New Hope in Cancer: A Panel Discussion - Dr Richard Sidebottom, Sanjay Popat and James Larkin

It's a real pleasure to be here. I've sat in the audience many times, but I'm so pleased to be on the stage. Uh, we have a, a wonderful array of, uh, two professors and a doctor tonight. And as about, um, a third of you have heard the talks, I would try to give like a 32nd summary of each of their talks just to get everyone on a baseline. Um, and as this is really a panel discussion, I really want to make sure that this is interactive tonight. So we'll do, um, about 40 minutes of me questioning the professors. Um, and then, um, at about 20 minutes ago, I'm gonna open it to the floor. So please do think of questions you'd like to ask. Uh, ask this both online as well. Uh, and Nicole, maybe you can wave at me, uh, at 22. So I, I know when the time is up. The Gresham College lecture that you're listening to right now is giving you knowledge and insight from one of the world's leading academic experts, making it takes a lot of time, but because we want to encourage a love of learning, we think it's well worth it. We never make you pay for lectures, although donations are needed. All we ask in return is this. Send a link to this lecture to someone you think would benefit. And if you haven't already, click the follow or subscribe button from wherever you are listening right now. Now let's get back to the lecture. Okay, terrific. Um, so let's just go through the talks, uh, very quickly. I'll start with, uh, James. So James, uh, I think gave a very moving opening to the challenges of what it used to be like to have, uh, a solid tumor, which was, you know, a very poor prognostic situation, um, at the start of your career. Um, and you talked about, um, how kind of chemotherapy had its time and then targeted therapy. But really then, um, in, in recent years, um, with Jim Allison, uh, and the invention of CTLA four checkpoint inhibitors came along. We had the second checkpoint inhibitor, PD one inhibitors, and really how finely people with solid tumors, uh, metastatic setting, uh, uh, started to have hope and, and high cure rates. So you talked about, um, checkpoint inhibitors as one example of immunotherapy. You touched briefly on this very tantalizing topic, hope we can get into with neoantigen, uh, vaccines as well, T-cell, uh, t-cell therapies or autologous. Um, in fact, I think you mentioned this, uh, treatment by ivans, which even in the four weeks since, uh, you, you mentioned it, have, have, has had FDA, uh, accelerated FDA approval in America. So we could probably hear a little bit about that too. Uh, you bemoan the fact that, um, these checkpoint inhibitors are not without, uh, not without toxicity. Um, and there are challenges in finding biomarkers of patients who respond to that. And, uh, I think that points to the direction that we all need to go in. Uh, but one of the main messages was that although checkpoint inhibitors aren't the cure for all patients with cancer, it really kinda woke up the community to the, the value of, uh, immune oncology, which was, I think, a really important thing that's happened in the last decade. Um, and then, um, as if, by kind of beautiful compliment, you didn't talk about, uh, targeted therapy, but, uh, you did Sanjay. So you started with a reminder of the central dogma of DNA to RNA, uh, to protein. Um, and, uh, the reason I think that was relevant, you, you introduced three imported innovation sequencing itself of the, the whole mutational landscape, the discovery of kinases, and then the discovery of, uh, kinase inhibitors. You mentioned gefitinib, I think is the first example. Um, and you told this great story about how suddenly, um, lung cancer patients started to be cured, but not all of them, uh, particularly women from Asia, were the subpopulation that were responding. And so this kind of brought about this advance of the need for companion diagnostics and strata stratifying patients. Um, and that's challenging 'cause you have to, uh, sequence patients in the clinic. And you talked wonderfully about the operational challenges of sequencing in the clinics and how the advent of liquid biopsy where instead of sequencing from a surgical biopsy, you sequence from a circulating tumor or cell free tumor, DNA and peripheral blood, that that's actually, uh, bringing its significant, um, operational improvements, not just to diagnosis, but to treatment selection and minimal residual disease follow up. So that, that was a nice kind of move from therapeutics to, uh, to diagnostics. And then finally, as if, uh, by design, uh, we ended, uh, with, with Richard, who gave, I think a very ambitious, uh, cancer through the history of radiology. So X-rays, CT scans, MRIs, radio nucleotides, or radio, um, imaging, nuclear imaging. Um, and then you also coupled it with a cancer through, uh, machine learning, uh, which was very challenging to do both in one lecture. So you talked about, um, the advent of convolutional neural networks for, um, and computer vision for picking features in a kinda supervised setting out of x-rays and MRIs and, uh, other imaging modalities. And you ended with, uh, a brief touch on, um, the use of transformer technology, which, which kind of looked at all data types, uh, at the same time and, and potentially points to the possibility of, uh, multimodal analysis both in research and in the clinic. And you ended, uh, I think with a, a tantalizing comment that, um, the innovation's very exciting, but we actually haven't seen much of it in your clinic yet. So we, we should hopefully come back to that. But are those reasonable summaries of, of the talks? Okay, great. So I mean, laid bare in those three talks were the kind of much of the armamentarium that we have to fight, um, cancer, but maybe not all of it. I could kind of go down the row. Um, starting with you, James, are there any things that we haven't mentioned that you think are contributing to our kinda new, new hope and cancer? Uh, I think, I think, uh, I'd get into trouble if I didn't mention surgery or radiotherapy. Ah, as <laugh> Still there As treatment modalities. Yeah, absolutely. Um, in terms of curative intent for solid tumors, the, the mainstay remains surgery and radiotherapy. I'm, I'm a medical oncologist, so I'm not an expert on either of those things, but there've been incredible advances in both of those areas, um, in the last 10, 20 years. Robotic surgery, I guess is the obvious thing that you might pick out. And then with radiotherapy, it's all about, uh, again, so that's something my, my area of expertise, but really trying to focus the treatment just on the cancer with minimal margins, because treating the normal parts of the body is what causes the side effects. And there's an interaction there, obviously with radiology, um, and also what computers can do. So those are, those are probably the main things that we haven't talked about. I'd pick out big picture, Sanjay, I hope someone's gonna talk about early detection. Well, that's