And the first talk is entitled, How Strong and Sustained Activation of the Estrogen Receptor Mediated Anticipatory Unfolded Protein Response Kills Breast and Ovarian Cancer Cells. I introduce myself? Yes. All right. So good morning. My name is Shantanu Ghosh, and I'm from the University of Illinois at Urbana-Champaign in the lab of Dr. David Shapiro. And I will just get started. OK. Good morning, everyone. I'm very happy to be here. Before we get started, these are my disclosures. So today, I'm going to talk to you all about one of the great challenges in modern medicine, which is the challenge of metastatic estrogen receptor positive breast cancer. Now, we all know that even though there has been considerable therapeutic progress, there is currently no cure for metastatic breast cancer. And that is true for cancer in general. So today, I'm going to talk to you about a novel mechanism or way that we have approached this challenge. Through screening and chemical optimization, our research team at UIUC identified Urso, which is a novel kind of drug that has proved to be extremely effective against metastatic cancer. And let me show you an illustration of that. So this is a mouse that has been xenografted with MCF7 cells containing the most lethal of the estrogen receptor alpha mutations, the Y537S mutation, and as well as luciferase. And we have used bioluminescent imaging and CAT scans to visualize the tumor. So if this would just work, this mouse has a tumor on its upper spine, lung, liver, and lower spine. This mouse would be dead in a week if left untreated. So Urso, which is completely orally available, has managed to induce the regression of all tumors in just seven days. Now, I know what you're all thinking. What if there are a few cells remaining that can actually develop resistance or grow to be fully formed tumors? What we see is that if we leave the mouse untreated for four months, none of the tumors grow back. Now, this was a very good result. Urso has unprecedented levels of efficiency against primary and metastatic breast cancer, and we have seen similar results in ovarian, endometrial, and actually other types of common cancers as well. What is really unique about Urso is that even though it works through the estrogen receptor, it does not compete with estrogens. So current cancer therapies like OHT or tamoxifen, ICI of powerful vestibule, and these two other selective estrogen receptor degraders do not compare to the level of efficiency that Urso does. Urso induces very good regression of the tumor in just 24 hours. This is a 24-hour trypan blue dye exclusion assay. So what did we know about the mechanism of Urso before I started work on this project? So after years of research, we actually saw that the Urso forms a multi-protein complex with the estrogen receptor alpha and activates the endoplasmic reticulum stress sensor, the unfolded protein response. I won't go into the details of this pathway too much, but I would like to draw your attention to a few key features of this pathway. One is the intracellular calcium increase, which is the first step of this pathway, happens in under two minutes, as well as near-complete inhibition of protein synthesis as well as ATP depletion. We also observe rapid cell swelling and a necrotic mode of cell death, and we initially attributed this to the ATP depletion and protein synthesis inhibition. But that still left us with the question, how do the cells swell up? What is the exact mechanism of cell death, and what is the role of necrosis in all of this? So as you all might know, apoptosis and autophagy are very well-known orderly pathways that have clear biochemical markers and are not known to induce the immune system. But in contrast, necrosis, or anti-cancer drug-induced necrosis, do not have very clearly defined biochemical markers, and since necrosis is sort of literally ripping open the membrane and releasing its intracellular contents, it does, in fact, attract immune cells to the site of necrosis and induce immunogenic cell death. So to identify proteins that are important for this necrotic pathway, I performed a genome-wide CRISPR screen with negative selection against our anticipatory unfolded protein response hyperactivators. I won't go into the details of this technique in the interest of time, but I'm happy to talk about it afterwards. But essentially, what we're screening for are proteins which, if we knock out, would confer resistance to ERSO. And surprisingly, the top target from this screen was a protein called TRIP-M4. TRIP-M4 is a ion channel, as you can see from this model. It is a calcium-activated, ATP-inhibited, selective sodium ion channel. And since our pathway increases cytosolic calcium and depletes ATP, it was perfect. It fit our pathway really well. So what we were thinking is that due to the tenfold increased concentration of sodium on the outside of the cell compared to the inside of the cell, activation of the TRIP-M4 would cause sodium ions to rush in. And then chloride ions would follow suit through passive chloride channels to balance charges. And then the H2O would follow to maintain osmolality, causing the cells to swell up, the membrane to rupture, and this might have been the mode of cell death. So what evidence did we have that TRIP-M4 was playing this extremely pivotal role in this pathway? So I knocked out TRIP-M4 in five experimental models, and I'll show you data for one of them here. This is a flowchart of what I just described, and I will show you the data for these last two steps. We see that knocking out TRIP-M4 completely abolishes the effect of ERSO on cell swelling. As you can see here, these cells don't swell up, and also the knockout cells do not die at all. They're completely resistant to ERSO now. So this, however, did not tell us if we would observe a similar result in tumor models. So using a similar experimental model of knockout and wild type, we did this in mice and saw that this beautiful regression of the wild type tumors with ERSO and the knockout tumors just keep growing. They're completely resistant to ERSO. ERSO has unprecedented levels of efficiency against primary and metastatic breast cancer, and we have seen similar results, like I mentioned, in ovarian and endometrial as well. But this, however, led to a problem, led to a confusion, that even though we could say that the cell swelling would be the cause of rapid cell death, we still would be seeing protein synthesis inhibition and ATP depletion, and that would eventually lead to the long-term death of these knockouts, we were wondering. So I had to go back and really look at the pathway again and see whether knocking out TRIP-M4 is affecting the lethal sustained activation of the unfolded protein response, and what I saw was quite dramatic. I saw that knocking out TRIP-M4 completely also abolishes ERSO's effect on the UPR. We do not see inhibition of protein synthesis. We do not deplete ATP in these cells. So it really suggests that TRIP-M4 is playing a much more important role in these cells, that the cell swelling is somehow inducing this osmotic stress-induced UPR activation. Of course, this could also suggest that in knockout cells, the TRIP-M4 is somehow causing the ERSO to not bind the estrogen receptor at all, and we do not initiate the pathway through the increase in intracellular calcium. So I had to go back further and take a look at this pathway, and what I actually saw was that in both the wild type and the TRIP-M4 cells, we do in fact initiate the pathway as evidenced by the increase in calcium in these two cells, but in the knockouts, we do not sustain this UPR activity. So it's a sort of reciprocal mechanism, if you think about it, where the ERSO-induced increase in intracellular calcium is causing the cells to swell up, and this swelling-induced osmotic stress is causing the sustained activity of the UPR and killing the cancer