#412 ‒ The evolving science of cancer: emerging approaches to prevention and early detection, and the advances reshaping treatment | Siddhartha Mukherjee, M.D., Ph.D.

Hey, everyone. Welcome to The Drive Podcast. I'm your host, Peter Attia. This podcast, my website, and my weekly newsletter all focus on the goal of translating the science of longevity into something accessible for everyone. Our goal is to provide the best content in health and wellness, and we've established a great team of analysts to make this happen. It is extremely important to me to provide all of this content without relying on paid ads. To do this, our work is made entirely possible by our members, and in return, we offer exclusive member-only content and benefits above and beyond what is available for free. If you want to take your knowledge of this space to the next level, it's our goal to ensure members get back much more than the price of a subscription. If you want to learn more about the benefits of doing this, please visit www.thedrivepodcast.com. For more information about our premium membership, head over to peterattiamd.com forward slash subscribe. My guest this week is Dr. Siddhartha Mukherjee, a returning guest to The Drive. Sid is a physician, oncologist, researcher, and a Pulitzer Prize winning author of The Emperor of All Maladies, as well as other books, The Gene and The Song of the Cell. I wanted to have Sid back on because, frankly, there are few people who can move as easily, between the molecular details of a disease and the much bigger questions of why we age and what we might be able to do about it. We begin our conversation with cancer, as this is the 15-year anniversary, it's hard to believe, of his remarkable book, An Emperor of All Maladies. We talk about where it comes from, how much it is driven by genetics versus chance, the challenges of detecting it early, and how treatment has advanced, before broadening our discussion to what Sid calls regenerative failure. The declining ability of our tissues to repair and replenish themselves as we age. Throughout this episode, we discuss how genetics, environment, and random mutations contribute to cancer risk. We talk about the challenges of cancer screening and early detection across all cancers, but especially pancreatic cancer, why aging isn't simply the accumulation of diseases, but also the loss of our ability to regenerate, how declining stem cell function may contribute to osteoarthritis and other age-related conditions, the relationship between inflammation, the immune system, and regenerative failure, what it would take to meaningfully extend human healthspan and lifespan, and how AI may help us better understand and potentially address the biology of aging and cancer. So without further delay, please enjoy my conversation with Dr. Sid Mukherjee. Sid, awesome to see you, as always. And it's hard to believe it's been 15 years since Emperor of All Maladies. I'm so glad to be here. I'm so excited to be here. It's been 15 years since 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. 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 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 learned more about cancer in the middle. Let me divide it very simply into three large sections and ask the question, what do we know more about early diagnosis or prevention, early detection and treatment? So three broad categories. So prevention, what do we know about prevention? Dr. Well, good news and bad news. There's a lot of good news and bad news, but there's a lot of good news and bad news. There's a lot of good news and bad news. The good news is that we know a lot more about how 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, you know- Dr. Tobacco and- Dr. Tobacco, et cetera. 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. We know that it's not, that there is a kind of a gun and trigger effect, the gun being the inflammatory state, the trigger being the mutations that cause cancer, or a mix of some states like that. The bad news is that we are still early. I mean, we're still in that story. 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." 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 a high cholesterol for cancer. Almost certainly, we will find it. Dr. Tobacco and- We will find that high cholesterol for cancer, and we will find people who are at risk and 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 a single gene. It's a multiplicity of genes. We know what they are. I could actually quantify, really quantify by genetic sequencing your relative risk of having cancer. Dr. Tobacco and- In some cancers, breast, ovarian, and more and more, and more and more, some other cancers. We know almost with great certainty that obesity is a risk factor for certain kinds of cancers, endometrial being one of them. We know for sure that I'm not speaking in the realm of prevention again. We know almost certainly that most cancers are in mortality across the population in the United States are going down, so we are actually making progress. The idea that cancer is exploding and killing everyone in the population is nonsense. But, but 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. Another way of saying that is that 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 in the years before. We know that a couple of cancers are on the rise. I told you about colorectal cancer in younger people. We know that esophageal cancer, esophagus, is on the rise. We know that a couple of other cancers are on the rise in younger people. We don't know why. In the case of endometrial cancer in women, it's most likely because of increasing rates of obesity. But the rest of it is a mystery, and the colorectal cancer business is a complete mystery. The esophageal cancer is probably because of alcohol and tobacco mixed together, but that's, that's half a mystery. Especially given that, that the trend appears that people are actually drinking less today, although maybe that hasn't caught up to where the cancer's actually rising. Yeah, it's always a lag. Yeah, there's probably a big lag there. It's always a lot. It's always a five to 10 year lag. So, that's all we know about prevention. Big, big deals. We can talk about each of those individual pieces that I talk, that I spoke about separately, and each one of them is a full conversation. All right, let's talk about early detection. We know that there are tests in patients. For instance, that will enable 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, 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. An extra, you know, tests, more tests, following tests, et cetera. But those are not going to decrease, are unlikely to decrease the mortality from cancers. 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 are at high risk, for instance, those with carrying genetic mutations that put them at high risk, or those who have had previous cancers, those tests may actually yield very important results. And we know also that 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 people used to go and have their mammograms once a year. By people, I mean women above a certain age. The mammograms themselves were of relatively poor quality. The once a year was a discrete test. We know that potentially having mechanisms to follow over time, less in episodic ways, but more in continuous ways, is likely 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. We know since the last 15 years that immunotherapy, activating the immune system, is extremely productive against some cancers. 