Today is my last class with you. Awe, I'm sorry, too. You guys are
a lot of fun. This has actually been the most interactive 7.
1 I've ever had. Usually there are a couple of people who perk up and
say things, but you guys are great because all sorts of people are
willing to contribute. So, I've had a wonderful time and
it certainly seems like you guys have learned a lot.
What I'd like to do for my last lecture is pick up again a little
bit like I did with genomics and try to give you a sense of where
things are going. I always like doing this because I
get to talk about things that are in none of the textbooks that,
well, I mean, it's just stuff that many people working in the field
don't necessarily know. And that's what's so much fun about
teaching introductory biology is because it only takes a semester for
you guys to get up to the point of at least being able to understand
what's getting done on the cutting-edge.
Even if you might not yet be able to go off and practice it,
you might need a little more experience for that,
but you'd be surprised, it's not that much more.
Take maybe Project Lab and you'll be able to start doing it already.
It's really wonderful that it's possible to grasp what's going on.
And, in many ways, you guys may have an advantage in grasping what's
going on because, as I've already hinted,
biology's undergoing this remarkable transformation from being a purely
laboratory-based science where each individual works on his or her own
project to being an information-based science that
involves an integration of vast amounts of data across the whole
world and trying to learn things from this tremendous dataset.
And, in that sense, I think the new students coming into
the field have a distinct advantage over those who have been in it.
And certainly the students who know mathematical and physical and
chemical and other sorts of things, and aren't scared to write computer
code when they need to write computer code have a really
great advantage. So, anyway, all that by way of
introduction. I want to talk about two subjects today of great interest
to me. One is DNA variation and one is RNA variation.
The variation of DNA sequence between individuals within a
population, and in particular our population, and the other is RNA
variation, the variation in RNA expression between different cell
types, different tissues. And the work I'm going to talk
about today is work that I, and my colleagues, have all been
involved in. And it's stuff I know and love.
So, feel free to ask questions about it. I may know the answers,
but what's reasonably fun about these lectures is if I don't know
the answers it's probably the case that the answers aren't known.
So, that's good fun because it's stuff I really do know well,
and I love. So, anyway, here's some DNA sequence. It's pretty boring.
This is a chunk of sequence from, let's say, the human genome.
How much does this differ between any two individuals?
If I were to sequence any two chromosomes, any two copies of the
chromosome from an individual in this class or two individuals on
this planet, how much would they differ? The answer is that much.
That's the average amount of difference between any two people on
this planet. Not a lot. If you counted up, it is on average
one nucleotide difference out of 1, 00 nucleotides on average, somewhat
less than one part in 1, 00 or better than 99.9% identity
between any two individuals. Now, that is a very small amount,
not just in absolute terms, 99.9% identity is a lot,
but in comparative terms with other species. If I take two chimpanzees
in Africa, on average they will differ by about twice as much as any
two random humans. And if I take two orangutans in
Southeast Asia, they will on average differ by about
eight times as much as any two humans on this planet.
You guys think the orangutans all look the same.
They think you all look the same, and they're right. So, why is this?
Why are humans amongst mammalian
species relatively limited in the amount of variation?
Well, it's a direct result of our population history.
It turns out that the amount of variation that can be sustained in a
population depends on two things. At equilibrium, if population has
constant size N for a very long time and a certain mutation rate,
Mu, you can just write a piece of arithmetic that says,
well, mutations are always arising due to new mutations in the
population and mutations are being lost by genetic drift,
just by random sampling from generation to generation.
And those two processes, the creation of new mutations and
the loss of mutations just due to random sampling in each generation,
sets up an equilibrium, and the equilibrium defines an equation
there, Pi equals one over one plus four and Mu reciprocal which
equation you have no need to memorize whatsoever and possibly
even no need to write down. The important point is the concept,
that if you know the number of organisms in the population and you
know the mutation rate, those set up the bounds of mutation
and drift, and you can write down how polymorphic,
how heterozygous random individuals should be at equilibrium.
That is if the population has been at size N for a very long time.
Well, the expected amount of heterozygosity for the
human population -- Sorry. For a population of size 10,
00 would be about one nucleotide in 1300. We have exactly the amount of
heterozygosity you would expect for a population of about 10,
00 individuals. Yeah, but wait, we're not a population of 10,000
individuals. Why do we have the heterozygosity you would expect from
a population of 10, 00 individuals? We're six billion.
