Transcriber: ‌Behdad Khazaeli Reviewer: Zsófia Herczeg
It's great to be here, Minneapolis, my hometown.
(Cheering) (Applause)
(Laughter)
Movies.
We live in an amazing digital technology age.
Mobile devices and online retail are changing the way we live and work.
We are seeing the emergence of artificial intelligence
and with it the potential for self-driving cars.
But most importantly, we can make cat videos.
(Laughter)
This is my cat, Salt, being cute.
And I shot this movie using my phone -
very proud of that.
Movies are powerful because they capture motion,
and they can tell a story - oh.
(Laughter)
I could stop there and that'd be the best TED Talk ever.
(Laughter)
Thank you.
(Laughter)
What I want to tell you about tonight
is a story about how we are missing a great opportunity for using movies
to diagnose and treat cancer.
And I remember growing up in Minneapolis
and having the opportunity through a summer school program
to see through a microscope
microorganisms swimming in pond water that I had collected.
I became fascinated by biological motion,
and guess what, I still study it today
in my laboratory at the University of Minnesota.
We study cancer
and in recent years have come to focus on the aggressive brain tumor
known as glioblastoma.
Glioblastoma is a devastating disease.
Median survival is about 15 months
despite surgery, chemotherapy, radiation
and most recently, tumor treating electrical fields.
Ted Kennedy, Beau Biden, Eleanor Mondale - all succumbed to brain tumors.
And just this past summer,
Senator John McCain was diagnosed with glioblastoma.
See, you might think, as I did,
that since cancer is driven by the dynamic growth and movement
of cancer cells within the body,
and since we have such amazing digital imaging technology,
that clinics will be routinely filming cancer patients' cells.
Surprisingly, we don't.
Instead, clinical diagnosis relies on a combination of MRI and lab pathology.
MRI is incredibly powerful and providing spatial detail
and guiding surgical removal of tumors.
This individual was diagnosed with glioblastoma,
underwent surgery, chemo and radiation,
and entered a phase two clinical trial, which did not work,
and within six months had passed away from the disease.
Typically, for such patients only one or a few scans are collected
because the scans are quite expensive,
typically costing thousands of dollars per scan.
Compare that to an 800-dollar smartphone
that can capture thousands of movies, each having thousands of frames,
and in addition can be used for texting, web browsing, movie watching, gaming
and even phoning.
(Laughter)
People still do that.
(Laughter)
MRI, in addition to being expensive and capturing pictures not movies,
does not capture cellular level information.
Cellular information is obtained by so called histopathology,
where tumor tissue is chemically fixed and stained
and viewed through a microscope by a highly trained pathologist.
However, the method that is used today
is essentially the same method that existed nearly 100 years ago,
meaning that senator McCain's tumor
was diagnosed using the same technology that existed when he was born -
in 1936.
So I began to wonder: "Might live cell movies
be the weapon we've been missing in our fight against cancer?
Might they enable dramatic improvements in our ability
to understand the aggressiveness of an individual cancer patient's tumor
and enable us to better identify
the chemotherapies that will work for that patient?"
In fact, we can make these live cell movies.
This is what brain cancer cell invasion looks like.
These are proliferating glioblastoma cells
migrating into small microchannels
that we fabricated to mimic the tight confines of the brain
that cells have to squeeze through in order to invade.
And we imaged it using a camera chip
that is essentially the same as that in my phone.
Directly observing these dynamics
allows us to see these cancer cells and their biomechanical outputs
so that we can target our therapies directly towards those.
We can even see the tracks that cancer cells used to invade.
These green cancer cells are tracking along red blood vessels
as they invade into the brain.
And these dynamics are not unique to brain cancer.
Cell migration is important for the invasion and metastasis
of a wide range of cancers.
When we look at cancer cell migration through the microscope,
we see them dynamically extending and retracting protrusions.
These tentacle-like protrusions formed by protein self-assembly into filaments.
And that self-assembly pushes the membrane forward.
But at the same time, molecular motors pull the filament backwards,
away from the membrane and toward the cell body.
Those motor forces can be transmitted to the environment
through a set of protein bonds
that I will refer to as "sticky proteins."
Together the molecular motors act as tiny muscles
and the sticky proteins as tiny shoes
that allow cells to get traction in their environment to move through it.
And we are now trying to determine
what the sticky proteins are in brain cancer
so that we can control them.
Working with my University of Minnesota colleague
the late John Ohlfest,
who developed a mouse model for glioblastoma,
we found that the level of sticky protein influenced mouse survival.
What we found was that mice at either low or high levels of sticky protein
live longer than mice at middle levels of sticky protein -
kind of valley of survival.
When we looked at how fast cells proliferate,
we did not see any dependence on sticky protein;
it was independent of it.
Then we looked at cell migration.
And this is what we saw.
You can see the tumor mass in the lower left
and individual cancer cells migrating away from the tumor
into the surrounding brain tissue.
When we quantified the rate at which they are moving,
we found an inverse correlation:
fastest migration where there is poorest survival.
You can see that there is a peak in migration
that coincides with the valley of survival as indicated by the red arrow.
Then we looked at human data.
Each point on this graph represents a single cancer patient,
and they are plotted on the graph by the stickiness level they had
and how long they survived after diagnosis.
And you can see how variable stickiness is amongst the patients:
some are low, some are medium,
some are high.
So stickiness: good or bad?
Well, if we start at the low-level stickiness on the left
and follow the black curve,
survival gets worse as stickiness increases.
But then it bottoms out and rises again; it gets better.
Again, it means that worst survival is at the middle level of stickiness -
just like the mouse.
Then we looked at another human dataset,
bigger than this one, same result.
Middle stickiness, worst survival.
