Learn how advances in biomedicine hold the potential to revolutionize drug development, drug treatments, and disease prevention: where are we now, and what does the future hold? This course will present short primers in genetics and mechanisms underlying variability in drug responses. A series of case studies will be used to illustrate principles of how genetics are being brought to bear on refining diagnoses and on personalizing treatment in rare and common diseases. The ethical and operational issues around how to implement large scale genomic sequencing in clinical practice will be addressed. After completing this course, learners will understand 1. The ways in which genetic variants can contribute to human disease susceptibility 2. How to choose among drug therapies based on genetic factors 3. That the functional consequences of the vast majority of genetic variants discovered by modern sequencing are unknown. This course is targeted primarily at physicians 5+ years out of training. Other healthcare providers, medical/health sciences students, and members of the public may also be interested. Course launches January 15, 2016. * The information presented in “Case Studies in Personalized Medicine” is offered for educational and informational purposes only, and should not be construed as personal medical advice. If you have questions or concerns about a medical matter, please consult your doctor or other professional healthcare provider.
Using Genomics to Find New Drug Targets: Familial Hypercholesterolemia
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Case Studies in Personalized Medicine
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UNIT 3: CASE STUDIES IN PERSONALIZED MEDICINE, PART 1
In Module 5 we will begin to discuss specific cases as these apply to personalized medicine. We will first look very closely at a case of familial hypercholesterolemia as we investigate how we use genomic medicine to move from a rare disease to a common medication, using genomics to find new drug targets, and a discussion of the side effects of statin therapy. In Module 6 we will look at a collection of "high risk pharmacogenetics"cases that illustrate adverse reactions due to drug metabolism and variable drug responses.
Rencontrer les enseignants
Dan Roden, M.D.Professor of Medicine and Pharmacology
Assistant Vice Chancellor for Personalized Medicine
[MUSIC]
In this module, we're going to continue discussing this particular patient, but
from a different point of view.
And that is how we use information about
lipid regulation to identify new drug targets.
And then we'll talk a little bit about other Ways in which genetics can
inform the identification of potential new drug targets.
A very exciting and interesting area I think.
So, I've presented this case to you before, and here is his family tree
with lots and lots of Hypercholesterolemia odosominal dominant way,
so gets interested sequences is LDLR gene,
the gene that encodes the LDL receptor, expecting to find a mutation
in the family, and low, and behold nothing is found.
So, we see families that had clearly have autosomal dominant
familial hypercholesterolemia but do not have a mutation in the LDLR receptors.
So where else might a receptor might a mutant mutation be?
So one clue might be provided by genome wide association.
This is a genome wide association that points out loci that
regulate cholesterol in a group of 19,000 patients.
And you can see that LDLR receptor is on that plot,
and what that means is that there are common variance in the LDLR receptor
that contribute in a very, very modest way to variability and LDL cholesterol.
This interesting example that will recur throughout our discussions of how common
variance in specific genes might have very small effects on a particular physiologic
process, and rare variance might have enormous effects on that same process.
But this process is also identifies a set of candidate genes that might be
interesting in terms of looking at for rare variance in patients with familial
hypercholesterolemia that don't have mutations in the LDOR receptor in LDOR.
So that was 19,000 patients.
And that was in 2009.
So through collaborations across the world,
investigators have accumulated much much larger data sets, and
the results of that are shown here in almost 200,000 individuals.
And the relationship between genetics variation and
individual genes that control LDL Triglycerides,
HDL, and ultimately coronary risk which is not shown here.
You can see that there are sets of genes that contribute to variability in LDL but
don't do anything else.
There are other genes that contribute to variability in LDL and HDL but
not anything else.
And in this Venn diagram there's overlapping sets
that actually encompass almost every combination that you could think of.
So this creates a very, very large set of candidate chains for
familial hypercholesterolemia.
The other way of approaching candidate chains is to understand a fundamental
underlying physiology in the process that we're studying and
therefore understand what candidates are.
So Goldstein and Brown had already identified this idea of LDL receptors
as the key mediators of handling LDL from the outside of a cell into the cell.
And then signalling within the cell, and
then what it turns out that the LDL receptor itself, the protein,
it shuttled from the cell surface inside the cell.
And then dissociated from the LDL particle, and
the receptor itself makes its way back to the cell surface to be used again.
And there are a number of proteins inside the cell
whose function is to modulate that particular process.
One of which is a gene called PCSK9.
