 All right. Thanks, Jay. So cool. You can just write whatever you want and Jay will read it. Hopefully someone believes it. Anyway, so I'm just going to get this going. We don't have a lot of time for the intro, but I'll share my screen so we can see that. Right genetic so just go through this quickly so genetic interactions really just mean when you combine two mutations and you get something that's unexpected not additive so it's off the sort of the additive side of the equation. And synthetic lethality is a great example of that, where if you can delete a gene and cells might be perfectly viable because most genes are non-essential. And then you could delete another one in the same phenotype. But when you combine them, you might lead to a lethal phenotype. And that's sort of like a perfect example of synthetic lethality, although each mutation can have some kind of fitness defect. As long as the double mutant has a more extreme phenotype than either of the signals, that's called a genetic interaction. And synthetic lethality is descriptive. So we can explore genetics. We can explore this systematically using genetics and map things on a global scale. And in yeast, you can delete each gene in a diploid format, convert the diploid to a haploid, and ask if the genes are essential. And 80, you know, of the 6,000 yeast genes, 5,000 of them are non-essential and could contribute to synthetic lethal combinations. The essential genes, if you have a partially functional allele, hypomorphic allele, then those alleles or variations like that in those genes can lead to synthetic lethal phenotypes when they combine with other mutations. And the essential genes actually drive a lot of the synthetic lethal interactions on our, our, our global map. So we, Brenda and I got together and generally, and came up with an automated form of yeast genetics so that we could map these things systematically. And then we generated a global map for yeast. And in the end, what happens is you get this wiring diagram where you could see something like mutations. The 19S proteasome genes are essential, but we can have a partial loss of function mutations in any one of these. And they're all synthetic lethal with the APC complex. So we end up connecting these two modules. And this is the sort of the way we think genetics is wired underneath the hood. We also score positive interactions. So you can see this mutations in this cog secretion complex really have no phenotype when we compromise the function of the proteasome. Lee Hartwell articulated two major concepts about synthetic lethality. Lee Hartwell, and this was in the late 80s, early 90s, Lee Hartwell won the Nobel Prize for his description of genes driving the cell cycle that are conserved from yeast to humans. But he came up with this idea that you could take what was a very esoteric concept of a synthetic lethal genetic interaction and translate that into a cancer therapy because tumor cells might be loaded, have a mutational load, and we can take advantage of that and target what is normally a non-essential gene in a wild type cell and turn that into an essential genes through a synthetically lethal interaction, you know, in a tumor. He also articulated that genetic interactions may underlie much of our inherited phenotypes or a significant component. And we think that's true and we're trying to prove that, but we won't talk about that today. So here's the original paper. Actually, it was in the late 90s, 1997, that it was published, and Hartwell articulated that if you could take, you know, what are mutations that are often found in cancer cells, map synthetic lethal genetic interactions that are conserved from yeast up to flies, where you could do those types of experiments at the time, you might be able to then identify synthetic lethal interactions that could be used for cancer therapy. And there is an example of this. This is this PARP inhibitor that will kill BRCA1 cells or BRCA2 cells in a quite sophisticated manner through a synthetic lethal type of interaction. It's a little more complicated because the drug actually traps creates a toxic component, you know, drug-protein combination. And so that might be why this one's particularly effective. Several groups, including Jason Moffitt here, showed that, you know, now with CRISPR, we can actually map essential genes in cancer cells and look for cancer cell line specific vulnerabilities using CRISPR. And of course, the depth map is doing this across hundreds of different cancer cell lines. And you can see that YKT6 is an essential gene. It's past this sort of threshold here, but there's variation even in the fitness associated with disruption of this essential gene in various cancer cell lines. PTAR1 is a non-essential gene. It has a slight fitness defect. And again, you can see this variable fitness. And there's some cell lines where it's absolutely essential. And presumably this is through some kind of synthetic lethal genetic mechanism. With Jason, we've been mapping synthetic lethal interactions in a cancer cell line. We delete a gene and then we do a CRISPR screen and we look for genetic interactions that are specific to that query versus another one. And we're creating a map that looks very much like what we've made in yeast. And this is quite powerful. The difference between this map and the DEP map is that we know the synthetic lethal pairs. In the DEP map data, all you know is that if you delete a gene and it's non-essential in many different cell lines, but it's essential in this cancer cell lines, but it's essential in this cancer cell line that there's some underlying genetic mechanism, but quite likely it's not as simple as a digenic synthetic lethality situation. So we delete B-tar when in here, we don't know what the genetic mechanism is, but I suspect given what we've done in yeast, because we can do the same thing in yeast, delete genes in different cell, different strains that are of yeast that are found in different parts of the world. And we can find genes that are essential in one strain and not another. And when we look at the genetics underlying that, usually it's quite complicated. There might be two, three, and often four modifiers that lead to the synthetic lethal phenotype. So with that, that's just sort of the background I thought might be interesting to this group, since the DEPMAP is sort of cancer-related and synthetic lethality. But with that, I think we've got three incredible speakers here. Jay's going to introduce them, and away we go. Yeah, thank you, Charlie, for that excellent introduction. I'd like to introduce our fourth speaker, Dr. Stephanie Leroux. She's going to represent the clinical translation theme, research theme. She's a medical oncologist, clinical investigator, site lead of gynecology, oncology, and co-director of Beyond Chemotherapy Program at the Bernts' Margaret Cancer Center. As of July 1st, she's a talented associate professor, faculty of medicine at the University of Toronto. Her clinical and academic interests are focused in gynecology, cancer, and early drug development with an emphasis on translational research and have garnered two ASCO merit awards. She received the 2019 Career Development Award from the American Society of Clinical Oncology and the 2021 Young Investigator Award from the University of Toronto Division of Medical Oncology. Internationally, she's a co-chair of the Translational Research Committee and director representing Princess Margaret Consortium at the International Gynecological Cancer Intergroup. She's also a member of the Gynecological Cancer Steering Committees at the U.S. National Institute of Cancer. steering committees at the U.S. National Institute of Cancer. In the last five years, she has over 60 peer-reviewed publications and widely cited journals. And she's a principal investigator, a co-principal investigator in different clinical trials, and has several large-scale peer-reviewed funding opportunities from Canadian Cancer Research Institute, Concord Cancer Foundation, American Society for Clinical Oncology, OICR, Ovarian Cancer Canada, and the Terry Fox Research Institute that cover preclinical and clinical areas of interest. Dr. Leroux, thank you so much for joining us today. And the floor is all yours. Thank you so much for joining us today. The floor is all yours. Thank you so much. So thank you so much for inviting me, and thank you so much for this nice introduction. It's a real pleasure to be there with you today and to focus on synthetic lethality and clinical perspective with a