Whole Genome Approaches to Complex Kidney Disease
February 11-12, 2012 Conference Videos

Data Annotation to Identify Actionable Variants
Ben Solomon, NHGRI

Video Transcript

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ANDREY SHAW: Okay, why don’t we get started? The last speaker in this session is Ben Solomon, who is a pediatric geneticist and staff

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clinician in the Medical Genetics branch of NHGRI. Ben studies the natural history and genetic causes of rare disease like VACTERL, which I

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think we are going to hear more about today, and holoprosencephaly. He’s been employing whole genome sequencing as a tool and the title of his

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talk is “Actionable Annotation of Genomic Data.” BEN SOLOMON: Thanks very much for having

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me, it’s an honor to be here and hopefully we will all make it out of here in one piece with the weather outside. This slide makes it look like

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things are going to be much more organized than they actually are but I’ve tried to loosely frame things into three separate sections. One, I am

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going to try to talk about some background and frameworks; ways to organize data. Second, I am going to try to give you some kind of raw

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sense of the numbers of what you find and the types of things that you find, and then I am going to try to hone in on some concrete examples

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because I think in talking about this stuff it is very easy to get hung up on the abstract, and it’s very helpful to illustrate some of the points by looking

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at some concrete examples of what you find and what you have to deal with. So, I often put this slide in talks I do on this topic. One term I

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myself—hopefully you guys won’t look like this about five minutes into the session—but also to point out that if you guys want to shout out

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questions or comments or throw coffee cups at me, you’re welcome to. I think this picture is actually taken across the street at the military

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med school at USUHS; I’m not positive about that but I think that’s true. You can confirm or deny this; this is a med school lecture. That’s what I

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have to go through. So, the topic of this talk is Incidental Medical Information, and obviously this isn’t a new thing right? If you’re doing a study on

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scoliosis and you’re doing chest x-rays and you see a blip on the lung, then that would be incidental information that you would probably

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have to deal with. In this context of next generation sequencing and this high throughput sequencing, or whatever you want to call it, is

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sometimes called “genomic risk information,” and as you can see from this article that was published a few years back in JAMA—I like this

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cute term, “The Incidentalome”—but you can see by the subtitle there that there’s a lot of concern about what this is going to do and what is going

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to happen to research using these methods. I think the idea is that we’re opening Pandora’s Box here, right? That we are opening up and we’re

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going to find a lot of things that we didn’t want to find and we’re not going to be able to deal with them in any rational way and it’s just going to be

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honestly a disaster. I think that we have to be careful and we have to conscientious about it, but I think with careful thought and planning that

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this can be a good thing both for research projects and for study participants and for our patients. So, we have to do things very well but

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it’s not…my feeling, and this is up for debate, is that it’s not necessarily and Pandora’s Box. So, a lot of what I am going to talk about—just to give

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you guys some background—is in the context of this condition I studied, VACTERL association. I know nobody has heard of this or is interested in

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this, but just to let you know. This is a cluster of congenital malformations. By NHGRI standards, this isn’t rare at all, though it’s not a common

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disease. We don’t know much about causes, though we’ve had some good preliminary data using some of the exome methods, so I feel like

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we are cautiously optimistic that we’re starting to make inroads in some of this now. What I am going to talk about for most of this is the first

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case that we did exomes on because I’ve had the most chance to kind of look at this incidental or actionable medical information in this family and

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the most chance, I think more importantly, to think about it. So this was a set of monozygotic or identical twins and they were about one year old

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when they participated when they came to the NIH to take part in my study, and so, just keep that in the back of your mind: these are one-year-old

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kids that we’re finding this stuff on, this isn’t you or your parents, this is a one-year-old child, right? One had this condition; one did not have

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this condition. There is no family history, so we thought we’d do whole exome sequencing of the twin pair to see if there is any kind of discordant

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genetic explanations there. So, this is going to be a very dry slide here. I’m just kind of warning you that you can take some coffee here, but it’s going

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to be a little slow. I just want to give you a sense of what we do from start to finish with some of this actionable data. To start with, obviously, the

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patient takes part in my main protocol. This is a choice, but for us we select certain people who separately consent to go through some of this

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genomic sequencing. I know other protocols just blank consent everybody to do, you know, everything from genome sequencing to looking at

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one SNP and everything in between, but we select certain people and they sign a separate consent. I’m not going to go over this too much

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because some of the other very nice talks before me talked about why this might be helpful or not, but basically we do an array first and then if we

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decide it’s a good idea we do whole exome sequencing. So, these first and second passes, because of Jamie’s really nice tool…and I’ll say

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I’m not just trying to make an ad for this—maybe I kind of am—but it’s very fun to use and for someone like me who is not originally a

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bioinformatician and still not and you’re not a computer programmer, it’s very fun to kind of get your hands into this stuff and it’s very exciting.

