Judging a cancer by it’s
cover tissue of origin may be the wrong approach. It’s not a publication yet, as far as I can tell, but summaries are flying around about a talk presented at AACR 2011 on Saturday, in which 50 breast cancer genomes were analyzed:
Ellis et al. Breast cancer genome. Presented Saturday, April 2, 2011, at the 102nd Annual Meeting of the American Association for Cancer Research in Orlando, Fla.
I’ll refer you to a summary here, in which some of the results are discussed. [Note: I haven’t seen the talk myself, but have read several summaries of it.] Essentially, after sequencing 50 breast cancer genomes – and 50 matched normal genomes from the same individuals – they found nothing of consequence. Everyone knows TP53 and signaling pathways are involved in cancer, and those were the most significant hits.
“To get through this experiment and find only three additional gene mutations at the 10 percent recurrence level was a bit of a shock,” Ellis says.
My own research project is similar in the sense that it’s a collection of breast cancer and matched normal samples, but using cell lines instead of primary tissues. Unfortunately, I’ve also found a lot of nothing. There are a couple of genes that no one has noticed before that might turn into something – or might not. In essence, I’ve been scooped with negative results.
I’ve been working on similar data sets for the whole of my PhD, and it’s at least nice to know that my failures aren’t entirely my fault. This is a particularly difficult set of genomes to work on and so my inability to find anything may not be because I’m a terrible researcher. (It isn’t ruled out by this either, I might add.) We originally started with a set of breast cancer cell lines spanning across 3 different types of cancer. The quality of the sequencing was poor (36bp reads for those of you who are interested) and we found nothing of interest. When we re-did the sequencing, we moved to a set of cell lines from a single type of breast cancer, with the expectation that it would lead us towards better targets. My committee is adamant that I be able to show some results of this experiment before graduating, which should explain why I’m still here.
Every week, I poke through the data in a new way, looking for a new pattern or a new gene, and I’m struck by the absolute independence of each cancer cell line. The fact that two cell lines originated in the same tissue and share some morphological characteristics says very little to me about how they work. After all, cancer is a disease in which cells forget their origins and become, well… cancerous.
Unfortunately, that doesn’t bode well for research projects in breast cancer. No matter how many variants I can filter through, at the end of the day, someone is going to have to figure out how all of the proteins in the body interact in order for us get a handle on how to interrupt cancer specific processes. The (highly overstated) announcement of p53’s tendency to mis-fold and aggregate is just one example of these mechanisms – but only the first step in getting to understand cancer. (I also have no doubts that you can make any protein mis-fold and aggregate if you make the right changes.) The pathway driven approach to understanding cancer is much more likely to yield tangible results than the genome based approach.
I’m not going to say that GWAS is dead, because it really isn’t. It’s just not the right model for every disease – but I would say that Ellis makes a good point:
“You may find the rare breast cancer patient whose tumor has a mutation that’s more commonly found in leukemia, for example. So you might give that breast cancer patient a leukemia drug,” Ellis says.
I’d love to get my hands on the data from the 50 breast cancers, merge it with my database, and see what features those cancers do share with leukemia. Perhaps that would shed some light on the situation. In the end, cancer is going to be more about identifying targets than understanding its (lack of ) common genes.