How Plants in Space Might Be a Cautionary Tale for Precision Medicine

A version of this originally appeared in the Timmerman Report.

When President Obama announced the Precision Medicine Initiative (PMI) back in 2015, it was a moment not unlike when a pop star drops a new album. We know she’s working on something, but details are scarce until that midnight announcement on iTunes. Then Boom!

This was Precision Medicine’s coming out party, the moment when a sensible, but somewhat obscure biomedical research concept went mainstream. And it’s exciting! As someone who’s been on the Precision Medicine bandwagon for a while now, I’m glad to see the concept getting more attention.

And yet, I’ve found myself worrying that we haven’t learned from the past and once again we’re riding a hype rocket destined to crash and burn like the first (and the second, third, and fourth) attempt at getting that SpaceX booster stage to land on a platform in the Pacific. While we’ve learned an amazing amount about human genetics over the past few decades, there’s much more we still don’t know and understand. While genetics races ahead, we are often still stuck in neutral when it comes to our understanding, much less predicting, the effect of the environment on genes’ effects on phenotype. It’s not easy.

Think, for a second about autoimmune diseases like type 1 diabetes, celiac, or rheumatoid arthritis. We don’t know what causes most of them, or causes them to flare up, but evidence is pointing toward the complex interplay of genes responding to environment. Consider the research on autoimmunity in Finland, compared with that across the border in Russia’s Karelia territory. As Moises Velasquez-Manoff has described, despite having quite homogeneous populations, and a similar geographic environment, the Finnish side sees much higher incidences of autoimmunity and allergies. Finnish scientists are increasingly pointing to genetic variations that have allowed Russians in a less-hygienic environment to avoid some of the autoimmune conditions that have emerged in Finland.

Or consider some provocative cohort studies from Florida, which characterize the timing of diagnosis for inflammatory bowel disease (IBD) in Cuban immigrants. Over time the duration between arrival in the US and diagnosis for IBD has been decreasing. Immigrants who arrived before 1980 had an average time to IBD diagnosis of 31.77 years, whereas immigrant who arrived after 1995 had an average time to diagnosis of 8.30 years. Given that the genetic background of immigrants is expected to be similar over time, the evidence suggests environmental factors.

Changes to the environment, including subtle ones that might seem trivial, sometimes matter.

Basic research often has a hard time justifying its existence in a world that wants immediate payoffs, but this is where it helps to look to basic research for guidance. In specific, we can learn from plants in space. There’s something here for everyone who studies eukaryotes.

I used to work on Arabidopsis thaliana – a genetically complex plant model organism that non-scientists would call a roadside weed. My ears perk up whenever I hear a story about Arabidopsis in the news. A few years back, researchers sent seeds up into space and grew Arabidopsis in microgravity. According to this report, while plant morphology was generally the same, the secondary branches and seed pods grew out perpendicular to the stem. On Earth, branches and pods normally point upwards, making an acute angle with the stem.

Who knew? But more to the point, who would have predicted this? I suspect no one would have guessed. Studies like this in space may help us actually figure out ahead of time, eventually, what effect microgravity will have on the growth pattern of seed pods, not to mention the many other things going on that weren’t or couldn’t be measured. It might also help us confidently predict the phenotypes that wouldn’t be affected. But this will take a lot of time and much better predictive models for gene regulation and phenotypic expression.

Arabidopsis had its genome sequenced back in 2000. It has been the subject of much basic and applied plant biology research—at this point, it’s probably the best studied plant on the planet (sorry maize—you had a good run). Scientists took advantage of the relatively easy transformation methods of Agrobacterium-mediated gene disruption and the ability to do blanket mutagenesis screens to define dozens of developmental and metabolic pathways. Many of those findings were used to improve numerous crop species (something to consider, for those in the US Congress who scoff at spending on basic research). But even after all this investment and research, I don’t think any scientist would suggest we’ve solved Arabidopsis to the point where we can predict everything about how it would grow, develop and otherwise behave when introduced to a new environment. But it’s a simpler model and easier place to start than human biology.

Parenthetically, speaking of unexpected things that happen to organisms in space, I’d suggest you check out this great piece on sex in space by Maggie Koerth-Baker and how the birds and the bees (and the rats) don’t (ahem) function the same in space either.

