Good afternoon. I'm Carol Christ, Chancellor of UC Berkeley. It's my privilege and my pleasure to welcome all of you to this year's 109th annual Martin Meyerson Faculty Research Lectures. It is so wonderful to see you here in person and to be able to gather together for this event. For more than a century, Berkeley's Academic Senate has singled out distinguished members of our faculty whose research has changed the trajectory of their disciplines and of our understanding. These lectures shine a light on an essential part of our mission, the creation of new knowledge. The curiosity and creativity that fuel the quest to learn and to understand are at the heart of Berkeley's core commitment to making the world a better place through what we discover, through what we teach, and through the public service that we provide. This year's pair of lectures represent the continuation of a treasured tradition that is recurred annually with one exception. In 1919, in the wake of World War I and the influenza pandemic, the faculty research lectures were suspended. Unfortunately, there was no Zoom back then and our predecessors were unable to do as we did last year as virtual events were in vogue and these lectures went on. I should note that today's lecture will be recorded and available to view on YouTube or from the faculty research lectures website for those who are unable to join us today. Being selected to deliver a faculty research lecture is rightfully seen as one of the highest honors that Berkeley can bestow on its own, on a member of our faculty, to stand out among peers who exemplify academic excellence as no small thing. For students, members of the campus community, and the public we serve, this is all a unique and wonderful forum that offers access for all to scholarly research of the highest caliber. I'd like to ask that you join me in welcoming the past faculty research lecturers who are with us today. Professors, please stand as I read your name, but let's hold our applause until everyone has been recognized. The two individuals chosen by our Academic Senate to give the 2022 faculty research lecturers are David H. Rowley, who will speak to us today, and Timothy Hampton, who will deliver his lecture next Monday. A few words of introduction for Professor Rowley. David Rowley is distinguished professor of molecular and cell biology and the Esther and Wendy Scheckman chair in cancer biology. He also serves as faculty director of Berkeley's Immunotherapeutics and Vaccine Research Initiative. He's been recognized with many awards for his scientific contributions, including election as a fellow of the National Academy of Sciences and being named a distinguished fellow of the American Association of Immunologists. Our university has established itself as a center of breakthrough research in cancer immunotherapy, starting with the pioneering work of former faculty member and Nobel laureate Jim Allison. Today, David Rowley is not only carrying on this tradition of innovation and discovery, he's taking it in exciting new directions. Rowley's research addresses how the immune system recognizes and responds to cancer cells and virus-infected cells. While his early work focused on T lymphocytes, current research focuses on another cellular player in our immune system, the natural killer cell. Related to T cells, natural killer cells employ completely different strategies to attack cancerous and infected cells. And Relay has discovered essential ingredients of their capacity for recognizing and destroying many types of cancer cells. Relay's recent work aims to understand why in some cancer patients, natural killer cells fail to activate or become inactive. Part of his ongoing quest to decipher the mechanism of cell activation in order to devise therapeutic approaches that will mobilize natural killer cells to eliminate cancers. Today, he will present a lecture entitled Not All Killers Are Bad, How Natural Killer Cells Protect You from Cancer. Please join me in welcoming to the podium Professor David Rowley. Thank you. Thank you Chancellor Christ. And thank you for being our Chancellor by the way. We love you. Yeah, I'd like to talk today about natural killer cells and how they protect you from cancer and to explore ways that we may mobilize them for immunotherapy of cancer. To start with, I must disclose relationships with the biotechs that I have here. And I want to start by really acknowledging all the wonderful colleagues we have and that have made such an impact on the research in my laboratory over these I don't know, 30 years that I've been at Berkeley. So here are the colleagues in the division of Immunology and Molecular Medicine. It's really been an incredible experience to be around these people. They've had such an impact on our work and I'm really grateful for that. In addition to the current members I want to acknowledge former member Nelab Shastri who passed away recently and of course Jim Allison who Carol mentioned at such a formative role in our division and also in cancer therapy. And I also need to acknowledge the larger molecular and cell biology department I brought this slide from Rebecca Heald. So it's really a compilation of the much larger department and the impact of all the different kinds of research that goes on here at Berkeley and it's been incredibly informative for the work we do and I'm grateful for that as well. And finally of course I have to thank my laboratory and this is a historical talk. I'm going to cover some decades really of work that we've done in a sort of narrative form and I want to acknowledge all the people in lab who've done this work over these many years I think probably missing some people in these various group photos but this covers a lot of the time. So thank you guys. I don't know about everyone's work but the work we do is informed by the work that all these people did. And this is a recent group photo in a retreat that we had and I have to shout out to Lily Zong who's the manager of our lab who's been with me for 22 years now I think and really makes lab run well and she's I have to say puts up with me so that's a good thing. And in that light I have to acknowledge my family as well who are here. Michael and Gabriel and what they put up with me too and that's great and actually my sisters are here as well nice and recent and they put up with me for a really long time so thanks for that. So the main take away I'd like to make today for those of you who are not in the field is that curiosity driven studies of cellular and molecular mechanisms drive the invention of new medicines and I'll take you through our work that we hope is leading in that direction and mention as well some other work along the way. So I'm going to do a kind of deep historical dive here into the immunology and going back now to actually the 1800 when Jenner invented the smallpox vaccine and of course Pasteur invented several other vaccines in the late 1800s and what's remarkable about them is that they really had no concept of an immune system dedicated to protecting us against pathogens or cancer. They really didn't know how vaccination worked but did work and so they exploited it and it really wasn't until around 1899 or 1900 that antibodies were discovered by von Bering and over the next years immunity was attributed largely to antibodies these proteins that can recognize things or to phagocytes which are cells of course that eat other cells and it wasn't until the late 1960s that the era of cellular immunity was born really with the discovery of T-cells and these are cells that kill other cells in our body and the concept is that killing infected cells prevents a virus or other pathogen from replicating within that cell and therefore can limit the infection and killing nascent tumor cells obviously has obvious value as well. So this background I want to introduce T-cell recognition because it's really the starting point for my presentation so T-cells recognize what we call antigens and antigens are what the immune system recognizes so I think spike protein of COVID and COVID's been great for educating people about immunology a little bit but what's key in this recognition and different from antibodies is that it involves these proteins called MHC proteins that are displayed on the surface of every cell in our body so that's the blue thing here and they are antigen presenting molecules and what they do is they survey the otherwise hidden internal contents of cells for antigens and then they present them on the outside membrane of the cell for recognition by the immune system so in the example of a viral protein that's a long polypeptide chain it's degraded into peptide fragments which bind to this protein then they're displayed on the outside and then T-cells have a so-called antigen receptor which enables them to recognize that complex and they have different T-cells have different specificity for different antigens and when it interacts it's a... pathogens can run but they can't hide and inside cells is my point there so these antigen receptors are triggers and when they recognize and bind to the complex here it triggers them to dump cytotoxic molecules onto the surface of the target cell to kill it so that's really how both T-cells actually and antigen cells kill target cells although as we'll see the receptors used are different so now this was our understanding as around in the 1980s but it rapidly became clear there's many other molecules interacting between T-cells and other cells not just the T-cell antigen receptor but other molecules and many of them but there was some that were particularly interesting and that were being studied in the early 1990s by many of us and one that Jim Allison seized on was this receptor protein called CTLA4 and it's on T-cells and Jim proposed that it engages molecules on other cells and in so doing that it inhibits the T-cells so this was a different idea it was actually preventing killing of a target cell and he made this specific jump then to suggest that maybe this inhibits T-cell responses to cancer and that perhaps you could block that interaction with an antibody against CTLA4 and therefore unleash the T-cell to kill the cancer cell so this was the concept you block that interaction the inhibition is gone and now the T-cell can kill the target cell and remarkably that worked in the first animal experiments he did and that led to the development of this approach in industry and I have to give a shout out to my colleague friend Alan Corman is in the audience who was working at Metarex and then at Bristol Myers Squibb and who oversaw the development of a human antibody that did this and this of course turned out to be very important therapeutic the first checkpoint therapy these are called checkpoint receptors and this shows clinical trial data 10 years out for patients with metastatic melanoma with chemo there was very little help for these people but a fraction of people about 20% would survive essentially permanently with this therapy and indeed if it was later combined with additional similar approaches to block other checkpoint receptors the outcomes have been improved really substantially greater than 50% long-term survival so this really caused a revolution in cancer therapy I think I would argue it's the single biggest cancer therapy breakthrough in I don't know 50 or more years not more and of course Jim was awarded the Nobel Prize for this work but it's also caused a revolution in the field of cancer therapy and in the biotech industry as well so the I should point out that this therapy is also effective against numerous other types of cancer but not all cancers so there's still a huge unmet need for treating cancer patients beyond this so we were studying and I want to go back in time a little bit earlier so we were studying T cell development when this story I'm going to tell you was initiated and we wanted to define how killer T cells develop from immature cells and then become activated during an immune