Phase i trialists

This is an early, and longer, version of a piece that was published in the Lancet Oncology, March 2012, 13(3):236

Phase i trialists

There is a new breed of clinical trialist on the block in cancer research.  You might not have seen them yet - they won’t be knocking down your door in the clinic.  They don’t know what HIPAA stands for.  They don’t know what to do in a code.  They don’t wear a white coat, you’ll be lucky if they wear a tie.  They’re not biologists - if you ask them to change the media, they’ll likely bring you some music you haven’t heard.  They are Phase i trialists.

What’s a Phase i trial?  It’s a pre-clinical trial, but no cell, mice or rats will be harmed.  Before one begins killing cells in a dish, there is the step to decide how to treat those cells, or mice in a sensible, yet new way.  It is this step, before even stepping into a ‘lab’, where we are just now seeing an influx of other types of scientists - physicists, engineers, computer scientists and mathematicians.  Some of these folks have run out of problems in their field and have found fertile ground for their tools in the dizzying biological complexity of cancer.  Others have become frustrated by the esoteric nature of their first field - it takes a special mind to be happy studying things in other galaxies, or so small that you need a super collider spanning three countries to learn anything new.

And then, some are just naturally dreamers, or follow their hearts into a field that has affected their life (cancer touches so many of us).  It is these folks that I call Phase i trialists (i for the imaginary number, the square root of -1), they are the smartest bunch of scientists that you’ve never met.  Happy dreamers who turn coffee into biological hypotheses.  Mid-career scientists who trade their radiotelescope time for hospital badges.  And what do you get when you turn these folks loose in cancer research?  You get crazy ideas that you might be able to apply information theory to genetics or the helmholtz free energy to mitochondrian.  You get people who dream that biology can be explained by first principles - that we can build models on a chalkboard or a computer chip that can predict how a tumor will grow and evolve, how a person may live or die.  You get Phase i.

The biggest difficulty encountered right now is trying to get clinicians and scientists, many of whom are already dogmatized, to listen to a physicist with a crazy idea.  To be willing to bet some of their hard earned grant money and time on an mathematician’s model that very well might be nuts.  To be willing to think back 30 years to the last time they took a calculus class and bite down hard on whatever they can find until the testable hypothesis comes out from the equations.  But, without scientists on both sides listening to each other though, these crazy (and possibly transformative) ideas will never come to light, and we could miss out on something worthwhile.

It is becoming more and more apparent that cancer isn’t just a collection of mutated cells - but instead a richly varied combination of different cell types, both normal and tumor, producing different factors and with different skills.  We have known for a long time that tumors have mechanisms by which to hijack angiogenesis (the process by which new growing tissue gets blood vessels), but now we are finding out that it may hijack other ‘normal’ mechanisms as well: chemotaxis and motility, likely stolen from wound healing or the migration needed to form a sensible embryo; limitless replicative potential, likely stolen from stem cells; and most importantly, the ability to evolve under pressure, inherited honestly by cancer’s virtue of being mostly us.

Taken together, all of these concepts make the task of treating a tumor with a single cytotoxic agent (chemotherapy), or even targeted therapy seem like an unlikely strategy for success.  Indeed - most cancers are treated with at least two agents now, and some with many more.  It would be a daunting task to try all possible combinations of agents, especially when human lives are at stake in trials and it is a daunting task to understand how all of these attributes combine to make a whole.  Fortunately, we aren’t the first minds to try to tackle this sort of complexity.  In many quite salient ways, this level of complexity is akin to an ecosystem - there is symbiosis between different players, competition for space and resources and various environmental factors that affect the whole game.  Why not then climb up on the shoulders of some giants and look around?  Why not try the tools that our conservation ecologists use to manage invasive species?  That macroeconomists use to understand predatory business strategists? That agronomists use to manage pest infestations?

Well, these Phase i trialists have, and continue to.  They have hijacked the beautiful differential equation system proposed by Lotka and Volterra to understand predator-prey systems to try to understand how the dynamic interplay between healthy and normal is effected by various traits or strategies1.  They have used Maynard Smith’s evolutionary game theory to tease out the relationship between the shift to aerobic glycolysis (the Warburg shift) and cancer invasion2.  They have studied the prisoner’s dilemma to understand cooperation between tumor cells of disparate lineage3.

Sometimes, the use of a new concept just raises more questions, but the questions are often ones that can be answered with relatively simple experiments that can elucidate new "big picture" information.  It was once said that if an experiment in physics needs statistics to prove its validity (as we expect every biomedical experiment to need) then it is not a real result! (credit to Ernest Rutherford I believe)  As an example, using physics to understand hematogenous metastasis: the first thing a physicist would do is draw a diagram.  Drawing the circulatory system as a flow network immediately highlights organ system connections that explain the preferential spread of some, but not all cancers4.  Cancers of the gut preferentially spread to the liver - the next stop; prostate cancers however, spread almost always to bone first, necessitating a tortuous route through several capillary beds.  How do these cancers differ?  Can we measure, as we would electric current, the relative amount of circulating tumor cells (the vector of metastasis) in each portion of the network?  Do conservation laws exist, like Kirchoff’s loops?  These types of questions have not been answered to date, and only very recently even have they been asked!  The list of insights from Phase i trials keeps growing.  The field of cancer research has been dominated by a single group of scientists for a long time and, like a field farmed for one crop, we have seen yields diminishing.  Letting the field lie fallow is certainly not an option - so we must sew new seeds and tend them with teams of scientists who have different viewpoints to realize the full potential of the harvest.

