FEMALE SPEAKER: Dr. David Solit.
I have to start with a personal note.
He's part of my family.
And so I've always been excited about David
and who he is as a person, and I'm excited about his work
as well.
He's my nephew.
But more importantly than that, he's
the director at the Kravis Center for Molecular Oncology
at the Memorial Sloan Kettering Cancer Center in New York City.
He's both a researcher and clinician,
and works on all areas of cancer.
And today he's going to talk about an aspect of his research
that I find fascinating when I understand it.
And so thank you so much David, for coming and talking to us.
DAVID B. SOLIT: Thanks for the invitation to present.
I am out here this weekend riding in a cancer fundraiser
called Cycle for Survival.
So since I was in town, I guess I had the opportunity
to create this Google YouTube stream
and speak to you guys today about what we've
been doing at Sloan Kettering.
So really what I want to talk to you about today
is how we're trying to use a big data computational
approach to identify new drug targets,
and how we're actually studying what we
call extraordinary responders.
These are patients who really beat the odds.
These are people who have a cancer that most patients die
of, and oftentimes very quickly.
But these are those rare individuals
who overcame those odds and are alive many years later.
And what we try to do is analyze tumors from those patients
so that we can learn lessons from that
and hopefully identify new drug targets
that we can apply in the broader population of patients.
So I've been at Sloan Kettering now it's 18 years.
And I run both the research lab.
I also see patients as a medical oncologist.
And really what the focus of my lab's efforts is
is to really try to identify these drug targets.
And so we try to accelerate this target discovery.
And it's a pretty simple paradigm that we follow.
It's really up here on this first slide here.
We try to define targets.
And I'm going to spend a few minutes talking
about what these targets are and how we find them.
We then, in the laboratory, try to identify
a drug that selectively will inhibit
that altered target that's making the cancer cell grow
or something downstream from that drug target
that'll kill the cancer cell.
And then what is a major challenge--
and I'll talk to you about that for the second part
of the talk-- is how do we identify
the individual patients who have an alteration in their drug
target so that we can give them a particular drug?
And that's really what we try to do.
So what are these targets?
There's really two main ways to identify these targets.
One's called a genotype to phenotype approach.
And then I'll mention to you the opposite, which is a phenotype
to genotype approach.
So the genotype to phenotype approach-- or G to P--
is where we take a large group of tumor samples
and then we retrospectively do molecular profiling on them.
And this is usually what's called next-gen sequencing
at this point.
There are three billion base pairs
of DNA within the human genome.
And what we try to do is sequence
that genome in these individual tumors
and then use a statistical approach
to find things that are commonly altered in multiple patients
with the same cancer.
So if you see the same mutation again and again
in a particular cancer type, that
gives you a statistical clue that that mutation maybe
is important for the growth of that cancer.
And so this was really the paper that really changed my career.
I was what was called a postdoctoral fellow working
in the laboratory of someone else.
I hadn't gotten my own laboratory
when this paper came out in a journal called
"Nature," which is one of the big biology
journals in the field.
And it was a very simple experiment that they did.
What they did is they took 1,000 cancer samples--
some tumors, some cell lines that were tumors
that they now had growing on plastic--
and they sequenced a common pathway that's
activated in cancer called the MAP kinase pathway.
And then what they were looking for
was genes that hadn't been known to be mutated that were mutated
in these tumors commonly.
And so when they sequenced that pathway,
they found this gene called BRAF.
And this was interesting because at the time,
nobody had studied BRAF.
There's actually three different types of RAF proteins.
There's ARAF, there's BRAF, there's CRAF.
CRAF is also called RAF1, and it's the one
that everyone had been biologically focusing on.
And it turned out that people were really missing the boat.
And this non-biased approach, by just sequencing 1,000 tumors,
identified that BRAF mutations were found in about 80
of those, so in about 8% of all those tumors.
And that was about 50% of melanomas,
but also scattered throughout other tumor types.
What was notable about these BRAF mutations
is that about 95% of them were the exact same change
in the DNA.
So it was a substitution of one amino nucleotide
for another nucleotide, which causes
a single base pair amino acid change in this protein.
And when you do that, you actually activate that protein
constitutively.
So that's why these people get cancer.
They have an abnormal protein that's always turned on.
And when you have a situation like that,
if you can develop an inhibitor of that activated enzyme,
you oftentimes have a new cancer drug.
And that's what we tested in the lab after that.
So what we did is we took a panel of cancer cell lines.
We looked to see which ones had BRAF mutations, which ones had
a mutation upstream called RAS that had already
been discovered a few years prior,
and then which ones had activation
of this, what's called MAP kinase pathway,
but due to upstream activation.
And what we found in the laboratory
was that these tumors that had BRAF mutations
were selectively dependant upon BRAF.
Because if you have a mutation, and the cancer cell is not
dependant upon that mutation, then developing a drug
that targets that mutation's probably not
going to have too much of a dramatic effect.
If you actually look at the chronology here,
this experiment was done in 2002.
We did this biologic experiment in 2006.
And then it took about til 2010 til
we had a good drug that we could actually test in patients.
And this is just some early data from the phase one trial.
That's the first in human study of a drug that selectively
inhibits RAF.
And this is a patient with very advanced melanoma.
All of these dark dots here on this PET scan are cancer.
And you can see 15 days later, after being
on this selective inhibitor of BRAF,
this patient's had a complete response on our PET scan.
And this is a CAT scan showing regression
of those tumors in this patient's lung.
And ultimately in a randomized study,
we were able to show that this drug,
if you give it to patients with melanoma who
have this BRAF mutation, that those patients live longer.
And actually we were able to show in the laboratory
that if you give this same drug to cells
that lack the BRAF mutation, those cells actually
grow faster.
So it really becomes critical that
for each individual patient, before we treat them,
we know whether they have the BRAF mutation
or whether they don't have the BRAF mutation.
Because if we give them the BRAF inhibitor,
they're likely to benefit if they have a BRAF mutation.
If we give them the BRAF inhibitor
and they're BRAF wild type tumor,
their tumor may actually grow faster
and they may die quicker.
So really what the challenge has become somewhat in our cancer
population is how do we figure out
who's got what mutations within their tumor?
Because again, these mutations can
be scattered over this entire human genome, which
makes up about three billion different base pairs.
So that's one way to approach this problem.
That way is really a statistical approach.
It takes large numbers.
It takes a large consortium.
And there's an effort called the Tumor Cancer Genome
Atlas that's trying to do that across the country.
And that's one way to do it.
And we actually came up with the opposite idea.
Can we actually take patients who were on cancer drugs
and they responded incredibly well to those cancer drugs--
most patients didn't, or maybe nobody else didn't-- and can we
find out what was the mutation that made them respond?
Because if we could figure that out,
we already have a good drug because it worked
in that individual already.
And if we could just find other patients just like them,
we have a drug ready for those individuals.
So could we expand out the number
of patients who would benefit?
And the real inspiration for this work
was this clinical trial that we ran in Sloan Kettering
a few years ago.
So this was a trial we ran back in 2008.
We did this trial in patients with bladder cancer.