The heart of Precision Medicine is giving patients tailored treatments based on the molecular fingerprint of their diseases or conditions. There will be better efficacy and fewer side effects of new drugs because the treatment will be more specific. It’s no mystery why the current forefront of Precision Medicine is in cancer, where the strong causal connection between genetics and phenotype means therapeutics tailored for specific mutations in specific oncogenes or other biological processes provide a clear, straightforward path to Precision Medicine. If you have mutation X in gene Y, then take drug Z, which was developed to target that precise pathway.

This simple path works best, however, if the genetic penetrance is nearly complete and is not affected by environmental factors. Outside of cancer there aren’t as many examples of common genetic risk factors that have strong causality, even in combination. When you don’t have that kind of tight causal relationship, Precision Medicine is harder to pull off. The recent Omnigenic model proposed by Evan Boyle, Yang Li, and Jonathan Pritchard, which suggests most genes in a given tissue influence disease susceptibility, and the resulting debate about the applicability and value of GWAS moving forward shows the ongoing evolution in how genomicists and clinicians look at the interplay between our genes and phenotypes. The implication of a model like this is that even when looking only at the genetic side of things, strong causality driven by a small, testable number of variants for many diseases may just not be how biology works.

Adding to this, the plants in space example suggests to me that even as we learn more about genetic contributions and subsequent gene-by-environment interactions, some of that knowledge has an expiration date because of how our environment is changing.

Look at China. Thanks to decades of coal-burning for energy needs, China is currently experiencing a frightening air pollution challenge in some of its larger cities. How is that environmental phenomenon affecting gene expression and penetrance of variants? How will it affect development and gene expression and development of chronic diseases in the future? The airborne irritants and continual exposure might have no effect at all. Or they may have dramatic effects that will render some potential therapeutic pathways more or less effective in that population five, 20, 50 years from now. If, let’s say in a crazy hypothetical situation, which our government is assuring us will never happen, the global temperature was on track to increase by an average of 2 degrees Celsius or more over the next century, with all the cascading environmental changes that would cause, how would our genes respond?

I guess this just reduces down to a call for some circumspection. The very fact that Precision Medicine has entered the common lexicon is a reason for biomedical researchers to be cautious. Overpromising has already happened, but it could get a lot worse. At a time when funding for science is under siege, we don’t need examples at which naysayers can point as instances where scientists promised they’d cure cancer, but didn’t.

The science is already amazing. There are terrific things coming down the biopharma pipelines. And I think we’ll get a handle on gene-by-environment interactions—enough, at least, to meet the goal of creating specific health solutions based in part on each person’s genome for some diseases (although if the late Susan Lindquist’s HSP90 hypothesis turns out to be correct, many bets are off). But health and environment are moving targets. If we want Precision Medicine to be like the successful SpaceX launches, we need to keep an eye on things outside the controlled environments of our labs and clinical trials and do everything we can to embrace and understand the conversation our genes are having with the world outside.

 

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It’s time for biopharma to embrace public health

This piece first appeared in the Timmerman Report.

Some years ago when I was working for a large biopharma, I heard a story. It seems a senior scientific executive had visited and given a seminar in which he described the company’s portfolio of drugs for type 2 diabetes. The company was projecting great uptake and profits. A member of our site raised his hand and said, “But if people just ate less and exercised a little more, they could prevent type 2 diabetes and the market would disappear.”

The answer: “Yeah, but they won’t.”

Harsh! But that executive was right. The Institute for Health Metrics and Evaluation (IHME) recently published a paper in JAMA describing how much different health conditions contribute to private and public health spending in the US. Number one? Diabetes. Following that were heart disease and chronic pain. These are chronic lifestyle diseases with big environmental and behavioral components, and the data make me wonder if there’s an opportunity here for the industry to zig and do some things that, in the long run, may make drug development more sustainable.

I think it’s time for biopharma to get involved in public health. Continue reading

An Open Standard for APIs Could Lead us to Better Health

There’s a parable about the elephant and the rider that’s been used by Chip and Dan Heath, and that originated with Jonathan Haidt, to describe how humans make decisions. A person’s mind can be thought of as consisting of a rider, representing the rational part of human thinking, and the elephant she’s riding, representing emotion. Both of these play a role in how a person decides things, and many of us believe the rider–the rational part–is in charge. The rider taps the elephant with her guide stick, and the elephant obediently moves in that general direction or does a specific task, like carrying lumber from place to place.