focus here today on the high-grade ovarian cancer. So here my disclosure. So in terms of the high-grade serous ovarian cancer, so every treatment that we develop is usually based on biology. So we need to know about the disease biology to develop new drug. And about high-grade serosuvae in cancer is currently still a lethal disease with an unmet therapeutic need, specifically in the platinum-resistant setting. And what do we know about high-grade serosuvae in cancer is usually characterized by genomic instability and the copy number change. We know that there's some precursor lesion that has been identified as tick lesion that can be there for a few years before having this advanced cancer, which unfortunately is usually diagnosed at late stage with metastatic disease. We also know that there's some metastases that have distinct niche with different chemosensitivity profile. And it's also known to have some chronic chronal heterogeneity and some immune invasion. Unfortunately, in high-grade serosuvae in cancer, we do not know yet how to target the immune system to have the response that was seen with other disease with some immune therapy. So in high-grade serosuvae in cancer, there's a posse of driver mutation that we can target therapeutically. The main mutation that is known for high-grade serosuvaein cancer is the TP-563 mutation, and it's also known for having an early emergence of drug resistance. But recently, with the TGCA, we know that what we call the gene that is involved in the DHR deficiency, that can be mutated or altered in the high-grade serosuavian cancer. As you can see here in the TGCA graph, that there is nearly half of the portion of high-grade serosuavian cancer that can be defined as a monologous repair deficient by BRCA1 or 2 mutation in the germline context. We also have some tumors that are BRCA1 or 2 somatic mutation. There is also some BRCA1 methylation that has been defined, and other mechanisms that can lead to the homologous repair deficiency. So here, the high-grade serosuavian cancer, the new therapeutic, has been trying to target the DNA repair. So the main chemo that we use is a platinum. So the platinum induces some double-st strike break in the DNA. And if you have this homologous repair pathway, you can actually try to repair this double strike break. And you use all the machinery from the homologous repair deficiency. And we can be qualified homologous repair deficient by BRCA mutation, as you can see below. There's also the gene RAF51, which is also important. But here in high-grade serosuvaein cancer, we know that the HR is deficient for nearly half of the high-grade serosuvaein cancer. So here the concept of synthetic lethality, if you are deficient in this specific pathway to repair the DNA because of the cytotoxic agent, you will rely a lot on the alternative DNA repair, which is the single break. And actually here you can target the PARP inhibitor treatment. And it's what have led to the concept of synthetic lethality in high-grade serosuvae in cancer. If you have a hemoglobin repair deficient, and if you target BRCA, and if you target the PARP, you can have this concept of synthetic lethality. And that has been the success of PARP inhibitors. So you have the patient who have a pneumologous repair deficiency, and you target the alternative DNA repair pathway by targeting PARP. And this early stage clinical trial showed benefit of the PARP inhibitor treatment, such as Olaparib. But there is other PARP inhibitor that has been developed. And they initially develop in the early phase one and two with some signal of activity, and then move as maintenance post-platinum-based chemotherapy in the platinum-sensitive recurrence. And now, given the benefit that happens in observing our patient, we have the PARP inhibitor treatment approved in first-line setting. And I will take a few minutes here today to go with you about this potential treatment that now is being integrated in our practice. So in clinic, what do we do now? We have now access to the first-line maintenance for all our patients. Here, I target most the maintenance with a PARP inhibitor treatment, knowing that there's another maintenance that can be approved with bevacizumab, but it's not the topic of the session today. So, what we have been seeing in terms of clinical trial. So, here's a trial that was done in the first line for patients with a high-grade serous or high-grade endometriosis advanced stage, stage three or four. And they were patients with a deleterious BRCA mutation identified either in the tumor or in the germline setting. All patients were treated with a platinum-based chemotherapy and were in response. After response to platinum-based chemotherapy, the patient was randomized to the PARP inhibitor Olaparib or placebo. And the treatment was given for two years. And then they look at the primary endpoint, which was a PFS in this trial. When they look at the PFS, definitely the patient who got the Olaparib have a median PFS that was not reached at the time of this initial presentation versus 13.8 months with the placebo. So definitely here, we have a clinical validation of the concept of synthetic lethality. You have patients with a BRCA mutation who have then the homologous repair deficient. They're responding to the platinum-based chemotherapy, and you target the alternative DNA pathway with a PARP inhibitor treatment. So here, definitely a positive trial. What is interesting is to have a follow-up. Here, the updated PFS analysis was a five-year follow-up. So when your patient was followed for five years, despite they stopped the treatment at two years, you see that the median PFS was significantly in favor of the Olaparib for 56 months versus the placebo at 13.8 months. So definitely a benefit of the PARP inhibitor maintenance for patients who have a BRCA mutation after response to platinum-based chemotherapy. What is interesting is to see that actually this benefit was also seen for patients who were considered with a lower risk of recurrence. So you take the patient who actually have a complete surgery at the complete response post-platinum-based chemotherapy, and still, this patient who have a lower risk of recurrence still benefit definitely of a PARP inhibitor maintenance with a placebo, despite having complete response, have a median PFS of 15 months with a placebo, and it's not yet reached with the olaparib. So definitely, that is now part of a standard of care. But homologous repair deficiency is definitely characterized by BRCA1 and 2 mutation, as expected, but we have seen that the HR deficient can be implicated with other gene or other mechanism. So this trial is a PREMA trial, which assess the same population, but beyond BRCA mutation. So it was patient with a high-grade serous, high-grade endometrioid advanced stage of cancer, which gave a complete response to partial response to platinum-based chemotherapy, and were randomized between the other PARP inhibitor, niraparib, versus placebo. and were randomized between the other PARP inhibitor, niraparib, versus placebo. As part of this trial, the patient was stratified according to the specific HRD status, the homologous repair deficient status, that has been tested with a specific test. The primary endpoint was also the PFS. As part of this trial, in terms of the PFS in the overall population, having nirapib is beneficial with a median PFS of 13.8 months compared to the placebo at 8.2 months. So in all population, there is a benefit of the PARP maintenance. But that differs according to the HRD status, as you can see here. The patient who has an HRD deficient by a BRCA mutation, the hazard ratio was 0.4. So definitely the patient who have a BRCA mutation benefit from a PARP inhibitor maintenance first line. When you have patients who are this HRD deficient by the test, but they're not characterized by the BRCA mutation and actually are BRCA wild type, there is a benefit of hazard ratio of 0.5. So definitely a benefit. And this homologous repair deficient is due to other mechanism than the BRCA mutation. And then you have this group of HR proficient. So it means that the specific HRD score is negative and the patient are characterized as proficient. And here the hazard ratio was 0.68. So still a benefit, but definitely not at the same amount as the other patient characterized by homologous repair deficiency. Another trial that was presented at ASCO last week is the ATENAT trial. The same population again, not focusing on BRCA. It was all patients with advanced ovarian cancer, high-grade, responding