Hi, I'm Judy Cole, the executive vice president and CEO
of the MIT Alumni Association.
And I'm delighted to welcome you to this web production
of the MIT Alumni Association.
Please welcome to the stage Eric Alm.
[APPLAUSE]
Hi, thanks.
It's really an honor to be here today,
and to share some of our work with you.
What I want to talk to you today is some work
that we've done on the human microbiome.
And we're all familiar with bacteria
as vehicles for infectious disease.
But what we're starting to learn is
that the bacteria and the microorganisms that
live in and on our bodies are really required.
They're required for human life, and they contribute directly
to not only disease, but also to health.
And I'd like to talk to you about some work
that I've been fortunate enough to take part
in that really spawned from a remarkable gift
from a former alumnus incorporation
member, Neil Rasmussen.
So Neil Rasmussen gave a remarkable gift
to MIT that allowed us to start the Center for Microbiome
Informatics and Therapeutics.
And this has really led to not only
cross-departmental collaborations,
but it's really made MIT a hub for a lot
of the medical research that's going on in the Boston area
in the particular field of microbiome.
So one of the questions I usually ask folks
is, what was it, why-- if you've seen microbiome in the news,
you see there's a lot of excitement around it,
and it's all very recent.
So what was it that sparked the emerging
interest in the microbiome?
What was the technology that allowed
us to probe the microbiome?
Was it a new method of growing these bacteria
in the laboratory so we could study them?
Perhaps a new microscope?
Any guesses?
It was DNA sequencing.
So we had an idea a few decades back
that if only we knew all of the possible drug targets
in the body, if--
we didn't know how many genes there were.
Some people thought there were 100,000,
some people thought there were 5,000.
Turns out there's about 25,000.
But if only we had the DNA blueprint
for all of those genes, we would have all the drug targets.
And that would really spur drug development.
And what you see here is, we put a lot of effort into that.
And we've got very, very, very, very good
at sequencing DNA really fast and really cheap.
And this is logarithmic plot.
And so you see, the cost of sequencing DNA
is going down faster.
It's super exponential.
It's growing faster than Moore's law.
But this is the number of drugs that we're
getting per billion dollars of R&D spent in pharma.
And so we haven't yet seen the gains.
And one of the reasons why we haven't seen the gains
is because there's a lag time.
And a lot of the folks in biological engineering
are working on systems biology.
And that's helping us understand-- now
that we have all the parts, how do they work together?
How do they form circuits like we've
heard about earlier today?
And how do those circuits ultimately impact human health?
And so I think this curve is going to reverse.
But there's a lag time.
We need to fully understand the system
before we can take advantage of the fact
that we know what all the genes are.
However, with the microbiome I think the story is different.
And this is basically our idea.
This is our idea that Neil Rasmussen
has funded us to pursue here.
And the idea is quite simple.
We know these bacteria are a fundamental part
of the human body.
So we're using the fact that we can sequence DNA
very cheaply and very quickly to take lots and lots of people,
figure out what bacteria are in their bodies
by sequencing the DNA, and getting a molecular bar code.
So we have a list for each person
that we look at, what are the names of all the bacteria
that are in them?
And then we can compare the healthy people
and the sick people.
And if these sick people are missing one key bacteria that's
in all the healthy people, now we have an idea.
We don't need a complicated model
to figure out what that drug looks like, we just
need to find that bacteria.
We put it in a pill, we feed it to patients,
that's a clinical trial.
So we've gone from DNA sequencing directly
to a hypothesis that we can test in the clinic.
And the best part is, these drugs probably
have low toxicity in humans because they are a natural part
of the human body.
They're a natural part of our personal ecosystem.
So part A is DNA sequencing.
The other half of this diagram is is culture.
We're isolating all these bacteria in the laboratory.
We're putting them in our freezer.
We have over 6,000 now from 11 different people.
And we're continuing to grow that.
We're actually traveling around the world
to get bacteria from indigenous peoples in isolated
human populations so we really have
the complete set of bacteria that grow in human beings.
There's a lot of diseases that have been
correlated with microbiome.
This is a very, very incomplete list.
And it's almost every week we see
new diseases that are significantly
associated with the microbiome.
