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Welcome to Decodit, the podcast where we explore how data, technology, and human insights are reshaping the cancer worldwide.
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I'm Yochy Camblon, biology streamer and founder at Software Genetics.
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Today my guest is a too heavyweight in our feet, someone who combines both world-class science and category-defining innovation.
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Educated at Yellen John of King's Keith, it's director of clinical research at Mass General Hospital Cancer Center in Boston and professor of medicine at Harvard Medical School.
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Kit is also a leader who puts human interactions at the center of his life, is an avid reader of philosophy, and brings that depth of thought to everything he does.
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So you've been recently elected uh president of ACR, can you tell us what is the mission of ECR?
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Yeah, well, the very succinct mission of ACR is to prevent and cure cancer through research, education, communication, collaboration, advocacy, and funding.
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So a dense mission statement, but I think the way that I unpack that is that basically, on one hand, AECR is the single largest convener in the cancer field.
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And I think people naturally think of academic researchers as you know the central purpose, if you will, in terms of convening.
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But actually, it's not so simple, and I mean that in a good way, in that throughout my career, I've watched ACR expand its embrace to make sure that everyone in the cancer research community, certainly those in the private sector, a broad domain of public sector researchers, so including those who work in government and IHNCI specifically and other agencies.
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And I would say, even though it's got the term American in front of it, it's always had aspirations to be a global organization.
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And that's particularly been true over the past 10, 15 years or so, as you can point to multiple examples of how it is that convening function has spread worldwide.
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So I think the convening is a major piece, but it's not just convening to have conversations, it's really to kind of turn that into action ultimately in the form of research and education and communicating to the lay public our purpose and what you know, what it is that we're trying to do as a field, how we do it.
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And then importantly, you know, advocacy to our funders.
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Our biggest funders, as you well know, are the public.
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The U.S.
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in particular is the single largest contributor to the funding of biomedical research, so so taxpayers, and their representatives in Congress are a major audience, if you will, for AACR's outreach work and advocacy work.
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That's a lot of domains.
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And of course, there's a large family of journals, you know, for disseminating data and science as well as at conferences.
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So that convening function really does encompass a broad range in its own right.
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And do you have a sense of how many members are a part of AACR today?
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Yeah, yeah, it's about the current number, generally carry it around in my head, because of course it's been continually rising throughout my career.
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But the current number, I would say, is I want to say 40 some thousand.
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You know, I'm not going to get it entirely correctly.
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But I would emphasize that number, which is large, encompasses not only the domains that I tried to mention, kind of you know, US and abroad, which is certainly true.
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But it turns out the single largest pool of members are young scientists and young researchers broadly.
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And in their training phase slash earliest phases of their professional careers, and that's true in both the nonprofit and the for-profit environments.
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That's been very gratifying because I can speak to my own experience at the very dawn of my career, beginning to engage with ACR, not just at the annual meeting, but importantly, there was sort of a secondary meeting, so-called molecular targets meeting.
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We'll probably come back to that phrase, I'm sure.
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That was a meeting that was still in its relative infancy in the ACR sort of pantheon of conferences.
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I would point back to my first annual meeting and my first block your targets meeting in the same year, it's being absolutely eye-opening, transformative, direction setting for me.
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And so I can really underscore just how critical it is that ACR reach the youngest members of our field, who obviously have the most fragile careers, particularly in the current environment.
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Thank you, being a European for seeing American but global.
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In today's world, we really need this mindset.
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So you studied at Yale and John Hopkins, and I remember you telling me uh one day uh your happy place is understanding or was understanding what was happening in within cells.
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Why did you choose medicine and why in particular oncology?
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Yeah, so sort of a long journey, but you know, it spanned from well, 1989 is when I started my undergraduate years at Yale.
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So 89 to 93, and then medical school 93 to 97, just to kind of give you a framing of where was science at the time and medicine, as I was beginning to learn some of the tenets of the science behind medicine, undergraduate, and then of course in medical school, a little bit more the applied science.
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Basically, you know, what I would say is really for me, I had a very crude interest in science, biological sciences and human-relevant science, that drew me as an undergrad to pick up what was still a hard copy course of study book, very thick one at Yale.
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And I saw that they had a neuroscience major, which struck me as remarkable at the time.
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Again, we're talking 1989.
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I learned in those years that really Columbia, which was the real front runner in neuroscience at the time, and Yale were really the two places that committed to recruiting a faculty in neuroscience and really making a concerted effort to do research in that domain, obviously educate and build kind of a thematic program there.
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And I thought, well, the biology of the mind, I mean, that's just what could possibly be more fascinating than that.
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I've stuck with that fascination, I think, my whole adult life now.
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But in any case, neuroscience to me was just, again, like infinitely interesting.
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And as I was kind of going through the paces of the key disciplines for that major, of course, that included biology and chemistry, but it was really cell biology, which I got to in my sophomore year of college.
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That was the aha moment because it was really, you know, it was early days in terms of really understanding the repertoire of growth factors in particular, growth factor receptors, and then the intracellular circuitry that mediated signaling downstream of those receptors.
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Multiple discoveries had been made just in that signaling unit, if you will, as you well know, that then as we decoded the cancer genome as a field, we then learned how much cancer sort of takes over, co-ops exactly that signaling axis.
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So there's a lot of genetic evidence in support of that.
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And of course, that's the successes we've had in the domain of so-called targeted therapy have really hovered right in that space.
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I didn't see that coming so much.
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It was more that I was just utterly fascinated by kind of this increasing understanding of how cells are wired, and really just the incredible complexity of a single cell, you know, which could be a single cell organism, or obviously when you get to complex systems and ultimately animals, humans, even more so.
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So, yeah, so it was the convergence of neuroscience and cell biology at you know at the time that really just absolutely opened up my mind and my imagination.
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And then I'll just quickly jump to the second part, which was that when I got to medical school, I had an intent to pursue neuroscience slash neurology, you know, the clinical medicine, if you will, of neuroscience.
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And so I organized my early clinical exposures, my first rotation, to be in neurology, my very first one, which we were able to kind of customize the order at Johns Hopkins.
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And that was an incredibly disappointing period of weeks as I was seeking mentorship and conversation with faculty in neurology.
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I had already met some in neuroscience in my preclinical years at Hopkins.
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Neuroscience was quite strong there as well.
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And in any case, what became very clear to me is there was a huge gap, huge gap between neuroscience and neurology.
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Just like complete and total gap.
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And I thought, well, okay, it would be maybe exciting to try to bridge that gap.
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But it just it forced me, I was just paralyzed, I guess I would say, by that.
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And it forced me to consider like, might there be a domain of medicine where the gap could be like more easily imagined to be filled or narrowed.
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And it was still a hypothesis, to be clear, we're talking in the mid-90s now.
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It was still a hypothesis of mine that oncology could be that.
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But as I was learning more and more about what was happening in terms of applying, particularly in molecular biology, as you well know, those tools were developed and deployed in cancer first and foremost.
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And so seeing that, like in the scientific literature and even as I was being taught as a medical student, I thought, okay, well, if oncology is at the forefront of understanding cell circuitry and then again how cancers apparently rewire themselves, then that's a good candidate for tying all these strings that I just mentioned together.
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You had a lot of instinct.
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We will realize that as we cover the interview today.
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Around 2000, you specialize in melanoma.
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What is different about this cancer type?
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Well, I mean, honestly, I would say the word instinct is absolutely right in terms of how I navigated my early career.
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And I would say, as I then, you know, the year 2000 was specifically when I was entering oncology for the first time as a fellow, and then I was at the University of Pennsylvania for my fellowship and stayed there on the faculty in total for nine years, between two years of fellowship and seven years on faculty.
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And so following my instincts further in like my ver very first maneuvers in oncology, I'd say maybe three things about melanoma.
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One was that it was clearly a very high unmet need cancer.
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So essentially nothing to offer patients with advanced melanomas.
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Thankfully, melanomas commonly could be discovered early and surgically excised and cured.
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That's good.
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But once cells began to spread, lymph nodes or beyond, we just had effectively nothing, was my take as I was kind of running the field.
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So, and then of course it's a very aggressive cancer in its advanced form.
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So, like just as bad as glioblastoma, pancreatic cancer, and you know, are the the ones that we continue to fear the most.
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And so I thought that makes it an important problem to focus on.
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And it also felt like a wide open landscape.
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The other thing was that at Penn, and then the WISTAR Institute, which for those who've not been on the Penn campus, you might not know that the WISTAR Institute's a freestanding science institute right in the middle of the Penn campus.
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So it's like literally landlocked in the middle of the Penn campus and right across the street from the hospital, University of Pennsylvania.
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And so the Penn WISTAR programs in a combined fashion had really the most impressive melanoma research team that spanned basic science and even into sort of you know early clinical translation, not yet with therapeutics, but in terms of understanding determinants of the disease.
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And importantly, at the WISTAR, for anybody who's been around melanoma science, you would know that there was this proliferation of cell lines, melanoma cell lines, the WISTAR melanoma lines, that were created that created this kind of workhorse platform or system.