cells. So, because we're researchers and questions never really end, we had to ask ourselves, is this really only applicable to our system, or is it actually a generally applicable mechanism to other systems as well? And currently, we have four experimental models of cancer therapies that are all largely abrogated by the knocking out of TRIP-M4, and I'll show you data for one of them. This is data for LTX315, which is an oncolytic peptide that initially attacks the mitochondrial membrane and induces immunogenic cell death. And we see that the ability of LTX315 is largely reduced in these knockout cells to kill these cells. So this suggests that we're really starting to unravel this widely available necrotic cell death pathway through which a lot of different cancer therapies might funnel through, and this has certain consequences, and one of these consequences is the immunotherapeutic potential of this pathway. So let's talk a little bit about that. Immunotherapy, as we all know, has revolutionized cancer therapy, but it is not without its problems, and one of the problems is immunologic deserts, which are these regions in solid tumors that do not have infiltrating immune cells. And so what that means is even if you activated the immune system, you do not have cells, the immune cells, in or around the tumor to actually induce immunogenic cell death. So in this experiment, I have used ERSO-treated media, not the cells, the media from ERSO-killed breast cancer cells, and I've shown that it has a very robust effect in causing the migration of immune cells across a membrane in comparison to Raptonil, which is an apoptosis inducer, and we see that even in comparison to the knockout cells, the ERSO-treated cell media causes incredible migration of differentiated THP1 human monocytes. So in conclusion, ERSO has a remarkable ability to induce regression of ovarian, endometrial, and breast cancer cells, and actually other types of cancers as well. It works through a novel necrotic mode in which TRPM4 plays a pivotal role in sustaining the UPR as well as causing rapid cell death. We have seen that diverse necrosis inducers all converge at the level of TRPM4 with different activation mechanisms, and this means that we are really starting to understand anti-cancer drug-induced necrosis, and this actually might help us to select for patients that would most likely benefit from these therapies as well as enhance immunotherapy in these patients. These are my acknowledgments. I am very grateful to each and every one of them, and I will take any questions. Thank you. I'm Surajit from Hormel Institute. My question is, you said that it acts through the estrogen receptor. So is TRMP4 acting without the estrogen receptor? Or have you looked at any breast cancer cells where there is no estrogen receptor, but there is TRMP4? And do you see the same kind of effect or not? Yes, that's a very good question. We have published that ERSO exclusively works through the estrogen receptor. That is the beginning of the pathway. The initiation is through the estrogen receptor. And we have knocked down the estrogen receptor to see that the ERSO doesn't work. We've seen that ER negative cells don't work with ERSO. We have knocked down, we have knocked in estrogen receptor in ER negative cells to see that now ERSO miraculously works again. So yeah, these two components are important in this pathway. So we knew about estrogen receptor, and now we know about ERSO. But ERSO has far more far-reaching necrotic implications. Any more questions? I think we could still have one more. No? Thank you, then. Thank you. Thank you. OK, our next talk is entitled Steroid Receptor Coactivator Complexes Regulate Metabolic PFKFB Enzymes to Drive Therapy-Resistant ER-Positive Breast Cancer. Let me set the clock here so you get a one-minute warning. Go ahead and introduce yourself. So my name is Tu Truong, and I'm a postdoc fellow at the University of Minnesota in Dr. Carol Lang's lab. And I'm going to be talking about a collaborative project between Carol Lang's group and Julie Ostrander's group, who's also at the University of Minnesota. So ER-positive tumors tend to relapse late compared to other subtypes. And what I mean by that is recurrence that occurs more than five years after diagnosis. And this subtype tends to respond well initially to endocrine therapies, but up to 40% of patients will eventually develop resistance that can then progress to metastasis. Now, contributing factors to late recurrence in metastatic spread include the cancer stem cell population. And these cells tend to be poorly proliferative and can exist as minority populations in resistant breast tumors. And then I also want to mention a couple of other resistant mechanisms, and those being deregulation of co-regulators, and then altered metabolism. And we also believe that those contribute, at least in the context of our work, to driving cancer stem cell survival and self-renewal. Now, this is going to bring me to the key players of our story, which are seroreceptor co-activators PELP and SRC3, and how they cooperate in ER-positive breast cancer with metabolic enzymes known as the PF-KFBs. So PELP was first identified as an ER co-activator, and it's primarily found in the nucleus and normal breast tissue, where it acts as a co-regulator. It can also dynamically shuttle to the cytoplasm, where it acts as a scaffolding protein. PELP is overexpressed in more than 80% of breast tumors, and altered localization of PELP to the cytoplasm has been seen in up to 50% of PELP-positive breast tumors. And because of this, cytoplasmic PELP has emerged as a marker of increased breast cancer risk, and also aggressive tumor behavior. And so the goal of our studies was to kind of understand what contributes and promotes oncogenic PELP signaling in ER-positive breast cancer. So in order to study signaling in the context of cytoplasmic PELP, we generated a model in MCF7 cells, where we knocked down endogenous PELP, and then re-expressed either wild-type or cytoplasmic PELP by mutating the nuclear localization signal, and that mutation is shown above the immunofluorescent images. And so this approach has been used by our group and several others in order to slow the rate of nuclear translocation, and then to ultimately increase steady state levels of PELP in the cytoplasm. And down on the bottom, I just have some representative immunofluorescent images showing that our cell line models do behave as we would expect them to, with wild-type PELP being primarily nuclear, and then the cytopelp-expressing cells containing a larger fraction of PELP in the cytoplasmic compartment. And then just to make a long story short, we performed a tap-tag mass spectrometry study in order to be able to identify SRC3, which is another ER co-activator as a unique cytoplasmic binding partner of PELP. And we confirmed these results in co-IP assays. Using tumor sphere assays, which are an assay to measure stem and progenitor activity and perform in the 3D culture conditions, we do see that in the cytopelp-expressing cells that they have increased ability to form tumor spheres relative to the vector control and wild-type PELP-expressing cells. And then when we knock down SRC3, we see a reduction in PELP-induced CSE populations. And in addition, when we treat with SI2, which is a small molecule inhibitor of SRC3, we see that there is a disruption in that co-activator complex. And so based on these results, we wanted to further define how that PELP SRC3 co-activator complex acts as a mediator of pathways that are involved in CSE-driven therapy resistance. So I've mentioned before that breast cancer stem cells are a minority population, and this makes it somewhat difficult to detect specific changes in gene expression. When we culture our MCF7 PELP models in 2D-adherent or 3D tumor sphere conditions, we can see that it's really those 3D conditions