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"Altered RNA Splicing: a driver event in pancreatic cancer” and "Defining New Pathways in Colorectal Tumors with Mismatch Repair Deficiency"

April 28, 2022

Yale Cancer Center Grand Rounds | April 26, 2022

Presentations by: Luisa Escobar-Hoyos, MSc, PhD and Rosa Munoz Xicola, PhD

ID
7767

Transcript

  • 00:00Just a minute after noon, so I'd like to.
  • 00:05A few people are still arriving,
  • 00:06but I'd like to to welcome everybody today
  • 00:10to the Cancer Center ground grand Rounds.
  • 00:13And for those of you who who don't know me,
  • 00:15I'm Mark Lemmon.
  • 00:16I'm stepping in for Eric Weiner today
  • 00:20because Eric is otherwise engaged.
  • 00:23I'm mark them and I'm deputy
  • 00:25director of of the Cancer Centre.
  • 00:28And so I'm I'm I'm channeling.
  • 00:33Actually, which is why I won't do it so.
  • 00:36But the great,
  • 00:37great to have you all here and great to have.
  • 00:40Louise Escobar Hoyos and rose and
  • 00:44we know Zikula to talk with us
  • 00:48today and we so we begin with with
  • 00:52Doctor Louisa has Escobar holes.
  • 00:55Who is an assistant professor
  • 00:57of therapeutic radiology.
  • 00:59She received her Masters degree in
  • 01:01Biomedical Sciences at the University
  • 01:02at Del Valley in Cali in Colombia.
  • 01:05And then as a Fulbright Scholarship,
  • 01:06pursued a cache in in cancer,
  • 01:09molecular and cellular pharmacology
  • 01:10at Stony Brook University,
  • 01:11where she was mentored by
  • 01:13Doctor Kenneth Schroyer,
  • 01:15then completed her postdoctoral training
  • 01:16at Memorial Sloan Kettering Cancer Center.
  • 01:18Commented by Doctor Stephen Stephen
  • 01:21Leach and Omar Abdel Wahab,
  • 01:23and the overarching goal of Doctor Escobar.
  • 01:27Hoyos's lab is to develop new
  • 01:30approaches to tackling pancreatic
  • 01:32and lung cancers with lots of.
  • 01:34Really very exciting work.
  • 01:36Exciting new work going on and lots
  • 01:39of innovation and specifically her
  • 01:41team is currently trying to seeking
  • 01:44to understand and target somatic
  • 01:46mutations and importantly aberrant
  • 01:48RNA processing in these tumors in
  • 01:50order to to develop novel therapies.
  • 01:53So it's a great pleasure to have
  • 01:55you start us off.
  • 01:57Louisa and I look forward very much
  • 01:59to your talk, so please take it away.
  • 02:03Wonderful thank you Mark. Let me just
  • 02:06can everyone see my presenter mode.
  • 02:09Sorry not my presenter. My full slide
  • 02:12slide that's all very good perfect. So
  • 02:14thank you Mark again for that Nice
  • 02:16introduction and thank you everyone
  • 02:18for participating today in in the
  • 02:20Cancer Center grand rounds and I'm
  • 02:22excited to share with you a little bit
  • 02:24of the research that we've been doing
  • 02:26in our lab in terms of finding how
  • 02:28altered our release by splicing is a
  • 02:31driver event for pancreatic cancer.
  • 02:33So if your disclosures I'm part of the
  • 02:36Scientific Advisory Board of QDX Diagnostics,
  • 02:38I won't be presenting the work
  • 02:40that I have with them today.
  • 02:42I'll be talking about this compound.
  • 02:43This small molecule inhibitor of
  • 02:45splicing that has been provided to
  • 02:47us by age 3 biomedicine and currently
  • 02:49we're in discussions to launch a
  • 02:52clinical trial using this compound.
  • 02:53Based on the research that I'm
  • 02:55going to show you today.
  • 02:56So for many of you,
  • 02:59it's not unheard of that pancreatic
  • 03:01cancer is a very lethal malignancy,
  • 03:04and in here is just plotting
  • 03:07the survival over time.
  • 03:09For the major cancers and what we
  • 03:11can see in this evidence is that
  • 03:13unfortunately we haven't been able to
  • 03:15make that much improvement in the five
  • 03:17year survival rate of pancreatic cancer,
  • 03:19and this could be attributed to many reasons.
  • 03:21It's it's a. It's a disease that
  • 03:24is diagnosed once it has been.
  • 03:26It's already systemic.
  • 03:27The first line,
  • 03:28chemotherapy and immunotherapies,
  • 03:30are ineffective,
  • 03:31and the available targeted therapies
  • 03:33are only available to 1% of
  • 03:35the cases that have actionable,
  • 03:37actionable mutations.
  • 03:38So there is a really.
  • 03:40Strong clinical need to understand more of
  • 03:42these tumors and develop new treatments.
  • 03:45So just to introduce a little bit
  • 03:46of the mutational landscape for
  • 03:48these tumors and mainly today,
  • 03:49I'm talking about pancreatic
  • 03:51ductal at no carcinomas or petax,
  • 03:53the most common form of pancreatic cancer,
  • 03:56so we know that there are driven by a
  • 03:58first mutation, first hit mutation,
  • 04:00activating mutations in cameras,
  • 04:02and also is very common to find
  • 04:04T53 mutations,
  • 04:05so we'll talk about a little
  • 04:06bit more of these mutations.
  • 04:08There is also sometimes it appears that
  • 04:10other tumor suppressors are mutated,
  • 04:12and then after these four top genes.
  • 04:15There's really a sea of mutations
  • 04:16that appear at a very low frequency,
  • 04:19so using mouse models over the
  • 04:22last 1520 years or so we have been
  • 04:24able to kind of dissect a little
  • 04:26bit the genetics of this disease.
  • 04:28We know that the normal pancreas,
  • 04:30if we engineer key rest mutations
  • 04:31into the US and ourselves,
  • 04:33these mice will start develop,
  • 04:35panning or pancreatic intraepithelial
  • 04:38lesions that will progress into
  • 04:41pancreatic cancer if we add
  • 04:43an additional mutation in P50.
  • 04:45And so basically,
  • 04:46this is this tumor follows kind of a
  • 04:50two hit hypothesis notes and model
  • 04:52and all with the with you know to
  • 04:55enhance the activity of kieras over the time.
  • 04:59Now for many years we've known about
  • 05:01these two mutations driving this disease,
  • 05:02but really we haven't made much effort
  • 05:05to understand how these oncoproteins
  • 05:07cooperate in the case of pancreatic cancer,
  • 05:10and even other tumors.
  • 05:11So a few years ago when
  • 05:13I was gonna start as a postdoc at MSK,
  • 05:15these studies came out in the molecular
  • 05:18subtypes of pancreatic cancer with the
  • 05:21squamous and basal subtype being the
  • 05:24most aggressive molecular subtypes.
  • 05:26And when you look into what genes are
  • 05:28differentially expressed into the subtype,
  • 05:31there is a small subset of genes that it's
  • 05:34overexpressed in this molecular subtype.
  • 05:36And when you look into their mutation status,
  • 05:38they're highly associated with
  • 05:39gain of function mutant, P. 53.
  • 05:41And these genes that are being
  • 05:44enriched are the majority are codeine
  • 05:46for splicing regulatory proteins.
  • 05:49So after I read these reports I kind
  • 05:51of got interested on understanding
  • 05:52a little bit more alternative RNA
  • 05:55splicing and just to remind everyone
  • 05:57during alternative splicing.
  • 05:59Not only we remove the introns of genes
  • 06:01but also there could be a selective
  • 06:04retention or skipping of exons and this
  • 06:06can lead to proteins that had opposite
  • 06:09functions or no protein being formed.
  • 06:12And we all have our favorite gene,
  • 06:13and sometimes we don't study the
  • 06:16alternative splicing of these gene
  • 06:17and this pathway in general is a
  • 06:19very potent and plastic that can
  • 06:21actually explain a lot of the features
  • 06:23that happen in cancer cells.
  • 06:25So based on those reports I
  • 06:29asked the question,
  • 06:30is there a connection between mutations
  • 06:31and P53 and alterations in RNA splicing
  • 06:33and so first we took the RNA sequencing
  • 06:36from many patients and we divided
  • 06:38them into three different groups,
  • 06:41either if they had truncating.
  • 06:42Mutations in P53 meaning loss
  • 06:44of function mutations of P53,
  • 06:47gain of function mutations or
  • 06:49mutations that make the protein
  • 06:51going from a tumor suppressor to
  • 06:53an uncle protein or wild type P53.
  • 06:55And we compared this glycine.
  • 06:57Differences between these tumors and
  • 06:59in the case of pancreatic cancer,
  • 07:01the most common hotspot missense
  • 07:04mutations are these four listed here.
  • 07:06So here are the 1st results.
  • 07:08So in in the X axis you have
  • 07:11a measurement of alternative.
  • 07:13Slicing of axons and the different tumors,
  • 07:15and here is each one of the mutations.
  • 07:1853 compared to the wild type P53 tumors,
  • 07:20and each one of the hotspot mutations
  • 07:23compared to truncated P53 and what you
  • 07:25are can appreciate is that all these
  • 07:27hotspot gain of function mutations
  • 07:30change alternative splicing and the
  • 07:32R175 one of the most common ones,
  • 07:33actually changes the most.
  • 07:35So based on those correlation
  • 07:36studies we started asking well.
  • 07:38Is P53 changing splicing
  • 07:40and pancreatic cancer?
