Hey everyone, welcome to the Drive Podcast. I'm your host Peter Attia. >> [music] >> Siddhartha, awesome to see you um as always um and it's hard to believe it's been 15 years since uh Emperor of All Maladies. I remember reading it as soon as it came out and that I think makes me feel a little old that 15 years has gone by so quickly. Um if we take a step back, what would you say is true today in the field of oncology that was not true
or maybe phrased another way, what do we know today about cancer that we did not know when you published this uh remarkable book? >> Well, Peter, first of all, thank you for having me. It's been a pleasure throughout. We've spoken in the middle and um learned more about cancer in the middle. Um let me divide it very simply into three large sections. And ask the question, what do we know more about about early diagnosis or um prevention? Uh early detection and treatment. So, three broad categories.
So, prevention. What do we know about prevention? Well, uh good news and bad news. The good news is that um we know a a lot more about how um cancer can arise not just out of being exposed to the traditional carcinogens, the cancer-causing agents that you and I know very much about, you know, x-rays and benzene and and you know, tobacco and >> tobacco etc. Um So, we now know that there is almost certainly inflammatory states that cause cancer. We know a lot about the nature
of these inflammatory states. Um we know that it's not that that there is a kind of um uh a gun and trigger effect, the gun being the inflammatory states, the trigger being um the mutations that cause cancer um or a mix of some some some states like that. Um the bad news is that we are still early in that story. Um in other words, I can't look at a person, a patient, my own patient, and say to them, "Oh my goodness, I know that you're in
an inflammatory state. You have that inflammatory state, which is going to be pro-carcinogenic." Um I don't have a biomarker in the same sense that I have a biomarker for, let's say, cardiovascular disease being hypertension or high cholesterol levels. I don't have I don't have a high cholesterol for cancer. Um Almost certainly we will find it. We will find that that high cholesterol for cancer, and we will find people who um are at risk and um and put them in uh put them in clinical trials to
follow them and figure out how we can prevent cancer. The second piece of good news in prevention, we're still in prevention, is that we now know much more about genetics. We know much more about people who are at higher risk. It's not, for instance, in breast cancer, we now know it's not just BRCA1 carrying patients, mutation-carrying patients, or BRCA2 mutation-carrying patients, but actually many other genes that are involved in breast and prostate and other cancers. In fact, in some cases, it's not one a single gene,
it's a multiplicity of genes. We know what they are. So, I could actually quantify really quantify by genetic sequencing your relative risk of having some cancers, breast, ovarian, and more and more and more and more some other cancers. Um We know almost with great certainty that obesity is a risk factor for certain kinds of cancers, endometrial being one of them. Um we know for sure that I'm I'm not speaking in in the realm of prevention again. We know almost certainly that most cancers are in uh
mortality across the population in the United States are going down. So, we are actually making progress. The idea that cancer is exploding and and killing everyone in the population is nonsense. But, but, there's a very important but, we know that colorectal cancer is on the rise in young people. We know that the gains that we had made over the last 20 years in breast cancer have started to plateau. So, something is going on. Um another way of saying that is that, you know, breast cancer mortality
was coming down year after year after year after year, and now it's beginning to sort of not come down as rapidly as we had hoped um in the years before. We know that a couple of cancers are on the rise. I told you about colorectal cancer in younger people. Um uh we know that uh esophageal can cancer esophagus is on the rise. Um we know that cancer of um a couple of other um cancers are on the rise in younger people. We don't know why. In
In the case of endometrial cancer in women, it's most likely because of increasing rates of obesity. Um and um but the rest of it is a is a mystery and the colorectal cancer business is a complete mystery. The esophageal cancer is probably because of alcohol uh and tobacco mixed together um but that's that's half a mystery. >> All right, especially given that that the trend appears that people are actually drinking less today. Although maybe that hasn't caught up to where the cancers actually arise. Yeah, there's
there's probably a big lag. >> There's always a 5 to 10-year lag. So, that's what we know about prevention. Big big deals we can talk about each of those individual pieces that I talked that I spoke about separately and each each one of them is a full conversation. All right, let's talk about early detection. Um we know that there are tests in patients. For instance, blood tests um that will enable uh the early detection of some cancers. We know that if you just at least for
now, we know that if you just take those tests and unloosen them on a general population. So, without selecting for high-risk people, without selecting for people who have had cancer before um we know that those tests are probably not going to work. They're going to lead to false positives, they're going to lead to extra biopsies and extra um you know, tests more tests following tests etc. But those are not going to decrease um are unlikely to decrease the um mortality from cancers. Um that's the bad
news. The good news is that probably if you had used those tests or if you were to use those tests in patients who pre- who are at high risk, for instance, those with carrying genetic mutations that put them at high risk, or those who have had previous cancers, um those tests may actually yield very important results. Um and we know also that um we have we're inventing a whole host of new ways of detecting cancer early using imaging technologies that are new. Whether they will work
or not, I don't know. So, one example of this is what's called more continuous rather than discrete imaging. So, by discrete imaging, I mean you know, used to people used to go and have their mammograms once a year. By people, I mean women above a certain age. They used to go and have their mammogram mammograms once a year. The mammograms themselves were of relatively poor quality. The once a year was a discrete test. We know that potentially having mechanisms to to follow over time more less
in in episodic ways, but more in continuous ways is likely um to bring a different set of answers. So, that's in the realm of early detection. In the realm of treatment, there's lots of things. Um we know since the last 15 years that immunotherapy, activating the immune system, um is extremely productive against some cancers. Um just to give you one example, I had sort of written a personal note to myself saying that this was 15 years ago, that there would be that I would not live
to a time where I would where there would be a medicine that would potentially extend the lifetime of a um smoker with lung cancer. That's not true anymore. Immunotherapy is now known to extend the lifespan of smokers with lung cancer if given to the right population of people. Um these are drugs that unveil or unleash the immune system to recognize cancers as foreign foreign cells and enable treatment of cancers. Um we know that in some cases um activated T cells, genetically engineered T cells, so-called CAR-T
cells can cure liquid cancers such as leukemias, blood cancers such as leukemias and lymphomas. And by cure I mean not make you live 5 years, but make you live 15 years or 20 years after your diagnosis. There are people walking around on Earth that have had CAR-T cell therapy for re- un- um responsive leukemias and lymphomas who are 15 years post-therapy. Um we know, and we were talking about a little bit before, that early that early science, that metabolic therapies against cancer, so using the idea
that cancer metabolically is different from normal cells um is a potential way to treat cancers. Um we know that there are um second-line and third-line therapies in certain cancers that will truly um expand your lifespan. So, two good examples are the use of um a drug um called Enhertu in breast cancer which works even after um the um even after many, many lines of therapies have stopped working. Um in myeloma, there are three or four new medicines. Um in lymphoma, there are two or three new
medicines. So so um in prostate cancer, there's two or three new medicines. Um and in and we know that there are now treatments surgical treatments for many of these breast, prostate, and other cancers which are much more um um sparing of normal tissue so that you can regain functionality more easily and better and yet remove as much cancer as possible. And finally uh in treatment um we know that there are radical new set of medicines on the horizon. You've seen um cases of pancreatic cancer treated
with the um the Daxon Rasib a new medicine against the one of those killer genes called Ras. Um it's extended the life of patients. Um we've seen videos of these patients on television. They're now living with pancreatic cancer, you know, months if not years after they're uh of the cancer has spread which would have been impossible um 10 15 years ago. So immunotherapy, novel um biologics, and novel um small molecule therapies are they are here. They're on the horizon. They're not just they're not just, you
know, making people live longer by 1 month or 2 month 2 months. They're sometimes actually making people live longer than, you know, into the into the 5-year range. Um and extending survival meaningfully. So um that's kind of my my that's kind of what the landscape looks like right now. >> I mean, so that's fantastic because everything you've covered there is sort of where we want to go with one exception. We didn't touch on AI which we will touch on and I'm sure but um let's go
back and look at something that we learned in the last 15 years uh courtesy of of Brett Vogelstein and his colleagues. So, in 2015 they published a paper in Science. Both you and I remember this very well. The entire oncology community was sort of divided a little bit on this. In 2017, they came out with another paper that kind of revisited it, but the the gist of the paper was that if you looked at all cancers by tissue type, if I'm remembering the math correctly, said
about 5% of the mutations, because again, cancer is a genetic disease, it is driven by genetic mutations, about 5% of these mutations were hereditary. Um and you already alluded to some of these, you know, her2neu and uh pardon me brca1 etc. etc. But but there there are many others that we don't necessarily talk about as frequently. On the order of 30% of the mutations appeared to arise from environmental triggers or carcinogens. So, when you smoke, you're increasing your risk of a mutation. When you're exposed to
