David Haussler: All right.
Thanks Adam, thanks for the opportunity to be here.
I decided to talk about something completely different that -- not in my area of expertise
in the lab but something that I think is good -- much more focused project than the grand
“let’s understand all of life” projects that we’ve been hearing about but still
I think right.
So, many of you are familiar with the way the adaptive immune system works.
The vertebrates are happy to have a unique type of immune system, and there are T cells
and there are B cells.
B cells, be thinking about antibodies and so forth.
The T cells are what’s called the cell mediated response.
And there’s a couple of different types of things but, you know, this cartoon shows
the, you know, the kind of interaction that we’ll be talking about, specific type of
T cells, CDA positive.
It’s going to react to this Class 1 MHC, and I’m not going to go through all the
details.
So basically the background is that almost every cell in your body presents antigens
through its HLS molecules, as you see in this picture.
And this peptide, the green bar, is just 8 to 10 amino acids, and it’s chop that out
of one of the proteins that’s being made inside of the cell.
And so you can think about these things as the cells being forced to display a snapshot
of what’s going on inside them at any point in terms of all of these little peptides.
And when a T cell has a T cell receptor that recognizes that peptide, then this other interactions,
and Sue, that I won’t talk about, but some of those can lead to destruction of that cell
if, indeed, something bad is going on.
And that’s the idea.
We’re constantly having a surveillance for that.
And they’re other -- you know, you could say, well, if you know what if you don’t
have anything presenting at all, and there the natural killer cells are actually suspicious
if you’re not displaying anything at all.
Then it’s a different system that gets suspicious.
So you’re -- there’s kind of an interrogation system that’s going on all the time, and
that’s a very powerful thing.
So I think the immune system has enormous potential, and I think we’re really on the
brink of understanding how to control that potential, which makes a large scale resource
type project very appealing at this point because the accuracy of our impending control
of the immune system will depend on us understanding what kind of antigens are displayed in this
way, what -- and what the response is precisely.
The common atorics [spelled phonetically] of this kind of antigen display by the Class
1 and the CDA positive cell recognition are simplified from the fact that this is a short
peptide, 8 to 10 amino acids.
And while we can make an enormous number of T cell recognition molecules, we’re finding
that we’re able to also tap into the diversity of those and actually start to think about
documenting that.
So I think it’s a large commenatorial [spelled phonetically] space but not so large that
we couldn’t attack it with a coordinated approach, and it would certainly be better
to start with T cells than the B cell antibody thing that’s much more commenatorially large
space.
So control: what would it mean that we can control the cell-mediated response?
It would have profound applications in infectious disease.
Obviously it’s the first thing you think about in terms of body’s response to that
-- any agent that actually is able to infect a cell and create abnormal peptides.
Cancer is another case, and if you were at the AACR meeting, the immunotherapeutic approaches
to cancer were the darling of the meeting.
What can I say?
It’s a new -- it’s a very new, exciting area, after being shunned for many years as
being hopeless.
Now this is really -- had a spectacular resurgence.
And of course autoimmunity, aging, stem cell transplant -- there are many, many applications
across medicine, if we could control the cell-mediated immune system, we could do amazing things.
So I think it’s a worthy scientific challenge to think about this.
It’s clearly a genomics proteomics hybrid project.
But the genomics part is very important.
So, for example, you can think of the concrete question, given its genome and its RNA expression,
predict the peptide antigens that will be displayed by a cell.
And many of you may not know, there are already software that’s doing this quite well.
I particularly like the program Net Image C, out of the Danish group.
There is -- I’m hearing from colleagues that are actually in the field using these
things, that it does a pretty good job.
So you got -- you can decide -- there are various processes in the cell that decide
which of these atemers [spelled phonetically] are going to actually be chopped out and which
will successfully be displayed.
That is a tractable problem that we could get very, very good at if we tried.
So that’s definitely not a hopeless thing.
You want to know also that the, you know, which HLA.
We can figure out that from the genome and whether all the pieces are in place to display
these peptides.
Second part is predict whether a T cell receptor will recognize a given displayed peptide antigen,
given the sequences of the receptor, the peptide, and the HLA molecule that displays it.
This is a much more ambitious but I think not out of reach question.
