 I want to thank both Suburban and the Genome Institute for inviting me and thank you for the introduction. Actually in the work that I'm going to talk about today I'll probably speak, I'm really not going to talk about any of the work we do in the lab but really give an overview of how genomics and genetics are, have and are currently impacting the treatment and diagnosis of breast cancer and how this is likely to be expanded in the near future. So to begin this lecture what I'd like to do is for the purposes of my talk define what I mean by genomic medicine and simply what I mean is the use of molecular genotype which usually means the sequence of the DNA, the sequence of the genes and the molecular phenotype which is either the expression either measured by messenger RNA or by protein expression to predict disease incidence, outcome and or dictate treatment. Now in cancer biology there are two genomes and it's important to keep this in mind. One is the tumor genome which is referred to as a somatic genome and the other is the patient genome or the germline. And last month you heard from Larry Brody about germline mutations in genes like BRCA1 and BRCA2 that predispose to the, that predispose patients to getting breast cancer. Today most of my talk is going to focus on using the tumor genome to dictate decisions about management of patients although I will touch on germline mutations as well later on in the talk. Now in the past treatment was based on clinical features of breast cancer and this is true for pretty much all tumors that features such as size, pathologic grade, the spread to the regional lymph nodes but also in the past it involved expression or genetic abnormalities so genomic features of the tumor but of only a few genes in the tumor. Now this is not a new story and in fact the really the story begins with this publication which I would say is the first therapy based on tumor phenotype. It's measurement or it's affecting the estrogen receptor which of course is still used today in the management of breast cancer is a really important feature of the management of breast cancer but this paper by George Beatson was published in the Lancet in 1896 about a novel treatment for patients with inoperable breast cancer. So this is the only case history I'll describe today but the patient he saw was a young woman 33 premenopausal who several months earlier had presented with a very large locally advanced tumor it was 11 by 8 centimeters there was clear skin involvement and she underwent a mastectomy. Three months later when he saw her she had diffuse chest wall involvement she had ulceration of her skin involvement of lymph nodes and apparent metastatic disease in her thyroid so this patient at this point was unresectable and at the time of the 1890s there was no other treatment for this patient but what he did is he removed her ovaries and a month later and over the next couple of months she developed a complete remission in her breast cancer and actually survived for four years before dying from breast cancer. So why did Beatson do this? Well what he was known at the time from both animal studies that he and others had done was that the ovaries somehow controlled the growth and differentiation of the breast. What was also had been observed but never really acted on was that patients who had breast cancer and then went through menopause sometimes had spontaneous remissions of their breast cancer so his reasoning was well gee maybe inducing menopause will cause a remission in this patient and of course we now know that that's absolutely correct. So why does this work? This works because of the tumor phenotype. Breast cancers in about two-thirds of all cases express a protein known as estrogen receptor which sits in the cytosol of cells as an inactive protein until estrogen comes along and binds to the receptor and that creates an active complex which one of its primary functions is to go into the nucleus and act as a transcription factor where it regulates the expression of other genes and these genes drive the growth and proliferation of cells. So what Beatson did of course was remove the estrogen so turn the system off and and of course therapies we use today are designed at either removing the sources of estrogen in patients or interfering directly with the function of the estrogen receptor but so this is molecular medicine in a very real sense it's targeting the tumor phenotype. Now Beatson of course didn't know any of this and it took about 70 or 80 years to work out these pathways in the time after he did the experiment but it works because the molecular phenotype and manipulation of that phenotype this the first therapy based on tumor genotype is also not a new story this is a therapy that began about in the 1980s when Dennis Slayman and his group identified amplification of a gene known as HER2-NEW. So this is just one piece of data from their paper but it also shows what is old-style genomics and low throughput genomics these are southern blots these are blots where DNA is run on a run on a gel and transferred to a filter paper and then a probe is used that identifies a specific target in this case the gene known as HER2-NEW. Now in every cell in a normal in a normal individual there should be two copies of any any normal gene and so all of these should look identical from normal cells and you can immediately see that these are 70 or so tumors and there's tremendous variation in the intensity of the signal meaning the copy number of genes in these tumors varies which of course is not normal and you can see for example in tumor number 18 there's a tremendously increased amount of signal this signal in lane three represents a normal diploid cell so what you can see is there's tremendous variation and in about a third of the cases he found what was defined as genomic amplification increased copy numbers of the genes he also found another striking feature when he looked at the prognosis of patients the patients with gene amplification especially high levels of amplification did worse than those patients who who had normal copy number of HER2 and this has been confirmed in I think more than a hundred studies in the time since that now what's HER2? HER2 is a protein it's shown in red here it sits in the membrane of cells it's a growth factor receptor and when it's activated it becomes an active tyrosine kinase it phosphorylates other proteins and as I'll show you in a minute that activates pathways important to the growth and survival of cells it's a member of the epidermal growth factor receptor family which has four members EGF receptor HER3 and HER4 and a whole host of ligands which bind to these proteins HER2 works as a partner with the other members of the family so shown in red you can see it will dimerize with either EGF receptor HER3 or HER4 and in response to ligands it turns on pathways that are important for growth or survival of cells and it leads to a whole bunch of outcomes that makes sense when you think about this as a bad prognostic feature it causes proliferation it protects cells from cell death causes invasion migration it affects angiogenesis so all of these features makes sense that it that it's in fact a bad prognostic feature more importantly because of this it's also become a very important target in the roughly 15 to 20 percent of patients who have amplification of this protein we can interfere with the