Exploring new frontiers in pancreatic cancer treatment with spatial biology

TRANSCRIPT

Hello everyone and welcome to today's DDN Webinar. I'm Marnie Willman, the assistant science editor for DDN, and I'll be moderating our discussion today. We have an exciting webinar planned. Our speakers, Dr. Yana Zavros, and Dr. Tad George will discuss how they combined spatial biology and patient derived organoids to uncover a distinct cell population driving therapy resistance and disease recurrence in pancreatic ductal adenocarcinoma. After the talk, Dr. Zavros and Dr. George will participate in a live Q&A session. To submit your questions or comments, simply enter your question into the Q&A chat box to the right of your screen. We'll try to get to as many of these as possible today.

I'd like to take this opportunity to thank our webinar sponsor, RareCyte Inc. RareCyte delivers innovative solutions for spatial biology and liquid biopsy. The Orion platform enables high plex single round, whole slide spatial profiling, empowering researchers to rapidly analyze protein expression, disease progression, and immune response for comprehensive biomarker discovery and translational research. Our sponsor has provided us with some helpful handouts related to today's webinar. You can find and access these in the handouts section located on the right hand side of your screen. Also in the handout section you'll find your certificate of attendance for participating in today's live event.

With that, let me introduce our speakers for today. Yana Zavros is the leading researcher in gastrointestinal disease and pioneered the use of human gastric and pancreatic organoids to study cancer progression and therapy response. She received her PhD from the University of Melbourne where her research focused on gastrointestinal disease. Previously she established the biology development and research of organoids core at the University of Arizona and led research initiatives at the University of Cincinnati advancing organoid based translational research. Now professor at the University of Georgia School of Medicine, she directs the research center focusing on TME interactions and personalized drug screening. Her recent work extends to Cushing's Disease where she developed the first pituitary neuroendocrine tumor organoid model securing a National Institute of Health RC-2 grant to advance research in this field.

Tad George is the senior vice president of biology at RareCyte where he spent eight years working closely with the engineering team to develop numerous products and applications, including the Orion technology for spatial biology. George has over 15 years of startup experience dedicated to creating scientific markets for novel instrumentation platforms that span basic research, drug discovery and clinical applications. Prior to joining RareCyte, he held similar positions at Biodesy Inc and DVS Sciences and was the director of biology at Amnis Corporation. George completed his bachelor's degree in biochemistry from the University of Texas, a PhD in immunology from UT Southwestern Medical Center and a postdoctoral fellowship at Immunex Corporation.

Let's take a moment to ensure the slides are working correctly before we begin. Alright, perfect. Dr. Zavros and Dr. George, you may proceed with your presentation.

Tad George

Thank you again for joining our webinar today. Yana and I are looking forward to discussing the application of spatial biology tools and techniques for cancer research and treatment. In terms of the agenda for the webinar, I'll give a brief introduction to one of the platforms that Yana's lab has used in their spatial biology studies. The bulk of the webinar though Yana will be discussing how her lab is addressing the challenges inherent to the treatment of pancreatic patients with pancreatic ductal adenocarcinoma. So when we're talking about applying spatial biology tools in the context of cancer, we're typically looking for platforms that can address the complexity adherent to tissue analysis. Because as you know, tissue consists of lots of different cell types, each with different functions and in different functional states and their spatial arrangement within the tissue can have a dramatic impact on patient health status.

So with spatial biology tools, you're using imaging technology, but you also need high plex at the same time. And if you're using fluorescence as your detection modality, that presents a challenge because there's only so many fluorochromes that you can address in a single round, and that typically translates to low throughput due to long staining and/or acquisition times. So at RareCyte we really developed the Orion technology to address that clinical translational gap in this space by providing a platform which can actually do high plex staining and imaging in a single round. And what we're talking about with Orion, that's 20 channels of data obtained with one shot staining and imaging approach. So this dramatically accelerates sample throughput. It also comes with low run costs while also preserving tissue and it works pretty much for any tissue type and indication and its whole slide.

And I really encourage you to look at probably the most fun part of our website here. Click on this link at the bottom, it'll take you to our interactive data sets. You can look at some of the examples of tissues and applications that we've done. For example, if you look at the mouse ileum one here, you can get a sense for the data quality super high. It's also a whole slide. I forgot to mention also, the single round approach is amenable even to doing same section H&E where we can align sort of morphological information from the H&E to the molecular information inherent within the IF staining. So you can see goblet cells hard to see by IF, but very clearly identified by the H&E. And it's of course not just made for one or a few samples. You can do large cohort studies. This is a large cohort colorectal cancer study that was done with our platform. So I really encourage you to look at that part of our website.

