cc  >> Hi, welcome to the  Biotechnology Center for  Wednesday Nite at the Lab.  I'm Cathy Vrentas, an outreach  specialist here.  And today we're welcoming  Michael Gould to talk to us  today.  And he's a professor in  oncology.  And he'll be talking to us about  his work in his lab about genes  that affect susceptibility to  breast cancer, as well as the  work on anticancer drugs.  He's also done some pioneering  work on developing  knock out rats to work on using  the rat as a model for cancer  as opposed to the mouse.  He has his background  in education here at UW Madison  including a PhD from here,  and did his postdoctoral work at  the Argonne National Laboratory.  So he'll be telling us today  about his work on breast cancer  genetics and more.  So join me in welcoming Michael.  (applause)  >> Well, thank you for all  coming. 

 What I will be telling you about  today is a little bit about  genes that control  susceptibility to disease.  I'll just use breast cancer as  an example.  Instead of putting breast cancer  in the top risk you can think  about your favorite disease that  you're interested in whether it  be diabetes or any of the common  diseases.  And that's a key word, common  disease.  So we're talking about what the  general population might suffer  from.  All of us suffer from something  or most of us suffer from  something whether it be high  cholesterol or it being  high blood pressure.  Be it blood sugar problems,  or in some cases cancer.  And most of these are diseases  that affect all people.  And each of you has an  individual risk for each of  these diseases.  We're going to talk a little bit  about that genetics. 

 And some of you may have seen  advertisements or articles  in "Time Magazine" or the  "New York Times," or maybe even  the local paper where there are  companies who would like you to  send them your DNA and in return  "National Geographic," for  example, will tell you what your  family's origin, that they all  are Irish or they're all Russian   or they're all a mixture of  these things.  In addition, this same type of  technology is being used now, at  least they suggest it's being  used, for companies like 23andMe  where you can send your DNA away  and supposedly they survey your  whole genome, and that's correct  they will look at about a  million locations over your  genes, and they'll be able to  tell you how you and I differ at  each of these million locations.  And based on some studies that  people are doing now over the  last two years, and some of  these I'll talk about, we can  start getting some ideas of  which of these differences  between you and me and our  millions copies of these  differences will lead to certain  diseases.  The conclusion of this  really is, to give you some idea  of how these are going to be  used, and some questions that  are current.  Again, let me know if I'm not  speaking loudly enough.  And also tell me, as a  New Yorker, I tend to talk very  rapidly so tell me to slow down.  >> Put the mic over here.  >> Good we'll put this close to  my mouth and that always helps.  I'm usually being accused by my  wife of being too loud.  So we'll see how that goes. 

 There used to be a pointer  around here.  There's several of them that I  have my choice of.  So we're going to talk about  risks and common disease.  And in this case we'll talk  about breast cancer but you can  substitute any disease you're  interested in because the same  rules are going to apply.  What I'd like to talk about  tonight and some of these slides  look technical but my  explanation hopefully will be  not too technical.  So stop me if you don't  understand anything.  I used to teach high school so  I'm used to being badgered and  interrupted.  So that's good for me.  So what we're talking about when  we talk about common diseases is  an interaction between  environment and genetics.  So all of us inherited certain  genes from our parents and that  will also give us a  predisposition to disease. 

 So, in other words, if many  members of one's family has had  diabetes, you're more likely to  have diabetes.  But now we can look specifically  to see whether you inherited  that trait or not.  However, none of these work  without interacting with the  environment because like breast  cancer maybe 30% or 40% of the  reasons somebody might get  breast cancer is due to  inheriting susceptibility or  resistance genes, but also  environment plays a big role.  And environment can negate the  genetic effects or they can make  the genetic effects worse.  Why is this the case?  Because we're all adolescents,  genetically we're all  adolescents since being humans.  We're all very young.  So we haven't really stabilized  our genetic programs.  And what has happened over the  last century really is while  we're trying to stabilize our  genetics, and we've done a  pretty good job of that over the  years, what we haven't  stabilized is our environment.  Over the last 100 years or so,  and probably over the last 25  years, we've really made our  environment unstable.  What do I mean by that?  For example, we have a ready  supply of foods so a lot of us  tend to eat too much. 

 If I don't go to the gym I  balloon out.  So again we have that, we have  different environmental hazards.  And our genome didn't grow up  overeating our genome probably  grew up under eating.  So some of our current  environments, our genetics  aren't ready for us.  So genes that would normally  might be good for us in a  primitive environment are really  bad for us now and so that's how  we get a negative genetic  environment interaction.  However, we could also talk  about a positive environment.  So our environment might be  taking drugs to lower our  cholesterol.  So in some ways if we do that we  can negate genes that make us  susceptible to high cholesterol  by taking drugs.  So again there's an environment  and genetic interaction.  So we cannot really tell you  individual risks without knowing  both environment and genetics  and that's the types of things  we try to study. 

 The other thing I'd like to  distinguish there's two types of  risk when we talk about risk to  disease.  One is risk to a population.  So we might say we're looking at  all the people in Wisconsin  which we study and we want to  know what their risk is as a  population to breast cancer with  certain genetic marks this  population may be of high risk  or this population may be of low  risk, but the thing I want to  emphasize here is this is the  population so I can collect your  DNA and maybe I can collectively  tell you something about people  have a genetic marker what their  chance is of getting breast  cancer is as a member of a  population.  But the important thing to  remember is currently, we can't  tell you what your risk is.  We can tell you whether you  might have a slightly higher or  lower risk but we really say  that with really a grain of salt  because we don't know what your  environment is, we don't know  how your genes interact.  So when we do these studies we  do them on populations of  people.  So our conclusions really are  about populations.  So what good is that to you?  Why would you even care about  populations?  All you really care about is  what your risk is.  The reason you care is because  if we can put you in a category,  for example, of being at high  risk to breast cancer and you're  40 years old that might say you  should get mammograms.  Is it because your individual  risk is high?  No, but you have a chance of  having a high risk because you  belong to a population that has  a high risk. 

 We can can't definitively say  you have a high risk therefore  you should get a mammogram.  What we'd be saying is you  belong to a population with high  risk and therefore you might  considering getting mammograms  earlier than other people.  Yeah?  >> You said Wisconsin might be a  one population but that's very  diverse.  >> What I say is we study  Wisconsin women.  So we're not going to talk about  women.  The population I'm going to  really talk about here because  when I give you real data is  going to be Caucasians of  European descent.  And when we get to that point  you can ask questions of why I  chose to do that.  [INAUDIBLE]  >> For example, my aunt by  marriage died of breast cancer,  and two of her daughters did.  Does that often reinforce the  susceptibility that they have?  >> Yeah, in fact that's why they  use the genetic data that they  get from the new technologies is  to combine it with what we  already know.  The normal risk factors for  breast cancer would be relatives  who have breast cancer or many  other things about weight and  things about maturation time and  other things. 

 So it's not to be used  independently; the best way to  use all data is put it together.  But today I'm going to stress  the genetics, other days I can  stress the environment.  So I think we spent enough time  on this slide but all I want to  say before I get off of it is  when we talk about genetics, and  that's what we'll be  concentrating on, always  remember environment can screw  up what I'm saying.  And the second thing remember is  all of us have both  susceptibility alleles and  resistant alleles.  So when we talk about disease,  whether it be breast cancer or  some other disease, all of you  in this room probably have  hundreds of pods of your genome  or genes that give you a better  chance of being resistant to  breast cancer but you also have  hundreds of genes that give you  a higher susceptibility to  breast cancer.  So when these act together this  is what gives you your risk.  And I'll just show you some  ideas of how we identify these  things because most of this has  really been done over the last  year or two.  >> Are you able to speak a  little more slowly?  >> I will try but keep reminding  me because I revert to my New  York background if not told  often.  Okay, let's see if I can make  this thing work.  Somewhere here, so hopefully you  can see that. 

