The Peter Attia Drive - #213 ‒ Liquid biopsies and cancer detection | Max Diehn, M.D. Ph.D.

Episode Date: July 11, 2022

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter’s Weekly Newsletter Max Diehn is a Professor of Radiation Oncology at Stanford and a clinical radiation oncologist specializing in lung cancer. Max’s research focuses on developing novel methods for detecting circulating tumor DNA in the blood of cancer patients and on elucidating the molecular pathways and genes associated with cancer. His interests also include uncovering biomarkers that can predict patient survival, responses to therapy, and disease recurrence. In this packed episode, Max discusses the history of blood-based cancer screening and the importance of understanding the predictive value of tests—sensitivity, specificity, negative predictive value, positive predictive value – and how these metrics play into cancer screening. Max then goes in depth on the topic of liquid biopsies, including the history, current landscape, and possible future of liquid biopsies as a cancer detection tool. He discusses how these non-invasive blood tests can detect DNA/RNA from tumor cells released into the blood as well as the different methods one can use to predict if a cancer is present. He gets granular on the topic of cell-free DNA/RNA signature, methylation patterns, and the importance of knowing mutation information, and he ends with a discussion on the exciting future of liquid biopsies and how we can possibly get to the panacea of cancer screening. We discuss: Max’s training that planted the seeds for development of liquid biopsies [4:30]; Max’s decision to specialize in radiation oncology [11:45]; A culture at Stanford that values research and physician scientists [17:00]; The motivation to develop liquid biopsies [19:15]; History of blood-based cancer screening and understanding the predictive value of tests [25:30]; Current state of lung cancer and the need for better screening [32:45]; Low-dose CT scans: an important tool for managing lung cancer but with limitations [42:00]; Using liquid biopsies to identify circulating tumor cells [47:00]; Liquid biopsy research moves from circulating tumor cells to cell-free DNA [1:03:00]; Zeroing-in on circulating tumor DNA in cell-free DNA [1:10:48]; Cell-free RNA and Max’s vision for cancer detection from a blood sample [1:22:00]; Methylation patterns and other informative signatures found in DNA [1:24:30]; Mutation-based methods of liquid biopsies [1:26:30]; Understanding the sensitivity and specificity of a diagnostic test [1:30:30]; Existing clinical liquid biopsy tests and their limitations [1:37:30]; The future of liquid biopsies [1:44:00]; How we get to the panacea of cancer screening [1:52:00]; More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

Hey everyone, welcome to the Drive Podcast. I'm your host, Peter Atia. This podcast, my website, and my weekly newsletter, I'll focus on the goal of translating the science of longevity into something accessible for everyone. Our goal is to provide the best content and 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 peteratia MD dot com forward slash subscribe.

Now without further delay, here's today's episode. My guess 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 Forsyte Diagnostics, a precision medicine company developing novel liquid biopsy tests for measurement of minimal residual disease. He's also the co-founder of CyberMed, a company that applies data science for biomarker discovery. Max also consults and advises a number of companies

in similar spaces. Max is current research involves the development of novel methods for detecting circulating tumor DNA 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 discussions 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 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 in particular how they play into cancer screening, especially when it comes to understanding prevalence and pre-test 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. 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 got 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 self-redeNA RNA signatures. We talk about methylation patterns. We talk about the importance of knowing

mutation information. We 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 D. 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 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 will 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 the time was revolutionary.

But 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 and I worked on many different projects. It was a very unique time in the lab and path lab as well as in labs and general

where 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 are 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 sore lymphocyte, that are a 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 to see how they go up and down after you perturb cells. And so we were very interested to see what genes have 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 costimulatory signal. And so you would look at the T cell, it's stimulated, are you actually measuring protein? RNA, you're making it RNA. Yeah, the technology 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 downregulate,

turned off when you activate T cells using different signals. And that catalog then, of of course has been very helpful for subsequent studies in 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 gonna become relevant as we start to talk about the differences between self-read DNA and RNA. But at the time, how difficult was it to keep all of this RNA intact as you cataloged the generation of mRNA from DNA as this signal 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'll measure a single RNA 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 they are, So there are relabatory 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 cell 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 subs of the RNA that was stuck to these organelles called the endoplasmic reticulum. There's 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 structured at various levels in a test tube. And that required a lot of work in a cold room where, you know, you're busy. You test tubes into a four degree room, but so is the experiment.

