor – we have pathway diagrams which detail sub-systems, but they are next to imposible to link together. (I’ve spent a few days trying, but there are likely people better at this than I am.)
Thus, every time I use a pathway diagram, I find myself looking at the “choke points” in the diagram – the proteins through which everything seems to converge. A few classic examples in cancer are AKT, p53, myc and the Mapk’s. However, the more closely I look into these systems, the more I realize that these choke points are not really the focal points in cancer. After all, if they were, we’d simply have to come up with drugs that target these particular proteins and voila – cancer would be cured.
Instead, it appears that cancers use much more subtle methods to effect changes on the cell. Modifying a signaling receptor, which turns on a set of transcription factors that up-regulates proto-oncogenes and down-regulates cancer-supressors, in turn shifting the reception of signalling that reinforce this pathway…
I don’t know what the minimum number of changes required are, but if a virus can do it with only a few proteins (EBV uses no more than 3, for instance), then why should a cell require more than that to get started?
Of course, this is further complicated by the fact that in a network model there are even more ways to create that driving mutation. Tweak a signaling protein here, a receptor there… in no time at all, you can drive the cell in to an oncogenic pattern.
However, there’s one saving grace that I can see: Each type of cell expresses a different set of proteins, which affects the processes available to activate cancers. For instance inherited mutations to RB generally cause cancers of the eye, inherited BRCA mutations generally cause cancers of the breast and certain translocations are associated with blood cancers. Presumably this is because the internal programs of these cells are pre-disposed to disruption by these particular pathways, whereas other cell types are generally not susceptible because of a lack of expression of particular genes.
Unfortunately, the only way we’re going to make sense of these patterns is to assemble the interaction networks of the human cells in a tissue specific manner. It won’t be enough to know where the SNVs are in a cell type, or even which proteins are on or off (although it is always handy to know that). Instead, we will have to eventually map out the complete pathway – and then be capable of simulating how all of these interactions disrupt cellular processes in a cell-type specific manner. We have a long way to go, yet.
Fortunately, I think tools for this are becoming available rapidly. Articles like this one give me hope for the development of methods of exposing all sorts of fundamental relationships in situ.
Anyhow, I know where this is taking us. Sometime in the next decade, there will need to be a massive bioinformatics project that incorporates all of the information above: Sequencing for variations, indels and structural variations, copy number variations and loss of heterozygosity, epigenetics to discover the binding sites of every single transcription factor, and one hell of a network to tie it all together. Oh, and that project will have to take all sorts of random bits of information into account, such as the theory that cancer is just a p53 aggregation disease (which, by the way, I’m really not convinced of anyhow, since many cancers do not have p53 mutations). The big question for me is if this will all happen as one project, or if science will struggle through a whole lot of smaller projects. (AKA, the human genome project big-science model vs. the organized chaos of the academic model.) Wouldn’t that be fun to organize?
In the meantime, getting a handle on the big picture will remain a vague dream at best, and tend to think cancer will be a tough nut to crack. Like my own work and, for the time being, will be limited to one pathway at a time.
That doesn’t mean there isn’t hope for a cure – I just mean that we’re at a pivotal time in cancer research. We now know enough to know what we don’t know and we can start filling in the gaps. But, if we thought next gen sequencing was a deluge of data, the next round of cancer research is going to start to amaze even the physicists.
I think we’re finally ready to enter the realms of real big biology data, real systems biology and a sudden acceleration in our understanding of cancer.
As we say in Canada… “GAME ON!”