"Thoughts on the Nature of Breast Neoplasia"

Transcript

• 00:00You can see it. Fred.
• 00:04So with no further ado on
• 00:06behalf of Doctor Nita,
• 00:08Hoosier Interim Cancer Center director,
• 00:10and actually this time next week,
• 00:12your good friend Doctor Eric
• 00:14Wyner will be sitting in that
• 00:16position as our permanent director.
• 00:18We're really excited to have you here,
• 00:21even though it's not in person,
• 00:23as the Julia Patricia Kingsbury
• 00:25Memorial lecturer and lectureship
• 00:27that's been sponsored for.
• 00:28You know well over 2 decades by
• 00:31their family for me to introduce.
• 00:33Doctor Norton is like.
• 00:34Introducing a Rockstar.
• 00:35Obviously he's a senior vice president
• 00:37in the office of the President,
• 00:40Memorial Sloan Kettering,
• 00:41the medical director of the
• 00:43Evelyn Lauder Breast Center.
• 00:45I think you're also the founding
• 00:48and incumbent N Norna Serafin
• 00:50clinical chair in oncology.
• 00:52Career started,
• 00:53you know,
• 00:54undergraduate and undergraduate at Rochester,
• 00:56then went on to Columbia for medical
• 00:58school in Albert Einstein and the NCI
• 01:01for Training and Medicine and Oncology.
• 01:03And really,
• 01:04you know the first time I was I was
• 01:06privileged to meet Doctor Norton
• 01:08was in 2002 at the old CLG or
• 01:11the cancer and Leukemia Group B.
• 01:12And you know that committee which
• 01:14you chaired for such a long time and
• 01:17then passed on to doctor Doctor Weiner again.
• 01:20Our incoming director and Doctor
• 01:22Hudis just to see how masterfully.
• 01:24The research,
• 01:25the work,
• 01:25the care of your patients over the years,
• 01:28and then you know more recently in the
• 01:30last decade you know being involved with
• 01:32the breast Cancer Research Foundation,
• 01:34which you and Evelyn Lauder,
• 01:37the late Evelyn Lauder,
• 01:38and Leonard Lauder,
• 01:39you know,
• 01:40put together really bringing over 200,
• 01:43I think,
• 01:43probably close to 300 of the
• 01:45world's leading investigators
• 01:46and breast Cancer Research.
• 01:48Really, for the cure,
• 01:50as defined as the founding
• 01:52scientific director.
• 01:53Gosh,
• 01:53this is such a.
• 01:54A great day for for a Yale and I
• 01:57know everyone is really excited
• 01:59to hear your thoughts on the
• 02:01nature of breast neoplasia.
• 02:02So thank you Doctor Norton
• 02:03for making the time.
• 02:04OK, thank you. Thank you very much.
• 02:06I hope everybody can hear me and thank you
• 02:08for that really very gracious introduction.
• 02:10You know it's a it's it's totally shame.
• 02:13In the old days when you give a lecture
• 02:14ship like this, you'd come in person.
• 02:16You'd have a dinner you'd meet with a
• 02:17lot of people, one on one, and so many
• 02:19of my great interactions in my career
• 02:21started really by those kinds of events.
• 02:23And so it's a. It's a shame that
• 02:25we have to do this electronically.
• 02:26But it's a great pleasure to be
• 02:28here and and speak with you.
• 02:30I'm, you know,
• 02:31my neighbors in the Northeast about some of
• 02:33the things that that I've been thinking of.
• 02:35What I've been doing,
• 02:37it's called mathematical insights,
• 02:38but for those of you who are math phobic,
• 02:40please don't don't run away screaming.
• 02:43You know it's a.
• 02:44I'm only going to show one equation,
• 02:47and it's not important really for the talk.
• 02:48Basically, it is mathematical thinking
• 02:50and a lot of people don't know math.
• 02:52Don't realize that what math
• 02:54is is is not the equations.
• 02:56The equivalency would be sheet
• 02:58music for music.
• 02:59The sheet music is not the music,
• 03:00it's the sound.
• 03:02And and with mathematics it's it's
• 03:04the the insights that you gain which
• 03:05you know in terms of how things.
• 03:07In this case, how they grow,
• 03:08how they shrink,
• 03:09why they grow that way and and and so on,
• 03:11how we take advantage of that.
• 03:12The the equations are not really the
• 03:15mathematics for many years when I was
• 03:17giving this talk I I skipped over a
• 03:19lot of the early stuff that I did,
• 03:21but then I realized a few years back
• 03:23that a lot of the younger people
• 03:25are unaware of that early stuff.
• 03:26And that really stuff is really
• 03:28very important for understanding
• 03:29the later things that we're doing.
• 03:30So I am going to be talking about it.
• 03:33I mean,
• 03:33it really happened to me a bunch of
• 03:35years already that I was a visiting
• 03:37professor and somebody presented a
• 03:39case and and said this patient dose.
• 03:41Dense chemotherapy with AC and Taxol, Dr.
• 03:45Ordinary familiar with that regimen.
• 03:47And that's when I realized that
• 03:49that perhaps I should really cover
• 03:51some of the early things that I
• 03:52that that I've done and and how it
• 03:54relates to to the bigger picture.
• 03:56So I'm going to talk.
• 03:57About growth models and of
• 03:59course the the premier growth
• 04:00model being from Howard skipper,
• 04:02I'll talk about the work that I did
• 04:05in the 70s interpreting that growth
• 04:08model in with the appreciation for
• 04:11understanding a different pattern of
• 04:12the way that cancers grow than how it
• 04:15skipper and and colleagues had shown.
• 04:17How it led to the concept of dose
• 04:19dense sequential therapy and what
• 04:20are the results of that that we
• 04:22just fairly recently summarized
• 04:24by the Oxford overview?
• 04:26And then talk about self seating
• 04:28theory and how it relates to all of
• 04:30that previous work and that will
• 04:32bring me into the area of fractal geometry,
• 04:34which is where where another topic
• 04:37in math comes in and how our that's
• 04:41informing our current work on the tumor,
• 04:45infiltrating leukocytes,
• 04:46and the interpretation of their firm.
• 04:48And I don't know if David Rim is,
• 04:50you know, here among us today, but,
• 04:52but we've had a number of early
• 04:54conversations a few years back about.
• 04:56The importance of fractal geometry
• 04:58and understanding biology from
• 04:59a pathology point of view,
• 05:01and then how that relates to concepts
• 05:04of drug resistance and and the use of
• 05:08immunotherapeutic agents and and lately.
• 05:10Our work that we're doing on antibody
• 05:12drug conjugates in that regard,
• 05:13but all informed by mathematical thinking.
• 05:16Let's just start back with Hippocrates,
• 05:18the father of us all.
• 05:20The parent of us all in, in in,
• 05:23in, in medicine.
• 05:24The actual quote translated from
• 05:25the Greek is an illness is once
• 05:27you keep two things in mind to be
• 05:29useful rather than cause no harm.
• 05:31That's frequently misquoted,
• 05:32as as first of all, do no harm.
• 05:34That's not quite what he said.
• 05:36What he said is don't be neutral,
• 05:37but but, but, but be useful,
• 05:40and that is,
• 05:42is is a very important quote
• 05:45because it relates very.
• 05:46Very directly to one of our major topics
• 05:48that we have to deal with in clinical
• 05:50oncology all the time, which is OK.
• 05:52I have a drug that works.
• 05:53How should I use it and dose level of
• 05:55course is a mix between the efficacy
• 05:56of the drug that you're giving and the
• 05:59toxicity that you're causing from it.
• 06:00And I spent almost all of my youth
• 06:02learning to be a medical oncologist
• 06:04learning how to to avoid or manage
• 06:06toxicity of of the agents and pick
• 06:08out the right dose level quotes
• 06:09in the modern world has gotten
• 06:11much more complicated than that.
• 06:13We have to not only look at
• 06:14at the at the dose level,
• 06:15but also the schedule.
• 06:17The duration of Therapy will give it
• 06:20impulses and that leads to various
• 06:22changes in efficacy and toxicity.
• 06:24Toxicity is not just acute toxicity,
• 06:26but late toxicities chronic
• 06:28toxicities that may arise.
• 06:30The personal cost to the patient and the
• 06:32personal goals for the patient have been
• 06:34taken into account and and in planning,
• 06:36dosing and scheduling as but also
• 06:39societal cost that everything that
• 06:41we do is going to have implications
• 06:43basically to all of our society.
• 06:44All of our patients and society in general.
• 06:47And how does all of this relate to a
• 06:49very rapidly evolving therapeutical
• 06:50landscape so so dosing is scheduling
• 06:53is actually a very germane topic
• 06:54in in the modern era,
• 06:56even when we're talking about
• 06:57some of the newer agents.
• 06:58And and what we've learned in
• 06:59looking at the older agents, IE.