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The two things that we do with VarSifter quickly for this actionable data is we look through some of these databases to see what’s out there, and

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these are databases like Jamie mentioned, the Human Gene Mutation Database, dbSNP, and at the same time…this used to be a second step but

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I think Jamie puts out a new version of VarSifter about every week, so with these latest versions what’s really nice is that you at the same time can

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filter out artifacts, and that’s absolutely critical because you don’t want to be confirming and thinking about thousands of artifacts that aren’t

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really there, even if they seem like they might be disease-causing. This one’s in red and there’s going to be another one in red and my point here

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and one of the Take-Home points from this talk is that it’s really great and very important and necessary to automate things to a great extent,

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but there’s got to be some steps where you have to think about things where there has got to be some manual curation, and I don’t think that can or

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should ever go away. So, the first manual review is doing what we would all do when we see something new, is looking at OMIM, looking at

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PubMed, looking at what you can find online. Then, if we think this meets our criteria—and I’ll hold off on that because I am going into what

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those criteria are—we would go ahead and confirm those in our lab just using regular old Sanger sequencing, and then if there is still a

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question in my mind and in the mind of our team, then I individually contact—and this is another manual step—some experts on these disorders

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or on these genes. I know a few genes really well but I certainly don’t know all 20,000-30,000 genes and wouldn’t know what to do with certain

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variants in some of them, so it’s another manual step there. Then we actually have a formal working group where folks like Sarah Hull,

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bioethicists, genetic counselors, clinical and molecular geneticists to go through some of these kinds of gray zone pieces of information. We

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CLIA confirm things, and that’s a requirement; we have to confirm things so you can return this data to the participants and we return it, and then I

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think in my mind if you return it to the participants you’re obligated to do some kind of follow-up and that follow-up might just be: let’s make sure your

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primary care doctor gets this. Or, it might be something in place that if you have to test relatives you might also have that, but you can’t

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just kind of give them the information and say, you know, “Best of luck with that mutation,” there’s got to be something in place there and that’s up to

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you. So, the Take-Home Message from this is that you have to start with a clearly defined, carefully formulated algorithm. Recently in the last few

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months reviewed grants for some very good places and I won’t mention any of them because probably some of you guys are from some of

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these places and they have in these statements that, “We’re just going to manage these things, we have a team that will meet and we’ll talk about

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these things,” and I thought, great. You’ve got to have a team and you’ve got to meet and talk, but you also have to have an algorithm in place or

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else you’re going to look like…this is a cartoon version of what I look like with my coffee drip in front of my computer all night while my wife

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screams at me and wonders why I took this job. So here is the algorithm for my protocol. I’m not going to go through this; it’s summarized here.

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There’s two ways to look at how we decide what to return. First, this is kind of how actionable things are. I know that’s not

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necessarily a good word but that’s the word that I use in my mind, so, “actionable.” First of all, things must be of urgent clinical significance,

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meaning that you need to know it and you need to know about it NOW in the context of the age of the patient there. I know some protocols say

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they’re going to re-examine things as people age but you have to take into account the age of them, and you have to be able to do something

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about it. It shouldn’t be just, “Here’s your mutation, now go stress about it for 30 years until symptoms do or do not show up.” You have to

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have some kind of treatment or intervention that’s going to make a difference, and it should be better to know sooner from the genetic

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information than it should be later when symptoms arise. Knowing sooner has to have an advantage than if they were just to find out and

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go to the doctor on their own saying, “Oh, I have this or that.” Number four obviously doesn’t give you a lot of information but it should be better to

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know than not know. Then for recessive disorders… and this is a controversial point. I certainly know investigators who are much

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higher up the totem pole than I that say we should not return anything that’s recessive. We, from my protocol, happen to set the bar at 1 in 40,000. So,

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to give you a sense of what that means, if you do the math it means 1 in 100 people would be carriers for these. So, this would allow things like

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cystic fibrosis, phenylketonuria, sickle cell disease, but actually you wouldn’t return a lot of stuff that’s pretty common—for example,