It's also fully quantitative. So with quantitative image analysis, it's pretty simple pipeline. You essentially segment the image into cells which allow you to quantify a primary data table. So each of these cells will now have intensities for each of those biomarkers along with locations, size and shape based information. This allows you to classify the cells into different populations and with their locations that allows you to derive spatial biomarkers that hopefully have predictive or prognostic value.

I got a couple examples to share with you in the colorectal cancer space. Of course, Yana's going to take quite a bit about applying this technology to pancreatic. But I have a couple of a prospective and a retrospective trial that was done with the Orion system in the colorectal cancer space. This was actually from a drug company that had found empirically that if they administered dual checkpoint inhibitor therapy to patients, they could dramatically reduce the size of the tumor prior to surgical resection, but they really didn't know what the mechanism of action of that dual therapy was.

So they enrolled 12 patients in a prospective trial where they were able to obtain pre-treatment core needle biopsies. You can see at the top, put the patients on therapy for about a month and then obtain post-treatment surgical resection samples and then processed those with a 13 plex Orion IO bias panel and then perform quantitative analysis to obtain, you know this is probably the simplest and most popular spatial biomarker, which is density of cell types within a pathologist drawn region, which in this case would be the cancer.

So if we look at one of the surgical resection samples, a huge sample here, this part of the tissue is quite normal, normal colonic mucosa, you can see PanCK crips in yellow, T cells raid within them and around them. What's not normal is have this swarm of T cells to the right and if we keep panning to the right a little bit, we can see this is where the cancer is. So cancer polyps here highly proliferative with the red K-67 nuclei, but you can see that the CD3 cells are really invading that tumor and also killing it off as you can see up here at the top. So treatment definitely caused the recruitment of T cells, but we want to quantify that. So in this case, in blue is the pre-treatment biopsy in gold is the post-treatment surgical resection, and you can see that the treatment caused recruitment of most of the major cell types into the tumor.

Also, another example of applying the system or in a prognostic space. In this case, one of our customers at Harvard actually applied a 17 plex panel to samples from a really large cohort colorectal cancer cohort, which they had clinical outcome data for. With this panel, they could actually simulate the gold standard CLIA prognostic test for colorectal cancer progression. But they were also able to trial many different candidate sort of prognostic combinations of these markers and found quite a few that theoretically outperformed that test. So it's a good example of combining that plex plus the throughput to derive statistically robust Kaplan Meier curve statistics, which can give you that confidence that those spatial biomarkers are actually prognostic or predictive.

So just to highlight in terms of translational space or biology, you really need a platform that meets all of these criteria, right? So that single round sample integrity really provides that high quality data that underpins the quantitative data. You also need complete spatial context. Many on average, those colorectal cancer resections from that prognostic study were probably four to five square centimeters and you wouldn't get all those spatial biomarkers by looking just at TMA core holes, as you can see drilled here, you need the sufficient plex for your biomarker discovery. But most important is that throughput is absolutely necessary to get through that large cohort of samples for that statistical power.

Of course, panel flexibility is critical. So the sort of panel billing blocks within the Orion portfolio involve antibodies directly conjugated to Argo floors. We have over a hundred biomarkers available from a catalog. They're all IHC validated and known to work on the system. We offer panels, 16 human, 6 mouse for now. This is an example of a diabetes panel. What's nice about the panels that each one of the biomarkers, all the biomarkers are supplied as separate tubes. So if you were say to start with this diabetes panel and if you weren't interested in B cells and you could say, take CD20 out, put DC-Lamp in, and build an order that way. So very nice mix and match nature of the panel design with the platform. Of course, we offer conjugation kits and services for custom biomarkers. The fluors are really easy to attach to antibodies with simple amine conjugation chemistry, requires pipettes and spin columns, very easy to reproduce your IHC results after labeling. And yeah, so I think as mentioned, Orion is just one of the tools that Yana is using to advance therapeutic approaches the treatment of pancreatic cancer. And we're very happy to have Yana here today to talk about the work her lab is doing to address this very challenging cancer. So thank you and Yana, take it away.

