 Well, you know, it's 12.02, Barbara. Why don't we get started? And I know there are folks still logging on. So for those of us who are here, thank you for joining Kansas Center Grand Rounds. I hope all of forward to year's end and hopefully celebrating a better 2021. But we're really very fortunate to have two exceptional speakers today. And I'll start by introducing our first speaker who, frankly, needs no introduction. Dr. Barbara Burtness is a professor of medicine, co-leader of the developmental therapeutics research program, and leader of the head and neck cancer program at Yale. Barbara is internationally known for her leadership in clinical development and research and understanding the biology of head and neck cancer. And among her many accolades, we can add now only in the past month is the principal investigator of the Head & Necks Corps, of which we are just so proud of both Barbara and the entire team being awarded this really coveted and elite grant, which I think Barbara, if I'm not mistaken, there are only two Head & Necks Corps now in the United States. You're the leader of one of them, which is an extraordinary distinction for the people who work in this space at Yale. So Barbara was kind enough to share with us the work she's doing on head and neck cancer. And Barbara, thank you for joining us today. Well, thank you for the invitation and for all the support that's gotten us this far. So what I wanted to do was talk about P53 mutated head and neck cancer, which is something I have a longstanding interest in. And obviously, P53 is a very critical tumor suppressor gene. It's meant to be the cell's way of reacting to cellular stress signals, and among these are many that we know are important in head and neck cancer, so hypoxia, DNA damage, replicative stress. Ideally, in response to these, p53 is activated and promotes the transcription of target genes in domains of cell cycle arrest, DNA repair, apoptosis, and others. However, in head and neck cancer, we're aware that p53 functionally disrupted in the majority. In HPV-associated head and neck cancer, p53 is wild type, but its degradation is fostered by viral oncoproteins. And in HPV-negative head and neck cancer, over 85% have genomic disruption of P53, including in-frame mutations, truncating mutations, and missense mutations. And you can see here that many of these are clustered in the DNA binding domain. And we know that this type of mutation is deleterious for the natural history of head and neck cancer. So this figure comes from a large trial that the Legacy ECOG Cooperative Group ran. Over 500 resected head and neck cancers, all resected to margin negativity, and all offered appropriate risk-based angioman therapy as was standard at the time. And then P53 was sequenced. And you can see here that long-term outcome was worse for those patients who had P53 mutation. And if you classify the mutations as disruptive or non-disruptive, it was worse for those with disruptive mutation. And the definition that was used in this paper for disruptive was a mutation that was either truncating or in the DNA binding domain. So on the basis of these outcome data, we were interested in the ECOG Akron Head and Neck Committee, which I now chair, in studying intensification of therapy for these poor prognosis patients with disruptive P53 mutation. But the first thing we wanted to do was examine how we really should be calling the P53 mutation. So we started with what we called the Poeta rule. So those were the rules from the paper I just showed you. And we compared them to 14 other classifiers that are out there, many of which are based on in silico predictions of disruption, some of which are based on experimental evidence, actually, of the decrease in WAF1 activation for every specific mutation. And then we also examined our Poeta rules augmented with information about splice site mutations. And you can see that this very simple definition of truncating or DNA binding domain actually outperformed in terms of clinical prognosis all of the other indicators. And so in our clinical trial, we moved forward with this Poeta rules plus splice site mutations. And the trial that we're now about halfway through is a randomized phase two trial of postoperative therapy for patients who meet the criteria for radiation, but who have negative margins, don't meet the criteria for chemotherapy. And then we want to ask in those patients with disruptive mutation, do we see an advantage for the addition of platinum that we don't see in other patients? This is supported by an enormous BFSCWIT grant that takes care of all of the sequencing. And we have two investigators who are doing the mutation calling in real time. So continue to support this trial and see this as kind of an important resource in terms of all the sequencing information that we're going to have on top of the clinical outcome. We also have support for a clinical trials planning meeting at the NCI, which is gonna happen in January. The goal of this is to write trials, both for locally advanced and recurrent metastatic disease, identifying promising therapies for p53 mutated cancer. We also want to develop a national infrastructure for the sequencing and mutation calling with a consensus approach that all of the groups within the NCTN will accept. The breakout groups for this have been meeting for about five months now. I can tell you that the focus is very strong on immunotherapy and synthetic lethal strategies, and I'll mention both of those in the remaining minutes of this talk. So head and neck cancer is one of the cancers where it appears that increased tumor mutation burden is predictive of response to immunotherapy. And we know that this is a cancer with a higher number of non-synonymous mutations, particularly in the HPV negative cancers. And