what I was gonna say. Great. Wonderful. Uh, because, you know, at the end of the day, you can only cut out a tumor if you pick it up at a small enough point at which the surgeon can really, uh, do a great job and cut it out cleanly because there's no point by and large for a tumor going in there and leaving it behind. That's the fundamental thing that we have to recognize is that surgery is only viable if the tumor can be completely cut out, and it can only be completely cut out if it's caught at an early enough stage. Now, some cancers grow on the outside of a body. We know that there's a problem with the skin. We can palpate or feel if there's a problem with the breast. However, for internal cancers, we've got an issue because they grow silently within our bodies. So how do we pick these up at a early point at which they can be cut out before they start causing any problems? And this is, I think, one of the great challenges we're gonna have to try and face over the course of the next few years, how we pick up patients before they know they've got cancer on Board. Yeah. Okay. And just to put that into plain relief, can you give the audience, as you're a lung specialist, a sense of the survival difference from the stage shift of catching someone at stage one versus stage four? In lung? In lung Cancer stage stage four is an incurable disease. By and large. We do have patients who have long-term survival, and that long-term survival can sometimes be measured in many, many years. And in some, uh, situations, people are alive for in excess of five years. But that is an uncommon scenario. And for many patients still with stage four lung cancer, the survival is unfortunately still measured at less than 18 months or 12 months. However, with stage one lung cancer, this is a curable scenario. The patient will undergo surgery and the cancer, by and large will never come back. And that's exactly the type of scenario we need to be. Yeah, okay. That's very promising. Um, and Richard, anything that you would like to discuss as new tools that? So I suppose the area that I do with, with breast screening, um, there's, there's real opportunity to move from what we do of quite a crude kind of, not quite one size fits all, but it, uh, essentially it's population screening and then very high risk screening. Um, and with some, uh, some more kind of detailed and nuanced analysis we could move on to, uh, alternative kind of stratified screening strategies. And, and these sort of evolving AI tools kind of really offer us some, some opportunities just, just to do what everyone thinks will probably be helpful, um, but was kind of too difficult to achieve. And so I think that it might be move that into, into the area that becomes doable. So when you're talking about stratification, you, you mean identifying some women that would benefit from starting their mammogram to screening or their screening at 40 rather than 50? Yeah. Or alternative modalities for imaging, because in their breasts who might not see them, for example, uh, you know, the cancer might be there and, and not detectable, um, on a mammogram. Uh, yeah, there, there are various scenarios. Okay. So there's a huge amount of energy being put into early detection. There. There's, uh, there's lots of other things to bring to bear. We haven't spoken much about metabolomics. There's also innovations in the clinical trial space. Um, and one might think that with all of these tools at our disposal, that cancer is essentially has been reduced to an engineering problem that we need, that we now know everything we need to know about the biology of cancer. And is just a case of, uh, kind of, uh, delivering operationally in the clinic. Uh, I expect, actually, if I said that, I'll get some pushback from you. So where, where do you think there are areas of biology that still need elucidation and, and true research? Um, sand do you wanna start? Yeah, I mean, I think, you know, to an extent it isn't an engineering problem, but really that's really not, that's simplifying the problem too much, really, because actually our level of knowledge about the biological aspects of cancer, particularly if I talk about genomics, is, is huge. And we've come leaps and bounds in what we understand about the genetic basis of developing cancer on what we understand about cancer's genes and how we can drug them. However, there are huge amounts of unknown knowledge. We don't really know how the regulatory aspects of how genes function contribute to the genes function and how they contribute and individualize a cancer prognosis from a very aggressive cancer to a very indolent cancer. They may look completely the same, but have very different gene function. We don't really understand how that works together. And neither do we really understand how patients develop genetic abnormalities within the tumor in the first place. We know that when we look at the genes of a cancer, not the genes of the individual, the genes of the cancer, we find the usual suspects of about 15, 20 genetic abnormalities. Sometimes we even find fingerprints of long runs of genetic abnormalities that occur within patient's cancer, but why is it the same? And what causes them to develop in the first place? Has the patient done something that puts them at risk, or is this just sheer stochastic bad luck that it's come to them in the first place? We're starting to piece together some of the epidemiological evidence that suggests that there may be environmental factors that contribute to this, but really it's not a very clear, uh, association between environment and the genetic problems that then result in, in the cancer developing in the first place. So there's a big biological piece missing to try and work out how we ended up with the cancer in the first place from the genomic view. Okay. So that's why we got cancer in the first place. James, I know you deal a lot in the metastatic setting, so do you feel that we understand why cancer's become often, often long periods of inance invasive yet? No, I mean, uh, you know, to, to answer the original question, I still think there's much more that we don't understand than we do understand. Um, maybe pick out a couple of examples of that. I mean, the tumor microenvironment, so what that means is that the cells around the cancer blood vessels, uh, immune cells, supporting cells, how they interact with cancer cells, can we use that therapeutically? Um, there's a lot of efforts to studying that, but I'm not really sure there's much coming out therapeutically at the moment. So I think that's an obvious area where we, we don't understand it very well. The, the other thing is resistance and sensitivity say to drug treatment or radiotherapy for that matter, do we really understand why some cancers are sensitive or why they're sensitive to start off with and then become resistant at, at, at a sort of top level, yes, but in detail, no. And then, um, the next consequence of that, if you're treating a cancer, say with a drug, it becomes resistant. What