And like every other bladder cancer trial
we've done over the past 20 to 30 years,
this was a negative study.
So we enrolled 45 patients on to this clinical trial.
Only two of those patients responded.
And based upon that result, statistically one would say OK,
this drug which we were testing here
called Everolimus-- it's made by a company called Novartis--
that this drug doesn't work in patients with bladder cancer.
So let's maybe move on to the next idea.
But despite the fact that in the entire population
it wasn't impressive, this patient
here-- and I'm showing her CAT scans-- really
jumped out at us.
And so just to give you her history briefly,
her history actually begins off the slide here.
In the summer of 2009, she presented
with a bladder cancer that had already
spread to her lymph nodes.
So it was already what we call metastatic.
And what we try to do in these patients is we give them
chemotherapy, we try to shrink the tumor down,
we try to cut everything out, and then
in a percentage of those patients we cure the cancer.
But unfortunately, by the fall of 2009,
her cancer had come back, it had spread back to the lymph nodes.
And you can see here in January 2010, she had this CAT scan.
And really this area here that the red arrows are pointing at,
that's her tumor.
And that spread out through her abdomen.
And actually in this situation, a patient with bladder cancer
who's had a prior line of chemotherapy,
we have no standard of care.
And the average survival of these patients
is only nine months.
And so we really encourage these patients
to go on clinical trials.
And for no particular reason other than it
was an open clinical trial at Sloan Kettering at the time,
this woman enrolled on the Everolimus study.
And despite the fact that this drug really didn't work well
in anybody else, by April 2010 she
had achieved a near complete response.
By July 2010, a complete response.
What I mean by that is that all the tumors had completely
resolved.
If you look at July 2011, still in a complete response.
'12, '13, '14.
She was actually just back in our clinic a few weeks ago.
And she's now at her five year anniversary
being on this compound without any evidence of disease,
despite the fact that this again,
in the overall population, was a negative study.
In fact, every other patient on this study
has died of their disease.
She's the only one who's alive.
And not only is she alive, her cancer's completely gone away.
So again, what makes her unique?
So at the time, back in 2008, 2009, the technology
that we had available really allowed us to look at one gene
at a time for mutations.
And that was pretty limiting.
We could have a hypothesis based upon the biology
around Everolimus that maybe this gene or that gene
was important for this response.
So we did that type of approach.
We looked at a few genes and a candidate approach.
Couldn't find what really was unique about this patient.
But what happened along the way is that technology improved.
And this was both what we call sequencing technology
and then also just the computational power,
our ability to analyze this data expanded to the point
where we could now do whole genomes.
And so actually, the first whole genome sequence
of a normal cell was not completed
until about the year 2000.
The first cancer genomes were only about five years ago.
And those first cancer genomes were
costing on an average hundreds of millions of dollars
to complete in their entirety.
The technology eventually progressed to the point where,
for just a small amount of money at the time,
for this paired genome for sequencing
both their tumor and their normal cost about $20,000.
That's already down to about $2,000
today, just a few years later.
But again, by being able to sequence her whole genome
and being able to analyze all that data,
we're able to figure out every single place in her tumor
genome where there's a mutation and it's
different than her normal genome.
And if you do that analysis, you come up
with 17,000 mutations that are in her tumor that
are not in her normal.
And you say well, how are you going to sort
through 17,000 data points?
But it's a real testament to how much
we've learned about the biology of these genes
over the past several decades that we're
able to very quickly bioinformatically map
every single one of these 17,000 mutations
to what we call the reference genome.
We can ask, which of these mutations
are present in a coding region of a gene?
Which of these mutations would be
predicted, based upon an algorithm,
to change the protein function?
And which of these genes that change a protein function when
they have a mutation would be predicted to be associated
with Everolimus response?
And when you do that, you can take
all 17,000 of these mutations and actually boil it down
to just two-- one called TSE1 and the other called NF2.
And the reason these were so interesting
is that when you mutated both TSE1 and TSE2,
you activated a protein TORC-1, which
is the direct target of Everolimus.
So if you look at this patient in retrospect,
she was the perfect patient to get this drug.
We simply didn't know it going into the clinical trial,
because we didn't know what genes
were mutated in her tumor.
Actually, at about the time-- I'll just go back--
that we were getting these results back,
there was actually a clinical trial
published that this same drug Everolimus works
in patients who have a condition called tuberous sclerosis.
This is an inherited disease where patients get cancer
at a very young age due to germ line mutations
in the same gene, TSE1.
And so these are people who are born with already one
defective copy of this gene.
If they lose the other copy in a normal cell,
they then get tumors.
And because normally you have to lose both copies of a gene,
if it's a tumor suppressor, to get a tumor,
these people are very susceptible to getting cancer
and they get cancer at a very young age.
So we've now taken this extraordinary responder
paradigm and we've applied it to other cases.
And I just want to mention one other quickly here today.
This is a second patient who was a young woman in her 40s.
She presented with a mass in her abdomen.
It was a localized tumor, so the surgeons opened her up.
They went ahead and cut out this tumor.
And if you look at this tumor under the microscope,
this patient has what's called a small cell
cancer of the urothelial tract.
And this type of tumor has a particularly grim prognosis.
So if you look at our series of this type of cancer
at Sloan Kettering over the past 10 years,
essentially all patients with this disease
die of their disease.
And so given that really grim prognosis, the doctors who
were treating her went ahead and gave her
some chemotherapy, the [INAUDIBLE],
even though she didn't have any cancer.
And we call this adjuvant therapy.
It's the type of chemotherapy you
give try to prevent the cancer from coming back.
We do this very commonly in breast cancer and in colon
cancer.
But unfortunately, despite getting this chemotherapy,
just a few months later, her cancer came back.
It had now metastasized to the kidney.
She now had metastases, and so spread to the lymph
nodes in the abdomen.
And so what they went ahead and did is they actually went,
took her back to surgery.
They cut all this cancer out again.
Usually that doesn't work, but they were really desperate
and had really no other options.
And unfortunately, just a few months later, the cancer
had come back again in the abdomen
and had now spread to the bone.
And so this was really not a good story.
This is one that probably was not going to end well.
And so what we recommended is that we just
don't have good standard treatments for you.
We referred her for a clinical trial,
and they referred her to what's called a phase one
clinical trial.
This is really the first [? in-man ?] human study where
we're not even sure what's the side effect of the drugs,
what's the right dose to give.
And so she ultimately again, for no particular reason
other than it was an open study at the time,
was enrolled on the study of a chemotherapy called Irinotecan,
and a second drug called AZD7762,
which is an inhibitor of a kinase-- or an enzyme,
you could say-- within the cell that's
important for DNA repair and checkpoint
to allow for DNA repair.
And the hypothesis with this trial
was that the cancer cells would be more susceptible to DNA
damage from the chemotherapy.
This kinase inhibitor would prevent this DNA repair
from happening in the cancer cells selectively,
and that would thus enhance the effects of the chemotherapy.
And it was a good idea.
There was some evidence in the laboratory
that this could work.
But unfortunately, they saw a lot of side effects
with this combination.
And they really weren't seeing a lot of activities.
So midway through treatment on this clinical trial,
the company AstraZeneca actually discontinued
the entire program.