It's a reflection of our history.
Because remember I said that was the statement about what the
population heterozygosity should be at equilibrium?
We haven't been six billion people except very recently.
The human population has undergone an exponential expansion.
It used to be a relatively small size, and then it very recently
underwent this huge exponential expansion. If you actually write
down the equations, the amount of variation in our
population was determined by that constant size for a very long time.
And then a rapid exponential expansion that's basically taken
place in a mere 3, 00 generations, it's much too rapid
to have any affect on the real variation in our population.
What do I mean by that? What's the mutation rate per nucleotide in the
human genome? It's on the order of two times ten to the minus eighth
per generation. In a mere 3,000 generations,
a tiny mutation rate like two times ten to the minus eighth is not going
to be able to build up much more variation.
So you might as well ignore the last 100,000 years or so.
They're irrelevant to how much variation we have.
The variation we have was set by our ancestral population size.
Now, don't get me wrong. Eventually it will equilibrate.
A couple million years from now we will have a much higher variation in
the human population as a function of our size, but the population
variation we have today is set by the fact that humans derive from a
founding population of about 10, 00 individuals or so.
And that means that the variation that you see in the human population
is mostly ancestral variations, the variation that we all walked
around with in Africa. And, in fact, that makes a
prediction. That would say that if most of the variation in the human
population is from the ancestral African founding population then if
I go to any two villages around this world, in Japan or in Sweden or in
Nigeria, the variance that I see will largely be identical.
And that prediction has been well satisfied.
Because when you go and look and you collect variation in Japan or Sweden
or Africa and you compare it, 90% of the variance are common
across the entire world. Most variation is common ancestral
variation around the world, and only a minority of the variance
are new local mutations restricted to individual populations.
This is so contrary to what people think because there's a natural
tendency to kind of xenophobia, to imagine that world populations
are very different in their genetic background.
But, in point of fact, they're extremely similar.
So, anyway, there's a limited amount of variation.
That's why we have such little variation in the human population.
Now, that variation, humans have a low rate of genetic variation.
Most of the variance that are out there are due to common genetic
variance, not rare variance. If I take your genome and I find a
site of genetic variation at the point of heterozygosity in your
genome, what's the probability that somebody else in this class also is
heterozygous for that spot? It turns out that the odds are about
95% that someone else in this class will also share that variance.
So that the variance are not mostly rare, they're mostly common.
And it turns out that some of this common variation,
that is most of this variation is likely to be important in the risk
of human genetic diseases. So human geneticists have gotten
very excited about the following paradigm.
If there's only a limited amount of genetic variation in the human
population, actually, if you do the arithmetic,
there are only about ten million sites of common variation in the
human population, where common might be defined as
more than about 1% in the population. There are only ten million sites.
Folks are saying, well, why not enumerate them all?
Let's just know them all, and then let's test each one for its
risk of, say, confirming susceptibility of diabetes or heart
disease or whatever? After all, ten million is not as
big a number as it used to be. We now have the whole sequence of
the human genome. Why not layer on the sequence of
the human genome all common human genetic polymorphism?
Now, that's a fairly outrageous idea but could be a very useful one.
Some of these variance are important, by the way.
We know that there are two nucleotides that vary in the gene
apolipoprotein E on chromosome number 19. Apolipoprotein E is also
an apolipoprotein like we talked about before with familiar
hypercholesterolemia. But, in fact, it turns out that
apolipoprotein E is expressed in the brain. And it turns out,
amongst other tissues, that it comes in three variances,
the spelling T-T, T-C and C-C at those two particular spots.
And if you happen to be homozygous for the E4 variant,
homozygous for the E4 variant, you have about a 60% to 70% lifetime
risk of Alzheimer's disease. In this class 13 of you are
homozygous for E4 and have a high lifetime risk of Alzheimer's.