you want to be able to do in clinic is do a biopsy or circulating, um, test of some nature, and then rationally based on what that's shown, choose another treatment. Are we close to that at the moment? Not really. I don't think in general. There's a few, um, examples of that. And then to, to get back to the whole, do we need to understand more, uh, or is it just an engineering problem? Well, there's still a lot of cancers where actually outcomes haven't really improved much at all over the last several decades. Pancreas, um, brain tumors are two examples of that. So I would've thought until we can say that those diseases are 90% curable, we still need to understand better. And pediatric as well. There's huge biological gaps in pediatric. I totally agree with, uh, those statements. Thank you. Um, Richard, um, moving on to kind of a question just for you. So you talked, um, a little bit about multimodal analysis. Uh, you just touched upon the, the, uh, the opportunity for it. May, maybe you could, um, explain to the audience what that means and, and how you might imagine an MDTA multidisciplinary team meeting of the future where, um, we have different disciplines from genomics and imaging and pathology coming together, looking in a slightly less siloed way at, at a patient's progression. I mean, an MDT is, is a, um, an example of getting together different experts to think about the same problem, isn't it? And, um, it seems possible. I don't know whether it's likely or, and it's certainly not yet, is that these, these new AI models that we've heard all about over the last few months, maybe able to make kind of real progress, uh, at, at looking at all of these different things. The, the genomics and the imaging of both in terms of the radiology macro imaging and the pathology, um, slides along with actually what's gone on with the patient in the, in the background, how healthy they are to give us some more kind of quantified input into those decisions that we need to make, um, giving us better ideas about treatment response, um, and about potential toxicities. Uh, so I hope that, that by gathering kind of hospital data together like that, we can, we can start to work on those questions. And it's not just, um, pulling the data together technically. Don't you think this may help to solve a cultural problem, which is often today gene geneticists working completely different buildings and speak a completely different language to say pathologists or radiologists. Do you think there's some hope that it actually may bring the cultures of these disciplines together? I don't know about that. That's an interesting thought. I mean, I think the idea of an MDT is that you do bring the cultures together. Um, I'm always cautious about people expecting AI solutions to deliver stuff that they hope that to deliver, like keeping the wheels on services. Um, so I think I might put that hope into that kind of box about, I'm not sure that's, I think that might be something for us to deal with Yeah. In a more human way, More old fashioned way. Yeah. Okay. I, I see where you're coming from. So Sanjay, one of the things that you said about the operational challenges of, uh, bringing genomics into the clinic, uh, really, really resonated with me having been at genomics England for four years. Um, and, um, but I think that's, that's kind of a view of the clinic and the things behind the scenes in pathology that not many people have any insight into. So I'd, I'd love you to just talk to the audience a little bit more about that. And I suppose just see if you can, uh, address the question. Um, you know, science has progressed significantly. Is the NHS ready to embrace these new innovations? Yeah, I mean, some of the logistical challenges we have is that, you know, what we want to do is to take a biopsy of a patient's tumor and sequence it, put it through a genetic analyzer, work out the sequence, and give that sequencing report to the oncologist to tell them what the genomic abnormalities are in the tumor. And the oncologist can make a rational, uh, decision on what type of drug treatment they're gonna have. And that sounds straightforward in principle, right? Because we've got genetic sequencing technology, which is pacing ahead really, really fast. We've got computers which are getting faster and quicker all the time. But meanwhile, putting all of these pieces together is really quite complex because the patient's gotta have the biopsy, the biopsy specimen, the tissue has to go to a pathology lab. That pathology lab is usually in a separate place from where the biopsy was taken. The pathologist then needs to understand that the specimen needs to then go to a molecular lab. So the molecular lab then needs to receive the specimen, the molecular lab's in a completely different place to the pathology lab. The molecular lab need to know, know what to do with that specimen. Uh, and the person that's actually actioning what that specimen, the oncologist has never even met the patient yet who's heard about them in a multidisciplinary team meeting. But what happens in the lab, it may not be what the oncologist wants. So the lab just do what they think is the right thing on the specimen. And then the science of what goes on in the sequencing laboratory needs to be translated from the complicated language of genomics to something that's really simple for a oncologist to understand. And that report needs to reach the oncologist, whom the molecular lab don't know who that person is yet, because they've not been attached to that patient's cases at the moment. So that's a chain of events that all need to align in the grand scale of things to work very smoothly. Now, that is an operational issue, and that undoubtedly can be resolved through a variety of different engineering constructs, whatever, whichever way you want to look at it. And I have no doubt that it will be, uh, resolved. In fact, today there's been a large meeting with NHS England looking at the operational aspects of how we resolve this particular issue, because this affects perhaps a handful of tumor types. Now, undoubtedly, it will affect the vast majority of tumors in the course of the next 10 years as genomics for cancer gets operationalized across the entire sector of, of cancer. And the way we solve this, I think, is complex and is not going to be the same solution in one particular environment, that it might be 20 miles down the road or 200 miles down the road, but the challenges are quite similar. The solutions will be different, but undoubtedly we will resolve this because it's the only way we're going to have the right treatment to the right patient at the right point in their journey to really maximize the health economics of making people better. Yeah, that makes a lot of sense. Um, James, um, I I'm gonna ask you, my second question will be about the operational challenges, but before I go there, perhaps I can first talk to you, uh, ask you about new antigen vaccines and the, and the whole prospect of