function of this protein either with antibodies like trastuzumab or pertuzumab which bind to the outside of the cell and block the function of the protein here or small molecule inhibitors such as lipatinib which will bind to the protein and inhibit the kinase activity so in a very real sense genomic medicine that's been going on now for quite some some number of years but both of these examples are examples of looking at one gene at a time in the patient's tumor and that's really not where we are today we still use clinical features we still use the individual gene estrogen and progesterone receptor and HER2 new amplification as measures of prognosis and also measures of what would be the most effective treatments for the patient but we've also started to incorporate more global genomic measures in of tumor prognosis and and predictive measures of therapies for the treatment of cancers and I'm going to talk about two today in a little bit of detail the recurrence score and gene expression microarrays so let's start with the recurrence score this was developed to stratify the risk and relapse and the need for chemotherapy in early stage patients who are hormone receptor positive node negative and could be treated with hormonal agents such as tamoxifen so why did why were people interested in doing this in this group well we know this is a group of patients who by and large do well but some of them relapse and yet over the course of this say the 90s and the early part of this this century we started using more and more chemotherapy so that we are treating a larger fraction of these patients who otherwise do well with chemotherapy in spite of the fact that only few of them really needed treatment so the question was could you stratify the risk of relapse and identify the patients are more likely to benefit from chemotherapy with some sort of test based on their genome what they did is they developed a 21 gene set assay by starting by culling the literature and microarray experiments and I'll define what a microarray is later and then they designed a quantitative reverse transcription PCR assay from formal unfixed paraffin embedded tumor tissue so first as an aside I want to explain what I mean by quantitative reverse transcription PCR so I think you'll hear about this more and more in the lectures and and probably as well in the literature so this is an assay that starts with RNA in this case from a tumor sample on a slide and uses an enzyme known as reverse transcriptase to turn that RNA into a copy of the RNA known as cDNA or it's a DNA copy of the RNA in the next step of this assay you can bind a complementary piece of DNA shown in green to the to the DNA that you've just copied and that probe has two molecules attached to it one is a fluorescent molecule shown as a reporter in R and the second is a molecule that quenches the fluorescence so this at this probe is actually silent at this point but then you do polymerase chain reaction where you copy the DNA into multiple copies and what happens in the course of that reaction is that the DNA in this green probe gets degraded and that frees up the reporter molecule from the quenching molecule and it becomes fluorescent and this can be measured in a fluorometer as you repeat rounds of PCR you get more and more freed reporter and the amount of this reporter is proportional to how much starting material you have and so you can measure this actually continuously as you're doing these assays so another term you'll often see is real-time PCR but that's synonymous with quantitative and you have quantitative reverse transcription PCR because you're starting with RNA so this is a very effective way of measuring the amount of a protein so what did they do they measured from the literature search and array experiments they did they found 21 genes that were useful in this assay and these are just the list of the genes some of them are related to estrogen receptor the estrogen receptor progesterone receptor and some other targets these are actually good prognostic features her to and a related gene that's amplified with for her to often grab seven a number of genes known to predict high proliferation rates and tumors genes that are known to be involved in evasion some other genes which can't easily be categorized in these categories and then a number of reference genes just as no internal normalizations and what they do is they perform this assay on all of these genes normalize them to the standards and then they they have an equation that accounts for all of these genes and results in a score some of them are give you negative values so these are good prognostic features and they lower the score these are bad prognostic features and they raise the score as are these and these and so what they wind up with is a linear not a linear but a continuous predictor of recurrence so this is a study where one of the features of the study that was so powerful is they designed this to use QPCR in formal and fixed paraffin and benetition which means they can immediately go back to large studies that had been done over the last 20 or 30 years take samples from that and apply this test to the to that those samples and validate whether their predictor is a true measure of recurrence and so this is just the recurrence rate this is the recurrence score and you can see in blue that the higher the recurrence score the higher the likelihood of recurrences and this is from a randomized study that was done by nsabp in about 19 in the early 90s now what they did is they then sort of grouped it they just bend this data into three groups what they'll call a low risk group and intermediate and a high risk group and when you do that what you see is it predicts prognosis so this is again going back to a study that had looked at patients treated with tamoxifen actually the study asked its tamoxifen decrease the rate of recurrence and that study which is a multi-center study showed in fact that tamoxifen was beneficial in early stage no negative hormone receptor positive patients so when they looked at the patients who had received tamoxifen what they found is those patients that they call low risk with a low recurrence score less than 17 less than or equal to 17 they had a very good outcome there were patients though who had a score above 31 that had a worse outcome a higher likelihood of relapse and a group in between and these are the numbers down here so about half of the patients in this group that we would normally would say these are good this is a good prognostic group half of them in fact have a very low risk at ten years of recurring only only six or seven percent if treated with hormonal therapy on the other hand about a quarter of them have a fairly high risk one in three of those patients will recur so this data becomes very useful for saying okay these patients may not need more therapy and we'll come back to that in a minute whereas these patients do now this this allows us to stratify risk beyond what would normally be done in the clinic so again just to show you two parameters it's it's well known that small tumors tend to do better than large tumors when you look at as a as a clinical staging of patients but when you look and that's borne out by this test so if you look at the patients who do well have a low risk it's higher in the smaller tumors than it is in the larger tumors but what's clear is they're small tumors