in the platinum refractory setting, both for pembrolizumab in this early single-arm trial and for drivalumab in a randomized phase three trial, also in the platinum refractory setting. In both cases, we see that as tumor mutation burden rises, the likelihood of benefit from immunotherapy increases. So working with my long-term collaborator at Fox Chase, Erica Gilemas, we wanted to examine whether or not mutations in not only p53, which is the most commonly mutated tumor suppressor in head and neck cancer, but also CDKN2A, which is mutated in slightly over half of HPV-negative cancers as well, see how these related to DNA damage as reflected in tumor mutation burden with the idea of establishing whether or not p53 mutated cancers would be particularly susceptible or appropriate for study with immunotherapy. We had access to a data set of 1,010 HPV negative cancers that had been profiled at Karis Life Sciences. Their gene panel is about a 600 gene panel. They exclude HPV-associated cancers with standard methods. And then the CDK N2A mutations that we saw were almost invariably truncations or deletions, so we included all of those. But for p53, we were interested, once again, in what's the best way of calling meaningful mutations. So we started with the American College of Medical Genetics variants calling. This included essentially all the p53 mutations that we saw. We then looked for consensus between the ACMG and two other variant calling algorithms, Intervare and Clinvare. We used the International Agency for Research on Cancer guidelines for what was dominant negative or loss of function. We then looked at the variances defined by the Poeta rules that I just alluded to. And then we called out those patients who seem to have gain-of-function mutations, most of which are defined experimentally across a range of publications. And TMB was measured just by counting all non-synonymous missense mutations across the about 1.4 megabases that are included in this panel. This shows you the patient characteristics. So predominantly oral cavity and oropharynx cancers, as we see in the clinic, males outnumbering females. And as you see at the bottom, the number of patients who had P53 mutation by the CARIS was higher than if we looked at the consensus calls or the disruptive call. Gain of function was less than 10% of all of the mutations that we saw in p53. And indeed, it turned out that either p53 or CDKN2A mutation was associated with an increase in TMB. Here we look for a threshold of 15 per megabase as being likely predictive of response to immunotherapy. And you can see that across the board, having both genes mutated was associated with higher TMB than having one or the other. And the only exception here was that those patients with gain-of-function mutations in p53 did not have an increase in tumor mutation burden. So, you know, we concluded that mutation of p53 or CDKN2A is associated with increased tumor mutation burden. This is highest when they're damaging mutations in both genes. And so just to kind of segue to the next part of the talk where I'm gonna talk a little bit more about synthetic lethal strategies, P53 mutated head and neck cancer, I think remains a really important subject for study because it's common, it has a poor prognosis. We still don't, after many decades of people examining this have agents which directly target mutated p53. And so the increasing evidence that synthetic lethal strategies might have promise in these patients has kind of attracted our attention in the lab. And so one of the things that we know about disruptive p53 mutation is that the cell loses the ability to perform cell cycle arrest at the G1S transition. And as a result, it becomes much more dependent on transition at G2M. And so we, I mean, obviously many people have been interested in this across many cancers. But we were interested in examining some of the potential targets that regulate G2M. We know that aurora's increased, and I'll show you a little bit about this. We know that aurora expression is increased in head and neck cancer. And aurora content will go up at the end of G2. Its activity is required to localize CDK1 to the centromere to foster mitotic entry. Aurora also, in addition to its roles in centrosome maturation, it also has the property of activating the CdC25 phosphatase, which removes an inhibitory phosphorylation from CDK1. And on the other hand, it's important to know that that inhibitory phosphorylation is placed by the mitotic checkpoint kinase, WE1. And so both WE1 and Aurora are recognized as upregulated in head and neck cancer. of synthetic lethality in p53 mutated cancers, but they appear to have kind of contradictory or opposing roles. And so the data that I'm going to show you now will try to make the case that by co-treating these cancers with an aurora inhibitor, which will lead to abnormal spindle formation, defective cytokinesis, but by inhibiting the Roro will lose the ability to remove the inhibitory phosphorylation from CDK1, and that will result in cell cycle arrest, that we can counter that by inhibition of WE1 so that phosphorylation isn't placed and accelerate these cells into mitosis where, given the spindle disruption that's been caused by the aurora inhibition, they will be unable to complete a normal mitosis and instead will apoptose or undergo mitotic catastrophe. So it's been recognized that aurora content is increased in the face of loss of p53. And there are a host of publications which demonstrate that increased aurora levels are correlated with poor prognosis. I'll show you some of our work. This is a panel of cell lines that we use in the lab, all of which have either mutated or P53 null status. And you can see that all of them increase the expression of Aurora A relative to either fibroblasts or normal