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 smoker with lung cancer. That's not true anymore. Immune therapy is now known to extend the lifespan of smokers with lung cancer if given to the right population of people. These are drugs that unveil or unleash the immune system to recognize cancers as foreign, foreign cells and enable treatment of cancers. We know that in some cases, activated T-cells, genetically engineered T-cells, so-called CAR T-cells, can cure cancer. We know that in some cases, activated T-cells, genetically engineered 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 five 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 unresponsive leukemias and lymphomas who are 15 years post-therapy. We know, and we were talking about it a little before, that early science, that metabolic therapies against cancer, so using the idea that cancer metabolically is different from normal cells, is a potential way to treat cancers. We know that there are second-line and third-line therapies in certain cancers that will truly expand your lifespan. So two good examples are the use of a drug called N-HER2 in breast cancer, which works even after many, many lines of therapies have stopped working. In myeloma, there are three or four new medicines. In lymphoma, there are two or three new medicines. So in prostate cancer, there are two or three new medicines. And we know that there are now surgical treatments for many of these breast, prostate, and other cancers, which are much, much, much better. more sparing of normal tissue so that you can regain functionality more easily and better, and yet remove as much cancer as possible. And finally, in treatment, we know that there are a radical new set of medicines on the horizon. You've seen cases of pancreatic cancer treated with the Daxonracib, a new medicine, against one of those killer genes called. It's extended the life of patients. We've seen videos of these patients on television. They're now living with pancreatic cancer, you know, months, if not years, after the cancer has spread, which would have been impossible 10, 15 years ago. So immunotherapy, novel biologics, and novel small molecule therapies are here. They're on the horizon. They're not just, you know, making people live longer by one month or two months. They're sometimes actually making people live longer than, you know, into the five-year range and extending survival meaningfully. So that's kind of what the landscape looks like right now. I mean, Sid, 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. But let's go back and look at something that we learned in the last 15 years, courtesy 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 gist of the paper was that if you looked at all cancers by tissue type, if I'm remembering correctly, Sid, 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. And you already alluded to some of these, you know, HER2 new and, pardon me, BRCA, BRCA1, et cetera, et cetera. But 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, et cetera. But the part that I think really was sobering for us was that two-thirds, 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 is 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 two-thirds 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, et cetera, et cetera. So how do we make sense of this beyond the observation? In other words, how do we take that observation and do it? And do something about it? So first of all, let's really take the observation seriously. Let's take it totally seriously. Five percent of mutations hereditary, that number is most likely no longer five percent. During Vogelstein's— Yeah, because that last paper, sorry to interrupt, was actually nine years ago. And I'm not—I don't know if there's been a follow-up, so yes. So, you know, Bert Vogelstein is a close friend, one of the great scientists. One of the greatest cancer scientists of the world. But obviously, the field changes, the field moves. That five percent is going to change. So how is it going to change? Well, that five percent was usually focused on single gene mutations. So BRCA1, BRCA2, you know, P53. I can rattle them off. Your listeners won't know them. They'll know BRCA1, they'll know BRCA2. So just to give you a—let's just take breast cancer. So hereditary breast cancer, where you know that there's a hereditary breast cancer. So you know that 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 mutations in single genes that increase risk. But we still—we know that about—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 a fraction, about a third, maybe a fourth, of hereditary breast cancer. So the question is, what's the other—so again, remember, we're talking about just hereditary breast cancer. So 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, et cetera, is 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 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 Bert wrote his paper. Yes, there's bad luck involved, but it's also bad luck plus something else bad that's going on in the body. Such as inflammation. And the best example of that. That is recent papers that have come out from many laboratories. I specifically want to point out Charlie Swanton's lab in the UK, but many laboratories that show that you can have cancer 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, and I don't smoke, you don't smoke, we would almost certainly find cells that genetically speaking, would bear all the hallmarks of genetic mutations of cancer. But you and I don't have cancer. So 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 had. It's something that's emphasized. It's something that's gone out of the scientific literature. It's sometimes very important, Peter, to publish an observation without explaining it. You know, I'm going to take a philosophical aside here. I'll come back to the real point in a second. I'm going to take a philosophical aside here. Our scientific publication strategy has become a form of magic storytelling. By magic storytelling, I mean you have to tell a full story about what you observe, 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, 1960s, 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 five 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 idea that they made very thorough, important observations. William Osler, perhaps the greatest of physician scientists, said, observe the patient first, explain later. So this is a philosophical observation that I want to make. So let's go back. So in the 1950s, a team of doctors, of scientists from Oxford published a paper which remained unexplained. And this was, it was a very simple paper. They took a carcinogen, let's call it whatever you want to call it, let's call it DMBA. It's 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? The 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. Dr. Andy Roark: Years and years later in experiments by Alan Balmain and UCSF, they genetically engineered a mouse to get cancer. So this mouse was genetically predisposed, just like a human being with BRCA1 may be predisposed to get a cancer. They made a, so, and sure enough, when the mouse grew up, it had four or five cancers. Dr. Andy Roark: There were four or five or six cancers where, you know, obviously where the gene was making cancer cells. And here's the interesting thing that happened. They made a little slit or a cut, an inflammatory wound in the mouse. And again, nothing very impressive happened. The next thing they did was very interesting, just by chance. And this is why science is very important. It's very important to do experiments. It's more to report experiments. Dr. Andy Roark: And it's important to 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? Dr. Andy Roark: It tells us something very important. It tells us that this whole idea that there's bad luck. Yes, there's bad luck. Let's say you, when I biopsy your lungs, I get cancer. Dr. Andy Roark: 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 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 the cells grew, 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. Dr. Andy Roark: 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 the word energy before Newton, or using the word atom before Dalton. When we 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." There's something very particular about the inflammation. We can talk a little bit more about it. Dr. Andy Roark: You had a particular kind of inflammatory response in your body. And one of your 2000 cells, which already was carrying a mutation, turned from a sleeper cell, a dormant cell, a doing nothing cell, into a tumor, into an active cancer cell. And from there on, you developed cancer. Dr. Andy Roark: I was lucky. I didn't have that inflammatory stimulation. My 750 cells remained dormant, remained quiescent, remained asleep, and I don't have cancer. So now let's go back to Bert Vogelstein. 