who have a high recurrence score and they're large tumors with a low recurrence score and that's also been known that while in general size predicts the likelihood of recurrence it's not a perfect predictor so this allows us to stratify risk further than the clinical parameter as another example grade is again a well-used parameter that predicts recurrence and high-grade tumors are more likely to recur than low-grade tumors but again within each grade of the tumor there are most of the low risk tumors in fact have a low recurrence score but some of them have a higher recurrence score and similarly most of the or the plurality of the high of the poorly differentiated tumors have a high recurrence score but still there's significant number that have a lower current score so this allows us to look at patients and start to stratify their risk further than the clinical parameters on the other hand if we look at her to new amplification the single gene that was has been established now for more than 30 years what we find here is that virtually all of the patients have a higher recurrence score so in fact if you have her to amplification you probably don't need to do a recurrence score because you already know that it's going to be high and this is out of the same set of data so you can see again about half of the patients overall have a low risk a quarter have an intermediate risk and quarter have a high risk but when you look at her to they're all biased towards the high risk so in some cases a single gene may give you as much information as the recurrence score but in the other cases we learn a lot about who may need more therapy now this has also been applied to know although it was developed for node negative tumors which are the patients who we clearly are over treating it's also true for node positive tumors so this is just showing study where they looked at recurrence score in node negative and node positive tumors and what you can see again in all cases node negative one to three lymph nodes or four positive lymph nodes that the patients there's a there's a link a continuous relationship between recurrence score and the likelihood of relapse what you can also see though is that at every score the node positive patients are at a higher risk of recurring so that when you stratify the patients again into these bins of low intermediate or high risk that this is from the data I just showed you these are the node negative patients again low risk do very well only a few percent of them recur whereas there are patients were about a quarter of the patients recur in the high risk group you can similarly stratify the node positive patients although again notice that all the numbers are lower so this is a group that has a worse outcome overall but even within that group you can start to stratify better and worse patients so this is good this as we can say this patient needs more therapy in some patients for example a patient in this group probably additional therapy is going to the toxicity will outweigh the benefit but is there direct data that says that this test actually can predict the outcomes of chemotherapy and again there are so what again taking advantage of the fact that they could go back to studies that had been collected prospectively to and this was a study designed to test whether chemotherapy was beneficial in node negative hormone receptor positive patients beyond tamoxifen and this is the result of that study which you can see the blue bar as chemotherapy and tamoxifen and the yellow bar the yellow line is the results of tamoxifen alone and overall there was a significant advantage to adding chemotherapy to this group but you can see that overall the group does pretty well and there while it's a statistical significant difference it's not a huge difference but when you stratify the patients by recurrence score what you can eat and remember about half the patients will have a low risk you can see that in the low-risk patients they do very well and there's no additional benefit to adding chemotherapy to these patients in in contrast when you look at the high-risk patients what you can see is that a much higher percentage of those patients will relapse somewhere between 30 and 40% of those patients relapse by 10 years and there's a tremendous advantage to giving those patients chemotherapy so most of this difference is driven by the effects in these patients there's also an intermediate risk group where at least from this study you'd say there's no advantage to giving chemotherapy to this group as well now so it's clear that you from this data that this group doesn't need chemotherapy this group benefits I will say that this group is a gray zone at the current time and the reason for that is twofold first of all remember this is a continuous variable so if this is a continuous variable it means that patients at this end with a 30 really aren't going to be appreciably different from this group that begins at 31 or patient at the low end of this group with a score of 18 really isn't going to be that different from a patient with 17 in this group so where to draw the line in this group becomes problematic and the second problem with this study is it was based on an older study where the chemotherapy certainly wouldn't be considered state-of-the-art or optimal chemotherapy for a patient today so that it's not clear that this group really doesn't benefit or where to draw the line and their ongoing studies asking just that question if you take a group in this intermediate a group of patients in this intermediate category and randomize them to what would be state-of-the-art chemotherapy do they benefit from chemotherapy or not now as a second type of test so that's a test based on PCR and it's based on and can take advantage of formal and fixed paraffin embedded tissue and that's something that's quite beneficial because and I think the penetrance of that test into the clinic is in part because most of our patients their samples go into formalin and they get fixed and so but we can still measure that but another type of test is using a CDNA microarray and the test that's been approved in this country is known as the mammoprint similar to the last test it was developed to predict risk of relapse in early-stage patients although in this case they were both hormone receptor positive and negative and node positive and negative and what this relies on is 70 genes from a CDNA microarray which started out by looking at essentially the entire genome 25,000 genes before I go into this test let's talk a little bit about what a CDNA microarray is though so so micro array technology is a very powerful way of querying the entire entire set of genes very rapidly what what you can do is you can using techniques that are very similar to the techniques used to make microchips in a computer you can print a known sequence of DNA and oligonucleotide onto a chip so that's just shown here as nine spots on this chip and so these oligonucleotides could for example be an oligonucleotide that would detect each of the genes in your genome and these chips the densities of these chips at the current time can hold I think hundreds of thousands of not millions of spots so you can essentially query the entire genome which is 20 which is approximately 25 or 30,000 genes in one experiment what you then do is make a probe in this case for CDNA microarray you would isolate RNA