Transcript Dr. Peter Kuhn - AI, Physics, and the future of early detection
When a Physicist declares war on cancer
Joelle Kaufman: So today on Kicking Cancer's Ass, we have a, a, a brilliant scientist who inspires me, who amuses me, who I have the pleasure of, uh, of working with from time to time, uh, when he wants to know what I think about something. But Peter Kon, Dr. Peter Kon is the university professor of, there's a long list here, biological sciences, medicine, biomedical engineering, aerospace and mechanical engineering and urology.
He is, uh, a founding member of the Michelson Center for Convergent Biosciences, a co-founder of the MI USC Mickelson Center for Convergent Bioscience Bridge Institute, and Director of the Convergent Science Institute in Cancer. So, uh, interestingly, Dr. Kon, and he'll tell you this too, I'm gonna call him Peter.
He's not an md, he's a physicist. And Peter, I wanna thank you so much for agreeing to come on kicking Cancer's ass and sharing your unique approach to that particular, that particular opportunity.
Peter: Thank you Joelle. Much appreciated. maybe I'll add a counter introduction to this really quick 'cause I just, uh, I was just thinking about this this morning. The first time I met you in the real world, uh, was we had assembled a group of what I think are the most brilliant minds to actually help us make progress in this. Problem of early, uh, breast cancer detection around a conference table. we had all just started having our first cup of coffee and you flew in as the last person to join. There was only one chair left. I still remember this like at this moment in time, chair left at the middle of the middle of the table. We had just done our introductions and naturally got to you as you sat down. And I'm like, Joelle, who are you? And I don't know whether you remember this, but you sat down and you were like, I was born to be here today.
Joelle Kaufman: I do remember that.
Peter: was awesome. That was so, is that that level of purpose, something that I just absolutely love about, uh, working with you and like us being in this space together of actually making a difference because that is what I believe it actually takes,
Joelle Kaufman: Yes.
Peter: to make progress.
Joelle Kaufman: Yes. And, and for people listening, part of the reason I started with that is as we go around the room of luminaries that Peter, um, assembled, we have people with multiple MDs and PhDs and people who have started massive companies, and people who are experts in lab operations, and how do we bring a, a new diagnostic to market?
And, and I show up and, you know, me, I, I mean I'm a tech, C-M-O-C-R-O, you know, kind of just a, a person. Uh, but unlike everybody else in the room, I had a BRCA one gene. And when we did that introduction, I hadn't yet been diagnosed with cancer. It was, uh, it was actually, I think before I was actually in, I. My own personal experience, but of course you knew about my parents, my mother, my sister, everything else.
I remember Peter when I told you about my diagnosis and I said my doctors were blown away because they looked at my MRI from June, and that was six months prior to diagnosis of a nine millimeter tumor. And then they looked at the one in January and they said there's nothing there in June. And what I remember is you were so deeply troubled that there was nothing there, and it was like a clarion call for why the work you are doing, the work you're doing to find other ways to detect cancers is just so important.
Peter: Yeah. And this is, um, and again, as you know from my own, from my own past, right? What, what bothers me the most, um, is, is really, that, is the unknown, is the uncertainty. what I hate about cancer is a surprise. Cancer in and of itself, it's inadvertent. It's part of aging, it's part of. part of process and, but, so that might be unavoidable, right?
But what is avoidable is really surprise, unknown, uncertainty. All of that is what we need to be able to eliminate through the process of science, right? Where I always, when I, when I, when I heard you say that to me, that was the ultimate statement of the failure of science. This is not the failure of your physician.
This is not the failure of you as a patient. This is not the failure of the technology that you're using. It's actually the failure of science and discovery at that front
Joelle Kaufman: Mm-hmm.
Peter: We haven't figured this out yet.
Joelle Kaufman: Mm-hmm.
Peter: It makes me mad, right. To be like, why haven't we, we have worked and with, as you said, with some of the best minds in the world. have been at this and we are trying to crack this. And, and it's just, it's a very, it's a very, it's a complicated, it's a very complicated problem, but by us, um, coming up just from different angles, I think is how we are actually making, making progress, um, in this entire world of detection, uh, early detection, and then understanding how do we actually deal with it as we go forward and then projecting out into the future.
Joelle Kaufman: So that actually leads to a great question. You know, you've de dedicated your career to attacking this problem, to addressing this failure of science. But you're not a doctor, you're a physicist. So tell the story a little bit of what brought you. To, I wanna use physics and this cross-functional, uh, cross subject approach to cancer research.
And how do you think your unique background and the way you collaborate has really changed your approach to cancer detection?
Peter: It's, uh, reading your book is actually fascinating, Joelle, right? Because it's, our story is actually where we are coming from is incredibly similar almost the exact same age. When your mother was diagnosed, my mother was diagnosed, and, and I think at, at that age, at this teenage age, you're like, I have no idea what to do with this. I know that there emotions, I know that there are consequences, beyond that, I'm like, I don't even, I don't even know how bad, what is, what does bad mean? And again, this was decades ago, right? So the level of knowledge was a totally different, was a totally different story. And, um. So as, as you work your way through that, um, you are taking in information and you're trying to understand how do I process what's happening to my mother? What's happening in this around me? the same time, of course, your own life is still progressing. You became that. other CMO mind you, right, when I first read Joelle Kaufman, CMO, I'm like, oh, chief medical officer, this is great. I don't know what she wants to talk to me about as a chief medical officer.