 They never use the full screen  here.  Okay, so the question is how do  we identify these loci.  Can you still see me on the  camera if I wander over here?  They taught me to stand close to  the podium.  So we want to start off with how  do I identify susceptibility  loci because that's essentially  what I'm going to be talking  about today.  And the idea is that when we  identify them why do we want to,  one is really to try to get new  biological insight.  And the other is to get some  more personalized medicine.  But what I'm going to be mainly  talking about here, and the  reason we want to do this is  because if we can understand the  various places in your genome  that controls risk, one in the  first instance which is  something that will be able to  do fairly soon is we're going to  be able it tell people what  their individual risks are to  disease.  And again, first as a population  and later on as an individual.  And this is marker so people can  take, if they have a lot of high  risk markers they can take some  prevention action, which in  breast cancer might be having  more mammograms and possibility  taking an anti-breast cancer  drug to prevent the breast  cancer.  Ultimately, what we'll be able  to do is look at everybody's  profile. 

 So I told you there are hundreds  of genes that can make you more  susceptible and hundreds of  genes that can make you less  susceptible.  But when we look at your genome  you may have a predominance of  bad actors.  And hopefully when we get into  the next era of molecular  medicine, not only will we be  able to tell you that you're at  high risk because you have these  two or three bad things but  we'll be able to give you  medications specifically  targeting this drug to be able  to prevent your individual type  of disease.  And this is something that is on  the horizon and something we  hope that's happening soon.  In fact, in breast cancer we're  almost there because we know  certain markers that might  predispose you to breast cancer  that's called estrogen positive  breast cancer and if that's the  case one can try to put people  on a prevention drug which is  commonly available which is  called tamoxifen or --.  So we're making progress towards  individualized medicine but as  you see we're not quite there  yet.  So what kind of genetics is  there for breast cancer?  And there's two types of genes  that predispose people to  disease.  One is we call high penetrance  predisposing genes.  And these are luckily they're  fairly rare in the population.  You may have heard of them in  breast cancer they've been  studied for many years. 

 BRCA1 and BRCA2.  Have you guys heard about those  yet?  So you know women that carry  mutants of these alleles are at  very high risk of breast cancer.  And that's what we mean by high  penetrance.  If you have a change in these  genes, you have a better than  50/50 chance of getting breast  cancer probably an 80% or 90% of  getting breast cancer.  So they're highly penetrance  because if you have it, you get  it.  Luckily, these are fairly low  risk in the population, in fact,  only 1% or 2% of all breast  cancer cases will have these  genes.  Am I speaking loud enough?  Good, I hope.  20% of cases, if you have family  history and somebody asked me a  question about family history,  first degree relatives with  breast cancer, yes you have a  higher risk.  But of that risk we can only  explain 20% of that risk by  talking about these highly  penetrant genes.  And what I'm mainly going to be  talking about and what's been  happening over the last two or  three years with disease is  we're able to start studying,  for the first time about two  years ago, identifying low  penetrance modifier genes. 

 And what I mean by this is they  contrast this luckily hopefully  most of you don't have high  penetrance genes for any  disease.  Being that a lot of you are my  age that's probably the case  because we're still around.  But all of us do carry low  penetrance modifier genes and  that's what we're trying to  study now.  And what is this?  How does this contrast?  The first thing is unlike the  high penetrance, which are rare,  I said most of you carry low  penetrance modifier genes.  So they have a moderate to high  population frequency.  And what I mean by that is maybe  10% to 15% of the population  will carry one of these genes.  Again there are so many of them  they have to balance out.  And they have low penetrance.  That means if you only have one  of these hundred it's not going  to influence your risk very  much, maybe 5% to 10%.  As opposed to the high  penetrance genes that affect  your risk by hundreds of  percent. 

 So this is a major concept I  want you to think about.  Is high penetrance genes, most  of you don't have them, low  penetrance genes is what's going  to control all our risk to any  common disease you can think of.  And this is a diagram that I  used to teach my classes.  Hopefully it's not too  complicated.  What we've talked about is  Mendelian disease, these are the  things that follow the laws that  Mendel put down, the same thing  he studied in peas.  This is how we find BRCA1 and  BRCA2.  Again, as you can see here, to  review what I said in the last  slide, these have a high genetic  penetrance.  That means if you have these  dominant Mendelian genes you're  going to have a very high risk  of getting the disease.  And to contrast that, the other  thing that we're going to be  talking about now are going to  be things that are called  discovered by genome-wide  association studies.  This is the stuff you probably  read about in "Time Magazine"  and "New York Times" over the  last two years when we come out  and they say they have seven new  genes for diabetes, eight new  genes for prostate cancer. 

 This is where they're getting  them.  Most of them are very low  penetrant.  So until we know what the  majority of them are we won't be  able to tell you your risk.  Right now we can only account  for a small percent of genetic  risk for any of these diseases  with what we know now.  However in the future we'll be  able to account for most risks.  Again, some of these other  categories we really aren't  dealing with yet.  But these are areas to be  investigated in the future.  But again what we talked about  was Mendelian disease, I won't  talk about that anymore, I'll  mainly be concentrated on these  fairly common alleles that each  of them by themselves only  modify breast cancer risk and  disease risk by a little bit  but when you add them up, some  will have a positive influence,  i.e., they'll prevent disease,  others will have  a negative influence,  they'll cause disease.  They'll interact with the  environment and ultimately when  you do all the additions and  subtractions and interactions,  they'll give you your risk for  any common disease.  Yep?  >> Can you just say a few words  about how those low penetrance  genes are identified. 

 >> That's going to be the rest  of the talk.  So you'll get hopefully your  fill of it but keep asking!  That's if I can keep the  keyboard going without my  glasses on.  I went the wrong way.  Okay, so this is how we find  them.  So the question was and the  answer, what we're going to be  talking about is how do we find  these low penetrant genes.  So the first thing we have to do  is get some idea of what we want  to test.  In other words, a candidate  gene.  So most of you or all of you  have about 23,000 genes.  We like to try to figure out  which of these genes have small  differences in them which we  call polymorphisms that might  affect your risk to disease.  So there's three ways we can try  to do this. 

 One is the candidate gene  approach.  So professors used to like this  because they can use their  intelligence so they would  figure out that if it was breast  cancer, they might think about  an estrogen gene.  Or if it was something like  heart disease they might think  lipid gene.  Or if it's something like  diabetes they might think of  something like a sugar gene.  Unfortunately, even with the  greatest and cognitive intent  this was an approach that people  abused for over 20 years and it  really didn't tell us anything.  That's why we never really knew  until about two years ago what  these types of low penetrant  risk alleles would be.  Because people generally just  pick genes that seemed to be  logical or functional candidates  as we used to call them.  In the current world, over the  last two years, we're using two  methods.  One is called GWAS or  Genome-Wide Association Studies.  And the other we use rodents in  a very similar way and then go  to humans. 

 So the difference between this  one that didn't work and these  two that do work is that this  one is referred to as a biased  method.  In other words, I use my  intelligence and come up with  bias list of genes that I might  want to study because I think I  can figure it out.  The answer is I can't  figure it out.  These two methods are different  because we call them unbiased  because it's like throwing dots  at a board.  We just take the whole genome of  whether it's an animal or a  human and we scan, now with  modern technologies we can look  at the whole genome and try to  find out areas of genome that  might be associated with the  disease.  So this is what I'll focus on  since this was not a winning  idea.  So this has only been available  to us over the last two years in  a positive sense.  So this gives us our targets for  breast cancer association sites.  In other words, they don't prove  one another but they give us  some good ideas of what to test  for.  And if we just use human DNA  we're doing that because we have  DNAs in our laboratory of over  7,000 women in Wisconsin and we  also use women over in England  again concentrating most on  Caucasians of European descent. 

