#213 ‒ Liquid biopsies and cancer detection | Max Diehn, 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, full stop, and we've assembled a great team of analysts to make this happen. If you enjoy this podcast, we've created a membership program that brings you far more in depth content. If you want to take your knowledge of this space to the next level, at the end of this episode, I'll explain what those benefits are. Or if you want to learn more now, head over to peterattiamd.com subscribe Now without further delay, here's today's episode. My guest this week is Max Dean. Max is a Professor of Radiation oncology, Vice Chair of Research, and Division Chief of Radiation and Cancer Biology at Stanford University. Max is a co founder of Foresight Diagnostics, a precision medicine company developing novel liquid biopsy tests for measurement of minimal residual disease. He's also the co founder of Cybermet, a company that applies data science for biomarker discovery. Max also consults and advises a number of companies in similar spaces. Max's current research involves the development of novel methods for detecting circulating tumor DNA and in the blood of cancer patients. He also works to understand cancer cells by identifying molecular pathways and genes associated with disease, and he's interested in uncovering biomarkers that can predict response to therapy or predict patient survival and return of disease as early as possible, which is something we'll get into in the discussion so you can understand why it's so important to predict recurrence as soon as it happens clinically. Max is a radiation oncologist and he specializes in lung cancer. He he manages a broad clinical research portfolio and he focuses on improving these personalized therapies for patients with lung cancer. In this episode, we talk about a lot of things. First of all, Max and I were also classmates in medical school, so we catch up a little bit on that and we talk about his background and how he became interested in liquid biopsies. We go into great detail here on sensitivity, specificity, negative predictive value, positive predictive value. These are things that everybody needs to understand if they want to be smart on diagnostics and if they want to understand cancer screening. We talk about why these things are important and and in particular how they play into cancer screening, especially when it comes to understanding prevalence and pretest probability. We spend some time talking about lung cancer, which is the number one killer for both men and women. And it's not just a smoker's disease. Remember, 15% of people who die of lung cancer have never smoked a cigarette in their life. So this is an important cancer, whether or not you are a smoker or not. From there, we dive really deep into liquid biopsies. The landscape, the history, the possible future of liquid biopsies. The for me, this was the high point of this interview. In fact, in preparing for this interview, I myself had to get a lot smarter in liquid biopsies. Certainly I know more about them than the average bird, and I've spent a lot of time looking at them over the past two years. But I think in this episode we get a lot more granular around the nuances of the different ways in which not just we can look at circulating tumor cells versus cell free DNA, but when looking at cell free DNA, what are the different methods that we can use to predict if a cancer is present? In other words, how can we look at the actual genes from the actual cancer that we know we're searching for versus in a screening situation when we don't know the gene that we have to look for other clues. So we talk about these cell free DNA RNA signatures, we talk about methylation patterns, we talk about the importance of knowing mutation information, talk about the difference in some of the screenings being approved by the FDA versus those that are being permitted to use for patients without FDA approval. Formally, there's a lot packed into this episode, but it is truly one of the most important subjects given the difficulty in treating cancer when it becomes advanced. So without further delay, please enjoy my conversation with Max Dean. Hey Max, thanks so much for making time today. So wonderful to see you again. It's been a lot of years, huh?

It's been a long time, actually. I can't remember the last time we saw each other, but we started together in medical school at Stanford, but then finished at a little bit different times. I think it's been a while since we saw each other.

As I was sort of joking earlier, we're going to have like a whole subset of the Drive podcast, which is based on the Stanford MSTP students. Between Carl, you and Josh kind of speaks to the quality of people that were a part of that program. Let's tell folks a little bit about kind of work you did. So as you mentioned, obviously we started in medical school together. We have some really funny stories from the beginning of medical school, which I think we'll refrain from telling at this point in time. I could have a whole podcast just on some of that stuff. And then after the first two years of medical school, I went right off into the clinical stuff and then you went off into the lab. Tell folks about whose lab you went to and what it is that you started pursuing and frankly, how you even.

Made that decision, as you pointed out, as part of the MD PhD program, the way it is done in most programs, you split the curriculum with splitting medical school in half, doing the first two more classroom based years first, then doing the PhD work in the laboratory and then going back for the clinical work. And there's an important transition point there when you're finishing the classroom part of medical school and deciding what lab to work in. I ultimately chose to do my dissertation with Pat Brown, who is a professor in biochemistry here at Stanford. He is no longer. A lot of your listeners may know of him though, because he's now a CEO of Impossible Foods, or was until recently. I think he just transitioned to a slightly different role. Founder and CEO initially of Impossible Foods. But he was here a faculty member. And what really attracted me to his lab was that he had around that time invented technology for measuring the expression of basically all the genes in the genome with a technique called DNA microarrays. And that at the time was revolutionary. Before that, we were always measuring everything in one or a handful of genes at a time in experiments. And now we could measure tens of thousands in one experiment. And it just seemed to me that this was opening up whole new fields that we would be able to learn so much. And that's why I chose to pursue that lab.

So what did you do for your actual dissertation? What was the project that you worked on?

I had a little bit unusual dissertation. Then I worked on many different projects. It was a very unique time in the lab and Pat's lab as well as in labs in general where, and I've seen this happen since several times. But when there's a new technology that's developed that's sort of transformative, it opens up so many doors simultaneously that, you know, you have this new tool that no one's ever had before, a new lens through which to view biology. You can just immediately think of thousands of questions that would be interesting to ask. And so I worked on a variety of different things. Two main areas. One is immunology, the other oncology or cancer biology. And those were my two interests coming into medical school. And so I felt fortunate that I was able to do projects in some of both. One of the projects was focused on T cells, which are a type of course of white blood. Cell lymphocyte that are critical part of the adaptive immune system. So we had this new tool to measure all the genes in the human genome at one time and to see how they go up and down after you perturb cells. And so we were very interested to see what genes are turned on and off in T cells when you activate them, when you stimulate them through their receptor, the T cell receptor, either by itself or with a co stimulatory signal.

And so you would look at the T cell it's stimulated. Are you actually measuring protein rna? You're milking at rna.

Yeah, there's a technology that was a way of measuring rna, so transcripts, so the intermediate between DNA and proteins. Of course, we could see hundreds and thousands of genes changing as we manipulated the cells. And so building a catalog of all the genes that turned on or down, regulate, turned off when you activate T cells using different signals. And that catalog then of course has been very helpful for subsequent studies and better understanding and teasing apart the mechanism of T cell activation, which has gotten increasingly more interesting. Of course, now with the advent of.

Immunotherapy, there's a lot of things there that are interesting. One of them is the instability of rna. This is going to become relevant as we start to talk about the differences between cell free DNA and rna. But at the time, how difficult was it to keep all of this RNA intact as you catalog the generation of MRNA from DNA as this signal of, of gene expression? I mean, was that one of the big technical challenges of this technique?

That for sure is a hurdle in all work that's focused on rna, which as you mentioned, is chemically relatively unstable, particularly when you compare it to DNA, which is much more stable. One has to be very careful in any experiment, whether you measure a single RNA or where you measure 20,000 RNAs, to really try to preserve the sample in a way such that you minimize the potential chemical degradation of rna. So there are laboratory methods to do that, of course. So for example, if you're in that example of the T cell stimulation experiment, the cells are alive. The RNA of course is maintained. It's really once the cells die, the degradation issue starts happening. So you design the experiment in such a way that you're very careful to immediately add solutions that protect the RNA after you kill the cells at the end of the experiment, so that there's no time for chemical degradation processes. My other main project in PhD was actually, we really struggled with this because it was a separate project, my rotation project, which is the first project you do in a lab when you're kind of trying to figure out if you want to go there. Where we developed a method to isolate RNA that's stuck to the endoplasmic reticulum inside cells, because that is where RNAs go for genes that are secreted from the cells or that are surface proteins in the cells. And we were interested in cataloging those because those are interesting for diagnostic purposes and therapeutic purposes. So we had to purify the subset of the RNA that was stuck to these organelles called the endoplasmic reticulum. This is a very long procedure where you have to build special gradients and float, you know, gently slice the cells and then float the membranes that they're stuck to to various levels in a test tube. And that required a lot of work in a cold room where, you know, you're basically test tubes in a 40 degree room. But so is the experimenter, you know, you have to wear jackets and stuff because you're working basically in a fridge, all to maintain the integrity of the RNA as well as you can add chemicals to try to stabilize the rna. So this is something that was critical at the time to really try to work out methods to stabilize the rna.

And how much of an insight could you get into non coding sequences of genes where they're not making proteins? But we now know, of course, that these non coding segments can be very important as well. Could you gain any insights there with.

The technique we were using at that time? We could not directly, because we were focused on measuring the coding portions of the genes, of the transcripts that code for proteins. And we had to decide at the beginning of each experiment which genes we measured, because basically we had to create a probe for each.

You had to have the primers, you.

Had to have the primers for it, and so you had to make a decision. So at the time we were not focused on that. Subsequently, this approach was used in some of the early work on long non coding RNAs. And that was largely led by a former postdoc from Pat Brown's lab who was there while I was a grad student named Howard Chang, who is professor here at Stanford. As you may know, I think you.

Finished your PhD in about three years, correct?

Three, three and a half years, something like that.

And then you went back now and you started your clinical rotations. Did you have a sense when you went back for the last two years of medical school that you definitely wanted to be a clinician, which would mean not just finishing medical school, but then Doing a residency. Some people in the MT PhD program just say, look, I just want to be a pure scientist, not a physician scientist. I'm going to finish medical school and get my md, but I'm not going to do any clinical training. Where were you on that spectrum at the beginning of that final two years?

I was set on doing a residency. I wasn't sure yet in what, but I was set on doing it. My sort of decision point was, for me, was made prior to med school. Initially I thought I was going to do graduate school and go for a PhD and focus on research for my career. But then my father was diagnosed with lymphoma while I was a junior in college. And the journey that he went through and interacting with the oncologist and the medical team as part of that, and really seeing how little we knew about many things and how suboptimal treatments were, really convinced me that I want to be on the doctor side, not just on the patient side with my father, and be able to help people, help patients at the time of their life, what might be the worst time of their life, as well as to try to move the field forward to improve treatments that while they worked, somewhat obviously were not good enough.

And so does that mean that even coming into medical school, not only did you know you wanted to be a physician and a scientist, but did you already have kind of an inkling that oncology was where you wanted to be?

That's exactly right, yes. I knew I wanted to work in one of the cancer specialties. What I didn't realize when I was a first year med school is how many options there are in that you.

Were probably looking at it mostly through the lens of medical oncology.

That's right.

Whereas of course, surgical oncology, radiation oncology, and other sorts of avenues. Remind me, were you born in Germany, Were you born in the U.S. no.

Yeah, I was born in Germany. I was born in Munich and I didn't come to the US until I was 11 years old.

And you grew up on the east coast?

Yes, we initially moved to South Florida when I was a sophomore in high school. We moved to Connecticut, the Northeast, and then I went to Harvard as an undergrad. So I was in the Northeast until I came out here, but then never left anywhere. And now I've lived longer here than anywhere else in the world.

So you go into the clinic and now it's basically a decision of medical oncology, radiation oncology, or God forbid, something like surgical oncology, though I'm guessing that was probably third on the list of.

Three options, you know, I did honestly strongly consider it. I do like the procedural aspects of it. And that's the one reason why I chose ultimately what I chose.

So how did you ultimately decide on radiation oncology then?

I was sort of leaning towards medical oncology because that's what I'd seen through my dad's journey. I did a rotation in radiation oncology, actually, in large part because my wife, who was also a medical student at Stanford, and who you, of course, no, did a research year in the department of radiation oncology. She at the time was also interested in oncology. And so that actually is how I got exposed to the field. While we were dating, I would often visit her in the lab and so got exposed in that way to the department in the field. And then so it made me want to try it as a rotation. And then I really enjoyed it. From a patient care standpoint, it seemed to me that the radio oncologists had a little more time in clinic to spend with each patients. We generally see less patients in a day than might be seen in medical oncology. That seemed very attractive to me. The other thing I really liked was the technology aspects. I've always been interested in technology aspects. Both new technologies in the research space, but then also, you know, technologies for treating patients with radio oncology is very procedurally heavy. It's, of course, a field where we do a lot of imaging to see where tumors are in a patient's body, and then we have fancy robots that deliver the radiation very precisely. So it just seemed like a great field to combine my love of oncology and patient care with my interests in technology development. And then lastly, I kind of saw it as a field where they really compared to medical oncology. There wasn't as much work being done in the laboratory at the molecular level. And it seemed like an opportunity to make a difference in a field where there weren't so many people working.

One of the things I talked about with Carl was the challenge of keeping his hand in the lab during those last two years of medical school and then during his psychiatry residency. He really found himself in a great situation when he did his residency where he was effectively allowed to do kind of a postdoc. And he was freed from certain things. He didn't go to lab meetings and journal clubs and things like that, but he still got to do a little bit of work. Were you able to do anything like that during your residency, or were you really strictly focused on just the clinical training for. Was it four years?