And it would be fairly trivial to go across the street to anybody's
lab and test that. Now, I don't particular recommend
it, and I haven't tested myself for this variant because there happens
to be no particular therapy available today to delay the onset
of Alzheimer's disease. And, therefore,
I don't recommend finding out about that. But a number of
pharmaceutical companies, knowing that this is a very
important gene in the pathogenesis of Alzheimer's disease,
are working on drugs to try to delay the pathogenesis using this
information. And it may be the case that five or ten years from now
people will begin to offer drugs that will delay the onset of
Alzheimer's disease by delaying the interaction of apolipoprotein E with
a target protein called towe, etc. So, this is an example of where a
common variant in the population points us to the basis of a common
disease and has important therapeutic implications.
There are some other ones, for example. 5% of you carry a
particular variant in your factor 5 gene which is the clotting cascade.
It's called the leiden variant. Those 5% of you are going to account
for 50% of the admissions to emergency rooms for deep venous
clots, for example. The much higher risk of deep venous
clots. And, in particular,
there are significant issues if you have that variant and you are a
woman with taking birth control pills. Some of you were at higher
risk for diabetes, type 2 adult onset diabetes.
There's a particular variant in the population that increased your risk
for type 2 diabetes by about 30%. 85% of you have the high-risk
factor, so you might as well figure you do.
15% of you have a lower risk, et cetera. And one I'm particularly
interested in here, it turns out that HIV virus gets
into cells with a co-receptor encoded by a gene called CCR5.
Well, it turns out that if we go across the European population,
10% of all chromosomes of European ancestry have a deletion
within the CCR5 gene. If 10% of all chromosomes have that
deletion then 10% times 10%, 1% of all individuals are homozygous
for that deletion. Those individuals are essentially
immune to infection from HIV. They are not susceptible. It's not
through immunity, it's through lack of a receptor.
Yes? You certainly can. It's not hard. It's a specific known variant.
You could test for it. Absolutely.
Now, of course, that only helps the 1% of people who
have that variant. But what it did do was point to the
pharmaceutical industry that the interaction between the virus and
that variant is essential. And now companies are developing
drugs to block the interaction with that particular protein.
And that tells you that it's an important protein. Yes?
Over the whole world?
I just specified European population for that one.
That one, interestingly, is not found at as high a frequency
outside of Europe, and no one knows why,
whether that might have been due to an ancient selective event or a
genetic drift. By contrast, the apolipoprotein E
variant, at that frequency of about 3% of people being homozygous and
being at risk for Alzheimer's, is about the same frequency
everywhere in the world. So, there's a little bit of
population variation in frequency. Now, the HIV variant is found
elsewhere but at considerably lower frequencies there.
And that's an interesting question as to what causes that variation.
So the notion would be, I've given you a couple of interesting examples,
but, look, there's only ten million variants. Just write them all down.
Make one big Excel spreadsheet with ten million variants along the top
and all the diseases along the rows, and let's just fill in the matrix
and then we'll really, you know, this is the way people
think in a post-genomic era. Now, could you do something like
that? You would have to enumerate all of the single nucleotide
polymorphisms, or SNPs we call them,
single nucleotide polymorphisms. Now, to give you an idea of the
magnitude of this problem, as recently as 1998, the number of
SNPs that were known in the human genome was a couple hundred.
But then a project has taken off. In 1998 an initial SNP map of the
human genome was built here at MIT that had about 4,
00 of these variants. Then within the next year or so an
international consortium was organized here and elsewhere to
begin to collect more of these genetic variants.
The goal was going to be to find 300, 00 of them within a period of two
years. In fact, that goal was blown away and within
three years two million of the SNPs in the human population were found.
And as of today, if you go on the Web, you'll find the database with
about 7.8 million of the roughly ten million SNPs in the human population
already known. Now, that isn't all ten million.
And it takes a while to collect the last ones, you know,
collecting the last ones are hard, but we're already the hump of
knowing the majority of common variation in the human population.
Not just a sequence of the genome, but a database that already contains
more than half of all common variation in the population.
So, we could start building that Excel spreadsheet.
Now, it turns out that it's even a little bit better than that because
if we look at many chromosomes in the population,
here are chromosomes in the population, it turns out that the
common variance on each of those chromosomes tend to be correlated
with each other. If I know your genotype at one
variant, like over at this locus, I know your genotype at the next
locus with reasonably high probability. There's a lot of local
correlation. So, instead of looking like a scattered
picture like that, it's more like this.
If I know that you're red, red, red you're probably red,
red, red over here. In other words, these variations occur in blocks
that we called haplotypes. Here's real data.
