les biesecker:thank you very much for having me. thank you for the nice introductions. and for thoseof you who are here to see me the second time, thank you for your patience. as a few of youmay know, i had -- i lost an argument with a couple of intervertebral discs in my c spineon april 13, 2012, and was unable to deliver the lecture, although i heard that you gota great lecture. dr. elaine ostrander filled
low affect, in for me about dog genetics. i would haveliked to have heard that myself, actually. but i was horizontal for that day. so what i want to talk with you about a littlebit is some of the work we're doing in genomics and clinical genomics to try and understandthe spectrum of heritability in human diseases
and human traits, including disorders thatsome of you are likely dealing with. so what this is about -- some of you may haveheard this phrase thrown around. there's several phrases that are sort of the phrase du jourhere. they all mean pretty much the same thing. i use the phrase "individualized medicine."some people use the phrase "personalized medicine." some people are calling it "genomic medicine."but the notion is the same, regardless of what word you might choose to use, which isto think about trying to customize or individualize care based on individual risks instead ofpopulation risks. and we are currently in a phase of medicine where, to push it a littlebit, we sort of worship at the altar of the large, blinded, controlled trial. and we dothat because -- not because it's bad, but
we do it because it works, and it allows usto find and evaluate therapeutics in a way that works for large numbers, substantialnumbers, substantial fractions of large populations of people. and that is good. although, i will tell you, in the course ofthis little encounter i had with those discs in my spine, i was on the phone with a colleagueof mine, who, believe it or not, was a lawyer, and he was commiserating with me because he'shad some of the similar problems, and he just went on this long harangue about the anti-inflammatories,or oral pain meds. and he said, "you know, they'll tell you that this medicine worksin 40 percent and that medicine works in 40 percent and then the third medicine worksin 40 percent." and he's lecturing me, "don't
you believe it for a minute, because those40 percent aren't the same people." so here's a lawyer telling me that we should be practicingindividualized medicine. i thought that was pretty hilarious. so what we want to do is acknowledge that,that what he's saying is probably correct, and i think all of us know that from our practices,and we use as much information as we have at our command to try and make those decisionsabout how to apply which treatment to which patient, and we use our intuition, and ourhunches, and our clinical acumen to try and make those decisions. but it would be better,i think, if we had some data that would allow us to direct the treatments toward individualsin a rational way. so we want to do several
things, right? we want to apply the treatmentsto the patient where it's most likely to be efficacious in that patient and where it'sthe least likely to have toxicity or adverse effects. we also would like to begin to move towardtreating and preventing diseases before the patient actually gets sick. and this is alittle bit of a radical notion, and it has a lot of challenges associated with it, butit is a very important goal, and i think that's important in cardiology as well as in manyother fields, like cancer. it won't be easy to do. and lastly, though this isn't a very popularthing to mention, but there are certain times
-- and we, again, use our clinical judgmentto do this all the time in current practice -- but when we have a patient we're takingcare of where the treatment is futile, it's good clinical judgment to stop rendering thattreatment, in most cases. if it's not helping the patient and the disorder is going to progress,why are we doing this, especially, again, if there are adverse effects? and there'ssome great examples now in oncology where the oncologist can identify patients who areextraordinarily likely, essentially certain, to be refractory to certain treatments. andin those cases, it's the wrong thing to do to continue those treatments. and we haveto figure out who these people are. so to do this stuff, all of these things thatwe would like to be able to do with individual
patients, we have to be able to make predictionsabout the medical and physiologic attributes of those patients at the level of the individual,and the large, randomized clinical trial will never get us there. we have to do it a differentway. okay, so what are we looking for? we needthe ability to assay or test some attribute of our patient that defines either the presenceof occult disease or disease that is yet to manifest, future risk of disease, responseto a treatment we haven't yet tried, adverse effects, et cetera. and, you know, truthfully,we do this all the time, right? we use physical signs all the time to detect, primarily, occultdisease. splinter hemorrhages in the fingernails is a sign that, by itself, means essentiallynothing to the patient, yet that tells us
what's going on with that patient's cardiovascularsystem, right? so we're used to this concept at the clinical level, and what genomics isgoing to do is expand that into heritable disease and allow us to make predictions basedon that patient's heritable susceptibility or propensity to develop disease. but predictions is a tough business, and we'redealing with a complex system here, right? the human organism is a complex critter. andi like this quote from albert einstein. i'll read it to you. "occurrences in this domain-- " of course, he's talking about particle physics, right -- "are beyond the reach ofexact prediction because of the variety of factors in operation, not because of any trace-- any lack of order in nature." so we recognize
that the system is complex. we recognize thatwe won't be able to make precise predictions. but that doesn't mean that you can't makepredictions. so that's a nice way of saying this, which was the gentleman in the foreground,niels bohr: "prediction is very difficult, especially if it's about the future." someof you may have seen that. that has been misattributed to our friend yogi berra, but it was actuallyniels bohr who said that. and i just like the picture of the two guys together and thetwo quotes side-by-side, and i think it gives you a nice feel for this problem. okay, so -- and going back to health carepredictions, we can ask the question, "can we do this for traits, for diseases that havea substantial heritable component?" so what
are the tools we need to do that if that'swhat we want to do? so we need some sort of an assay that broadly assesses the risk ofthese traits or diseases. until recently, that was very difficult to do, because thetesting, the genomic genetic testing that we had until recently, was low-throughputand focused, and the clinician had to know what disease they were looking for to orderthe test to determine the susceptibility to the disease. and so that was a very intrinsicallylimiting problem. but now that the technology is changing, we can ask the question in anopen, prospective way with an individual patient, and extract that knowledge that we're lookingfor. all right. so what are we actually talkingabout here technologically? so we have to
do a little bit of genetics here, and so ihave a graph here. this is a theoretical graph that shows on two axes the frequency, whatwe call the minor allele frequency; that is, any position in the genome or in a gene wherethere is a difference, a variation in the population, the less common of the two states,the minor allele. frequency can range from zero -- almost zero to 50 percent. can't bemore than 50 percent, because then it's the major allele, right? so that's the frequencyof some allele in the population. and then we think about a heritable trait, and we canask the question, "if a person has the minor allele, how likely is it that that personhas the trait?" and that, we call "penetrance." and that can range from zero to one. so ifthat variation in every single person leads
to the trait being manifest, the penetranceis one. and if that variation has a very, very low contribution to that trait, and maybeonly 1 percent of people who have that variant have that trait, then that's a low-penetrancetrait. there's a general relationship for variationthat we know in the genome versus traits that sort of hovers in a cloud, if you will, alongan axis between here and here. all right. a lot of you have heard about snps and gwas,genome-wide association studies. what that kind of a study is doing is assessing commonvariation in the population -- so alleles that have minor -- variants in the genomethat have minor allele frequencies, from about 1 percent to up to 50 percent -- and askingthe question, "are those variants associated
with some trait?" and that has been done verysuccessfully for a number of traits, including cardiovascular diseases like atherosclerosis,lipids, blood pressure, et cetera. and you can find these variants, and you can discoverthe relationship between genotype and disease, and this is an incredibly fruitful and productivearea of science. then there's this stuff up here that jean[spelled phonetically] referred to that i have worked on in the past. and these arevariations in the genome that are individually extremely rare variations going down in frequencyto what is essentially one over the population size; that is, variants that are essentiallyunique, and we see that in the population. and these rare variants, many of them canhave very significant impacts on phenotype.