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. 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. You had to make the primers. We 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 and some of the early work on long-known coding RNAs and I 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, half four, 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 MTPHD program, you know, 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 M.D. 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 lymphomaire while I was 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, it 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 want it 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 the first year med students how many options they 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 or were you born in the US? 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 and I was going to move 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 I 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 what 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 dance

journey. I did a rotation in radiation oncology actually in large part because my wife, who was also a medical student at Stanford, who you, of course, did a research year in the Department of Radiation on Collegiate. 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 radiation call just had a little more time and clinic to spend with each patient. 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 and radiation callings very procedurally heavy. It's of course a field where we do a lot of imaging to see where tumors are in the 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. You know, 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 you know certain things he didn't go to you know 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, Racial College is, did I'm internship in medicine? Here at Stanford, and then it's four years of Racial College, 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 finish up work from the PhD. Array-Choncology residency programs have a research track for individuals who are interested in laboratory-based research called the Holman Pathway, which is an other field, it's 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 post-doc time of about two years during my four years of radiation oncology residency. During that post-doc 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 I'm 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. Radiation Oncology grew out of radiology, which is as listeners of Paranoid was College grew out of radiology, which is, as listeners, part of now 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, Rayson College was part of those departments, but then here are first chairman Henry Kaplan, who is a very famous Rayson College's infosition scientist, became the first chair of Rayson College 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

a radiotherapy, which around the 50s, where those were some of the first successes of taking patients, especially young patients with Hodgkin's disease, a type of lymphoma that was incurable previously, who now, 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 many, many patients we can't 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, oh, and now we've really solved it, but ultimately, you know, it gets more complicated. But that, of course, established the department as one of the very leading departments in the world. And it has continued that the department here throughout its history has had a very strong interest in laboratory research, as 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 liked 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 wanna 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 below the laboratory of a 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. 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 others might not.

That's one way I can be a useful contributor to the field in general. We always start with clinical areas of clinical need. 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 target-to-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 and 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 three, six, 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 out on CT scanners? Are we at 256 bid 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 one centimeter in diameter, maybe eight millimeters. It depends a little bit on the location of 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 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 an 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 one centimeter is problematic. But for the most part, if you talk about a lung cancer, liver cancer, pancreatic cancer, colon cancer, et cetera, breast cancer, one centimeter is irrelevant. At what point does a tumor become in and of itself threatening to the host?

I mean, you could argue by the time it's 10 centimeters by 10 centimeters by 10 centimeters, 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 a 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

were pretty much flying blind. In a relevant to the work similar to work that we're not doing, many cancers once they have metastasizer spread, which is really ultimately what ends up killing patients is usually when the cancer spreads. If the cancer is only localized, usually a surgeon or a radiational college just can cure that patient. But once the cancer spread in many places in the body, you can't remove it all or radiate

it all, right? So, if you think about a patient who has what's called micrometastatic disease or microscopic disease that has spread elsewhere in the body, you could have dozens or even hundreds of these micrometastatic 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 of blind spot of our imaging methods.

The disease is all spread out. We can't see them all. But that is one thing that one could potentially get a hand on if one had a test that could be measured, let's say, in the blood, where one could measure contributions from all these dozens of micrometastatic deposits and that that one might get a 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 CA199 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 CA199 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 the exactly examples you pointed out, which are the markers you mentioned, like PSAC in 1999,

are protein biomarkers. So these are proteins made by the cancer cells that can be shed into the blood, and that 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 that's just that's their baseline what the normal cells are making in their body. And that lack of specificities, 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, have been done in lung cancer previously looking at similar biomarkers, including CA, actually, which in some subsets 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 where we're 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 positive 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 100 cancer patients, 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 100. 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 well, 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 a hundred percent sensitive, meaning it catches every cancer patient. That is really unrealistic in the vast majority of not all cases. And I think this is something that's sometimes not 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 you know, 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 analyst 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 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 yeen 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 inside or a new advance where you can just increase sensitivity without also hurting specificity. I guess to finish off so again specificity since we haven't defined it so that is sort of the inverse of sensitivity which is that if for a patient who does not have cancers, 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, 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 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, especially if you're just in men or just in women like breast or prostate, but where actually outcomes are much better,

we can cure a very large fraction of patients with breast and prostate cancer. Of course, not everyone. And we're 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, a 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 in recent development. So 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 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 point out one of the point, which is 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 are related to pollution. You know, and that has been well described that pollution or other things in 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, 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'll never get rid of lung cancer, even if we could magically make smoking go away today, you know, across the globe, you know, 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 isn't 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 the time, it can sometimes be. Squamous cellocarcinoma, is that correct? The vast majority of the 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, seemed 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 versus non? We do not. 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, but that it's one of these,

as are many, of course, of our chronic illnesses that have genetic components, the cardiovascular diseases, for example, right, 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, they elevate the risk. And then they 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. Thank you mentioned, Raydon.