 And the reason we use only one  type of background is because we  need as much homogeneity as we  can in large populations.  But these studies are being done  in other institutes with other  populations.  And in this method what we do is  we take DNA from several  thousand people with cancer and  several people without cancer  and that's called the case  control study.  And then we ask how they differ  in a million positions.  So we can take a look at each of  these DNAs from thousands of  women using a technology which  you might have heard about here  which is based on these DNA  chips and we can look at a  million locations in your genome  where we know some of us differ.  And we can ask, and they come in  two flavors.  Each of these million things are  in vanilla and chocolate and we  can ask whether or not you have  the vanilla variant or your  neighbor has the chocolate  variant.  And then if these variants have  nothing to do with breast cancer  or the disease you're studying,  then you should find out that  when we look at the cases of  breast cancer or other controls  which we take from social  security files or driver's  licenses, randomly contained  controls, then we should find no  differences.  In other words, if the normal  ratio in the population of  people you're studying is 40%  chocolate and 60% vanilla and we  look at the breast cancer sample  and they're also 40% chocolate  and 60%, I'm getting that  backwards, or 60% vanilla, if  they're the same then that means  this one of the million  locations that genome has  nothing to do with breast  cancer.  Or whatever disease you're  looking at. 

 However, if we find a place in  your genome where the normal  population might be 50%  chocolate and 50% vanilla but in  the breast cancer cases it's a  40/60 ratio then we know we're  on to something.  If we can show that statistic  relevant in the many thousands  of women we study.  So that's how we do it using  this method.  We can directly use DNA which  has been kindly donated to us  from many women around the state  of Wisconsin.  And you might ask how do we get  the DNA?  And the answer is after we get  all the permissions and the  safeguards so confidentiality is  maintained in all of this, we  just send them a bottle of Scope  mouthwash, they rinse and spit  and we pull out the DNA so it's  a pretty simple procedure.  The other way we can do this  which is more complicated, but  I'll show you why it has a lot  of advantages, is we start off,  instead of starting off with  humans directly, we start off  with animals.  And the case of breast cancer  what you want is an animal that  gets breast cancer that's  similar to the human.  And most people would like to  use mice because they're  relatively inexpensive and small  but they don't get the same type  of breast cancer that women get.  So instead we had to use the rat  because it turns out the rat  gets very similar breast cancer  like humans do.  In other words, humans, you  probably heard, some respond to  hormonal therapy, some do not  respond to hormonal therapy. 

 The mouse doesn't have that  characteristic.  None of them would respond to  hormonal therapy because they're  not similar to human.  Where in the rat we almost get  the same ratio of responses to  non-responses.  And a lot of other reasons that  rat is a good model.  So what we can do in a rat since  they're inbred, we can take two  strains, one strain that gets a  lot of breast cancer, one strain  gets the other breast cancer,  and I'll show you an example of  this in a few minutes, and we  can map the genes and find out  where the locations are very  exactly.  Then we can move to humans and  ask whether humans have the same  location.  And you'll see later the  advantage this has.  So if we just go directly from  humans we can get markers  prediction but that's not much  more we can do with it because  as you'll see later we don't  know the exact mechanism or what  this one marker is doing.  However, if we come from a  rodent idea then we validated in  humans we have two species.  We have the rat species and we  have the human species who both  have the same difference yet  instead of just giving us  markers like you can do from  human DNA we can use the animal  models to start getting drug  targets, understanding  mechanisms, developing drugs. 

 So this is how we're going to  intervene using these models to  discover new drugs and new  mechanisms that might affect  breast cancer.  I'll show you an example of some  novel new mechanisms that we  would have never found if we  only used DNA from humans.  Even though it's very necessary  to use DNA from humans otherwise  we might be studying traits that  only rats have, and I wouldn't  want to spend your taxpayer  money doing that.  Okay, going on then what we're  going to try to tell you is how  this all works.  And we're going to start off  with the rodent models and the  human models.  So this is just an idea of what  we know about humans.  It's very little right now but  these are the locations in the  genome that have been identified  over the last few years by many  groups, and a lot of this has  been done by one of your  collaborators in Cambridge,  England, and what they've  identified now using just  directly from human DNA called  genome-wide association studies,  they have identified several  places in the genome that  associate with risk to breast  cancer.  So if you have vanilla, you may  have a high risk, chocolate, a  low risk.  And these are located on  different chromosomes.  And you can see that the allele  frequency, remember I said these  kind of modifiers have a high  population frequency. 

 And you can see the frequency of  these are generally over 10% and  some approach 50%.  So that's what I mean by they're  common.  However, looking at BRCA1 or 2,  they'd be very rare.  The other thing you can see is  the relative risk.  When we talk about BRCA1 or 2.  [INAUDIBLE]  For example, the top 20, 38%.  >> So 38% of women carry the  allele that modifies the risk  frequency.  Okay, again let me, if you want  a little bit more exacting  definition is everything in this  type of genetics we look at the  miner allele.  Obviously, I shouldn't say  obviously, but people have to  either have chocolate or vanilla  so these are always by  convention given by the minor  alleles.  The other alleles will obviously  be the difference of 38 minus  100. 

 So that would be the major  allele.  So this is all relative to the  minor allele.  And the minor allele is what's  associated with risk, either  increased or decreased risk.  And I'll show you specific  examples about this in a few  minutes.  So one thing is these are  common.  And second of all they have a  low penetrance.  So a relative risk means that  this is one of the strongest  ones we have and it only  increases a person's risk by  25%.  And again, that's a very  simplistic thing because  remember I said there could be  other genes that interact with  this and lower this risk.  Or there might be things in the  environment that change that.  So this is just when we try to  get very simplistic. 

 And again these give you some  ideas that we are dealing with a  population of what we call  modifiers or low penetrant, high  frequency alleles.  Now moving to the rat.  In the rat we can also find  these things.  And the way we find them in the  rat is we use inbred rats.  What's an inbred rat?  An inbred rat or an inbred  anything, and mostly you can  only inbreed mice and rats, is  animals that are very uniform in  their genetics.  They've been bred to each other  for a very long time.  And the reason we can do this in  rats and mice and not do it in  any other species is because  rats and mice normally inbred  because they live in little dens  so they don't get out to find  new mates.  So they're used to inbreeding  which means their genetics allow  us to get homogeneous strains.  If I talk about a strain of  rats, let's stay --, they have  strange names, or Copenhagen or  whatever, all of these animals  are genetically identical.  More or less like identical  twins. 

 So you can get thousands of  equivalents of identical twins  which allow us to study their  genetics.  So we can find some strains like  the Copenhagen strain here that  are resistant to breast cancer.  Here a -- strain that's also  resistant to breast cancer.  We can find others that are  susceptible.  And we can use these strains to  map, just like you might map  what makes corn better or what  makes a potato grow faster, I  hear you're going to hear about  that in the future.  So these traits are similar and  we can map them and we can find  out where they live, and I use  the animal model because using  the animal model I can start to  demonstrate the complexity of  what's going on here and show  you whether or not the same  things that affect rats also  affect humans.  And the answer is in many cases  yes, otherwise I wouldn't be  telling you this story.  So, again, you see a larger  change in the rats.  What you'll find out, for  example, and I'll use this as an  example tonight, MCS5 we call  it.  And this decreases risk in  general by 83%. 