lots of UV light, you're going to have a mutation etc. But the part that I think really was sobering for us was that 2/3, 66-ish percent of mutations were purely random. And this is where the idea that stuck from Vogelstein's paper was this idea of cancer as a bad luck disease, right? You can do everything right, Sid, and you more than anybody else knows this. You're a practicing oncologist. You can have the healthiest person who's done everything correct, and yet this random mutation occurs, and that
may explain up to 2/3 of the mutations we see. And we don't see that in cardiovascular disease, right? We don't see the perfect pillar of health who's done everything right, who's managed cholesterol and blood pressure etc. etc. So, what how do we make sense of this beyond the observation? In other words, how do we take that observation and and do something about it? >> So, first of all, let's let's really take the observation seriously. Let's take it totally seriously. 5% of mutations heredi- hereditary Um that
number is most likely no longer 5%. >> Mhm. >> Um um in at Vogel's during Vogel's team >> that last paper, sorry to interrupt, was was actually 9 years ago and I'm not I don't know if there's been a follow-up, so yes, yeah. >> So, so, you know, Bert Vogelstein is a close friend, one of the great scientists of the world, one of the greatest cancer scientists of the world. But obviously the field changes, the field moves. >> Yep. >> The five that 5% is going
to change. So, and what how is it going to change? Well, that 5% was a was usually focused on single gene mutations. So BRCA1 BRCA2 you know, P53, I I can rattle them off. Your your your listeners won't know them. They'll know BRCA1, they'll know BRCA2. Um so, just to give you an let's just take take breast cancer. So, hereditary breast cancer, where you know that there's a hereditary uh breast cancer, so you know that your your your mother had it, your mother's sister had it,
your aunt had it, your grandmother had breast cancer, and you sequence your genome and there's no BRCA1 mutation, there's no BRCA2 mutation. So now we've added many more mutations, single point single mutations in single genes, um that increase risk. But we still we know that about that that only covers we're now talking only about hereditary breast cancer. If you take all the single genes that we know increase the risk of hereditary breast cancer, that still only covers um a fraction, about a third, maybe a fourth,
of hereditary breast cancer. So, the question is what's the other mister So, again, remember we're talking about just hereditary breast cancer. So, what's the what's the other whatever percentage? And the answer now is that that other fraction in hereditary breast cancer, not breast cancer caused by random mutations, or by exposure to environments, etc., is um a combination of multiple genes, not one, not two, but probably hundreds of genes that are pushing your risk by a small percentage. So, the way I try to describe this to
my patients is I say, "In hereditary cancers, there are two kinds of mutations. I call them shove mutations and nudge mutations. BRCA1 is a shove mutation. It shoves you off the cliff and puts you at a ninefold, tenfold risk above the normal population to get hereditary breast cancer at an early age. BRCA2 is a shove mutation. It shoves you off the cliff. But there are also patients who have nudge mutations. They're not shoved off the cliff. They're just sort of given little nudges. Each of these
changes gives you a little nudge. And of course, there's environmental factors that come into it, but they give you a little nudge along with the environmental factors until you fall off the cliff and cliff and get cancers. That's hereditary. So, that number 5% is no longer the number 5%. Second big major point. Cancer as a disease of bad luck. Yes, it is still a disease of bad luck. But it's not a disease of bad luck in the same sense that we imagined it 10 15 years
ago when wrote his paper. Yes, there's bad luck involved. But it's also bad luck plus bets but plus something else bad that's going on in the body. >> Mhm. Such as inflammation. >> Such as inflammation and the best example of that is recent papers that we've that have come out from many laboratories. Um I specifically want to point out Charlie Swanton's lab in in in in the UK. But many laboratories that show that you can have cancer that that you and I right now if we
were to biopsy every cell in our lung if we magically had the capacity to biopsy every cell in our lung we would find almost certainly [snorts] and I don't smoke, you don't smoke. Um we would almost certainly find cells that genetically speaking would bear all the hallmarks of the of genetic mutations of cancer. But you and I don't have cancer. So, what's what's with that story? The answer to that story takes us back to the 1950s and this is a very important idea that I often
uh emphasize. It's something that's gone out of the scientific literature. It's sometimes very important Peter to publish an observation without explaining explaining it. You know, I'm I'm going to I'm going to take a philosophical side here. I'll come back to the real point in a second. I'm going to take a philosophical side here. Our scientific um publication strategy has become a form of magic storytelling. By magic storytelling, I mean you have to tell a full story about what you what you observe, what the what the
mechanism is, what the explanation is. And how it's working at a molecular level. That was not the case in the 1950s, in the 1950s, 1940s, 19 60s, you could say, "Listen, I don't know the answer. I don't know why this is happening. I can just tell you it's happening. You go figure it out 5 years later when you have the technology to figure it out." I can't emphasize how important that is. You know, we have writers. I'm a writer myself, you know this. We have writers
like Oliver Sacks whose legacy has depended on the on the idea that they made very thorough, important observations. Um William Osler, the perhaps the greatest of physician scientists said, "Observe the patient first, explain later." Um so, this is a philosophical observation that I want to make. So, let's go back. So, in the 1950s, um a team of of doctors from of scientists from Oxford published a paper which remained unexplained. And this was it was a very simple paper. They um took a carcinogen. Um let's call
it whatever we want to call Let's call it DMBA. It's a chemical carcinogen. Makes mutations. Should cause cancer. And they painted it on the back of a mouse. And sure enough, a few cancers grew. And then they went back and get this. This is a super interesting story. Then they went back and said, "Okay, fine. Just a few cancers grew. But we painted it on the entire back of the mouse." Why was it like four like four cancers grew? So they then after painting the back
with this chemical carcinogen, they added an inflammatory agent, croton oil. Something that you can buy from Amazon. And all of a sudden 80 cancers grew. Years and years later in experiments uh by um Allan Balmain in UCSF um they genetically engineered a mouse to get cancer. So this mouse was genetically predisposed, just like a human being with BRCA1, maybe pre- predisposed to get a cancer. They made a a a So and sure enough when the mouse grew up it had four or five or six cancers
where you know, obviously where the gene was making cancer cells. And here's here's the interesting that happened. They made a little slit or a cut, an inflammatory wound in in that in the mouse. And again, no- nothing very impressive happened. The next thing they did is very interesting. This is by chance. And this is why science is very important. It's very important do experiments. It's more than report experiments. And it's important report just observations. They stapled where the wounds were. And wherever there were staples, of
course, you got an inflammatory response. And exactly exactly the place where they put the staples outgrew massive cancers. Now, what does that tell us? It tells us something very important. It tells us that this whole idea that that there's bad luck. Yes, there's bad luck. Let's say you when I biopsy your lungs, I get you know, you have several million, maybe a billion cells in your lung. I'll find let's say 2,000 cells that have already obtained or carrying cancer mutations. Let's say I biopsy my cells
and I find that that number is not 2,000 but 750. You have some bad luck, I have some bad luck. Your bad luck is 2,000, my bad luck is 750. Those cells with mutations, most likely, and I'm just saying most likely because we don't know, most likely arose because you and I had bad luck and when we when the cells grew, they make they made mistakes. They caught up, they made genes that are cancerous and so forth. But those cells are dormant. I call them sleeper
cells. The real bad luck that happened between you and me is that you had some kind of inflammatory response. And it's not when we say inflammation, it's like using the word motion before Newton or using word energy before Newton or using the word atom before Dalton. When you say the word inflammation, you know, it's a loose word. It's not like, oh, you know, I had a fever, I had a cold, I had a virus, I There's something very particular about the inflammation. We can talk a
little bit more about it. You had a particular kind of inflammatory response in your body and your 2,000 in one of your 2,000 cells which already was carrying a mutation turned from a sleeper cell, a dormant cell, a uh doing nothing cell into a tumor, into an active cancer cell. And from there on you develop cancer. I was lucky. I didn't have that inflammatory stimulation. My 750 cells were remained dormant, remained quiescent, remained asleep. And I don't have cancer. So, now let's go back to Bert
Vogelstein. That the number 5% is not 5% anymore. It's a much larger number than 5%, and we can detect that larger number now with greater and greater accuracy. Not in all cases, not in all cancers. Breast cancer, yes. Ovarian cancer, yes. Uh maybe in some other cancers more and more. And that number and that number unlucky the cancer of of bad luck is no longer cancer of bad luck. Very, very soon, I hope. I can't give you a date. Very soon, I will be able to
tell that that that I will be able to tell you that you have bad luck and I don't have bad luck. And it's not bad luck, it's just the nature of our inflammatory response. So, this whole idea of dividing up a creating a typology of cancer. Every typology of cancer evolves. It's evolved since the beginning of any typology of cancer. And and so those numbers are all going to change and going to change in a way that makes is is sensible. So, Bert is not wrong.