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And that existed, you know, in the years before I'm describing.
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And so I saw that commitment and the resources, and it seemed to me that, okay, well, then this is like the substrate is there to then really understand the wiring diagram, if you will, how it's altered in melanoma, and then how we can use that information to therapeutic benefit.
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Those decisions were made in 2000-2001, and then it was understood kind of a high level that the map kinase pathway, which is one of the key growth factor receptor RAS effector pathways, seemed to be particularly utilized in melanoma cell lines.
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That was the biochemistry, if you will, had been, you know, it was reasonably well described there.
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And so then in 2002, which was the transition between fellowship and faculty, BRAF mutations right in the heart of the MAP kinase pathway were described in cancer.
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8% of all cancer, but nearly half of all melanomas in a nature paper that was the cover article of that nature issue.
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And there you have it.
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That then absolutely set my course, at least in the melanoma side of my life.
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I also had a focus on kidney cancer based on the angiogenesis, increased understanding of angiogenesis at the forefront of kidney cancer.
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That's its own parallel story, but in the case, that was you asked a melanoma question, so there's that answer.
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People in the field know that.
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And before uh interviewing you today, uh in my last episode, I have interviewed Olivier Mikelin, who is as well in the field of melanonoma.
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So for the audience, Keith is a king in Melanoa.
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Let's be clear, right?
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He's very humble, but he's a king of Melanoa.
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It looks like interestingly you end up starting there because of the surroundings and the environment, which I found fascinating as well.
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Because I think maybe as we cover philosophy, you will tell us a bit about that.
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Not always easy to basically define our destiny and serendipity in our life is an element that is important as well.
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But looks like to me you always followed really your instinct in everything you've been doing.
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You're reminding me in that comment that another instinct that I developed right at that time, very dawn of my career, as I was learning about how clinical research was done in oncology in particular, was it seemed to me that there was a great degree of siloing that was happening in all domains of research, cancer research included.
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It struck me that that was the case in terms of laboratory research.
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You know, the people were kind of, in a way, secretive until the point of finally disseminating data.
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And that I've been when it came to clinical research, as I was kind of reading the literature, over and over again you would see what were at the time were referred to as clinical trials, kind of put that in quotes, of 14 patients, or seven patients or 20-something patients, treated at a single center with some new approach.
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And I just over and over again was just struggling to understand why 14 patients, what was behind that?
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And I actually, during the course of my fellowship, just two years, actually began taking the courses that were in biostatistics, applied epidemiology, if you will, because the Penn had an excellent program in that regard, so I could like sort of sign up for those courses.
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I even took a couple of those in my very beginning of my faculty life to try to become conversant in how one thinks about when it comes to laboratory data as well as clinical data, just how do you understand data distributions, how do you then kind of analyze, compare, and ultimately when it comes to clinical trials, think about things like sample size and power calculations and the like.
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And in any case, all of this fits under the siloed behavior I learned, basically, which was that centers basically took great pride in doing whatever they could do by themselves.
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And so it was again very right at the dawn of my career, and as you're saying, as I was instinctively being drawn into these kind of two subfields, but particularly melanoma at Penn, the intuition that I had was that the joy that's going to be had in a career in clinical or translational research, a term that I used later in my career to describe what I was trying to do at the beginning, is to really try to build and operationalize, participate in team science, which is not an invention in the time that we're talking about, right?
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Other disciplines already had team science, but not clinical medicine, not oncology.
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And it was the dawn of that time.
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I'm not saying I invented it.
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That's not my point, but it was early days, to say the least, of the concept.
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We're still learning how to really operate in a way that is maximally impactful for our human constituents, you know, audience, if you will, in team science.
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It's incredible to think about it 20, 25 years ago, no genomic sequencing, clinical trials with 14 uh patients in a single hospital.
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So obviously things have evolved a lot.
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And today uh there are a number of uh drugs or therapies that are, I would say, well known by the public in cancer, like chemotherapy, now immunotherapy.
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But you really distinguished in targeted therapies and small molecules, if I'm not mistaken.
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Can you explain us what were your discoveries and how, from those discoveries, you've been actively involved in making them an innovation and so making them basically commercialized?
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Okay, well, I guess I'll start with an academic, you know, an answer that's based on my academic work because it then dovetails into why I gained interest and then conviction and ultimately sort of passion for also doing this work in parallel in the private sector.
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I mentioned a moment ago that what was emerging, even before we had comprehensive genomic analysis of a single cancer cell or a collection of cancer cells in a tumor, there had been efforts or discoveries made around individual genes.
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The puzzle was getting pieced together without pieces connecting, right?
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So it was like pieces were floating, if you will, on the table, trying to understand, well, like, you know, again, fundamentally, what does the wiring diagram of cancer cells look like?
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Early attempts at sequencing individual genes.
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And then finally, in the in the 2000s, as you well know, was the era in which finally the technology and the ability to actually sequence an entire genome, healthy or diseased, you know, first became possible.
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Then the puzzle started to begin to fill in much more rapidly.
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And so it was very clear that molecules inside of cells, signaling molecules, the molecules that are responsible for telling a cell its circumstances, if you will, its you know, its environmental circumstances, so the conditions outside the cell, and then its internal conditions as well, including the integrity of the genome, for example.
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But understanding nutrient availability, oxygen tension, you know, in its immediate environment, I mean, these are some of the critical functions.
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And then knowing when a cell is in an appropriate state to actually divide or not, because you know, harsh conditions, the like, some of the functions that are co-opted by cancer through genetic alteration.
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So, in other words, so some of the molecules that are involved in transferring this information from outside to inside, if you will, become co-opted in cancer by virtue of somatic genetic alteration.
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So put it another way, cancer cells are selected for that have these features.
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It's not like there's a purposeful gain of any of these mutations.
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You know, by all accounts, it's accidental.
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But the ones that are selected for that confer advantage to cancer cells turn out to be clustered in this domain that I was alluding to before of sort of growth factors, growth factor receptors, and then the intracellular wiring.
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I emphasize the word intracellular because that's one needs for the foreseeable future still, one needs drug approaches that are small molecules that can get inside of cells to be able to interact with and counteract the effects of these genetic alterations.
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Not always directly.
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So, yes, the early versions of targeted therapy, including ones that I worked on, like BRAF targeted drugs, for example, were direct antagonists of a mutated oncogene.
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But even a simple one step down below in that signal transduction pathway, BRAF activates MEC, MEK, MEC inhibitors actually preceded BRAF inhibitors, as it turns out, in the early, early ones anyway.
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Where to direct their potential application was not well known.
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But as it when BRAF mutations were discovered, it then became more increasingly clear that, oh, well, that actually, since BRAF activates MEC, that might be the best use.
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And that's it, so that's an example of an indirect in a way approach, right?
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Still targeted therapy.
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If you're a specific molecular target in the case of MEC.
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But in any case, the point is that it's intercepting the function, the co-opted function of those intracellular molecules.
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That's what I comfortably refer to as targeted therapy.
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I should mention that we've had significant success in targeting cell surface molecules on cancer cells and then on immune cells, or PD1 being the most famous example, of course.
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And these are targeted therapies in the sense that they have a specific molecular target and they're engineered to engage with that molecular target.
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But cell surface targeting is something that can be done with large molecules very specifically, leveraging human immunobiology and kind of taking over that chemistry, if you will, create very selective targeted molecules that are accessible, in that case to large molecules.
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But small molecule addressable targets has been much more my passion, if you will.
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And also what I learned in my academic work that then informed my interest in doing this, being part of building teams and being a part of teams in the private sector, is that basically it is hard work, right?
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Which is to say that to create the perfect small molecule, nature doesn't give us the same toolbox as monoclonal antibody development with this kind of permutation of hypervariable domains and achieving selectivity and affinity with antibody type molecules.
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We don't have that same sort of fast-forward toolbox, if you will.
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And so small molecule medicinal chemistry has always been hard work.
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There have been substantial gains made.
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I mean, this is where computational chemistry, AI in the form of chemistry, is increasingly revolutionizing how this work is done.
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But what I'm getting at is simply that I was watching molecules come forward for human application, and as an academic clinical investigator, I was working with many companies bringing such programs forward.
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And over and over again, I was seeing that the output of years worth of effort seeming to be suboptimal.
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And I was just I always, as an academic, not a chemist, wondered is there a way to kind of set up the enterprise in the first place so that you make a more concerted effort to produce something as close as possible to the perfect molecule.
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And if you can't, maybe you shouldn't be moving that forward for a potential human application.
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And so, yeah, so it was but same concepts in terms of the types of targets that I had been working on academically.
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It's just the companies that I began to found and the programs within them consistently had nothing to do with melanoma or kidney cancer, partly because those targets, the relevant targets there had been largely attended to.
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That's maybe a significant part of it.
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But there was just such a broad landscape of work to be done in terms of expanding our repertoire of such drugs.