So they stopped the development of this AZD7762.
And that was despite the fact that this patient had actually
achieved a complete response.
So all her cancer had gone away.
But she was really the only one where
we saw that type of response.
And so given the fact that they had discontinued the program,
she had to stop taking the drug.
They continued the chemotherapy for a few more months,
eventually had to stop that to give her a break.
They were expecting the cancer would come right back once they
stopped the treatment.
And that was about four years ago.
Her cancer's never come back and she's now
probably cured of her cancer.
So again, this is a woman who was cured of a cancer
that everybody dies of on a drug that
was so disappointing in the clinic
that the company discontinued it during the first clinical trial
that was being done.
So we went ahead and we sequenced this woman's
entire tumor genome.
And this is called a Circos plot.
It's a way for us just to represent graphically
the complexity of her tumor genome.
We just take the chromosomes, we line them up in a circle.
These line show translocations, so a breakage of one chromosome
where it's recombined into another.
This inner circle shows copy number alterations, so gains
or losses of particular genes.
And then this woman actually had 19,000 somatic mutations.
These are alterations that are in the tumor that
are not present in the normal.
So again, how do we sort through all this information?
Just to give you some hints of how we do that.
One thing we do is we can actually align these genes
to the homologues of these genes in other species.
So we can take particular genes that are mutated informatically
and align them to the same gene in a mouse,
or the same gene in a Drosophila fly,
or the same gene in a yeast, or the same gene in a bacteria.
And when you have areas where the gene is identical
in a human, in a mouse, in a dog, in a Drosophila,
in a yeast, in a bacteria, that's a part of the gene
that you simply can't mutate without altering its function.
So we can prioritize that mutations
in that part of the genome are more
likely to be functional or disruptive of the protein's
function.
And so when you go ahead and do that, again we go back
and we take the 19,000 genes, we match them
up to the coding regions.
We actually narrow it down to just a couple hundred
doing that.
And then we take those couple hundred candidates,
we align them evolutionarily.
And when we do that, there was a particular gene called RAD50
where there was a single base pair
substitution in that gene where it was in a part of the protein
called the D loop.
And that part is essentially the same in humans, in mice,
in Drosophila, in yeast, in bacteria.
And so that really allowed us to prioritize this particular gene
mutation for further functional validation versus for example,
this ATR mutation, which was also
mutated in this patient's tumor but was
not in a known functional area of the protein
and was not in an area of evolutionary conservation.
We can also take these different genes that
are mutated and based upon crystal structures of proteins
that have been made, we can actually model off the crystal
structure where the mutation is in the protein
and what this specific amino acid substitution is
potentially going to do to the interaction of that protein
with other proteins.
And so this is just showing in this patient who
had this unbelievable curative response, that this
is a protein complex of actually three different proteins called
the MRE complex.
And this mutation in RAD50 would actually
be predicted to be on the interface between RAD50
and another protein called MRE11, would actually
disrupt binding of these two proteins,
and would actually disrupt the function.
So based upon again, this evolutionary conservation
and this crystal structure modeling,
we're able to take these 19,000 mutations
and really focus on this one mutation that
seems most interesting.
But even there, it's really a computer prediction.
It's a guess.
And we still need to go back to the lab
to do functional studies to prove
that that hypothesis makes a lot of sense.
So what we did in this patient, to make a long story short,
is we took advantage of this evolutionary conservation
within this protein to actually do
a cross species validation of this mutation in a yeast model.
So because the yeast protein for this pathway
is identical in humans and in yeast,
we can knock this mutation into yeast
and see if it disrupts the protein function
and whether it would maybe explain
this woman's incredible sensitivity
to the chemotherapy.
And so that's really this experiment here.
And what we're showing in this experiment is
if you knock in this RAD50 mutation into yeast-
and actually yeast just have one copy of the genome,
these are haploid yeast-- you get some sensitization
to the chemotherapy here.
But if you actually knock in this RAD50 mutation and also
knock out the check one pathway--
and so MEC1 is the homologue of ATR,
which activates upstream check one--
you get what's called a synthetically lethal
interaction.
So you see a killing of these cells
when you have both the RAD50 mutation and knockout check
one when you give the chemotherapy that you don't see
when you give the chemotherapy to cells
that don't have this particular mutation profile.
So again, like the first patient I talked to you about,
this patient was the perfect patient
for this combination of drugs.
We simply didn't know it going into the clinical trial.
But one could now hypothesize that if we knew the mutation
status of all of these patients going
into these clinical trials, we could do these clinical trials
more effectively, hopefully get them done quicker,
and hopefully see better results going forward.
So this is really the paradigm of this extraordinary responder
initiative that's now gone nationwide through the National
Cancer Institute.
We take a clinical trial which was disappointing,
but for which there were some patients who seemed
to benefit from the treatment.
We take tumors from those patients.
We do some sort of genetic analysis
to figure out potentially what made those patients unique.
It's not showing up on the slide here,
but we always go back to the laboratory
to do functional validation of that biology.
And then what we need is we need an assay,
we need a test that will allow us to perspectively
now look for those particular mutations in everybody.
So we know who has which mutations
so that we can do an iterative clinical trial where we just
enroll patients with that mutation
onto this drug to ask how many of them are going to have
similar type of responses.
So how are we going to find the patients?
What is the assay that we're going to do
to actually do this analysis?
And so this is what we're doing at Sloan Kettering.
We run an assay called MSK-IMPACT-- IMPACT standing
for Integrated Mutation Profiling of Actionable Cancer
Targets.
And so this is an assay of 341 genes.
And I took out the slide.
And so what we know about cancer is
that genes are not necessarily distributed
in terms of the important mutations all
the way throughout the genome.
That there's certain parts of the genome, certain cancer
genes, that are more likely to be important than others.
And so what we can do to try to save costs,
to save money so that we can sequence more patients
and get more from our sequencing resource, what we can do
is we can take DNA from multiple patients, we can chop it up,
we can then attach a DNA barcode to each of those individual DNA
molecules, and then mix all those DNA molecules together
in the same reaction.
And then we can use those barcode DNAs
in a very powerful computer at the end
to actually figure out which DNA molecule came
from which patient.
And so by mixing all these in the same reaction,
that allows us to save money.
And I hope you've got a plug. [LAUGHS]
FEMALE SPEAKER: It's there.
I don't know why it's not.
Keep talking.
DAVID B. SOLIT: OK.
The other way we save money is we
can actually pull down the parts of the genome
that we're most interested in.
So we develop dates.
We develop DNA dates that will go and hybridize
with the parts of the genome where the cancer mutations are
most likely to be found.
And that again allows us to save money on the sequencing
resource in the future.
And so we've developed this assay.
It's a custom assay.
Yeah?
AUDIENCE: Question.
[INAUDIBLE]
DAVID B. SOLIT: So this is called next-gen sequencing,
and it's really a digital platform.
So we sequence each individual DNA molecule separately.
And what we try to do is try to sequence each of these DNA
molecules 500 or 1,000 times.
And the reason that we need to do that
is because the normal DNA is mixed in with the tumor DNA.