So there's lots of diseases that we
can go after where there's a difference
between the bacteria in healthy people
and the bacteria in sick people.
There's a lot of correlations.
As we know, correlation does not always mean causation.
But as long as we have a correlation,
we can think about diagnostics.
And we can think about diseases for which diagnosis
is difficult. And this is something
that we've done a lot.
So here's one example.
This is a hypothetical example of Ashley Davis.
She's an undergraduate here, and she's feeling sick,
she's feeling nauseous.
Her grades are going down, and she wants a treatment plan
immediately.
So she comes into the clinic.
She has some risk factors that point
toward inflammatory disease, some risk factors that point
toward non-inflammatory disease.
And really, the best thing to do is a colonoscopy.
Now let's say her father had a bad history with colonoscopy
and she's afraid to do it, but she still wants a treatment.
And what we really want is, we really
want noninvasive diagnostics for inflammatory disease.
So this is a very typical case.
And what we've been doing is trying
to harness the power of the microbiome
in DNA sequencing to build these diagnostics.
And when we built these diagnostics, it's big data.
It's not the type of data that a doctor usually orders in a lab
report, which you see on the left as sort
of the typical data that might come in.
Microbiome data is going to be too complex for any one
clinician to sit there on the fly and analyze.
So we also need to start thinking about algorithms.
And part of the diagnosis process,
even part of the therapeutic process
is going to involve algorithms.
And this is all new territory.
It's new territory for FDA to figure out--
how are we going to regulate algorithms when algorithms
are part of health care?
How are we going to change medical records so that doctors
can see the predictions of theoretical models and things
like that?
So we're starting to do this today.
So this is one of the studies that I first got involved in.
This is done with Athos Bousvaros
at Children's Hospital.
And he came to me, he said, Eric, we've got a problem.
We have a lot of pediatric patients
who come in with inflammatory disease,
and we can't send every 8-year-old kid
who has stomach pains out for a colonoscopy.
We need a noninvasive test that is sensitive.
And if that test comes up positive,
then we'll send them out for a colonoscopy.
So we collected stool samples from sick kids
who did not have inflammatory disease, we collected
stool samples from sick kids who had ulcerative colitis,
and then sick kids who had Crohn's disease.
And when you look at these data-- so on the rows,
those are all a different bacteria.
And the darker colors means there's more
in that particular patient.
And the columns are the patients.
Nothing really stands out.
But when we use machine learning algorithms
to train on all of these very weak predictors,
we were able to predict very accurately,
as accurate as the current gold standard noninvasive test,
whether or not the kids had inflammatory disease.
And the best part is, we looked at how good our predictions got
as a function of how many kids we diagnosed.
And the model keeps getting better.
So the more we use it, the better the test gets.
And that's not true for a standard ELISA test
or a standard laboratory test.
One of the things I'm particularly passionate about
is that patients need access to this data
that we're generating on them.
And so one of the studies that we're starting up now
is taking inflammatory bowel disease patients,
taking lots and lots of samples non-invasively
from them over time, and trying to build predictors
so we can tell not only what a patient is sick,
but when a patient is starting to get sick
so we can start to intervene before they get sick
and do preventative care.
We're starting to do this today.
And so I'll tell you about a study
that we performed in 2009.
And this was a study where we decided
to track 300 different lifestyle factors,
get patients to record every single morsel of food
that they ate, every time they woke up
in the middle of the night, if they traveled,
if they got sick.
300 different factors for a year.
And then we needed to collect a stool sample every day.
Nobody signed up for the study.
[LAUGHTER]
So my graduate student Lawrence David and I
took it upon ourselves to sign ourselves up
for the study for science.
And on the top you see my microbiome in 2009.
On the bottom is Lawrence's microbiome in 2009.
And if we zoom in a little bit closer to my microbiome,
you can see something remarkable happened about 3/4 of the way
in.
Went out to a local restaurant and I got the chef's
special french toast.
It came complete with a salmonella infection,
so I got food poisoning.
I went to the urgent care at the time,
I said, I think I've got food poisoning.
He said, nonsense, it's a virus.
So I didn't get antibiotics.
I got an IV, and I was sent home.
But now I've got the DNA evidence,
which I wish I had had at the time,
that it was food poisoning.