You know, should know it all. Right? Um, right. So, uh, our life progresses and for me, that was really becoming, becoming a physicist. My older brother became a physicist, followed in his footsteps. That seemed great. good stuff, no entrance exams. Most kids fail in the first year, and that's how you filter out.
So all of that seemed to work out great. My mom's initial treatment also worked out great. Okay. She actually was part of a clinical trial of brachytherapy. We are talking, you know, 40, 50, 50 years ago now and, um, 40 years ago. um, and she recovered well from that. So I'm like, okay, great.
Joelle Kaufman: What is that? What, what is b brachytherapy? I don't, I've not heard that word before.
Peter: um, sorry. This is, this is, um, this is when radiation therapy first came about. Um, essentially what happens is my mom had a lumpectomy, so the primary tumor was resected. And, and then essentially, um, a set of needles, um, uh, were put into her breast and a, a, uh, focused, um, uh, radiation therapy. Um, was, uh, localized around where the initial tumor was, and, and then you go through that radiation therapy.
And for her, at the time, that was, in fact the only treatment, um, she received.
Joelle Kaufman: Mm-hmm.
Peter: that was deemed, it was deemed early stage. It's all of the things that I had no idea what to do
Joelle Kaufman: Right.
Peter: Um, so, but after surgery, you know, I, I went into the hospital, saw my mom, my, I, I viewed my primary job as just bringing sunshine, sunshine, uh, into that, into that room and making sure that negative voices stay out of the room. I view that as my primary job as the youngest of four kids. And, um, but along the way, uh, over this next couple of weeks, then actually I started hearing, like, okay, there was this medical physicist who was actually doing the calculations for the radiation therapy. Oh, cool. And I was in the middle, in the middle of studying physics.
I'm like, huh, interesting.
Joelle Kaufman: Okay.
Peter: do something useful on the medical side. That was like that initial of like, this could really be, this could really be interesting. You park this. So I
Joelle Kaufman: Um.
Peter: as I went forward, um, again, parallel to your construct of processing the risk of cancer. For me, it was actually processing the risk of relapse for my mom. It was the understanding of her having confidence that that cancer is gone on the one hand, but no matter where the slightest pinch was in the body, when it happened to me, I'm like, oh, I strained the muscle. Shouldn't have stayed out this long last night. Should not have lifted that many Apple crates last night should have done this. But it doesn't occur to me that, hey, maybe I have cancer. For those of us not yet diagnosed is really, the emotions I think are actually completely impossible to actually understand. I saw it with my mom. I could always detect it it took me forever to actually start wrapping my head around Why is that right?
And why is that? Is because the way a recurrence, that cancer coming back, that thing that really nobody wants to ever happen once you're diagnosed oftentimes first experienced by the patient having a pain sensation.
Joelle Kaufman: Interesting.
Peter: Right, which is, which is sort of very similar to when you described that, you know, how many false positive signals did you have, right?
Because most of the time when you have exper, when you're experiencing pain, right? For my mom, for 47 years post treatment, those were all false positives. Okay? It was only three years ago, four years ago now that her breast cancer actually came back. so that process of like, wow, wait a minute, can we not, we really, we can't do any better that. We can't nail this in any other way than waiting for pain to show up. Um, so that is what really then over time, started defining my own pathway in terms of going from, call this a more traditional physicist and high energy physics, little bit on the engineering physics side. I really wanted to build cars.
I really wanted to work for Porsche. There's all kinds of fun things that I really wanted to do, but started guiding me down this path of biophysics, biomedicine, not quite knowing yet what that, what that means, but eventually getting me to this place where I am today. Still on that same mission, still haven't solved that problem.
It's still frustrating me because it is a problem that's incredibly difficult to solve. But in 2025, we live in a totally different world of actually starting to make real inroads in having specific tests, specific biomarkers that can help us monitor. Patients being free of disease, patients being at risk of disease, patients being at the verge of a recurrence of the disease. And so, so that is really what got me to this place where I am today. And now, is a physics degree a meaningful or useful degree? Um, well, I'm gonna, I'm gonna turn that around to you, Joelle. Right? I mean, this is when I, when you came and you came down and sat at the table, right? I'm like, Hey, this is not, this is no free lunch, okay? I need to learn from you because your CMO, that other CMO title is actually incredibly important because we actually need to also understand the people.
Joelle Kaufman: Well, in the effort to understand people, I'm hoping that you can help me and, and anyone who's listening understand, you know, people talk about tests like the PSA test or the gallery test, or you know, something Because I think any of us who live under the specter of cancer, you and I both have lived under it as the children of survivors.
Um, I have friends who are the children of people who died from it. And the specter of cancer is terrifying. But this desire, like, Hey, the PSA test, as long as your PSA is good, you don't have prostate cancer. Okay? Is that the best we can do? And there's nothing like that. For ovarian cancer or breast cancer.
Why is this such a hard problem?
Peter: Yeah. No, I love that. No, this is great. Why, why growth? This shouldn't be this hard. Peter
Joelle Kaufman: Come on. Like it's just a blood test, right?
