Cancer moon shots

M. D. Anderson Cancer Center announced a $3 billion research program today aimed at six specific forms of cancer.

  • Acute myeloid leukemia and myelodysplastic syndrome (AML and MDS)
  • Chronic lymphocytic leukemia (CLL)
  • Lung cancer
  • Melanoma
  • Prostate cancer
  • Triple negative breast and ovarian cancer

These special areas of research are being called “moon shots” by analogy with John F. Kennedy’s challenge to put a man on the moon. This isn’t a new idea. In fact, a few months after the first moon landing, there was a full-page ad in the Washington Post that began “Mr. Nixon: You can cure cancer.” The thinking was the familiar refrain “If we can put a man on the moon, we can …” President Nixon and other politicians were excited about the idea and announced a “war on cancer.” Scientists, however, were more skeptical. Sol Spiegelman said at the time

An all-out effort at this time would be like trying to land a man on the moon without knowing Newton’s laws of gravity.

The new moon shots are not a national attempt to “cure cancer” in the abstract. They are six initiatives at one institution to focus research on specific kinds of cancer. And while we do not yet know the analog of Newton’s laws for cancer, we do know far more about the basic biology of cancer than we did in the 1970’s.

There are results that suggest that there is some unity beyond the diversity of cancer, that ultimately there are a few common biological pathways involved in all cancers. Maybe some day we will be able to treat cancer in general, but for now it looks like the road forward is specialization. Perhaps specialized research programs will uncover some of these common patters in all cancer.

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True versus Publishable

This weekend John Myles White and I discussed true versus publishable results in the comments to an earlier post. Methods that make stronger modeling assumptions lead to more statistical confidence, but less actual confidence. That is, they are more likely to produce positive results, but less likely to produce correct results.

JDC: If some scientists were more candid, they’d say “I don’t care whether my results are true, I care whether they’re publishable. So I need my p-value less than 0.05. Make as strong assumptions as you have to.”

JMW: My sense of statistical education in the sciences is basically Upton Sinclair’s view of the Gilded Age: “It is difficult to get a man to understand something when his salary depends upon his not understanding it.”

Perhaps I should have said that scientists know that their conclusions are true. They just need the statistics to confirm what they know.

Brian Nosek talks about this theme on the EconTalk podcast. He discusses the conflict of interest between creating publishable results and trying to find out what is actually true. However, he doesn’t just grouse about the problem; he offers specific suggestions for how to improve scientific publishing.

Related post: More theoretical power, less real power

Flying to Mars in three days

Richard Campbell brought up an interesting idea in his recent Mars geek out show. Suppose you could travel to Mars accelerating at 1 g for the first half the trip, then decelerating at 1 g for the final half of the trip. Along the way you’d feel a force equal to the force of gravity you’re used to, and you’d get there quickly. How quickly? According to the show, just three days.

To verify this figure, we’ll do a very rough calculation. Accelerating at 1 g for time t covers a distance is g t2/2. Let d be the distance to Mars in meters, T the total of the trip in seconds, and g = 9.8 m/s2. In half the trip you cover half the distance, so 9.8 (T/2)2/2 = d/2. So T = 0.64 √d.

The hard part is picking a value for d. To keep things simple, assume you head straight to Mars, or rather straight toward where Mars will be by the time you get there. (In practice, you’d take more of a curved path.) Next, what do you want to use as your straight-line distance? The distance between Earth and Mars varies between about 55 million km and 400 million km. That gives you a time T between 1.7 and 4.7 days.

We don’t have the technology to accelerate for a day at 1 g. As Richard Campbell points out, spacecraft typically accelerate for maybe 20 minutes and coast for most of their journey. They may also pick up speed by slinging around a planet, but there are no planets between here and Mars.

Deniers, skeptics, and mavericks

Suppose a scientist holds a minority opinion. There’s a trend in journalism to call him a denier if you think he’s wrong, a skeptic if you don’t care, and a maverick if you think he may be right. If this had been the norm in Einstein’s day, he might have been called a Newton-denier.

“Denier” is an ugly word. It implies that someone has no rational basis for his beliefs. He’s either an apologist for evil, as in a Holocaust denier, or mentally disturbed, as in someone in psychological denial. The term “denier” is inflammatory and has no place in scientific discussion.