individualized medicines. Um, and then I'll go on to ask you about the, the enormous operational challenges of making that happen. But perhaps you could first explain what, what is happening in this, uh, new and, um, sexy field of immune oncology and new antigen therapies? Yeah, so this idea of an individualized neoantigen therapy, what does that mean? So it's a question of working out neoantigen means, uh, a protein that's on the surface of a cancer cell, uh, that isn't on the normal cells, um, that the immune system can potentially respond to. So the way to work that out is actually via sequencing. And the way you do that is you have to sequence the normal cells. You have to sequence the tumor cells as well, and you also have to find out from more sequencing, um, what the proteins are on the surface of the cancer cell. So that all sort of sounds complicated, and it is, but it's doable actually. That's the, that's the important bit. Um, and I showed one or two slides with my talk showing in a small trial in melanoma, that type of approach, making a, a personalized vaccine, if you want to call it that with, uh, messenger, RNA appears, um, to be better, um, in combination with immunotherapy than our standard treatment, which at the moment would be to have immunotherapy on its own. So there's a lots of trials ongoing to try and confirm that. And again, it was a small trial, uh, there wasn't that much follow up, but I, I guess the point of all of this is the complexity of doing this. And to Sanjay's point, I think, uh, honestly at the moment, um, pathology labs aren't remotely set up to do that. It's being done at the moment in the context of a clinical trial, um, supported by the companies that make the product. Uh, and if the trial turns out to show a real benefit in due course, which will be, I would imagine, two or three years, when we see the results of the trial, then if you like transposing that, if that's the right word, into our routine practice, assuming the treatment gets reimbursed, of course, I think could be quite challenging. Honestly, the, the specimen, Sanjay said this already, but it's worth repeating, uh, needs to be, um, uh, analyzed in a particular way. It needs to be processed in a particular way. If you don't do it in that way or we take too long to do it, then you don't get a good quality product. The turnaround times need to be quite quick. So there's, there's none, none of this is insurmountable. But the the point is logistically, uh, you can't just, if the drug kind of becomes, or the product becomes available in due course, you can't assume it's all that stuff's gonna happen. You actually need to put some work in to get organized to work out how you're gonna do it, otherwise the patients are not gonna end up getting the treatment. So there's a bit of work to do there right, as well for, um, vaccines in this space. Yeah, and I certainly know that the time tolerances, uh, that some of the, the leaders in that field are expecting, are very challenging. They, they want to go from the surgical biopsy, a LAC and RNA-Seq, um, to doing the neoantigen prioritization, designing the vaccine, manufacturing it, and getting it back into the patient's arm within I think, 30 days, which is, uh, something which is almost unimaginable in, in the modern clinic today. But it gives us the thing that we need to aim for. And, and I think I'm hopeful in the next few years we, we will be able to do that. Yeah, I mean, I think it's important to have a target. Yeah, and I mean, I talked a little bit about, um, cellular therapy as well, and one of the problems, if you like, historically with that was it took so long to generate a product. And with ivans, which is the example I showed, it's, it's down to three weeks now. So all, all this stuff's doable. Um, but we, we just need to, uh, figure out how to make it happen. The other interesting thing, actually, the other point I would make at the moment I've been another, have been talking about a, a small trial. One of the reasons to do a larger trial at more centers around the world is to try and actually get some traction on these issues during the trial, and to try and find out if you like, what's important or what isn't important to the size of the sample, how long it's in the lab, all sorts of other stuff. So, we'll, we'll get, we'll get more information on that. And I'm gonna make one more point about that as well, which I can't remember if I said it last time, is that this is a new potential class of therapy for cancer. And so one of the reasons to do trials is to demonstrate, hopefully it's better than what we've got at the o at the moment. But the other thing we new therapies is to understand side effects as well, because if the treatment gets approved for use, um, we actually need to understand the side effects. And if it's a completely new type of drug, who's to say that we might not see side effects that we weren't expecting or we hadn't seen so far? So I would emphasize that point as well. Yeah, I think you speak the way that, um, many of the, the wise immune oncologists that I speak to tend, tend to do, which is the immune system is so complicated, they're trying to anticipate rationally what it will do under certain conditions is just not feasible. And really the right way to answer these questions is just to run trials. And, and, and so are you pleased that this is happening, uh, in the clinic, in, in kind of ambitious programs like, like these no androgen therapies? Yeah, absolutely. Um, yeah, but, but it, I mean, I'll say that what you've just said, again, I think it's critical. You might think you can second guess what's going to happen in terms of side effects, but I think history teaches us I that you can't and you'll always gonna get surprises. And also, um, I mean, do we really think that we can understand all of this stuff from preclinical modeling, laboratory research? Of course, we can't, things happen when you actually start treating patients that you of course didn't anticipate. Yeah, we, we have cured, um, many cancers and many neurological diseases in mice many times that don't work in humans. So, um, so James is very keen on trials. Let's go back to the topic of AI and imaging. Um, I think you, you, you are one of the radiologists that I think is really embrace, embracing and enthusiastic about this space, which is not true for everyone in your field. Um, but what traction are you actually seeing today in the clinic and what, what do you think it would take before we start having, um, algorithms alongside radiologists? Um, hopefully improving not just the efficiency, but also the efficacy, the effect effectiveness of your, of your Well, absolutely. I'm really Keen diagnostics. We keep trying to improve outcomes with these technologies rather than trying to repeat what we can do, right? A little bit cheaper maybe. But, uh, we haven't got all that much yet. We've got actually impressed now the, there are some good studies coming out of Scandinavia over the last six months or a