Peter: it should just be a blood test. Okay, so, so let's deconstruct, let's deconstruct this. Let's deconstruct this a little bit. Um, let's, uh, let's think about anatomy for the moment. What we really want to find right is, um, the, the time point.
Um. So if, if we think about that early development of that cancer, um, the, the evolution from a pre-cancerous to a cancerous lesion, is a highly variable time period. We assume breast cancer it's likely years. Then you have the time period of malignant transformation, meaning something has now turned into cancer, now it's growing locally in the breast, it's growing locally, and that is when we want to catch it. is when we want to treat it. We can talk a little bit about also what interception would mean prior to that. Right. But for
Joelle Kaufman: Yeah, that would be cool.
Peter: That would be super cool. That's cool vision stuff that we should talk about because it's a pretty amazing space as well. But this is when we want to catch it. Okay. Well, so from that perspective, was, for a long period of time, a very, uh, uh, uh, strong supporter of, Hey, it doesn't make sense to detect this one in the blood because I wanna detect it when it has not yet left the breast. Okay. So a blood test, if I take it that strictly doesn't make sense. Okay. Now then we did 10 years worth of research. Okay? this is what I love about that scientific process, which sometimes drives people, of course, a little crazy that are not part of this process, right? But part of this process is truly discovering the unknown and oftentimes, sometimes. That discovery process actually proves you wrong. In this case, it proved my hypothesis wrong that there are no early signals of cancer out in the wider blood circulation. mentioned PSA, it's a super interesting, it's a super interesting test, right? Gets good rep, bad rep, Because as you, as you said, right, we equate PSA with prostate cancer, the challenges that you can have an elevated PSA for reasons other than prostate cancer, and there are some subtypes of prostate cancer that are not PSA positive, okay?
So you have the same false positive, false negative challenge that you know you have gone through as well. So. So that's what makes these tests really difficult to develop, really hard to put into the right, into the right spot. And I'm going to, I'm going to go to gallery in a moment because that's, you know, one of the, uh, one-off a suite of tests that we call multi cancer early detection tests.
Now, because what happens, of course, every test has to be put in the context of the individual. And again, I'm using your own case,
Joelle Kaufman: Feel free.
Peter: you have, as you have described this in your book, right? So, um, you are as a, as a BRCA one carrier, um, as you said yourself, it, it is barely a question if, but when. Okay? So your single highest risk cancer post taking care of the ovaries was really breast cancer. Okay. So you need the most highly tuned set of tools that allow you and people like yourself because again, good news is there is many like you, right? So we can start learning from one another, right? But you need a highly tuned set of tools that help like yourself prior to diagnosis and post-diagnosis. So that's the camp I live in, right? That's the camp that's the problem I want to solve. Now, there is also, um, now everybody is at risk of cancer, right? There is nobody who is not at risk of cancer. So we also need some broader tools, right? We need some tools that allow us to screen for cancers broadly. Right again, for. We need to, again, recognize and appreciate that when we say breast cancer, even that is a set of half dozen different subtypes. Um, depending on how finely I wanna subset the subtypes as
Joelle Kaufman: Well, I did the math just for fun and came to, I, I stopped when I got to 120 different combinations.
Peter: So, right. So, so this, this is, then, this is super tricky. So, and what we have learned in the past, um, is as we, you know, traditionally we have, we have looked for the silver bullet. We have looked for the silver bullet of finding cancer. We have looked for the silver bullet of treating cancer. What you have just said is the simplest way to think about there is no silver bullet and this is a suite of diseases happening at a huge range of human backgrounds that define your personal risk. I'm gonna come back to these multi cancer early detection tests because I do believe that they will play an important role as we go forward in society, um, to really be a routine test a broad set of cancers at well understood sensitivity and specificity. will actually drive, I think, a beautiful level of clinical innovation as well.
They will teach us a lot about this cancer revolution as we go forward. Um, but what they're not meant to do is really be that finely tuned tool. For women like yourself at risk of breast cancer or at risk of recurrence. So that finely tuned tool where we make use of all of the biology we possibly can about a particular cancer is where we get to just much higher precision. So in, in general, what you will see evolving or what you what is already happening is really a combination of tool sets. And that goes from imaging, whether that is mammography or MRIs, um, down to blood tests that look for particular proteins like PSA or they look for particular pieces of DNA they look for cells or cell fragments as we do the end.
All of that has to be part of this much broader tool set and the tool set that then gets assembled in a custom way for each patient to become this. Cancer detection pathway that is, has to feel absolutely natural along your standard healthcare pathway. And that, I think is that next big, that next big level of innovation that has to make its way through research, the process of science, and then into clinical practice.
Joelle Kaufman: So I have so many questions that come out of this, but let me start with something pretty simple. You used the word sensitivity and specificity, right? And the word precision. Now, I think those words for you are as easy as apple and orange are for everybody else. But I'm not sure those of us that aren't living in the science world and in the diagnostics world actually understand what those words mean and why they matter.
So could you educate us?
Peter: No, ab. Absolutely. And, um, and so here's the, the beauty, the beauty of the world we live in, in 2025, actually, and I would always, I, I'm, I have a, a, a, um. An interesting relationship, with the large language models, but what the large language models, uh, whether that is the being co-pilot or chat chip pt, what they're actually really good at is really for these types of purposes.
