 Thank you to Royal Society for this immense privilege and honour to stand here today and sharing with you the work and the journey we've taken in exploring mutations in cancer. Many in my team are here today. Many of you have been alongside us in our research endeavours. Thank you. I speak today on behalf of us all. I'd like to start by first acknowledging that we are standing on the shoulders of giants. The giants of DNA biology and chemistry, the visionary leaders that thought about the human genome project, completed it, and shared data, all that enabled the development of modern chemistry techniques that accelerated our ability to read the human genome, and that's DNA sequencing. Without that prior context, we cannot have derived the insights that you will hear about shortly. For in the last few decades, the speed and scale of DNA sequencing has increased by orders of magnitude, and thanks to the advent of modern sequencing technologies, massively parallel sequencing, it's now possible to read the entire human genome sequence in one sitting, in one experiment in a single day. That's 3,000 million base pairs of the human genome, so it's a really handy tool for cancer, because human cancers are full of genetic changes of mutations, and if you could read that whole human cancer genome, you could learn a lot about what has driven each person's cancer. For as a cell turns from being a normal cell into a cancer cell, etched into the DNA of the cancers are thousands of mutations, the scars of historical events that have driven the cell into a malignancy, and for the members of the audience that are not scientists, I'll try to visualise the extent of mutations in cancer. Here are the human chromosomes at conception, 22 pairs of autosomes and a pair of sex chromosomes, but at the point of cancer diagnosis, the chromosome complement can be so markedly evolved from its original state, so there's extra copies, there's lost copies, there are unusual chromosomal combinations as well. Now the fact that this scrambling of chromosomes, these chromosomal mutations were actually reported as far back as the turn of the century, turn of the last century, and in 1902 Theodore Boveri first observed using a good old microscope before we even understood about DNA, he observed that in sea urchins that chromosomes were likely to be important units of heredity. They had to be present in the right quantities for normal cellular development, and he suggested at that time that cancers probably arose from cells that had had its chromosomes scrambled. Now this first notion was dismissed initially and it took 13 years for others to verify Boveri's hypothesis. Had Boveri made the suggestion 100 years later, it would have been validated by someone else in 13 months in nature cell of science, or if he was very good on social media he might have been able to deal with it through Twitter in 13 years. 13 minutes, but 13 years for someone else to verify this idea. So chromosomes, although they provided some insights into cancer mutations, they are rather crude, very low resolution, and you can unpack and unravel the chromosomes to reveal the DNA building blocks. That's adenine, cytosine, guanine and thymine, ac, g and t. And just like the chromosome level mutations, which we call copy number changes, you can have a penoply of different types of mutations. So substitutions are single base changes that's when you change one nucleotide for another. Insertions or dilations is when you remove some or you add additional nucleotides. Rearrangements are when you have a break in that double helix that sugar phosphate backbone breaks off, relocates elsewhere and forms a new mutation that's called a rearrangement. When we do whole genome sequencing, you will be able to see them all, all these different mutation classes and all the mutations of those different classes. For decades in cancer research, we have focused on identifying a small handful of mutations in each person's cancer, so-called driver mutations, because we believed these were causally implicated and drives the development of cancers. That's a fitting notion because these could be therapeutic targets. So here are a few examples of known driver mutations in cancer for which there are effective drugs that have been created, tested and are in clinical use today. But beyond that sort of one to five driver mutations present in each person's cancer, there's thousands and thousands of passenger mutations. That which was not thought to be useful, just mutational noise, random events that are scattered throughout the genome. And what whole genome sequencing technology allows us to do is it gives us access, it allows us to see not just that small one to five mutations but all the thousands of passenger mutations. And that's sort of the thing that I started doing in my research training at the Sanger Institute. Now, I was sat down a couple of times and I was sort of advised that most people are studying driver mutations in the world today, Serena, and you're exploring passenger mutations. You're foraging round the bin of mutational detritus. But in fact, you can learn a lot by foraging round people's bins. You can learn who's eating too many pizzas and drinking too much alcohol. We're not here to judge. We just understand a bit more about how each person lives. And so what I'd like to do in this section is to tell you a bit about how this tons of data that we get per patient from whole genome sequencing, how we, and it was only 21 cancers that we first sequenced, which doesn't sound like much. But at that time was a loss of data. I'm going to share with you three major biological insights that we gained by just squeezing the information, the biological insights out of these first 21 whole genomes. So the first thing is about this DNA graffiti, mutation patterns. For as a cell turns from being a normal cell into a cancer cell, there are these mutational processes that are constantly occurring and some of them may be perfectly normal. As your cells divide, you will acquire errors in DNA. As you sit here listening to me speak, your cells are exposed to oxygen and water. Those are the most essential elements of life, but they're also the most mutogenic. Oxidation and hydrolysis is happening in your DNA all the time. Now there may be mutational effects from just being exposed to various things in the environment. So tobacco smoke is a good example of that, and we know that that can cause lung cancer. Ultraviolet radiation is also damaging and can give you skin cancer. But there are DNA repair pathways in all your cells that are constantly mitigating this damage. Now DNA repair pathways can go awry as well and that can cause mutations. There may even be mutational processes with unknown causes for now, and we are constantly learning so we