Yana Zavros

Thank you for joining us today. Pancreatic tumors overall respond poorly, not only to standard of care chemotherapy, but also to immunotherapy. And a recent study has shown that pancreatic ductal adenocarcinoma continue, the incidence continues to increase and pancreatic ductal adenocarcinoma (PDAC) has a poor overall clinical outcome. Despite ongoing research, the five-year survival rate of patients with this disease is approximately only 10%, and the improvement over the years has only been incremental. So tumors evade immune surveillance through several mechanisms and one mechanism is through the expression of PD-L1 that interacts with PD-1 on T-cells and subsequently inhibits CTL proliferation and defector function. Patient response to immunotherapy has been unpredictable or poor, and in many clinical trials the results have been inconclusive. And our earlier studies sought to really understand the immune suppressive mechanisms that are present within the tumor microenvironment in order to develop therapeutic strategies to increase efficacy of checkpoint of immunotherapy. So overall PDAC, although patients may express PD-L1 within their tumor microenvironment, only 5% of those patients will respond to immunotherapy and even then the patients will still have a high recurrence.

So our earlier studies that were published in 2020 showed that myeloid-derived suppressive cells or m DSCs that are found within the PDAC tumor microenvironment play a key role in inhibiting optimal efficacy of checkpoint inhibitors. So in our earlier studies using the GeoMX digital spatial profiling, and this was in the early studies where we had in this case a protein panel, we found that in particular the infiltrating immune cells that were marked here by CD68 expressed markers that were high in CD11b, CD33, CD15, and Arginase1 and low in CD14 expression. And based on our flow cytometric analysis and also our findings from the digital spatial profiling data, we found that these cells were in fact myeloid derived suppressive cells. So this is important because MDSCs use iNOS, they convert the cells, convert L-arginine to ornithine and cystine sequestration to inhibit T-cell function or induce T-cell death within the tumor microenvironment.

So when we identified the myeloid derived suppressive cells within the PDAC tumor microenvironment, we asked the question of whether if we depleted the MDCs from the tumor microenvironment, could we increase the efficacy of immunotherapy in patients? And at the time we turned our attention to Cabozantinib, which is a tyrosine kinase inhibitor. It targets several signaling pathways among the c-MET, AXL and VEGFR are among the main targets abundantly expressed in the myeloid-derived suppressive cells. And earlier studies used Cabozantinib in combination with immunotherapy for the treatment of various cancers. One of the earliest reports was prostate cancer. And then following that, there are reports that have shown combinatorial therapy with metastatic renal cell carcinoma, urothelial cancer and thyroid cancer. In our studies we found that Cabozantinib consistent with what had been previously reported does in fact target myeloid-derived suppressor cells within the tumor microenvironment by depleting these cells from the TME and in addition, induces CD8+ T-cell proliferation. And recently we have also identified a mechanism whereby Cabozantinib has the potential to also inhibit stromal cell viability within the tumor microenvironment.

So from these preclinical studies working together with Dr. Rashna Schroff at the University of Arizona Cancer Center, we developed a phase 2 clinical trial to test the safety and efficacy of combination therapy with Atezolizumab plus Cabozantinib in metastatic refractory pancreatic ductal adenocarcinoma patients. And this clinical trial, this phase 2 trial, gave us the, enabled us to access tissue in the same cohort of patients longitudinally, such that we were able to collect tissue, tumor tissue from liver and lung metastatic lesions, but from the same patients we were able to collect tumor tissue either pre-treatment and then 9 weeks on treatment with carbo and atezolizumab. So today I won't be discussing the outcomes of this phase 2 clinical trial. However, what I am going to discuss is the tissue, the analysis of the metastatic tissues or core biopsies that were collected from patients at the time of enrollment of the study and screening, so pretreatment.

So our lab has generated an optimized protocol whereby we implement patient derived organoids in order to study tumor behavior. And at the time of tissue collection, we also ask the patient if they are willing to volunteer a sample of blood. And from there we are able to culture out from the peripheral blood mononuclear cells, autologous dendritic cells, CTLs and myeloid derived suppressor cells. So once we have this autologous system, we're able to set up a co-culture with the patient's own PDAC organoids in order to study these key interactions between the immune cells and the tumor cells of the patient. And what you're seeing here is a high magnification of the co-culture showing cancer stem cell markers, CD44v9 shown in green, and then infiltrating CD8+ CTLs in the co-culture. And the higher magnification shows infiltration or migration of these CD8+ cells towards the tumor cells in the organoid.