There aren’t that many combinations, and I think similar approaches -- large-scale,
big data approaches could approach that.
If we could do that, then we could possibly create a master reference for human T cell
recurrent for human recurrent T cell recognition events accompanied by perhaps live cell models
where we could actually then experiment with these recognition events and learn how they
are controlled, and ultimately create a predictive model for this process.
So what would be the data elements that NHGRI would actually supply?
First of all, we need accurate sequencing at the MHC region and understanding of the
key molecules, the HLA molecules, and the other immune-related molecules that are contributing
to this process.
We still aren’t as good at sequencing the MHC region as we should be.
We would need mutant cell panels so we could do the experiment with this, and there are
many methods to do that.
It’s exciting that we can do single cell RNA sequencing now because you would really
want to know at that level because as we learned looking at a bee for a brilliant paper recently,
that are so many different ways in which cancer cells, for example, vary from one to another.
And it’s great to be able to do that on a single cell level.
If we can then determine computationally from whole genome sequencing and all this other
information, what peptide antigens are displayed then we would be in the next -- at the next
level.
We would like to do this, have information about local immune environments and time courses
and vaccination, infection during cancer treatment and so forth.
Who would participate?
Obviously NIAID is a great collaborator in this case, and NCI increasingly because cancer
immunotherapy is so hot at this point.
You might even consider going outside of the NIH and collaborating with some of the patient
advocacy organizations that are concerned with diseases that have a profound immune
component or that may have an immunotherapeutic solution.
I tried to get all the projects that I could find that are already going along these lines,
and I apologize deeply if I left you off the list.
Rick Myers is here.
He’s got a great project at Hudson Alpha.
So there are already several groups that are doing T cell receptor sequencing.
In other words, you can take a blood sample and I can tell you all of the different clonal
expansions of T cell receptors that are in that blood sample.
And that gives you the, you know, essentially a snapshot of what the patient’s T cells
are configured to do at that given point.
And this is a great project that’s doing that in many different areas.
There are two commercial companies that do that as a service as well.
Mark Davis at Stanford has through NIAID funding a genome corps that is collecting these data,
and other data that would be relevant to this.
NIAID has an immune epitope database where you have examples of experimentally validated
cases where certain peptides were displayed in certain disease or normal conditions.
And I mentioned the Net MHC program, which actually creates very good prediction, which
is then validated by these experiments.
They have a large database called Import, and I found that to be a little bit of a grab
bag [unintelligible] I think we do much better informatics, we could do this right if we
really pulled all these data together.
The TCJ of course has all of these full tumor genomes, and we could predict intogen [spelled
phonetically] being displayed by all of the tumors from this process.
And we’re trying to pull together something that would allow you to pull in literature
and other ideas based on that so that [unintelligible].
So as I said, the common atorics are not all that outrageous.
Typical NHC Class 1 displayed peptides about nine amino acids, so 512 billion possibilities
but only a small fraction of these will actually have the affinity to nest in any particular
HLA molecule.
And we’re getting to the point where we can actually make that prediction quite accurately.
We sequenced the TCR beta chain and the highly variable and relevant part of that is the
CDR3 receptor part.
That’s coupled to the alpha.
If you look at the CDR beta alone, you can have one person with 100 million different
versions of that circulating in their blood but only a few of them are actually clonely
expanded, indicating that they’ve responded to something and then expanded maybe for a
purpose.
And we can measure that quite accurately.
So I think the common atorics of this, while large, it’s not so frightening for me putting
on my big data hat if you think about the problem of speech recognition or any of these
other problems that have a big data component.
We can make the machine learning methods work along with the experimental data.
We literally have to fill in a big table of all possibilities here, but we will be able
to extrapolate from what we learn.
And if this is all successful on the T cell, cell mediated response, we could go back to
the much more complicated antibody and B cell recognition problem where the antigens are
much more diverse, and the common atorics of creating antibodies also is much more diverse.
So you have much larger repertoires for orders of magnitude larger, but still possibility.
Okay, that’s it.
How’d I do for time?
Okay?
We have time for questions?
Male Speaker: There’s time for one or two questions.
David Haussler: Okay, Eric.
Male Speaker: So can you help fill in for me the connection
in terms of -- presumably you need a lot of data of the form this TCR sequence recognizes
this peptide.