that drive the increase of known breast cancer stem cell markers, such as the aldehyde dehydrogenase activity and the CD44 high, CD24 low population. And so then using these conditions So then using these conditions in order to enrich for that CSE population, we perform an RNA-seq study in order to be able to identify specific changes that are related to cytopelp-regulated pathways, and then also to distinguish any differences between 2D and 3D culture in the cell line models that we test. And so the Venn diagrams here just represent and illustrate that 3D versus 2D comparison. And so what we did next was we then took the cytopelp-specific up and down-regulated genes and performed pathway analysis on that. And so our IPA upstream regulatory analysis revealed that 3D-cultured cytopelp cells are predicted to up-regulate HIF-activated pathways, particularly those that are involved in cellular metabolism. And just over here in the right, I have a representative heat map just illustrating those 3D-specific cytopelt-regulated pathways. And then the red arrows are just pointing to genes of interest to us, and we ultimately decided to focus on PFKB3 and PFKB4, which are known metabolic bifunctional kinase phosphatases with roles in breast cancer. QPCR analysis shows that mRNA levels of EPAS1, so that's this genome for HIF-2, PFKB3 and PFKB4 are up-regulated in the cytopelt-expressing cells in 3D but not 2D conditions. And then additionally, when we perform C-HORS cell energy phenotype tests, we do see that cytopelt-expressing cells result in the increase of mitochondrial respiration and glycolysis, so suggesting the potential for some metabolic plasticity up here as well. Now taking this into account, we hypothesized that the PFKBs would be important components of that PELP SRC3 complex. We do see that in co-IP assays, that the PELP PFKB3 and the PFKB4 interaction are increased in the cytopelt-expressing cells. And then when we treat with PFK158, which is a PFKB3 inhibitor, and 5-NPN, which is a PFKB4 inhibitor, we see a reduction in the PELP SRC3 interaction, and we measure this using a proximity ligand assay, and we also see a reduction in MCF7 PELP-induced tumor spheres as well. And so we wanted to take what we knew so far and see if any of this PELP SRC3 biology that we had discovered was, you know, in any way applicable to chemotherapy and endocrine therapy-resistant models. And so for those studies, we utilized a paclitaxel and a tamoxifen-resistant cell line. The first thing we did was we analyzed endogenous PELP localization, and both attacks are in the TAMR lines. And we did this by culturing the cells in 2D adherent or 3D major gel conditions. And so I want you to first focus on the 2D conditions. And so, and both attacks are in the TAMR lines. We stain for PELP and then analyze PELP localization from immunofluorescent images. And we do say that in the 2D conditions that both attacks are in the TAMR lines contain increased endogenous cytoplasmic PELP relative to the matched parental control. And that trend is further enhanced when the cell line models are cultured in 3D major gel conditions. Additionally, we also see an increase in endogenous PELP SRC3 coactivator complexes in both the Taxar and the TAMR models. When we evaluate mRNA expression levels, specifically the PFKB3 and PFKB4 levels, we do see an increase as we observed in our MCF7 and PELP model within both the Taxar and the TAMR lines, particularly in 3D culture conditions. And then of course, we have also evaluated their behavior in a tumor sphere assay to measure CAC populations. And so we do see in both the Taxar and the TAMR lines that the basal tumor sphere formation is increased in both the Taxar and the TAMR lines relative to their parental control. And then when you treat with inhibitors of SRC3 and the PFKFB inhibitors, you see a reduction in the tumor sphere formation as well. And so I just want to leave you with a few key takeaway points from the studies that I've shown you so far. Our studies do demonstrate that PELP localization in the cytoplasm alters signaling with metabolic enzymes known as the PFKFBs in breast cancer cell models. When we block PFKFB activity, we're able to disrupt that PELP SRC3 coactivator complex from forming, then ultimately any PELP-induced CAC that arise from that. And then finally, we demonstrate that in chemotherapy and endocrine therapy resistant models that a lot of that PELP SRC3 biology that we saw in our MTSM and PELP re-expression models is phenocopied in the Taxar and the TAMR lines, indicating that that coactivator complex biology is an important mediator for therapy resistance. And so I just want to end with a little bit of preliminary studies that we've done so far to extend our published work. And so we have done the RNA-Seq study for specifically the Taxar model to compare 3D versus 2D conditions. And our initial pathway analysis reveals a couple of pathways of interest to us. I'll first point out the SOX2-OCT4-NANOG axis, and that's a known stem cell pathway. But then additionally, we also identified that progesterone-mediated pathways are predicted to be upregulated in the 3D culture Taxar cells. And this was of interest to us because other previous work from our lab has demonstrated that PR expression contributes to CAC populations in ER positive breast cancer models. And so when we either perform a Western blot analysis or flow cytometry, we do see that in 3D-cultured Taxar cells that both total and phospho PR levels appear to be increased in the Taxar lines relative to the parental control. We do think that this seems to be estrogen-independent. So our ongoing studies are now focused on looking at the cooperation between PR, the co-activator complexes, and then their associated binding partners such as the PFKFBs in order to drive paclitaxel resistance in CAC populations. And so with that, I want to end with my acknowledgments, thanking both my post-doc mentor, Carol Lang, and the people that were involved in this study, and that would be Kyla, Carlos, Betsy, and Ying, my longtime collaborator, Julie Ostrander, who is co-senior author on the studies that I've discussed today, and then our collaborators and our funding resources. David Shapiro, Illinois. Very nice talk. So if you reverse this effect with the inhibitor, do you also have any effect on tamoxifen resistance or Trax resistance or just the metabolic parts? We, you mean like in terms of... In your TamR and TaxR resistant models, does resistance reverse at all if you block this pathway? Yeah, so when we block this pathway in tamoxifen resistant... Sorry. Okay, yeah, so... It's possible you have it. Yeah, yeah, we need to look at that. Beautiful talk too. Becca Riggins from Georgetown. I was curious, do we know how these cytoplasmic complexes lead to increased expression of the PFKFB enzymes? We don't know specifically, and so that's what we're looking at. We're hoping to use like a combination of CyTOF studies to look at that as well. Eric Nelson, University of Illinois. So very nice. I have a question on the last part where you're showing that there's increasing PR. Do you know if there's a shift in the PR isoforms at all? Yeah, we haven't looked at it in terms of just PRA or just PRB. We're just looking at it in both of them so far, but that would be good to distinguish between the PRAs and PRBs just because we know that those behave differently, at least in the CSE population. Right. And in silico anyways, I guess you're thinking that PALP1 is directly interacting with PR maybe? I do think that it is acting as a scaffolding protein to get PR activated, just because we do see an increase in specifically the serine 294 site, which we have tracked that in a separate study to CSE populations. Cool. Thanks. Last question. Hi, my name is Taylor Baker. I'm a student at Case Western Reserve University in Cleveland, Ohio. Awesome work. And