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Hard work, but Neurocay is extremely successful work with basically real innovations with products that are being in the market, are in the market, and with two biotech companies that are being acquired, as I mentioned in the intro, respectively Locto and Scorpio by Eli Lili for$8 billion and$2.5 billion.
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So this is very remarkable.
00:23:16.480 --> 00:23:20.319
You've also funded other companies like Strata Oncology.
00:23:20.480 --> 00:23:26.079
And what I would like to understand is from your experience, how different have been these journeys?
00:23:26.400 --> 00:23:26.720
Yeah.
00:23:26.960 --> 00:23:38.000
Well, Strata is one example I do like to point to because but I there's an another company that I've founded that has different, but I've learned similar lessons.
00:23:38.079 --> 00:23:39.440
I'll try to speak to those.
00:23:40.000 --> 00:23:50.400
Everything I've talked about seems to kind of point to the need for the drugs and like kind of, you know, create the perfect purpose-built drugs for a specific molecular task, if you will.
00:23:50.640 --> 00:23:55.200
But the reality is it's the application of them to exactly the right patients.
00:23:55.359 --> 00:24:03.440
You know, this is a kind of the full concept of precision medicine as it pertains to oncology, anyway, is really trying to match the therapy and the patient population optimally.
00:24:03.519 --> 00:24:14.480
And this doesn't just relate to mutated oncogenes matched with drugs that directly antagonize them and then hunting for mutations in those in tumors, and then having that be your basis.
00:24:14.640 --> 00:24:33.680
There are much broader concepts in terms of how to try to understand cancer biology, the complexity of cancer biology and therapeutic vulnerability of tumors in a way that I would say we already have enough evidence to say that like there are going to be multiple other ways of characterizing a population, you know, an optimal population for therapy.
00:24:33.839 --> 00:24:35.599
So simply put, this is diagnostics, right?
00:24:35.680 --> 00:24:37.519
But it's diagnostics for the purposes of therapy.
00:24:37.599 --> 00:24:51.200
So, and when you match up therapy ultimately with a diagnostic, and both the diagnostic and the therapy are approved together, so-called companion diagnostic, that's like the ultimate definition of the role of a diagnostic in this endeavor.
00:24:51.359 --> 00:25:07.519
And, you know, simply put, in US, Europe, and Asia Pacific, which is the major markets as we call them, this is not less of an issue anywhere else in the world, diagnostics are simply not valued in the quote unquote marketplace the way that doctors and patients value them.
00:25:07.680 --> 00:25:21.680
I often, using like highly semi-quantitative language, I should say, often summarize the state of science by this statement that basically diagnostics are basically half the problem, right, before us, and the other half are the therapeutics.
00:25:21.839 --> 00:25:31.200
And when I say half the problem, I mean half the value, if you will, right, to a doctor/slash patient is knowing, not like just that you have lung cancer versus colon cancer versus breast cancer.
00:25:31.359 --> 00:25:33.519
That's of limited utility, to be honest.
00:25:33.680 --> 00:25:38.400
It's knowing what makes that cancer tick and what therapy would be most impactful.
00:25:38.640 --> 00:25:40.480
That's a real diagnostic.
00:25:40.640 --> 00:25:42.079
And so it's incredibly valuable.
00:25:42.160 --> 00:25:51.920
And if you don't have that information, you're back in the year 2000, certainly, but you're relatively blind in terms of how to think about advising a patient, navigating them to the best potential outcome.
00:25:52.000 --> 00:25:55.279
So it's such a huge problem, that's the case, right?
00:25:55.359 --> 00:26:12.559
Because then how is it that you discover and develop the types of diagnostics I'm alluding to, including so-called complex diagnostics that involve more than one marker and sometimes more than one marker that are continuous variable features to them in terms of not having a discrete kind of black and white result?
00:26:13.200 --> 00:26:21.920
You know, basically the idea of developing such tests would not impress a lab scientist or, for that matter, a statistician, or a pathologist.
00:26:22.160 --> 00:26:25.680
This is not rocket science, but it turns out to be a huge challenge.
00:26:25.839 --> 00:26:44.480
And that's like maybe the point that I'm trying to emphasize here in terms of the lesson learned, in terms of how to try to describe and create that value in a way that motivates the transition from public sector to private sector research and the scaling ultimately that's needed to deliver any product to the marketplace.
00:26:44.640 --> 00:26:52.559
There are diagnostics, admittedly, that actually can be operationalized in a fairly broad way without, you know, large-scale commercial investment.
00:26:52.880 --> 00:26:54.640
But in general, that's a major challenge.
00:26:54.799 --> 00:27:00.160
And then the therapies to date has never been possible without ultimately investment and engagement in the private sector.
00:27:00.319 --> 00:27:06.880
So trying to balance, if you will, these two domains, that has been well, it's not just a challenge.
00:27:06.960 --> 00:27:19.599
I would say it's like remains for me a huge area of conviction and I would dare say passion to really help unlock the value that diagnostics have in what as it pertains to therapy.
00:27:19.759 --> 00:27:27.279
And so that's and Strata's been on that journey now nine years and really beginning to realize the therapy opportunity.
00:27:27.440 --> 00:27:34.319
But I've over and over again, I've been sort of obsessed with this problem because it creates a real inefficiency.
00:27:34.400 --> 00:27:48.720
There's just a lot of science that isn't being translated to medicine in both the diagnostic domain, even more in the diagnostic domain, frankly, and then similarly in the therapeutic domain, because there's so much unmet need that remains in cancer, like we can't really tolerate that quote-unquote inefficiency.
00:27:49.119 --> 00:27:49.759
Absolutely.
00:27:50.000 --> 00:27:55.359
Well, it speaks to me, and you and I and others will work to change it to make a difference.
00:27:55.599 --> 00:28:05.039
So, Keith, we get to be introduced by a common friend, and now I would like to speak about your work in India with Scarakino Selfcare.
00:28:05.119 --> 00:28:10.240
You said that we're not just there to help India, we are there to learn from them.
00:28:10.480 --> 00:28:14.640
Why is India so critical for the future of oncology around the world?
00:28:14.960 --> 00:28:57.119
There's so many parts to the answer to that question, but the simple component to pull forward from what I said a little while ago is that I am convinced now for five or almost six years now that I've been partnering with, collaborating with, engaging with the leadership of Carkonos, and then through them, a large swath of the Indian cancer care and research community in oncology, of course, that basically team science is and will be more possible in that environment because the motivations, incentives are just they're fundamentally different than in our environment.
00:28:57.200 --> 00:29:01.839
And again, I would call our environment US, Europe, Asia Pacific region.
00:29:02.160 --> 00:29:10.480
That's the global domain of where biomedical research and private sector interface as well, obviously largely focuses.
00:29:10.880 --> 00:29:39.279
And anyway, the point being that India is very much behind the curve with regard to access to technologies, access to therapies, importantly, cost considerations are you know a major impediment for sure, but there's a very concerted will that's shared across all domains, including government in India, to basically to work collaboratively to address this gap.
00:29:39.440 --> 00:30:37.920
But what I'm trying to suggest is that actually think that what's going to happen in India, because there is this tremendous collaborative energy and a very well-educated clinical and research community, and of world-class technology, and like certainly when it pertains to like sort of high-tech software engineering like type of technology, uh, absolutely world-class, where basically one can already see happening the creation of a massive data ecosystem that wraps around effectively the entire Indian cancer care environment that would allow us to then create multimodal data sets and interrogate them with artificial intelligence, machine learning tools in a way that we have been talking about in the West, but we will be forever impeded by our siloed behavior in terms of actually unleashing the potential power of those approaches.
00:30:38.160 --> 00:30:42.319
Simply put, that is what I think is already happening in India.
00:30:42.400 --> 00:30:57.119
And because of the speed at which it's happening and because of the speed which knowledge can be gained by applying such tools to multimodal data, I actually envision that India is going to be teaching us a fair amount about how we think about next generation precision medicine and oncology.
00:30:57.680 --> 00:31:02.880
100%, I and others who are enlisted in this effort are enlisted to help India.
00:31:03.039 --> 00:31:06.640
17% of the world's population, they're vastly underserved.
00:31:06.720 --> 00:31:11.039
They're not so far away from a capability perspective in terms of closing that gap.
00:31:11.119 --> 00:31:21.759
I'm very hopeful that some of the tools and approaches we develop there actually will be exportable and relevant to less well-developed economies as well that are have an even bigger care gap.
00:31:21.920 --> 00:31:29.839
To me, it's this type of collaborative team science that, and particularly public-private sector partnering in such domains.
00:31:30.000 --> 00:31:34.079
That is something that we are hard pressed to motivate in our research environment.
00:31:34.160 --> 00:31:42.720
And therefore, I think they're going to get ahead of us in a lot of discovery science that pertains to what I'm referring to as next generation, which is medicine.
00:31:42.960 --> 00:31:54.319
So you're just being speaking about AI, you're a man of deep science, but as I said as well, you read a lot of philosophy and put human interactions at the center of your life.
