Nazneen Rahman: So good morning.
Thank you very much for the invitation.
I very much enjoyed this meeting.
For those of you who don’t know me, which I think is pretty much everyone, I’m predominantly
a researcher.
My group identified genes that are predisposed to cancer.
I’m also a clinical geneticist, and I run the Cancer Genetic Service at the Royal Marsden
Hospital.
And I’m really going to speak to that today.
We are particularly passionate about trying to realize the potential of new sequencing
technologies to maximize patient benefit and to bring that into mainstream practice.
So when we go the pre-questionnaire, I was amused I think to see that on the front page
it said that there weren’t going to be questions about the BRCA genes because the clinical
interpretation of those sequence variants was pretty much sorted.
So I sort of first laughed, then I cried, then I started foaming at the mouth.
[laughter]
And I really wish that that were true, but the reality is that despite the fact that
it’s been over 15 years since those genes were discovered and they are some of the most
intensively investigated genes both with respect to clinical practice and scientifically, we’re
still very far from having a consistent way of dealing with sequence variance in those
genes.
And I think the potential for chaos and harm as more BRCA mutation results are available
is quite hi.
So, this graph will be known to everybody.
With respect to breast cancer, the situation is that there are about 24 common variants
found through GWAS with small affects.
There are BRCA1 and 2 which have very high risks of disease.
And then my group has been particular involved in identifying genes in this area of -- [off
mic] -- I shouldn’t do that because I can’t move.
You can use your imagination -- oh God, now got trouble.
[laughs] -- in this intermediate region.
So these genes are very like BRCA1 and 2.
They are characterized by rare inactivation mutations, but instead of having risks of
10- to 15-fold, they have risks of about 2- to 6-fold.
And the discovery of these genes has really impacted on how I think about missense variants
and VUSs, so I just wanted to take a couple of slides just to describe that to you.
So the way that we find these genes is by undertaking large-scale familial case control
resequencing studies.
So we sequence the full gene in typically 1,000 to 2,000 familial cases and the same
number of controls or more.
And when you do that kind of experiment for this type of gene, there’s a very clear
signal between your cases and controls with respect to truncating mutations.
But when you look at the missense mutations, the data’s really quite interesting.
So you will always see multiple rare, usually singleton missense mutations that are only
present in your cases.
And these are just the type of missense variants that are called VUSs or in gene discovery
papers where controls are not analyzed, they will often to be assumed to be pathogenic
because you found them in your cases.
But if you are sequencing the full gene in controls, what you will see is -- and what
we have seen for all of the genes that we’ve found of this type is that there are multiple
singleton rare missense mutations that are in controls only.
And if you try and work out which may be pathogenic and which aren’t, what you find or what
we have found is that there is no significant difference between those two groups.
You can’t pull them apart either in terms of their frequency or their position or their
conservation or their predictive functional impact.
And it’s very different from those truncating mutations.
So what is that telling us?
Well, I think it’s telling us the same as lots of other types of data, the data that
Jonathan presented yesterday and Les also would, I think, speak to this.
And that’s that most missense variants in genes, particularly those in which we know
that the pathogenic mutations are inactivating are not disease-causing.
Also, most missense variants that are predicted in silico to be deleterious in some way are
not disease-causing.
And that cauterization is deleterious, which is often assumed to be the same as pathogenic,
just isn’t.
It’s incredibly difficult to pick out disease-causing missense mutations even in genes which we
know cause disease.
And so I think the sort of bottom line is the VUSs should be considered, and we consider
them, as innocent until proven guilty.
And I think that everybody does know that when you talk about it, but actually instinctively,
a lot of people, even the most sort of expert in these fields, are actually when they see
those variants, they actually manage them as guilty until proven innocent.
And I think that that’s a problem.
So, with respect to clinical utility, we’re not using the GWAS common variants.
And I don’t think that we’ll be doing that in a meaningful way anytime soon.