that is, when they're present, 100 percentof the people, essentially, who have that variant have that trait. now, there are -- this cloud exists becausethis stuff down here is stupendously hard to figure out. if a variant is uncommon inthe population and it has a low effect on the trait, statistically it's really hardto find. so this, we are sort of thinking, for all practical purposes, until we can assaythe entire population on the planet, is really unknowable. we just can't figure that out.this, between here and here, so these rare diseases that we currently know about andthese common variants that lead to low-penetrance traits, there is a huge cloud here, and thisis currently unknown, but it is becoming knowable
because of the technological advances thatwe're now seeing. and the notion is, what we have to do in genomics and clinical geneticsand in medicine is to connect these two clouds of variants and understand this full rangeof genomic variation and the relationship between genotype and phenotype in the fullspectrum of frequency to penetrance. okay, so to summarize again, you can thinkof, in general, our current understanding is there's two classes of genomic variation:common variation and rare variation. so common variants are relatively easy now to assayand analyze. these are done -- this is done by what are called snp chips, so chips -- dnachips that assay two million, sometimes upwards of five million different common variationsacross the genome. and then statistical testing
is done to relate those variations with sometrait. and there's -- that's all very straightforward to do. now the statistics are all worked out,and we know how to do that. the problem is, again, it requires large cohort sizes, right?this makes sense, because if the effect of the variant is small, you're going to needa large population of people to find the relationship. that's plain, old-fashioned statistics. youcan't escape from that. so, again, it goes back to this notion of the large trial andaveraging across patients. it is incredibly useful and has illuminated a number of differentrelationships that we didn't previously appreciate, connecting genes and the proteins they codefor with traits and diseases, and has been a real boon to understanding the pathophysiologyof human disease. the problem is, is that
for us. as clinicians, it doesn't necessarilyhelp us with individual patients, because, again, each of these variants is relativelypoorly predictive of phenotype. rare variants, until recently, were nearlyimpossible to assay genome-wide, but now, because of sequencing, they are getting mucheasier to find and generate the actual variants, but it's still hard to analyze them. i'lltalk a little bit about that. the associations, because they're powerful, can require smallernumbers, so that's a good thing for us as clinicians. and what i really like about itis we can then bring this into the clinic and start to think about assaying the genome,the individual patient, and making a prediction about an individual person that is highlylikely to be correct.
okay, so a little bit of background on genes.genes have a number of parts. this is a strand of dna. this dark part here, these green blocksare the parts of the dna that encode for protein. genes have elements within them that controltheir expression, when and how they're expressed. those are called promoters and enhancers.and then there's lots of dna between the genes. that is spacer dna; it used to be called "junkdna," but that's no longer correct because we know all of it has function. so these piecesof the gene are then spliced together. here's an individual gene spliced together. an openreading frame is what makes a protein. the protein is what does the job in the cell. so common variants are, for the most, notin the protein coding parts of genes. and,
in fact, a fair number of them aren't in genesat all. they're in this dna outside of genes. the rare high-penetrance variants that we'retalking about, that stuff in the upper left-hand corner of the graph, turns out almost allof those are in these green blocks, these coding parts of these exons, and the coolthing about that is, even though there's 20,000 genes and 300,000 exons across the entiregenome, this green stuff only comprises about 1 percent or 2 percent of the dna. so if you'reinterested in the high-penetrance, individual prediction parts of genes, you only have tolook at 1 to 2 percent of the dna to extract that information. and the technology that allows us to do thisis incredibly cool. it's very complex, but
you can show the concept here, which is thatwe take dna from the patient, shear it up into little blocks, and then we basicallytake artificial dna that's complementary to those green parts of the genes, mix that withour patient's dna, and it has little beads on it that allow us to extract that dna with,believe it or not, actually a magnet, which is kind of cool, so you magnetically separateout the dna you're interested in, and then that's the dna which represents those greenparts of the genes. then you take each of those pieces of dna and adhere it to a slide,and hundreds of millions of these pieces of dna are added to a slide at one time, andthen that is -- each one of those molecules is sequenced simultaneously in a reaction.so that's why this sequencing, it's commonly
called "next-generation sequencing." morecorrectly, it should -- it's called "massively parallel sequencing." it's massively parallelbecause you're running hundreds of millions of sequencing reactions all at the same time.and then each one of these sequences is read off of the slide, and then it's fed into thecomputers that analyze those sequences, find which part of the dna, of the genome, theycomprise, lines them up, stacks them up, and reads the base pairs. so sequencing instruments actually look likethis, these nice boxes with the cool glowing blue lights. that makes them really cool,right? and these things are really awesome, right? so you can now sequence a whole genomeor six to eight exomes -- and exomes is just
that 1 percent to 2 percent -- in about threedays. okay? ten years ago, this took -- the first genome took about 15 years to sequenceand cost $1 billion to $2 billion. so now it takes about three days, costs about 10,000bucks for a whole genome. but you can do the green parts, the exome, or the exons, forabout 1,000 bucks or less, which is a stupendous drop in cost, which is starting to make thistechnology comparable in cost to a lot of tests we order on patients all the time. andthat allows us to simultaneously, in a single reaction, in a single assay, evaluate allgenes. and that's the tool we need, from the previous slide, to make these predictionsand assay these genes, again, without knowing what disease the patient has.
