Translational Research in Breast Cancer: Emerging Role of Immunotherapy, Novel Targets and a Hidden Dimension of Cancer Biology

Transcript

• 00:00Both in cancer and and obviously
• 00:03immunology and response to viruses.
• 00:06And we'll start with our first speaker,
• 00:09doctor lash boost.
• 00:10I allows is, you know,
• 00:12is a professor of Medicine at
• 00:14the school of Madison at the US.
• 00:17Will Medison Co.
• 00:18Leader of the genetics,
• 00:20genomics and epigenetics research program
• 00:22and director of Translational research
• 00:24for breast cancer in our Cancer Center.
• 00:26Having received his medical degree
• 00:29in Budapest and his subsequent
• 00:31doctorate at the University of
• 00:34Oxford blouse is really committed.
• 00:36His career to really understanding the
• 00:39biology of breast cancer and leveraging
• 00:42that understanding to fundamentally
• 00:44improving our way to deliver more efficient,
• 00:48more effective,
• 00:49and more successful clinical care.
• 00:51You know,
• 00:52through his laboratory work,
• 00:54his work in translation,
• 00:56Medison And frankly,
• 00:57his leadership of clinical
• 00:59trials in clinic research,
• 01:00which have not only investigated new
• 01:02drugs but also leverage new technologies
• 01:04to better define and predict how
• 01:07women will best respond to therapy.
• 01:09He really has been the triple thread
• 01:11and actually have academic Medison,
• 01:13and we're pleased to have him today
• 01:15to share his work in translation and
• 01:18research in breast cancer so loud.
• 01:20Thank you for Evergreen speak today.
• 01:23Thank you Charlie, for them the
• 01:25opportunity to to give a brief update
• 01:28on some of the translation of projects
• 01:30that we actually have been involved in
• 01:32the past few years here at Yale. Um?
• 01:42So this is my a disclosure slide
• 01:45and I would like to cover 3 areas.
• 01:49When is exploiting the emu micro environment
• 01:51of a breast cancer for therapeutic purposes,
• 01:54some potential metabolic vulnerabilities
• 01:56in breast cancer I think exists and I'd
• 02:00like to challenge you to think and you
• 02:02knew very about cancer predisposition
• 02:05or cancer risk category cancer risk.
• 02:07So the road from an idea to a clinical trial
• 02:11or clinical trial result is really very
• 02:14long and often times very sort of tortuous.
• 02:17So as an example,
• 02:19about 10 years ago we published this
• 02:21paper very show at the presence of
• 02:24immune cells in primary tumors,
• 02:26either ER positive or triple negative,
• 02:28or her two positive breast cancer service
• 02:30usually associated with prognosis.
• 02:32Let's outcome in patients receiving
• 02:34order surgery.
• 02:34Now the treatment and surgery so
• 02:37that people was published in 2010.
• 02:39I'd like to remind you that in 2010
• 02:41there is no effective, you know,
• 02:44therapy in any disease type.
• 02:45So we cause this the closing sentence.
• 02:48This paper was, well,
• 02:49it remains unknown whether the
• 02:51Indians selectivity is simply
• 02:52associated with with a better outcome,
• 02:54or it's really the cause of
• 02:56the battle outcome in disease.
• 02:58So around the same time in another
• 03:01project with a postdoc in my lab,
• 03:03we also looked at what sort of biological
• 03:06processes or or gene expressions,
• 03:08signatures or patterns are
• 03:09associated with the response.
• 03:10Show me legend.
• 03:11Chemotherapy.
• 03:12Sony rejoin chemotherapy is really a
• 03:14perfect setting where you can correlate
• 03:16a particular biomarker with sensitive
• 03:17to the treatment because you can
• 03:19directly measure the effect of the
• 03:21treatment at the time of the surgery.
• 03:23Patients have no residual
• 03:24cancer after the chemo.
• 03:25They tend to do really,
• 03:27really well and we call this
• 03:29pathological complete response.
• 03:30So be selling.
• 03:31This is again a number of immune signatures.
• 03:33Are Indian related markers fell
• 03:35out so obviously the big question
• 03:37is so is this a mere Association
• 03:39or A cause and effect and?
• 03:41Right around that time,
• 03:42in the second half of 2010,
• 03:45the 1st paper came out in the New
• 03:47England Journal of Medicine and eating
• 03:49woman at that have shown that actually
• 03:52manipulating an immune checkpoint,
• 03:54so we'd illumi map could improve the
• 03:56survival of patients in metastatic Melanoma,
• 03:59so that really created an opportunity to test
• 04:01this Kozera fact versus Amir Association.