00:31:54.400 --> 00:32:01.759
So, how do you think we as humankind can ensure that humanity will remain in the world of AI?
00:32:02.000 --> 00:32:02.960
That's a great question.
00:32:03.119 --> 00:32:11.039
I mean, it's I think about that as much in the domain of science and medicine as I do and of all of all of human existence.
00:32:11.279 --> 00:32:58.240
And what I was touching on in terms of learning about myself, learning about my intellectual instincts, like kind of natural curiosities, what I learned about myself as a young, late adolescent, young adult, in terms of the joy that I derived in sort of thinking about problems in a visual fashion, entirely in my own head, and then trying to then imagine, kind of in an experimental framework, but also just when it comes to like navigating to higher order complexity problems, like trying to co-develop diagnostics and therapeutics to match a high-and-mandy population where we've got an emerging sense of therapeutic vulnerability, these higher order problem-solving endeavors, this is to me just incredibly joyful.
00:32:58.319 --> 00:33:02.000
Like this is intellectually such satisfying work.
00:33:02.319 --> 00:33:12.559
But I guess what I'm trying to emphasize is that what we will be able to preserve and what we should be focusing on preserving is a life of the mind, right?
00:33:12.640 --> 00:33:17.839
And that again, there's ramifications in science and medicine, but obviously much more broadly than that.
00:33:18.240 --> 00:33:36.960
I don't see a problem with having machines help us alleviate human suffering by closing some of the gaps that I've alluded to in terms of untranslated science to being able to kind of close the gap in remaining high-end need and truly suffering-inducing diseases like cancer and many others.
00:33:37.119 --> 00:33:51.599
The fact that that might change job descriptions or how it does one thinks about their education and training and the need to continually retrain and still be able to offer something creative, if you will, right, that human minds are uniquely good at.
00:33:51.759 --> 00:33:56.799
I mean, that like is just pattern recognition, yes, but ultimately this forward-looking creativity.
00:33:56.960 --> 00:34:01.279
It plays out in the arts and other domains, obviously in terms of philosophy as well.
00:34:01.599 --> 00:34:02.960
I'll conclude the answer with this.
00:34:03.119 --> 00:34:06.559
I mean, such a broad question, and one I think about an awful lot.
00:34:06.799 --> 00:34:21.039
You know, as I have meditated on how it is that the human mind has evolved and how it is currently sort of constructed to function, we are incredibly attuned and wired to interact with each other.
00:34:21.199 --> 00:34:29.920
We can read each other with just incredible depth and rapidity in terms of how it is that we interact with each other, interpret each other, obviously using language.
00:34:30.000 --> 00:34:32.880
You know, this is beyond just even verbal language that I'm alluding to.
00:34:33.119 --> 00:34:42.719
In that domain, that's going to remain for us as an area of human thriving that machines don't frankly tinker with.
00:34:42.880 --> 00:34:50.880
And so my broad answer to the question is let's focus on alleviating suffering, which includes alleviating poverty.
00:34:51.039 --> 00:35:03.840
Let's focus on elevating the life, the quality of life of everyone, and then allowing people to be in a position where they can use that life to live this joyful life of the mind.
00:35:04.079 --> 00:35:29.760
I mean, for me, as I just keep digging in philosophy, including ancient philosophy as well as modern, this is where my mind goes, which is I can think about these problems, I can listen to this 2000, this thinker from 2000 years ago for the rest of my days and derive great joy in terms of being able to understand across the divide of that span of time and cultural evolution, that kind of connection.
00:35:30.000 --> 00:35:35.840
What's to complain about in terms of what it is that AI and ML have to offer when we get to keep doing that?
00:35:36.159 --> 00:35:40.960
When we last talked, you told me that you started in the stone age of precision oncology.
00:35:41.440 --> 00:35:49.360
Knowing everything you know now, the challenges, the jail data, but also the incredible tools we have today.
00:35:49.599 --> 00:35:52.000
Would you sign up for this journey all over again?
00:35:52.320 --> 00:35:53.199
Yeah, for sure.
00:35:53.360 --> 00:35:57.119
I mean, I would need to educate myself a little differently, right?
00:35:57.280 --> 00:36:21.039
I mean, I've said this to my mentees for the longest time that like my analogy of taking courses in biostatistics in the early 2000s is now a computational education, computational biology, computational chemistry, even just mathematical modeling, you know, kind of more broadly, exactly for the purposes of being able to at least be conversant in how artificial intelligence models are built and operate.
00:36:21.199 --> 00:36:30.400
I think to be effective now in applying next generation tools to persistent problems, that's an example of sort of the vocabulary one's going to need.
00:36:30.559 --> 00:36:34.800
But the bottom line is it's enlisting in this human endeavor with others.
00:36:34.960 --> 00:36:44.000
So again, partnering with other people in the form of team science as I've alluded to, where the tools that need updating and evolve over time, that's not so important.
00:36:44.079 --> 00:36:54.320
It's really this it's the opportunity to work on important problems, jump out of bed in the morning, pinching yourself that like, wow, I really get I get to do this with my dayslash my life.
00:36:54.480 --> 00:36:55.440
That's the endeavor.
00:36:55.599 --> 00:36:58.480
I mean, as a young person navigating towards early professional life.
00:36:58.639 --> 00:37:04.559
And would I sign up for this same domain, updated as it is, fast forward in 2026?
00:37:04.800 --> 00:37:05.920
100% I would.
00:37:06.079 --> 00:37:13.039
I'm sure there's other domains as well where life of the mind similarly is allowed to run free similarly.
00:37:13.199 --> 00:37:16.960
But science and medicine are absolutely that domain now.
00:37:17.199 --> 00:37:20.480
So I would encourage any young person to jump in with both these.
00:37:20.960 --> 00:37:24.159
Keith, thank you so much for joining me on Decody today.
00:37:24.320 --> 00:37:35.840
But sounds out from this conversation, it's how cancer drug development is evolving from trial and data world to data-driven design and from isolated breakthroughs to systems at scale.
00:37:36.159 --> 00:37:49.280
You've shown us that real innovation in oncology isn't just about discovering new targets, building an ecosystem, but discovery, trial, evidence, and access actually work together for the fit-in-finity factor.
00:37:49.760 --> 00:37:58.000
Because better outcomes don't come from better science alone, they come from making the whole system ecosystem to freedom alone.
00:37:58.079 --> 00:38:06.719
If you enjoy this episode, subscribe on Spotify and follow wherever you respond and share it with someone who cares about your oncology heading.
00:38:06.960 --> 00:38:09.360
Until next time, thank you for listening.
00:00:01.280 --> 00:00:08.160
Welcome to Decodit, the podcast where we explore how data, technology, and human insights are reshaping the cancer worldwide.
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I'm Yochy Camblon, biology streamer and founder at Software Genetics.
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Today my guest is a too heavyweight in our feet, someone who combines both world-class science and category-defining innovation.
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Educated at Yellen John of King's Keith, it's director of clinical research at Mass General Hospital Cancer Center in Boston and professor of medicine at Harvard Medical School.
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Kit is also a leader who puts human interactions at the center of his life, is an avid reader of philosophy, and brings that depth of thought to everything he does.
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So you've been recently elected uh president of ACR, can you tell us what is the mission of ECR?
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Yeah, well, the very succinct mission of ACR is to prevent and cure cancer through research, education, communication, collaboration, advocacy, and funding.
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So a dense mission statement, but I think the way that I unpack that is that basically, on one hand, AECR is the single largest convener in the cancer field.
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And I think people naturally think of academic researchers as you know the central purpose, if you will, in terms of convening.
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But actually, it's not so simple, and I mean that in a good way, in that throughout my career, I've watched ACR expand its embrace to make sure that everyone in the cancer research community, certainly those in the private sector, a broad domain of public sector researchers, so including those who work in government and IHNCI specifically and other agencies.
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And I would say, even though it's got the term American in front of it, it's always had aspirations to be a global organization.
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And that's particularly been true over the past 10, 15 years or so, as you can point to multiple examples of how it is that convening function has spread worldwide.
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So I think the convening is a major piece, but it's not just convening to have conversations, it's really to kind of turn that into action ultimately in the form of research and education and communicating to the lay public our purpose and what you know, what it is that we're trying to do as a field, how we do it.
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And then importantly, you know, advocacy to our funders.
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Our biggest funders, as you well know, are the public.
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The U.S.
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in particular is the single largest contributor to the funding of biomedical research, so so taxpayers, and their representatives in Congress are a major audience, if you will, for AACR's outreach work and advocacy work.
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That's a lot of domains.
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And of course, there's a large family of journals, you know, for disseminating data and science as well as at conferences.
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So that convening function really does encompass a broad range in its own right.
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And do you have a sense of how many members are a part of AACR today?
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Yeah, yeah, it's about the current number, generally carry it around in my head, because of course it's been continually rising throughout my career.
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But the current number, I would say, is I want to say 40 some thousand.