may start to uncover the etiologies of some of those mutational processes. Whatever are the different mutogenic processes, each one leaves a characteristic imprint, a mark or a signature on the genome. When you sequence a cancer genome, what you see is a final portrait. It's a composite of all those signatures added together, so it does look like a random collection of mutations, this mutational detritus. But in fact, when I was working in the lab of Mike Stratton at Sanger Institute together with Ludmille Alexanderov, we showed that if you had multiple tumours of the same tumour type, in this case it was the 21 breast cancers, you could use mathematical methods to extract the patterns, the mutational signatures, that's the pink triangles, and then you could even quantify the amount of each signature in each person's genome. That's determining the size of the arrows. Now I'd like to show you some examples of what mutational signatures look like. Now there's about 150 of these today, so I don't have time to go through all of them. I just want to give you a flavour. We started with substitutions, single-base changes. So here are some examples of substitution signatures. We classically present them like this. There's six main substitutions, as you can see, in the six different colours, and for every nucleotide, your neighbour's matter. So we take five prime ACGT, three prime ACGT, four by four is 16, 16 by six is 96. A signature is this 96-channel pattern. And for this particular signature, where you see the tall bars, these are C to T mutations, which occur at CPGs. So that's when a C precedes a G guanine, these are methylated CPGs, and they are constantly deaminating. This is a normal mutational process. It's happening in all your cells right now as you sit here listening to me speak. And this is an intrinsic process, perfectly normal. There are some other intrinsic processes like these two caused by a family of enzymes called apobex. I hope you can see that they do produce quite distinct patterns. These C to T and C to G mutations from apobex tend to occur at a five prime T preceding the C. That's just how the apobex work. Now, here are some signatures that are due to environmental agents. The one at the top is due to UV light that occurs in skin cancers. The one in the middle is due to tobacco smoke, and you find those patterns in lung cancers. And the ones at the bottom is aerosolocic acid, and that's seen when you ingest aerosolocic acid that's seen in liver and kidney cancers. So I hope you can see that these patterns are really quite distinctive and recognisable. And that's a bit like street art, like graffiti art. There are cases of street art that nobody recognises, but there are some where you can know who the artist is. So this is seen, and he's been doing graffiti since 1970s, and he does comic book street art. It's very distinctive, and it's something you see a little bit in New York. This is Reevoq. He's got a very particular style. So recognisable, unfortunately, that he does have legal consequences to his art. And then this is Ben Ein. He's famous for his distinguishable alphabet letters that he's put all over shutters in East London. So he's celebrated for his street art. And in 2010 British Prime Minister David Cameron gave Barack Obama one of Ein's canvases as a gift on a visit to the US. And just like in human cancers, mutations are there to be read. And in here, in this message, there is a message there. I don't know whether you see it. If you do, you can tell me about it later at the reception. So this is Banksy, personal identity unknown, yet graffiti style, very recognisable, iconic even. And there is some political or social commentary behind it. And just like street art with DNA graffiti, you can tell who done it. You can say from looking at the signatures, you can know what the etiology is. But sometimes you can't. A bit like Banksy. We don't know his personal identity. But the significance of the signature in cancer can be delineated. So, really, the point I wanted to make there was that the power of data from whole genome sequencing can help to reveal these mutation patterns, these graffiti-like signatures, which may tell you something about the causes of each person's cancer. The second thing I wanted to highlight was that that graffiti-like patterns, they don't just have to be evenly distributed through the genome. You can have some areas where there are localized mutations, hypermutations even. So, again, let's visualize the data together. So let's say we lie our chromosomes down one to chromosome X and number every single mutation from one to X. And we calculate something called an inter-mutation distance. And that's a distance from one mutation to the one immediately preceding it in the reference genome. Now, most cancers have about 3,000 mutations. And the human genome, as I said, is 3,000 million bases. So, roughly you would have a mutation every million bases. And that's roughly what you're seeing. You can plot this information like this. And on the horizontal axis, we have the inter-mutation distance, and that's on a log scale. And on the horizontal, we have the mutation number. And what you get is a cloud of dots like so sitting at around one million base pairs. And these four dots, they're in that tiny little region. Right. You can color the mutations like so to make it look a little prettier. But if you did have mutations that were clustered together, that were localized and there's focal hypermutation, you'll find much shorter inter-mutation distances like this. And you might see it as a strip of mutations. Oh, yes, I have a pointer. You have a strip of mutations like so in this sort of rainfall plot. Now, when we examined the 21 genomes, we found this. This is a really dramatic example, but here are 800 mutations. They're all red dots. They're all C to T mutations. They're all in one place in the genome. In fact, there's another little strip here. So, this is something we call Cataeges, localised hypermutation. Now, the wonderful thing about the whole genome sequencing data is it's digital. You can zoom out, but you can also zoom right in. So, those 800 mutations are on chromosome 6. So, we're going to look at chromosome 6 together. So, here's chromosome 6 lying on its side. That's a short arm and that's a long arm. There's chromosomal coordinates. And this is your standard rainfall plot. Remember I said in whole genome sequencing, you get all classes of mutations. So, these are substitutions. Let's add on some other classes to our surprise. We find that it co-localises, these sort of storms of mutations co-localise with another class of mutation called rearrangements. So, that's two different classes, but they're all occurring in the same spot. Now, let's look at this in higher magnification. We're