  • 07:42So we went ahead and developed.
  • 07:43Three different model system patient
  • 07:45derived organoids where we can
  • 07:47actually shut off the expression of
  • 07:49the mutant P 53 and after doing deep
  • 07:52RNA sequencing and splicing analysis,
  • 07:53we can see that there are these different
  • 07:56exons that are either preferentially
  • 07:58retained in red or preferentially
  • 08:01spliced out in the context of mutant
  • 08:03P53 in complex to complement this
  • 08:06model we generated a murine cell line,
  • 08:09also with the same capacity
  • 08:11to shut down mutant P 53.
  • 08:14And again,
  • 08:14we were seeing these changes
  • 08:16or swapping of axons.
  • 08:17And lastly, we took a
  • 08:20pancreatic precancer mouse,
  • 08:22panning organoids where we knocking
  • 08:25the mutation of our 175 and again we
  • 08:28were seeing that even in early stages
  • 08:30after early 19 of the mutation we were
  • 08:32seeing this differential splicing of exons,
  • 08:35so we wanted to ask,
  • 08:36what are the specific features
  • 08:38of these exons that are being
  • 08:40either retained or skipped?
  • 08:41And we found that there is this this that.
  • 08:44The retention of these axles is not random.
  • 08:47All the promoted axons after splicing
  • 08:49those ones that are going to be
  • 08:51retained in the mature M RNA are highly
  • 08:53enriched for seas while they're repressed,
  • 08:55exiles were highly enriched in a S&G's,
  • 08:58suggesting that this was pretty much
  • 09:01a program established in these cells,
  • 09:03so we wanted to focus on what were
  • 09:06the MRE's that were being coded by
  • 09:08these gain of of policy axons and so
  • 09:11here we're summing into one of these.
  • 09:14Barneys Gap 17.
  • 09:16We're in mice and human.
  • 09:18This Exxon 17,
  • 09:19which is a policy Axon,
  • 09:21is alternatively spliced,
  • 09:23and here are the raw sequencing of
  • 09:27the of the reeds of this Axon and
  • 09:29what you can appreciate is that
  • 09:30whenever the mutant P 53 is present,
  • 09:32there is higher rates versus
  • 09:34when you knock it down.
  • 09:35There is a decrease on the
  • 09:37retention of these Axon,
  • 09:38but not the neighboring axons,
  • 09:40and we saw this pattern across
  • 09:42the marine cell line that we had
  • 09:44engineer in the panning organized.
  • 09:45That we had also crisped.
  • 09:48So from here we actually went and said,
  • 09:50well,
  • 09:51let's go back to patient derived
  • 09:53samples and let's see if the retention
  • 09:55of these policy accounting gap 17 is
  • 09:57it exclusive for the R175 mutation,
  • 09:59or is it also found in other
  • 10:01gain of function mutants of P53?
  • 10:03And the answer was yes,
  • 10:04it's actually retained and not only are 175,
  • 10:07but other gain of function mutant P53.
  • 10:10As you can appreciate here from this
  • 10:13targeted PCR. So then the question what?
  • 10:15It was well,
  • 10:16what is the consequence of incorporating
  • 10:19policy exons into M RNA's over time,
  • 10:22and so when we started looking at all
  • 10:24of the M RNA's that were incorporating
  • 10:26policy axons in the presence of mutant P.
  • 10:2853,
  • 10:29we found that a family of proteins
  • 10:31called the GPA,
  • 10:33the GPA's activating proteins or gaps
  • 10:36were actually gaining these policy Axon.
  • 10:38In fact 25% of total gaps encoded by
  • 10:41the human genome were gaining policy
  • 10:43exons and just to remind everyone.
  • 10:46The gaps do.
  • 10:47They actually accelerate the GTP
  • 10:49hydrolysis of Ras proteins so
  • 10:51they bring it from the on state,
  • 10:54which is bound to GTP to the
  • 10:57off state bound to GDP.
  • 10:59And just to give you a sense of what,
  • 11:01how was this exon impacting the
  • 11:04protein we were seeing that these
  • 11:06policy actions were inframe and when
  • 11:08they got translated they encoded.
  • 11:11For prolines,
  • 11:12highly rich proline tails in the
  • 11:15sea terminus of the parties and
  • 11:17here is just an example to show
  • 11:20you that actually these are
  • 11:22different molecular weights of the protein.
  • 11:25Here is the promoted by
  • 11:26P53 with the policy Exxon.
  • 11:28And here's the repressed
  • 11:30P53 isoform of gap 17.
  • 11:32Without the policy Axon,
  • 11:33so both can be produced and
  • 11:36translated in in the self.
  • 11:37So at this point we were
  • 11:39faced with the question well.
  • 11:41What happens with the CARAS state of either
  • 11:46GTP bound state or GDP bound bound state?
  • 11:49Whenever you have a plus policy
  • 11:51gap 17 or a minus policy gap 17,
  • 11:55and so we did this in cell experiments
  • 11:58where we actually first overexpress
  • 12:01either the policy gap 17 or the minus
  • 12:05policy gap 17 and we actually did a
  • 12:08pull down to capture GTP bound K rest.
  • 12:11And then we did.
  • 12:12We used an antibody that it's only
  • 12:14recognizing the mutant form of kiras
  • 12:16to determine the the the levels of
  • 12:20active cares in these cells and
  • 12:23what we found was interesting,
  • 12:26which is in the presence of policy gap 17.
  • 12:29The isoform promoted by mutant P.
  • 12:3153.
  • 12:31The levels of active care as were
  • 12:35actually maintained.
  • 12:36However, as soon as we overexpress
  • 12:39the minus policy gap 17 that.
  • 12:41Isoform that is repressed by mutant P 53.
  • 12:44We saw that the levels of active
  • 12:46care has decreased and also the
  • 12:48active levels of Arc which is
  • 12:50downstream of of cameras were
  • 12:52also significantly decreased,
  • 12:53and so it was interesting to see kind
  • 12:56of like the different effects on the
  • 12:59active form of Keras in the presence
  • 13:02or absence of this gap 17 isoforms.
  • 13:04So then we went and did a
  • 13:07self free essay where we took
  • 13:09while type carrots or mutant.
  • 13:11The arrest,
  • 13:12and we incubated it with either the
  • 13:15policy gap 17 or the minus policy gap
  • 13:1717 and what you can see is that there
  • 13:20was no much difference in the cell.
  • 13:23Free assays in terms of their capacity
  • 13:26to hydrolyze GTP bound cameras,
  • 13:28and this was very odd and surprising
  • 13:30to us because actually in the cells
  • 13:33they were maintaining different
  • 13:35levels of Keras bound to GTP.
  • 13:37So this made us go back to the drawing
  • 13:39board and start thinking about.
  • 13:41What happens in the context of
  • 13:44cells in the activity of gaps?
  • 13:46It turns out that gaps are
  • 13:48usually cytoplasmic proteins that
  • 13:50when calls to deactivate Keras,
  • 13:52they go to the membrane and
  • 13:54that's when they actually promote
  • 13:56the hydrolysis of the GTP.
  • 13:58What we were seeing was the following
  • 14:00and the presence of mutant P53.
  • 14:02When you have the policy gap
  • 14:0517 being expressed,
  • 14:06the gap mainly localizes into
  • 14:08the title plasm of the cell.
  • 14:10Even when we gave it signals
  • 14:12to go to the membrane.
  • 14:14However, when you knock down P.
  • 14:1653 out of the cells,
  • 14:18you can see that there is this the the
  • 14:21gap 17 that is now not expressing policy.
  • 14:25Exxon now can more likely reach
  • 14:28the membrane and promote the
  • 14:30hydrolysis of Keras.
  • 14:32And so we were excited to find
  • 14:34these because that led us to
  • 14:36a model where we had for the
  • 14:37first time kind of discover
  • 14:39how these two owner proteins,
  • 14:41mutant cares and mutant 53
  • 14:43synergizes in the following.
  • 14:44OK, our model suggests that in the presence
  • 14:47of a wild type B 53 or the loss of P53,
  • 14:50the cells actually lose policy
  • 14:53axons across multiple M RNA's,
  • 14:56mainly the gap in RNA's and after
  • 14:59this M RNA gets translated.
  • 15:01It encodes gaps that actually are
  • 15:04efficient at reaching the membrane and
  • 15:06being more efficient at hydrolyzing GTP
  • 15:10bound cameras and promoting tumor growth,
  • 15:12but not as much as.
  • 15:14When you have the hotspot mutant,
  • 15:16because now this time you are
  • 15:18gaining a policy Axon and when that
  • 15:21mRNA gets translated it has now
  • 15:23these reach domain of prolines that
  • 15:26prevented from reaching the membrane.
  • 15:29Maintaining an active care
  • 15:31estate and more tumor growth.
  • 15:33So just to go back to our model and
  • 15:35the genetics of pancreatic cancer.
  • 15:38So I've told you before that you
  • 15:40needed Karras and mutant P 53 and
  • 15:42what our findings had suggested is
  • 15:44that in the presence of just mutant.
  • 15:46The rest you still have the active rest,
  • 15:48but then when mutant P 53 comes specifically,
  • 15:51the gain of function mutant of P53,
  • 15:53you Now have an altered RNA splicing
  • 15:57and and a feedback loop that now
  • 15:59prevents the gaps from being active.
  • 16:01And then in this way you can enhance the
  • 16:04oncogenic signaling and activity of key
  • 16:06areas and this is our model system currently.
  • 16:09So that was great and we published
  • 16:11this a couple of years ago.