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ascertained by newborn screening, okay? Then, an important point is that there has to be an OPT IN/OPT OUT. Some people are going to want to

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find out some of this stuff, some people are going to say, “Absolutely not, no matter what, I don’t want to know it,” and so there needs to be some

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participant or patient or study cohort decision making in that process as well, at least that’s what I think. So that’s kind of the actionable side

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of things in terms of the variant side of things, and I know there’s been a number of talks about this today, but you have to set the bar high. You

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have to have good evidence that the gene is associated with human disease. We all know that there are studies out there that are just---pardon

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my language, but—crap, right, about what’s determined with what. So, you have to have good evidence that the gene is associated with

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human disease. Then the variant you find, the nature of that variant, has to be such that either just by itself it predicts pathogenicity in a high

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confidence manner, like a nonsense mutation in a loss of function disease, or in the case of, say, a very polymorphic gene where you’re going to find

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a lot of variants that that exact same variant has really good evidence to be associated with human disease. And I think this is a nice article,

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so if… probably many of you aren’t interested in this topic, but for those of you who are and are going to be working on this, I would highly

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recommend this article as maybe the one thing you take away from my talk. This is mainly by these folks down at Chapel Hill that I work with

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on some of these questions. From this article, this is a very nice table that put things in tabular format, kind of what I just said in words, and

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what they do is this idea of “binning.” So, they bin the variants into different categories along the lines of what I was talking about. Here’s just

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another and I’m not going to go through all these little boxes, but just to point out that this is an algorithm that a group applied to a genome that

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they sequenced and they just kind of went through how they decided what was clinically relevant and not. So again, if you’re interested in

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this, another good article to read. It’s interesting, too, because this was two years ago and so it’s interesting to see how things have changed

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since then. So at this point, I just want to share a couple of examples. Maybe horror stories is an exaggeration; maybe it should be “Tales of

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Caution” or something, but just a couple things to drive home some of the points about, for example, what other speakers said before me

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about why the databases… why you need some manual looking at them, why you can’t just depend on looking at what’s on the computer. So

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in these twins, when I first sequenced…when Jamie and his team first sequenced them, I was first looking at the data. We saw quickly that they

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had…one of the first mutations I saw was this frame-shift mutation of PMS-1, and in this semi-reputable journal Nature about 20 years ago, they

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published that mutations in this gene caused a common form of hereditary colon cancer. So I thought, “Oh gosh, I found this in two one-

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year-old kids.” There’s not much of a cancer history in this family but I don’t really focus on cancer histories, or there could have been—I

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could have missed it—and I was just really not excited about finding this. One of the genetic counselors that’s involved with me in this study,

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one of his main areas of research is actually colon cancer and other kinds of cancer, and I talked to him about this and he said, “Oh yeah,

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that’s very interesting, they publish this and you notice there’s not a lot else published on this gene,” and it turns out that in those families

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where they found this, they’re actually segregating mutations in a different gene but nobody really wants to publish that stuff quite as

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much; it’s not quite as exciting. Then, I guess the other post-script of this is that with some of the really new artifact filters—the really nice artifact

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filters, rather, that were added later—I could quickly see that what we found here was an artifact, so that saved some time and stress

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there. Another one, this one is one of the few examples that’s not from these twins. his is from a different family that I did exomes on recently.

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Within the last two months we found that this woman, who was right about my age—so, in her mid-30s—had a missense change in VHL, which

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is—you guys being renal folks probably know—Von Hippel-Lindau, this relatively horrible cancer syndrome. We had done exomes on other

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members of the family so we knew her dad and paternal relatives didn’t have it, but she had a couple of kids that could have had this and her

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mom who could have had it, her siblings could have had it, multiple maternal relatives, and if you look at these very nice databases, like the Human

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Gene Mutation Database, that exact variant was listed as “yep,” from this article. This is associated with this very horrible cancer

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syndrome; kind of a worst case scenario of what you could find in terms of incidental medical information. So, with one of the post-docs in the

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labs, we designed, sent off for some primers so we could confirm this, and then thought about how we were going to tell the family, and so on.