going to go down 100 fold and 100 fold, 1 megabase, 10 kb windows, and now you're looking at individual DNA molecules. The yellow and blue rectangles are individual DNA molecules and the red bits are mutations. I hope you can see that there are some DNA molecules where all the mutations are close together and they're all on the same DNA molecule as well. This is giving us insights into how mutations arise. So, if I draw it out for you as a schematic, by looking at whole genome sequencing data, you can see that these are C to T mutations. Interestingly, those C to T mutations are almost always preceded by a T thymine. You saw this before, I mentioned apopex. This is probably caused by apopex. And not only that, those mutations are all happening on the same strand. Now, apopex happen to require a single strand at DNA. Apopex evolved to get rid of viruses. If it sees single strand at DNA, it thinks it's virus, it's going to deaminate it, it's going to mutate it. So, this gives us some really lovely insights into how mutations arise, some insights into new, into mutational biology. And that's really the second point I wanted to make, is that you can also learn quite a lot about new biology from this whole genome sequencing data. My last point is about that digital nature of the whole genome sequencing. Because for every position in the genome, you get many, many sequencing reads on each site. So, you get digital information at each location. So, let's say you've inherited a mutation from your mum, or from your dad even. Here are your chromosomes, a pair of chromosomes for everyone. So, let's say you've inherited a mutation from your dad. If we zoom into this site, because one in two alleles is mutated, one in three is 50% of the sequencing reads are going to carry a mutation. That is if you have inherited it from one of your parents. In a cancer, you'll see a slightly different situation. Because when you take a cancer piece, you will also capture some normal cells, some lymphocytes, some stromal tissue, some fat. And so, there will be some reads that are from normal cells. And let's say in this case 70% of the reads are coming from the tumour. And if you have acquired a mutation in your cancer, on one of two alleles, half of 70% is 35%. So, you know what to expect for chromosomes that are present in two copies. So, now you can plot this information here. And we know, for example, in this patient, chromosome 10 is present in two copies. We expect to only see a cloud of mutations here. But I hope you can see that some additional clouds down here, additional mutations, not present at where you expect it to be. And this is evidence that, in fact, in your cancer, as it has developed, some new subpopulations are arising, new subclones are arising in the tumour. And those clouds of mutations down here is evidence that there's new mutations happening on chromosome 10 in those little minor subpopulations. You can do that for all the chromosomes to infer phylogenetic trees to try to understand the evolution of cancer. So, this is a gross oversimplification for the reason of time, but basically you can use mathematics to model the mutations that you know have happened very early in cancer evolution, and you can also model the mutations that have happened in the branches later. You can construct a phylogenetic tree, and that's from just one sample if you have whole genome sequencing data. So, these are the three main things I just wanted to communicate early on, on the whole genome sequencing data. In that first goal, we were able to demonstrate mutational signatures as graffiti-like patterns so we can understand biology, we can explore cancer evolution. And this I did in the labs of Mike Strathen and Peter Campbell, both Fellows of the Royal Society. But since then, there's been a huge explosion in the field. So, we're not the only ones doing this. Everybody's doing this now. What I just told you is when you get whole genomes and people are now taking multiple samples per patient, you can drop phylogenetic trees that way, you can take samples that are separated temporarily over time. So, these are just different ways that you can drop phylogenetic trees. And in the field of mutational signatures and DNA graffiti as well, from 21 genomes, we then went to 500, and there was landmark paper there, and then the field has just exploded. There's loads and loads of new data all the time. Now, I'd like to also point out that it's early days. We are still learning. Nothing is dogma. That knowledge and understanding will change because we started with very few samples. And as you have more data, you'll get more knowledge as well. So, there's no pride in it. We'll be changing our views and changing our thoughts about certain things as we go along. There's also a lot of experimental work to support a lot of the mathematics. Isn't just mathematical hocus pocus? There actually is some evidence that you can reproduce these signatures, these graffiti-like patterns in a dish. And I'd also like to lastly point out that we could not have done this without sort of the visionary and ambitious international sequencing endeavours that share their data. A lot of the work that we do is enabled by the fact that everyone shares their data. So, I've sort of galloped through a first section of my talk. I'll give you a little bit of a breather with a tiny anecdote. Our work on cartridges we just made it on the cover of Cell, which was a highlight of my life, and I shared this with my mother. My mother is a Chinese lady who doesn't really care about nature, cell and science, and she just wanted a daughter who was an NHS consultant. So, you know, she was most unimpressed, but she did say, you know, explaining mutation signatures and patterns to her, and she goes, so you have a PhD in pretty patterns. You are not an NHS consultant. You are earning pittance. How does this benefit mankind? And parents are good for grounding you. But this brings me on nicely to my second section, which is how we've now taken this data and tried to create algorithms to try to get clinical value for patients. And to do that, I'm going to walk you through more data, more visualizations of cancer-holding data. So, I'm going to orientate you. Here's a patient. She's anonymized. We have her chromosomal ideogram. Chromosomes 1, 2, 3, 4, 5 all the way around to X and Y. And then we have the substitutions. Remember the drivers I mentioned? She's got one, P53. But beyond that one driver, she's got 4,500 other mutations with interesting patterns in them, including apobag. Now, insertions and deletions. We can summarise it like so. We can see that she has an excess of a particular kind of deletion, which other people have shown to be associated with having a defect in a DNA repair pathway or a homologous recombinational