• 04:042012,
• 04:04which is actually the year
• 04:06when I joined the air.
• 04:07We proposed the new edgmont trial to be
• 04:10an S and ask them would repeal me map.
• 04:12It could improve the pathological
• 04:14complete response rate when
• 04:15combined with chemotherapy.
• 04:16So PMS said, well,
• 04:17now that's a good idea,
• 04:19but it's really too toxic and
• 04:21it's way too early.
• 04:22So it went to look and proposed them
• 04:25the same thing and you ain't even prior,
• 04:27but also for good measure,
• 04:29he asked them whether they would
• 04:31be interested in Edgmont Prial,
• 04:32since since a lot of immune cells
• 04:34in the microenvironment predicted
• 04:35better outcome of the surgery.
• 04:37So for the new regiment they actually
• 04:39sent me this lovely letter that I
• 04:41thought it's interesting to read, right?
• 04:43So read the actual date first,
• 04:45so it's 18th of September 2012.
• 04:47The ordinary nice,
• 04:48polite rejection that well,
• 04:49we are unable to provide
• 04:50either funding or drug for this
• 04:52project because of the unclear.
• 04:54FDA regularly path forward.
• 04:56Either there is well,
• 04:59sort of tried the road for edge
• 05:01event registrations and they agreed
• 05:03to do an agent trial which led to
• 05:06this spoke as 1418 konnakol trial,
• 05:08so not as 40 actually means
• 05:11that it was started in 2014,
• 05:13so it took about two years to the NCI
• 05:15to really put together this large of
• 05:18the randomized registration trial.
• 05:20After Merck supported it in
• 05:222012 in principle.
• 05:23So I'm kind of tenacious and not
• 05:26very aggressive, but I I tenacious.
• 05:28So I supposed to say my dear again
• 05:30to Med immune in the same year,
• 05:33maybe even at that time was
• 05:34a startup company.
• 05:35They had do volume app and now they're
• 05:37part of easiest is Erica and being
• 05:40smaller than they actually said?
• 05:41Yeah, why not?
• 05:42So we propose them to studies
• 05:44and they agreed to both.
• 05:46What was the single agent,
• 05:47I mean a single arm phase, one phase,
• 05:49two trial at Yale, and the randomized trial.
• 05:52So the I spy consortium combined it all,
• 05:54Apple event added to pocket back.
• 05:56So our study at year was just simply.
• 05:58So.
• 05:59Hum.
• 05:59So I could be presented last
• 06:03year the results of the year
• 06:06phase one phase two trial.
• 06:09There's a historical interest because
• 06:10this was the first knew edgmont even
• 06:13oncology trial anywhere in the world.
• 06:15So the first patient was enrolled in 2015.
• 06:17It was a phase one phase two trial,
• 06:20and because this has never been
• 06:22done in the collective setting
• 06:23combined with chemotherapy food,
• 06:25though,
• 06:25is chemotherapy before surgery FT
• 06:27require that the first three patients
• 06:29is watch for 9 months each week
• 06:31for the next lot could be entered.
• 06:34So it looks like it was a long
• 06:36time to complete.
• 06:37The results showed that the
• 06:39pathological complete eradication
• 06:41rate in the rest was about 44%.
• 06:43The same chemotherapy regimen
• 06:44in a similar trial,
• 06:46and by by the sea by smog.
• 06:48Southwestern college group resulted
• 06:49in a PCR or pathological covered
• 06:51response with different 29%.
• 06:53So we also noted that there,
• 06:55as you would expect,
• 06:56you cancel each other higher PD
• 06:59leg and one expression or more
• 07:01sites at a higher PCR eight more
• 07:03closer to 60% rather than.
• 07:0544 So remember,
• 07:07parallel with this and the other study was
• 07:10running through the I spy that I was so.
• 07:13Lad and he showed the results of the
• 07:16Plenary session of the EC are this year on.
• 07:19The study shows 9 the randomized setting
• 07:21that indeed leave this door vulnerable.
• 07:23A product combination improved the
• 07:25pathological can't response rate
• 07:26in both the triple negative group,
• 07:29which was the results were
• 07:30very eerily similar in terms
• 07:32of the PCI rate, 47 versus 44% in
• 07:35our little negative study at Yale,
• 07:37and we also notice that
• 07:39actually this drug also work.
