Last week's Nature had a big paper from the Sanger surveying human protein kinase gene hunting for somatic mutations in cancer. The paper (and a News&Views item; alas, both require a subscription) has deservedly received a lot of press coverage, but a few notes.
First, it is important to underline that this is a first discovery step which associates these mutations with cancer, but it certainly can't guarantee that they are involved. Tumors generally have very battered genomes; indeed, the study noted more mutations in tumors likely to have undergone extensive mutagenesis (defects in DNA repair, smoking-related lung tumors, melanomas from skin exposure, tumors in patients treated with mutagenic oncologic drugs). A useful filter is to compare the ratio of synonymous to non-synonymous mutations, that is those mutations which do not change the amino acid coded in the message vs. those that do. Synonymous mutations should be (to the first approximation) not selected against, so they can be used to estimate the background mutation rate. If a non-synonymous mutation is seen more often than expected, it is inferred to have been selected as advantageous.
One interesting side observation is that in some tumors there is an excess (vs. random chance) of mutations at TC / GA dinucleotides (or TpC / GpA as written -- a common convention to specify that this means T followed by C and not any dinucleotide containing T and C). Such a pattern was not observed in germline (normal) samples from the same patients nor has it been observed previously, suggesting a tumor-specific mutational process.
Protein kinases are an obvious set to look at because so many are already known to implicated in cancer and drugs targeting kinases have already been found useful in the clinic. Indeed, this week the FDA gave the first approval for Tykerb, another small molecule drug targeting oncogenic kinases. The kinases found in the study include many kinases already well implicated in disease. For example, the same group had previously found BRAF mutated in many melanomas. I've discussed STK6 (AuroraA) in this space previously, and STK11 (LKB1) is a well studied tumor suppressor. But there are some interesting surprises. For example, in the list of the top 20 kinases ranked by probability of carrying a cancer driver mutation, there appear to be at least two kinases that are essentially completely uncharacterized in the public literature, MGC42105 & FLJ23074. Another interesting hit is the top one in the list: titin, a protein that hugely deserves its own post. Titin's functions in muscle are well characterized, but a role in cancer would appear to be new. A mutation in KSR2 which resembles kinase activating mutations in other kinases is interesting, as KSR2 has at least sometimes been thought to be an inactive pseudokinase. AURC, whose role in anything remains controversial, shows up with a mutation in a key part of the ATP-binding pocket (P-loop).
There will be a lot of work to actually nail down the role (or lack thereof) of these kinase mutations in cancer. Many other experiments, such as RNAi, have been targeting kinases to try and identify roles in cancer. Most of the mutations observed here were seen in only a few tumors, so there will be lots of work to screen more tumor samples and important cell lines for these mutations. Finally, there are a lot of other genes and gene families (e.g. small GTPases) worth looking at.
However, this is all still very expensive (though the total sequence data, while huge by most standards -- 274Mb of final sequence, pales next to Venter's metagenomics cruise of 6.3Gb). An important question is to what degree should research dollars be invested in these studies vs. other important functional studies (such as RNAi & conditional mouse models, to name just 2). While new sequencing technologies will bring down the costs of large scale sequence scanning, the cost will not go to zero. Balancing the approaches will remain a great challenge for the cancer research community.