to platinum-based chemotherapy. As part of this trial, they look at different maintenance treatment, but here at ASCO, they reported the maintenance with Recaparib, which is another PARP inhibitor, versus placebo. So here, we present that result, and again, the same result. In the overall population, the PFS was in favor of the maintenance recuperative, which is the PARP inhibitor treatment versus placebo. So definitely, again, in a benefit of PARP maintenance. And again, when you look at the population of HRD, which was the same test that was used before, the recuperative median PFS was 28.7 months versus 11 months in the placebo. So definitely now part maintenance is in practice for the first-line treatment in high-grade cell-surveying cancer. But we still have some limitation. As you can see, this trial in the primary endpoint was in all population, but we know that the one who benefits the most are the patients defined as homologous repair deficient, as the biology shows us, because of the concept of synthetic lethality. So the tests that were used as part of this trial are the HRD score, which is based and performed by Myriad, which is a composite score based on the loss of heterozygosity, the telomeric allelic imbalance, and the large-scale state transition. So it's a combined score. And from retrospective study, the score serol has been set at 42. So if you are based on this score, you are proficient or deficient. There's a lot of effort to see if we can improve this test because usually this test is performed on the archival tissue on the initial diagnosis, and that may not be reflecting the current status of the disease. And here's an example of another HRD test that was developed by the Leuven Group, which is an academic laboratory-developed HRD test, which also involved three components. And here they tried to test this new HRD score versus the Myriad score as part of another trial and showed that actually there is overall agreement about 91%. And the Leuven HRD test was slightly more HRD positive patient compared to the Myriad test. But overall, an agreement of 91%, but was still able to actually separate the one who had the best benefit of PARP versus the other one. So a lot of effort currently ongoing to define the HRD. But now we need to think as a clinician of what we will do, because as you can see in the first line, most of our patients now will have a PARP inhibitor treatment as maintenance post-platinum-based chemotherapy. So two concepts that is important. First of all, as you can see, all the trials in the first line setting have a specific duration of the PARP maintenance, either two years or three years. So PARP exposure will not mean necessarily PARP resistance. And that was already proven by a recent study showing that the PARP exposure is not equivalent to the PARP resistance. What we know is that PARP resistance is usually platinum resistance. But we know also that when we give a PAP inhibitor treatment to a patient, you may have a different response to the subsequent chemotherapy. And here's an example. The olaparib maintenance treatment was assessed at the time of recurrence. And at the time of recurrence, patients are usually re-challenged with a platinum-based chemotherapy. And if they didn't have a PAP before, they can have a PAP. And the PAP maintenance and at the time of recurrence, patients are usually re-challenged with a platinum-based chemotherapy and if they didn't have a PAP before, they can have a PAP and the PAP maintenance is until benefit. And actually as part of this trial, it was Olaparib versus placebo. And when patient progress and was re-challenged with a platinum-based chemotherapy, the patient who got placebo got a better response to chemotherapy compared to the one who have Olaparib. Despite the overall survival was in benefit of volaparib, but it showed that when we treat a patient with PARP, the response to subsequent therapy can be different. So that we need to learn more about that. And that is just an example that we have seen in our patient. As you can see here, with our patient, we have the archival tissue. Then the patient unfortunately relapsed several times and then got the prolonged benefit to the PARP inhibitor treatment. And we were able to have the tissue at the time of PARP progression. What we have learned by whole exam sequencing and NRA sequencing, we have seen that on the initial tumor, which was PARP-naive, the patient did not express BRCA, which was not related to a BRCA mutation. And at the time of PARP-resistant, she restored the BRCA function and the expression. In addition, at the time of initial diagnosis, she didn't have a cyclin E overexpression, but at the time of progression, when she began to be pap resistant, she actually has a cyclin E amplification. So definitely with treatment, the tumor evolves, and we will need to target the tumor differently. So if the patient begin to have resistance to treatment, what we will do, and that is currently a lot of investigation and a lot of effort currently. That is just a small example of patients who progress in clinic on PAP inhibitor treatment. So patients got treatment and progress on PAP inhibitor treatment. And before, for all the treatment for ovarian cancer, we distinguish platinum sensitive and platinum resistant. But when patients are treated with PAP, does it mean anything still? So here we enroll patients with platinum sensitive and platinum resistant. All patients got the biopsy at the time of PAP progression. And when they progress on PAP, we re-challenge with a PAP, but combining to an anti-angiogeny to see if we can overcome the resistance. What did we find? We find that actually some patients, despite progressing on PARP, can benefit of a rechallenge with PARP in combination with anti-angiogenic. Some patients benefit, but some do not. And that was interesting as well. You can see here in blue, it was a duration of the prior PARP and then the duration of the PARP rechallenge with an anti-angiogenic. What we have learned is that by sequencing the tissue at the time of PARP progression and sequencing the tissue at the time of the initial diagnosis, chemo-naive and PARP-naive, we see, again, some evolution of the cancer. So here we have seen that at the time of PARP progression, there's some reversion mutation in this HRD gene, including BRCA1 and 2 and RAD51. We have seen some amplification of the cyclini, and we have also seen at the time of PARP progression, a progression of the multidrug efflux pump ABC1B. Interestingly, the patients who have this reversion in HRG have really worse outcomes and do not benefit of a recharge of PARP. And what we have seen as well is that one patient can actually have multiple mechanisms of resistance to the PARP inhibitor treatment. So different roads for the PARP inhibitor resistance, but the same destination for people. So we know that the PARP inhibitor resistance can be related to the restoration of the homologous repair pathway. It can be also an up-regulation of the drug efflux. It can be also a senior transduction pathway, but also now, and it's why it's a huge focus for high-grade psoriasis in cancer, is a replication for protection. So in practice, what we will do, we need to identify this mechanism of resistance in real time, because the tumor, the initial diagnosis may not be reflecting currently what's going on with the patient. We will need to do tumor biopsy, but sometimes can be challenging. Can we develop new tools, for example, secreting tumor DNA? And we will need to redefine our treatment strategy. Can we prevent the resistance when we actually give the PARP? Or if the patient progresses on PARP, can we overcome resistance? Or actually, can we pass by the resistance with all this ADC new development? So that is one of the hypotheses that we will test. And this trial will be open to Princess Margaret hopefully in one or two months' time, is a phase two trial with ovarian cancer where we will take patients to progress on PARP inhibitor treatment or receive prior a PARP inhibitor treatment and progress. And we'll try to see if we can adapt the treatment in a real-time assessment, knowing that the genomic can evolve. So this trial is supported by OICI OVN Cancer Canada, and we will take patients who have high-grade ovarian cancer who previously got a PARP inhibitor treatment, regardless of the platinum status. All patients will undergo tumor biopsy and ctDNA, and they will be rechallenged with a PARP inhibitor treatment with an anti-angiogeny because we know that some