• 07:01Chemotherapy is directly related to how
• 07:03we're going to be applying our newer agents,
• 07:06and as I close the talk,
• 07:07I hope I'll be addressing some
• 07:09of these points.
• 07:10But the central dogma that led most of us in
• 07:13our careers in medical oncology is this.
• 07:16If you want to kill more cancer cells,
• 07:18you have to use higher dose levels.
• 07:19So you want to use the highest possible dose
• 07:22level you can to kill more cancer cells.
• 07:24Because the more cancer
• 07:25more cancer cells you kill,
• 07:26the more benefit to the patient
• 07:28either eradicating the cancer.
• 07:29If if if that should actually be possible,
• 07:32or just buying time 'cause we have smaller.
• 07:34Buying more tumors can take longer to regrow,
• 07:36and that's going to be translated into
• 07:38into improvement in duration of Disease
• 07:41Control for the patient and hence our
• 07:43training was all about determining and
• 07:46treating at maximum tolerated dose.
• 07:50I'm going back to another great
• 07:53teacher of medicine, William Ostler.
• 07:55The greater their ignorance,
• 07:56the greater the dogmatism,
• 07:57and I believe that this this this.
• 07:59This dogmatism is really dominating
• 08:01us even to the present day
• 08:02when we have targeted agents,
• 08:04and yet we're still trying to
• 08:05achieve maximum tolerated dose.
• 08:07Thinking that we're going to be
• 08:08benefiting the patient by doing so,
• 08:09and I'd like to really question
• 08:11that this this concept really came
• 08:13from the work of Howard skipper,
• 08:15Franckesche Bold and Griswald and others
• 08:17at at at Southern Research Institute.
• 08:19It was an extremely important part of my
• 08:22education in in in medicine and oncology,
• 08:24in in in the 60s.
• 08:26The concept was based on mooring,
• 08:28leukemia and Howard skipper made
• 08:30the observation that if you if you
• 08:33inject a certain number of cells and
• 08:35the mouse died at a certain time,
• 08:37that could tell you basically
• 08:39how many cells you injected.
• 08:41'cause it took a certain very
• 08:42reproducible amount of time for those
• 08:43cells to reach a lethal number.
• 08:45So all of his work was not really
• 08:47measuring cancer cell numbers,
• 08:48it was actually measuring animal.
• 08:49Death and extrapolating back
• 08:51to cancer cell members.
• 08:52And that's common,
• 08:53not commonly appreciated is is
• 08:55that it was all extrapolation,
• 08:57but the the fundamental observation that
• 08:59he made is that if you kill cancer cells,
• 09:01you can extend lifespan and the
• 09:04extension of lifespan was a.
• 09:06Basically it took time for lethal
• 09:07number of cells to arrive and you can
• 09:09go back and extrapolate from that in
• 09:11terms of how many cells you killed
• 09:13because it would take that certain
• 09:14number of cells that were left or
• 09:16residual to lead to eventual or lethal
• 09:18number and and the death of the mouse.
• 09:21This led to a concept that is shown
• 09:24here by one of the one of the.
• 09:27Very very often in in my youth,
• 09:30especially reproduce figures is
• 09:31that if you start off with a large
• 09:33number of cancer cells and you
• 09:35give a certain dose of therapy,
• 09:37you kill and will go back over this
• 09:39a constant fraction of the cells
• 09:40that are present with each dose.
• 09:42Each chemotherapy core skills,
• 09:43in this case,
• 09:442 logs of gotta get rid of this pop
• 09:47up two logs of kill means means you're
• 09:50you're killing 99% of the cells,
• 09:5290% is 1 log killed,
• 09:5499% is a two log kill and you
• 09:56could drive to cure unless you get
• 09:58the emergence of drug resistance.
• 09:59Of course,
• 10:00if you stop treating when the cancer
• 10:02is disappeared, that's not enough,
• 10:04because because there's plenty of
• 10:05cancers left and they can grow
• 10:07back and and the concept of roses
• 10:09that if you start a small of tumor
• 10:11size that you can actually get rid
• 10:13of the cancer cells before this
• 10:14emergence of drug resistance arises
• 10:16and hence the concept of Azure and
• 10:19chemotherapy came from this Vince
• 10:20Davida long associated with Yale
• 10:22was my great teacher and still
• 10:24is my great teacher and
• 10:25in oncology took these concepts and use
• 10:27them to develop the MOP chemotherapy
• 10:29regimen and those of you who have not.
• 10:31Read Vince on his book of the Death of
• 10:34Cancer about the early days where this
• 10:37figure was extrapolated into the cure
• 10:39of a solid tumor Hodgkin's disease.
• 10:41You really should read it because it's
• 10:43it's an excellent book and it really
• 10:45captures the excitement of those early
• 10:46days in oncology and the application
• 10:48of this mathematical model to the
• 10:50development of a curative regimen.
• 10:52Well, this was led in the in in in the
• 10:5560s to the concept of dose escalation.
• 10:57This way if you have no therapy and you
• 11:00have simple exponential growth like this,
• 11:02you give one drug.
• 11:03You get certain log kill two drugs.
• 11:05You get you double that.
• 11:06If this causes one log kill and this
• 11:08causes one log kill then you get 2
• 11:10log kill 90% sale killing here and 90%
• 11:12sale killing here since 99% sale of
• 11:15killing three drugs should be should
• 11:17should cause disease eradication.
• 11:19Therefore,
• 11:19four drugs should certainly cause
• 11:21disease eradication, which was very.
• 11:22Influential,
• 11:23they're thinking about Rob chemotherapy,
• 11:25but it also applies to doses.
• 11:271 dose, 2 doses, 3 doses,
• 11:30all increased cell killin,
• 11:32causing DC radication.
• 11:33So the 70s was a decade of enthusiasm.
• 11:36Fueled by this confidence in the skipper.
• 11:38In the skippers model,
• 11:39there are many drugs that came along,
• 11:41such as the Cyclones,
• 11:43the platinum agents,
• 11:44the concept of combination chemotherapy
• 11:45as I've just demonstrated to you,
• 11:47arose from these thinking.
• 11:49And indeed we're getting successes.
• 11:51Cure simply nysm.
• 11:52And and leukemia is
• 11:54infamous testicular cancer,
• 11:55it really looked like we were
• 11:56moving in the right direction.
• 11:58Getting high response rates and many
• 12:00other tumors including breast cancer.
• 12:01The field that I eventually specialized
• 12:03in the concept of postoperative attribute
• 12:05chemotherapy rose from that period.
• 12:07Based on that on that mathematical idea
• 12:10and an enthusiasm for those those level
• 12:12escalation which we led to a lot of
• 12:15enthusiasm for a mega dose escalation,
• 12:18which is bone marrow transplantation.
• 12:20This enthusiasm was so was so.
• 12:23Pronounced that a mentor, not Vince,
• 12:26is another mentor in 1976 cents Me Larry,
• 12:29you still got a chance.
• 12:30You're young enough to change
• 12:31your career path,
• 12:32you're not.
• 12:32There's not gonna be any field
• 12:33of oncology in a few years.
• 12:35All these combinations.
• 12:35All these agents are just going
• 12:37to come together and cancer will
• 12:38be disappeared in a few years.
• 12:39You better think about training
• 12:40and something else.
• 12:43Well, I persisted against that
• 12:45advice and kept working on cancer.
• 12:47And as you know it hasn't been that easy
• 12:49and a lot of that enthusiasm is still
• 12:51there and we definitely making progress.
• 12:53No question about it,
• 12:54but the rate of progress really
• 12:56has has slowed even with the
• 12:58addition of of newer agents.
• 12:59And we're not getting cures of
• 13:01metastatic colon cancers and
• 13:02and and and stomach cancers.
• 13:04And and and and and many
• 13:06lung cancers and so on.
• 13:07But certainly breast cancer as
• 13:09readily as we would have hoped.
• 13:10Metastatic disease is still a big problem.
• 13:13So what went wrong?
• 13:14And this is my number one favorite
• 13:16quote and kind of a kind of you know,
• 13:19model for my life.
• 13:19It's not what you don't know
• 13:20that gets you in trouble.
• 13:21It's what you know that for sure
• 13:24that turns out not to be true.
• 13:26And the thing that we knew for sure
• 13:28was that the skipper model worked
• 13:30because cancers grow exponentially,
• 13:31but they don't grow exponentially,
• 13:33nor do they grow in a strictly
• 13:35linear fashion.
• 13:36And we know this because
• 13:37if that were the case,
• 13:37from the time of initial diagnosis to
• 13:40the time that that the cancer would
• 13:42cause problem would be too long.
• 13:44Exponential growth also doesn't
• 13:45make sense because for the
• 13:46time of initial diagnosis,
• 13:48the time of lethality would be too short.
• 13:50It's got to be somewhere
• 13:52in between and indeed,
• 13:54Benjamin Gompertz in 1825.