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You know, I'm not going to get it entirely correctly.
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But I would emphasize that number, which is large, encompasses not only the domains that I tried to mention, kind of you know, US and abroad, which is certainly true.
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But it turns out the single largest pool of members are young scientists and young researchers broadly.
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And in their training phase slash earliest phases of their professional careers, and that's true in both the nonprofit and the for-profit environments.
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That's been very gratifying because I can speak to my own experience at the very dawn of my career, beginning to engage with ACR, not just at the annual meeting, but importantly, there was sort of a secondary meeting, so-called molecular targets meeting.
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We'll probably come back to that phrase, I'm sure.
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That was a meeting that was still in its relative infancy in the ACR sort of pantheon of conferences.
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I would point back to my first annual meeting and my first block your targets meeting in the same year, it's being absolutely eye-opening, transformative, direction setting for me.
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And so I can really underscore just how critical it is that ACR reach the youngest members of our field, who obviously have the most fragile careers, particularly in the current environment.
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Thank you, being a European for seeing American but global.
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In today's world, we really need this mindset.
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So you studied at Yale and John Hopkins, and I remember you telling me uh one day uh your happy place is understanding or was understanding what was happening in within cells.
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Why did you choose medicine and why in particular oncology?
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Yeah, so sort of a long journey, but you know, it spanned from well, 1989 is when I started my undergraduate years at Yale.
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So 89 to 93, and then medical school 93 to 97, just to kind of give you a framing of where was science at the time and medicine, as I was beginning to learn some of the tenets of the science behind medicine, undergraduate, and then of course in medical school, a little bit more the applied science.
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Basically, you know, what I would say is really for me, I had a very crude interest in science, biological sciences and human-relevant science, that drew me as an undergrad to pick up what was still a hard copy course of study book, very thick one at Yale.
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And I saw that they had a neuroscience major, which struck me as remarkable at the time.
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Again, we're talking 1989.
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I learned in those years that really Columbia, which was the real front runner in neuroscience at the time, and Yale were really the two places that committed to recruiting a faculty in neuroscience and really making a concerted effort to do research in that domain, obviously educate and build kind of a thematic program there.
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And I thought, well, the biology of the mind, I mean, that's just what could possibly be more fascinating than that.
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I've stuck with that fascination, I think, my whole adult life now.
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But in any case, neuroscience to me was just, again, like infinitely interesting.
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And as I was kind of going through the paces of the key disciplines for that major, of course, that included biology and chemistry, but it was really cell biology, which I got to in my sophomore year of college.
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That was the aha moment because it was really, you know, it was early days in terms of really understanding the repertoire of growth factors in particular, growth factor receptors, and then the intracellular circuitry that mediated signaling downstream of those receptors.
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Multiple discoveries had been made just in that signaling unit, if you will, as you well know, that then as we decoded the cancer genome as a field, we then learned how much cancer sort of takes over, co-ops exactly that signaling axis.
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So there's a lot of genetic evidence in support of that.
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And of course, that's the successes we've had in the domain of so-called targeted therapy have really hovered right in that space.
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I didn't see that coming so much.
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It was more that I was just utterly fascinated by kind of this increasing understanding of how cells are wired, and really just the incredible complexity of a single cell, you know, which could be a single cell organism, or obviously when you get to complex systems and ultimately animals, humans, even more so.
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So, yeah, so it was the convergence of neuroscience and cell biology at you know at the time that really just absolutely opened up my mind and my imagination.
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And then I'll just quickly jump to the second part, which was that when I got to medical school, I had an intent to pursue neuroscience slash neurology, you know, the clinical medicine, if you will, of neuroscience.
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And so I organized my early clinical exposures, my first rotation, to be in neurology, my very first one, which we were able to kind of customize the order at Johns Hopkins.
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And that was an incredibly disappointing period of weeks as I was seeking mentorship and conversation with faculty in neurology.
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I had already met some in neuroscience in my preclinical years at Hopkins.
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Neuroscience was quite strong there as well.
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And in any case, what became very clear to me is there was a huge gap, huge gap between neuroscience and neurology.
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Just like complete and total gap.
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And I thought, well, okay, it would be maybe exciting to try to bridge that gap.
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But it just it forced me, I was just paralyzed, I guess I would say, by that.
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And it forced me to consider like, might there be a domain of medicine where the gap could be like more easily imagined to be filled or narrowed.
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And it was still a hypothesis, to be clear, we're talking in the mid-90s now.
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It was still a hypothesis of mine that oncology could be that.
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But as I was learning more and more about what was happening in terms of applying, particularly in molecular biology, as you well know, those tools were developed and deployed in cancer first and foremost.
00:09:40.240 --> 00:09:58.559
And so seeing that, like in the scientific literature and even as I was being taught as a medical student, I thought, okay, well, if oncology is at the forefront of understanding cell circuitry and then again how cancers apparently rewire themselves, then that's a good candidate for tying all these strings that I just mentioned together.
00:09:58.799 --> 00:10:00.240
You had a lot of instinct.
00:10:00.399 --> 00:10:03.840
We will realize that as we cover the interview today.
00:10:04.159 --> 00:10:07.039
Around 2000, you specialize in melanoma.
00:10:07.279 --> 00:10:10.000
What is different about this cancer type?
00:10:10.559 --> 00:10:16.080
Well, I mean, honestly, I would say the word instinct is absolutely right in terms of how I navigated my early career.
00:10:16.159 --> 00:10:31.919
And I would say, as I then, you know, the year 2000 was specifically when I was entering oncology for the first time as a fellow, and then I was at the University of Pennsylvania for my fellowship and stayed there on the faculty in total for nine years, between two years of fellowship and seven years on faculty.
00:10:32.000 --> 00:10:39.519
And so following my instincts further in like my ver very first maneuvers in oncology, I'd say maybe three things about melanoma.
00:10:39.679 --> 00:10:44.000
One was that it was clearly a very high unmet need cancer.
00:10:44.080 --> 00:10:47.440
So essentially nothing to offer patients with advanced melanomas.
00:10:47.600 --> 00:10:51.919
Thankfully, melanomas commonly could be discovered early and surgically excised and cured.
00:10:52.080 --> 00:10:52.720
That's good.
00:10:52.960 --> 00:11:02.159
But once cells began to spread, lymph nodes or beyond, we just had effectively nothing, was my take as I was kind of running the field.
00:11:02.240 --> 00:11:05.440
So, and then of course it's a very aggressive cancer in its advanced form.
00:11:05.519 --> 00:11:12.799
So, like just as bad as glioblastoma, pancreatic cancer, and you know, are the the ones that we continue to fear the most.
00:11:12.879 --> 00:11:16.399
And so I thought that makes it an important problem to focus on.
00:11:16.480 --> 00:11:18.799
And it also felt like a wide open landscape.
00:11:18.960 --> 00:11:30.960
The other thing was that at Penn, and then the WISTAR Institute, which for those who've not been on the Penn campus, you might not know that the WISTAR Institute's a freestanding science institute right in the middle of the Penn campus.
00:11:31.039 --> 00:11:36.480
So it's like literally landlocked in the middle of the Penn campus and right across the street from the hospital, University of Pennsylvania.
00:11:36.720 --> 00:11:56.080
And so the Penn WISTAR programs in a combined fashion had really the most impressive melanoma research team that spanned basic science and even into sort of you know early clinical translation, not yet with therapeutics, but in terms of understanding determinants of the disease.
00:11:56.240 --> 00:12:12.399
And importantly, at the WISTAR, for anybody who's been around melanoma science, you would know that there was this proliferation of cell lines, melanoma cell lines, the WISTAR melanoma lines, that were created that created this kind of workhorse platform or system.
00:12:12.480 --> 00:12:15.440
And that existed, you know, in the years before I'm describing.
00:12:15.840 --> 00:12:31.120
And so I saw that commitment and the resources, and it seemed to me that, okay, well, then this is like the substrate is there to then really understand the wiring diagram, if you will, how it's altered in melanoma, and then how we can use that information to therapeutic benefit.
00:12:31.360 --> 00:12:45.840
Those decisions were made in 2000-2001, and then it was understood kind of a high level that the map kinase pathway, which is one of the key growth factor receptor RAS effector pathways, seemed to be particularly utilized in melanoma cell lines.
00:12:46.000 --> 00:12:49.840
That was the biochemistry, if you will, had been, you know, it was reasonably well described there.
00:12:50.000 --> 00:12:59.200
And so then in 2002, which was the transition between fellowship and faculty, BRAF mutations right in the heart of the MAP kinase pathway were described in cancer.
00:12:59.440 --> 00:13:06.799
8% of all cancer, but nearly half of all melanomas in a nature paper that was the cover article of that nature issue.
00:13:07.039 --> 00:13:08.320
And there you have it.
00:13:08.399 --> 00:13:12.720
That then absolutely set my course, at least in the melanoma side of my life.
