Investigating Lineage Plasticity in Organoid Models of Prostate and Bladder Cancer

March 18, 2021

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Yale Cancer Center Grand Rounds | March 16, 2021

Michael Shen, PhD

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00:00Good afternoon, my
00:03name is Katie Politi and I'm an
00:07associate professor of pathology
00:09and of medicine here at the Yale
00:12School of Medicine and Awesome Co.
00:16Leader of the cancer Signaling Networks
00:19Research program at the Yale Cancer Center,
00:23and it is my pleasure to introduce
00:27Doctor Michael Shen today.
00:29Doctor Shen is a professor of medicine,
00:32genetics and development,
00:34urology and systems biology at
00:37Columbia University Medical Center.
00:39He received his undergraduate
00:42degree from Harvard University and
00:44his PhD from Cambridge University.
00:47He then pursued his postdoctoral
00:50training with Phil, Doctor Phil,
00:53leader at Harvard Medical School,
00:55before becoming an independent investigator
00:58at Rutgers in 1994 and moved to
01:02Columbia University Medical Center in 2007.
01:05He is currently the Co leader
01:07of the Tumor Biology and Micro
01:10Environment Program at the Herbert
01:12Irving Comprehensive Cancer Center,
01:14as well as the director of Graduate
01:17studies in the Columbia Department
01:20of Genetics and Development.
01:22Over the past 26 years,
01:25Doctor Shen has investigated fundamental
01:28mechanisms of mammalian development
01:30in cancer using in vivo analysis of
01:34genetically engineered mouse models.
01:36Recently his lamp has generated novel
01:38culture conditions for mouse and human,
01:41prostate and bladder organized noise,
01:43which have led to the creation of a bio Bank
01:46of patient arrived bladder tumor organoids.
01:49Current work in the lab focuses on
01:53understanding molecular mechanisms of cell
01:55type differentiation in the normal as
01:57well as the transformed prostate epithelium.
02:00The epigenetic regulation of
02:02linear plasticity in both
02:04bladder and prostate cancer,
02:06and the role of the tumor micro environment
02:10in modulating treatment response.
02:13Doctor Sen,
02:14it really is a pleasure to have
02:16you here today and have you visit.
02:19I'll be at virtually from
02:20Columbia University,
02:21so thank you very much and we look
02:23forward to your presentation.
02:27Well, thank you Katie.
02:28It's a real pleasure to have
02:31this opportunity to speak to.
02:33This audience that yell and I
02:35wish this were in person, but.
02:38Make do as best as we can,
02:40so I'll go ahead and
02:42share my screen. Um? And.
02:50Hopefully. You can see my presentation.
02:54Is that true? Can everyone see my?
02:58Yes we can see it.
03:00Thank you. Yes excellent.
03:01So today I'd like to talk.
03:03Take the opportunity to discuss published
03:06work, but also a lot of work that can
03:09be construed as work in progress.
03:11Much of it focusing on the
03:14issue of linic plasticity.
03:15And we've been studying this through
03:18in vivo analysis in mouse models,
03:21but also using organoid models,
03:23and we've been studying this in
03:26both the prostate and the bladder.
03:30So to start with, what is plasticity?
03:32So if we consider that plasticity
03:35in the most general definition is
03:37the ability of a cell to change
03:40from one identity to another,
03:42we can think of this in the.
03:45You know, perhaps cliched Waddington model.
03:48As you know,
03:49sort of balls rolling down a Hill.
03:52We start with a stem cell and we
03:55have various differentiated cell
03:56types and the ability of cells too.
04:00Basically change their identity
04:02is considered to be plasticity
04:04and so there are different forms
04:07there sort of reprogramming back
04:09to a more progenitor state.
04:11There can be transdifferentiation changing
04:13from one identity to another etc.
04:16So this is a process that has been
04:19studied extensively in both development
04:22and cancer and it's important to
04:25think about when we talk about
04:27plasticity in cancer to consider that.
04:30In order to study plasticity,
04:33it's also essential to understand the
04:36normal pathways of differentiation
04:38so that one can distinguish what
04:41happens in the normal context from
04:43what might happen in a tumor context.
04:46So over many years we've been studying
04:49these issues in the prostate,
04:51and more recently in the bladder,
04:53and to start with, I just like to consider,
04:56you know,
04:57sort of a basic review of the
05:00prostate in the mouse.
05:01The prostate has a distinct anatomy there.
05:04It sort of has lobular structure.
05:06There are four different lobes in the mouse.
05:10There's the anterior prostate,
05:11the dorsal prostate, the lateral prostate,
05:14as well as the ventral prostate.
05:16However, in the human there is
05:19something a little bit different.
05:22The human prostate does not have
05:25a distinct lobular structure.
05:27Instead, it can be distinguished at
05:30the pathological level as having you know,
05:33sort of architecture of different regions.
05:36So there are zones that have
05:39been defined pathologically.
05:41The peripheral, central,
05:42and transition zones,
05:44so the distinct anatomy of the
05:47mouse and human prostate has been.
05:50Um?
05:50Of note,
05:51for many years and has been used sort
05:54of as an argument as underscoring
05:57the inability perhaps of the mouse
06:00to truly model human prostate cancer.
06:03Now we've known from studies over many
06:05years that there are many conserved
06:08signaling pathways in the like,
06:10but the relationship between the
06:12mouse and human prostate still
06:14remains somewhat mysterious,
06:15both in terms of normal development as
06:18well as the cell types can contain.
06:21Within the prostate. No.
06:23The prostate in the mouse is formed
06:26at late stages of fetal development
06:28and organic Genesis
06:30primarily takes place at neonatal stages.
06:33The prostate form,
06:34through a process of ductal budding
06:36budding from the urogenital sinus
06:38and initial prostate bugs are marked
06:40by expression of the homeo box.
06:42Jinan kicks 3.1 here,
06:44visualized by Beta Galactoside.
06:45ASA Valax Enoch in that we made a
06:48number of years ago and you can see
06:51even early on at the time of birth.
06:54Their buds that correspond to distinct
06:57lobes and at least initially and kicks 3.1,
07:00is expressed by all of the
07:03epithelial cells in the prostate.
07:05Prostate formation of course
07:08requires androgen signaling,
07:09but the requirements for androgen receptor
07:12are actually fairly complex and they
07:16involve epithelial mesenchymal interactions.
07:18So androgen receptor is actually
07:21required in the urogenital
07:23mesenchyme for prostate formation.
07:26So if you delete androgen receptor in
07:30the urogenital mesenchyme here in a
07:33TFM una testicular feminize mutant.
07:36And you perform a a tissue recombination
07:40assay as was first shown through.
07:43The studies of Jerry Kunia about four
07:46decades ago now the prostate will not form.
07:50However,
07:50if you delete androgen receptor in the
07:53epithelium now you will form a prostate.