• 13:56Then invented a curve of human mortality,
• 13:59which we call the Gompertz curve is
• 14:01and kind of sigmoid curve sigmoid.
• 14:03Because you see as as as shape and
• 14:05and others had shown that that
• 14:07Gompertz curves actually applied to
• 14:08the growth of experimental tumors.
• 14:10I got into this in the mid 70s and
• 14:13this early paper I wrote in nature.
• 14:16In 1976 these are two rat tumors.
• 14:19This is a mouse tumor and what we
• 14:21found are working with Richard Simon
• 14:23is that if you have a few early
• 14:25measurements that it actually fits
• 14:27a pattern and you could predict
• 14:28later measurements that gone.
• 14:29Protein growth was really very predictable.
• 14:31This is really an important
• 14:33observation that just sort of sat
• 14:34there and up until the present day,
• 14:36but it actually is rather
• 14:37meaningful but nest.
• 14:38But but indeed,
• 14:39Gompertz equations are applied
• 14:40and then not exponential,
• 14:42and so my my work was basically
• 14:44to see how do we apply the
• 14:47skipper Schaible principles.
• 14:48To the.
• 14:50How do we apply skipper Schaible
• 14:52principles to gum persien curves
• 14:54and papers in the early days about
• 14:57this and they eventually led to
• 14:59the concept of sequential therapy
• 15:00and then dose dense therapy?
• 15:02And this was the work in the 70s
• 15:05and and again Vince DeVito was
• 15:07was working closely with Johnny
• 15:09Bonadona in those days and and
• 15:11some of these ideas got translated
• 15:13and indeed this was actually a
• 15:16competition in the adjutant setting
• 15:18between a other modelers.
• 15:20Called Goldie and Coldman and
• 15:22and myself and and Richard
• 15:23Simon that they predicted that if you
• 15:25have agents like doxorubicin and see
• 15:27at math you should use them in an
• 15:29alternating fashion because that would
• 15:31get this drug in these all the drugs in
• 15:34sooner to limit the emergence of drug
• 15:36resistance by random mutation whereas my
• 15:39modeling which I'll show you in a second,
• 15:40suggested that it would be
• 15:42better to use them sequentially.
• 15:43So we'll go over that modeling because
• 15:46this is buried in ancient literature
• 15:48and was published before many of you
• 15:50who are listening to this lecture were.
• 15:52Warren, so you're not aware of
• 15:54this work is that, of course,
• 15:55it's always better if you've got
• 15:57two agents or two combinations,
• 15:59you gonna use them simultaneously if you can,
• 16:01that's going to give you maximum cell kill,
• 16:03but you can't really do this in most
• 16:05situations without such toxicity that you
• 16:07have to reduce the dose levels of the drugs.
• 16:10And by reducing the drugs,
• 16:11dose levels of the drugs,
• 16:12you're not going to get the maximum
• 16:13efficacy from any of the drugs.
• 16:14So the question is,
• 16:15what can you do if you can't give
• 16:17them in a simultaneous combination?
• 16:19The Goldie Coleman idea
• 16:21is you alternate them.
• 16:22And by the Norton Simon modeling
• 16:24when we did it, we found well,
• 16:26we got a very inferior cell
• 16:28killed than if we did them.
• 16:29Obviously by simultaneous therapy.
• 16:31But if you give them in
• 16:33an alternating fashion.
• 16:35You can actually get cell killed.
• 16:37That's better than you can get by
• 16:38giving him an alternating fashion,
• 16:40so the bonadona experiment which
• 16:42started in the mid 80s was a was
• 16:45basically a competition between
• 16:46this approach and this approach.
• 16:48And of course this approach one.
• 16:50Then, with the advent of grants,
• 16:52iconic stimulating factor where
• 16:53you can actually squeeze the
• 16:55doses of drugs closer together,
• 16:57you can actually get maximum self
• 16:59kill and even a better cell kill.
• 17:01Then you can just with the simultaneous
• 17:03combination by by the application of GCSF.
• 17:06So you can make a through cycle,
• 17:08for example into a two week cycle.
• 17:10Tax oil can be given even
• 17:12without GCSF in a one week cycle,
• 17:14and that's also a dose dental
• 17:16regimen as well.
• 17:17So that's the understanding of dose density,
• 17:19which is kind of lost in
• 17:20history a little bit.
• 17:21And a lot of people don't appreciate
• 17:23really where it came from.
• 17:24Water million means.
• 17:26Hence we designed this regimen
• 17:28which basically started.
• 17:30In 1997, nineteen 9741 with Mark Citron,
• 17:36who we just lost very recently
• 17:38from a from from a neoplasm.
• 17:40A great great Lawson,
• 17:41and really a great clinician and
• 17:44and and a great clinical scientist.
• 17:47And this was A and the regimen
• 17:49was a two by two design,
• 17:51and you'll notice those of you who
• 17:52are doing cooperative group studies.
• 17:53We don't do two by two designs
• 17:55very much anymore,
• 17:56and I really wish we did because
• 17:57it would answer a whole lot of
• 17:59questions faster than doing doing just
• 18:01some of the some of the
• 18:02regimens that we're now doing,
• 18:03which is comparing 2 treatments or
• 18:05now even not comparing it at all, but
• 18:07basically comparing it to historical data.
• 18:09Which is, you know, Noninferiority designs.
• 18:15For the topic about the the wisdom of that,
• 18:17but, but certainly certainly these two
• 18:18by two designs get a lot of information,
• 18:21and we gave a. We gave adriamycin and
• 18:25cyclophosphamide AC with paclitaxel.
• 18:27Either all the drugs in in in in a Q3
• 18:32week regimen, in a sequential fashion,
• 18:34or the AC together 'cause you can do
• 18:37that without having dose modifications.
• 18:40That's why it makes sense in a
• 18:42three week Red room or squeeze these
• 18:44together in a two week regimen.
• 18:46In a sequential way and squeeze
• 18:48these together and this.
• 18:49This of course became the standard
• 18:51because the the AC from axle 2 weeks
• 18:53was better than AC for Taxol 3 weeks
• 18:55I would just emphasize that this
• 18:57regimen and this regimen really came
• 18:59out the same and so they were pulled
• 19:01together for the analysis and and if
• 19:03you can't give the cyclophosphamide
• 19:04with the age of my son together
• 19:05and if you give it the end,
• 19:07it would be just as effective.
• 19:08I have done this in certain situations with
• 19:10patients we're running into into issues.
• 19:12For example.
• 19:13The other thing that I've done is
• 19:15basically substitute Murph for adriamycin.
• 19:17In in this regimen,
• 19:18if there's issues related to
• 19:20cardiac toxicity,
• 19:20'cause we know that CMF and
• 19:22AC really are the same.
• 19:24In terms of efficacy in
• 19:25in the action setting,
• 19:27and retrospectively I'll say I think
• 19:29it was a shame that we did not do a
• 19:33comparison between AC dose, dense AC.
• 19:35Those dents followed by Taxol.
• 19:38Those stands compared to CMF.
• 19:40Those tents followed by Taxol
• 19:41'cause I bet you they come out
• 19:43the same and we wouldn't have all
• 19:45the drama about the potential for
• 19:47cardiac toxicity with anticyclone.
• 19:48Sorry I fear that we're throwing
• 19:50the baby out with the bathwater
• 19:52often when we're not using AC.
• 19:54Taxol,
• 19:54because we're afraid of cardiac toxicity
• 19:57and using other regimens that avoid
• 19:59the anti cycling and in doing so,
• 20:01we're also leaving out those density.
• 20:02I think that's a mistake and I'll show you
• 20:06why I think that's a mistake in a second.
• 20:08I just also want to emphasize and
• 20:09I just wanna mention this quickly.
• 20:11Is that the doses of the drugs
• 20:12we use did not come from nowhere?
• 20:14We actually studied the doses and
• 20:16we found out that moderate levels of
• 20:19the CF combination was equivalent
• 20:21to higher levels that going higher
• 20:23doses was not better.
• 20:24Half doses were inferior.
• 20:26This network that Dan Budman did,
• 20:28and in this in the cancer Community,
• 20:31Group B,
• 20:32and so the whole idea of going higher
• 20:34with doses to get more so killed
• 20:36was just not borne out by the data.
• 20:38The same thing was done
• 20:40with cyclophosphamide.
• 20:40And with Bernie Fisher in the NSA,
• 20:42BP,
• 20:42where they looked at higher
• 20:44and higher doses of
• 20:46cyclophosphamide in the CF combination
• 20:48and and it did not add in in the in in
• 20:52the in in the various regimens they
• 20:55went to higher and higher doses and they
• 20:58did not add so that and and Eric which
• 21:01I he told me he'd be listening today
• 21:03did the same thing with paclitaxel,
• 21:05going to higher dose of the 175 not
• 21:07showing any advantage in a study.
• 21:09So this notion that just going
• 21:11higher and higher?
• 21:11Doses you can get more cell kill.