So radio Oncology, did an internship in medicine here at Stanford and then it's four years of radiation oncology, so five total during internship, of course, as you I'm sure remember, there is no time. I do remember trying to write some papers in the call room to finishing up work from the PhD. Rachel. Oncology residency programs have a research track for individuals who are interested in laboratory based research called the Holman pathway, which is in other fields kind of called short tracking or fast tracking. It's a similar idea where basically one can trade in some of the clinical training time for research time. And so that's what I did, which gave me a postdoc time of about two years during my four years of radiation oncology residency. During that postdoc time I had clinic activities about half a day to a day a week and the rest of the other days were in the lab. So it actually was a really good preview of what my life would be like once I finished all the training and was a faculty member myself. That allowed me to keep a foot in the clinic while mainly focusing on the postdoctoral research.

So when you finished your training, obviously the first decision you had to make is do you want to stay at Stanford? I'm guessing that between the connections you already had there, Jenny was probably by this point already out of her training and in practice that was probably a very high activation energy to leave. Tell me about the department of Radiation Oncology at Stanford. Was it a natural fit for the types of problems you wanted to solve on the research side?

The department here at Stanford has a very long history. It was actually one of the very first departments of radiation oncology in the US Racial oncology grew out of radiology, which is, as listeners probably know, is a diagnostic arm of radiology where you do imaging only to diagnose disease. Initially back in the 50s, 40s, 50s, it was part of radiology, was part of those departments. But then here our first chairman, Henry Kaplan, who is a very famous radiation oncologist and physician scientist, became the first chair of radiation oncology and sort of led the movement to develop it as its own specialty. He quickly put the department on the map because of his work in curing Hodgkin's disease with radiotherapy, which around the 50s where, you know, those were some of the first successes of taking patients, especially young patients with a Hodgkin's disease, which is a type of lymphoma that was incurable previously, who now, you know, the majority could be cured with radiation. At the time there were articles about how now Radiation will cure all cancer. And look, we're on the path that of course didn't turn out to be that way. We still have a many, many patients we can cure. But it sort of was one of the first examples of this. The hype that you get at each time a new therapy comes where you know, then think, okay, now we've really solved it. But ultimately, you know, it gets more complicated. But that of course established department as one of the very leading departments in the world. And it is continued that the department here throughout its history has had a very strong interest in laboratory based research. This is all thanks to Henry Kaplan, who was doing laboratory research at the time also while seeing patients. So there were already faculty that were laboratory based, even just PhDs who were fully laboratory based, which wasn't the case in many radiation oncology departments. And so I really like that here that within radiation oncology this was a place where I could see that the kind of research I wanted to do was valued. There was already mentors that had done it and successfully. And those things are important when you're a young physician scientist, to have mentors that can show you, if you run into trouble, how to overcome obstacles.

At what point in your evolution did the idea of liquid biopsies start to become of interest? Obviously, that's something I really want to talk about today because it's something that I think people are just starting to hear about. I mean, it's something I've been following for about six years. Obviously you've been following it for a lot longer than that. But I think even in the sort of broader public eye the past year, I think a lot has happened to bring this to the fore. But I still think for many people it's still a bit of a black box. How many years ago did this sort of become something that landed on your screen as an area where you wanted.

To put focus, as these things often happen. I did not start my lab with the idea of focusing on liquid biopsies. I really got there by following the results we got from some experiments. And I think one of the keys to being a successful scientist, whether you're a physician scientist or a purely laboratory based scientist, is following the data, following the data that you generate to where it leads you, as opposed to, you know, saying I have to work in one area. So I was actually going to work on a different area. Initially. I started this line of work in my laboratory out of a clinical need I saw. And that's in general how I run my laboratory. All the research projects we do, we start with a clinical need or a clinical, it's suboptimal we're doing in the clinic, whether it's for diagnostic purposes or treatment purpose or whatever. You know, that's of course one of the things that having being a physician and taking care of patients is one of the things that I spend a lot of years learning about. And also what I have insights into that I others might not. That's one way I can be a useful contributor to the field in general. And so we always start with clinical areas of clinical need. And in this case, it was one of the main things I struggled with as a junior faculty member, which is that I decided to specialize in treating lung cancer clinically. So one day a week, once I was on faculty, I was doing clinic one day a week and I saw lung cancer patients exclusively. And I was very frustrated by the fact that after I treated patients. So I treat a lot of early stage lung cancer patients. These are patients that are stage one or two lung cancer, which means as far as we can tell, the lung cancer hasn't spread yet, it's just in the lung where it started and hasn't spread anywhere. With targeted high dose radiation, I can cure the majority of those patients, but there's let's say about 20, 25% of those patients who ultimately develop recurrence, where the cancer comes back after I finish my treatment in that first follow up visit, let's say three months after I do the radiation, I could not tell who was going to ultimately have the cancer come back and who had been cured. So state of the art at the time, and actually, you know, is still state of the art today in many ways is just to see the patient every 3, 6, 12 months as you get further out and to do scans and see if the cancer has come back without any way of predicting in whom it will. It's a very reactive approach and very unsatisfying. And this is something we went through with my dad.

Also give people a sense of the resolution we have with traditional imaging. Where are we at on CT scanners? Are we at 256 bit or what's the resolution and speed of a CT scanner today?

You may ask somebody who works in that specific field of building those scanners. But to rephrase your question a little bit is, you know, how small of a tumor can we see?

That's where I'm really going, which is if you can see a one by one by one centimeter or a one centimeter diameter tumor, you're Doing pretty well, right? You can probably see a little smaller than that.

That's exactly right. So it is hard to see things much smaller than 1 centimeter in diameter, maybe 8 millimeters. It depends a bit on the location in the body. Some areas, of course, it's easier to see smaller things than others, but that's generally true.

And so how many cells is that?

The general sort of rule of thumb is that's about a billion cells already, which is, of course, a vast number of cancer cells. Again, at the time when we can just barely detect it in a patient.

And I think it's important for patients to understand the non linearity of this. So you have everything shy of a billion cells is by conventional detection methods, undetectable. And then outside of the most rare tumors, anything at a centimeter is not posing a direct threat to the organism. You know, maybe a really badly placed brain tumor at 1cm is problematic. But for the most part, if you talk about a lung cancer, liver cancer, pancreatic cancer, colon cancer, etc. Breast cancer, 1cm is irrelevant. @ what point does a tumor become in and of itself threatening to the host? I mean, you could argue by the time it's 10cm by 10cm by 10cm, the burden of tumor itself is fatal. And that's how many cells there. Roughly when you're at the point where the burden of the tumor is actually.

Fatal. Of course, whether a tumor is fatal or not, as you point out, greatly depends on the location. In certain areas, one can have a ton of tumor without it being fatal. And in other areas, you have very little room for that. It's a volumetric. It grows by the cube, by the radius, multiplied by itself three times. And that is, of course, non linear. It grows much faster. The number of cells in a mass are much more than just the linear increase in the diameter of the.

Lesion. Yeah, and I think that's the part that's just hard to understand. We think linearly pretty well. We don't think exponentially very well. But again, I think I want people to just anchor to this idea that from zero to a billion cells, we're pretty much flying.

Blind. And relevant to the work, some of the work that we're now doing. Many cancers, once they have metastasized or spread, which is really ultimately what ends up killing patients, is once the. Usually when the cancer spreads, if the cancer is only localized, usually a surgeon or a radiation oncologist can cure that patient. But once the cancer spread and is in many places in the body, you can't remove it all or radiate it all. So if you think about a patient who has what's called micro metastatic disease or microscopic disease that has spread elsewhere in the body, you could have dozens or even hundreds of these micro metastatic deposits in different.

Organs. Right. That are under half a centimeter. And so you technically have tens of billions of cells and it's still.

Undetectable. Exactly right. That is a critical issue where that's a limitation or blind spot of our imaging methods. Since they're all spread out, we can't see them all. But that is one thing that one could potentially get a handle on if one had a test that could be measured, let's say in the blood and where one could measure contributions from all these dozens of micro metastatic deposits and that that one might get higher sensitivity. And that exactly kind of was the motivation for the liquid biopsy work in my lab. I had no idea how I was going to do that when I started.

It. Now let's hit pause for one second, Max. Let's also give people a little bit of the historical context of where there are at least a couple of areas where this was sort of being done, although it wasn't great. So maybe explain for people how PSA, CEA and CA19 9 have been used historically. Because even when we were in medical school, long before you and I got to medical school, those three biomarkers were used. Now you were always cautioned that at least the CEA and the CA19 9 could never be used for screening, but they could be appropriate for following. PSA has a very complicated history which we could go down the rabbit hole of about how it could be used for screening. But you can be misled. But tell folks a little bit about what those things are and how effective you think they were for what they were trying to do and which elements of those you wanted to replicate for lung cancer and where you wanted to improve upon.

It. When we started thinking about developing a blood based test for initially lung cancer, we of course thought about. Exactly. Examples you pointed out, which are the markers you mentioned, like PSA, CN99 are protein biomarkers. So these are proteins made by the cancer cells that can be shed into the blood and that where one can then measure them in a non invasive fashion. A large issue with these protein biomarkers is that they are actually not that specific for cancer. Meaning that these are also proteins that normal cells can make. Now, cancers often tend to make more of them, which is why they are potentially of interest. But the problem becomes that having low levels of any of those markers, when a patient has that, you can't know whether that means they have a little bit of cancer in the body or whether it's just that's their baseline, what the normal cells are making in their body. And that lack of specificity is what it's called, meaning you're not so sure when you see it that it's cancer or not. Cancer was the major Achilles heel of the protein based biomarkers. And a lot of work, of course, had been done in lung cancer previously looking at similar biomarkers, including CA actually, which in some subset of patients can be a good biomarker if the tumor just pumps out a lot of it and the patient has a decent amount of disease. So the total level is significantly higher than what a normal patient would have. But in most patients that was not the case. And so that was protein based approaches were really state of the art, but had this major weakness of a lack of.

Specificity. Let's maybe use this as a moment to explain to people sensitivity and specificity, which are going to become very important as we then factor in prevalence when it comes to screening to understand positive and negative predictive value. But I think for now, if we can just start with helping people understand what specificity and sensitivity are in terms of probabilities, of false positives and false negatives. I find this stuff to be so important, and yet if you don't explain it over and over again, it's easy for people to kind of get lost in the details. And yet it's so important for what we're about to talk about today that we'll probably end up doing this twice. Three.

Times. Yes. These are very important topics, words that are thrown around a lot and can be confusing. Sensitivity is something that's also known as, or a synonym for. It is true positive rate, which is basically the likelihood of having a positive test when the patient has the actual condition. So let's say in the example of cancer, sensitivity is you have 100 cancer patients, you have a new blood test. Out of those hundred cancer patients, you know how many of them is the blood test positive? That's sensitivity.

Right? So a perfect test would obviously have 100% sensitivity, which means 100 out of 100 would test positive. In reality, to give people a sense of this, a mammography might have an 85% sensitivity, which means if you do a mammogram on 100 women who are known to have breast cancer, it might only tell you that 85 of those hundred do. So it's correctly identifying 85. It's erroneously. It's giving you a false negative in 15 of the.

Hundred. In general, it's important to realize that there are no perfect tests, really, particularly when you start pushing the envelope to seeing, trying to see smaller and smaller tumors. So while it would of course be great to have a test that's 100% sensitive, meaning it catches every cancer patient, that is really unrealistic in the vast majority, if not all cases. And I think this is something that's sometimes not unique, understood by patients, where you go get your mammogram or your cat scan for lung cancer screening, and patients sometimes still develop a cancer even though they did all those things. And why is that? And that can lead to a lot of frustration. But that's because these tests, they're not always going to pick up a cancer, even when it's.

Present. One of my former analysts, Bob kaplan, came up with a great thought experiment to explain this to people, which was the reason you can't have perfection is if you drive something to a very, very, very high sensitivity, the specificity must go down. And here's the silly example, and this is his example. So I don't want to take any credit for it, but it's brilliant. If you wrote a letter max to a thousand women randomly and told every one of them they had breast cancer with no additional knowledge, you technically have a test with 100% sensitivity. Because let's assume 50 of those women have breast cancer. You have correctly identified them all. There are no false negatives in that group. So you have a 100% sensitive test. The problem is your specificity is in the toilet. You have so many false positives that the test is utterly useless. So you can have 100% sensitivity if you really want to push that envelope. But it has no clinical utility. And of course, I can play the experiment the other way. You could send a thousand women a letter that says you absolutely do not have breast cancer. And technically you have no false positives, but you clearly are going to have false negatives. So I love that experiment because it explains that if you pull full throttle on sensitivity or specificity, that's fine, but it doesn't make for a good test because you've undoubtedly compromised the other.

Parameter. It's a great example to explain how that works, actually. It explains a lot of what happens in the research, the diagnostic research field in general. You know, you try to push sensitivity, but that never is meaningful to report if you don't also Report the specificity to look at specificity, because it's kind of like a yin and yang. If you push on one, the other, you know, usually one goes up most of the time, the other goes down. Unless you really have a dramatic new insight or a new advance where you can just increase sensitivity without also hurting specificity. I guess just to finish off. So again, specificity, since we haven't defined it. So that is sort of the inverse of sensitivity, which is that for a patient who does not have cancer, really is a true negative, what we call it, and doesn't have cancer. The test reports that the patient doesn't have cancer. So it's sort of the exact inverse of sensitivity. But for the patients who don't have cancer, and as you mentioned, they are connected, you can game it. You can always push the sensitivity higher if you kind of ignore the.