 That looks like a higher  penetrance than we're normally  talking about.  But you'll see when we actually  dissect this in fine detail  that's it's going to have many,  many different loci in it and we  can start to identify individual  loci.  So this thing instead of having,  so this is a collective peak  that in it may have many, many  genes that affect risk.  And each of those genes affect a  percentage of risk.  >> With 83% is the ones that are  low susceptibility?  >> Yeah, so you see that an  animal that has the -- flavor,  the chocolate flavor of this  gene, has an 83% lower chance of  getting breast than it has, and  I didn't show you the name of  this rat it's called --, the  other flavor.  And I'll show you it's not as  simple as it looks here but by  using this example I'll show you  how we break these things down  and how we go the human to find  out if they're true in humans.  >> Would you explain each of  these columns?  >> Okay.  So this is the name we  arbitrarily give them.  MCS stands for mammary carcinoma  susceptibility and we just  number them.  [INAUDIBLE]  The allele is the gene?  >> So this is the name of the,  QTL, quantitative trait locus,  which is the location in the  genome. 

 And we just named this area  which sometimes millions or  hundreds of genes in them.  Okay?  Then this is the flavors,  vanilla, chocolate, strawberry,  whatever.  Most of them decrease the  frequency of risk to breast  cancer but some also increase  it.  Even though, as you'll see  later, these animals by  themselves get absolutely no  breast cancer.  They still harbor some genes  that increase the frequency but  the other genes can fight them.  So these four, three genes will  fight this one and you actually  see a decrease in breast cancer.  I'll show you that in more  detail.  This column here is just talking  about the model, and I'll get to  that later.  But we produce models that only  carry, so we'll move this very  small region from this animal  onto a background and we bred  them for 10 generations so  they're very pure, but I'll show  you that in a more exacting way  in a second.  And this is a change in risk. 

 And the last column we don't  have to worry about in this  talk.  Okay?  Does that answer that somewhat?  I think you have to see my  example.  So I'll give you an example of  just one of these.  It's my favorite of today anyhow  because we've just had some good  results.  But we can also generalize  something from the animals to  humans.  So the first thing we learned  from using the animals and these  are just some we have more of  them that we have details and it  looks like it's going to hold up  in the human also.  When we first got into this kind  of stuff we judged from the  Mendelian types of things like  BRCA1 and BRCA2 that what was  different from women who has  BRCA1 high risk and low risk is  a mutation in the gene.  But what we're learning now is  the things that affect  susceptibility to common  diseases, these low penetrant  genes, don't really change, in  most cases, I won't say in all  cases, but generally don't  affect the gene, therefore it  doesn't affect the structure of  the protein that the gene makes.  What it does it changes  something else and we believe  these are areas that control  gene regulation.  So instead of breaking a gene  completely, what these things do  is they just say to the protein,  not that you're going to be  different as a protein but we're  going to make more or less of  you. 

 And these are the regulatory  units.  They're found between genes and  sometimes they're even found in  what we call gene deserts.  And this is also found in humans  where a gene deserts is millions  of base pairs that just have  nothing in them and a big  challenge now is learning how  they affect risk diseases  whether it be in humans or mice  or rats.  So that's the first principle  that we've gotten from studying  the animals is that we really  have to look in humans and other  things into regions that don't  code for protein but regions of  the genome that regulate how  a gene functions, i.e., how much  protein it wants to produce.  [INAUDIBLE]  >> This one here?  >> Yeah.  >> Okay, this just tells you  these are loci.  And you'll see they're some loci  from what I showed you before.  But I'll get into that.  So we're going to talk about  MCS5A today and what this says  is this, I'll stick with this  one.  I'll show you this example but  if you take MCS1A, for example,  it says that it's located in the  middle of a gene desert. 

 It says if you take MCS5C, it's  located between two genes.  If you take 1C, it's located  between two genes.  The point to make these are all  regions that don't have sequence  that codes for proteins.  Mostly these regions code for  the regulation of gene  expression, how much of a  product we're going to make, not  the, so it's quantitative,  rather than qualitative change.  Does that make sense?  Hopefully.  And I'll get a little technical  here.  Okay, so the question is how  were we going to try to use the  rat model to locate human breast  cancer risk because we care  about us, we don't, I mean we  care about rats but we're really  not going to invest all of our  research time to save rats.  So what we can do is we can find  human orthologs that means if we  go into any location of the rat  genome or the dog genome or the  chimpanzee genome we can find  the equivalent regions in  humans.  So that's called an ortholog.  And we identify the areas to  study in the humans based on our  animal studies. 

 Because the animal study, as I  said before, is an unbiased  method to look for these.  Why do we want to do this rather  than study the whole genome  because if anybody here is up on  statistics, if you study a  million locations in the genome  like you might do when you have  direct DNA you have to correct  for every one of those billions  of comparisons because if I do  100 experiments one of them will  always be interesting.  If you do a million experiments  a lot of these will give us  false positives.  But when you work with the rat  to find it out, when you go with  the human you have to test very  few of these markers so instead  of testing a million markers you  might test one to five markers  so that gives you much better  what we call statistical power.  Okay, so this is complicated and  I'm not going to make you  understand it all but I just  want to show you that MCS5  remember that was the locus we  were going to talk about.  This reduced the reduced the  breast cancer in the animals by  about 80%.  But it's a very complicated  locus and it will keep getting  more complicated as I talk  because it divides in A, B, C  and now we've also found D and  possibly E.  So there are many things in this  area that add up to give you the  80%.  And the one we're going to  mainly focus on tonight will be  just one example, this MCS5A.  And what I show you here is ways  that we locate this, I won't go  into any detail, but basically  we use lines of animals that  only have, the colored pieces  come from the resistant animal,  the rest of the genome, so this  represents only less than  probably 1,000 to 1% of the  genome is in the color. 

 The rest of it is a background  which comes on a susceptible  animal.  So what I do by breeding, just  like you breed potatoes, over  ten generations so I can move  this location, the small black  box, for example, from the  resistant animal on to a  susceptible background.  So all I'm really studying is  this is isolation.  And that's what I'm going to  talk to you about tonight is  just an example of this thing  called 5A in isolation.  What is it?  Is it true in humans?  Can we sought to learn how it  works so we can intervene?  And how do we find this?  And it's very complicated.  It even gets more complicated so  the message I'm trying to give  you is complexity.  That none of these things are  simple.  So this is the 5A region.  And we've gotten it down to  about 100 kilobases, 100,000  nucleotides.  That's a very small region. 

 And yet we can divide it into  two pieces.  And what we found is we need a  piece here and a little piece  here and they have to talk to  each other in order to give us  the phenotype or the trait of  being resistant to breast  cancer.  And this is fairly new.  This is the first demonstration  in mammals that such a type of  mechanism exists.  They've showed this before in  fruit flies but not in mammals.  And I won't show you the details  of how we discovered that.  That would take me past my bed  time.  Okay, so but the next thing we  want to do is try to find out  what we discovered in 5A in the  human.  Remember that had two regions.  Can we find similar regions in  the human, which is yes, we can  by homology, but the question is  do they also control risk for  human breast cancers, they might  control risk for animal breast  cancer. 