00:13:12.799 --> 00:13:19.279
I also had a focus on kidney cancer based on the angiogenesis, increased understanding of angiogenesis at the forefront of kidney cancer.
00:13:19.360 --> 00:13:24.799
That's its own parallel story, but in the case, that was you asked a melanoma question, so there's that answer.
00:13:25.279 --> 00:13:27.039
People in the field know that.
00:13:27.120 --> 00:13:35.279
And before uh interviewing you today, uh in my last episode, I have interviewed Olivier Mikelin, who is as well in the field of melanonoma.
00:13:35.679 --> 00:13:38.559
So for the audience, Keith is a king in Melanoa.
00:13:38.720 --> 00:13:39.519
Let's be clear, right?
00:13:39.679 --> 00:13:42.000
He's very humble, but he's a king of Melanoa.
00:13:42.080 --> 00:13:48.639
It looks like interestingly you end up starting there because of the surroundings and the environment, which I found fascinating as well.
00:13:48.799 --> 00:13:52.480
Because I think maybe as we cover philosophy, you will tell us a bit about that.
00:13:52.720 --> 00:13:59.519
Not always easy to basically define our destiny and serendipity in our life is an element that is important as well.
00:13:59.679 --> 00:14:04.320
But looks like to me you always followed really your instinct in everything you've been doing.
00:14:04.639 --> 00:14:25.919
You're reminding me in that comment that another instinct that I developed right at that time, very dawn of my career, as I was learning about how clinical research was done in oncology in particular, was it seemed to me that there was a great degree of siloing that was happening in all domains of research, cancer research included.
00:14:26.080 --> 00:14:28.799
It struck me that that was the case in terms of laboratory research.
00:14:28.960 --> 00:14:34.399
You know, the people were kind of, in a way, secretive until the point of finally disseminating data.
00:14:34.480 --> 00:14:50.639
And that I've been when it came to clinical research, as I was kind of reading the literature, over and over again you would see what were at the time were referred to as clinical trials, kind of put that in quotes, of 14 patients, or seven patients or 20-something patients, treated at a single center with some new approach.
00:14:50.799 --> 00:14:56.960
And I just over and over again was just struggling to understand why 14 patients, what was behind that?
00:14:57.120 --> 00:15:11.440
And I actually, during the course of my fellowship, just two years, actually began taking the courses that were in biostatistics, applied epidemiology, if you will, because the Penn had an excellent program in that regard, so I could like sort of sign up for those courses.
00:15:11.519 --> 00:15:28.720
I even took a couple of those in my very beginning of my faculty life to try to become conversant in how one thinks about when it comes to laboratory data as well as clinical data, just how do you understand data distributions, how do you then kind of analyze, compare, and ultimately when it comes to clinical trials, think about things like sample size and power calculations and the like.
00:15:28.879 --> 00:15:38.639
And in any case, all of this fits under the siloed behavior I learned, basically, which was that centers basically took great pride in doing whatever they could do by themselves.
00:15:38.799 --> 00:16:09.440
And so it was again very right at the dawn of my career, and as you're saying, as I was instinctively being drawn into these kind of two subfields, but particularly melanoma at Penn, the intuition that I had was that the joy that's going to be had in a career in clinical or translational research, a term that I used later in my career to describe what I was trying to do at the beginning, is to really try to build and operationalize, participate in team science, which is not an invention in the time that we're talking about, right?
00:16:09.519 --> 00:16:14.720
Other disciplines already had team science, but not clinical medicine, not oncology.
00:16:15.200 --> 00:16:16.799
And it was the dawn of that time.
00:16:16.960 --> 00:16:18.159
I'm not saying I invented it.
00:16:18.240 --> 00:16:21.759
That's not my point, but it was early days, to say the least, of the concept.
00:16:21.919 --> 00:16:33.039
We're still learning how to really operate in a way that is maximally impactful for our human constituents, you know, audience, if you will, in team science.
00:16:33.360 --> 00:16:42.879
It's incredible to think about it 20, 25 years ago, no genomic sequencing, clinical trials with 14 uh patients in a single hospital.
00:16:43.039 --> 00:16:45.120
So obviously things have evolved a lot.
00:16:45.360 --> 00:16:54.960
And today uh there are a number of uh drugs or therapies that are, I would say, well known by the public in cancer, like chemotherapy, now immunotherapy.
00:16:55.200 --> 00:17:01.360
But you really distinguished in targeted therapies and small molecules, if I'm not mistaken.
00:17:01.679 --> 00:17:14.640
Can you explain us what were your discoveries and how, from those discoveries, you've been actively involved in making them an innovation and so making them basically commercialized?
00:17:15.039 --> 00:17:30.640
Okay, well, I guess I'll start with an academic, you know, an answer that's based on my academic work because it then dovetails into why I gained interest and then conviction and ultimately sort of passion for also doing this work in parallel in the private sector.
00:17:30.799 --> 00:17:44.640
I mentioned a moment ago that what was emerging, even before we had comprehensive genomic analysis of a single cancer cell or a collection of cancer cells in a tumor, there had been efforts or discoveries made around individual genes.
00:17:44.799 --> 00:17:48.319
The puzzle was getting pieced together without pieces connecting, right?
00:17:48.400 --> 00:17:58.160
So it was like pieces were floating, if you will, on the table, trying to understand, well, like, you know, again, fundamentally, what does the wiring diagram of cancer cells look like?
00:17:58.400 --> 00:18:01.599
Early attempts at sequencing individual genes.
00:18:01.759 --> 00:18:12.559
And then finally, in the in the 2000s, as you well know, was the era in which finally the technology and the ability to actually sequence an entire genome, healthy or diseased, you know, first became possible.
00:18:12.720 --> 00:18:15.680
Then the puzzle started to begin to fill in much more rapidly.
00:18:15.839 --> 00:18:31.839
And so it was very clear that molecules inside of cells, signaling molecules, the molecules that are responsible for telling a cell its circumstances, if you will, its you know, its environmental circumstances, so the conditions outside the cell, and then its internal conditions as well, including the integrity of the genome, for example.
00:18:32.000 --> 00:18:38.960
But understanding nutrient availability, oxygen tension, you know, in its immediate environment, I mean, these are some of the critical functions.
00:18:39.119 --> 00:18:50.640
And then knowing when a cell is in an appropriate state to actually divide or not, because you know, harsh conditions, the like, some of the functions that are co-opted by cancer through genetic alteration.
00:18:50.720 --> 00:19:00.160
So, in other words, so some of the molecules that are involved in transferring this information from outside to inside, if you will, become co-opted in cancer by virtue of somatic genetic alteration.
00:19:00.240 --> 00:19:04.559
So put it another way, cancer cells are selected for that have these features.
00:19:04.720 --> 00:19:08.480
It's not like there's a purposeful gain of any of these mutations.
00:19:08.559 --> 00:19:10.640
You know, by all accounts, it's accidental.
00:19:10.880 --> 00:19:23.519
But the ones that are selected for that confer advantage to cancer cells turn out to be clustered in this domain that I was alluding to before of sort of growth factors, growth factor receptors, and then the intracellular wiring.
00:19:23.680 --> 00:19:38.799
I emphasize the word intracellular because that's one needs for the foreseeable future still, one needs drug approaches that are small molecules that can get inside of cells to be able to interact with and counteract the effects of these genetic alterations.
00:19:38.960 --> 00:19:39.920
Not always directly.
00:19:40.079 --> 00:19:50.000
So, yes, the early versions of targeted therapy, including ones that I worked on, like BRAF targeted drugs, for example, were direct antagonists of a mutated oncogene.
00:19:50.240 --> 00:20:01.519
But even a simple one step down below in that signal transduction pathway, BRAF activates MEC, MEK, MEC inhibitors actually preceded BRAF inhibitors, as it turns out, in the early, early ones anyway.
00:20:01.680 --> 00:20:04.799
Where to direct their potential application was not well known.
00:20:04.960 --> 00:20:12.720
But as it when BRAF mutations were discovered, it then became more increasingly clear that, oh, well, that actually, since BRAF activates MEC, that might be the best use.
00:20:12.799 --> 00:20:16.559
And that's it, so that's an example of an indirect in a way approach, right?
00:20:16.799 --> 00:20:17.759
Still targeted therapy.
00:20:17.839 --> 00:20:20.319
If you're a specific molecular target in the case of MEC.
00:20:20.559 --> 00:20:27.359
But in any case, the point is that it's intercepting the function, the co-opted function of those intracellular molecules.
00:20:27.519 --> 00:20:29.920
That's what I comfortably refer to as targeted therapy.
00:20:30.160 --> 00:20:40.480
I should mention that we've had significant success in targeting cell surface molecules on cancer cells and then on immune cells, or PD1 being the most famous example, of course.
00:20:40.640 --> 00:20:46.319
And these are targeted therapies in the sense that they have a specific molecular target and they're engineered to engage with that molecular target.
00:20:46.400 --> 00:21:02.000
But cell surface targeting is something that can be done with large molecules very specifically, leveraging human immunobiology and kind of taking over that chemistry, if you will, create very selective targeted molecules that are accessible, in that case to large molecules.