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, particularly 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 epimulox 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 non-linear that curve is as well versus, you know, the 40 pack

year smoke or the five pack year smoker, etc. 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 can't measure it in a completely unbiased way. The way it just soaks your patient to understand how a doctor tries to figure out how many pack years of 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, you know, 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 set it there is an increase in the as you go from zero to higher levels. But then around 20 or 30 pack years, some studies argue that then kind of plateau

is that you may be sort of saturating that don't have the exact slope. So those lines I don't know if top of my head, but it is not a perfect variable. What are the data say about second hand smoke, which I would imagine is even harder to quantify than pack years? For example, I have some patients who grew up with parents who smoked. They themselves have never smoked.

Let's say those parents continue 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 for example with low-dose CT and things like that It's very difficult to measure. That's even harder to measure than the packers 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 that that 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 secondhand 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 and buy a marker 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 Lodocet. So, second and second, it's not considered the way that we decide on those criteria for who should get this test and covered by insurance and who shouldn't. It's really about trying to enrich for us the highest risk population. You want at least a certain percent, half percent, a percent of risk at the can so that you're going to screen for it,

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, second hand smoke, that we no increase the risk. We don't, then 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 will 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 with a pre-test 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. 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. You know, there have 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 an 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 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, right, 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 significant lower rates of lung cancer deaths. I mean, I have a relative risk reduction of about 20% 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 percent.

I don't remember it off the top of my head. But it was single digit percent, so let's say it was 5 percent, 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 percent? Maybe it's 0.5 to 1 percent based on that argument. Now that gets complicated also because you'd think doing a CT scan, you either see a nodule, you don't, so it should be pretty easy. But of course, it's not that easy.

CT scans are complicated thing to read. And so how you interpret the scan, and what you consider a positive will, of course, affect your sensitivity, that's a physicality, and will affect the number needed to treat downstream. So the way that we now read scans, the load of 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 studies. Lotus CT is not my area of expertise. I don't have all the numbers of 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 it's 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

milyseverance of radiation the Loto CT provides? I'm guessing it's like one milysevered 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. Graduation can cause cancer, so it's the risk worth it, right?

It's called low to 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 mle seavers 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 one to two 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? Do you start to worry at 20 mle cvts 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, 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 harms. It's actually very difficult to quantify exactly how much risk of, let's say, a second malagency, there a future malagency, there is from a given protocol of a CT scan or whatnot, just because to get that data and you could never do it randomize all those things, right?

The way that medical engineers 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? Well, this helped 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 it. 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, we'll kill them, right? And so if it's going to come back, if we don't catch it early, where we still might have it as a second chance of cure, so that's how we do the calculus. It's not that there's badges by patients wear, they track this so that if you get to a certain point, you couldn't do no more imaging. It's always 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 there clinically as we would say, any D, 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 100 people who are currently any D 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 host, right? Because we'd have to wait until they had full-lating, 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 cancers. 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 or 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's it called preclinical models, in which you try to grow tumors in the animals and then see if you could have an hypothesis

for what might be 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 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 to recollect in 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 this is why I want to study it or the outset who's bearing the risk financially? When you're new faculty member, when you start a new lab, when you get the 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 have an extra 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, I'm not sure your listeners, I've heard it 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 is, sounds great, but you have no proof that it works at all, they're not going to give you money. So I use not any that I had for my startup funds. So yeah, so I just start collecting blood. I started thinking about what biomarkers would be good. So I read the literature on the protein biomarkers as we talked early.

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, you know, at the time of their follow-up scans when they're coming back to CS. Protein bi-market 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 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, you know, 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 right or reasons. Two biggest ones, probably are, or maybe 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 it in needle and 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 have the blood sample drawn. That makes it very difficult to build up bio banks of frozen cells, frozen samples that you could, you know, get a large cohort to 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-circumular tumor cell methods were using found circulating cells.