More recently we've identified that these, our patient-derived organoid cultures are far more complex than what we had originally observed in that we observed that in the co-culture system or in the organoid co-cultures, we were able to observe adherent cell populations. So some of these adherent cell populations are more of the metastatic tumor cells that are able to migrate out of the three dimensional organoids. So you can see here the three dimensional organoid and then migrate out of that organoid and attached to the base of the cell culture plate. So in addition to that, however, we noted that this heterogeneous population of adherent cells also showed morphological similarities to cancer associated fibroblasts. So to further validate that we had cancer associated fibroblasts within our patient derived organoid cultures, we converted the three dimensional organoids onto a two dimensional monolayer. And what we observed was the cells self-organized such that we saw that the tumor cells centered here were surrounded by morphologically similar cells, that similar to cancer associated fibroblasts and running a single cell RNA sequencing on these cultures, we confirmed that there were genes expressed in a cell population that was consistent with the expression of cancer associated fibroblasts within the cultures.

In addition, the monolayers were stained for key markers for CAFs. In this case, what we're seeing here in this particular patient derived monolayer, we see the expression of alpha smooth muscle actin and Interleukin6 that's indicative of the presence of inflammatory CAFs. In addition to further validate the presence of CAFs in the patient derived organoids, we took the organoids and orthotopically transplanted them into non skid gammamine. And then the tumors were used to perform multiplex immunofluorescence using an Orion antibody panel. And in this case, we customized the panel to express human specific CD44v9, and then also GFAP. And what we found was in this region of interest here, 1B, we found a strong expression of human specific CD44v9. And interestingly the expression of smooth muscle actin that you see here in yellow showing that indeed there were CAFs that were carried forward from the patient derived organoids and engrafted within the mouse pancreas. Interestingly, what we found was the expression, also in close proximity to CD44v9+ cancer stem cells, GFAP. And this was consistent with our observation of the existence of Schwan cells in our patient derived organoids and also through spatial transcriptomics, which I will talk a little bit about later on.

So we use these organoid co-cultures to then use them as a predictive model of whether a patient is going to respond to combinatorial therapy. And I want to show you the next experiment. So this is a co-culture experiment whereby an organoids were generated from, in this case a patient that did not respond to Cabozantinib plus atezolizumab. And these organoids were generated from a liver metastatic lesion prior to the combination therapy and we monitored change in organoid circumference over a 3 day period. And on day 3, we stained the cultures for propidium iodide as a measure of cell death. And then the organoid cultures were also fixed and stained for EDU as a marker for proliferation. So compared to our vehicle control where we see an increase in organoid growth over the 3 day period, cabozantinib interestingly almost consistently decreased the overall circumference of the organoids in culture.

However, when we stained for propidium iodide, there was not a lot of expression. And what was even more interesting was that the organoids continued to proliferate even though we did see mitotic catastrophe that was induced in response to Cabozantinib. Pembrolizumab had very little effect on the circumference of the organoids over a 3-day period. The circumference continued to increase over time in response to Pembro, and we also found a significant increase in proliferation in these cultures. Combination treatment with Cabo and Pembrolizumab of the organoid cultures significantly induced organoid death in culture. However, this organoid death and apoptosis was only observed in a subset of tumor cells within the cultures, and in fact, there were cell populations within the culture that continued to show high proliferation.

So we then asked the question, what is the mechanism driving therapy resistance and recurrence in these patients? And working closely with Dr. Schroff and our clinical collaborators we're able to access liver core biopsies of patients with refractory and metastatic pancreatic ductal and adenocarcinoma. And accessing these precious samples has enabled us to reveal potential mechanisms of disease recurrence and a cell population that really persists through post standard of care and exhibits therapy resistance.