I was really curious if you've investigated the role of PALP1 in ER negative cancers, specifically like I'm thinking more triple negative breast cancers as they are typically become resistant to pacletastic. So I wasn't the one that looked at it specifically, but people in Carol Lang's lab have looked at PALP in triple negative. So I believe that they do see an increase in PALP expression, and that appears to be also correlated to increase HIF1 and HIF2 expression as well. And so I wouldn't say the same thing, but there does seem to be a correlation, at least in the triple negative. Cool stuff. Thank you. Thanks. Thank you. Thank you. Okay. Our next talk is entitled Analysis of Testes in Rich Genes in Estrogen-Dependent Genome Regulation in Breast Cancer. Go ahead and introduce yourself. Okay. Good morning, everyone. First of all, I would like to thank the organizers for giving me this opportunity to share my lab work. And I'm an assistant professor and separate scholar in cancer research in the Department of Molecular and Translation Medicine at Texas Tech University Health Science Center at El Paso. Thank you. Sorry. As the title suggests, I'm going to talk about how the testes in rich genes, majorly which are transferred from chromosome X, and they are often upregulated in cancers. They regulate estrogen-dependent genome regulation. And I have nothing to disclose. As we all know, 70% of the breast cancer diagnosed are positive for ER receptor. And around 20% are positive for HER2 receptor. And around 15% are for triple negative. However, what I would like to emphasize here is even though they have very good clinical outcomes, but 50% of them become hormone refractory over a period of time. So what we are trying to understand is, are there any novel molecular determinants which can influence or which can regulate estrogen-dependent signaling in ER-positive breast cancer? And what we are trying to address is one specific question. So far, as you all know about cancer testes antigens, here we are trying to understand some of the novel genes which were hidden because of the lack of genomic analysis in the past. And we think that the genes which are transcribed from chromosome X, especially which are expressed in testes, they can regulate estrogen-dependent genome regulation. What is unique about them is they are very specific to immune-privileged organs. When I say immune-privileged organs, they can be eyes, testes, placenta, or brain. And mostly they are present in the gene deserts, which it was not possible before to identify them. And they are localized on chromosome X. And the majority of them are linked RNAs or long non-coding RNAs. When I say long non-coding RNAs, that means we don't know its potential to code for proteins. Since they are conserved only in primates and humans, they are not even conserved in mouse, even at the position on sequence level, not even like 1% similarity. So, we used an integrated genomic approach, and we identified all possible genes which are transcribed from chromosome X, and the list included coding and non-coding genes. And they are regulated by estrogen. I'm going to talk about one specific example, which we named it as upregulated in ER-positive breast cancer. And what is unique about this is, this is robustly upregulated by estrogen in a dose-dependent manner. And moreover, when you were to fractionate MCF7 cells into cytoplasm, nucleoplasm, and chromatin, what we found is, with or without estrogen, it is localized, majorly localized in chromatin, and partly localized to nucleoplasm. So, further to confirm what my post-doc, Ramesh, has done is, we used a unique technique, which is RNA in-situ hybridization, which has like 20 prose tiles with the RNA, and it presents as a dot aggregated signal from all the 20 probes. And we used RNA-ish, it's not fish, in-situ hybridization on a tumor microarray, which consisted of like around 20 normal samples, and around 50 ERPR-positive breast cancer tissues. And shown here is a representative image of normal, which is not, and it's not present in normal tissue, and it's present in a punctate manner. More, what we, the RNA-ish also provides you the copy number of this particular link RNA. It's majorly localized in nucleus, as well as it's present in one or two copies, or probably three copies in some other tissues, which confirms our fractionation RNA-seq. And this tumor microRNA, this tumor microarray has linked to clinical data, and when we plot the survival curves, what we found, that expression of this ULAB correlates with poor outcome in luminal A breast cancers, as well as ER-positive breast cancer tumors. Now, this is at the tissue level. Now we wanted to find a cell-lines model where we can verify, or what is, to address the mechanism. So, we went ahead and performed RNA-ish in several of the breast cancer cell-line, ER-positive breast cancer cell-line, MCF7, T470s, ER75-1. Here is a representative image of ER-positive MCF7 cell-lines. It is a negative control, positive control, and what we see, what we have seen in tissues, that it is majorly localized inside the nucleus. It's co-localized with the hematocycline, and it is present in a punctate manner. Somewhere you may see like a bigger dot. That means that concentration is high, and moreover, it's present in four or five copies. And then, we looked at the expression level across different cell-lines. Its expression is comparable in normal ER-positive and ER-negative cell-lines, and we designed specific siRNAs to knock it down in ER-positive cell-line and ER-positive MCF7 and T470 cell-line, shown here is the efficiency of knockdown. And what we see is, upon knockdown, in the absence of estrogen, it could knockdown growth of ER-positive MCF7 cell-lines. With estrogen, it also inhibited the growth of ER-dependent signaling. Also, it inhibited estrogen-dependent growth of T470 cells. This is by siRNA knockdown. Then, we took a gain-of-function approach. We created ectopic overexpression of ULIP in MCF7, T470, and 468 cells. This, we took it as a negative control, shown here is the efficiency of overexpression. What we found is, this acts as an amplifier of estrogen signaling, and without the estrogen, it could promote the growth of ER-positive cells. And also, when you add estrogen, it amplified the estrogen-dependent growth of MCF7 cells. Then, in T470, even in the absence of estrogen, it promoted the growth of T470. However, it had no effect on MDA and B468 ER-negative cells. So then, we took these cell-lines, put it in xenograft model. What we see is E2-dependent growth of MCF7 xenografts. It's all done in the presence of estrogen and in the presence of doxycycline. We tried to doxycycline to express this particular link on a physiological level. And what we see is E2-dependent growth of MCF7 xenografts, and also E2-dependent growth of T470 xenograft cells. Then further, to confirm, again, we deleted the promoter of this particular link on a ULIP. It's a 500 base pair deletion. And we confirmed through RT-QPCR, showing that we completely knocked out this gene. And we took these cells and put it in xenograft. And we saw a completely opposite phenotype, what we saw in over-expression cell-line, that when you knock this one out, it inhibits E2-dependent tumor growth of MCF7 cells. Further, as we saw, it amplifies estrogen-dependent signaling, and also it can work without the estrogen. So we feel that, we think that it works, it acts downstream of estrogen ER receptor or estrogen signaling. So we performed a total RNA sequencing. It's not a poly-sequencing, we performed total RNA sequencing, just the ribosome RNA depleted. So that since it is localized to nucleus, we want to interrogate all the enhancers, as well as the promoters, and the open regions. So even in the absence of E2, what we found, that the genes which are induced upon expression of this particular link RNA, those genes