Breast Cancer, Moving Ever Closer to Cure for All

October 25, 2022

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Yale Cancer Center Grand Rounds | October 25, 2022

Presentation by: Dr. Lajos Pusztai

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00:00I'm doctor Mary. I'm lustberg.
00:02Thank you for joining in person and
00:05for those of you joining online.
00:10I'm pleased to introduce Doctor Louis
00:14Pushti as today's ground round speaker.
00:19Doctor Pushki is professor of medicine.
00:22And Co director of the genomics,
00:25genetics and Epigenetics
00:26Research program here at Yale.
00:29He received his medical degree
00:32from Semmelweis University of
00:34Medicine in Budapest and his Doctor
00:37of Philosophy degree from the
00:39University of Oxford in England.
00:42His research group has made
00:44important contributions to establish
00:46that estrogen receptor positive
00:48and negative breast cancers have
00:51fundamentally different molecular,
00:53clinical and epidemiological characteristics.
00:58He's been a pioneer in evaluating
01:01gene expression profiling as
01:03a diagnostic technology.
01:05To predict chemotherapy and
01:09endocrine therapy sensitivity.
01:11And as shown that different biological
01:14processes are involved in determining
01:16the prognosis and treatment response
01:19in different breast cancer subtype.
01:23His group has also developed new
01:26bioinformatics tools to integrate
01:28information from across different
01:30data platforms in order to define
01:33the molecular pathways that are
01:35disturbed in individual cancers
01:37and could provide the basis.
01:40For individualized treatment strategies.
01:45Doctor Pushki is a trusted colleague
01:48here at Yale and is a principal
01:50investigator of several clinical
01:52trials investigating new drugs,
01:55including immunotherapies for breast cancer.
01:58He's published over 250 scientific
02:02manuscripts in high impact medical journals
02:05and is among the top 1% most highly
02:09cited clinical investigators in medicine
02:12over the past 10 years.
02:15Today he will speak on breast cancer,
02:18moving ever closer to cure for all.
02:21Thank you so much Doctor Pushkar.
02:29You can go ahead and start using this.
02:31Thank you, Mary.
02:32I'm so if you're OK with you,
02:34I will take this mask off because
02:36having a mask, my accent and my
02:37voice would be really serious.
02:39Triple hit against me from the get go.
02:42So I hope it's OK with you.
02:44It's delighted to see that some
02:46people are in the auditorium because
02:47I actually forgot how to get here.
02:50So I really sympathize with those of
02:52you who are actually online with this.
02:54So I think I need to start
02:58with my disclosure slides.
03:01And then before I start my slides,
03:03I would actually like to make a
03:05confession to you and admit a weakness.
03:07It's not chocolate,
03:08but I do feel like a child in a
03:10in a candy store surrounded by a
03:12lot of really delicious and very
03:15interesting scientific questions.
03:16So my weakness is that I have a really
03:18eclectic and very broad range of interests.
03:21And don't be scared,
03:21I'm not going to talk about all
03:23of these questions,
03:24but these are the type of questions that.
03:26My group has been studying in the
03:28past few years and I showed this here
03:30for you to forgive me and understand
03:32why I don't show up to most of the.
03:37Administrative meetings,
03:38so these studying things like
03:40cost effectiveness,
03:41what's the best cost effective
03:42strategy in the new adjuvant
03:44setting for for breast cancer,
03:46why some preoperative chemotherapy
03:47regimens produce high response rates
03:49but very little improvement in survival
03:52and other regiments to the opposite
03:54small improvements in response,
03:56large improvements in survival.
03:57Why there is some women develop breast
04:00cancer 20-30 years before the median age?
04:03Could we develop some sort of a tool to
04:05sum up all the genomic abnormalities?
04:07From germline and somatic regions that
04:09would actually describe the capture
04:11the totality of abnormalities in atom.
04:14How comes that summer stragen receptor
04:16positive cancers recur as they are negative?
04:18You know some ER positive cancers
04:20are not fully ER positive,
04:223040% positive.
04:23So what are the rest of those cells
04:25which are ER negative?
04:26What's their relationship to the
04:28ER positive cells?
04:30What novel therapeutic strategies one could
04:32dig out from high dimensional genomic data.
04:35So what is the molecular phylogenetic
04:38relationship between different
04:39metastatic lesions and the primary tumor?
04:41Is these different for synchronous
04:43mats against asynchronous?
04:44That's you know why some Kansas are
04:47immune reaction immune poor was the
04:48difference between the immune rich ER
04:50positive and PR negative terms is there
04:52a difference in the microenvironment
04:54that's race influence this so really
04:57study all of these things and.
05:01You can look at the publications on them.
05:02So I'm only going to focus on a
05:04few which I think have a longer
05:06trajectory and contributed to the to
05:08this remarkable events that happened
05:09in the past 20 years that breast
05:12cancer survival and mortality decline,
05:14mortality decline by about 50%.
05:17I think this is primarily driven
05:19by new treatment strategies based
05:20on better understanding of the
05:22disease and the new
05:23classes of drugs that we developed.
05:25And I think the journey is
05:27just just about to begin.
05:29So how new treatment strategies
05:32could influence outcome?
05:34So in the early 2000s,
05:36I was in the right place at
05:38the right time at MD Anderson,
05:40we were interested to explore
05:42period preoperative chemotherapy
05:43for women who actually had operable
05:45disease and we assumed that they
05:46would end up with a better cosmetic
05:48outcome as smaller disease.
05:49And at that time,
05:51it was a pretty controversial idea
05:52and there was really no good way
05:54to either define the response.
05:56How do you measure the efficacy
05:57of these preoperative regiments?
05:58Do you measure it by response?
06:00On imaging or we measured by
06:02the extent of residual disease.
06:04So we proposed the the definition
06:06which eventually become the standard
06:07of care definition that you have
06:09no residual invasive cancer in the
06:11breast or lymph nodes and that's kind
06:13of the best outcome that you could get.
06:15So with this definition it pretty
06:17quickly become available become
06:19obvious that individuals accomplish
06:21this complete pathological response.
06:23It really well regardless of what
06:25type of breast cancer they had,
06:26they are positive or negative
06:28or too positive.
06:29Those who had residual disease didn't do so.
06:31And this immediately defines you what you
06:33actually want to accomplish in the clinic,
06:35right?
06:35You want to put more patients
06:37into these pathologic CR category
06:38and you want to hurt harm.
06:40Do you wanna help those who are
06:42in the residual disease group?
06:43So we did that in the past 20 years.
06:45So you see the evolution of the chemotherapy.
06:50Regiments,
06:50in 2008 when we published this
06:52paper on the survival curves,
06:54the best chemotherapy was
06:55Taxol anthracyclines.
06:56It produced about a 3035%
06:58response complete response rate,
06:59in particular negative disease
07:01and now we have doubled that.
07:03So now we actually accomplish
07:04about a 63% complete response rate
07:07by adding an immunotherapy drug.
07:09And you also learn that adding other
07:11chemotherapy agents like carboplatin
07:13improves the pathologic CR rates.
07:15We have regiments that don't
07:16include the anthracyclines that
07:18some of my colleagues think that.
07:19Is the chemical incarnation of the devil.
07:22Also there are even single agent therapies,
07:25targeted therapies like PARP inhibitors
07:27that produce pretty respectable
07:29pathology company eradication of
07:31the cancer before surgery in in
07:33germline Brockhampton patients.
07:34But we also made him really important
07:37improvements for in the life of
07:39those who have residual disease.
07:41So those are three randomized clinical
07:43trials that established the value
07:44of giving capsidae in chemotherapy
07:46for those and the residual disease
07:48with triple negative cancer.