Except that’s not how a lot of decisions actually get made. Instead, the elephant sees a bunch of bananas, or a herd of other elephants, or a nice cool river to bathe in, and goes that way instead. And the rider…well, the rider can’t do much about it except, after the fact, rationalize how she always wanted to go in that direction to begin with. Yeah, it was time for a bath, sure

This framing has stuck in my mind for years and it’s a really helpful way of looking at many of the odd things that people do or say, ranging from climate change denial, to believing genetically modified organisms are inherently evil, to smoking despite everything we know about the harms that result, to even saying that Paul Blart, Mall Cop II is really, you know, not that bad–really. And it also speaks to one of the more vexing problems we have in human health. Why do people keep doing things they really probably shouldn’t, and know they shouldn’t, if they want to stay healthy?

I’ve touched before on how the power of digital tools can help make it easier for us to make good decisions. OPower is doing this for power consumption and conservation, and with the advent of tools like Apple’s Healthkit and the proliferation of activity trackers, the time is right to do this for health. Continue reading

Making Change

And now for something completely different! Short fiction in honor of the recent unveiling of the Apple iWatch and Healthkit.

“I wouldn’t eat that if I were you.”

Sylvia paused, bacon cheeseburger halfway to her mouth, and peered at the neon green band wrapped around her wrist. The wraparound touchscreen was currently showing a cat emoji. It had a frowny face, expression halfway between puzzlement and alarm.

“What did you say?”

“I’m just saying,” said her Best Buddy wristband, “that when we met a few weeks ago, you mentioned wanting to keep your weight in a specific range.” The emoji shrugged. “Little friendly reminder. You know?”

Sylvia carefully put the burger back down and resisted the urge to lick grease off her fingers. She fumbled for her napkin, her fingers leaving translucent streaks on the thin, white paper.

“I–well, yeah. But, I mean, you’ve never said anything like this before like when–” She broke off, remembering the milkshake, the onion rings, the King-size Choconut bar…

“Well it’s not the first thing you do, is it? When you meet someone and you’re just getting to know them?” The cat had morphed into a light pink, animated mouse, standing on its hind legs, bashfully kicking one leg. “But now, we’re friends!” Continue reading

Baseball, Bayes, Fisher and the problem of the well-trained mind

One of the neat things about the people in the baseball research community is how willing many of them are to continually question the status quo. Maybe it’s because sabermetrics is itself a relatively new field, and so there’s a humility there. Assumptions always, always need to be questioned.

Case in point: a great post by Ken Arneson entitled “10 things I believe about baseball without evidence.” He uses the latest failure of the Oakland A’s in the recent MLB playoffs to highlight areas of baseball we still don’t understand, and for which we may not even be asking the right questions. Why, for example, haven’t the A’s advanced to the World Series for decades despite fielding good and often great teams? Yes there’s luck and randomness, but at some point the weight of the evidence encourages you to take a second look. Otherwise, you become as dogmatic as those who still point to RBIs as the measure of the quality of a baseball batter. Which they are not.

One of the thought-provoking things Arneson brings up is the question of whether the tools we use shape the way we study phenomena–really, the way we think–and therefore unconsciously limit the kinds of questions we choose to ask. His example is the use of SQL in creating queries and the inherent assumptions of that datatype that objects within a SQL database are individual events with no precedence or dependence upon others. And yet, as he points out, the act of hitting a baseball is an ongoing dialog between pitcher and batter. Prior events, we believe, have a strong influence on the outcome. Arneson draws an analogy to linguistic relativity, the hypothesis that the language a person speaks influences aspects of her cognition.

So let me examine this concept in the context of another area of inquiry–biological research–and ask whether something similar might be affecting (and limiting) the kinds of experiments we do and the questions we ask.

Continue reading

Baseball analytics, arthritis, and the search for better health forecasts

All opinions are my own and do not necessarily reflect those of Novo Nordisk.

It’s Fourth of July weekend in Seattle as I write this. Which means it’s overcast. This was predictable, just as it’s predictable that for the two months after July 4th the Pacific Northwest will be beautiful, sunny and warm. Mostly.

Too bad forecasting so many other things–baseball, earthquakes, health outcomes–isn’t nearly as easy. But that doesn’t mean people have given up. There’s a lot to be gained from better forecasting, even if the improvement is just by a little bit.

And so I was eager to see the results from a recent research competition in health forecasting. The challenge, which was organized as a crowdsourcing competition, was to find a classifier for whether and how rheumatoid arthritis (RA) patients will respond to a specific drug treatment. The winning methods are able to predict drug response to a degree significantly better than chance, which is a nice advance over previous research.

And imagine my surprise when I saw that the winning entries also have an algorithmic relationship to tools that have been used for forecasting baseball performance for years.

The best predictor was a first cousin of PECOTA. Continue reading