Specificity. So now let's go back to your clinical problem. By the way, Max, you know, we've talked a little bit about lung cancer on this podcast, but I can never resist the opportunity. Given the prevalence of lung cancer and the what I refer to as the loss given default, meaning the severity of the disease. Do you want to give people just a sense of where we are today in 2022 with lung cancer? Is it the first or second leading cause of cancer deaths for men and.

Women? It's the number one cause of cancer death. I think a lot of people don't realize because it doesn't get nearly as much attention, at least historically, as some of the other cancers that are maybe more common. If you do look just in men or just in women, like breast or prostate, but where actually the outcomes are much better. We can cure a very large fraction of patients with breast and prostate cancer. Of course not everyone, and we still need to make more advances. But the difference with lung cancer is that the vast majority of patients historically can't be cured. And so that's why it is the number one cause of death, cancer related.

Death. Is that going down or going up or flatline? I mean, because smoking rates are coming down and about 85% of lung cancer by incidence I believe is smoking related. So some combination of smoking related, 15% is in non smokers. So what's the trend line on lung cancer.

Mortality? Both. The incidence of lung cancer has been going down in both men and women, initially lagged in women, but it's also coming down in women now. And this is all related to, as you mentioned, the uptake and then now decrease of smoking in the population. So that means because there's less smoking and because smoking is such a strong risk factor for developing lung cancer, there's less lung cancer overall. On top of that, our treatments have gotten much better, as has now slowly the advent of screening. We have a way of screening for lung cancer, which is relatively recent development. So those, those things combined have also decreased the number of patients who die from lung cancer once they get it. Even with all that said, it still is the number one cause of cancer deaths. But the trajectory is good. It's starting to head this way. And of course we're very eager to accelerate that slope, make it even less of a killer of patients. I want to one other point which often comes up in these kind of discussions, which is that while smoking is by and far away the largest risk factor, it is not the only risk factor. And really anybody who has lungs can get lung cancer. You know, that can happen for reasons that related to pollution, you know, and that has been well described, that pollution or other things in the environment like radon gas that people are exposed to can cause lung cancer. And there's genetic associations also, for example, Asian populations, there's a much larger incidence of non smoking individuals who get lung cancer, particular females. And so we are never going to get rid of lung cancer. Even if we could magically make smoking go away today across the globe, make all cigarettes disappear and never make a new one, there will still be lung.

Cancer. Let's go over kind of the distribution of how these things work out. You have small cell and non small cell. Is the big division.

Correct? That's.

Right. And then within non small cell you have adenocarcinoma, you have squamous cell carcinoma. Large cell and carcinoid, are those the big ones on the large cell or in the non small.

Cell? I'm sorry, carcinoid is not considered a non small cell lung cancer. It's its own category, but it's not a small cell. So that's true. Still, the most common lung cancer is the non small cell subtype of which the majority have adenocarcinoma, but also a significant fraction of squamous cell.

Carcinoma. When a non smoker gets lung cancer, it's usually adenocarcinoma, is that.

Correct? The vast majority of time it can sometimes be squamous cell carcinoma or even occasionally small cell, but that is extremely.

Rare. And as you mentioned, being a woman, being Asian seem to elevate risk. In the case of women, it seems to be potentially related to differences in estrogen, testosterone. That's been proposed as one plausible explanation. Do we know what genes might account for this in Asians vs.

Non? We do not. And that's sort of one of the big mysteries that we have not identified the main genetic drivers. That's not for lack of trying, of course. It's multifactorial that it's one of these, as are many of course of our chronic illnesses that have genetic components. The cardiovascular diseases for example, where it's not about a single gene, the vast majority of the time it is contributions probably from multiple genetic variants that in aggregate elevate the risk. And then there may also be of course on top of that then some further environmental factors that aren't smoking that may then interact with that genetic.

Background. I think you mentioned radon. We talked about that as definitely a big environmental exposure. What do we know about the PM2.5 exposure? I've certainly seen data that show that people who are in and just for folks listening, particulate matter less than 2.5 micron is small enough to make its way into the most distal part of the lung. There's clearly an increase in all cause mortality associated with higher PM 2.5 exposure. Do we know if that's also true specifically for lung cancer? Is that something that we can peg to an increase in lung.

Cancer? There are associations with that. So people have looked at individuals living in cities versus rural areas and correlating with the particulate contamination and that is also associated with more lung cancer in the cities that have more of the particulate in the ambient environment. There's definitely an association in that regard. You know, as with epidemiologic studies, to really prove it in a human is difficult of course, but it does seem quite strong and there's some biological rationale for that where that if that is causing irritation and chronic inflammation, those kind of things, of course we know that those things are associated with developing cancer in many organs. So it all makes sense. I think it's likely true. But there also are of course chemicals in smog that you know, sort of along the lines of what's in tobacco smoke, similar class of chemicals that can be direct.

Carcinogens. Do we have a sense of what the dose response curve is in pack years of tobacco to relative risk increase in lung cancer? So does a person who smoked a pack a day for 10 years so they have a 10 pack year history relative to a non smoker? Is that 2x the risk? 50% more risk? I mean do we have a sense of how nonlinear that curve is as well versus you know the 40 pack year smoker, the five pack year smoker, et.

Cetera. There are associations between the number of pack years and the risk of developing lung cancer. Now a pack year is not an ideal clinical variable because you can't measure it in a completely unbiased way. The way just so your patients understand how a doctor tries to figure out how many pack years a patient has smoked, meaning we basically multiply the number of years of smoking, how many years they smoke for, by the number of packs per day that they smoked. Well, how do we get that information? Well, we have to ask the patient. And the reality is, number one, patients smoke different amounts over their time and number two, they don't always remember perfectly. So it's not a great metric in that regard. So it's difficult to really pin it down perfectly. I have seen studies that have for sure suggested there is an, you know, increase when you go as you go from zero to, to higher levels, but then around 20 or 30 pack years, some studies argue that then kind of plateaus that you may be sort of saturating. I don't have the exact slopes of those lines. I don't know off top of my head. But it is not a perfect.

Variable. What do the data say about secondhand smoke, which I would imagine is even harder to quantify than PAC years? You know, for example, I have some patients who grew up with parents who smoked. They themselves have never smoked and let's say those parents continued to smoke all the way until those kids, now adults, went to college. We have a sense of how to quantify what their smoke exposure is and should they be treated as former smokers. In terms of cancer screening, for example, with low dose CT and things.

Like that, it's very difficult to measure. It's even harder to measure than the pack years we just talked about. We know that certain professions that were exposed to a lot of smoking, like waitresses and waiters or stewards and stewardesses, there was increases. You know, epidemiologically of course there's always other things that go along with that issue also. So I think the link is quite clear how to quantitate it and how to know has one had enough second hand smoke exposure that one's risk is significantly elevated? I think that's a major problem. Actually an error would be very useful to have a biomarker that could be quantitatively measured to actually measure like a clock of amount of exposure you had. We don't have such a thing currently. That is not one of the criteria that would get A patient eligible for lung cancer screening by low dose ct. So second, and smoke is not considered. The way that we decide on those criteria for who should get this test and covered by insurance and who shouldn't is really about trying to enrich for us the highest risk population. You want at least a certain percent, half percent, a percent of risk, the cancer that you're going to screen for. Because otherwise you will screen lots and lots of patients who will never get it. And then the specificity becomes a problem we talked about earlier. Even though there are things like exposure to pollutants, secondhand smoke that we know increase the risk, we don't. And it can be frustrating enough for patients who are in those categories to know whether why can't they get access to screening? But there are public health reasons for.

That. Yeah. Which we'll then get to something we'll talk about in a little bit, which is you can know the sensitivity and specificity of a test, but you then need to know the prevalence or the pretest probability to know how to interpret the result. Without the prevalence, you can't impute the positive and negative predictive value, which is what tells you when you have a positive, how confident are you it's positive. And similarly when you have a negative, how confident are you there? Let's talk a little bit about low dose ct, because I would say this has been certainly in the last 10 years, one of the major changes in how we manage lung cancer. It's been about 10 years since the data have made the case for the use of that as a screening.

Technique. Yeah, there had been a long effort at trying to develop a screening test for lung cancer, given that it is the number one cause of cancer deaths. And there were a lot of failures along that road. A lot of initial studies focused on doing chest X rays when that was the main thing that was available. So a much lower resolution way of imaging the lungs where those studies were unable to show a benefit. But then there was a landmark study called the National Lung Screening Trial. In that trial, they took patients that randomized them either to get a low dose C CT scan, which is now a more high resolution way of looking at the lungs that can see smaller nodules versus getting a chest X ray, which was this technique that previously already we had kind of known that didn't work. And if you do that, the patients who get the low dose CT scan, the high resolution imaging in that group, there was significantly lower rates of lung cancer deaths. You know, relative risk reduction of about 20% that argued that that's a major win for a screening test to actually be able to decrease the number of deaths from the disease you're screening.

For. Do you remember what the absolute risk reduction.

Was? That was small. It was in the single digit percents. I don't remember it off the.

Top of my head, but it was single digit percents. So let's say it was 5%. Your NNT would be 20. You could save a life for every 20 people. That still seems pretty.

Good. It's actually lower than.

That. So maybe it was like 1 to.

2%. Maybe it's 0.5 to 1%. Based on that argument. Yeah. Now that gets complicated also, because you'd think doing a CT scan you either see a nodule or you don't. So it should be pretty easy. But of course it's not that easy. A CT scan is a complicated thing to read. And so how you interpret this scan and what you consider a positive will of course affect your sensitivity specificity and will affect the number needed to treat downstream. So the way that we now read scans, the low dose CT scans, is different than what the initial study did because of a high risk of false positive the way that they was doing the original study. Lotus CT is not my area of expertise. I don't have all the numbers at my fingertips, but those are the sort of salient issues that are in that field. So even though we have this test and it is great and is this major home run for screening because we can save cancer deaths, it's not a perfect.

Test. Do you know by any chance how many millisieverts of radiation the low dose CT provides? I'm guessing it's like 1 millisievert or less.

Maybe. I think that's right. Yeah. I don't know that off the top of my head, but it's significantly less than the scans that we routinely do for patients who already have cancer. And this is an important concern that patients have radiation can cause cancer. So is the risk worth it? Right. It's called low dose CT because the amount of radiation used is much, much, much less than was historically done for a CT scan. And so therefore the risk of causing a cancer is much, much.

Lower. You know, the NRC says that we shouldn't be exposed to more than 50 millisieverts in a year. Is that sort of like saying you shouldn't drink more than 10 drinks in a day? Like, do you have a sense of what would be your personal threshold for how much radiation you would want to receive a year from imaging? Because you're going to get radiation from being on airplanes, although that's relatively small. You know, just being at sea level is probably 1-2-millisieverts a year. If you live in Colorado, you probably double that. But when do you start to think about how much radiation a patient is getting from a practical standpoint? You know, do you start to worry at 20 millisieverts in a year, which is obviously pretty easy to do with a PET CT scan, for.

Example. You know, we don't routinely track that at the individual patient level. It's tracked in a less, you know, I guess, systematic way in that we don't do imaging tests unless we feel that the benefit of doing that test outweighs the known, but really low and actually very difficult to quantitate harm. It's actually very difficult to quantitate exactly how much risk of, let's say, a second malignancy there is from a given protocol of a CT scan or whatnot. Just because to get that data and you can never do it randomized, all those things, right? The way that medical practitioners who order imaging studies, which of course most or many fields, in many fields they do, not just radiology, the calculus is always, do I need this test? Right? Will this help me manage the patient in a way that's beneficial to the patient? We absolutely should not be doing imaging. If there's no point, like, if no matter what the scan shows, I'm not going to change what I'm doing, then I should not be ordering that scan. The question comes up, should I image? Should I not? And the way I and all of us in these areas think about it, is, is it worth doing? And so some patients who have lung cancer, particularly when they get radiation therapy, which is very high doses of radiation, they have astronomical amounts of radiation that if you were not a cancer patient, you would never want those amounts of radiation. But the vast majority of those patients will not get a cancer from their imaging or radiation treatment. And their cancer, if we leave it untreated, will kill them. Right. And so if it's going to come back, if we don't catch it early, well, we still might have a second chance at cure. So that's how we do the calculus. It's not that there's badges that patients wear that track this so that if you get to a certain point, you couldn't do no more imaging. It's always a sort of a case by case.

Basis. So let's go back now to the clinical problem that is at the root of this whole situation, which Is you have a patient who has either had a resection and or radiation to follow. They're clinically, as we would say, ned, no evidence of disease. But you know that the recurrence rate is there. It's, let's say it's 25% actuarially, 25% of these hundred people who are currently ned, are going to have a recurrence. And we know that the sooner we catch the recurrence, the better the odds are for treatment. The lower the tumor burden, the lower the burden of mutation in the patient, the greater the odds of therapy. So if this was 200 years ago, we'd be hosed, right, because we'd have to wait until they had folating fungating masses, they were coughing up blood. I mean, obviously that would be crazy. And we've already now just put in great detail that look, even giving high resolution CT scans, at best, we have to wait until a billion cells are cancerous. That's the best case scenario. So now we want to talk about something else. So walk us down the path of how you would or how you did think about going after another type of biopsy, a liquid.