  • 16:13So then we came back into the
  • 16:14lab and we started thinking,
  • 16:16well,
  • 16:16how can we target RNA splicing
  • 16:19and pancreatic cancer cells?
  • 16:21And so recently,
  • 16:23this small molecule compound,
  • 16:26H3 B 8800 it started being tested
  • 16:29in phase one clinical trials,
  • 16:31and they got interested in our research
  • 16:35with pancreatic cancer and mutant 53.
  • 16:37So basically what H3 B 8800 does.
  • 16:40It binds to one of the.
  • 16:41Course splicing proteins as F3V1 and
  • 16:44prevents this whole machinery the
  • 16:46spliceosome to bind and recognize
  • 16:48fully the M RNA's, and so we were.
  • 16:51Our hypothesis was well,
  • 16:52if mutant P53 tumors really
  • 16:54depend on ultra RNA splicing,
  • 16:56they'd be more sensitive to toddler.
  • 16:58They would be more sensitive
  • 17:01to any perturbation into the
  • 17:03splicing machinery with the AD 800,
  • 17:06so we launched what we call a
  • 17:09mouse trial where we took mice.
  • 17:11That either had tumors that had
  • 17:14mutant P53 in them, so that's red,
  • 17:17or that lacked mutant 53 inone
  • 17:20which are blue.
  • 17:21And then we randomize these animals
  • 17:23to either receive 8800 or vehicle.
  • 17:26And what you can appreciate is
  • 17:28that the solid lines,
  • 17:30which are the animals that receive 8800,
  • 17:32they all benefited from having
  • 17:35from receiving the compound.
  • 17:36However,
  • 17:37the animals that survive and
  • 17:39benefited the most were those
  • 17:41ones that had mutant P53 in them,
  • 17:43suggesting that these mutations
  • 17:45sensitizes these tumors.
  • 17:46To this lysine modulator.
  • 17:48And when we did RNA splicing
  • 17:52analysis and after we treated
  • 17:54these tumors with the 8800,
  • 17:55we can nicely see how.
  • 17:58H3 B 8800 was repressing the
  • 18:00retention of that policy Axon in the
  • 18:03cells in a function as a function
  • 18:05depending on the concentration.
  • 18:07So you know other words.
  • 18:09This compound was reversing the key
  • 18:11splicing events that we had seen
  • 18:13in the presence of mutant P. 53.
  • 18:15Lastly, we have now established a human
  • 18:18model where we have isogenix cells that
  • 18:21express different forms of mutant P.
  • 18:2353 and we know that when we
  • 18:25treat them with this compound,
  • 18:27the mutants and particularly are
  • 18:29more sensitive to these compounds
  • 18:31when you compare them to the
  • 18:33counterparts when they don't have P53
  • 18:35or when they have a wild type P53.
  • 18:37So based on these results we
  • 18:39are now in discussions with HB,
  • 18:41biomedicine and row Invad sciences,
  • 18:44who recently bought the 88.
  • 18:46100 compound because we would
  • 18:48like to start a phase.
  • 18:49Two clinical trial where
  • 18:51we combine Gemma Broxton,
  • 18:52which is one of the first
  • 18:54gamma standard of care,
  • 18:55chemotherapeutic lines for pancreatic
  • 18:57cancer patients and start escalating
  • 18:59doses of 8800 for patients whose
  • 19:02tumors have gained a function.
  • 19:04Mutant of P 53 so hopefully
  • 19:06we can launch this soon.
  • 19:08So let's back.
  • 19:09Let's go back to you know what are
  • 19:12the mutations that Dr pancreatic cancer?
  • 19:15What have we understood
  • 19:16and how can we target?
  • 19:17To drive personalized medicine,
  • 19:20so,
  • 19:20as I mentioned before,
  • 19:22we now understand that KIERAS
  • 19:24mutations are the most common
  • 19:25mutations and they are required
  • 19:27hit mutation to form tumors.
  • 19:29We know that 10% of the cases have
  • 19:32familiar pancreatic cancer and
  • 19:33most of them have mutations in ATM
  • 19:36and when they have these mutations
  • 19:38they are giving PARP inhibitors
  • 19:40and that's why we do molecular
  • 19:43molecular profiling industry rumors
  • 19:45to identify this cohort of patients.
  • 19:47To have actionable mutations.
  • 19:50We also know that,
  • 19:52as I mentioned before,
  • 19:53that 30% of the sporadic pancreatic tumors,
  • 19:56which are the most common ones are
  • 19:58driven by gain of function 53.
  • 20:01And as I mentioned before,
  • 20:03we're hoping to start a clinical
  • 20:05trial using 8800.
  • 20:06These glycine inhibitors to see if
  • 20:09we can bring a targeted therapy
  • 20:12for these sporadic tumors.
  • 20:14Now we're still facing the challenge
  • 20:16that we still don't understand.
  • 20:1830. What is the mutation?
  • 20:20That drives the other 30% of
  • 20:23pancreatic tumors because we also,
  • 20:26because we already know that the other
  • 20:2830% is driven by loss of function P.
  • 20:3053.
  • 20:30So for these two last groups.
  • 20:32Unfortunately,
  • 20:32right now we don't have any targeted therapy
  • 20:36or any trials that are going to launch here,
  • 20:39so we were curious to know
  • 20:40well what is sporadic,
  • 20:42what other mutations, Dr,
  • 20:44sporadic tumors of pancreatic cancer,
  • 20:47and so to answer that question
  • 20:49I mentioned before that.
  • 20:50You know there is a such a a
  • 20:53large number of mutations that
  • 20:55appear in very low frequencies,
  • 20:58but it's hard to study each one of
  • 21:00these mutations to understand well.
  • 21:01Are they driver mutations or
  • 21:03are they passenger mutations?
  • 21:05And so we took an inform
  • 21:07approach where we went
  • 21:08back to the basic contact concept.
  • 21:10Sorry of mutual esclusiva,
  • 21:12so just to remind everyone we know that
  • 21:15mutations may be mutually exclusive,
  • 21:17meaning that if P53 is present.
  • 21:20The mutation in P53 is present,
  • 21:22then it would turn into a viable tumor cell.
  • 21:25If other mutation is present but not P53,
  • 21:28it could be viable,
  • 21:29but if both mutations are present,
  • 21:31it could be synthetic, lethal,
  • 21:33and the mutual exclusivity of these
  • 21:35mutations is because sometimes these mutual
  • 21:38exclusive mutations either have the same
  • 21:40function or impact the same pathway and
  • 21:42that's what makes them driver mutations.
  • 21:44So we started conducting a mutual exclusivity
  • 21:48analysis by taking advantage of C.
  • 21:50Bioportal.
  • 21:51Which has the.
  • 21:53Mutation signatures of over 3000
  • 21:56patient samples of pancreatic cancer,
  • 21:59and so the 1st results that we
  • 22:01derive from this analysis are are
  • 22:03shown here in this volcano plot.
  • 22:05So on the right hand side we have the
  • 22:08mutations that Co occur with mutant P.
  • 22:1053 and in the left hand side we
  • 22:11have the mutations that are mutually
  • 22:13exclusive for P53 and this was the
  • 22:16side of the volcano that we were
  • 22:18interested in because this potentially
  • 22:19could tell us what mutations were
  • 22:22driving this disease aside from mutant.
  • 22:2453 so as a as a proof of concept,
  • 22:27key results in the middle is
  • 22:29the first mutation.
  • 22:30It appears for all of the tumors,
  • 22:32but here's where it got surprising to us.
  • 22:34One of the most mutually exclusive
  • 22:36mutations to P53 was mutation in SF3B1.
  • 22:39It's a score splicing protein.
  • 22:42Then it on this same side we
  • 22:45have mutations in RBM 10,
  • 22:47which is another splicing factor.
  • 22:49But on the core occurring
  • 22:51side we had a U2AF1,
  • 22:53again another mutation in another.
  • 22:54License factor,
  • 22:55so if our hypothesis was true,
  • 22:57it's potentially that
  • 22:59mutually exclusive mutations,
  • 23:01meaning S3B1 and RBM 10 could be
  • 23:04drivers of pancreatic cancer and just
  • 23:07assuming what type of mutations are
  • 23:10present in S4B1S4B1 in pancreatic
  • 23:13cancer has a driver mutation.
  • 23:16Very hot spot mutation in case 700.
  • 23:18E RBM 10 is mainly truncating mutation.
  • 23:21So basically you're losing the
  • 23:24function of RBM 10 and U2AF1 has
  • 23:27a hotspot mutation in S34F.
  • 23:30So we our question was,
  • 23:32are any of these three mutations
  • 23:34driving pancreatic cancer?
  • 23:35And so we took advantage and we
  • 23:38started generating genetically
  • 23:39engineered mouse models.
  • 23:41So here's the case.
  • 23:42C model system where it's only
  • 23:45driven by ACARAS mutation.
  • 23:47And what we found and what we
  • 23:49expected was that these animals
  • 23:50should only form pannings or
  • 23:52pancreatic and triphenyl neoplasias,
  • 23:54those precancer states.
  • 23:56So then we cross this KC animal with a
  • 24:00U2AF1 mutant animal for the S34F mutation.
  • 24:04And what we found is that
  • 24:06actually there are pannings,
  • 24:07but not as much as we expected.
  • 24:09And most importantly,
  • 24:10there was no Peacock in these animals.