With respect to these intermediate penetrance genes, I think once sequencing makes that
cheap and quick, we will be able to use them.
But BRCA1 and 2 is clinically usable and in many ways offers all that you could hope for
from a disease gene.
So I think with respect to unaffected women, this is -- people are very on top of this
and they understand this.
These women are at high risk of breast cancer, ovarian cancer.
We give them increased surveillance.
They will often have their ovaries removed after completing their family.
And many of them will also have bilateral mastectomies.
I think surprisingly it’s underappreciated just how important and how helpful it is to
know the BRCA status in somebody who has cancer.
So if you have breast cancer and you’re BRCA positive, you’re at high risk of having
bilateral breast cancer.
You’re at high risk of having ovarian cancer.
Increasingly there are genetic-tailored treatments such as PARP inhibitors which are close to
being in widespread use, that are tailored to that genetic defect.
And this is making a sizable contribution to this cancer incidence.
So if you take unselected ovarian cancer just walking into the clinic, over 10 percent of
those will have a BRCA mutation.
And all of those are at high risk of breast cancer if nothing else.
It’s a smaller proportion of unselected breast cancer, though in certain subgroups
such as triple-negative, it’s over 10 percent.
And I would certainly like to see, and what I hope the new sort of technology will allow
us to do is to offer BRCA testing to all women with breast and ovarian cancer.
So what are the challenges to the implantation of that?
Well, we need cheap, quick testing.
And this should be achievable.
Obviously there’s also the issue of patents which I’m not going to talk about today,
partly because I’m not able to talk about it unless I’ve drunk quite a lot of gin.
[laughter]
So, [laughs] but I’m assuming that that bit will be solvable.
We need to have quick, simple report of the results.
And that will have to be readily understandable by non-genetic experts.
And we will need that to be then triaged into what the clinical actions should be.
So where are we with that?
We’re a long way off from that.
So, we saw this slide yesterday from Les.
This is one of the ways in which classification systems for BRCA variants are used.
We don’t use this.
Why don’t we use it?
I think there are a number of different problems.
It is a fairly arbitrary system based on various assumptions.
That sounds like a criticism.
I don’t mean it as a criticism.
I think that sort of taking your best guess is the sort of bedrock of clinical practice
and science.
I don’t have any problem with that.
I’m not sure that if you are guessing that you should quantify that to three decimal
places.
And I think that if you are having an arbitrary, pragmatic system, you should try and have
it as simple as possible.
And it should be tailored to the aims you’re trying to achieve.
And I think this is very laborious, and many variants you just don’t have the data in
order to classify it.
But I think the main problem is there are five variant classes, but there are effectively
only two management things.
You either can manage it as a positive or a negative.
And therefore, you have to -- if you’re trying to have this system, you have to be
able to tailor that into your management strategy.
So I get daily emails, I think, from people around the world where they’ve got variants
that are in classes two, three, and four, and they don’t know what to do about them.
And, again, going back to these things that most people sort of think things as being
guilty until proven innocent, there have been hundreds of women who have had their breasts
removed on the basis of variants that are highly, highly likely to not be increasing
their risk of breast cancer.
So, in part to manage my inbox, and in part because we wanted to think about trying to
make this more user-friendly, over the last year, we’ve been using a different system.
And this is the system that we use.
So we have a variant category, but it’s very much tailored towards going to a management
category.
And then the management category, it’s either negative or it’s positive.
And I think the key difference, which was a slight moment of epiphany in my group, I
think, was that instead of trying to classify every variant individually, there was a default
category that, when you first have a variant, if you can’t do anything else to it, it
goes into what we call our variant 2 category.
So if there’s a missense variant that’s never been seen before, you actually can’t
move it into pathogenic.
You can’t move it into full [spelled phonetically].
And that just goes straight -- that’s in 2 and it goes straight out to getting our
report.
