DNA Methylation in Aging and Cancer

Transcript

• 00:00Morgan is an assistant professor of pathology
• 00:03and Epidemiology at the school of Madison.
• 00:06She's a member of the combined
• 00:09program in computational biology,
• 00:11environ fanatics, as well as the Center
• 00:14for research on Aging and her work.
• 00:17Her multidisciplinary work has
• 00:18really been integrating new
• 00:20methods of statistical genetics,
• 00:22computational biology,
• 00:23mathematical demography to develop,
• 00:25sort of a new high dimensional mix
• 00:28approach to aging in both humans and
• 00:31animal models and applying those.
• 00:34Efforts to a variety of major
• 00:36chronic disease,
• 00:37most notably cancer,
• 00:38and so Morgan really pleased to hear
• 00:40about your work and looking forward to talk.
• 00:43Thank you so much.
• 00:49OK, maybe we can see that yes.
• 00:55And let me make it bigger on my screen.
• 01:02OK, um, so today I'm going to talk
• 01:04about some of my work on in developing
• 01:08biomarkers using DNA methylation data
• 01:10to study aging and diseases like cancer.
• 01:17Why isn't it? I'm so I usually like to
• 01:21kind of remind people what the biggest
• 01:23risk factor for most major cancers is,
• 01:26and I like to illustrate this often
• 01:28using something like lung cancer.
• 01:30So a lot of times when asking students what
• 01:32the biggest risk factor for lung cancer is,
• 01:35they'll say something like cigarette smoking,
• 01:37which we know increases the risk.
• 01:39The incidence and death from lung
• 01:41cancer by about 15 to 30 fold.
• 01:43But in reality,
• 01:44aging itself is actually much bigger risk
• 01:46factor for developing lung cancer, so for
• 01:49individuals who are 25 to 29 years old.
• 01:51About one in 200,000,
• 01:53you have about one in 200,000 chance
• 01:55of Belton lung cancer, however.
• 01:58Nearly 400 and 100K,
• 02:01so it UH-80 full increase risk for the
• 02:05OR 800 fold increases for those 75 to 79.
• 02:09And this is the case across
• 02:11a wide variety of cancers.
• 02:13We see, UM, in general,
• 02:15an exponential increase with age in both
• 02:17incidents in mortality risks from cancer.
• 02:20And you know,
• 02:21some people have thought that this
• 02:23is just commit probability with time.
• 02:25So at the longer you live,
• 02:27the more time and the more likely
• 02:30they will develop cancer.
• 02:31But really,
• 02:32what we think is that it's actually
• 02:34the molecular.
• 02:35Another changes that accompanied
• 02:37the aging process that are
• 02:39actually playing a causal role.
• 02:41In the ideology of major
• 02:42diseases like cancer,
• 02:43so I like this kind of New Yorker Cartoon,
• 02:46which says you're deliberately putting
• 02:49yourself at risk avail help by being over 65.
• 02:52So one thing that my lab is really
• 02:54interested in is can we actually try
• 02:56and quantify some of these aging
• 02:58changes that might underlie risk
• 03:00for things like cancer or other
• 03:02major chronic diseases?
• 03:04And so this is where kind of
• 03:05biomarkers of aging come in.
• 03:07Uh, so aging is.
• 03:09Not an observable,
• 03:10it's it's this latent concept.
• 03:12So it's actually hard to define.
• 03:14But biomarkers can actually serve as
• 03:17useful proxies that we can estimate
• 03:19the agent Ness of a cell or tissue,
• 03:22or on the whole Organism level.
• 03:24So they serve a variety of purposes.
• 03:27They can be used as clinical trial
• 03:30endpoints for interventions to try
• 03:32and slow the rate of aging there.
• 03:34You can also be used for basic
• 03:37biology to understand aging.
• 03:39And also for risk stratification
• 03:41and the goals in developing some of
• 03:43these biomarkers is that you should
• 03:45have a biomarker that differentiates
• 03:47between a 20 year old an 8 year old,
• 03:50which is pretty easy.
• 03:51You can even use facial image to do that,
• 03:54but probably the harder thing is,
• 03:56can you actually differentiate
• 03:57risks among individuals of the
• 03:59same chronological age?
• 04:00So can you identify who might be aging
• 04:03faster or slower and then in turn,
• 04:05does that have implications for the
• 04:08risk of a future morbidity mortality?