epithelial tissue. a aqua assay, an in situ fluorescence assay for aurora that could be applied to tissue microarrays. And so you see here that green is for keratin defines where these head and neck cancer nests are within the tissue core. Blue is for dappy, so that will be your nucleus, and red is for aurora. And in this aurora high cancer, what you can particularly appreciate is the high level of expression of aurora within the nucleus. When we looked at nuclear aurora in the tissue microarray, first for all cases, we saw that high aurora expression was associated with worse survival. This is also true just as a reflection of natural history in those patients who had had no post-operative treatments and never been exposed to any DNA damaging agents. And we were able to show that this was entirely driven by the HPV negative cancers. So on the basis of this, these data we went to millennium and argued that Aurora rate for aurora monotherapy in head and neck cancer turned out to be about 9%. And given the increasing experimental evidence that aurora inhibition may always be intrinsically limited by this kind of compensatory cell cycle arrest. We were then interested in what would be a rational combination with Aurora A inhibition that could optimize the targeting of what we continued to think was likely to be an important target in this disease. And so Eddie Mendez and colleagues at the University of Washington, together with Del Yarbrough, our former colleague here, had undertaken a functional kynomic screen in p53 mutated head and neck cancer. And actually, Aurora came out of that screen. But another thing that came out was this mitotic checkpoint kinase that I just alluded to, WE1. And people have been interested in the idea that inhibitors of G1 will abrogate the G2 checkpoint. The G1 checkpoint is already abrogated by p53 mutation, and that this might accelerate cell death, particularly in the presence of DNA damage, such as you might generate with cisplatin. in the presence of DNA damage such as you might generate with cisplatin. And they showed in animal models that the V1 inhibitor MK1775, which is now known as adavacertib, was synergistic with platinum in p53 mutated head and neck cancer models. Eddie Mendez then took this forward as a window trial in head and neck cancer. So a small number of patients treated with a davisor tip together with low dose weekly chemotherapy. And you can see here that the majority of patients had some diminution in tumor size and a number of them had rather major pathologic responses. And most intriguingly, you can see that there was evidence of target engagement. And so among those patients who had responses, there was a decrease in phosphorylation of CDK. There was a decrease in phosphohistone H3. And potentially, you could see some increase in gamma H2AX. potentially you could see some increase in gamma H2AX. They also were able to correlate both pathologic and clinical response with the presence of p53 mutation in the HPV negative cancers. And across the board, these p53 mutations are disruptive or deletions. are disruptive or deletions. So we've been exploring whether or not you can combine Aurora A and WE1 inhibition and observe synergy in p53 mutated head and neck cancer. And you see here a picture of Zhang Wuli, my colleague in the lab, who has done the majority of these experiments. And so you'll see MLN, that's the Aurora inhibitor, AZD1775, that's the WE1 inhibitor. And we see synergy in terms of cell viability, soft agar, oncosphere formation. And this was present in two separate HPV-negative head and neck cancer cell lines that bear P53 mutations. Trying to figure out whether or not our guess about the mechanism was correct. You can see here that when you give the Aurora inhibitor, there's a dramatic increase in phosphorylation of CDK1. This happens on a slightly different timeline in the two different cell lines, but seems to be a reproducible phenomenon. And that's abrogated by the addition of the WE1 inhibitor and completely abolished when you give the two together. This results in an increase in the number of mitotic figures that's abnormal to the presence of really only single-digit normal mitotic figures in the presence of the combination. So if you just walk through here, these are the normal mitotic figures. When you give the WE1 inhibitor, you get some disaggregation of chromatin reflected here in the phosphohistone H3 stain. When you give the aurora inhibitor, you get the formation of these multipolar spindles, three to four spindles per cell. And when you give the two together, you get an abnormal catastrophic mitotic figure. We also showed in using an Exin-5 flow and looking for cleaved PARP that there's an increase in apoptosis. And we wanted to compare this to Aurora B inhibition which completely cuts off mitotic entry by abrogating the phosphorylation of histone H3. And there was no synergy between these two agents. And you can see there the lack of phosphohistone H3 and increase in DNA damage. DNA damage. Taking this into xenograft models here at either of two doses of the WE1 inhibitor, the standard dose of the Aurora inhibitor, tumors continued to grow, not too differently from vehicle, but when we gave the two together, there was control of tumor growth and actually statistically significant improvement in survival for the animals. Looking at the tumors under the microscope, when we gave the two agents together, there was a decrease in proliferation reflected in decreased Ki67. There was increased cleaved caspase, and there was a decrease in phosphocdk1 within tissue. And if we did AQUA for phospho-CDK1 and counted the amount of phospho-CDK1 signal in the tumor leading edge, you can see this was dramatically decreased. Alacertib has been a difficult drug to work with in the clinic. It's