We can't usually get a pure population
of just cancer cells.
And so if the tumor that we cut out from the patient and then
smushed up and extract the DNA out
of it, if that's 70% normal cells and 30% tumor cells,
we need to get better sensitivity
for finding the mutations.
We want to have very deep coverage.
And that means sequencing that same part of the genome
again and again and again to make sure we
don't miss the mutation.
Yeah.
FEMALE SPEAKER: [INAUDIBLE].
DAVID B. SOLIT: So we've created this initiative
at Sloan Kettering.
And really the initiative is pretty simple.
What we're trying to do here is we're
trying to sequence every single patient
with advanced metastatic cancer at Sloan Kettering using
this assay.
And so we see about 45,000 unique patients
with cancer at Sloan Kettering every single year.
About 75% of these patients are cured.
About a quarter of them develop recurrent disease
and are thus at highest risk of dying of their cancer
and thus the most in need for novel therapies.
And so we've got about 10,000 to 12,000 patients
like that at Sloan Kettering every year.
And so we are going to attempt, and we've
begun this effort just over the past year,
use this assay to sequence every single one of those patients.
We actually sequence both the tumor
and we also sequence the normal DNA.
And by sequencing the normal DNA in all these genes,
there's actually the potential to identify a mutation that's
in the normal cells that was actually
the reason that the patient got the cancer in the first place.
So genes like BRCA1 or BRCA2, these
are risk factor genes that make it more likely that you're
going to get cancer.
What's the major hurdle to doing this?
Previously up to this year, it was technology.
But now it's really money, like many things.
So it's very expensive to do the sequencing.
And that's why I was focusing on how can we
bring the cost of the sequencing down by mixing DNA
from multiple patients together and just sequencing
parts of the genome?
And it's both expensive and it's not
standard to do this type of testing in most patients
with cancer.
So we can only bill insurance companies
to do this type of testing in six adult tumors.
So patients with lung cancer, colorectal, melanoma,
GIST tumors, thyroid, and patients
with diffuse gliomas, which are brain cancers.
So people with breast cancer, folks with prostate cancer,
endometrial cancer, ovarian cancer, bladder cancer, these
are not tumors that we can get any sort of insurance
or Medicare reimbursement for doing the sequencing.
So to be able to do this sequencing, we need to go out
and we need to raise funds.
And I'm actually here this weekend
riding with Cycle for Survival to raise funds
to do this type of sequencing.
And so our plan is to try to do this as a
what we call a research non billable test,
try to sequence everyone, and then use this information
to try to drive patients to the most appropriate treatment.
We've also made a decision at Sloan Kettering
that we're going to make the data that we generate
using this assay available to everyone at the Center
in real time.
So what we've done is we've created this web-based portal
and database called the cBioPortal for Cancer Genomics.
And there's both a private version
of this portal and a public version that you can access.
But this is really almost cancer bioinformatics for dummies.
It really is a way for biologists
who don't have a computational degree, maybe
knowledge of how to do a Unix command prompt
or other training, to be able to browse this data.
So if you're a clinician and you're not an informatician,
or you're a biologist and just don't
have a bioinformatics degree, this type of portal
lets you figure out very quickly where
mutation's found in particular cancer types, what's
the co-mutation pattern, how often
are those mutations found.
And this data's now available to everyone at Sloan Kettering.
And this is the summary page of that portal.
And I just blew it up here so that you can see it.
It lists how many of each cancer that we've done.
441 patients with breast cancer, 393 with lung cancer,
222 with colorectal, all down the line.
And we're not just doing common cancers.
We're also doing very rare cancers.
We've done one patient for example with a Wilms' tumor.
This is a very rare pediatric cancer.
We've done a couple patients who have Fallopian tube cancers,
another very rare cancer.
And so we're doing both common cancers and rare tumor types.
There's a patient centered view for these tumors.
And you can see this is a patient who
had a colorectal cancer which had one of these BRAF mutations
that we now have a drug against that I mentioned
to you a little bit earlier.
But the patient also has a number of other mutations
that are picked up by the assay.
And so this type of sequencing may
allow us to understand maybe why some patients
with BRAF reputations who get the BRAF inhibitor
do better than others.
So some patients may have a co-mutation
that makes their tumor less dependent upon BRAF
and thus less sensitive to that inhibitor.
We've also created a database which
is out there to actually educate the clinicians as to what
this information means.
So many of the oncologists who treat these patients really
just never got the education in cancer genetics
to even understand what we're talking about when we talk
about all these gene mutations.
And so there's 20,000 plus genes in the human genome.
I don't know off the top of my head
the four digit code for every single one of those
and what that gene does.
And so we almost need a computer tool
to be able to, when this data comes back,
be able to give us data.
This gene has this function and when
you have this particular mutation in the gene, that's
a functional mutation and causes sensitivity
to a particular drug.
So this database is something that's a work in progress.
But something that actually doesn't exist publicly
is something we're actually hoping
maybe we can get together with other major cancer centers
and do a Wikipedia type of model where everybody puts
in their specific knowledge expertise
and eventually you then crowd source
this sort of important data for all 20,000
plus genes in the genome.
But currently, we have this data just
for the genes that we have in our assay.
And even there, it's only partial.
So it's a real work in progress, and a real problem.
Because when this data comes back, most oncologists again,
they don't know what these particular mutations mean
and what they do.
So again, this is-- yeah, go ahead.
So if we actually go back one slide here before this
comes up.
AUDIENCE: [INAUDIBLE]
DAVID B. SOLIT: Yeah.
Yeah.
OK, so can we compare our database
to 1000 Genomes or the Sanger database and others?
And cBioPortal has become a public database in some ways.
So all the Tumor Cancer Genome Atlas that's publicly available
is now searchable in the cBioPortal.
Other published databases are now
brought into the cBioPortal.
And so this has actually become, I
think, the primary tool for most people
who are not cancer bioinformaticians to really
sort through the data.
But you know, the problem is is that not everyone even
makes their data publicly available in a way
that we can bring it in to these databases.
So just to give you one example, there's
a company that does germ line testing for BRCA called Myriad.
And they've probably tested close to 200,000 plus patients
with breast and ovarian cancer for these germ line
alterations.
But they consider that data to be proprietary.
They don't release it, and so we don't have that data.
So it really is going to be important
that anything public gets into these type of databases.
And it's not something that a cancer center necessarily
was in the best position to do.
I mean, that's not our expertise.
And so we've created this initiative at Sloan Kettering.
But I'll tell you, we'd love to reach out
to people who have better experience putting together
these type of databases to make these tools even better.
So I think that remains a problem.
There's a lot of proprietary data out there,
but I think there's enough in the public domain
that if you really consolidated it into one source,
it would be incredibly valuable.
So again, that's how we're going to find the mutations.
And now I just want to talk for a few minutes
about how are we going to do the clinical trials that
are actually going to take this genetic information to really
test these new drugs?
And so there's really two main ways
we can do these clinical trials.
One is called an umbrella protocol or a master protocol.
And these protocols are usually centered
around a particular cancer type.
So this would be a master protocol for patients with lung
cancer, as an example.