can benefit. And then we will adapt based on the genomic sequencing on the tumor biopsy and the CT DNA. If patients have benefit, they will continue the treatment. If they do not benefit, then we need to adapt according to the alteration observed in the tumor or the blood. If there is no mechanism of resistance observed, they do not have sufficient benefit, so we'll add an immune therapy to try to target the microenvironment. If they have a nomologous repair gene reversion or a second overexpression, we need to change our treatment, and we know that the rechangery spark will not be benefit. And this patient will undergo chemotherapy. So this kind of trial is a pilot trial to see if we can adapt the treatment based on the timely assessment. But definitely, we need to overcome resistance. And there's a lot of new DDR target in the DNA repair network. So we will discuss just a few slides after the V1 inhibitor treatment, because we know the DNA repair is also dependent on the cell cycle. And we know also that the high-grade cell-surveying cancer is always B53 abnormal. So there is a lot of target in the DDR that we can look at. The V1 is one. There's also the ATR inhibitor that is currently on development in a selected population or in combination. So just to finish, definitely targeting the cell cycle is important in high-grade serosurveying cancer. And that was a randomized phase 2 trial that our group conducted based on the rationale that it's not synthetic lethality, but it's a methotic catastrophe. When you target all the point of the cell cycle, you can lead to a kind of a synthetic lethality. So high-grade serosurveillance cancer, if P50 is remutated, so you increase your reliance of the G2M checkpoint here. So you can block it by a WE1 inhibitor, for example, the AZD1775. And then you can target the X phase with a gemcitabine, which is an anti-metabolite. So you target all the checkpoint of the cell cycle to go to the methotic catastrophe. So here we did the trial based on this hypothesis in platinum-resistant high-grade psoriasis cancer. We randomized the patient between gemcitabine plus the AZD1775 versus a gemcitabine plus placebo. Here are the clinical results. So with this combination of the W1 inhibitor plus gemcitabine, we have increased the PFS and we have an overall survival benefit with a combination of 11 months versus 7 months with gemcitabine alone. So clinical benefit, but also some potential synergy is term of objective response rate with a combination led to a 23% response rate versus 6% with a gemcitabine. What is also interesting as well, you can see that some patients have prolonged benefit to this combination. So we were able to actually identify potential biomarker of response because all patients that undergo this trial have a baseline biopsy. And you can see here the patient in the group of the WE1 inhibitor plus gemcitabine, the one who had the best response actually were the one who had the sacclinic amplification. So definitely, this hypothesis is that the patient who has a replication stress and has a sacclinic amplification may benefit from this kind of treatment strategy. So in terms of the TECOM message, so definitely the DDR defect causes potential vulnerability for high-grade serosuvae in cancer, but also offers actually a therapeutic opportunity. If there's alteration, you can actually leverage this vulnerability by a new treatment approach. But also, I wanted to show that the synthetic lethality has been validated by the PARP inhibitor treatment, specifically in the HRD population, but it's definitely broader than that. And there's different way to have this synthetic lethality concept. Definitely with the cell cycle and the replication stress is an opportunity. In challenge for clinic, we definitely still have challenge in defining the HR deficiency patient. Who are there? Because we know that the HRD test is not completely accurate. So there's a lot of effort to try to define this homologous repair deficiency in clinic. We know also that we need new treatment option for the patient who are HRP. You can be HRP at the initial diagnosis, but you can also become HRP by different mechanism of resistance. So we need to develop new treatment for overcoming this resistance. And definitely something that we'll follow in the discussion is how we can target this replication stress in ovarian cancer. So I want to conclude by thanking all our patients, as you can see some of them here that participate in clinical trials that allow us to answer some specific questions. And thank you, our research team and our research friends. So thank you. a specific question and thank you, our research team and our research friends. So thank you. Thank you, Dr. Leroux. Our next speaker is Dr. David Gallo, who's going to represent the adaptive oncology team, research team. David completed his PhD at the University of Toronto in the Department of Biochemistry, studying the DNA replication stress response using yeast as a model organism. From there, he moved to Dr. Dan Turoce's lab to complete a postdoc where he uncovered the mechanism of how PKMYT1 loss is synthetically built with CCNE1 overexpression. Currently, he's a senior scientist with Rare Therapeutics, helping guide the PKMYT1 inhibitor RP6306 through clinical trials. Dave, thank you for joining us and the floor is all yours. Hi Jay, yeah, thank you for the kind introduction and the opportunity to present some of the work and how we identify new drug targets from synthetic lethal interactions and how we attempt to translate that into the clinic. So today I'll tell you a little bit about, first I'll start by talking about utilizing and understanding synthetic lethal screens to identify new drug targets. I'll then move into a little bit about how we can expand patient populations of synthetic lethal drug targets using chemogenomics and other screening techniques. And finally, I'll finish off with trying to understand how we can use biom but CRISPR-Cas9 genome editing technology has really revolutionized the way that we do genomics in human cells. So briefly, basically what you need to understand here is that Cas9 is an enzyme that cuts DNA and it binds to synthetic guide RNA. And the synthetic guide RNA will guide the Cas9 enzyme to specific regions of the genome where it introduces the double-strand break. If that double-strand break is repaired in a mutagenic fashion by the non-homologous end-joining pathway, you create indel mutations, which then result in a non-functional RNA, which is decayed. So essentially, what happens is if you target the targeting region of the genome where the SGRNA goes, ends up in a gene deletion, the equivalent of a gene deletion. So what has happened is there's been many synthetic guide RNA libraries that have been created to systematically knock out every gene in the human genome. And so we use this technology to screen for synthetic lethalating cancers. And so basically, this takes the form of a pooled lentiviral dropout screen. So basically, what you'd start with is a synthetic guide RNA library. You introduce this into your cells at a low titer. So you get one guide into the cell. So this library will have three or four guides for each gene in the human genome. So yeah, you transduce at a low titer so that you can make sure you get one guide into each cell. So each cell then harbors a single gene deletion. You grow that out, and then these are all barcoded. So then you can do a barcode next genome sequencing and understand which genes when knocked out are essential in that background okay so that's the idea of a pooled dentivial dropout screen but how do we use this to find synthetic lethality well there's two methods I think think that, you know, two main methods to do this. One would be to take a cancer cell line. So here, shown here is a bunch of different cancer cell lines that are well characterized, and then do your screen in that cancer cell line. An alternative approach is to create an isogenic cell line. So in this case, you take a wild type cell and you introduce a cancer mutation into it, and then you screen both isogenic pairs with the genome wide library to figure out which genes are essential in the cancer cell line, but not in the wild type cell line. And so these two approaches have both been used. However, cancer cell lines are often, have multiple mutations, multiple different copy numbers of genes, which makes it very difficult to do CRISPR sometimes in them. And also it complicates identification of synthetic lethal pairs due to this kind of