These patients don't have cancer. So, they were picking up something else, so there was a lack of specificity. So, they were technically complicated. It seemed like it's going to be hard to get this into the clinic anytime soon, so there was a lack of specificity. So they were technically complicated. It seemed like it's going to be hard to get this into the clinic anytime soon, and there was some weakness. So that's them 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-cancer 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 lung cancer patients, and in ones where it is, it can even be a marker for occurrence, like PSA, can be a 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 CEA and a few of these other markers, like C9-9, some of the ones that have been previously reported to be markers and 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 it was an artifact of that cohort or something else was going on where, 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. How 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 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 to definitively prove something doesn't work quite as well as initially reported, one actually has to do very large studies so that the what are called air bars are an estimate of the sensitivity that 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 of thousands of dollars. So, there is these decisions one has to make. Is this really worth it and what's the opportunity cost? But it's interesting when you think about it, because it means that we have a biased 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 in a way 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, you know, 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 say 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 unlimited resources and time, even to publish such a study would take, let's say, a grad student's, but could still take six months of their time to get that out. But now, they're not spending those six months on their main project, which is to develop the new task. And I've spoken with graduate students who will say, look, it's really gonna be harder for me to get my PhD

publishing negative studies. I mean, I'm gonna 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. This selection 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, right? 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. It means yourself that something is really real. It's really helpful to see multiple sites 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 take somewhere between 10 to 20 ml 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 at 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 what we didn't do, but another group in the field, like Helen Dive from the University of Manchester, where they actually took blood from the draining veins at the time of the surgery, the lebeck to me for lung cancer,

and did circling tumor cell assays, and found evidence that they could find more signal, 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 two 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 circumtumor 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 circumtumor cells, most assays, use, uh, 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 panor white blood cell marker. On the flip side, for a positive marker, what it should be expressed in the cancer cell, but not the white blood cells, they look for things like cytokaritins, 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 cytokaritin 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, 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 gonna treat you like your stage three or worse. That's absolutely where we wanted to get that exact situation on the main motivations for starting liquid biopsy,

working in my lab. And yes, with very high levels of circuent tumor cells, one could have envisioned if one were to do a very large studies to definitively prove that these patients were the 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 developed 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 circling 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 said, and we'll talk about shortly, but more development is required

if we're going to be able to use that clinically. What do you 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 noncellular 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 Lo, and Hong Kong, and Steve Quake,

it was actually here at Stanford, and then engineering department, basically showing that in pregnant mothers can detect DNA from the fetus in the woman's blood, cell-free DNA, and not inside cells, but outside of cells. So it's pretty obvious that we can do that in pregnancy than, 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 sulfur 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 yourself. You could enrich for it again. And that might actually be a future-instained research direction. But the problem is, currently, if you were to just take some of that cellular compartment, And you could enrich your cell for it. You could 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. It's only about on the healthy inhibitor, about one to five nanograms per mill 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 hematocryter 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 for med school,

Matt McCormick, who is my roommate, used to always refer to me as having a 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. 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 for 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 that there's a double-spinning experiment

along those lines. These are a double-stranded DNAs, we think. We know those are don't experiments along those lines. These are a double-stranded DNA molecules. They are rather short, the self-redued DNA molecules. How many? Yeah, it's about 170 basis, 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 is 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, and 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, and it's these DNA enzymes, and probably probably important because if you ever work with DNA, if you get to high concentrations of it, it becomes very sticky kind of a snot-like substance,

which would be very probably bad to have in your micro-rasketure 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 in an animal, what we know is that most, if not all tissues, contribute DNA to the self-reduce 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, historically a lot of most of the reviews and textbooks would argue that 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 programs. 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 program 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. It also is critical for getting rid of six cells, because some of us, I'll get sick, there has to be a way of killing it. So amortosis is a programmed way of cells dying dying and as part of that, once that's activated, the cells get a chop up, their proteins as well as their DNA, basically. It's sort of a tight, 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 factor is now we have much better understanding that there's also DNAs that's just floating in the plasma that are also chopping up DNA. So over the 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 assuming 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, 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 sulfur DNA work. The concentration is really low, like single-digit nanograms per mill. And often when we do laboratory procedures, we like to work with at least micrograms of DNA. So this nanograms is 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 do I expect for healthy individual?