Across 111 kilobases of DNA there's a bunch of variants,
but it turns out that the variants come in two basic flavors.
98% of all chromosomes are either this, this, this,
this, this or this, this, this, this, this.
Then there tends to be sites of recombination that are actually
hotspots of recombination where most of the recombination of the
population is concentrated. And you get a couple of
possibilities here. So, the human genome can kind of be
broken up into these haplotypes. Blocks that might be 20,
30, 40, sometimes 100 kilobases long in which within the block you tend
to have a small number of haplotypes, or flavors as you might think of
them, that define most of the chromosomes in the population.
So, in fact, I don't actually need to know all the variants.
If they're so well correlated within a block,
if I knew this block structure I would be able to pick a small number
of SNPs that would serve as a proxy for that entire block of inheritance
in the population. So, what you might want to do is
determine that entire haplotype block structure of hwo they're
related to each other, and pick out tag snips.
And it turns out that in theory, a mere 300,000 or so of them would
suffice to proxy for most of the genome. So, you might want to
declare an international project, and international haplotype map
project to create a haplotype map of the human genome.
And indeed, such a project was declared about a year and a half ago
through some instigation of scientists and a number of places,
including here. And this is $100 million project
involving six different countries. And, it is already more than
halfway done with the task, and it's very likely that by the
middle of next year, we will have a pretty good haplotype
map, not just knowing all the variation, but knowing the
correlation between that variation, being able to break up the genome
into these blocks. By the next time I teach 701,
I should be able to show a haplotype map of the whole human genome
already. That will allow you to start undertaking systematic studies
of inheritance for different diseases across populations.
And in fact, people are already doing things like that.
Here's an example of a study done here at MIT like this,
where to study inflammatory bowel disease, there was evidence that
there might be a particular region of the genome that contained it,
and haplotypes were determined across this, and blah,
blah, blah, blah, blah, blah, blah. And this red haplotype
here turns out to confer high risk, about a two and a half or higher
risk of inflammatory bowel disease.
And it sits over some genes involved in immune responses,
certain cytokine genes and all that. And, things like this have been
done for type 2 diabetes, schizophrenia, cardiovascular
disease, just right now at the moment, a dozen or two examples.
But I think we're set for an explosion in this kind of work.
In addition, you can use this information to do things beyond
medical genetics. You can use it for history and
anthropology as well. It turns out rather interestingly,
that since the human population originated in Africa and spread out
from Africa all the way around the world arriving at different places
in different times, you can trace those migrations by
virtue of rare genetic variants that arose along the way,
and let you, like a trail of break crumbs, see the migrations.
So, for example, there are certain rare genetic variants that we can
see in a South American Indian tribe, and we can actually see that they
came along this route because we can see that residual of that.
In fact, we can do things with this like take a look at Native American
individuals and determine that they cluster into three distinct genetic
groups that represent three distinct migrations over the land bridge.
And, you can assign them to these different migrations.
You can do this on the basis of mitochondrial genotype,
etc. You can also, for example, determine when people talk about the
out of Africa migration, there's now increasing evidence that
there really were two, one that went this way over the land,
and one that went this way following along the coast into southeast Asia.
And, it looks like we're now beginning to get enough evidence of
these two separate migrations by virtue of the genetic breadcrumbs
that they have left along the way.
So, it's really a very fascinating thing of how much you can
reconstruct from looking at genetic variation, both the common variation
that allows us to recognize medical risk, and the rare genetic variation
that provides much more individual trails of things.
None of this is perfect yet. There's lots to learn. But I think
anthropologists are finding that the existing human population has a
tremendous amount of its own history embedded in pattern of genetic
variation across the world. You can do other things.
I won't spend much time on this. Well, I'll take a moment on this,
right? There's some very interesting work of a post-doctoral
fellow here at MIT named Pardese Sebetti who has been trying to ask,
can we see in the genetic variation in the population,
signatures, patterns of ancient selection, or even recent selection
in the human population? Now, hang onto your seats,
because this will get just slightly tricky.
But, hang on. It's only a couple of slides. Here was her idea.
You see, when a mutation arises in the population,
it usually dies out, right? Any new mutation just
typically dies out. But, sometimes by chance it drifts
up to a high frequency. Random events happen. But it
usually takes a long time to do that. If some random mutation happens,
and it happens to drift up to high frequency with no selection on it,
then on average it takes a long time to do so.