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And then, looking and designing primers and looking a little harder—and this takes a couple of hours, you don’t immediately find this stuff—it

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turns out that this exact variant, even though it was published here, is not associated with dominant Von Hippel-Lindau syndrome; this exact

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variant is associated with an allelic disorder, a recessive polycythemia disorder. So, this woman is a carrier for a rare disorder, it’s not going to

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affect her health, almost certainly not going to affect her kids’ health, but you could see how one could easily return this data if you didn’t take

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a little extra time, and you’re dooming this family because they are going to believe you. This was a very smart family but they are going to buy

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what you say—you’re the geneticist—if you had returned it from them. So, Take-Home Message 2 is that you’ve got to take everything out there

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with a grain of salt, even the stuff that’s in the best curated database; you’ve got to be really careful about it. And along those lines, my

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Take-Home Message 3 is that, because of some of these situations that have happened to me in trying to manage some of this data, I have

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evolved or devolved, or at least changed the way I look at things. So, I amended my protocol formally to actually make sure I was promising

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less to research participants in terms of this incidental medical information, because I would have hated to do something that I wasn’t totally

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sure about and violate the “do no harm” idea. So to give you a little sense about some raw numbers here, and the numbers are a little lower

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you’ll notice here than what Jamie quoted. These exomes were done a couple of years ago, so you get more variants now, if that’s correct,

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Jamie, if you do exomes. You can consider this kind of one person, right? These are monozygotic twins so we did both of them, but they’re

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monozygotic. We started with about 80,000 variants, you get rid of a lot of stuff that looks like bad data, you get down to about 65,000. If you

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look purely at the variant types—so, nonsense, missense where it looks like a bad quote-unquote “bad missense” and we can talk about that in a

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second—frame-shift, etc., you get about 8,000 potentially pathogenic variants. Of those, 400 are in some of these databases as known

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disease-associated variants. When I say disease-associated—maybe that’s a misnomer—maybe it should be health-associated, because

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they could be associated with a risk of asthma, a risk of diabetes, a risk of high blood-pressure. There are some that were risks for good things,

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like if you have this, you might have a better memory than the average person. Then, 32 that were in disease-associated genes, and by the

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nature the variant, looked like they could be pathogenic. So, we applied our filter and we get down to about three that we thought might meet

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criteria for return of information. After talking about that in some of the groups it turned out that one of them didn’t meet criteria in talking to some

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experts about that, so we were down to two, and of those two, one I think is returnable—and I will show you what these are—and then one I’m

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honestly still on the fence and I don’t know if I’ll ever have… I don’t know if I’ll ever decide until the science is much better on this particular gene.

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Another way of thinking about things is that they break down to kind of three categories of variants. One is carrier states and a lot of those,

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depending on how your protocol is setup, you may or may not choose to manage that, and I think there are arguments either way, obviously.

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The other is susceptibility variants and a lot of those we don’t return to folks, and I will kind of give some rationale for that, and then the

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interesting ones are the ones where they should have the disease, where you see something, whether you’re sequencing an infant or an

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elderly person where they should have the disease, and those are ones that we think about. So, here are some examples of things that are

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carrier states and I am a card-carrying, board certified pediatrician and clinical geneticist and some of these I haven’t even heard of, so these

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are very rare. To give you an example, we found that these twins were both carriers for this what looked like a bad pathogenic mutation in ORAI1. If

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you have two mutations, one from mom and one from dad, you have a pretty horrible immune deficiency. What you can see from this

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pedigree—I don’t know if you guys are familiar with pedigrees like this—this is very consanguineous, so a very inbred pedigree. This

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has only been described, this syndrome, in one consanguineous Turkish family, so it’s probably not something that most people would return to

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their research participant,s unless of course they are members of this consanguineous Turkish family, but these guys were not Turkish; they are

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probably not going to be consanguineous, and so on. You get a lot of things that are these possible susceptibilities. I know it’s probably hard to read

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these but you get a lot of things from various GWAS studies or other types of studies. You’re getting a lot things that say, oh, you’re at slightly

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higher risk for autoimmune disorders or infertility or having low bone density; you get a lot of this stuff. My feeling is, that unless the evidence is

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terrific and you have a clinical link to this, this should be weighted against returning. So for example, in our twins we found this variant in this

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gene that’s associated with a risk of schizophrenia, so having this variant raises the risk of schizophrenia, right? I’m going to make up

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these numbers and correct me; I’m sure I’m wrong. So, let’s say the risk of schizophrenia in the general population is—I don’t know—1 in 500,

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so maybe having this gives you a risk of 1 in 400. Again, I’m making up these numbers, but it’s not like it’s going from 1 in 500 to 1 in 2, right? With a

00:19:01,700 --> 00:19:12,133
lot of these when you look them up, the data is mixed, right? It hasn’t necessarily been validated, there are not follow-up studies, maybe it actually