• 21:13It's just not borne out
• 21:14by empirical evidence,
• 21:15and we have to keep that in mind as
• 21:17we question the original dogma that
• 21:19led to a lot of what we're still
• 21:21currently doing in in our application.
• 21:23Medicinal chemistry to the treatment
• 21:25of cancer of the present day.
• 21:28Well, this led to 26 randomized trials
• 21:32over 37,000 randomized patients looking
• 21:34at various permutations at dose and schedule,
• 21:36and this was published in The Lancet.
• 21:38I'm just summarizing all this work is that if
• 21:41you use and they talk about intensity here,
• 21:44but there's a very big choice of terms,
• 21:46but nevertheless that was the
• 21:47consensus that we use the term.
• 21:49It's really dose density,
• 21:51standard schedule rather than
• 21:53using a dose dense schedule,
• 21:55you get recurrences,
• 21:56reduced breast cancer mortality.
• 21:58Reduced and this is over 37,000
• 21:59randomized patients, so this is hard data,
• 22:02no.
• 22:02No increase in death without
• 22:03recurrence and there is no incremental
• 22:05toxicity from our agents by
• 22:07using them in dose dense fashion,
• 22:09and indeed all 'cause mortality
• 22:11is reduced because reducing cancer
• 22:13specific mortality as you see here so
• 22:16clearly it's shown that the concepts
• 22:18of those 10s therapy work and are
• 22:20applicable and the reason why I'm
• 22:22saying this is oh and by the way,
• 22:24is that and paclitaxel 80 weekly is superior.
• 22:2875 and it's a dose 10 schedule and
• 22:30the sideman showed this because
• 22:32it's being given every week.
• 22:33Rather than reading every three weeks
• 22:35and the dose response relationship
• 22:37for paclitaxel as Eric Weiner showed,
• 22:39is not is not steep and that you are
• 22:43accomplishing at least 1/3 as much
• 22:45efficacy with 80 as you are with 175.
• 22:49So the reason why I show all this most first
• 22:52of all is to catch some historical facts.
• 22:54For those of you who are not familiar
• 22:56with them but also make this point,
• 22:57it's gone pretty and growth is true.
• 23:00And you can use growth gun purchasing
• 23:02growth kinetics to improve cancer therapy,
• 23:04which leads us with the big question
• 23:06what is the etiology of gun?
• 23:07Pretty and growth?
• 23:08I was at something called the Ideas
• 23:10Festival in Aspen one year and I was
• 23:12having trouble parking my car so I was
• 23:14blocking somebody else from getting out
• 23:16of her parking spot and she got really
• 23:18angry and she came running up to me.
• 23:19With her hands on her hips,
• 23:21and I pulled down my window and she and she.
• 23:23And she was really angry, said,
• 23:24what's your problem and I said
• 23:26my problem is the etiology of gun
• 23:27protein growth what's your problem?
• 23:29She obviously thought I was a lunatic,
• 23:31which I probably am and she
• 23:32walked away from me.
• 23:33But this has been my preoccupation.
• 23:34For many years is understanding what
• 23:36is the etiology of compresion growth.
• 23:39And so,
• 23:40thinking about this in the early
• 23:422000s I I got a phone call from a
• 23:45Jean massage my great collaborator
• 23:47here at Morrison Kettering.
• 23:49He had just published his paper
• 23:50by Andy Mineo,
• 23:51was about to publish his paper by Andy Min,
• 23:54where they were looking at the etiology,
• 23:56molecular etiology and metastasis,
• 23:58and found this tumor.
• 24:00You know,
• 24:00which is an
• 24:04MMDA MB 231. Sometimes is rushing ahead,
• 24:07which had a certain gene
• 24:09expression profiling.
• 24:09Machines being locked, these genes
• 24:11being on and the tumor sticks here.
• 24:13But occasionally you get along with tax
• 24:15assist and if you get a long metastasis
• 24:17and you take the cells out of the lung,
• 24:19wash them and put them back into
• 24:21the memory fat pad several times,
• 24:23you can develop a cell line 4175,
• 24:26now called the lung metastasis signature,
• 24:28which has a signature which
• 24:30predicts lung metastasis because
• 24:32the mouse develops long itassis.
• 24:34He's done this for other
• 24:36other organs as well.
• 24:37Well in this paper they they had
• 24:39this very interesting figure.
• 24:40It showed that the tumor that goes
• 24:42to the lung more readily that has
• 24:44this gene expression profile also
• 24:46grows faster in the mammary fat pad.
• 24:49The one that doesn't go to
• 24:50the lung doesn't grow as fast,
• 24:51and the intermediate steps
• 24:54have an intermediate.
• 24:56Intermediate growth rate in terms of memory,
• 24:59fat pad.
• 24:59So the question that I was
• 25:01asked in on a phone call is.
• 25:04Number one, is it true?
• 25:05As a clinician that cancers that are
• 25:06metastatic tend to be faster growing?
• 25:08And I said that's true and he said Larry,
• 25:10can you figure out why and the
• 25:12answer was really rather obvious.
• 25:14Is that yes,
• 25:15they're getting metastatic to distant sites.
• 25:17Why?
• 25:17Why were they stop them from getting
• 25:20metastatic back to the original site?
• 25:22And he and the so the query was
• 25:24but the S phase fraction the KS
• 25:2667 was not different and I said
• 25:28that's makes a whole lot of sense,
• 25:30because basically if the tumor
• 25:32that goes metastatic,
• 25:33let's say to the lung,
• 25:35also gets meta meta static back to itself,
• 25:38then in this case we have like 3
• 25:40lumps that are growing independently
• 25:41and each of them growing at 5%.
• 25:43Let's say you're still going to
• 25:45have a growth fraction of 5%,
• 25:46but you're going to grow three times faster.
• 25:48'cause three things going at
• 25:505% each is going to grow faster
• 25:52than one thing growing at 5%.
• 25:53And so therefore it makes sense that
• 25:55they carry 67 would not be different,
• 25:57and yet you would get faster growth.
• 25:59And because it's being metastatic
• 26:00back to back to itself,
• 26:02so Jean message and I labeled
• 26:04this self seating and and did
• 26:07subsequent work in this.
• 26:08This was a hypothesis me on Kim.
• 26:11Did this work published in 2009?
• 26:13And this was just a brilliant experiment
• 26:15of the exact same tumor implanted
• 26:16in two different fat pads but with
• 26:18different fluorescent proteins in them.
• 26:20So they're different colors.
• 26:21And indeed they exchange.
• 26:23And this would be this.
• 26:24The left side of tumor.
• 26:25This would be the right side of tumor.
• 26:26This started green and and then turn
• 26:29red because red cells moved over.
• 26:31This started red and moved and
• 26:32and developed green.
• 26:33'cause green cells moved over
• 26:34and there's an exchange of cells
• 26:36between the two tumor sites.
• 26:37On much more work was in this paper.
• 26:40Obviously if you inject a non
• 26:42seeding tumor here and then inject
• 26:44the LM 2 seating tumor to the heart,
• 26:47it will see that tumor.
• 26:49Here's an interesting observation which
• 26:50I still think is very provocative.
• 26:52When you inject the tumor cells,
• 26:54they they light up the whole body obviously,
• 26:55but then over a period of 42 days
• 26:58they grow in the implanted tumor.
• 27:00That's not metastatic on this side.
• 27:03Why is this interesting?
• 27:04Because you're not developing
• 27:06lung metastases.
• 27:06In other words,
• 27:07the tumor is citing the the
• 27:09cells that you're injecting,
• 27:11which were developed to siedlung are
• 27:13not going to the lung 'cause they
• 27:16preferentially going to the tumor,
• 27:18and indeed to follow this out.
• 27:20If you give the tumor cells into a
• 27:22tail vein injection and get lung
• 27:24metastases first and then implanted
• 27:26tumor that implanted tumor will
• 27:28then suck cells out of the lungs.
• 27:30As you can see,
• 27:31the recipient tumor of the
• 27:32cells will grow at.
• 27:33Here 'cause it's sucking sells out
• 27:35a lung and these mice can actually
• 27:37live longer because they you can
• 27:39live longer with a subcutaneous tumor
• 27:40than you can with a long list full
• 27:43of metastases and and these these
• 27:45these have really profound implications.
• 27:47We think not all of which we followed up
• 27:49on in terms of therapeutic implications,
• 27:51but perhaps if we have time we
• 27:53can talk about them.
• 27:56What I want to focus in on, however,
• 27:58is that if cancers are growing at
• 28:01least partially by cells that are
• 28:02spreading and coming back to the tumor
• 28:05mass from the outside in rather than
• 28:07just growing from the inside out,
• 28:09as we always anticipated that
• 28:10they would grow, it would grow in
• 28:12this fashion like a snowflake,
• 28:14and this is this.
• 28:15This pattern of growth is with the
• 28:18skinny Franz is reminiscent of
• 28:20what the physics physicists called
• 28:22Diffusion limited aggregation,
• 28:23and it's because a water molecule.