from your sample of interest in this case a breast tumor label it with a fluorescent probe and hybridize it to this chip and if the message is exists in the pool of messenger RNA is from the from the sample it will hybridize and give a signal and that signal intensity will be proportional to the amount of copies of the gene of the RNA that are expressed and then this can be read in a chip reader which then gives a result that in the raw data looks like that which isn't particularly interpretable but that data can then be digitized and this is an example from the original mammoprint paper so these are 70 genes across the bottom it's about 300 patients so each row represents a patient each column represents a gene and the way the data is presented is the intensity of the signal compared to normalization control shown here in the middle as black is either lower or higher so low is green and high expression is red so very simply it's I guess a molecular Rorschach test if you look at this these are the patients that did well and these are the patients that had early relapse and just looking at this while it's not important to look at any one spot you can see that there's a pattern that the patients fall into so for example the good patients have low expression of these genes and generally high expression of these genes and the patients who do poorly it's a little more heterogeneous but again higher expression of these genes and lower expression of these genes but you can take any patient then and simply ask if we do this microwave quantify the data does the patient cluster with this group statistically or with this group and when they and when they did that what they found is that these signatures could predict the outcome so the patients with a good signature do relatively well and this is both relapse and overall survival and the patients with a poor signature do poorly and since this test was designed to look at both ER positive and negative and node positive and negative patients in fact the data hold up for both in this case I'm showing no negative patients or node positive patients but again the signature predicts the likelihood of relapse of these patients so this is again a very powerful test it allows us to stratify patients we know that some patients do well we know that some patients do poorly this allows us to start to identify which patients are more likely to be in in those groups and therefore target therapy to the patients shown in red who are more likely to need it now this is actually I think used more in Europe where it was developed it was developed in the Netherlands by a group that's headed by Renee Bernards and one of the one of the problems with this although I think that's likely to be overcome in the near future is that this in general needs fresh frozen tissue to do these sorts of microarrays in the current state of the art that requires that people at the very beginning in the operating room say okay this sample has to go out into formalin but into into a non-fixative solution in Europe they're actually managing to do that in studies that are ongoing I think in this country that can be done and it is done in many cases and studies but I think within the near future my understanding is they'll be able to do these assays out of formalin fixed tissue as well so that may not be a limiting feature now how do these tests compare to one another and this was a study published in the New England Journal a few years ago where a group compared the recurrence score to this 70 gene profile and to two other tests which use in this case more than 400 genes and again all of these tests were developed in slightly different for slightly different uses this was developed as I said for node negative hormone receptor positive patients this was developed really for all comer early stage patients this test looked retrospectively at a group of patients this actually wasn't developed for cancer at all it was looking at what happens to fibroblasts when you induce a wound and the response in fibroblasts to wounding but it was recognized that many of the signatures you saw and wound response look like signatures you see in cancer cells the point is that all of these tests identify better patients and and worse patients in terms of outcome and when they were compared it turned out if you took any patient if it was predicted to be a more likely a higher likelihood of relapse in this by this test all of the tests seem to behave similarly they all predicted outcome more or less more or less equivalently interestingly very little overlap in the genes that were used there 25,000 genes in the genome and yet there's almost no similarity in the genes that were used in these these tests what was similar though were the processes they all looked at invasion they all looked at proliferation they all looked at things that prevent cell death so while it's not it wasn't important what gene was chosen they all seem to focus on the same processes so I'm gonna so this is where we are today we're already using genomic measures of gene expression in tumors to decide who should and shouldn't get chemotherapy who needs further treatment who doesn't need further treatment but where where are we going in the near future so again treatment will be based on clinical features of the tumor and it continues to be based on that we continue to use estrogen receptor in her too but I think the measures of multiple gene expression are gonna become more powerful as we go forward they're gonna allow us to stratify risk they're gonna they're also gonna start to inform pharmacogenomics and of course I'm gonna talk a little bit about whole genome sequencing so I think that's really ultimately where a lot of this is headed so this is the probe one of the profiles that used 400 genes and it's probably it's a study done by sorely at all from a group that took a number of tumors where the outcomes were known and did a microarray analysis they focused on 400 genes which allowed them to stratify the patients into several groups as seen here in the color code and what they found was first of all so the luminal patients shown here in blue orange and light blue are all the hormone receptor positive patients so they cluster together so the the assay asks are there genes which associate tumor samples with one another and it doesn't put in the data that these patients were ER positive or her too positive but in asking are there genes which can identify subgroups in fact what fell out were the ER the ER positive patients cluster together the her to amplified patients cluster together shown in I guess this purple color and then in the red at the very end patients we call triple negative breast cancer can't pancancers that don't express hormone receptors and don't express her to amplify or have her to amplification cluster together so this non supervised clustering identified the subsets we already knew were important but we gained more information and and that has prognostic significance as the survival curves show this is probability of survival in the patients and you can see that the different groups have different survivals but if there's an important distinction here or important thing that comes out of the study when we look at ER positive patients that all of these patients in these first three groups are ER positive and yet it's known again that not all ER positive patients respond to hormones