Okay? So while I will, I will speak, uh, to sensitivity and specificity, but it's always, this is what, this is what these models are actually great for, just asking those questions then you can fine tune the answer to like, hey, up, you know, up the complexity of the answer a bit or reduce the complexity.
It's actually great, but to really answer your question, Joelle, so sensitivity and specificity really has to do with, how confident am I in the result being truly or actually having overlooked something. so specificity speaks to, the, the number of true positives, meaning that if, if a test says you likely have cancer set, the specificity gives me how likely that is, right?
So a specificity of 99% means that 99% likely that you actually have cancer, whatever cancer it is. Sensitivity gives me the confidence, bless you. Sensitivity gives me the confidence in the negative answer, meaning your cancer free.
Joelle Kaufman: Okay.
Peter: Think of it as, you know, how good is my warranty and how long is my warranty?
Period.
Joelle Kaufman: it. Got it.
Peter: And so both of those are incredibly important because they also relate as a patient, what does that mean for my next step. So in breast cancer, for example, and this is always complicated when, full disclosure, Joelle said, I'm a physicist. Okay? That means I have no bedside manners. I typically live in a basement.
I talk to machines and all the rest of it. Now, the last thing you want a physicist, a male physicist to do is actually talk about breast cancer and treatments. Okay? So, um, but, um,
Joelle Kaufman: What you do.
Peter: which, but I do, but I do. Um, so this is just like me apologizing. If I hit some something wrong, the wrong tone, please just forgive me on that.
But in, in, in breast cancer, I'll give the contrast to pancreatic cancer in a moment. In breast cancer, um, uh, as a woman, you have to ask yourself what is most important to me? Um, because a false positive means that you have to come back in for an additional ground of imaging as you, again, Joelle, as you described in your book, right? And you might even have to come in for an extra biopsy, and anytime somebody sticks a needle into your body, not a pleasant experience, right? And it's painful, et cetera, et cetera.
Joelle Kaufman: And it's, and my end, I think even worse is it's very stressful. There's a lot of anxiety about I have to go back. What does it, what does it mean? When will I know? Um, and you know, just to put some context, I mean, mammography is a great, great diagnostic tool, very pro it, but the level of false positives, besides the fact that it only detects 80% of the cancers, the false positives is really high.
And the number of biopsies, thankfully, that come back. As malignancies is fairly low, but you don't feel that or think that when your doctor calls and says, we need to have you back for a biopsy, I.
Peter: And, and so, and this is, this is where we get caught in a, in this natural dilemma of math, that specificity and sensitivity relate to one another. Okay? So we both live in California, okay? So it's reasonably, our weather is reasonably predictable, If I now say I want to have a birthday party tomorrow, and I want to do it outside, so I need nice weather. Now, if I'm going to say I must have perfect weather tomorrow, you have to guarantee me that I have perfect weather tomorrow, which is 71 degrees and sunny I. I will never have, I will never have that birthday party. Right. So a hundred percent specificity is, is I will overlook my opportunity have my birthday party right.
Or find that
Joelle Kaufman: Mm-hmm.
Peter: right, because I'm paying the
Joelle Kaufman: Right. This is per perfect. Perfect is the enemy of, of really, really good.
Peter: Correct. And so we have to strike that balance and, and, but then also, continue to never be satisfied. Because mammography, as it has made progress, right, is only one tool in the tool set. That's why we are saying, Hey, we need to build a set of blood tests that gives me a different lens into that disease. And then we can all go alongside each other to do this, I'm going to give you the counter example on the pancreatic side where, um, of course, specificity is much more complicated, right? Because the biopsy is much more invasive. So in breast cancer, right, you can throttle specificity and sensitivity because again, while it is anxiety driving, you know, while it is painful, et cetera, et cetera, the risk of, uh, physical harm is really, really low. Whereas if you, um, if you actually do a, uh, a biopsy of the pancreas, it's a different, it's a different story. It's much more, it's much more complicated. So that is this balance between sensitivity and specificity. That's why we try to establish those values very carefully, very thoughtfully. There's a lot of discussion around whether those are the best parameters, et cetera, but this at least gives us a description of how good a test performs in a particular, in a particular setting.
Joelle Kaufman: Now, I know you've always been cross discipline, all of your work, and you were just talking about your relationship with the LLMs. Now, when we were working together on the project, we talked about how AI is a revolutionary catalyst for your work. Um, and I'd love for you to talk about that. Is, is one of the big possibilities of AI is that it's gonna change cancer detection and what, what can it do?
That is so much harder for human beings or human beings with computers that aren't ais. And what are the limitations that, you know, we need to be aware of here?
Peter: Yeah. No, abso absolutely. So the um, um, actually just goes, uh, credit to um, uh, a graduate student about an hour ago, um, because we just talked about this, right? Where the LLMs are, are really good at the way he put it. Interpolation making connections, they're quite poor still at extrapolation. So they're not finding new things. They can't fill in big white spaces of the unknown. can't, is no data there. That's really, really dangerous because gets produced that is completely not meaningful. So they're quite poor when it comes to all of that. Um, however, they're really good at this interpolation connectivities.