He is just early. >> And and maybe there's another point to just double click on this, Sid, which is the the way that that paper described it suggested that these are discrete types of mutations, but as you pointed out, and I love I'm going to borrow this, by the way, so thank you. This idea of shove versus nudge mutations, they can both arise as hereditary mutations, but here's where the environment overlaps. So, instead of saying that hereditary mutations are a discrete bucket, environmentally acquired or triggered
mutations are a discrete bucket, it could be no, you if you have a bunch of shove mutations, or pardon me, a bunch of nudge mutations that are acquired, you know, or pardon me, that are passed through the germline, um the environmental mutation is really a push or a gentle nudge. >> Yeah, exactly. So, in other words, these are not discrete, they're going to start to overlap a little bit more. >> Absolutely. And and this is super important because this is not just theoret- theorizing, right? This
is these have very practical implications for patients. Because if I could figure out, you know, where you are in the nudge versus shove cascade, I could tell you I could quantify your risk. I could tell you what your risk is, and you should be maybe in a clinical study. You should maybe be in a a a a prevention program or or a trial prevention program. So, these are not just sort of abstractions, these are realities. Um the the last thing we didn't touch upon is the
metabolisms. We talked about inflammation. Yeah. We didn't talk about metabolism, so metabolism is becoming very important. We know I mean, just to give you the most obvious example, you know, we know that there's a a a a enormous risk between uh and we don't know why exactly, but an enormous risk that connects, for instance, endometrial cancer with obesity. Um and endometrial cancer is one of those cancers which is either plateauing or maybe on the rise in in women, maybe because of obesity, but um what's the
connection? Is it through inflammation again? Is it because there is a hormone that's making, you know, these sleeper cells alive? Um obesity itself is unlikely, I would say, to cause mutations. It's not a obesity is not a mutagen, but obesity is a is a is a physiological state. And those physiological states, of course, are important because we've already seen in the case of inflammation, a physiological state that can take a dormant cell and convert it into a cancer into a cancerous or cancerous tumor cell. >>
And Sid, how much of it do you think is obesity per se with its adiposity versus insulin resistance, hyperinsulinemia, and the sort of metabolic milieu that accompanies obesity often, but not always? >> So, in humans, we don't know, but um about 5 years ago, Lou Cantley and I wrote a paper in Nature, which actually you were um you inspired in some ways. Um where we know that in at least in animal models, you don't need to be obese um in order to unleash um or at
least in order to have a cascade that sustains a cancer. I I have to be careful with my words because that paper was really a a paper about treatment. Um but what what we what we can surmise from that paper, and it's well, I'm I'm being careful with my words, surmise, um is that it's not being obese. It is the insulin and it's the cascade of insulin and what follows the insulin cascade that is responsible at least for the resistance of cancer cells to therapy. >>
Mhm. >> Now, from that you could extrapolate, and I'm using again being careful with my words, you can extrapolate the idea that insulin and insulin resistance and the presence of chronic insulin response um may have something to do with releasing cancer cells um in the body. But, if that's an extrapolation, it would be a complicated experiment, but a doable experiment in which you chronically exposed animals to hyperinsulinemia in the way that obese people are ex- posed to hyperinsulinemia without the appropriate insulin response which normal non-obese
people have. Um and figure out whether it was truly that that was causing the cancerous cells to develop. The general hypothesis I have, and again, I have to say this is a hypothesis, is that that's that's the case. >> Yeah, Mitch Lazar at Penn wrote a paper. It's been It's been about 15 years as well. And he looked at the incidence of cardiometabolic disease. And he he sort of created a two-by-two, right? So, you were either obese and or metabolically uh unhealthy. But, there were states,
as you know, where you can have lean individuals who are still metabolically unhealthy. You can actually have obese individuals who are metabolically healthy. And when you when you do the two-by-two of that, uh what Mitch's paper found, again, this was focused on cardiovascular disease, not oncology, Um but it showed that the prevalence of disease didn't track with the adiposity, it tracked with the hyperinsulinemia and everything you just described that accompanies it. So, it's It I mean, that would at least suggest plausibility that the same thing
could be true in oncology, although of course the diseases are different. >> Yeah, the diseases are different, but but again, you know, as I said, our work has been and it's now a full-fledged clinical trial, we can talk about the trial. But our work has focused on treatment. Um and we know that um chronic insulin tone I use the word tone like you know, like the tone that of of muscles. But chronic insulinemic tone um has the capacity to make cancers grow and and be become
or be or remain resistant to some forms of therapy. And that you need to remove that chronic insulinemic tone for those cancers to become vulnerable. That These are facts. From those facts, you may extrapolate that chronic insulinemic tone is also responsible for the sustenance and maintenance of cancerous cells that have mutations and in the same way that inflammation is making those cells sustain, maintain, and grow, that chronic insulinemic tone maintains those cells in a chronic um a state. Now, I would be so happy if someone
did that experiment. It's not It's It's not a It's not that complicated an experiment to do. It It just takes a lot of very careful controls and careful It's It's It's It's a fussy experiment to do. And good experimental people can do fussy experimental science. >> So the GLP-1 agonists, while potentially in the very near term, will transiently raise insulin levels to aid with glucose disposal, ultimately tend to reduce hyperinsulinemia. It's not entirely clear why. Of course, it could be that with enough weight loss and,
you know, a reduction in energy balance, that insulin hyperinsulinemia goes down. But but has anyone proposed doing a study of using a GLP-1 agonist as an adjuvant to conventional cancer therapy, where you would just have a simple randomization of standard of care versus standard of care plus GLP-1 agonist? >> No, many people have thought about it. I don't know if there's been a formal proposal to either, you know, Novo or Lilly or the NIH. This is a This is a fundable and and thoughtful study. You'd
have to do it Again, it's a fussy >> the right cancer, you know. >> You'd have to pick the right cancer. You know, I would pick endometrial or ovarian or some of these really >> potentially. >> Or breast. Yeah. >> Yeah. >> And and really think through it. This is a You want to do this thoughtfully. You don't want to just run into this and just do it in in a in a silly way. >> Yeah. >> You want to do this thoughtfully. I think it's
a beautiful study. It would be a beautiful study. You would You would randomize patients with on GLP-1 versus on GLP-1. You would not GLP-1. You would study their insulinemic response versus their weight loss response. >> Yep. >> And with AI, which we'll talk about in a second, you could probably dissociate those two and disconnect them and ask the question, which one is leading and which one's following? But it would It's a beautiful study. And, you know, Look, we could do a very broad um sweeping epidemiological
study which you could do in 5 years or 10 years. So, that's just a little bit too early. And just ask the question, listen, if uh you know people who are on GLP-1 versus not GLP-1, you know, is there a lower risk of endometrial cancer? Now, >> Uh see, I prefer the experimental approach here, Sid, because I Yeah, not just because of time. I think there might be too many confounding factors if we just do it observationally. >> Of course. I mean, of course. Uh but
the observational study is much easier to do. It's you know, you don't have to >> Yeah, you don't have to intervene. >> You don't intervene. No, no. So, yeah, of course, we all prefer the real proper study. And I think someone should fund the study. I mean, you know, if it's not the NIH, if it's not the Eli Lilly or Novo or whoever else is now making you know, any of the GLP-1s, you know, any of the people anyone in the audience who's uh you know,
interested in in longevity. Cuz I I'm interested ultimately in >> Yeah. >> you know, what what these drugs do, not just to cancer, but to the overall um physio-inflammatory state, which is certainly um pushed by insulin and and I would love to ask the question, what happens to longevity markers? And what happens to you know, so so this is a study that's that that one of the many very many people in um who have the means and capacity to do do so can can fund through
a you know, a remarkable institute like UCSF or Columbia or Stanford or Harvard I'm going to I'm just going to name some names, but there are many. >> Yeah, yeah. Um and and by the way, there is a bit of a precedent on some of this. I don't know if you've been following the literature on osteoarthritis, but >> Oh, I I I this is you know, one of my favorite topics. >> Yeah, it it's again, maybe just explain the observation to people and how it points
or shines a light on inflammation independent of weight loss because it's quite fascinating. >> Well, so so osteoarthritis osteoarthritis is is my neck of the woods because my laboratory discovered the first osteoarthritic stem cells. >> Mhm. >> We published that in Cell in and then subsequently in Nature and Nature Medicine. Um I have never believed from the beginning of the time I began to study osteoarthritis. I never believed that osteoarthritis was an obesity disease. Um there are non-obese people who have osteoarthritis. There are obese people
who have osteoarthritis. It's It's much larger. But the idea there was something kind of like it it didn't make sense and also it was sort of like, let's blame the victim. Let's say fat people get osteoarthritis. And let's just say, oh, you know, that's a fat people problem. And it just didn't make sense. It doesn't make sense for many reasons. Look, I'll tell you some of the reasons. So yes of course the weight-bearing limbs, the knees, the hips get osteoarthritis and people complain about knees and
hips and they get hips replaced, knees replaced, etc. Great. Fine. Makes sense. Usually people who are obese, usually women who are obese. Point number one, it seemed to be like many things that we've done in in in medicine for women, a a backwards problem, which is to say, oh, you know, they have they're they are obese and therefore they must have weight-bearing problems and therefore it's a weight it's a weight problem. But then you you meet a person who's you know, just completely has no obesity,
a woman. And their osteoarthritis is in their hands. They have these massive nodes, you know, inflammatory nodes, inflamed nodes in their hands. The hand is not a weight-bearing um joint. Um some of them have, you know, degeneration of their upper neck joints. Their complaint is upper neck pain, shoulder pain. And they're and they're not obese. And you say, "Well, this this observation can't be true. This It just doesn't make any sense." So, now we know that there that osteoarthritis is almost certainly a combination of two
problems. One that we've that my lab has been a big proponent of, which is that the stem cells that grow cartilage in the joints, over time, those stem cells there's a balance. So, during our lifetime, cartilage is being generated and is being destroyed, generated being destroyed, generated being destroyed. If that gen- gen- genesis and and destruction is in adequate balance, then we maintain cartilage and we don't get osteoarthritis. If the genesis gets stopped with the destruction remains the same, then of course, you know, you won't
get new cartilage and over time you'll eat up the cartilage that you have, and you'll get osteoarthritis. So, the question then becomes, well, what causes this imbalance? So, the the If you imagine them as a as weights on a scale, remember, normal is equal weight, genesis and genesis and destruction, genesis and destruction, genesis and destruction. In women who have obesity and in very older men and women, genesis goes down, destruction remains the same or goes up, and all of a sudden you have an imbalance. So,
the question becomes, well, what's driven genesis down? Well, we now know, at least in animals, what's driven genesis down. Then, the stem cells that form cartilage, the cells that create real cartilage, have gone down. We've measured it. We know this is this is a fact. Now, the question is, why have they gone down? Well, may- some of it is age. Age because, you know, in over age, all of our stem cells go down. Skin stem cells go down. Your bone marrow stem cells that make blood
go down. So, some of it is age. If you inject in a young animal, young mouse, if you inject an inflammatory response into a joint, and it's a young animal, has no reason to have any other problem, we know that that inflammatory response will cause the stem cells to stop making cartilage. >> And sorry, when you say inflammatory response, do you mean like LPS or bacteria? >> could be it could be it could be a like a a bacterial mimic. >> Yep. >> LPS. >> So,
something to activate the immune system. >> Uh something to activate the immune system exactly. And we now know that if you activate the immune system either using a small molecule or using a some kind of stimulation, the inflammatory response will you'll start an inflammatory response in os- and it will create it will mimic almost exactly mimic osteoarthritis in the animal. The balance, remember again the weight scales genesis destruction. The destruction rem- goes up, the genesis goes down, and you have osteoarthritis. >> And so let's think
through why that might be happening, because that's also not entirely intuitive that an immune response, which serves the purpose of trying to eliminate a foreign pathogen, would also in its wake have its host's stem cells uh reduce their output. >> Now you're asking a really hard question, and the answer is I don't know. I just I don't know. I think that um I think that it's actually not uncommon in the stem cell world across different kinds of stem cells, skin, bone marrow, blood stem cells, and
in this case cartilage-forming stem cells, that certain immunological responses tend to make the stem cells reduce their capacity to regenerate. And we It's a common theme. This is not an uncommon >> I mean, there must be some at least teleologic explanation that I'm just missing. >> know, but the the problem, as you know, Peter, teleology teleology is not biology. >> No, it's rationale. It's a way we tell our story to ourselves that we like. >> It's like Rudyard Kipling's just so story. Yeah. Teleology is like
saying, "Oh, I think it's because in ancient times in ancient >> Yep, yep. >> when you know, when people were hunting and gathering and fighting lions and running from woolly mammoths or whatever, you know, crazy nonsense. >> Yeah, yeah. >> [laughter] >> You know, stem cells had to shut down because they had >> That's right. They They were were energy demanding, and when the immune system Yeah yeah exactly. >> I mean, you know, wait, all Some of this can be some of this can be observed.