PCSK9 actually showed up on the G WAS that I just showed you and
on that large overlapping set of Van diagrams as a regulator.
Of LDL cholesterol.
It's function and
regulation was actually not well known when this particular story evolved.
But its clear that mutations in the PCSK9
chain cause autosomal dominant hypercholesterolemia.
The initial idea was tha if you had a mutation,
somehow or the other, the function of that gene would get destroyed and perhaps that
would mean a change in LDL receptors coming to the cell surface.
It turns out that the mutations that cause familial hypercholerstolemia
probably result in actually fewer, fewer LDL receptors of the cell surface.
It looks like what PCSK9 does is it binds to the cell surface
binds to the receptor inside the cell.
And once bound, the receptor and the PCSK9 complex are actually degraded.
So an overactive PCSK9 results in fewer cell surface receptors.
We didn't know that when this paper came out in Nature Genetics.
There's a lot of speculation about the mechanism and
it turns out that that's probably the mechanism.
And here's why we know a little bit more.
This is a really interesting experiment.
This experiment took place in something called the Dallas Heart Study.
A study that recruited thousands of patients from the Dallas Heart area,
Dallas area, and studied many, many things.
But one of the things they studied was cholesterol handling and also the presence
or absence of coronary disease, these are patients who are all middle aged.
Now there's a very large African American cohort and
there's a large Caucasian cohort.
So what the investigators did was they said well, if we think PCSK9 is
important for cholesterol handling, let's sequence the PCSK9 gene.
And let's find variation in the gene
that we know disrupts the function of the encoded protein.
So the way you do that is you sequence the gene and you find Polymorphisms
that result in early truncation of the protein, so called stop codon.
These are nonsense variance, that's the other term for them.
And they found a group of 85 patients with nonsense variance
compared to about 3200 controls that did not have nonsense variance.
And what you can see on one panel is the distribution
of LDL cholesterols on the top among the 3,200 without the variants.
And on the bottom, among the 82 with the variants.
And you can see that the patients who had the variants had much lower LDL
cholesterols.
They had had low LDL cholesterols all their lives.
And the other panel shows that they had much, much less coronary disease.
They had a little bit but much,
much less in patients who didn't have these non sense variants.
So the assumption is that by knocking out PCSK9 function, and these
are patients who only have one allele knocked out, the other allele is normal.
There's a lowering of LDL cholesterol and
a decrease in the incidence of coronary disease.
So if you're a drug developer, you look at these data and
say well, if I had a drug that could interfere with PCSK9 function,
I might actually be able to lower LDL cholesterol And control coronary disease.
And in fact this paper appeared in The New England Journal of Medicine in 2006 and
in 2012 the results of the first clinical trials of
an antibody against PCSK nine were reported in the same journal.
And what you can see is that as a function of dose, these are patients who were given
a single dose of an antibody against PCSK9, the larger the dose, the longer
the effect, but some of the patients developed persistent 70% reductions
in LDL cholesterol that last weeks and weeks and weeks after a single dose.
So, this proves that inhibiting PCSK9 function either through genetics or
through a drug results in striking lowering of LDL and the hope of
course is that the drug would then result in a decrease in the prevalence and
incidence of atherosclerosis and it's complications like heart attacks.
Those trials are underway and of course everybody would like to have a pill
rather than an injection of an antibody, but
this trial is really important because it demonstrates the proof of principle that
that's what that gene Interfering with that genes function does.
So we had this story, I've shown you this slide before in one corner we have
data obtained from genome wide association studies Is gwass where the variants we
find are common across a population and the effect sizes are very modest.
An odds ratio of 1.2, 1.5.
Maybe 1.7 if you're really lucky too and
then at the other end of the spectrum are rare variants that occur in families.
We call those mutations that can confer really
amazing human phenotypes like LDL cholesterol values of 352.
Now, we start to fill in the middle.
We're starting to see the idea that there are rare variants, not so
rare as the familial hypercholesterolemia that confer large effects.
The Dallas Heart study showed that there's a small group of patients among
African-Americans, 2%, so that's not really rare, but
it's not really common to have pretty large effects, large enough to catch
our intention in terms of cholesterol regulation and certainly large enough to
catch the attention of a drug developer to develop a new drug based on the genetics.
So the story that I've just told you is a story of how rare variation
can be a real clue to understanding what a new drug target might look like.
These are all promissory notes.
So here's another example.