• 07:41That is combination worked.
• 07:42It was added to chemotherapy. You have to.
• 07:45I mean it hormone receptor HR stands
• 07:47for hormone receptor resolution,
• 07:49receptor positive disease.
• 07:50However, the result might this reason
• 07:53why this was picked for a plenary
• 07:55session is actually in the next slide,
• 07:58so we stumbled upon a remarkably sort of
• 08:01simple and clear way to isolate out among
• 08:04the estrogen receptor positive tumors,
• 08:06the ones which really benefited
• 08:08versus those who did not.
• 08:10There was no editing benefit.
• 08:12So if you split these estrogen
• 08:15receptor hormone receptor positive
• 08:16group into two more likely subtypes,
• 08:19let me call your MP1 and MP2MP one
• 08:22this crap sort of Mamma print.
• 08:25The lower end of the moment I grew
• 08:27up in the MP two is the Momma Prince
• 08:31Super High score on my printer.
• 08:33Similar sort of predictor that we used
• 08:35to to identify patients who benefit
• 08:37from Edgmont chemotherapy in hormone
• 08:39receptor positive diseases score.
• 08:41So what we're showing here that this
• 08:43core itself has a meaning and just
• 08:45being called high risk or or benefiting
• 08:47from chemotherapy is valuable,
• 08:50but you actually can also split this look
• 08:52into that really super sensitive to adding.
• 08:55Uh, in Indian checkpoint
• 08:57inhibitor to the chemotherapy.
• 08:58So in that group,
• 09:00the pathological CR equals 64% versus 22%.
• 09:03In the chemotherapy alone.
• 09:05So what is this MP group?
• 09:07So this NP group actually is
• 09:09the group which has a very high
• 09:11proliferation and the relatively
• 09:13low estrogen receptors signaling,
• 09:15or estrogen receptor sort of activity.
• 09:17Read out that you can capture
• 09:19Biostar generating wearing jeans,
• 09:20and that's an important sort of
• 09:22piece of information to design the
• 09:24follow-up registration trial for
• 09:26base that we have working with.
• 09:28Because this guy proliferation
• 09:30most regions signaling group is
• 09:32the group that is the least likely
• 09:34to benefit from endocrine therapy.
• 09:36Gotta hand it most likely to
• 09:38benefit chemotherapy,
• 09:38and we think that this benefit
• 09:40could be further augmented by
• 09:42by adding indoor follow map.
• 09:44So we want to get back to this
• 09:46letter in September 2012 from work.
• 09:49So a year later, in September 30th,
• 09:512013,
• 09:51the FDA approved the first sort of
• 09:53drug to be based on pathological
• 09:56company response rate in breast cancer,
• 09:58and that was purchased.
• 09:59So purchasing have improved the
• 10:01pathological computer response rate
• 10:02in her two positive disease and
• 10:04lettered registration of this drug.
• 10:06So not respecting the idea,
• 10:08and they actually lounge the large
• 10:10randomized trial with a pathological CRS,
• 10:12their endpoint, and to their credit,
• 10:14they invited me back.
• 10:16To the app.
• 10:18Leadership of the trial and the
• 10:21results were actually published this
• 10:23year in the new invention of Madison
• 10:26because it did confirm that indeed,
• 10:28adding parallelism after chemotherapy
• 10:31improves the pathological computer
• 10:33education rate improved the
• 10:35recurrence free survival even after
• 10:3818 months of median follow up.
• 10:40So this is the research
• 10:42study that sort of was
• 10:43largely based on these observations.
• 10:45Remember 10 years ago, so it took six.
• 10:48It looks 40 years to actually start this
• 10:50study another six years to complete it,
• 10:52and it would have been completed by now,
• 10:55not for the coded.
• 10:56So it has accrued 923 patients out of 1000
• 11:00and the results probably will be become
• 11:02available in the next two to three years.
• 11:05So so some cancers that high,
• 11:08you know, you know.
• 11:10Union presence know why
• 11:11so often suffer numbers.
• 11:13Medical student at Yale took on
• 11:15this project to actually look
• 11:17into the molecular background,
• 11:19or why sometimes have a
• 11:21lot of lymphocytes at this.
• 11:22Last one is now medical country
• 11:25fellow at Sloan Kettering.
• 11:26So we did these families pcga let
• 11:28me show that many other people
• 11:30did before that triple negative
• 11:32cancer's had a higher notation count,
• 11:35highly antigen mode and more
• 11:37cytotoxic T cells.