07:49And the Olympia study showed that
07:51that whole party improves the
07:53response within a similar population
07:55if the average germline Broca's.
07:56And the Catherine study did the
07:58same for the record TDM one or
08:00Godzilla for her to post the disease.
08:02But I want to spend a few minutes on
08:04how do we get there, in particular,
08:06how we actually came about to establish
08:11the value of immunotherapy in.
08:14In breast cancer. So the roots of
08:16this idea that immunotherapy might
08:18work in breast cancer has been
08:20long rooted in preclinical studies.
08:23But also in the early 2000s a number
08:25of of groups reported that even in
08:28patients who only receive surgery,
08:30the amount of immune cells in the tumor
08:33microenvironment is hugely prognostic.
08:34So this is what the the first half of
08:36this slide shows you survival curves
08:38for patients who did not receive
08:40any other treatment than surgery,
08:42they were stratified into three groups.
08:44Little high immune presence,
08:46intermediate in presence or low
08:47immune presence and you see that
08:49that the the immune cells have a
08:52massive prognostic value in all three
08:53categories of of breast cancer subtypes
08:56including the the ER positive patients.
08:58And what we used in this particular
09:00study was gene signature to define
09:01the immune richness.
09:02They're in the same time German
09:05investigators showed that that
09:07the presence of immune cells also
09:09predicts the probability of complete
09:11pathological response.
09:11But this slide shows you 32 important things.
09:14One is that in the red circles you
09:17see the pathologic computer response
09:19rates by tumor infiltrating into side.
09:23Presence.
09:23So they grouped the cases into
09:25no lymphocytes, some lymphocytes,
09:26lymphocyte predominant and you
09:28see that the pathologic CR rates
09:30these numbers in the in the little
09:32blood red circles increase as you
09:33have more and more lymphocytes.
09:35So for example in the blue,
09:37so the square or highlighted
09:40area and ER positive disease,
09:43we know lymphocytes,
09:44it's a very small 6% PCR.
09:45If you have a lot of lymphocytes,
09:47it goes up to a respectable 23% and you see
09:50this same trend across all the subtypes.
09:52So of course these observations lead
09:54to a lot of other questions then.
09:55So why some breast cancers are immune,
09:57originalists don't is the immune
09:59microenvironment differ between
10:00the primary system and the maths,
10:02it's a different by ER subtype or by race?
10:05And ultimately the the most important
10:07question is this a causal relationship
10:09or immune cell presence is actually
10:12responsible for the good outcome or
10:14it's just an association that reflects
10:16some other underlying biology.
10:17So when these papers were published,
10:20you couldn't really test this in people,
10:21there were no chemotherapy drugs.
10:23But now we have and we actually have
10:24the answer to most of these and I
10:26put there some of the publications
10:28that that address these these issues.
10:32So I want to share with you some results
10:34which I think really informed a lot of
10:37my thinking about the the value of the
10:39role of immune system in breast cancer.
10:41So a few years ago Anton Sofronoff
10:44was a medical student here at. Yeah.
10:47At that time took on this project,
10:49but downloaded all the CG data or an
10:51AC DNA copy number, mutation data,
10:54germline snips and ask this question.
10:56So what drives the immune infiltration
10:58and breast cancers?
10:59So we looked at Chrono Heterogeneity,
11:01mutation load, new antigen load,
11:03copy number variations,
11:04germline snips,
11:05single gene somatic mutations,
11:07pathway level abnormalities,
11:09which of these is associated with
11:11high immune presence,
11:12whether you think the results showed?
11:15So. Gosh.
11:22So the results are actually
11:25quite counterintuitive.
11:26So what this shows you is a correlation
11:28matrix of about 12 immune gene
11:29signatures that we use to define the
11:31immune presence or absence or in your
11:33richness and about 6 genomic features.
11:36So the darker brown shows a higher
11:39correlation value and the darker
11:41blue shows a negative correlation.
11:44And you see right away that
11:45the immune gene signatures are
11:47highly correlated one another,
11:48whereas they are not correlated
11:49very closely at all. In fact,
11:51they are anti correlated with many of the.
11:53Economic features.
11:53So and you see this across the
11:56board in all the three subtypes.
11:58So in in primary breast cancer greater
12:01chromo heterogeneity and higher mutation
12:03and neoantigen loads are associated
12:05with lower immune infiltration.
12:07So there was such a weird finding
12:08that we actually teamed up with
12:10with the A colleague from Germany,
12:12Thomas Cohn to really confirm this
12:14in an independent data set data sets
12:17and we find the same same result.
12:20So why is this interesting?
12:23Because even though we found no share
12:25genomic alterations that drive the
12:27immune infiltration in breast cancer,
12:28we really find a strong supportive
12:30evidence that there is an active
12:33immune editing in early stage disease,
12:35right.
12:35So a lot of immune cells in actually
12:37called remove chromo heterogeneity
12:39and that's why you have a chromoly
12:42simple tumor and actually a lower
12:44your antigen load because the cancer
12:46cells with the high neoantigen load
12:48are removed by the immune system.
12:49So that's really attractive.
12:51Hypothesis and it makes testable predictions.
12:54So one prediction is that even tumor cells
12:57sort of undergo medical transformation.
13:00Some of it could be eliminated
13:01by the immune system.
13:02So if that's really true,
13:03then then actually immunotherapy
13:05should work as chemoprevention.
13:07Of course, it's too toxic to do that,
13:08but the concept is important.
13:10So we're going to test this in
13:12an ongoing large event trial that
13:14uses symbolism for a year to see
13:16whether it alters contralateral
13:17breast cancer events and also
13:19whether it alters breast density.
13:21Which is sort of a somewhat
13:24validated risk predictor.
13:25But the most important consequence is this
13:27that when we actually diagnose these cancers,
13:29there may be a quasi equilibrium fight
13:32between the immune system and the cancer.
13:34So when there are a lot of immune cells,
13:35it's kind of indicate that the
13:37immune system is having almost upper
13:39hand and that's why it actually is
13:42associated with better prognosis.
13:43But at that stage you might actually
13:45help tip the balance towards the
13:47immune system by chemotherapy or by
13:49immune checkpoint inhibitors and then.
13:51Do not have the drugs to test this.
13:53And we actually launched 4 studies
13:54to to address these questions
13:56and three of them have results,
13:57and I'll show that to you.
13:59But the third prediction is also interesting,
14:02right?
14:02So if you really follow this logic,
14:04then the metastatic disease should
14:06really arrive through an immune escape.
14:08So we did a series of studies
14:10to compare primary
14:11exams and maths, and it's among the
14:13first groups to show that actually
14:14metastatic lesions in breast cancer
14:16are profoundly immunocompromised.
14:18And we also looked at whether there
14:21is subtle variations by sight.
14:23So now these are all sort of
14:25relatively valid accepted principles.
14:26I I thought I showed this to you,
14:29especially for those of you
14:31who are younger investigators.
14:32So there are risks of being coming up
14:34with an idea too early or too late.
14:36So this particular idea came
14:37on a little bit too early.
14:38In 2012, about a month of Tiki came here.
14:41I approached Merck to do 2 large
14:43studies in the curative setting.
14:46What was the neoadjuvant trial to see
14:48whether we could actually push the PCR?
14:49It's up based on the associations that
14:51I showed you to test the causality.
14:53The other one was an adjuvant study.
14:54We could actually improve the outcome by
14:56giving people liberalism out and eradicate.
14:58Micromedex and this is what they said,
15:00sorry you're unable to avoid the drug
15:02and the monetary support at this time
15:04due to unclear regularly path forward.