HR. Brach1 is a gene in the HR pathway, and this woman has inherited a Brach1 mutation. So, this fits, this pattern fits with the fact that she's got a Brach1 deficiency. Going inwards some more, these are chromosomal copy aberrations, what I started with. So, green means she's got gains in these chromosomes, and pink means losses. And as you can see, she's got losses throughout the genome. Again, another pattern, a signature of Brach1 deficiency. And now, rearrangements. She has over 300 of them. They are beautifully distributed around the genome. They're all of a particular class called tandem duplications. And this, again, is a signature, a pattern of Brach1 deficiency. These are all patterns that are classic of Brach1 deficiency, they are the graffiti of Brach1 deficiency, and sometimes referred to as Brachanus by the community. And there are specific drugs that have been created for patients who have inherited mutations in Brach1, that have Brachanus. Drugs like platinum and parp inhibitors in particular. So, if I showed you these patients again, I've just showed you her tumor, she's got inherited Brach1 cancer. Hopefully, her tumor is going to be sensitive to parp inhibitors. And I showed you these two other women who are not related to her at all. And I said to you that one in the middle has acquired a Brach1 mutation and the one at the end is a generic way of turning off her Brach1 gene. I think you'd agree that they might all be sensitive to parp inhibitors. And although they don't share a single mutation in common, they each have tens of thousands of mutations, not a single one shared in common, but by taking this holistic cancer genome profiling approach, you can instantly recognise the biological stage of a cancer as being Brach1 deficient, even if how they become Brach1 deficient is different. The other women are the graffiti-like mutational signatures. They are pathoglamonic and distinguishing. And this is the sort of information that is perfect for machine learning. Now, I don't have time to go through lots and lots of different models. I just want to sort of communicate the principle. When you have this sort of data, it's perfect for trying to teach models to be able to classify tumors for us so that we can enable the next generation to do cancer whole genome interpretation. I want to tell you about HR detect, which is an algorithm that was designed to find Brachanus, because I've just told you that these tumors might be sensitive to parp inhibitors. So the algorithm's HR detect, we applied HR detect on a cohort of 560 patients with breast cancer. Now, to our surprise, we found a greater proportion of breast cancers that had high HR detect scores indicative of Brachanus than we had expected. Brachanus wasn't limited to just 25% of the cohort, which is what we expected. It was present in 22% of the cohort. So that's one in five breast cancers. Now, I know a lot of breast cancers are cured, but not all of them are cured. And that's one in five breast cancers. This is a mix of breast cancers, the sort of thing that you get in the population. So that was a little bit surprising. So we dived into it a little bit more. And we found the 22 women that we knew had inherited positive controls. We identified another 33 new families with germline mutations in BRCA1 and BRCA2. So we opened a can of worms there. We found 22 patients who had acquired BRCA1 or BRCA2 defects. But one third of the cohort, we can see the graffiti-like patterns. We can see the signature, but we cannot find the genetic or epigenetic cause. So you cannot find the driver, but you can see the patterns. It's clinically valuable or not. So at this point, we have an algorithm. An algorithm that has been developed through data science on the rich whole genome sequencing data sets that are available in the community today. But to take an algorithm through two patients, you need to demonstrate clinical value. We need to show that it can either prognosticate that is to provide some sense of outcome or better still be predictive that it can tell you whether someone is going to be sensitive to a particular drug. But this is the hurdle. This is the slow step between discovery science and implementation in patients. This is the slow bit before getting to clinical utility. Nevertheless, we collaborated with colleagues in Sweden because they have fantastic collections of clinical data, treatment and outcome data. And that permits us to try to answer the question of prognostication. So this was a new cohort of triple negative breast cancers. It's a kind of breast cancer that tends to have a poor outcome. The project is called SCAN B in Sweden and it was led by Ockabog and Ewan Staff from Lund's University. Critically, they have clinical information, treatment and outcome data. And this was critical because it allowed us to show that HR detect could prognosticate. It could distinguish patients who are going to do well from the ones who are going to do badly. And this was irrespective of whether we could find a driver, a genetic or epigenetic driver. So even in cases where you couldn't find a driver, the signatures, the graffiti-like patterns were able to prognosticate. And the next step in collaboration with colleagues in London, that's Nick Turner at the ICR, we explored our algorithm in a small proof of principle phase two clinical trial. We applied HR detecting patients with newly diagnosed treatment naive triple negative breast cancer. They had been given a window of parp inhibition right at the start, very unusual and we did the genome sequencing at the start and then Nick was collecting blood samples throughout. Now if your tumor is sensitive to the parp inhibitor, the circulating levels of tumor DNA is going to drop and that gives you a low CDR15 ratio. And sure enough here you can see HR detect high scoring cases have got low CDR15 ratios. Now this is a very small proof of principle study but it allows us now to go towards a phase three randomized clinical trial. This shows us nicely that there might be early predictive capabilities of our algorithm as well. So I really use an example of one clinical algorithm to try to communicate that actually there are these additional steps before you can take it to patients. But it's important to do them because otherwise they don't get to patients. 