• 11:38However,
• 11:38when you look at the actual group On its own,
• 11:42like triple negative disease or ER
• 11:44positive cancer, her two positive cancer.
• 11:46These associations suddenly flip.
• 11:47This is a correlation matrix.
• 11:49I don't expect you to see the numbers,
• 11:52but the colors indicate you the
• 11:54the positive correlation when
• 11:56it's Brown and it's blue,
• 11:57it's negative and anti correlation
• 11:59and the deeper the color,
• 12:01the higher the correlation value.
• 12:02So you can see that the Indian signatures
• 12:05are highly correlated with one another.
• 12:07But on the other hand,
• 12:09is Genomic metrics of generic
• 12:11complexities such as.
• 12:12Mutation load or New Antigen Lord
• 12:14or deletions or amplifications,
• 12:16loads or como complexity.
• 12:17We actually are inversely associated
• 12:19with immune Indian presence in
• 12:21triple negative disease and disease,
• 12:23so that was pretty counterintuitive.
• 12:25Counter intuitive in 2017.
• 12:27OK,
• 12:27so you're moving in says you have a simpler.
• 12:31We can suggest the fewer than
• 12:33your hand surgeons are.
• 12:34So when I see things like this,
• 12:37I try to confirm it.
• 12:39So we reached out collaborator and
• 12:41friend Thomas card and ask him to
• 12:44actually replicate this or with a
• 12:46different methodology and Thomas
• 12:48actually find exact same thing that
• 12:50in primary triple negative disease,
• 12:52the fewer the immune cells they hire.
• 12:55The Genomic heterogeneity and
• 12:56the worst prognosis. So worse.
• 12:58Prognosis means that you have a
• 13:00higher probability for methods.
• 13:02This is so and let us do this hypothesis
• 13:05that maybe the metastatic lesions
• 13:07are actually immune refugees or or escapes.
• 13:10So there are more immune in Earth
• 13:13that Michael Environment is more
• 13:15immune inert and these three
• 13:16brilliant woman took on this project
• 13:19or parasitically was a visiting
• 13:21scientist from Hungary or so with.
• 13:23Smoking baby that his lab in Charlotte.
• 13:27With me and.
• 13:28More Gerstein so we rounded up samples
• 13:30which will pair primaries and meds and
• 13:33also a separate Court of primaries.
• 13:36And that's not from the same patient,
• 13:38and subjected them to a whole series
• 13:41of molecular studies to test it in.
• 13:43You micro environment is the
• 13:45same or different,
• 13:46so this is just really
• 13:47examples of three sort of
• 13:49simple and straightforward
• 13:50findings with teal comes the tumor.
• 13:52Infiltrating info sites are lower in
• 13:54maximum primary tumors in matched
• 13:56and unmatched chords be dealing.
• 13:58Expression is the same, it slower.
• 14:00In in the matter equations and
• 14:02also a whole lot of different
• 14:04Indian signatures are all lower,
• 14:06consistently showing that there are less
• 14:08Indians and less activity in the breast.
• 14:11Cancer metastatic micro environment.
• 14:12Why don't was really interesting though?
• 14:15Is that while most of the
• 14:17emu markers went down,
• 14:19some of the Indian targets actually
• 14:21remain high or or even increased
• 14:23in the meta static environments.
• 14:25And these IO targets are potential
• 14:27good set of candidates for offer.
• 14:30Testing them in the meta static setting,
• 14:32either alone or in combination.
• 14:34Also in combination with established agents.
• 14:37So we selected the group of this
• 14:39preserved IO targets for a clinical trial
• 14:42that we hope to conduct this support.
• 14:45Clinical trials it's called rustic
• 14:46and this is sort of a scheme of it.
• 14:49And again,
• 14:50this takes this immune targets that we
• 14:52preserved in the meta static setting
• 14:54and testing in the clinic whether
• 14:56they really have a functional role.
• 14:59In suppressing the new Micro Hood,
• 15:00so because of lack of time I
• 15:02can't really talk about all the
• 15:04other Indian projects every day,
• 15:06but I just thought I list them here
• 15:08so we did compare changes in the
• 15:10micro environment before and after
• 15:12therapy and the shouts only published.
• 15:14This give us some ideas what to add
• 15:16to Pembrolizumab Order Volume app to
• 15:18make the treatment even more effective
• 15:20than you actually and setting.