If you want, I could write a stochastic differential equation
that would say that, but just take your gut feeling that
if something has no selection on it and it's a rare event that'll drift
up, when it drifts up it's kind of a slow process. It was a slow process.
Then over the course of time that it took to drift to high frequency,
a lot of genetic recombination would have had to have occurred many
generations. And the correlation between the genotype at that spot
and genotypes at other loci would break down.
And there would only be short-range correlation. So,
in other words, the amount of correlation between knowing the
genotype here and the genotype here, maybe allele A here and a C here.
That is an indication of time. It's a clock almost. It's like
radioactive decay, right, that genetic recombination
scrambles up the correlations. And, if something's old, the
correlations go over short distances. But suppose that something happened.
Some mutation happened that was very advantageous.
Then, it would have risen to high frequency quickly because it was
under selection. If it did so quickly,
then the long-range correlations would not have had time to break
down, and we'd have a smoking gun. A smoking gun would be that there
would be a long-range correlation around that locus,
much longer than you would expect across the genome.
Things even out of this distance would show correlation with that,
indicating that this was a recent event.
So, we just measure across the genome, and look for this telltale
sign of common variance that have very long range correlation that
indicate that they're very recent. So, a plot of the allele frequency,
common variance, sorry, if something has a common high frequency and
long-range correlation, you wouldn't expect that by chance.
So, something that was common in its
frequency and had long-range correlation would be a signature of
positive selection. So anyway, Pardise had this idea,
and she tried it out with some interesting mutations,
some mutations that confer resistance to malaria,
one well-known mutation causing resistance to malaria called G6 PD
and another one that she herself had proposed as a mutation causing
resistance to malaria, variants in the CD4 ligand gene.
And to make a long story short, both the known and her newly
predicted variant showed this telltale property of having a high
frequency and very long range correlation.
Well that's very interesting because she was able to show that each of
these mutations probably were the result of positive selection.
But what you could do in principle is test every variant in the human
genome this way: take any variant, look at its frequency, and compare
it to the long range correlation around it, and test every single
variant in the human population to see which ones might be the result
of long range correlation. Now, when she proposed this,
this was about a year and a half ago or two years ago,
this was a pretty nutty idea because you would need all the variants in
the human population, and you would need all this
correlation information. But in fact, as I say, that
information's almost upon us, and I believed that this experiment,
this analysis to look for all strong positive selection in the human
genome will in fact be done in the course of the next 12 months.
So, I'm hoping by next year I can actually report on a genome-wide
search for all the signatures of positive selection.
Now, this doesn't detect all positive selection.
It will detect sufficiently strong positive selection going back pretty
much only over the 10, 00 years. When you do the
arithmetic, that's how much power you have. Of course,
10,000 years has been a pretty interesting time for the human
population, right? The time of civilization and
population density, and infectious diseases,
and all that, and I think we'll have an interesting window into
the change in diet. All of that should come out of
something like this. So, there's a lot of really cool
information in DNA variation to be had. All right,
that's one half. The other half of what I would like to talk about is
totally different. It's not about inherited DNA
variation. It's about somatic differences between tissues in RNA
variation. So, let's shift gears.
RNA variation: let me start by giving you an example here.
These are cells from two different patients with acute leukemia.
Can you spot the difference between these? Yep? More like bunches of
grapes and all that. Yeah, it turns out that's just a
reflection of the field of view you have if you move over
to look like that. But I mean, that's good.
It's just that it turns out that that isn't actually a distinction
when you look at more fields. Anything else? Yep? White blood
cells like different. They look broken. There's more of
them in this field of view. But you look at 100 fields of view
and it turns out that's not either. Well, the reason you're having
trouble spotting any difference is that highly trained pathologists
can't find any difference either. I generally agree there's no
difference between these two if you look at enough fields of view.
But you can convince yourself if you look that you see things there.
But these actually are two very different kinds of leukemia.
And, these patients have to be treated very differently.
But, pathologists cannot determine which leukemia it is just by looking
at the microscope, it turns out. This is the work of this man,
Sydney Farber, namesake of the Dana Farber Cancer Institute here in
Boston, who in the 1950s began noticing that patients with
leukemias, some of them seemed different in the way they responded
to a certain treatment, and he said, look, I think there's
some underlying classification of these leukemias,
but I can't get any reliable way to tell it in the microscope.