Biopsy. As I mentioned, I was frustrated by not being able to diagnose recurrence earlier, and that was an unmet need. So I wanted to start part of my laboratory working on that problem. But to be able to do that, there's two ways you could think about doing that. You could start with mouse models. To build mouse models, let's say what they're called, preclinical models, in which you try to grow tumors in the animals and then see if you could have a hypothesis for what might be a good biomarker, try to test that and see if it works in a mouse, and then ultimately go back to the human. And the approach that we take in my group is more very tied to the clinic, very what's called translational research. And so, so for us, really, the human being is the model organism. It's the final model. Of course, it's the model we want to get the answer for. I thought we should do this work directly on blood samples, because if we find something, then it likely will be directly applicable. Otherwise you'd always still have that second step where it works in the mouse, let's say, but you don't know. So I started just by collecting blood samples. I actually didn't know what I was going to do with.

Them. And by the way, who was funding this work at the time? Max, was this something you went to the NIH and said, I want an R01 and then this is why I want to study it. Or at the outset, who is bearing the risk financially when you're a.

New faculty member, when you start a new lab, when you get that first job, you get startup funds, which is basically funds your department or university gives you, provides you sort of as a, to kickstart you because you don't have grants at that moment. You're just starting, you haven't had a chance to write them. And so I was using that money basically. So I was footing the bill on that pot of money I got to start my lab because as you know, and I'm sure your listeners have heard many times that one of the things with academic research and funding is that oftentimes you kind of have to show it already works before you can get funding for it. And that's a very common story because grant funding is limited. And so organizations that fund research are often quite conservative that if the idea sounds great, but you have no proof that it works at all, they're not going to give you money. So I used money that I had for my startup funds. So yeah, so I just started collecting blood. I started thinking about what biomarkers would be good. So I read the literature on the protein biomarkers as we talked earlier.

And sorry, did you collect blood in patients prior to resection and post resection or just post resection, both prior to.

Resection or radiation, whatever the treatment was, and then in subsequent follow up visits, usually at the time of their follow up scans when they're coming back to see us. Protein biomarkers didn't seem very useful. I then spent maybe about a year looking at something called circulating tumor cells, which is one subtype of what this field called liquid biopsy, which is actually looking for intact cancer cells that can circulate in the blood of patients. I tried that with a couple of different techniques and quickly realized something that many others in the field have also observed, which is that while if one could really do that, it would be super powerful, it is very complicated to actually measure those cells for a variety of reasons. Two biggest ones probably are. My three biggest ones are the cells are not very abundant when they are there, there's very few of them, so they're very difficult to purify. Like a good purification. They might still only be 1% or less of the cells in your purified sample because it's just so hard to purify. It's finding a needle in a haystack. And because it's intact cells. You have to process the samples really quickly. You have to process them basically the same day or within an hour or two of the patients having the blood sample drawn. That makes it very difficult to build up biobanks of frozen cells, frozen samples that you could get a large cohort you could actually study because you have to process them immediately. So. And then the last thing was that we did some control experiments. This is critical. Obviously you always want to ask is the thing I'm working on, really working, and where we drew blood from healthy individuals who don't have cancer. Some of these tumor, circulating tumor cell methods we're using found circulating cells. Those patients don't have cancer. So they were picking up something else. So there was a lack of specificity. So they were technically complicated. Seemed like it's going to be hard to get this into the clinic anytime soon. And there was some weakness. So that's then when I went back and tried to really see what else could we.

Do. So let me go back to the first one. Let's just talk about the protein, because that's makes sense as the obvious place to start because we already kind of do that with psa, notwithstanding the limitation that normal cells, normal prostate cells, non cancerous cells, make prostate specific antigen. Same with CEA. CA99, did you find any proteins that were made that were expressed by lung cancer cells that were unique to.

Them? We at the time did not. We did not do a holistic screen ourselves, but other people had. There's techniques now called mass spectrometry, where one can take a sample of proteins, let's say purified from the blood, run them through this machine, and it basically tells you what proteins were there. And those studies had not found convincing markers that were unique to lung cancer cells. Now they found some markers that in some patients were elevated. Actually CEA is one of them that can be elevated in lung cancer patients. And in ones where it is, it can even be a marker of recurrence like PSA can be in prostate cancer. But we already knew that CEA has problems with specificity. Right. So that was sort of the main concern. But the main protein work we did was to actually I drew some CA and a few of these other markers like C99, some of the other ones that had been previously reported to be markers in lung cancers in some of my patients, because there were some studies that claimed really great performance in the literature, but quickly realized those studies were maybe as an artifact of that cohort or something else was going on because we could not reproduce those.

Results. Let's talk about that a little bit, Max, because I think what people don't understand is how difficult it can be to reproduce results. Help people understand what that process is. Like you or one of your graduate students or one of your postdocs reads a paper where somebody says, hey, we've identified this process protein. It seems really specific. It looks fantastic, et cetera, et cetera. Presumably, these people are collaborative. You can read the paper and get the methods. You can call the lab, they can tell you how they did it. Now you do the experiment. What do you.

Find? In many cases, unfortunately, one finds that one can't reproduce the result. That can be. Not that it doesn't work at all, but that just. It doesn't work as well as it did in the initial study, but it works so much less well that it's not going to be clinically useful. That's a very common event. And so why does that happen.

Now? Do you still publish that result.

Max? The lack of validation? Yes, sometimes it depends. Yes, sometimes we do. Often people don't. The reason is complex, of course. One issue is that, you know, to definitively prove something doesn't work quite as well as was initially reported. One actually has to do very large studies so that the, what are called error bars or estimate of the, say, sensitivity, that's what we care about, is very accurate. But even though by doing a smaller cohort, you can already tell it's not going to work out. But to really ultimately prove it to the level of rigor that one would need for a publication would require another year and thousands of dollars. Right? And so there's these decisions one has to make. Is this really worth it? And what's the opportunity.

Cost? But it's interesting, right, when you think about it, because it means that we have a bias system in the publication realm. We are biased towards positive findings that are often untrue because let's say one person publishes that, they can make this thing work. Okay? And again, we're going to throw out fraud or anything like that, but we're just going to assume that in good faith. These are people who did something, but they had an artifact, they didn't catch it. The experiment looked like it was positive, they publish it. Let's assume that you and four other labs independently try to reproduce it, and none of you can reproduce it, but you all come to the same conclusion, which is, well, we only did it this one way and it didn't work technically, I would need to do it three or Four other ways to be really sure. I'm going to put those resources of time and money elsewhere and away I go and everybody comes to the same logical conclusion. But of course that means that that one finding gets disproportionately propagated as a positive finding, when in reality four labs have now found it to be highly unlikely and that doesn't get published. Of course, I'm not being critical of you or anybody, Max, other than just the system, which is I wish there was a way that the incentive structure could be such that we prioritize negative findings as much as positive.

Findings. And I agree with you 100%. In general, we don't have a system where one lab could know that three other labs had done the same thing. Maybe eventually you find out over dinner at a conference or something, right? But because there is no place to look up to see what other people have done, there is no way to know. It would be great if we could fix it. It's a very difficult problem, of course, because of many factors, including the economic parts of limited resources and time. Even, you know, to publish such a study would take, let's say a grad student, but could still take six months of their time to get that out. But now, you know, they're not spending those six months on their main project, which is to develop the new.

Test. And I've spoken with graduate students who will say, look, it's really going to be harder for me to get my PhD publishing negative studies. I mean, I'm going to quickly turn my attention to where I think my committee is going to be looking at me more favorably. I think the problem is pretty.

Significant. The selection positive of the positive results is a major issue. And so this is why reproducing positive results is so critical and something that also not everyone likes to do it because reproducing something someone else already did is not making a brand new discovery, but more in the medical fields we do do quite a bit of right. There is quite a bit of studies that try to reproduce positive results and then publish it when the results are also positive. And that's really to really prove something works, convince yourself that something is really real. It's really helpful to see multiple studies from multiple groups, ideally with multiple methods that find the same thing. And that's sort of how you can read through the literature and really try to see where is likely a robust.

Finding. All right, so let's go to the CTCs now. So protein out circulating tumor cells. Okay, so talk to me about a patient who shows up with a big, juicy stage 2 lung cancer prior to resection? How many milliliters of blood do you take out of that patient.

Preoperatively? So that depends greatly. But these kind of tests, whether we're doing them for research purposes or ultimately, you know, if you were to develop something for the clinic, usually takes somewhere between 10-20mls of blood, so a few.

Tablespoons. So right out of the gate, how many CTCs do you see preoperatively when you should be at your highest right, that should be your greatest circulating tumor.

Burden. So in stage one to two, non small cell, most often.

Zero. Oh, my.

God. That was what we found. When we looked, that meant the sensitivity isn't very.

Good. Did you have positive controls of stage four.

Patients? We did, and we did see some patients where we saw quite a lot. So you can definitely see patients that have massive amounts. Occasionally you even see early stage patients that have quite a lot. This is work we didn't do. But another group in the field, led by Carolyn Dive from University of Manchester, where they actually took blood from the draining veins at the time of the surgery, the lobectomy for lung cancer, and did circulating tumor cell assays and found evidence that they could find more signals, since it was better in that context, because the hypothesis being that you're basically measuring the blood outflow of the tumor, you're going to catch more cells that way because once they hit the systemic circulation, they're getting diluted all over the entire body. So there is some evidence along those.

Lines. Any findings about the differences in those early stage? So if you took a person with stage 2 cancer who is your typical patient, who has no circulating tumors even preoperatively, then you said there might be some who had a reasonably high burden. Does that predict who's going to recur, even though, based on TNM staging, they're both considered identical.

Postoperatively? Absolutely, there is evidence of that. There are studies that have shown that when you do see high levels, that that is a negative prognostic marker, a bad thing, and that those patients are at higher risk of recurrence. Now, one complication in the CTC field, the circulatory cell field in general, is that, as I mentioned, even non cancer patients can have circulating cells that look like circulating tumor cells using the markers that are generally used, which are, you know, when it's looking for circulating tumor cells, most assays use stain the cells, meaning they look for markers on the cells they want the cells to not express. Markers from lymphocytes from white blood cells. So there's a marker that's highly expressed on white blood cells that should not be expressed on the circling tumor.

Cell. Which one do you.

Use? CD3 or people usually use CD45 as the most common marker, which is sort of a pan white blood cell marker. On the flip side, for a positive marker, what should be expressed in the cancer cell but not the white blood cells? They look for things like cytokeratins, which are structural proteins that are specific to epithelial cells. But as we and others have found, healthy patients can also have cells that have cytokeratin expression, but no white blood cell marker expression in the circulation. And other people have gone further to do single cell sequencing of those to show that their genomes are actually normal. Those cells have normal genomes. They're not mutated like the cancer cells are. So it definitely appears that there can be epithelial cells circulating in us that are not cancer cells. Those cells can't divide forever, so they will not ever set up shop anywhere. There's not very many of them. But that makes it complicated to look for the presence of certain tumor cells if one doesn't have a very specific way of getting. Removing that.

Signal. Yeah. So that right away tells you this is never going to be. At least that exact approach can never be a cancer screening tool. And it makes you worry that even in patients who are without disease but who have been resected, it might not be a great predictor of disease recurrence. So far, the only thing that sounds somewhat promising from this approach is it might help you determine the course of adjuvant therapy in a patient. Correct. So if you took a patient who was resected, radiated stage two, had high circulating tumor burden versus one who didn't, you might take that former patient and say, even though the textbook says you don't need chemotherapy, we're actually going to give you chemotherapy because we're going to treat you like you're stage three or.

Worse. That's absolutely where we wanted to get. That exact situation is one of the main motivations for starting liquid biopsy work in my lab. And yes, with very high levels of circulating tumor cells, one could have envisioned if one were to do very large studies to definitively prove that these patients with a certain threshold, Even though they're a small minority, that is really very highly associated with recurrence, that could work, but it would miss most patients who ultimately develop recurrence. And so that was the negative. It's not to say in the Future, we might not get there. There's lots of work still going on in the circulating tumor cell field. So I don't want to be too negative about it. It's just not as far along as the thing that we ultimately settle on. We'll talk about shortly. But more development is required if we're going to be able to use that.

Clinically. What did you go next? Did you look at cell free.

DNA? After that we went to what's called cell free DNA, which refers to DNA molecules that are found in the circulation but outside of cells. So they're circulating in what's called the blood plasma, which is the non cellular liquid portion of the blood. It's called cell free because it's outside of cells. Free of cells. Of course, it comes from cells originally, but it's now circulating by itself without membranes around it. The reason I got interested in that was actually largely from reading about a different field, which is prenatal diagnostics, where around this time there was a lot of work being done, led by a number of individuals like Dennis Lowe in Hong Kong and Steve Quake, who's actually here at Stanford in the engineering department, basically showing that in pregnant mothers can detect DNA from the fetus in the woman's blood cell free DNA. Again, not inside cells, but outside of cells. So it's pretty obvious that we can do that in pregnancy. Then, you know, couldn't we try to do the same thing in cancer.

Patients? Just to be clear, Max, when you spin this down, when you take a tube of blood and you spin it, you get all of the cellular matter below the buffy coat and then the plasma is above is the cell free DNA in the clear plasma and not stuck within the cellular material it manages to.

Correct. The cell free DNA is in the plasma. And that's exactly where we isolated. That's actually a critical technical aspect.

Of. Yeah, because if it were in the cellular matter, it would be a disaster to find.

