This official statement is rather odd as others have pointed out. Anyway, the biggest problem with the original paper is that they leave out breast and prostate cancer, the two most common cancers. Maybe these would support their hypothesis, but they leave them out and they don't really explain why. It's probably because we aren't sure what the true incidence is, due to ascertainment bias from increased screening. Whenever I read a broad ranging conclusion in life sciences research, I remind myself to stick <Except when it isn't> on the end (eg Most cancer is due to bad luck - except when it isn't.)

The paper also technically sets out to explain why cancer incidence varies between different tissues. An analogy is correlating the average temperature with distance from the equator. It is clear that most of the difference in the mean temperature of Equador compared to Iceland is explained by distance from the equator. You can create categories like number of days with rain, number of days with a temperature > 30 degrees C, and each of these values will be highly correlated with distance from the equator. But it does not follow that this strong correlation means that there are hardly any environmental influences on the temperature in St Petersburg tomorrow, and that we can fire all the meteorologists.

(Disclaimer for what follows: I do life sciences research, so I may have an overly pristine view about physics.)

The really interesting thing about all this I think is the collision of maths/physics/engineering and life sciences! Vogelstein wrote this paper with a mathematician. They basically did a back of the envelope calculation, an approach that is much favoured by engineers but completely alien to life science researchers. These simple calculations are useful because in the physics/engineering paradigm, abstractions are incredibly powerful and non-local. For example, many important physical laws that apply equally across many orders of magnitude, can be derived from thinking about falling apples or billiard balls. Paul Dirac predicted the positron by simply exploring other possible solutions to an equation! To me, this is an extraordinary and winning moment for quantum physics, where the theory is so powerful, that it can drag us screaming towards completely unintuitive and otherwise inaccessible conclusions. To a biologist, this kind of thing is ridiculous and alien. The same abstractive power is almost completely absent, and both experimental and theoretical models are extremely limited.

There are many reasons for this. You could argue that biology is not amenable to the often time-invariant abstractions so useful in the other sciences. Or perhaps biologists aren't trained to think that way. In any case, it is interesting to watch when an engineer encounters biology, and this paper is an example. I am still not sure whether we need more engineers in biology or not?

I think it is abundantly clear though that breakthroughs in biology don't come from professors having epiphanies while walking amongst the pine cones on a cloudy autumn day. Human intuition alone is failing to get us very far in biology. We need a paradigm that can deal efficiently with uncertainty, incomplete yet massive data, noise, simulation of highly parallel processes on long time scales, causality in networks of highly correlated actors etc - I hope that some future melding of computer science, biology and staggering computational resources will give us more useful ways to investigate life.