07:56However, the prostate is not entirely normal.
07:59For example,
08:00it lacks secretory protein production.
08:03So what are the cell types that are
08:06found in the normal adult prostate?
08:09Well in both the mouse and the human
08:12there's an array of different cell
08:14types in both the epithelium and in the
08:18stroma that are just now really being
08:20able of being characterized in some detail,
08:23but historically we've considered
08:25the epithelium as containing
08:26three basic cell types.
08:28There are the luminal cells which are
08:31the tall columnar secretory cells.
08:33That produce the the prostate secretions.
08:36There's an underlying layer of basil cells.
08:39These cells are androgen receptor
08:41low or negative,
08:42and express basil cytokeratins
08:44unlike the luminal cells,
08:46which are a are high and
08:48expressed lumenal cytokeratins.
08:49And then there is a rare third type
08:53known as neuroendocrine cells.
08:56These have been very understudied and
08:58I'll touch upon these later in my talk.
09:02So this is sort of the way we thought
09:04about the prostate epithelium for
09:07many years now, and classically,
09:09basil cells have always considered have
09:11been considered to be more interesting.
09:13They appear to have more stemlike
09:16properties or does luminal cells have
09:18been considered to be somewhat boring,
09:20but I think you know for many years
09:23we've thought that luminal cells
09:25are actually the interesting cells.
09:27And now with the advent of single
09:30cell analysis,
09:30we see that there's considerable complexity.
09:33In the lumenal population.
09:35So what do basil cells do?
09:37Well, we believe that there is sort of.
09:41A conserved ancestral role for basil
09:44cells and that's in wound repair.
09:46So here, for example, in the prostate.
09:49If we damage the luminal cells by
09:52deletion of idcat here in inducible
09:55deletion of ekit here and the
09:58epithelial cells will Slough off.
10:00And undergo a notice and the basil
10:02cells will actually differentiate
10:04into luminal cells to replace
10:06the loss luminal cells.
10:08So this is shown in cartoon form here.
10:10So there's sort of a basil to luminal
10:13differentiation that takes place,
10:15so we think that this is a conserved
10:18function of basil cells in many tissues,
10:20and more recently,
10:22work from Cedric Web Con Slab has
10:24demonstrated that is in fact the case.
10:27What about luminal cells?
10:29Well, luminal cells, as I mentioned,
10:31have been considered to be somewhat boring,
10:34but in fact,
10:35in ex vivo assays at least one
10:37could see that there are luminal
10:40progenitors that are by potent.
10:42So if we perform lineages marking
10:44of luminal cells and generate
10:46organoids that are wholly composed
10:48wholly derived from luminal cells,
10:50then we can see that there are basil cells
10:53that can be formed in these organoids,
10:56and these basil cells are marked.
10:58Indicating their lumenal origin.
11:01So of course this is an ex vivo
11:05assay and ex vivo cells often
11:08display more plasticity than is
11:11found in during normal development,
11:15and certainly in.
11:17Normal context luminal cells
11:19are usually you'd impotent,
11:21but we think that normal
11:24developmental processes,
11:25among other things or,
11:26can be constrained by the micro
11:29environment and ex vivo assays such
11:32as organoid formation can reveal
11:35developmental potential that can
11:37be displayed in specific contexts.
11:40So for example,
11:41we think that the ability of
11:44luminal cells to display by potency
11:47to be able to generate basil
11:50cells is actually an ability that
11:53occurs early in organic Genesis.
11:56So this is sort of a linear
11:59hierarchy as is currently understood,
12:02in which there are by potent basil
12:05progenitors generating both basal and
12:07luminal cells during organic Genesis.
12:10But.
12:10Luminal progenitors were
12:12generally thought of unipotent,
12:13but in recent studies we found that in
12:17fact there is a BI potent luminal progenitor,
12:20so if we lineages mark luminal cells.
12:24At early postnatal stages
12:26using an inducible CK 8 driver,
12:30we can then mark luminal cells
12:33and then analyzed later.
12:36Luminal cells are marked,
12:37but also basil cells,
12:39and it's it's a reasonable fraction
12:41of basal cells that are marked,
12:44and this by potent progenitor,
12:46is fairly short lived.
12:47It's fairly transient,
12:49but it can still be detected at
12:51a week after birth, but again,
12:54the ability of this by potent progenitor
12:57quickly disappears thereafter.
12:59So interesting, Lee,
13:00both the by potent basil progenitor and
13:03this by potent luminal progenitor do
13:05not require androgen receptor function,
13:08so if we delete androgen receptor.
13:11In basil cells,
13:12we find that there's no effect on
13:15the formation of luminal cells,
13:18and similarly if we delete a
13:20are in the luminal cells,
13:22we don't see any effect on the
13:25generation of basil cells.
13:28So in cartoon form then what we
13:30think is going on is that there is
13:34a urogenital epithelial progenitor
13:36that gives rise to both basal
13:39and luminal progenitors,
13:41and initially the basil progenitor
13:43can be by potent as as well
13:46as the luminal progenitor.
13:48But that this by potency is fairly transient,
13:52and then in adulthood both
13:54luminal and basal progenitors are
13:56generally unipotent.
13:58However, this period of by potency
14:00is actually occurring in the
14:03first four weeks after birth,
14:05and Interestingly, this is a time
14:08when androgen levels are very low.
14:11At these pre pubertal stages.
14:14Now there is one other interesting
14:16aspect of luminal cells,
14:17which is that in a series of studies
14:20we've shown that they are favored as
14:23cells of origin for prostate cancer,
14:25so we've shown this in a number of
14:28different transgenic mouse models,
14:30as well as a hormonal carcinogenesis model.
14:32If we mark Basil cells in these models,
14:35for example in the hymec model or a tramp
14:38model which are well characterized,
14:40transgenic models of prostate cancer,
14:42the basal cells are marked.
14:44But they don't really contribute to tumors.
14:47However, we mark Lumenal cells now they
14:50readily contribute to tumor formation.
14:53So. What we think is going on then
14:56is that if we mark luminal cells,
14:59they will contribute tumors,
15:00and this argues that luminal cells are
15:03a cell of origin for prostate cancer.
15:05If we mark Basil cells,
15:07they don't contribute to tumors.
15:08However, if you explant these cells,
15:11they will.
15:11You know in this sort of ex vivo context.
15:14For example, in a graft undergo a
15:16basil to luminal differentiation,
15:18and now they can form tumors.
15:20So we consider basal cells to be a
15:23celeb mutation, whereas luminal cells.
15:25Are the true cell of origin.
15:27And again,
15:28we've analyzed this in multiple
15:29mouse models of prostate cancer.
15:31So because we've observed the
15:33same result every time,
15:35we think that luminal cells are
15:37generally favored as a cell of origin.