And so what we do here is we sequence all the patients
with lung cancer, maybe at multiple institutions.
And then depending upon what mutation they have,
we put them on different drugs based upon that.
And many of the doctors like this approach,
because as I mentioned earlier, it's
very hard to find the funds to sequence the patients.
And so if you have a clinical trial like this,
you can ask the drug companies maybe that make these drugs--
the EGFR inhibitor, the BRAF inhibitor, the MAC inhibitor--
to maybe pay for the sequencing.
So this is one way that we try to do this.
And there are some master protocols open
for patients with lung cancer and some other tumor types.
There's a few problems with this approach though.
One is that oftentimes it's not the same drug
company that makes each drug.
And so drug company one might make the EGFR inhibitor,
drug company two may make the BRAF inhibitor,
and so on and so forth.
So sometimes you can spend a year or two negotiating
with different drug companies to get
the different drugs into this one giganto clinical trial.
And by the time you've actually secured the drugs
and opened the trial, maybe one or two of these drugs
have already failed in another context
and they're not really the best in class drug that's available.
So that's one downside.
The other downside I'm going to focus on
is that oftentimes these are small clinical trials.
And maybe you're sequencing in this clinical trial
200 patients.
And if you've got a mutation in the population
that you're studying that's only 1% or 2% or 3%
of the population, you're not going
to get enough patients on to this clinical trial
with that particular mutation to really answer
the primary question, which is if you have this mutation,
do you respond to this specific inhibitor of that drug?
So that's one approach.
But if you could find the right patient ,
you can sometimes see these incredible effects.
This is a patient with lung cancer.
And actually in lung cancer, these BRAF mutations
I mentioned to you earlier only make up about 1% to maybe 2%
of the population.
But if you can find that rare patient who has a BRAF mutation
and give them the same drug that works
in melanoma-- in this case, [INAUDIBLE],
which is a selective inhibitor of RAF--
you can see these type of incredible effects.
This is a patient who's been on this drug for over two years
with a disease that most patients progress very quickly
and die within a year or two.
So again, if you can match the right patient
on to the right drug by some mechanism, this can work.
But for these rare mutations, this type of master protocol
design is really inefficient.
So what we've tried to do is create a different type
of trial design that works for mutations where the mutation is
found in a lot of different cancers,
but is not common in any of them.
And so an example of that is a gene
called ERBB2, which is mutated in pretty much every different
cancer.
But usually about 1% or 2% or 3%.
So there's a lot of these mutations.
If you add them up, there's probably
10,000 patients in this country a year
who have a ERBB2 mutant tumor.
But in most cancers, like breast cancer, it's only 2%
of the population.
So if you wanted to just test for it
and then enroll a breast cancer trial,
you'd have to test 100 patients to find
two who have the mutation.
And that's a very inefficient way to do that.
So what we've decided to do is we've
created this new type of trial that we call a basket trial.
And what a basket trial is it's a trial
that's focused around a specific gene or mutation and not
around a particular tumor.
So we can take any patient.
It doesn't matter what kind of cancer you have,
as long as you have the mutation we're interested in.
And for this study of a drug called Neratinib,
we're looking for patients with a ERBB2 mutation.
And it doesn't matter if you have a bladder cancer or colon
cancer, an endometrial, gastric, ovarian, breast.
It doesn't matter.
If you have a ERBB2 mutation, you can go on to the study.
We always have another arm for these studies
as well, because maybe there's some other rare cancer
type that we didn't even know had ERBB2 mutations.
But we find one, we want to give the opportunity
to test whether that patient's going to respond to the drug.
And we've seen some incredible responses with this approach.
So this is a patient who has one of these ERBB2 mutations.
And we generally don't test for these mutations in breast
cancer, which is what she has.
And this is a woman who'd been through many lines
of chemotherapy, none of which were
working because they weren't targeting the right mutation.
And you can see from her PET scan,
she had this incredibly high burden of disease.
All of these are actually metastasis from her cancer.
This is her brain, this is her bladder.
Some of this is her heart, but most of these
are all mutations.
And actually see less uptake in the heart
because so much of the PET tracer here-- the glucose
that we inject, the radioactive glucose tracer--
is going up in the tumor.
And so she was started on this drug on this basket study
in July of 2004.
Within two months, she's had a complete response.
But the key issue here is not that it's surprising
that this ERBB2 inhibitor, which is what Neratinib is,
would work in this ERBB2 mutant patient.
The real problem is that almost all patients who
have this mutation-- this V77L missense mutation in ERBB2--
are unaware that they have this mutation
because we don't test for this mutation.
So since nobody's testing for it, we can't find the patients.
We can't put them on the trial and we can't
prove that the drug works.
So it's really a chicken and the egg problem.
How do you prove the drug works without knowing
who has the mutation?
How can you actually justify doing the testing unless you
know that the drug works?
Because otherwise, the insurance company
won't pay for the testing.
And so to try to break this chicken and the egg cycle,
we've just made this commitment that we
are going to test everyone using institutional funds until we
can essentially enroll enough patients onto these trials
to prove that these drugs work or don't work.
It's a little more complicated than I'm making it out to be.
There are multiple different ERBB2 mutations in cancer.
They may all respond a little bit differently to the drug.
And so what we do with the basket study, for example,
is we can take this breast cancer arm.
We can initially enroll all the patients with ERBB2 mutations.
Since we've already seen a positive result
on the overall arm, we can then do sub-baskets
where we enroll a cohort of patients
that have this S310 mutation, a cohort of patients
with an L755 mutation, a cohort with V77L on down the line.
And again, we always have another arm
for these rare mutations to give access
to the drug to these patients.
And the question is, can we eventually get to a point
where we've got a rare enough mutation disease
combination where almost everybody's responding
that the FDA would simply allow us to change standard of care
at that point?
So typically, the FDA has required us, for cancer drugs,
to do randomized studies.
Where one half of the patients or so get the drug,
the other half get a placebo or an older standard drug
that doesn't work very well.
And the question is, can we avoid
those type of randomized studies by very carefully selecting
the patients so that the benefit is so high on a small study
that it compels the community to just change standard of care?
Because you could imagine if we were seeing this type of result
in 80% or 90% percent of the patients
with this mutation in breast cancer,
a patient with that exact mutation in breast cancer
doesn't want to go on a placebo.
And it might sound ridiculous to even consider that possibility.
But the [INAUDIBLE] study that I told you about earlier was
that exact situation.
And despite that, we actually had
to do a placebo controlled study by the FDA.
In that case, there were enough patients to do that trial.
I don't even think there's enough patients in the country
to do a placebo controlled study of just V77L ERBB2 mutations
with Neratinib.
So hopefully, the regulators will accept this and change
the way we do things.
Question?
So I guess the question was are we
potentially missing important mutations or other alterations
by just sequencing the 410 genes that our current assay is
looking at?
And the answer is absolutely correct.
But my initial answer that question would be this
is an example of not letting the perfect be
the enemy of the good.
So since we can't do whole genome sequencing
on every single patient, that shouldn't
prevent us to do 410 genes on every single patient.
So we're doing what we're feasibly able to do right now.