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. Maybe in some other cancers, more and more. And that number unlucky, a cancer of bad luck, is no longer a cancer of bad luck. Very, very soon, I hope. I can't give you a date. Very soon, 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. Dr. Andy Roark: Okay. Dr. Andy Roark: So this whole idea of dividing up and creating a typology of cancer, every typology of cancer evolves. It's evolved since the beginning of any typology of cancer. And so those numbers are all going to change, and going to change in a way that is sensible. So Bert is not wrong, he is just early. Dr. Andy Roark: And maybe there's another point to just double-click on this, Sid, which is the way. Dr. Andy Roark:. 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. If you have a bunch of shove mutations, or pardon me, a bunch of nudge mutations that are acquired, or pardon me, that are passed through the germline, the environmental mutation is really a push or a gentle nudge. Spinal shove. Yeah, exactly. So in other words, these are not discrete. They're going to start to overlap a little bit more. Absolutely. And this is super important because this is not just theorizing, right? These have very practical implications for patients. Because if I could figure out where you are in the nudge versus shove cascade, 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 prevention program or a trial prevention program. So these are not just sort of abstractions. These are realities. The last thing we didn't touch upon is metabolism. We talked about inflammation. 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 an enormous risk between, and we don't know why exactly, but an enormous risk that connects, for instance, endometrial cancer with obesity. And endometrial cancer is one of those cancers which is either plateauing or maybe on the rise in women, maybe because of obesity. But what's the connection? Is it through inflammation again? Is it because there is a hormone that's making, you know, the sleeper cells alive? Obesity itself is unlikely, I would say, to cause mutations. It's not, obesity is not a mutagen. But obesity 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, well, in humans, we don't know. But about five years ago, Luke Cantley and I wrote a paper in Nature, which actually you were, you inspired in some ways, where we know that in, at least in animal models, you don't need to be obese in order to unleash, or at least in order to have a cascade that sustains a cancer. I have to be careful with my words, because that paper was really a paper about treatment. But what we can surmise from that paper, and I'm being careful with my words, surmise, 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. 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, may have something to do, with releasing cancer cells 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 exposed to hyperinsulinemia without the appropriate insulin response, which normal, non-obese people have. 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 the case. Yeah. Mitch Lazar at Penn wrote a paper, it's been about 15 years as well, and he looked at the incidence of cardiometabolic disease, and he sort of created a two by two, right? He looked at the incidence of cardiometabolic disease, and he sort of created a two by two, right? And he looked at the incidence of cardiometabolic disease, and he sort of created a two by two, right? So you were either obese and or metabolically 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 do the two by two of that, what Mitch's paper found, again, this was focused on cardiovascular disease, not oncology, 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, 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 again, as I said, our work has been, and it's now a full fledged clinical trial, we can talk about the trial. Our work has focused on treatment. And we know that chronic, insulin tone, I use the word tone, like, you know, like the tone of muscles, but chronic insulinemic tone has the capacity to make cancers grow and 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. These are facts. From those facts, you may extrapolate that chronic insulinemic tone is also responsible for the sustenance and maintenance of cancer 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 state. Now, I would be so happy if someone did that experiment. It's not, 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 a fussy experiment to do. And good experimental people can do fussy experimental science. Sid, 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. To pick the right cancer, you know. You'd have to pick the right cancer. You know, I would pick endometrial or ovarian or some obesity- Even breast, potentially. Oh, breast? Yeah. Yeah. And really think through it. This is a, you want to do this thought fully. You don't want to just run into this and just do it 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 randomize to patients with on GLP-1 versus on GLP, with not GLP-1. You would study their insulin, 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, sweeping epidemiological study, which you could do in five years or 10 years from now. It's just a little bit too early. And just ask the question, listen, if, you know, people who are on GLP-1 versus not GLP-1, you know, is there a lower risk of endometrial cancer? Now- See, I prefer the experimental approach here, Sid. Of course. Because I, yeah. And not just because of time. I think there might be too many, too many confounding factors if we just do it observationally. Of course. Of course. I mean, of course. 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. No, no. So yeah, of course, we all prefer the real, proper study. And I think someone should fund this 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, to anyone in the audience who's, you know, interest in longevity because I'm interested ultimately in, you know, what these drugs do, not just to cancer, but to the overall physio-inflammatory state, which is certainly pushed by insulin. And I would love to ask the question, well, what happens to longevity markers? What happens to, you know, so this is a study that one of the many, very many people in who have the means and capacity to do so can fund through, you know, a remarkable institute like UCSF or Columbia or Stanford or Harvard. I'm just going to name some names, but there are many. Yeah, yeah. 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 have. This is, you know, one of my favorite topics. Yeah. 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 osteoarthritis is my neck of the woods because my laboratory discovered the first osteoarthritic stem cells. We published that in Cell and then subsequently in Nature. And Nature Medicine, I have never believed from the beginning of the time I began to study osteoarthritis, I never believed that osteoarthritis was an obesity disease. There are non-obese people who have osteoarthritis. There are obese people who have osteoarthritis. It's much larger. But the idea, there was something kind of like, 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, et cetera. 