It. You can't get it right. And actually we've thought about this idea. Could there be more cell free DNA mixed in with the cellular compartment? And I think it's quite likely that there could.

Be. If you could put a gradient on that and try to somehow extract.

It, get it out, that you might be able to get it out and.

You could enrich your cell for.

You. You can enrich for it again. And that might actually be a future interesting research direction. But the problem is currently, if you were to just take some of that cellular compartment, there's vastly more DNA in the cells than there is in the cell free space, you would basically lose the signal. You would just have all the vast, vast majorities from the cells in that compartment. So we take the aqueous part, the plasma part at the top of the tube after you spin it, and there's very low levels, you know, it's only about, on a healthy individual, about one to five nanograms per mil of cell free DNA in the.

Plasma. By the way, you know what's interesting? I have beta thalassemia minor, so my phenotype is normal. Well, I shouldn't say that my hemoglobin hematocrit are normal, but my phenotype is that I have very small RBCs. So my MCV is in the 50s or 60s or something like that. And my RBC count is very high. So that's how I have a normal hematocrit because I have a lot of small red blood cells. So our mutual classmate from med school, Matt McCormick, who was my roommate, used to always refer to me as heaven shite for blood. And I just learned something very recently I've spun my blood a billion times. I mean more times than I can count. And I've always thought, man, I must be dehydrated, Man, I must be dehydrated because relative to everybody else's blood you'll get cellular matter this much buffy coat plasma, they're about the same volume. And you can even estimate hematocrit by spinning in a lavender top and looking at the separation. Well, you can't do any of that in my blood because my plasma volume is tiny and my cellular fraction is enormous. And so finally I spoke to a hematologist who said, oh, the reason is because you have all those little crappy red blood cells, they're actually keeping and trapping a lot of the plasma within the cellular body at the bottom. So it's not that you don't have a normal plasma volume, you do, but you just can't get it out of the separation. Which means I'd be a lousy patient for this because I'm cutting my sample size down so much by reducing my plasma.

Volume. So you might have to donate larger amounts of blood to get the right amount of plasma. But I think that's a very interesting observation. I think it kind of does suggest this phenomenon of there potentially being some cell free DNA among the cells. You're the exceptional outlier. But it's probably also happening in the average patient to a lesser.

Extent. Now again, it's DNA. So just to make sure everybody remembers this is A double stranded or single stranded.

DNA. These are largely double stranded DNAs. We think we know there's some experiments along those lines. These are a double stranded DNA molecules. They are rather short, the cell free DNA.

Molecules. How many.

Pairs? Yeah, yeah, it's about 170 bases. 165, 170. Exactly the length of DNA you would expect if the DNA were wrapped around the core histones. Histones of course are for your listeners, are proteins that package the DNA in our cells. They're sort of the scaffold that lets the DNA, which if it were linearly stretched out would be extremely long, fit into the compact nucleus of a 10 micron cell. It has this special packaging scaffold which consists of the histones. And so the DNA in the circulation is wrapped still around this core histones. And we think that's critical because there's actually high activity of enzymes that chew up DNA in our blood and our extracellular fluid. And so the DNA is only there temporarily and it's temporarily protected by these histones. So the DNA that's bound to the histones is present at a higher frequency in the blood than the DNA that's between the histones. The histones are like pearls on a string. And the DNA regions that are between two pearls are relatively depleted from the blood because these enzymes are chewing up the DNA constantly, which is what contributes to these very low levels of DNA, because of course we have lots of cell deaths, so you might imagine we should have higher levels of DNA in our blood, but we have a system to clear it out in this DNA enzyme. And probably important because if you ever work with DNA, if you get high concentrations of it, it becomes very sticky, kind of a snot like substance which would be very probably bad to have in your microvasculature for stroke, et.

Cetera. Does this cell free DNA primarily come via apoptosis or what is the predominant method by which or mechanism by which the cells are evicting their.

DNA? That is very poorly understood. That's a great question. It's still one of the mysteries. And it's a mystery because it's really difficult to study. Because in a human being or even an animal, what we know is that most if not all tissues contribute DNA to the cell free DNA pool. You know, it's coming from everywhere, so it's very difficult to study release of something from everywhere because it is chopped up in these small histone bound fragments. There was historically a lot of most of the reviews and textbooks would argue that it's largely apoptosis that does that. Because in apoptosis, of course, there is a laddering of the DNA or chopping up of the DNA as part of the.

Program. Tell folks what apoptosis is so they know what we're talking.

About. Apoptosis is a mode of cell death. So how cells in our bodies can die, it is a controlled programmed cell death, meaning that there is a mechanism in our cells that is basically a suicide program that can get activated. This is critical for development, our development, because at certain points, as we're developing from the fertilized egg up to a full infant, and even then later in life, cells sometimes have to get killed to make room for other cells. And that's just part of our normal developmental homeostasis. So it's something that we have evolutionarily built. And it also is critical for getting rid of sick cells, because if a cell gets sick, there has to be a way of killing it. So apoptosis is a programmed way of cells dying. And as part of that, once that's activated, the cells chop up their proteins as well as their DNA. Basically it's sort of a tide to kind of clean up after themselves in a way. But because of this chopping up of the DNA that happens during apoptosis, it was long thought that that likely could be a major mechanism. I think it still could be the complicating factors. Now we have a much better understanding that there's also dnases just floating in the plasma that are also chopping up DNA. So if a long chunk of DNA were released through a non apoptosis process, it would likely also become chopped up. And so just because it's chopped up, assume it comes from apoptosis. We don't know for sure. I think that's an important area of research. We think it's likely multifactorial apoptosis, necrosis, you know, basically any way that a cell can die and release some of its contents into the.

Blood. Okay, so let's say we're talking about a normal person. You've taken 10cc of blood, you take the plasma from them. How do you quantify how much cell free DNA you would expect to get out of that? And then furthermore, how are you distinguishing what is potentially cell free DNA from a normal cell, which presumably is the bulk of it, versus cell free DNA, that is from the cell of interest, the cancer.

Cell. Once we purify the DNA, once we have the plasma, so the non cellular compartment, there is laboratory procedures for isolating DNA that have been well worked out for over many Decades that we use in all different fields of biology. And so we apply some methods there that have been optimized for these low concentrations of DNA. That's one of the unique things about the cell free DNA work. The concentration is really low, like single digit nanograms per mil. And often when we do laboratory procedures, we like to work with at least micrograms of DNA. So there's nanograms, a small amount. And that was one of the challenges early on in the field to try to overcome that. So we purify the DNA and then we can quantify how much there is. There's a variety of laboratory tools. We have the most commonly used one in our lab is a fluorescent based method where you basically add a fluorescent dye to the solution, it binds to DNA and that gives a specific fluorescence and then you can read that out in a machine that can read that fluorescence. So you can then have a standard curve and read off how much DNA you have. And what I'd expect for a.

Healthy individual, do an institute.

Hybridization. This is just purely a dye based method. It's much simpler than that. You could do more complicated things. The more complicated thing that we would do next when we do sometimes in certain situations is called quantitative pcr, where basically you use an enzymatic method that can amplify DNA. You again have a standard curve where you know how much DNA is in there and you compare how the amplification curves grow. And that can let you read out how much DNA was in your sample. But most time we can use this more simple method, which is just add a die, put it in the machine and get your.

Result. It's very fast and just give me some quantity. So in 10 milliliters of plasma, how many nanograms would you, how many nanograms per milliliter in any sample would you expect to have of cell free.

DNA? 10mls of plasma would be 20mls of blood we'd collect because plasma in most individuals is about half. From 20 milliliters of blood, we get 10 mls of plasma. Those 10 mils in healthy individuals would have somewhere between 1 to 5 nanograms per mil. So that would mean 10 to 50 nanograms of DNA at the end. But in some conditions, in some advanced cancer patients, patients who have trauma, have an infection, the levels can be much higher, much higher than 5 nanograms per mil, because there's active cell death happening in the body. And so when that happens, we sometimes see levels, you know, in the hundreds of nanograms per mil that's rare, but it can.

Happen. So now the big question is, how do you separate the good actor and the bad actor in that.

Pile? The way that we and others in the field do that is to focus on a unique molecular property of cancer cells that normal cells generally don't have, and that is the mutations that cause the cancer. Of course, as many of your listeners will know, cancer is a disease that is caused by mutation mistakes in the DNA of a normal cell that accumulate and ultimately lead to that cell not being responsive to cues to stop growing or to kill itself and just to grow uncontrollably. And so then those mutations, both the ones that cause the cancer, as well as many thousands that are along for the ride in most tumors, serve as really exquisitely specific markers of the cancer cell, because these mutations are really only present in the cancer cells, not in the patient's normal cells. So this is a really attractive biomarker, and for a variety of reasons. One is you're tracking the cause of the disease. Really the molecular cause of these is a mutation. That is what you're tracking. What better biomarker can there be for an illness? You're actually tracking that. And the second thing is the specificity, particularly when you compare it to the proteins like psa can be made by normal prostate cells and by prostate cancer cells. But the mutations in a prostate cancer would not be present in the normal prostate cells. They would be present in the cancer cells. How do we tell then if there's circulatory tumor DNA present is that we look for mutations in these short DNA molecules. So we look for mutant molecules. Most of the molecules in the blood come from the healthy cells of the patient, and they don't have mutations, but a small subset. And that can range from. An advanced lung cancer patient might have 1% of the circulating DNA is from the cancer. So still a small amount, and that's high levels. In early stage patients, it's often well less than 0.01 or 0.001% of the DNA that's from the.

Cancer. How do you even detect that without. Do you have the resolution to detect 0.1% of the cell free fraction, which itself is already very small. And know that you're not seeing an.

Artifact. The methods we use to try to detect mutations, and there's a variety of them, but the one that we chose to use, and which the vast majority of the field has moved to since as well, is next generation sequencing, which is these high throughput molecular methods for sequencing, sequencing DNA where you can identify the actual sequence of the bases. The AGTC bases that make up DNA can get the exact sequence for hundreds of millions of molecules, or billions of molecules, even in parallel in one experiment. It's as if we had a microscope that's so good that we can just read off the basis. It's not a microscope, it's a complex molecular biology procedure. But the end result is the same. We get a data set of millions of DNA sequences that are all about 170 bases in length. And we can and then compare those sequences to the patient's germline DNA. Let's say that we purify from buccal swab or from the cellular compartment of the blood. And we can say the vast majority of molecules look identical to the patient's germline, meaning their normal healthy cell DNA. But some small proportion of the molecules have mutations. And in many of our experiments, the way that we can be very certain that's the case is because we start by actually sequencing the tumor. So let's say, particularly for this initial problem we had of telling who is cured or not, those patients all had treatment, they all known to have cancer. So we have a piece of their tumor, we can sequence that and identify what mutations that tumor has compared to the patient's healthy cells. Let's say we find in a particular panel we might be using, let's say we find 10, just for sake of example. So 10 mutations the patient's tumor has that the healthy cells don't. We can then go look in the blood in the sequencing data set we have for those mutant molecules that carry one of those 10 mutations that turns out to be exquisitely specific and sensitive for the presence of the cancer. If we see even one or a handful of molecules that carry the mutation, one of these mutations has borne out that that basically means there's still cancer cells in the.

Body. Now the other thing that I want to make sure I understand here is we're talking about relatively short fragments of DNA. Call it 150 bases.

Correct. 170, maybe closer to.

170. 99.9% of all of that cell free DNA lines up to some segment of their germline. But no two of those are the same either. It's like if you could lay their entire germline genome out, it would span miles and miles and miles. You probably have no two segments that are the exact same. So you have a whole bunch of 170 base segments that line up. So somewhere on that problem, that's a pretty interesting computational problem. To me, because 170 is not that big. Presumably some of that 170 also is present in the cancerous, in the mutated. A cancer is only going to have maybe 100 mutations in the coding.

Regions. Exactly. I'd say 100 mutations in the coding regions in the total genome. If you include the regions between genes, maybe 10 to 20,000 or something. It depends. Of course there's a range. But having more than tens of thousands of mutations in the whole genome would be.

Unusual. I guess what strikes me as almost improbable is given the few number of mutations that really exist in the cancer, if only 0.1% of the entire cell free DNA, which itself are very short segments, it seems to me you could very easily just miss it. Right. You wouldn't happen to find a segment that lines up with a mutation. Presumably there must be a way to do the. I mean I feel like if you gave me an hour I could figure out the numbers. It's just a statistics and probability problem. So how infrequent is.

That? That is exactly one of the major challenges in the field. Our first paper in this field that started really this whole part of my Laboratory back in 2014, we addressed that problem. So what? There of course had been some prior work in cell free DNA and cancer prior to us. You know, one is always building on the shoulders of former researchers when one does the next project. Maybe just for brief history lesson, you know, mid of last century was the first observation that there is cell free DNA present by a French group because they had very, very insensitive methods. So they could only tell it, see it in some very, very advanced cancer patients. But even then they already had the foresight to say, well this could be potentially useful. But then for the intervening, I don't know, 50, 60 years, there wasn't that much progress because of this hurdle of there being so little of it and the methods just not being there to measure it. What people had been starting to doing around 2005, 2010 in that range was to actually look, use very sensitive PCR methods to look for single mutations. Let's say, you know, the patient has a PT3 mutation, you go looking for that PT3 mutation. There weren't ever that time then methods that could do that more sensitively. But that ran into the problem that you mentioned, which is that, well, now you're looking for one mutation, but often there's less than a cancer genome present in the blood tube and so you have lots of negative samples just because that mutation isn't There it's not in the blood draw you took. The way that we overcame that was actually fairly very simple, which is just to say, well, let's use this next generation sequencing technology where we can get sequence for millions of molecules. And instead of looking for a single mutation that looks for dozens of mutations from the patient's cancer in parallel, compared to looking for one mutation, looking for 10 actually increases your sensitivity tenfold by whole order of magnitude. Because I don't have to see all 10 mutations to know the cancer is still there. If the mutations are specific, I just need to see one of the.