associated with myelosuppression, and there's been a negative phase three monotherapy trial in lymphoma, and so we were concerned that the development of that agent might not go forward. However, there have been a number of second-generation aurora inhibitors that have come forward, and we've had access to a compound from Tahoe called TAS-1119 recently been acquired by Vitrac and it's going to be called VIC1911 moving forward and once again across a range of p53 mutated cell lines we see dramatic synergy for the two agents once again we see synergy in xenograft models this is confocal microscopy that again shows you the multipolar spindle formation with the use of TAS119 as the aurora inhibitor. But with the cells really arresting in that or becoming quiescent in that multipolar spindle state and as they then attempt to enter mitosis in the presence of both the WE1 inhibitor and the Aurora inhibitor, developing these very catastrophic mitotic phenotypes. And I notice that I'm sort of running out of time here, so I won't march you through this, but the mechanism looks to be identical here as what we saw with L-acertib. Working with Erica Gilemes' lab at Fox Chase, we undertook a high-throughput screen to see if we could find additional partners that would be both hindering and fostering mitotic entry, again, with the attempt to exploit these multiple regulators of G2M. And another hit that appeared very strong was the CHECK1 inhibitor prexacertib. This is an agent that's not really moving forward in the clinic because of its toxicity. But I wanted to show this just because with very low dosing and a single dose, we saw a profound clonogenic survival effects that make us hopeful that with a number of these pairs, one might be able to go to very low doses in the clinic. So TAS 119 has completed two clinical trials. There's a recommended phase two dose. The toxicity seems to be very manageable with diarrhea and eye disorders, probably the most prominent side effects. And so we are moving forward with a window trial in HPV-negative head and neck cancer that will have both an initial dose escalation, looking at the combination of VIC and a DAVA-CERTIB, and then followed by a dose expansion. And that will be part of project two of our head and neck spore. So I wanted to leave a couple of minutes for questions, but I didn't want to end without, first of all, calling out all of the fabulous colleagues who were part of the team that put the head and neck spore in, and then acknowledging all the people whose work I've just talked about, particularly Jongwoo Lee, Janaki Parameswaran, and Teresa Sandoval-Shafer in the lab. So thank you very much. Well Barbara, thank you. That's fabulous work. Congratulations on all of it. And folks can submit questions on the chat box of Zoom. But I wanted to ask, you know, with regard, as you look through the combination of an aurora kinase and WE1 inhibitors, do you have a sense of, first, what might emerge? I mean, even when you're getting response, because of the complexity of those pathways, what might emerge as mechanism resistance that will occur when you have dual inhibition, if any. And then the second question I have is, what do you anticipate will be the toxicity profile or the therapeutic window for the combination clinically? So the WE1 inhibitor has been quite tolerable, because I'm going to take the second question first, because I've already wrestled with that a lot. The WE1 inhibitor has been quite tolerable in the clinic, but when it was combined with PARP inhibition, diarrhea really became the dose-limiting side effect. And so this second-generation Aurora inhibitor did have about a 25% rate of high-grade diarrhea at the recommended phase two dose. So the two things that we're sort of hoping is one, that we'll get away with lower doses as we have in the animal models. And second of all, the diarrhea as a dose limiting toxicity is one of the easier ones to manage. And so that if we're on top of this with an imodium regimen early on, hopefully that will be helpful. In terms of resistance mechanisms, this is not something that we've really gone into with the combination yet, but is well studied for both of the agents independently. And one of the resistance mechanisms to the Aurora agents has been a kind of conformational dependence on the inhibitors. So the inhibitor binds to the activated form of Aurora A. And if you get an adaptive process where the cell just generates more inactive Aurora, the current generation of inhibitors may not work as well. And there is a group, Kevin Shokat's lab, has been developing novel aurora inhibitors that may be more able to bind the inactive confirmation as well. And in terms of we want inhibitors, there is a suggestion that the DNA damage effects of the we want inhibitors may have an S phase could actually up regulate some checkpoints that are earlier in the cell cycle but it's a good question probably something we should devote more effort to yeah well I'm sure it'll it'll definitely emerge as you get samples from your trial. So it's really exciting and congratulations. So I know we're at 1231, Barbara, so why don't we turn now to people can submit questions to Barbara online, but we'll turn now to our second speaker and very fortunate to have another valued member of our faculty speaking. Dr. Elizabeth Klaus is a professor of biostatistics and neurosurgery, focused not only on brain tumors, but also the epidemiology, most notably the genetic epidemiology of these malignancies. She received her MD and PhD from Yale and completed her surgery here in nurse surgery. And through her work, she really has been an international leader in the investigation of the epidemiology of CNS malignancies, most notably serving as the leader of the Meningioma Consortium, the Meningioma