90% of algorithms don't get to patients so this is the limiting step. Now in recent years 100,000 genomes projects is an England-wide project that's sought to do whole genome sequencing in many rare diseases and in cancer. And they've produced the largest cohort of whole genome sequence cancers in the world of over 17,000 NHS cancer genomes. Remember I started with 21? This is a huge number now. And together with all the other whole genome sequence cancers in the world there's over 20,000 of them. And here again I'd like to pause for reflection to make three points. I think we should acknowledge it's a spectacular feat by the NHS Collective that which we've been hearing such negative things about in the press. And I think it is pretty remarkable that if you invest in something for the medium to long term you can mobilise a willing and able group of people. And these NHS cancers were recruited from all over England and Wales. So you can make amazing things happen. And I'm in awe that they did this project. On the backbone of that research project the UK then led the way in implementing a national infrastructure called the Genomic Medicine Services, GMSs. And yes, it's early days and beyond being an academic I am an NHS clinician. I am at the gritty end of the implementation spectrum and I appreciate that there are issues but beyond the operational issues I'd like you to see potential. The potential for a continuous cycle of learning, of using and reusing the ever growing data of making new discoveries of creating new algorithms and then performing the clinical studies in getting patient impact quickly. This is a system and resource like nowhere else in the world. And the last thing I wanted to make at this point was that if your cancer was used in any of these studies the data are all anonymised. When I've showed you plots they've all been anonymised. I don't need to know your name I don't need to know where you live but the data from your cancer genomes and the associated clinical information are incredibly powerful for academic learning. We can use it to turn them into tools and test them quickly and we can bring benefits to patients faster. So in our most recent endeavour mining the extraordinary treasure trove of nearly 20,000 whole genome sequences including the NHS cancers we're able to see such an incredible diversity of DNA graffiti of nutritional signatures that are present in human cancers can be used to provide very precise insights regarding how we might treat cancers more effectively per patients. And with that I'm going to come to my last section about how we gain new knowledge from large cohorts but the idea is to bring benefits for individual patients and I hope you've seen that every cancer is highly individual. So we're going to walk through some real patient whole genome sequencing data. So here is a patient with a very typical breast cancer that you've seen these whole genome sequencing plots. So beyond the driver mutations a patient also has the signatures, the graffiti-like patterns of BRCA2 deficiency. Now if your clinician looking at whole genome sequences is not very confident apply one of the algorithms and what you get is a nice high score to help you in case you're uncertain. So this is great we have got a BRCA2 deficient cancer this tumour might be sensitive to parking but it's great, easy one. Here's a uterine cancer we don't usually look for BRCA2 deficiency in uterine cancer but here look it's got BRCA2 deficiency it's got a p53 driver BRCA2 signatures a high HRD tech score but no BRCA1 or 2 mutations found. In today's clinic you would only be treating this patient for a p53 mutation which is basically no treatment at all you wouldn't even see the BRCA2 deficiency. What could this patient get instead? What are we not giving patients that what are we not exploring soon enough? Here's a colon cancer and this is an abnormality that's usually hunted down which is mismatch repair deficiency these are common in colon cancer relatively common in colon cancers and there's a driver mutation to go with it an inherited MLH1 mutation Now we have many different algorithms we've got another one called MMR detect so this patient has a high MMR detect score and then you know you can apply any other genomic acid it's not just ours anybody's in the world and there's something called tumor mutational burden TMB this is an FDA approved marker biomarker to identify patients with mismatch repair deficiency because these tumors tend to be sensitive to immunotherapies so that's great that's an easy one here's a breast cancer again with mismatch repair deficiency high MMR detect score it's got all the signatures high TMB there's something we look for in breast cancer patients so this patient will again not get necessarily the drugs that you might give patients with MMR deficiency right from the outset you might discover it in due course but these patients are basically treated as breast cancers they're not treated as a breast cancer with a mismatch repair deficiency that's really the message okay here is a uterine cancer with the graffiti like patterns of signatures of polymerase dysregulation she has the acquired polymerase polyemutation as well she's got a very high TMB this is a classic polymerase dysregulated cancer these cancers are sensitive to immune checkpoint inhibitors that's an easy one and here is an oral cancer oral cancers are on the rise in young adults in the UK we don't really know the reason behind it but it's also polymerase dysregulated and it would be missed it's got a low TMB score so it doesn't even fit that criteria so again here might be a tumor that could be sensitive to immune checkpoint inhibitors that might just not be detected okay last few this is a lung cancer I'm showing it mainly because the bluish tinge of tobacco smoke is very very clear very clear graffiti sign of tobacco here this is a kidney cancer of a patient who has had exposure to something called a risolocic acid it's got the signatures interestingly the patient doesn't think they've had any exposure to risolocic acid so this is a public health thing is banned in the UK and this is a skin cancer malignant melanoma with the signatures of ultraviolet radiation, ultraviolet damage this is a very typical looking malignant melanoma the red tinge high numbers of mutations high TMB very clear keep that in your mind if I showed you this tumor which was from a metastatic lung cancer there were lots of lesions in the lung primary was unknown but if the primary is unknown and you're looking at this wouldn't you think that this tumor might have come the primary from the skin and last but not least I'm going to end on a clinical case so I have the permission of the patient to tell this story so he's a 30 year old male with an inherited genetic abnormality called xeroderma pigmentosum or XP when you have an XP mutation you can't fix damage from UV very easily so these patients tend to have photosensitivity they have a lot of skin damage and they have an increased risk of skin cancer and he presented with a lesion on the medial aspect of his left eyebrow and this turned out to be an angiosarcoma that's a rare cancer of the blood vessel lining so initially he had no metastases wide local excision and you can see the skin grafts trying to heal the