• 04:11So most of the biomarkers in my lab
• 04:13works on a more epigenetic biomarkers
• 04:16and specifically involved in DNA methylation,
• 04:18so I like to think of the meth alone as
• 04:21kind of the molecular operating system
• 04:23it instructs else how they should
• 04:25behave and respond is involved in a
• 04:28number of different cellular processes,
• 04:30but a really interesting thing that
• 04:33was pointed out more than I think
• 04:3530 years ago is that there does
• 04:37seem to be genome wide patterns.
• 04:39Um that emerge in terms of
• 04:41changes in Maculation with aging.
• 04:43So you gotta change net in the maculation
• 04:47landscape as a function of age.
• 04:50And based on this, uh,
• 04:51a number of labs,
• 04:53including ours who developed what
• 04:54we call these epigenetic clocks.
• 04:56So because they have been very precise,
• 04:58age changes that have been observed.
• 05:01We actually use machine learning
• 05:02to predict the age of a sample
• 05:05based on the DNA methylation level.
• 05:07So you can take a sample from whole
• 05:09blood from tissue in a cell culture,
• 05:12and we often measure metalation at
• 05:1410s of thousands to now up to 850,000
• 05:17different CP G sites across the genome.
• 05:20And then what people have done is applied
• 05:22supervised machine learning methods
• 05:24to actually develop age predictors.
• 05:26So most of the early clocks were trained
• 05:29to predict things like chronological age,
• 05:31the first Clock being published in 2011.
• 05:34However, more recent clocks have actually,
• 05:36which we call the second generation at.
• 05:39The generic clocks were
• 05:40developed to predict age coral.
• 05:42It's so not chronological age,
• 05:44but things like mortality or physiological
• 05:47processes that change with aging.
• 05:48So mostly that was.
• 05:50Our Clock is one of the second
• 05:52generation clocks inside the John Clock.
• 05:55And the second generation clocks actually
• 05:57tend to be much better predictors of
• 06:00future disease and mortality risk.
• 06:03Uhm,
• 06:03but first I just want to show kind of how
• 06:06these clocks look across different tissues.
• 06:09So this is an example of five different
• 06:12epigenetic clocks in a variety of
• 06:14different tissue are fluid samples.
• 06:16On the X axis,
• 06:18I'm showing chronological age on the Y axis.
• 06:21Is this predicted at the genetic age?
• 06:24These two clocks by Horvath were
• 06:26actually trained using multiple different
• 06:28issues simultaneously pulled together,
• 06:30so that's why you get much better
• 06:33agreement across the tissues in
• 06:35terms of their predicted ages,
• 06:37whereas the other three clocks are
• 06:39actually all trained in whole blood,
• 06:42but still do predict still do show.
• 06:45Very heists age correlations.
• 06:46In other tissues, and actually,
• 06:48if we were to show this within tissue,
• 06:52a lot of these age correlations
• 06:54are above .8 two point 9.
• 06:57But the interesting thing is you also,
• 06:59if you actually took the time to
• 07:02map these colors out is kind of
• 07:04these divergent issues tend to be
• 07:06samples from brain or these tend to
• 07:08be non bring samples and we actually
• 07:11think that it's important to have
• 07:13differences in Appleton at age
• 07:15between tissues because we all know
• 07:17to choose don't age at the same rate.
• 07:19So we actually shouldn't be forcing
• 07:21similar epigenetic gauges across tissues.
• 07:26And then we can actually also
• 07:28show that epigenetic age is also
• 07:30differentiates normal tissue from tumor.
• 07:32But that is not the case
• 07:34across all the clocks.
• 07:36It tends to be the case across
• 07:38these second generation clocks,
• 07:40where we can see that in the normal
• 07:42tissue you get significantly lower
• 07:44epigenetic age compared to the tumor,
• 07:46and these are all adjusted
• 07:49for chronological age.
• 07:50Um, so on our Clock and also the Clock
• 07:52by Yang Show these differences across
• 07:55a variety of different tissue types.
• 08:00So one question that we've
• 08:02really been dealing with is,
• 08:03you know all these clocks for
• 08:05developed to predict the same thing.
• 08:07To capture this kind of epigenetic or
• 08:10metalation based change with aging.
• 08:12Yet they seem to be perhaps
• 08:13capturing different parts of
• 08:15this epigenetic aging signals.
• 08:16So basically,
• 08:17can we identify the individual
• 08:19components and decompose the
• 08:21signal to adapt to figure out
• 08:22what the different parts are and
• 08:24how they map onto disease risk?
• 08:26So this is kind of an illustration
• 08:28of taking the clocks apart.