 So we need very large-scale  populations to study this and  what we, and this an early study  and now we have a lot more cases  in Wisconsin.  But this was a few years ago.  We had about 3,000 women in  Wisconsin were participating in  our study.  Now we have over 7,000 women.  And we also worked with a  colleague who joined us in these  studies from England at that  time had about 9,000 women.  Now has probably 15,000 women.  And we worked together on this  because we study women of  European descent so we can merge  these populations.  So what we do then is we test  the -- in humans of the MCS5A  orthologous region.  What this means is I look for  little markers in this region in  humans, which I told you we can  study a million of but in this  region we might only study two  or three.  And we ask whether or not they  suggest a difference in risk,  i.e., do we change the  percentage in the control women  from 50/50 to maybe 60/40  in the breast cancer women. 

 And the answer is, again, I  wouldn't be showing you this  unless we had positive results.  And what this is this is the  equivalent in the human of what  we found in the rat.  We had two regions here, we  thought we needed something here  and something here.  And the rat we really can figure  out exactly where they live.  And the in humans since we have  a lot more women to study than  rat strains.  Remember when we study two rat  strains it's like looking at two  individuals.  Now we're looking at 12,000  women in this study.  And what we can find is  something here and something  here.  And when we look at these  markers which are called SNPs or  single nucleotide polymorphism,  in other words they differ by  one base or another.  What we can do here is ask if  there is normally 50/50 does it  change?  And the same thing in this  region. 

 And when we find this, we find,  for example, over here we have  an allele frequency of 12%.  Here's the allele frequency 26%.  So you can see in the humans  these are the common alleles,  i.e., greater than 10%.  And when we look at this we can  find that the minor allele, the  one that's at 12% is associated  with a 14% reduction in breast  cancer risk.  So it has a low penetrance but  it's definitely there.  And it turns out that about 22%  of women have this.  And this protects a woman from  breast cancer if she has one or  two copies of this gene.  So now if we wanted to we could  test woman and test whether they  had this or not.  And then this other allele,  again, and this is very similar  as we've found in the animal we  called the -- rat strain.  And this is the susceptible rat  strain. 

 And this allele actually causes  an increased risk but again it's  on the minor allele, what the  major allele would do  resistance.  And this is a recessive trait so  you need to have two copies.  And if a women carries two  copies of this, which about 6%  of women do, than those women  would have about a 20% chance  increase of breast cancer.  So this shows you that we're  finding the animals are also  true in humans.  So now what gives us a great  advantage of study is if we can  use humans for relevance and  also because they have a finer  genetic map and we can use  animals to study mechanism, we  cannot only use these are  markers, so if we only study  humans and found these we then  have markers of risk.  But having the animal model too  allows us understand what's  going on.  So it will give us some new  insights of what might be  causing and preventing breast  cancer.  And ultimately give us some  ideas of how we can prevent  breast cancer which we couldn't  get.  The other nice thing about both  rats and humans is we're not  using artificial things.  You've heard about artificial  things such as knock out animals  and transgenic animals and  animals that were given  chemicals. 

 But here what we're taking  advantage of is natural  variation, and that's important  because, and it's also why we  chose to use animals and humans  together because if something is  similar in rats and humans that  means it's survived for millions  of years of evolutionary  distance.  If something survives well over  that very long period of time,  it's not going to be toxic.  So for example, even if, let's  say this gene controlled the  mammary gland to make even a  half percent less milk, over  time that would be lost because  it gives the animal a  disadvantage.  So this doesn't have a  disadvantage and that's why it  makes it more likely to be a  future drug target for  prevention because we would  predict since it's a natural  variant that has withstood  millions of years of evolution  it will not have a disadvantage  if we target it with a drug.  In other words, can we make the  susceptible lady more like the  resistant lady by giving a  certain drug.  So, again, these are examples of  what we found.  And that one I'm going to show  you is how we use animal models  to try to understand what they  do.  And this is what you can only  get if you have animal models.  So for people who always worry  about should we be using animals  or can we get everything from  human DNA, the answer is no, we  can't get it all from human DNA.  We need to have some animal  models in order to really find  out what these are doing. 

 And without finding out what  they are doing, and even with  hindsight after looking at what  these things are doing, I would  have never suspected it if in a  hundred years.  So these things can't be guessed  at.  You need animal models.  I'm going to skip to the next  slide.  So again, we had several  possibilities.  So this is just that one region  where we had four possibilities  of genetic variation that could  theoretically lead to breast  cancer.  But some of them might not be  functional.  So in the human study we found  all of these.  But we have to find out which  base change is really important.  And what we found in the major  resistant allele that by  changing this base to a C we  bind a unique protein. 

 And then if we, in the minor  allele, if we change, we have a  small deletion of four bases.  When you have the deletion  nothing binds, but when you, I'm  sorry when you have the  insertion of four bases nothing  binds but when you have a  deletion you get another protein  binding.  So in some ways going from this  allele, which is associated with  resistance, to this allele,  that's associated with higher  susceptibility, we're changing  it.  And now we know the differences  when we go from protein one to  protein two or vice versa  protein two to protein one.  And if we can understand and  we're working now to try to  identify what types of proteins  these are by chemical methods.  If we can identify these we  might already have targets that  we might be able to modify to  change risk.  So this is where we might be  going.  But, again, what this just tells  you is some biology.  But you also have to know what  these things are doing to the  organism.  So the first thing we want to  find out is in general it's  assumed that if we get genetics  to change breast cancer the  effect is happening in the  breast. 

 Or if we have things that affect  diabetes maybe it's affecting in  the beta cells or in the  pancreas or if change to heart  disease maybe it's arteries that  are being changed.  But in this case we need to be  rigorously test that.  And what we can do since we have  an animal model and it's inbred  we can transfer resistant  mammary cells to susceptible  animals and susceptible cells to  resistant animals and ask the  question of whether or not the  resistance goes along with the  breast.  In other words, is this  something that's happening  locally in breast or is it  happening somewhere else in the  host?  And to answer that question it's  very interesting.  While everybody would suspect  based on assumptions that would  be working in the breast since  it's a breast cancer gene it  turns out it's not working in  the breast.  And I won't show you all the  data but we can conclude from  the data that this locus is  actually not working in the  breast and it's called, it's not  mammary gland autonomous; it  works somewhere else in the  organism, not the breast.  And now we have to find out  where else in the organism it  can happen.  So you hypothesize it might be  some tissue that could affect  breasts.  So what can affect breasts?  It could be something in the  ovary, for example, that could  make different hormones.  Something in the pituitary gland  in the brain that could make  different hormones. 

 Or maybe something in the immune  system that's different.  And these would all be new ideas  because everybody always assumes  that these types of genes affect  breast.  And to make a long story short  we did some studies close by  genes to see if there were  changes in expression were down.  And the only places we found  expression changes in these  genes were found in immune  tissues in the spleen and in the  thalamus.  So this suggests to us very  rapidly that we need to start  looking in the immune system.  And this is something different  because it's saying perhaps all  of us are born with slightly  different changes in our immune  system that either increases our  chance of developing breast  cancer or lower or chances of  getting breast cancer.  And this is pretty exciting  because it gives us something  that we didn't expect, a new  target to think about  prevention.  We don't have to just think  about breast anymore, we can  think about immune cells.  But again this is only  association data.  We wanted to see if this is  really true. 

 Is it really in the immune  cells?  So the first experiment we did  here is what we can do with  animals.  We can move bone marrow.  So most of you have probably  heard about bone marrow  transplants in humans.  Is that true?  So we could also do this in  these inbred animals that have  an inbred background.  So we can move susceptible bone  marrow cells and bone marrow  cells make the immune system,  into an animal just like in the  human transplant so when they  use human bone marrow  transplants the first thing they  do is destroy the immune cells  and the person is to receive the  bone marrow using radiation or  chemicals.  Then they transfer bone marrow  sometimes from a related  individual.  And that's essentially what  we're doing in these rats.  And when you do this you can see  that what we did this particular  experiment, and this is an  experiment, we moved, we had  bone marrow recipients that were  resistant to cancer and we asked  if we took away their bone  marrow or their immune system  and gave them an immune system  that was sensitive could we  actually just transfer this  whole resistance and  sensitivity.  And the answer is yes.  Because the larger fraction of  the immune system that we move  from a sensitive animal and  resistant animal more sensitive  the animal becomes. 