So in order to identify potential biomarkers in this cell population in order to develop a targeted therapeutic approach, we first implemented the CosMX Spatial Molecular Imaging approach or SMI, and we used patient samples that were part of a tissue microarray. And this was a commercially available TMA that allowed us to analyze not only surgical biospecimens but also adjacent cancer samples. And then in addition to that, we also separately analyzed surgically resected tumor tissue that we obtained from the tissue acquisition core at the University of Arizona Cancer Center. And of course, leveraging the biospecimens from the phase 2 clinical trial pretreatment core biopsies of liver metastasis, we were able to analyze the expression of resistant cell populations within the liver metastatic lesions. So using the SMI approach, we were able to identify these broad categories of cell populations across all tissue. So we found that there were cells that could be categorized under cancer stem and progenitor cells, the stroma that consisted of SAFs and fibroblasts endocrine cells. There was a high infiltration of immune cells, predominantly our immune suppressive cells, and then also pancreatic and liver cells that included hepatocytes and acinar cells. Hepatocytes were predominantly or found in the liver metastatic lesions. And interestingly, as I had mentioned earlier, we found a large population of Schwann cells within the tissue.

What's important to note is that from this analysis, just sorry. Oh, so what's important to note is from this analysis, if we take a look at the niche analysis across all tissues for surgical resection and liver metastasis, we find that the tissue expressed 7 niches or cell neighborhoods and within each niche. So here they're labeled 1 through 7. Within each niche there is a distinct cell distribution. And what's important to note is that this cell distribution within each niche changes or varies between surgically resected tissue and liver metastasis.

So if we take a look at this closely, so this is a representative spatial maps of tissue that represents surgically resected tissue. We find, for example, niche 1 is representative of more sort of cancer associated fibroblasts together with progenitor or stem cells. Niche 2 had a high Acinar ductal metaplasia phenotype such that although we found Acinar cells within this tumor microenvironment, we also found ductal cells that highly expressed cancer stem cell markers and also markers of this metaplastic phenotype. And then niche 4 was interesting because it was very distinct in that it expressed genes that were indicative of progenitor and also cancer cells including cancer stem cells. When we looked at some of the genes that are highly expressed in those niches and cell populations, we found that CD44 and TROP2 were among the genes that were highly expressed in that particular cell neighborhood.

When we looked at the metastatic lesions or the metastatic tissue, on the other hand, we found here I'm representing niche 3 and niche 7, whereby we find, as I mentioned, niche 3 was predominantly made up of the hepatocytes and Kupffer cells, which is as expected given that this is a liver metastatic site, but then a large infiltration of mesenchymal stem cells as well. And then in niche 7 there was again emphasizing an abundance of mesenchymal stem cells. When we compare this to the feature plot of the same tissue, we find that markers of cancer stem cells, including CD44 and TROP2 were enriched at the metastatic sites when compared to the surgically resected biospecimens. Importantly, what I want to also mention is that across all biospecimens there was a consistent expression of T regulatory cells, cancer stem cells, the Schwann, a large Schwann cell population, and then also different subtypes of cancer associated fibroblasts that shifted across spatially across the tissue.

So because CD44 was enriched in the area of the tumor of the tumor cells, we wanted to ask or identify what was the alternative splice variant of CD44 that was present within those tumor cells likely to be cancer stem cells. And we identified CD44v9 given our previous work in gastric cancer and also our interest in the role of CD44v9 in various solid tumors. But at the time, the role of variant nine was unclear and not well studied in PDAC. So CD44v9 is known to stabilize the XCT cysteine glutamate transporter. And through this mechanism it acts as a protection against reactive oxygen species in order to facilitate cancer cell survival. So in the case of chemotherapy and radiotherapy in patients, this accumulation of glutathione within the cancer cell renders those cells resistant to reactive oxygen species induced apoptosis. The other splice variants including CD44v6, is important in promoting cell cancer cell proliferation. And then also v9 is known to regulate cancer associated fibroblast phenotype.

So to validate the expression of CD44v9 in particular across the same tumor tissue that we analyzed by SMI, we implemented the Orion multiplex immunofluorescence approach. And what you're seeing here is a slide scan of the core biopsy collected from patient 009 from a liver metastatic site. And the same section you're seeing below here is the same section stained with multiplex by multiplex immunofluorescence. When we take a higher magnification of region of interest 1 for example here you can see that there's the infiltration of tumor cells within the region of the liver that's surrounded by CD11b+ cells. And in particular, I wanted to highlight the expression, the strong expression of CD44v9 in that same region. The same region also highly expressed GFAP and also SPP1. So you can see here that the high magnification clearly shows expression of GFAP in regions that are in close proximity to CD44v9 cells. So if we look at another representative region in this case ROI 1 B, we see a similar expression pattern of CD44v9, and again this really beautiful staining of GFAP, which is indicative of infiltration of schwan cells that are in close proximity to where we also observe the CD44v9+ cancer stem cells.