Doing it suits you hybridization or? This is just purely a die-based method. It spreads 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're going to 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 of the time we can use this more simple method, we just add a die, put in the machine, get your results very fast. And just give me some quantity. So in 10 ml of plasma, how many nanograms would you, how many nanograms per milliliter in any sample would you expect to have a self-read DNA?

Ten miles of plasma would be 20 mls of blood. We collect because plasma in most individuals is about half. From 20 mls of blood we get 10 mls of plasma. Those 10 mls in healthy individuals would have somewhere between 1 to 5 nanograms per mill. So that would mean 10 to 15 nanograms of DNA at the end. In some conditions, in some advanced cancer patients, patients who have trauma, have an infection, the levels can be much higher, much higher than five 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 mutations, 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 long 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. You're really at the molecular cause of these is the mutation.

That is what you're tracking. What better biomarker can're really at the molecular cause of these ismitation. 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 them if there's circumcline to mediate a 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 circling DNAs from the cancer, so still a small subset, and that can range from an advanced lung cancer patient might have 1% of the circling DNAs 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 in 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 DNA, where you can identify the actual sequence of the bases, the AG, TC bases that make up DNA, can get the exact sequence for hundreds of millions of molecules or billions of molecules,

even in parallel and in one experiment. This is 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 then compare those sequences to the patient's germline DNA. Let's say that we purify from a buckle swab or from the cellular compartment of the blood. And we can say,

the vast majority of the molecules look identical to the patient's germline, meaning they're 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 will have treatment. They all know 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 a 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, we can mark as a 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, that 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, close it on 70. 99.9% of all of that self-read DNA lines up to some segment of their germ line. 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 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. The cancer is only gonna have maybe a hundred mutations. In the coding regions, exactly. I'd say a hundred mutations in the coding regions, in the total genome, if you include the regions between genes, maybe, you know, 10 to 20,000 or something, it depends, of course, as a range, but having more than 10s of thousands of mutations

in the whole genome would be unusual. I guess what strikes me as almost improbable is given that 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 self-redean and cancer prior to us. We 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 self-redefree DNA present by a French group. This had very, very insensitive methods, so they could only tell it, see it in some very, very advanced cancer patients, but even then they were already at the foresight to say, well, this could be potentially useful. But then, for the intervening, I don't know,

50, 60s 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 P3 mutation, you go looking for that P3 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 10 fold by a whole order of magnitude, because I don't have to see all 10 mutations to know the cancer is still there, 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 voting or non-coding, and we've successfully used both. What is beneficial is to have what's called a truncle 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 the 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 this else. 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, it gets it 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 it.

So let's now talk about, is there such a thing as self-free RNA, or is that just so unstable that it's not even worth thinking about? And the self-free DNA, the only place to be thinking about this? No, absolutely not. There is such a thing as self-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, 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 that gets into the future, that is what 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 PDL1 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 I have it to do with signaling, epigenetic reasons, et cetera. You can't tell by looking at the sequence in DNA of PDL1, 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 a lumb 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 promarious ults. 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, 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 kind of modifications, can influence which genes are turned on and off. You can envision that in a long cell, it needs genes for surfactant production or making a VLI or whatever needs those genes on, but it doesn't need the genes on that cell to make fat because it's not doing that. Some genes are methylated and turn 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 of years looking at methylation marks to do that. And it has some potential advantages because unlike mutations, it's such a 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 as a pan screen.

For screening purposes, exactly, which is 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 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, as they 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 sort of our third the mutation-based methods are much more sensitive. So we recently published a paper, a new method, that's sort of 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 where the prior methods were kind of bottoming out at about 0.01 percent, the prior mutation-based method around 0.01 percent, so 1 in 10,000, this new method can get to 1 in a 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, we've lots of patients where we had sample leftovers, so we could use our first gen assay, and now come to this new assay, and we leftovers that 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 circling 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. It's a 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 ml of blood and we're just going to look for all the self-free DNA. We're going to identify if there's cancer or non-cancer in there, if 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%. 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 what 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, right? You could include one stage one patient and 999 stage 4 patients and say I have sensitivity of blah from stage 1 to 4. It's important to break it down by stage.