15:40So.
15:42We now have this view that lumenal
15:45cells are in fact quite interesting,
15:48so recently we decided to explore
15:50the heterogeneity of the prostate
15:52epithelium using single cell approaches.
15:55For this we had to really learn how to
15:59dissect the mouse prostate properly.
16:02You might think that's a bit of
16:04an over some bug, an exaggeration,
16:06considering we've been studying
16:08the prostate for over 20 years,
16:09but in fact it's really not
16:11a trivial matter of two.
16:13Dissect the individual mouse
16:14lobes all the way down to their
16:17junction with the urethra.
16:18So this is sort of a view of sort of how
16:21we can dissect the weight mouse lobes.
16:24This is actually in a green mouse,
16:26so you can see the lobes that are dissected.
16:30And then we subjected these two
16:33single cell RNA sequencing both
16:35the whole prostate as well as
16:38the individual lobes to analyze
16:41the results we collaborated with
16:43Roll Rabadan's lab Raul has.
16:47Developed you know,
16:49amazing algorithms that are based upon
16:52rather sophisticated mathematical
16:53approaches for analyzing single cell data.
16:57So in particular,
16:59his laboratory has developed approaches
17:01based upon random matrix theory,
17:04which demonstrate that these
17:06large arrays of data,
17:08for example as one generates using so
17:12single cell RNA sequencing are mostly noise,
17:16so.
17:17If you consider.
17:19These as giant matrices,
17:21well in fact the distribution of
17:24eigenvalues follows a sort of conserved
17:27mathematical distribution known as
17:29the Marchenko Pastur distribution,
17:31and the deviation for this distribution
17:34is actually where the signal is,
17:37and typically it's only
17:39about 2 to 3% of the data.
17:42So this is a hypothetical distribution.
17:45This is actually real data.
17:48This is one of our prostate datasets.
17:51And again, here is the signal,
17:54so his laboratory is developed randomly,
17:57an algorithm to isolate these data.
18:01And this proved to be very
18:02useful in our analysis,
18:04because it allowed us to identify a
18:06cell population that would have been
18:08very difficult to identify other ways.
18:10So when we examine an aggregated data
18:13set of whole prostate, we observe.
18:18Five different lumenal populations.
18:20First of all,
18:21we only identify one Basil population.
18:24So basil cells are actually
18:26not heterogeneous,
18:27but instead there's heterogeneity
18:28in the lumenal population.
18:30So, first of all,
18:32there's four distinct lumenal
18:34populations that correspond to the
18:36that are located distally in each
18:38prostate lobes alumet population
18:40distally in the anterior prostate
18:43loom D in the distal dorsal prostate
18:46lumel the lateral movie and eventual.
18:49Then there is a population that
18:51we call Lumpi for proxamol.
18:53It is similar in all four lobes and
18:57it is it is found more approximately.
19:00Finally,
19:01there is a population that
19:03we call Paraurethral.
19:04This population has both
19:06basal and luminal properties,
19:08and it is found in the region
19:11most adjacent to the urethra.
19:14So these are the distinct epithelial
19:16properties that we've identified.
19:18We've also identified heterogeneity
19:20in the stroma,
19:21but I won't speak to that further.
19:24So,
19:24as you can note from this
19:27sort of illustration,
19:28there is diversity along
19:30the proximal distal axis.
19:32So to give you an idea of what we
19:35mean by proximal and distal axis,
19:37here's an anterior prostate that's
19:39been sort of splayed out and cut in
19:42histological section when we refer to distal,
19:44we're actually referring
19:45to this whole region here.
19:47That is more than 90% of
19:49the volume of the prostate.
19:51The proximal region is just this region here.
19:55And these regions are quite distinct at
19:57the level of marker expression as well as.
20:00Histology,
20:01so here if we go along the
20:04proximal distal axis,
20:05we have the paraurethral
20:07region has specific markers.
20:08Here is the proximal region.
20:10It has a distinct Histology and is
20:13marked by specific gene expression
20:15of specific genes such as this one.
20:18PPP, one R1B.
20:21Then we have in the distal
20:23region other markers that are
20:25specific for distal luminal cells.
20:27The lume population here,
20:28but you will note there are also
20:31proxamol cells that are scattered about.
20:34They're not very common,
20:35but you can definitely find them in
20:38the distal region and then in between.
20:40There appears to be a boundary
20:44where these regions meet.
20:46If you perform electron micrography
20:48micrography of sort of the boundary region,
20:51you can see that.
20:54These lumit lumayan lumpi cells actually
20:57appear to be distinct cell types,
20:59so the lume cells again are
21:02tall columnar secretory cells.
21:03The loopy cells,
21:04on the other hand,
21:06have a more cuboidal appearance,
21:08and they don't seem to be
21:12particularly secretory.
21:13At the transcriptomic level,
21:15you can analyze the sort of relationships
21:18of these populations with each other.
21:22So Luis Aparicio, postdoc INR Lab,
21:25who who's done these computational analysis,
21:28has used. An approach based upon optimal
21:32transport theory to calculate Wasserstein
21:34distances between these populations,
21:36and then relate these
21:38populations to each other.
21:39You can see that Lumpi is sort of,
21:43you know, sort of at the center related
21:46to the distal lumenal populations,
21:48and then PR you and basil.
21:52So in order to investigate the
21:56function of these populations, we've.
22:03And so, as you might imagine
22:05from this sort of relationship,
22:07we observed that there is greater
22:10projector potential in the loom PPR.
22:13You and basil populations versus
22:15the distal lumenal population.
22:16So here's an organoid formation assay.
22:19You can see that the distal lumenal
22:22populations all have a low efficiency
22:25of formation of organoids lumpi as a
22:28much greater efficiency and PR you in
22:31Basel have a greater efficiency yet.
22:35We've also isolated these populations
22:37by flow cytometry and performed renal
22:40grafting assays so in combination
22:42with urogenital mesenchyme,
22:44these cells were formed renal grafts
22:46all be it with different efficiencies,
22:49and then we can analyze the sort of
22:53cell types present within these graphs.
22:57So using the markers that we've identified
22:59that is specific for each population.
23:02In brief,
23:03the loom a another distal luminal.
23:05Cells can make more of themselves,
23:07but not the loom PNP Ru populations,
23:10where is the loom PNP?
23:12Ru populations can make all of
23:14the other different populations,
23:16so this supports a sort of a projector.
23:21Increased progenitor potential
23:23for loom PNP RU populations.
23:26So finally one can ask what
23:28is the relationship between
23:30the mouse and human prostate?
23:32After all,
23:33anatomically they are quite different
23:34and histologically as well,
23:36so we've.
23:38Analyzed three independent benign
23:40prostatectomy specimens at the
23:41single cell level, and again,
23:43we can see that there is a
23:46single basil population,
23:48but they're different lumenal
23:49populations that we can relate to.