Genome Wide Association Study, and also a leader of the Yale Acoustic Neuroma Study. And again, we're so pleased to have talented people who bridge the gap of epidemiology and biology of cancer. And Elizabeth, thank you so much for sharing your work with us today. Thanks very much. Can you see my slides okay? Yes. Great. So I'm gonna talk a little bit about something we've been working on. And I do want to note that this is work done in collaboration with Jeff Townsend's group, who I think you all know very well, and Vincent Canotero, as well as Stephen Gaffney. So despite all the things that we've attempted to do, we still don't know much about risk factors for glioma. And we wanted to take a look and see if there were different methods that we could use to see if we could tease out both environmental and then also another hot topic is sex-specific signatures of glioma causation. So you all know that gliomas are the most common type of malignant brain tumor, accounting for about a third of all brain tumors and the majority of malignant tumors, but they've about a third of all brain tumors and the majority of malignant tumors, but they've proved to be very heterogeneous. And we have not done a great job at identifying risk factors, be they genetic or environmental for glioma. And so we were interested in doing that, particularly in light of the poor outcomes that we see with this group of patients. in light of the poor outcomes that we see with this group of patients. So we do know that there are sex-specific differences in glioma risk and outcome. And the plots I have here are for all gliomas and then glioblastoma or sort of an IDH positive, excuse me, IDH negative tumor and then lower grade gliomas, the males being the blue, tumor and then lower grade gliomas, the males being the blue, the females being the red. And it's interesting in that we see this sex specific difference across the entire age range. So it's a little bit different than we see with, for example, meningioma, where we see the women having greater risk, but the risk difference decreasing once women pass through the menopause. but the risk difference decreasing once women pass through the menopause. Whereas here we see the sex differences for glioma across the age spectrum and across all subtypes. And so that obviously suggests that other mechanisms, in addition to sex hormones, must be behind the difference. Men are at greater risk of being diagnosed with the disease. And again, that's across pretty much all the subtypes. And they also have lower survival in general than for females across all subtypes. So we've looked at this a little bit, and I've been lucky enough to collaborate with a group of individuals called the Glioma International Case Controls Consortium, and that's led by Melissa Bondy, initially at MD Anderson, then at Baylor, now she heads up the epidemiology section at Stanford. But we were able to gather over 10,000 cases and 10,000 controls. And so these are essentially looking at constitutional or germline risk alleles by sex. So if I can draw your attention to the table over here, these are the variants that we found to be significantly different, males versus females, males being the blue and females being the red. So we did certainly find differences at the germline level, but we were also interested in looking at things at the tumor or the somatic level. So sex is a biologic variable. As I mentioned, this is a very hot topic now. We obviously know there are biologic differences between males and females. There's also some thought as to whether there's variation in the prevalence of risk factors, and then also whether there's a difference in sort of a gene by environmental interaction. So for example, and this has long been postulated, but it's really been pretty difficult to prove that males in particular are more likely to be exposed to work like toxins that might be associated with risk. And so that was one of the things we wanted to look at as well and in part why we divided our analysis by sex. So there's two goals and what I'm going to talk about today. We wanted to look at the relative contribution, and this is based on some of the work that I know you've already appreciated with Jeff Townsend, but we're applying it specifically now to glioma. But looking at the relative contribution of cancer cell lineage proliferation and survival of single nucleotide mutations. And we divided our study subjects up by IDH mutation. And as most of you know, IDH mutation is one of the key dividers into the higher and the lower grade tumors and certainly a prognostic factor, as well as a factor in response to treatment. We also wanted to quantify, and this is something that is a little bit new to epidemiology in terms of how we've tried to identify risk exposures. Typically, we've done things like large case control studies where we look at large numbers of people that have the disease, compare them to large numbers of people without the disease, and look at things like questionnaire or work pick exposure, and see if we can figure out differences between the cases and controls. So what we're doing now, and this is sort of an emerging field in cancer epidemiology, is to look at the cosmic cancer mutational signatures in tumors and see if we can then backtrack, match it to possible risk exposures. And one of the things we're hoping to do in the future is to go back to our cohorts and studies for which we collected good occupational data and see if we can match it up to mutational signatures. So the methods I'll talk a little bit about, I am highlighting here Jeff's paper that he had in JNCI two years ago, and I think you've seen some of these sorts of methods applied in particular to actually head and neck cancer. including the Glioma Longitudinal Analysis Consortium, or