messy mutations in the background. So the preferred method that we like to use at Repair and many others is to use this isogenic pair method where we use an immortalized non-transformed cell lines so like a human like an h-transform cell line or a mortalized cell line sorry and these cell lines have a lower mutational burden relative diploid ploidy which results in a better gene editing efficiency and it's easier to track back your genetic interactions because ideally you're only looking at one mutation that you've introduced into the isogenic pair. Okay, so to do this, you have to first create a nice isogenic pair, a nice isogenic cell line with your mutation of interest. So keeping in the concept of what Stephanie was explaining, we chose to use cyclin E overexpression as an isogenic model for our initial screens here in this paper. So this has all been published in Nature recently. And so basically what we did is we created a cyclin E overexpressing cells. So we picked two different clones here. And you can see then the first step to this is characterization of the isogenic model. So we can see that indeed they do overexpress cyclin E. They also express markers of replication stress, which is what we'd expect when we overexpress cyclin E. And they have this S phase accumulation in the cell cycle, which is another phenotype of cyclin E overexpression. So we've now got a very nicely characterized isogenic model system to screen and so we carry forward the screen and basically so the screen's done the same way I just explained so basically you take a cancer cell line your wild type cell line you do your screen and at the end of the day after your NGS sequencing of the barcodes, you can then score which genes or which synthetic guides drop out of the mutant cancer cell line, but remain in the wild type cell. So these would be your synthetic lethal interactions that are specific to the cancer cell line. And in this case, it's cyclin E overexpression. Okay, so what does that look like? So basically what we've done here so we use our favorite workhorse cell line which is the RPE1H church. These are all p53 knocked out so we always work in a p53 mutant background and so in this case we've done the screen here with the TKOV3 library which was developed in Jason Moffitt's lab at the Donnelly Center and so what you can see results from the screen here is that we get so what here I'm plotting is two different scoring algorithms to score essentiality and we get this kind of plot here where you can see the synthetic lethal interactions up here in the top right corner. So this is a lot of genes here so we want to think about different ways that we can narrow down this list to a more manageable set of interactions to follow up on. And so to do that, we actually just do more screens because that's what we do. And so in this case, what we've done is we've taken multiple different clones. So to get a nice robust screen, it's good to screen a couple of different clones of your overexpression. And we've screened it with a few different libraries. So a few different iterations of the TKO library. And in this case, now we've narrowed it down to about five genes that look interesting up here. So we can see that these now become more interesting hits in the screens. And from there, you can actually go one step further and start data mining. So we can use DETMAP, which Charlie introduced from the Broad Institute. And so we can do an in silico screen where we actually ask what genes are essential in these cancer cell lines that overexpress cyclin E. So when we look at cyclin E overexpressing genes, PKMET1, cyclin E, and CDK2 come out at top. So we can see that PK-Met1 is a common theme among these genes, or among these screens that we've done. And so this now becomes a very interesting hit, which we ended up following up on. So if you're a biochemist, you look at this and you say, oh, PK-Met1 is a kinase. So that's actually a perfect druggable target. And so I think Richard will explain a little bit more about this part of the drug development process later. So suffice to say, we ended up targeting going after PK-MET1 in this for follow-up. And indeed, we were able to create a potent and selective orally available inhibitor of PK-MET1 called RP6306. So RP6306, yeah, it's a PK-Met1 inhibitor, and we are actually now currently in phase one clinical trials with this drug for cyclin-E amplified tumors. But so now we've got an inhibitor, and we've got a nice inhibitor, and we've got a synthetic lethal interaction, but how can we use screening to expand our patient population? So we're always thinking, what else can we use this drug in? We always want to look for new patients that we think the drug will work well in. So to do this, we actually turn back to more screening, of course. So from here, what we can then do is what's a chemogenomic CRISPR screen where you then use the PKMet1 inhibitor in combination with the screening campaign to look for additional patient populations where loss of function of these genes will then cause sensitivity to the PKMet1 inhibitor. So we can use chemogenomics to do this to expand our patient population from cyclin E to other loss of function mutation. And so by doing this with the PKMET1 inhibitors, so we did an RP6-0-6 chemogenomics screen, we actually uncovered a loss of function of FBXW7 to be another synthetic lethal interaction with PKMET1 inhibition. And when you look at this, it actually makes sense because FBXW7 is the adapter that guides cyclin E for degradation. So when you lose FBXW7, you get accumulation of cyclin E protein. So this makes sense at the mechanistic level as well. So that's one method to do a chemogenomics screen. So, however, there's lots of genetic space available for chemogenomic screens. And so when we're doing CRISPR and activating screens, we're really looking at loss of function and activation of tumor suppressors. And the genetic alterations in cancers here might be biallelic truncations or epigenetic silencing. And that's what we did as a CRISPR and activation genome-wide screen. However, there are other genetic alterations in cancers that are not loss of function. So for instance, we can have gain of function or loss of function, small nucleotide variants. And this looks like missense mutations, hotspot mutations, or splicing mutations. And so we can't use a loss of function screen to identify these, but we can use other technological CRISPR approaches to screen for these, which might be base editors. So we can use CRISPR to introduce small nucleotide variants and screen that way. Alternatively, we also have overexpression, so driver or passengers in cancers. So what we see is this can be seen as amplification of genes, tandem duplications, or epigenetic upregulation. And to screen this, we can use a CRISPR activation screening library. So I'm not going to get into the details of these, but suffice to say there are other methods available to use in the chemogenomics space to identify new patient populations. Okay. So finally, an additional way that we can screen is not by CRISPR screening, but there's this new trend of using pooled cell line screens in the chemogenomics approach. So this has been kind of championed by the Broad, of course, by, and what they do is this screen called the PRISM. And so PRISism is they basically taking a whole bunch of cell lines, so 578 cell lines in this case, and they barcode each cell line. So then, then they do a pooled cell line screen. So they pool the cell lines based on the growth rate. And then you basically can do a pooled cell line screen with your drug on board. And from there, you can get an idea of which cancer cell lines in these panels drop out when you have your when you have your drug on board. So basically this offers a nice fast message to screen for drug sensitivity in multiple well-characterized cell lines so these are these are well-characterized cell lines, subtype mutations, mRNA protein expression levels, copy number, methylation status. So they're well characterized. You can also use these, you can use this method to find mechanistic analysis of your drugs. So you can use whatever your outputs here are and do a correlation to match genetic screens or other drug screens. And so if your drug behaves like another drug or like another gene deletion, then you can use this guilty by association kind of idea to gain some mechanistic insights. Furthermore, this also allows data mining to find new patient populations. So if you see a bunch of cell lines drop out of your