They're very, very good at processing, actually similar to just a super fast thinking human right? So the human can't create data. If you make stuff up, will live in a made up world that has nothing to do with the real world, and really bad things will happen because you will make really bad choices because you're making those choices on non-existing data. So that's all scratch. As humans in this process of scientific discovery, right? We experiments, we take the data, we analyze the results, and then we interpret, we do this over and over and over. So as we are then accumulating data, um, we get experience, which means that our interpretation becomes better and better and better. So in the world that we live, where AI is an absolute revolution is when we look at a single blood test. The way we prepare these samples is essentially a big blood smear on a glass slide, and then we look at millions and millions and millions of cells. And we try to find that outlier cell that shouldn't be there. As humans, we have now enough evidence, right, that I can enrich that population and I can can, I can present that to the human and the human gets really good at figuring out which cell shouldn't be there. But of course, I can only do this at a call this at a certain throughput, couple of hours per sample. And that limits my ability to actually also learn fast enough. It's not just about making it cheaper in the clinic, but it's actually limits my ability to learn because I can also have a hard time learning across un. Now, this is something that AI is fundamentally really, really good at. Taking images, right? And I mean, if you, if you type cat into, um, uh, into a, into a search, it doesn't have to be a large language model, even just into a search, right? You will get lots of cats, right? And you see them, right? So now think about, think about 30 years ago, you go to the library and, and to physical library and you're like, I'm gonna go through picture books and find cats. Right? That's a, a, a really slow process. so that is a process that we have now put on steroids now all of a sudden, right? We can ingest just enormous amount of true data. That's really, really important that all of this is based on true data from patients, and we can learn on that data. And that is what these AI models are really, really good at.
They learn just like the human brain would learn, right? The human brain looks at an image and interprets the image. with AI, we just can do this incredibly fast. And that is what is really changing, changing the world of pathology, right? The, the world of pathology that's looking at a, um, uh, uh, at a piece of human tissue um, and then establish a diagnosis from there. That entire world is being wonderfully. Propelled forward, uh, with the new AI methods, which is then incredibly empowering set of tools for the physicians, uh, who are treating patients. But it's equally as powerful for us in the research world of really accelerating research.
Joelle Kaufman: So is there something you think breakthrough wise that you feel like is going to come faster because we're learning faster and you know, not that I'm asking you to guarantee the future or predict, but you know, what are you most excited about? What are you seeing happening here? 'cause you look very animated and charged up.
Peter: Yeah, yeah, yeah. No, I mean, this is, um, you know, we live in complicated and complicated world and in complicated times, but if we just focus for the moment on science where, and where science can really get us today, it is just absolutely striking. Um, and, and that has to do with, uh, with both the bigger picture of cancer and cancer research where we are going there. Um, the fact when we, when we now get together within the breast cancer setting, that, and I mentioned this earlier in the conversation, I said this idea of interception, right? Um, when we had these conversations, you know, six, seven years ago, it was, you know, a couple of knuckleheads, you know, in like a dark room in the basement.
Be like, dude, this would be amazing. Okay. Fast forward just 6, 7, 8 years, and all of a sudden, right, it's a conference room. Full of 2025. Just incredibly thoughtful minds, physicians and scientists sitting together and being like, okay, here are the setup survey of observations in the clinic. Here is the set of preliminary early data in the lab. Here is what worked. Here's what hasn't worked. Here's why it's complicated. here is where if we want to catch it, what does that actually mean? How do we, how do we do this? How do we define this moment in time? Right? And, and the fact that we are having these conversations, meaning that our data and our intellect is at a place where we can start. designing experiments around it is, is, is just absolutely brilliant. at the same time, if we then drill down first further, right, and we ask the question around detecting cancer early, detecting breast cancer early with just a blood test, um, I, I feel unbelievably good us breaking through these barriers over the next. Three, four or five years. And I'm saying that not only because of our own data, I'm actually saying this because of this just awesome worldwide competition. This is just great. I mean, there are some of the, I mean, you know, when we are, when we are in private, we call each other friends, you know, and we hang out together, you know, in public, right?
We are competing, but we are competing all for common cause, right? So it doesn't really matter who of us actually breaks through right by us, but by us working together, competing with one another, right? Us wanting to be out the first ones with the next round of data, meaning we all, we all learn from each other. And what we are learning in terms of the insights of, of breast cancer is just striking. There are, there are surprises every day where like, hmm. I see that one coming. That's a huge surprise. have to, you know, rethink it is that I'm actually looking at. Um, how do I put that back into context?
You redesign, you have to redesign the next study. And the beauty of the, all of the technologies we have today is actually that many of these new studies we actually do in the setting of. Patients. So we now run our, uh, what we call intercepted trials around the country, uh, to really look at patient samples, analyze them again, we publish our data.
There are other groups who then run model, what we call model systems. So laboratory systems, they take our data and they put them into their context of the laboratory systems, and then they try to figure out to see whether they can recapitulate it. Because if they can recapitulate it, they can actually test the biology, right? So, and then, and then we keep, we keep running that flywheel of, of scientific discovery, and that's what I just feel incredibly good about because we have never been in such a good spot as we are today.
Joelle Kaufman: That's amazing. And one of the things I remember, uh, Peter, is you're very passionate about both sharing that data, like open sharing of data across science, but also about being very inclusive. Uh, there's shocking how much, uh, scientific and medical research doesn't include women or people of color or people from different, uh.