10. And you don't care if they're coding or non.

Coding. We don't care if coding or non coding, and we've successfully used both. What is beneficial is to have what's called a truncal or clonal mutation, meaning a mutation that's present in every cancer cell. So as cancer develops, as we talked about earlier, by acquisition of mutations, now the cell starts healthy, it gets first mutation, and then ultimately gets to be a cancer which has tens of thousands of mutations. Those happen over time, right. Years. Generally, many of those mutations are present. Many of the 10,000 are actually present in all cells of the tumor, even though they're not driving the cancer. Just because it's the history, the evolutionary memory of that cancer. Right. That the cell that ultimately was able to continuously divide already had these 10,000 mutations from the precursor process. And so once it then gets the final mutation, that lets it divide forever, it keeps all those to other 10,000 mutations. So we don't care. We just want the mutation to be present in all the.

Cells. And it's interesting because driver mutations have surprising heterogeneity. Right. I was actually talking to Steve Rosenberg about this on the podcast last year, and I was amazed at the non overlap of driver mutations. And so, yeah, a lot of people had P53, but it wasn't necessarily the driver, so it was present. And also, I mean, this gets back into your other thing that you're interested in, which is the immunology, is how often it was not a mutation.

That was antigen inducing, that was immune response.

Inducing. Yeah. So let's now talk about is there such a thing as cell free rna or is that just so unstable that it's not even worth thinking about? And is cell free DNA the only place to be thinking about.

This? No, absolutely not. There is such a thing as cell free rna. That field is much more nascent than the DNA.

Field. What advantages would it offer given the instability of the.

Molecule. I think there's some critical questions initially about what is the stability of the molecule, you know, how much of it is there and how stable is.

It. And then you miss the non coding mutations as well.

Right. If you're looking for mutations, I don't think it has advantages really. My vision is that ultimately we want to get to the point where we can, from a blood draw, determine as much as possible about a patient's cancer, ideally eventually to the point where we don't even need to biopsy it. Now, that is science fiction currently. But if you extend the line of work decades into the future, that is where we're trying to get. If we want to get there, we have to be able to measure things other than mutations. Mutations are important, they're critical, but they're only a small part of the puzzle. Measuring RNA could tell you about which genes are on and off in the cancer, theoretically. And so that is critically important. For example, in the immunotherapy field that you mentioned, there's some markers like PD L1 that can be expressed in the tumor cells that can basically hide the cancer from the immune system. That marker is. That's not a mutational process. In the vast majority of patients, it's expressed from the promoter, you know, just turned on for reasons having to do with signaling, epigenetic reasons, et cetera. You can't tell by looking at the sequence in DNA of PD L1 whether the tumor has this marker. It's not a mutation marker. So we'd like to be able to measure something like that from the blood. We'd like to be able to tell the difference between an adenocarcinoma and a squamous cell carcinoma, or between a lung cancer and a breast cancer from the blood. Some of those things you can do with DNA potentially, but a lot of them you can't. And they might be better done with rna. It's a fascinating area. My group is working in this area. We haven't published our work, but we will hopefully soon. We have some exciting primary results. I think in the future, RNA will be a big part of the liquid biopsy puzzle. That doesn't replace DNA. It offers complementary pieces of information that you can't get from.

DNA. Now, is there a difference between cell free DNA and circulating tumor DNA? Sometimes I've heard people use those interchangeably. Are they interchangeable or is there a.

Distinction? They are different. Generally, cell free DNA refers to the total DNA in the circulation that includes both the healthy cell derived DNA and the cancer cell derived DNA and.

Circulating tumor is just the sub fraction of.

That. That's the tumor, that 1% that's from the. Or whatever. That's from the tumor. Exactly. So that's the difference in the.

Terminology. Are there any other signatures that can be helpful here? For example, looking at methylation patterns on the DNA, can that be informative at.

All? Absolutely. DNA methylation refers to a chemical modification of the DNA molecules. So again we have these about 170 base pair molecules in the circulation. And at certain of the nucleotides there can be chemical modifications that are put on by enzymes, but particularly a methylation, a methyl group, a chemical group. And that is put on not the same in every cell. Different cells based on largely what tissue they're from, have different patterns of this methylation. And it has to do because these methylation marks, these chemical modifications can influence which genes are turned on and off. You could envision that in a lung cell that needs genes for surfactant production or making alveoli or whatever needs those genes on, but it doesn't need the genes on, but a fat cell to make fat, because it's not doing that. Some genes are methylated and turned on or off in different cells in different ways. So they can be exquisite marks of the origin of the DNA. And so there's been a lot of work in the last couple years looking at methylation marks to do that. And it has some potential advantages because unlike mutations, it's actually much more stereotyped methylation profile. Lung cancer of two lung cancers is more similar than their mutation.

Profile. So in other words, you can use the mutations to identify and distinguish between tumor and non tumor and you use the methylation to hopefully zero in on tissue of.

Origin. You could, for.

Example. Yes, if you were using it as a pan screen for screening.

Purposes. Exactly. Which is a little. It's a different scenario than this initial scenario of saying, I have a patient who I've treated with surgery or radiation, I want to know if they're cured. Now I think an important thing to recognize is that the sensitivity of the methylation based approaches is significantly inferior, inferior to that of the mutation based approaches. But from a practical standpoint, the methylation approaches have some advantages for the screening question, but the sensitivity and the practicality aren't aligned. You'd want the most sensitive assay possible for screening because the tumors are tiny. But that is not the current state in the field is that the mutation based methods are much more sensitive. So we recently published a paper, a new method that's our third generation mutation based method that's about 100 times more sensitive than our prior methods as well as other methods in the field that can now get down to one part in a million. So we can detect maybe with the prior methods we're kind of bottoming out at about 0.01%, the prior mutation based method around 0.01%. So 1 in 10,000, this new method can get to one in a minute.

Million. So a two log.

Improvement. Two log improvements. A log improvement is a dramatic improvement in a diagnostic assay. And so with that assay we have lots of patients where we had sample left over so we could use our first gen assay and now come to this new assay and we had false negatives with the first gen assay that now we can pick up. With this newer method. It wasn't that there was no ctdna, there was circulating tumor DNA, it just was not at the level that the prior generation could detect with the methylation based assays. It's actually the data is still not very mature to know exactly what their sensitivity is, but it's probably closer to one in a thousand. So 0.1% the.

Sensitivity. Of course to be clear, what makes the mutation based method so much more sensitive is you know what you're looking for, you have the mutation a priori in the methylation method. You don't. If you're doing a pan screen for someone who doesn't have cancer, by definition you can't know what mutations to look for. So how does that gap get closed? Because if you look at a test like Grail for example. So Grail is I believe, looking at exactly this method, right? They're using, I think they're looking at methylation patterns of cell free DNA, but they're doing it as a pan screen. So it's give me 10, 20 milliliters of blood and we're just going to look for all the cell free DNA. We're going to identify if there's cancer or non cancer in there. If there's, there is cancer in there, we're going to try to predict the tissue of origin. How good can that get? Because right now the sensitivity for all stages is probably 50% with a specificity of about 99%. It's above 99%. But if you look at stage one, if you're really trying to catch an early cancer, we're talking about a sensitivity of maybe 20%.

Right. This gets into a very interesting part of this diagnostic field in general, particularly this liquid biopsy field with regards to early detection, which is how results are presented. And you put your finger on it. We care about sensitivity and specificity. We want specificity very high, 95, 99%, something in that ballpark. And we want high sensitivity. When studies report sensitivity across stages, I have an issue when that is done because you can easily bias that by just including more stage four patients. You could include one stage one patient and 999 stage four patients and say I have sensitivity of blah from stage one to four. It's important to break it down by stage. And actually even within stage one, it should be broken down because for like a lung cancer, let's say we break stage one into stage one a, stage one B and even stage one A one, stage one a, two stage one. So it gets very granular. And that all relates to smaller and smaller tumors that have better and better outcome that are the ones you want to catch in lung cancer. Actually, the sensitivity for stage one lung cancer in the grail data that's been presented is actually 5% or less for stage one lung.

Cancer. Let's make sure people understand the math on that. If it's bigger than stage one, you don't need a liquid biopsy. You can do a low dose CT and you'll see it in theory. The only reason you'd care about a liquid biopsy is if we're talking about fewer than a billion.

Cells. If you had a patient, you could do either test. That's true. There are some potential advantages of the blood based test as a way of getting more patients screened. I think there are some practical reasons why you could argue if people have less access to care access or they're hesitant to get the.

Scan. Yeah, fair enough. But let's just say I want to make sure people understand the following. If a test has, and this is going to be, I think, amazing for people, 50% sensitivity, 99.5% specificity. If you said that that was your stage one performance in an unbiased sample, I think people would be like, wow, that's pretty good. For people who don't have cancer, for whom we believe don't have cancer, in whom we don't have any mutational information, I have a test that is 50% sensitive and 99.5% specific. Do the test. Okay, here's how I explain this to my patients. What is the pre test probability that you have cancer? So if you're one of my patients and you're 45 years old and you're not a smoker and your family history is relatively Normal. And, oh, by the way, your colonoscopy is negative. In other words, your pretest probability is 1%. And I encourage everybody to do this. And for the show notes, we're going to include my calculators. I built a little calculator, but there are apps that do this. I just do it in Excel. But you can download an app that will allow you to do this. You plug in sensitivity, specificity, and prevalence, and it will spit out positive and negative predictive value. Well, if you use the numbers I just gave you, 55%, 99.5% specificity, 1% on the prevalence. If my memory serves me correctly, your negative predictive value goes to 99.7%. Let's just say you put in 99% specificity, it goes to 99.5% negative predictive value. And they say, well, that's really good, isn't it? And I say, yeah, but it's only a little bit better than what it was at the outset. Your pretest probability was 99% negative. I just took you from 99 negative to 99.5 negative. And by the way, your positive predictive value is 40%. If I get a positive test in you, it's more likely than not a false positive. I don't want to be the guy that's raining on the parade because we are using these tests in our patients, but never in isolation. That's a lot I loaded into that. So feel free to comment on any and all of it. But where I want to go with this, Max, is without the mutational information that makes the type of testing you're doing in patients with known cancers so sensitive. How can we make these things more than a parlor trick in patients who don't have cancer, at least at the.

Surface? Excellent point. You laid out some things that I often do in my talks about these things. This is something that's even not well understood, not just by patients, but by a lot of researchers and physicians. This issue that something that sounds great. 50% sensitivity, 99% specificity, even ignoring for the fact that the stage one sensitivity is actually less than 5%. I just say on the surface, the 50% where we're at, that if you already basically have no risk of cancer, you're in less than 1% chance of having cancer. The test isn't really moving the needle for you significantly and has a high risk of catching false positives, as you mentioned, because the positive predictive value isn't so good, that leads to lots of further testing anxiety. Often we can't find anything. But then patients are worried for years that is there something brewing? Is it just the next scan will show it or the next time? So I have lots of concerns about that. I'm one of the researchers that's really focusing on this field and I very much see these issues and am concerned about them. This is why I really strongly feel that we as the field need to ultimately really do studies that prove cancer specific survival benefits of these tests. And that is currently not really on the roadmap. The commercial efforts, of which there are many, they're not planning on doing this. Large randomized trials like the low dose CT study we showed, we talked about earlier, that proved that you save lives from lung cancer. Why not? Well, they're expensive and take years and it's not attractive from an investment standpoint. But if we really want to be sure that we are helping our patients and we're not just adding costs to the healthcare system, that's what we have to do. And we don't know if the sensitivity as of the best tests as they currently are, are good enough to save lives. And there's multiple reasons for that. But an important reason is that it gets very complicated. But you got to look at by stage because you can game that even within stage one. You got to look by stage one, a one, one, a two, one, a three. But even once you're at that point, it gets even more complicated because there's actually really. Let's say we have stage one lung cancer patients. There's two types of stage one lung cancer patients. There'S a stage one lung cancer patients where the cancer cells have not left the lung. That's where they are. That's the only place they are. And the surgeon removes the tumor and the patient is cured. That's one type of stage one lung cancer patient. The second type of stage one lung cancer patient looks the same, has the exact same CT scan, has the same surgery, but already has microscopic cells in the liver and the brain that we don't know about. They're stage one. We call them stage one because that's all we can see. So stage one is actually heterogeneous. There's two subtypes of it. And in order for a screening test to be useful, it has to catch a significant fraction of the first type of stage one, the stage one that doesn't have micrometastases which the surgeon can.

Cure. Because I think what's implicit in your comment, Max, is that the surgeon's not going to cure the second.