• 08:30And then figuring out which each
• 08:32part of the Clock is doing.
• 08:35So the way that we did this is we
• 08:38applied something called WG CNA,
• 08:41so it's a weighted network analysis
• 08:43and we did this a cross using six
• 08:47different issue in fluid datasets.
• 08:49So we had uh samples from dermis,
• 08:52epidermis, breast dorsolateral
• 08:53prefrontal Cortex Colon, an full blood.
• 08:55And the goal here was to identify Co
• 08:59maculation modules that are shared
• 09:01across all these tissue or sample types,
• 09:05and from this we were able to identify
• 09:0816 of these Co maculation modules
• 09:10using Skeggs from the clocks which
• 09:13word starting with about 1600.
• 09:19I'm so the next thing we did is we actually
• 09:22looked at how these different modules
• 09:25are impacting the overall Clock scores.
• 09:27So in this I've color coded all the
• 09:3016 modules and you can see that in
• 09:33our Clock and this Clock by Hannum a
• 09:36large proportion of this is actually
• 09:38driven by this yellow module,
• 09:40whereas the two clocks by Corvette seem
• 09:43to have relatively similar proportions in
• 09:45contributing to the overall Clock score.
• 09:48But the interesting module that
• 09:49I'm actually going to talk about
• 09:52today is this Brown module,
• 09:53which actually is shown in most
• 09:55of these clocks and has a pretty
• 09:58similar proportion of about uhm.
• 10:0010 to 15% in each of the
• 10:02clocks to the overall signal.
• 10:06So the other thing we can do is not
• 10:09just look at what proportion of the
• 10:11clocks is explained by each module,
• 10:13but whether what their capturing
• 10:15is actually the same signal.
• 10:17So this is all the modules,
• 10:19but I'm going to really focus
• 10:21just on 2 for right now,
• 10:23so basically this is the part of
• 10:25each Clock that that's represented
• 10:26by Stevie jobs in this Brown module.
• 10:29And what you can see is that for these,
• 10:32epigenetic clocks have really similar
• 10:34or high agreements in terms of
• 10:36their epigenetic age signal here.
• 10:38However, just a contrast this
• 10:39on this purple module,
• 10:41you can see that in in two of the clocks
• 10:43what the proper module is contributing
• 10:46to is considered accelerated aging,
• 10:48whereas in the other two
• 10:50clocks or three clocks,
• 10:51it's considered decelerated aging.
• 10:53So this is an example of a module is
• 10:56differentially waited and might be
• 10:58contributing to differences in the
• 11:00performance by the various clocks.
• 11:02But for the rest of the talk,
• 11:04I'm going to focus on this Brown module,
• 11:06which seems to be the one that's
• 11:08most important in terms of cancer.
• 11:12So now what we can do is we can look at
• 11:15instead of looking at the entire Clock score,
• 11:18look at the individual modules.
• 11:20So is there a part of the clocks for this?
• 11:23Actually driving this kind of these
• 11:25associations that we're seeing?
• 11:26So in this case I'm looking at just
• 11:29the part of our Clock that's captured
• 11:31by CP GS in this Brown module.
• 11:34So this is just 21 CP GS over all,
• 11:36and what we can see is we can kind of
• 11:39recapitulate the finding with the tumor
• 11:41versus normal across these different issues.
• 11:44However, in this case it's actually
• 11:46more significant when we're just
• 11:47considering this Brown module.
• 11:51We can also look up this is in normal
• 11:53breast tissue and we do see that this
• 11:56module is significantly correlated with
• 11:58age in normal breast, suggesting that.
• 12:02Perhaps as women age,
• 12:04their breasts as she develops.
• 12:05The more of this accelerated aging phenotype
• 12:09which could predispose them to cancer.
• 12:12And this is actually, uhm,
• 12:14what we can observe when we
• 12:16look at this is all data from
• 12:18normal breast tissue from women,
• 12:21either with or without breast
• 12:23cancer prior to treatment.
• 12:24This is a collaboration with others at
• 12:27Yale and we validated this in the original
• 12:30study and then also in another study.
• 12:33Or you can see that women with breast cancer,
• 12:36their normal tissues seems to
• 12:38be epigenetically older when
• 12:40we look at this Brown module.
• 12:42And women without breast cancer.
• 12:44And this is all age matched our age
• 12:47adjusted and adjusted for things like BMI,
• 12:50smoking another potential confounders.
• 12:55Uh, we also had a really small
• 12:58data set where we had, uhm,
• 13:00this Brown module measured in tumors
• 13:03and we had information on survival,
• 13:06so this is a data set with
• 13:09only 51 samples an over.