• 28:25Or sell coming here is more likely to
• 28:27stick here than work its way into the middle.
• 28:29And if you do that,
• 28:31you actually get a pattern of growth.
• 28:33That's from Purtian because
• 28:35as objects get larger.
• 28:37The ratio of their surface to their
• 28:39volume decreases and we're going to talk
• 28:41more about that in in in a few minutes,
• 28:43and you could actually.
• 28:44Here's my only equation.
• 28:45I'm going to show you you could
• 28:46actually write an equation that's
• 28:47called the Norton mass gay equation,
• 28:49which basically summarizes that,
• 28:50and it's been much more subsequent
• 28:52mathematical work on this equation.
• 28:54And really what it means.
• 28:56But what it really means to me now,
• 28:57and I want to get into this topic.
• 28:59You know,
• 29:00with the limited time that we have,
• 29:02is that that this pattern of
• 29:04growth explains a lot of.
• 29:06Things that we know already about
• 29:08clinical medicine and not the least
• 29:09of which is the pattern of growth.
• 29:11For example, take a look at this MRI.
• 29:14This is a breast cancer MRI we
• 29:16we see this all the time and we
• 29:18call these satellite lesions.
• 29:20But frankly, it's all satellite lesions.
• 29:22This is a lesion.
• 29:23This is a delusion, this illusion.
• 29:24This illusion.
• 29:25It's got long skinny tendrils
• 29:26sticking out like a snowflake.
• 29:28It's the pattern of growth of what
• 29:29you'd see if the cells are coming in
• 29:31from the outside and so self seeding
• 29:33actually explains a lot about what
• 29:34we see in in the anatomy of cancers.
• 29:37At least the gross anatomy.
• 29:38And we'll get into the
• 29:40microscopic anatomy in a second,
• 29:41because this pattern of growth
• 29:44is called a fractal,
• 29:45and a fractal is repeated patterns
• 29:48at different scales and fractals
• 29:50have what's called a dimension.
• 29:52So now I'm going to go to a discussion
• 29:54of dimensionality because I think
• 29:55this is very important for some of
• 29:58the work that we're doing right now,
• 29:59and is implications particularly
• 30:02tumor infiltrating leukocytes.
• 30:03Now in Euclidean geometry.
• 30:05Dimensions are simple.
• 30:07A point has no dimension.
• 30:09A straight line only has length as one
• 30:11dimension a a sheet has two dimensions,
• 30:13length and and and and and
• 30:15and height and a cube.
• 30:17A solid cube has three dimensions.
• 30:18You're adding you're adding the depth.
• 30:20That's simple dimensionality
• 30:22in Euclidean space.
• 30:24In fractals it's a little
• 30:25bit more complicated.
• 30:27Let's just take one of our
• 30:28sheets that we had before that
• 30:30had a civil dimension of two,
• 30:32and let's look on it and cross section.
• 30:34Well, if you start to crumble it up,
• 30:35if you if you crumple up the sheet,
• 30:37it's going to be a little bit more than
• 30:39just a flat sheet and dimensionality.
• 30:41Here is actually 2.1 number of
• 30:43flat sheet is a dimension of two.
• 30:45If you crumple it some more it
• 30:47gets a higher dimension. 2.3.
• 30:48Now let's say that it's really getting
• 30:51more and more crumpled overtime.
• 30:53Well, it starts to. Have the appearance
• 30:55of something that's thicker.
• 30:56This is a dimension of 2.6.
• 30:58This is dimension of 2.8.
• 31:00A dimension 3 would mean you're
• 31:02prompted so much that it's now just
• 31:04a solid mass of of sheet material,
• 31:07but it's in a solid mass, so now it's a.
• 31:09It's a it's it's got.
• 31:10It's got the dimensionality
• 31:11of a 3 dimensional object or
• 31:13having dimensionality of three.
• 31:14So these are things to keep in mind and
• 31:17and it's a big difference between a 2.6
• 31:19and and and a 2.8 dimensionality you
• 31:21can see in terms of the thickness well.
• 31:24Fractals occur in nature all the time.
• 31:26These are artificial fractals
• 31:27on top of various sorts.
• 31:29These are the kinds of fractals
• 31:30that occur in nature all the time.
• 31:32Plants and animals and and
• 31:34and and diffusion in in.
• 31:36In substances like like ice or plastic,
• 31:40these these the fractals
• 31:42are just common in nature.
• 31:44Mandelbrot was discovered,
• 31:45there's been more Mandelbrot
• 31:46and written extensively about,
• 31:48and there's been an extensive
• 31:49explosion of literature in this.
• 31:50Written this in this regard.
• 31:52So what we've done is.
• 31:54We we've looked at this in the context
• 31:56of self seating and the context of
• 31:59leukocytes and why leukocytes because
• 32:01as me and Kim showed in this paper,
• 32:04we join mask and colleagues is an
• 32:07unseated state compared to a seated state.
• 32:09This would be an unseated tumor and
• 32:12this would be a tumor that's received.
• 32:14Received cells that have come
• 32:16from the outside,
• 32:17Ellen,
• 32:17two cells in the blood vessels
• 32:19are brought in with the seeds and
• 32:22they're mostly bone marrow derived
• 32:24endothelial cell precursors that
• 32:26close that blood level of growth.
• 32:28But I was particularly fascinated
• 32:29by the fact that that that when you
• 32:32get seating and this is is these are
• 32:34seated cells that they're staying green,
• 32:37the green for some protein.
• 32:38Not they have for some protein not staying,
• 32:41but they're obvious here in this
• 32:43particular setting they bring
• 32:44white cells in with them.
• 32:45CD 45 cells in with them,
• 32:46and so the seating process bringing
• 32:49bringing brings white cells in with them.
• 32:51Well,
• 32:51if it's bringing white cells in
• 32:53with them from the outside,
• 32:54perhaps the growth the pattern of
• 32:56white cells that we're going to see
• 32:58in a tumor is also going to follow,
• 33:00or fractal geometric pattern,
• 33:02and so with, with Matthew, Hannah,
• 33:05and and and and, and George Reese,
• 33:07Philo, Hannah when Ebro,
• 33:08G and and and and others we've looked at
• 33:13this by actually looking at at tumors.
• 33:15Conventional tumors.
• 33:16These are triple negative breast cancers
• 33:19and using image analysis in this acute pack.
• 33:21Roughly available image analysis
• 33:23program visual image analysis program
• 33:25to actually segment the white cells
• 33:27from the tumor cells so that we can
• 33:29actually measure the number of cells
• 33:31in each region of interest and then
• 33:33using various mathematical techniques,
• 33:34mathematical tricks that we've developed.
• 33:36We can then calculate the fractal dimension
• 33:38of those white cells and what we found
• 33:40in this is preliminary work and much
• 33:42more work is going on in this topic,
• 33:44so this is not a take home message
• 33:46just to show you that we've done it is.
• 33:48We looked at.
• 33:49This is the very first experiment
• 33:50that we did three cases of.
• 33:52Triple negative breast cancer
• 33:54and not neoadjuvant.
• 33:55These are patients treating
• 33:56the agent setting.
• 33:57They're small tumors versus non
• 33:58cases without recurrence at the
• 34:00fractal dimensions are different
• 34:02and in fact the fractal dimension
• 34:04of the of the white cells in the
• 34:06cancer that that that that recurred
• 34:08or that became metastatic was 2.77
• 34:11on the average and it was 2.65.
• 34:13So it's like 2.8 verse 2.6 like I
• 34:15showed you in previous diagram and
• 34:17a statistically significant P tire.
• 34:19Much more work is going on in this direction,
• 34:21but I think this is a very.
• 34:22Interesting area for us to think about
• 34:24the application of fractal geometry
• 34:26motivated by the concept of self
• 34:27seeding in terms of analyzing tills,
• 34:30and of course we're doing much more work
• 34:31in terms of characterizing those cells
• 34:33and and and other aspects of this work,
• 34:35that would be a separate talk.
• 34:36Hopefully at another time.
• 34:38What are those white cells doing in there?
• 34:41Well,
• 34:41previous work again from John Massage shop.
• 34:44In this one area,
• 34:45Korea published.
• 34:46This shows one of the things that they're
• 34:48doing is that they can actually provide
• 34:51resistance to chemotherapy the white cell,
• 34:53and this is work that's published here
• 34:55in cell in 2012 under stress of any sort
• 34:58releases a substance that causes TNF alpha.
• 35:02This pop up just driving me crazy.
• 35:05Right,
• 35:06right?
• 35:06So I'm gonna now gonna go move with the
• 35:08lightning speed and custom stuff out.
• 35:10I have no idea why that happened,
• 35:11but nevertheless here we are is that
• 35:14when you do when when we when I showed
• 35:16you about self seeding and when you
• 35:18have self seeding white cells come in.