00:19:12,133 --> 00:19:21,099
has to do with a different gene, and so you have to take these again with a big grain of salt. Some of the interesting ones are we find things that

00:19:21,100 --> 00:19:27,766
they say, when you find it, you should have the disease. I got a little cute with this slide. I should have eliminated some of these but I left some of

00:19:27,766 --> 00:19:34,732
them up because, when I looked through the artifact filter, a couple of these drop away really quickly, okay? But if you look at some of these,

00:19:34,733 --> 00:19:42,099
these are bad things, that neither these kids nor anybody in the family had, like neonatal/severe cataracts or congenital diabetes and mental

00:19:42,100 --> 00:19:50,966
retardation, cardiomyopathy and psychiatric disturbances. So they didn’t have these, but their exomes said that they should have these and

00:19:50,966 --> 00:19:56,199
these are very interesting, so there’s a lot of possibilities, right? One is that the gene isn’t associated with the disease. One is that this is

00:19:56,200 --> 00:20:02,833
not a pathogenic allele. One is that these people are going to develop this and they don’t have it yet, or it’s somehow much less penetrant. It’s

00:20:02,833 --> 00:20:10,633
very interesting what you find here, and then you go back and look at the papers and you think, wow, something doesn’t match up here. I left this

00:20:10,633 --> 00:20:17,933
slide on. When I give a similar talk to less medical folks I use this to explain it, and I am not going to harp on this point at all. You guys know that,

00:20:17,933 --> 00:20:25,233
basically, having the same amino acid substitution, one or two amino acids later could have a very different effect, so it’s very hard to

00:20:25,233 --> 00:20:34,299
extrapolate from a different variant to the next. There are tools that Jamie talked about that are very nice, and I think they’re very nice primarily

00:20:34,300 --> 00:20:41,633
for the primary research target—what is the cause of this rare condition—but in terms of this actionable secondary, or whatever you want to

00:20:41,633 --> 00:20:48,166
call it genomic risk information, maybe these shouldn’t be depended on quite as much. As Jamie says, these depend on things like

00:20:48,166 --> 00:20:55,599
conservation and functional motifs and so on. As you guys know, there’s also ones that are freely available on the Web, but again, these are great

00:20:55,600 --> 00:21:03,800
for kind of our primary research target, looking for candidate genes, but maybe for when you’re talking about actionable stuff these aren’t quite as

00:21:03,800 --> 00:21:13,133
useful here. I just wanted to run through—I’m not sure how I’m doing on time—a few concrete examples from our data just to kind of give you

00:21:13,133 --> 00:21:19,733
food for thought here. The Take-Home Message from this is that you can have, again, the best algorithm, the most carefully designed algorithm

00:21:19,733 --> 00:21:26,566
with the world’s most brilliant bioethicist and IRB members as we do, but they’re still going to permit gray areas and I think it’s good to have

00:21:26,566 --> 00:21:31,899
some of those gray areas because there are going to be some that are going to be completely up for discussion until our genome is well

00:21:31,900 --> 00:21:40,966
understood, which will be probably never. This is the one that I still don’t have an answer for and I know other groups looking at this variant have

00:21:40,966 --> 00:21:48,066
come up with different answers for this, but in any case, in these twins we found that they had this missense variation, that was predicted to be

00:21:48,066 --> 00:21:55,299
functionally significant by software, where you’re susceptible to malignant hyperthermia, hypokalemic periodic paralysis. Has anybody

00:21:55,300 --> 00:22:04,400
here worked on this gene or disorder by the way, just out of curiosity? It’s mostly—and people get into trouble here, not always but mostly—when

00:22:04,400 --> 00:22:11,233
they are exposed to certain anesthetics, but it can be a life-threatening horrible thing that’s treatable, that’s pre-treatable; that could make a

00:22:11,233 --> 00:22:18,333
difference. This variant that we found in our twins hasn’t been seen in controls. It also hasn’t been seen in the disease population. This is a

00:22:18,333 --> 00:22:25,366
very polymorphic gene, meaning it has a lot of changes in it, so it’s hard to know. There’s no family history of this, and I called the family. I

00:22:25,366 --> 00:22:34,099
didn’t spill the beans but I just went into a very careful history of anything related to this, and nothing really popped up. So I took the next step,

00:22:34,100 --> 00:22:41,566
and I think the point of this isn’t, “Look what I did,” but the point is that it does take time to do these things and so you have to think about that.