bit more, uh, that look very promising for real prospective work. There's lots of work in the retrospective, uh, context, but that's, you know, needs to be treated with caution for various reasons. Um, and, and you, it's useful to guide prospective work, but you really need prospective work before you start to be able to rely on that sort of change. Um, yeah. Else elsewhere in radiology, there's, there's, there's little bits and bobs and, and, and there's, uh, good things with nodule detection and things like that in lung. But, um, it hasn't made a big change yet, but there's a lot of work going on. And I think, uh, I think there's a lot of potential in a lot of areas, but some of it will not deliver For sure. Well, I mean, if it, if it all delivered, we wouldn't have to run trials, really. Um, Sanjay, I, I know that you are professionally very committed to liquid biopsy. You do a lot of work in r and d in the field alongside your clinical practice. Um, it, it's such an important revolution in the cancer space. I'd love you to explain it a little bit more.'cause I think it really is bringing a lot of hope and I think it's bringing hope in, in four settings that we've talked about before. So if you could just kinda run through the four clinical settings, uh, that liquid biopsy work. Well, I mean, I, so liquid biopsy is a really very broad term. Yes, it simply means, it's a terrible term actually. It is, but it, it does, it, it seeks, solves its purpose. It basically means taking a blood sample, um, or a sample. It could be a urine sample, frankly. But, uh, we generally use it to talk about a blood sample, um, and understanding the tumor better from it. And the fundamental premise is that one takes a sample of blood and within the blood, it's essentially a swimming pool full of things that the tumor has gotten rid of as the tumor has grown that we can then analyze. And there are lots of little tiny fragments of the tumors, debris within the blood that can then be analyzed. The one that we are currently looking at, and the one that we really have a lot of data on is the DNA of the tumor that we can find in very, very small, tiny fragments in a patient's blood. And as a cancer cell grows and divides, some of these will die, and as they die, they release their genes within the blood. And we can detect that at very, very small level. So if we can take a blood sample and detect fragments of the tumor's, DNA, that really gives us a great understanding of what's going on in the tumor's. DNA, without putting a big needle into somebody's chest or a big bronchoscope down their, uh, mouth, or a big camera on their bottom, it's a, it's a great advantage to take a blood sample.'cause we can also do that at various time points as well. And this is the really critical point because we can track the evolution of cancer along a patient's journey at various time points. So for example, if a patient, uh, uh, has a diagnosis of early lung cancer, it can be used perhaps to detect that cancer in the first place. And there's a lot of work that's currently ongoing to look at liquid biopsy as a screening tool to take people from the street to do a blood draw to find if it's got cancer, if they've got cancer in them or not on the basis of this blood test. Now, whether that will pan out or not, we can discuss later on. You can take a patient who's known to have early stage cancer stage one and show that you can detect their DNA prior to their surgery. And then you can take the cancer out and you can do the same test afterwards to check whether that DNA has been cleared. And that is called molecular resolution of that particular cancer. Now, what's really interesting is you can follow that patient up. We know that even if we cut out cancers, many people are cured, but in some patients, the cancer comes back. If we do serial blood sampling, we can sometimes detect the fragments of the tumor's, DNA reappearing often, many months before we detect the cancer on a scan. So that gives us an opportunity to intensify our treatment because maybe those patients with residual cancer after the surgery are the ones that need more treatment than just the surgery. Maybe they need James's immunotherapy, maybe they need my targeted therapy, maybe they need chemotherapy, maybe they need radiation, maybe we need to hunt more to see if there's any other tumor there to really convert them into cures because that's what it's all after. And of course, if they're unfortunate enough for the cancer to come back, we can then use that same blood draw to then analyze the DNA of the cancer that we know that has then come back to then work out again, what is the right drug therapy for that particular cancer to work out the achilles heel that we can really slay that cancer or really topple the tree by cutting it out the roots to try and eradicate it now. And sometimes we treat a cancer and then the cancer then evolves, and then we can do another blood draw to check what the evolution of that cancer is, to see how the genes have changed to work out. Whether it off offers up a new opportunity for a new targeted therapy or a new immunotherapeutic. I think undoubtedly we are gonna be doing more and more of this over the course of the next 10 years. For me, I think this is really the key area that is going to change over the course of the next 10 years for implementation and routine clinical care to really change our drug decision making over the course of the patient's lifetime. So that, that was a, a beautiful description, including early detection. I want to ask, uh, all three of you about early detection, but to make a link between what you do and what you do, Richard. Well, uh, you, you mentioned that we can look for genetic, um, features of DNA in, in the liquid biopsy setting. We can also look for these chemical modifications called methylation and from methylation patterns in, in an all comer population. So people who have not yet been identified with cancer, you can also map that methylation to something called tissue of origin. So we can say your elevated risk for cancer, and by the way, it is in your brain or it is in your liver. And then we can follow up with imaging. Uh, so there, there will be an imaging cascade to follow up and as a confirmatory test if that exists there. So, I mean, maybe starting, uh, with you, but I'd love to hear all three of your views on that. In, in five or 10 years from now, what can you see a world where every year, every adult, um, goes and has a blood test, um, gets cascaded down, a series of follow up pathways, maybe with radiology, maybe with polygenic risk scores. Um, how do you see the future panning out that way? I think you've gotta be exceptionally careful with that kind of approach. I mean, you, because you've gotta know what your questions are and how you can deal with the implications of what you find. So I suspect your five or 10 year timeframe wouldn't give us enough time to, uh, to clarify what we can do and what we can discover. Um, so, and, and screening