And he put many years into working this out, first by noticing certain
difference in enzymes in the cells, and then people noticed certain
things in cell surface markers, and some chromosomal rearrangements.
And nowadays, there are a bunch of test that can be done by a
pathologist when a patient comes in with acute leukemia to determine
whether they have AML or ALL. But it turns out that you can't do
it by looking. You have to do some kind of
immunohystochemical test of some sort in order to do that.
So this is a triumph of diagnosis. After 40 years of work, we can now
correctly classify patients as AML or ALL. And they get the
appropriate treatment. And if they don't get the right
treatment, they have a much higher chance of dying.
And if they do get the right treatment, they have a much higher
chance of living. So, this is great.
There's only one problem with the story. It took 40 years,
40 years to sort this out. That's a long time. Couldn't we do
better? Surely these cells know what they are.
Surely we could just ask them if they are. Well, here's the idea.
Suppose we could ask each cell, please tell us every gene that you
have turned on, and the level to which you have that
gene expressed. In other words,
let us summarize each cell, each tumor by a description of its
complete pattern of gene expression to 22,000 genes on the human genome.
Let's write down the level of expression, X1 up to X22,
00 for each of the 22,000 genes of the genome. So,
ever tumor becomes a point in 22, 00 dimensional space, right?
Now clearly, if we had every tumor described as a point in 22,
00 dimensional space, we ought to be able to sort out which tumors are
similar to each other, right? Well, it turns out you can
do that now. These are gene chips, one of several technologies by which
on a piece of glass are put little spots, each of which contains a
piece of DNA, a unique DNA sequence. Actually, many copies of that DNA
sequence are there. Each of these is a 25 base long DNA
sequence, and I can design this so whatever DNA sequence you
want is in each spot. The way that's done is with the same
photolithographic techniques that are used to make microprocessors.
People have worked out a chemistry where through a mask,
you shine a light, photodeprotect certain pixels; the pixels that are
photodeprotected you can chemically attach an A, then re-protect the
surface. Use a light. Chemically photodeprotect certain
spots. Wash on a C. And in this fashion,
since you can randomly address the spots by light,
and then chemically add bases to whatever spots are deprotected,
you can simultaneously construct hundreds of thousands of spots each
containing its own unique specified oligonucleotide sequence.
And you can get them in little plastic chips.
And then if you want, all you do is you take a tumor.
You grind it up. You prepare RNA. You fluorescently label the RNA
with some appropriate fluorescent dye. You squirt it into the chip.
You wash it back and forth. You rock it back and forth,
wash it out, and stick it in a laser scanner. And it'll see how much
fluorescence is stuck to each spot. And bingo: you get a readout of the
level of gene expression. I guess each spot, you should
design it so that this spot has an oligonucleotide complementary to
gene number one. And the next one,
an oligonucleotide matching by Crick-Watson base pairing
complementary to gene number two and gene number three.
So, if I knew all the genes in the genome, I could make a detector spot
for each gene in the genome. And of course we know essentially
all the genes in the genome. So you can make those detector
spots and you can buy them. So, you can now get a readout of
all the, I mean, this is like so cool because when I
started teaching 701, which wasn't that long ago because I
ain't (sic) that old still, the way people did an analysis of
gene expression is they used primitive technologies where they
would analyze one gene at a time, certain things called northern blots
and things like that, right? And, you know,
you'd put in a lot of work and you get the expression level of a gene,
whereas now you can get the expression of all the genes
simultaneously, and it's pretty mind boggling that
you can do that. How do you analyze data like that?
So, we still use northern blots. It's true. So,
every tumor becomes a vector, and we get a vector corresponding to
each tumor. So, this line here is the first tumor,
the second tumor, the third tumor, the fourth tumor.
The columns here correspond to genes. There are 22,
00 columns in this matrix, and I've shown a certain subset of
the columns because these genes here have the interesting property that
they tend to be high red in the ALL tumors, and they tend to be low blue
in the AML tumors, whereas these genes here have the
opposite property. They tend to be low blue in the ALL
tumors and high red in the AML tumors. These genes do a pretty
good job of telling apart these tumors.