15:06But it was three years later they
15:08actually realized that there is a
15:10path forward and they actually run
15:11both of these studies or or agree to
15:13do it and they to their credit they
15:15actually invited me back to their
15:17steering committee of the new adjuvant
15:19trial and I lead the adjuvant trial.
15:22So what do these studies show it?
15:24This is just the selection that is
15:26representative of the findings from
15:28the neoadjuvant immunotherapy trials.
15:30And they were lounged in triple
15:32negative disease because of the
15:34really strong association of immune
15:36cells with pathologic CR or strong
15:38strong association with prognosis.
15:40And by and large triple negative
15:41cancers have a higher in your presence.
15:44So all these studies took place
15:45in in that space except one,
15:47the ice spy all talk to you a
15:49little bit more about it.
15:50So what this study shows is that the
15:51the computer response rates improved.
15:53Didn't have as much as we thought.
15:55So the largest study keynote 5 to 2,
15:57the Merck study showed improvement
15:59about 7 percent, 56 to 63.
16:01Really underwhelming because chemotherapy
16:02trials could do double digit improvements.
16:05Yet the chemo studies actually
16:06didn't really improve the event
16:08free survival that dramatically.
16:09Oftentimes it didn't deal with
16:10it all to a significant extent.
16:11But keynote 522 did.
16:13You see the same in an even smaller study,
16:15paranormal.
16:16They're also showed a 9% even PCR rate.
16:18Not even significant,
16:20but the event free survival was significant.
16:22The other?
16:23Important finding in this sort of
16:25or observation from these studies
16:27is that in metastatic disease,
16:29again parallelism have improved the
16:31outcome when combined with chemotherapy.
16:33But this was only seen in the pediatric
16:35and positive patients whereas in
16:36the early stage setting you don't
16:38need to have Pedialyte and one.
16:39So that confuses a lot of people.
16:41But I think there is a really
16:42simple and elegant
16:43explanation and it comes from the
16:44slide that I showed you previously
16:46from the fact that the metastatic
16:48lesions are immunocompromised or really
16:50immunosuppressed immune attenuated so.
16:53And the only stage setting I think a
16:54small amount of immune presence that
16:56you could miss with the biopsy and they
16:57actually miss it oftentimes with biopsy.
16:59So this is a work that Adriana Khan,
17:01one of our fellows showed and we presented
17:03the San Antonio Breast Cancer meeting.
17:05So even a few period like in one positive
17:07cells that are intermixed with the
17:09micro environment and missed the initial
17:11biopsy could be enough to actually
17:12ignite an immune response and the same
17:15way chemotherapy ignites sort of like
17:17one expression in the more massive scale,
17:20but you don't see the same thing
17:21in in in the metastatic setting.
17:24So the other question was this really.
17:27This thing observation that why small
17:29improvements in Pathologic CR really lead
17:32to large improvements in survival whereas
17:34in other setting it doesn't happen.
17:36So that brings me to another sort
17:38of debate that used to rage and
17:40the the breast cancer community and
17:42we spent a lot of time on it.
17:44It's really prompted by the 1st
17:46initial new adjuvant trials and shovel
17:47power to show improvement in PCR,
17:49but was woefully underpowered and
17:51included all subtypes to to really
17:53show improvement in survival.
17:55So this matter analysis by the FDA
17:57and showed very little in fact
17:59no relationships at all between
18:01improvement in PCR and survival.
18:02They confused a lot of people,
18:04but it would have to fly against
18:06the totally common sense.
18:07Observations, Taxol improved pathologic,
18:09sciarid improved survival receptive
18:11improved Pathologic CR,
18:13it improves survival.
18:14Platinum improved Pathologic CR
18:15it's and now we know that it
18:17improves survival as well.
18:18And of course the immune checkpoint
18:21inhibitors improved pathologic
18:22security improve survival.
18:23But nevertheless it's really true
18:25that at the individual trial level
18:27the relationship between the PCR
18:29change improvement and the improvement
18:31in PFS is hugely variable.
18:33So that's the next question to
18:34study why and I actually have a
18:36good explanation for you.
18:37And I think it's very elegant and simple.
18:39But to understand that you need
18:42to familiarize yourself with this
18:44concept of of a continuous metric of
18:46of outcome or pathological response.
18:49So again in 2007 we developed this
18:52metric called residual cancer burden
18:54to capture the pathological residual
18:57disease as a continuous variable.
19:00We did that because continuous
19:01variables are more powerful to
19:03identify genes that would be associated
19:05with outcome or not but.
19:07So eventually it took sort of
19:09traction in the form of categories,
19:11so you can use this continuous score to
19:14create bins of 0 being complete response.
19:17Another bin.
19:18That's the minimal residual disease
19:19or RCB 1 moderate amount or CB2
19:22and a large amount of RCB 3.
19:23But the truth is that this is really
19:26a continuous scroll and that's
19:27why we did it so.
19:28Be teamed up the deal I spoke
19:31to investigators because
19:32this continuous sort of score,
19:35I thought actually could reveal
19:36some really interesting things
19:37about how different drugs work.
19:39So what you see here is actually a pretty
19:42cool picture of the continuous RCB scores in
19:45seven different arms of the eye spy study.
19:48So the eye spy is randomized trials,
19:50the control arm is always staxel ACC,
19:52and but you see here is the RCB values
19:55from zero to 50 is complete response.
19:58Five is expensive.
19:59Single disease.
19:59This kind of shows you the the the
20:01prevalence of the density or the
20:03frequency with which you encounter a
20:05particular RCB value in the trial arm.
20:07So the black is the control and the dotted
20:11lines are various experimental drugs.
20:12I just want to look at you the two
20:15panels which are labeled so I don't
20:18think I can use a A.
20:21Sort of a pointer,
20:22but you probably see there
20:23that the bottom panel,
20:24which is regimen 7,
20:25you have a large improvement in PCR rates,
20:28right, because the the initial
20:29zero values are much higher.
20:31That's where the curves start.
20:33But you also see a massive shift towards
20:35the smaller values across the board.
20:37If you look at the Regiment 3 on the
20:39top instead of right hand corner,
20:42then you see that that regimen
20:43also improves PCR rates.
20:44But it does it by moving the RCB 1,
20:47the little residual disease group,
20:49into the PCR company response.
20:52And that is very unlikely to
20:53affect survival like it doesn't.
20:55But this particular regimen didn't
20:56affect at all the higher residual cancer.
20:59So we thought that actually measuring
21:01the the distribution of the differences
21:04in residual cancer burden scores could
21:06capture the efficacy of a regimen.
21:09And we developed a new statistical tool
21:10that you can find in this paper and
21:12you can even play with it if you have
21:14a breast cancer on this open website,
21:16we call it treatment efficacy
21:17score and it basically compares
21:19the distribution of RCB scores.
21:21Cross through trial arms in that
21:23particular metric actually really
21:25correlates quite well with event
21:27free survival which is what you see.
21:29There's a significant difference.
21:30There is an event free survival improvement.
21:32Is that all significant improvement
21:33in this test score then you don't
21:36have significant improvement
21:37in event free survival.
21:39So we're going to validate this
21:40within with the other groups.
21:42So we're not move to this other question
21:44that these studies show up, right.
21:46So pembrolizumab is expensive and 15%
21:49of the patients have severe toxicity,
21:51so.
21:51He entered into this race to find
21:54predictive markers that define the
21:57patients who need pembrolizumab and
21:59this is a slide from from us from
22:02a group in Germany civil libel.
22:03And one of my former lab members Thomas Kuhn,
22:06who leads their translational research arm.
22:08And what they show in this randomized
22:11immunotherapy versus chemotherapy alone
22:13ARM study that there are a number
22:15of molecular variables that predict
22:17response to any if you have them like
22:19high commutation burden or a high.