response so this I would say was curiosity driven basic science question and the question we asked was what role does MHC play in T cell development and to do that we wanted to test it in mice useful animal for immunology research with mutations so that they were in MHC components so that they did not have any MHC on their cells so we were fortunate that we collaborated with Rudolf Janisch a colleague of MIT where I was at the time and Janisch was one of the first to successfully generate a so-called knockout mouse where you could take a normal lab mouse and through a feed of genetic engineering and a lot of time and effort you could generate a mutation in a component of MHC for example and then generate a pup that lacked that finally a mouse strain that lacked MHC proteins on all their cells so this was a very important technique and it's been replaced by Jennifer Doudness CRISPR technology more recently which is much easier to do so it took it by storm quickly so we studied these mice in collaboration with the Janisch lab and we showed that MHC deficient mice lack killer T cells altogether so not only is MHC important for presenting antigens but it's important for the development of T cells from immature stem cells and that was an interesting and important finding and we wanted to understand it better and so we did so and of course this led to a chance observation that diverted us and this was worked by a graduate student in the lab, Mark Bix and a postdoctoral fellow, Nancy Leal and they decided to investigate the following question do the T cells themselves need to have functioning MHC genes or do they need to interact with MHC displayed on other cell types and so this was important for various reasons and to ask this question we decided to perform bone marrow transplants from MHC deficient mice to normal mice so this was a way you could have bone marrow from one source and the rest of the animal from another source and so the idea was to prepare bone marrow cells from MHC deficient mice and that has all the stem cells that form all the bullet cells and then you would take a normal mouse that has MHC on all its cells and you treat the mouse to destroy most of its own blood cells and then you give it the transplant to replace the blood cell system with the donor type stem cells and then they would of course develop into mature cells with time and you could then see what happened well we did this experiment and much to our much to our surprise the mice receiving the MHC deficient marrow cells became very ill 7 to 10 days later and we had to euthanize completely unexpected we really didn't expect that to happen so the question was did the what happened here and did the MHC deficient bone marrow cells fail to engraft and the symptoms and the timing of this syndrome were consistent with bone marrow failure where donor bone marrow cells fail to engraft and you ultimately get anemia as a consequence so we thought perhaps the MHC deficient marrow cells were being rejected by the normal mice sort of transplant rejection type response but bone marrow cells the bone marrow cells we were using lacked the MHC molecules and normally you need MHC molecules for graft rejection that's what T cells actually target in graft rejection and also the recipient mice should lack T cells due to the pre-treatments we perform so they don't have any T cells in theory at least so it seemed unlikely that T cells were responsible but there were hints that another type of cell was involved the so-called natural killer cells and I'll tell you more about that in a minute before I introduce natural killer cells in a little bit more detail so what are they? they were discovered in the mid-1970s as cells lymphocytes like T cells present in mice or in humans and they killed tumor cells that was how they were initially defined they were called natural because they were present without previous exposure to the cancer cells as late as 1990 with the understanding of how they recognized cancer cells we had nothing about the receptors they were basically as we say naked cells no known antigen receptors we had no idea how they would recognize any target cell they killed numerous types of tumor cells which you would think would be exciting but actually immunologists at the time didn't like that because they were fascinated at the discriminatory powers of T cells or antibodies and so they considered these cells boring if you will and as a result there was really not that much interest in them and others were actually even skeptical that they existed they thought there was some sort of artifact or some kind of weird T cell or something like that so they were the Rodney Dangerfield of cells and I was raised in that environment where we disrespected natural killer cells and I was not particularly interested in that but we thought maybe they have a role though in this bone marrow rejection phenomenon and to test it we asked whether if you deplete NK cells from the recipients do you reverse the rejection and indeed that was the case so the way we could do this is you can deplete NK cells by injecting specific antibodies and a day later all the NK cells are gone so here's the experiment we can quantify the amount of graft acceptance here and when we transfer normal bone marrow to normal recipients you know very nice graft acceptance but again the MHC deficient marrow was actually completely rejected but if we depleted the NK cells immediately before the transplant the grafts were accepted perfectly well so that made the strong case that NK cells were indeed rejecting these MHC deficient bone marrow cells and suddenly they seemed extremely interesting to us it was claimed that they were nonspecific and did not recognize MHC but clearly MHC did influence the rejection of the bone marrow cells by NK cells so MHC was recognized in a way but the mode of recognition was very different because immune cells were thought to recognize the presence of foreign things right that's how we generally think about it but NK cells somehow detected the absence of a self thing so really they flipped around in terms of what was going on here so how can that even work it was unexpected at the time so the hypothesis arose and by the way we didn't do all this work in isolation there were many people that were working in the field but I don't have time to call out everyone in the field but there was a hypothesis that NK cells have a new class of receptors that bind MHC and the idea was that these receptors instead of activating the NK cell inhibited them and this by the way was before Jim Allison's work so the notion of checkpoint inhibitory receptors was not out there if anything Jim may have been influenced by this kind of work but this concept was completely new that there was inhibitory recognition and that the MHC binding would actually inhibit NK cells and prevent them from killing target cells so this is one way this could work and this was called a missing self recognition where self is self MHC so other groups actually came up with the first good candidates to be these receptors right there was a search for receptors like these and the live 49 and here I won't burden you with the names of these things but they were identified subsequently and they were good candidates but it was worked by a postdoc in the lab Werner Held who's now a professor at the University of Lausanne who that really demonstrated that these function in vivo to inhibit NK function and he did it by introducing one of the receptor genes into a mouse chromosome in a manner that resulted in all NK cells expressing that one receptor and that receptor bound to a specific MHC variant and he found that NK cell killing of target cells with that MHC variant was prevented so this was a genetic demonstration of the receptor inhibited recognition this worked both in cell cultures and in intact mice so this supported this idea of inhibitory recognition so what's the biological purpose of it well killer T cells recognize viral antigens and tumor antigens presented by MHC proteins and this exerts a selective pressure for loss of MHC by mutation for example and you can imagine that happening in a cancer cell mutations in MHC and they no longer express that they become invisible to the T cells and viruses it turns out frequently interfere with MHC expression to try and hide from the T cell component of the immune system in doing so the cells become sensitive to being killed by NK cells and so you can appreciate that NK cell killing of cells like MHC is an evolutionary countermeasure that we've evolved over the years okay so that was really important steps that we and others made in the field to get this model of inhibitory recognition but is that the whole story and we thought it couldn't be we thought NK cells must have activating receptors too because in order to be triggered immune cells generally need some kind of activation receptor and so the hypothesis was there was some unknown activation receptor on NK cells that engaged some kind of molecule on a target cell and just for nomenclature purpose a thing that a receptor binds to is called a ligand so I'll use that term subsequently a ligand is what the receptor binds to so we thought there must be activating receptors and we thought that they must recognize ligands on target cells and we wanted to identify both of those but NK cells don't have T cell antigen receptors and they don't have antibodies so is there something else involved that was the question so here's where great graduate students make a big difference and I've had many great graduate students some of them are here and this one took advantage of what was happening in terms of gene sequencing in the late 1990s before the full genome sequences were done random gene sequences in the late 90s began to be deposited weekly in large numbers into online databases and Russell Lantz who many of you know is our colleague here at UC Berkeley an esteemed colleague in our department at the time was a graduate student in the lab and he searched the database weekly for genes with sequences related to the inhibitory receptor family he was working on he did this on his own volition not on my suggestion and one day up popped this sequence NKG2D and NKG2D was fascinating because it had features suggesting it might be an activating receptor and we really immediately took interest and began to pursue it so we asked ultimately does NKG2D bind to something on tumor cells is it a tumor recognizing receptor and this is how we did that this is the notion of a membrane a plasma membrane of an NK cell and here's the NKG2D receptor anchored in it well it turns out by engineering you can readily generate a version that's truncated and is soluble it's not anchored in the membrane anymore and then by doing a little bit more engineering you could create a what we call a multimer but it was basically just several of them and also it's attached to a fluorescein reagent so it makes it light up and it's fluorescent so this could be a reagent as we call it to stain cells and if it stained cancer cells that would suggest that NKG2D binds to cancer cells so this was such an experiment and this is the machine we run to test this called a flow spectrometer and on this axis is the intensity of the staining and this is just the number of cells in each channel and the main point is that the normal cells had negligible staining with this reagent but the tumor cell line shown here stained very intensely all the cells did essentially so this suggested indeed that this receptor does bind to tumor cells and in fact it bound to 11 of 14 tumor cell lines that we tested and most primary tumors were stained as well so this is very frequently on tumor cells whatever it's recognizing and this immediately made clear that NKG2D sees cancer cells and then of course it becomes very interesting to ask what does it see exactly on the cancer cells and we wanted them to identify that so to quote the Taj Mahal the musician, many fish bite if