So together the mouse data and the human data told us
that the worst survival is at the middle stickiness level.
And in the mouse, that is where cell migration was fastest.
In fact, this is what our theory had predicted.
We built a cell migration simulator
using simple biophysical rules,
and we built from earlier seminal work
by Doug Lauffenburger, now at MIT, and his students.
And this is what the model looks like.
Here you can see individual protrusions
extend and retract
according to Newton's laws of motion.
And the individual sticky bond proteins engage and break under load
as determined by a random-number generator on the computer.
And with this we can predict how fast cells migrate
as a function of the stickiness level.
On the left, at low stickiness,
cells send out protrusions, but they don't stick very well.
They slip as if they are on ice in Minneapolis winter.
(Laughter)
Tough break, kid.
(Laughter)
It has happened to me.
On the right, you can see the protrusions go out.
It is as if they are stuck; they can't let go in any direction.
It is as if they are bogged down in mud in all directions.
Too bad for the mud guy.
But in the middle, these protrusions just can't get enough grip,
but not so much that they can also let go,
which allows them to move fast -
kind of like fitness man here.
So we got three cases:
low, medium and high, but only the middle case is fast.
And that's exactly what we saw with the mouse data.
To move fast you need to have both the muscles to power you
and the right footwork to match the track that you are on.
So what does all that mean for cancer patients?
Our cell migration simulator would predict
for the clinical trial of a stickiness protein blocking drug
that patients in the middle level would actually do well with this drug.
The reason is they are at their peak performance -
optimal stickiness -
and if we block it, we knock them off their game,
and they start to slip,
as if they are on ice.
That is good.
But if we take the patients who are at low stickiness to begin with
and treat them,
they are already slipping on ice - so probably no effect.
But think about the third case:
the patients at high stickiness level.
If we don't completely block them and drive all the way through that peak,
they might end up at the middle and get worse.
Now if you take all three of those cases together -
no effect,
got better,
got worse - and average them,
no effect overall.
In fact, recently a phase three clinical trial was completed
for a stickiness protein blocking drug for glioblastoma patients.
Now, phase three clinical trial is the most expensive trial you can do;
it builds on years of research
and typically costs all together on the order of one billion dollars.
That is for reference equal to all of the budget
that has been allocated towards the cancer moon shot.
That trial failed.
And sadly we would have predicted it would have failed.
And the reason is the trial did not discriminate
between low, medium and high stickiness levels.
Instead, they treated all the patients the same.
We would have recommended treating the middle group -
meaning that patients need to be classified
and treatment personalized
based on the predictions of the simulator.
In recent years, simulators have become commonplace.
Airplanes, chemical plants, computer chips,
they are all extensively simulated
according to known physical chemical rules.
Before the risk is taken
and the investment is made to send up a test plane,
to actually build the chemical plant,
to actually fabricate the computer chips.
Even golfers have simulators.
And this guy's grip looks a little wonky to me,
(Laughter)
but that's the beauty of the simulator.
He can play around with this grip; he can be wonky;
he can figure out which clubs work for him.
That is not what is happening in cancer and drug discovery.
Instead,
a trial and error approach is being used instead of simulating,
and the result has been low success in the clinic -
about 25% success rate in phase two clinical trials,
5% success rate in brain cancer trials.
And then correspondingly high drug costs
to pay for the costs of failure in each and every one of those failures.
So we have left not one but two major weapons
against cancer sitting on the table.
One, live cell dynamics
from cancer patients' tissue,
and, two, mathematical computational models,
such as the cell migration simulator informed by these movies
with which to predict individual patient prognosis
and potential therapeutics strategies.
The good news is that technology to do that exists right now.
I mentioned my phone and the camera chip -
my lab group routinely stores and process terabytes of live cell movie data.
High intensity illuminators such as LEDs and lasers
are relatively cheap thanks to consumer demand for video graphic displays
and also for telecom.
And we use them to make high quality fluorescence movies.
And what about the simulations?
They require high performance computing.
Here again the consumer market comes to the rescue.
Thanks to video gaming -
thank you, gamers!
(Laughter)
Yes, graphical processing units, or GPUs,
were built for gamers.
So you get high speed graphics from highly parallel processing.
Well, guess what,
that technology is now driving self-driving cars.
And self-driving cars
rely on the two things I mentioned for cancer,
movies plus models,
to predict and control car behavior in complex dynamic environments.
This is exactly the approach
that is not happening in cancer and drug discovering development.
Without even realizing it, by making our cat videos,
by watching movies online, by playing video games,
you've helped set the stage for a new era of cancer:
one driven by live cell movies and mathematical computational modeling.
So here in Minneapolis at the University of Minnesota,
we are doing it.
With funding from NIH's National Cancer Institute
for New Physical Sciences and Oncology Center,
we are collecting live cell data
and using it to inform our biophysical models.
Working with surgeons and oncologists,
we are currently enrolling patients at the University of Minnesota hospital
to collect their tumor tissue
and use it for our research.
But the thing we do that's different,
that is not standard in the clinic,
where the first step is to either kill or discard that tumor tissue,
we try to keep it alive as best we can.
So here we are more like an organ transplant surgeon.
But instead of transplanting to a recipient patient,
we transplant the tissue from the operating room to our microscope,
where we collect live cell movies overnight
in biologically realistic environments -
in many cases, while the patient is still in recovering from surgery.
We view tumor tissue as a vital, precious living entity
that potentially contains valuable dynamic information within it.
Finally, we use that live cell data
to inform our simulators to predict patient outcomes.
And in the process, we aim to transform oncology,
driven by an engineering approach
that fully leverages the exponential growth
of digital imaging, computing and physics space modeling
to stop cancer in its tracks.
Thank you.
(Applause)