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 medicine for women, a backwards problem, which is to say, oh, you know, they have, they are obese and therefore they must have weight-bearing problems and therefore it's a weight problem. But then 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, and they have a lot of weight. They have a lot of muscle. They have a lot of, you know, inflammatory nodes, inflamed nodes in their hands. The hand is not a weight-bearing joint. Some of them have, you know, degeneration of their upper neck joints. Their complaint is upper neck pain, shoulder pain, and they're not obese. And you say, well, this observation can't be true. This, it just doesn't make any sense. So now we know that their, 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 genesis and destruction is an adequate balance, then we maintain cartilage and we don't get osteoarthritis. If the genesis gets stopped or 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, as weights on a scale, remember, normal is equal weight, genesis, genesis and destruction, genesis, destruction, genesis, 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, some of it is age, age, because, you know, in overage, 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? Yeah, it could be. It could be like a bacterial mimic? Yep. Yes. So something to activate the immune system. Yeah, 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, you'll start an inflammatory response in, and it will create, it will mimic, almost exactly the same response to arthritis in the animal. The balance, remember, again, the weight scales, genesis, destruction, the destruction 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 hosts stagnate. 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, 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 immunosuppressants, 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 theme. Yeah, yeah. I mean, there must be some at least teleologic explanation that I'm just missing. No, there's a, you know, but the problem, as you know, Peter, teleology, teleology is not biology. No, it's rationale. It's a way we tell ourselves. We tell our story to ourselves that we like. It's a just-so story. It's like Rudyard Kipling's just-so story. Yeah. Teleology is like saying, oh, I think it's because in ancient times. Right, right. Exactly. 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. You know, stem cells had to shut down because they had. That's right. They were, they were too energy demanding. Yeah, obviously. The immune system. Yeah, yeah, exactly. I mean, you know, 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 or a pre-human being or a monkey or a God knows ancestral common ancestor in some stress condition 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? The more relevant question is how. If we knew the how, then we could intervene because 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 should say this as a, I'm not taking off my hat as a, as, as an immediate scientist and talking to you as a science philosopher. Why questions are very hard to answer in science? How questions? Why 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 were to ask me why, 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 lives in the depths of, of very abstruse physics. And by the way, you know, I don't know. biology is so many degrees removed. If you ask me, you know, why does a stress response in humans shut down blood-forming stem cells? I'd say I have no clue, but I can tell you how. I can tell you exactly the molecules. I can tell you the molecules that are shut down. I can tell you who's moving. But why? Why is not a science question? 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 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, we- No, we have a remarkable immune system. Yeah. So we have this incredible immune system. And we have a remarkable immune 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 my mind, which was that 80, 8-0% 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. I want to spend some time talking about T-cell biology and what do we think it's going to take? 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 been around the block too many times. The answer is likely all of those things, but let's talk about neoantigens for a second because we need to really think about this. Steve Rosenberg, who I know very well, was a pioneer in the field. Years ago, Steve Rosenberg and I had a very public argument on stage at a very prominent cancer conference. He said, well, there are neoantigens in cancer. I said, but where are they coming from? He said, well, some of them are coming from cancer. Some of them are coming from mutations. I said, well, come on. There are like 30 mutations, 50, 100. 100. Yep. Call it 100. It doesn't explain it all. It doesn't explain anything. Now, there's beautiful work from Tyler Jack's lab and I would say it's something from my lab that says that that's not the real neoantigen. The real neoantigens are coming because when cancer cells start deregulating, dysregulating, 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 making proteins. 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 neoantigens that are coming? And number two is why aren't T cells recognizing them? The second part of the problem is more interesting, which are 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. And 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 a T cell. And that's why we don't have desperate, terrifying autoimmunity. 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 one. It eats the pancreas alive. Think of a B cell, 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 their 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 anthropomorphizing a cancer. Right, but that's okay. It's a safe spot for that. This is an evolutionary process, right? 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 create flags that enable them to tell the T cells, and these 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 regeneration, 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 it. Yeah, yeah. So how much is that sci-fi and how much is that something that we might see in our lifetime? Well, now we come to AI. One of the most interesting talks I heard recently was from someone at Harvard who was looking through CAT scans of patients and asking the question about wellness, longevity, disease, cancer, et cetera, using AI and asking the question, was there anything in the scan of their bodies that was correlative, not causative, that 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 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 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. You can propose, 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, as, 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 has 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. Could we make a T-cell booster? What better T-cell booster to make than re-implant the kindergarten where T-cells grow up and learn to become T-cells? This study very clearly points to the idea that that's one of the places to start. There was a paper that came out, I don't think it was quite 10 years ago, but probably around then. I say this with no disrespect to the authors or anything. It was a bit of a clumsy paper aimed at thymic regeneration. It was a little bit of a kitchen sink approach. They gave these patients growth hormone and metformin and a whole cocktail of things. On imaging, they showed, hey, look, in the patients who got this. By the way, it was even worse because I think it was an open label study, so there wasn't even a randomization. They said, look, the thymus grew. This sort of idea of thymic regeneration became very popular. 