you've got good bait well we had this staining reagent that's the bait and we could try and use it to pull out the fish which would be the gene that encodes the ligand the thing that the receptor binds to so basically the way that works is that the messenger RNA from the tumor cell is complex there's 10,000 different kinds and messenger RNA encodes proteins and we reasoned that maybe there was a protein that was being recognized and but only one or a few of those 10,000 different kinds would bind to NKG2D so it's a needle in a haystack and you have to find a way to find the one or few that are in there that actually do that and so the way we do this without going through details is what's called expression CDNA cloning and we did that and we succeeded in pulling out some gene so this is a method to use the bait as the NKG2D to pull out the proteins that bind to NKG2D so we pulled out a couple of different proteins one called ray 1 and H60 and again don't worry about the names I'll collectively call them NKG2D ligands what was remarkable was that there's a whole bunch of them ultimately once they were all defined each of us humans has eight different versions and they're distant relatives of the MHC proteins that I've already told you about and they all bind to NKG2D and what we then showed was that one of the genes was introduced into rare tumor cells that lack all of them the tumor cells are now killed by the NK cells so that was a kind of principle and indeed if we put those tumor cells into mice the mice rejected those tumor cells so another line of evidence that this is a NK is a cancer recognizing system came from a graduate student Amanda Jamison who's now a professor at Brown University and what she did was to generate a monoclonal antibody that binds to NKG2D the idea was if NKG2D is an activating receptor involved in cancer cell recognition then an antibody that binds it may block the tumor cell recognition and tumor cell killing so she made a monoclonal antibody she took six tries in two years of spending a lot of time in the tissue culture hood she finally succeeded and she named it Mission Impossible 6 or MI6 and it proved to be a quite useful antibody and this is one example where what we're doing here is asking whether NK cells kill tumor cells so we're adding in a tissue culture well we're adding more and more NK cells from left to right and then we're testing the amount of killing of the tumor cells in the tissue you can see in blue that goes up now you repeat the experiment but in the presence of the antibody and you can see it completely blocked the killing so it doesn't always block this successfully because it turns out that NKG2D is one of several tumor recognizing NK activating receptors but one of the more interesting one okay so that was nice finding currently in this system we could say NKG2D ligands are kill me signals and then the question becomes what turns them on and they are self proteins they're encoded by our own genes so it's not foreign recognition the genes are off in normal cells but on in tumor cells and so what cellular signals turn them on in tumor cells and why became a question now we knew that cancer cells are highly dysregulated in numerous respects and this leads to cellular stress and the idea emerged in the field that maybe stress pathways induce NKG2D ligands and we decided to really begin to explore that so to get a little more detail cells have what are called pathways or mechanisms to counter stress excessive heat will activates the so called heat shock stress pathway unfolded proteins or amino acid starvation activates the integrated stress pathway and damage to the genome to DNA activates the DNA damage response stress pathway and we wondered whether those pathways were involved here so a postdoctoral fellow Stefan Gasser joined the lab and he decided to investigate this and he began to specifically explore the role of the DNA damage response pathway so let me just summarize that pathway briefly what happens here is that there can be errors in DNA replication as cells divide but also there can be damage to DNA from irradiation or DNA damaging chemicals those can cause breaks in the DNA in the genome and these are these lesions these damage is detected by some sensor proteins that are called ATR and ATM so they detect the damage and then they activate a cascade of underlying events phosphorylation events and this leads to two things first of all you stop the cells from dividing so that you can do some work on them and you induce the expression of DNA repair functions which attempts to repair the damage and repaired cells can then go on their merry way but the cells that are too seriously damaged may undergo senescence or program cell death so that is the general features of the work that emerged around the time we were doing this these studies showed that this pathway is often activated in cancer and the reason is that in cancer DNA replication in cancer is unscheduled in effect and errors occur frequently in DNA replication and cause damage and that can activate this pathway constitutively in many cancers it is on all the time in many cancer cells so that became quite interesting that this could be a way to detect cancer and what Stefan showed that indeed this pathway induces expression of NKG2D ligands on cells and therefore killing by NK cells and this was a really interesting finding because it connected this pathway which had been seen really as kind of an intrinsic pathway to mobilization of an immune response for the first time so here's a way you could link cancer to expression of these kill me proteins and Stefan showed this in two different ways one was by starting with normal cells and then damaging the DNA in them and showing they gain NKG2D ligands and that depends on these ATR and ATM proteins or you take cancer cells which express NKG2D ligands all the time and then you inhibit these sensor proteins or you temporarily knock them out or something like that and you sow loss of NKG2D ligands so that together made the case that indeed this pathway is important for expression and so you have this picture then of a normal tissue with a lot of cells and then there's some tumor formation one of these cells goes awry and begins to accumulate and that generates DNA damage stress that causes expression of these ligands and enables NK cells to now try and kill those tumor cells and then work by two subsequent members of the lab graduate student Benjamin Gowen and graduate student Haeyun Yong investigated other kinds of stress and showed that the unfolded protein stress response would induce one of the ligands or hyper proliferation would induce another of the ligands so this seems to be a system to detect different kinds of stress and to enable NK cell killing as a consequence of that okay so then there's a picture emerge then of NK cells with both activating and inhibitory receptors and in stress cells you increase the expression of the stimulatory ligands and that can activate the NK cell to kill but you still have these inhibitory receptors which can either partially or even completely prevent that but then many tumor cells lose MHC1 it turns out and so then that inhibitory signal goes away and you have a killing due to lack of inhibition in this case so it turns out basically that loss of MHC can be sufficient for being killed cells that strongly express these ligands that can be sufficient for being killed and when both things happen the cells are really killed very efficiently they're the best targets for NK cell killing okay so the next question we asked does NKGGD play a role in immune surveillance so the notion that the immune system often stamps out newly arising cancer was first proposed by I believe by Paul Ehrlich in the early 1900s and this idea was wax and waned over the years but by the 1970s a study appeared suggesting the answer was no and it kind of lost favor and for 30 years no one thought much of that idea but then around the year 2000 Robert Schreiber and his colleagues basically resurrected the theory and specifically implicated T-cells in immune surveillance of cancer in some cases so that was an important finding but building on our findings we hypothesized that NKG2D and NK cells play a role in immune surveillance of cancer in some instances and we wanted to test that so Nadia Guerra a postdoctoral fellow joined the lab and she's now a faculty member at the ICRF in London and she decided to investigate this question by disrupting the gene that encodes NKG2D in mice so here again we use the same method I showed earlier where you can disrupt a specific gene and in this case she disrupted the NKG2D gene so these mice she could make a mutant mouse strain that specifically lacks just the NKG2D gene now to study cancer incidents in these mice we would have had to look at probably thousands of mice to get to look at fully spontaneous cancer so the way to get around that the way we do the experiments is to equip the mice with oncotrans genes and these are basically oncogenes cancer causing genes that increase the incidence of specific types of cancer and then to ask the question if we compare the normal mice that have normal NKG2D or they're NKG2D deficient but they have the oncotrans gene in both cases would this lead to more cancer or more severe cancer so you age the mice and you monitor cancer incidence and severity so Nadia did this study and first she investigated a model of prostate cancer and what we found is after 10 months with this model there's a low frequency of massive early arising adenocarcinomas in the mice with normal NKG2D but in the mice that lack NKG2D it was several fold higher incidence so really making the case that NKG2D is suppressing cancer and then she also did it in another system this was a model of B lymphoma and in this case with time the mice with normal NKG2D get all get lymphoma but they get it faster in the NKG2D deficient mice so both of these studies support the idea that NKG2D recognition helps to eliminate some cancers spontaneously but obviously not all of them so if NK cells can kill cancer why do they so often fail that becomes the question and what can we do about it so we think that one reason for failure is likely to be poor initiation of the NK cell response and that's because immune cells generally when in the absence of an infection or a tumor or etc. are in a so-called naive state where they have little functional activity they're kind of minding their own business they really have to be activated to become active killer cells and that wasn't actually thought to be the case for NK cells but we developed evidence that it clearly is the case that NK cells require this sort of event as well and the signals that do this activation are called innate immune signals and they emanate from either the recognition of pathogen molecules or other kinds of events that signal something is wrong and that can result in the production of goodies if you will you know cytokines and other signals that lead to the activation of the NK cell cytokines I should say are basically hormone like proteins that regulate immune responses so we think we began to study this and again it's great that we have such great colleagues here while Russell Dan is now a faculty member and Dan Portnoy both members of our department have done pioneering work in elucidating what's called the Sting Pathway and this is one of these innate immune pathways where the presence of pathogens through a series of events results in the production of what's called a second messenger a small molecule inside cells it's called a cyclic dinucleotide and I'm just going to call it a CDN from now on and those small molecules bind to another protein in the cell called Sting stimulator of interferon genes and that leads to the production of all these goodies and those goodies activate immune responses so that really is kind of how innate immunity works more broadly but the role of this specific cyclic dinucleotides is specific