not all ER positive patients do well and you can see that the group that he's calling luminal a in this study which are the patients that generally would have high levels of hormone receptor expression they do very well in terms of outcome but the patients in these other two groups of hormone receptor positive patients don't do so well so the power of this is it allows us to again look and to look at the patients who have hormone receptor and say well not all of them are equivalent some of them are going to do well some of them aren't going to do well and really what we need to be focusing our attention on is why don't these patients respond to hormones and do as well as these patients again similarly just to make a point the triple negative patients are known to do poorly and again this identified a group of patients that are predominantly the triple negative he called them basal characteristics will come back to that in a second and they in fact have the worst outcome in this graph this data was generated before the introduction of herceptin so the purple line here are the her to positive patients before the introduction of herceptin so they do very poorly in this group currently this group would be somewhere up in this range with the introduction of herceptin now what do they mean by luminal and basal so again this is a definition where the array the gene expression of luminal cells look like the lumen of the breast duct so if you looked at the cell that line the ducts of the breast they would have a gene expression profile similar to these tumors if you the basal cells are the cells that line the outside of a duct in the breast and they would have a gene expression profile similar to these tumors doesn't to be careful it doesn't mean that these tumors arose from luminal cells it means they look like luminal cells the question of where these cells come from and what the source of any of these cancer cells are is still an open question but they look at the end of the day like the cells in the lumen of the breast and these look like the ones in the basal area of the breast so focusing on this group for a second we know this is a group that does poorly we can't don't have targeted therapies for them as yet they're triple negative they can't use hormones because they don't have hormone receptors we can't use her to because they don't have her to amplification so the question is can we use array data to start to get better information about these patients and this is a recently published paper from Jennifer Pytin polls group at Vanderbilt and the answer is yes so this is that group of triple negative patients and you might expect that a group defined by the lack of markers is going to be a heterogeneous group and in fact it is she could identify seven subsets so again this is just microarray data and just without worrying about so looking at this data these are the genes each row is a gene and going across each column is a patient and it's not important what the genes are but you can see that they cluster into groups so for example the second group has high expression of these genes third group has high expression of these genes and using what looks to be about a thousand genes or so you can identify patients in that these patients fall into one of seven different subsets now this is all again preclinical data but as this data was an analyzed it became apparent that some pathways were more important in one or another of these and so what they did is they took cell lines again this is preclinical that represented different subsets of these patients and treated them with different drugs and in fact there is differing response to standard chemotherapeutic agents or targeted agents in the different subsets of triple negative of cancers so while this is all early in development what it means is it's certainly in terms of hypothesis generating is that if a triple negative patient falls into one of these different categories maybe the therapies that we should be using in these patients should be different now of course that's a that's a question that has to be tested clinically but it begins to allow us to ask questions about are there ways to treat these patients more effectively and again not it individualizes treatment even further than just saying they're triple negative it's saying they're triple negative in one of these seven categories I'm sure this is not likely to be the end of the story and that there are probably to be other characterizations of the of these sorts of patients but again it's the power of using multiple genes to dissect the molecular phenotype of the tumor now what I want to do now is talking about another topic where I think we're going to impact very profoundly in the foreseeable future which is pharmacogenomics and again as a definition it's using genetic information either the sequence of the genes or the expression of those genes the genotype or phenotype to predict efficacy or toxicity and again I just want to remind you that in a tumor they're two genomes there's the tumor genome and the patient genome so let's focus first on the tumor genome and pharmacogenomics in terms of that so here it's the presence of a therapeutic target predicts the treatment benefit well we already know this but this is in fact pharmacogenomics although wasn't necessarily thought of in those terms if a patient has the estrogen receptor we use hormonal agents if they have her to new amplification we use her to targeted therapies so in many respects the expression of specific genes in the tumors predicts efficacy and in fact the absence of these markers predict the lack of efficacy in those tumors of these agents so this is something that's already being used in the clinic as a second example I'm going to go back to something that Larry Brody talked about last month when he discussed the BRCA 1 and 2 mutations so this is a case where these aren't necessarily the targets but they potentially predict what therapeutic intervention may be beneficial so if you remember his talk the BRCA 1 and 2 genes a large part of their role in a cell is to is to help repair DNA damage of a specific type and those are double-stranded DNA breaks caused by things like ionizing radiation or other genotoxic agents and this is the predominant mechanism those breaks are repaired by in a normal cell and it depends on having a normal copy of BRCA 1 or 2 around there are other ways to repair this DNA damage it's just they are less efficient and more likely to cause errors but in a patient who has who has one defective copy of BRCA 1 this pathway still works because all you need is is a copy of BRCA 1 or BRCA 2 to work but in the tumor you've lost both copies so in the tumor cells from a patient that has BRCA mutation they have no functional homologous recombination because this pathway has lost two of them or one or two of the critical components and so this pathway becomes much more important to the repair of this DNA and Larry and at the end of his talk showed the results of one study but the idea is if you take then a tumor that's dependent on this and interfere with this pathway the tumor cells should be very susceptible to to to any kind of DNA damage either spontaneous or induced whereas normal cells which still have a functioning copy of BRCA 1 or 2 should actually be relatively spared and in fact that's the idea between behind a new generation drugs known as