  • 24:13But surprisingly,
  • 24:14the animals that had them,
  • 24:16the Keras mutation and the SFRB 1 mutation,
  • 24:19foreign pancreatic tumors,
  • 24:21same as the animals that we cross to have K,
  • 24:26res,
  • 24:26and RBM ten loss.
  • 24:28So here's just the quantification
  • 24:30done by our pathologist,
  • 24:32who you can see that there is only pdac
  • 24:34and the animals that have the nutrition
  • 24:37in S4B1 and the nutrition in our BM.
  • 24:3910 There is more pannings also in these
  • 24:41animals and they succumb to the disease.
  • 24:43Very early on,
  • 24:45so we are now in this hypothesis
  • 24:48that we're trying to further test which is.
  • 24:51We believe now that pancreatic cancer
  • 24:54cells that have a mutant carras
  • 24:57actually require a splicing switch
  • 24:59in order to become tumor cells,
  • 25:03and most likely the majority
  • 25:05of these of these tumors will
  • 25:07develop through a mutant P53,
  • 25:09which I showed you before how it
  • 25:11drives alternative RNA splicing,
  • 25:13but we're now fathering.
  • 25:16Starting how these SF 3B1 mutation,
  • 25:20and RBM ten loss also drive the the
  • 25:23the disease based on a splicing change,
  • 25:26and these animals are now being
  • 25:29characterized by a couple of
  • 25:32postdoctoral fellows in my lab,
  • 25:34and so I just want to quickly mention
  • 25:37that they have obtained really
  • 25:38interesting results in terms of
  • 25:40what are the splicing defects that
  • 25:42these proteins mutated proteins.
  • 25:44Are leading to.
  • 25:46They are very similar to the
  • 25:48P53 splicing changes.
  • 25:50We do tons of deep RNA sequencing
  • 25:53into this model systems.
  • 25:55We do several.
  • 25:56We run several algorithms to
  • 25:58determine the splicing changes into
  • 26:01not only the marine model systems,
  • 26:03but also patient derived samples.
  • 26:07I just want to skip quickly through
  • 26:09this just so I can get here to
  • 26:11how are we going to target these
  • 26:13mutant splicing factor proteins.
  • 26:15So similarly,
  • 26:16we use the 8800 compound and we are
  • 26:19now finding that also these mutant
  • 26:21cells are very sensitive to this compound.
  • 26:24We are also finding that these
  • 26:27mutations confer sensitivity to certain
  • 26:30chemotherapeutic agents. In this case.
  • 26:33In particular, the case 700 E.
  • 26:35As of Feb,
  • 26:36one is more sensitive to
  • 26:37gemcitabine than it is to five FU.
  • 26:39So this is important because these
  • 26:42mutation profiling can also help
  • 26:43to decide what would be the best.
  • 26:45Chemotherapeutic agent who assigned
  • 26:47to a particular patient and when
  • 26:50we did combination studies on
  • 26:52mixing gemcitabine with 8800 in
  • 26:56mutant versus wildtype cells,
  • 26:58we can see that the mutant cells are
  • 27:01more sensitive to the combination
  • 27:03of jam and 8800 more so than the
  • 27:06wild type cells suggesting that this
  • 27:08combination of therapy could be
  • 27:10important to treating the patients
  • 27:13that have these K 700 E mutation.
  • 27:15That's up 31,
  • 27:17so I just want to finalize by
  • 27:20saying that I'm currently based
  • 27:22on our findings on Mutant P.
  • 27:2453 and mutant SFB.
  • 27:25One and RBM 10 laws as the drivers,
  • 27:28as pancreatic cancer.
  • 27:30All of these mutations leading to
  • 27:32changes in alternative splicing.
  • 27:34We're hoping to also bring into the
  • 27:37trial patients eligible patients
  • 27:39that are case 100 mutant or have RBM
  • 27:4210 lost to be eligible for this.
  • 27:45Glycine anti silicene therapy that
  • 27:47we wanna lounge in combination
  • 27:49with Gemini and Gemini 8800 and
  • 27:51so with that I wanna wrap up by
  • 27:53saying thank you to everyone here
  • 27:55for your attendance today to all the
  • 27:58people in my lab who are leading
  • 28:00this effort to all our collaborators
  • 28:02and also to our funding sources.
  • 28:05Thank you very much and I'll
  • 28:07take any questions. Thank you.
  • 28:09Thank you so much Teresa,
  • 28:10that was really fascinating
  • 28:13work at great, excellent stuff.
  • 28:16If people have questions for Louisa,
  • 28:20please put them in the chat and
  • 28:23I can read them to her and she
  • 28:26can go ahead and answer them.
  • 28:28I had one quick question.
  • 28:31While people are formulating their thoughts,
  • 28:33which is it is intriguing that
  • 28:37the mutations and the and effects,
  • 28:38and indeed the the the.
  • 28:438800 are all focusing on you two.
  • 28:46Do you have some? He's out.
  • 28:51It may be migraines,
  • 28:52but what does that mean, mechanistically?
  • 28:55Yeah, thank you Mark.
  • 28:57So basically the compound targets
  • 29:00mutant SF 3B1 and so it's the
  • 29:03tumors have mutant SF 3B1.
  • 29:06They are more sensitive to this compound,
  • 29:08so that's the case for S3 one.
  • 29:10But we also know that the if if
  • 29:13tumors highly depend on splicing.
  • 29:15There there are more sensitive to
  • 29:18this compound because they cannot
  • 29:20tolerate a double perturbation of the
  • 29:23splicing changes and the splicing.
  • 29:25Machinery and so that's how we are
  • 29:29attributing the sensitivity of of
  • 29:32mutant P 53 and mutant RBM 10 to 8800,
  • 29:35and I think more dissection of the
  • 29:39mechanism of of the drug within you.
  • 29:42Know RBM 10 and P53 can further elucidate
  • 29:45why are they so sensitive to this compound,
  • 29:48at least for P53.
  • 29:50We know that in certain cases
  • 29:52it reverses the effects.
  • 29:53The splicing changes that mutant P 53.
  • 29:56Is promoting.
  • 29:59Make makes sense and the other
  • 30:01question I had was that we're going
  • 30:03back to the gap 17 story. Do you see?
  • 30:06In other circumstances the if you look
  • 30:09through other cells and and indeed
  • 30:12tumors that aren't don't have the
  • 30:15the gain of function P53 mutations.
  • 30:18Do you see the the gap 17 with
  • 30:22the policy Exxon in other places?
  • 30:25Yeah, so that's a good question.
  • 30:27So for example.
  • 30:28We've looked into other cancers
  • 30:30that are not key, rest driven,
  • 30:32but have this mutant form of P53,
  • 30:34and we see that indeed the
  • 30:37splicing changing gap 17 occurs.
  • 30:39Now we have also seen some other tumors
  • 30:42where mutant P 53 is not present and
  • 30:45we still see the splicing change,
  • 30:47and we think that this is attributed
  • 30:50to the overexpression of a splicing
  • 30:52factor called H&R AMPK that today
  • 30:54I didn't have time to go into,
  • 30:57but we think that this splicing.
  • 30:59Regulator also promotes the policy.
  • 31:03The the policy acts on inclusion in M RNA,
  • 31:06so we think that there is not
  • 31:08a single pathway to to promote.
  • 31:10The policy acts on retention in gaps.
  • 31:15This is fascinating and I have a
  • 31:18question in the chat from from
  • 31:20Timothy Robinson who says great talk.
  • 31:23I agree with the way you described
  • 31:26using mutually exclusive analysis to
  • 31:27find events within the same pathway.
  • 31:30Did you look at Gap 17?
  • 31:32Aberrant splicing based on mRNA
  • 31:35directly to identify other drivers?
  • 31:39So I'm not sure if I'm
  • 31:40understanding the question.
  • 31:41If I if we looked into into
  • 31:44other pathways that are not
  • 31:47linked to to the cares pathway.
  • 31:49I think the question is
  • 31:50actually did you look?
  • 31:51Did you look at aberrant
  • 31:53splicing based on M RNA to
  • 31:54identify other drivers that
  • 31:56might be other than gap 17?
  • 31:57I think that's
  • 31:59yeah. So all the splicing changes
  • 32:01that we identified are based on mRNA
  • 32:04sequencing and based on splicing
  • 32:06analysis that we conduct. But if I.
  • 32:09But I can also mention that the gaps are
  • 32:12only 5% of the events that mutant be 50,
  • 32:15three, 5% of the event is splicing
  • 32:17events that mutant P 53 is triggering.
  • 32:20So there are other M RNA's that affect
  • 32:22other pathways that are being impacted by
  • 32:25the aberrant splicing by mutant P. 53.
  • 32:27So we just went with the gaps to start
  • 32:30with because of course of the relevance
  • 32:32and the path and the Keras pathway.
  • 32:35But we are there is a student in the lab
  • 32:37who's actually trying to understand.
  • 32:39What?
  • 32:40What is the role of the other splicing
  • 32:42changes in other M RNA's that are not gaps?
  • 32:47And presumably, in that context,
  • 32:49I mean, it's kind of interesting
  • 32:50that the gap 17 effect is so
  • 32:53kind of singular in a sense,
  • 32:55and you presumably in the other cases
  • 32:57it's really going to be a combination
  • 32:59that's going to be the constellation
  • 33:00of those changes that are key,
  • 33:01which is going to be interesting
  • 33:03but tough to tease out
  • 33:04exactly. So, as I mentioned before,
  • 33:06we are seeing that 32 gaps encoded
  • 33:10by the genome of of 120 dots that
  • 33:13are encoded are being differentially
  • 33:16spliced, we manipulated.