Ancient understanding of tides

In his essay On Providence, Seneca (4 BC – 65 AD) says the following about tides:

In point of fact, their growth is strictly allotted; at the appropriate day and hour they approach in greater volume or less according as they are attracted by the lunar orb, at whose sway the ocean wells up.

Seneca doesn’t just mention an association between lunar and tidal cycles, but he says tides are attracted by the moon. That sounds awfully Newtonian for someone writing 16 centuries before Newton. The ancients may have understood that gravity wasn’t limited to the pull of the earth, that at least the moon also had a gravitational pull. That’s news to me.

How things break

Venkatesh Rao wrote a blog post today Stress Failures versus Decay Failures. It reminded me of three other resources I recommend on how things break. The first is about how things literally break. For example, why the steel in the Titanic was brittle.

The other two are about how complex systems break.

The cult of average

Shawn Achor comments on “the cult of the average” in science.

So one of the very first things we teach people in economics and statistics and business and psychology is how, in a statistically valid way, do we eliminate the weirdos. How do we eliminate the outliers so we can find the line of best fit? Which is fantastic if I’m trying to find out how many Advil the average person should be taking — two. But if I’m interested in potential, if I’m interested in your potential, or for happiness or productivity or energy or creativity, what we’re doing is we’re creating the cult of the average with science. … If we study what is merely average, we will remain merely average.

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Chaotic versus random

From John D. Barrow’s chapter in Design and Disorder:

The standard folklore about chaotic systems is that they are unpredictable. They lead to out-of-control dinosaur parks and out-of-work meteorologists. …

Classical … chaotic systems are not in any sense intrinsically random or unpredictable. They merely possess extreme sensitivity to ignorance. Any initial uncertainty in our knowledge of a chaotic system’s state is rapidly amplified in time.

… although they become unpredictable when you try to determine the future from a particular uncertain starting value, there may be a particular stable statistical spread of outcomes after a long time, regardless of how you started out.

Emphasis added.

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Eat, drink, and be merry

Almost every bit of health advice I’ve heard has been contradicted. Should you eat more carbs or fewer carbs? More fat or less fat? Take vitamin supplements or not? It reminds me of this clip from Sleeper in which Woody Allen wakes up after 200 years of suspended animation.

Offhand I can only think of a couple things on which there seems to be near unanimous agreement: smoking is bad for you, and moderate exercise is good for you.

Here are a couple suggestions for evaluating health studies.

Be suspicious of linear extrapolation. It does not follow that because moderate exercise is good for you, extreme exercise is extremely good for you. Nor does it follow that because extreme alcohol consumption is harmful, moderate alcohol consumption is moderately harmful.

Start from a default assumption that something natural or traditional is probably OK. This should not be dogmatic, only a starting point. In statistical terms, it’s a prior distribution informed by historical experience. The more a claim is at odds with nature and tradition, the more evidence it requires. If someone says fresh fruit is bad for you, for example, they need to present more evidence than someone who says an newly synthesized chemical compound is harmful. Extraordinary claims require extraordinary evidence.

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Approximating galaxies as spheres

A couple days ago I compared different ways of approximating Earth and other ellipsoids by spheres. The earth is so nearly spherical that the difference in the approximations would only matter when you need fairly high accuracy. Elliptical galaxies, however, can be much more eccentric than Earth and so the difference in approximation approaches can matter more.

The Hubble classification of elliptical galaxies uses a scale E0 through E7 where the number following ‘E’ is 10(1 – b/a) where a is the major semi-axis and b is the minor semi-axis. An E0 galaxy is essentially spherical. The most common classification is near E3. The limit is believed to be around E7.

Hubble photo of galaxy M49

The image above is a photo of Messier 49, an E4 galaxy, taken by the Hubble telescope.

For an E3 galaxy, the minor and major axes are around 7 and 10 in some unit. The average of these is 8.5. A sphere with the same volume would have radius 8.88 and a sphere with the same surface area would have radius 8.98, about 5.7% larger than the average of the axes.

For an E7 galaxy, the minor and major axes would have a ratio of 3 to 10. This gives an average of 6.5. Matching volumes gives a radius of 6.69 and matching surface area gives a ratio of 7.67, about 18% larger than the average of the axes.