all right. this is not all goodness and light,all right? there's some bad news to this stuff too. this is not as easy as it looks. it generatesstupendously large amounts of data. so for every genome we put into one of these instruments,the typical output is about three million variations. so any two of you i were to sequence,i look at your two genomes, you will differ in about three million nucleotides. some of-- most of that is benign variation; some of that is variation that is associated witha disease. the trick is to figure out which is which. nontrivial. so interpreting thisis a huge challenge. we're just scratching the surface of this. a small fraction of itcan be interpreted, and then the glass is half full, the glass is half empty. half emptyis, we can only interpret a small fraction
of it. glass half full is, yeah, but the fractionthat we can interpret is useful. and i'll show you how that is useful. so, as these instruments were being developeda few years ago, a group of us got together and said, "well, you know, the biologistsare using this to understand the biology of genomes. why don't we docs get together andfigure out how we can use this to help take care of patients?" and so we put togethera project called clinseq, which was a translational research project to use genome sequencingin clinical care, clinical research to figure out the relationship with disease and buildan approach to developing this as a clinical assay. so we set up a study. our initial targetwas to recruit 1,000 people into the study.
our initial phenotype was cardiovascular disease.we thought that was a great trait to start with because it has a lot of attributes thatare amenable to this kind of an approach. we know that cardiovascular disease -- atherosclerosis,myocardial infarction susceptibility, hyperlipidemia -- has a high degree of heritability. we allknow that. there are common variants, common variations in the genome that lead to low-penetrancesusceptibility to lipid levels, as well as rare variants that lead to high-penetrancelipid syndromes, other cardiac phenotypes as well. so what we wanted to do was develop a cohortof people who had a range of phenotypes for atherosclerosis, from completely unaffectedto affected, and then assay those patients
by whole genome sequencing. so we did whatwe called "binning," which was we recruited patients into the study based on framinghamrisk scores, 250 each of each of these categories, the framingham risk scores, and one bin ofpatients who have the disease, and we're still recruiting for patients who are affected withatherosclerosis and have had myocardial infarctions. sequence them, and then we do the follow-upstudies. we interpret the variations that we can extract from their genomes, validatethem, return them to patients, and try and start managing these patients based on thesevariants to, again, test the model of individualized medicine. the recruitment was for folks that were between45 and 65 years of age. it was open to any
ethnic group, both sexes. we did want to excludesmokers. in phase i, we did require that they have a primary care physician, recognizingthat we were going to find things in their genomes that would need to be evaluated andfollowed up by their physicians, and so we wanted to make sure they had that in place.people -- and we were looking for people who wanted ongoing involvement in the study. andwe also have set it up so that the patients themselves don't have direct access to thedata. this has actually come to be an interesting issue, which we could talk about if peoplehave questions. so, clinically, what do we actually do tothe folks who enroll in the study? we take only a brief history, because here's the problemwith the genome. we can assay all 20,000 genes,
but no clinician can do a history or a physicalthat evaluates a patient for all 20,000 gene traits. that is just not possible. so whatwe have to do is set this up in a way where we do it iteratively. that is, we start withsome brief phenotyping and history-gathering, bring the patients in, consent them for ongoinginvolvement, and say, "we're going to come back to you after we look at your genome.when we find variations in this, that, or the other gene, we're going to phenotype youfor traits related to those genes, and do it in a directed, iterative way." so the information we gather is pretty minimal,by my standards, to tell you the truth. brief history just related to cardiovascular disease,a family history, a few anthropometrics, electrocardiogram,
echocardiogram, coronary calcium, a prettybroad panel of chemistries, which you can see here, for your reference, and then someresearch samples, dna, rna, and we make cell lines for the patients. okay. so here we have these patients who areinterested in doing this. we have the genomic or exomic data sets on the patients. we havethe baseline phenotypic data. how do you actually go about, then, using these data to find conditionsin patients? so we set out to do some pilot studies. and this is one of three of our earlypilots. and what we decided to do was to screen a set of patients for cardiovascular traitsthat were not related to the reason why they enrolled in the study. and this is a reallyimportant caveat. we enrolled patients for
atherosclerotic heart disease, and what we'redoing here is assaying them for something other than atherosclerotic heart disease. so we had an exome set of 572 patients thathad been exome sequenced, and we asked the question, "how many of these patients havea gene variant that predisposes them to have either a cardiomyopathy or a rhythm -- cardiacrhythm disorder." so we did a literature search, searched the textbooks, and came up with 41genes that have been found to be related, highly related to cardiomyopathies of varioustypes, and i listed those here. some of the folks in the room, i'm sure, are more familiarwith some of these traits than am i. and then a number of rhythm disorders, like atrialfib, long-qt syndrome, et cetera, and asked
the question, "how many patients have variantsin those diseases?" so when you take the exomes, again, you findenormous amounts of variation. remember, this is 572 people. and it is -- i think it is63 genes. yeah, 41 plus 22. so you take just short of 600 folks and 63 genes. how manyvariations do you find in their genomes? about 1,200. right? so that's an average of twovariants, two genetic variants per person, in a set of traits that we know are not commontraits. these are rare disorders. so what that's telling us is that there is much morevariation in the genome than there are pathogenic causative mutations for these traits, andthat means that only a small subset of them are actually causative. so which ones arethose? and that's the trick of the interpretation
at genome-scale interrogation. so what we do is what we call filtering. soyou take those 1,200 variants, and you begin to look at them and ask questions about them,and basically do exclusions if those variants have attributes that make you think that theyare not pathogenic. so, of course, one of the first ones is, if we can look and validatethe sequence technology, the values, the quality values that are coming off the instruments.if we're not convinced about that, we can eject the variants. frequency's a big one. so we use frequency.and this is a little bit of circular reasoning, but it's practical, and it works, which isthat if a variant -- so let's take any one