for things can cause a lot of harm as well as a lot of good. Uh, so yeah, I don't know, but I'm cautious and I think five to 10 years is optimistic. Interesting. We, I will challenge that ever dinner, uh, <laugh>. I look forward to that. Um, James, what do you think? Uh, yeah, I think the technology will exist to do that in five to 10 years. Uh, will it be widely implemented? I'm not sure about that. Um, but Richard makes an important point as well. You know, you're picking up these early things, you need to understand what they mean. Um, and so that requires study.'cause it might be you pick something up and it's sort of incidental. It's in the background for a number of years, not causing any trouble. And to Richard's point, um, you know, if you then start doing biopsies and things like that, then you risk causing problems with that. So that, that, that, that all needs to be properly investigated, but that will happen. Um, just on the subject, um, of early diagnosis, uh, more generally, I think it's, it, it's critical. I mean, I, all of us as oncologists would prefer not to have to treat established cancers or cancers that have spread. Um, and I, I might, it's probably slightly off topic, but I might just mention prevention as well.'cause I mean that's a, that's an important aspect I think of all of this. Uh, you know, there are some incredibly well characterized risk factors for some cancers. So, uh, ultraviolet light sunburn for melanoma is well known. Um, does that mean that if you go to the Mediterranean, you summer holidays, all the kids on the beach are wearing rash fests and they're out of the sun in the middle of the day wearing factor 50? No. Have we had that information for decades? Yes. Um, so I'm making the point the knowledge is there, but actually there's a whole, a whole range of chain of events that need to happen for it to, to kind of make it into, um, people's day-to-day lives. And then the other thing, smoking, I don't think, I can not mention smoking. I don't treat lung cancer, Sanjay does. But in terms of, you know, an incredibly well established, uh, risk factor, uh, preventable for cancer, uh, and there are others for different cancers. So I think that has to be part of the picture As well. So in other words, saying that is don't wait for clever people like these guys to come and, uh, cure your cancers. Prevent them by not smoking, <laugh> by putting sunscreen on. And, um, yes, uh, try, try not to be overly morbidly obese as well. Um, Sanjay, a a final comment from you and then we're gonna open to the floor. Uh, yeah, Well, you know, I'm a glass half full guy, right? And I think that we will be implementing these tests, uh, over the course of that, uh, 10 year timeframe. Uh, we're currently using the first generation methylation signature tests at the moment. And then HS England is piloting these in a trial. They do pick up cancer, uh, not very frequently, but they do pick up cancer. The problem is that a lot of the cancers they pick up are already metastatic. And that's the real challenge that we've got with our first generation technologies. But listen, this is the first time we've dabbled with this, right? Whilst we are using first generation technologies, we're already aware that there are other generation of methylation signatures, including other aspects like fragments coming through, which have the possibility of making much more sensitive, much more specific assays available. I have no doubt that we will be using some sort of blood testing over the course of the next 10 years routinely in some form of clinical care. Now, whether that will be adjunct to standardized screening programs that we already have or whether that will be, well, I'm gonna pop into Sainsbury's to buy my bananas and I'll have a blood test while I'm still at it. Uh, I don't really know, but I, I think that we, there will be some form of blood testing at some point, which will identify high risk population to then either go straight to scanning or some sort of other risk modulation to then pick up cancers earlier. Okay. Fascinating answers. We've had the whole range of risk appetite, um, in the panel. And to put this in perspective, um, many of these liquid biopsies are being extensively used, uh, clinically in America today. So, um, uh, and so it shows how often, um, cultures and the health economics of, of health economies, um, uh, change the pace of, of innovation. So with that, um, I would love to open the floor. I hope you've all thought some good questions. Um, we do need to ask you to use the microphone 'cause there will be thousands of people online listening to this in real time. But someone have a microphone. We have a question right at the front here, if we can get her a microphone. Nicole, maybe posh your microphone ever. Thank you. And there should be some revving mics. Um, Um, would somebody like to talk about the potential value of proton bean therapy? They've had quite a lot of su success in some other countries, particularly the states with certain types of cancer. And it's, um, even can help people with stage four cancer, but it's not widely used here. I know it's very, very expensive to install. Is it the cost or the lack of research, or is it going to be used more here or, or what, Who wants to hit that? I can, uh, not my area of expertise, but, um, yeah, proton treatments pretty well established in certain situations. So eye melanomas are treated with, um, protons and some childhood cancers as well. I, I, I've already said I'm not a radiotherapist, but some of this is about the characteristics of protons, where they go in the body and what they can do. Um, so, uh, certain cancers already are treated, um, but I think more research is needed to try and understand that better. But it, it's not a panacea. It's an example I would say of a type of radiotherapy, um, that's a well understood role in certain areas and probably needs more investigation in others. And yes, it's expensive. I think there's two proton machines in the uk. There are two NHS proton machines, but there are several private ones as well. And the nearest one is walking distance from here. Um, not widely used in metastatic cancer 'cause it's a very, very focal treatment. Um, but, um, as, as James has, has some very strong application. Yeah. Hi. Thank you very much. Um, just so you, you picked up on the use of ctdna, um, liquid biopsies in kind of monitoring, um, ongoing disease. I was just wondering to kind of expand that out a little bit and kind of particularly in the context of a very capacity constrained service. Like what do you see as the big kind of opportunities or challenges in terms of with more patients hopefully being in remission, potentially cured, how those patients are kind of supported, informed, and managed in that kind of longer term space? How do we manage that in a, in a kind of functioning health system? That's a good question. Uh, Yeah, please. Um, It's a, it's a complex, um, scenario. I think the one, I think the one area where I