Ethnic backgrounds and socioeconomic and, uh, those interceptor trials, if I remember right, you were very deliberate. I think one was in a Native American setting and, and one was a heavily, uh, bipoc and, and I kept saying, you know, don't forget the BRCA people. Turns out, uh, Laura Esserman, Dr. Esserman educated me that the Barca people are not just Ashkenazi Jews.
There's actually large clusters in the African American community. And, uh, we can certainly be inclusive while using the prevalence. So I always thought that was terrific. Now, you talked about uncovering new information. One of the things that you, your research uncovered was new information about how cancer metastasizes, right.
Because when you finish treatment, they say, congratulations, you're cancer free. And if you're like me and you had a bilateral mastectomy, you say, so what do we do next? They're like, oh, we'll see you in six months. And then what are you gonna do? We're going to use our hands. We recommend you use your hands, but these are, these are the most effective tools.
Now, I'm glad that these are effective tools, but like seriously, that's what we've got. So what are a couple of the key discoveries about how we understand metastasis and how do you see that knowledge being applied in a patient care setting?
Peter: Yeah. So I'm gonna touch briefly on what you mentioned before. Um, this, the problem that we are facing, probably that you just outlined, that potential of a future relapse. How high is that risk? does it change over time, et cetera, et cetera. That's a darn hard problem to solve, and I really don't care who needs to be around the table to solve the problem. have to get every stakeholder and every potential brain cell that can contribute to this around, uh, the table. And that is also, even from that perspective, lots of different backgrounds, lots of different perspectives, lots of different approaches to science, right? In addition to working with a broad set of patients, because again, we should do the experiments in the most meaningful group of patients. mind you, right, just like what Dr. Reman was saying, right? You have these clusters of BRCA. Um, uh, mutations, right? In particular patient populations. So if I wanna understand the consequences of BRCA one, BRCA two mutations, and how do I start defining risk? I measure something else that tells me where you are? And the question that you asked, right? like, it's not about if, but it's when my goal is to give every woman a date or a date range at least, right? And be like, okay, you know, live your life at somewhere in there. We're gonna start. We, we will measure stuff. And as we measure stuff, we will start giving you a date range, right?
And that can start informing you, right? So this is super important. And obviously you would do this in a population that is, I. That is enriched for that, right? But it helps everybody, right? Because it exists in smaller proportions in everybody else as well, right? So this is all part of how we design our scientific experiments in such a way that they're most impactful, most meaningful, and we learn the fastest, the absolute fastest way possible. So if we go back in terms of metastatic metastatic relapse, right? Uh, there was a whole bunch of interesting questions. You know, one was, is it random? Yes. No. Right Where it goes next. Okay. That was an interesting question, which we did not know. We actually teamed up with a group of applied mathematicians who came out of aerospace and mechanical engineering who took a really interesting, um, an old, an old data set, on, uh, of, of autopsy, uh, uh, autopsy data, right?
And mapped this out and proved mathematically that it's actually not random. Okay. So that's one important observation, right? Because now we're like, huh, okay. It's, there's something purposeful about it. Okay. Then the next question is what you just, what you just asked as well, what you hinted at, right?
You're like, well, wait a minute. Double mastectomy. I thought it's all gone, isn't it? So there is actually just, I wanna say about six months ago or so, little while ago, an interesting paper was published, and I'm bringing these up, these examples, right? Because this is we as scientists do, right? We look at other people's data. And it's so important that goes out into the public, right? we consume that. We ingest that and we put it into our own context. Okay? So they asked the question of, uh, patients with similar breast cancer, comparing lumpectomy to mastectomy, to me to double mastectomy. And, um, and it was, it was the out the outcome was, was, was very, very interesting where lumpectomy and, and mastectomy made very little difference, but that had to do with a subtype of breast cancer as well that was used, right. Um, and the double mastectomy, interestingly enough, um, uh, of course reduced, makes sense, the recurrence in the other breast, right? Um, and it delayed, um, a recurrence overall, right? But in the end had a similar lifelong risk.
Joelle Kaufman: Interesting.
Peter: Right. So this becomes all super interesting because, and then there's the other data that, um, was asking about, um, lymph node resection as well, right? Because if you have lymph node involvement, then you have a, um, then you're at higher risk of relapse, right? You are a higher stage of cancer. Um, and, but in fact the resection, um, doesn't make a huge difference in changing the, prognosis, right? So you're like, okay,
Joelle Kaufman: Hmm.
Peter: We always thought that the lymph nodes would actually be the reservoir, but, Hmm. I guess they're not. So again, I'm just giving you another
Joelle Kaufman: Mm-hmm.
Peter: of what we understand, right? We don't understand, uh, where these cells are actually hiding. another colleague of ours, uh, Julio Gizo at Albert Einstein is looking at, um, what we call, uh, cancer cell dormancy. So cells that essentially can shut down. And they can just sit around for a very, very, very long period of time. And then the question is, what is it that awakens them? And that, is that our so source of recurrence, right? Because obviously that future recurrence to be cells that have left the primary before we have the chance to cut the primary out, So that construct is really important to recognize, right? So that's why we, that's why we're doing systemic therapy, That's why we give chemotherapy even after surgery. It's not only to clean up whatever was left locally, because there really wasn't, the surgeons are doing an amazing job at lean margins and taking every last little bit of cancer out, right? But. We need to do a systemic cleansing, to try to kill last cancer cell that's somewhere disseminated. The problem is, of course, that every cancer drug relies on the cancer cell being active. So if a cancer cell now just sits there on the shut down, it's really hard to attack it. that's that next level of problem that we are now trying to address.