are implicated in pneumelin breast cancers, and which are generally down-related in basal, that means triple-negative breast cancer. So further to interrogate its mechanism, we took a different approach. We interrogated the chromatin regions for open and closed chromatin regions in ER-positive cells. For that, we used ATAC-seq, which assess for, as is shown in the diagram, closed as well as open chromatin. This is the assay for transposase accessibility. And following that, we sequenced the entire DNA after ectopically overexpressing ULEP, and ectopically overexpressing GFP. And surprisingly, what we found, that we took all the genes induced. We looked at the promoters, enhancers of the genes which are induced upon ectopic overexpression of ULEP, and we found that when you overexpress ULEP1, there's an open chromatin conformation at the peak center, as well as the transcription start sites of ULEP-induced genes. So far, so our working model. So the rest of the data which I'm not showing here is, we are looking at the in vitro reconstituted chromatin stop assay, as well as the histone chaperone assay, whether it can modulate the nucleosome assembly in vitro per se. And we found that it interacts with histone H1 and HMGB1 kind of chromatin proteins. So our working hypothesis is estrogen induces the expression of this link RNA and leads to the open chromatin conformation. When it is not present, it leads to close chromatin conformation. So we think that it's an amplifier of estrogen-dependent assembly. So this is one of the example. We have other example, which kind of works in the same manner. And here are some of the key messages we would like to share with you, that these genes are present in reproductive tissues, specific to immune-privileged organs. They can be regulated by estrogen. And they have specific clinical outcomes where they are expressed, which cancer they are expressed. They are localized to nucleus, as well as chromatin. And they modulate chromatin structure to regulate gene expression. And at the end, I would like to thank all the organizations which support our research. Here is our lab group. And especially, I would like to thank Enrique and Ramesh, who are spearheading this project. Now I would like to take any questions you have. Thank you. Hi there. Eric Nelson, University of Illinois. I'm going to get my steps in, just going from my seat over here. Very interesting work. I have two questions. First is on the last chromatin analysis you're looking at. Can you go in and see, are there enriched sites for various transcription factor writing? We did. And what we have seen is mostly the open chromatin regions we saw at the TSS, Transcription Start Site. And some of them also co-localizes with estrogen response elements. So the next word, we are integrating this data with the ER-alpha ChIP-seq and also STK27 acetylation and 4-monomethylation ChIP-seq. See that, whether it's just a localization or whether it affects the recruitment of those factors as well. Yeah, that leads into my second question, which is, if you could speculate on mechanism, what's it doing? Is it acting as a scaffold RNA, or how's it opening up the chromatin? So what we feel is that it interacts with histones, right? It retrieves the histones from the regions, which target regions, like upon estrogen. But we have also seen that without the estrogen, it could do that function. However, it needs a signal, and we think that signal is the estrogen. So for that, we are reconstituting in vitro nucleosomes and putting this link RNA with the histones and see whether it can upload it on the template, or it can unwind it. Thanks. Thank you. So if you, David Shapiro, Illinois, so if you take breast cancer cells, say, that are fulvestrate-resistant, is there still an effect of ULAB on gene expression and growth? Or do antagonists, especially ER degraders, actually block its action? So we have, so we try to do this in two ways. So we took the commercially available tamoxifen-resistant cells. And what we have found is those cells are similar to kind of like ATCC cells. What I see is, I'm going to tell you, that they are mostly resembles triple negative. So we made our own tamoxifen-resistant cell line, which took like more than one year or so. And in the presence, the expression, it's like a five-fold high in tamoxifen-resistant cell line compared to tamoxifen-sensitive cell line. So we are going back and trying to do the same asses to see that how it exactly functions in those cells. Very interesting. Thank you. Hi, Chris Glass from UCSD. Thank you for that interesting talk. Normally, in female cells, there would be two copies of X, one inactive and one active. So I'm wondering what the basis of upregulation of this would be in cancer, and whether you've looked at the possibility that there might be defects in X inactivation. Yes. So that's a very good question. We think it's a loss of heterozygosity. And so the other piece of data which I have not shown you is very specific triple negative cell lines, not M468, not HCC1143. If you take M157, M1159, which completely resembles real triple negative. So it acts as a tumor suppressor. And we think that when there's a loss of heterozygosity, the tumor expression is gone. Then it loses that. And that's how it is regulated. Through loss of heterozygosity. Yeah. OK. Many breast cancer cell lines don't have X inactivation. So if they don't, they lose it again. We're over, but we can take one quick question. Very fascinating talk. Sayi Anak from Illinois. Just thinking about ULIB expression, you said is it restricted to the immune privilege area, like the eye and the? Eye, brain. And the placenta and testis. So do you think it's just specifically regulating some proliferative cues there? I'm just trying to understand all the cool mechanistic things link RNA ULIB could be doing, for instance, in retinoblastoma, which may not be ER associated. Yes. So you think it's a proliferative cue? I'm just. Possible, because we see exactly opposite mechanism in glomer cell lines. Yeah. Thank you very much. Thank you. Our next talk is entitled Genomic Interrogation of the Phytoestrogen Genostine in Ovarian Cancer Cells. All right. Hello, everybody. My name is Tim Kuo, and I'm representing University of Texas Southwestern Medical Center. All right. All right. So before I begin, I just want to give acknowledgment to my postdoc mentor, Dr. Lee Kraus. He has been instrumental in supporting my scientific career and supporting this pet project that I have started about three from four months ago. So if you are a graduate student or even just a junior postdoc who's interested in the research that we're doing in Kraus' lab, please find him and go talk to him and at least meet him and shake his hand. So why am I interested in ovarian cancer? So one of the nice things about working at UT Southwestern and at the Green Center for Retroductive Biology Sciences is the fact that we have gynecological oncologists fellows who rotate through the lab. So working with them and just getting to talk to them, obviously, they work on ovarian cancer, and that's how I got interested in ovarian cancer and the basic science of ovarian cancer. So although ovarian cancer is relatively rare in terms of the cancers in females, it is actually quite deadly because of the fact that a lot of these ovarian cancers detected late in their disease stage. So survival rate is actually not very high. Now, talking to these clinicians, when I was talking to them about hormone receptors, what they told me was that a lot of these ovarian cancers, in the clinics at least, they don't consider them to be a hormone-dependent cancer just because of the fact that they are detected at a later stage in a female life, so mostly postmenopausal. However, if you look at the literature, the induction in biology of ovarian cancer is actually related to exposure to estrogen. And