  • 33:17One, but if you imagine manipulating
  • 33:20several of them and forcing
  • 33:22policy axons to be excluded,
  • 33:24the the effect might be synergistic
  • 33:26in terms of the the the cell
  • 33:29proliferation and the tumor growth.
  • 33:33Sure. Any other questions in the chat?
  • 33:39So we don't seem to have at
  • 33:40the moment of so that we could,
  • 33:41and we we we should probably move on.
  • 33:44So thank you very much, Lisa.
  • 33:46That was a fascinating stuff with
  • 33:48the enormous about everyone.
  • 33:49And to think about it. Thank you.
  • 33:52So, so let's move on for the second
  • 33:57half to Doctor Rosa Vinod Zikula.
  • 34:01So Doctor Zickler is an assistant professor
  • 34:04of medicine and digestive diseases.
  • 34:06She received her PhD from the
  • 34:08university app Autonomo de Barcelona.
  • 34:11I apologize for my pronunciation
  • 34:13and oncology and her postdoctoral
  • 34:14training at the Institute of Cancer
  • 34:16Research at the University of
  • 34:18Illinois and at Yale University.
  • 34:20Dr Zickler's long term goal is to decipher.
  • 34:22Known genetic alterations that
  • 34:24predispose to colorectal cancer
  • 34:26development and her research focus
  • 34:29is on understanding molecular and the
  • 34:31molecular characterization of sporadic
  • 34:34and hereditary colorectal cancer
  • 34:36with an interest in understanding
  • 34:38the biological differences among
  • 34:40racial groups to develop her
  • 34:42translational research doctors.
  • 34:43Zigler is a key player in several
  • 34:47repositories and consortia that
  • 34:49recruit cancer patients and
  • 34:51then collecting biospecimens.
  • 34:53And and clinical data and Doctor
  • 34:55Nikola will will tell us about defining
  • 34:59new pathways in colorectal tumors
  • 35:01with mismatch repair deficiency.
  • 35:04So thanks so much Rosa for doing this.
  • 35:05I really look forward to your talk.
  • 35:08Thank you, let me share.
  • 35:15Can you see properly?
  • 35:18OK, so thank you so much for giving
  • 35:20me the priority to show you all our
  • 35:22most recent data on the topic of
  • 35:25mismatch repair, deficient tools.
  • 35:27So the outline of the talk is going to.
  • 35:30I'm going to explain you give you an
  • 35:32overview of the mismatch repair and the
  • 35:34phenomena of microsatellite instability.
  • 35:36And then I will explain you the
  • 35:38clinical phenotypes and challenges in
  • 35:40the molecular that I diagnosis of the
  • 35:43tumors that have mismatched efficient.
  • 35:45Then I will explain you the association.
  • 35:49That we are describing between
  • 35:51deficiency of RAQUE and DNA helicases
  • 35:54in Lynch like syndrome cases.
  • 35:56And then I will show our most
  • 35:59recent publication that describes
  • 36:00the identification of tumors with
  • 36:02a high likelihood development and
  • 36:05immune response through mutational
  • 36:07signature profiling.
  • 36:10So here in the left you can see that
  • 36:12it's a cartoon that shows the the mosque
  • 36:15important for the main proteins that are
  • 36:18involved in the mismatch repair system.
  • 36:20The mismatch repair system is the inner
  • 36:23repair system that identifies mismatches like
  • 36:26single base base or like larger mismatches.
  • 36:29And there's two main complexes,
  • 36:31the mute test that it's formed by message 6
  • 36:34and Message 2 and Message 3 and a message 2.
  • 36:38So these proteins are the
  • 36:39first ones to recognize them.
  • 36:40As my tools and then the mute L complexes
  • 36:44recruited to help fix the the mismatches
  • 36:48and mutl is formed by PMS two and MLH 1.
  • 36:51So in the genome there are these
  • 36:54sequences that are called microsatellites
  • 36:56that are prone to acquire alterations
  • 36:58when any of the proteins of the
  • 37:01mismatch repair not working.
  • 37:03So here you can see here you can
  • 37:07see sorry this is on the way.
  • 37:09Here you can see a microsatellite
  • 37:11microsatellites are short and repetitive
  • 37:13sequences present in coding and
  • 37:15non coding regions of the genome.
  • 37:17And when the any of them is not
  • 37:20working this Microsoft.
  • 37:22That's accumulate deletions or insertions.
  • 37:24So when the size of the microsatellite
  • 37:27cannot be properly kept during
  • 37:29replication of DNA in the cells,
  • 37:32the phenomenon of microsatellite
  • 37:34instability isn't identified in tumors.
  • 37:40So I MSI can be identified
  • 37:42in a variety of tumors,
  • 37:44but as you can see here on the table
  • 37:46and in the graph in the material tumors,
  • 37:48colorectal and stomach are the
  • 37:51tumors that have a higher incidence
  • 37:54of microsatellite instability.
  • 37:56So, in colorectal tumors,
  • 37:58about 10% of a sporadic tumors have
  • 38:01mismatch repair deficiency and these
  • 38:03deficiencies due to CPG island promoter
  • 38:05musculation of the gene mileage one,
  • 38:08which I show you that it's a.
  • 38:10It's one of the two proteins
  • 38:12that form the metal complex,
  • 38:14so when there's a promoter methylation,
  • 38:17there's an addition of transcription
  • 38:19of the gene and it and resulting in
  • 38:22the loss of expression of the protein.
  • 38:24So here you can see the difference.
  • 38:26Between normal expression by
  • 38:28immunohistochemistry and loss
  • 38:30of expression and a significant
  • 38:32number of these tumors,
  • 38:35they also present this hot spot
  • 38:37mutation in the Bureau of Gene.
  • 38:39Here you have the mutation and these
  • 38:42two are molecular events are used
  • 38:44to differentiate and tumors that
  • 38:47develop through a sporadic events.
  • 38:50Then the tumors that develop MSI
  • 38:52but they are developing in the
  • 38:55setting of hereditary disease.
  • 38:58So Vince Syndrome is the the tumor.
  • 39:02It's cancer syndrome.
  • 39:03There is due to germline mutations
  • 39:06in this mismatch repair genes.
  • 39:08It's actually the most common
  • 39:10cancer syndrome of all it's present.
  • 39:12It's estimated that one in 270 people
  • 39:14in the US carry one of the mutation
  • 39:17in one of these genes and these
  • 39:20individuals have present this syndrome
  • 39:23presents as penetrance about 70 to 80%,
  • 39:26which means that.
  • 39:27That in in 70 to 80% of the cases
  • 39:30individuals that carry a mutation,
  • 39:32they end up developing
  • 39:33cancer and when they develop,
  • 39:34cancer is usually associated
  • 39:36with an early age of onset.
  • 39:38So clinically,
  • 39:39Lynch syndrome patients present with
  • 39:42fewer polyps than other colorectal
  • 39:44cancer inherited syndromes,
  • 39:46and the tumors localized in
  • 39:48the right side of the column.
  • 39:50And this lynch patients.
  • 39:51They have a high risk of
  • 39:53developing multiple cancers.
  • 39:55Colorectal cancers are
  • 39:56diagnosis or over time.
  • 39:58And another clinical feature
  • 40:00that it's important to remember.
  • 40:02Sorry,
  • 40:02remember from these patients is
  • 40:04that the Lynch syndrome is actually
  • 40:07a multi cancer syndrome affecting
  • 40:09different organs and here you can
  • 40:11see the list and it's significantly
  • 40:13important to remember that because
  • 40:15actually female lynch patients they
  • 40:17developed for example like in the material,
  • 40:19they have a higher incidence
  • 40:21of developing endometrial than
  • 40:22colorectal cancer.
  • 40:25So because I explained you that
  • 40:27Link syndrome is the most common
  • 40:29cancer syndrome and because of
  • 40:31all this clinical features that
  • 40:34these patients have nowadays,
  • 40:35all in the midfield and Jay
  • 40:38cancers are are supposed to be
  • 40:40tested for the for Lynn syndrome.
  • 40:43So how this works is all these cancers.
  • 40:46They are tested with immunohistochemistry
  • 40:49for the expression of the four main
  • 40:52proteins of the mismatch repair if.
  • 40:54Because of the expression in MSH
  • 40:572 MSH 6 or PS2 is identified,
  • 41:00then the patient should be referred
  • 41:02to cancer genetics for testing and
  • 41:05contrary if the loss of emulate
  • 41:07one or PMS or the conduction
  • 41:09of emulate one and PMS two is
  • 41:11identified by immunohistochemistry,
  • 41:13then there is the one.
  • 41:15Methylation should be tested and
  • 41:17if there is no methylation then
  • 41:19the patient should be referred
  • 41:21to cancer genetics and in any
  • 41:23way if anyone in the identifies.
  • 41:25MSI case, but there was no.
  • 41:28I'm even Histochemistry tested,
  • 41:30but the physicians have a clinical concern.
  • 41:33Then these patients should be
  • 41:35preferred to cancer genetics.
  • 41:39So in general, in the cancer genetics
  • 41:41clinic was we've been facing,
  • 41:43is that about 50% of the suspected link
  • 41:46syndrome patients that are referred.
  • 41:48They actually test negative for Jim for
  • 41:52having germline mutations in the genes.
  • 41:54And this case is where name as Lynch
  • 41:56like syndrome because they are similar
  • 41:59to lynch like but there's no mutations.
  • 42:02So as as a definition these lines
  • 42:04like syndrome patient patients,
  • 42:06they develop tumors at the MSI.