screen, you can go in and mine those for genetics and various other characteristics that they have to do data mining to find new patient populations in this way. So that's great. So now we have a drug and we found a whole bunch of patient populations that we think are going to be, that we might have efficacy in the clinic. And so that's the first part of the CRISPR screening platform to find synthetic lethality in cancer. However, the next part is how do we find these patients in the clinic, right? So we can have a great synthetic lethal interaction. We can have a great pair. But if we can't find the patients in the clinic to treat the drug, we can't do anything with it, right? So really, this is where the challenge in synthetic lethality is translating into the clinic. So to do this, we really need robust biomarker development to find the right patient in the clinic. And then we also have to think about figuring out effective strategies, either in the mono or combination therapy space, using these biomarkers that we've developed. So how do we go about that? Okay, so there's many different assays to measure biomarkers. So basically, I'm going to use cyclin E as an example, because that's what we did in our manuscripts, and that's what we're going after in the clinic. So to score cyclin E, manuscripts, and that's what we're going after in the clinic. So to score cyclin E, first of all, you might think about looking at protein levels of cyclin E, which is an IHC assay. Advantages to this is you have low tissue requirement, it's three to five days to get a result. However, you can only get a single biomarker out of an IHC, and it's got a relatively small dynamic range. So you can see here, these would be graded from zero to four, and these are basically just scored on this scale. So this is great for when you're looking at amplified protein amplification, but if you already have a low level of expression and you're looking for something that's going down, this can be a bit challenging through the small dynamic range. Alternatively, we can use FISH to find cyclin E amplification, so fluorescent in situ hybridization. So we can use a probe for cyclin E and look in the nuclei of these cells. And we can see that basically the more dots they have is the more amplification, gain or amplification of cyclin E they have. And so again, this is a low tissue requirement, three to five days to results. But again, we have the same problem with only being able to run a single biomarker at a time. This has a bit of a higher dynamic range. So really what the field's kind of moved towards is an NGS, so sequencing techniques. So this is looking at the genomic level for amplification or deletion of genes. And so this, however, it takes a higher tissue requirement, a longer time to get the results. However, we do get the whole genome in this case. So we'll get, we can test multiple biomarkers for amplification or deep deletion if you're looking for BRCA, for instance, and we have a really high dynamic range here. So the problem with this though becomes is what, how do we proceed if the expression and genomic level data are not aligned? So in the case of cyclin E, you can think of a scenario where, and this is a scenario actually in the clinic, where you see high amplification based on an NGS, but then you look by IHC and you have low protein. So we might be actually picking patients if we're picking on NGS that don't actually end up with high protein level. So we might be treating patients that might not respond. actually end up with high protein levels. So we might be treating patients that might not respond. Alternatively, a patient could have high cyclin E levels but not be amplified at the NGS level. So we might be missing these patients if we're only looking at NGS. And so I don't have a good explanation as to how to go about this. We're still working through this in the clinic, but these are the kinds of things we have to consider when we're looking at biomarkers and how to select patients. Okay, so finally, I'll just end with thinking about some effective combination strategies to consider. And so thinking about moving into the clinic, we want to really consider what's an effective combination strategy. So combinations, you know, are very important in the clinic. So how do we go about doing that and considering the biomarker? So basically, the way we think about this is it's kind of three factors that come into play. So first is the mutation or the mechanism. So the mutation being cyclin E and the mechanism of action. So, the mutation being cyclin E and the mechanism of action. So, well, okay, let's take high-grade serious ovarian carcinoma as an example. So, as Stephanie said, you can be HR defective, or you could have another gene such as cyclin E that's an oncogene-induced replication stress. So, based on that mutation or that mechanism of action, you know, we could think about how to make a combination partner based off that. So does the MOA of the synthetic lethal interaction align with the MOA of the combination? So another important thing is the indication that we're going into. So this could be, you know, if we find this mutation in these patients, what's the indication that we find them in? And so which indications have the high prevalence of the patient selection biomarkers. And really what this has to do with combinations is that each indication could have a different standard of care. And so depending on the standard of care in each indication and which partners are already approved, that might influence the way that we pick a combination partner as well. So we will use high-grade serious ovarian carcinoma as an example here. So as Stephanie said, many of the high-grade serious ovarian carcinomas are homologous recombination defective. However, some are cyclin E-amplified and others are in the other pool. So if you come and so you turn up in the clinic and you're homologous recombination defective by NGS, as Stephanie mentioned, this is right to treat with PARP inhibition. And we might consider a combination with an ATR inhibitor in this case based on the mechanism of action. So this would be a targeted plus targeted combination therapy. So, however, if you do turn up with cyclin E amplification, you might just, the gemcitabine would be a better combination partner, as Stephanie said, because you're combining the replication stress phenotypes together, which creates problems for cyclin E amplified cells. And in this case, we would come in with our PKMET1 inhibitor because based on our mechanism of action, we think that PKMET1 inhibition would be more synthetic lethal when you combine with gemcitabine. So this would be a chemo plus targeted. I'll just say that PKMET1, I didn't get into the mechanism of PKMET1, but PKMET1 is the sister of WE1. So you can think of PKMET1 inhibitors similarly to WE1 inhibitors. So alternatively, if you move away from high-grade serosylvarian carcinoma, cyclin E, and from cyclin E, we know, like I said, from our chemogenomics screens, we're able to identify FBXW7 mutations as being sensitive to PKMET1 inhibition, and these are more prevalent in colorectal cancers. And so, and colorectal cancer, full flurry is commonly used as a chemotherapeutic. So in this case, we might consider using a full flurry PK-Met1 inhibition, PK-Met1 inhibitor combo to address this in the clinic. So taking together, I think, you know, synthetic lethality, I think we start with a screen and we're able to identify, you know, nice synthetic lethal interactions with screen. However, the real challenge becomes translating this into the clinic. So how do we then, you know, find these patients in the clinic and how do we combine these and translate this into effective combination strategies to help treat these patients? So I'll stop there. Thank you very much, Dave. Our next speaker represents a therapeutic innovation team. It's Dr. Richard Marcellas from OICR. He's currently a scientific advisor in the drug discovery program. And prior to OICR, he worked in the pharmaceutical industry for over a decade at Geminex Pharmaceuticals in Montreal. Richard has broad experience in therapeutic development, spanning the early drug discovery and development process from target identification and validation to IND support. His expertise includes protein production and characterization, acid development and compound screening, drug target confirmation, and project management. Richard, thank you for joining us today, and go ahead. Thank you for the introduction, Jay. Glad to be here. I'll