1.  Gatenby RA, Vincent TL. “Application of quantitative models from population biology and evolutionary game theory to tumor therapeutic strategies” Molecular Cancer Therapeutics 2003;2(9):919-927

2.  Basanta D, Simon M, Hatzikirou H, Deutsch A. “Evolutionary game theory elucidates the role of glycolysis in glioma progression and invasion” Cell Proliferation 2008;41(6):980-987

3.  Axelrod R, Axelrod DE, Pienta KJ. “Evolution of cooperation among tumor cells” PNAS 2006;103(36):13474-13479

4.  Scott JG, Kuhn P, Anderson ARA.  “Unifying Metastasis”, Nature Reviews Cancer Jul 2012, 12 445-446.

Whose model is it anyways?

Whose model is it anyways - a fundamental barrier to progress in the age of biology?

We are now well into the ‘age of biology’, which is following the ‘age of physics’ that was the 20th century - a time that marked immense theoretical and experimental progress in fundamental physics.  This new ‘age of biology’ has been characterized so far by large leaps forward in experimental technique: the dawn of genomics, epigenomics and proteomics to name a few.  Because of these inventions, there has been a sea change in the way we collect data: from simple hypothesis driven experiments where the answer was typically a clear yes or no, to very complicated experiments where the answers are much less clear - very similar to the early experiments of Rutherford and colleagues clearly showing alpha particle scattering (eventually yielding the Bohr model of the atom) as compared to the data coming out from the Large Hadron Collider.  The ‘age of physics’ saw it biggest gains when theorists like Feynman and Gell-Mann joined the fray, using primarily mathematical methods to understand complex interactions, and to make predictions to drive future experiments.  We contend that the ‘age of biology’ can not yield the results it promises until the analogous situation arises -  when quantitative, theoretical biological modelling is fully embraced by experimentalists and trialists.

This realization is not new, indeed many funding agencies are now, and have been for some time, pushing to fund research at the interface between theoretical and experimental disciplines.  Surprisingly, despite all this, there are still fundamental misconceptions that we feel are affecting how interdisciplinary research is evaluated and, potentially, seriously hampering its progress.

A recent question from an experimental scientist highlights what we think is a fundamental misconception about the relatively new, interfacial, discipline that is mathematical biology.  

He asked:

“What information do you need to put into your [mathematical] model?”

While this is a well-meaning question, and one asked in the spirit of interdisciplinary science, we contend that it illustrates a widely held, incorrect, opinion about mathematical models: that they are fundamentally different models compared to experimental ones.  While it is true that the mathematical (or in silico) models that we build look quite different than the models used in a biology lab (in vitro and in vivo), we contend that they are a part of the self-same scientific process, that they also are fundamentally distillations of a central model, and that it is only semantics getting in the way.

To clear the air, let us agree that every scientist doing work in biology has a logical construct (a model) in his or her head to explain the phenomena in question - if they didn’t, they couldn’t possibly be doing hypothesis driven research.  This model is, if the project is truly a collaborative effort, the same as the one being held in the minds of each other scientist working on the project, whether they be theorists or experimentalists.  While each of these scientists may have a slightly different interpretation or depth of understanding, the central model should be the same.  What is different then is the approach that each scientist takes to inform the central model - whether it be an in vitro or in vivo experimental approach, or an in silico quantitative approach.  

Further, it is exactly these differences that gives each approach its strength. An in silico model can never show a relationship to be biologically relevant the way that an in vivo one can, and experimental techniques run into difficulty when the relationship or interaction between factors is non-linear (as we believe most are in biology), and it is exactly here that quantitative techniques shine.

So a better question would have been:

“What sort of questions has your [mathematical] approach generated about our model that I could test?”

In order to truly be interdisciplinary scientists, and to realize the promise of the ‘age of biology’, we have to realize that we are all part of the same team, seeking the answer to the same question - trying to understand the same model - just with different tools.

**

n.b. I wrote this piece with help from Philip Maini and Alexander Anderson, two of my DPhil supervisors.

The Tomorrow's Doctor Initiative

In my opening post, I mentioned that I gave a talk at last year's TEDMED meeting.  Giving the talk itself was a great experience, but more important to me were (are) the relationships that I made.  It was such a strange and lovely collection of people - lots of square pegs - that I felt right at home in a way I rarely have before.  I mentioned meeting Jonathan Eisen in my first post, but this time, I am going to focus on an initiative that the two other 'young guys' and I started.