  • 42:09They don't have resolution of image one.
  • 42:11They don't have the hotspot be
  • 42:13600 imitations and they don't
  • 42:15have a germline mutations either.
  • 42:17So what are these things like cases they
  • 42:20actually could be Lynch syndrome cases,
  • 42:23but that due to difficulty on
  • 42:25identifying mutations or because they
  • 42:27have like encrypting mitigations.
  • 42:29Maybe we have not been able to
  • 42:30then defy them,
  • 42:31or they could actually be heritary
  • 42:34cases that they might be due to general
  • 42:37mutations in other genes and that.
  • 42:39They end up developing MSI as a driver
  • 42:44effect, not as a cancer driver effect,
  • 42:49sorry.
  • 42:51Sorry that they developed because
  • 42:54other germline mutations but they
  • 42:57actually the MSI was an effect of
  • 43:00the development of cancer but they
  • 43:02could just be sporadic cancers.
  • 43:05So to address these these challenges,
  • 43:08we have developed 2 main projects,
  • 43:11one and the general level and
  • 43:13another one at the semantic level.
  • 43:15The general level with our aim was
  • 43:17to identify the current deficient
  • 43:19DNA repair genes and the cellular
  • 43:22consequences that contribute to the
  • 43:24development of colorectal cancer in
  • 43:26lines like patients and at the somatic level,
  • 43:29we aim to define molecular factors
  • 43:31in the three types of mismatch.
  • 43:34Deficient tumors the lynch lynch like
  • 43:36and the viraf methylated ones which will
  • 43:39contribute to diagnosis and treatment.
  • 43:44So so our collaborations with the
  • 43:47correct with the current concerns,
  • 43:50we were able to describe the patients have
  • 43:52a higher frequency of family history of
  • 43:55colorectal cancer than sporadic cases,
  • 43:58and you can see here how the standardized
  • 44:01incidence ratio was 2.2 for the links in
  • 44:04comparison to 0.48 for sporadic individuals,
  • 44:08and and and these incidents
  • 44:11of family history was.
  • 44:13Actually lower than lead,
  • 44:14so this kind of foods.
  • 44:16The Linge like phenotype and
  • 44:19in between between.
  • 44:21Lynch and Sprite.
  • 44:23We're also able to to show that the
  • 44:27average age of diagnosis for Lynch like is
  • 44:31significantly younger than sporadic cases.
  • 44:33So these two features are suggest
  • 44:36that a potential unidentified genetic
  • 44:38predisposition induced in this in a group,
  • 44:41at least in Group of Lynch
  • 44:44like syndrome patients.
  • 44:45So to address this,
  • 44:47and because we believe that that is the case,
  • 44:49we develop a a study including 654
  • 44:55individuals from our Chicago Colorectal
  • 44:58Cancer Center consortium cohort,
  • 45:00and we performed that link screening
  • 45:03testing that I mentioned before
  • 45:06we identified 23 suspected links.
  • 45:09Lynn syndrome.
  • 45:12So from those we were able to have
  • 45:16germline DNA from 15 of them and
  • 45:18we perform XM sequencing and we
  • 45:20identified that four of them were
  • 45:23actually engaged and eleven were links
  • 45:25like were classified as Lynch like
  • 45:28because we didn't find limitations.
  • 45:30So then we take it one step further
  • 45:33and we wanted to identify if if
  • 45:36any of these links, like patients,
  • 45:39had mutations in other DNA repair genes.
  • 45:42So we analyze 162 DNA repair genes and
  • 45:46we were able to see that this links,
  • 45:49like patients.
  • 45:50They had the higher mutational burden
  • 45:52and comparison to lynch to the TCG,
  • 45:55a colorectal cancer cohort,
  • 45:57and to control without cancer.
  • 46:00So specifically,
  • 46:01we identified four loss of function variants,
  • 46:03one in body, one one in Werner,
  • 46:06one in MCPH one and one in Rev 3.
  • 46:11So then after this first study
  • 46:13that we identified that links
  • 46:15like were in bridge with mutations
  • 46:16in the inner river jeans,
  • 46:18we include decided to include two
  • 46:21different independent series of lines,
  • 46:23like patients to try to identify
  • 46:25genes that maybe would be recurrently
  • 46:28mutated in this in this phenotype.
  • 46:31So when we did that in the first
  • 46:34series with unified 6 genes that
  • 46:36were mutated and had lots of
  • 46:38function variants and interestingly.
  • 46:40We found the same splicing variant
  • 46:43in two different patients in the
  • 46:45regular 5 gene and we actually
  • 46:48perform a kinship analysis to show
  • 46:50that and to prove that these two
  • 46:53patients were not genetically.
  • 46:55And they were not genetically.
  • 47:02There were no this related.
  • 47:05Sorry, because these patients were
  • 47:07both coming from from Spain and we just
  • 47:10wanted to make sure that there was no
  • 47:12any family relation that we don't know.
  • 47:16And then when we developed the analysis
  • 47:18of the other series of patients,
  • 47:20we again identified another loss
  • 47:23of function variant in regular 5.
  • 47:26So with that, if you've been able
  • 47:28to follow my my talk and the the
  • 47:31the notification of mutations
  • 47:33in our original serious,
  • 47:34we have identified 4 different
  • 47:37mutations in genes that belong to
  • 47:40the Dracula DNA helicase family.
  • 47:43So here you can see the five the
  • 47:47five proteins that are in this
  • 47:49family regular one bloom Werner
  • 47:51regular four and regular 5 and all
  • 47:54of them share the same helicase.
  • 47:57I mean.
  • 47:58So these are the individuals
  • 47:59that we have identified the two
  • 48:01individuals with the same splicing,
  • 48:03one with the insertion and
  • 48:05from the original cohort.
  • 48:06We also identified this individual
  • 48:09with a mutation.
  • 48:11So after that we were interested in
  • 48:13knowing if maybe the mutations in
  • 48:16this in this family of genes were also
  • 48:19recurring in other cancer friendships.
  • 48:21So to do that?
  • 48:23First took individuals that were
  • 48:25referred to the Smilo Cancer
  • 48:27Genetics and Prevention program
  • 48:28that when they were referred,
  • 48:31they and they were tested
  • 48:32and we in the clinic.
  • 48:34They didn't find any mutations,
  • 48:36say many known cancer predisposition genes.
  • 48:39So we perform XM sequencing in 156
  • 48:43breast cancer patients in 75 individuals
  • 48:46that had different types of tumors
  • 48:49that were not breast breast tumors.
  • 48:52We'll sync clouded,
  • 48:54MSH and PC.
  • 48:55These are very rare type of familial
  • 48:59colorectal cancer that affects individuals
  • 49:02in different generations and that
  • 49:04they develop cancer at the young age.
  • 49:06But these individuals don't have MSI RMS.
  • 49:10And lastly,
  • 49:11we also identify mutations in the DC G.
  • 49:14So with this analysis we were
  • 49:16able to see that actually,
  • 49:18like the higher a little frequency variants
  • 49:20in DNA repair genes that are not the.
  • 49:23Compare and then they are not know
  • 49:26well established cancer predisposing
  • 49:28genes and we all the identified
  • 49:32mutations in the REQ DNA helicases
  • 49:35in the lynch like phenotype.
  • 49:38So then we went back to the
  • 49:42families that we were able to.
  • 49:44Contact again to in the defy
  • 49:47if the mutations were shared.
  • 49:49If these mutations with shared
  • 49:50with other family members,
  • 49:52so here these are the three families that
  • 49:54will have with mutations in the right QL.
  • 49:56So family A&B are the the ones that
  • 49:59share the same splicing variant,
  • 50:01so here this is the program that
  • 50:03developed for family aid that
  • 50:05developed colorectal cancer.
  • 50:06At 63.
  • 50:07We were also able to sequence
  • 50:09the tumor of this individual and
  • 50:11we also found a missense variant
  • 50:14in the in the tumor of this.
  • 50:16Of this patient,
  • 50:17and then the brother of this program
  • 50:20had a small bowel cancer and he was
  • 50:22also a carrier of the mutation.
  • 50:24The family we we.
  • 50:26This was the program that they
  • 50:28are of collector cancer at
  • 50:2964 very strong family history
  • 50:32and then we tested the two sons.
  • 50:35That one was a carrier and the
  • 50:37other one was not a carrier and the
  • 50:40rate of diagnosis was under 40s.
  • 50:42And lastly this last one we.
  • 50:44This was the program developed
  • 50:47colorectal cancer at 66 and we
  • 50:50tested this son that also had
  • 50:52sorry also had the mutation.
  • 50:55But however, these individuals
  • 50:57in the second generation,
  • 50:59because they are in their 40s,
  • 51:01they might have not been able
  • 51:03to develop cancer yet.
  • 51:04So this this course aggregation
  • 51:06study was not definitive.
  • 51:10So then we wanted to to test what was
  • 51:13the effect of having a heterozygous
  • 51:15loss of function barrier in intestines.
  • 51:19So to do that we went back to to our
  • 51:22contacts in Spain and we extracted cells,
  • 51:26extracted blood samples from the two of
  • 51:29these songs that I show you in family.
  • 51:32That one was a carrier and
  • 51:33the other one was not.
  • 51:35And we extracted that RNA.
  • 51:37We did the red transcription and qPCR.
  • 51:40To show that actually the the level of gene
  • 51:43expression was significantly lower in the in,
  • 51:46in the brother that had the mutation.
  • 51:50And to test the effect in the Warner,
  • 51:53we had to use a different approach because we
  • 51:56didn't have access to that family anymore.