PARP inhibitors PARP is one of the enzymes involved in these alternative repair pathways if you inhibit this pathway the tumor cell will be more sensitive and will die and in fact in early phase studies where these have been used in BRCA 1 or 2 mutant breast and ovarian cancer patients as a single agent has very high response rates and so this is actually very promising and if you think about coupling this with DNA damaging agents it's it's likely that this is a therapy that will be effective so why is this pharmacogenomics it's because the mutations in the tumor are predicting what's likely to be effective therapy in these patients now turning to patient pharmacogenomics and this is a different topic and again it's a very broad topic I know people like Doug Figg give a whole lecture on this alone but the idea is that not only does the genotype of the tumor matter or the phenotype of the tumor the genotype of all of us matter in terms of a response to drugs and in this case the presence of genotypic or phenotypic markers in in any individual patient can predict again the drug toxicity or efficacy now remember these are normal all of these are the normal genome so all of us in this room if we were to sequence our DNA would have differences in many different genes typically or most commonly single nucleotide changes in those genes but before we talk about that let me just talk about a few how this can be used is already being used in the clinic so when we think we don't always think of it in terms of this but in in a patient who's being treated with hormonal agents for an ER positive breast cancer we think about whether the patient is pre or post menopausal because the sources of estrogen are different in a pre and post menopausal patient in a pre menopausal patient the predominant source of estrogen is the ovarian hypothalamic pituitary ovarian access whereas in a post menopausal patient the the estrogens that are produced are produced by converting adrenal androgens to an enzyme known as aromatase into estrogen so this is pharmacogenomics in a pre menopausal woman her phenotype is such that we can target ovarian estrogens this is what beats and did over a hundred years ago he removed the ovaries and so we can continue to do that either with ooferectomy or we can use drugs that turn off the ovary on the other hand this would do no good of course in a post menopausal woman and conconversely in a pre menopausal in a post menopausal woman we use enzyme inhibitors of this enzyme aromatase to block the production of estrogens by the conversion of adrenal androgens and again just as aromatase inhibitors won't work for pre menopausal women targeting the ovary it doesn't work for post menopausal women and finally if we target directly the estrogen receptor that can be done in in either a pre or post menopausal women but again this is looking at the patient's phenotype this has nothing to do with the tumor itself to decide what is the best treatment for this patient the other more classic way of thinking of pharmacogenomics is metabolic enzymes that may affect it's for example acetylchrome P450 enzymes and again this is what I was referring to most commonly these are single point point changes in the basis they're not mutations so much as their variation between any two people in the population and these can be measured so I'm going to come back to a little bit of technology just as we did microarrays to look at expression of genes you can imagine printing a microarray but now these spots don't represent probes for individual genes they could be probes for very single nucleotide polymorphisms in the same gene so all of these nine spots are probes for the same gene but shown down below the gene has sequence variations from individual to individual again these are normal people these are normal genes but these sequence variations can affect the activity of metabolic enzymes and again using these arrays you can measure again hundreds of thousands if not millions of these in one one setting so if you knew a particular cytochrome that was important you could just sequence that but as we go forward what can be done is an array that will allow us to look at many polymorphisms and it's conceivable in the future that this will be something done on on most patients because it's not just cancer drugs but drugs for almost any disease we treat will be affected by metabolic enzymes and you can imagine having a profile and saying in this patient we need to be careful with this drug so the best example I know of this there there are no good examples of single nucleotide polymorphisms in breast cancer as yet but there's a drug called six mercaptopurine use predominately in pediatric tumors and there are good metabolizers and bad metabolizers of that drug and if you carry a polymorphism that makes you a bad metabolizer you're much more likely to have toxicity in the dosing of the drug is affected and there are a number of drugs where that's true for at this point so this is something that will affect again our decision about what drugs are the best drug for that patient will there be undue toxicity in some cases drugs need to be activated by the metabolic enzymes and is this a patient where the drug will or won't be activated and so I'd like to end then with what is something that again is being done already but not really in a clinical setting and that's whole genome sequencing and this schematic from a Greg Frero's recent New England Journal paper that summarizes molecular techniques and genomic medicine talks about the difference between sequencing what I guess is traditional sequencing and the next generation sequencing so this is just a schematic but in traditional sequencing you clone a piece of DNA and then you sequence it and this can be done in a reasonable throughput fashion so that you can sequence maybe a hundred copies of the gene of a hundred different pieces of DNA at a time and you get about a hundred thousand bases of sequence and it would take probably if you had a really good person doing this probably a week or two to do this and it would take thirty thousand experiments like this to sequence the genome once so this is you know not practical way to sequence the genome but the current sequencers don't rely on the old methods they rely on either solid phase or fluid phase methods that sequence not a hundred fragments at a time but millions of fragments they do relatively short reads on those fragments but you generate about 30 gigabases I'm sorry a hundred gigabytes of data so if you think about it the human genome is three billion bases this is covering the human genome 30 times over in one experiment and practically speaking I think this data can be acquired probably in a week or less from the time you have a DNA sample to the time you have the data at the end of the day so this would allow you to acquire the the sequence on many many many genes in the entire genome of the tumor or the patient what you've probably seen in the literature is the thousand dollar genome the cost of this is rapidly falling to the point where it will cost about a thousand dollars to do this we're not at a point where we can use this data so the analogy Larry Brody actually gave me it's a little bit like a CT scan CT scans useless without a radiologist to read the CT scan and tell you what you're looking at so this data can be generated very quickly the problem right now