• 35:20So we hypothesize that the white cells
• 35:22that come in the CD 45 positive cells
• 35:24here are coming in as a reflection of
• 35:26the seating process and we can uncover
• 35:28that by looking at their fractal geometry.
• 35:31And indeed we've looked at
• 35:32this is work with Matthew,
• 35:33Hannah and colleagues.
• 35:34We've we've done this with a.
• 35:36Two paths you know,
• 35:37method for being for segmenting
• 35:39between white cells and cancer cells.
• 35:41And indeed it is indeed fractal and
• 35:43the fractal dimension is different
• 35:44in triple negative breast cancers
• 35:46that recur then triple negative
• 35:48breast cancers that don't recur.
• 35:49Much more work is going on in this direction,
• 35:51and I discussed I described
• 35:52it a few minutes ago,
• 35:54but I can't go back over it now.
• 35:55We'll have to do another lecture
• 35:57on this particular topic.
• 35:58One of those white cells doing one
• 35:59of the things that they're doing
• 36:01is providing drug resistance.
• 36:03His work of sworn ally Acarya,
• 36:04and John Messages Laboratory.
• 36:06If you stress the cancer cells.
• 36:09And with anything chemotherapy or radiation,
• 36:13or or even heat,
• 36:14you can get the secretion of TNF alpha,
• 36:17which causes the secretion of CXCL one
• 36:19which goes through receptor on white cells,
• 36:22which causes the release of S 100
• 36:24proteins and can save the cancer cell
• 36:27as a mechanism of drug resistance.
• 36:29We showed this by actually showing that
• 36:31the inhibited by itself does nothing,
• 36:34but that if you give a stress
• 36:36in this case a chemotherapy,
• 36:37you can up regulate the loop.
• 36:39And kill cancer cells,
• 36:40but some are being saved by this loop.
• 36:42And by Ablating that loop we can get
• 36:44a much higher degree of cell kill.
• 36:46So one of the things that those
• 36:48infiltrating white cells is doing is
• 36:50providing a mechanism of drug resistance.
• 36:52We've also and I I told I I gave
• 36:56you a a wonderful anecdote here.
• 36:59That was a that that is lost.
• 37:02Now for very history about how
• 37:03why we did this work.
• 37:05But we looked at the at at those white
• 37:07cells that are infiltrating human cancers.
• 37:09And we found that very often indeed,
• 37:11in most cases they have leukemia
• 37:13genetic mutations in them.
• 37:15Tumor infiltrating leukocytes
• 37:16are not genetically normal,
• 37:18they are mutant,
• 37:19and they,
• 37:19however known leukemia Jennifer
• 37:20mutations and not only that,
• 37:22but if the patient is followed.
• 37:24In developing secondary leukemia
• 37:25much later in the future,
• 37:27those secondary leukemias have have
• 37:28the same mutations that you found in
• 37:31the tumor infiltrating leukocytes.
• 37:32In many cases many years earlier,
• 37:34there's something else that we're
• 37:36exploring and and doing work on on
• 37:38what role mutant white cells may be
• 37:40playing and actually and actually growth.
• 37:42Promotion of the cancer,
• 37:43as well as providing a potential
• 37:46mechanism for drug resistance.
• 37:47The last point I made in this
• 37:49regard or second to last point,
• 37:50I made this regard that you missed
• 37:52is that the that all of this could
• 37:56be exploited because circulating
• 37:57cancer cells in self seeding
• 37:59can only return to the cancer.
• 38:01But you can have circulating cancer cells
• 38:03going from one metastatic site to another,
• 38:05and it's been shown in both xenografts
• 38:07by Jonathan Weissman and also
• 38:09been shown in in in lung cancer.
• 38:12Clinical lung cancer specimens as
• 38:13well as some breast cancer specimens
• 38:15obviously can't read the details now.
• 38:17But this could all be exploited by
• 38:20giving some form of local therapy to a
• 38:22tumor to cause secretion of antigens,
• 38:24which then you can use checkpoint
• 38:27inhibitors and checkpoint inhibitors
• 38:28to get stimulation and and
• 38:30theoretically in this concept kill
• 38:32circulating cancer cells that are
• 38:34self seeding being drawn back to
• 38:36the area of inflammation that's
• 38:38caused by this particular
• 38:39procedure. We did this with with Becky
• 38:41Weights did this with Jim Allison when Jim
• 38:44Allison was at Memorial Sloan Kettering,
• 38:46where we looked at an animal.
• 38:48Model, in this case,
• 38:49the one that's growing in in green.
• 38:51If you just give anti CTA forward,
• 38:53nothing happens.
• 38:54If you just a BLT,
• 38:55a contralateral tumor, nothing happens,
• 38:58but the combination of ablation and
• 39:00and anti CTA 4 gets a 90% cell kill,
• 39:04and Heather MacArthur,
• 39:05who's now in Dallas has been exploiting
• 39:08this in a number of interesting studies.
• 39:11This is a work that she did
• 39:13at Memorial Sloan Kettering,
• 39:14where a primary breast tumor was ablated
• 39:16with crir ablation and the remaining tumor.
• 39:19Inside the two is profoundly
• 39:21Immunogen IK and we showed and
• 39:23published in several papers.
• 39:24Now in a new paper coming out of Elizabeth,
• 39:26Coleman has just a first authored
• 39:29that that you can increase the
• 39:32immunogenicity of that residual tumor
• 39:33by giving immune checkpoint inhibitors
• 39:35and indeed combinations work better.
• 39:37And this is now being looked at in
• 39:39terms of therapeutic implications.
• 39:42Coming and this could be done with
• 39:44radiation as well as with prior ablation
• 39:46which we're currently exploring.
• 39:47Combinations of immune checkpoint
• 39:49inhibitors and other thoughts
• 39:51related to educated T cells in
• 39:53terms of car T cells, for example,
• 39:55as well as inducing trans genes that
• 39:58actually may make the the the inflammation
• 40:00that were causing even greater.
• 40:02So, so this is where I left off
• 40:04and I just want to give you one
• 40:07other quick thought about geometry.
• 40:09You all remember that a sphere,
• 40:11something I mentioned to you earlier,
• 40:13is that the surface area is related
• 40:15to the square of the radius,
• 40:16whereas the volume is related to Cuba.
• 40:18The radius.
• 40:19This explains why mice are furry
• 40:21because they're very small and so they
• 40:23have a very high surface area related
• 40:25to their volume and therefore they
• 40:26lose heat easily and they need to be
• 40:28very furry to hold their heat in.
• 40:30You get to a large animal like an elephant.
• 40:32Is bald and doesn't need for her
• 40:35because its surface area is very
• 40:36low related to its volume.
• 40:38Its problem is getting rid of heat,
• 40:40which is why orphans Jen tend not to
• 40:41want to run very very quickly because
• 40:43generating heat is uncomfortable
• 40:44for them 'cause they don't get rid
• 40:46of heat very very very readily,
• 40:47and that is something else that we
• 40:50can exploit therapeutically because
• 40:52the fact is that as tumors grow
• 40:55just 'cause they're getting bigger,
• 40:56the ratio of their surface area is there,
• 40:58volume drops comes down.
• 41:00So you're converting basically a mouse.
• 41:02Into an elephant,
• 41:03it comes down faster for well
• 41:06differentiated cancers than for
• 41:08poorly differentiated cancers,
• 41:09and this is because of fractal geometry.
• 41:12You know if they're interested in that,
• 41:13we could talk about the reasons why,
• 41:14but that's the reason why.
• 41:16So that actually,
• 41:17if you have a tumor that's growing
• 41:20and you do A and the surface area
• 41:23decreases related to the volume
• 41:25while it's growing and then shrink
• 41:27it with chemotherapy.
• 41:29That the surface area to volume
• 41:30ratio is going to rise,
• 41:32and since we when when we're
• 41:34talking about immuno immunotherapy,
• 41:36we're talking about a relationship
• 41:37between the surface of the cancer and
• 41:39white cells that are trying to kill
• 41:41the cancer is that is that the best
• 41:43time to use this kind of ablation
• 41:45would be after an initial induction.
• 41:47And to take this idea and exploit
• 41:49it by inducing small tumor first,
• 41:51increasing the surface area to volume
• 41:53ratio and then coming in with your
• 41:56oblated therapy and then combining that with.
• 41:59Combining that with your.
• 42:02Your immune checkpoint inhibition.
• 42:05Now,
• 42:06the same concept can apply to in
• 42:09to one of the really most exciting
• 42:11areas in terms I think most exciting
• 42:13areas in terms of of modern medicinal
• 42:16therapy of cancer,
• 42:17which is the antibody drug conjugates,
• 42:19as we all know,
• 42:19they attacked a target antigen in the cancer,
• 42:21so they have increased payload delivery,
• 42:24but their penetration could be
• 42:25poor and this is something
• 42:27that has to be exploited when
• 42:29they're internalized that the the
• 42:30payload is reduced, it is is is
• 42:32released in 'cause the cancer cell.