of those genes for those traits. if a singlevariant for that trait is present in the cohort at a frequency that is substantially higherthan the trait itself -- so let's say the trait affects one in 1,000 people, all right,and let's say there's 100 variations that cause that trait. you know that any one geneticvariation that can cause that trait is a subset of that one in 1,000. if any variant is presentat a frequency that's as common as the trait, you know it can't cause that trait, becauseotherwise the trait would be much more common than it is. you have to be a little carefulwith that, but that's how we can start filtering. that pushes out an enormous number of variants. there are certain types of variation thatare more likely than others to actually be
pathogenic. so we can exclude some of theones that aren't -- that don't have those attributes. and then it gets down to sortof brute force, good old-fashioned pulling up the literature and analyzing the casesof the patients who have been reported to have those variations or very similar variations,and determining, based on clinical judgment, if those reports of causation are, in fact,true. and that's a challenge, but works quite well. all right. so when we look at 63 genes and572 people, what do we find? well, turns out you find pathogenic variants. so about 1 percentof these patients have pathogenic mutations in one of these genes. one of these was dilatedcardiomyopathy. this is a gene that's called
phospholamban, and this exact variant hasbeen found to be present in patients with dilated cardiomyopathy. hypertrophic cardiomyopathy,two different patients, each with their own unique variation. these are -- for those ofwho aren't familiar with this, these are standard mutation nomenclature. this means there'sa stop mutation in the gene, so that gene protein product is truncated prematurely.that's a severe mutation. this is a mutation -- remember how there were the little greenblocks in the gene that were spliced together? this is a mutation of one of the sequenceelements that causes that splicing not to occur, so the gene is never put together correctly.and this is a change in an amino acid, so this is what we call "missense variant" ina gene. that has been shown to cause this
trait. then some of the rhythm disorders.we had three patients who had cardiac rhythm disorders. variants have all been describedin several families, and one of which i'll tell you a little bit more about. okay, so then we go back and, again, lookat the patients, and do this in an iterative way. so for the cardiomyopathy patients, whenwe went back and pulled their echoes, they did not have current evidence of cardiomyopathy.okay? so that can mean one of two things. either we're wrong and these variants do notactually cause these diseases, even though they're published as being causative variants,or what we have done is exactly what we set out to do, which is to find disease susceptibilitybefore the disease manifests. right?
when we look at the family histories, we findseveral of these individuals have a striking family history of unexplained cardiac deaths.now, that could be attributed to a number of things, and those diagnoses are hard toassess, because these are secondhand reports of disease and death. but we were quite impressedat how many people have relatives who have unexplained symptoms and deaths. and thisis that last patient i mentioned, who was a very interesting patient in our study. thisis a lady who enrolled at the young end of our age eligibility, late 40s, and she hashad ongoing problems with unexplained syncopal episodes. she has a left bundle branch blockthat is not explained by a known coronary artery or other cardiac disease. she has,on our electrocardiogram, a clearly abnormal
qt corrected interval. and she also has achild who also has had episodes of unexplained palpitations. so here is a patient who cameinto our study to be enrolled for atherosclerosis, doesn't have atherosclerosis, but instead,when we sequence her, we find a variant that i think is highly likely to be pathogenicfor a serious cardiac rhythm abnormality, and we have diagnosed this disease in a patientwho didn't know that she had it. so what is going on here? this is actuallypretty radical stuff, because it flies in the face of how, essentially, all of us wereclinically trained. so what we did is we took a cohort that was not selected in any wayfor the presence of cardiomyopathy or dysrhythmia, or for family history of sudden death, right,because that's how we normally do genetics.
we try to go out and find patients who havethese rare phenotypes or have family histories of these disorders, and we sequence them.we're not doing that here. we're taking unselected patients and just screening their genomesand asking the question, "what tiny subset of this population has this trait?" we sequencedall genes and then selected the genes retrospectively to look at and analyze, and there was no indicationfor doing this testing in these people. and what we found is that more than 1 percentof our cohort have apparently pathogenic genes in these diseases that we consider to be raremonogenic forms of non-atherosclerotic cardiovascular disease. so, again, this was done without a chief complaint,without a history, without an exam, without
any clinical testing to suggest a disorderwas there, without a family history, and ordered every test -- ordered tests for every genein the genome. and i can tell you -- and i think most of you would say the same thing-- if i had even suggested doing such a thing when i was in my clinical training, they wouldhave practically hit me with sticks. you don't do that. what i was trained to do is thati only ordered tests when i knew that the patient had an indication for the test, thati understood what the test was for, and that the alternative outcomes of the test wouldchange the management of the patient i was taking care of. that's what i was taught todo. we are completely throwing that head over heels and saying the exact opposite. so thisis a radical thing to do. but, again, if we're
serious about wanting to do individualized,predictive medicine, we do have to be willing to throw these things overboard and try somedifferent approaches. so, again, this is contrary to everything we've been taught and is a newway to think about how to practice medicine. the interesting thing is, when you stop andconsider our old model, our old model works, but it's kind of perverse, in a way, isn'tit? what we're basically telling people is, "you know what? we are not going to understandyour disease until you're either already sick or people in your family have died. untilthat happens, leave us alone, because we're not going to take care of you." that's ourcurrent paradigm. and you have to ask the question: is that really what we should bedoing? and there's good reasons why we practice
medicine the way we do. and i'm not here tosay that the chief complaint, the history, the exam, the differential diagnosis is useless,because you and i all know that it is absolutely not. it is essential. it will always be used.it'll always be useful. that skill set will always be important. but now it's not theonly way to do it. there is another way to do it. all right, so, getting back to the originalfocus of the study, let's think about dyslipidemia. so this is not a surprise, right? if you recruitpatients into a cohort and you want to study atherosclerosis and you select for patientswho have disease, you darn well better find people who have dyslipidemias, because weknow that dyslipidemias cause atherosclerotic