Some of this can be experimentally tested, but you can't put a human being in, you know, prehistoric times. You know, 100,000 years ago and or or a pre-human being or a monkey or a God knows ancestral common ancestry in some stress condition and and ask the question, "Well, why do their stem cells not stop working?" >> Yeah, the more relevant question The more relevant question is how, right? If we knew the how, then we could intervene cuz that's what we have to figure out. >> Well,
you know, again, so let's let's talk philosophically. Biology and and and maybe I I should say this as a I'm now taking off my hat as a as as an immediate scientist and talking to you about as a science philosopher. Why questions are very hard to answer in science. How questions are are what science science can do. I can tell you how something happens. I can tell you if I I can tell you how an apple falls from a tree and with what velocity and and
how. Now, if you would ask me why >> [laughter] >> why does the apple fall from the tree and go towards the ground? I would say, "I have no idea." Who knows? Why are planets attracted to each other? I don't know. I mean, you know, this is like lies lives in the depths of of very abstruse physics and biology is so many degrees removed. If you ask me, you know, why does a stress response in in humans shut down blood-forming stem cells? I'd say, "I have
no clue." But I can tell you how. >> Mhm. >> I can tell you exactly the molecules. I can tell you the the molecules that are shut down, I can tell you who's moving But why? Why is not a science question? Why is a Why is a metaphysical question? >> So let's talk about the how. Especially as it pertains potentially to the most important question, which is Well, before I go there, I want to I want to ask you a question that might set the stage
for this. So We have a remarkable immune system. You've already alluded to it a number of times through the lens of immune-based therapies. And while we didn't go into great detail >> No, no, we have a very remarkable immune system. >> Yeah, so we have this incredible system and we've danced around the success of this class of drug called checkpoint inhibitors, which come along and remove the brakes on the immune system and unleash the immune system on the cancer. Suggesting that our immune cells at least
have the potential to recognize cancer as non-self. And in fact, when I had Steve Rosenberg on the podcast a couple of years ago, he shared some unbelievable data. Blew my mind. Which was that 80 80% of solid organ tumors had novel neoantigens that could be recognized by self. Now, think about that for a moment if you're the average person trying to make sense of this. What that means is 80% of the cancers that are most lethal, the ones we think of, breast, colon, lung, pancreas, these
are the really bad cancers. 80% of these are immune cells, our T cells know that that is bad and it's not self. Then the question is, why can't they mount on their own enough of a response to do the job? Sometimes they can, that's very rare. We now have a drug that can take a subset of those and this is the checkpoint here. But we're still sort of wandering in the desert with cancers that are non-self and an immune system that just can't make it happen.
So let I want to spend some time talking about T-cell biology and what what do we think it's going to take? Is it Is it simply the age of the T-cells and is it the case that if you're 10 years old your T-cells are doing this automatically, that's why 10-year-olds rarely get cancer, but by the time you're 70 they've just they've they've been around the block too many times. >> The answer to this all of the likely all of those things, but let's talk about new
antigens for a second because we need to we need to really think about this. Um So Steve Rosenberg, who I know very well, was a pioneer in the field. Um And years ago Steve Rosenberg and I had a very public argument on stage. Um at a very prominent cancer conference and he said "Well, there are new antigens in cancer." I said, "Well, where do they come from?" And he said, "Well, some of them are coming from mutations." And I said, "Well, come on, they're like 30
mutations, 50, 100?" >> 100, yep. Call it 100 and it doesn't explain it all. >> It doesn't explain anything. Now there's beautiful work from Tyler Jacks lab and just I would say to some extent from my lab that says that that's not the real new antigen. The real new antigens are coming because when cancer cells start deregulating disregulating the way genes are their genetic apparatus is decoded they make mistakes. They go wacko. And all sorts of cryptic antigens cryptic pieces of genes that should not have
been expressed made into proteins, are now coming out and becoming made and make it making proteins. We Tyler's done Tyler Tyler's lab has done at MIT has done an incredible job at decoding what this is. It's an incredible story. I would encourage people to read about it. But we now know what those are. We can map them. That's the first part of the problem. So, you know, as you know, there are two parts of the problem. Now, one part of the problem is what are the
new antigens that are coming and number the number two is why aren't T cells recognizing them? The second part of the problem is more interesting which or equally interesting. The T cells don't recognize them because of all the reasons that we've talked about. There's not one checkpoint. There are dozens of checkpoints in cells. Um and the what's a checkpoint? A checkpoint is a gene or a flag that a normal cell floats on its surface saying, "I'm normal. Leave me alone." to to a T cell. And
that's why we don't have desperate terrifying autoimmunity. Um when we know that patients people who have mutations in some of these checkpoints are consumed by T that T cells eat them alive. We have enough T cells in our body to eat ourselves alive. Think of a disease like, you know, a T cell autoimmune disease like diabetes type 1. It eats the pancreas alive. Um think of a B cell. Um again, another immune cell disease like lupus. It eats our body alive. Think of scleroderma. Think of
a patient that patient is eaten alive by our own autoimmune cells. But you and I are not. Why not? And that's because all of our cells are carrying not one flag, not two flags, but dozens of flags saying to a B cell and T cell, "I'm normal. Don't eat me. Don't kill me. Don't create an autoimmune response against me." So, cancer cells, of course, figure this out by themselves. I'm I'm anthropomorphizing and cancer >> Right, but that's okay. It's a safe spot for that. >> This
is an evolutionary process, right? Uh the cancer cells that survive are the cancer cells that are able to put up these flags. So, that is the non-anthropomorphic variant of what I'm just saying. Cancer cells that survive um uh create um flags that enable them to tell the T cells and B cells not to attack them. And there's not one, there's not two, but a dozen such response elements. And these response elements are ultimately responsible for the fact that even though cancers have now, we now know,
not one, not two, but dozens of neoantigens, that they're still not attacked by the immune system. >> And so, do you think that along the lines of the inflammation angle, which I want to spend more time talking about, do you think there's a world in which T cell generation, uh regeneration, I'm sorry, just as people are starting to talk about how do we regenerate cartilage, how do we regenerate corneas, things of that nature, are we going to get to the place where we have serious considerations
of medications, drugs, therapies that say, "Look, if an 80-year-old could be armed with the 20-year-old version of their immune system, their mortality is going to plummet. Because not only are they going to be able to combat cancer, but also infections, right? I mean, think about Yeah, yeah. So, how how much is that sci-fi and how much is that something that that that we might see in our lifetime? >> Now, we come to AI. One of the most interesting talks I heard recently was from someone who
um at Harvard who was looking at looking through um CAT scans of patients and asking the question about wellness, longevity, disease, cancer, etc. Using AI. And asking the question, was there anything in the scan of their bodies that was correlative correlative not causative correlative correlated with any of these markers. And they found one marker. And it was the presence or absence of an organ called the thymus. So, the thymus, T, the thymus is the reason T cells are called T cells, because the thymus is the
you can imagine it in metaphorical ways as the baby nursery or the kindergarten where T cells grow up. And people across thousands of people across thousands of disease states people who had an intact thymus uh in this study, it's been well published, well noted. People who had an intact thymus tended to have less diseases such as autoimmune disease in and cancer. People who lost their thymus as they grew older, because the thymus naturally involutes and dies, tended to have more. Now, this is a correlative study,
it's not a causal study. They You can propose I I right now you can think of 10,000 ways that you can probe this. But it's super interesting. Because all of a sudden it says, "Oh, maybe just like Lipitor for cardiovascular disease or hypertension for cardiovascular disease, maybe I have a marker. Maybe the marker is the size of the thymus or the presence of the thymus." The real test, of course, is starting with animals, take an older animal whose thymus is gone away, put a new thymus
in, and see if you can restore their capacity. These are difficult but important studies. But you're on the right track. You know, could we make a T-cell booster? Well, what better T-cell booster to make than reimplant the kindergarten where T-cells grow up and learn to become T-cells. And this study very clearly points to the idea that that's one of the that's one of the places to start. >> You know, there was a paper that came out I don't think it was quite 10 years ago, but
probably around then, and it was uh and I say this with no disrespect to the to the authors or anything. It was a bit of a clumsy paper aimed at thymic regeneration, and it was a little bit of a kitchen sink approach, right? They gave these patients growth hormone and metformin and a whole cocktail of things. And on imaging they showed, "Hey, look, these you know, these in the patients who got this." And by the way, it was even worse cuz I think it was an
open-label study, so there wasn't even a randomization. But they said, "Look, the thymus grew." And this sort of idea of thymic regeneration became very popular. Um I haven't actually followed it closely enough to say, "Hey, has the field moved to a place where there are legitimate inquiries?" because I don't even recall the rationale for Metformin growth hormone and DHEA or whatever they were they were giving. Um, have you followed this space at all and are there are there molecular attempts at this? >> I had yeah,
you know, again, we we the good news is we're going from a human generally speaking the kind of study that I like is what I call a closed circular study. You start with a human epidemiological observation. And it's an observation. This goes back to my old philosophy. Publish observations, you don't need to publish explanations. The observation is humans who retained their thymuses um tended to have fewer various diseases. This was picked up by an AI this picked up by AI AI reading of multiple tens of
thousands of cat scans. >> Mhm. Great. >> Now take that into an animal model in which you can do an experimental implantation of the thymus. This is possible to do. You can actually open a chest, put a new thymus in and ask questions. You could ask very detailed questions. It'll take some time but it will happen. And then slowly work yourself back up to the human and close the loop and find out, you know, what are the thymic growth factors? I'm sure there's some. >> Yeah.
>> Otherwise, you know, why would you why would let's say your thymus you and I let's say are the same age. I don't know if we are. But uh your thymus is gone and my thymus is not gone. Can I find the growth factor that made your thymus involute and mine and not involute? Great. I'll replace yours with a growth factor in but you know, putting in random stuff Metformin it's it's is That's just a That's just a mess. So, um Look, science is systematic. And
we need to follow systems. It's great when we follow systems and when we understand systems. Sometimes we have to make a few leaps of faith. It's okay. Um sometimes there's a lot of insight, there's some intuition, blah, blah, blah. It's okay. But ultimately, it's just it's a systematic way of thinking. And there's nothing more to it. It's a systematic way of thinking. So, yes. The answer is we'll find the both we'll find the hormones there's I'm sure. Look, I'm sure there's a hormone. There's some hormone
that sustains your thymus and doesn't sustain mine. Yours is there, mine is gone. I need to keep mine alive. I'll keep mine alive and then I'll have a new way of preventing many diseases including cancer. >> Before we leave the immune system, I want to go back to something you brought up which has been one of the great successes of the past 15 years which has been the the the success of CAR T therapy, especially around the the B-cell lymphomas and leukemias where to your point
we were talking about durable cure. We're not talking about extension of median uh survival. Um maybe just spend a a little moment explaining what the CAR T is. Like what is a chimeric, you know, T-cell, etc. But where I really want to go with this said is will we ever or do you believe that there's going to be a future where the same technology that has given us a CAR T cell to treat B9, you know, CD19 B-cell disease is going to allow us to treat
a solid cancer like colon cancer, breast cancer, pancreatic cancer? >> Okay, so let's start with what a CAR T is. A CAR T is So, remember we said T-cells are able to recognize cancer cells and kill them provided that the that the cancer cell doesn't have enough flags on its surface. Um that prevents it from being recognized. Okay, so we get that. That's standard immunotherapy. Keytruda is one example of that. There are other ones. Um Okay. A CAR T is a slightly different variant of that.