One. Good Point, Yes. So the surgeon can cure what he can see or she can see what they can cut out. If there's micrometastatic metastases in the liver and the brain, the surgeon isn't cutting those areas because they don't know about them. And so those will eventually grow back. That is why simply saying a test increases the number of patients diagnosed at earlier stages doesn't automatically prove that that test will save lives. When you look at the very granular level, you consider this issue about that there being different kinds of stage one patients. You know, you can envision tests that mainly catches the patients with micrometastatic disease. And actually we have hints that the CTDNA assays preferentially catch those patients because of the things we talked about at the beginning with there being now lots of microscopic deposits spread throughout the body. If you were to put those all into one place and add them up, you would have many billions of cells. Therefore overall you have more circulating tumor DNA and you can detect it. But you think they're stage one because that's all you can see on the scan. How do we prove that the tests are not falling into that pitfall is we have to do randomized studies. That's the only way we can prove it. And to me that's a major concern. And there's this push and pull about, well, these are great tests, they're very promising, and obviously I wouldn't work on it if I didn't think ultimately these will save lives. But should we withhold them from.

Patients? From non cancer patients.

Especially? Okay, we don't know yet if they're going to save lives. But isn't it unethical to not give them? Because, you know, if we later find out, if we 10 years from now or five years from now find out, they save lives. Now there's five years of patients who didn't have the benefit. That is an argument that's often put forward. The problem with that argument, which is why ethically it sounds of course, very reasonable, is that if by that argument we would never test anything new that we develop in medicine because there's always a possibility that that thing could improve.

Things. And that's, I think, why the FDA has taken the posture, because right now there are only four approved liquid biopsies, Gardent and three.

Others. Correct. But those are for very different things. Right? Those are for genotype. It's something we haven't really talked about. Those are for identifying what mutations are present and patients with advanced disease. That's what those tests are let's actually talk about.

That. Tell folks what gardent does. It's an FDA approved diagnostic, but explain to people what it's used for. Because I get asked this question all the.

Time. So the garden assay and they're one of the first companies in this field, they use a very similar method to what we use next generation sequencing to measure circulating tumor DNA. Their initial test, the one that's approved, was developed for a very specific clinical problem. You have a patient with metastatic cancer, let's say lung cancer, multiple parts of the body, and you want to identify what mutations that patient has. Because in lung cancer we have.

Drugs that work for patients with certain treatments based on.

Mutations. It's what we call actionable. If we know what the mutation is and if we have a drug for it, that is the first thing we should treat the patient with. Historically, we always had to do a biopsy or surgery to get the piece of the tissue to identify the mutations. We, you know, the field garden we others have shown is that in many patients with advanced disease because like say 1% of the circling tumor DNA is from the cancer, that's enough that you can identify the mutations without needing to do the biopsy. So that's potentially very useful, particularly in patients who can't have a biopsy or where they had a biopsy but all the tissue is used up. There's lots of cases like this or where the biopsy missed the tumor. That happens a lot, you know, a certain subset of the time. In those cases it can be super useful to have a blood test to say, oh yeah, this patient does have an EGFR mutation. So they should, should get on the egfr, tyrosine kinase inhibitor. That's what those tests were developed for. Now that is the easiest problem in the liquid biopsy space because you're in stage four patients that are known to have cancer, they have lots of disease in the high tumor burden, high tumor burden. And so those tests are optimized for that. They work well for that. They have good agreement, a high 80% agreement with biopsy. They can sometimes find mutations that the biopsy misses. Because of technical issues with the biopsy. As I mentioned, they're good tests for that problem. They are not designed for, number one, early cancer screening or detection where the levels of circum tumor da are many logs lower. And they're not designed for this question of is the patient cured or not after treatment, again, where the levels are much lower. They are designed for it is one thing, and that's very Important to recognize that test. If you were to go to the doctor and say, you know, I want the test to see whether I'm cured or not. FDA approved tests don't do.

That. Is that true of. Is it Cell Search and Foundation one? Are there a couple of the others that are approved.

Right? Yeah. Foundation has a liquid biopsy test very similar to what we developed in Garden, and they do the same thing. Now. Cell Search is actually a circulating tumor cell assay. It was FDA approved, actually was approved before any circulating tumor DNA assay. We talked about the circulating tumor cells at the beginning, at the very beginning of the liquid biopsy field, like in the early 2000s. That was the focus. Everyone was focusing on the cells. And so that's why that was the first test. It is FDA approved. No one really uses it because it doesn't provide you actionable information. Your doctor runs that test. Whatever the result is, doesn't impact how the patient is treated. And so therefore, that has grown out of favor. And there is a difference between FDA approval and actually clinical usefulness that is important to.

Recognize. Now, when you look at a test like Grail, for example, the FDA has not approved it yet, but it is still permitted in use. Explain that.

Distinction. Now, this is a complicated area in diagnostic space in the US where there's really two kinds of ways tests can be regulated by the government such that they can be used on patients. One is the one that's well known as FDA approval, which is of course, an agency that focuses on approving drugs and diagnostic tests and evaluates them carefully and then allows companies to gives them approval to market them if they pass the bar of what you need to show. The second way is actually a much easier way to get assays out to patients, and that is by setting up the assay in a lab that is compliant with the CLIA act, an act of Congress that focuses on regulating laboratories that do diagnostic tests that is independent of the fda. The way that roughly works is that the lab itself gets this designation at yes, you. You guys are doing things in an appropriate way. They get inspected every year or two and make sure they're doing things appropriately. And then those labs have the blessing to develop any tests that they want using the procedures that they're supposed to follow with, you know, running control, those kind of things, and then offer them to patients. So the FDA never reviews those. It's really a much less highly regulated because basically the individual tests aren't regulated. It's the laboratory that's regulated. And that is what Garden did before they got their FDA clearance. That's what Grail's doing. That's what all the diagnostics companies, they start by building a clia laboratory and then building their assays in that frame because they're much less regulated, so you can start selling them to patients and providers long before you'd ever get FDA.

Approval. So from a technology standpoint, I mean, There are like 30 other companies that are out there doing this right now. And not to use the word grail again, but the holy grail is the blood test. That's going to take a patient who doesn't think they have cancer, screen them with a high enough sensitivity and specificity that I do the blood test and it says, peter, we're afraid that you have colon cancer. What? I had a colonoscopy two years ago. Well, it looks like you have it again. Go get a colonoscopy next week. Lo and behold, there's a tiny little adenomatous polyp there that is an early stage one. And gosh, I need just the smallest little partial colectomy and I'm cured, because that is a hundred percent curable cancer. Or, you know, a woman gets a blood test and it says, you have breast cancer, and she says, that can't be. I don't feel a lump. My mammography was negative six months ago. Understood. Maybe you should go get a diffusion weighted image MRI and look at that breast a little more closely. And sure enough, there it is. What would really be amazing is if we had the confidence in these things that even if they were never showing up on imaging, you could treat them. Now we're getting back into science fiction. What's science fiction? Science fiction is, in five years you're going to have a 1cm pancreatic adenocarcinoma. You and I, Max, both know that even a 1cm pancreatic adenocarcinoma has a 25% five year survival. That is a death sentence of a cancer, unfortunately, even at stage one. So waiting until you can see a billion cells on the CT or MRI is not going to be the way to remedy pancreatic cancer. It's either going to be much earlier detection or treatments that actually work. So today we have neither of the above. Is there a path to this sci fi world that I'm dreaming of where using the blood alone, we can actually start to develop a high enough sense of confidence that that patient does indeed have cancer, but they might be three or four years away from it being clinically.

Detectable? I am Hopeful that we could get there. It's not going to come in one step, but we are already taking steps towards that. So building the bricks on top of each other. And the reason I say that is that the easier application again is in the patients who already known to have cancer, where we have these super sensitive tests that we can then run after and see who's cured or not. So that we now have, we have these tests that can detect the state that's called minimal residual disease, meaning microscopic cells that are residual after your treatment and that have currently at least very high positive predictive value, meaning that if you detect the ctdna, that patient is very likely going to have the cancer grow.

Back. And is that guiding therapy? Is that immediately now sending you to adjuvant therapy right.

Away? That's exactly where it's going. There's now studies, clinical trials underway to do that. So we've recently launched a couple here at Stanford in lung cancer, where we're taking patients with early stage lung cancer. They can have one of the trials, it's very broad. They can have radiation or surgery, whatever is appropriate. If they need chemo for their standard of care for their stage, they get that too. When they're done with every standard thing, they get the blood test and we do this minerals agilities test. If it's positive, we give those patients immunotherapy. We can't see any cancer anywhere in the body, but we know they have microscopic cells left behind, and that is the moment in the patient's lifetime with their cancer that they will have the least number of cells they'll ever.

Have. And the fewest.

Mutations. Exactly. The lowest heterogeneity. Right. The lowest chance of having resistance. So I'm very hopeful that by doing that we will be able to cure more patients. And we already have evidence of that because we know adjuvant treatment, even given the brute force way, like we've done for decades, where we basically give it to everybody with a certain stage where most are.

Cured. Yeah. The same drug in adjuvant is more effective than the same drug in.

Metastatic. Absolutely.

Yeah. I think this is a very important point that gets lost on the part of many people in the field, which is early detection absolutely matters. And you have to look no further than that simple point which has. I've never seen it refuted. Less tumor burden, less heterogeneity equals better outcomes. It's an.

Axiom. It's absolutely true. And that's because we now understand much better that why cancers initially respond and become resistant is because of this genetic heterogeneity that they already have mutation. They're not all the same. The cancer cells. Actually all two, no two cells are probably the same. They're all different. They all have slightly different, a handful of mutations that are different between each cell. And the more cancer you have, the higher chance that one of those mutations will make the cancer resistant to whatever therapy you're using. So this idea of guiding adjuvant therapy based on ctna, mrd, I think is the first step towards the future that you're envisioning or dreaming.

Of. So let's think about which cancers that's going to be relevant in. It will not be relevant in pancreatic, because everybody's going to probably need adjuvant. I mean, if you're really being honest, it certainly is relevant in your field of lung cancer. It's certainly going to be relevant in breast cancer. It's certainly going to be relevant in colorectal cancer. You could potentially argue prostate, although the treatment is pretty bad. That really takes care of kind of the big five right.

There. I throw in bladder, which is whatever the six or seven most common cancer. Some of the more rare ones would be useful with melanoma, of course, it's much more rare, of course. But where we also give adjuvant. The way that I think of it actually is it's going to be useful in the cases where a minority of patients develops recurrence. If you have a situation like pancreas cancer where the vast majority of stage one patients will develop recurrence, that's not the place to start this kind of developing, this kind of a test. Because like you said, the tests aren't ever going to be 100% perfect. No test gives you 75% chance of recurrence. That's not the place to do it. It's to do it in places where there's a subset that recur and, and in many cancers actually that maybe at certain stages you don't need it. Stage three colon cancer, well, maybe we don't need it.

There. Yeah, we know the probability of recurrence is high enough. We should just treat as.

Accordingly. Maybe we should just treat it, but stage one, two, that's a different story. Right? The minority recur there. So I think that's how I think of it. Where to first focus.

On. We've talked a lot about where we are on call that stage one program one, identifying the high risk patient who needs adjuvant therapy. Great progress on lung. What is the state of the art for that exact problem on breast, prostate and colorectal.

Cancer. So there are active studies underway. Colorectal cancer actually I would argue might be the furthest along. There actually are some medicare approved tests using CTDNA from a company called Natera for colorectal cancer that I believe can be reimbursed in some patients actually, even though there hasn't been proof of benefit of that treating based on it matters. But that's something that got approved recently. So I think in those patients, at least a subs of them, it can already be done in a doctor's office. But there are many trials going on in colorectal cancer actually many more than in lung cancer currently. And there are trials going on in breast cancer to also do similar things. Now breast and prostate are actually more challenging for a couple of reasons, but one is they have very low levels of circulating tumor DNA. It's it seems that there's difference between tumor types and how much circulating tumor a day they shed for a certain amount of volume and they seem to have lower levels. So it's a little more challenging. So you need to use these very, very sensitive mutation based methods and probably even the subset of mutation based methods that's most sensitive. So I know there's studies going on in breast cancer. I'm actually not aware of prostate cancer studies in the CTDNA space because of this issue of the very low concentrations. But I wouldn't be shocked if there are now some places that are doing a.

Few. Yeah, prostate seems to be a very privileged site. Even the Grail test, I think they say, really it doesn't screen effectively for prostate because you're just not getting enough cell free DNA in the circulation or.

Breast. Actually the Grail's development plan initially was largely around breast, but they then pivoted because the early results were that breast also they didn't do very.

Well. Would it be that the next stage of patients you're interested in treating are patients who are say post adjuvant therapy or who were deemed low enough risk to not need adjuvant therapy, but who are still higher risk than the general population in whom you're screening for recurrence? Would that be kind of the next group you would want to develop a liquid biopsy to.