So in another core biopsy, again taken from an individual, a patient with liver metastatic disease, in this case this is patient 010, we find representative areas where there is sort of this inhibition or expression of CD8+ CTLs in this case along the periphery of the tumor microenvironment that is enriched in CD44v9, and then in another region where in this case a region of interest 4, you can see there's this clear separation between infiltrating tumor cells, again, highly expressing CD44v9, and then another area where you can see the expression of hepatocytes and Kuppfer for cells, Kuppfer cells labeled here with CD163 in yellow, but again, a higher magnification nicely showing sort of this trapped CTLs that highly expressed CD8 within in this case areas of hepatocyte and Kuppfer cells.

So if we go back to our data that we saw in an earlier slide whereby we found that a combination treatment of the organoid co-cultures with Cabozantinib and Pembrolizumab does induce organoid death in a subpopulation of cancer cells in culture, there was a persistent cell population that was highly proliferative. And we went back and took a look at this data again, and in this case asked the question of what does the expression of CD44v9 look like in these cultures? And indeed, we did observe that cell population that express that expresses variant nine persists through these treatment groups and almost becomes enriched with the combination treatment. So in addition to that, we also sustained the cultures for fibronectin and found that interestingly Cabozantinib did seem to deplete or decrease the expression of fibronectin in culture. However, what the expression of fibronectin in these co cultures is also showing is that it further supports the expression of cancer associated fibroblasts within the co-cultures that are involved in deposition of the extracellular matrix.

So what we found is that when we compare surgically resected tissue specimens by spatial transcriptomics and also multiplex immunofluorescence to the metastatic tumor tissue, we find that post standard of care there is a population of cells, tumor cells, that persists at the metastatic sites that highly expressed CD44v9. In further support of this, we also asked what cell populations and what niches or what cell distribution among these 7 niches are present in the adjacent cancer tissue. And this was quite interesting because what we're seeing here is again, is a representative cell distribution across all adjacent cancer tissue whereby niche 2 highly expresses two populations of Acinar cells with different markers of cancer stem cells. And then niche 7 highly expressed markers that were supportive of the presence of Acinar to ductal metaplasia. And this is a critical step in progressing to pancreatic ductal adenocarcinoma. Within niche 7, we also found an expansion of cancer cells and also ductal cells, again expressing cancer stem cells including CD44.

So by immunofluorescent staining, we confirmed that there was indeed within adjacent cancer tissue, the presence of a cell population that is consistent with the Acinar ductal metaplastic phenotype. So what we're seeing here is in a region of interest 1 the expression of areas where we find Acinar marker amylase co-expressed with CD44v9, and in another area where ductal marker CK19 is co-expressed also with CD44v9. In region of interest to however ROI 2 we find in this case an area where all three markers are co-expressed. So we have an Acinar ductal marker that's co-expressed along with CD44v9. And what this data is showing us is that even in the tissue adjacent to the tumor, there is the infiltration of CD44v9+ cancer stem cells but v9 is co-expressed on cells that are expressing both pancreatic acinar and also ductal markers that suggest that v9 is expressed on the metaplastic cells.

So in conclusion, using spatial biology and organoid technologies, we find that there is a subpopulation of cancer stem cells that are persistent through progression of disease and importantly resistant to standard of care therapy, but also to Cabozantinib with an immune checkpoint inhibitor in potentially indicative of patients that are at high risk of recurrence of disease. So in particular, the cancer stem cells, we identified strong expression with CD44v9, and also TROP2.

Now I want to show you the interactive dataset, and this is from patient 009. This is the core liver biopsy that we analyzed by both spatial transcriptomics and also the multiplex immunofluorescence. And what I want to highlight here is basically what you're seeing here is the PAN-cytokeratin where you're seeing a high expression of a tumor cell population within normal liver areas. But this area down here is an area of the tumor that we saw was highly expressing CD44v9. And what I want to show you here in this case, this is the same region where it was highly expressing CD44v9, and also GFAP in this case, what we're looking at here is PAN-cytokeratin. The importance of using this interactive dataset and implementing the Minerva stories is that the multiplex immunofluorescence can be compared back to or benchmarked back to the same section H&E in order for us to then understand the expression of these biomarkers back onto the patient's pathology.