• 42:34But more than that.
• 42:35In terms of the activity of these
• 42:37payloads on killing the cancer cell,
• 42:39they often leak out and they can kill
• 42:42adjacent cells that don't necessarily
• 42:43have that particular target.
• 42:45This or this work of Josh Drago.
• 42:47Well, this can be exploited by the same
• 42:50way is that when you if you used your
• 42:53antibody drug conjugate to a large tumor,
• 42:55you get down regulation of the target,
• 42:57and that's not not what you
• 42:59want to optimize the effect.
• 43:01So one of the things we're exploring,
• 43:02and this is not in the clinic yet.
• 43:04This is just an experiment experiment
• 43:05that we're doing right now,
• 43:06preclinical in preparation for clinical
• 43:08experiment is by giving a non ADC induction.
• 43:12First we can increase the
• 43:13surface area to volume ratio.
• 43:15And then come in with the ADC as a
• 43:17late intensification and therefore
• 43:18it should be even more active in this
• 43:21area to get tumor volume eradication.
• 43:23And if the animal experiments work,
• 43:25I think there's something else that
• 43:27could be exploited extremely easily
• 43:28in the clinic 'cause we have a lot
• 43:29of drugs in breast cancer that can
• 43:31cause tumor volume regression that
• 43:32are not Adcs and then instead of
• 43:34waiting for the tumor to grow and
• 43:36using using your Adcs in as a salvage
• 43:38if you use them at time of maximum
• 43:40tumor volume regression and this,
• 43:42by the way,
• 43:43could be determined not just by actually
• 43:44watching the cancer shrink with imaging.
• 43:46But also by by the burden of
• 43:48of circulating cancer and DNA,
• 43:50which would be another way of
• 43:52actually when that plateaus,
• 43:53you know you've achieved your maximum
• 43:55volume regression would be the best
• 43:56time to come in with your ABC's.
• 43:59Last slide and I'm not going
• 44:01to talk about this, obviously,
• 44:02is that we're exploiting exploiting
• 44:04all of this in much more sophisticated
• 44:07mathematics with a number of
• 44:09mathematical collaborators.
• 44:10I don't album and Jodeci in particular.
• 44:13Arena Elkin and jungle in terms
• 44:15of actually looking at this same
• 44:17mathematical concepts in terms of gene
• 44:20gene interactions and their networks.
• 44:22The same thing that works at the cell
• 44:23level and the the tumor of brain
• 44:25leukocyte level may work at the gene level.
• 44:27This would have a different fractal
• 44:28dimension than this, for example.
• 44:29'cause we have a lower fractal dimension.
• 44:31This would have a higher fractal dimension.
• 44:33You can look at gene networks in
• 44:34the same way as another term for
• 44:37this cord curvature.
• 44:38Obviously I can't get into it,
• 44:39but this is giving us some great
• 44:41insights and we recently published a
• 44:43paper in ovarian cancer that actually
• 44:45showed that the the structure of the
• 44:47gene gene interaction network has
• 44:49predicted values in terms of response
• 44:51to immune checkpoint inhibition.
• 44:53In this situation and,
• 44:54and indeed that you can actually
• 44:56predict which patients with ovarian
• 44:57cancer there's not supposed to
• 44:59respond to immune checkpoint ambition.
• 45:01Will respond on the basis of the the
• 45:04mathematical analysis of of their.
• 45:06Gene Gene interactive networks.
• 45:08So what I've been able to do,
• 45:10I hope in this lightning talk made even
• 45:12more lightning by the loss of the Internet.
• 45:17It's just described where this all came from.
• 45:19Skippers model being modified to the
• 45:22compression growth model and leading to
• 45:26a clinical advance and then why tumors
• 45:29grow in that kind protein fashion.
• 45:31The whole self seating concept which led
• 45:33us into the concept of fractal geometry,
• 45:36which is now one of my most active areas.
• 45:38Investigation how,
• 45:39how can we actually quantify tills and
• 45:41what is the prognostic significance
• 45:43of them using fractal geometry?
• 45:45How does all of this relate
• 45:46to drug resistance?
• 45:47And optimizing immunotherapy and optimizing
• 45:49new agents such as antibody drug conjugates.
• 45:52Forgive me for speaking too fast,
• 45:54but I I know we have to end on time
• 45:55and thank you all for listening.
• 45:57I apologize that we lost the Internet.
• 46:00Thank you Larry. That
• 46:01was if we have a couple of minutes let
• 46:02you know if I can do a couple of talks.
• 46:04I can stay later if people want to
• 46:06stay late. We have a couple of questions.
• 46:08First of all, thank you so much for you.
• 46:10Know it just to me. It's amazing for
• 46:13a Conservatory trained musician to be
• 46:16such mathematician at the same time,
• 46:18I don't know how both sides of the border
• 46:20link they get. They link together music
• 46:21and math is the same, the same the same.
• 46:23You know part of the brain. So
• 46:25we have some questions from Pat Larussa
• 46:28and David Rim to start with there.
• 46:30Kind of on the same pattern on
• 46:32the fractal pattern differences
• 46:33between hormone receptor positive
• 46:35and triple negative breast cancer.
• 46:38Are there differences that you see
• 46:39not only for the tumor, but the tills?
• 46:41And then does that work in
• 46:43terms of the agency used
• 46:45colleagues?
• 46:45That's what we work in progress,
• 46:47but but the answer is almost
• 46:49certainly so because you know,
• 46:50I'm not doing anything with the
• 46:52fractal geometry that the pathologist,
• 46:54they scope pathologist is doing
• 46:55with their eyes. You know,
• 46:57a skilled pathologist looking and says,
• 46:58hey, this is well differentiated.
• 46:59This poor differentiated differentiation.
• 47:01Is poor differentiated means
• 47:03a high fractal dimension,
• 47:05whereas a well differentiated
• 47:06means a low fractal dimension?
• 47:08And so basically I'm just
• 47:10basically just quantifying.
• 47:11I'm quantifying something that that the eyes
• 47:13of the beholder have seen is seen already.
• 47:15So clearly we're gonna see this.
• 47:17But you're talking here about fractional
• 47:19dimension of the cancer cells,
• 47:20which is obviously something we're exploring.
• 47:22I was talking about fractal
• 47:23dimension of of of the tills,
• 47:25but it all relates together and and I think
• 47:27what makes it really intriguing to me.
• 47:30Just personally,
• 47:30maybe nobody else, but to me.
• 47:32Is that it relates to this
• 47:33concept of a pattern of growth?
• 47:35This self seeding pattern of growth?
• 47:37One thing you gotta know about math is that
• 47:39you know even if self seeding didn't happen.
• 47:42If if things anatomically look the way
• 47:44they would happen were it to happen,
• 47:47it still is biologically significant.
• 47:49That's that's the way that's the
• 47:51way math mathematics works alright.
• 47:52You don't have to have the
• 47:53the example you know the the.
• 47:55The same mathematics works for
• 47:57gravity and for magnetism,
• 47:58even though the mechanisms are different,
• 48:00we don't understand the mechanisms,
• 48:01but we know they're different at the same,
• 48:03the same, the same.
• 48:04The same.
• 48:04You know the same mathematics
• 48:05you know works for the theory of
• 48:07universal gravitation works the same.
• 48:08So so once we actually understand
• 48:10the mathematical principles,
• 48:11they can generalize even if the
• 48:12thing that got us into that which is
• 48:14substituting concept is not valid.
• 48:16But I really do think the self
• 48:18seeding thing is valid.
• 48:19But based on the on the accumulating
• 48:21body of evidence that we're seeing,
• 48:22so I'm just basically trying
• 48:23to quantify that.
• 48:25Thank you we have another question
• 48:27on the implications of Gumpertz and
• 48:29growth for the rate of survival
• 48:31and proliferation of cancer cells.
• 48:32What are there implications and then
• 48:35does it imply that proliferation slows?
• 48:37And if so, why are the clinical
• 48:40implications of that slowed growth?
• 48:42All
• 48:42right? You know, you're asking for
• 48:44treatise and a little bit comma,
• 48:45and I wrote a really nice review
• 48:47article about the clinical implications
• 48:49of cancer self seating so you know,
• 48:51COMEN and Norton.
• 48:52You can Google it and go with that paper.
• 48:54Really, really, really very quickly.
• 48:55When we go into all that in great depth,
• 48:57first of all,
• 48:58gun person growth has to happen.
• 48:59'cause if it didn't happen we we
• 49:01would have no chance against cancer
• 49:03because with exponential growth I
• 49:05mean from the time of diagnosis,
• 49:06time of death would be a matter of of of
• 49:09of weeks at even even for solid tumors.
• 49:11So we know there's gotta be a tailing
• 49:13off of growth rates and it really
• 49:15has great profound implications
• 49:16in terms of our understanding,
• 49:18growth and and and planning for therapy.