 So if we have no, if we only  have resistant bone marrow we  only get less than one tumor on  average.  Where if we move almost 100% of  the resistant animals we get  about three or four tumors which  is what we'd expect.  So again we can move this  resistance by moving the immune  system of the animal.  So this mechanistically suggests  then that we're working with  bone marrow.  And we've done other    experiments, which I won't have  time to tell you, that sort of  confirm this result.  >> You destroy the immune system  and then you.  >> And then build it back again.  >> If you destroy it wouldn't  they all come out to be same?  Why would there be a difference?  >> Okay, so here's the  difference.  So we take an animal that's  resistant to breast cancer.  So it has resistant bone marrow,  we hypothesize, or resistant  immune cells, okay?  We kill those all off. 

 Or we kill most of them off.  Different percentages.  And then we replace the ones we  kill with sensitive immune  cells.  [INAUDIBLE]  >> Yeah.  >> You said different  percentages.  >> What I mean by is we don't  always get 100% replacement.  So here, for example, we get 50.  So this animal would have half  bone marrow from the susceptible  and half from the resistant.  This animal would have 80%,  these animals would have 80% of  the animals from the sensitive.  And more bone marrow comes from  the sensitive, the more mammary  tumors we get. 

 That help?  So again this is actually  functional data as opposed to  just correlative data.  And we have other things that I  won't have time to tell you.  So now we want to understand the  mechanisms.  So I won't go into a lot of  detail here.  All I'm going to say is that we  had two genes with different  expression, and I won't spend a  lot of time on them, but the  important thing to say is that  this one, which we don't know  the function of, was the one  that is controlled in the same  way that breast cancer  susceptibility is controlled.  In other words, you need a piece  here and a piece here to change  its expression.  With the other gene you can  change just by having one piece  changed.  So this is the gene we chose to  study.  We want to ask in several cases,  you'll see this is not the whole  story but this gives us a good  marker because it's a biomarker  in some ways.  It marks resistance. 

 So we know if an animal is  resistant to breast cancer it  will also have a drop in this  particular gene which is one of  the genes closest to this area  in the genome.  It doesn't say it's causative.  It just says it's a marker.  Okay, and then I won't have time  to do this in detail but through  a lot of experiments we were  able to show that the type of  cell in the immune system that's  important is called the T cell.  So there's B cells and T cells  and many other components of the  system.  T cells are cells that often  recognize bacteria and kill them  or recognize tumor cells and  sometimes kills them.  So the activity using this  marker says that the activity or  the genes being active in the T  cells as opposed to the mammary  gland or other parts of the  immune system.  And I won't go with this data  right now.  The other thing that was  interesting to us that we said  you needed a piece here and a  piece here in order to have  things go.  What we showed later on that not  only did you have to have a  piece of this color and a piece  of this color to have the  resistance, but they also had to  be on the same chromosome. 

 So this gives you resistance but  if we move them to different  chromosomes they don't.  If they're on different  chromosomes you might have herd  heard of things called  transfactors which means they  can move from here to here very  easily so that rules out the  idea that it could be a  transcription factor which you  might have heard of.  A chemical or a protein that  gets made here that could go and  act here.  So that's not what's happening.  So when you get something like  this, even though it's a new  field of study, we talk about  maybe it's something that's  physically interacting.  So the hypothesis is something  here physically interacts with  something here.  And there's a new method that  was developed a few years ago to  study this.  And I won't go into.  But what we feel is happening.  Here's something and here's  something. 

 Even though they seem far apart  like my hands in reality they're  not this far apart because we  hypothesize the DNA folds and  brings these two interacting  partners close together.  We wanted to see if that was  really true.  Are we out like this or do we  fold them together?  What we think is happening is  there's something here and  something here that normally  would be this far away.  But if they loop together they  get much closer together.  And we can test this, and I  won't go into a lot of details,  but we can test this and find  out that's true.  And we find in reality this is  what's happening.  We get a loop.  And I won't show you the details  of how we find that.  But this is the final model you  get.  Instead of having what we call,  these were some of the  polymorphisms we found the  humans are located in position  one and these are located  position two and normally these  would be about 60,000 base pairs  away but because we formed this  complex loop they come closer  together and we get this  physical interaction. 

 So again, and the gene that we  were studying was this gene and  you can see how close it is.  And we found the differential  expression of that gene.  This gene is a very low copy in  T cells and it might not be that  important so we propose that  maybe, and there was only one  report in literature, that maybe  these types of structures can  talk very far away.  In fact, this is what I'm going  to show you is probably the  first look at whether these  types of structures, which are  called insulator loops, can  actually activate genes on other  chromosomes.  There have been one or two  reports that yes, they can  activate even though this might  be chromosome five they can go  to chromosome one and activate  something.  But with the advent of new  technologies that have come over  the last year that we're about  to buy for Wisconsin which is  called high genome sequencing  equipment, we could actually  look at the whole genome and say  where do these genes touch?  Do they touch places in the  genome.  And I won't go into a lot of  this detail here.  But any how we can use this and  what we find is something that  looks like this.  So instead of just touching  areas that are on the same  chromosome and close by which  would be right around here,  i.e. this area here, we can  show that this looping that  forms in the chromosome  five area here actually touches  many chromosomes.  And this is really a first  demonstration using high  sequencing that we can get a  catalog of all these things. 

 And I didn't have time to show  you data but we also find that  where these things touch they  also change the expression of  these genes in a similar way.  So we're finding a brand new  architecture in the genome  instead of just thinking about  transcription factors.  This is a first clear  demonstration that a physical  thing can move closer to other  chromosomes and control their  regulation and that's what we're  currently studying in the lab.  >> Would everybody in this  configuration?  >> Yeah, it looks like both rat  types have the same  configuration.  So this is necessary but not  sufficient.  >> So now what are the purple  lines?  >> The purple lines are just  your other chromosomes.  This is just figments.  So this is just saying that this  can talk to places all over the  genome.  I won't show you details but  we're finding hundreds of genes  that they go to.  And it's a big -- problem which  I won't have time to go into  unless someone wants to know  about it. 

 And this just is an example  here's one place in the genome  that you can see that when we  have the configuration of this  loop that we get a lot of hits  to this part of the genome and  you can see the gene closest by  which is given by this name is  actually differentially  regulated.  And there are many hundreds of  examples like this.  So we're seeing instead of  transcription factors we're now  seeing a physical thing that can  act as a transcription factor.  And this is really the first  study, none have been published  so far anyhow, that talk about  this global control.  So we're developing this kind of  a model now where this type of a  loop structure can control local  gene expression but also can go  and touch other chromosomes and  perhaps affect their expression.  And this is the hot area of  investigation in our lab.  And we're looking for this in T  cells and this somehow affects  breast cancer susceptibility.  So some things you might take  home is if we had a whole list  of these things that we  currently have, I showed you six  or seven of these types of parts  of the genome that might control  breast cancer risk, you might  want to ask what your breast  cancer risk is.  We can obviously measure these.  But will it really tell you  breast cancer risk?  And the take home message is  it's not useful yet for  individual risk because we have  to know a lot more of these  things. 