GLAS, which is led by Roel Verhoek at Jackson Labs, and which I'm also a member of. So these groups have identified the most common genetic changes in primary glioma tumors, including TP53, IDH, EGFR, but the relative importance of these mutations and how they relate to tumor genesis is not well known. So one of the things that we've been working on, and Jeff has been a leader in, is defining this cancer effect size. So this metric of the relative overabundance of variants due to their contributions to survival and division versus what you're actually seeing in the tumor. So we're quantifying the cancer effect size. We're using single nucleotide mutations. And then we basically do a scaled selection coefficient for the different variants. We look at it by sex and by IDH subtype. And so we're trying to get a feel for whether this would help us explain any differences in the glioma risk and outcome that we see by sex. And then we're going to move on to the cosmic mutations. So I won't go into the gory statistical detail. This is drawn from Jeff's paper, but basically you're comparing expected to observed. So expected number of synonymous mutations, and then we're looking at the rate at which the mutations actually occur. The data that we're using here, our whole exome sequencing data from a pretty good size data set in terms of glioma, so about 1,100, and these are all adult patients. There's no pediatric patients in here. And we drew it from the Cancer Genome Atlas Study. And as I mentioned, I know some of you may be aware of what GLASS is. So it's an effort, as I mentioned, led by Roel Verhoek, but which Yale is also a member of, looking at not only the initial tumors, but the tumors over time. So how do they change in terms of their genetic makeup when we do nothing to them, when we do chemotherapy, or when we do radiation or a combination of all of the above? And what changes do we see and what do we learn from that in terms of what we should or should not be doing? And then we also used a lot of data. All of this is readily available off the internet, but we use tissue-specific mutational covariates, and this helped us figure out what sort of mutation rate calculations we should use, gave us a little bit of information about replication timing, and some of the other data sets that are listed here. So here's some of the results, just to take you through it a little bit. So I have it divided by tumor type, and it's by sex and by mutation. So the wild type tumors who would be considered the higher grader, primarily the glioblastoma tumors are in the first two rows and the IDH mutant, which would more typically be the lower grade tumors. And then I have males versus females, males versus females. And then there's sort of a cancer effect size here. The blue is non-coding region and the red is coding. So you can see the patterns are quite different for what might be called the low and the high grade. The IDH mutant tumors had few unique recurrent substitutions. All of them were in coding regions, whereas the wild type tumors, and obviously this is in part what makes them so hard to manage, is they exhibited many substitutions, but they were primarily in non-coding regions. So here's another picture, a little busy, but divided once again. The IDH mutant or the lower grade tumors are presented first. The wild types are second, and there's female, male, female, male. And so what we're looking at here is that items that top the list are the most important. The size of the circle that is attached to them measures the prevalence. So there can be kind of this disconnect as to what is important and how frequently it occurs. So we can see that in the low grades, it's pretty much as expected. Previously reported mutations in IDH1 and 2, TP53, some of the other classics were confirmed. But what's interesting is if we go here to the IDH wild type tumors, the most important with respect to cancer effect gene is this low prevalence, right? You can see that the circle that matches up to it is small, not large, like we see for IDH, is this BRAF V600E. So we know that it's important. It turns out that it looks like it's the most important, but obviously it doesn't occur that frequently, but interesting what drives some of these gliomas here. The other thing we looked at is do males and females show the same pattern of what's significantly overburdened? And there were a lot of similarities. The way that we have this broken up here is each panel is a gene. The mutants come first in each panel. And then within each panel, we've got the females and the males. So we did see some differences, although overall, most of the things the males and females showed were similar. But we did see differences in the PI3K pathway. were similar, but we did see differences in the PI3K pathway. So in IDH mutant tumors, the PIK3CA mutations were located in the helical domain for females and the kinase domain for the males. And so that's up here, this panel here. Okay, so it's the mutant and the non-mutant. And then the variants of import also differed by sex for PIK3R1. And so that's interesting in part because we know that the way in which these areas are targeted by various chemotherapies does differ. We looked in the literature and we don't see too much reported on this. We did find a paper by Dan Cahill et al. at Mass General, and although they didn't report it as such, they found something similar, where the females tended to have variations in the helical domain and the males had them in the kinase domain. And so, as I said, although both domains are involved with gliomagenesis. There's differential amounts potentiated by these two regions, and obviously there's different sensitivity to various treatment types depending upon domain. So back to environmental exposure. We have searched, and