22:22Energy and expression or high P like in
22:24one expression or high till comes you
22:26have higher PCR rate with chemotherapy,
22:28chemotherapy but also with
22:30chemotherapy plus immunotherapy.
22:32But the improvement by immunotherapy
22:34happens in both groups,
22:36the remediation low and high,
22:38the PD low and high or the field
22:40count low and high groups.
22:42So these are these one of these
22:44metrics are selective to identify
22:46who actually needed the panel,
22:48but we have an idea who actually
22:50might benefit from Pedro.
22:51So we teamed up with the investigators.
22:53On the build who previously suggested
22:55that MH subclass 2 expression in tumor
22:58cells might actually identify a group,
23:00the group of patients who
23:01really need it Pembroke.
23:03So I need to see class to is is
23:05mostly expressed in immune cells and
23:08participates in antigen presentation,
23:10but it can be induced to be expressed
23:12in cancer cells and epithelial cells
23:14by interferon gamma, for example, so.
23:17Have you run this immunity chemistry,
23:20a simple immunity chemistry for
23:22emission classical expression on
23:23cancer as opposed to the immune cells.
23:26And we actually confirmed that what
23:29Justin Balko originally reported
23:32that the cancers which were positive
23:34for MHC Class 2 expression actually
23:36had a higher pathologic CR rate when
23:38Pembroke was added in the ice spy study.
23:41But the pathologic CR,
23:42it was the same whether they were
23:44MHC Class 2 high or low if they
23:46only got chemotherapy and so.
23:48They really strong interaction,
23:50marker treatment interaction
23:51in that study and parallel with
23:54this completely independent.
23:55Another set of former lab member of mine,
23:57Jean-paul Bianchini showed the
23:59same thing in their new adjuvant
24:01trial without the salesman.
24:02You know,
24:03I highlighted for you the
24:05interaction between Italy,
24:06the expression on epithelial cells that
24:09actually predicted higher odds ratio for PCR.
24:12Vidot is always the map but didn't
24:14have any sort of significant other
24:16ratio with chemotherapy alone,
24:17but the same.
24:18Study our immune cells didn't carry this.
24:21So it's a really cool project there
24:23and we just got funding from the NCI
24:25to kind of test this and validate this
24:27in a larger trial them S 1418 that I,
24:31I mentioned to you earlier.
24:33But again,
24:34so this study is the fascinating thing
24:35about science is that every advance
24:37actually throws up new questions,
24:38even more interesting questions.
24:41So one question is why some cancers
24:44are important in reach, right?
24:46A lot of people are struggling
24:47to find answers,
24:48how you make a cold against the heart.
24:51But we thought we ask something a
24:52little bit more original and maybe
24:54something that that could be easier to crack.
24:56And that's the question,
24:57why doesn't all immune high
24:59cancers actually accomplished PCR?
25:01Why is the PCR only 63%?
25:03And 100 or 90 that's a project
25:05that Kim actually came women led
25:07and we compared the immune reach
25:09triple negative disease that had
25:11the PCR versus those that did not.
25:13And we find really pretty interesting
25:15stuff that I think could lead
25:17us to some leads about what
25:19combination therapies,
25:21immunotherapies could really be
25:23make embolism and more effective.
25:25So just to summarize this let's we
25:27found that the teacher have better if
25:29one teacher beat is high in the immune
25:31microenvironment even if you are in reach.
25:33You don't accomplish PCI and a
25:35lot of innate immunity markers
25:36are also associated with it.
25:38The innate immunity markers actually
25:41are macrophage and K markers and when
25:43you look at the cytokine milieu then
25:46you really see this very strikingly
25:48so cancers it raises your disease.
25:50The dominant cytokines are actually
25:52cytokines which are involved
25:54in chemotaxis and activation of
25:56neutrophils and macrophages.
25:58So we hypothesized they're blocking.
25:59Some of those would actually improve
26:01the outcome or the efficacy. Of.
26:05You actually went pembrolizumab.
26:07So interestingly I just put that
26:09asterisk for you to to that.
26:11It's so beautiful because it congruent.
26:13So we find that a lot of these very
26:15same cytokines that we see highly
26:17present in immune rich non responding
26:20TNBC at the very same chemokines
26:22and silicones that we find in the
26:25microenvironment metastatic disease
26:26right in that paper that showed that
26:28the metastatic microenvironment
26:29is more immuno attenuated.
26:34Just instead of finish these sort of
26:36series of questions and immunotherapy off.
26:38So if immunotherapy works
26:40beautifully entrepreneur disease,
26:41could it actually work in a
26:43subset of ER positive cancers.
26:44And we think that it will work because
26:47we noticed in the eye spy trial data
26:50that in three arms that included
26:52immunotherapy including the door volume up,
26:55Olaparib arm, the Iliad,
26:57the Penrose Metaxa arm and the pembrolizumab
26:59and it's all like receptor antagonist.
27:02Arm in all of these three arms
27:05independently we saw that among the
27:07ER positive here we call them HR
27:09hormone receptor positive cancers.
27:11There is a group that is characterized by
27:15routinely reported sort of molecular feature,
27:18the ultra high mammaprint status.
27:21So all of these patients had
27:22to have high mammaprint result.
27:23High MAMMAPRINT defines patient
27:25superficially benefit from chemotherapy
27:27but within that high mountain
27:28group you can devise an agent,
27:30they actually introduce their system.
27:32The device to group smaller print
27:34high high and some Withrow high.
27:36So the small print we throw higher
27:38MP two group is the subset among the
27:40ER positive patients who benefited
27:41and it's really, really elegant.
27:42You can't see that right.
27:44So the HR positive MP1,
27:46there's no difference whether
27:47you get chemo plus durva,
27:49but if you are MP two then
27:50Nirvana improves your PCR.
27:51It's same for pembrolizumab
27:53with the other two arms.
27:55And what's even nicer when you
27:56look at the molecular features
27:58of these empty two patients,
27:59the area are positive but
28:01their ER signaling and.
28:02Yeah,
28:03sort of the gene signatures that typically
28:05associated with endocrine sensitivity,
28:07this is low.
28:07So that's the group let's see
28:09are positive but least likely to
28:10benefit from endocrine treatment.
28:12They have sort of a higher proliferation
28:14signature which also makes sense.
28:15So they are more sensitive to chemotherapy
28:17and we also saw this in the the,
28:19the chemotherapy arms and but
28:21we didn't really see a major
28:23difference in the immune micro
28:25in in immune signature genes.
28:27So again we hope to launch the prospective
28:31study that would validate this concept.
28:32With the routinely available essay we
28:35could actually identify a group that
28:37will benefit from the same way as
28:39triple negative disease benefited from
28:42including immune checkpoint therapy.
28:44So just to summarize these clinical
28:46partially the paradigm shift that
28:47happened in the past sort of 20
28:49years is that the best way to treat
28:51most stage two and stage three
28:52triple negative patients is new
28:54adjuvant chemotherapy and the best
28:56PCR rates are accomplished about
28:57two third of the patients having a
28:59competent navigation of the cancer,
29:01the same happened in her two
29:02positive disease.
29:02Don't talk about this because it's
29:04really predated at least by 1015 years,
29:06the immunotherapy revolution
29:07and there are a lot of really
29:10interesting studies that will push
29:12the survival even further among
29:14those who have residual disease.
29:16So there are new studies that
29:17are launched in that space that
29:19I kind of highlighted for you.
29:21So what's next,
29:22right.
29:22So what's going to be the
29:23next paradigm shift in the next 10 years?
29:25And I think the this is really.