• 15:22Homes we also compared to same
• 15:24day the immune reach,
• 15:25triple negative and the energy or
• 15:28positive Kansas to see that there is
• 15:30differences in their micro environments
• 15:32and that was done by Paso mirror
• 15:35medical students who is now a resident
• 15:37at the Harvard system the same way.
• 15:40So we did similar comparisons
• 15:42by by race and King blindness.
• 15:44Scientists in my lab is working or not.
• 15:47Of data from a number of different new
• 15:50agent trials that seems to do well.
• 15:52Map kind of what precisely
• 15:54defined predictors,
• 15:54and he rosenblit is Medical College
• 15:56of fellow with a very nice people and
• 15:59actually looking at in a large pool
• 16:01of data from Foundation Medicine.
• 16:03Be like an expression across different
• 16:05meta static sites in breast cancer.
• 16:07And there are some really substantial
• 16:09differences in people like an
• 16:11expression depending on what site
• 16:13you are actually sampling.
• 16:14I'm going to move on to something else
• 16:17that really got me excited in the past.
• 16:19So if you a few months so.
• 16:21Many metabolic processes are catalyzed
• 16:23by multiple different isozymes or or
• 16:25proteins that really capitalizes.
• 16:26Same enzymatic reaction.
• 16:27Normal cells usually have many of these,
• 16:30and oftentimes in cancer you actually
• 16:31see that one of the isoforms become dominant,
• 16:34so that's the schema on this figure, right?
• 16:37So normal tissue is both sides.
• 16:39When I sent to our expressed in cancer,
• 16:41I just have one becomes a dominant
• 16:44and the other
• 16:45one is lost. So we asked how often do we
• 16:47see this in cancer and do this sort of isis
• 16:51and expression changes could could harbor.
• 16:53Or or include enzymes that we could target
• 16:57metabolically somaca March secret visiting
• 16:59scientist faculty from from a Polish
• 17:02University to this project on and device.
• 17:05This sort of strategy to
• 17:07look at humanizing forms.
• 17:09Isozymes managed matched primary
• 17:11tumors in the metastatic lesions
• 17:14sorry match the normal tissue.
• 17:16The DC area across 14 different cancer
• 17:19types where this was available than the
• 17:21validated the results in cell lines.
• 17:23Make sure that this is really
• 17:25observed in the purest system and
• 17:27not just an artifact of difference.
• 17:29He still different issue compositions
• 17:31and then says the functionality
• 17:33in this depth map data which is
• 17:36basically complete knockdown of all
• 17:37human jeans in about 7 or 8 cell
• 17:39lines and then the conference hits.
• 17:41We validated in the manual screen.
• 17:43This is an example for you
• 17:45how this exactly looks.
• 17:47404 Kansas so this is Csea enzyme.
• 17:522 forms ACA&B.
• 17:53Plots show you that how they still the ACA,
• 17:57which is the red and be which is the blue,
• 18:00had the expression a normal and an action.
• 18:03Cancer tissues and you can see that
• 18:05the Blues all go down and cancel.
• 18:07That means that the expression
• 18:09of this is lost,
• 18:10whereas the red remains stable
• 18:11and the red is the sea.
• 18:13So this thing this is a
• 18:15potentially interesting target.
• 18:16So when you look at this across
• 18:18different cancer types and
• 18:19indeed app map validation data,
• 18:21then actually what really fell out on the
• 18:23top is this questionnaire carboxylase.
• 18:25Which show this loss of isoenzyme
• 18:28diversity intro different cancer
• 18:30types and was socially but nine
• 18:33different cell lines and each cancer
• 18:35types in in the depth map so that
• 18:38map has like several dozens of cell
• 18:40lines for a particular cancer type,
• 18:43like breast cancer.
• 18:44Nine of these showed the same
• 18:47loss of heterozygosity,
• 18:48loss of diversity as we
• 18:50saw in the human data.
• 18:52Different cancer types.
• 18:54And in this case is also validated,
• 18:57so most of the cell lines there
• 18:59where the CSC was knocked out.
• 19:01If it had the dominant expression,
• 19:03it really impact viability.
• 19:04But the real kicker is that when we
• 19:07look up what is known about this,
• 19:09it turns out that Pfizer has a drug that
• 19:12they put through phase one and phase
• 19:14two trials for diabetes and fatty liver,
• 19:17and actually showed all the
• 19:19metabolic effects that we expected.