not just our group, but many groups have searched long and hard for environmental and genetic risk factors for glioma. In terms of genetic risk factors, we have found small numbers of families with high risk, but typically that does not relate to the general population. And so no genetic risk factors really explain a large proportion of inherited risk. And other than high dose radiation, to which not many people thankfully are exposed, we really haven't found much of an association between environmental risk factors and glioma risk. There has been reported a fairly consistent but low effect in inverse association with history of allergy. So the question comes, why haven't we found anything? Is it that there is no association or is it basically statistical power that there's so few cases of glioma relative to other things we've looked at? For example, I started my work with breast cancer and even just using the state of Connecticut as a base, you would have enough cases for a large study. For glioma, that is not true. And also likely a lot of the exposures that we think are causing risk are themselves rare. So one of the things that people have been thinking about doing is there another way to do this now? So now that we have these mutational signatures that are listed in the catalog of somatic mutations in cancer or cosmic, can you use that as a way to match up to exposure, particularly if you have previously obtained environmental or other exposure history in the patients? So we did that here with the 1,100 cases that we mentioned. We grouped exonic SNVs and tried to match them up to what is in COSMIC. And so you know that the COSMIC catalog is rapidly changing. New things are always being added, but we looked at what existed at this point. And obviously, they have previously found a matchup of environmental exposure to signatures, not only in head and neck cancer, but smoking and lung cancer, UV exposure. And so we looked at that for glioma. And so again, here's our slide here, again, broken into IDH mutant, which is the top row, IDH wild type, bottom row, and then females are first column, males are second column. What each of these bar charts relates to is the proportion of our cases for whom the majority seem to be associated with a certain signature. And the overall news is a little bit depressing in the sense that the primary molecular signature identified was age-related mutagenesis. Basically, the older you get, the more at risk you are. But we did find one thing that was quite interesting, particularly in the light of there's such a paucity of risk factors identified for glioma, and that was occupational exposure to something called haloalkanes. Pretty much true across whether you were male or female and whether you were IDH mutant or not. We did find a little greater rate of the signature showing up in the males versus the females, but we certainly saw them in both. And then we also saw, which we haven't quite figured out how to explain yet, these UV light signatures. And it's interesting because glioma has been associated in a number of instances with melanoma and also with the BRAF. So we're trying to sort out whether that has anything to do with why we're just seeing some of those signatures. So haloalkanes are basically used for many industrial and day-to-day purposes. Of interest, they're seen in refrigerants, fire extinguishers, flame retardants. And we thought this was very interesting because there's always sort of been this theory that in some of these occupations, including for firemen, and that has been reported, that there's an increased risk of glioma. And so whether or not this ties things together or not is unclear. So the signature was basically developed by looking at cholangic carcinoma in a group of workers that were exposed, known exposed to haloalkans in Japan. And so essentially they had 111 workers that were exposed, 17 developed what you all know to be a pretty rare cancer. So it was quite unusual that this number of individuals was diagnosed with it. They all were working in printing companies and they all were known to have occupational exposure. And so essentially what they did was they took the tumors from these individuals, looked at the molecular pattern and developed the signature. So that's essentially how the signature was initially identified. And so that's what we're seeing basically in our data. So conclusions here, that the majority of cancer-causing mutations in these gliomas we're seeing primarily as a consequence of endogenous rather than exogenous exposures. We did think it was interesting that different domains of genes in the PI3K pathway were different for males and females. For those of us that have searched long and hard for some of these risk factors for glioma, we are excited that at least potentially there's a new means to try and identify, even if rare, these environmental risk factors. And it's sort of a whole new aspect of glioma that we're looking at. So some of our future directions, we're looking now to partner with colleagues who have worked with us both in the meningioma consortium and the glioma registry, looking at cohorts in which we have a good occupational history. So the San Francisco Bay Area Adult Glioma Study, which is led by Margaret Wrench and John Wienke, they collected extremely detailed occupational history for their cohort, and they have all the tumors. So we're going to try and go back and genotype those tumors and see if we can confirm these associations, which they found with firefighters and glioma. And also they found it with painters as well. And so we're also collecting glioma patients with occupational histories and just sort of throwing it out to people. If you're aware of any firefighters or similarly occupied individuals with glioma, I would love to try