area but unfortunately over a relatively short period of time he really didn't respond to any of the standard treatments for angiosarcoma and this is the usual furrow that we plough we have an angiosarcoma we're going to treat it for an angiosarcoma this is the protocol and at this stage his man is really really very ill it's sort of terminal disease at this stage and we do a whole genome sequence and you can see that tinge of red all around that's ultraviolet radiation damage you saw this earlier but that's not a surprise because he's an XP mutant you expect to see this what's really killing this patient is the polymerase this regulation that's present but not in a very large amount in a relatively small amount we even find the acquired polymerase epsilon mutation that was caused by UV damage but it's present in a very small amount of sequencing reads remember the phylogenetic trees we talked about present in a small variant allele fraction this was present in a subclone of the tumour so remember the phylogenetic trees I talked about he's got his main tumour and then he's got his little subclone that has acquired this polymerase defect now these tumours tend to be sensitive to immune checkpoint therapies and it was not easy trying to get this drug it takes a village and all the people at the bottom and who are in the audience today were involved in trying to get the patient the drug and we did all sorts of things including staining of his primary sample which did not show any evidence of antibody staining to PDL-1 but there was a lot of two and pro in the sort of discussions between us and finally PDL-1 staining was done in a metastatic sample cos it's the met that's killing him and indeed that was all positive so now we have really good evidence to support giving this man immune checkpoint therapy I've made it sound very easy this was a really hard battle but here are his scans pre-treatment he's got tumour everywhere in his cranium, in his lymph nodes in his chest, in his liver it was really everywhere and he was really sick, he was in hospital requiring pleurodesis and that's when you have to re-inflate the lungs now here are scans three cycles in after receiving Pembrolizumab and after seven cycles he walks home in fact today he's in the audience he's alive and well thank you very much for being here I'm sure he'll be happy to take any questions so he exhibited a spectacular response it's a great story but what he did is this that in our sort of interactions with this patient what he communicated was how he felt that he was treated as an individual with this cancer as opposed to being another angiosarcoma on the standard sort of protocol so with that ladies and gentlemen I have reached the end of my scientific narrative and all the work that you've heard about today cannot have been put together without the generosity of the patients the families and all the healthcare professionals that collected all the samples which enabled our work there's an extensive list of collaborators from really all over the world that have been unwavering in their support there's so many I cannot list them all on the slide it takes a village it really really does and there's the adage behind every man is a great woman but behind every woman is a critical group of mentors and sponsors that have looked out for me that have put me forward for all sorts of things these are great men and great women of all ethnicities that have supported, advised pushed and challenged me over numerous coffees and time meals thank you for taking a chance on me I don't think I was your typical person who would have gone into a PhD or sort of in my thirties with two small children I didn't have the most fantastic scientific pedigree so thank you for giving me that opportunity our generous funders that have enabled my team and I particularly to our hearts content one or two of these funders go beyond just funding you do put in huge efforts into growing and developing us it's very hard to quantify how valuable that is but it really is very valuable now my team past and present a nicer bunch of people I cannot find a refreshingly diverse incredibly committed thoughtful, sincere, secure deeply interested group of people even when you diso baby and do loads of extra experiments and additional cell lines and way too many replicates I am internally delighted your curiosity is what will drive innovation and never let that be extinguished I want you to know how much I am inspired by you thank you and last but not least the other team the family and friends that are the sustenance of life your solid grounded perspective I deeply respect I feel so lucky to be able to bounce the daily grind of you packaged with your humour and zest for life you provide the foundation from where I have grown now my parents are no longer with us today my mother was a very strong grounding force and my father was a person who opened up my mind and I boarded a plane to the UK at the age of 17 I had never left Asia at that point and my mother said it's all very well to get an education but you must come home and marry a Malay boy I didn't do that but my father said you know what whatever you hear behind you whatever you hear from society don't look back the opportunities are ahead of you they are over there so grab those opportunities when they come because that's the place that's where you're going to learn new things don't look back and with that I'd like to thank the Royal Society again for this amazing recognition out of all the opportunities that I've had in the UK thank you very much thank you so much for an utterly inspiring lecture fantastic science straight into clinical medicine and the background of your group and your family I've rarely heard such an inspiring lecture it was wonderful thank you so much so now we have plenty of time and we have plenty of time for questions so once online we'll come on to the iPad here and as I see them I'll put them to our speaker but let's start with some questions from the room there's a roving microphone there and there's a question here hi thank you so much for inspiring talk I was wondering the dream of personalised treatment like how many decades do you think it will take the UK to get there well that's a huge question thank you very much for the question of the kind words you know we already today have some evidence today in the audience we have some evidence of it already in play but it's too small I think you're right I think what your question is when is it just going to become something that is part of the norm and I think we are unusual in that we have that infrastructure that NHS Genomic Medicine Service infrastructure where we can recruit patients we have that information and we can send it back out very very quickly so you know if you told me when I started