Tonight's Advance Online Publication section of Nature Genetics contains a new study with an enormous author list (including three former colleagues of mine at Millennium) which surveys 238 oncogenic mutations in 1000 tumor samples from 17 types of cancer. This is a big study, but it should be kept in mind that this is the warm-up for grander schemes.
Alas, Nature Genetics isn't cheap & I don't have access to an electronic subscription, so I haven't read the paper. But from the Abstract, Tables & Figures (JavaScript link on the Abstract page), Supplementary Items and Nature Genetics' blog entry, one can get the gist of the story.
First, some foundation. It is important in this context to think of cancer as an evolutionary disease. Many cells acquire mutations, but only those that acquire mutations that lead to the loss of appropriate growth controls can lead to cancer. Fully progressing to a a tumor requires multiple mutations in almost certainly a stepwise fashion; the odds against all happening simultaneously are too high. Presumably one mutation gives the cell a small advantage & it proliferates. A second favorable mutation within that population leads to a new winner, which proliferates again. And so on, until a full fledged tumor arises -- and then it continues further to select for more and more aggressive variants. Chemotherapy or radiation therapy adds new selective pressures, which now enhance or reduce the fitness of various mutants. It is likely that at all times the tumor is really a population of cells with different genotypes, with constant selection for more 'fit' (i.e. more likely to kill the patient).
There are many mutations that can contribute to cancer, but this paper concentrated on point mutations which activate oncogenes. There are several reasons for this focus. First, high throughput technologies exist for screening point mutations whereas translocations can be complicated to screen (because their molecular details may be quite different between examples). Second, there tend to be a small number of possible activating mutations in oncogenes, whereas there are many ways to inactivate a tumor suppressor. The false negative rate (calling a gene normal function when it is fact abnormal) is therefore going to be much lower for oncogenes.
One focus of the paper is apparently searching for oncogenic mutations that either frequently co-occur or seem to be mutually exclusive. This is summarized in Figure 2. Why would you find such associations?
Frequently co-occuring mutations suggest that they are in some way cooperative. For example, if the tumor can result if two pathways are turned on, but not either one alone (a molecular AND gate), then an expectation is that mutations activating both pathways would frequently co-occur.
On the other hand, mutually exclusive oncogenic mutations would suggest participation in the same pathway -- if one is turned on, you don't need the other one two. For example, if they are in two branches which converge, and activation of either one will create a tumor (a molecular OR gate), then it is unlikely that both will occur. Another case would be for one mutation to be upstream of the other; if the effect of both mutations together is the same as either one alone in activating the pathway, then there would be no selective pressure for both.
Supplementary Figure 3 shows nicely how which gene is mutated strongly depends on the tumor type. This is a well-known phenomenon, but cannot be said to be well understood: why are specific tumor types so driven by particular mutations. In the tumor suppressor world it can be even more stark: why do mutations in BRCA1, which encodes a critical gene for proper DNA maintenance in every cell type, lead to tumors primarily in female reproductive tissues? This is a general phenomenon: there is a long list of tumor suppressors which have been discovered by very tissue-specific cancer syndromes yet are parts of central cellular machinery shared by all cells.
Supplementary Figure 4 gives an overview of how much of the cancers are explained, at least in part, by the mutations surveyed, and Figure 1 shows how much of each tumor is explained by each mutation. The 3D figure has some merits, but personally I would have tried to combine Figure 1 with supplementary figures 3 & 4 in one combined figure (3 might be a stretch, but S4 would fit nicely placed next to the gene axis).
For example, 100% of the pancreatic cancers surveyed had at least one mutation. Given that mutations in KRAS are very common in pancreatic cancer, this isn't totally surprising. 75% of polycythemia vera (PV), a leukemia-like condition were explained; if you go back to Supplementary Figure 3, the JAK2 column is all marked PV. This is a known association, and perhaps one of the more explainable ones (JAK2 is a key regulator of differentiation in the cell lineage that goes haywire in PV).
On the other end of the scale, only 1% of kidney or prostate cancer had a mutation. So in these tumors, something else is going on. In both cases, it is likely that mutations in tumor suppressors explain many of the cases; both tumor types are known to often be mutated in certain suppressors. There is also a big middle: 36% of breast, 50% of colorectal, 32% of lung, etc. Again, in some cases mutated tumor suppressors may be at least part of the story, but there may be other oncogenes unexamined by this study playing as well.
The future promises many more such studies. There are other technologies which can type many thousand point mutants at a time (though there may be other trade-offs; I'm not an expert on this). Ultimately, many investigators want to just sequence away; pilot studies have already been published (if you have a Science subscription, there is a nice letters firefight in the current issue on the topic). But that is a ways away; even with $1000 genomes, the mixed genetic nature of any tumor will make life challenging. But in the meantime, one can expect to see more studies such as this one, but with more mutations and more tumors. More mutations should help fill in the gaps, whereas more tumors would allow much deeper probing of co-occurring and mutually exclusive mutations, as well as detect rare mutation-tumor pairings (such as those listed in Table 1).
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