to this particular pathway so that was an important pathway for recognizing pathogens but evidence some of it from our lab showed that in fact this happens in cancer as well and it happens downstream of the DNA damage that I've already told you about that happens in cancer that you can generate signals that activate CDN production and activate this pathway so Asaf Marcus a postdoctoral fellow joined the lab and he showed that that pathway this Sting pathway is essential for NK cell responses to tumors for the spontaneous NK cell response to tumors so that suggested that this really was an operative pathway for activating NK cells in response to cancer but his evidence also suggested that this was a relatively weak and often insufficient pathway to strongly activate the immune response the good news was that there are other ways to activate the pathway and work from Tom Gajewski's lab and also from the group at Aduro Biotech including Sarah McWhorter but also Tom Dubenski and others showed that if you inject cyclic dinucleotides into tumors in sufficient quantities you actually super activate that pathway and you get very powerful activation of the immune response so this is a potential therapeutic approach for cancer then alright but before I show you some data another reason that NK cells fail we believe is the process of desensitization so here is basically years of work we've done that shows that persistent unresolved stimulation leads to NK cell dysfunction so the way you think about that in the context of cancer is you get an active NK cell it goes into a tumor if it can't completely eliminate the tumor it's persistently stimulated and they give up you know they they give up NK cells give up and they're dysfunctional they can't really kill tumor cells anymore well what we were investigating were ways to wake up these NK cells and that by the way it's still a very active area of research in my lab I think there's much more to be done here but Michela Artilino who's a post doc in the lab now a professor at the University of Ottawa decided to investigate whether he could wake them up and he tried to do it in collaboration with Chris Garcia a colleague at Stanford with a so-called superkind so these I mentioned cytokines are like hormone like proteins well Garcia's lab engineered in especially active form of one of these cytokines called interleukin 2 and he calls a superkind and Michela's work showed that this were versus NK cell dysfunction so we could restore some NK cell activity by injecting this superkind into animals so that led to the idea then that we could maybe do both things we could strongly initiate the response with the CDNs we could prevent NK cell desensitization with the superkind it's called H9 MSA or better yet we could combine the two things and hopefully get synergistic effects so these were studies initially carried out by Chris Nicolai and Natalie Wolfe to show that this maybe could work and the idea then was to inject CDNs into tumors and then inject superkinds into the animals and see if we could see therapeutic effects against cancer models and so this turns out works great for NK cells as I'll show you but it works very well for T cells too and we like the idea that we're getting sort of a 1-2 punch from both NK cells and T cells in this approach so here's just this is my last data slide where it could be applied in a very difficult to treat colorectal cancer model MHC deficient tumor cell there's no MHC on it so T cells can't even recognize it and it's very hard to cure but so what Natalie did in this experiment was inject the tumor cells the tumor becomes established and then she applies therapy beginning at day zero and with no therapy the mice are all going to succumb we euthanize them before they do but the tumors are growing with the CDN or with the superkind however there's massive delay in tumor growth so that was good news and in fact we cured about 20% of them in both cases so that was also promising but the most remarkable results were when they were combined where we basically eliminated the tumors and the mice were cured curative 100% taking these mice out for many months and there's no evidence of cancer and this was mediated solely by NK cells T cells played no role in this response so we were quite impressed and happy that NK cells could do such a good job on their own so then Christina Blage investigated an even harder to treat kind of cancer this is a sarcoma cancer induced in mice by carcinogens and sarcomas are an unmet need the existing checkpoint therapies that I told you about are ineffective in most sarcomas and you know so there's not much if they're advancing there's not much to do about them and indeed in this sarcoma model that Christina investigated the tumors would grow and as I said the checkpoint therapy was completely ineffective in even delaying the tumor formation but what was nice was that the CDN in particular had a major effect in delaying the tumors super kind maybe had a small effect but we didn't get any cures here the tumors grew out and all the mice but when we combined them we began to see this again this synergistic effect and about 12% of them lived long term seemed to be cured so that was encouraging and then we decided to say when you we knew that checkpoint therapy is most effective when the immune response is actually ongoing so the idea is we're making an immune response go here so maybe now the checkpoint therapy will help if we add it to the mix here and so we did that and that turned out to be quite remarkable when we combined the three agents we saw 44% cure rate in this model so that was really very encouraging so this you know we're obviously and we're actually exploring many different ways to to generate NK dependent immunity I should point out that this rejection was mediated by both T cells and NK cells so we're getting that you know one-two punch they work together in this case neither alone is sufficient so we're really mobilizing both kinds of immune responses okay so I'll just summarize here and I'm not really summarize but just make some closing remarks about discovery science because I think I think that I think that's a way to generate effective therapies when I was a grad student there was one biotech I believe Genentech and not much coming out of that at the time and developing new treatments was a dream we all had but it was rarely a reality we just didn't know enough and today there are thousands of new therapeutics that we treat myriad disorders we basically take them for granted now and at each step I would say the main driver has been discovery science curiosity driven so that's really the way these discoveries are made we had to learn to have an eye towards applications to ask what does this finding suggest a potential therapeutic approach Jim's example was a good one he asked that question instantly in his work and I think all of us now realize that we can do this now and try to do that but there's many more of these approaches to come probably accelerating dramatically I would imagine and I would point out the most important ones will still be based on curiosity driven research and the reason is we still have so much to learn occasionally you can do anything like we've done enough basic science now it's time to just cure things well that's stupid because there's so much we don't understand there's so much more research to be done and that's where all these new findings will come from and Berkeley is good at this I have to say when you think that I would say the two major therapeutic breakthroughs in the last I don't know how many years came out of Berkeley Jim Allison's work on cancer Jennifer Dowden's work on CRISPR which is going to have many many applications it's really Berkeley's an impressive place and not to speak of the work of everyone else here at Berkeley okay so I'll stop here and just acknowledge again I think I've shouted out all these people and I'd be happy to take any questions thank you very much Dave the the interleukin to the Super Chi I assume that's also promoting proliferation of the NK cells so is there any thought to from any given patient to take out their NK cell culture them so that the total number of NK cells that you could put back into the person to go after the tumor might that might be a strategy or not well there's many efforts now to expanding K cells ex vivo to take them out of patient expand them in tissue culture make them better even engineer them to make them better and then put them back in huge number of efforts like that are underway so yes that approaches is widely being explored not going to be able to hear you Doug so we know the T cells have a lot of all bad side effects because a lot of times you kill the patient trying to treat him with a T cell I'm sorry I don't hear very well too many rock concerts a lot of times the checkpoint therapy kills the patient so in the NK cells what are the sort of bad side to this great therapy you described yeah so there is toxicity associated with checkpoint therapy I don't know about a lot of times I think because I understand clinicians have become better and better at recognizing toxicity and you know backing off when necessary but there may be actually the notion in the field is that NK cells are likely to be much less toxic than T cell therapy but I think that remains to be seen in actual practice but they could apply therapy because they make cytokines that cause you know problems so called cytokine storms but clinicians have just gotten very good at recognizing that and backing off when necessary we have time for one more question hello professor thank you very much for your time and for speaking today I have a lot of questions but I guess like one that I would actually curious to know is that have you ever been in like I know you've been experimenting on mice in your research and there has been ever some times that when you're studying mice that research has failed and at what point when you are studying a certain study that there's enough failures that you just decide to pull the plug and decide to do some other research I'm sorry could you repeat the last part of that sorry I'm not making sense here but yeah at what point if there's enough studies that turned out that experimenting on mice have failed that you decide to just pull the plug on such research at what point does it fail did you say enough time I'm sorry I didn't understand I apologize oh sorry it's my bad I just don't sound systems here are never very good and I don't hear very well sorry I said again how can you judge a failure ah well when nothing happens that would be one one indication I mean it's I mean it's certainly the case that when these drugs are translated into humans they sometimes don't work often don't work actually so that's obviously a key step but of course that's not something we do in a university laboratory that has to be done usually outside and usually in an industry actually so that's a key step that there's often issues with because human cancer is a little different but again one of the reasons one of the ways we try and deal with that models of cancer in mice that are perhaps more similar to human cancer by for example the carcinogen model I mentioned is it really involves initiation of the cancer in a natural environment and that's really more similar to human cancer than some of the other models that people use in mice