• 13:12I totale I are over 3471
• 13:15person Montes or 20 deaths.
• 13:18And what you can see,
• 13:20we need to validate this given
• 13:23those small sample where we do
• 13:25see that this Brown module 1
• 13:28standard deviation increase in
• 13:29this module it's associated with
• 13:32about 2.25 fold increased risk of
• 13:34mortality over this time period,
• 13:37and that's adjusting for things like age,
• 13:40race, ethnicity, tumor grade,
• 13:41ER and also chemotherapy tree.
• 13:47So I went looking more specifically
• 13:49at what's in this Brown module.
• 13:51Um, these are the individual CP
• 13:53GS in the Brown module and we
• 13:56can actually relate each CVG to
• 13:58some of the outcomes I discussed.
• 14:01So this first column is whether it
• 14:04differentiates in normal breast tissue,
• 14:06women with breast cancer versus controls.
• 14:08The second column is whether it
• 14:11can differentiate breast tumors
• 14:13from normal breast tissue and
• 14:15the third column is the survival.
• 14:18I'm finding and basically what we can see is.
• 14:22There's about a group of 12 CP GS for
• 14:26which hypermethylation so increased
• 14:28maculation in these 12 CP GS is
• 14:32associated with either cancer and
• 14:34normal tissue or or tumor versus
• 14:38normal or lower survival rate.
• 14:41And from the these are the jeans that
• 14:44these DVD's are in an there actually
• 14:47almost all in promoter regions in
• 14:49these jeans and we can use just ease
• 14:5212 to estimate an overall score.
• 14:54So we use PCA across these three
• 14:56samples and we can take PC one of
• 14:59those 12 jeans and follow up with that.
• 15:04So the other thing is that we also
• 15:07find that these 12 genius seemed
• 15:09to have specific characteristics,
• 15:11so they seem to be associated with
• 15:13polycomb group targets and also HT
• 15:15K27 trimethylation occupancy and see,
• 15:17and they tend to be ensues.
• 15:1912 pound jeans.
• 15:20So this is these 12 selected jeans.
• 15:23These were all the jeans that were
• 15:25in the original ground module and
• 15:27these are all the CP GS that we have
• 15:30measured in all of our samples.
• 15:33So about 20,000 CP GS over also.
• 15:35This is kind of the background.
• 15:37So about um 65 to 70% of them
• 15:41are orange juice 12 pound jeans,
• 15:44about 50% are Co locating with H2K27
• 15:47trying Appalachian and similarly
• 15:4950% with Polycom group targets.
• 15:53And Interestingly,
• 15:54this Association is actually not news,
• 15:57so there's some dating back about
• 16:0013 years of evidence that these
• 16:03polycomb mediated methylations does
• 16:06seem to be important in cancer and.
• 16:10Basically,
• 16:10Polycom group proteins are
• 16:12involved in repression of jeans
• 16:14that are required for salt.
• 16:16A stem cell differentiation.
• 16:19Um,
• 16:20so finally we also looked at
• 16:22these in non breast cancers again,
• 16:26so this is in colorectal cancer
• 16:29and again we find using this 12
• 16:32PPG DNA methylations score that
• 16:34we can significantly differentiate
• 16:37normal tissue from cancerous tissue.
• 16:41And Lastly, probably to me,
• 16:43the most interesting thing is
• 16:45we can look at this.
• 16:47A trustee PG score in completely
• 16:50normal tissue across a bunch
• 16:52of different tissue types.
• 16:53And basically we see really strong
• 16:56correlations with chronological
• 16:57age across all of these.
• 16:59So in brain whole glide colon,
• 17:01dermis,
• 17:02an epidermis which to me suggests
• 17:04that these might be changes that are
• 17:07naturally occuring with aging and
• 17:09that that might be predisposing.
• 17:11Some of these tissues to tumor Genesis.
• 17:14I'm so something that we're really
• 17:16interested now is in terms of
• 17:18kind of a primary or secondary
• 17:21prevention approach.
• 17:22Can you identify people who are
• 17:24scoring higher for their age then we
• 17:27would expect an are those boots are?
• 17:29Are those people more at risk
• 17:31of developing cancer in these
• 17:33specific tissues down the road?
• 17:35The other thing we're interested
• 17:37in is comparing across tissues.
• 17:39So are people who seems to be
• 17:42aging faster in blood also aging?
• 17:44Faster and something like breast or colon.