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 giving. Have you followed this space at all, and are there molecular attempts at this? I have. I have. You know, again, the good news is we're going from a human. Generally speaking, we're going from a human epidemiological observation to a human epidemiological observation. 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 thymus tended to have fewer various diseases. This was picked up by an AI. This was picked up by AI reading of multiple tens of thousands of CAT scans. 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 can 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. Otherwise, you know, why would. Let's say your thymus. You and I, let's say, are the same age. I don't know if you are. But your thymus is gone. My thymus is not gone. Can I find the growth factor that made your thymus involute in mine and not involute? Great. I'll replace yours with a growth factor. But, you know, putting in random stuff, metformin, it's just. That's just a mess. Yeah. Yeah. It's just a mess. So 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. Sometimes there's a lot of insight, there's some intuition, blah, blah, blah. It's okay. But ultimately, 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 growth, we'll find the hormones, there's. I'm sure. 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 success of CAR T therapy. Especially around the B-cell lymphomas. B-cell lymphomas and leukemias, where, to your point, we were talking about durable cure. We're not talking about extension of median survival. Maybe just spend a little moment explaining what the CAR T is, like what is a chimeric T cell, et cetera. But where I really want to go with this, Sid, 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 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 cancer cell doesn't have enough flags on its surface that prevents it from being recognized. Okay. So we get that. That's standard immunotherapy. Okay. There's one example of that. There are other ones. 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. The virus just basically inserts it. So I'm going to do that. I'm going to put a gene into the T cells. I'm going to grow those T cells 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. This has been a great success in leukemias, B-cell leukemias, and B-cell lymphomas. Okay. Okay. So what's the next step? So the next step is to start using the T cells. So then there's no physical reason why we should not have a car team. cell against breast cancer then? Breast cancer is a hard example. You've chosen a pretty hard one. Pancreas is a hard one because there's, you know, there's not, there are 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, which really, 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 ovarian cancer cells have an antigen. It's very well known. They have more than one antigen. And there are multiple attempts at making CAR T cells against them. One of them is going to be successful. Good example. What percentage of pancreatic cancers have KRAS mutations? Aren't most of them? Most of them have KRAS mutations. And does the KRAS mutation produce an antigen that we could target with a CAR T? Not really. It produces an antigen in some patients, but most of KRAS is hidden inside. Most of KRAS is not a CAR T target. Now, KRAS, I'm talking about KRAS, KRAS is a great target for a small molecule, 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, Daxon racib, nasty side effects. You've seen the senator with those 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 get over, but as in find new medicines 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 one diabetes have no pancreas, or they have no- Effectively. No endocrine. Yeah, yeah. No endocrine. Endocrine pancreas, yes. People with a Whipple surgery- Yeah. They lose enough pancreas. I mean, plenty of patients have Whipples with a significant- I have no pancreas. So yes. So yes, if we could eat up every pancreatic cell, and people would still live, yes. But we haven't found one yet. That's one direction we could go in. Another direction we could go in and say, listen, we can't eat up the whole pancreas, including pancreatic cancer, but we can shut off the gene that drives pancreatic cancer, KRAS. That's on RASP, does that. Nasty side effects, but maybe we can live with that. And then, and that's another direction to go. What do you think it is about- Sorry, go ahead. That's about immunotherapy. Yes, yes, correct. 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 five years after they were diagnosed with an adenocarcinoma of the pancreas didn't have an adenocarcinoma of the pancreas. So the argument that those people are making, which again, I don't personally agree with. I don't think that's true. 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 Halsteadian 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. But if we could detect of those 95% who were caught too late, could we catch them earlier? Where do you sit on this spectrum? So look, I mean, so I agree with you on this. I think, so as you know, the pancreas is a long organ. It looks like a big leaf. It's sitting sort of in your gut. Fundamentally speaking, there are two kinds of pancreatic cancer. The ones that are in the head, one set in the head are closer to the parts of your intestines. The ones that 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. I think that surgically speaking, if you can find the ones in the tail, they're less involved. They're less involved with other organs. It's an anatomically distinct kind of cancer. So I think surgically speaking, some of those patients can actually live longer. 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, you know, it's like a traffic accident. It's probably the most complicated real estate in the entire avenue. Below the neck, I would say it's the most complicated. 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's surgical cures of patients with, I have patients who lived 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 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 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 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 neoadjuvant therapy. Adjuvant because it is an accompaniment to the main therapy, in which case, which in this case was the removal of the bulk of the tumor. The real interesting question is what's Daxon RASP 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 cancers, found to have a big lump goomba in the, in the traffic intersection of all of this, goes through a Whipple. Now, most of the visible cancer is out. And now we put them, they have KRAS mutation. We put them on Daxon RASP and then ask the question, are they better or worse off? Is that trial, is that trial being done? It'll be done in, if, if, if it's not in rolling right now. I mean, is it in the works is what I mean? Is it, it must be? It is in the works. Absolutely. Okay. Yep. In, in, in thousand ways. And by the way, that's, I mean, that's such an important example of a principle in oncology that has some exceptions, but not many, which is- Some exceptions. 