If a variant has been seen before and has been previously categorized, obviously it
goes into that category.
If a variant is a frame-shifting mutation, you can just code that.
That will go straight into pathogenic because we know that it doesn’t matter whether you’ve
seen it before or not.
If it’s causing premature protein truncation, it is pathogenic.
And then we have the possibly assignable is for the group where there is some information
in the literature, but -- or available, but it’s not sufficient to cause -- to say it’s
pathogenic.
And here, we’re using genetic information because at least for the BRCA genes, there
is no robust in silico predictive information or functional information unless it’s showing
that it’s the equivalent to a frame shifting mutation that will allow you to be -- to call
that pathogenic.
So those are the categories that we use.
This is -- so following Howard’s example, this is one that I did this morning because
I had one in my inbox.
So, this is a variant here which has not been reported before.
In fact, the in silico predictions and conservation wouldn’t suggest that it was pathogenic.
And so that goes straight out to our variant 2.
And this is what those reports say, manages a negative BRCA test.
The consultee should be informed that the BRCA test was negative.
I think the next bit would be a point of discussion, possibly a point for transatlantic difference.
A discussion of variant is not required.
There’s a management recommendation.
Breast surveillance should be recommended appropriate to family history, and we have
that goes to protocol.
We have a number of protocols on there.
The only rule of thumb is that no protocol can be more than one page.
No predictive testing should be offered for this variant.
No additional analyses are currently required.
So is this model transferrable or scalable?
I think it probably is both.
At the moment, our reports are going back to geneticists or genetic counselors because
those are the people who are doing those tests, but I could see how it could go into oncology
if the oncology people were doing the tests.
The negatives would just be negatives.
In terms of the positives, the report would be slightly different.
You would have the information that the oncologist potentially would be using, and then there
would be another thing that would say “Refer to genetics.”
So here, instead of genetics having to see everybody, we’re seeing the positive people.
We will do the genetic information.
We will be doing the cascading.
And we can manage that in terms of hours.
What we can’t manage is seeing everybody who’s going to be negative.
I think there probably are other genes and systems for which it is also transferable.
What -- we’ve often been asked, what would you need?
What I would really like, I said yesterday, I think we need more data.
I think we need population data.
One of the major areas where we’re doing mismanagement is when you see the variant
that’s been seen in cases from a particular ethnic group.
It’s assumed to be pathogenic because it’s been seen several times from a particular
ethnic group, but the equally plausible and I would say more plausible thing is that that
is a population-specific polymorphism.
And unless we have really good data on these low-hanging fruit key genes from lots of populations,
we are going to keep making that mistake.
And I’m not clear that we’re just going to get that data from unless we directly try
and get it.
I think it also does give you better information about the spectrum of mutations across there.
And because [unintelligible] into our old genes, there’s virtually no sequence data
from control individuals in BRCA1 and 2 because it was sort of done before, and it’s still
quite a herculean enterprise, but it certainly was then.
We clearly need much, much better predictive in silico methods.
That’s a given.
What I’d really like to see is some standards for deciding for us as a group or some group
to decide if genes or variants are definitely pathogenic, so really thinking about which
ones we classify into that group.
I appreciate that all the others we need to sort out as well, but at least if we can sort
that out.
And I want to just finish.
On a sort of cautionary tale to exemplify that, so RAD51C was a gene that was published
in “Nature Genetics,” very high profile, about 18 months ago.
And it was published as a high penetrance breast/ovarian gene comparable to BRCA1 and
2.
It’s got [unintelligible], BRCA3.
I’m aware of one woman who’s had prophylactic mastectomies because they have a mutation.
It was a little unusual, this paper, because in it, the truncating mutations were only
found in breast cancer cases if they were in families with both breast and ovarian cancer.
And if you looked at families with just breast cancer only, there were no mutations.
And that’s obviously odd for a high penetrance breast cancer gene.