A CAR T is a variant in which I say "Okay I'm going to override all of those signals. I'm going to take cells from your body, genetically modify them to recognize cancer cells using a genetic modification system based on a virus. Um the virus just basically inserts a gene into the T cells. I'm going to grow those T cells in either in the body or outside the body, re- inject them in the body or change the T cells in the body. And now those weaponized very
important word, weaponized T cells will go and kill your cancer. Um this has been a great success in leukemias, B cell leukemias, uh and B cell lymphomas, um producing not just small cures, but durable cures. Um just as a plug for what one of the things I do, I I created the one of the first CAR T cell companies in India where we sell the therapy for 1/20th the price that it's uh that's available in the United States. We've treated almost 150 odd patients. Uh maybe
coming up to 200 soon. Um So, that's what a CAR T cell is. And now your question is, "Why can't we do that for colorectal cancer and other tumors like brain tumors and And the answer is quite subtle. Um And if you're a listener to this podcast, I want you to pay very attention very close attention to the subtlety of the answer because the answer is extremely subtle. Um Why could we cure B cell and B cell lymphomas and B cell leukemias and why have we
struggled to cure brain cancer and others with these CAR-T cells which are weaponized T cells which ignore, by the way, ignore all these red flags from cancer cells. They just go kill them. The answer lies in the fact that no one and I'm sure that no one in your program has really talked about and very few people talk few people talk about. So, the CAR-T cells that kill B cell leukemias and lymphomas don't just kill B cell leukemias and lymphomas. They kill all B cells. I'm
going to repeat that fact. They just They don't kill B cell leukemias and lymphomas. They kill all B cells. And it just happens to be the case for reasons that we don't know, evolutionary reasons and others, that human beings can live with B cells killed. The problem and that's great for so for B cell leukemias, B cell B cell lymphomas and other cancer some some other >> we can a woman can live without any mammary tissue. >> So, a woman could live without her ovaries >>
let's kill every breast tissue in her body when she has breast cancer. >> live with A woman could live without her ovaries if >> Yep. >> I mean, pleasant thought, but a woman could live with her without her endometrium. Not a pleasant thought, but yes. >> Yep. >> So, CAR-T cells really should be thought about as lineage, not cancer-specific. >> Yep. >> Lineage and tissue-specific ways of killing cells. Now, you can't live with obviously one wonders, but obviously you can't live without a brain. So, you
can't create a CAR-T cell out to a brain cancer because your brain cancer will almost certainly be almost similar to a normal brain cell. But, you can live without an ovary. You can live without a men and a women >> you can you can live without a pancreas if push comes to shove. >> without a pancreas. You can live without a prostate. >> Yep. >> You can live without So, there are a bunch of tissues that you can live without. We just need to find CAR-T
cells to be able to kill those cells. >> And what's limiting us? What do you believe that there are unique antigens on memory cells, pancreatic cells? >> Well, I don't know believe we studied this to the nth point. Um and the answer is yes, there are. >> So, then there's no physical reason why we should not have a CAR-T cell against breast cancer then. >> Breast cancer is a hard example. You've chosen a particularly hard one. Um pancreas is a hard one because there's you know,
there's not they're not very many unique antigens in a pancreas cell that is not present in normal cells. But, I'll give you an example that's not so hard. Ovary. Ovarian cells uh which re- ovarian cancer cells which really are fallopian tube cells. We now know that ovarian cancer is not an ovarian cancer. It's really a fallopian tube cancer. Long story, we can talk about it later, but um ovarian cancer cells have an antigen. It's very well known. Have more than one antigen. And there are multiple
attempts at making CAR-T cells against them. One of them will going to is going to be successful. For example >> What what percentage of pancreatic cancers have KRAS mutations? Aren't most of them? >> Most of them have KRAS mutations. >> And does does the KRAS mutation produce an antigen that we could target with a CAR-T? >> Not not really. It produces an antigen in some patients, but most of KRAS is is hidden inside. Most of KRAS is not a CAR-T target. Um no. KRAS told my
KRAS KRAS is a great target for small molecules that shut off its function. So, now we're not talking about the immune system, we're talking about small molecules, locks and keys, keys that go and shut off the lock. Daxsonrasib a nasty side effects you've seen the senator with >> Yes. >> lesions in the face. I'm sorry about those nasty side effects. We'll get over I think we'll get over most of them. Not not get over, but as in find new medicines that that that no longer have
those effects. But yes. KRAS I think the way to attack pancreatic cancer is either to find a unique pancreatic antigen that a CAR-T can attack. And if it takes all of the pancreas away, great. People with type 1 diabetes have no pancreas. Or they have no >> Effectively. No no endocrine. Yeah, yeah. No endo >> endocrine pancreas, yes. People with a Whipple surgery >> Yeah, they lose enough pancre- I mean, if you have I plenty of patients have Whipples with a signifi- Yeah. >> pancreas. So,
yes. So, we So, yes, if we could eat up every pancreatic cell and people would still live, yes. But, we haven't found one yet. Um That's one direction we could go in. Another direction we could go in and say, "Listen, we I we can't eat up the whole pancreas including pancreatic cancer, but we can shut off the gene that drives pancreatic cancer KRAS. Daxsonrasib does that. Um the nasty side effects, but maybe we can live with that. Um, and then and that's another that's another direction
to go. That's not immunotherapy. >> Yes, yes, correct. What what is it you think about pancreatic cancer that makes it so devastating, right? So, you know, there's an argument that's been made and I'm not sure I agree with it, by the way, but I've heard it made by some, which is any patient who is alive 5 years after they were diagnosed with an adenocarcinoma of the pancreas didn't have an adenocarcinoma of the pancreas. >> I only have a thumb too. >> Right. So, so they so
the the argument that those people are making, which again, I don't personally agree with and that's probably my bias coming from a surgical program that treated this, was that their argument is there is no such thing as early detection of pancreatic cancer. The moment that cancer is present, it is a fait accompli, it has spread, you are dead. The other argument would be the Halstedian argument would be no, if you catch this at an early early early stage one and you do the appropriate Whipple procedure,
a subset of these patients can live and yes, it's probably only 5% that will be surgically cured of all comers. Um, but if we could detect of those 95% who were caught too late, could we catch them earlier? How how Where do you sit on this spectrum? >> So, look, I mean, this so I agree with you on this. I think So, so as you know, the pancreas is a long organ. It looks like a big leaf. It's sitting sort of in your in your gut.
Um, the fundamentally speaking, there are two kinds of pancreatic cancer, head the ones that are in the head, one in the tail. So, if you think about it as a leaf, the ones that are in the head are closer to the parts of your intestines, the ones that are in your tail are further away. The tail ones tend to be detected incidentally. The head ones tend to be detected with pain and jaundice and other things. Um, I think that surgically speaking, if you if you can
find the ones in the tail, um they're less involved, they're less involved with other organs and your it's an anatomically distinct kind of cancer. So, I think surgically speaking those some of those patients can actually live longer. Um and I agree with you. The ones that in the head, the problem is that it's all there's a lot of anatomy there. Your intestines are going down, your blood vessels are crossing. It's in a place it's sort of like in the it's like a you know, it's like
a it's like a traffic accident. >> It's probably the most complicated real estate in the >> Yeah, it's the problem with most of >> below the neck. Below the neck, I would say it's the most complicated area. >> there are two there are two there are three truly complicated real estates in the body. One is right here. One's right here. And the third one is right there. So, real estate-wise, you know, it's like the traffic intersection of everything going everywhere and you have an accident. And
that accident it's not that that accident ain't going to resolve easily. So, yes, you're absolutely right. There is surgical cures of patients with I have I have patients who live 10 years with a tail pancreatic cancer. Not in the traffic accident area, far away from it, had a Whipple. >> And it was incidentally found and >> Incidentally found. And and so Um so so the answer is yes, it depends on the anatomy. Now, by the way, what we don't know, and here's what's important. So, cancer
therapy almost always moves in the following way and you need again, your listeners need to know about this. We can talk about longevity later or in a different podcast. But listen, cancer therapy almost always begins with therapy directed against diffuse metastatic cancer. So, the senator has diffuse metastatic prostate cancer, is on the drug, is living great. The next step in in cancer therapy is to take patients take most of their cancer out surgically. And then give them the therapy after most of the cancer has been
treated surgically or with radiation to now now kill the remaining cancer cells, and that's called adjuvant or neo-adjuvant therapy. Adjuvant because it is an an accompaniment to the main therapy, in which case which which in this case was the removal of the bulk of the tumor. The real interesting question is what's Dacogen R acid going to do to adjuvant therapy. So, a patient comes in, not the senator, the patient comes in, has painless jaundice, classic example of pancreatic cancer, is found to have a big lump
Goomba in the in the traffic intersection of all of this, goes through Whipple, now most of the visible cancer is out, and now we put the they have KRAS mutation, we put them on Dacogen R acid, and ask the question, are they better or worse off? >> Is that trial being done? >> It'll be done in it it if it's not in the works >> it in the works is what I mean. Is it >> It it is in the works. Absolutely. >> Okay. Yep. >>
In in in 1,000 ways. >> And by the way, that's I mean, that's such an important uh example of a principle in oncology that has some exceptions, but not many, which is >> Some exceptions, but not many. >> the burden of the tumor matters. A lower burden of tumor is a higher probability of success. Again, we can think of a few exceptions. Papillary thyroid of the cancer doesn't seem to matter. These handful of really remarkable checkpoint inhibitors often don't matter. But for the most part, fewer
tumor cells is a better outcome. And it's a greater >> you know and and and this is this is where again our friend Bert Vogelstein says says it very succinctly. He says that there are three zeros between 1 million and 1 billion. And that means if you if you have 1 billion cancer cells and you take them out and there are only 1 million left, that means that there's one in a thousand you've reduced the chances of one in a thousand that you'll have a cancer
cell that's resistant to your therapy by 1,000. >> It's huge. >> Like by 0.001 degrees. Whatever you want to call it. >> Yeah. Yeah. You touched on something at the outset, said that um I am not seeing a enough hoopla about this and that is the staggering and upsetting rise of colorectal cancer in young people. Now, I've taken an unbelievable amount of for this personally over the past 10 years, which is driving towards earlier and earlier initial colonoscopy. And it's been very pleasant to see that
the formal recommendations have now moved from 50 to 45. But if you do the math, you'll realize there are still many people dying of colorectal cancer under the age of 45 and therefore I believe that really we should start screening even average risk adults at 40 and obviously earlier if they have any of the known hereditary drivers. I know why questions are a fool's errand at times, but as an epidemiologist, if I ask you to put on your epidemiology hat, what are some of the possible
explanations for why this particularly lethal cancer, it is the third leading cause of cancer death. Um, why are we seeing this go up? Is it antibiotic use, gut health, obesity, you know, where where where do you think the culprit is? >> So I so the if I was to be totally honest, I would say I don't know the answer. Um, if I was to be a little bit more speculative, I would say it has to do the our colorectal milieu is rapidly change and has rapidly
changed in the last few decades. >> Mhm. >> Um, we are ingesting more and more forever chemicals. Um, and there is strong evidence from animal models that some of these forever chemicals including the ones that are ingested in food are very pro-inflammatory. So brings us back to the inflammation theory of cancer. Um, we have strong evidence that they that these are multiplied by changes in the microbiology of the gut, the gut microbiome, um, which is being disrupted by all sorts of things antibiotics and other foods
it included. Um, and this is I'm sorry, this is again, maybe for your audience uncomfortable territory. But um, Burkitt, Dennis Burkitt, who went to Africa, the person who discovered Burkitt's lymphoma, went to Africa in the 1950s, 60s, I don't remember when, but from Oxford. And he said that he virtually found no evidence of colorectal cancer, especially in young men and women because they um, because the diet was so fiber rich that virtually everyone was essentially evacuating their colorectal >> contents. >> content um twice a day.