And actually, even within stage 1, it should be broken down because for like a lung cancel, let's say we break stage 1 into stage 1A, stage 1B, and even stage 1A1, stage 1A2, stage 1, so it gets very granular and that all relates to small 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 1 lung cancer in the grilled it's

been presented is actually 5% or less for stage 1 lung cancer. Let's make sure people understand the math on that. If it's bigger than stage 1, you don't need a liquid biopsy. You can do a low-dose CT and you'll see it. But in theory, the only reason you 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 patient screened.

I think there are some practical reasons why you could argue. If people have less access to care. Access or there has a tend to get the scan? Yeah, fair enough. But let's just say, I wanna make sure people understand the following. If a test has, and this is gonna 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 pre-test 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 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% and a half, 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 pre-test probability was 99% negative. I just took you from 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 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 was actually less than 5%, like they're still at the surface of the 50% where it's where we're at. That if you already basically have no risk of uncancer, you're plus a month percent channel having cancer, the test isn't really moving the needle for you significantly. And as 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 there's something brewing. Is it just the next scan we'll 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 I'm 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 Lotus CT study we talked about earlier, that proved that you save lives from lung cancer. Why not? While 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 1a1, 1a2, 1a3. But even once you're at that point, you guess even more complicated because there's actually really, but let's say we have stage one lung cancer patients,

there's two types of stage one lung cancer patients. The 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 as the exact same CT scan as 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 randole level, you consider this issue about that there being different kinds of stage 1 patients. You can envision a test that mainly catches the patients with macromanastatic 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, you know, lots of microscopic deposits spread throughout the body.

If you were to put those all into one place and that them up, you would have many billions of cells. Therefore, overall, you have more circling tumor DNA, and you can detect it, but you think they're stage one, because that's all you can see on this scan. How do we prove that the tests are not falling into that pitfall is we have to do random my 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. We don't know yet if they're going to save lives, but isn't it unethical to not give them? Because if we later find out, if we'd 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 it'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. Garden and three others. Correct. But those are for very different things. Those are for genotype.

We haven't really talked about those are for identifying what mutations are present in patients with advanced disease. That's what those tests are. Let's actually talk about that. Tell folks what garden 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 an extraneous sequencing to measure circling 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, 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's the first thing we should treat the patient with. Historically, we always have to do a biopsy or surgery to get the piece of the tissue to identify the mutations.

The field, garden, we others have have shown is that in many patients with advanced disease, because like, let's say 1% of this 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, certain subs of the time. In those cases, it can be super useful to have a blood test. They say, oh, yet this patient does have an eG-farmutation, so they should get on the

EGFR tires and 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 at the biopsy misses because of technical issues with 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 circumtumorin-day are many logs lower. And they're not designed for this question of, is the patient cured or not after treatment? Begin where the levels are much lower. Their design for this 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 proof test, don't do that. Is that true of is it cell search and foundation one or the 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 circling tumor cell assay. It was FDA approved, actually was approved before any circling tumor DNA assay. You know, we talked about certain tumor cells at the beginning. At the very beginning of the Lycopybibes, you feel like in the early 2000, that was the focus.

They were everyone's 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 has 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 give 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 a 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 that yes, 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 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 is doing. That's what all the diagnostic companies, they start by building a Clia laboratory and then building their asses in that frame because they're much less regulated. 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, low and behold, there's a tiny little adnomitus polyp there that is an early stage one. And gosh, I need just the smallest little partial collectime. And I'm cured because that is a 100% 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 science fiction? Science fiction is, in five years, you're going to have a one centimeter pancreatic adenocarcinoma. You and I max both know that even a one centimeter 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? We're 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 know the 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, the 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 and lung cancer where we're taking patients with early stage lung cancer. They can have one of the trials that it's very broad. They can have radiation or surgery, whatever it's 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 or dualities 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 for most or cure. 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 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 are become initially

respond and become resistance is because of this genetic heterogeneity that they already have mutations, they're not all the same, the cancer cells. Actually, no two cells are probably the same, they're all different, they all have slightly different, handful of mutations that are different in between each cell. The more cancer you have, the higher the chance that one of those mutations will make the cancer resistant to whatever therapy you're using. This idea of guiding adjuvant therapy based on CTN MRD, I think is the first step towards the future that you're envisioning or dreaming of.