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- And, 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 that if you, if you have 1 billion cancer cells and you take them out and there's 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, Sid, that I am not seeing 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 shit 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, why are we seeing this go up? Is it antibiotic use, gut health, obesity? Where do you think the culprit is? If I was to be totally honest, I would say I don't know the answer. If I was to be a little bit more speculative, I would say it has to do, our colorectal milieu is rapidly changing and has rapidly changed in the last few decades. We are ingesting, more and more forever chemicals. 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 it brings us back to the inflammation theory of cancer. We have strong evidence that these are multiplied by changes in the microbiology of the gut, the gut microbiome, which is being disrupted by all sorts of things, antibiotics and other foods included. And this is, I'm sorry, this is, again, maybe for your audience, uncomfortable territory, but Dennis Burkett, who went to Africa, the person who discovered Burkett's lymphoma, went to Africa in the 1950s, 60s, I don't remember when, but from Oxford. And he said that, "We virtually found no evidence of colorectal cancer, especially in young men and women, because the diet was so fiber-rich that virtually everyone was essentially evacuating their colorectal content twice a day." In the United States, we've reached a place where we're doing that once every two days or three days. And that's what's happening. We're doing that once every two days or three days. That's what's happening. So my recommendation for people who want to avoid colorectal cancer is triple or double your fiber intake. Obviously, reduce exposure to these forever chemicals. And based on no evidence whatsoever, use probiotics. And probiotics are cheap. I don't mean go buy very fancy. I just mean have live yogurt. Yogurt, yeah. Yeah. Yeah. At least once a week so that you can replace your probiotics. So we've touched a little bit on AI, and I know that it's something that we are both thinking a lot about. I want to talk about it through the lens of maybe drug discovery. So 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, there are lots of reasons for why CT scans are a beautiful environment for AI to shine. But let's talk a little bit on the drug side. Now, AlphaFold, 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, this is a very exciting area. So 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 fantastic. 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 speed things up. So are there other areas you're thinking about this in? And then 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. I'll just give you a couple more unlocks. I'll give you one example in the beginning, which is. Yeah. using AI as an epidemiological scanning tool. So that's how researchers found the thymus correlation, right? AI for the recruitment of clinical trials. We haven't talked about that. AI for the monitoring of clinical trials. So it's AI all the way. My personal arena has been AI in drug discovery. So as you very well know, every medicine, every drug, aspirin, Lipitor, Prozac, works by binding to something. It's a key and a lock. Lock and a key. The protein that it binds to is the lock, and the drug is the key. It opens or shuts down some function of the protein. Lock and key is a little bit miscommunicated. It's a little bit miscommunicated. It's a little bit misleading because the proteins are actually moving in space. They're wriggling around. So it's a wriggling lock. 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 know the lock. Lipitor is a key. We know the lock. Prozac is a key. We know the lock, and so forth. All right. So 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 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 the smartest chemists in the world were required to create these keys to the lock. And another way to do this is to do it 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 call synthetic molecular design, generative molecular design, and the second one I'll call 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 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. So 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 you have to have an inferential way of taking. So in other words, let's boil it down to simple language. You start with. Yeah. 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, this is my guess, it will require quantum computers able to do that level of inferential reasoning. Because 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 more than a billion billion. So that's a huge shortcut. It's more than a billion billion. But that's still an enormous undertaking. Yeah. The second way, which is the usual way that we are doing drug discovery, is to look at the inside of the lock and ask the question, "What kind of key will fit into this lock? How do I design a key?" And synthetic molecular chemists working in laboratories, in pharmaceutical companies, et cetera, they have rules. They know that if there's a crevice here, I'm going to put a certain kind of molecule there. If there's a hollow surface there, I'm going to put a certain kind of molecule there. If there's a hollow surface there, I'm going to put a certain kind of molecule there, et cetera, et cetera. So this is generally speaking, it's generative chemistry. Generative because you're making it from scratch. You're not just searching and fitting. But you need to know the rules of chemistry and physics. This is not biology anymore. This is pure chemistry and actually really is pure physics. The good news is that you can teach an AI all those rules, Mm-hmm. and that's what we've done at Manus. 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? Because 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, that is what we do. We look at the shape, the geometry, the electrostatics, all the elements that form the pocket where you want to put your key. And we design 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 chat, it is not a image-making bot. 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 very briefly touched on how much more complicated biology is than physics or chemistry. Rules are not as neat. So is this. There are barely any rules. Yeah. So is this actually something we can aspire to? Yeah. 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. Right. It has some resemblance, of course. Metabolically, it's the same, blah, blah, blah. But its job is completely different. Its job is to transmit information, sequester information, storage, whatever it might be, right? A cell in your skin is a completely different kind of cell. So I'm a little skeptical of. 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 there's a very mechanical and relatively straightforward functional determination of what it can do. Whereas the neuron, just to use that example again, completely different. 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, oh, this ion moves through this channel against this gradient, and when this gradient switches, it does this. Yeah. So. So. So listen, I think 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 cell by cell level, is it going to be. Is it going to work? No, I'm a skeptic, but skeptics are often wrong. Sid, if we're sitting here in 15 years, which 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? Oh, I think that, you know, we've covered a lot of it. New antigens, 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, you know, as I said. Where do you think it will translate to the most life saved? Do you think it will be that. Do you think there's a chance in 15 years? The answer is always going 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 chemoprevention? I was 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 chemoprevention. Do you actually think there will be a day when we have molecules that will prevent cancer? Yes, there will be chemoprevention. I just. I, it is not clear to me that the chemoprevention is going to be, it's not clear to me that chemoprevention is going to be as simple. So remember, Lipitor blocks decreases your LDL levels. 