It was salmonella.
Now the remarkable thing I want you to notice about this slide
is, the different rows are different bacteria.
The x-axis, of course, is time.
And the warmer colors means there's
more of that bacteria on that particular day.
The blue colors indicate there's less on that particular day.
And so after I got salmonella, all of those bacteria
in the top right disappeared.
They went extinct from my gut.
And they were replaced with that block of red bacteria.
And that means we can think about engineering
the microbiome.
We can reprogram it.
Hopefully not by giving people food poisoning,
but it says it can be changed.
We're thinking about other things,
too, not just sequencing the DNA.
But we're starting trials with wearable devices,
and profiling the immune system like Darrell just talked about,
and integrating all of these data
to get a more complete picture of patient health.
So if we think about engineering the immune system,
one of the things that comes to mind in terms of microbiome
is fecal transplants.
And if folks saw the booth outside by Open Biome, then
you've heard all about fecal transplants.
Or maybe you've heard about them on the news.
But what we have is a case where, often patients
will come to the doctor's office,
they'll get antibiotics for a condition
that has nothing to do with Clostridium
difficile infection.
In the hospital they will acquire the most common gut
infection for patients to acquire,
is C. diff, Clostridium difficile.
And so what do we do?
We give them antibiotics because now they have a gut infection.
But it was that antibiotics that sort of--
if we think about the ecosystem as this lovely forest,
and at the top of the figure, were
clear-cutting the forest when we give
these patients antibiotics.
And that's what allows this invasive species C.
diff to grow up.
So now we have a patient with C. diff, what do we do?
We feed them antibiotics.
We clear-cut that forest again, and they come back
with C. diff.
So what do we do?
We give them more antibiotics.
And once we do it three times, now they're
classified as recurrent C. difficile patients,
and now we can give them another treatment.
And that treatment is fecal transplants.
So we take some of those healthy bacteria from healthy donors.
And it turns out, even for patients
who have failed standard antibiotic therapy three
or more times, often many, many more times,
there's a 90% recovery rate for re-inoculating them
with healthy bacteria.
So this is called fecal transplantation.
We were lucky enough to get involved
with the study at the Broad Institute and at MGH.
And so we took 20 patients who had C. difficile.
We had four donors.
Those four donors contributed healthy bacteria
to the 20 patients.
About 90% of them recovered.
And this is what it looks like.
So the different-- each row here,
the first one is our patient before the fecal transplant.
The second row is the donor, and the third row
is the patient after fecal transplant.
And the different columns and the different colors, those
are different bacteria.
And the darker the color, the more bacteria the patient has.
What I want you to do is look at this graph, and figure out,
what is the rule?
What's the algorithm that you could look at the patient
before fecal transplant, and look at the donor,
and how would you figure out which
bacteria are going to ingraft?
The reason this is important is because I said
in the beginning, what we want to do
is, we want to take these bacteria from our freezer,
we want to put them in a pill, and feed them to patients.
But if those bacteria don't ingraft in those patients,
if they don't become part of that patient's new ecosystem,
then that drug's not going to work.
And so we have to be able to understand which
bacteria are going to ingraft.
We tried AND gates, we trade OR gates, we tried adding them.
There were no simple answers.
But then we turned it over to a machine learning algorithm.
And this was the prediction we got.
And we found out we could predict it
about 86% of the time.
And we're starting to learn the rules of which bacteria will
actually ingraft in a patient, in which bacteria
make good candidates as drugs.
So we're not just kind of limited to the academic sphere.
In 2013, a really talented graduate student
in my lab, Mark Smith, together with a Sloan School student,
James Burgess, a post-doc in my lab, Zain Kassam, and Carolyn
Edelstein who's outside manning the Open Biome booth,
started a nonprofit to take these fecal transplants,
and disseminate them across the country to folks who need it.
And I'm happy to say that today they've
treated tens of thousands of patients for this disease.
In fact, the number of recurrent C. diff patients
that now get treated by fecal transplants
from this MIT spin-off nonprofit is greater than 50%.
So greater than 50% of the folks with this disease
are now being treated by the nonprofit.
So I think I'll end there, and I'll turn it over
to Angie Belcher.
[APPLAUSE]
Thanks again for joining us.
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