29:28I I see two really potentially very
29:30high impact fields which we could
29:32improve again survival within the
29:34next 5 to 10 years and which is.
29:36So wait a second.
29:41Yeah. So what is coming up with this
29:44concept that could we detect molecular
29:46relapse in solid tumors the same way as
29:49we detect molecular relapse in leukemia.
29:51So if you see that with PCR that
29:53your genomic abnormalities returned,
29:55then a second round of treatment
29:56at that point would actually
29:58cure some people from leukemia.
29:59So could the same paradigm apply to
30:01to sometimes it didn't really have
30:03good ways to catch this and we didn't
30:05really have good effective drugs
30:07either 5610 years ago to test this,
30:09but now we have we have most molecular.
30:11Essays that can pretty reliably
30:13identify and the SEC DNA is
30:16particularly tumor informed C DNA.
30:18So if you have a high C DNA level
30:20that's starting to rise while you
30:22are in the surveillance of follow
30:24up stage of the initial curative
30:26therapy as the city then rises,
30:28unfortunately it's almost sure bad that you
30:30will have a recurrence clinical recurrence
30:32within the next seven or eight months.
30:35So could we intervene at that point
30:37when people are still sort of
30:39micrometastatic but the micrometastasis
30:40is raising its ugly head?
30:42So that's an idea of a second line.
30:44I look in therapy and we
30:45actually lead a study.
30:47We have a study in that space that that's
30:49exactly this idea in your positive
30:51patients who are receiving endocrine
30:53therapy but start to have a rising CDN,
30:55they randomized the full
30:56Western public cycling and.
30:58And we'll just continue with their standard
31:00of care treatment and get treatment
31:03when they become clinically symptomatic.
31:05So the other potentially paradigm
31:07shifting idea is really that they
31:08could cure some metastatic disease.
31:10So you have metastatic disease kind of the
31:12current dogma is that you will die from it.
31:14It may take many, many years,
31:15but ultimately people die.
31:17I'm not sure that this actually
31:18has to happen like this.
31:20So what happened in the past five,
31:22six years is that you really
31:24understood much more clearly that
31:26only that there are multiple.
31:28Different types of meds,
31:29not just some medicine.
31:30Disease doesn't exist.
31:31There's a homogeneous entity,
31:33just like the breast cancer doesn't
31:34exist to looking. It doesn't exist.
31:36It's a useful concept.
31:37But practically really these
31:38are all very there are many,
31:39many different types of leukemias that
31:42require different approaches and treatments,
31:44different types of breast cancers.
31:45And the same way like metastatic
31:47disease is also heterogeneous.
31:49So the novel stage for disease is unique
31:51because it never received any prior therapy.
31:54That's obviously very different
31:55from somebody relapsing and
31:56having a metastatic disease.
31:58After they went through all the
31:59treatments that I showed you
32:00in the new adjuvant setting,
32:01the chemotherapies was embolism and whatnot.
32:04So curing those folks with
32:06existing therapies is a long shot,
32:08but curing those folks who never had
32:10any therapy with the combination of
32:11drugs is probably not such a long shot.
32:13And there are many case reports and
32:15oncologists who practice for a long time.
32:17All have anecdotal cases of
32:19metastatic patients,
32:20particularly with her two positive
32:21disease because her two positive
32:23disease had the best drugs initially
32:24the her two targeted drugs,
32:26but now we have good drugs for
32:27for triplet disease as well.
32:29And also for your poster disease,
32:31so this paradigm that really
32:32kind of put into the mind of many
32:35practicing physicians that some her
32:36two positive cancer can be cured.
32:38I think it's kind of increasingly
32:40applicable to the other subtypes as well.
32:43So we hope to do a study that would
32:45actually focus on covad especial
32:47group of her of metastatic patients,
32:50they de Novo newly diagnosed
32:52metastatic patients particularly
32:53with oligo metastatic disease,
32:54so that we could really get rid of all
32:57the known homicides and what's left.
32:59Is micromass,
32:59but we can deal with micro Mets.
33:01That's the success story that I showed you.
33:03That's how adjuvant therapy improves
33:05survival after removing the the primary
33:07breast cancer in the lymph nodes,
33:09the systemic therapy.
33:10Washes and and and kills them
33:12at the Micromax.
33:14So I think this better than probably
33:15will hold up in stage four disease
33:17and the vision is very simple.
33:18So in in five or ten years you don't
33:20call these patients the oligo metastatic
33:23stage four patients stage four,
33:24but you call them stage 3C.
33:26Because they are deep, sorry.
33:28Because then they will be curable.
33:31So I'm going to move on to some other
33:33projects that I also find amazing and I
33:35just wanna share you some of the results.
33:37So why do some women develop breast
33:39cancer 20-30 years earlier than the
33:41average or median age even in the
33:43absence of any germline mutation?
33:45Actually that's the majority of
33:47young women with breast cancer.
33:48It's only a minority who has broken
33:51mutations rather identified mutations.
33:52So we had two ideas.
33:53One was that each is the strongest non
33:56genetic risk factor for breast cancer.
33:58So could you actually sort of
34:00hypothesize that young women?
34:02Could be breast cancer actually
34:04experience an accelerated epigenetic
34:06age of their breast.
34:07So this was an idea that Erin Hofstatter,
34:09our former colleague picked up and
34:11we did a series of publications
34:13that actually suggests that this
34:14is indeed happening.
34:15So it shows you this insert from
34:17the the clinical epigenetics paper
34:19in 2018 shows this the most sort
34:22of simply and clearly.
34:23So what you should what you see
34:25there is each acceleration in the
34:28normal breast tissue of women who
34:30had breast cancer later and the.
34:32Epigenetic age acceleration of people
34:34who never develop breast cancer.
34:35So we did this with the Susan Comment
34:37Tissue Bank and with some tissues from here.
34:40So you see that there is a
34:42significant acceleration.
34:43So epigenetically speaking based
34:45on the methylation signature,
34:47the breast normal breast tissues of
34:49woman who subsequently developed breast
34:51cancer is older than their chronological age.
34:54And we don't see this to such
34:56extent in the control patients.
34:58And then and then we had some follow
34:59up patients which really kind of
35:01papers that explained that it's mostly.
35:03Polycom related genes whose
35:04methylation pattern is associated
35:06with this age acceleration,
35:08and this last paper on the review in
35:11science advances shows that actually every
35:13cell proliferation adds a little bit of
35:16epigenetic aging to to to the tissues.
35:18And there is a share of epigenetic
35:21signature between cancers and and normal
35:23cells and it relates to aging and it
35:26relates to ultimately cell divisions.
35:28But it's probably not the full story though.
35:31So what's the rest of the story?
35:32So family history is a predictive risk
35:34factor even in the absence of any
35:37detectable hyper reference gene mutations,
35:38right? So something you inherited
35:40increases your risk,
35:41even if it's you can't see it so.
35:44Polygenic risk scores that use individual
35:47snips that are individually associated
35:49with risk to a very small extent,
35:52sum them up and you've made them
35:53by the risk that they confer.
35:55That's a polygenic risk score.
35:56However,
35:56even the best ones today using several
35:59100 risks polygenic risk and have
36:01a lot of missing heredity in them.
36:03So they don't explain this complete story.
36:05So we have this other idea that could
36:08the combination of non recurrent rare
36:10germline variants and cancer relevant
36:11genes determined individual risk.
36:13So because they are not recurrent.
36:14Missed them in in indigenous studies,
36:16right,
36:17because they start out finding individual
36:19snips that are associated because
36:20they are recurrent in the mental
36:22state of India's cancer population.
36:24But if it's not recurrent,
36:25you won't see it.