share my screen. Okay, can you see those slides? Let me zoom up. How's that? Looks good. Okay, I'll try and go fast so we don't run out of time here. So I'm coming at this very much from a drug discovery perspective. So we can see lots of interesting synthetic lethal combinations, but ultimately we have to find things that are druggable. So in our definition, a target for me is some component of the cell or some biological component that we can modulate with an external agent. In our group, it's always a small molecule agent that can be a direct modulation or sometimes an indirect modulation. But the idea being we can alter the course of the disease or the condition with this small molecule treatment. When we talk about validation in our terms, we really would like to start with some evidence that modulation of our target is first of all possible, it's a druggable target, and will give us this desired phenotype to help alleviate the disease. So for us, what we're looking for for for a small molecule binding site, is really a protein needs to have a deep hydrophobic pocket. Drugs are almost always hydrophobic. They're not too charged or they won't get into a cell at all. So the active sites of enzymes are good examples of those kind of pockets. Protein-protein interactions are historically considered undruggable, but there are more and more examples now where if a protein-protein interaction does involve deeper pockets, they can be druggable, and we've done work in that area quite a bit. Generally speaking, small molecules are going to inhibit your target. So there's only a few examples where you can actually activate a target. So generally, we think of them as inhibitors. Now, what we can't do is go after highly polar surfaces like DNA binding domains, microRNAs or long non-coding RNAs. That would be much better suited to nucleic acid type therapies. Small molecule drugs that are polar enough to interact with those things are not going to have the properties we need to dose patients. And as I sort of alluded, we can't replace missing functions with a small molecule. We can shut something off. Maybe we can activate something in some cases, but we're not going to replace a protein's function. So we're not going to fix a tumor suppressor loss. When we look at the new targets, we tend to throw them on a kind of a grid like this, where we're thinking, okay, how likely is it that it's druggable? So that's our x-axis, our druggability score. And how good is our target validation? So if we're sitting up in the top right corner there, we know something's druggable, such as a kinase. It's got really good validation from the clinical genetics. That's going to be a very competitive space and the pharmaceutical companies are going to sit up there. Biotechs will tend to want to have good validation. They want to know who their patients are going to be, but they'll tolerate more of a druggability risk. So they're in the upper left there. A lot of work we do actually with collaborators is in the bottom right. So we go after things we know we can drug in a reasonable time frame, but we don't really know its full biological role. That hasn't been worked out. So we'll do things like probe development to help develop the tools to allow us to assess the validity of the target. Genetics is great, and we use genetics a lot, but genetic ablation of a target, especially a CRISPR, even knockdowns, is not the same as binding a compound. They hopefully will correlate, but the correlation is not sometimes as high as you would think it should be. The bottom right there is the very risky stuff. We do a bit of that, but we try and get away from the things that are going to be too risky. It's a long road to develop a high affinity and specific inhibitor. So I'm going to start from a clinical genetics perspective, mining big data, something we have people doing quite a bit, taking all this RNA sequencing and DNA sequencing from tumors and mining that data, looking for the overexpressions, translocations, etc. that are seen in cancer cells versus a normal, looking for the activations, which are presumably the oncogenic drivers or the things supporting the tumor phenotype, and also the inactivations, your tumor suppressors and things like that. There's the primary target itself which is showing up in these screens with the activating mutations for example but we also pay attention to their interacting partners because we do have quite an interest in the protein protein interaction space. Sometimes if the the key protein you wanna go after is not particularly druggable, there may be a partner protein which would give you a similar outcome. It'd be more druggable. So we always pay attention to that. And from all of this, we're generating some kind of hypothesis and then we'll get to work setting up our assays, doing our screens, et cetera. So I'm gonna start with an example, just using C-BioPortal, which is a pretty accessible way to get into the clinical data sets from all these sequencing projects. So here I've just selected the top, the MSK impact sequencing set done at Sloan Kettering. That's almost 11,000 patients that have had a lot of targeted tumor sequencing done. So we can look and see what these patients have. So, oops, one more. And you can see that it's a lot of different solid tumors in this set. There's no heme. So you can see the breakdown up here in the left, what kind of tumor types are there. And then down below on the lower level, you can see which mutations they have. You see P53 is there at the top. 41% of these patients have a P53 mutation, pretty standard. KRAS mutated, but inactivating mutation in this case. So P53, of course, generally inactivating mutation. KRAS is activating. Telomerase, your TERT gene, activating. So you can see a lot of your drivers are going to be there. And of course, some of the tumor suppressors getting shut off with point mutations. On the middle, you've got your fusion genes, which is generally a type of amplification in that two genes fuse together and express one of the components at a much higher level than it should be expressed, sometimes change its properties. Tempris-ERG is a pretty well-known fusion between those two partners that activates a transcription factor. Very nice validation, very nice biomarker, but very difficult to drug Tempris-ERG. And the last column is the copy number aberrations. And I'll go and focus in on that. And you've heard a bit about some of these. So in copy number aberrations, this is your deletions, generally of things that are stopping a tumor from forming, your tumor suppressors and things, and the amplifications of things that are supporting tumor growth with your driving oncogenes, MYC, a lot of the growth factor receptors you'll see amplified. Now, the most common homologous deletion you see in cancer across most types actually is CDKN2A. So that locus, 9P21, harbors, that gene actually codes for two proteins. One of them is a CDK4 inhibitor. So P16-INC is a CD4K inhibitor and P19-ARF inhibits MDM2. So it is both functions to restrain the cell cycle at the G1S interface. That's the CDK4 is pushing you through that. And of course, MDM2 shuts down P53. So you want to, in a tumor cell, does not want P53, wants P53 to be shut down. So you don't want the inhibitor of MDM2. So we are focused in the little example I'm going to give you here, we're focusing on the CDKN2A. So I, instead of doing an actual isogenic screen here, I'm going to show you a bioinformatic way that we will use. So if I go into, let me see, next slide. If I go into what you've heard many times now about the DEPMAP, an extremely useful tool, is ask, split all the cell lines essentially into those that harbor the CDKN2A loss, and these are the ones on the right, and those that do not harbor a CDKN2A loss. So we've taken the 700 or so cell lines, split them into two groups, and then ask which vulnerabilities we can find. And what showed up with a decent correlation score at 0.37 was PRMT5, protein arginine methyl transferase 5. So that is an enzyme that's showing up. Here's just a scatter plot showing the sensitivity to PRMT5 on the y-axis and the expression of the CDK N2A on the x. So you can see as it gets lost, you're more sensitive, you're going more negative in your score. So your dependency score is getting better with that gene. So this combination of going in with some kind of genetic idea, and then we don't have dependency data, of course, from our genetic data sets. We just see what the tumors have. We don't know for sure they've created