The part where we are now playing in is that when we build our original, um, uh, blood test for cancer detection, it was all built around the cancer cell. And what we have now realized through the research that we have done, we and others have done over those past, uh, two decades, is that in fact it's not just a cancer cell that we see that's actually a minority. Most of what we see are actually fragments and cancer cells out of, uh, and sorry, and, uh, tumor cells out of that environment of that lesion. Okay? So when you think about that initial cancer growing, you have a handful of cancer cells and then you have what is a fairly normal environment. So this gets us a little bit into this perspective of a cancer cell by itself ain't gonna do anything if that cancer cell is sitting in a super healthy environment. Nothing is gonna happen. healthy environment, that immune system is just gonna keep that thing in check. If it tries to replicate, it's gonna take it out. so it is only when the environment around that cancer cell is actually now amenable to growth or even that pre-cancerous cell when it's amenable to growth. And evolution is when that growth starts happening. Now you have that tumor building and that tumor will still have a minority of cancer cell, and the rest of it is what we call these tumor microenvironment cells. And because it gets its nutrition from the blood, the blood vessels get destabilized. That's how it gets more and more nutrition. But that means that all of that can spill out into the blood. And we realize that with the newest technologies that we now have, we have such exquisite. Sensitivity that we can actually find these cells. And this is now what we are, what we are really after because it is pretty clear that those are the earliest indicators of a cancer growing. And by extension it makes sense that that is also the earliest indicators of a cancer recurring.
Joelle Kaufman: It'll be very interesting as you continue down that research if we're able to isolate or understand any of the patterns that preceded that. Um, I consider myself in many ways very, very lucky, but one of my areas of luck was that my little tumor that, you know, showed up out of nowhere, right, happened to grow adjacent to a lymph node.
Um, and didn't, it didn't, the lymph node highway didn't seem to get started, but because it was touching it, I qualified as stage two and that gave me access to, um, with the brand name is called Keytruda. Keytruda, but it's an immunotherapy, and correct me if I'm wrong, that was explained to me as it's not really treating your current.
Systemic, the chemotherapy is going after that. What we're trying to do is actually train your immune system so that they are on the lookout and your immune system is kind of optimized to take out future, uh, future. These, if these dormant cells become active again, the immune system is, it's not gonna elude the immune system because part of the theory as it was explained was cancer is eluding the immune system.
It's somehow getting away from your body's own natural ability to fight off anomalies and invaders.
Peter: No, that is absolutely right. So, um, and so think about, think about that lymph node. Right. So your lymphatic system, right, is your drainage system, right? And the lymph nodes are very, very good at taking the trash out. Okay? So for an unknown amount of time, but probably for a long period of time. As the cancer cells get drained through the lymphatics, the lymph nodes are actually taking the trash out. Our immune system also for quite some time, works just fine at keeping the cancer at bay. Somewhere along the way, either the cancer becomes too strong, or the immune system just too weak. But remember, we don't need much of an imbalance. I'm not, this is not about something radical or dramatic or this or that, right? as long as the immune system has like, you know, a 51% control over the cancer, right? The cancer is gonna stay smaller, right? If, if the cancer, if the immune system drops down to 49% control, right? cancer will start growing, right? So the question that arises is, well, wait a minute. Um, there is this intrinsic assumption, which again makes total sense, right?
That the immune system can in fact control the cancer. over time, the question that arose in the thera in the therapeutic development arena is we can either take a chemotherapy and broadly take out everything that is replicating, or we take a, what we call targeted therapy. Meaning we have a target on the cancer cell and we only take out that particular cancer cell. And now the third set of therapies, which are these immunotherapies that you just spoke about. what they do is they don't really, they don't kill the cancer cell. They just what we call a checkpoint on the cancer cell that would normally signal to the immune system that I'm, I'm an okay, I'm a healthy cell.
Don't eat me. If I now have an immunotherapy that covers that checkpoint, all of a sudden an immune cell comes by and you says like, wait a minute. an unhealthy cell. I'm gonna start eating you, If I start eating you now? Right? Enabled by that immunotherapy, this immune cell now gets educated.
It a flavor for the taste of that cancer. And it's like, okay, that was not bad. Okay. And you're gonna do that a couple of times over and over, right? And, and immune cell. And with that immune system getting better and better and better at recognizing it, which then gets us to this construct of a, a durable response, a long-term response. And, and again, this is, um, it's an, it's an amazing, it's an amazing world. We are still, even though we have these huge successes, I still believe we're actually early on in our understanding of all of that and making this work across, uh, the full spectrum of disease and for all patients, uh, lots of hurdles ahead of us. But again, that scientific process, that flywheel, that flywheel is rolling.