36:28So this is an idea that really kind of
36:30wanted me for quite a while since this
36:32paper came out from the 1000 Genome Project,
36:34which showed that all of us
36:35here have different faces.
36:36And the reason we have different faces
36:39is this amazing set of variation in
36:41Snips and Jermaine Snips and other
36:44genomic variations that we are born with.
36:48So an average person carries about
36:5020 and 50 to 350 genes that have
36:54a loss of function.
36:55That's probably the reason why I have
36:56this poor voice and small stature.
36:58But anyway,
36:59so the point is that this low
37:01frequency events that occur in unique
37:04combination individuals might set the
37:06stage that what additional events
37:08matter or cause the transformation.
37:11So it's a combinatorial effect, right?
37:14So.
37:16We put these hypothesis forward
37:18that really that functional germline
37:20variants as potential Co oncogenes.
37:22And this actually I think there's
37:25something that covers on the screen.
37:27Yeah, so you can't see this well,
37:29but this model,
37:30the the nice thing about models is
37:31they predict testable hypothesis, right.
37:34So this particular idea that the
37:36Germans polymorphisms all of them
37:37together said this theme stage for
37:39what counts as an oncogenic event and
37:41eventually this is the totality of
37:43abnormalities that lead to cancer.
37:45So it's this sort of testable leads
37:48to this testable hypothesis,
37:50right that cancers in younger patients.
37:52This is correct.
37:53They should have more germline variants
37:55because they need fewer somatic
37:57events to reach a threshold, right?
37:59The sexual disturbance that
38:01pushed them over to to
38:03become malignant.
38:04And theoretically you could also
38:06use this idea to develop a cancer
38:09gene systems integrity score that
38:10captures how far a cell or tissue is
38:13from this malignant transformation.
38:15So we started to study that.
38:17And this is a paper that
38:19touching postdoc in my lab did.
38:21So we asked this really fundamental
38:24simple thing that amazingly not a
38:26lot of people actually studied before
38:27that what's the relationship between
38:29the person's age of that each of your
38:32diagnosis of cancer and the germline
38:35variant load in cancer relevant genes.
38:38So what are cancer relevant genes?
38:39So we just put from the literature
38:41and from from review articles about
38:431500 genes which are experimentally
38:46validated that they alter.
38:48They've played an important
38:49biological role in cancer.
38:50And when you see here,
38:51it's actually pretty obvious and
38:53it's really beautiful, right.
38:54So people who develop cancer at
38:57an older age have fewer germline
39:00alterations in these cancer relevant genes.
39:02People who develop cancer at
39:03younger age have a much higher,
39:05these are age bins by years of 10 and the
39:08opposite is seen in the somatic space.
39:10So people will develop cancer at their ages.
39:12Prostate cancer folks
39:13have a lot of mutations,
39:14whereas people who develop cancer
39:15at an early age have fewer somatic.
39:18Positions,
39:18and we knew this from the
39:20pediatric literature actually.
39:20Pediatric cancers don't have
39:22a heck of a lot of mutations.
39:24So that's actually a really nice story
39:26that that supports this idea that
39:27somehow that's the combined effect.
39:29And if you have a lot of germline hits,
39:30you need need a fewer random
39:33somatic hits to push you over.
39:36In this paper view,
39:37it kind of did you think a
39:39little bit deeper and you know,
39:40so cancers which actually are highly
39:43linked to environmental factors for
39:45lung cancer for example that they
39:47actually tend to have a lot more
39:49somatic events and some somatic
39:50mutations from somatic origin,
39:51from germline in other cancers
39:53kind of coffee.
39:54So in between and some of them are
39:56actually like testicular germs,
39:57atoms are dominated by germline
40:00hits rather than somatic hits.
40:05But then this this location OK so
40:07why 1500 genes so probably are
40:09there more genes related to cancer.
40:12So we we asked this question whether
40:14what's the what's the totality
40:16of cancer relevant human genes
40:18and the name we came up with the
40:21really simple concept that if.
40:23Core cancer genes are important
40:24and we define core cancer genes
40:26actually from a clinical panel,
40:28the MSKCC impact panel that's clinically
40:30used to define actual permutations.
40:33So these hypothesized the
40:35genes that interact in a.
40:38Putting putting interaction network or
40:40the string network that there's a lot of
40:42different ways to measure interactions.
40:44So genes that interact with the core
40:45genes will be somewhat important and
40:47genes that interact with this one step
40:49remove genes will also be important
40:50to some extent but probably less.
40:52And then those which are three four
40:54steps removed are even less important.
40:56So we wanted to test this hypothesis,
40:58but as you get closer to the close genes
41:00then you would have increasing connectivity.
41:02That's one mathematical way to measure
41:05the importance of gene as you get closer.
41:08So one step.
41:09Both from from core cancer genes
41:10then it's going to be more important
41:12than survivability.
41:13We can check this in genome
41:15wide CRISPR and ASARONE screens.
41:17Also predicted genes which are
41:18one step removed,
41:192 steps removed are more important than
41:21those which are three steps removed
41:23in terms of having large number of
41:25somatic mutations in in Kansas and
41:27that they will be under a stronger
41:29negative selection in the germline,
41:31right,
41:32because they are important.
41:33And in many of these genes that
41:34are important,
41:35cancer are important in many other things
41:37and that's exactly defined in this paper.
41:39And this just shows you the numbers though.
41:40So one or two step remove genes in
41:43our genome is about 10,000 genes.
41:45So actually probably the cancer 11
41:47genes space is much much bigger,
41:49just don't know about a lot of these
41:51and of course they're importance is
41:53not as important as a P53 mutation but
41:56nevertheless they contributes very
41:58likely contribute to the biological disease.
42:01So where are you going with this?
42:03So what you actually want to do
42:05really is so address cancer as a
42:08cellular transformation as as a a
42:10defect in a in a in a complex system.
42:13So complex systems fail through unique
42:16combinations of individual non lethal events.
42:18I mean just think about this if
42:20you would run the statistics on
42:21what's causing plane crashes,
42:22even find anything.
42:23Because even though flying through
42:25a storm is a risk,
42:26but many many planes fly through
42:28storms have any problem, you know,
42:30pilot sleeping or not.
42:31Been trained,
42:32it's a lot of happens that that
42:34despite of this sort of human errors,
42:36the plane survives,
42:37you don't even know about it.
42:39So it's really a unique combination
42:41that brings down points.
42:42And so that's the thing that we
42:43actually try to see whether we could.
42:45So some of these unique combination
42:47of Germany and some
42:48of the events into a score and
42:50they ultimately visualize it.
42:51They did a little bit of a sort of
42:54preliminary kind of effort in this
42:56few years ago with wavey she trying
42:58to kind of map all the molecular
43:00abnormalities that particular cancer.
43:02As and visualize it in a standardized way
43:04in in these papers we try to resurrect
43:07this really delighted that Susan Coleman
43:09actually accepted this challenge for
43:11their hecaton in March next year.
43:13So we're going to lead A-Team to to
43:16try to develop this Kansas score. Umm.
43:22So the new classes of drugs, right.
43:24So that's the last piece that I'm
43:26actually going to talk to you a little
43:28bit because I'm so excited about it.
43:30So metabolically, right,
43:31rewiring is a major hallmark of cancers,
43:34yes. Yeah, we don't have any
43:36drugs that exploit it.
43:37So remember, a lot of chemotherapy
43:38drugs interfere with DNA synthesis
43:39because you need to double your DNA,
43:41but you need to also double your lipids.
43:43You also need to double your proteins.
43:45So why don't we have drugs in that space?
43:48So we started off with the computational
43:50biology project to look for.