in my hands, at least, ovarian cancer, they do respond to estrogen. So at least at the basic science level, there is a response to estrogen. So I'm interested in dietary molecules and dietary bioactives because in my PhD, I was working on fish oil and anti-inflammatory effects of fish oil. So working in Li's lab and being interested in estrogen receptor, of course, I was going to look for a compound that's already known to interact with estrogen receptor. That would be an easy project to start off with. So genistein is a naturally occurring esophageal bone with bioactive properties. So it's found mainly in soybeans. And because of its structural similarities to 17-beta-estradiol, the main estrogenic compounds, it has been studied in the context as a selective estrogen receptor modulator. And several epidemiological studies have suggested a correlation between high soy consumption and reduction of ovarian cancer risk. So for example, in the Asian population, women who consume a lot more soy have a reduced incidence of ovarian cancer. As a population as a whole, Asian female population have, again, a lower incidence of ovarian cancer. So again, the focus of genistein has been on estrogen receptor beta and interplay between the beta and alpha. But I wanted to go beyond that. So I wanted to use an unbiased method and approach to study genistein. So again, we have hormones. We have our diet. And of course, we're interested in how that leads to biological outcomes. So in Li's lab, we're very interested in how receptors engage enhancers and how that ultimately leads to targeting expression. So focusing on enhancers, what are enhancers? So enhancers are these DNA elements that are littered across the genome that act as nucleation sites for transcription factor binding, recruitment of co-regulators, and ultimately controlling targeting expression. So we can characterize enhancers with different chromatin marks, different transcription binding factor recruitment, as well as machinery that gets recruited. And of course, if you went to my talk yesterday, I'm interested in enhancer RNA and using enhancer RNAs as markers of active enhancers. So I won't go into too much details about the molecular properties of enhancers, but just that I'm using enhancer RNA and enhancer transcription as markers and genomic tools to study what are the enhancers that are activated by exposure to genistein and what are the possible transcription factors. So here is just a browser track. This is in MCF7, or sorry, 231 cells expressing ER alpha. We did ChIP-seq on it, as well as GroSeq. So in GroSeq, what we do is we incubate nuclei with labeled nucleotides so that we can capture nascent transcripts. So the advantage of using ProSeq and GroSeq is that you can look for unstable transcripts, such as enhancer RNAs. And as you can see here, we see tracks that correlate, overlap with ER alpha binding. So these are enhancers that are bound by estrogen receptor that also have enhancer transcription. We also have tracks or regions where there's enhancer transcription, and they're not bound by ER alpha. So we can use motif search to look at what could be the possible transcription factors that are bound there. Another nice thing about using GroSeq and ProSeq is that you can actually also look for targeting expression. So here, looking at this, you can see the induction of the gene upon exposure to estrogen. So not only can you look at the non-coding genome, but you can also look at the coding genome as well. So I just started off using OVCAR3s and OVCAR4 ovarian cancer cell lines and just treated them with genistein and to see whether genistein had an inhibitory effect. And indeed, they did in both cell lines. So I used the concentration of 25 markimolar because that's the EC50 that I discovered in these cell lines. And this is actually quite high. It's five times the level of circulating plasma concentration in women consuming just a typical soy diet. However, looking at some clinical data, there has been reports of using at least 16 micromolar genistein. So perhaps this concentration, although it may not be physiologically achievable in a normal diet, perhaps there may be some, I guess, clinical reasons for using a higher diet like that. So just concentrating the OVCAR4 cells for now, I did DMSO treatment versus genistein for 48 hours. I didn't want to do anything longer because at that time point, the cells are dying. It's harder to harvest the dying cells. Prepare ProSeq libraries. Use DREG, which looks for bidirectional transcription. So one of the hallmarks of enhancer transcription is the fact that there's bidirectional transcription. So DREG is developed by Charles Danko at Cornell. It's just an algorithm looking for this bidirectional transcription. And then, of course, I looked for intergenic enhancers because I don't want to. It just keeps the data cleaner so that it's not overlapping or messaging RNA transcription. So I found about 5,000 genistein-specific intergenic enhancers, pulled those out, did a motif search, and what did I find? So again, I get a list of potential transcription factors that may be bound at those enhancers. What do they do? So again, this is very preliminary. So I just looked at GTEx data versus TCGA data. And about three, sorry, not about, but three transcription factors in that top five actually are slightly increasing normal versus tumor cells. So this is quite interesting because perhaps genistein directly or indirectly activate a group of transcription factors such that they can act and inhibit ovarian cancer cell growth. And of course, I have to do a lot of bench work to validate some of these targets as well. As I was preparing this talk, I realized I should have gone back and looked at whether genistein actually increased any of these expressions. So kind of a feed-forward loop. So of course, not only can I look at non-coding transcription, I can also look at coding transcription. So that's what I did, looked at counts for the genes. So we get up-regulated genes, down-regulated genes, and we're following through studying some of these as well. So just a summary. So genistein inhibits cell proliferation in ovarian cancer cell lines. And using PROCYP, we can identify previously unknown transcription factors that are regulated by genistein directly or indirectly. Of course, I still have a lot of work to do to define exactly what these targets are. And of course, working with clinicians, it would be great to validate some of these targets. And one of the interesting gene ontology biological processes is the fact that genistein may regulate translation. And our lab has reported very recently how PARP1 can regulate translation in both breast and ovarian cancer cells. So I'll direct you to these two papers if you're interested in that. So with that, I would be happy to take any questions. OVCAR 4, is it low or is it just not there? So in my hands, OVCAR 3s, OVCAR 4s, and I also had a SKOV 3, all of them express ER alpha and ER beta. My POSIT controls MCF 7, so my MCF 7 expresses pretty high levels of ER alpha. Compared to that, it's like minuscule, essentially. But they do express both. Okay, and then, so you showed data for OVCAR 4. Is it similar to OVCAR 3, or have you done that analysis? I have. It's messy. I'll just leave it at that. So are patients. Thank you. Hi, Salma Khan from Loma Linda University, California. Very nice talk. I have like two questions there, because there's individual variation, like here. So how do you address that? And another question I have is that, how do you approach the cardiotoxicity? As you know, isoflavone test, cardio, like, you know, for in clinical setup, what would be your approach? So the first question regarding individuals. So of course we know that individuals have SNPs, right? SNPs occur in both genes, as well as intergenic regions. So one possibility may be that a certain