• 17:49And then last, um, basically,
• 17:51we also looked at this using a cultured
• 17:54fiberglass and basically we have,
• 17:57uhm, the early passage controls.
• 17:59We haven't immortalized transform fiberglass
• 18:01where you can see an acceleration of
• 18:04this epigenetic score immortalized,
• 18:06and we also looked in cellular senescence.
• 18:09So on pigeon induced, in essence,
• 18:12an replicative senescence,
• 18:13and these are near near senescence that
• 18:16were passage together so prohibitive.
• 18:19But they, uh, show high snacks and story,
• 18:22associated beta gal.
• 18:23And basically what you can see is
• 18:26compared to the early passes cells.
• 18:28We can recapitulate this
• 18:31Indies cultured fiberglass.
• 18:33So In conclusion, uhm,
• 18:34there are different kinds of DNA
• 18:36methylation changes in aging that are
• 18:38captured in the different epigenetic
• 18:39clocks and by deconstructing then
• 18:41we can start to understand the
• 18:43functionality of the signals that
• 18:45are captured in these clocks.
• 18:47And specifically,
• 18:48the Brown module seems particularly
• 18:50interesting in terms of cancer.
• 18:52Is one of the biggest shared signals
• 18:55across all the epigenetic clocks and
• 18:58a distinguishes tumor versus normal
• 19:01in a variety of different issues.
• 19:04Uh,
• 19:05differences to normal breasts
• 19:06are also observed for women with
• 19:09cancer versus those without,
• 19:11and the signal from these from the model
• 19:14and tumors associated with survival.
• 19:17We can that also narrow it down
• 19:20to $12.00 that are really driving
• 19:22the signal in this Brown module
• 19:25there mainly capturing promoters,
• 19:27TPG island hypermethylation that tend
• 19:29to be marked by Polycom extricate
• 19:3227 trimethylation and sues 12.
• 19:34We can observe acceleration in culture,
• 19:37fiberless, appan,
• 19:38immortalization transformation
• 19:39and also so there's no sense.
• 19:42But to me out again,
• 19:44really interesting thing is that we
• 19:46actually see linear changes in this
• 19:48signal across the adult range in
• 19:50a bunch of different issues which
• 19:52actually might be informative.
• 19:53So overall,
• 19:54I think this may represent an opinion
• 19:57about genetic aging change that
• 19:59explains the increase cancer risk.
• 20:02With that I want to acknowledge people
• 20:05in my lab and also my collaborators,
• 20:08both at Yale.
• 20:11And elsewhere, as well as my funding.
• 20:15Working, thank you.
• 20:16That's a terrific presentation
• 20:18in a really interesting work.
• 20:20And we actually have a number of
• 20:23questions that have been put forth
• 20:25on the chat or let me just run
• 20:28through a few Dan Demayo ask you
• 20:31make see that people have recently
• 20:33described meth elation of RNA M RNA.
• 20:36Specifically, does that change as well in
• 20:38the context of what you've been describing?
• 20:43So we haven't looked at that here.
• 20:45I know people are looking at that, um,
• 20:47there's a group at Harvard who is actually
• 20:50looking at that in terms of aging,
• 20:52but it for now what I'm discussing
• 20:54here is just CG metalation in DNA.
• 20:58Um, one another question sort of.
• 21:00Have you looked at this in the
• 21:02context of progeria patients,
• 21:04which is sort of a really interesting
• 21:06question as it relates to aging,
• 21:09is curious if if you are folks
• 21:11she worked with it worked
• 21:13in that space and so we we've
• 21:15looked at the overall Clock scores
• 21:17in progeria and not all of them,
• 21:20but some of them do show
• 21:22acceleration in fridge area.
• 21:24We haven't looked at this specific modules
• 21:26for the Brown module or the 12 PPG.
• 21:29Part of the Brown module in progeria,
• 21:31but that is actually an interesting
• 21:33thing and progeria something we
• 21:35we have plans to look at all the
• 21:37different modules to see if there
• 21:39are certain parts that are that are
• 21:41picking that up because again some
• 21:43clocks seem to pick up the progeria
• 21:45acceleration whereas others don't.
• 21:47Thank you Marcus has a
• 21:49question which as you can see,
• 21:51he said for the for the 12 CP GS that
• 21:53you've identified their individual
• 21:55basis as opposed to islands in
• 21:58any variation of those sites.
• 22:01Uhm, I actually haven't looked at
• 22:03whether there snips um at those sites,
• 22:05so they are individual CP GS,
• 22:07so 12 individuals seeking.