00:21:02.079 --> 00:21:05.759
But small molecule addressable targets has been much more my passion, if you will.
00:21:05.920 --> 00:21:16.400
And also what I learned in my academic work that then informed my interest in doing this, being part of building teams and being a part of teams in the private sector, is that basically it is hard work, right?
00:21:16.480 --> 00:21:31.599
Which is to say that to create the perfect small molecule, nature doesn't give us the same toolbox as monoclonal antibody development with this kind of permutation of hypervariable domains and achieving selectivity and affinity with antibody type molecules.
00:21:31.759 --> 00:21:35.440
We don't have that same sort of fast-forward toolbox, if you will.
00:21:35.599 --> 00:21:39.680
And so small molecule medicinal chemistry has always been hard work.
00:21:39.759 --> 00:21:41.359
There have been substantial gains made.
00:21:41.440 --> 00:21:50.160
I mean, this is where computational chemistry, AI in the form of chemistry, is increasingly revolutionizing how this work is done.
00:21:50.319 --> 00:21:59.759
But what I'm getting at is simply that I was watching molecules come forward for human application, and as an academic clinical investigator, I was working with many companies bringing such programs forward.
00:22:00.480 --> 00:22:07.599
And over and over again, I was seeing that the output of years worth of effort seeming to be suboptimal.
00:22:07.680 --> 00:22:22.240
And I was just I always, as an academic, not a chemist, wondered is there a way to kind of set up the enterprise in the first place so that you make a more concerted effort to produce something as close as possible to the perfect molecule.
00:22:22.400 --> 00:22:27.119
And if you can't, maybe you shouldn't be moving that forward for a potential human application.
00:22:27.200 --> 00:22:32.640
And so, yeah, so it was but same concepts in terms of the types of targets that I had been working on academically.
00:22:32.720 --> 00:22:44.720
It's just the companies that I began to found and the programs within them consistently had nothing to do with melanoma or kidney cancer, partly because those targets, the relevant targets there had been largely attended to.
00:22:44.799 --> 00:22:46.559
That's maybe a significant part of it.
00:22:46.640 --> 00:22:53.119
But there was just such a broad landscape of work to be done in terms of expanding our repertoire of such drugs.
00:22:53.440 --> 00:23:13.759
Hard work, but Neurocay is extremely successful work with basically real innovations with products that are being in the market, are in the market, and with two biotech companies that are being acquired, as I mentioned in the intro, respectively Locto and Scorpio by Eli Lili for$8 billion and$2.5 billion.
00:23:14.079 --> 00:23:16.240
So this is very remarkable.
00:23:16.480 --> 00:23:20.319
You've also funded other companies like Strata Oncology.
00:23:20.480 --> 00:23:26.079
And what I would like to understand is from your experience, how different have been these journeys?
00:23:26.400 --> 00:23:26.720
Yeah.
00:23:26.960 --> 00:23:38.000
Well, Strata is one example I do like to point to because but I there's an another company that I've founded that has different, but I've learned similar lessons.
00:23:38.079 --> 00:23:39.440
I'll try to speak to those.
00:23:40.000 --> 00:23:50.400
Everything I've talked about seems to kind of point to the need for the drugs and like kind of, you know, create the perfect purpose-built drugs for a specific molecular task, if you will.
00:23:50.640 --> 00:23:55.200
But the reality is it's the application of them to exactly the right patients.
00:23:55.359 --> 00:24:03.440
You know, this is a kind of the full concept of precision medicine as it pertains to oncology, anyway, is really trying to match the therapy and the patient population optimally.
00:24:03.519 --> 00:24:14.480
And this doesn't just relate to mutated oncogenes matched with drugs that directly antagonize them and then hunting for mutations in those in tumors, and then having that be your basis.
00:24:14.640 --> 00:24:33.680
There are much broader concepts in terms of how to try to understand cancer biology, the complexity of cancer biology and therapeutic vulnerability of tumors in a way that I would say we already have enough evidence to say that like there are going to be multiple other ways of characterizing a population, you know, an optimal population for therapy.
00:24:33.839 --> 00:24:35.599
So simply put, this is diagnostics, right?
00:24:35.680 --> 00:24:37.519
But it's diagnostics for the purposes of therapy.
00:24:37.599 --> 00:24:51.200
So, and when you match up therapy ultimately with a diagnostic, and both the diagnostic and the therapy are approved together, so-called companion diagnostic, that's like the ultimate definition of the role of a diagnostic in this endeavor.
00:24:51.359 --> 00:25:07.519
And, you know, simply put, in US, Europe, and Asia Pacific, which is the major markets as we call them, this is not less of an issue anywhere else in the world, diagnostics are simply not valued in the quote unquote marketplace the way that doctors and patients value them.
00:25:07.680 --> 00:25:21.680
I often, using like highly semi-quantitative language, I should say, often summarize the state of science by this statement that basically diagnostics are basically half the problem, right, before us, and the other half are the therapeutics.
00:25:21.839 --> 00:25:31.200
And when I say half the problem, I mean half the value, if you will, right, to a doctor/slash patient is knowing, not like just that you have lung cancer versus colon cancer versus breast cancer.
00:25:31.359 --> 00:25:33.519
That's of limited utility, to be honest.
00:25:33.680 --> 00:25:38.400
It's knowing what makes that cancer tick and what therapy would be most impactful.
00:25:38.640 --> 00:25:40.480
That's a real diagnostic.
00:25:40.640 --> 00:25:42.079
And so it's incredibly valuable.
00:25:42.160 --> 00:25:51.920
And if you don't have that information, you're back in the year 2000, certainly, but you're relatively blind in terms of how to think about advising a patient, navigating them to the best potential outcome.
00:25:52.000 --> 00:25:55.279
So it's such a huge problem, that's the case, right?
00:25:55.359 --> 00:26:12.559
Because then how is it that you discover and develop the types of diagnostics I'm alluding to, including so-called complex diagnostics that involve more than one marker and sometimes more than one marker that are continuous variable features to them in terms of not having a discrete kind of black and white result?
00:26:13.200 --> 00:26:21.920
You know, basically the idea of developing such tests would not impress a lab scientist or, for that matter, a statistician, or a pathologist.
00:26:22.160 --> 00:26:25.680
This is not rocket science, but it turns out to be a huge challenge.
00:26:25.839 --> 00:26:44.480
And that's like maybe the point that I'm trying to emphasize here in terms of the lesson learned, in terms of how to try to describe and create that value in a way that motivates the transition from public sector to private sector research and the scaling ultimately that's needed to deliver any product to the marketplace.
00:26:44.640 --> 00:26:52.559
There are diagnostics, admittedly, that actually can be operationalized in a fairly broad way without, you know, large-scale commercial investment.
00:26:52.880 --> 00:26:54.640
But in general, that's a major challenge.
00:26:54.799 --> 00:27:00.160
And then the therapies to date has never been possible without ultimately investment and engagement in the private sector.
00:27:00.319 --> 00:27:06.880
So trying to balance, if you will, these two domains, that has been well, it's not just a challenge.
00:27:06.960 --> 00:27:19.599
I would say it's like remains for me a huge area of conviction and I would dare say passion to really help unlock the value that diagnostics have in what as it pertains to therapy.
00:27:19.759 --> 00:27:27.279
And so that's and Strata's been on that journey now nine years and really beginning to realize the therapy opportunity.
00:27:27.440 --> 00:27:34.319
But I've over and over again, I've been sort of obsessed with this problem because it creates a real inefficiency.
00:27:34.400 --> 00:27:48.720
There's just a lot of science that isn't being translated to medicine in both the diagnostic domain, even more in the diagnostic domain, frankly, and then similarly in the therapeutic domain, because there's so much unmet need that remains in cancer, like we can't really tolerate that quote-unquote inefficiency.
00:27:49.119 --> 00:27:49.759
Absolutely.
00:27:50.000 --> 00:27:55.359
Well, it speaks to me, and you and I and others will work to change it to make a difference.
00:27:55.599 --> 00:28:05.039
So, Keith, we get to be introduced by a common friend, and now I would like to speak about your work in India with Scarakino Selfcare.
00:28:05.119 --> 00:28:10.240
You said that we're not just there to help India, we are there to learn from them.
00:28:10.480 --> 00:28:14.640
Why is India so critical for the future of oncology around the world?
00:28:14.960 --> 00:28:57.119
There's so many parts to the answer to that question, but the simple component to pull forward from what I said a little while ago is that I am convinced now for five or almost six years now that I've been partnering with, collaborating with, engaging with the leadership of Carkonos, and then through them, a large swath of the Indian cancer care and research community in oncology, of course, that basically team science is and will be more possible in that environment because the motivations, incentives are just they're fundamentally different than in our environment.
00:28:57.200 --> 00:29:01.839
And again, I would call our environment US, Europe, Asia Pacific region.