individual have a certain SNP, and that results in a transcription factor that is, can't bind or can bind strongly to that, right? So that individual may be, may have a SNP that may be protective, or, you know, pathological. And people, a lot of people have done a lot of work on that. So that may account for the individual differences. The second part in regards to the cardiotoxicity. So it's, so we have to study diet in terms of the whole biological system, right? So genistein, there's evidence that it's actually bad in terms of breast cancer. It can actually increase cell proliferation. So I would say it has to be case by case, you know? Pumping women with 25 micromolar genistein is probably not achievable. But at least, you know, with the studies that we're doing, we can identify targets that we can develop better targeted therapy for that. Thank you. Hi, very nice talk. Lindsay Trevino from City of Hope in the Los Angeles area. I was wondering if you're aware of the spontaneous model of ovarian cancer, the chicken. So it has been the studies that have shown that genistine does reduce development of ovarian tumors in the chicken. And we're using that at our institution. So just letting you know. Oh, thank you. Yes, I did see the literature. That's why I felt very comfortable going down this route. So thank you. Thank you very much. Thank you. Our last talk is titled, Rilazole Suppresses Growth and Enhances Response to Endocrine Therapy in ER-Positive Breast Cancer. I'll give your name and affiliation. How do I put my slides forward still? Matt, you hit this button to advance. It does have a link. All right. Thank you very much. My name is Ayodeji Okoya. I'm in Dr. Rebecca Riggins' lab at Georgetown University. And today, our talk is going to be on how Rilazole suppresses ER-positive breast cancer in, yeah, oh, sorry. All right. All right. So as we all know, the prevalence of breast cancer, one of the important frameworks in which we talk about or think about breast cancer is in terms of its hormone receptor status. And the two major statuses are the hormone receptor positive and the hormone receptor negative. And for the case of this talk, I'm primarily going to be talking about the estrogen receptor. And this classification or framework is important because it helps us to inform treatment. And so based off of the studies on the receptor types, we're able to develop endocrine therapy. And as you can see on the slides, the three major groups right there, and those groups affect the estrogen signaling in different parts of the estrogen signaling. However, as previous speakers have mentioned, endocrine therapy resistance is an issue that typically develops after a period of time. However, another important framework we need to consider when thinking about endocrine therapy resistance is the framework based off of histological classification. And there are two major classes of that. We have the invasive ductal carcinoma. We have the invasive lobular carcinoma. And as you can see, the ILC are pretty unique from the most common type, which is the IDC. The ILC, they account for 10% to 15% of breast cancer. They have the CDH1 mutation, which as you can see from the two pictures, you can see it affects and contributes to the linear growth you see right here. And the ILC also metastasize to different unique areas like the ovaries, GR tract, and the peritoneum. And they're nearly always ER. They're nearly mainly ER positive. And there's a higher risk of late recurrence post-diagnosis. So all of these characteristics lead us to believe that studying endocrine therapy resistance in this context can be very useful. However, there are some challenges that we face modeling ILC breast cancer. And one of them is the limited number of cell models available. And another challenge we face is that most of the ILC genetically engineered mouse models are typically ER negative. So you're left with either subcutaneous or mammified injections or patient-derived xenograft. However, previous work in our lab, we were able to develop a tamoxifen-resistant variant of one of the cell lines, the SOM44. And when we performed some molecular analysis on the pair of cell lines after treatment, when we compared both cell lines molecularly, we found out that in the tamoxifen-resistant variant that there is higher levels of GRMs, which are metabotropic glutamate receptors. And these receptors are mainly common in the central nervous system. And there are different groups of them, group 1, group 2, and group 3. However, literature has suggested that some of these groups have been implicated in breast cancer. And so what the GRMs do is they bind to glutamate, and then they're able to perform their function. So this led us to the idea that if we can treat with a glutamate blocker, maybe we can offset some of the changes we see in the resistant variants as opposed to the sensitive cell lines. And so this led us to the goal of testing the ability of realazole to inhibit ER-positive breast cancer growth alone and in combination with current endocrine therapy that we have. And so we performed this in vitro, in vivo, and also using an explant model. So first off, what we wanted to do is we wanted to treat with a various dose of realazole on several cell lines, both ILC and IDC. And this led us to selecting an IC50 value. And we went with the 10 micromolar, which was the concentration where majority of the cell lines we tested were about at the IC50. And so as you can see on the next slide, what we then did is we treated the several cell lines, which include a pair of ILC cell lines, the SOM44, which are the resistant, and then the LCC10, which are the tamoxifene, the SOM44, which are the sensitive, and then the LCC10, which are the resistant variant. And then we had the MM134s, and then the MM134 long-term estrogen-deprived cells. And we also had a pair of IDC cell lines, and then we had the MCF10As as control. And so what we did is when we treated with 10 micromolar of realazole, we can see the effect of realazole on the resistant cell lines. So you can see the SOM44, and you can see the effect of treatment with 10 micromolar realazole on the resistant pairs across the board. And as you can see, when we treated with the MCF10As, which were the control, we don't see the inhibition of growth we see in the other cell lines. And so we then move forward to see what the effects would be on cell cycle. And so we treated with the same concentration, 10 micromolar realazole. And after 48 hours, we performed cell cycle analysis. And as we can see, there is a differentiation in how both groups of histological cell types behave. So like we can see with the ILC, there was a G2M arrest as opposed to the IDC, which we see a G0, G1 arrest. However, you can see that with treatment with realazole, it affects the cell cycle of the cell lines. And so we then went further to see what the effect would be on cell death. And we chose to test this in apoptosis and pharoptosis. And when we treated with the 10 micromolar realazole concentration that we've been using, we can see that there is apoptosis in the SOM44s. But you can see that there is a higher impact than that on the LCC10, which is the resistant phenotype. And so we then went on to test pharoptosis, another form of cell death. And when we treat with realazole, we can see the effect on cell viability. And what we then did to confirm this is we then treated with ferrostatin, which is an inhibitor of pharoptosis. And so you can see when we have treatment with realazole, we can see the effect. And then when we treat with ferrostatin, we can see the reverse of the effect. And then we validated that using Western blood analysis. And what you can see to the far right is when we treat with realazole, we see an increase in MDA, which is a molecule that is increased during pharoptosis. And then when we treat it with ferrostatin, we see