• 19:20But they discontinued development
• 19:21last year or two years ago
• 19:23because of thrombocytopenia,
• 19:24which is wonderful.
• 19:26Because don't beside opinion
• 19:27through megakaryocytes really rely
• 19:29on the normal lipid synthesis,
• 19:30because from both sides bought off and
• 19:33every time I turn both sides come off
• 19:36from the surface supermodel career site,
• 19:38it takes lipids.
• 19:39Membranes made it so we answered
• 19:41the proof that is really works
• 19:43the way it's supposed.
• 19:45So the Anthony to collaboration with
• 19:47Pfizer to to do some additional
• 19:48preclinical studies and bring
• 19:50it in the clinic
• 19:51if it validates so,
• 19:52we simply throw validation is pre
• 19:54clinical validation is falling on
• 19:56the shoulders of Julia fold even
• 19:58offer Medical College of Fellows and
• 20:00finish with scientists in my lab.
• 20:02So before the coveted broke we had a
• 20:04chance to look at 10 different cell
• 20:06lines and you see that in the human
• 20:09sort of achievable concentrations
• 20:10that you can get in the human serum.
• 20:13It says a pretty broad inhibitory effect.
• 20:15And the army is not for coffee.
• 20:18I could probably show you,
• 20:20said the combinatorial screen results
• 20:22from the high throughputs combinatorial
• 20:24screen that we initially is doing in
• 20:27our core facility at the West campus
• 20:29and also collaborating in jacks
• 20:31to test this drug in PDX models.
• 20:33And we hope to bring this to the clinic.
• 20:37Maybe the year 2.
• 20:38So finally the last five minutes I
• 20:41wanna spend on an idea that we kind
• 20:43of stumbled upon off awhile back.
• 20:45This is not our paper,
• 20:47it's the people from nature of it shows
• 20:49you the distribution of different sort
• 20:51of mutations in large cities of Kansas.
• 20:54So the striking thing about this
• 20:56is that there are these set of
• 20:58jeans that affected more than 65
• 21:00cases out of close to 3000 Kansas.
• 21:02And even in this sort of very modern
• 21:05and high sort of accuracy study,
• 21:07about 9% of Kansas said
• 21:08no driver alterations.
• 21:09English challenge you to think about anyway.
• 21:11What you think you mean by a driver, Jean.
• 21:14So is it a statistical construct
• 21:16from any sort of statisticians
• 21:17in computational biologist?
• 21:19It is actually a statistical construct,
• 21:20but of course many of you think about
• 21:23this is gene that caused the cancer.
• 21:25The way I think about this is
• 21:28actually it's just a narrative tool.
• 21:30To kill a good story.
• 21:32So this is actually from the same paper
• 21:34from but from the supplementary figures,
• 21:37but it shows you the enormous amount
• 21:39of model of Genomic abnormalities
• 21:41that any particular cancer
• 21:42has so retro transpositions,
• 21:44a few dozen number of structural variants,
• 21:47several dozen to several, several 1000.
• 21:49So these are big chunks of the DNA
• 21:51chromosomes missing very larger than
• 21:53the thousands of in Dallas and 10s of
• 21:56thousands of single included variance.
• 21:58Incidentally,
• 21:58you also see this actually in the CIS,
• 22:01which is a premalignant lesion,
• 22:03so these services, all the.
• 22:05Well,
• 22:05marks off of cancer,
• 22:07except it's not really cancer,
• 22:08but it has the same B 53 mutations
• 22:12clarifications or not.
• 22:13Just the game keeps you big pools,
• 22:15better weather really.
• 22:16The function of this these jeans
• 22:18and then the individual jeans is.
• 22:20So this is a people that many
• 22:22years ago we did nearby Sunday.
• 22:24She was that he was a medical student.
• 22:26That year.
• 22:27Now is a faculty at Sloan Kettering
• 22:29and what I want to illustrate here
• 22:31is that every single cancer which
• 22:32is a column as really a different
• 22:35combination of abnormalities.
• 22:36So if you think about it that way,
• 22:38maybe it's really the reason.
• 22:39Why cancel the have different layers?
• 22:41Because because of this combinatorial
• 22:43difference that they have.
• 22:44So if each of these contributes
• 22:46something then their net effect is
• 22:48really really heterogeneous behavior.
• 22:50But maybe it's even more interesting.
• 22:52Is this work with DVR? She was.
• 22:55The students at that time at Yale
• 22:57and now it's a medical student pad.