So let's think about which cancer is 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, the lung cancer. It's certainly going to be relevant in breast cancer. It's certainly going to be relevant in col 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 what are the six or seven most common cancer. Some of the

more rare ones, a little bit beautiful melano, 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 those stage one patients will develop recurrence, that's not the place to start this kind of a, 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 in many cancers actually that maybe it's 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 recurrences high enough. We should just treat as accordingly. Maybe we should just treat it. But stage one, too, 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, unbreast 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 at treating based on it matters, but that's something that got approved recently. So I think in those patients, like the 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 seems that there's difference between tumor types and how much circling tumor days 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 substantive 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 self-redean-A in the circulation. Or breast, actually.

The grails in development plan initially was largely around breast, but they then pivoted because the early results were that breast also 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 in 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? We can get proof of principle of that this approach works in these early studies. I think the logical step would be exactly what you say, which is you can 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 adren until you see a signal that the test is positive. So maybe that way you minimize missing patients and you can still 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 a month before they have clinical recurrence. I think it's very likely that that kind of an approach

where you basically only give adjuvant the test is positive, but you do the test longitudinally 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 overtreating 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 of people need treat is quite high because most patients don't already cure a metasurgeon and don't have macromatostatic disease. And then the treatments are imperfect, so the ones even have macromatostatic disease, if a number don't benefit. So actually, we have a huge problem with over-treatment, and this approach that I outlined with a 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 ml 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 and 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 long clip for clippest 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, self-read DNA, and the leukocyte DNA. The reason we do that is because we've learned that in pretty older individuals, there are mutations present in the leukocyte's often that have been acquired through age,

through a process called clonal amout of poysis. So those 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 you wanna get rid of, that's not coming from a lung cancer or a 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-free DNA molecules are a little bit shorter than that 170 bases. There are 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 for DNA is coming from white blood cells. And that the other 10 or 20% comes from solid organs as well as cancer. Probably that DNA has to traverse longer to get into the blood, right? If you could die in the tissue

and then you 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 and 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 self-reduined a 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 a 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. 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 unfortunate, because every cancer is unique, and 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. 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. Again, the 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 of their distribution. It likely will be a humble 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 someone could imagine doing on hundreds and 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 datasets, right? So fragments, 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-and-a-million sensitivity, apply that to a large cohort 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 CT DNA in how much in stage 1. So that experiment will tell us what is actually the lay of the land for the the amount of circumtumoridane shedding in stage 1 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 this, 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 to ago within stage one, we could get to about 20% sensitivity, including in stage one, A, non-small cell lung cancer, and evaluation cohort. And what was the specificity? The specificity was 99, 98%. I think it was two-in-a-half analysis, 98%. Again, I know that's early stage.

It's still just not going to make sense for people who's incoming pre-test probability is one to two percent. It's just still too low. It's too low. My back at the envelope, Math Max, is that if someone has a pre-test 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, OK, we're not there, but maybe in the 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%, 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 percent specificity because it has a 99 percent specificity that has 20 percent sensitivity. Now you drop the specificity to 80. Your sensitivity management goes up by 20 percent, right? Because just the dumb luck part. And your negative predictive value goes out the windows. 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 bars, of course, much lower. We know that mammography, even low-dose CT, may 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, I'll of course say, you know, there's 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 the reason 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 bars lower because 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 bars lower, because we don't have a predicate. Cancer is 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 Lotus CT, for example, we know tiny minority of patients who are eligible are currently getting it in the US. Medicare pays for it,

but the best 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 this concern of radiation exposure or the difficulty of having to book another appointment to go to an imaging facility, access to the immune facility. So if you're already a department care doctor, you're getting your Pymglo-Mae1c tested or whatever, it's probably easier to draw another couple of 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 for us, I guess.

So, that's sort of high- would ration develop it, but we still need to prove, again, I think it's 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 a 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 clear environment I think all the companies are working to try to improve their tests constantly, because they're being run in the clear environment. It's actually much easier to change the tests than it is in the FDA and approval. I think people are trying hard. I haven't seen it solve. That includes my group. 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 have 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 2012-ish, you know, the first-pokish in 2014, when I was going to meetings around those times,

I would often be the only person talking about circling 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 studies section where one scores the grants, that the scores of the NIH uses to fund research.

And it's a lot of work as you get to read dozens of grants. And you spend days of your week reading other people's grants and commenting on them. But it's a service we do because, of course, otherwise, the system doesn't work. But now in the study 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, and 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 work out. 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 cleaned 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 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, man.

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