think, um, liquid biopsies can help in the operable setting is in patients that have undergone surgery to risk stratify them into those that we think do not need any more treatment at all. And those that need consolidation, that is the most obvious benefit that it's going to bring that. Uh, and I, and I think, you know, we've already got some good examples where that really works. You know, stage two colon cancer already has randomized phase three level one evidence that, that that benefits, uh, patients, um, that is not yet seen in many other cancers probably because the type of CTD NA analysis we are using aren't sensitive enough to make that differential impact. And again, as we've got more sensitive tests that come through, we may be in a scenario to broaden that out to other cancers, not just stage two colon cancer. The challenge with following up patients with a blood test is what is the blood test bringing that's beyond what the routine scan is gonna be bringing. Because if I pick up the cancer six months earlier, am I really gonna meaningfully change the trajectory of that patient? Am I really going to cure them at that stage? Or has the patient already developed disseminated disease and picking it up six months earlier isn't gonna make an impact on their quality of life or their overall survival? And that's why, as James has said, we just need to run the trials. We need to generate the data to say what does the routine blood testing of a patient after surgery bring compared to the standardized CT scanning that they would be having afterwards to try and work out what the value of that is. Does it make people live longer? Does it make people have better treatments or does it allow us to withhold some treatments from patients that don't need it? And that, I think will bring together the value of that investment to then allow us to better operationalize it, to determine how that then gets, um, pitched into the routine clinical care. Because you're right, capacity is a problem, but unless we really know where it fits, we're not gonna be able to operationalize it properly. Maybe I could say something about that as well. A couple of concrete examples was in, in melanoma. So this business about adjuvant treatment, I talked about it when I was, um, when I, when I did my lecture. And at the moment, let's say in melanoma, we don't really have any sophisticated tools at all to choose who to give a year of drug treatment to after surgery. But if say on a circulating marker, we could say, well actually you don't need to have this treatment. It might be 80% of the people in the clinic don't need to have the treatment. Obvious resource saving, side effect saving, cost saving. Uh, and then I'm gonna just make a technical point as well, Sanjay, um, if I may, which is that targeted therapies for melanoma in the established metastatic setting spread of cancer abnormalities on scans are not curative. But in the earlier setting, adjuvant, they seem to be potentially curative. And so maybe there is something in there about the actual number of tumor cells in the body. And actually maybe our treatments work a lot better if we've got this minimal residual disease. I'm not sure it's been that well tested yet, but again, if we could stop people getting metastatic cancer with targeted therapies, 'cause we're picking up minimal residual disease, you're gonna cure more people, you're gonna have life expectancy and so on and so forth. So I actually see major opportunities in both directions. Can I ask, what are the biggest challenges in translating from the science into the clinic? And also why is like in terms of a scientific question, like why is cancer so hard to cure and what are the biggest like features that want to target in order to get the therapy across? Wow, that's two very big questions. I love it. Uh, how do we make science work in the clinic and why is cancer hard to cure? Uh, who wants to have a go at that? Yes, please. James. Why is cancer hard to cure? Um, 'cause it's lots of different diseases. They're heterogeneous. There's enormous variation, um, over space and time. Um, and also cancer cells, unlike let's say viruses or bacteria relatively similar to our normal cells. So the kinds of drugs that we use to eradicate viruses and bacteria we can use at doses where you don't get the same level of side effects and you don't get that, at least with drug treatment, you don't get that advantage with cancer cells. Uh, and cancers grow and evolve over time. And so if you're talking about drug treatment and fixed selective pressure, like targeted therapy, if, sorry, if I'm being a bit too technical, then almost always cancers will get around that selective pressure and evolve. And we, we know that very well. There's lots of different examples, um, of that. Uh, and then the other bit was about, um, translation between the lab and the clinic. Well, so what I'd say about that is, at the end of the day, uh, laboratory systems models are just that with the best will in the world. There's things that you can understand, there's discoveries that you can make, but at the end of the day, day they're by definition model systems. And once you go into human biology or animal biology, it's loads more complicated. Parker has mentioned this already. Uh, the immune system's an example of that. And so what I would say with my, my own bias I suppose is that it's beholden on all of us to do absolutely everything we can, um, to understand cancers and their treatment actually in humans. And that some of that's about getting samples. Some of that's about scans actually as well. And in the old days, I think I did say this before, we didn't do any of that stuff, but now we've got the tools at our disposal, um, and if we analyze those data, I, I think that's one of the ways that we're gonna understand why treatments work and don't work. So as a, as a go Actually, yeah, it's, it's a very rich scene. Uh, I'm gonna come to you at the front in a second, but I just wanna ask one question after the iPad 'cause we, uh, we've got so many I won't be able to get through them. But a question for you, Richard. So, um, I thought this was on your topic. How protective do individuals need to be about their personal medical data in the future? Um, This is something that I think is really important, um, because, uh, from, personally speaking, I'm very protective about my personal data. I really do much on social media and I don't allow WhatsApp access to my contact lists, which means it's complete pain to use. But I just, I just don't like people knowing about me. But as a society, if we can share our medical information, then I think we get huge advances in terms of helping each other with the information about ourselves. But that needs to be very carefully protected. And actually we have I think, really good regulation in this area that, that that is incredibly strict. And, and as everybody, all the researchers working in my kind of area kind of wrestle with making sure the systems, uh, work with