that its value goes down a little bit. And then we were also interested in seeing what some of the possible downstream signaling that is affected with treatment with realazole. And so we performed a phosphokinase array. And I've highlighted some of the pathways we thought were interesting. However, we decided to further test the focal addition kinase, one, because it's implicated in a lot of cell survival, and cell proliferation, and overall cell development. And also because it's important in cell addition. And as we've seen previously with ILC cell lines, they have the CDH1 mutation, which affects the addition. So what we saw when we validated the FAC pathway using the Western blood, we can see that according to this, we're supposed to see a decrease, which is what we see when we did the Western blood analysis. So we then moved this further to see what the effect would be of realazole in combination with known endocrine therapy. And the result I'm showing right here is the effect of realazole in combination with fulvestrant. And so as we can see from a majority of the cell lines that we've tested, the combination of realazole and fulvestrant seem to have a greater effect on inhibiting cell growth. And as you can see, it doesn't have any significant difference in the control MCF10A cell line that we tested. So we then moved this further into an in vivo. We wanted to move this to an in vivo analysis. And we used the HCI013, which is an ER positive E2 responsive ILC PDX model. And so there was two variations of the model. We have the HCI013 and the HCI013 estrogen-independent model. So since we've been testing the effect of realazole on, since we've seen previously that realazole has been doing very well in the resistant cell lines, in vitro we decided to go with the HCI013 estrogen independent model. And so what we can see from our results, when we treated the mice with the combination treatment of realazole and fulvestrant, what we can see is that we can see that the combination seemed to inhibit growth a lot sooner than the known endocrine therapy fulvestrant. And as you can see from the slope figures for the individual growth figures right here, we can see that the combination had a significantly higher slope from fulvestrant alone. However, the results we saw here were not as robust as we had seen in the cell line. And so we wanted to further test this in a hybrid model, which was the explant model. And so when we treated explants with realazole, we then stained for PCNA, which is a marker for proliferation. And as you can see from the images right here, you can see that the combination treatment reduces PCNA staining. And as you can see from the estimation plot right here, that when we had the combination treatment, we can see that there is a decrease, which is in line with some of the results we had seen in our in vitro studies. And so in summary, we're able to see that realazole inhibits the growth of ER-positive breast cancer. And we're able to see that realazole in combination with fulvestrant inhibited this growth in cell lines in our PDA model and also in our in vivo model. And with that, I want to acknowledge our lab and our collaborators. And with that, I'll take questions. Thank you. Hi, Eric Nelson, University of Illinois. Thank you. That was really interesting. Thank you. In your xenograft study, it looked like when you combined fulvestrant with the R compound, you were actually getting regression, which is rare for a CERD, at least in the MCF7 model. So my question is, have you followed those mice out? Do you continue to get regression? And so in that study that I showed here, when we treated with Realzol, even after we stopped treatment on some of the mice, we let them grow out to see if the tumors will come back. And in the short period that we did let them grow out, we didn't see any regrowth of tumors. So yeah. That's great. Thanks. Thank you. Matt Sikora, Colorado. Really nice talk. So if I understood right, so you see Realzol suppressing proliferation across IDLC and IDC, but the difference in cell cycle response is intriguing. Does that correspond to selective killing? Is Realzol killing ILC by apoptosis, but is it not killing IDC? I'm not sure, but I think that's something that'll be interesting follow-up, especially the cell cycle data, the differences in how they respond. I don't think we've followed that out yet, but I think that's something that's interesting and something we're definitely looking to follow. The last two questions were kind of related to mine, but I wasn't clear from your answer. Since you're getting an additional type of apoptosis, when you take the mice off treatment, does your combination of Fulvestrin with the Realzol take longer to start to recur, or did they both not recur? So in the study that I showed, when we took them off treatment, we didn't see any tumor recurrence in the combination treatment. Or the Fulvestrin only, or both? I can't remember exactly, but we're really excited about the fact that we didn't see any of that in the combination. But I can't remember if that was the case in the Fulvestrin one as well. And you haven't done a non-ILC, or non-lobular ductal model? No, we haven't done it in vivo. Thank you. Hi, Marina Holes, New York Medical College. So in your differential phosphorylation profile, you see FAC, but also mTOR and the KT signaling, which are regulated by FAC. So you think there'll be differential sensitivity to KT or mTOR inhibitors? Since they're related, you think that's the case. But we haven't tested any of those pathways that you mentioned. But definitely something to look into. Hello, great. Great talk. Very nice. Thank you. I do have a general question, because I'm not as familiar with ILC and IDC. Are there known disparities in either incidence or mortality in these subtypes? Yes, some literatures have shown that there are some differences in both subtypes. And I can't remember the study I'm thinking about, but there have been some differences in overall survival down the road in the difference. But are there differences like racial, ethnic, that kind of thing? So we know there are disparities in breast cancer in different subtypes. I don't know. That's a good question. Definitely something we could look into. Do we have back one? Yeah, there's nothing else to add to this for a while. Hi, really good talk. I really enjoyed it. Thank you. So I actually had a question. So initially, you showed that amoxifen-resistant cells have an increased expression of GRMs, and that is helping the result to actually function. So fulvestrin, which is an ER degrader, I would assume that it has a sort of negative effect on that sort of resistance mechanism. So have you seen that in cells treated with fulvestrin, the level of GRMs actually go back down? I think we haven't taken it into checking for some of the gene expression results quite yet. Right. It just felt a little counterintuitive, because the resistance to amoxifen happens because of the estrogen and the BCL-XL ratios. And so if you degrade the ER, I would assume that that sort of resistant mechanism, which is eventually causing the overexpression of the GRMs, would also go away. But it did actually work, but I'm just curious as to what the actual mechanism might be. Yeah, we've not followed the study into the exact mechanism of how the drug works. I think that some of the studies that we're planning and we're in the process of doing right now, but it would be definitely interesting to see. OK, thank you so much. Thank you, everyone, for coming. Thanks to the speakers for doing a great job and for the great audience participation. Thank you.

gene ULAB

estrogen-dependent genome regulation

breast cancer

poor outcomes

knockdown of ULAB

growth inhibition

ER-positive breast cancer cells

glutamate blocker RLZ

cell cycle

tamoxifen-resistant

focal adhesion kinase 1 pathway

fulvestrant