heart disease. so here's another participantin our cohort at the higher end of the age scale, 65-year-old female. she was diagnosedwith high cholesterol at the age of 25 years. she was very, very well-managed. and you cansee her numbers were in good shape. although, she is clearly suffering from this dyslipidemiaand has a stupendously high coronary calcium score, although she has not yet had an mi. so here's a lady that we evaluated and wefound that she had a pathogenic mutation in the low-density lipoprotein receptor, whichis a very well-known cause of hypercholesterolemia. and we then talked to this patient, and itturns out she had several family members who had said, "oh, yeah, i think some of my relativesalso have high cholesterols." and we went
through the family and identified four otherpeople who have this trait as well. and this is a really interesting phenomenon, becausewhen i talk to practicing physicians, internists, family practitioners, pediatricians, theywill tell me -- and they're very comfortable being frank with me. that's what the diplomaticscall it, a frank and honest exchange. and they say, "i don't need no freaking genetictest to tell me if my patient has hypercholesterolemia." right? you can do that biochemically. theydon't need it. well, actually, i would say that we do needit, and here's why. for every patient we've discovered that has a genetic cause of a dyslipidemia,we have found, by looking at them and their relatives, between four to eight patients,relatives of them, who have hypercholesterolemia
that is either undertreated -- either completelyundiagnosed or significantly undertreated. and yes, it is true that you don't need toanalyze the genome or the proband to understand that they have hypercholesterolemia, becauseyou can diagnose that, you can treat it, and you can manage it perfectly fine. but we canactually leverage this, because what happens is we can identify multiple other individualsin the family, and that marginal cost of identifying those other people is very, very small. andwhat i'm beginning to understand is, i think, actually, the genomic or the genetic resultforces us and our patients, because it occurs to the patients at the same time, to ask aquestion, "oh, this is a genetic trait, and it's a simple genetic trait. we understandexactly how it's inherited. so, doc, who else
in my family could have this?" and then thedominoes start falling. and, in fact, i think what genomics is doing is forcing a conversationto occur that we are currently ignoring. all of us are trained to take care of the patientin the office. our colleagues in family practice are, arguably, a little better at this thanmost of us -- pediatricians, internists, ob-gyns, et cetera, are -- because we need to startthinking about the family as the patient. there are more patients than one. and including this family, because here thisgrandson, if i remember correctly, was 10 years old and had wildly abnormal cholesterol.and, as some of you may know, the american academy of pediatrics has now recommendedinstitution of statin therapy for children
with familial hypercholesterolemia, not garden-varietyhypercholesterolemia, but familial hypercholesterolemia, starting at age eight, because it is clearthat this lifetime burden of cholesterol is what leads to the buildup of atherosclerosisover time. and this lady's calcium of 1,700 is because she wasn't diagnosed until herthird decade of life, partly attributable to that. and so we need to start treatingthese people much earlier. in fact, we thought this was so clever, but, of course, when wego into the literature, it turns out in several scandinavian countries they have these wonderfulsingle-pair health care systems and public health systems where every person who getsa diagnosis of familial hypercholesterolemia, a public health nurse is sent to that person'shome. they get a family history. then they
go back to the unified medical record systemthat they have; they pull up all their relatives. they go to their houses, and they bring theminto the clinic, diagnose them with familial hypercholesterolemia, and treat them. so they'vebeen using this for about 10 or 15 years, and it works, and they have a very low incidenceof this trait in the population now. okay. okay. so i've told you about two examplesof how we can use genomics clinically. and so we've diagnosed these six cardiomyopathies,nine dyslipidemias. we've also gone through this cohort and asked the question, "how manypeople in this study have cancer susceptibility syndromes?" as some of you may know -- andi think you've had a lecture on cancer genetics already -- there are a number of inheritedcancer susceptibility syndromes where individuals
in these families have an extremely high rateand early onset of cancers. and we, again, did the same 572 people, and eight individualsin those families have an early onset cancer syndrome, and interestingly, only half ofthem were known at the time that they enrolled in this study. and half -- the other halfwere individuals who had either negative family histories, because the family was small or,in the case of several of them, they had hereditary breast and ovarian cancer gene mutations inthe family, but just by chance, these families had a preponderance of male births insteadof females, so there just weren't that many people to manifest. and, again, that getsto that notion of, "we're not taking care of you until you have the disease or yourrelatives start dying of the disease, and
we can find them prospectively." two patients in the cohort have malignanthyperthermia susceptibility syndrome, which is a very important trait, and good, easymedical interventions to eliminate and reduce the risks of that phenotype. three patientswith a peculiar form of neuropathy that's supposed to be rare, that i can't explainwhy it's so common in our cohort, but there it is. and we've also defined one patientwith an occult metabolic disorder that is -- was previously thought to only affect children.now we know it can affect adults as well. and really here, we're just scratching thesurface. this is a decent amount of people. so, again, 500, 600 people. five percent ofthese people have an occult or unrecognized
rare disorder that we thought only affecteda few families here and there but, in fact, is scattered throughout the population ata significant level. and i would actually ask all of you to consider, for those of youwho are in active practice, if you have 2,000 or 3,000 people in your practice and we sequencethem, these data would suggest that 5 percent of those patients, so 50 of your patientsin your practices right now, have diseases like this. and, for the most part, we probablydon't know about that. that's a concerning thought to me. and, again, we're just scratchingthe surface, so really, there's more than that in there. and while clinical sequencing,routine clinical sequencing is not indicated yet, it's going to be soon. you're going tostart seeing patients who have this done,
and we're going to be able to find this. so there's lots of other traits. this is onlya small fraction of genetic traits. there's hundreds of other dominant diseases in humansthat we are going to start going through. pharmacogenetic data can be extracted fromexome and genome sequencing that will allow us to begin to better select drugs, and sopatients who, for example, have a rare variant in a gene that gives them a myopathy fromatorvastatin, we can find that. we communicate that to patients and providers. those drugscan be avoided. as well as all of us have mutations for which we are heterozygous carriersthat have reproductive risks for our descendents, and we should consider that as well.