But the expectation is that this sequencing
is going to become cheaper over time,
and the computational power to do the analysis
gets cheaper and quicker over time.
And everybody I'm sure in this room
knows of Moore's Law, which is the rate
at which computational power becomes more powerful and less
expensive.
The rate at which sequencing is becoming more powerful
is actually much dramatically faster
than what you see with Moore's Law with semiconductors.
So we're seeing this incredibly fast drop
in the cost of sequencing.
And there's companies like Illumina
and others that are making these machines that
are driving this improvement.
And it's probably a few years away
from a point where we will be getting that whole genome
sequence on every patient.
We just feasibly can't do it now.
But there are probably some patients
who are going out and having their whole genome sequenced.
There might be a few patients of means that can
convince somebody to do that.
My lab does it in this retrospective approach
for these rare, extraordinary responders.
But each one of those not only costs the lab tens of thousands
of dollars to do, but it takes actually
a team of informaticians weeks to do the analysis.
So we need the cost of the sequencing
to come down and then what we need
is better algorithmic approaches for the analysis
so that you can take these billions of data points
and be able to analyze them much more quickly.
And one way to do that is to have
this central database of all known variant
changes that you're able to reference.
And so the fact that we've only done
a small number of whole genomes so far
make it difficult for us to know whether something
found in a particular genome is important.
Because the more you do, the more often you
see these patterns that allow you to know
that something is real or not.
But it's a great question.
So what are the advantages of this basket approach?
I'm a clinician, but I'm also a cancer biologist, a geneticist.
What this allows us to do is really
test that defined biologic hypothesis.
Does a ERBB2 inhibitor work in patients with a ERBB2 mutation?
How often does it work?
If it works in some patients and not others,
we can collect tumors from all those patients,
we can maybe do whole genome sequencing
in that whole cohort, and figure out
what are the co-mutations that determine that heterogeneity
within even that small class.
We're also doing on this trial what's
called a co-clinical trial, where we enroll patients not
just with known activating mutations
but also patients who have novel mutations that have never been
biologically characterized.
And what we do is while those patients are
on the clinical trial, we biologically
characterize those mutations in the laboratory.
And we may find that for some of them,
they're not functional mutations.
So if I go back to this slide of ERBB2 mutations,
these hot spots have now all been biologically validated
and are real.
As we get more and more sequencing data,
some of these rare mutations will become hot spots.
But some of these are just what we call passenger mutations--
the random changes in the DNA due to the toxin that
induced the DNA damage.
And they're not driving the cancer.
And so we don't have that knowledge for all
of these mutations so far.
We just have it for the most common.
But as patients come on to the study,
we study individual mutations that have not already
been validated.
What are the challenges with the approach?
The primary criticism we get from drug companies,
clinicians, regulators, is that we
fail to identify patients who could respond
but they don't have the bio-marker we're testing.
So in the case of ERBB2, you don't necessarily
have to have a ERBB2 mutation to benefit from Neratinib.
We actually know that's true.
Maybe you can have an activated receptor from a ligand
that's binding.
So while that's true, it's not the point of the study,
and we can always study those things in later studies.
I call it the sad fact, and that's in academia,
it's sometimes difficult to get the lung doctors to work
with the colon doctors to get them to work with the bladder
doctors.
They're actually not used to doing clinical trials that way.
So the way they train, the way they practice over decades
is you just do breast cancer trials,
and they're separate than doing lung cancer trials,
and they're separate than doing colon trials.
And the concept of doing a trial where any type of cancer
can be in that trial is something that's
foreign to the community.
But I think that's starting to change with some
of the success we've seen.
And again, I'm just going to mention
that the primary hurdle really remains that identifying
the patients is a challenge.
The vast majority of patients with cancer in this country
don't get any testing.
There's only a few institutions doing this right now.
The scale, that number is going to keep going up over time.
But most patients in the community don't get this.
There are some companies that are now specifically opening up
to do this type of testing.
One's called Foundation Medicine.
They charge $5,800 for the test, and generally insurance
doesn't cover.
So for most patients, it's not really
feasible to get this test performed.
I just have two more slides.
I just want to point out that the testing's not just
for selection of what we call targeted therapies, which
are drugs that inhibit a specific mutant protein.
This was one of the first patients of mine
that I see in the clinic which I did this testing for.
And this was a patient with prostate cancer
who did very poorly with all the standard treatments.
And so we went ahead and we biopsied his cancer
after it had become metastatic and had spread to other sites.
And then we did the testing, and what
was notable about his tumor is that he actually
had 29 mutations identified by the assay.
And that's surprising in prostate cancer,
because most prostate cancers only have a few mutations.
So prostate cancer's known for a very low mutation rate.
But this guy had a very high mutation rate tumor.
And he actually had two genes that were mutated.
The red bars are a little off.
One's called MLH1, the other's called MSH2.
And these were both truncating mutations.
And what's interesting about these genes
is that when you get mutations in these genes
in the normal cells, you have a condition called
Lynch syndrome, which leads to colorectal
cancers, endometrial cancers.
Not typically prostate cancers, but you
get these hypermutated tumors.
And what we've learned is that if you test immunotherapies--
these are a new class of drugs that harness the immune system
to attack the cancer-- these type of hypermutated tumors
are more likely to respond than tumors
that have fewer mutations.
And that's probably due to the fact
that these hypermutated tumors are expressing
proteins that are seen by the immune system as being foreign.
They're not self, they're not the normal proteins
for that individual.
They've got a slight change and so they're seen as foreign
and it's easier to stimulate the immune system to attack them.
So we actually have a basket study
where you can have any type of cancer.
If you have one of these type of hypermutated tumors,
this basket study design provides access
to this type of immunotherapy.
In particular for this trial, a drug called MEDI4736.
This is a drug that binds to a protein called PD1, which
leads to a checkpoint effect and a stimulation
of the immune system to fight the cancer.
And I can say that this patient here is actually
seeing early benefit with this anti PD1
therapy with this hypermutated prostate cancer.
These type of hypermutated tumors
were not something we saw when we did a retrospective
analysis of early tumors.
But notably, we've found already about five or six
of these patients out of the first 150 or so
of the prostate cancers that we've done perspectively
at Sloan Kettering.
And it's possible that these hypermutated tumors
are overrepresented in those advanced metastatic patients
because these hypermutated tumors are
less likely to respond the standard treatment
and more likely to recur and progress to metastatic disease.
So again, figuring out in the end what
is driving an individual patient's tumor,
we're hoping to use that information to really guide
the treatment of patients.
Just one more point and then I'll finish up.
This is this company that I mentioned
that's been doing this type of testing.
This was a report that was sent to me from a patient at Johns
Hopkins asking if they could be on this ERBB2 basket study.
And they had a ERBB2 mutation, they
had an S310F ERBB2 mutation.
But there was this note on the report that
was what's called subclonal.
And so what that means is that it's likely
at least you can infer informatically
that this mutation is not likely present in every single cancer
cell.
And so to just show you from our data what that means,
here's a patient with breast cancer
who has an L755S mutation.
The allele frequency here is the number
of reads in the tumor that have the mutation
versus the normal reads.