is now you have a hundred gigabyte of data or a huge amount of data it's interpreting that data really that takes a long time but just to show you some of the things that have been done with it and again this is all experimental and not clinical this is from a paper published by Bert Vogelstein's group in science a few years ago where they took 11 breast cancer and actually 11 colon cancer cell lines and sequenced the entire all of the coding sequence in that so the sequence that turns into messenger RNA not the entire genome what this is a graphical representation their chromosome one through chromosome X so all the human chromosomes are arrayed this way and the little dots and peaks represent mutations seen this is just the breast cancer sample in in the samples and everywhere you see a dot there's that means there was a mutation in that gene and if there's a little purple hill it means there is more than one mutation more than one sample had a mutation and what immediately falls out are several things first of all they're two really high peaks in this this is p 53 so out of the 11 samples something like seven or eight of them had mutations in p 53 so that's nothing that we didn't already know p 53 is one of the most frequently mutated genes in the human genome this is another gene known as PI3 kinase it's a it's a kinase that's involved in lipid metabolism but it's also very important in signaling in cells especially towards survival pathways again that's a peak that came out from this data but there's another thing that comes out of this data this is 11 samples this would be the equivalent of sequencing 11 patients you can immediately see there's something like 10 10 or 20 mutations on average per patient so there are a lot of mutations and one of the difficulties then in deconvoluting this data is well which of these are really meaningful and which of these are noise so what for Fogelstein which are drivers of the tumor phenotype and which are passengers is are the terms he's applied to this but we can hone in on the PI3 kinase example with a little more detail so out of the 11 samples this is that peak this is showing both the breast and the colon the blue is the breast so out of 11 samples half of them five of them had mutations in this particular gene in this protein and it would turn the protein on and that would actually drive the proliferation and survival of the cells but this schematic is the PI3 kinase pathway and so we really have to think not in terms of genes also but in terms of pathways and in terms of more system type analysis of these tumors because what the rest of the little circles in blue and red for colon breast and colon respectively show is that this pathway is targeted in more than more than just hitting this particular protein so that you see that their mutations throughout this pathway and most of them the net effect is to turn this pathway on so a lot of the little hills you saw still target this pathway so actually if you said if instead of showing individual genes you said well which of these hills represent this pathway that would have been a very tall peak now why is this useful information again not ready for routine use in the clinic but it turns out that there are data that support that activation of this pathway make a patient resistant to hormonal therapies or to her to targeted therapies so that means that you might predict these patients won't do as well with her septin for example or with or to moxifen or aromatase inhibitor and importantly that these patients may benefit from combinations of therapy that block this pathway and then target the more traditional pathways so this is very useful information the problem with the genome data right now is culling the data down to meaningful clinical information and that's really probably the long part now acquiring the data is very rapid so with that I'll end with the summary slide the past was really looking at tumor characteristics and one or one or a few genes we currently are already looking at genomic using genomic medicine we're looking at arrays that look at anywhere from 20 to 70 genes of breast cancer but the future really is going to be to expression of hunt hundreds of genes at a time and and also sequencing and snip arrays to decide what are the best choices of drugs for a given patient for a given tumor who needs treatment who doesn't need treatment stop there take questions go ahead you know so chemotherapy is mutagenic and so people have looked at that clearly there you know I can't give you a specific example but clearly if you look at microarray data of resistant tumors it's different if you look at genomic data there are acquired mutations there have been data I don't remember if the patient was treated or not but for example people have done whole genome or exome sequencing on a lobular cancer both from the original cancer and from metastasis of that cancer clearly there are mutations that are there at the get go that also show up in the in the metastatic disease but there are new mutations in the metastatic disease and you would imagine that therapies whether it's going to do two things therapy is genotoxic most of the chemotherapy we do causes that genetic damage radiation is genotoxic but also we're going to select it's it's just like bacterial resistance we're going to select for pre-existing clones that may not have been detectable that have a mutation so it's hard to know if the therapy always did it but it probably causes it so if you look at the original p10 patients it's not as good okay so the question was what about p10 mutations in breast cancer and I'm trying to remember if they showed up in this slide yeah so two things first of all if you look at the regional paper identifying p10 p10 is a tumor suppressor gene it's a negative regulator of this same pathway p10 is down here it turns off the PI3 kinase pathways of it's a phosphatase that removes the phosphate that PI3 kinase puts on these are lipid phosphate phosphorylation events not protein and if you look at the original cloning paper it actually was found as a progression feature in both glioblastoma but breast cancer so the original identification p10 said it would occur in breast cancer but not that commonly and in Burke-Vogelstein sequence two out of the 11 samples had p10 mutations so I think that's certainly likely to be true these are inactivating mutations would but all of these data say well inhibiting this pathway in one shape or form might be a good idea obviously that the genes that are inactivated are hard to think about as targeting because their activity is lost but I think that is going to be something it's not going to be a common mutation but really the theme here is not necessarily that p10 is common but PI3 kinase pathway mutations are probably more common than we know and this set of tumor cell lines very by a set it was half of the cells had a p10 PI3 kinase mutation go ahead what is the mortality rate of breast cancer at this time as per say 1960 what is the mortality rate of breast cancer so the two things so first of all the overall mortality rate of breast cancer so one the way I look at that is so the question is what's the mortality rate of breast cancer now compared to 1960 I have to confess I'm not really the epidemiologist in the in the audience but in general it's lower now than it was then if you look there has there was a relatively