okay. so this all look really easy, right?i love this picture. [laughter] there's so much unsaid. what i really likehere is you can just barely see in the background all these guys just sort of standing around,and i just can't imagine how annoyed those guys are. all the hard, hard, awful work that went intomaking that beach ready for this guy to sort of prance on with the photographer in frontof him. he makes it look easy. all the hard work has been done before he even got there.and there are a lot of criticisms to this notion of individualized and personalizedmedicine.
so there are some statistical analyses thatsay that heredity is not as good as we think it is at predicting these traits. and thisis an evaluation of identical twins that suggests that our power to do this may not be quiteas high as we think it is. and one of the big reasons for that is, remember that penetrancegraph i showed you? all of those data on penetrance and variation are based on ascertaining rarefamilies. so rare families are selected for the attribute, that when they have this singlevariation, they manifest this disease, which means they probably have some other geneticattributes that make the disease highly likely to be manifested in those families, and thenif you go outside of those highly penetrant families, that what you will see is that thepenetrance of the same variant will drop off,
and so that we may be overestimating that,and that will make our predictions not as strong as we think. we've been dealing with these kinds of problemsfor a long time, though. all of us are used to this, right? we have, in almost all testswe do, a significant probability of false positive results. i've told you, in sequencing,you know, we found more than 1,000 variants in these cardiomyopathy and rhythm genes,and all but six of them were probably benign or unlikely to be pathogenic. in every clinicalpathology test we do, the same thing is true. we use normal ranges, statistical normal rangesthat mean that at least one out of 20 of every test that we do has a false positive justby statistical variation, right? the famous
chem-20 that we order, the odds are prettygood that one of those 20 values is out of whack; just for statistics, not for pathology.and certainly in imaging -- my goodness. we do coronary calcium scores by ct scanning.the rate of false positives in incidental findings in ct scanning is more than 5 percent.plenty of false positives and abnormal findings there. so the next question is, there's a lot ofchallenges for geneticists and for clinicians. are patients ready for this? what is goingto be involved in sitting in a room and talking to a patient about a whole genome test? so,again, genome generates enormous numbers of results, millions of variants. we know, fromclinical experience, it can be a challenge
to communicate one test result to a patientin the clinic. can you imagine talking to them about three million? not even conceivable.so we have to figure out how to cope with this information overload, because that's-- we are swimming in data here, and you cannot bring that into clinical practice. so we'regoing to have to develop new approaches for how we use those data, how we parse them out,how we roll it out to patients over time. and to do that, we need to know what the patientsthink, what they want, and how they use it. so we're studying our participants in clinseqalso to understand how they view the information and what utility they are making about it. so we did what's called qualitative interviewing,open-ended questions to ask patients what
it is that they're looking for, what theyexpect, how they imagine using that. and what we found was two basic clusters of answersto these very open-ended questions. first was -- and this is a wonderful thing aboutbethesda and the nih when we recruit patients in -- patients, these people are really altruistic,and it is absolutely amazing to me how willing these people are to help us understand whatwe're trying to study, even if it doesn't benefit them. they also, though, are equallyinterested in their own health. they believe that they will -- we will find and that theywill receive from us information about their genomes that will allow them to change somethingabout their health care that will help them or their family members. and what's importantabout this -- and this is important methodologically
here -- this is not asking people, "wouldn'tit be interesting to think about having your genome sequenced?" these are people who you'reactually bringing in, and they have to put their arm out, and we take the blood, andwe're doing it. so this is the real thing, and this is what we can really expect peopleto want to do. we also assess their preferences of what they'llwant from exome and genome sequencing at their baseline enrollment and then following theconsent, and then gave them four scenarios for what kinds of classes of results we mightfind and evaluated them on that. and what was interesting, these are self-selected,very interested, eager folks. nearly all of them said they wanted to learn their results.six of them, interestingly enough, were uncertain
about it. even signing up for this study,they weren't sure they wanted their genome results returned to them. they were a littleanxious about what that might mean. they were interested in using it for prevention, andthey felt they were very committed to the notion that they -- having -- them havingthis information would better equip them to either prevent or manage diseases when theydid manifest. there were also some comments about preventive measures that could be implementedby their docs, and then also using the results to change their environmental exposures, theirdiet and exercise, et cetera. about a third of the patients -- a third ofthe population were just curious. it's really -- there's an intense curiosity about ourheritability, about our families, about our
genomes. and these people are of the opinion,"all knowledge is positive." i think that's a really interesting thing, because i wouldbet that every clinician in this room would say, as i would say, that's just not true.it is not the case that everything i can find in all these genomes will be a positive thing.there are things that we can learn that will be wrong. there are things we can learn thatwe will understand there's nothing good we can do about. not all information is positive,yet the patients hold stupendously highly optimistic views of this, and that's an issuewe're going to have to deal with in matching the optimism of patients to the reality ofthis testing. about a third of them wanted the informationbecause they wanted to understand something
either about a trait that they thought wasfamilial and in their family history, or things they wanted to use to transmit to their childrento help their children plan for their futures. and some of them actually came to the studywith a specific condition in mind, and that may be the explanation for why we're seeingsome rare disorders perhaps a little more commonly than we ought to, is a very subtleform of self-selection, is people having this vague but potentially correct feeling that,"you know, there's something going on in my family, and i think you guys can figure itout." and we are then stumbling across those traits. most of it was related to heart disease,again, which is appropriate with the focus of our study. but it may be more general thanthat, and i think that's an interesting thing
to consider. so, again, they're very enthusiastic aboutlearning all range of results, even results that we would say are of uncertain clinicalsignificance. the patients are interested in that and desire that. they do recognizethe distinction among the types, even though they're generally enthusiastic, and they wantthese, what we call, actionable results, genomic results that they can take to their doc anddo something with. knowledge of the patients. again, if you recruitfrom bethesda, you're going to get a knowledgeable and sophisticated group of folks, and theyhad very high levels of knowledge pre- and post-counseling, so they have a long geneticcounseling session, 45-minute session, where
we explain to them what the sequencing isand what it is not. and we -- even though they came in highly educated, there was anincrease in understanding about the power and the limitations of genomics pre- and post-counseling,so that is an important part of it. so the big picture here is i want to challengeyou. i'm a very -- i'm very proud, and i think i'm pretty darn good at phenotyping patients,but i also recognize that i am not -- i am far from perfect at it. and when i sequencea patient's genome, i learn things about my patient that i didn't know before i sequencedit. and i can figure out things that i'm pretty sure i would have never figured out withoutthe genetic data. and i think, because we have only had our diagnostic abilities tofind disease in the past, we rely on that
exclusively, but that doesn't mean that that'sthe only way that we can do this. i think we practice a lot more trial-and-errormedicine than some of us would like to admit. again, that's all we can do, and so we dothe best we can. we have good data to try and tailor medicines and treatments to patientsbased on their attributes, but we're not that good at it. we can do better. our abilityto currently predict disease onset, disease susceptibility, the severity, and the courseof the disease; we would also like to be able to predict that in patients. efficacy andside effects of treatment is, again, limited and is ripe for improvement. and the way to think about this: is genomicsgoing to perfectly solve all of these problems?
