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DNA Methylation in Aging and Cancer

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DNA Methylation in Aging and Cancer

May 26, 2020

Morgan Levine, PhD

Yale Cancer Center Grand Rounds | May 19, 2020

ID
5243

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.