um In the United States, we reached a place where we're doing that once every 2 days or 3 days. um So, my recommendation for people who want to avoid colorectal cancer is triple or double your fiber intake. um Obviously, reduce exposure to these forever chemicals. And um based on no evidence whatsoever um use probiotics to and probiotics are cheap. I don't mean go buy like very fancy. I just mean have live yogurt. >> Yogurt, yeah. >> Yeah, um at least once a week so that you
can replace your probiotics. >> So, we've touched a little bit on AI um and I know that it's something that we are both thinking a lot about. Um I want to talk about it through the lens of maybe drug discovery. So, you've get you've already given a great example of how AI can do things on a CT scan read that no human can do. And of course, lots of reasons for why CT scans are a beautiful environment for AI to shine. But let's let's talk a
little bit around the drug side. Now, AlphaFold, the the company that has garnered a lot of much deserved credit for using AI to be able to predict the sequences of amino acids, which has allowed them to more rapidly develop drugs. Uh this is a very exciting area. So, this if we would say this is one area in which AI is going to fundamentally change drug discovery. You could say, "Okay, another area would be AI could help us propose better hypotheses and or design better experiments." That
would be >> Different idea. >> Different idea. And then a third idea, and this is the one that I think personally is the biggest unlock is having real simulated biology that would dramatically shorten the preclinical period, but more importantly increase the probability of success as we go from preclinical to clinical. And then potentially a fourth layer would be extracting better insights from the actual clinical trials to to speed things up. So, are there other areas you're thinking about this in? And and then where which of
these do you think is the biggest unlock? >> I think all of them are big unlocks. I don't think any of them is any less unlock than the other. I think we need the entire ecosystem to unlock. Because every one of these is a potential unlock. Um I just give you a couple more unlocks. Um I give you one example in the in the beginning, which is using AI as an epi- epidemiological scanning tool. So, that's how we had that's how we that's how researchers found
the the the thymus correlation right? Um AI for the recruitment of clinical trials. Um we haven't talked about that. AI for the monitoring of clinical trials. We haven't talked about that. So, it's AI all the way. Um My personal um arena has been AI in the um in um drug discovery. So um as you very well know, every medicine, every drug, aspirin, Lipitor, you know, Prozac, works by binding to something. Um it's a key and a lock. Lock and a key. The The protein that it
binds to is the uh lock and the drug is the key. It opens or shuts down some function uh, of the protein. Um, lock and key is a little bit mis- can be a little mis- misleading because the the the proteins are actually moving in space. They're wriggling around. So, it's a wriggling lock. >> Hm. >> And so, the key has to be a little bit of a wriggle of a key that goes in. But, basically, it's all Aspirin is a key. We I know we
know the lock. Lipitor is a key. We know the lock. Um, Prozac is a key. We know the lock. And so forth. All right. So, um, in the past, we had two ways of looking for keys. Either we could design them from scratch. So, we could look at the protein and say, "What What looks What is likely to look like a key?" And we needed an enormous amount of knowledge of chemistry and chemical biology to do that. This is why, you know, the the the smartest
chemists in the world were required to create these keys to the lock. And another Another way to do this is to take is to do a little bit more like a search function. So, let's make a library of a billion keys and try to see which one fits. Now, both of those, the first one I'll I'll call synthetic molecular design, generative molecular design. And the second one I'll call search and fit. Uh, search and fit molecular design. Both of them are very amenable to AI and
computation. So, search and fit is a little bit harder because it's the number that I quoted to you, a billion, is not a billion. It's actually 10 to the power of 20. >> Which is not that's technically not computational, is it? >> That is technically beyond the level of any computational >> there must be an inference that allows this to happen. >> That's right. You have to do some inference that allows it to happen. That's right. So, so you have to do you have to have
an inferential way of taking. So, in other words, let's boil it down to simple language. You start with some some keys and you begin to infer the properties of the key based on what fits and doesn't fit. It's turned out that that is computationally too expensive right now. And it probably require I mean I would this is my guess it would require quantum computers >> Mhm. >> able to do that that level of inferential reasoning. Cuz you probably need to fit at least a billion keys
to learn enough about the kind of key that you need to really open the lock. >> Which is amazing because it's really a a it's more than a billion billion. So, that's a huge shortcut >> It's more than a It's more than a billion billion. >> But that's still an enormous undertaking. >> Yeah. >> Yeah. >> The second way, which is the usual way that you know, that we are doing drug discovery is to look at the the inside of the lock and ask the question,
what kind of key will fit into this lock? Um how do I design a key? And synthetic molecular chemists working in that's in laboratories and pharmaceutical companies, etc. have they they have rules. They they know that if, you know, if there's a if there's a crevice here, I'm going to put a a certain kind of molecule there. If there's a hollow surface there, I'm going to put a certain kind of molecule there, etc. etc. So, this is called this is generally speaking it's a generative chemistry.
Generative because you're making it from scratch. You're not just searching and fitting. Um but since you you need to know the rules of me chemistry and physics. This is not biology anymore. This is pure chemistry and actually really is pure physics. Um the good news is that you can teach an AI all those rules. And that's what we've done at Manifold. We've taught AI all the rules of synthetic molecular chemists and said, "Let's take one synthetic molecular chemist and make them into 50,000." Cuz that's what
AI does. AI can scale. And if you have one, you can make 50,000. And so, the answer to the question is we that is what we do. We take we we look at the shape, the geometry, the electrostatics, the all the elements that form the pocket where you want to put your key and redesign a key from scratch using basic molecular principles. And if you can do that, you can essentially recreate you can make the smartest generative molecular chemist that ever lived. And the last plug
I'll make for this is that this is the best, to me, the best use case, proximal use case of good AI in the world. This is not a chatbot. It is not a image making bot. It It is making medicines for human diseases. >> How difficult do you think it will be to teach AI the rules of biology to thereby simulate actual experiments? I mean, you you you very briefly touched on how much more complicated biology is than physics or chemistry. The rules are not as
neat. So, is is this >> There aren't really any rules. >> Yeah, so is this is this actually something we can aspire to? >> Yeah, I I think it will come. I'm a little Look, I'm personally skeptical of how useful it's going to be because Look, the biology of a kidney cell a kidney cell has a very particular biology. Its job is to filter out salts. >> Right, it's quite well-known. Like, we actually understand It's very discreet. >> A cell in your brain has no resemblance.