  • 51:58But we were likely to acquire
  • 52:01Lymphoblastoid cell line from family that
  • 52:04had there were these one mutation and
  • 52:08heterozygosity and from a control also.
  • 52:11And this mutation is the one
  • 52:12that the cell lines have.
  • 52:14And it's just like a loss of function
  • 52:16mutation just for an amino acids
  • 52:18down the line from the actual.
  • 52:20Colorectal cancer mutation that
  • 52:21we found in one of the patients.
  • 52:23So we extracted.
  • 52:24We grow the cells.
  • 52:25We extracted proteins and we show that
  • 52:28again that there is an effect on the
  • 52:31heterozygous and the protein expression.
  • 52:34So with these we show that when
  • 52:36there is a when these individuals
  • 52:38have a heterozygous well as a
  • 52:40function in these genes,
  • 52:42they actually have a downregulation
  • 52:44of the gene and protein.
  • 52:47So then we were interested in knowing
  • 52:49well if there is a downregulation,
  • 52:51what's happening with the activity
  • 52:53on the activity of the genes,
  • 52:56and how is that?
  • 52:57How are these sales managing DNA damage?
  • 53:00Because again,
  • 53:00remember that these are DNA repair genes.
  • 53:03So to do that we grow the cells and
  • 53:06we perform a flow cytometry analysis
  • 53:08that was actually testing the
  • 53:11quantity of forceful relation of the
  • 53:14serene 139 residue of the history.
  • 53:16Age to ax as an indicator of the
  • 53:19damage and DNA double strand breaks.
  • 53:23So when we did that,
  • 53:24we determined the phosphorylation
  • 53:26at different time points and the
  • 53:29black are the wild type cells and
  • 53:31the and the and Gray are the the
  • 53:33ones with the headers I use,
  • 53:35so it's true that that the first time
  • 53:38the first time point might be a delay
  • 53:41on the on on the phosphorylation we see
  • 53:44that on the other time points there
  • 53:47is a higher dose of the frustration and
  • 53:50therefore an indicator that these cells have.
  • 53:52The higher DNA damage
  • 53:55and here as you can see,
  • 53:57this is the difference
  • 53:59between the heterozygote,
  • 54:00the the heterozygous that has
  • 54:02like a higher phosphorylation so.
  • 54:07So right now we are also doing more
  • 54:10analysis and we are testing for for example,
  • 54:13for the effect of these variants
  • 54:15in cell cycle because some of our
  • 54:18preliminary data showing that maybe
  • 54:20these cells are actually arrested in G1,
  • 54:23but we have not had this data yet.
  • 54:27So in conclusion from this aim
  • 54:29I we believe that
  • 54:30heterozygous loss of function
  • 54:32variants in DNA repair genes
  • 54:34such as Warner and regular five,
  • 54:37could predispose to tumor development
  • 54:39because they are enriched among
  • 54:41the lines like cancer phenotype.
  • 54:43They lead to gene down regulation
  • 54:46and they increase DNA damage.
  • 54:49So now turning it to the end
  • 54:51two at the somatic level.
  • 54:53Had to do develop these aim.
  • 54:55We also included two different
  • 54:57independent series of tumors that
  • 54:59mismatch repair deficient tumors
  • 55:00from the three different types,
  • 55:03and we develop exam sequencing
  • 55:05to identify somatic variants
  • 55:08and loss of hydrazoic events.
  • 55:11And with with this data,
  • 55:12with this excellent data,
  • 55:14we also were interested in defying
  • 55:17the contribution of mutational
  • 55:19signatures to these tumors.
  • 55:21So mutational signatures are like
  • 55:24a fingerprint of of the portrait
  • 55:27of the mutations that the tumor has
  • 55:30acquired over the development of the
  • 55:33tumor and they some of them are well
  • 55:36established and they are associated to,
  • 55:37for example,
  • 55:38exposure to carcinogens and other ones.
  • 55:41Associated like in the case of the mismatch,
  • 55:43repair to deficiency on DNA repair pathways.
  • 55:47So they the these are the six
  • 55:51current well established signatures
  • 55:52that are associated with deficiency
  • 55:53of the mismatch repair.
  • 55:55So when these tumors have,
  • 55:57when the tumors have deficiency and you
  • 55:59analyze the the mutational signatures,
  • 56:01you can see this one so so we
  • 56:03were interested in knowing what
  • 56:05was the contribution of these
  • 56:07signatures to each of the tumors.
  • 56:09So let me explain.
  • 56:11So this colorful graph here.
  • 56:13So we first read identified what
  • 56:15were the mutational signatures that
  • 56:17were contributing the most to each
  • 56:19of the tumor and then we perform
  • 56:21clustering to see whether the groups
  • 56:23of whether the tumors that have
  • 56:26a similar contribution of those.
  • 56:29So here each each row is 1 tumor
  • 56:33and each column is rotational.
  • 56:35It's a contribution of to
  • 56:37the mutational signatures,
  • 56:39and here we are also having the phenotypes.
  • 56:42So in here you can see the tumor
  • 56:45is linch light lynch or the MSI?
  • 56:48Isolated.
  • 56:48And then in the last column here we
  • 56:51are showing that which is the protein
  • 56:53that each of these tumors have.
  • 56:56Most of the expression.
  • 56:57So when we perform this analysis,
  • 57:00you can see with identified 2 of the
  • 57:03that mutational signatures based on
  • 57:05the the contribution of SBS 26 and 15,
  • 57:10which are very well established.
  • 57:12Mutational signatures associated with
  • 57:13deficiency of the mismatch repair
  • 57:15identified first the two clusters
  • 57:17that are in breach with the Lynch.
  • 57:19And the lynch,
  • 57:20like and then we then defied this
  • 57:23cluster that has a higher contribution
  • 57:25of the tumors that are missing.
  • 57:28MSI,
  • 57:29MSI dated.
  • 57:32So here I'm I'm showing you the
  • 57:35different features associated
  • 57:37with each of the the clusters,
  • 57:39and as I mentioned,
  • 57:40cluster two is enriched with MSI dated
  • 57:43and also this cluster has specific
  • 57:46clinical features that are well
  • 57:48established with this type of tumors,
  • 57:51which are that they develop preliminarily
  • 57:54and female patients at an older
  • 57:56age and that they are associated
  • 57:58with the bright side location.
  • 58:02So as a molecularly,
  • 58:03as I as I explained you,
  • 58:05this cluster is associated with
  • 58:07thousand expression of mutl and mainly
  • 58:10due to the manipulation of image
  • 58:13one and and then there's tumors.
  • 58:16They also have the higher
  • 58:18number of frames if mutations,
  • 58:19even though there is no difference in
  • 58:22tumor purity that could be affecting this.
  • 58:24But we didn't see that there
  • 58:26was a significant difference,
  • 58:27and they don't have a significant
  • 58:28difference in their own TMB.
  • 58:29So to like two more.
  • 58:31Additional burden,
  • 58:32so it's specifically to the friendships
  • 58:34and what this suggests is that the
  • 58:36the the tumors in this cluster.
  • 58:38They actually have the higher
  • 58:40level of Microsoft the instability.
  • 58:44So one of the results of having
  • 58:46a higher level of microsatellite
  • 58:48instability could be that these tumors
  • 58:50have a different new antigen load,
  • 58:53so new antigens are these peptides
  • 58:56that are generated after somatic
  • 58:58mutations arise in the tumor.
  • 59:00And as you can see here,
  • 59:01you can see that the normal protein and
  • 59:03this is a missense mutation in the tumor,
  • 59:05so this is going to be 1 amino
  • 59:07acid different from the self.
  • 59:09The regular normal protein,
  • 59:11but no antigens that are that are there.
  • 59:15Develop from frame.
  • 59:16Frameshift mutations are significantly
  • 59:19different from the normal because
  • 59:21they introduce a lot of well.
  • 59:25Insertions and deletions.
  • 59:27So these proteins.
  • 59:29These peptides are significantly
  • 59:31different from cells,
  • 59:32and these new antigens which represented
  • 59:35here by this dot are presented from
  • 59:38through the HLA 1 receptor to the TCR.
  • 59:43To the T cell receptors,
  • 59:45and this is obviously a very simplified
  • 59:47version of what's happening,
  • 59:49but then when this is when when this
  • 59:52is happening then the T cells identify
  • 59:56the tumor cells as as non self,
  • 60:00and then they're going to
  • 01:00:02start the immune response.
  • 01:00:05So we wanted to see how these new
  • 01:00:09antigens and the direction of the HLA.
  • 01:00:12Image of the patient were
  • 01:00:13occurring based on the different
  • 01:00:15clusters that we are defined.
  • 01:00:17So to do that we use several
  • 01:00:21bioinformatics pipelines.
  • 01:00:22We use Poly solver to predict
  • 01:00:25the HLA one alleles that we know
  • 01:00:28that there's three of them using
  • 01:00:31the germline XM sequence data.
  • 01:00:34Then we use unaware tool to
  • 01:00:36annotate all the mutations that
  • 01:00:39we had identified in the in the.
  • 01:00:42More excellent sequencing and
  • 01:00:43then we use net,
  • 01:00:45MCA,
  • 01:00:46MHC pan that actually identifies
  • 01:00:49what are what.
  • 01:00:51What are the interactions between
  • 01:00:53the HLA's and the new antigens?
  • 01:00:57And then we took it one step
  • 01:00:58further and we use narrow pred.
  • 01:00:595 that actually this algorithm
  • 01:01:03computes the recognition potential.