a dependency. This is a way of sort of bridging that. Of course, these are just cancer cell lines. They're not primary cancers, but many things we'll track through to them as well. Okay. So if we then go back into the debt map, but now take PRMT5 as our query and say, okay, what's happening when we knock down PRMT5 across 700 cell lines? So here it's just a summary. You can see an RNAi or the shRNA. You can see it's moderately left shifted from the zero, but not strongly. But you can see there's quite a big tail coming off the left side, really pushing into the area of strong dependency. So it gets scored as a strongly selective in RNAi. In CRISPR, however, it is a common essential. This is a very important enzyme. You CRISPR it out, you're going to kill almost all cells. And what you see, one way I use to assess the function of a protein and figure out how well the literature is reporting on the role of that protein and its binding partners is look at the codependencies. So in CRISPR, the top codependency is a protein called WDR77. That also showed up in the RNAi at the top codependency that is an obligate binding partner for PRMT5 so that makes a lot of sense these two things work as a complex we'd expect them to show similar phenotypes and a correlation of 0.5 out of the depth map it is a fairly noisy system so a 0.5 I consider to be extremely good correlation makes sense in the RNAi which is showing quite a bit more texture I think we're getting better information out of the RNAi, which is showing quite a bit more texture, I think we're getting better information of the RNAi. You can see a bunch of other things that from the literature would be potentially predictable. MAT2A is the enzyme that makes a substrate for PRMT5. It makes SAM. Rheokinase is a binding partner for PRMT5 involved in ribosome biogenesis. So I see a pattern here, which looks really good, makes me trust the data. So if I dig a bit deeper into the PRMT5 data set, look at all the correlations, what I now find is that in fact, CDK N2Aa which is the original thing that drew me there certainly their copy number loss of CDK and 2a and B correlates quite well however it's not the best correlation best correlation is a loss of a different gene called mtap and you can see it's correlating at very high correlation score 0.56, extremely high. Again, you see the binding partner there and MTAP showing up both as expression and copy number. Above is the plot. So as MTAP expression drops towards these X axis, you can see the sensitivity to PRMT5 is leftward shifting. So it's a pretty good correlation. I've looked at a lot of this stuff, and this is a very good example for a univariate kind of sensitization. So if we go in and take M-TAP as our deletion and we again break our cell lines into those on the right have lost M-TAP, the ones on the left have not, and again go and see which dependencies are showing up. PRMT5 is expected is there, WRDR77 is there as well, shows up in both data sets, CRISPR and shRNA, which again is another nice validation for me. There's a lot of noise in the data set so I like to see the same answer coming out of both of them. Okay, MTAP deletion is found in lots of different cancers. GBM particularly high, almost 50% in GBM. So we got to get interested in MTAP. And then went back to C-BioPortal, and you see that MTAP and CDKN2A actually are in the same place. They're right beside each other. So if I look at the MTAP deletions, if I pull out all the patients that are MTAP deletions, almost 99% of them also have the CDKN2A deletion. If I flip it around and look at the CDKN2A deletion, I'll see that 84% of them have an MTAP deletion. The tumor cell wants to delete CDKN2A. That harbors those two types of tumor suppressors, as I described. MTAP, however, is coming along for the ride most of the time. There isn't a good reason for a cell to delete MTAP. In fact, it's actually partially detrimental. So this example is all coming out of just data mining. However, just before the DETMAP came out, this was discovered the way David had described it. This group at Agios, led by Kevin Marks, had done an isogenic screen. They were very interested in, not actually in the CDKN2A itself, they were very interested in this metabolic enzyme, MTAP. And they had created an isogenic strain, two lines where they had deleted MTAP or not, done the screen and came up with the same answer. They'd waited about a year, they could have done this all a lot faster, but nobody knew the DEPMAP was coming at that point. So this loss of the tumor suppressor is in about 15% of cancers. Almost all of them, 80% to 90% of them are also going to co-delete M-TAP. So you have a nice biomarker. That's something that's commonly looked for. We see that PRMT5 vulnerability. And the way this all works is kind of interesting. So MTAP is just a metabolic enzyme. It's involved in scavenging or salvaging adenine and methionine. And when you've deleted it, the substrate accumulates. And that substrate looks a lot like, in fact, it looks identical to half of SAM. SAM is a methyl donor that this arginine methyltransferase uses. And in fact, all the methyltransferases use SAM is a methyl donor that this arginine methyltransferase uses, and in fact, all the methyltransferases use SAM as their methyl donor. Turns out, though, that PRMT5 is one of the, of all the methyltransferases, it's one of the weakest for binding SAM. So this excess production of MTA is more detrimental to PRMT5. PRMT5 is a very essential protein in the cell. You wouldn't want to permanently delete this, but in a situation where it's already partially inhibited by excess MTA, any other perturbations on the pathway, either hitting MAT2A, which makes SAM, or knocking down PRMT5 a bit or its binding partner WDR77, will the two factors together. You wouldn't need complete inhibition of PRMT5. Ideally, in a clinical sense, you'd want sort of a modest decrease of PRMT5 to spare your normal cells. However, just target the cells that have this lesion. Going after PRMT5 with a SAM competitive inhibitor would be likely to not have a very good therapeutic index. But we feel that perhaps partial inhibition in the context of MTAP deletion could be effective. So that was interesting. I have searched a lot of different genes through this sort of analysis, and I must admit, I haven't found that many that come up this beautifully, but hopefully there's more there to find. So, as David has made pretty clear, getting clinical biomarkers for patient selection is really important. We have to know who we want to treat for a clinical trial, and obviously the physicians like Stephanie have to know what patients to treat once something's been approved. So we've been mining this big omics data sets here at OICR, trying to get some ideas. And as I said, repairing damaged cellular functions is beyond the ability of our tools with small molecule drugs. So we're really concentrating on taking advantage of synthetic lethality as it might emerge because of these lesions. And just keep in mind that the vulnerability due to the tumor suppressor loss, that might be the source of your synthetic lethality. But deleting genes is quite messy. And often there's important stuff that's also accidentally lost. And that can be leveraged as well. And often there's important stuff that's also accidentally lost and that can be leveraged as well. And our feeling here is that, I mean, oncogenic drivers, we know them very well. They've been studied since all of our viral days back in the 90s and 80s and been well-mined for anti-cancer therapeutics. And we feel that a lot of the future is going to be more in the realm of the acquired dependencies and synthetic lethalities, which hopefully will also give us good therapeutic windows. And that's it. Thank you very much.
Synthetic Lethality in Cancer Therapeutics - OICR Mondays
Moderator: Dr. Charlie Boone, University of Toronto

10:12 - Clinical perspective: Success of and development of resistance to current synthetic lethal drugs (PARP inhibitors) and emerging synthetic lethal drugs in the clinic. (Dr. Stéphanie Lheureux, UHN)

34:12 - SL target identification and biomarker perspective: Screening methods to identify synthetic lethal interactions and development of biomarkers for patient stratification and/or combination therapies. (Dr. David Gallo, Repare Therapeutics)

55:40 Druggability perspective: Opportunities and challenges in drugging new synthetic lethal drug targets – not all genes are created equally. (Dr. Richard Marcellus, OICR Drug Discovery Program)