It has some resemblance, of course. Metabolically is the same, blah blah. But, its job is completely different. Its job is to transmit information, sequester information, store it, whatever it might be, right? A cell in your skin is a completely different kind of cell. So, I'm a little skeptical of >> Well, and it's also just to add one more layer there, Sid. Sorry to interrupt. It's It's that there are certain cells in the body like a muscle cell where it's a there's a very mechanical and um
relatively straightforward functional determination of what it can do. Whereas, the neuron, just to use that example again, completely different. Like, what's happening in the neuron is not as mechanical as what's happening in the myocyte or what's happening in the nephron where you can actually just see or this ion moves through this channel against this gradient and when this gradient switches, it does this. >> I think there's some there's some use to building biological models of cells. Do I think it's going to be easy? No. Do
I think it's going to be until you get down to every, you know, cell by cell level is it going to be is it going to work? No, I'm a skeptic, but you know, skeptics are often wrong. >> Sid, if we're sitting here in 15 years, which I I hope we are going to be both you and I personally sitting here having another discussion in 15 years looking back and saying, "God, I can't believe it was 30 years ago you wrote Emperor of All Maladies." If
you had to predict what we're going to say in 15 years that is true about cancer that is not yet true today, what do you realistically think that might be? >> Well, I think that, you know, we've covered a lot of it. New antigens, uh new theories of how cancers arise because of inflammatory states or other states, the role of obesity, the role I mean, all of this will become clear. >> Where do you think it will translate to the most life saved? Do you think
it will be that Do you Do you think there's a chance in 15 years >> answer has always been to be prevention. So, the answer is always going to be prevention. Now, the question that's a real question is can we do general prevention? Is prevention some kind of like habit change, behavioral change, lifestyle change, or is it chemo-prevention? >> just about to say, will there be a drug? Just as I can give you Lipitor today long before you have a heart attack, and I can give
it to you with an ACE inhibitor if your blood pressure is high, these two molecules will prevent you from having a heart attack. It's chemo-prevention. Do you actually think there will be a day when we have molecules that will prevent cancer? >> Yes, there will be chemo-prevention chemo-prevention. I just I It is not clear to me that the chemo prevention is going to be it's not clear to me that chemo prevention is going to be a simple So, remember Lipitor blocks um the decreases your uh
LDL levels. Um and Losartan or whatever ACE inhibitor or ARB inhibits your capacity to have high blood pressure by acting on your kidneys hormonal system. It's not clear to me that it's going to be sort of a a relationship that's like that. >> Although the what you described with inflammation there's there's actually there's actually an analogy in cardiovascular disease. As I'm sure you know, we are now starting to check for chip in patients. And while our therapies are still are still limited, we have colchicine. Now,
it's a crude drug, but there's evidence that if you have you know, if if you're >> better than colchicine, by the way. >> Yes, I'm saying in the car in the cardiovascular side the but but I think that there's this is an opportunity in cardiovascular medicine to go after residual risk where you identify clonal hematopoiesis in a patient that's posing risk for CVD, we treat that preemptively, and that's really our first immune-based therapy for heart disease. So, there's a model, I think, that I would be
very excited if we're doing that in 15 years for oncology. >> Yeah, I mean, you know, as I said, we have and we have some tools. We have the GLP-1s, for instance. >> Yeah. >> In in obesity and potentially in in inflammation, so that's a trial that I would like to watch very very carefully. >> I hope someone listening to us today, by the way, has taken has has become motivated by that idea. >> And you know, you can use you don't have to go all
the way. You can use breast and endometrial cancer as your sort of two simple not simple, but two and like models, yeah. Models, yeah. Um there's anti there there are a variety of anti-inflammatories um of which we know some. Um but again, you know, Peter, I know you care about things like longevity and and I do too. You're not the only person who cares about longevity, obviously. It may turn out that the answers are is orthogonal. It may turn out that the answer is that that
And and we know some extent this is we already know the answer to this question. That again, I'm going to use I'm going to use my words carefully here. The prevention of pathological disease cancer, heart disease, the two biggest killers is not the answer to the prolongation of longevity. So I'm going to use I'm going to say that sentence again. I'm going to say it very slowly. And I'm happy to have be pushed back on it. The current model of longevity is the prevention of diseases
that kill us. Cancer and cardiovascular or one current model the only current model. One current model of longevity is the prevention or the elimination or the treatment or the cure of diseases that kill us, of which obviously the two big ones are cardiovascular disease and cancer. But the orthogonal way to look at longevity is to say I'm not I'm actually not interested in the diseases, cancer and heart disease, the two most common killers, I'm actually interested in a physiological state that promotes the elimination or the
decrease of not just those diseases, but all diseases, including diseases of degeneration, osteoarthritis, immune failure, bone marrow failure, inf- inflame- chronic uh unopposed inflammation, et cetera. That's a question which is a deep deep deep question. And when I say orthogonal, I mean maybe if we could reduce if we could change the physiological state of the body, reduce the inf- inflammatory state, reduce the chronic insulinemic tone, and there's a big maybe there, cuz otherwise everyone's going to come and attack me and tell me I'm a crackpot.
But maybe it is that the reduction of the chronic inflammatory insulinemic tone and the chronic tone that comes from thymic reduct- I mean, all the things we talked about before. It wellness instead of illness. Maybe that's the path to longevity. >> You know, Seral I think about this all the time, and >> know you think about this more than I do. >> No, no, no, but I'll so I'll I'll yeah, I'll share my thoughts. So so I I agree with with the broad statement, for sure.
Um my concern is that different diseases behave differently. So when it comes to atherosclerotic cardiovascular disease, which is indeed the number one killer, not just in the US, but globally, there is actually a world in which we can turn that into an orphan disease because the causal drivers are well known and we have treatments for all of them. >> you can you can take aspirin and Lipitor and you know PCSK9 you can take like 17 drugs now. >> That's right. You So if you don't smoke,
if you take whatever drugs are necessary to stay insulin sensitive, if you obliterate your apoB and LDL-C down into the physiological levels you were born with, and if you ever develop hypertension you immediately nip it in the bud with a drug, you you can't get heart disease. You just can't. The pathophysiology inside the artery wall can't happen. So you're going to die of something, but it won't be the number one cause of death. I don't have that degree of confidence that there's anything we could do
that will eliminate all cancers. So even if we were completely insulin sensitive and we never smoked and etc. etc. etc. we still have enough genes, we still have random mutations, we still need to rely heavily on early detection and treatment. >> All fair all fair except let me give you the only counterfactual to that which is that people who are um super uh super centenarians >> Yes. >> People who are you know more than >> 110 yep 110 >> they don't die of cancer. What they're
dying of is failure of skin regeneration, their skin falls off. They're dying of bones being broken. Basically they're they're dying of regenerative failure. >> But they also they believe it or not they still die of atherosclerosis and they still die they still die of infections like their their immune cells are breaking down. Yeah. >> But infections is immune failure. >> Yes yes yes yes. >> So so but but they what they're not dying of, I mean, assume assume for a second that they took, you know,
every anti-atherosclerotic drug which they don't, by the way, because they're in Greece or some island in Corfu or some, you know, somewhere else, god knows. But, what they're not dying of is cancer. They're dying of bone breaks and skin failure and immune failure and blood failure and, you know, all of these other things. That's the only That's the important counterfactual I'll present to you, which is to say that there are two worlds. It's almost like there are two worlds. There's one world in which humans die
at age 70, 80, 90 of >> The usual suspects, yeah. >> The usual suspects, you know, the three major suspects, neurological disease, cancer, heart disease. >> Uh in opposite order. >> Yeah, whatever. And there's another world in which once you're not dying of those diseases, you can sail into your hundreds, not sail, get into your hundreds. And there, your killers are not those diseases. There, your killers are immune failure, blood system failure, bone breakage, basically regenerative failure. Um you know, I've been writing a lot about
thinking a lot about Elie Metchnikoff, uh who the Russian Parisian scientist who discovered the innate immune system. And Metchnikoff in the 1890s, get this, in the 1890s, had a theory, he was the first person to pre- to propose that we study death systematically. He pre- He created the the word thanatology. The study of death, which of course the opposite of which is the study of longevity. Mechnikov argued that that if you could remove the proximal causes, the pathological causes of death cancer, heart disease, neurological disease,
and of course accidents >> Accidents and infections, yeah. >> Accidents and infections, etc., etc. That human beings could live to 100, 102 and they would at some point of time would lie would naturally want to die just like trees at a certain age hundreds of years finish their sap and die. It's a It was a very radical idea in its time. And I often come back to this thought thinking maybe that's true. Maybe that's where maybe that's where we are. Maybe in eventually we need to
think about it instead of thinking about oh let's pick off the buds one by one. Atherosclerosis, cancer, etc. Or maybe we keep picking off the buds. It's okay. >> Right, but we need something that goes after the foundation of aging. >> We need to go to some That's the point. Yeah. >> Yeah. And so how what is your degree of optimism that cuz if you if you what is the what is the sort of AI buzz say that everybody's saying, "Look, AI is going to cure
this. We're going to be immortal. We just have to not die for the next 10 or 15 years and we're going to become immortal." And I'm struggling to come up with a steel man for that argument. >> No, no, no, no, no. You know, I think as I said regenerative failure is a real phenomenon. And treating regenerative failure is not easy. It's going to be another that is a huge new frontier in biology. We haven't even faced that frontier biology. Why do What is you know
What is regenerate failure of cartilage? I don't know. What is regenerate failure of the brain? I don't know. What is regenerate failure of the immune system of the thymus there's no thymus. How can I make a new one? I don't know. So the new frontiers of longevity will have to deal with not just sort of oh you know let's change our diet and make ourselves I don't know somehow with dietary perfections or inject ourselves with some random peptides and make ourselves By the way everyone knows
this but let me just remind you there are 20 amino acids per individual molecule so um the combinatorics of injecting yourself with pep with a random group of peptides the chances you'll find the right peptide at the right concentration are basically zero. Like zero not it's like there's no mathematics this is a mathematical truth. But anyway >> Well it goes it goes back to what you said about the billion billionth chance like a billion billion chances it it it yeah. >> But but on the other
hand if you find if you were to find the right peptide I'd be I'd be rooting for you but not you but rooting for the person who found the right peptide I'd be great. Listen the problem is that yes I think there is a hack as the Silicon Valley people want to call it to longevity. The hack but the hack is not just a simple hack. It's not going to be peptides It's going to be finding out systematically how to address these regenerative failures and you
separate it out from the the the diseases that kill you at 70 and 80. Let's separate those out. >> Yeah, we need we need both of these happening in parallel. >> Exactly. Yeah, they're both in parallel, right? So, you remove the diseases and you have a good example. Atherosclerosis is a good example, right? You remove atherosclerotic atherosclerotic heart disease using all the mechanisms all the medicines that we have now, including behavioral change. Most importantly, behavioral change. And then you're left with the question of regenerative failure.