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 relationship that's like that. Although what you described with inflammation, there's actually an analogy in cardiovascular disease. As I'm sure you know, we are now starting to check for CHIP in patients. Yes. And while our therapies are still limited, we have colchicine. Now it's a crude drug, but there's evidence that if you have, you know, if you're. Oh, we have better as an otocine, by the way. Yes, I'm saying in the cardiovascular side, 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, in obesity and potential heart disease. Potentially 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 become motivated by that idea. Yeah. 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. Models, yeah. Models, yeah. They're a variety of anti-inflammatories of which we know some. But, again, you know, Peter, I know you care about things like longevity and I do too. You're not the only person who cares about longevity, obviously. It may turn out that the answer is orthogonal. It may turn out that the answer is that, and we know to some extent this is, we already know the answer to this question, that, again, 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 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, not the only current model, but 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, inflammation, chronic 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 inflammatory state, reduce the chronic insulinemic tone, and there's a big maybe there, because otherwise everyone's going to come and attack me. But if we could change the physiological state of the body, reduce the inflammatory state, reduce the chronic insulinemic tone, and there's a big maybe there, because otherwise everyone's going to come and attack me. But if we could change the physiological state of the body, reduce the chronic insulinemic tone, and there's a big maybe there, because otherwise everyone's going to come and attack me. And tell me I'm a crap bot. But maybe it is that the reduction of the chronic inflammatory insulinemic tone and the chronic tone that comes from thymic reduction, I mean, all the things we talked about before, wellness instead of illness, maybe that's the path to longevity. You know, Sid, I think about this all the time. I know you think about it more than I do. No, no, no. But yeah, I'll share my thoughts. So I agree with with the broad statement, for sure. 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 take aspirin, Lipitor, and PCSK9. You can take like 17 drugs now. That's right. 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 physiologic levels you were born with, and if you ever develop hypertension, you immediately nip it in the bud with a drug, 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 et cetera, et cetera, et cetera, 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 super, super generians, people who are more than 100, 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 dying of regenerative failure. But believe it or not, they still die of atherosclerosis and they still die of cancer. They still die. They still die of infections. Their immune cells are breaking down. Infection is immune failure. Yes, yes, yes, yes. But what they're not dying of, I mean, assume for a second that they took every anti-atherosclerotic drug, which they don't, by the way, because they're in Greece or some island in Corfu or somewhere else. God knows. But what they're not dying of is cancer. Yeah. They're dying of bone breaks and skin failure and immune failure and blood failure and all of these other things. 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 usual suspects. Neurological disease, cancer, heart disease. 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. You know, I've been writing a lot about thinking a lot about Elie Metchnikoff, 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 propose that we study death systematically. He proposed that we study it. He created the word thanatology, the study of death, which of course the opposite of which is a study of longevity. Metchnikoff argued that if you could remove the proximal causes, the pathological causes of death, cancer, heart disease, neurological disease, and of course- Accidents and infections. Accidents, infections, et cetera, et cetera. That human beings could live to a hundred, two, and they would at some point of time would naturally want to die. Just like trees at a certain age, hundreds of years, finish their sap and die. 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 we are. Maybe in eventually, we need to think about, instead of thinking about, oh, let's pick off the buds one by one, atherosclerosis, cancer, et cetera. 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. And so what is your degree of optimism that, because if you, what is the sort of AI buzz system? 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. 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 in biology. Why do, what is, you know, what is regenerative failure of cartilage? I don't know. But what is regenerative failure of the brain? I don't know. What is regenerative failure of the immune system? Okay, fine. 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 the immune system, but the immune system as just sort of, oh, you know, let's change our diet and make ourselves, I don't know, somehow 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 the combinatorics of injecting yourself with a random group of peptides, the chances you'll find that the right peptide at the right concentration are basically zero, like zero. It's like there's no, this is a mathematical truth. Well, it goes back to what you said about the billion billionth chance, like a billion billion chances. Yeah. But also- But on the other hand, if you were to find the right peptide, I'd be rooting for you, but not you, but rooting for the person who found the right peptide, but 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- Yeah. How to address these regenerative failures and separate it out from the diseases that kill you at 70 and 80. Let's separate those out. Yeah. 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 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, that's a matrix, red pill, blue pill. Yeah, beyond my pay grade. It's beyond my pay grade, but it's true. Fair enough. You can imagine there's a red pill, blue pill, matrix, longevity universe that 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. A 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, they need systematic, and we're beginning to find out. AI is helping us find out. I give you one example, but there are many. But this is not really a priority of the NIH. I mean, the NIH is not putting the resources into studying the hallmarks of aging and geroscience. Because to your point earlier, Sid, if you want to start answering these fundamental questions, you must do them systematically, right? And you started with a the 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 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 would argue that it nearly lies outside the current NIH mandate. And we need a new NIH, if we were to do this, which we should or could, it needs a new NIH mandate. The problem with zero science is, remember, again, history is very important here. What really changed cardiovascular science? It was the fact that we found biomarkers for- There were proxies for disease. 