• 22:08Geez, what we're interested now
• 22:10is actually looking at the whole
• 22:12region and looking at it like
• 22:14variation across the regions,
• 22:16but we haven't done that yet.
• 22:18But yeah, I should go back and
• 22:20actually look at whether they're
• 22:22adjacent snips that would be.
• 22:26One question I have is, uhm, you know.
• 22:29Looking at your data and realizing
• 22:32that beyond aging there are,
• 22:34you know many sort of behaviors,
• 22:37environmental exposures for lack of
• 22:39a better phrase that drive cancer.
• 22:42Breast colon, certainly. And should have.
• 22:46Is there an opportunity to study sort of,
• 22:49uh, the behavior of of these individuals
• 22:52overtime that would drive the signature
• 22:54in a way that you know they are sort of.
• 22:58They have a greater component of that.
• 23:02At Methylations signature that not
• 23:04only is reflective of promoted aging,
• 23:06but increase risk of cancer. Yeah,
• 23:09so we can see we have UM shown in
• 23:11the overall Clock scores that you
• 23:14do get accelerated at genetic age
• 23:16in Association with things that we
• 23:19think of as normal risk factors,
• 23:21so cigarette smoking obesity I need in
• 23:23some socioeconomic factors seem to map
• 23:26onto differences in these aging rates.
• 23:28We haven't looked again specifically
• 23:30at this module, although I will
• 23:32say from some of our other work,
• 23:35it seems like the Brown module is
• 23:37not particularly picking up smoking.
• 23:39But that might just be when
• 23:41measured in blood,
• 23:42whether it is in long or or some other
• 23:45samples that might be different,
• 23:47whereas it seems more like that purple
• 23:50module that it didn't really go into.
• 23:52It's actually picking up
• 23:53more of those smoking,
• 23:55and the influence was smoking
• 23:57in when measured in blood.
• 24:00Another question is that the methyl
• 24:02lation that of the 12 jeans in breast
• 24:05and with regarding the breast in memory
• 24:07you can obviously the questions you
• 24:09can see is that breast tissue is.
• 24:13A combination of various cell types
• 24:15and have you narrowed down sort
• 24:17of the epithelial, fibroblast,
• 24:18other cell types with regard
• 24:20to what you're finding. Yeah,
• 24:22so unfortunately we just have bulk samples
• 24:25so we can actually narrow it down to
• 24:28which cell type this is coming from,
• 24:30but I think because breast is
• 24:32so heterogeneous we actually the
• 24:34age correlation with our measures
• 24:36actually much weaker and breast,
• 24:38I think because it's a little bit
• 24:40confounded by the cell composition.
• 24:42However, you know,
• 24:43part of the reason we did to
• 24:45follow up in the culture fiberglass
• 24:47was to make sure we weren't just
• 24:50capturing something about cell
• 24:51composition changes with aging.
• 24:54And the other interesting thing
• 24:56is that at least the Brown module
• 24:58seems to be pretty conserved across
• 24:59cell and tissue types,
• 25:01so I don't think it is picking up
• 25:03something from a specific tissue type.
• 25:05It it would be interesting to look
• 25:07at epithelial versus fiberglass
• 25:08and see if one of those is driving
• 25:10the signal more than the other,
• 25:12but right now we don't have that data.
• 25:15And then the last question before
• 25:17we break is if you looked at
• 25:19expression of of the individual jeans
• 25:22a particularly as they relate to
• 25:25potentially classic tumor suppressor
• 25:26genes or other typical mechanisms.
• 25:30I'm so that is the follow up that
• 25:32we're actually doing right now,
• 25:33so everything I showed today is either on
• 25:36the first part of the talk is impressed.
• 25:38The second part is in progress,
• 25:40so it's kind of early days still on this.
• 25:42But yeah, our goal is then
• 25:44to move to expression.
• 25:45We have looked at human protein at listen.
• 25:48Do see some associations in terms of.
• 25:51Answer and expression in the jeans in
• 25:54our 12 CG set so we are optimistic
• 25:57that we'll see differential expression.
• 26:01Well thank you were or just now at
• 26:03the top of the hour and I want to
• 26:06thank Morgan and Marcus for two superb
• 26:08talks that it really elucidated.
• 26:10Gray science being conducted
• 26:12at our Cancer Center.
• 26:13Thank you all for joining us again for
• 26:15virtual grand rounds and look forward
• 26:17again to seeing you all again next week.
• 26:21Great. Thanks, thank you.