So, here's a new tumor. Patient came in. We analyzed the
RNA, squirted it on the chip. Can somebody classify that? Louder?
AML. Next? Next? Congratulations, you're
pathologists. Very good. That's right, you can do that.
It works. And in fact, in the study that was done that was
published about this, the computer was able to get it
right 100% of the time. Not bad. So now you say,
wait, wait, wait, but you're cheating.
You're giving it a whole bunch of knowns. Once I have a whole bunch
of knowns it's not so hard to classify a new tumor.
What Sydney Farber did was he discovered in the first place that
there existed two subtypes. Surely that's harder than
classifying when you're given a bunch of knowns. And
that's true. So, suppose instead,
I didn't tell you in advance which were AML's and which were ALL's,
and I just gave you vectors corresponding to a large number of
tumors, do you think you would be able to sort out that they actually
fell into two clusters?
Could you by computer tell that there's one class and the other
class? Turns out that you can. Now, I've made it a little easier
by not listing most of the 22,000 columns here.
But think about it. Every tumor is a point in 22,
00 dimensional space. If some of the tumors are similar,
what can you say about those points in 22,000 dimensional space?
They're going to be clumped together. They're near each other.
So, just plot every tumor as a point in 22,000 dimensional space,
and your question is, do the points tend to lie in two clumps up in 22,
00 dimensional space? And there's simple arithmetic you
can learn using linear algebra to get some separating hyperplane and
ask, do tumors lie on one side or the other? And,
it turns out the procedures like that will quickly tell you that
these tumors clump into two very clear clumps. They're not randomly
distributed. And so, if you get these tumors,
and you do gene expression on them and put the data into a computer,
the amount of time it takes the computer to discover that there were
actually two types of acute leukemia is about three seconds marked down
from 40 years. That's good. So, you can reproduce the discovery
of AML and ALL in three seconds. Now you know what the pathologists
say about this. They say, oh, give me a break.
It's shooting fish in a barrel. We know there was a distinction.
Big deal that the computer can find the distinction.
We knew that there was distinction there. I know the computer didn't
know it and all that. Tell us something we don't know.
That's a fair question. So it turns out that you can ask
some more questions. You can say, suppose I take now
just the ALL's. Are they a homogeneous class,
or did they fall into two classes? It turns out that extending this
work, folks here were able to show that we can further split that ALL
class. There was a hint that you might be able to do so because
there's some ALL patients who have disruptions of a gene called MLL.
And this tends to be a little more common in infants,
and tends to be associated with a poor prognosis.
But it was really very unclear whether this was simply one of a
zillion factoids about some leukemia patients, whether this was a
fundamental distinction. So, what happened was folks took a
lot of ALL patients, got their expression profiles,
and lo and behold it turned out that ALL itself broke into two very
different clusters. This is an artist's rendition of a
22,000 dimensional space. We can't afford a 22,000
dimensional projector here, so we're just using two dimensions.
But, the two forms of ALL were quite distinct from each other,
and so actually ALL itself should be split up into two classes,
ALL plus and minus, or ALL one and two, or MLL and ALL.
And it turns out that these forms are quite different.
They have different outcomes and should be treated differently.
It also turns out that a particularly good distinction
between these two subtypes of ALL is found by looking at this particular
gene called the flit-3 kinase. The flit-3 kinase gene, whatever
that is, was of great interest because people know that they can
make inhibitors against certain kinases. And so,
it turned out that an inhibitor against flit-3 kinases,
against this flit-3 kinase gene product.
If you treat cells with that inhibitor, cells of this type die,
and cells of this type are not affected. So in fact,
there's a potential drug use of flit-3 kinases in the MLL class of
these leukemias, and folks are trying some clinical
trials now. So, not only did the analysis of the
gene expression point to two important sub-types of leukemias,
but the analysis of the gene expression even suggested potential
targets for therapy. So, I'll give you a bunch more
examples. I have a bunch more examples like that there.
They are examples of taking lymphomas and showing that they can
be split into two different categories, examples of taking
breast cancers into several categories, colon cancers.
Basically what's going on right now is an attempt to reclassify cancers
based not on what they look like in the microscope,
and based not on what organ in the body they affect,
but based on, molecularly, what their description is, because
the molecular description, as Bob talked to you about with CML
and with Gleveck, turns out to be a tremendously
powerful way of classifying cancers because you're able to see what is
the molecular defect and can make a molecular targeted therapy.