• 49:20I I think it's a shame that we haven't
• 49:22used those dense sequential therapy
• 49:24for more tumors beside breast cancer.
• 49:25There's been a little bit of work
• 49:27in lymphomas in this regard.
• 49:28A little bit of work in other tumors,
• 49:29but we haven't optimally exploited it,
• 49:31and I think that we could actually
• 49:32do better even with existing agents
• 49:33if we were able to take some of the
• 49:35principles we learned with breast cancer,
• 49:37move them into that setting.
• 49:38But right now where I'm focusing
• 49:40in on instead,
• 49:41is how do we use some of the newer agents,
• 49:43particularly Adcs,
• 49:44and apply some of the things we've
• 49:46learned from chemotherapy to it
• 49:48using gun protein growth and using
• 49:50our concepts with tumor geometry.
• 49:53And maybe the last question is from
• 49:57Doctor Bafan. Thoracic surgery?
• 49:59Is the self seating limited to cancer
• 50:02cells or do other employee put in stem
• 50:05cells from normal cellular turnover,
• 50:07preferentially land and tumors,
• 50:09for example to gastro intestinal stem cells?
• 50:14Go on to blood, still some lines and other.
• 50:17Yeah, it's a great question.
• 50:18It's a great question because it's
• 50:19something that that that we are on verbal.
• 50:21Yeah, stem cells and seeds I think
• 50:23is the same thing. Basically,
• 50:24I think that's the capacity of stem cells
• 50:26is being able to move around and and.
• 50:28And frankly it's not such a stretch.
• 50:30'cause that's what happens in Embryology.
• 50:31I mean, that's that's how
• 50:32the embryo forms is,
• 50:33that is that the the stem cells move
• 50:35from one spot to another in a very,
• 50:37very logical kind of fashion.
• 50:39It isn't that and people
• 50:40always ask that you know,
• 50:41you know what draws them self to that site.
• 50:43It isn't drawn to that site.
• 50:45We know this from this from the self seeding
• 50:46work that's been done in the laboratory.
• 50:48The cells go all over, it's just
• 50:50where they stick that really matters.
• 50:51So it looks like it's drawn to that
• 50:52site only 'cause they stuck there and
• 50:54and it's that sticking their stickiness
• 50:55that I think is something that that
• 50:57that that's being scored by a number
• 50:59of by a number of investigators.
• 51:02You know that particular phenomenon,
• 51:05but I'm sure this happens in general.
• 51:06Look at wound healing.
• 51:07I mean wound healing.
• 51:08You know you heal your wound,
• 51:09your surgeons,
• 51:09you don't heal your wound because of the
• 51:11cells that are right there where you cut you.
• 51:13The cells are brought in there,
• 51:14you know,
• 51:14married to rod cells are brought
• 51:15in there and that's what allows.
• 51:16The wound to heal so that so that I I
• 51:18think seating is a general biological
• 51:20phenomena and a lot of things that we're
• 51:22doing in cancer may relate to other things,
• 51:24such As for instance, wound healing.
• 51:25Uhm, uh,
• 51:26that that we're starting to think you know,
• 51:29you know about the cytokine release
• 51:31syndrome that we're seeing with COVID-19,
• 51:33and how that relates to the mobility of
• 51:34of of white cells in that regard as well.
• 51:36In response to inflammation.
• 51:37So so it may be a much
• 51:39more general phenomenon.
• 51:40The cool thing for me,
• 51:41and again,
• 51:42I'm just speaking for me,
• 51:42is that the mathematics we
• 51:44workout in one area may relate
• 51:45to all these other areas as well.
• 51:46And that once we understand,
• 51:48developed these mathematical principles,
• 51:49that we can actually use them to generalize
• 51:52beyond cancer into heart disease.
• 51:53We know that colonial meta polices cells
• 51:56are are important in arteriosclerotic
• 51:57heart disease as well as as as we
• 52:00just discussed with cancer as well,
• 52:02so that these principles may generalize
• 52:04and and and have much more replicability.
• 52:07Can we squeeze one more question.
• 52:08This is from an Chang former
• 52:11memorial colleague who are now
• 52:12our Chief Network Officer.
• 52:14She's asked, can we exploit Atascosa
• 52:16specific or other gene processes in
• 52:19the tumor microenvironment to prevent?
• 52:21Self seating in the niche of growth.
• 52:23Yeah yeah, great another great question.
• 52:25Another great hot topic.
• 52:26You know something that Joe and I,
• 52:28Joe and Megan.
• 52:29I thought a very early days when we
• 52:30started doing this when we started
• 52:32doing this work and I remember that
• 52:34we published the paper with 2009,
• 52:35so it's been a lot of time this
• 52:37past and and and and you know,
• 52:38we know that that that cytokines in
• 52:40flammatory cytokines are important
• 52:41for the process and that's already
• 52:43that's already been demonstrated and
• 52:45that may be why inflammation is is
• 52:46such a problem is related to cancer.
• 52:48But I want to get the things that
• 52:50are more targetable than that.
• 52:51And so that's one of the reasons
• 52:53why that very last slide that I
• 52:55showed you that very complicated
• 52:56mathematical slide is is is we we,
• 52:58we are right now doing a number of
• 53:00different studies looking at gene
• 53:02interactive networks using the
• 53:04same basic mathematical principles.
• 53:06In fact,
• 53:06trying to see what are the gene
• 53:08interactions that may may underlie that
• 53:10process, because that will tell us what,
• 53:12what,
• 53:12what genes we maybe have
• 53:14development chemicals to,
• 53:15medicines to to be able to be able to target,
• 53:17to interfere with this,
• 53:18the just there's something in that
• 53:20regard I think is is really important.
• 53:22Is that?
• 53:22We focused on so much of our energy
• 53:24in terms of medicinal chemistry on
• 53:26targeting genes or gene products,
• 53:28and one of the things we're learning by
• 53:29using that mathematics and looking at
• 53:31gene interaction networks is this is yes,
• 53:33indeed is the action of individual genes,
• 53:35but it's not the action of
• 53:37individual genes by themselves.
• 53:37They're all interacting with each other,
• 53:39and it's the whole network of
• 53:41genes that actually forms a a
• 53:43meaningful biological entity and
• 53:44not just the individual genes.
• 53:46So we're going to have to target
• 53:48target those interactions rather
• 53:50than target the genes themselves,
• 53:51and that's not something that we commonly.
• 53:53Do you?
• 53:54Although we we probably do it,
• 53:56we don't realize we do it with
• 53:57with with therapy.
• 53:58When you give steroids to a patient
• 54:00for all the reasons that we give
• 54:02Google Corticoids for a patient you
• 54:04you're attaching to Google Corticoid
• 54:05receptors all over the place,
• 54:07not just in a particular place,
• 54:09and you're basically affecting
• 54:10cell cell interactions all over
• 54:11the place and you're affecting
• 54:13gene interaction networks all over
• 54:14the place by using some of the
• 54:16most powerful drugs that we have
• 54:18actually are not targeted therapy,
• 54:20it's starting to question the notion of
• 54:22are we really better off using targeted
• 54:24therapy when we're dealing with complex?
• 54:26Processes or should we be able to
• 54:29target the complexity itself so so
• 54:31that's one of the things that we're
• 54:33zeroing in on on that particular thing.
• 54:34Now,
• 54:35how do we find those drugs is is that?
• 54:37Basically,
• 54:37if you understand the networks and you can,
• 54:40you could then screen a lot of
• 54:42different drugs and see how it
• 54:44affects the network and so you can
• 54:45actually as as possible that even old
• 54:47drugs could be repurposed for this reason.
• 54:49And you may not be able to put your
• 54:51finger on exactly why they work,
• 54:52but you could just show that
• 54:53they are working in the show.
• 54:54They have clinical utility.
• 54:56And and and that's that's really
• 54:58kind of a very different way of
• 55:00thinking about medicinal chemistry.
• 55:01Rather than saying I,
• 55:02I'm gonna go after the specific
• 55:04target to actually go after
• 55:06basically the biological effect.
• 55:07Or you know,
• 55:08in in general with your agents
• 55:09and then and then move them into
• 55:11clinic on that kind of basis.
• 55:12So those are some of the things that
• 55:13we're thinking about right now.
• 55:15Thank you Larry.
• 55:15This is been really great and
• 55:17we really appreciate your time.
• 55:18I know next year Eric will want
• 55:20to have you here in person again
• 55:23to talk to us and this was really
• 55:26just phenomenal lecture.
• 55:27Even though you dropped
• 55:28off for a few minutes,
• 55:29if you were able to bring everything back
• 55:31and and and please thank thank
• 55:32the person who actually called me
• 55:33on my cell phone so that I they
• 55:35got to me so I was able to come
• 55:37back in I I appreciate it.
• 55:38Thank you all very much for
• 55:39listening. Thank you Larry.