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-talk studies, we found that those medicines actually reduce heart attacks. You don't have that for cancer. And that's a separate question. You don't have it for aging. You certainly don't have it for longevity. So there's talk of biological flocks. So here's my question back to people who are studying. Okay. To people who study longevity science, an important science, very important science. Find me a reliable biomarker. The problem with finding me a reliable biomarker is that the biomarker study itself will take 15, 20, 30 years. Because 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 interviews. We could go back and look at the interventions testing program, which is that great NIH program that looks at all the molecules. 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, is very big science. It's very big science. And it's a reliable science. The only problem with it or the question with it that I would argue is that we can't screw it up because- 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've found it, again, remember Lipitor, remember cholesterol, remember hypertension, you can do dozens of studies to think about its systematity. But until that's found, we're sort of stuck. You know, it's funny. Just a couple of weeks ago, I was talking to a friend of mine, who I'm sure you know, David Sabatini, good friend of Lou's. Yes, and a friend of mine. 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 his point, which I think is absolutely spot on, is the effort must be done. The effort must have a significant engine in the basic science. It cannot go straight to drug discovery, which is the temptation. When these things are started as for-profit companies, you have to return to shareholders quickly. You've 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 loop is quite clear. You start with human observation, you go backwards, forwards, you understand the basics. And David's absolutely correct. And, you know, he's been studying this for years with Lou, with me, with others. 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, et cetera, for aging, we're going to be lost. And it requires a 30-year. Yeah. 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. The current NIH. I don't think the NIH ever will. And I think, yeah, I think there's many reasons why this is not going to be within the purview of NIH. Unfortunately, it should be, but it won't be. And therefore, I think. I think you, me, Lou, David, and I are going to have to brainstorm some ideas here. You, me, Lou, David, and the bunch of us are long overdue for a dinner to brainstorm some ideas here. Well, happy to have the dinner. And as I said, if there are people who are truly interested in this without the temptation of let's make a. We need a drug. We need a drug within six years. Yeah. Yeah. If there are people, then we should really launch this, launch it systematically. There is a National Institute of Aging, as you know. They could, if they wanted to, launch it. Yeah. And 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. Yeah. It's not. It's. And that is a great thing. We've done great things with it. And it has produced incredible things from vaccines to Lipitor. Great. That's wonderful. It's not the current mandate. Well, Sid, it sounds like I need to get up to Boston or New York and we all need to grab dinner. Well, as you know, I mean, 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, the idea was initially proposed by you to Lou Cantlie and to me. And then Lou Cantlie and I sat down for dinner in a restaurant in the Upper East Side. And I was like, "Well, I don't know what's going on." And then Lou Cantlie and I sat down for dinner in a restaurant in 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's work in the 1900s, the cancer cell, the metabolism of a cancer cell is not the metabolism of a normal cell. And every time, you know, you're saying something is different about a cancer cell than a normal cell, that makes it possible. It's an opportunity. It's an opportunity. So in this case, we used that opportunity to make new kinds of drugs that would particularly target cancer metabolism and less-so-normal metabolism. And that led to a company called Phyeth, Phyeth Therapeutics. It went public maybe two weeks ago, four weeks ago, God knows when. But it's in the middle of trials. And early on, they're 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 no longer know anything about the internal information of a company. But the last we heard, the last public disclosures that we heard, 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 owe you a dinner in Manhattan. Yeah. I remember that dinner very well. It was sushi, and it was a great sushi dinner. Are you working on another book? You mentioned your writing. Yeah, I'm working. Well, this is why I talked about a lot of this. I'm working on a book on death 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 systematize? How do we really systematically think about longevity, systematically think about death? What is your process for writing? I'm just going to have to embarrass you a little bit, 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'm not just saying this, Sid. I think you are 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 appreciate that. And I appreciate that, too, Sid. And I really mean that, Sid, you. 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. And I don't want to suggest that you're just gifted, and it doesn't come down to how hard you work. But my guess is it's a bit of both. I think you have a way with words, and you obviously work very hard at it. So tell me a little bit about the process by which you're writing this book. Look, you know, 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. 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 words for it. thinking about what the book will sound like. Who's driving the narrative? Because there has to be a who. Where is it placed? where does it go? Where is it geographically? What time is it in? What mood does it convey? Who's the reader and why is he or she being moved by it? 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? 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. And once you've gotten that piece straight, it turns out that writing a book, I mean, it's complicated. You know, you have to do a lot of explanation, blah, blah, blah, blah, blah. But then the book just kind of moves along much more quickly. So in other words, you know, musicians often say this and you have to listen to the musicians because 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 notes are just a mechanism to drive the mood of the book. By the same logic, you need to know the mood of the book. And the skeleton, once you have the skeleton, you can hang meat on the skeleton. You can hang pages on words, et cetera, et cetera. So my advice, process, whatever it might be, whatever you're going to want, don't write the first word before knowing anything about all of this. Once you know all of this, you can write the first word to the last word for 600 pages, 700 pages. It'll just come to you. So you're writing, the actual period of pen to paper is short. It sounds like relative. My writing is actually short. My editing is not. I edit very great. 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. She's edited Pynchon and Roy and Stephen King and God knows what. She's a great editor. We work together. Editing is not a slow process. Writing a book is a fast process. So lots of thinking, speeding through the writing, slow editing. Yes. Yeah. Fast on this right away, slow on the curbs. Yeah. That's what I think about it, yes. Sid, it's always a pleasure. Always 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 I owe you a dinner. Talk soon. Thank you for listening to this week's episode of The Drive. 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