23:52The lumenal populations
23:53that we see in the mouse,
23:55so there isn't a snare population
23:58that seems more closely related to
24:00the distal lumenal populations.
24:02A ductal populations more proxamol,
24:04as well as a PR you like population.
24:08Again,
24:09using analysis of Wasserstein distances,
24:11this time in across species way we can
24:14we can definitely show this relationship.
24:18So the acinar cells are actually interesting.
24:22Lee most closely related to
24:24lumenal L cells in the mouse,
24:27the ductal cells to loom
24:30P&PRU 2P RU in Bloom,
24:32P, etc.
24:33So this analysis highlights loom L as
24:37a population of interest in the mouse.
24:41It perhaps is most closely related to
24:44the bulk of the human prostate epithelium.
24:48Yet the lateral lobe is understudied
24:50in the mouse,
24:52and particularly analysis of cancer models.
24:56So now I'd like to turn to cancer a
25:00little bit and think about plasticity
25:04in advanced prostate cancer.
25:07So in the current spectrum of of.
25:12Prostate cancer where we have.
25:15Treatment of with potent anti
25:17androgens that are very efficient at
25:20suppressing energon receptor function.
25:23Now,
25:23castration resistant prostate cancer is
25:26displaying a range of different entities.
25:29As sort of a spectrum that can be
25:33distinguished perhaps by different
25:35differential lineages, plasticity.
25:37So there is prostate cancer that is
25:40remains a our pathway positive it
25:43still expresses androgen receptor
25:46despite the presence of anti androgens.
25:49And then at the other extreme
25:53we have neuroendocrine prostate
25:55cancer which is a are negative and.
25:59He's most extreme forms can display
26:01a small cell phenotype that's
26:03very aggressive and lethal,
26:04so there's been considerable interest
26:06in the mechanisms of neuroendocrine
26:08differentiation in CR, PC, and so.
26:10There have been studies of CR PC that are
26:13trying to distinguish the different entities,
26:16and, for example, there is something
26:19considered double negative.
26:20That is a are negative and
26:22neuroendocrine negative,
26:23which is defined more by what it
26:26is not rather than what it is,
26:28but the relationships between these.
26:30Distinct entities is unclear,
26:33and it may be simple, maybe more complex.
26:39But there is widespread agreement that
26:41there must be a role for epigenetic
26:44reprogramming in this process, and so.
26:47A range of studies have provided evidence
26:50that there's increase in ezh two as PRC.
26:54Two activity in this spectrum of
26:57plasticity and recently flybot
26:59John Cody's lab has shown that PRC
27:01one activity is elevated in double
27:04negative prostate cancer etc.
27:06But this is all involved studies
27:09either in cell lines or in human
27:12prostate cancer specimens,
27:13so it's been difficult to sort
27:17of study these things.
27:19You know at a more detailed level
27:22and in terms of mechanism as well.
27:26So to approach this,
27:28we started with a mouse model that we
27:32had been analyzing in collaboration
27:35with Korea Body Schenz lab.
27:38So the NPP 53 mouse model uses
27:42inducible deletion of P-10 and P53.
27:45Of these animals develop a castration
27:49resistant prostate cancer that will.
27:52Display features of neuroendocrine
27:53differentiation and we were able to
27:56distinguish what we called focal
27:58neuroendocrine differentiation in
27:59which the neuroendocrine cells are
28:02not proliferative from overt nor
28:04endocrine differentiation which often
28:06displays a small cell phenotype.
28:08These are highly proliferative
28:10or endocrine cells.
28:11However, since we use lineages marking here,
28:15we could show that the neuroendocrine
28:17cells are derived from a luminal cell,
28:20so the initial.
28:23Tumor induction was from luminal
28:26cells and then we have.
28:29Alright,
28:30formation of CR PC neuroendocrine
28:33differentiation and then there is
28:35some type of proliferative switch
28:37that we don't understand that
28:40results in this highly proliferative
28:43neuroendocrine prostate cancer.
28:44So because we use linear tracing here,
28:48we provided evidence that in fact
28:51this was a transdifferentiation of
28:53luminal cells to neuron can cells.
28:56So when we think about transdifferentiation,
28:59there's sort of a fundamental
29:01question both in developmental
29:03as well as cancer context,
29:05which is what is really going
29:07on in terms of the pathways that
29:10result in transdifferentiation.
29:12Well,
29:13it's possible that this change in
29:15identity occurs through a transition that
29:18happens normally during development.
29:21Alternatively,
29:21it's possible that this change
29:24in identity occurs through a
29:26transition that is wholly or at
29:28least partially novel that doesn't
29:30really occur in normal development,
29:33so there could be a hijacking of
29:36an alternative pathway or or some
29:39other pathway or process that
29:41doesn't occur in normal context.
29:44To understand this,
29:45of course it is first of all important
29:49to discover how neuroendocrine
29:52cells differentiate normally.
29:54So.
29:55It's remarkable that there's very
29:57little in the published literature about.
30:00Origin in fact,
30:02features of neuroendocrine cells
30:03in the normal prostate,
30:05enlarged likely because they are quite
30:08rare and what is also interesting
30:11is that there are different models
30:13have been put forth for their origin.
30:17So one model says that they
30:19actually are of epithelial origin
30:21and arise from Basel progenitors.
30:24Another model is that they arise from neural
30:27Crest and so Cedric Pompons lab published.
30:31That they came from basil
30:33cells and a more recent paper.
30:34From that they're from their old Crest
30:37in both these papers use linear tracing.
30:39In fact, we believe that both of
30:41these papers are incorrect and most
30:44likely they arise from an early
30:46urogenital epithelial progenitor.
30:48So neuroendocrine cells are very rare.
30:50But what is make some particularly
30:53in the mouse, prostate is they're
30:55highly asymmetrically distributed,
30:57so they are mostly found in the proximal
31:00region, which I showed you earlier.
31:02So nearly all the owner can
31:05cells are proxamol.
31:06They're very rare distally.
31:09And remarkably, neuroendocrine cells,
31:11despite their rarity,
31:12are heterogeneous.
31:13So about 80% of neuron can cells
31:17have adluminal like phenotype.
31:19They actually express androgen receptor
31:21remarkably and they express lumenal
31:24cytokeratins and then another 20% of
31:26your endocrine cells are basil like
31:29they expressed basal cytokeratins and P.
31:3263.
31:34They can be detected very early
31:37in organic Genesis at burn.
31:46Many, most perhaps all Durand Prince cells
31:49are actually formed at prepubertal stages,
31:52and since neuroendocrine cells I didn't
31:56mention this on previous slide are.
31:59But never divide there.
32:00They appear to be post mitotic.
32:03We believe that they are made and
32:06are not subsequently generated.
