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"Thoughts on the Nature of Breast Neoplasia"

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"Thoughts on the Nature of Breast Neoplasia"

January 25, 2022

Julia & Patricia Kingsbury Memorial Lectureship/Yale Cancer Center Grand Rounds | January 25, 2022

Presentation by: Dr. Larry Norton

ID
7376

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.