00:22:41,566 --> 00:22:49,032
Fortunately, there is a great national/international consortium on this disorder, so I talked to and had some very interesting e-mail exchanges with

00:22:49,033 --> 00:22:57,799
folks in New York, France, and Canada about this and we had lots of discussion and we ultimately felt that, given the family history, given the clinical

00:22:57,800 --> 00:23:06,400
history of these kids, given the nature of the variant, it’s unlikely to be disease-related, but this is, again, one of these things that honestly do

00:23:06,400 --> 00:23:13,366
keep me up at night sometimes, and this is one of the ones I think about. The Take-Home Message 5 from that is that you have to share the wealth,

00:23:13,366 --> 00:23:21,532
and that’s sharing the wealth both with some of these individual experts in some of these conditions as well as to kind of advertise

00:23:21,533 --> 00:23:29,966
ourselves: folks who are bioethicists and IRB members and clinical geneticists, and are kind of used to dealing with some of these questions.

00:23:29,966 --> 00:23:39,899
So, just to kind of end up, I thought this is a nice story of something that we found of a perhaps clinically actionable piece of information. So, we

00:23:39,900 --> 00:23:47,000
found they had this missense variation in this gene, in this enzyme, in this gene that encodes this enzyme in the urea cycle. And so, if you’re

00:23:47,000 --> 00:23:55,400
homozygous or compound heterozygous from mutations here, you have this horrible biochemical disorder and most folks die. If you live you need a

00:23:55,400 --> 00:24:02,466
liver transplant and you’re usually severely and neuro-cognitively impaired. So, the software predicts that this is a very deliterious change, and

00:24:02,466 --> 00:24:08,732
I talked to this very, very, very nice gentlemen in Spain who was very sure and had some preliminary data modeling that said this is, indeed,

00:24:08,733 --> 00:24:19,933
a pathogenic allele. It’s very rare, this disorder, so less than 1 in 100,000, so unlikely that their kids could be affected, even though they are both

00:24:19,933 --> 00:24:25,399
carriers, but what’s interesting is, starting about 10 years ago, it was found that there’s very good data published in the New England Journal and

00:24:25,400 --> 00:24:34,033
other journals like that, that carriers from this can be at risk for pulmonary hypertension, especially as babies, right? What was interesting about

00:24:34,033 --> 00:24:41,266
these guys…so the kid who had VACTERL had a tracheoesophageal fistula—an abnormal connection between his trachea and his

00:24:41,266 --> 00:24:47,999
esophagus. His esophagus doesn’t go down to his stomach and so he had to have surgical repair on the first day of life to fix that. So, I

00:24:48,000 --> 00:24:58,066
remembered from my pediatric training, and I talked to some NIQUE folks about this. This tends to be not a big deal in these kids. They get their

00:24:58,066 --> 00:25:04,866
surgery, they hang out in the NICU for a couple weeks kind of learning to eat, learning to use their esophagus, and then they get discharged and

00:25:04,866 --> 00:25:10,932
they do fine, but this kid completely crashed after surgery, and I reviewed his record and talked to his parents and he just fell apart. He was in the

00:25:10,933 --> 00:25:17,733
NICU for several months and on all—for those of you who are clinicians—sorts of pressers and drips to keep this kids alive, and I’m surprised he

00:25:17,733 --> 00:25:25,966
made it out of there, and I’m surprised that he is doing well now. The reason that he fell apart was he had very severe pulmonary hypertension. So

00:25:25,966 --> 00:25:35,266
the idea here and what we are pretty confident happened, is that he had a hypomorphic allelein this gene and what that does is it drives down

00:25:35,266 --> 00:25:43,299
your Citrulline and Arginine, and that makes nitric oxide less available, and this is part of the Nobel Prize winning stuff, this system here but I think it

00:25:43,300 --> 00:25:51,900
was more related to Viagra then this. The thing that’s interesting is that having surgery when you look at the literature—and I didn’t know any of this

00:25:51,900 --> 00:25:58,033
ahead of time—is it completely washes out or some types of surgery that the type of this kid gets—some approaches—wash out your

00:25:58,033 --> 00:26:05,566
Citrulline and Arginine stores, and so you basically get severe pulmonary hypertension. This kid is never going to be a baby again but he

00:26:05,566 --> 00:26:15,666
has siblings and he’s going to have kids probably some day, and if any of them need surgery in the future as babies, this would be something that