• 23:00You sequence all the human kindness
• 23:01ease in 90 two breast cancer,
• 23:04only to see whether there are any.
• 23:06Lowering additional kindness is that it?
• 23:08I guess we didn't find any,
• 23:10but we really observe though
• 23:11is that there is a very large
• 23:13number of high functional impact.
• 23:15Variance in kindness is bigger germline.
• 23:18I'm still think about for a second,
• 23:20so you actually carry a bunch of germ line
• 23:22so the mutations that inactivator overactive.
• 23:25It kinda seems like PSC kinase or whatnot.
• 23:27So what does it mean?
• 23:30So please give us this idea
• 23:32that maybe it's really.
• 23:34He just focused too narrowly
• 23:36on driver mutations,
• 23:37which are only four of five in a cancel.
• 23:40What actually would be probably also
• 23:42helpful is to look at the context
• 23:44in which this is happening and
• 23:46the constant Israeli hundreds of
• 23:48additional variants that come in from
• 23:50the somatic or the germ line angle.
• 23:53So he proposed this idea that
• 23:55really functional German variance,
• 23:57conkle, jeans and it's the totality
• 23:59of the functional impact.
• 24:00High functional impact German
• 24:02variants in cancer Lady Jeans.
• 24:04They could actually determine cancer risk.
• 24:06So we know that there are
• 24:08a few very high penetrance.
• 24:10Cancer is chains like Bronco,
• 24:12but it's really is the minority
• 24:14women who carry this even very,
• 24:16very strong family history.
• 24:17So what's accounts for dismissing heredity?
• 24:20You think it's the totality of
• 24:22the defects that actually are.
• 24:24Embedded in a whole lot of
• 24:26individually non sort of.
• 24:28Cannot translate that.
• 24:30And then the next iteration of this,
• 24:33that's really the combined effect
• 24:35of the germline and somatic events
• 24:37that really lead to the malignant
• 24:39transformation rather than a few
• 24:41individually dramatic effect.
• 24:42And it's a project that talking
• 24:45is pursuing in my lap towers
• 24:47visiting post doc from from.
• 24:49And that is so if it's really true,
• 24:52then we would expect that woman who
• 24:54developed cancer the younger age will
• 24:56have a lot more sort of deleterious
• 24:59germline events in cancer jeans then
• 25:01people who develop cancer the later
• 25:03age because it's ultimately the
• 25:05combined effect of the acquired and
• 25:07the inborn errors that lead to Kansas.
• 25:10So if you are born with a
• 25:12lot of errors to start with,
• 25:14it gonna take a fewer or shorter time
• 25:17to get to reach this critical level.
• 25:20And long, behold,
• 25:21that's exactly what he observed in a
• 25:23bunch of large series like that ECA or
• 25:25the UK biobank and now just published
• 25:28this paper in Nature Communications.
• 25:30I want to just point out to you because
• 25:33time is short on this very last figure,
• 25:36which is the the comparison,
• 25:38the relationship between the
• 25:39mutation than in the cancer versus
• 25:41the variant, the German
• 25:43variant burden by age groups.
• 25:45So he he means younger than 30 and the
• 25:49other advocating speak louder than 80.
• 25:53The installation ship is actually remarkable,
• 25:55so yo patience young who are who
• 25:58have cancer at a younger age and
• 26:00it's it across all the cancers that
• 26:03letter TCG ahead or the UK biobank.
• 26:05We also get consider the 30s, forties,
• 26:0850s have a much higher germline
• 26:10variant burden in cancer Lee jeans,
• 26:12and this is like 5 or 6 only jeans then.
• 26:16Then people who get cancer the older
• 26:18age and on the other hand most
• 26:21folks have a much higher mutation
• 26:23somatic mutation so they can.
• 26:25So so then that led to us
• 26:27another idea that So what?
• 26:29Actually the cancer jeans are in this thing?
• 26:32That it's probably a lot broader
• 26:34sort of repertoire then then
• 26:35no one Canonical cancel jeans.
• 26:37So you could think that this study?
• 26:40The Dom Hussein,
• 26:41who is a PhD student in the
• 26:42computational biology program and
• 26:44supervised by Mian Mar Gerstein too.
• 26:46So how many jeans are actually connected,
• 26:49one step or two step or three step
• 26:51away in a pretty important interaction
• 26:53network from from a cancer gene.
• 26:55So if they are immediately next to it,
• 26:58then they probably influence
• 26:59the affective for cancer gene.