and get a cohort together. The other thing that was just sort of luck this past semester, so I teach over at the School of Public Health and everything has been remote. And so as I was meeting via Zoom with one of my students for her final project, And so as I was meeting via Zoom with one of my students for her final project, she revealed that she was actually the principal project director for the Firefighters Cancer Cohort Study. So we're also hoping to pair. I know NIH has a big directive to try and look further at environmental exposures in cancer. So we're hoping that we can partner with some of these folks to look at individuals either living or dead that may have been diagnosed with glioma that we know have this exposure. So thank you all for your time. I wanted to also thank Jeff Townsend, Vincent Canotero, who was a postdoc in Jeff's lab, but now is an assistant professor in biology up the road a little bit at Emanuel College. And I have to thank him. He made all the beautiful pictures. And Steven Gaffney, who also works in Jeff's lab. Thank the various brain tumor associations, including the ABTA and the NBTS, as well as Loglio and a Dutch group called Stop Brain Tumor for their support. And then also thank you for Dr. Roel Verhoek and the Glass Consortium who allowed us access to the data. So happy to take any questions. Elizabeth, thank you. That was a terrific summary of your work. And obviously we'll open it up to questions on the chat line. But let me ask i found it interesting the the observation i guess from asia about the association of halal canes with cholangiocarcinoma you may know there's there's a biologic difference between intrapatic and extrapatic cholangio where extrapatic actually get IDH mutations, but intra don't. Were the cases that they found in Asia, were they extra hepatic? You know, I don't know the answer to that. I gave a similar talk at UCSF, and they mentioned this as well. So we're trying to gain access to some of that information, but I don't know at present um and then um with regard to the finding of the potential differential in mutations within pick 3ca by gender by sex is there an understanding of why those two domains would be different between men and women no and you know we've started to look at that a little bit and we collaborate a bit with Dan Cahill, as I mentioned up at Mass General. So we don't know yet, but he's going to try to take a look into that. He presented the data but didn't note the differences. So he's going to try to take a look and see what that might entail. And then my last question, and this is going to show my naivete and understanding brain tumors, but in sort of the natural history of the low grades, is there an evolution of the somatic events such that they look more like high grades? So it depends. They generally remain quite different. The IDH mutation stays constant throughout. And so that's sort of been one of the issues is what you show up to the party with tends to be what you stay with and that makes it a little bit different to manage them. We didn't find in some of the glass consortium work that we've looked at that really things changed that much whether you gave them treatment or whether you didn't give them treatment. So I was a little bit disheartening, but we're going to try to look a little bit further at that. Yeah. Yeah. You know, judging by the, what you described for the presence of hella alkanes, you could imagine they may be more ubiquitous in our environment than we might otherwise appreciate given all the things they're in. Absolutely. And it doesn't have to just relate to glioma, you know, could relate to lots of different things. Right, right. Well, very interesting. You know, I know we're just about out of time and really appreciate, oh, actually, no, Joe Contessa has a question, forgive me. So Joe's question is high dose radiation therapy delivered to pediatric patients can lead to glioma. Have you found evidence that medical imaging and radiation exposure in this setting is associated? So there's actually and you probably even know of these two studies. There's a big cohort from Australia as well as a second cohort from England. as well as a second cohort from England. And they did find that even exposure to head CTs at an early age in children was associated with a, I mean, it's a very small increase in risk, but a definite increase in risk of both glioma and meningioma. And then anything we looked at, we did find it was a fairly hotly contested topic. We did find exposure to bite wings was associated with an increased risk of meningioma, but that sort of exposure level in terms of dental x-rays generally doesn't exist now. But yeah, in terms of head CTs, the two big cohorts from Australia and England do suggest that. Although, even though the risk is increased, the absolute numbers are small. Of course. And then Antonio Mura asks, great talk. Have you looked at thyroid hormones, thyroid disease and differences between men and women? So we haven't, but one thing that is very interesting and it relates a little bit more to meningioma is a gene that we found and this is a constitutional gene on chromosome 10 we've found to be associated with meningioma, breast, ovarian and also now thyroid tumors. Interesting. Well Elizabeth thank you we are at the top of the hour appreciate both your talk and Barbara's really outstanding work. Thank you for sharing all of it with us. And to everyone who joins us today, thank you for taking the time to join Grand Rounds and we'll see you all again next week. Have a good day.
Yale Cancer Center Grand Rounds | December 1, 2020
"Synthetic Lethal Therapy of TP53-mutated Head and Neck Cancer" and "Environmental and Sex- Specific Molecular Signatures of Glioma Causation"

Barbara Burtness, MD and Elizabeth Claus, MD, PhD