Joelle Kaufman: And you know, I'm hopeful that as all that progress is happening, we are trying to capture what was the environment, what was going on, um, in the person's life, in the person's health. Uh, because the real name of the game is, can we prevent this? Can we understand? And it's such a complicated disease, and the body is.
I, I mean, I think it's glorious, but it's unbelievably complicated. Even what you just said, the immune system can, it, it adapts. It morphs. It's a learning system. Like how incredible is that? Like it, you know, your immune system cells are just going, Hmm, I think I'll change him and pick up this signal now. I mean, that's incredible.
So, Peter, I, I could talk to you for hours, but I wanna ask my, my last question and then we'll have you on again sometime, I'm sure. But, um, what would you say, you know, in the theory of we're kicking cancer's ass, what is maybe one or two things people can do to help kick this disease's butt?
Peter: Um, thank you, Joelle. I mean, it's a, um, it's participate, participate, participate, uh, participate in your own health, um, uh, participate in the process of science around you. There is oftentimes there is ways of either volunteering and helping in clinical studies. There is participation in clinical studies. There is, uh, when you get the opportunity to participate in a new diagnostic development, somebody might offer you to participate in a blood test development, when you get your mammogram done. it's that participation. It is, it is, it is really putting, putting that notion out with each other as well.
Right. That is all of us around the table. Right. All we can do as scientists, right? Invest our lives and our careers into this, In the end, it comes down to the patients actually participating with us in this incredible and this incredible journey, and incredible. Now put any, any given word, incredibly hard, incredibly challenging.
Incredibly frustrating. Incredibly exciting. Sometimes we get excited about cancer, right? Because we're like, wow, look at what it did. That's amazing, huh? Right. So incredible, right? Um, but this incredible journey is something that we all have to participate in. We have to have that trust in the data, right?
It has to be a data-driven approach so that as we go forward, again, on an individual choice.
Joelle Kaufman: Mm-hmm.
Peter: Everybody has the option, right, to participate in this very personalized framework of understanding where they are in this journey so that a patient certainly never unexpectedly faces that challenge. And if at all possible, we can with prevention measures, push out any lethality of disease well beyond our normal lifetime.
Joelle Kaufman: That'd be good.
Peter: that is the goal. That is the goal for all of us.
Joelle Kaufman: So I know, because I know Dr. Esserman about the wisdom study, which is a way for women who haven't had breast cancer and are older than 30 years old to participate and it's free. How else can people find opportunities to participate, to be part of the scientific process? And you know, I'll put out to people, sometimes what you discover in science is that that didn't work.
Or you know, that's not the answer. Uh, and what I love about scientists is you look at that with a smile. You're smiling now 'cause you're like, oh good. Something that didn't work, something new to go explore. That sounds great. Right? And. Um, I would would love for you to share with people how they can participate, participate, participate, because it, it sounds like I, other than the wisdom study, I wouldn't know where to tell somebody to go look for how to participate.
Peter: Yeah, no, it, it's, it starts with ideas like the Wisdom study. Um, it continues with organizations like the Breast Cancer Research Foundation. it goes all the way to, um, to your visit with your OB GYN, um, and asking you which GYN being like, Hey, participating in any of those early cancer detection trials?
I would really be interested. Now your OB, GYN might be like, oh, no, no, I don't have time for that. And you as a patient might be like, yeah, but it would really be interesting. And then you ask, then you ask 10 of your friends like, Hey folks, when you get your mammogram done by that same ob, GYN, ask that same question, right?
Because if you ask the individual, you have an enormous amount of power. It does, might not feel like that, right? But in the end, you have an enormous amount of power, right? If you, if you go to your, to your provider and you're like, no, I, I would really like, what do you mean? What do you mean you don't participate?
There's these other people who are participating. I want to participate, right? That is how we get this whole process, how that is how we get this whole process rolling. Obviously, you know, lots of just like Joelle, just like you found us, right? There's a little bit of that. Like, hey, reach out to your friendly neighborhood physicists, you know, reach out, reach out to people, and sometimes we will give you a very long answer. And you're like, oh gosh, Peter, no, please go on, tune it back. Simplify this. Right? And my honest answer is like, when, when I give you something really complicated, it means that I don't fully understand
Joelle Kaufman: Mm-hmm.
Peter: Right? It's a recognition of our own limitation of where we are today, where we wanna get to, right?
It's providing a simple answer. But that is, again, it's, it's, it's, it really is incredibly complex. And we have, we are hammering at this as fast as we can because it, there's nothing that gives us more, while I'm smiling about things that go sideways, right? The ultimate joy is actually breaking through
Joelle Kaufman: Right.
Peter: new knowledge to this space, right?
And enabling a better outcome for every patient.
Joelle Kaufman: Well, Peter, you have already contributed so much, and I have no doubt that with your enthusiasm, curiosity, you are going to keep contributing to the space and I know. Personally and for my family, I am very thankful that this is your life's passion. Uh, as for participate, participate, participate. I think it would be awesome if we could get all 330 million Americans, maybe, you know, everybody in the world to just participate.
Right? It's whatever it is. Look for any opportunity to be part of the scientific process and contribute. Uh, because with more data, the now that you have the machines also working tirelessly for you, more data accelerates the time to insight.
Peter: Absolutely.
Joelle Kaufman: Great. Thank you so much, Peter.