steady mortality of breast cancer one way to look at it very simply is there about 200,000 cases of breast cancer every year and about 40,000 deaths so the the mortality due to breast cancer is about 20% of all comers and of course varies based on what your stage at presentation is and so forth in your molecular features and that was actually very constant I don't think it increased over the 60s or 70s and and the primary driver of that work of the raid was that if you detected it early and did surgery those patients did well but it didn't change all that much starting at about 1990 however the rate has been decreasing a few percent so I think that the rate of mortality now is probably two or three percent lower than it was in 1990 the so it's not a dramatic effect but it's clearly going down and the decrease has been attributed to two things one is increased use of screening and the other half is the increased use of advanced chemotherapy in more patients so it's thought to be both the so both of them targeting early stage patients to my knowledge that the mortality for metastatic breast cancer hasn't changed essentially we except for anecdotal data we don't care patients who have metastatic breast cancer so all of the decrease in mortality can be attributed to defining it earlier and also treating the early stages more aggressively to deal with micrometastatic disease that's spread yeah so about half of that decrease is attributed to screening due to mammograms and and I guess given the date of onset it's really not MRI or more advanced screening techniques but it screening in general has thought to decrease the incidence the estimate again these are epidemiologic estimates it was a New England Journal article Don Berry was I think one of the main authors that estimated that that decrease was was primarily 50% due to screening and about 50% due to more aggressive therapy the other thing that's made a dramatic difference in the incidence of breast cancer in the last few years and I think Larry Brody showed the slide was when the women's health study showed that hormone replacement therapy not only didn't decrease heart disease but increased breast cancer people very rapidly stopped using hormone replacement therapy and within two or three years there was a fall in the incidence of breast cancer which also will drive a fall in the mortality but really since 1990 there's been a steady slow but steady decline so the question is looking at I guess pre or the breast prior to the malignancy or I think there's a lot of work on that it's clear from work of people like Mina Bissell that the tumor really arises it's an organ and arises in an environment and the environment has a major impact and she's for example shown data that you could take tumor cells and if you put them into one environment they form a tumor and if you put them into another environment the tumor is suppressed so it's clear that the environment has an impact it's clear that the genotype has an impact so if you carry a BRCA one or two mutation your chances of developing breast cancer over the course of your life are many fold higher the average the number that's quoted is roughly one in nine women will develop breast cancer if you carry a BRCA mutation the penetrance is anywhere from 40 to 80 percent so much higher risk so that's telling you the genotype of the of the non malignant cells matters in terms of what other features matter we know that there are environmental factors that can increase your risk of breast cancer there are people who are looking at this I don't know of any data that I could cite to you that says okay this is something that's going to predict someone's risk but that's something people would like to do can you ideally using a non-invasive method look at a person's tissue in the breast before they ever develop cancer and predict their risk people are doing that they're doing Dr. Lovage people are doing biopsies and higher risk patients to see if they can identify markers I don't think anything's ready for use aside from the normal risk factors BRCA mutation family history but those are being looked at clearly the breast right and I think people are doing that so people are looking at at the breast it's the question is do you have enough natural history in any given group of biopsies do you have samples from patients so if you've had a breast the better would be breast reduction surgery where you get samples because then the patient still has a breast and if they develop breast cancer you can say these people had breast reduction and didn't develop breast cancer and that's being done I don't know of any data that says there's something we could really use for that today but those are ongoing studies that I'm sure that are being done in a lot of places good question so my knee jerk would be I'm sure it's been done I don't know I haven't seen any data that specifically look at that we treat men with breast cancer like we treat women because we we don't really have a lot of data to guide us there are clear data that things like tamoxifen are beneficial there's a lot of controversy what about aromatase isn't a guy just like a postmenopausal woman and the answer is what we don't know so we don't know what to do so that's that's an interesting question I haven't seen a publication of that sort I can't imagine it's it's not been done obviously the cases are rare I should say that men with breast cancer it's depending on how you look at it it's one it's about 1% of all breast cancer so since breast cancer so prevalent it's not an it's it's uncommon but it's not incredibly rare so a lot of people there's a lot of research going on on just that and it's I'm sorry how does the stroma impact the tumor so first of all let me just make a point most of the expression analysis that's done is done on the tumor which means tumor cell and stroma because tumors in Oregon and that was a decision made for example the group at Stanford who really started and blaze the way on this Joel Gray's group they made that decision really added a practicality it was easier just to grind up the whole tumor than it is to try to micro dissect the tumor from the microarray and their feeling was you would get information from this and in fact you do because when you look through the data the best example that comes to immediate mind in lymphoma there is prognostic information that comes from looking at the whole tumor but some of the prognostic information is an immune signature of the response to the tumor and not the tumor cells themselves so and that's true in breast as well so you're getting information but people are micro dissecting they're looking at stroma versus normal there are clearly data that suggests the stroma is not is different in tumors their data that go back a long time probably 10 or 15 years and prostate cancer if you take fibroblast from patients with prostate cancer versus patients without they support the growth of prostate cancer cells very differently the normal don't and the tumor fibroblast do so people are looking at that because that becomes another target and the at treat the intriguing part of that is the normal cells their genomes are more stable so if you start to target the normal cells maybe you won't have as much problem with development of resistance that you do in the tumors but that's an active area of research there are a lot of people looking at the stroma