• 27:01If they're two step removed,
• 27:02they still probably influence over less,
• 27:04so there actually a whole lot of jeans, but.
• 27:07Half of all human jeans are
• 27:09actually connected.
• 27:10One or two steps away from
• 27:12from cancer hub gene.
• 27:13Most of these are not implicated in cancer,
• 27:16and if you look at their sort of
• 27:18functional importance in this gap
• 27:20database and it turns out that the
• 27:22further away you get from the cancer hub,
• 27:25the less important they seem to be
• 27:27in survival in the depth map data
• 27:29which supports the idea that there
• 27:31are lot more jeans involved in
• 27:33cancer than what you think you see
• 27:35the same when you look at weather.
• 27:38There is an evolutionary pressure to to
• 27:40preserve truncating mutations in these jeans,
• 27:42and again the further away.
• 27:43So the jeans which are one Step
• 27:452 step three step away from a
• 27:47concert Hall gene and less and less
• 27:50evolutionary pressure on them into
• 27:52four through 4 to exclude truncations.
• 27:54And it was so carry actually
• 27:56somatic mutations. So that's.
• 28:02Personalized sort of Jim Langley scored.
• 28:04That's sums up all these effects that
• 28:06people are born with cancer chains,
• 28:08and that's a project that
• 28:10that we actually got to ask.
• 28:12Or young investigator award
• 28:13to pursue together with cow.
• 28:15And we say this too
• 28:16gruesome pictures out here.
• 28:18Just remind you guys that
• 28:19this is how airplanes crash.
• 28:21They actually don't crash because
• 28:23there is a statistically significant
• 28:25losses of the wings or the engines
• 28:26or not every single plane crashes
• 28:28caused by a different combination
• 28:30of individually nonlethal events.
• 28:32So they fall into groups like human error,
• 28:34which is almost always there,
• 28:35but it's not the same human error.
• 28:37It's a different kind of human error.
• 28:38There's always some kind of a
• 28:40mechanical or instrumental failure,
• 28:41but it's never the same instrument.
• 28:43So that's my final slide,
• 28:45really studying the new micro environment.
• 28:47Let us to some some useful ideas
• 28:49about clinical trials are very
• 28:51excited about exploring metabolic
• 28:52adaptations for therapeutic targets,
• 28:54and we submitted the DOD grant with this
• 28:56with a group of other investigators,
• 28:59and I think I really think that the
• 29:01universe is functionally cancel.
• 29:03Event chains is much larger than
• 29:05we think it is interested in.
• 29:07More stuff at the bottom of the
• 29:09slide shows takes you through the
• 29:12list of publications by our group.
• 29:14So thank you, and this is my lab.
• 29:18Each other.
• 29:21Wow, thank you.
• 29:22That was a an impressive array
• 29:24of work on so many fronts.
• 29:27And congratulations on all of it.
• 29:29I know where we're a little late on time,
• 29:32so let me just offer up one question.
• 29:35You know? I think you oppose.
• 29:37Obviously a very good case that
• 29:39it's a combination of germline
• 29:41asmatic events and I'm curious,
• 29:43do you think breast is is different?
• 29:46Breast cancer is different than other
• 29:48solid to malignancy's because obviously
• 29:50germline and semantic events are.
• 29:52Install cancers,
• 29:52but you think breast is
• 29:54somehow different that respect.
• 29:55Yes, it's different. It's a matter of fact,
• 29:58so you can group really Kansas even
• 30:00in this paper that that I refer to
• 30:03be looked at different cancer types,
• 30:05and this Association starts to fall apart
• 30:07in Kansas that actually have a very high
• 30:09environments or customer on exposure, right?
• 30:11Because that in this sort of
• 30:13message of this new relationship.
• 30:15So this relationship is less strong
• 30:17in lung cancer, bladder cancer,
• 30:19and some other cancers.
• 30:20So the real picture of course, is nuance.
• 30:23It's much more nuanced.
• 30:24And the same way.
• 30:25So the jeans we chat important so the
• 30:28cancer gene is probably also vary from
• 30:31some tissue types of tissue type.
• 30:33So these are the refinements
• 30:34that we are actually working on.
• 30:36Is just that I wanted to give you
• 30:38a repertoire of things that we do,
• 30:40but that's exactly what we actually
• 30:42addressing in this project right now.
• 30:44Thank you and I know just for time
• 30:46will will move on, but obviously
• 30:48folks can certainly email allow us to.