all those regulations. So, um, providing we keep this good regulation and, and we make it work for everyone, then hopefully as an individual, you shouldn't have to be too worried about your personal data. What I want, what I'd love is for everyone to be absolutely sure that their personal information that could be related to them as an individual stays safe. And yet we can use the information about that information about that individual in an anonymized way to inform treatments for everyone else. Okay. Thank you. And we can have a whole talk about this, but there's clearly a social contract, uh, in it that would help. Here. Can I go to you at the front please? Is is progress being made in being able to treat the more aggressive cancers that seem to not respond to treatments as well?'cause obviously more you're making progress. I think with the less aggressive We need to personalize every cancer that we treat because a hormone sensitive breast cancer for one individual is a highly aggressive cancer, but for another individual is relatively indolent. And how do we tell the difference between these two particular cancers, which on the surface look exactly the same? And that is about better understanding the genomics of the cancer, better understanding the, um, imaging context of the cancer, better understanding the tumor microenvironment of the cancer, better understanding its immunogenicity and many other factors that contribute to making personal decisions about that individual's cancer. Because how one person's cancer will be treated, I suspect is gonna be somewhat different to how we treat the next person's cancer. We're already doing that to an extent, which is contributing to driving up a survival standards. But that level of complexity and individualization undoubtedly over the course of the next 10 years become increasingly more complex and increasingly personalized so that we can give more patients the consolidation treatments that they need at that particular point. Or in other patients say, you don't need that consolidated treatment, you can just have the letrozole and that will be fine. So we are gonna make progress, we are already making progress in that area. And I think at Parker over the next 10 years, it'll become increasingly more complex. Yeah, absolutely. I think that's a good answer. And sorry about your mother. And, um, I think one of the confounding things is that cancer is a disease of aging and our population is aging. So even though we are making much more progress with cancer, we do see more incidents absolute, um, absolute levels of cancer in society. I was going, we have Another question. If you successfully treat more cancers in more people, but presumably you are going to keep 'em alive to get more recurrences, um, because those people will continue to age, the original cancer may come back or other cancers may because it's a degenerative disease. Well, certainly we, we, we will all die. Uh, that is the one, the one thing I'm absolutely certain of, if not of cancer, of, uh, of neuro degeneration. Um, I actually, I've got a a another question for you. Um, Sanjay, which is up on the iPad. Is there evidence to suggest that the frequency of certain biomarkers, and they given alk and r as examples, common lung biomarkers are decreasing in lung cancer with, um, with transitions to next generation sequencing? And why? I think the, the, the, the frequency of the markers, uh, in the population are pretty static, right? Yeah. Because these are, these are particular, um, uh, genetic, um, subsets of, in this particular case, lung cancer. Um, the issue is that we are perhaps picking up more of these, uh, subtypes because we are doing more in depth sequencing of cancers. I mean, that's the fundamental, uh, uh, issue. We are now learning much more about our cancers than we ever did five years ago, two years ago. Uh, and now we are picking up way more, uh, genomic abnormalities than we ever would before this, this afternoon I had an email from a colleague who said, um, well, I found this particular EGFR mutation, what should I do in this particular context now that would've never been picked up five years ago? Because the next generation sequencing technology to identify it didn't exist, or if it did exist, it wasn't implemented at scale. So we are now identifying a whole bunch of new genomic abnormalities that we now need to try and figure out what we do. So part of the, part of the answer is we are finding new abnormalities because we are doing much more in depth sequencing, but the, the, the frequency of these markers isn't changing, we are just actually picking them up. Yeah. The more you look, the more you find indeed. Okay. I, we have time for one more question from the floor. Uh, make it a good one for the panel, uh, lady here in the red. That's a good one, <laugh>. Thank you. Um, with the liquid biopsies, my understanding is they're, they're like a snapshot of potential cancers in the blood. Um, in, in the, in, let's say you are, you are using a liquid biopsy in a sort of a monitoring setting. So someone's gone through treatment and you want to see how their, you know, the, the future is progressing. How frequently would they have to, would they, would they be carried out because, you know, an aggressive cancer could come back quickly? So you could, if, if you did it, let's say once a year, you know, within that year, it could come back very quickly. Or six, you know, how, what, what sort of timescales or does it, would it vary? That's a good question. I mean, I, I don't think we know the answer to that question to, to be quite honest with you. And I think it will differ. The question, the answer to that question will defer by where you are in that journey on follow up, what your risk of a priority risk of relapse is in the first place.'cause the, a priority risk of relapse is gonna be determined by characteristics of the tumor that was cut out. You know, how much of it was on board, how many lymph nodes were involved, how aggressive did it look under the microscope? Um, and some cancers you might want to, uh, sample annually. Some cancers you might wanna sample a bit more frequently, but fundamentally, we don't even know where the sampling them makes a difference at all. So we really do need to start at base one to try and figure out what are these blood tests which can detect blood count, uh, uh, tumor earlier than the scans. What are they really bringing to the equation? Are they just causing seven months of worry until the cancer pops up and then we do anything about it? In which case it's completely waste of time, frankly. Or is it really going to change the survival outcome because we can intervene early and make a difference? That's the data we need to generate. Sanjay, thank you. I think that's a good place to end. It is clear that we are all on a, uh, a kind of exploratory journey together. Patients, professors, doctors. Um, I just would like to end by thanking the panel for doing such a good job. Thank you everyone.