32:08By lineages tracing, we believe
32:10that they have an epithelial origin,
32:13so using an NCX Cree driver we can
32:16mark most of the prostate epithelial
32:19cells and in fact the vast majority of
32:22neuroendocrine cells are marked by NCX Creek,
32:26so we believe they have a
32:29prostate epithelial origin.
32:30So there's more than 95% of the
32:34neuroendocrine cells are marked
32:36in this experiment.
32:38Can finally neuroendocrine cells you know?
32:43Likely arise,
32:44as has been shown in the lung
32:47through a pro neural pathway,
32:49in which sort of the master regulator
32:53at the top of of this of the sort of
32:58transcription factor hierarchy is ASE L1.
33:00So if we delete ACL one in
33:04the mouse prostate,
33:05we can actually recover mice that
33:07have prostates that completely
33:09lack your endocrine cells.
33:11And yet the prostate appears to be normal
33:14and there is a normal proximal region.
33:17So we do have a rare escaper cells in
33:21the Periorbital region which are likely
33:25due to incomplete deletion by index 3.1.
33:29So our current model for the
33:31origin of your endocrine cells is
33:34that they likely arise from your
33:36original epithelial progenitor,
33:38although we haven't excluded
33:39the possibility they arise from
33:41an early lumenal progenitor.
33:44But in either case progenitor activity
33:46coincides with the developmental stages
33:49in which androgen levels are very low,
33:51and we're currently studying.
33:54The molecular properties of
33:56normal neuroendocrine cells.
33:58To understand you know in more
34:02detail their regulation.
34:04Moving on to cancer,
34:06we've used the NP 53 mouse model
34:10to generate organoid lines that
34:13displayed neuroendocrine phenotypes so.
34:17This is work from a talented
34:19postdoc in my lab, Jolly.
34:21She has used NP 53 tumors and established
34:24a large number of organ would lines,
34:27some of which have neuroendocrine features.
34:29As you can see here,
34:31this is the sort of primary tumor,
34:34and you'll note that it's heterogeneous.
34:36These are the organoids
34:38are established there,
34:39green because of the linear smirking.
34:42Here's a different line that you can
34:44see the region of small cell Histology.
34:47And these organoids are heterogeneous.
34:49You can see that they have a
34:53neuroendocrine region as well
34:54as a non ***** can region that
34:57is mesenchymal in nature.
34:58So this can be more clearly
35:01revealed by marker analysis.
35:02So here's a line that's
35:04relatively homogeneous.
35:05It expresses Synaptophysin and Chromogranin
35:07a so it has a neuroendocrine phenotype.
35:11Here's a different line that is more
35:14heterogeneous there that has sort of
35:16mixed expression of neuroendocrine
35:18markers as well as androgen receptors.
35:21To some extent,
35:22it actually has a double positive
35:25or African phenotype.
35:27This is a heterogeneous line that
35:30I showed you earlier,
35:31so it is it has intermixing of
35:34neuroendocrine cells and non your
35:36endocrine cells that express
35:38androgen receptor.
35:39Here is a different heterogeneous line
35:41again with a similar intermingling.
35:44So these new rendering lines,
35:47whether homogeneous or heterogeneous
35:49or highly stable during passaging,
35:51they can be passaged for
35:53more than 20 passages,
35:55and the heterogeneous lines will
35:58maintain their heterogeneity.
36:00So we can analyze the heterogeneity
36:02using single cell RNA sequencing and
36:04so this is the first line I showed you,
36:07and Interestingly the clusters
36:08are sort of grouped together,
36:10so we have an ARPU C cluster on any PC
36:14cluster and and and and a grouping of DN PC.
36:18And this is this is the
36:20other heterogeneous line,
36:22and we see a similar arrangement of clusters.
36:26So the heterogeneity of these organize
36:29is striking because it suggests that
36:32we've been we're able to capture
36:34much of the spectrum of CR PC within
36:39organoids established organoid lines.
36:41So what can we do with these organoid lines?
36:44Well, we can do a number of things.
36:47One thing is we can examine, you know,
36:50sort of the epigenetic marks that are
36:52displayed in these organoid lines.
36:54So for example,
36:55we've been pursuing cut and tag
36:58analysis here,
36:58looking at H3K27 trimethyl.
37:00So this is the mark deposited by PRC 2.
37:04And so I'll just show you just
37:07little this little tidbit here.
37:10What's interesting here is that
37:12actually the non neuroendocrine
37:14lines appear to be have a somewhat
37:16higher level of H3K27 trimethyl
37:18than the neuroendocrine lines.
37:20So that's something interesting that
37:23we are currently following up on.
37:26We've also been collaborating
37:28with Andrea Califano's laboratory.
37:31Which has developed a set of
37:34computational systems approaches
37:35to identify master regulators
37:38that drive biological processes,
37:40and so one of the sort of.
37:46Analytical approaches that they've
37:47developed is known as Meta Viper,
37:50where we can take single cell
37:52RNA sequencing data and analyze
37:55this to infer protein activity
37:57at the single cell level.
37:59So Alessandro Vasi Evo in Andre's lab
38:02postdoc in Andre's lab has done this,
38:05and again using the same organoid
38:07line as I showed you earlier
38:10here by protein inference,
38:12we can see again clustering of ARPU.
38:15See any PC and NPC.
38:18The the RPC cluster is elevated.
38:24Using an androgen receptor signal is
38:27is enriched for androgen receptor
38:30signature the any PC cluster is
38:32enriched for a neuroendocrine signature
38:34and when we can predict master
38:38regulators using this sort of approach.
38:41Notably one of the newer endocrine master
38:44regulators that's predicted is ACL one.
38:48So this is a way that we are.
38:51This is a method that we're employing
38:54to identify candidate intrinsic drivers
38:56of neuroendocrine differentiation
38:57that we're currently seeking to
39:00validate in functional assets.
39:02So.
39:02One of the sort of interesting
39:05questions we can ask is does trans
39:08differentiation occur at some level
39:11in organoid cultures and we have
39:13some evidence that that it might
39:16one way that we've been looking at
39:19this is using single cell a tax seek
39:22to examine chromatin Accessibility,
39:24and we can see that this is the
39:28same line again by single cell,
39:30a taxi there, seven clusters,
39:33and these clusters are.
39:34Have open chromatin at chromogranin.
39:36A neuron can marker these in a R and what
39:40you can see is that there is one cluster
39:43here that has open chromatin for both.
39:46Chrome Grande are you can
39:48see this more readily.
39:51Looking at the genomic locus so this
39:54cluster seven has accessible chromatin
39:57at both chromogranin, A&AR and so.
40:01This cluster, we believe,
40:03corresponds to a potential transitional
40:05population in the process of
40:08neuroendocrine differentiation.
40:10We've also been trying
40:12to assay this directly,
40:13so this is very preliminary data,
40:16but we can isolate non your
40:18endocrine cells by flow cytometry.