43:52Most of isoenzyme diversity in cancer
43:55compared to corresponding normal tissue.
43:57So isoenzymes kind of more or less sort of
44:00could catalyze the same chemical reaction.
44:03But they are different genes and sometimes
44:05they are located in different compartments.
44:07So what you want to look at is is a
44:09particular isoenzyme becomes cancer dominant.
44:12So this isoenzyme diversity gets lost because
44:14out of the three or four isoforms that
44:17produce the same sort of chemical reaction,
44:19one becomes dominant.
44:20That may be actually important.
44:22Analogy.
44:22So if you're looking for is this sort of
44:25change that the normal cell has kind of fun,
44:27actually both sides of enzyme one
44:28and two and the cancer actually
44:30one of these becomes dominant.
44:32So we asked how many are these
44:34in the human genome?
44:35So we again went to the TTC share
44:38data and called all the human enzymes
44:40which have less than 5 isoforms
44:42to find to look for a pattern that
44:45showed this cancer dominance.
44:47Once we find this,
44:48then we looked whether we can see the same
44:50in the CLA the cancer cell line encyclopedia.
44:52Just to make sure that this is really
44:54happening at a cellular level,
44:55not at the tissue level because the
44:57TCG's tissue level and it also then
45:00once we confirm those that they are
45:02also dominant in a cancer cell line
45:04that enabled us to really check
45:06whether this particular isoform is,
45:08is survival critical in the depth
45:10map data which is CRISPR.
45:13You have no card database.
45:15And then the final hit you wanted to confirm,
45:17so this is what we found.
45:18So there are about 136 cancer breast cancer
45:22dominant isoenzymes that we find in the CG.
45:25About 81 of these are also cancer
45:27dominant in breast cancer cell lines,
45:29but 53 are important for survival.
45:33When you knock it out,
45:34you can sell lines, survival improves.
45:37And about 44 of these,
45:38the locking out the the particular
45:40isoform is more important than knocking
45:42out the other one and then you actually
45:44meet all these three criteria then
45:46you end up with about 17 potential
45:49targetable isoenzymes in breast cancer.
45:52But we did this for a whole bunch
45:54of cancer types and the the most
45:56shared sort of cancel them in a
45:58nicer form turned out to be a C1
46:00or acetyl coenzyme carboxylase.
46:03And this little uncertainty,
46:05the things the right side for
46:09you shows the
46:10the actual pattern expression pattern, right.
46:12So the red one is a potential target and the
46:15first column or the first sort of set by
46:17the line start is the normal tissue and the
46:19second column is the corresponding cancer.
46:22So you see that the blue goes
46:23down because it's lost in cancer,
46:24but then the red stays up.
46:26So we actually looked at why this is
46:27happening. It's maturation driven.
46:30And but what is a C?
46:31So C1 and C2 are actually the first literally
46:34the enzymes in fatty acid synthesis.
46:37They pre they are immediately
46:39before fast or fatty acid synthase.
46:42They convert acetyl coenzyme to
46:43Malaya coenzyme and this C1 is
46:46actually in the cytoplasm.
46:47C2 is the mitochondrial membrane
46:49also regulates fatty acid breakdown.
46:51So if you block ACC,
46:53you block fatty acid synthesis and
46:56accelerate fatty acid burning.
46:58So it turns out that actually this
47:00wasn't real skin of pharmaceutical
47:02companies for a long time because
47:04because as a target for Nash,
47:06which is non Alcoholics started
47:09hepatitis or fatty liver and it's also
47:12actually one of the major targets for
47:14herbicides that we use in agriculture.
47:16Turns out that Pfizer actually had a drug
47:18that worked amazingly well in people.
47:20They put it through several clinical trials
47:22and they established that it actually works,
47:25it blocks the novel fatty acid synthesis
47:27as you see on that curve that ports.
47:29The percent of the noble lipogenesis
47:31in people, it was also safe,
47:33except for one thing.
47:34It caused a little bit of
47:36hypertriglyceridemia and made and
47:37caused a drop in platelet counts.
47:39You know, we play the games on 400,000,
47:41so the politicians,
47:42not the 200,200 thousand is actually,
47:44it's a 50% drop.
47:45But we don't even count this as a
47:48toxicity in chemotherapy because
47:49it's a very safe level.
47:50Nevertheless,
47:51Pfizer felt that that this
47:53warrants discontinuing the drug.
47:55So we reached out to them and we actually
47:57got the right to test this drug.
47:59In.
47:59In preclinical models and hope
48:01to bring it back to the clinic if
48:04these little promising,
48:05but did the preclinical model look promising?
48:08So I don't really invitro data
48:11because the invitro,
48:12you know metabolism is highly sort of.
48:14Dependent on how much fatty acid
48:16and one that you have in the media.
48:17So this is the in vivo data in mice.
48:20So this is PBX to macros that we
48:22contracted out for Jackson lab and
48:24you see that this ACC inhibitor
48:26actually inhibits the growth although
48:28doesn't strike completely.
48:29The MDA MB 468 Genographic did here
48:32at Yale shows the same thing but the
48:34most striking thing was synergy,
48:36the doxorubicin and Vina Robin and
48:38also with the collaborator is
48:40interested endocrine sensitive CVD
48:42and resistance to develop the food.
48:45Strand resistant MCF 7 cell line,
48:48she also showing you know xenograft
48:50model that there are actually
48:51inhibited the growth.
48:53So this looks pretty promising to
48:54us and we do some additional studies
48:57to really figure out more about the
49:00synergy between chemotherapy agents and
49:01we hope to get this back from Pfizer.
49:05But how does this work?
49:07So the most interesting thing was that
49:09when we looked at what transcriptional
49:11changes occur after exposure to this drug,
49:14what really was.
49:16Striking is the that there was a
49:20dramatic increase in genes that are.
49:22Mediating and involved in unfolded
49:25protein response and upregulate
49:27endoplasmic reticulum stress.
49:29So our working hypothesis thereby
49:32inhibiting the Novo fattiest synthesis,
49:34you actually alter the membrane
49:36composition of the endoplasmic reticulum.
49:38You know proteins have to find a threat
49:41through the membrane to get into the
49:44endoplasmic reticulum for secondary
49:46modifications and we think that by
49:48changing the endoplasmic reticulum lipid
49:50composition we change this process.
49:52Of of protein synthesis and in user
49:55unfolded protein response which
49:56eventually overwhelms the cell.
49:58So that's the project that we do in the lab.
50:00Look at the lipid membrane composition
50:02of of the endoplasmic reticulum as as
50:04far as we can and the lipid alterations
50:06in the cells exposed to this and also
50:09some some reporter systems to nail
50:11this as the mechanism of action.
50:15So I'm going to summarize this really.
50:18So for those of you who are clinical fellows,
50:20you know every clinical dilemma
50:21that we discussed in a tumor boards,
50:23it's a research question asking for a study,
50:25some movies disheartened then
50:26people come about saying that OK,
50:28what should I research?
50:29I mean what you should
50:30research is all around us.
50:32You just need to open your eye.
50:33And so recognizing the prognostic
50:35importance of Pathologic CR residual
50:37disease has left new treatment
50:39strategies and improved survival in
50:40triple negative disease and her two
50:42positive disease and I showed you how so.
50:45Molecular offices of these issues
50:46also gives some idea that how
50:48we could make it even better by
50:50studying the difference between
50:52the nonresponders and responders.
50:54So immunotherapy established its
50:55value in breast cancer and Robinson
50:57is now approved as as neoadjuvant
50:59therapy together with chemotherapy
51:01for all three primary disease.
51:03It's also approved as first line
51:05therapy for PD like 1 positive
51:07metastatic breast cancer.