  • 01:01:05So what it does is it provides a likelihood
  • 01:01:09that this interaction is going to occur.
  • 01:01:12And it's based on on the immune epitope.
  • 01:01:15It's it's.
  • 01:01:16It's this prediction is based
  • 01:01:19on the TCR receptor rapporteur,
  • 01:01:21that it's that it's.
  • 01:01:25That he's present in the
  • 01:01:27immune epitope database.
  • 01:01:29So with that we took this likelihood
  • 01:01:34and this recognition potential,
  • 01:01:36and we score them,
  • 01:01:38and we identified the ones that
  • 01:01:40were at the highest 10% tile and
  • 01:01:42the ones that were at the lower 10%.
  • 01:01:45So we assume that if there is no selection,
  • 01:01:49then the these interactions in
  • 01:01:51the temple in the top percentile.
  • 01:01:54Between the new antigens and the HLA,
  • 01:01:57one should be the distribution of
  • 01:01:59these alleles should be similar to
  • 01:02:02the distribution of the patients
  • 01:02:04and the little frequency in the
  • 01:02:06patient population.
  • 01:02:07So to test this hypothesis we we
  • 01:02:11compare the actual frequency of the
  • 01:02:14alleles in the patient population for
  • 01:02:16each of the different clusters and
  • 01:02:19the frequency and the distribution
  • 01:02:21of the alleles in the ones that
  • 01:02:23are selected as having the higher.
  • 01:02:25Likely for the recognition and
  • 01:02:27what we
  • 01:02:28identified is that actually there was
  • 01:02:31one specific allele B702 that were
  • 01:02:34significantly in breach in this in
  • 01:02:37the top 10% recognition potential,
  • 01:02:40which that was not happening in
  • 01:02:43the lower set of of interactions.
  • 01:02:46So we think that the specific actually
  • 01:02:50wanna leaves like the B702 could promote
  • 01:02:53stronger immune immune response.
  • 01:02:55And these tumors that are the ones with
  • 01:02:58the higher microsatellite instability.
  • 01:03:00And we believe that these down the line
  • 01:03:02could be affecting the immune response
  • 01:03:06of these tumors to and and how to
  • 01:03:11immune immune checkpoint inhibitors.
  • 01:03:13So obviously this is the the beginning
  • 01:03:16of like expanding this work in
  • 01:03:19the area of immune response by the
  • 01:03:22immune checkpoint inhibitor response.
  • 01:03:25So in conclusion,
  • 01:03:26for him two molecular differences between
  • 01:03:28the three different types of mismatch repair,
  • 01:03:31deficient tumors could have a direct
  • 01:03:34implication and immune response specific.
  • 01:03:37One else could be driving the presentation
  • 01:03:40of neoantigens among mismatch repair
  • 01:03:42deficient tumors with the highest
  • 01:03:44level of microsatellite instability.
  • 01:03:46We probably specially this work,
  • 01:03:48so overall the take home message is that
  • 01:03:51our studies show that there's novel
  • 01:03:53molecular heterogeneity among these.
  • 01:03:55Under the efficient tumors and
  • 01:03:57that understanding the clinical
  • 01:03:59pathological features associated with
  • 01:04:01this heterogeneous heterogeneity is
  • 01:04:03essential to accurate diagnosis and
  • 01:04:06prediction of treatment response in
  • 01:04:08the setting of personalized medicine.
  • 01:04:10And our future directions.
  • 01:04:13It's to understand the molecular
  • 01:04:15mechanism that associate trequel 5
  • 01:04:17and Warner deficiency with this type
  • 01:04:19of tumors identify immune regulators
  • 01:04:21that determine response based on the
  • 01:04:24type the specific type of mismatch
  • 01:04:27repair deficiency,
  • 01:04:28and investigate also the treatment
  • 01:04:30response to immune checkpoint
  • 01:04:32inhibitors based on this type of
  • 01:04:34mismatch repair deficiency.
  • 01:04:36So with that,
  • 01:04:37just acknowledge our funding sources
  • 01:04:39Martinek Albuch that is the the first
  • 01:04:42dog in my lab that has one done most
  • 01:04:45of the work and my collaborators
  • 01:04:47in the US and also in Spain.
  • 01:04:49And I'll be happy to take any questions.
  • 01:04:53Thank you very much Rosa.
  • 01:04:55A terrific work that's very interesting and
  • 01:04:57we do have a couple of questions in the chat,
  • 01:05:00so which hopefully I can read properly.
  • 01:05:05So the first is from Jeffrey
  • 01:05:08Townsend and Jeff asks,
  • 01:05:10is the association of BRAF V600E
  • 01:05:14with MSH mutation purely mutational?
  • 01:05:17Or is there some more complex biology
  • 01:05:20to the association and he asks because
  • 01:05:22the trinucleotide signature in.
  • 01:05:24Used by MSH is especially likely to
  • 01:05:27make the B Rav 600 to E mutation. Yeah
  • 01:05:30so so. The BRAF mutation in colon cancer
  • 01:05:33is associated with the serrated pathway,
  • 01:05:36so that's like the more biological.
  • 01:05:38It's not this type of tumors,
  • 01:05:40but for the for the Ms,
  • 01:05:42I believe it's more like a
  • 01:05:44motivational association,
  • 01:05:45but the one that has been more
  • 01:05:49described biologically is the one
  • 01:05:51that the the the serrated pathway.
  • 01:05:54Had tumors that they were writing.
  • 01:05:56Passwords are developing,
  • 01:05:58but this is like more like.
  • 01:06:01Mutational that we used to
  • 01:06:03mainly separate the sporadic
  • 01:06:05from the hereditary ones.
  • 01:06:09OK great thanks.
  • 01:06:10And then the next question is
  • 01:06:13from Ryan Jensen and Ryan asks.
  • 01:06:16And one of the potential roles of
  • 01:06:19of REC QL 5 is to prevent aberrant
  • 01:06:22homologous recombination by displacing
  • 01:06:24RAD 51 off single stranded DNA.
  • 01:06:27And he wonders if in tumors from
  • 01:06:29patients with loss of function,
  • 01:06:31mutations in REC queue do you see
  • 01:06:34increased chromosomal aberrations?
  • 01:06:36Sister chromatid exchanges,
  • 01:06:37or perhaps increases in microsatellite
  • 01:06:40contraction or expansion.
  • 01:06:43So all of these we have, we.
  • 01:06:46There's so the the work done in Q L5
  • 01:06:50and colorectal cancer is not very vast.
  • 01:06:53So so right now what I can say is that
  • 01:06:57we we just engineer a cell line that is,
  • 01:07:00that has these mutations,
  • 01:07:01which rupees per so we are going to
  • 01:07:03have the cell lines that have like the
  • 01:07:05heterozygous and homozygous and Val types.
  • 01:07:07So we are going to be testing these
  • 01:07:10kind of events that Brian is suggesting.
  • 01:07:13So I don't have that information
  • 01:07:15yet where I know that, for example,
  • 01:07:17for Frank L5 is that there there's been one.
  • 01:07:20There was one old paper that was
  • 01:07:22showing that interestingly regular 5
  • 01:07:26downregulation was identified in MSI tumors,
  • 01:07:29and so I think that there is more
  • 01:07:32than we can be learning about this
  • 01:07:35and and I think that that's going
  • 01:07:36to be one of our like next steps.
  • 01:07:40Great thank you and I had one quick question.
  • 01:07:43When you were going
  • 01:07:45through and looking at the.
  • 01:07:46The the red queue and other
  • 01:07:49mutations in the Lynch like syndrome.
  • 01:07:51I didn't have a sense for for whether
  • 01:07:54it was clear whether that they're
  • 01:07:55all loss of function mutations,
  • 01:07:57like for example the T31K in that one
  • 01:08:00family is that is that some is that a is
  • 01:08:03that one so that one was mutation that
  • 01:08:05we found in the tumor of that patient.
  • 01:08:08We have not been able to
  • 01:08:10test the other individual.
  • 01:08:11So the germline variants that
  • 01:08:13we are even defining in the
  • 01:08:15germline are all loss of function.
  • 01:08:17But we only have been able to test
  • 01:08:201 tumor from these individuals.
  • 01:08:23I can tell you, not for AQL.
  • 01:08:25I know a lot of the data for
  • 01:08:27one not a lot few data from
  • 01:08:30Warner mutation somatically.
  • 01:08:32There is the there identifying loss
  • 01:08:35of function mutations and actually
  • 01:08:37these tumors that have loss of
  • 01:08:40function mutations in Werner they
  • 01:08:43have a significantly higher number
  • 01:08:45of them in comparison to the ones
  • 01:08:48that don't have mutations there.
  • 01:08:49MSI. So again another kind of.
  • 01:08:53Another clue that there have there
  • 01:08:55might be some some association between
  • 01:08:58deficiency in these genes and MSI.
  • 01:09:01However, association doesn't mean causality,
  • 01:09:04so this is what I think that
  • 01:09:06is what we actually need to do.
  • 01:09:07More research to figure out
  • 01:09:09these needs one or the other.
  • 01:09:12Good, well I think it's been a great session.
  • 01:09:18And lots of good questions
  • 01:09:19and and two fantastic talks.
  • 01:09:21So I'd like to just to finish
  • 01:09:23by by thanking Luisa and Rosa.
  • 01:09:25Very much for really giving very
  • 01:09:27stimulating and exciting talks,
  • 01:09:28great grand rounds and thank
  • 01:09:30you very much everybody.
  • 01:09:32Thank you. Bye bye bye.