Sup guys, it's me, it's Jacob. Welcome back to another reaction video. It's it's actually been a few hours since we watched episode number or since I watched episode number two But we're about to watch episode number three of three of the YouTube original series By Bazinga about Bazinga called how to be Bazinga and before we get into it There really is only one thing And one thing only that's that's left to be done Ba-ba-ba-ba-ba-ba-ba Let's go Here's how I became who I am. Teenage cancer trust have asked me to run under marathon And there's no other answer than yes. When I started this journey, there really was no athletic ability inside of this body And for often when it runs, there's no way to go to class. Come on, let's go! He had a point to prove, both to himself and the world I feel like an adventurer My left leg feels like I'm packing it I'm officially in the pits Oh boy Okay So he goes off-road in this episode So I've been a hunk because Now a bit of context A wee bit of context If you weren't here for the third to last video Or the second to last or no sorry The fourth to last, third to last or second to last You will not know yet that the reason why I'm looking this guy up And I might not look like him right now Possibly if I had a beard And I put these on I might look a bit more like him But look Many people out there have said And I'm going to say it's probably because of my display picture Not Because of what I actually look like in person Probably most likely because of my display picture Come to think of it It looks very similar to this man here His name's Ethan Payne He's doing a marathon for cancer For children's cancer Which is a massive achievement in itself He's gotten through a lot in his life That I've learned about in the last few hours And I would not have had any clue In fact looking at him right now Knowing that he's into fitness He did a marathon I think I would have thought that he'd been fit his whole life And that is absolutely incorrect He wasn't You just saw him before When I first saw that body I thought that's not the same guy But it is So he's got over a lot This documentary series has covered A lot of his life actually And it's been Really entertaining If I didn't like it I wouldn't have watched episode 2 or 3 We're watching the final episode He's going to run the London marathon He's doing it through Covid Which just adds another hurdle to jump over And I'm excited to see how he goes I really am The reason why I'm doing this The reason why I've looked this guy up Is because all of you guys keep on saying He looks like me Either I look like him or he looks like me Or I look like his dad And with that being said BAM I felt a little bit ill My fever went up, my temperature went up I was really hot and sweaty Sorry, not only are they Covid times But he got Covid Which means your lungs are compromised It takes a lot to recover from And he's about to do a marathon Let's go It wasn't pretty But I decided to take a Covid home test A swab test I had to swab the back of my throat And use the same swab To swab my nerves So now it's time to I thought that The only thing we had in common was our looks But we've certainly got a lot more than that In common and Whether that be Being absolutely and utterly obsessed With YouTube For years on end But still not finding happiness Or whether that be Running our first marathon within a month of each other There's a lot It's so annoying It's so annoying You know what's funny I thought that I wasn't going to I thought I wasn't going to look anything like him with the hat on He hasn't worn a cap this entire Video series But as soon as I put one on He does This shit's scary I shivered up and hide in my Herd and stuff What's funny in the words Literally undercooked But he's looking like I probably will Will I be ready in time for up to over the fourth I've told you anything Is that you can pull it out of the depths Also the whole documentary I've been screaming that I wanted to do a four hour marathon Can I even do a four hour marathon Well he did a tour He did an hour and 56 Half marathon I don't even know My own school We found every single classroom This room has changed Miss Collins Ethan in the car School has a kid Ethan was very funny Very very mischievous You know the rule of technique How Ethan was difficult to work with Like being with his mates Like doing what he liked doing What the curriculum wanted him to do That's what he was trying to get him to do The teacher went out of the room and everyone was like I heard him do something funny I went and stood on the teacher's desk My pants fell down He was my class clown Wanted to make everybody laugh The infamous Ethan science book story This is where it all happened Again from Marshall's Ethan had set off All the fire extinguishers after school How? So we figured out one day That you can Off these off the wall And I kept saying there's no way Ethan would do that And then the ceiling tiles started I'm sorry but someone seems to be Missing me non-stop and I don't know why Sorted He's not that silly And you don't really know another Found it hilarious Apparently the classroom was full of a bum The staff got very frustrated with him These are the drama rooms In the second door here I did the performance of the live tap I was quite creative and even the ones that had me doing actual things Was something I excelled in He was always the focus of attention This is my exact rhythm I still remember I was meaning that I just smashed it My drama teacher said yeah you've done a good job here The thing is I'm great at performing Basically my actual emotions I'm probably a little bit shitter Which is quite ironic What do you say Cease did you go? Ah none, well I've got a C And then once you're like okay I'm now reliant This is going to pay for the next stage You're like oh Probably should have done something Fuck me too bro Me too Although I didn't Heavily rely on it But I did want to get into university And the fact that I left high school Halfway through my last year Halfway through my last year of high school After killing it For at least three of those five I just completely and utterly lost interest Due to smoking weed every day But we move on We've been mind boggled At the thought of doing a marathon 26.2 miles So 12 years later We're here Are we at race day? We're all right then So throughout lockdown I messaged Finley and I said it would be really good to get a training session So we've organised A little bit of a special session So it's going to be a good one Finley, lovely to see you How was your lockdown? In terms of running Okay, sweet, so a bit of context Ethan's doing this for child cancer His mother Fought cancer This fella on the left Had A very rare tumour behind his eye At the age of 15 A cancerous tumour He too fought and beat cancer So he reached out to Ethan Said I've got a similar story Running the marathon too Let's loan cup Ethan said yes we will And now they have I wanted it to be Pretty much the same No gyms but we're doing a training session Like I said we would However we're up in the ante And this time we've got Double the Olympic champion Dan Kelly I'm looking forward to it So here we are with Dan Kelly The paces today I don't know So I've brought you here today To Olympic Park for a bit of a training session So we've got a one mile circuit So the key thing for that is your pace So set it as nine minutes You're not going to have a watch for this though So I'll time you when you get back to that mile Let's see how close you are There might be a few extras in there So a nine minute mile effectively 26 miles, 926s What's 1026s 260s What's 4 hours, 240s Because I'm not sure if you know my background As being a physical training instructor in the army Oh right Okay guys so you ready Remember it's a nine minute mile don't go off too fast Okay 3, 2, 1 Let's go I think I've got a nine minute mile in us mate It should be alright It's a bit faster than that Well done, not too bad A bit of pace this guy actually In the hip flexes We haven't seen the zinger sprint yet Until then In fact I want to give him a bit of clap Let's watch that form up the hill again That was quite good Have a look at this He's alright He's only 25, anything's possible So pull them up that's where you get tight across the chest Come on Yes you're feeling tired But you're going to keep going no matter what No one is going to stop you Good, keep it going now again There we go I'll try to simulate what it's like When you get tired throughout your run So legs get heavy See he's got big thighs right He used to get shit for having big thighs Because he had a big stomach Now that he's lost the stomach and he still kept the thighs They're just as powerful And we can see that So this is it you're on your line This is never going to be as hard as the journeys you've already gone through It becomes a mental game when you're running A year before I won my two gold medals in Athens I had the biggest breakdown of my career Because of emotional turmoil Or being injured and lots of other things that's gone on And I was getting many For a world champs And at that point I could have given up I wanted the floor to open up Jumping it and not see tomorrow But I did, I ran And I got silver medal at those world championships And no one knew what I was going through Our minds are so strong And experiences can make us stronger You know what I'm going to say is you're doing it for a fantastic cause You're also doing it for yourself Kind of going do you know what I've done something really important In my life that I put my mind to I set myself a goal I didn't give up on it and I accomplished it So I wish you the best of luck I think it's just inspiring Let's go boys I'll tell you what If he can pull out a four hour marathon I mean I'll be proud of him He's done so well He's incredible to hear And that four hour goal Is a really realistic title for him Four weeks away from running the marathon Not the most pristine circumstances But you know what it doesn't matter It's mindset, it's a mind game You just get through it, you get it done That's exactly what he was just told See all these experiences they've hit him with Throughout the six months leading up to the marathon All about Building your self esteem It's about building the belief in yourself Whatever happens you can do it At the end of the day all you need to do is finish this thing No one cares about the four hours It's all about getting from A to B Just like he said Let's go son Ten days until the marathon I've been wearing the same clothes All week Probably longer than a week now I'm ready to break free I'm ready to leave There isn't much left for me to watch I sit and watch YouTube all evening So I have just returned Because people don't know how it really affects people He said it's hard to Definitely put the thing down and say Nothing will happen He said that being up to the marathon now That I should try and just get in A little bit more of long distance running Analyse how I feel And into my training So start with skipping I'll tell you what He's been committed He's done well through Covid In the last one we saw him doing real dealt flies With books And that's something I've never seen in my life He's even got the doctor on his side I mean come on He's doing it virtually I was going to say How the hell would they have held it in October of last year Unless it was virtual Five best UK marathons For first timers If he's going to complete this task He deserves to run the full thing Out in the open Whatever course it's going to be A marathon at the end of the day is all the same distance Doing it somewhere else will just be like refresh rate It's like a change of scenery I feel like I've run enough of London by now That's a nice fade Okay so there's one thing I do need to confirm What is this necklace Because everyone seems to be wearing it I feel like that's probably That might be the Sidemen logo Yes it looks Looks like it is X What is that in Roman numerals Okay let's have a look X-I-X Sidemen Meaning Sidemen were formed on the 19th of October 2013 So it stands for the 19th Being 10 I-X 19 19th of October Boom Or should I say The sidelines Boom Even though I'm doing it by myself I feel like other people will be doing it by themselves And other people will be going through emotions It's like a group effort around the world Jesus This series took some real Twist and turns I'm gonna say It's not about that I suppose It's alright After today I've got my stuff laid out Time to get ready This is exactly how I thought It's exactly