00:26:15,666 --> 00:26:21,666
would be reasonable, I think, for them to know about because there are things that can be done during or before surgery to prevent this from

00:26:21,666 --> 00:26:29,832
happening. Dad was the carrier. This just shows this, that dad was the carrier and the other twin had it. I won’t go into this but the

00:26:29,833 --> 00:26:40,699
bottom line is that there are a lot of issues here and I think since I was invited kindly to speak here I’ll go ahead and give a little more of my two cents

00:26:40,700 --> 00:26:47,933
here which boils down to first, do no harm, and the point here is that you’re promising… if you’re telling your research participants, your patients,

00:26:47,933 --> 00:26:56,099
that you’re going to look through genomic data for this clinically relevant risk information, it takes a lot of time to do a thorough job and a lot of this

00:26:56,100 --> 00:27:03,400
stuff can be automated and I am working on an automated tool. I know lots of others are working on automated tools that will probably be much

00:27:03,400 --> 00:27:10,100
better than mine. So, things will get more automated, be faster, but are still going to take a lot of time, and it still should take a lot of time. It’s

00:27:10,100 --> 00:27:19,266
very easy, as some of the other speakers talked about, to miss potentially significant variants, and it’s equally easy to overcall benign variants. So, I

00:27:19,266 --> 00:27:29,332
think it’s a fine thing to do but if you’re going to do it, you want to design things very carefully beforehand and do it conscientiously and well.

00:27:29,333 --> 00:27:46,199
Thank you. I’m happy to take questions. FEMALE: So, I’ll ask a question. I’m over here,

00:27:46,200 --> 00:27:56,100
sorry. Thank you for your presentation. I’m sorry I missed the first few minutes, so I hope I am not overlooking anything. You know, there is a subset

00:27:56,100 --> 00:28:04,033
of people out there in the world—research participants—who are making this claim of, “That’s my data and if you’re going to know

00:28:04,033 --> 00:28:08,133
something about me, then I damn well get to know about it too.”

00:28:08,133 --> 00:28:11,299
BEN SOLOMON: Yeah. FEMALE: How do you think about that claim in

00:28:11,300 --> 00:28:13,800
light of some of your experience here with these patients?

00:28:13,800 --> 00:28:20,533
BEN SOLOMON: I don’t have a perfect answer. I think there’s arguments that could be made either way. I can tell you my opinion is that, as a trained

00:28:20,533 --> 00:28:30,733
clinical geneticist, I have a lot of trouble managing this data and I think, you know, just giving someone everything on a disc or giving someone

00:28:30,733 --> 00:28:38,333
everything to their primary care doctor, I think then you’re really opening up Pandora’s Box. I think this stuff… just like you wouldn’t give an MRI

00:28:38,333 --> 00:28:46,766
to a person who came in for a possibility of cancer; you’d interpret it for them and I think it’s very similar to that. I think the other problem that,

00:28:46,766 --> 00:28:55,099
maybe this will change, but still these days is that so much of the information you get has to be confirmed through other methods that if you’re

00:28:55,100 --> 00:29:01,600
giving it to them you’re not going to be able to separately confirm it. They’re not going to go set up a lab in their house and do Sanger sequencing

00:29:01,600 --> 00:29:07,566
to confirm the findings. I think for now we have to be the confirmers, as well.

00:29:07,566 --> 00:29:14,766
MALE: Just to be clear, when you talked about those “should’ve known” variants—I think there were 11 of them—they were previously

00:29:14,766 --> 00:29:21,866
published, those particular variants in the genes but not previously published, exactly.

00:29:21,866 --> 00:29:28,266
BEN SOLOMON: Exactly. So, those exact variants…great point. Thank you. So, those exact variants weren’t published, but when you look at

00:29:28,266 --> 00:29:38,332
the alleles, they had high…you would expect them to be pathogenic. These were things like nonsense or frame-shift, most of those and

00:29:38,333 --> 00:29:44,933
things like that. FEMALE: Thank you. That was a great talk. I

00:29:44,933 --> 00:29:58,033
wanted to see your two cents and raise it to four. Your last slide you said “if” you promised results like this to your participants it’s a lot of work, and

00:29:58,033 --> 00:30:05,599
you’ve demonstrated that very clearly and I’m aware of a lot of what’s been going on behind the scenes, but I’m curious what you think about

00:30:05,600 --> 00:30:16,533
that “if” and if what your advice would be to investigators who aren’t equipped to do the kind of analysis that your group had been doing. Is it