00:29:02.160 --> 00:29:10.480
That's the global domain of where biomedical research and private sector interface as well, obviously largely focuses.
00:29:10.880 --> 00:29:39.279
And anyway, the point being that India is very much behind the curve with regard to access to technologies, access to therapies, importantly, cost considerations are you know a major impediment for sure, but there's a very concerted will that's shared across all domains, including government in India, to basically to work collaboratively to address this gap.
00:29:39.440 --> 00:30:37.920
But what I'm trying to suggest is that actually think that what's going to happen in India, because there is this tremendous collaborative energy and a very well-educated clinical and research community, and of world-class technology, and like certainly when it pertains to like sort of high-tech software engineering like type of technology, uh, absolutely world-class, where basically one can already see happening the creation of a massive data ecosystem that wraps around effectively the entire Indian cancer care environment that would allow us to then create multimodal data sets and interrogate them with artificial intelligence, machine learning tools in a way that we have been talking about in the West, but we will be forever impeded by our siloed behavior in terms of actually unleashing the potential power of those approaches.
00:30:38.160 --> 00:30:42.319
Simply put, that is what I think is already happening in India.
00:30:42.400 --> 00:30:57.119
And because of the speed at which it's happening and because of the speed which knowledge can be gained by applying such tools to multimodal data, I actually envision that India is going to be teaching us a fair amount about how we think about next generation precision medicine and oncology.
00:30:57.680 --> 00:31:02.880
100%, I and others who are enlisted in this effort are enlisted to help India.
00:31:03.039 --> 00:31:06.640
17% of the world's population, they're vastly underserved.
00:31:06.720 --> 00:31:11.039
They're not so far away from a capability perspective in terms of closing that gap.
00:31:11.119 --> 00:31:21.759
I'm very hopeful that some of the tools and approaches we develop there actually will be exportable and relevant to less well-developed economies as well that are have an even bigger care gap.
00:31:21.920 --> 00:31:29.839
To me, it's this type of collaborative team science that, and particularly public-private sector partnering in such domains.
00:31:30.000 --> 00:31:34.079
That is something that we are hard pressed to motivate in our research environment.
00:31:34.160 --> 00:31:42.720
And therefore, I think they're going to get ahead of us in a lot of discovery science that pertains to what I'm referring to as next generation, which is medicine.
00:31:42.960 --> 00:31:54.319
So you're just being speaking about AI, you're a man of deep science, but as I said as well, you read a lot of philosophy and put human interactions at the center of your life.
00:31:54.400 --> 00:32:01.759
So, how do you think we as humankind can ensure that humanity will remain in the world of AI?
00:32:02.000 --> 00:32:02.960
That's a great question.
00:32:03.119 --> 00:32:11.039
I mean, it's I think about that as much in the domain of science and medicine as I do and of all of all of human existence.
00:32:11.279 --> 00:32:58.240
And what I was touching on in terms of learning about myself, learning about my intellectual instincts, like kind of natural curiosities, what I learned about myself as a young, late adolescent, young adult, in terms of the joy that I derived in sort of thinking about problems in a visual fashion, entirely in my own head, and then trying to then imagine, kind of in an experimental framework, but also just when it comes to like navigating to higher order complexity problems, like trying to co-develop diagnostics and therapeutics to match a high-and-mandy population where we've got an emerging sense of therapeutic vulnerability, these higher order problem-solving endeavors, this is to me just incredibly joyful.
00:32:58.319 --> 00:33:02.000
Like this is intellectually such satisfying work.
00:33:02.319 --> 00:33:12.559
But I guess what I'm trying to emphasize is that what we will be able to preserve and what we should be focusing on preserving is a life of the mind, right?
00:33:12.640 --> 00:33:17.839
And that again, there's ramifications in science and medicine, but obviously much more broadly than that.
00:33:18.240 --> 00:33:36.960
I don't see a problem with having machines help us alleviate human suffering by closing some of the gaps that I've alluded to in terms of untranslated science to being able to kind of close the gap in remaining high-end need and truly suffering-inducing diseases like cancer and many others.
00:33:37.119 --> 00:33:51.599
The fact that that might change job descriptions or how it does one thinks about their education and training and the need to continually retrain and still be able to offer something creative, if you will, right, that human minds are uniquely good at.
00:33:51.759 --> 00:33:56.799
I mean, that like is just pattern recognition, yes, but ultimately this forward-looking creativity.
00:33:56.960 --> 00:34:01.279
It plays out in the arts and other domains, obviously in terms of philosophy as well.
00:34:01.599 --> 00:34:02.960
I'll conclude the answer with this.
00:34:03.119 --> 00:34:06.559
I mean, such a broad question, and one I think about an awful lot.
00:34:06.799 --> 00:34:21.039
You know, as I have meditated on how it is that the human mind has evolved and how it is currently sort of constructed to function, we are incredibly attuned and wired to interact with each other.
00:34:21.199 --> 00:34:29.920
We can read each other with just incredible depth and rapidity in terms of how it is that we interact with each other, interpret each other, obviously using language.
00:34:30.000 --> 00:34:32.880
You know, this is beyond just even verbal language that I'm alluding to.
00:34:33.119 --> 00:34:42.719
In that domain, that's going to remain for us as an area of human thriving that machines don't frankly tinker with.
00:34:42.880 --> 00:34:50.880
And so my broad answer to the question is let's focus on alleviating suffering, which includes alleviating poverty.
00:34:51.039 --> 00:35:03.840
Let's focus on elevating the life, the quality of life of everyone, and then allowing people to be in a position where they can use that life to live this joyful life of the mind.
00:35:04.079 --> 00:35:29.760
I mean, for me, as I just keep digging in philosophy, including ancient philosophy as well as modern, this is where my mind goes, which is I can think about these problems, I can listen to this 2000, this thinker from 2000 years ago for the rest of my days and derive great joy in terms of being able to understand across the divide of that span of time and cultural evolution, that kind of connection.
00:35:30.000 --> 00:35:35.840
What's to complain about in terms of what it is that AI and ML have to offer when we get to keep doing that?
00:35:36.159 --> 00:35:40.960
When we last talked, you told me that you started in the stone age of precision oncology.
00:35:41.440 --> 00:35:49.360
Knowing everything you know now, the challenges, the jail data, but also the incredible tools we have today.
00:35:49.599 --> 00:35:52.000
Would you sign up for this journey all over again?
00:35:52.320 --> 00:35:53.199
Yeah, for sure.
00:35:53.360 --> 00:35:57.119
I mean, I would need to educate myself a little differently, right?
00:35:57.280 --> 00:36:21.039
I mean, I've said this to my mentees for the longest time that like my analogy of taking courses in biostatistics in the early 2000s is now a computational education, computational biology, computational chemistry, even just mathematical modeling, you know, kind of more broadly, exactly for the purposes of being able to at least be conversant in how artificial intelligence models are built and operate.
00:36:21.199 --> 00:36:30.400
I think to be effective now in applying next generation tools to persistent problems, that's an example of sort of the vocabulary one's going to need.
00:36:30.559 --> 00:36:34.800
But the bottom line is it's enlisting in this human endeavor with others.
00:36:34.960 --> 00:36:44.000
So again, partnering with other people in the form of team science as I've alluded to, where the tools that need updating and evolve over time, that's not so important.
00:36:44.079 --> 00:36:54.320
It's really this it's the opportunity to work on important problems, jump out of bed in the morning, pinching yourself that like, wow, I really get I get to do this with my dayslash my life.
00:36:54.480 --> 00:36:55.440
That's the endeavor.
00:36:55.599 --> 00:36:58.480
I mean, as a young person navigating towards early professional life.
00:36:58.639 --> 00:37:04.559
And would I sign up for this same domain, updated as it is, fast forward in 2026?
00:37:04.800 --> 00:37:05.920
100% I would.
00:37:06.079 --> 00:37:13.039
I'm sure there's other domains as well where life of the mind similarly is allowed to run free similarly.
00:37:13.199 --> 00:37:16.960
But science and medicine are absolutely that domain now.
00:37:17.199 --> 00:37:20.480
So I would encourage any young person to jump in with both these.
00:37:20.960 --> 00:37:24.159
Keith, thank you so much for joining me on Decody today.
00:37:24.320 --> 00:37:35.840
But sounds out from this conversation, it's how cancer drug development is evolving from trial and data world to data-driven design and from isolated breakthroughs to systems at scale.
00:37:36.159 --> 00:37:49.280
You've shown us that real innovation in oncology isn't just about discovering new targets, building an ecosystem, but discovery, trial, evidence, and access actually work together for the fit-in-finity factor.
00:37:49.760 --> 00:37:58.000
Because better outcomes don't come from better science alone, they come from making the whole system ecosystem to freedom alone.
00:37:58.079 --> 00:38:06.719
If you enjoy this episode, subscribe on Spotify and follow wherever you respond and share it with someone who cares about your oncology heading.
00:38:06.960 --> 00:38:09.360
Until next time, thank you for listening.