Assess? If we can get proof of principle that this approach works in these early studies, I think the logical concept would be exactly what you say, which is you could envision a future where maybe even you move into the more high risk patients with higher risk of recurrence where there's still a substantial number that don't recur. But you do this test repeatedly, let's say every three months and you don't give adjuvant until you see a signal that the test is positive. So maybe that way you minimize missing patients and you can still catch them months before they have clinical recurrence. I think it's very likely that that kind of an approach where you basically only give adjuvant if the test is positive, but you do the test longitudinal to make sure that you don't minimize missing people, that that would very likely have equivalent outcomes to giving everybody in that group treatment actually maybe even better because you're not giving toxicity to a big chunk of patients. So that's one. The second one, which I think is really exciting is analogous to that basically which is to it's the same set of trials, but I think it's looking at it the other way around, which is to say that patients, you would avoid over treating patients. So one big problem we have right now in adjuvant therapy in places, like many places, but breast cancer is a good example of it, the number need to treat is quite high because most patients already cured by the surgeon and don't have micrometastatic disease. And then the treatments aren't perfect. So the ones even that have micrometastatic disease, a significant number don't benefit. So actually we have a huge problem with over treatment. And this approach that I outlined with the repeat testing could get us away from that over treatment situation where we only give the treatment to the patients where we have substantial evidence that they still have cancer cells in the.

Body. Those are three great use cases for liquid biopsies that are all going to benefit from a very important insight which is you know, the mutations of the cancer at the outset. So you get to pair a very important piece of information, which is tumor identification genetically with cell free DNA. We now need a different approach if we're going to move to the sort of panacea of cancer screening, which is every person once a year goes to their doctor, gives 10 or 20 milliliters of blood and a test comes back that says one, do you have any cancer? Yes or no. Two, if you do, it's this organ, let's go work it up. Which either means in the early stages, more diagnostics in the later stages means direct to treatment. That would be again what we talked about earlier. It seems to me that one way to think about that is if you were going to try to do that based on mutation analysis, you would have to develop the world's most robust database of mutations for every given tumor. Is that even.

Possible? So we, about a year and a half ago published a paper where we developed actually a mutation based lung cancer screening method using ctdna. We call that method lung clip for Clippus cancer likelihood in plasma. And it's a method that is purely based on mutations. The way it works actually is you sequence the plasma and the white blood cells of the screening patient. The sampling on the patients you're trying to screen, you sequence both the CF DNA cell free DNA and the leukocyte DNA. The reason we do that is because we've learned that in particular older individuals, there are mutations present in the leukocytes, often that have been acquired through age through a process called clonal hematopoiesis. So those mutations, mutations that are in the white blood cells end up showing up in the plasma most of the time because the white blood cells die in the circulation and release their DNA. And that's part of the cell free DNA. So those we want to get rid of, that's not coming from a lung cancer or breast cancer. Right. So we do both. We subtract the things that are in the white blood cells and then we use a machine learning algorithm that looks at the mutations that are left in the plasma and looks at things like how long is the cell free DNA molecule? We haven't talked about it, but we have and others have shown that the cancer derived cell for DNA molecules are a little bit shorter than that, 170 bases. They're a few bases shorter on average. The reason we think is because most of the DNA in the blood comes from white blood cells. We know that's true. About 80, 90% of the cell free DNA is coming from white blood cells and that the other 10 or 20% comes from solid organs as well as cancer. Probably the DNA has to traverse longer to get into the blood. Right. If you die in the tissue and then you to get into the blood takes a while and that's probably.

A longer time to be exposed to the.

Enzymes. That's right. So we think that's part of the problem. It may also be that some of the way the histones in the chromatin is configured may contribute to it. But for whatever reason that is true, we look at is a mutation present. What gene is the mutation in? How long is a cell free DNA fragment? Is this mutation one of the mutations that is caused by smoking? All these different things put that in a machine learning model. And the machine learning model isn't just saying, have I seen this mutation before in lung cancer? But it's looking at these other properties to say do the properties of the mutations match what you'd expect in a lung cancer? And then the model ultimately spits out a probability that that blood sample was from a patient with lung cancer or not. So that is I think the way that I would envision doing it, using mutation based approach where you're not actually doing this catalog of mutations. Because that won't work, unfortunately. Because every cancer is unique and you can, you can have a mutation in any gene in any.

Position. I assume you can use machine learning to basically predict what should be a cancer mutation versus.

Not. That's what we're doing. Exactly right, yes. You try to use as much information as you can get beyond just the mutation being there in follow up work. We are trying to extend that by adding more such features. Right. The more features you have, that link with cancer, the better it is. The other question is, can we combine that with methylation? Maybe the combination of the two, you could kind of leverage the best of both worlds. Can we combine it with other analytes, other approaches? So that's how I think we're going to move forward to try to develop ultimately the best screening test. And it very likely will be a combination of not just methylation or just mutation or just what's called fragmentomics, which is this size of the molecules or their distribution. It likely will be a combo method. That's still very much in the early research phase. One has to prove which of those things combine nicely and usefully. And then once you know that, then you have to think about, okay, can I develop an assay that's affordable that one could imagine doing on hundreds of millions of people. But we're still at the discovery.

Phase. Do you have a sense of what the theoretical limits look like? Assuming you can take information from each of those data sets? Right. So fragment size or features of the fragment length, methylation patterns, predictive mutations, et cetera. And based on what you know about the frequency with which you can identify cell free DNA. And given that we're now exclusively talking about patients that do not have disease on any other imaging modality, let's just limit it to pre stage one or say at best stage one, early stage one. Realistically, where do you think the sensitivity and specificity could be pushed.

To? I think we don't really know yet. I think one critical experiment, which is what we're currently doing, is to take the most sensitive method we have this third gen Method I mentioned that has the one in a million sensitivity. Apply that to a large cord of stage one patients and see how many of those patients can we now detect circulating tumor DNA in when we know the mutation. This is not the screening question, but it's sort of the preamble to it where we can definitively say these patients have CTDNA and how much in stage one. So that experiment will tell us what is actually the lay of the land for the amount of circontermid shedding in stage one lung cancer. Once we have that, we will know what sensitivity do we have to get to with a naive approach, meaning we don't know what the patient has cancer with this integrated approach. Before I can say, basically, I need to see that data, but I'm hopeful we can push it above the 5% we talked about earlier in our study from a year or two ago. Within stage one, we could get to about 20% sensitivity, including in stage one a non small cell lung cancer in a validation.

Cohort. And what was the.

Specificity? The specificity was 99, 98%. I think it was tuned in that analysis.

98%. Again, I know that's early stage. It's still just not going to make sense for people whose incoming pretest probability is 1 to 2%. It's just still too.

Low. It's too.

Low. My back of the envelope math max is that if someone has a pretest probability of 1%, I mean, it sounds crazy. You're going to need 80% sensitivity and 99.5% specificity to say this is a test that matters. This is a test where, okay, now my positive predictive value is high enough that I can do something with this. And just as importantly, my negative predictive value means I can breathe.

Easy. It's possible we can't get there with this approach. If you had asked this question in the 50s with some of the protein biomarker work, which was exciting, people would have said, well, okay, we're not there, but maybe in 10 years we.

Will. Do you think this is a decade.

Away? We don't even know what the real distribution of CTDNA is because most of the experiments have used insensitive method where most of them are negative. Well, then we can't see what's negative. Right? So I'm hopeful we can increase it significantly. I'm hopeful we can get to at least 50%, you know, 50 to 75%. Can we get to 80, 90%, 50 to.

75. If it's truly early stage one, that's a big step.

Forward. Agreed. It would be a big step.

If you can keep your specificity north of.

99%. Correct. So some liquid biopsy groups that have kind of done that by saying, okay, let's go with 80% specificity. Because if you ever tested 99% specificity that has 20% sensitivity, now you drop the specificity to 80, your sensitivity magically goes up by 20%. Right. Because just the dumb luck part.

And your negative predictive value goes out the.

Window. So that's the problem. Right. So for the foreseeable future, there's going to be sort of two main areas. The way I think about applying these. One is in the cancers that have no screening tests currently, the bar is of course much lower. We know that mammography, even low dose CT, colonoscopy is a little bit of an exception, but none of those are perfect obviously, yet we do them. And at least for the case of lung cancer and the colon screening, we're very confident they save lives. We think for breast cancer, Love corsairs. You know, there's contrast controversy around that. But in the cases where we don't have screening tests, if we can even get a test that's kind of like that, not perfect, but can get us some, that could be helpful. It would be ideal in the case like pancreatic cancer, but for reasons we talked about earlier, we don't have to do with them and I don't think that's the right place to do it. But other cancers, you know, that we don't screen for, there's that group and there may be the bar is lower because we don't have a predicate cancers that we already have screening tests for. I think it's really important how the tests compare to the existing screening tests. Obviously if they're as good or better than what we're already doing, well then you could argue, well then we should switch to those. Right. Even if they're not perfect. But if they're worse, which is currently the situation, the liquid biopsies aren't as good as those tests, then you really have to think hard about how you would use them. And then I think the main use has to start to be something that's more around practical considerations or health system considerations, like low dose ct, for example. We know tiny minority of patients who are eligible are currently, currently getting it in the U.S. medicare pays for it, but the vast majority of patients who are eligible don't get it. So why is that? Well, there's many reasons, but part of it could be you know, this concern of radiation exposure, the difficulty of having to book another appointment to go to an imaging facility, access to the imaging facility. So if you're already at your primary care doctor, you're getting your hemoglobin A1C tested or whatever, it's probably easier to draw another couple tubes of blood and send those off, right? So, but that's not the dream. That's helpful, but it doesn't solve the problem. So that's sort of how I would ration develop it. But we still need to prove again I think very important that these tests decrease cancer specific death. Even though they're exciting technologies and we're very hopeful that they will, we shouldn't just give them a pass because they're high tech and not do the right clinical.

Science. Do you know of any companies out there that are actually in pursuit of what we're talking about as the dream scenario where you have an actual high sensitivity, high specificity test for low stage, early stage.

One? I think all the companies are working to try to improve their tests constantly because they're being run in the CLIA environment. It's actually much easier to change the test than it is under the FDA and approval. I think people are trying hard. I haven't seen it solved. That includes migrate. We're working on it hard. We have some ideas we think are promising, but it's too early to know how much they're going to push the bar over what we showed in that prior paper or what Grail showed in their recent.

Papers. How much money is the NIH putting into this on a global.

Scale? That's a great question. Actually. I've never seen a number of how much funding are they doing for a liquid biopsy research? I can tell you that it's increased dramatically. So just in general, when I started working in this area, like probably 2012ish with the first publication 2014, when I was going to meetings around those times, I would often be the only person talking about circulating tumor DNA. There would be a bunch of other diagnostics, imaging and a lot of circling tumor cell work at that time that was being presented. Whereas now the vast majority of presentations at meeting and stuff is focused on liquid biopsy. The same thing I've seen at the NIH in study section, you know, reviewing grants. Many of us, including myself, serve on these NIH study sections where one scores the grants that the nih. The scores that the NIH uses to fund research. And it's a lot of work because you got to read dozens of grants. And you know, you spend days of your week reading other people's grants and commenting on them. But it's the service we do because of course otherwise the system doesn't work. But now in the size section I sit on, which is focused on cancer biomarkers, we see lots and lots of liquid biopsy work being submitted and often scoring high. So I think it's substantial. It's increased dramatically from 2012. There's a lot of interest at NCI and NIH. They see the value, just like many of us do, that this is an area we absolutely have to push forward and deserves more.

Funding. Max, this was super interesting. I mean, this is a topic I've been interested in for years and I learned a lot. Today I really have a much better sense of the landscape and the history of how it got where it got. So I'm positive that everybody listening to this will, even if they came in with less information than me or more information, is going to have gleaned something. Thanks so much for making the time and setting aside a few hours of your day to talk about this and to catch up in general. It's just.

Great. Yeah, it was really a pleasure chatting with you. It's great to see you again. Thanks for all the insightful questions. I really enjoyed the conversation. I hope your listeners got something out of.

It. I'm positive they will. Thanks Max. Thank you for listening to this week's episode of the Drive. If you're interested in diving deeper into any topics we discuss, we've created a membership program that allows us to bring you more in depth exclusive content without relying on paid ads. It's our goal to ensure members get back much more than the price a subscription. After that end, membership benefits include a bunch of things. One Totally kickass comprehensive podcast show notes that detail every topic, paper, person, thing we discuss on each episode. The word on the street is Nobody's show notes rival these monthly AMA episodes or Ask Me Anything episodes. Hearing these episodes completely access to our private podcast feed that allows you to hear everything without having to listen. Listen to spiels like this the Qualys, which are a super short podcast that we release every Tuesday through Friday highlighting the best questions, topics and tactics discussed on previous episodes of the Drive. This is a great way to catch up on previous episodes without having to go back and necessarily listen to everyone. Steep discounts on products that I believe in but for which I'm not getting paid to endorse, and a whole bunch of other benefits that we continue to trickle in as time goes on. If you want to learn more and access these member only benefits, you can head over to peterattiamd.com subscribe. You can find me on Twitter, Instagram and Facebook all with the ID Peteratti md. You can also leave us a review on Apple Podcasts or whatever podcast player you listen on. This podcast is for general informational purposes only and does not constitute the practice of medicine, nursing or other professional healthcare services, including the giving of medical advice. No doctor patient relationship is formed. The use of this information and the materials linked to this podcast is at the user's own risk. The content on this podcast is not intended to be a substitute for professional medical advice, diagnosis or treatment. Users should not disregard or delay in obtaining medical advice from any medical condition they have, and they should seek the assistance of their healthcare professionals for any such conditions. Finally, I take conflicts of interest very seriously. For all of my disclosures and the companies I invest in or Advise, please visit PeterAttiamD.com about where I keep an up to date and active list of such.

Companies.