And we never see this as 100%, because typically you only
mutate one copy of the gene.
And also there's this stromal contamination
of the normal cells, which are obviously
normal for the mutation.
But what's notable, it's 58% here, 20 something percent
for the other two mutations.
This is probably a mutant where there's
two copies of the mutation for ERBB2
and one copy for these other two genes.
If I look at this other patient, this
is a patient with bladder cancer where
can see this P53 mutation's in 57% of the reads,
the TSE1 is in 61% of the reads, but the ERBB2 mutation's
only in 3% of the reads.
And the way you can statistically get that
is if the ERBB2 mutation is just in some of the cancer cells
and not in all the cancer cells.
And one could imagine that if the mutation that we're
targeting is not in every single cancer cell,
when we give the drug, the cells that don't have the mutation
are probably going to quickly grow out and become resistant.
And this type of what we call clonality analysis
is very difficult to do when you have limited amounts of data.
So if we only have a small list of mutations
because we're only sequencing a few genes,
it's very difficult to do this.
But if we do whole exome or whole genome sequencing,
we can actually define this clonality much better.
It still takes a huge amount of computational resource
to do this type of analysis.
But as this technology gets better,
this is the type of data we're going to be
getting for every patient.
And it's not going to simply be do
you have the mutation as a binary variable, yes or no,
but is that mutation what we call an early truncal mutation?
Is it present in all cells or is present in just some?
This is an interesting patient where we have
four tumors that we sequenced.
And this is how the cBioPortal shows that.
And you can see for these four genes,
these four mutations were found in all four tumors.
But you could see the ERBB2 mutation was just in tumor one.
This PIK3CA mutation was just in two and four.
We actually have drugs against both of these targets.
The EGFR mutation was only in tumor three.
The P10 only in tumor two.
So these are all druggable mutations,
but they're not in every single tumor cell.
And so this is an additional level of complexity
that is going to make this difficult.
And is really going to require to eventually use
combinations of these inhibitors to really see maximal effect.
So I'm just going to leave it there
and just acknowledge a number of people.
I really want to acknowledge Gopa Iyer, a very talented-- he
was initially a postdoc fellow, now junior faculty member who
was in my lab and really started with me
this extraordinary responder initiative.
The center I run, which is the Kravis Center for Molecular
Oncology at Sloan Kettering, I've
got three associate directors.
One, Mike Berger, who developed this impact assay and really
deserves a lot of credit for that.
Another, Agnes Viale, who runs our operational sequencing
core.
And then Barry Taylor, who we actually
recruited back from UCSF just recently
to head what we call computational oncology.
And this is really a new field that didn't exist
before the last couple years.
And so what's computational oncology?
These are people who use computational tools to answer
clinical and biologic questions.
So we now have a whole group of scientists
that that's what they're going to be identified as,
as computational biologists or computational oncologists.
And then just note several other people.
Marc Ladanyi, who runs our clinical sequencing lab
with Maria Arcila.
Dave Hyman, who runs a lot of these basket studies with me,
and Jose Baselga, who's our physician in chief,
and has really provided a lot of leadership
to get this sequencing effort up and running.
So thanks again for the invitation.
Happy to answer any questions, either now or formally later.
So thank you.
[APPLAUSE]
AUDIENCE: You showed a slide where one person had you
said, 20 some mutations in the prostate.
DAVID B. SOLIT: Yes.
This is--
AUDIENCE: And then you had a previous slide further back
where you did a single tumor.
And you had 19,000 somatic mutations.
DAVID B. SOLIT: Yeah.
AUDIENCE: And from what I read actually,
that's more the common case versus the cancer
that you have very few mutations.
The common case is that you have tens of thousands
of mutations in a single tumor.
DAVID B. SOLIT: So let me just clarify this
as an issue maybe I wasn't clear on,
which is really differences in the denominator.
So this assay that I'm showing you here,
this was an assay of just 341 cancer genes.
So this was our capture based approach where we just
pull down parts of the genome.
And when we do that, the scale's obviously different
because we're only sequencing a small part of the genome.
We're sequencing the part of the genome
with all the known cancer genes in it,
but it's a very small fraction of the total genome.
And so this is a lot of mutations for sequencing
just that small part.
AUDIENCE: OK.
DAVID B. SOLIT: The 19,000 referred
to sequencing the entire genome.
AUDIENCE: Right.
DAVID B. SOLIT: And within that 19,000,
that patient probably had about 140 coding mutations.
And if you actually just ran IMPACT,
you probably only would have found about eight or nine
mutations.
So it's really a difference of what fraction of the genome
are we sequencing?
Those numbers are going to be very different, yeah.
AUDIENCE: So maybe the premise of my question is wrong.
But my question was if you've got so many--
you kind of boiled it down to a much smaller number--
but if you've got 19,000 somatic mutations, how many, if you
were to look at so-called normal cells in a normal person,
a typical assay, how many mutations would you
find in normal cells?
DAVID B. SOLIT: So the normal cells
have no mutations by definition, because we're
using that as the reference for each individual patient.
So there is the normal coding sequence,
which is really created by an amalgamation of many people.
And then there are variations from individual to individual.
So because there's these polymorphisms, or differences
in the genome between individuals,
we actually don't want to use this reference sequence
for the actual calling of a mutation that's somatic.
We want to actually compare this individual patient's
normal to this tumor and actually then
digitally subtract it.
If you don't do that, you'll end up
with thousands of additional polymorphisms
between any individual and actually
the reference sequence.
Now when we sequence the normal, we don't sequence one cell.
We sequence what is DNA extracted out of thousands
or tens of thousands of cells.
And so if you actually sequenced just one individual cell,
you took hundreds of them, you might even
see some mutations that are slightly
different from this cell to that cell.
But we don't pick it up with this approach
because we're averaging that out and then that detail's lost.
AUDIENCE: But it's radically different in a cancer tumor,
right?
DAVID B. SOLIT: But you know, yeah.
AUDIENCE: So a cancer tumor, radically more.
DAVID B. SOLIT: Dramatically higher, yeah.
AUDIENCE: So how do you explain that
on a fundamental biological when you're looking at first causes?
You would think, for example, or I
would think-- just kind of a lay person--
that if you imagine that cancer is caused
by some initial somatic mutation,
why does that single mutation not dominate and survive
throughout the cancer?
So why do you have tens of thousands?
This is a real mystery.
And it seems like a real problem to this approach.
Because if you've got tens of thousands--
and some places I've read they say hundreds of thousands
of mutations in a single tumor--
DAVID B. SOLIT: Yeah.
So--
AUDIENCE: Then how?
DAVID B. SOLIT: So we actually have a lot of sense for this
now with the Tumor Cancer Genome Atlas that's been done.
There's very well characterized whole exome data.
So not a lot of whole genome data,
but whole exome data, showing that, for example in melanomas,
it's not uncommon to have 3,000 or so exomic mutations.
And that's the tumor that has the highest mutation rate.
There are many pediatric cancers-- lymphomas
and leukemias-- that have an exceedingly low mutation rate.
And some of it has to do with the pathogenesis of the cancer.
So what was the toxin that caused it?
So ultraviolet light is what's causing these melanomas.