how I felt man I was making a vlog series Up to the time that I did my marathon I got up that morning I think it was six o'clock I went outside, had a coffee, looked outside Beautiful day Had my shit ready last night Had my number Although he's probably not gonna have one And I actually used these very shoes Believe it or not These very shoes Right here I bought them a week before going You guys warned me New shoes, new shoes Don't do it But it all worked out fine They look good Legs ready Holy shit look at those fucking We're all ready to run Legs ready Yeah he's got some muscles I don't know how scared or bad Nerves but I suppose not many people have done it So it's definitely a feat We are about ten minutes away from Destination and it's time to warm up And get focused and ready To put this 26.2 months And being able to do something for teenage Cancer Trust is one of the main Things I'm looking forward to I've just spoken to him on the phone He's all prepared, he's excited He's had a poo, so it was good I'm on my way He hasn't got a clue Oh nice One of the most exciting things about this Is the fact that the London Marathon Have actually enabled this run to count Because I've been adhering to Covid quarantine rules And they're allowing other runners And myself to do the run within the first two weeks And doing that you'll be able to get your medal I said four hours and a half But if I'm scoffing too much We'll try and step to the pace We'll try and step to the time and see if we can do it Okay so originally I thought that he was doing some cross-country training But that was before I knew That he was doing a virtual Off-site marathon It seems that he's doing A bit of cross-country Which is exactly what I did So he's going to do real well to get under four hours But if he does man I'll tell you what, if he This is what happened to me In the first 21 21.1km Which is 13 miles In Two hours and one minute And I thought right I'm feeling good My music is blaring I'm going well If I can just do that again I'll get four hours Now what happened was About 5km after that My time started going from 3 minutes per mile And whatever I did I just couldn't bring it back I mean I was so fatigued Is he going to have some fuel Because the thing that I didn't do Is take on enough fuel I didn't take on enough water And what that meant is that I was not only You know Completely out of energy But I was also out of Electrolytes And what that meant was that I started cramping Really bad at the 32km mark Originally which was 4 hours To about 4 hours 30km And then to 4 hours 55 minutes And in the end all I wanted I was actually running through cramps Which if you've had to do that I mean you don't do it, it's impossible You have to stop, but I had a goal in mind To finish this marathon Under 5 hours And I did it But it took some fucking doing I'm telling you that So let's go If I were to give you any inspiration It would be run, you big chubby bastard Good luck, you deserve it This is going to be another one In the bag You'll get through it if you pace yourself Keep going, if you don't smash it No matter where we are in the world We will always get you back Go on Ethan, you got this Go on, do us proud And always keep that bit grin on your face He knows in his heart he can do it Oh my god I'm almost crying bro I'm a little bit of an Indian I could promise you that Let's do it Imagine hearing that right before you hit out Like seriously Oh god Lovely bit of motivation Fuck you Just everyone, myself Everyone, it's a cool one It's a cool thing, celebrate your friends Celebrate each other, enjoy Everyone's got their achievements And this will be mine It's here now, it's just Point A to B It's a long way His mum's going to be there at the end Oh god Oh mate, I'm almost crying already Actually I am Finally got this That's really interesting When I was training It would take me about 10 minutes To start sweating Usually And that would be Around about 1.8Ks Or something like that 1.9 maybe even 2Ks 1.9Ks But that Jogged my memory because of the fact that It was pretty much like clockwork The instant I started getting those beads on my head It would have been about 10 minutes Into the run Interesting Now it gets pretty Here we go Look at that We're in the grasslands Terrain's going to be a bit slipping out Actually it's 4 miles here This is mental Yeah, that's not easy That's not easy Not at all That'll certainly make it harder Now this is This is going to slide me down Look at this Oh no Oh Please I should have discussed it I feel like I need to poo I feel like I'm going to have to poo But desperately Holy shit Unless you have to do it It's so easy I'm going to burn back That's a wrap I'm going to stop recording It's safe this It's safe My wagon one was the night before Running a marathon Well he ain't making 4 hours I tell you that much He's running on sand Oh my god he hasn't made it He has not made it easy on himself Not whatsoever Oh no Just over 14 miles done But definitely In the head now Your feet hurting We've got to go through the different terrains again But yeah, it's going to be tough Ethan likes to be on the win inside But he's also ready to accept When he's lost My feet are killing me Feet are f**king rote Feet are f**king rote Just as I got to the 15th mile It happened Done, absolutely finished My left lower calf was just complete spasm It was just like stiff My left leg feels like it's packing it Not great I feel like I'm officially in the pits Bro I tell you what If you've got some cramps going on I mean where's your Where's your water bro I'm sure people are giving you water You've got a camera crew with you but sh** I mean this is going to be interesting Oh boy He's out there in some really heavy Muddy terrain So I'm a little bit anxious that he's tiring him out Bro This is not ideal This terrain is absolutely Caning my feet He wants to push himself He knows he can do it The calves mate, the calves Okay, so they're following him On a quad bike with the camera Got it That makes sense My left leg's going My left leg's going up Decent bit of a gradual hill here F**k he's limping pretty hard isn't he So people get injured in these things man They just keep pushing, keep pushing Because you've got to finish but you'll push yourself through an injury You know what I mean He's just so determined To prove all the doubters wrong And prove to himself That he can do anything F**k man, I'm the same My legs are completely done But with anything He'd be thinking how far he's come Or that he's achieved Definitely thinly My cancer journey or what we went through It'll all be running through If I cry don't hold him against me Because I know what it's like to finish one of these things man But earlier This hill was going to be hell It's going to be hell It is essentially like a landmark In his self-improvement And this is the proof of that work Confirm my legs sputhing My groins My hammer strings My groins My hammer strings At this point the main thing for him Is how can I improve myself 24 miles My groin, my hamstrings That actually reminds me The cramps actually Travelled across different muscle Groups in my legs First they started in the calves Then they went to my hamstrings Oh my god, I tell you what That ain't good Then they went to my adductor Then my quad And then back to my calves And in the end I was running through calf cramps For the last 2.2 miles So I feel his pain man If he gets under 5 hours At this point I was like fuck man If I get under 5 hours I'm happy And I did 4 hours 55 I got So let's see how he does 2.2 miles, that's 3 3.5k's He's going to be the hardest 3.5k's he's ever done I'm telling you This is it baby Lots chance He shared this journey Yes Yes To start this journey let alone see Run it Party man come on What's the time 4 4.40 4.40 Moving time 4.23 4.23 I feel great So good That was more than that That's the moving time He must have taken some stops I'll give him 4.23, why not I did all this over the last 2 years And then I put myself Literally through the trenches To get him out The running and the achievement Is almost like a self validation Even though I know I should have doubted myself That's what he'll do at times Right he would have been going through some pain man He really would have So respect brother An achievement you sort of sit down and you think Don't doubt yourself again Now it's time to just help people That have been in similar situations And try and push out a positive message And get people to make the right choices Now make a choice, make a decision Follow it through So we're just taking one step at a time I'll tell you what, if I'd never run a marathon Before in my life This would spur me on to do it That really would And I hope anyone else out there That has thought about it But thought no I could never do that Has watched this and watched my vlogs And just challenge yourself man Do it You only need to do one You're trying to help life He's bringing people along On his journey of finding himself And understanding himself Even a half marathon, I'll let you away with that Bearing himself every day And inspiring really to watch as a friend And I'm sure it's inspiring for a lot of viewers too The three words That I would use to describe Ethan Would be Inspirational, exciting And Powerful Four and a half hours Four and a half hours you know Amazing Amazing mate and the reason why I just teared up At the end there was his friends man, his mates You know what I mean like Backing him Having you know Yeah mates can give each other shit But at the end of the day They'll always back each other And that's what mates are about That's what family's about And that's what this journey was about I'll see you in the next one There's nothing else to say guys Peace out


A palm tree, when it's surrounded with other trees, creates an oasis. A palm tree on its own cannot produce a lot of shade. But when it's surrounded with other trees, it will create an oasis. We see that in Israel when they traveled through the wilderness, and they didn't have any water, and then they found bitter water, and God healed that water. And after that, they came to this new place in Getty, where they saw an oasis. A lot of palm trees, about 70 palm trees. This speaks to us that one of the successes of life is being surrounded with other people like mind, who have same values, same dreams, and passions, inspirations as you. What it does is it creates a blessing, it's a success.  We just had a snow this week that fell a few weeks ago, and we saw something very unique about snow. One snowflake is the most fragile thing you could ever come in contact with. So fragile that your warm body, warm temperature, that snowflake will fall on your hand and it will lose its characteristic. It will turn into just a drop of water. But when snowflakes combine together, and they link up, they become so powerful that they shut down schools. Flip cars over, cause you to slide, cause you to be afraid, cause you to put on chains on your car, cause your vehicle to lose its stability on the road. How can something so fragile become such a big, big force to be reckoned with when it links with other fragile things? You can be small by your own, but when you connect yourself with other people, next thing that happens is you become a force to be reckoned with.  That's why home groups exist in the church. That's why we as Christians believe that we should not leave our company with one another. You shouldn't plan to skip church. You should plan to be in the church and not just come in and walk out, but able to meet other fragile, beautiful, awesome snowflakes because united we stand and divided we fall. Amen? One of the most successful things you can have in your life is to have a physical family and a spiritual family. You might not have a lot, but if you have these two things, if you have eternal life and you have a group where you can grow, where you can mentor and be mentored, you already successful person. Someone say amen.