So, these sorts of tools are quite cool, and I've got to say,
in the last year we've begun using these expression tools not just to
classify cancers, but to classify drugs.
We've begun an interesting and somewhat crazy project to take all
the FDA approved drugs, put them onto cell types,
and see what they do, that is, get a signature, a fingerprint,
a gene expression description of the action of a drug.
And then we hope, here's the nutty idea,
that we can look up in the computer which drugs do which things and
might be useful for which diseases, because we'd put the diseases and
the drugs on an equal footing. All of them would be described in
terms of their gene expression patterns. So,
I'll tell you one interesting example, OK? This is an interesting
enough example. I don't even have slides for it yet.
It turns out that these patients with ALL that I've been talking
about, some of the patients with ALL will respond to the drug
dexamethasone. Some won't. If you take patients
who respond to dexamethasone, and patients who are resistant to
dexamethasone, and you get their gene expression
patterns, you can ask are there some genes that explain the difference?
And you can get a certain gene signature, a list of,
say, a dozen or so genes that do a pretty good job of classifying who's
sensitive and who's resistant. Then you can go to this database I
was telling you about of the action of many drugs and say,
do we see any drugs whose effect would be to produce a signature
of sensitivity? If we found a drug X,
which when we put it on cells turned on those genes that correlate with
being sensitive to dexamethasone, you could hallucinate the following
really happy possibility that when you added that drug together with
dexamethasone, you might be able to treat resistant
patients because that drug could make them sensitive to dexamethasone,
and that you could find that drug just by looking it up in
a computer database. So, we tried it and we hit a drug.
There was a certain drug that came up on the screen,
yes? That's very much in the idea too. We found a drug that produced
the signature sensitivity, and tested it in vitro. In vitro,
if you take cells that are resistant and you add dexamethasone,
nothing happens because they're resistant. If you add drug X,
nothing happens. But if you add both drug X plus dexamethasone,
the cells drop dead. It's now going into clinical trials
in human patients. It turns out drug X is already a
well FDA approved drug, so it can be tested in human
patients right away, so it's going to be tested.
So, the gene expression pattern was able to tell us to use a drug which
actually had nothing to do with cancer uses in a cancer setting
because it might do something helpful.
Now, what's the point of all this? We can turn up the lights because I
think I'm going to stop the slides there. The point of all of this,
which is what I've made again, and I will make again,
because you are the generation that's going to really live this,
is that biology is becoming information. Now,
don't get me wrong. It's not stopping being
biochemistry. It's going to be biochemistry. It's not stopping
being molecular biology. It's not stopping any of the things
it was before. 45:57 But it is also becoming
information, that for the first time we're entering a world where we can
collect vast amounts of information: all the genetic variants in a
patient, all of the gene expression pattern in a cell,
or all of the gene expression pattern induced by a drug,
and that whatever question you're asking will be informed by being
able to access that whole database. In no way does it decrease the role
of the individual smart scientist working on his or her problem.
To the contrary, the goal is to empower the
individual smart scientist so that you have all of that information at
your fingertips. There are databases scattered
around the web that have sequences from different species,
variations from the human population, all of these drug database,
etc., etc., etc., etc. It's a time of tremendous ferment,
a little bit of chaos. You talk to people in the field,
they say, we're getting deluged by data. We're getting crushed by the
amount of data. I don't' know what to do with all
the data. There's only one solution for a
field in that condition, and that is young scientists because
the young scientists who come into the field are the ones who take for
granted, of course we're going to have all these data.
We love having all these data. This is just great, couldn't be
happier to have all these data. We're not put off by it in the
least. That's what's going on. That's what's so important about
your generation, and that's why I think it's really
important that even though it's 701 and we're supposed to be teaching
you the basics, it's important that you see this
stuff because this is the change that's going on,
and we're counting on this very much to drive a revolution in health,
a revolution in biomedical research, and we're counting on you guys very
much to drive that revolution. It has been a pleasure to teach you
this term. I hope many of you will stay in touch,
and some of you will go into biology, and even those of you who don't will
know lots about it and enjoy it. Thank you very much. [APPLAUSE]