00:30:16,533 --> 00:30:25,399
okay to say, “No, we’re not going to return these kinds of results”? What is your feeling about a sense of obligation to do this for investigators

00:30:25,400 --> 00:30:30,233
who are generating exomic data? BEN SOLOMON: I think it’s a fantastic question

00:30:30,233 --> 00:30:39,233
and a very hard question, obviously. To give you a little context I certainly know investigators who say that we should be only looking at the primary

00:30:39,233 --> 00:30:46,233
research target, it’s unethical to look at this other stuff because the reason people take part in our studies isn’t to find out about this other stuff, it’s

00:30:46,233 --> 00:30:58,199
to find out about why I have a renal disorder or why I have whatever I came to you in the first place for. My response now is different than it

00:30:58,200 --> 00:31:05,600
would have been two years ago, and now I honestly think that if you’re very clear about things, it’s okay not to promise to manage some of

00:31:05,600 --> 00:31:13,566
that data. But having said that, I still think you have to have an algorithm and a plan in place, because you can try not to see that data but if

00:31:13,566 --> 00:31:19,799
some post-doc in your lab comes to you and says, “This is so weird, I found this stop mutation in this BRCA…I’ve never heard of this BRCA1

00:31:19,800 --> 00:31:26,100
gene…what does that mean?” there has to be an algorithm still even if you’re not going to plan to return that to manage it, because these situations

00:31:26,100 --> 00:31:34,366
are still going to arise. No matter what you want to do you unfortunately can’t be an ostrich in the sand.

00:31:34,366 --> 00:31:42,399
MALE: I applaud you for being very thoughtful about these issues and suddenly tomorrow I am sure there is going to be more discussion, and

00:31:42,400 --> 00:31:54,633
there is going to be a breakout session about what we do in nephrology practice. Now, I can’t help but totally agree with you--everything that

00:31:54,633 --> 00:32:03,699
you said in the pediatric space—but I want to caution that when we move into the adult space, when we actually deal with the concept of

00:32:03,700 --> 00:32:13,800
autonomy, and it was mentioned before that people increasingly feel that their medical information, not just the genome, and certainly

00:32:13,800 --> 00:32:22,700
eventually also the genome sequence, belongs to them and they are going to take it wherever they want to take it, and we lose control; we, as those

00:32:22,700 --> 00:32:30,700
who have so far been sort of the people who’ve been sitting on the medical information on the genome. So, this is a trend that is happening and

00:32:30,700 --> 00:32:45,333
it’s not going to be reversed, but getting back to…I would like for you to comment on your perspective on this for the adult medicine space

00:32:45,333 --> 00:32:56,166
and most of the renal disease patients we are dealing with on adult space, and perhaps your reflection may be that the bar may not need to be

00:32:56,166 --> 00:33:04,999
that high, particularly considering that we’ve now done structured interviews with hundreds of patients and people in the community, and

00:33:05,000 --> 00:33:18,733
overwhelmingly after long discussion and so on, people really do want to know what’s in their genome. So, just before you close can you

00:33:18,733 --> 00:33:22,966
reflect on your bar for the adult space, if you could?

00:33:22,966 --> 00:33:30,932
BEN SOLOMON: I think it’s a fantastic point and these points about patient or participant autonomy I think have to be very carefully considered, and I

00:33:30,933 --> 00:33:42,066
absolutely agree that there’s a strong argument to be made that “it’s my genome, don’t hide it, give me what I need.” I don’t, again as I think I’ve said

00:33:42,066 --> 00:33:48,266
for every answer, I don’t have a perfect response but I think one thing that people are moving towards and groups are moving towards

00:33:48,266 --> 00:33:59,099
is that if you’re setting the bar a little lower, which I think is fine and there are certainly arguments for that, is that it’s very easy to cripple a

00:33:59,100 --> 00:34:06,966
research endeavor because all your resources suddenly get shunted towards dealing with things that you frankly don’t have an interest in or

00:34:06,966 --> 00:34:14,699
expertise in. I think that one of the models for managing some of this stuff is to, within in institutions whether it’s NIH or the BROAD

00:34:14,700 --> 00:34:23,700
Institute or CHOP or wherever else—to set up independent but collaborating centers who manage some of this data, and I think that might

00:34:23,700 --> 00:34:33,766
be some of the answers to being able to lower the bar to the point where people are more comfortable in participating. Thanks.

Date Last Updated: 9/18/2012

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