It isn’t always true for science, but it sure seems like the more we learn, the more complicated the knowledge becomes. Take breast cancer for an example. Every few months a new study is published that announces the discovery that this that or another gene is ‘linked to breast cancer.’ Likewise there is a stream of news about studies finding correlations between various environmental influences – food, smoking, alcohol, pollution, and lifestyle – also related to breast cancer. Typically these studies conclude by saying something like, ‘although this study is preliminary, the linkage between “x” and breast cancer may lead to new treatments and a potential cure.’

Given that there are millions of women (and a few men) for whom such hope is paramount, each one of the announcements may be greeted with enthusiasm. Even if each of these discoveries doesn’t turn out to be ‘the cure,’ surely collectively they must indicate progress toward finding a cure? Well, yes they do, but the question has to be, how much progress?

That gets me back to the original problem of knowing more. A team of scientists at Washington University (St. Louis, Missouri USA) led by Matthew Ellis performed a feat of massive computational proportions by sequencing the whole genomes of 50 women with breast cancer. The resulting paper, to be published in Nature magazine was previewed as a news article. [ Fifty genome sequences reveal breast cancer's complexity] The research entailed sequencing 50 genomes from the tumor cells, and 50 genomes from healthy cells and then comparing them, looking for alterations in the genome. They found mutations, lots of them.

They found 1,700 genetic mutations. That might be classified as the good news. The less than good news is that most of the mutations were unique to each woman’s tumor. Only three mutations occurred in 10% or more of the women. The genetic changes also appeared to be the result of almost the entire kit of mutation: Single-nucleotide variations (copy errors, radiation changes); frame shifts (where the boundaries of genes are broken); translocations (genes get moved to the wrong location); and deletions (which sounds like it is).

The positive spin for the study was that three genes were common, sort of: MAP3K1 (10%), PIK3CA (43%) and TP53 (15%). These may represent a toe-hold on correlating genetic mutation and breast cancer. Of course, that leaves over half the women in this study with breast cancers from various configurations of relatively rare mutations. Remember, this study was conducted on a small number of women, who had only one type of breast cancer (estrogen-receptor-positive). The next research will be on a thousand women with different kinds of breast cancer.

How to interpret this? Here’s the opinion of pharmaceutical chemist (and blogger) Derek Lowe:

The Nature piece contains some brave-face material about how this study has uncovered a whole list of new therapeutic targets, but sheesh. What are the odds that any of these will prove to be crucial, even for the low percentage of women who turn out to have them? No, instead of making me yearn for ever-more-personalized targeted therapies, this makes me think that early detection and powerful, walloping chemotherapy (and surgery) must be the way to go for now. I mean, this was still only fifty patients, and uncovered this much complexity: how tangled must the real world be?

[Source: In the Pipeline]

No question the genetic sequencing technology is becoming better by leaps and bounds (and cheaper). No question that studies like this one are racking up a lot of new data correlating genetic change and cancer. It’s possible that one, or a simple combination of genetic mutations could be fingered as the cause for breast cancer – and treatments developed. Unfortunately, studies like this one make it more likely that genetic mutations are part of a much more complex web of causation – probably with elements scientists don’t even know about yet. I can hear scientists like Derek moaning, “We’re not going to test for and make treatment with 1,700 genetic errors!” Like I said, we just need to learn more.