this with 21 cancer genomes that we would have 20,000 today I would have been surprised I would have thought no there's no chance we would have done that in a decade I think we are a country that is unusual and you have the infrastructure to be able to do with this kind of personalised medicine pretty quickly so I'd like to think that in 10 years time we'll be a lot further forward than we are we have the infrastructure and we are building people you have the GMSs and Genomics England I think it's possible to take it a good distance we need to do the clinical studies so just following up on that how do the health economics stack up how does it affect the cost of treating each patient again another enormous question and I think sort of systematic studies on this probably need to be done I think we need to look at costs I think a lot of people will cut very concerned about the cost of genome sequencing initially that was very expensive and that actually has dropped dramatically the limitation is the analysis and interpretation now and then storage of the data because we have a centralised and national infrastructure storage of the data is less of an issue for all the different sites in the UK but the analysis and interpretation is the tricky bit and the costs there I think are something that we need to understand but then what we need to balance that with is what is the cost for people when they are given months and months of the wrong treatment or treatment that's not perfect we give them rounds and rounds of certain chemotherapies, they're out of work they're not able to contribute to the economy we need to do those calculations there's also the cost and the quality of life because if you give somebody a drug and they're really sick with it put dollars against that these are all very important issues we need to address so that we can move the community for I think there are still many people who think this is still quite esoteric stuff I think we need to move people and shift that mindset we can read a whole genome we just need to start doing it Thank you that was an absolute tour de force and incredibly inspiring as an oncologist to listen to many of the drugs that we use in the clinic and their approval require a sort of a registration trial with a companion diagnostic usually developed by a pharmaceutical company within that trial can you say a little bit about how you could see this infrastructure and this whole genome sequencing triaging or informing how we move people perhaps not to the immediate use of the treatment providing the triage into the trial that provides the evidence because that's the gap I think that's sometimes missing Yeah absolutely so for the audience that some what Professor Touch is alluding to there is using this national infrastructure perhaps we can do a whole genome sequencing for every cancer triage at that point you get the read out and then you triage them into clinical trials so that you can get them through these registration trials faster the UK is an unusual place to think that is exactly the sort of thing that we can do this sort of study we'll need to talk to the regulators and and also convince our colleagues our clinical colleagues who will be doing all those clinical trials that this is something that can be done but to do those of course are the operational issues we need to be able to make sure we can get samples which are not necessarily flash flows and snap flows and these are operational things we can get results quickly we can feed back meaningful information so there's some operational issues that we need to deal with but I think that notion that you've raised and something we have discussed I think it's a fab idea I think that's the way to try to get things through clinical validation studies and two patients as quickly as possible and there's nowhere else that we can do it in the UK, the UK is one of them if you kind of think about mismatch repair deficiencies in colorectal cancer 45,000 colorectal cancers in the UK 8% are mismatch repair deficient you'll be able to get thousands of colorectal cancer in a year and prove that you can give immune checkpoint therapy right at the start so it's what we can do and we should do one here and then one there Serena, amazing talk but not a surprise to me that you delivered that my question is about prevention of cancer so you focus on risk factors that have a major effect which obviously is where you start with any investigation into disease so tobacco ultraviolet light so how far away are we from being able to look at risk factors that have more modest effects but which either over time or when they're additive may increase someone's risk of cancer so for example environmental pollutants and obviously in my area of interest diet so how far are we away from being able to pick up the effect such that we can prevent some of these cancers because we say you know what when you add that additive to food you increase the risk of this type of cancer and so we're at the Royal Society and we get Royal Society level questions from members who are FRS so what a fantastic question the information I showed you today has been based on thousands of samples to get that level of ability to be able to predict and to advise people and to intervene and for something that has a smaller effect you need a bigger population, you need bigger many more samples and then we need to do the data collection because lots of these especially at the start were people's favourite samples in the freezer to do the clinical studies I have to have a prospectively collected right I need to have the treatment and outcome data all done together so what do we need to do to get to that ideal we need to do a study large study and you don't have to worry about privacy because it's going to be anonymised hopefully and you would collect environmental information which is available you can get met office information you can know what the pollutant levels are you can know how much pollen is in the air you can know whether someone is going to get too much asthma in the population at any one time so I think we are at an age where we should start to collect data and samples and data from all sorts of things not just the clinical data the environmental data collect information about diet, behavioural scores there have been one or two studies that have started to do this co-hort which is a European wide thing but also we've got an established epic co-hort in Norfolk that's a really good example of something that tried to do something like that started off in that way what's different today is that we know how much data we can collect and it's a lot and we need to structure that data so that we can get the next generation playing with that data asking questions of finding the models developing the models