Hello, everyone, and welcome to the next talk for the Room 3, me. I am delighted to introduce Tariq Barada, who is going to be talking to us about how to use maths to produce the optimal wedding seating as somebody whose wedding is in a month from today and did maths. I'm really excited to hear this talk. Tariq, where are you calling in from today? Good morning. My name is Tariq, yes. Thank you for the introduction. I'm calling from Vienna, Austria. And I'm very happy to have the opportunity of giving this talk at the EuroPyton 2021. Well, we're delighted to have you. Without any further ado, I'll let you take it away. Thank you very much. So hello, I'm Tariq and I'm a data scientist at the Treyport, specialised on forecast and optimisation models for energy markets. But today, I'm going to take you to a wedding. And hopefully, well, as you know, we're like in the eye of the storm, right between two probable lockdowns. But right now, parties are a load. And for this reason, we can marry. And more specifically, we look at a seating arrangement problem. And you probably know if you've already been at a wedding that to preserve the piece of the wedding, you're going to see the different guests in a way that minimises conflicts. And we'll see how to achieve this in Python using the wonderful PiOMO optimisation and modelling package. It's an open source package. And there is a notebook to this talk, which is available on GitLab. There is a link here on the schedule and probably on the chat. You don't have to download it now, but we'll have a look later and you can look it up by yourself. So you know this kind of situation, right? People invited to a wedding. You've got these brothers-in-law, sister-in-law that can't really go well along with each other. You've got this weird uncle that keeps telling embarrassing jokes and that you have to somehow neutralise. Here we're invited at a wedding with 16 guests. You might argue 16 guests is not a lot, but on the other hand, you probably won't be allowed to invite much more in these times. And you want to allocate them onto four tables with four seats each. Each guest is characterised by a corona index. It's a number between minus five and plus five, which measures the opinion of a guest with respect to the corona pandemics. Ranging from, well, minus five would be like the absolute conspirator who is deeply convinced that corona is just an invention by big pharma and big Bill Gates. And on the opposite of the spectrum, you've got the total science freaks, positivists who can't wait to get their third shot of this Pfizer-BioNTech. And you'd like to, let's say, have rather homogeneous tables where people with relatively, well, compatible opinions sit together. The other property that you have that each guest is characterised by his or her gender for simplicity will keep it binary and say that females are one and males are zero. And the question is, how can we arrange the guests so that people with relatively close opinion with regard to corona sit at the same table? So for that, we compute a corona distance for each, well, for each pair of guests, G1 and G2, we just take the difference of the absolute value of the difference of the scores. And we can display it on this heat map. So it's a kind of representation, which you've probably already seen from actual maps when you have the distance between two cities. The darker the cell, the higher the distance. And what you want to avoid is a situation where Sandrine Flipe and Lucius Motif, for example, would keep arguing on corona the whole evening. So we define the distance for a given table as the sum over all pairs of guests of the corona distance. And the factor of one half here is simply to avoid counting the same pair of guests twice. So how could we solve this? Well, there is obviously a simple solution, which is to sort the guests by their corona index and fill the tables accordingly. If you do this, well, you'll see that indeed it works. But does it still work if we add new constraints? And which are these new constraints? Well, first, we'd like to achieve some kind of gender mix, gender balance. So we want to have at least one female and one male guest per table. And most importantly, we've invited the Krasinski. They used to be a couple long time ago, but they're diverse and they hate each other, quite frankly. It's not an option to not invite any one of them because otherwise the other one would be terribly offended.  So we see already that by simple sorting, it doesn't work anymore. So what could we do about it? A solution we'd always work is brute force iteration. So we simply go through all the possible ways of arranging, of seating the people and see whether it works or not. Well, how many combinations are there by the power of factorials? If you want to seat 16 people on 16 chairs, you've got roughly 21,000 billion ways of doing this. That's a big number. However, if we say that the order of the guests on the given table does not matter, we've got like only 60, 63 million distinct table arrangements. So one solution would be to go through each of these 63 million table arrangements. Check if it is a valid one in the sense that we have enough male and female per table and that the Krasinski don't sit at the same table, please not. And if it's a valid arrangement, measure the corona distance of this arrangement and compare it to the best one we found so far. And if it's better, then we would save this solution as the best one so far. If we iterate through all the solutions, then eventually we'll find the best solution. So in the notebook you can check. I'm making an estimate. I'm estimating the time it would take. So if you really go through the 21,000 billion combinations, redundant arrangements, these would take years. However, if we already find a way to determine this distinct arrangement, then it would roughly last on my laptop half an hour. So it works, but it's quite time consuming. And for this reason, there is actually another way of dealing this kind of problem. And it's by recognizing them as a so-called optimization model under constraints. What we want to do to rephrase it is to minimize the total corona distance on each table. This is something that we can call our objective function, subject to a certain number of constraints. And these constraints are that each guest sits at one and only one table. There are no more guests than seats on each table. There is at least one male guest at each table, at least one female guest as well. And Julius and Samantha Krasinski do not sit at the same table no way. These are the ingredients of an optimization model. Typically, such a model consists of variables. So these are the things you do not know in advance and that you would like to optimize to find the optimal value of the decision variables. For example, for a given guest and a given table, whether the guest sits at the table, yes or no. So it would be a binary decision variable. You've got parameters which are given numbers that specify your problem. For example, the number of seats per table. They're known in advance, right? You've got constraints which are equations or equations which link variables and parameters together. You've got an objective function. Here it's our corona distance that we want to minimize, but you could also maximize something. And eventually there is something which is not strictly speaking necessary to an optimization model, but it's very convenient to have sets of indices to index the other modal components on. We'll see an example in a second. So it's common to express these models in a pretty standardized way and to pass them to a so-called optimization solver, which is an external program which is capable of ingesting these standardized forms. So it's basically big matrices. Solve, find the optimum using some clever algorithms and return you the result. So here we're using to model a problem. You will be using Pyomo. So it's the Python. Well, Pyomo probably stands for Python optimization and modeling package. It's a wonderful package full of tools to solve to express optimization problems. So essentially you write down equations in math and they're turned into code to send the problem to a solver. And when it's solved to collect the result and bring them back to Python. And as a solver, we're using the CBC coin or a solver. So BC stands for branch and cat. This is the algorithm that's used for this type of optimization problems. And both Pyomo and CBC, the CBC solver are currently hosted or taken care of by the coin or our computational infrastructure for operations research foundation. So it's all open source. And operation research is the branch of mathematics that deals with this kind of problems. So let me walk you through how such a Pyomo model looks like. So here I'll show you an exemplary, very simple model. It's not complete. The syntax is I've simplified the syntax. So what you see on the screen wouldn't compile wouldn't run. However, if you look in the notebook, you'll see a running example. So you first define an empty model, a Pyomo model, and then you can add ingredients by ingredients. You can add the sets. For example, it's very convenient to have tables, a set of tables, simply the name of the table so you can, you can, you have a handle to them. Then you can add parameters which may or may not be indexed on a set. For example, the table capacity, the number of chairs per table is a parameter which is indexed on the set of tables. Avocado table has four seats, etc. You can add the variables, the one that you're going to look for. And for example, one variable we'd be very interested in is this binary, this set of binary variables called guest seats at. It's indexed on the Cartesian product of guests and tables. And it tells you it's binary one or zero and it tells you whether a given guest sits at a given table, yes or no, right. And this is what you are going to need. Well, this defines a seating arrangement, a combination of these binary variables. You can add constraints, which again may or may not be indexed on the sets you've defined previously. An obvious constraint is we want one table per guest, so each guest is assigned to one and only table. And you see that such a constraint object, well, first it's indexed on a, on a, sorry, on a, on a set. And then it contains a rule. This rule is an equation, a mathematical equation written in Python. This one tells us that if you sum the binary variables guest seats at for a given guest, but if you sum it over all the table, then this must be equal to one. It's like a one-hot encoding. For a given guest, this variable will be zero for all tables, excepted one. And this way we ensure that the guest is only assigned to one table. We add eventually the last bit of the model. The model is the objective function. The objective function is again a function, an equation. Well, it's actually a relationship, an equation, which can be outsourced, sort of defined in a function that would return such kind of equation. So you can use all the usual Python object, which measures the total corona distance. So the sum of the distances of the corona distances on each table. And you have to supply the direction of the optimization. So here we want to minimize the disagreement between guests and ensure a peaceful wedding. Your model is ready to be solved. You pass it to a solver. It's a handle to an actual solver program. And when it's solved by the solver, you can retrieve the values, the optimal value, meaning the values of the variables that minimize the objective function. And you can check, for example, that Julius Krasinski is sitting on the coconut table. But that Samantha Krasinski isn't, which is a great source of relief. So now we're going to switch and go to the notebook. So again, I said this notebook is zoom a bit. Hope you can see, hope you can see well. So as I said, this notebook is on the, should be on the, or even the chat, and you can also execute it if you want. So I'm going to quickly go through the beginning. So it's all things we've already discussed. But in the notebook, it's explained in many more detail. And come to the part where we expressed, we write down the model using Pyomo. So I won't go into the details of all the constraints, obviously. But here, at least you see like the real syntax of Pyomo. So we declare a model. We declare our sets, a set of guests and a set of tables. We declare our parameters, some