Subsequent data, nobody was finding any truncating mutations, but they were finding missense
mutations.
So they thought, all right, the missense mutations are causing breast cancer.
That wasn’t the original hypothesis, and no controls were studied.
For a variety of things I haven’t time to go, and we were working on another gene which
is an ovarian cancer gene.
Anyway, we’ve looked at this, and this is hopefully shortly going to be published, but
it is also consistent with all of the available literature, including the original paper I
should say.
So we did a large case control resequence study looking at both cases and controls.
RAD51C is an ovarian cancer gene.
It has a decent size risk of ovarian cancer, about nine percent lifetime risk, but it doesn’t
predispose to breast cancer.
So what’s going on?
I think it’s fairly old fashioned to ascertain [unintelligible] bias.
If you’ve got a gene that causes phenotype A, in this case ovarian cancer, and you look
at phenotype B, but the -- which is breast cancer in this case, but the only breast cancer
cases you’re looking at are relatives of your phenotype A, you are bound to see an
association unless you correct for that.
But if you look up for phenotype B on its own, which they did, you’re not going to
see an association.
So 18 to 24 months after that first gene report, you know, the cancer is clarified.
It’s not a high-risk breast cancer gene.
In fact, it doesn’t cause breast cancer.
But you can see a potential where if we’d had the whole genome data in the electronic
patient records and that data was already available and the gene comes out and you’re
thinking, right, it’s high-risk breast cancer.
You can see there’s a situation where a lot of women might have thought, right, I’m
going to have prophylactic surgery or act on that.
And I think we’re in the era where there’s going to be lots and lots of new gene discoveries.
When you’re writing a paper, it is perhaps natural that one’s optimistic about the
clinical utility of that.
And there’s often in that paper, but I think there needs to be standards and that -- or
principle by which people can look to see whether it has passed a bar where it is ready
to be used in the clinic.
And I think we need to think about that now.
And I’ll finish then.
[applause]
Female Speaker: All right, I think we used up our question
time, unless somebody has -- Heidi, do you have like a quick one?
Heidi: Just a quick question.
You mentioned recommending no management in cases where there’s a non-assignable BRCA1
variant.
You know, we study cardiomyopathy which is a very similar paradigm in terms of dominant
inheritance and high risk for sudden cardiac death and a need to manage those family members.
And we would always recommend familial testing to look at segregation in order to determine
the clinical significance of those VUSs.
And once you get a high enough LOD score, you can say with statistical confidence that
that variant is pathogenic and then use it in a predictive fashion.
So I’m a little bit concerned about not encouraging those physicians to pursue familial
studies which can be extremely informative in terms of pathogenicity.
Nazneen Rahman: So, I think that with respect to those Class
3 ones, we certainly do do that.
All the Class 3 ones, we then would recruit into our research and we would do that more
broadly.
I think this does speak to things what should you do with just any VUS, and it then also
speaks to the fact that are they innocent until proven guilty or guilty until proven
innocent.
Now, the one I talked to you about, in fact, all the in silico things would say that it’s
benign anyway.
And we know that they’re mostly benign.
We’re not doing that for synonymous variants.
So I think in terms of going -- often you can’t do the segregation.
The families are too small.
The chance of being able to really sort that out in terms of a LOD score for breast cancer,
which has got a high phenocopy rate, which also impacts in that so it makes it very difficult.
It’s quite difficult.
So I think the problem that has as people go back to them and you’re telling them,
if people knew that it was innocent rather than proven guilty and they were doing it
on a purely exploratory fashion, I wouldn’t have so much problem with it.
But I think what happens is people are assuming that if the doctor’s telling you this or
the doctor is actually telling that it is likely to be pathogenic, that we have a problem
there.
And so, currently, we’re doing more harm than good.
So it’s part of that balance, but I appreciate it’s a difficult issue.
Female Speaker: Great, thank you.
And we’ll definitely have time after the panel’s all done for more questions.