 We only know maybe ten of these  things and we really need to  know hundreds of these things.  But over the next few years I  think we're going to start  making approach but then the  hard thing is going to be  looking to see how it interacts  with individuals and we're  using, in another project lab  that I don't have time to tell  you about, we're using a thing  called systems genetics to try  to be able to interact  environmental factors with these  genes to find out how they play  out.  We also suggested that it's  important to be able to use  comparative genomics.  So if you want to do a good job  on any disease you both have to  have human resources and human  DNA samples but you also have to  have appropriate animal models.  Because merging the systems  together are much more powerful  than each individually.  And the interesting thing in the  lab is by studying this in a lot  of detail suggests some real  novel ideas for cancer  prevention.  So we would have never thought  about maybe modifying immune  cells.  All the drugs we have today are  all directed to breasts  themselves, but we now might  able to think about modifying  the immune system.  And this might also have an  advantage, we haven't proven it  yet, but modifying the immune  system could protect against  other cancer cell types as well.  It's something we'd like to  study. 

 And the other thing we learned  which is unique from a molecular  biology viewpoint is one, that  many of these things  unsuspectably are not affecting  genes themselves or proteins  they're affecting the regulation  of these genes and the amount of  protein that's produced.  And finally the newest thing in  the lab is we're starting to  learn about these  interchromosomal gene regulatory  processes and this is really in  its infancy, it's only about two  months old and we're moving fast  on this project.  And this can only be done using  high sequencing and luckily the  federal government is going to  buy a dice machine for us at the  University of Wisconsin.  We just got a grant funded to  buy one of these machines so we  can do these things locally  instead of mailing them out all  over the country, in fact all  over the world.  We've had them done in Denmark.  We've had them done in Holland  and part of Michigan.  So anyhow we're getting there.  So I'll conclude just by  thanking some of the people that  did the work.  Jill Hagen has been a long time  person in the lab and she's done  all the animal studies.  Dave -- was a postdoc in lab. 

 He graduated.  He now has his own lab in  Louisville.  He always brings me  up a bottle of bourbon every  time he comes so he's still  appreciated.  Bud Smith is a Dutch postdoc in  the lab but he's done most of  the DNA work on folding.  Charisa is a postdoc we got from  Johns Hopkins and she's been  involved in some of the DNA  binding studies.  And then we have collaborators  in the UW as well as in the  United States and throughout the  world.  So these are just some of them.  So anyhow, thank you for your  attention.  [APPLAUSE]  >> Why don't you look at  Icelandic people?  Because they really have  similar.  >> Could you repeat  the question?  >> Yes. 

 So the question is why don't we  study Iceland.  And the reason I don't study  Iceland is because two reasons,  one is they have more uniform  population.  [INAUDIBLE]  And the second thing is they  have what we hope to get here  is a single payer issuance.  So they have medical records for  all the women in Iceland and the  people in Iceland have voted to  make these records available to  a company called Decode that can  then do these studies.  The answer is Decode has done  these studies.  They were first done by the  group in England to find some of  these things.  And the Decode studies out of  Iceland have confirmed some of  these things.  The negative thing about  studying the Icelandic  population is because they are  such a small population and  they're a very stable population  they may not have all the  polymorphisms that all  Caucasians have.  They may have a subset of them.  So we can't discover all of them  looking at this population. 

 It's easy to find them but you  don't get them all.  So I guess that's the answer.  Any other questions?  Yep?  >> On the whole range of these  markers, how do you, when you're  looking at the differences, how  do you -- that this difference  is a breast cancer gene versus,  because there must be  differences.  >> So essentially what we do  just to go over it again.  So the question is how do we  zero in on these breast cancer  genes.  >> Identify them as breast  cancer.  Because you're saying you have  two sets and they differ from  each other.  But there must be multiple,  multiple sets that differ from  each other.  >> And let me just, I skipped  through it very fast.  So the question is all of us  have differences in our genome  so if I study anybody at a  million locations I'm going to  find lots of differences. 

 How do I know which ones are  related to breast cancer?  That's the question.  And the answer is that we have a  thing called a case control  study.  So half the women have breast  cancer and half the women don't  have breast cancer.  So we're comparing them, and if  something has nothing to do with  breast cancer, if the ratio of  vanilla and chocolate is 50/50  it will always be 50/50.  And the way we identify  something that's related to  breast cancer is instead of  being 50/50 it's something else,  so 30/70.  So if we find there's a  difference between the control  population and the breast cancer  population then we know we've  hit something.  So it's the unexpected change  because what it says is if a  women has more of a certain  thing she has a higher risk of  breast cancer that's a breast  cancer allele.  Does that answer that?  So what you're looking for is  how does the normal population  differ from the effected  population.  Because theoretically everything  should be the same.  Because we're talking about  thousands of women. 

 Okay?  >> In the human population you  might have a mutation that  causes susceptibility in this  area of the genome but there may  be 25 mutations in the human  population in that area that  lead to that susceptibility,  whereas in the rat you're  dealing with a single mutation.  >> Okay, so let me be specific  since it's a specific question.  So the question deals with now  we've zeroed into a very tight  region in humans, and in that  tight region there's lots of  changes between all of us in the  room.  In fact, there's a change every  800 bases.  So we zero into a region that's  a thousand bases there are  obviously going to be multiple  changes.  So the first thing we do is we  have this marker SNP that we say  is a susceptibility SNP.  And the thing to stress, this is  only a marker it's not  causative.  The big question is how do we  find the causative one.  So the first thing we do is by  definition the causative one has  to travel together with the  marker SNP.  So maybe of all of SNPs in my  interval maybe 20% of them  travel together with my marker  SNP. 

 And in our case one of the  regions, in fact there were four  things that travel together with  the marker.  So I know it had to be one of  those four things.  So this turns out to the be most  difficult problem and the way we  figure out which ones are  important we hypothesize since  they're regulatory that the  important changes would change  how they bind onto regulatory  proteins.  So we're able to use molecular  biology methods and we can show  that when you go from vanilla to  chocolate you bind this protein  on the vanilla side but you  won't bind it on the chocolate  side.  That suggests it has a function.  Next, which we aren't at, we  have to show that this protein,  the differential binding of  protein, really can link up to  breast cancer risk and that's  what we're working on now.  So this is a very difficult  problem.  We're further ahead than most  places because we have the  animal models to test this in.  But, again, it's a very hard  question.  And until we know that, and  that's why we need the animals  and the humans together, until  we now that all we have is a  marker which is important to  estimate risk but until we know  what it specifically is and how  it works we can't start to build  drugs and that's the key that  trying to do now. 

 >> In the example I used I have  an aunt by marriage who died of  breast cancer and two of her  daughters did but the other two  are not affected.  Now wouldn't those be the ones  to study because they  theoretically have 50% from each  parent.  >> Okay, so the answer is yes  for certain genes.  So those are how we discovered  the high penetrant genes.  One of us is buzzing.  That's how we find the high  penetrant genes.  In fact, BRCA1 and 2 are  identified through those  families.  Those are called linkage  studies.  But it turns out that because  each of these other types of  genes we're looking at low  penetrance genes that have such  a small affect and there are so  many of them, we can't use that  approach.  We need to have very large  populations of women because we  looked at a pair of people. 

 Because of two daughters versus  the other two daughters would  have so many differences there's  no way we could statistically  zero in on them for something  with a low chance of causing  breast cancer but we did use  that technique to find the high  penetrant genes.  Yes?  >> In the rat studies the  environment is held constant for  those rats that have cancer and  those that don't; it's the same  environment for both of them.  Now my question is under a  different environment might not  the ratio be different between  those who get cancer or not?  >> That's a good question.  So the question talks about one  reason we use the rats, but also  a disadvantage of the rats, is  we keep their environment  uniform.  They all eat rat chow, whatever  that is.  And they all live in very  expensive housing.  In fact, a lot more expensive  than most of us live in.  The humanity is controlled,  everything's controlled  actually.  It's a pretty sweet life.  But the problem is we don't all  have that same sweet life. 