And that's not a hack. That is a powerful systematic study of how you will remove the regenerative failure of people who are in their 70s, 80s, 90s, including the regenerative failure of the brain, which we know the least about. And yes, then I can conceive of physical longevity. Now, there's a whole other world in which you can talk about virtual longevity. >> Sure. Sure. Yeah. >> That's a different you know, that's a that's a you know, Matrix red pill, blue pill. >> Yeah, beyond my pay
grade. >> It's what it's beyond my pay grade, but but but but it's true. Fair enough. You can you can imagine there's a red pill, blue pill blue pill Matrix longevity universe that you no longer you no longer need your physical body or maybe robotics takes over most of it. God knows, maybe you're a brain in a box or whatever there. >> Mhm. >> Thousand Let's just put those aside for a second. I'm just talking about the physical body. And I think that those are not
hacks. These are system They need systematic and we we beginning to find out. AI is helping us find it out. I give you one example, but there many. >> But this is not really a priority of the NIH. I mean, the the the NIH is is is not putting the resources into studying the hallmarks of aging and gerontology because to your point earlier, Sid, um if you want to start answering these fundamental questions, you must do them systematically right? >> Yeah. >> And you started with
a human observation. It's a very important human observation. Now we need to go back and do the animal models that begin to look at the ways that one could actually perturb that system. And and then of course, you bring that full circle back into humans. But this is very big science. This is a Manhattan project of science. >> I I would argue that it nearly lies outside the current NIH mandate. Uh and we need a new NIH if if we were to do this, which we
should or could, it needs a new NIH mandate. The The problem with gerontology is remember, again, history is very important here. What changed what really changed cardiovascular science? It was the fact that we found biomarkers for uh >> They were proxies for disease. You can measure blood pressure, you can measure cholesterol. >> You can measure blood pressure, you can measure cholesterol, and very soon we said we don't need to wait for heart attacks. If there's a drug that reduces blood pressure, if a drug that reduces
cholesterol, we'll make that drug available. And in post-hoc studies, we found that those medicines actually reduce heart attacks. >> Yep. >> You don't have that for cancer, and that's a separate question. >> You don't have it for aging. >> for longevity. >> Right. >> Yeah. So, you you know, there's talk about biological clocks, there's talk So, so here's my question back to people who uh study longevity science, an important science, very important science. Find me a reliable biomarker. The problem with finding me a reliable biomarker
is going to is is that the biomarker study itself will take 15, 20, 30 years cuz you can't do it retrospectively. You can do it sort of retrospectively. You can say, "What is there in the blood of people who live at 100?" >> Yeah, and we can start with I mean, and I've talked about this with others on the podcast. I mean, we could go back and look at the interventions testing program, which is that great NIH program that looks at all the molecules. >> Yeah.
>> You could You could just begin by looking in the metabolome and the proteome of all the ITP winners versus losers, and we could at least get some clues. But yes, just finding the biomarker is big science. It's very big science. >> Very big science. It's very big science, and it's a it's it's reliable science. The only problem with it or the question with it that I would argue is that we can't screw it up. >> Right, it sets the trajectory of where we're going. >>
It sets the trajectory indefinitely. So, we need to find it, confirm it, find it and once we found it, again, remember Lipitor, remember cholesterol, remember hypertension. You can do dozens of studies to think about it systematically. But until that's found, we're sort of stuck. >> You know what's funny? Just a couple weeks ago I was talking to a friend of mine who I'm sure you know, David Sabatini, good friend of Lou's. And >> Yes, and a a friend of mine. >> And and so, David was
lamenting this. He said, "Look, what we really need is a Manhattan Project around this." And his view is it's not going to be the NIH stepping up. It's going to be a group of philanthropists that decide that we need to put money. But But his point, which I think is absolutely spot on, is the effort must have a significant engine in the basic science. It cannot go go to drug discovery, which is the temptation. When these things are started as for-profit companies, you have to return
to shareholders quickly. You got to go straight to drug discovery and whack-a-mole. Whereas David's view is, and I think he is correct, you have to have a continuum, and you must have basic science. >> He's absolutely correct. And not only that, he's absolutely correct because, you know, again, the the loop is quite clear. You start with human observation, you go backwards. You You understand the the basics, and and David's absolutely correct. And you know, you know, he's been studying this for years with Lou, with me,
with others. Um There's no short Unfortunately, there's no shortcut. And this is not a drug discovery >> That's right. >> This is a cholesterol discovery project. This is a hypertension discovery project. >> Yeah. >> And unless we discover cholesterol, hypertension, etc. for aging, we're we're going to be lost. And it requires a 30-year Manhattan project. Maybe 15 years if we're good with AI. Maybe you can shorten that down. And the NIH has no mandate to right now Maybe a new NIH will, but the current NIH
doesn't have a mandate for doing this. >> I don't think the NIH ever will, and I I I I think um Yeah, I I I think there's many reasons why this is not going to be within the purview of of of NIH. Unfortunately, it should be, but it won't be, and and therefore I think I think you, me, Lou, David, and the bunch of us are long overdue for a dinner to brainstorm some ideas here. >> Well, I'm happy to have the dinner and, you know,
as I said, if there people who are truly interested in this without the temptation of, you know, let's make >> a drug. We need a drug within 6 years. Yeah. >> If there people then then we should we we should really launch this launch this system systematically. You know, there is a National Institute of Aging, as you know. Um They could they they they could, if they if they wanted to, they could change some of their focus and there is a focus on well aging. The
problem is the NIH's mandate is illness, it's not wellness. It's not it's, you know, and that is a great thing. We've done great things with it. And it has produced in in in incredible things from vaccines to, you know, Lipitor. Um Great. That's wonderful. It's not the current mandate. >> Well, Sid, sounds like I need to uh to to get up to Boston or New York when we we all need to we need to grab dinner. >> Well, as you know, I mean that this metabolic
trial >> Yeah, you want to just I don't know how much you can talk about it. Can you give us Is there anything you can give us? >> Well, it's a public company now, so it's all good, but listen, you know, the idea was initially proposed by you to Lou and Lou Cantley and to me. And then Lou Cantley and I sat down for dinner in a res- restaurant on the Upper East Side and on the back of a napkin sketched out a potential strategy, which
is basically the idea that cancer cells must depend on particular metabolic inputs, which make them different from normal cells. I mean, know that from Otto Warburg Warburg's work in the 1900s, um the cancer cell the metabolism of a cancer cell is not the metabolism of a normal cell. And every time you say something is different about a cancer cell than a normal cell, that makes it possible. >> It's an opportunity. So, in this case, we used that opportunity to make a new new kinds of drugs
that would particularly target cancer metabolism and less so normal metabolism. And that led to a company called Five Five Therapeutics. It went public uh maybe 2 weeks ago, 4 weeks ago, God knows when. But it's in the middle of trials and early trials are very promising and we'll see. We'll have to see you know. It's a public company so I'm actually no longer privy to the internal information. I'm a founder but Lou is a founder. We and we no longer know anything about the internal information
of company. But um the last we heard the last public disclosures that we heard the the that the idea that targeting cancer metabolism in endometrial and breast cancer was working. So we're happy about that. >> Well we will continue to follow that story. >> And if it's a success we we owe you a dinner in in in Manhattan. >> Yeah, I remember that that dinner very well. It was sushi and it was uh >> Yes it was this >> It was a great sushi dinner. Um
are you working on another book? You mentioned you're writing. >> Yeah, I'm work Well, this is why I talk about a lot of this. I'm working on a book on death. >> Hm. >> And dying and longevity. But not this kind of usual oh you know, let's get to It's the kind of stuff that we talked about. >> Yeah. >> How do we really systematically think about longevity? Systematically think about death. >> What what is your process for writing? You know, we've I I I I'm
just going to have to embarrass you a little bit um because it's possible there are people listening to this podcast who haven't read any of your books. I've read all of them multiple times and I um I'm not just saying this Sid. I think you are uh not only a generational talent, you are the single greatest writer I have ever read when it comes to the discipline that I find myself in and I and I really mean that Sid. You I and I think I've told
you this before. When I was struggling to write my book I would curse you constantly and think god damn it. He does it so well. My book is going to be garbage. I'll never be able to do what he does. And I can't but that's okay. Um so so and I don't want to suggest that you're just gifted and it doesn't come down to how hard you work, but but my guess is it's a bit of both. I I think you have a way with words
um and you obviously work very hard at it. So, tell me a little bit about the process by which you're you're writing this book. >> The first part of the process, I mean this is what I generally tell people who ask me this question. It's going to sound like a generic answer. It's not generic, it's real. It's I think it's very important before starting to put the first word on the page is to spend a lot of time, I call it dream time. People can have
different different words for it. Thinking about what the book will sound like. Who's driving the narrative? Because it has to be a who. Where is it placed? What What Where does it go? Where is it geographically? What time is it in? Um what mood does it convey? Who Who's the reader and why is he or she being moved by it? Um What's the What's the structure? What's the anatomy? Do we Is it compartmentalized? What do the chapters seem like? What do the big sections look like?
Um And I think that before you put the first word down for a book, you should spend months weeks years thinking about all of that. Because that's what's the basis. That's the center of a book. Um And once you've gotten that piece straight it turns out that writing a book and I mean it's complicated, you know, you have a lot of explanations, blah blah blah blah but then the book just kind of moves along much more quickly. Um So, in in other words I I You
know, musicians often say this, and you have to listen to the musicians, cuz of course there's a lot of closeness between prosody and music. Musicians often say, "You need to know the mood of the piece you're playing before you play the piece, long before you play the piece." It's the mood of the piece that's important. The the notes are just a mechanism to drive the mood of the of of the By the same logic, you need to know the mood of the book. When the the
skeleton Once you have the skeleton, you can hang meat on the skeleton. You can hang pages on the words, etc., etc. So, my my um my advice, process, whatever it might be, whatever you're going to Don't write the first word before knowing anything about all of this. Once you know all of this, that you can write the first word to the last word for 600 pages, 700 pages. It'll just It'll just come to you. >> That's inter- So, your writing, the actual period of pen to
paper, is is is is short, it sounds like, relative >> My writing is actually short. And my editing is not. >> Yeah. >> I I edit very great I I give time. I have great editors. Nan Graham has been my editor from day one. She's an incredible editor, one of the story editors, you know. She's edited Pincheon and Roy and you know, Stephen King and God knows what. She's a great editor. We work together. Editing is not a slow pro- is not a fast pro- is
a slow process. Writing a book is a fast process. >> So, lots of thinking, speeding through the writing, slow editing. >> Yes. >> Yeah, fast on the straightaways, slow on the curves. >> Yeah. >> Hm. >> That's what we think about. Yes. >> Sid, it's always a pleasure. >> It was a pleasure. Thanks for having me. >> Thank you so much for everything. This has always been a great discussion. >> Great. My pleasure and good luck and and I owe you a dinner. >> Talk soon.
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