So if you had a ton of that data, you might bootstrap your way up.
But tell me how you’re going to get any of that data in any significant amount?
David Haussler: Yes, so that’s the key technology challenge
-- to get looped into this thing.
But currently that --
Male Speaker: In parallel.
I see you have to do individual experiments, but you probably want millions of, you know,
millions to hundreds of millions of data points, and as individual experiments that seems challenging.
David Haussler: Step one is absolutely to scale out --
[talking simultaneously]
Figure out a way to do that.
So that’s the step one technological challenge.
And --
Male Speaker: [inaudible]
David Haussler: Absolutely, yes.
So without some kind of experimentally grounded large data set, one would be shooting in the
wind.
Male Speaker: So a starting point might be to start with
known drug HLA interactions and work from there.
So is there a plan to use structural biology approaches to sort of understand how drugs
might interact with varying HLA alleles?
David Haussler: You could [unintelligible] drugs as modifiers
of this process would be --
[talking simultaneously]
Male Speaker: Well, so drug HLA interactions are pretty
important clinically.
David Haussler: Yes, absolutely, yes.
That would be a great way to get at an incredibly important, clinically important aspect of
it, yes.
Yes.
Male Speaker: That was great, David.
I think this is a terrific area.
And it does illustrate a kind of policy dilemma for the NHGRI, but it’s one that I would
encourage, you know, developing some approach to.
As you pointed out, you know, there are all these other parties.
NCI is very excited for good reason right now about these interactions.
And NIDDK kind of grabbed the Type 1 diabetes story, and NIAID has [unintelligible] as much
interest and HLA.
And so there -- I’ve followed this field pretty closely for 20 years, and there’s
always been the “somebody else is doing it” kind of attitude.
And I think that opportunity here for genome is just to sort of recognize as politely as
possible that much of that effort has simply failed to grab hold of the core genomic issue
that’s on the table and that is of interest to NCI, NIDDK, and so forth.
I mean, you look just backing up from the focus that you put on this, you know, on the
peptide recognition, the association, you know, between class 2 genotypes and Type 1
diabetes, you know, was [unintelligible] discovered in the 1960s.
It’s still one of the strongest allelic associations that we’ve ever found after
all the money that we’ve spent looking for tiny effect sizes.
This is a huge effect size.
And we still don’t know whether the primary lesion that leads to Type 1 diabetes happens
in the pancreas or the thymus.
So that’s not a very good record after 50 years of studying this problem.
And if you read the literature, you’ll see quite easily why the problem has not been
solved.
It hasn’t been attacked as a general problem in genetics, genotype, phenotype, and genomics.
It’s always been attacked from a narrower angle.
So there -- anyway, there’s an opportunity here.
And I think it’s a good model.
And there’s way too little discussion of HLA in genomic circles.
There’s this tendency that we inherited from our forefathers that, you know, it’s
very special.
It’s weird, it’s different.
It’s just another part of a genome that happens to be highly variable.
We can solve this.
David Haussler: Thank you so much, Maynard [spelled phonetically].
Yeah, my reaction, and I -- not being an immunologist, which is obvious, that it seems like a complete
cottage industry at this point that is waiting to mature the way genetics matured when we
got a hold of the Human Genome Project and made it happen.
It’s very, very frustrating.
[unintelligible] is another disease that I’ve started to study.
And you know that [unintelligible] in 90 percent of the cases have a particular variety of
HLAB, the HLAB 27 allele.
And it’s by far just a whopping signal, and you know that is displaying some peptide
in there, some reaction to that in these people.
But how could we have gone 30 years and not [laughs] figured that out?
Very frustrating.
Yes.
Female Speaker: So David, I like this too from the perspective
of tackling difficult regions of the genome.
I mean, we say we have the genome but we really don’t.
And some of the most medically important regions at a genome are quite challenging actually
even today.
So I think pushing areas like this that are so medically relevant in getting the right
resources there is very important.
David Haussler: Thanks, Debbie.
Yeah.
So if anyone has a solution to the issue that -- the little issue that Eric brought up [laughs]
[unintelligible] how to scale the key experimental part of this, that would break this whole
thing open.
Male Speaker: Thanks, David.
I think we have to move on.
[end of transcript]