 We all live in different  environments, have different  diets.  So the question that was asked  was if we took these rats and  each rat got put onto a  different diet, would we see the  same results?  And the answer is probably not.  One reasons for using the rat  model is because we can get rid  of this compounding factor  called environment and just  study their genes.  That's simplistic when we go to  humans.  And when we go to humans one  reason we get a slightly less  effect with the humans is  because humans are what we call  stratified.  They'll be some that eat potato  chips and others that eat  bagels.  Or some that actually eat good  food.  Salads.  So all this is going to matter.  And one of the reasons we get  not as good signal in humans and  why we have to use so many  humans is because we have to  average out all of phenomena. 

 So in some cases we're now  studying 20,000 women, hopefully  the environment will average out  over breast cancer cases and  controls.  But again it makes the data  noisy.  What we're doing in a completely  different project in the lab is  now trying to use the human  cells and the rats and  everything else to study the  interaction between environment  and breast cancer because it  turns out on our study of the  breast cancer women in Wisconsin  and the controls, that study  collaboration with Amy Trentman  Dietz who is an epidemiologist,  so we know a lot about these  women.  We know about what their diets  are.  We know whether they had  siblings with cancer so we can  now factor these in and try to  understand this.  But every time we do that it  just kills your statistics.  There's also a new study in  Wisconsin where we're collecting  the DNA from people and also  vacuuming the insides of their  homes so we can actually look at  chemicals that are in their  homes.  These things are all underway.  This is the new show project  that just got funded by the  state of Wisconsin with the Blue  Cross Blue Shield money.  So we're actually collecting DNA  from people around Wisconsin and  we're getting complete  environmental samples. 

 So this is where we want to go  in the future.  We want to be able to account  for environment because we can  in fact, if we can't account for  your environment we can't give  you individual risks, we can  only give you population risks.  >> Can you inject a specific  gene from a women into a rat?  >> Yes, we can.  >> What happens?  >> Pat does this all the time.  We haven't done this  particularly with these things.  We don't have enough knowledge,  in this case, what do inject  yet.  So that's something we might do  in the future.  Is put in, for example, if we  see that this looping turns on  these genes all around the  genome and by using what we call  systems biology to build  networks and things we may be  able to find things that are the  most important things and then  we can go in and take animals,  put that gene in and if it's  over expressed we can make what  we call a transgenic animal and  make more of that protein and  see if we get the effect, and if  we want less than we can take  one of the copies of the gene  out.  But again it's very complicated  to do it this way and it may be  turning out to be something that  we'll first do with computer  modeling and things of this  nature.  And if you see the big hole  across the street that's what's  going to go in that hole is a  lot of people who like to play  on computers and do these types  of things. 

 So hopefully we'll be working  with them.  Yes?  >> Do you have a connection with  the Women's Health Initiative,  that long range big study?  >> I don't have with that but we  do have a Wisconsin's Women's  Health Initiative and that's  where we're getting our DNA  samples from.  This is the Wisconsin study  which is different from the big  women's health study that was  nationwide.  >> Is it part of that big one?  >> It's very complicated  interactions.  But we're specifically studying  the Wisconsin women not because  we're against using other women  we just don't have the  infrastructure.  These infrastructures to collect  DNA are very, very expensive and  it was very hard to just get the  Wisconsin women on study  nationwide.  But we do collaborate with other  institutions to confirm our  results that have DNA.  >> Do you control for the type  of breast cancer that is the one  that is estrogen, progesterone  sensitive and thing that is are  not?  >> We're not currently doing  that because again it's all  about finances, whether we can  collect that data.  But the English group has been  able to do that so we know that  some of these risk alleles that  come from these human studies  are more likely to be associated  with estrogen responsive cancers  than with estrogen nonresponsive  cancers.  This is a very new area. 

 So the most valuable thing we  can do is get more and more  information on the women that we  have DNAs from.  But again a lot of this involves  making sure we have  confidentialities and human  subjects' permission and having  the money to collect one more  thing out of the record, let's  say estrogen responsive stuff,  is several hundred thousand  dollars for each thing we want  to add to it sometimes,  especially in retrospect.  The answer is people are doing  this we're just not set up to do  that locally yet.  >> Are any studies any place on  male breast cancer?  I remember seeing a few papers  where they were looking at --  genes and asking those sorts of  questions.  >> So male breast cancer is  actually more common than most  people think.  It's about 1% of the risk of  female breast cancer.  So it's really a problem.  And we've built some models in  other studies where we have a  model for male breast cancer  that we're starting to study a  little bit.  So the same type of genes we  were studying called -- that you  may -- for male breast cancer.  So male breast cancer is a real  problem. 

 And some of the same genetics  might give you male breast  cancer.  But to be able to study the  types of things we're studying  now with low penetrant things  would be a real challenge to be  able to get enough cases.  We'd have to get a worldwide  cooperative group to be able to  do that.  And at this point male breast  cancer isn't prevalent enough to  be able to get the resources to  do that.  But as we get better  technologies and these  experiments get less expensive  we might be able to go after  male breast cancer.  >> So animal models with rats in  males too?  >> Well, with have a specific  model where only males get  breast cancer and none of the  females do.  That's something we'd like to  study in the future.  Again, it's a matter of federal  people thinking whether male  breast cancer is a high priority  to spend funds on at this point.  But I think it's something that  would be interesting.  And we did publish a very  important paper in the area of  male breast cancer modeling and  animal studies a few years ago  in a journal called Cancer Cell. 

 >> Won't that breast cancer is  diagnosed in all different ages  in women, if you took a sample  of just 40 year olds you'd get  the ones that already have it  but you wouldn't know which ones  are going to get it when they're  60.  So how do you control for that?  >> So again, that's a good  question.  So the question is about women  get breast cancer at 40, they  get breast cancer at 60, they  get breast cancer at 80.  How do we control for that?  Because when we look at 40 year  olds we can never say that they  won't get breast cancer when  they're 60 even though we put  them in the column of not having  breast cancer.  That's a -- in the studies, but  the way we try to make that  better, and again it's not  ideal, is we try to get age  match controls.  So the way we get age match  controls if they're young, we  find them by randomly looking at  their driver's licenses.  So we survey all the driver's  licenses of everybody Wisconsin  and we ask them if they'd give  us DNA.  And for the older folk when we  want to control for let's say  70-year-old people we might use  the social security records for  that type of thing.  And this is all done under very  controlled circumstances so  there's no violation of  confidentiality.  >> How many 70 year olds have  already died?  >> Again that's a problem too. 

 So what we try to do is collect  all ages.  And, again, one of the things  with breast cancer, the only  reason we can do this, for  example, these studies would be  almost impossible with  pancreatic cancer.  The reason we can do this with  breast cancer is because so many  women are surviving because by  the time we find the records  from the cancer registry and  everything else we're sometimes  years beyond when they had the  cancer.  And the good thing for women is  they are living longer and  sometimes forever after being  fixed with breast cancer so we  can study those women.  Where in pancreatic cancer or  brain cancer they -- rapidly the  bureaucracy doesn't go fast  enough so we can get DNA samples  from them while they're alive.  So one of the things to do these  studies you have to have  diseases where people live  longer.  Okay, I think we're done.  Thank you all.  [APPLAUSE]