no way. but the other way to think about itis, in many respects, we're not as good at this -- nearly as good at this as we wouldlike to be. and even if we can improve it only a little bit in all of these attributes,that would make an enormous difference to medical practice. so, for sure, there is a lot more work thatneeds to be done. we have to do an enormous amount of work to really tighten up this relationship.we have to be able to precisely predict genotype -- predict phenotype from genotype and knowthe limitations of those predictions. we have to develop and test approaches to pre-symptomaticmanagement. we really don't have ways right now, effective approaches to managing a patientwho has hypertrophic cardiomyopathy gene mutation
before they have hypertrophic cardiomyopathy.we need to figure out how to do that. and we have lots of work to do to build the infrastructureand methods for managing and disseminating and using this information with patients andin our health care records. lot of arguments about whether we should orshouldn't do this, and some people are really critical of it. and the truth is, i thinkthose arguments -- that's now water under the bridge. this stuff is out there. genomescan be ordered clinically, and you will start seeing patients in your practices who havehad this kind of testing done, and that may be for reasons completely unrelated to yourcare of your patient. for example, you may be taking care of an adult who has been sequencedbecause they have a child with autism. right?
so when we do sequencing for things like autism,we usually sequence the kid and both their parents. you sequence the parents, you'regoing to find this other stuff whether you want to or not. we're going to have to learnhow to work with it. so it's coming, and not to mention the factthat there's 1,000 people in metropolitan washington who have been sequenced in clinseqnow. some of those may end up in your practice as well. so we have a lot going on, and weare going to have to figure out how to use these genomes clinically, and these patientsare there. there are other downsides here. genetic discrimination.it was really fun. we had a -- nih has a science in the cinema series, and they screened, ontuesday night -- or wednesday night, the movie
"gattaca" over at the afi in silver spring.good -- pretty darn good movie about predictive medicine and discrimination. we have to thinkabout genetic discrimination. we have passed a law called the genetic information nondiscriminationact that addresses some of these concerns, but it has not completely been put to rest.and we really, again, have to struggle with this notion of prediction, and so is geneticsgoing to be able to predict these things, or are we going to end up like these guys?right? here's a mode of prediction that isn't all that respectable anymore. and i wouldactually suggest that genetics is going to do much better than this. but other -- quotei like about predictions is, "the groundhog is like most other prophets; it delivers itsprediction and then disappears."
so, for better or worse, i think us genomicspeople are here to stay. i think we're going to be a thorn in the side for a while. buti think this stuff is going to work, and it's going to expand. we're going to build it out.and we'll be able to predict what's going to happen to our patients before it happens. so i'll stop there, take your questions. andthank you for your attendance. [applause] any questions? yes, sir. male speaker:[inaudible] look at the other side of the coin. in other words, if there's a very strongfamily history, and the individual doesn't
have a disease, is there something protectiveabout their sequence and not only the fact that they don't, or is it related to environmentalfactors? [inaudible] in other words, looking at it from a, "why don't they?" kind of thing[inaudible]. les biesecker:fabulous question. and that gets to this notion of the penetrance. that's exactly what you'reasking about. and there's two levels of that. the first is, absolutely, it's harder to figurethat out. we call that genetic modifiers, when it's other genes that modify the traits,right? and, you know, we sort of know that. sort of our intuition tells us that what you'resaying is true, and we see that when two families come together, family that has a high incidenceof some trait or character, and when they
marry into another family that is maybe geneticallyvery different from them, you can see that trait disappear in the descendents, and that'sexactly the phenomena that you're talking about. the other one is that we're actually -- there'sa great study going on called the centenarian study, and what they're doing is sequencingpeople who have lived to the age of more than 100 years. and the notion will be that thosepeople will have genetic variants that are uncommon in the rest of the population, andthose variants allow them to live into their 11th decade, and so it's a great question.and people are working hard on that right now. and environment is always important.always, always. i like to say it's the -- i
get the -- when i talk to people about thisin social situations, i always get what i call the "uncle walter story." the uncle -- "myuncle walter -- " and, you know, when i get this story, they have at least one hand ontheir hips, right, when they tell me. "my uncle walter ate bacon and eggs for breakfastevery single morning and smoked his cigar after dinner, and he lived to be 600 -- " youknow. people love telling those stories, and there are people who have those protectivealleles. and, you know, frankly, that'd be a good thing to know about. female speaker:idea about the costs? les biesecker:costs. okay. i gave two numbers for cost,
but you should be very skeptical of thosenumbers. those are research costs, okay? so my cost for doing a whole genome sequencein a research context is about $10,000 for an exome, and i think it's currently $850.when that is rolled out clinically, as you're well aware, costs go up because there's alot of things you have to do when you do clinical testing that you don't have to do when youdo research testing, and it makes it significantly more expensive. you can buy from at leastone commercial company that is selling clinical whole genomes. i believe that actually theirretail cost -- and you can order that today for about 10,000 bucks. so you can get retailgenomes for that price today. and people are ordering them, patients who have severe intractablecancer. some of you may have caught that series
in the new york times this past week. patientswho are having whole -- actually, they get two genomes done. they have their normal genome,their peripheral blood genome, done, and then they have their cancer genome done, and theycompare the cancer cells to the noncancerous cells to figure out what's going on in thecancer. so they actually have two of them done -- 20,000 bucks. male speaker:is rna done, too? les biesecker:say it again? male speaker:rna sequencing. les biesecker:and there's rna sequencing, and there's proteomic
analyses, so there's lots of these -omicsthat we can begin to think about doing. but i think you should think about this in the$1,000 to $10,000 range, is where this testing will be, which is a lot of money. we can'tpretend that that's cheap. but, again, it is in the realm of other medical tests thatwe do all the time, medical tests that can't tell us, in fact, as much as we can learnabout a patient from a genome, in some ways. so it's now come down to where you can startto begin to think about this, and you're going to start seeing it in practice. male speaker:those two patients who had the malignant hyperthermia, did they gave a clinical [inaudible], is thatwhat they [inaudible] to these studies?