The average speaker at TEDMED is, I would say, at least mid-career and crushing it.  Last year, there were three of us who did not fit this mold.  Me, you know, a PGY-3/PhD-2, sort of in medias res.  The other two, Ali Ansary (who talked about embracing technology in health care)

and Sandeep Kishore (who asked us to make sure we are considering the 'causes of the causes' in all of our health decision making) 

were rising 3rd year medical students.  To be fair, they both have other advanced degrees and have done some 'crushing it' on their own, but I digress.  

The three of us talked (on stage) about quite different topics, but our conversations backstage and during the breaks always gravitated to what we knew best - medical education.  We all felt that our experiences were really good, but that medicine was changing really fast, and the educational system wasn't keeping up.  We were lucky enough to engage folks from the AAMC and talk about changes they had planned already to the MCAT to try to capture a bit more about a student than just their hard science acumen, but we felt that we wanted to contribute a bit more to the discussion.

A series of discussions over Skype ensued and we came up with a sort of manifesto, which we published on the Huffington Post

and subsequently on The Health Care Blog.

These articles, in short, outline what we see is a set of problems facing medical schools and physicians preparing to practice in the 21st century.  What we don't think we have right now is answers and there are already nearly 30 medical schools pioneering new curriculums (all different!) and organizations from the AMA (10M$ grant!) and AHA (white paper) who have taken stands to stir up change.  So how the heck will we fit in?

What we hope to do is start a conversation that anyone can join in on about preparing the 21st century physician.  We started a website, www.tomorrowsdoctor.org

which solicits people's 'visions' of how to prepare the 21st century physician.  The TEDMED blog ran a post about this at the time, and it is currently only cataloguing visions, but it will (next iteration, a week or two we hope) have more functionality to include the ability to comment on individual visions and share them via social media.

We are going to then sit down in 6 months time with some collaborators and curate these visions and the conversations that they begin and use this 'crowd-sourced' vision to design a unified set of core competencies for the 21st century physician and the track required to prepare and admit them.

Will it be taken seriously?  I don't know.  The AAMC is interested in what we're doing, so that is a start.  And, anything that sparks a conversation about this important issue isn't a waste of time. So - go share your vision with us if you haven't already...

We have a meeting with the AAMC next week, if there's anything you'd like us to communicate with them, let me know in the comments. Otherwise, stay tuned.

Upcoming posts will include a conversation about stochastic models in biology, a real 'cancer connection' where I've helped a modern artist (www.raypaulart.com - @raypaul4) and a pathologist come together to make art and start a healing journey (see twitter feed for an example of the new art!) and my own, personal, EO Wilson rant...

Jake out.

Edit on 4/27/2013:  I was remiss in not including an initial word cloud from the first 40 or so visions.  Here that is:

Hello world

I've been meaning to start this blog for a long time now.  A year ago, at TEDMED 2012, I met a guy backstage, Jonathan Eisen, who I didn't know from Adam.  It turns out, he's a pretty big deal, and more importantly, a really nice guy, a great scientist, science communicator and now a mentor and friend.  He  writes a science blog I follow, The Tree of Life and gave a great talk at that meeting called 'Meet your microbes', where he talked about fecal transplants, among other things.  You can watch his talk here.

It was our first conversation backstage though, when he challenged me about my 'job' as a scientist, that I remember best.  It went like this:

Jonathan: Oh cool, you do theoretical biology research? What's your twitter handle?

Me: I don't use that stuff, I just do science.

Jonathan: Wait, what's your job as a scientist then?

Me: Uhhh... My job is to do science.

Jonathan: No, your job is to do and then communicate science.  And if you aren't using social media and a blog to do that, you aren't doing it as effectively as you could be.

**update - just after I started this blog, members of Jonathan's lab published this awesome paper in PLoS Biology to help teach scientists how to use social media... I'm trying!**

So, I signed up to twitter (follow me if you wish, I'm @CancerConnector) and vowed to start writing a blog about science. Last week, at TEDMED 2013, I hung out with Jonathan again, and...  well, I'm writing a blog now.

My hopes for this blog are to write about my disparate interests in a somewhat more casual way than I would for official publications.  If you don't know me, I'm a radiation oncologist (someone who takes care of cancer patients and uses radiation therapy to help in their treatment) who does theoretical research into the way cancer works.  This research is usually in the form of mathematical models, but I work closely with biologists as well to ensure that my models are testable.  Because of my background, I have other interests that may find their way into this blog as well that include medical education (here's a recent article on Huffington Post), patient-doctor communication, global health, care of veterans and random medical essays... so bear with me.

I'd be remiss if I didn't also embed my own TEDMED talk as it nicely summarizes ME and will give a sense of who I am and what I'm generally about.  Here I am:

So.  There it is.  I hope this will be useful and informative.  Talk to you soon...