51:08And I think we have a reasonably
51:10decent explanation why you need the PD
51:13ligand one in the metastatic disease.
51:14So we are about to launch studies
51:16to demonstrate that similar benefit
51:18could be seen in a subset of
51:20molecular defined subset,
51:21small subset of ER positive breast cancers.
51:24And we also have some promising
51:26markers that could actually make this
51:28whole strategy safer and more cost
51:29effective by tailoring the treatment
51:31to those who really needed it.
51:33But these you need validations and
51:35I think the most exciting sort of
51:37things on the horizon clinically is
51:39CDN surveillance and interventional
51:41homophone macular relapse that might
51:44ultimately reduce further metastatic
51:46recurrences and this understanding
51:48the molecular phylogeny of metastatic
51:51disease really prompted this idea
51:53that because the.
51:54Synchronous mats are very similar to
51:57the primary tumors might be they are
52:00responding to the same way and the
52:02micro mats that remain after eradicating
52:04those are also similar to the to them.
52:06So that the microbes that remain after
52:08the primary tumor is being resected
52:10that may be approaching the same these
52:12disease with the same strategy that
52:14we very successfully used in stage
52:16three disease might actually cure a
52:19small subset maybe 10% maybe 30% of of
52:22the Novo metastatic stage four disease.
52:25And.
52:25There's a really deep portfolio
52:28of new classes of drugs.
52:30And that's my last slide.
52:32I apologize ahead of time for people who
52:34actually didn't make it to the slide,
52:36but I ran out of space.
52:37But these are the various people
52:39who worked in my lab and contributed
52:40the work that I showed you and
52:42students and other collaborators
52:43and collaborators within Yale.
52:51So.
53:02Yeah, so. If you have any
53:05questions then feel free to.
53:07Ask yes, silly. I have.
53:14Saying that, we were going to.
53:18And you mentioned, right and when you
53:21talked about the model especially.
53:24Negative. I want to know if you will
53:28consider rate in that model and it's so.
53:33So actually Kim and and some other
53:35previous lab members did they really
53:38nice analysis trying to see whether
53:40there is a immune difference between
53:42triple negative breast cancer by race.
53:45The hypothesis was that that.
53:48Stress and this sort of this weathering
53:51that that unfortunately many people
53:53with African American or Hispanic race
53:55have to suffer would have an impact
53:57on your immune immune system, right.
53:59So the truth is that if there is
54:01such a thing, it's really subtle.
54:03We find some some really intriguing
54:05things around macrophages things,
54:07but whether this really holds up,
54:09I'm not quite sure yet.
54:10So I can send you the slides
54:12and we have some things,
54:13some references there and we we see
54:15some things but I'm not sure that it's.
54:18It's really detectable.
54:18There are other things that we haven't
54:20looked at but we plan to do which is
54:22like inflammatory markers in the blood.
54:24But that's also kind of
54:26biased by comorbidities.
54:27So if you have a lot of other diseases,
54:29then it's just going to be high anyway.
54:30And in terms of the models,
54:32you know,
54:33so Pathologic CI is equally good
54:35in terms of metastatic recurrence
54:38regardless of race.
54:39In fact,
54:40I personally have a really serious
54:42doubt that there is any major
54:43genetic sort of explanation
54:45behind disparities and outcome.
54:50So models that include in survival
54:53rates are problematic, right,
54:54because it perpetuated a risk
54:57factor that that maybe not true.
54:59So if your social,
55:00social circumstances change.
55:03Is there a question from online?
55:06I should call you back.
55:11So there's this question online that.
55:14Umm. Somebody's relevant regretting
55:17their choice that they're not breast
55:18oncologist and they agree with that.
55:20That's the do patients with inflammatory
55:22breast cancer have higher response rates
55:24to checkpoint inhibition and the agent
55:26setting regardless to applying results.
55:28Yeah, that's a good one.
55:29So you know inflammatory
55:30breast cancer is a misnomer.
55:32It's really, it's a clinical description
55:34that people came up and whatever
55:36maybe the 19th century and because
55:37the breast looks like inflamed,
55:39it's red and hot and and and swollen,
55:42it looks like a skin infection and
55:44very often primary care physicians.
55:46Give it antibiotics and it just gets worse.
55:48So inflammatory breast cancer
55:49actually is not particularly rich.
55:51In fact it's pretty poor in immune cells.
55:54But we did.
55:55Actually the first whole genome
55:57sequencing of inflammatory breast cancer,
55:59hoping to find something and
56:01disappointed we didn't find
56:03anything that actually defined this
56:06autonomically at the DNA sequence space,
56:08but we find some interesting things.
56:10Again, TGF beta macrophage
56:13related markers show up there.
56:16As potentially contributing
56:17to the poor outcome.
56:19But yeah,
56:20so inflammatory breast cancer
56:21is all the four subtypes and
56:23as far as we can tell today,
56:25there is really no proton
56:26nomical genomic alteration.
56:31So what type of preventive
56:33interventions do you foresee for
56:35patients with high cancer score.
56:36So if you already have validated and
56:40really effective prevention drugs,
56:42right, the moxen aromatase inhibitors and
56:45food and other drugs, the I type drugs,
56:48but they have side effects and and I
56:51think one way to use these cancer score
56:53would be to if you're high risk that
56:56you are close to this tipping point,
56:58I should say you that we
56:59don't have that score.
57:00It's working on it.
57:01But it's the idea that if you
57:02can tell that these biopsy,
57:03tissue biopsy shows that you are
57:05close to this tipping point and
57:07maybe you are willing to put
57:08up with some additional.
57:10Umm.
57:10Discomfort from a prevention drug.
57:18All right. Let's go ahead, Andrew.
57:20A lot of times with the
57:23people who have even PCR,
57:25they can relapse in the brain.
57:27And people sort of say that's
57:29due the blood brain barrier,
57:31but are there molecular alterations
57:34that predict frame labs or can you?
57:38No, I can't. But you know,
57:39I mean, that's the reason why I
57:40don't go to many of the meetings,
57:42because there are so many
57:43interesting things to study.
57:45I just enjoy them more but yeah so,
57:47so people tried that but they didn't find it.
57:49But what you bring up is illegal one right.
57:51So the pathologic CR is really good
57:53but it's not a perfect predictor and
57:55for for there are many reasons why
57:57there should be a disconnect with
58:00Pathologic CR improvement in survival.
58:01So you can't cure people twice.
58:03So if you enroll a lot of people
58:05that are on stage one breast
58:06cancer and the surgeon cure them,
58:07it doesn't really matter whether
58:09they are chemosensitive or not.
58:11But in terms of recurrences look to Silver
58:13Point out something that many oncologists.
58:15Even breast oncologists may
58:16not be totally familiar with.
58:18So there are a number of studies
58:20that show now that the first
58:22sight of recurrence of the PCR,
58:24half of the time it's the brain.
58:26When you have no PCR residual disease,
58:29then the brain is the first site
58:31in about 10% and it goes along
58:33with this idea that the brain
58:35is somehow a protected site.
58:36And the question is then how they
58:38actually can break this protection and
58:41really help avoid brain recurrences.
58:43There are some some really good
58:45initiatives in the in the her two
58:47positive space and some of the ADC
58:49may get in there triple 90 disease,
58:51but what actually would define
58:53high risk for brain recurrence
58:55in terms of molecular markers?
58:58But they could find that in a reproducible
59:00and accepted sort of widely accepted way.
59:06Thank you. Thank you for all
59:09of you who have joined both
59:12in person and virtually.
59:13This concludes our breast cancer
59:15awareness month grand rounds.
59:17Thank you so much.
59:39Yeah.