This example starts with a GWAS on pro-insulin
levels in several thousand patients.
You can see there's lots and lots of signals.
And one of them is in a gene called SLC30A8.
SLC30A8 encodes a zinc transporter.
And there is some physiological rationale for
thinking zinc transport my modulate risk of developing diabetes.
So not only does devariant common variance in SLC30A8
modulate pro insulin levels as you can see in the inset, but they also modulate
risk of type two diabetes with an odds ratio that is very, very modest.
1.14 with a P value that's pretty respectable.
So, it's a real odds ratio, but it's a very small effect.
Now, a group of investigators then asked the question,
well what happens if we looked for coding region variants
that disrupt the predicted function of SLC 30A8.
So those are coding region variance that result in early stop in the protein
just like the PCSK9 story, and here's the result of that study.
So the first thing to notice is that there is an enormous number of patients
that were studied, there were 30,000 cases of type two diabetes and 120,000 Controls.
The Allele frequencies for these various stop codon variance are shown
on that Y axis and they are as low as 0.05%, very rare
Here in the population and as high as 0.4% so then all of these are very rare and
that's why it lead very, very large numbers to get statistical significance.
There were really three kinds of variance that were studied that are shown on the X
axis.
Two of them are shown and the other is a group of other stop variance,
each one of which occurred once or twice in a population.
Most of these patients came from Iceland but
a number of them came from other parts of northern Europe, across Europe, and
occasionally from other parts of the world.
But mostly from caucasian populations.
And what you can see is that overall, carrying
one of these variants reduces the risk for type two diabetes by about 65, 70%.
So again, if you're a drug company and you look at those data,
you say type two diabetes is an enormous Medical problem.
We have a drug that prevented the development of type 2 diabetes.
That would be a blockbuster, important for human health and maybe this is the target.
So stay tuned, maybe this will turn out to be a target, maybe this wont.
One final example, is was featured in, not in the medical literature but
in New York Times.
This is a story from 2012 of a young woman in Georgia who cannot feel pain.
And you would say,
we'll that's a great phenotype to have because you break your arm and
you won't be in tears but in fact, you could break your arm and not even know it.
So it's not a very nice phenotype and that's showing on the bottom picture,
when she was a young child, she had injured her hands and didn't know it.
So these children can put their hands into boiling water and not know it.
Of course they can still hurt themselves.
The actual mutation that causes this particular illness in this child
is not listed in the New York Times article, but there are other
patients around the world who have been described to have this phenotype,
notably in an article in Nature in 2006 that described three families.
In which consanguineous marriages resulted in children with
the inability to feel pain.
The consanguineous marriages part is quite important because what it suggests
is that the father and the mother both have a rare variant,
that is perhaps prevalent in their family but not prevalent otherwise, and
by marrying each other they strikingly increase the risk of a child having
two copies of the rare variant and therefore the phenotype.
And that's exactly what happened here.
The children affected have two copies of a mutation in a gene called SCN9A,
which encodes a sodium channel that is important for sensing pain.
And that's the variance, all result in total loss of sodium channel.
Functioned for that particular version of the sodium channel.
And what's interesting is of course if you could develop a drug that could inhibit
function of that channel a little bit,
maybe you'd have a great analgesic to use after operations, or for anybody with.
Chronic pain.
Obviously these mutations are tolerated through life.
You can see the picture of this young girl on the cover of the New York Times.
She's developed normally and otherwise seems like a normal person so
you could take that drug.
The genetic data suggests that you can take the drugs that inhibit function of
that gene and not develop some other amazing side effects.
So these are clues, ways in which genetic data often obtained from very, very
rare patients with very unusual phenotypes can inform the drug development process.
So the take-home messages here are the studies of rare diseases,
once again, are highly, highly informative for personalizing medicine, not just for
the rare patient who has the really rare disease but for all of us.
Where do HMG-CoA reductase inhibitors come from?
Where are PCSK9 inhibitors going to come from?
Those are all coming and they will be important for large numbers of patients.
The other story of course relates to the fact that there are common variants
that predispose to diseases like.
High cholesterol or heart attack or type 2 diabetes.
And those common variants, while they don't tell
us very much about risk in an individual subject,
understanding the complicated pathways in which those
variants modulate, things like LDL cholesterol,
can make us smarter, not only in terms of developing drugs,
but in terms of predicting exactly what pathways
are important for developing high cholesterol and
therefore preventing the consequences of high cholesterol.
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