40:20Mark them with expression of RFP and
40:23then culture than honor in cells,
40:26and neurons can sell separately for
40:28several passages and they maintain
40:31their non ***** couldn't border and can
40:34phenotypes if we coculture them together.
40:36However,
40:37we now see their rare cells
40:39that that our RFP positive.
40:41But now express neuron could
40:43markers such as synaptophysin or
40:46Chromogranin A and interesting Lee.
40:48They maintain the expression of the Menton.
40:51So these appear to be a transitional cell,
40:55since phenotype seemingly corresponds
40:57to the what the a taxi might predict.
41:02OK,
41:02so these are some of the approaches
41:04that we've been employing to study
41:06language plasticity in prostate cancer.
41:09In the remaining 10 minutes or so,
41:11I'd like to switch over to bladder
41:14cancer and explain how we've
41:16been using organize to study
41:18plasticity in bladder cancer.
41:20So bladder cancer.
41:24Is of course a major health problem.
41:27It's quite understudied.
41:28The normal bladder contains again
41:31sort of three epithelial cell types,
41:34as it were basil cells,
41:36intermediate cells,
41:37and umbrella cells,
41:39and bladder cancer can be roughly
41:41divided into non muscle invasive
41:44disease and muscle invasive disease.
41:47Historically,
41:47these have been considered to
41:50be almost distinct entities,
41:52and it's unclear what the
41:54relationship actually is so.
41:56There are two forms of non
41:59muscle invasive bladder cancer,
42:01papillary and carcinoma insight,
42:03two and carcinoma insight two has
42:06been considered to be sort of the
42:08precursor to muscle invasive disease.
42:11However,
42:11there is also sorry there is also some
42:15evidence that papillary disease can
42:17progress to muscle invasive disease,
42:20so we've been interested in studying
42:23progression of bladder cancer and.
42:26To pursue this,
42:27we've actually established patient
42:29derived bladder tumor organoids,
42:31and these have been established through
42:33collaboration with urologists who
42:35perform transurethral resection's.
42:36So what they do is they sort
42:39of go in and extract,
42:42sort of like the tops of
42:44these of these tumors here.
42:47This is what they actually
42:49view through the cystoscope.
42:51This might look a little uncomfortable
42:53for men in the audience, but.
42:56This is how it's done and we
42:58take these samples and we can
43:01establish organoid lines in culture,
43:04which we can seriously passage.
43:06These organoids can also be grafted.
43:08Orthotopic Lee.
43:09This orthotopic grafting it uses
43:11ultrasound guided implantation into
43:13the bladder wall which is a very
43:16efficient process so we can readily
43:18interconvert organoids into Xena graphs.
43:20We can also take the Xeno grafts and convert
43:23them back to organoids, so all of these.
43:27Together with the parental tumor can be
43:31analyzed by sequencing or histopathology etc.
43:34So this just shows you some of the organoid
43:38lines that we established and what is,
43:41I think, evident is that the organoids
43:43and the Xeno grafts retained the
43:46characteristic characteristic
43:47Histology of the parental tumor.
43:50We also can establish organoid lines that
43:52have less common histological variants,
43:55such as squamous cell carcinoma.
43:57In recent unpublished work,
43:59we've now increased our bio bank to
44:02approximately 50 organoid lines.
44:05We've been able to establish organized
44:08lines from cystectomy samples,
44:09as well as from transferring for receptions,
44:13and have also a stab Liszt several
44:17lines that contain variant histologies.
44:20So in collaboration with David
44:22Solids Group at Memorial Sloan,
44:24Kettering Owen,
44:25the previous pathology was in
44:28collaborate with collaboration
44:29with him at all Media Memorial.
44:31We've analyzed these organoid lines
44:33molecularly using the targeted sequencing
44:36platform at Memorial MSK impact.
44:37We sequenced the organoids parental
44:40tumor and normal bloods and we can
44:43show generally that the organoids.
44:45Display mutational profiles that are
44:47concordant with that of the parental tumor.
44:50We can examine.
44:52Sort of the mutational profiles
44:54of these organoid lines,
44:56which really recapitulates sort of
44:58the distribution of of mutations
45:00in human bladder cancer.
45:02So we can see that among the common
45:05mutations we see mutations in a
45:07lot of epigenetic regulators which
45:10are frequently mutated in bladder
45:12cancer such as KDM 6A KMT 2C and 2D.
45:15As well as error 1A.
45:20Interestingly, we also see were also able
45:23to capture mutations that are relatively
45:25rare but interesting in bladder cancer,
45:28such as mutations and ERB B2 and of
45:31note we have very few nations in RB,
45:35so many bladder cancer cell lines were
45:38established from metastatic bladder
45:39cancer and contain RB mutations,
45:41whereas are organoids.
45:43Are generally established from non
45:46muscle invasive bladder cancer
45:48or earlier stages of muscle.
45:51Invasive disease and lack RB mutations.
45:55So what can we do with these organoid lines?
45:59One thing we can do is we can
46:02examine their drug response,
46:04and of particular interest were able
46:06to establish organoid lines from
46:08patients in a longitudinal fashion.
46:10So patients will often undergo
46:12transurethral resection, be treated,
46:14and then sometime thereafter,
46:16their tumors will unfortunately recur.
46:18And then we have an opportunity to
46:20establish another organoid line.
46:22So here's an example of a patient.
46:25And a pair of organoid lines where
46:28patient was not otherwise treated.
46:30So the tumor was removed,
46:32but the patient was not otherwise treated,
46:35and we can see in in terms of
46:37response to a range of different
46:40drugs that Interestingly,
46:41the organoid lines display nearly
46:43overlapping drug response profiles.
46:45However,
46:46in this case the different patient
46:48was treated with Mitomycin C and BCG.
46:52The tumor relapsed after over a year,
46:55and now the recurrent organoid line is much
46:59more resistant to a range of different drugs.
47:02However,
47:03displays similar responses to other drugs,
47:06so.
47:07It's of interest to us to understand how.
47:12Drug response has altered the
47:14properties of these organoids.
47:16So one thing we've started to do
47:19is to perform single cell analysis
47:21here of this recurrent pair,
47:24and Interestingly,
47:25the recurrent organoid line is
47:27actually much more heterogeneous
47:29than the sort of the parental of the
47:32order online from the parental tumor.
47:34And what is interesting is if you
47:37re aggregate these together now
47:40you can identify.
47:41A cluster that is actually in common
47:46between both the between the 1st
47:50and the 2nd organoid lines so.
47:54This cluster, we believe,
47:56corresponds to a transitional
47:58population and we can identify
48:01markers that are specific for
48:03this transitional population,
48:05so our hope here is that we can use
48:08utilized this pair of organoid lines
48:12and other examples of recurrent
48:15disease that we have to sort
48:18of replay in organoid culture.