And actually many of the mutations
are ones that are caused by ultraviolet light induced
changes.
Whereas in lung cancers, which is also
another hypermutated tumor, the mutation spectrum
is a different type of amino base pair
change than what you see in a ultraviolet induced melanoma.
So many of these are passengers.
How many are actually drivers, which
make the difference between a driver and a passenger
is a driver really is having a functional
effect in driving the cancer pathogenesis,
we really don't know in large part.
The presumption is the vast majority of them
are passengers, they're not doing anything.
But why are they there?
They're there because there's just
a stochastic randomness to where these mutations occur
in some ways.
And unless you hit on something that actually
is presenting a survival advantage,
it doesn't really matter.
So these mutations are accumulating in a stem cell,
and what happens is you get an early hit which
maybe makes a cell proliferate a little bit more
quickly than normal.
So that cell starts to replace the average population
of the normal cells.
But then in that, you get a lot of random mutations
that are not doing anything.
But then eventually in one of those cells that's
now built up more of those random changes
you get a second hit.
And then it accumulates.
And then eventually you can get a mutation
in a gene like MLH1 and MSH2, and these genes actually
are important for the DNA replication.
So once you get one of these DNA repair deficient mutations,
there's just going to be a huge number of now
additional mutations that are caused
because these the fidelity of DNA repair
or DNA reproduction is very low.
And so that's part of it.
I can't answer your question completely.
Yeah.
AUDIENCE: It sounds like eventually this
is going to go towards if you have cancer,
you'll get a screening upfront and figure out
what genomic alterations you have
and you can match them with a drug.
Do you think the rate limiting factor
on that will be technology or insurance or physician
adoption?
Which one of those is the biggest hurdle?
DAVID B. SOLIT: So until this past year, it was technology.
So even if you had unlimited amounts of money,
you simply couldn't do this.
It would take a team of dozens months or years
to do an individual genome.
And that's just not feasible.
But now my lab can do a whole genome sequence
on any individual for less than $10,000
if we spend X amount of time to do the analysis.
So it's getting quicker and it'll be turnkey more and more.
So as the informatic tools, the algorithms get better,
they're more automated.
We actually are limited by computational power
at Sloan Kettering.
You guys could laugh.
We have 1.5 petabytes of memory and that's all we got.
And we've got a cluster with this many processors.
And our thing is some of the analysis
is waiting to get on to the cluster.
We just don't have enough computational power
to be doing more than we can.
And we spent $2.5 million last year
alone on computational resources,
and that's a lot of money for a nonprofit hospital
to be paying.
So right now, technology is still limiting.
But that clearly is going to go away.
The other problem is doctor adoption.
I think even if the technology becomes completely
turnkey and inexpensive, and even if the insurance companies
start paying for some degree-- and they're not
going to pay for whole genome right away.
What they're going to do is pay for some number of genes
and then as the cost goes down, they'll
pay for more and more genes.
And then maybe eventually it's cheaper
to just do the whole genome in everyone.
But the other downside of this is
doing this type of sequencing somewhat
opens up a Pandora's box.
And that's because we all have probably 10 to 20, if not more,
polymorphisms in our normal cells that
put us at risk for something.
And it's not something that we all
want to worry about for our entire life.
So just to give you a quick scenario here,
we run our genetic test here for a patient with cancer
and the patient's cured.
One of the genes in our assay is called PARK2.
And this is a gene that's important for cancer.
It's a tumor suppressor.
It's a gene that was shown to be a tumor suppressor in a paper I
published with a colleague of mine, Tim Chan,
just a few years ago.
But the reason that the gene is called PARK2
is because it's found in the germ line,
so in the normal cells, of patients who have early
onset Parkinson's disease.
And so imagine the scenario of a patient coming in,
they get sequenced, we use the sequencing data to tell them
how best to treat their cancer.
And then the oncologist says, oh by the way,
you have a germ line mutation of PARK2
and you have an 80% chance of getting Parkinson's disease
by the age of 50, and we have nothing we can do about that.
So did you want to know that information?
And as we find more and more of those susceptibility genes,
ones that maybe are less penetrant, meaning they're
less of a phenotype or less dominant,
we're all going to have some of these.
And is this going to create so much anxiety in all of us
that some people are going to have
a hard time dealing with that?
So there is really a huge amount of worry and concern
in the community as we're doing this testing
to make sure that when we look at the germ line
data in particular, that we're very careful in how we counsel
the patients as to what this means,
and that we don't return a lot of information that is this
is a change in your germ line in a gene that could be important
but we don't know what it means.
And then patients are worrying about it.
So that's going to slow physician adoption of this
a little bit.
But eventually, I think all of us in the field
think eventually 10 years down the line, 15 years down
the line, you're just going to come in and in a day,
here's your tumor sequence.
And then we're going to match you up to certain therapies
based upon that.
But getting there is going to require
some significant technologic improvements.
Definitely better computational algorithms
for the analysis, better databases
of what all these mutations mean, not just somatically
but also in the germ line alterations, and then also
better biologic understanding of what
they mean so we can counsel people
that they don't worry about things that are not important.
And then probably even new laws, because if you
tell that patient that they have that PARK2 mutation, maybe
they can't get life insurance, they
can't get health insurance.
Their family may have the same mutation
and maybe can't get health insurance or life insurance.
So this test is maybe not just affecting you,
it's affecting your family members.
So there's a little ways to go until we sort out
all those issues.
AUDIENCE: When you identified the mutations
and the responders, do you also sequence
the genomes of the people that don't
respond to confirm that they don't have that mutation?
DAVID B. SOLIT: So for the paper we published in "Science"
on that Everolimus trial-- I didn't show it here--
we unfortunately didn't have tumor
for every patient from the study.
But we did go back and sequence a host of patients
from the study.
And what we found that even the patients who
had minor responses to Everolimus,
they also had a TSE1 mutation.
But what we found is that the co-mutation pattern
for those patients was different.
So this patient who had the extraordinary response
had an NF2 mutation with the TSE1.
And both of those, we think, cooperate
to make that patient exquisitely dependant
upon the target of that drug.
Other patients had a TSE1 mutation with other mutations
that we think diminish addiction to that target,
but maybe a combination would be helpful.
I think the learning experience from that was going back,
we realized we didn't have tumor from everyone.
So I think now we're trying to do a much better job of making
sure that when we run these studies,
we collect these tumors and have the material available
for these type of retrospective studies.
But actually some of the extraordinary responders
initiatives are not going to even be needed.
Because if we have the mutation data perspectively,
it just becomes very obvious sometimes why
somebody responded.
Well, they had the right mutation.
And so the key is again getting the tissue for study later on.
And it's a challenge.
I'm actually minimizing how big of a challenge it is.
It's not necessarily just good enough to have a piece of tumor
from five years ago from the patient.
If they've had 10 therapies along the way,
the tumor may have changed significantly during that time
and it's beyond what we're able to do for every clinical trial,
to get a new piece of tumor from every patient right
before they go on the therapy.
Because that's invasive, it's costly.
And so we work with what we have the best we can,
and it's not always perfect.
Well, thank you.
[APPLAUSE]