and going that's a high risk population over there, they need to come for colonoscopy that's a high risk, they need to have lodo CT but it's a big study I think the UK is one of those places that could do it but we need to have the patients and the public on-site because these are not people who are sick yet these are healthy people and so trying to motivate healthy people to come into a study is always a little bit harder but hopefully we should go out there and educate the public and try to get them involved Outstanding stuff Serena, thank you for the talk If money was no object what would be next what's your dream project I do not have unlimited money with me by the way He's from one of the funders What a great question Where shall I start I mean like I've got a massive list I'll have to call you separately So I think we've already heard some suggestions in the room today one of that is more towards using genomics or any kind of omics I focus on genomes today but actually you can use all sorts of omic data but actually you can use all sorts of omic data so one bit is to really improve precision medicine properly precision medicine right so getting those patients stratified up right up front, sorting them out into clinical trials where we'll need approvals and people to be happy at MRA level regulators etc and then also having the pharmaceutical industry involved as well where they're happy to give out the drugs in this context because what you heard from Professor Tsat there was people's studies in a very particular way they have a very set mindset and they also want to see the best results where patients are the sickest so if a lot of breast cancers are cured they don't really care, not really interested and we need to slightly change the mindset because yes 70% of breast cancers are cured but they're not all cured and so can we improve precision medicine for people with early disease but that prevention question was also a great one if we really want to improve health outcomes we know that we've got to hit we've got to detect cancers as early as possible so I think that would be a nice space to try to explore we've heard about the whole genomes there's still plenty more we don't understand about the whole genomes and the germline and so when we have if we're able to do a systematic study along the lines of what Professor Farouk has just raised I think that would be a really nice step forward I think that would be really enabling across the population across the England and Wales wide map this is great first one here and then one at the front here so the example you gave of the angios sarcoma as I understand it from what you've explained to us before actually the mutational signature had a lot in common with a malignant melanoma do you think that when metastatic cancers are sequenced and is this a frequent thing to end up with a mutational pattern that suggests that that is similar to another one and do you think that means that for instance a tumour like that actually did not actually arise from angioid tissue even though it looks like it histologically and actually what you've discovered is that it is actually just a very peculiar looking melanoma treat the biology rather than it may well have been a melanoma originally but we should treat the biology rather than treat it based on tumour of origin now we started with treating tumours based on tumour of origin but that is perfectly reasonable for that time that's the best we had and I think it's perfectly reasonable if you have a breast cancer you're going to send them to a breast person they're going to treat that appropriately if you have an angiosarcoma you're going to treat that appropriately but if you have new information that now tells you this angiosarcoma is not a typical angiosarcoma so the biggest limitations people will not come off the standard protocol they will stick to that guideline because this is the guideline and they're going to go through it and then they're going to say okay unresponsive to anything now let's look at the genome I think that's too late I think we need to start to shift the mindset we train our juniors to think like that but I think there's in fact you've raised almost two points really one is educating our next generation to think about how you adjust to have new information and this is quite new information but also just educating the workforce in general I think a lot genomics has not been slightly imposed on the NHS we have to sort of implement it and we have to do it but we haven't provided people with the tools to interpret, to analyse and to bring it back to patients then we haven't provided them with the tools to deal with the results so I think there's a workforce thing we have to do to try to upskill to educate the next generation but I think you're really right you hit this on an issue that I've hit recurrently almost weekly at the moment which is the mindset shift that needs to happen to get people to come off that sort of standard protocol thinking From a non-medical background I'm very concerned a family member, my brother four years ago he had colorectal cancer the initial treatment he had in a hospital in Leeds he lives abroad in Andorra so subsequently he's been monitored and lesions, nodules were found last year now should I possibly suggest genomics sequencing for him as a way to monitor potential cancers or is that too extreme for me to interfere and he's at a very premier hospital in Barcelona so I don't know if you can give me any advice that I can hand over to my brother so thank you for the question I'm very sorry to hear about your brother and he does sound like he's in very good hands now the vast majority of oncologists are doing fantastic jobs and if he's in Barcelona he's very likely to be in a very very good environment as well genomics is offered in many places actually and sometimes in colorectal cancers it is done and it's a little bit dependent on which country in which hospital so a lot of places offer it without even necessarily the patient knowing sometimes it's sort of just part of the in the NHS we do is so it may be that your brother and actually many NHS patients already get some level of genomics they may not get this level of genomics but they're already getting some level of genomics I'm sure your brother isn't very very capable hands but you know if you wanted to sort of have a separate conversation offline I'm very happy to have that conversation I think it's time for the next few weeks and congratulations so it's a great pleasure to present you with the Francis Crick medal and to thank you warmly for the wonderful lecture that you've given us this evening the food for thought and the vision of what treatment in the NHS for cancers might look like hopefully in the near future so congratulations go forward