les biesecker:great question. great question. so those two people, if i remember correctly, did not haveany hint in either themselves or any relative of malignant hyperthermia. one of those twomutations i'm absolutely sure is correct. it's on a panel of the 20 most common mutationsthat cause malignant hyperthermia in european-derived persons on this planet. it's a very commonmutation. it's rock solid. i will also, though, tell you that there is a third person in ourcohort who does have a family history of malignant hyperthermia. male speaker:[inaudible] medications to stay away from? les biesecker:yeah, so malignant hyperthermia is most commonly
caused by -- i think it's isoflurane. arethere anesthesiologists here? i think it's isoflurane and succinylcholine are the triggeringagents. and, of course, then the antidote, the treatment for that is dantrolene. andso these patients, we recommend simply that they wear a medic alert bracelet and haveit in their chart that they have malignant hyperthermia susceptibility. this costs nothing.the treatment really costs essentially nothing. and that if they do go into surgery, thatthe anesthesiologist has a vile of dantrolene sitting there and watches that patient's temperaturein the or, and if the temperature starts to go up, then you know. but the third patient is interesting. we havea guy in the cohort who absolutely has a very
strong family history of malignant hyperthermia.we sequenced him, his whole genome, whole exome, and he does not have a mutation inthe gene. in fact, he has a variation that we showed doesn't cause malignant hyperthermia.so that's a very important thing to recognize about whole exome sequencing, is that it isnot 100 percent sensitive. i mean, what of our tests are, right? but we have to remember,we use the word "whole" genome sequencing, and we use the word "whole" exome sequencing,and that's just a little bit of a fib, because it's not whole; it's not 100 percent. andso we know that that person, we missed it, even though we know he has it. so the onethat had the family history, we didn't find the variation. the two where we found a variation,they didn't have a family history. and again,
that sort of conflicts with how we're currentlypracticing medicine and genetics, when we require those two things to happen togetherand they don't. yes, sir. male speaker:so could you speak a little bit to your opinions -- oh, [inaudible] mic. les biesecker:yeah, i don't think you need it. i like that. male speaker:could you speak a little bit to your opinions on who should own individualized genomic informationand should have access to it?
les biesecker:so how i think the information needs to be managed is, i think the ideal system wouldbe what i call a two-key system. the information i think of as more sequence is a health careresource; it's not a test. if we think about a whole genome sequence as a test, it gets-- you get tied up into knots because you can't figure out what the heck to do withit. it's too many results to analyze. it's too many results to turn to a patient. it'sjust too much of everything. so what we have to do is take a step back and say, "this isa resource; it's not a test." and the patient will have this done for some reason or haveit done to them because of some reason in a relative. and that resource, then, becomesa part of their health care record or resources.
and then when that person has a need for someinformation from their genome, they and their clinician will have the ability to accessthat, if that both agree that that's a proper thing to do. and so i would envision a partnershipbetween the patient and the clinician where if they are in agreement about it, you useit, and if they're not in agreement, you don't. and it stays safe and secure and is integratedinto our health care system in a way that makes sense with our current practice analysis,because i am not one of these people who likes to go around saying that genetics is goingto revolutionize medicine, because what i've found is that most people actually don't likerevolutions. they're really kind of messy things. and let's try and evolutionize medicineinstead of revolutionize it, and use these
data in ways that makes sense with what wecurrently know how to do and can insinuate it into our practices in a way that's usefulto us as clinicians and where we know what we can do with our patient, and the patientswant it and perceive it as useful and perceive it as necessary, and then go forward thatway. maggie [spelled phonetically], behind you. male speaker:if the genome sequencing is repeated, let's say, at five-year intervals, how accurate,that is to say, how reproducible is it? les biesecker:great question. now, here's your classic, again, glass half full, glass half empty argument.so good, quality genome sequencing or exome
sequencing is between 99.9 percent and 99.99percent accurate. impressive number, glass half full. then you say, "okay, how big isthe genome again?" the genome is about three billion nucleotides, and so you can say, "that's,then, how many errors?" that's hundreds of thousands of errors. so those errors willcome up. the error rates in the models for analyzing them are constantly improving, sothat number will actually go down over time. but the dominator is so big that even verysmall error rates can have significant implications. for that reason, we are currently doing thingsin a way that all of our sequence data are generated in a research sequencing laboratory.and then if we are going to use any of those data to do anything to a patient, they arecompletely -- the variant that we're interested
in using for clinical care is completely replicatedin another testing setting to make sure that the two results are the same from the twodifferent methods. and that, then, dramatically lowers the error rate. but when you considerthe genome as a whole, there are errors in it. anytime you assay anything that big andyou're less than perfect, which we always are, there's going to be errors. that's agreat question. then that gets also into the whole questionof mosaicism. and we have this lovely fiction that every nucleated cell in our body hasthe same genome in it, right, because they're all identical. well, actually, it's not true.there's mutations that occur within different parts of our bodies, and there's variationwithin us. and we now know that that can cause
a number of diseases. cancer is the most extremeexample of somatic mutations, right? that's
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where there's hundreds of mutations in a cellor a tissue. but we know, actually, that extends down to much finer grades of variation andcan cause a number of other traits and diseases. so great question. thank you all very much for coming.