48:20The events that take place during
48:23the emergence of treatment.
48:25Resistance.
48:28So finally I'd like to address the
48:31issue of tumor progression in organoid,
48:34so non muscle invasive disease you know
48:37can be classified into two lumenal
48:39categories as well as a basil category.
48:42Muscle invasive disease is more complex
48:44and again as I mentioned earlier,
48:47the relationship between these
48:49entities has been somewhat unclear.
48:51However, there is a question of whether
48:53you know if we have progression,
48:56whether there might be a switch
48:59and subtype specifically.
49:00From sort of Class 2 luminal tumors to Basel
49:03to a basal squamous muscle invasive tumor.
49:06So we believe that we can
49:09recapitulate this in organoid culture.
49:11So many of our organoid lines are
49:14phenotypic least able and culture.
49:17If you look at different markers there
49:20stable and organoids as organoid Xena,
49:22graphs ansina graph derived
49:25organoids however.
49:26Sort of a little over a
49:29majority of the organoids from.
49:30Non muscle invasive tumors displayed
49:33this sort of phenotypic plasticity.
49:35This is a they start with the luminal
49:38phenotype here and they become
49:40basil during organoid culture.
49:42But notably this phenotype can be
49:44largely reversed by xenografting,
49:46so we believe this there's an effect
49:48of the tumor micro environment
49:50that can repress this basil to
49:53luminal differentiation.
49:54In fact can reverse it but if we
49:58remove the tumors again from the.
50:00In a graph and culture miss organoids,
50:03they will again undergo the
50:06space little differentiation.
50:07So John Kristen Terrific Post
50:09Document Lab has pursued a small
50:11molecule screen to examine organoid
50:14lines that display plasticity to
50:16see if it's possible to revert it,
50:19and he identified one big hit here GSK.
50:22Else one, which is a.
50:25KTM one inhibitor and it is able to
50:30partially revert this plasticity,
50:32so the.
50:43I'm not showing this here,
50:45but if he knocks out KTM one day,
50:47he can also see this effect. So.
50:52He is currently investigating
50:54the mechanisms by which KTM
50:561A regulates this transition.
51:01In parallel, he's also been pursuing a taxi.
51:04Can Alesis to examine sort of the
51:06epigenetic States and eventually
51:08the epigenetic marks that are
51:10associated with this plasticity.
51:12So if you examine, for example,
51:14these three organoid lines,
51:16this is adluminal line.
51:17This is a basil line and this
51:20is what we call a plastic line.
51:23The plastic line looks like a basil line,
51:26although it started with a luminal phenotype.
51:29So we've performed a taxi.
51:31Can Alesis and we can cluster
51:33these so you see that the
51:36lumenal lines clustered together.
51:38The basil lines clustered together
51:40and these are the plastic lines which
51:42in the first principle component
51:45cluster together with basil.
51:46But they're separated along
51:48the second principle component.
51:52So now if we look at, you know,
51:55sort of genomic tracks, you can see that
51:59at basil markers such as keratin 14,
52:02that the basil lines of course
52:04have open chromatin and the plastic
52:07lines also have open chromatin,
52:09which of course is to be expected
52:12because they have a basal phenotype.
52:14But what's interesting
52:16is that lumenal markers.
52:19Such as gotta three.
52:21We we see that the plastic organoid lines
52:24seem to have partially open chromatin,
52:27so they retain an epigenetic
52:30memory of their lumenal origin.
52:33So we are currently pursuing
52:35studies to determine whether we can
52:37actually detect this epigenetic
52:39memory in human prostate tumors,
52:42which might indicate.
52:44A specific pacifically base of the
52:47basal squamous category to determine
52:50whether they may have in fact
52:53originated from more lumenal tumor.
52:59Finally, we're also pursuing motif
53:01discovery approaches to identify
53:04candidate transcription factors that
53:06might be driving this transition,
53:09and we're coupling this with
53:12other computational approaches.
53:14We think that this really is modeling
53:18something that's happening during, you know.
53:21Sort of transition to muscle
53:24invasive disease because.
53:25As has been noted in breast
53:28cancer for Andy Walt,
53:29what we've found is at the at the
53:32invasive front of these tumors.
53:34Now we see the expression of a basal marker
53:37cytokeratin 14 at the invasive front,
53:40but this is not expressed within
53:43the tumor body.
53:45So in closing,
53:46then I'd like to underscore that we think
53:49that these organoids represent a model
53:51system for studying tumor plasticity.
53:54So we can identify transitional
53:56populations in patients in order rates
53:58from patients with recurrent disease,
54:01and we think that the sort of plasticity
54:04we're studying culture can reflect
54:06processings of disease progression in vivo,
54:09and we're using computational
54:10systems approach is to identify
54:13the drivers of plasticity.
54:15And more generally,
54:16we believe that organoid models.
54:19Are incredibly useful because they
54:22will allow mechanistic studies
54:24of complex questions in cancer
54:27biology that may be inaccessible,
54:29used using other approaches.
54:33Of course,
54:34the work that I've described
54:36involved large team of terrific
54:39scientists really did all the work.
54:41So notably,
54:42Laura Crowley is a graduate student.
54:45My lap who led the single cell
54:47analysis of the prostate epithelium
54:50together with Francesco Comma,
54:52Boolean, former postdoc Moshe Botton,
54:54now at George Washington University.
54:57The work on normal neuroendocrine
54:59differentiation was performed
55:01by graduate student gave alone.
55:04And the prostate ***** organized by
55:07Jolly a terrific postdoc in the lab.
55:11Then former lab members soup soup
55:14Lee started the bladder organized
55:16project and I mentioned John Kristen
55:19is a current post Doc who's played a
55:23major role in continuing this project.
55:25We've had terrific collaborators.
55:27Roll rabadan Andrea Califano for
55:29computational systems biology.
55:31Jim Mckiernan,
55:31whole team of talented urologists
55:34who provided samples.
55:35We've collaborated with Korea Body Shen,
55:38in analysis of Mouse models,
55:40which how Lewin, epigenetic analysis.
55:42Need help search for pathology as
55:45as well as Max load at Cornell.
55:49For pathological analysis and
55:51a great group of collaborators
55:53at Memorial Sloan Kettering,
55:56David Solid Hickman to Marty
55:58and Barry Taylor.
56:00So thank you very much and I'll
56:02be happy to take any questions.
56:05Thank you very much, Michael.
56:07That was a wonderful talk and really
56:10amazing to see all of these models
56:12and everything that can be done
56:14with all of these different models.
56:17I would like to remind the audience
56:20that they can type questions in
56:22the chat and I will read them
56:25out and ask them to Michael.
56:27I will I will get started
56:30with a question Michael.