being scalar, for example, the minimum number of females per table. It's a scalar number. But we can also define parameters which are indexed on a set of tables, for example. The corona distance is an interesting set because it's indexed on the cross product of guests with themselves, right? Because the distance is defined for each pair of guests with some trivial results, like the fact that the distance between yourself is usually zero, because you tend to agree with yourself. But you need to declare all these sets. And we declare the variables, again, telling what set they are indexed on and which domain they belong to, whether it's binary, it could be in some of the problem's continuous variables. And here we've got two sets of variables. We've already seen the guest seats at set of variable, telling us whether a given guest sits on a given table. But we also need a second set of variables called two guest seats at, which tell us whether a given pair of guests sit at a table. I'm going to need this because of the equations which compute the distance, because the distance is always defined within two guests, right? And then we'll add the constraints. We've already seen the constraint that we have one table associated to each guest. We have a constraint telling us that the number of guests sitting at a table might not exceed the number of chairs. We've got constraints to tell us that there should be at least a certain number, at least one female per table and one male. And then there is a slightly more complicated constraint, which is here to define this variable two guest seats at G1 and G2. Sorry, two guest seats at a given table. So G1 and G2, two guest seats at table T. Obviously, we would like to write down that this is the product of the binary variables telling us whether a given guest sits at a table. However, this constraint is not a so-called linear constraint. It's a product of two variables. And for reasons I don't want to go right now into, we prefer to keep it linear so that the problem remains a so-called mixed integer linear problem, which are known to be easier to solve. There is a trick for that. And eventually we write down the Krasinski exclusion principle, which makes sure that Samantha Krasinski and Julius Krasinski do not sit at the same table. Eventually, we define our objective function. As the total corona distance, well, we define it in two steps. First we define for each table the total corona distance on the table, which is obtained by summing the distance of all pairs of guests using these variables to tell us whether the two guests sits at the table or not. The sum will only contain terms when the two guests sit at the table, and we sum this over all the table. And now the stage is ready. We can pass the model to a solver and solve it. You already see that it takes some time. And by the way, when you solve a model, you can also pass as an argument the certain time limit, which is a time up telling you, well, after let's say two minutes, 120 seconds, just give me the best solution you've found so far. And we see here that after 17 seconds, the solver declares that it has found an optimal solution. And the objective function, so the total corona distance for this solution, presumably optimal solution, is 29.18. We can now extract the result, the optimized values from the model. And in particular, we are interested in how people seats. So let's look at this. So this is the actual result. So we see that each guest is allocated to one given table. So here I've ordered them by table that you can check that there is for each table at least one male and one female. And you can check that Samantha Krasinski is on the coconut table while you use Krasinski it on the avocado table. So actually by, well, you can try to convince yourself or to get some feeling about whether this solution is optimal or not by, for example, looking at the average corona index per table where you see that it fairly kind of evenly samples the space minus 5 plus 5. And if you look at the standard deviations of the dispersion of the corona index here on each table, you feel you see that it's rather narrow so that the model tries to achieve a relatively narrow distribution of corona index per table. And of course having to do a trade-off between minimizing the corona distance but still, for example, having the Krasinski is not sitting at the same table although they have actually very similar opinions on this matter. But actually how can you be sure that the solution is optimal? Well, it's pretty easy when you have a solution to check that it is valid. You can check the seating arrangements. You can check easily that there is one male, one female per table and that the Krasinski do not sit at the same table. But how can you check that it's optimal? Either you can prove it mathematically but you can prove that the algorithm which is implemented by the solvers so this so-called branch and cut returns an optimal solution. And apart from that, well, it would be the brute force way. What you can do, it's not a proof, of course, but what you can do is to collect some, let's say, anecdotal evidence. Well, you can try to generate that random seating arrangements, retain those who are valid and compute the objective function, the corona distance and check that indeed all the solutions you find have a worse, larger corona distance than the optimum. So first, let's just check that the optimal arrangement is a valid solution. We do kind of an independent check outside of the model and it is. It has a distance of 29.2 and let's look at 10 random seating arrangements which are valid and print the total corona distance and fortunately all of them are worse and very often much worse than the optimal arrangement. Of course, it's not a proof but it suggests that it's quite hard to beat the optimum and actually it's not possible to beat the optimum by definition. So coming back to the slides, I am glad to tell you that we've reached the happy end of this talk. So we have found an optimal seating arrangement after roughly 10, I think this started with 17 seconds on a laptop whereas by brute force iterating through all the solutions we would have taken roughly half an hour or 20 minutes. So it's faster. In practice, very often in real life what happens is that you have a limited budget and time. Your boss pops up into your office and says, well, I want the best solution within two hours, 10 minutes. So what you can do is to interrupt the solving after a certain time up and keep the best solution so far which very often happens to be good enough. But the cool thing about these optimization methods is that they usually give you a better solution because you could also brute force iterate and then solve after a given time. This would also give you a best solution so far but usually these algorithms would come closer to the actual optimum or the actual optima because there may be a difference. There may be a lot of degenerate solutions, so solutions which are equally optimal. The cool thing about PiOMO is that PiOMO is a modeling language. It translates math into code. There are other modeling languages around, but this one is written in Python and it leverages on the power of Python as a programming language. You can get data from Python into PiOMO, send to the solver, get the result back, process it in PiOMO, so it's really integrated. It's not the only one. As an alternative, I would cite PULP which is a very good alternative for, let's say, simple optimization problems, in particular so-called mixed integer linear problems such as the one we solved today. For what we've seen today, PULP would have been more than enough. However, I think that PiOMO has a much wider range of problems it can solve, including, and here I'm just dropping names, including nonlinear optimization and stochastic optimization. And eventually, what I'd like to give you as a take-home message is that many real-life situations, particularly in the industry, can be expressed as optimization models in logistics, in economics, finance. Here at Trayport, we're using these kind of techniques to optimize energy systems and energy trading. Eventually, it can be used to optimize the arrangement of the guests at your wedding. It's probably not the only issue you have to tackle at a wedding, but at least this one is solved and you can fairly hope that you'll have a peaceful wedding. With this, I thank you very much for your attention and I'd be very glad to answer your questions, whether now, if we have some time, or at our virtual booth, because Trayport is a sponsor of this conference and we'd be happy, my colleagues and me, we'd be happy to answer your questions and tell you more about Trayport. Thank you very much. Fantastic. I really, really enjoyed that. Thank you so much. I'll have to adopt some of that methodology for planning my seating plan. We do have a question on the chat from Fabian. Fabian asks, have you experimented with how many guests the model solver can deal with, or how those unlinked constraints of, for example, divorced guys influence running times if there were more of those? Thank you for the question. I didn't experiment systematically, but what we could say is that if you look at the brute force way or simply if you look at the number of combinations they are, it scales very badly. I think like even worse than exponential. So if you're looking at, let's say, the number of arrangements, assuming that you have a certain number of guests per table and which is constant, but you increase the number of tables, it will, well, if I didn't do anything wrong with the math, it's like M to the power, the number of tables to the power of the number of tables, so probably something like exponential. So brute force iteration scales really badly. How about the branch and kettle algorithm? And, well, I couldn't really give you a definitive answer. As far as I understand, there can always be pathological cases where it scales also really badly, but in practice, it's much faster in a lot of real cases, but still for this kind of problems, branch and kettle, even increasing, well, if you double the number of table, for example, you've got 10 to the 16 times more arrangement, so even in this case, branch and kettle wouldn't help you that much, to be honest. Thank you, very comprehensive answer. Ah, we have another question. Can pyome be used with a different solver than coin or CBC? Is that one you recommend? Okay, so yes, pyome can be used with any, well, all the standard solvers, there are commercial solvers, which are like Grobi or Cplex, which is very powerful, but commercial. CBC is a very good open-source, the best open-source solver I know, I'm aware of, so I would definitely recommend using this one unless you're limited. Otherwise, if you have a big problem that you can't solve on your computer, what you can do is to use remote infrastructure. You can use those program hosted by, I think universities across the world. It's also compatible with pyome, we just need the internet connection. It sends your problem through the internet and you get back the solution. So it's for like one-shot experiments, it's good. You wouldn't use it for your startup. Excellent. We have another question. It looks like you really need to know the underlying data structures for defining constraints, with the need to create supporting intermediate data structures. Do you know about a collection of common constraining patterns to make this easier? I'm not sure. I get the question. In my understanding, you need to really well define, yes, you need to know everything. You really set right down the equation, so you need to have a pretty clear idea of the data you're working with. Of course, like in which kind of Python data structure the data is, doesn't really matter. You just put it in the end in more or less dictionaries. I guess this is not really what you're asking for. My question is how far do you need to understand the maths behind it in order to be able to use Pyomo effectively? My guess would be you do need to know some of the theory fairly solidly. That's true. You need to write down the equations by yourself. Hopefully, you can also get some inspiration from existing models on the, let's say, your gallery on GitLab or books, et cetera. All right. I think that's all of the questions we had from the chat, and we are at time. Thank you so much, Tariq. Thank you very much. Happy wedding.