56:32One of the things that you see in
56:35the organoids and I was thinking in
56:38particular about the prostate cancer
56:41ones is there's a the heterogeneity
56:44and I was just wondering whether
56:47you see shifts in the heterogeneity
56:50or of the organoids themselves when
56:53you use different types of media.
56:56So can you see shifts based on
56:59how you grow them?
57:01And then I was also wondering whether
57:04you have applied certain types of
57:07therapies to those prostate cancer
57:09organoids and whether you see changes
57:12in that heterogeneity and shifts when
57:15you when you use different different
57:18types of treatments.
57:19OK, so Katie, that's a great question.
57:22Well questions so first
57:24question regarding media so.
57:26One of the things that I forgot
57:29to mention is that we use our own
57:33sort of homegrown media for all
57:35of these organoid experiments.
57:38This is a complex medium containing
57:42hepatocyte media and serum.
57:44You know we.
57:47Develop this years ago to grow
57:50mouse prostate organoids so it's
57:53quite different from the ENR
57:56based media that many groups
57:59use to pursue organoid assays.
58:02There are although.
58:05You might imagine that some of the
58:08growth factors involved are in common.
58:10There are undoubtedly differences
58:12between the media compositions in
58:14terms of what's actually going on.
58:17We know that, anecdotally,
58:19for bladder tumor organoids that
58:21it is probably easier to establish
58:24patient derived organoids using
58:26our media than in our based media.
58:31And we also know that you can transition
58:34organoid lines from one media to the other,
58:37but it may not be that straightforward.
58:40So we have some experience with DNR
58:43based media, but all the analysis
58:46that I've showed you today were done
58:49in our sort of homegrown media.
58:52So do we observe shifts in
58:55composition in different media?
58:58We have not really examined that.
59:02In part because it can be different,
59:05it can be difficult to transition
59:07organ lines from one medium to another.
59:10Be a composition to another.
59:13In terms of drug treatment,
59:15we have only started to do this with
59:19respect to the prostate organoids the.
59:22Bladder organizer, something that
59:24we've been examining in more detail.
59:28We've been particularly interested in
59:30mechanisms of cisplatin resistance,
59:32which, of course is of considerable
59:35translational interest.
59:37So those are studies that are that are,
59:41you know,
59:42currently being pursued to examine
59:45how cisplatin treatment alters.
59:48Or the phenotype,
59:49and perhaps the heterogeneity
59:51of the organized,
59:52but I don't have any results yet.
59:55To show you.
59:57Great, thank you.
59:58There's a question from Gefsky.
01:00:01Are have you been able to analyze human
01:00:04lumenal bladder tumors for plasticity,
01:00:07markers and correlate those
01:00:09results with subsequent development
01:00:10of muscle invasive tumors?
01:00:14So that's a great question.
01:00:16Obviously we want to extend what
01:00:18we've been doing in organoid
01:00:20culture to human specimens, so.
01:00:26I have to confess that this work that we've
01:00:28only recently gotten started, so we don't.
01:00:33Part of the problem is actually
01:00:35having a cohort of patients
01:00:37that's suitable for this so.
01:00:40Yeah, we are now in the process
01:00:43of trying to assemble patient
01:00:45cohorts where we can actually.
01:00:49Have samples are sort of launch eternal
01:00:54from patients who have progressed,
01:00:57say from high grade non muscle invasive
01:01:00disease to muscle invasive disease.
01:01:03Assembling these cohorts is very nontrivial.
01:01:06Fortunately we are part
01:01:09of a large collaboration.
01:01:12Led by Corey Body Shan,
01:01:14together with collaborators at Memorial
01:01:17Sloan Kettering and at Johns Hopkins,
01:01:20so collaborates Atmore Memorial
01:01:22include David Solid and colleagues,
01:01:25as well as you know,
01:01:28people like Jonathan Rosenberg,
01:01:30DeAndre Jordan, Barry Wagner,
01:01:32Bernie Buckner, and at Johns Hopkins.
01:01:35Led by David Mcconkey,
01:01:37Nojan and others to try
01:01:40to gather together the.
01:01:42Cohorts that are essential to
01:01:44address this type of question
01:01:46because they don't currently exist,
01:01:48and these types of samples are rare.
01:01:53Thank you we have another question
01:01:55from Mike Hurwitz who says great talk
01:01:58within the different luminal subtypes.
01:02:01Do you think some are more
01:02:03likely to develop into cancer?
01:02:06Any correlate in human prostates?
01:02:09OK, so this is a question about
01:02:11prostate and of course we're very
01:02:13interested in cell of origin.
01:02:15You know, we've always been sort of
01:02:17dissatisfied with our previous analysis
01:02:19of Cell of Origin because you know you
01:02:22have luminal cells and basil cells.
01:02:24There's only so much you can say,
01:02:26but we we think that there's still
01:02:29something to explore here because.
01:02:32This is well known phenomenon in which.
01:02:38You know 85 to 90% of prostate
01:02:41cancer patients.
01:02:45With sort of intermediate risk disease,
01:02:48you know a will actually have indolent.
01:02:53Prostate cancer, whereas the remaining
01:02:5610 to 15% of patients actually
01:02:59have aggressive disease and it's
01:03:01difficult to distinguish between the
01:03:04indolent and aggressive tumors and
01:03:06despite a lot of molecular analysis,
01:03:10they haven't tremendously improved over.
01:03:12Just simple histological police and grading.
01:03:15So we think that it remains
01:03:18possible that cell of origin could
01:03:21explain at least partially.
01:03:24The difference between indolent
01:03:26and aggressive disease.
01:03:27And so that's something we're very
01:03:30interested in pursuing. The.
01:03:31We we know already that you know from
01:03:35the literature that both proximal
01:03:38and distal luminal cells can be
01:03:42cells of origin in mouse models.
01:03:46But that does not necessarily answer
01:03:49the question because you know they
01:03:53may be different in terms of their
01:03:56phenotype or response to treatment or.
01:04:00Ultimate outcomes so you know we're in the
01:04:04process of pursuing these types of studies.
01:04:08It's not really clear what's
01:04:11going on in the human prostate.
01:04:15And again,
01:04:16I think we're just scratching the
01:04:17surface in terms of understanding
01:04:19the relationship between the
01:04:21lumenal populations in the mouse
01:04:23and the populations of the human.
01:04:25There's a lot more work that
01:04:27needs to be done there.
01:04:31Well, thank you very much Michael for
01:04:34this visit for this fascinating talk.
01:04:36I I know I it made me think of a
01:04:39lot of things or some parallels
01:04:41in in the world of lung cancer.
01:04:44So it was really great to think
01:04:47about this this so thank you very
01:04:49much for visiting us today and
01:04:51thank you everybody also who joined
01:04:54and have a wonderful afternoon.
01:04:56Well, thank you Katie. Thank you every.