So I would like to take this opportunity to thank the current members of the lab, Ugonna, Iris, and Abby in particular, Ugonna and Iris, who have worked closely with RareCyte to onboard this technology and also working together to establish the Minerva stories and the interactive dataset from our recent findings. And with that, I also want to acknowledge the past members of the lab, in particular, Loryn Holokai and Jayata Chakrabarti and Pritha Adhikary who were instrumental in providing the foundation for the preclinical research that I presented today and acknowledging the current members of the lab, in particular, Andrew and Rohan, who are part of the Medical College of Georgia and the Augusta University UGA medical partnership, and then also our collaborators at the University of Arizona Cancer Center, University of Cincinnati, Eugene, and Bill at the University of Georgia. And of course, we acknowledge the amazing support from the RareCyte team. And thank you, and I'm happy to take any questions.

Q&A

Thank you for that great presentation. Our speakers will now be available live for questions. Just a reminder that to submit your questions or comments, simply enter your question into the Q&A chat box to the right of your screen. We'll try to get to as many of these as possible. And I see we already have quite a few.

So let's get started with the first audience question is, it's great to see combining transcriptomic and multiplex if data in the same program, but how did you decide when to use Transcriptomics versus the Orion IF?

Yana
I'm assuming? I'll take that question. So we started with the spatial transcriptomic approach because we really wanted to identify first of all potential targets for that resistant cell population that we were observing in our preclinical model. From there, once we identified potential biomarkers, we really then moved that that approach into using the Orion, first to validate our biomarkers that we identified at the transcriptomic level, but then to further refine that panel into something that can be clinically implemented or implemented into the clinic and relevant to sort of stratification of patients that are at higher risk of recurrence and also resistance to therapy.

Fantastic, thank you. Another member of our audience has asked how long does it take to develop a custom panel?

Tad: Maybe I can take that one, Janna. Yeah, so I think it depends on whether or not the panel requires custom biomarkers. So the technology with Orion, we have a pretty large catalog of antibodies directly conjugated to Argofluors. The nice thing about those reagents are that they're already validated in tissue for the platform using the antigen retrieval that's common to all the panels that are run with the system. So if the panel is, it consists of biomarkers that are already available, really when you buy the reagents from us, the main thing you do is titrate those reagents on the tissue that you're applying them to. For example, Yana would titrate it on PDAC samples. We might not validate those reagents in pancreatic cancer, so the abundance of the biomarkers might be a little bit different. So it's really a titration and then you can start running if you do need to implement custom biomarkers, like I think your CD44v9 was something that was very specific to something you had discovered. It wasn't part of a regular catalog. That's just an exercise in validating the antibody by IHC first and then labeling and verifying that it works by IF. I would say in the lab, in our lab, we will typically do 4 or 5 of those in parallel. It takes us usually a couple of weeks to do 4 to 5 of those. So it's pretty quick. Thank you. Fantastic. Thank you. Another question has come in that says, how did you narrow down the spatial transcriptomics panel for multiplex IF? Yana: I'm trying to understand that question, could you please repeat it? Sorry. Absolutely. The question was how did you narrow down the spatial transcriptomics panel for multiplex IF? Yana: Oh, yes. So that's a good question. So at the transcriptomic level, we asked what genes were highly expressed in those regions of infiltrating tumor cells that we saw, and of course, taking a spatial approach, we're able to benchmark it back to the matched histology H&E stain. So from there, the genes that were highly expressed, in particular CD44, then we asked the question of, then we developed the panel around really validating those biomarkers at the protein level. Thank you. We've had someone else ask, how well do you think patient derived organoids replicate the in vivo spatial relationships observed in your tissue samples? Yana: That's a good question. Also, it's one that we would love to pursue and try to answer also, I think in the three dimensional state perhaps it's difficult to really answer that unless we have kind of matched three dimensions of the tissue with the organoids. But I think at least comparing the monolayers, so at the two dimension, comparing the monolayers to the tissue may help to try to answer spatially how the organoids really represent the tissue. So perhaps investigators should start at the monolayer level and then try to ask at the more complex 3D. Great answer. Thank you. We've had someone else ask, you had identified Schwann cells as neural cells, but have you observed any pancreatic neurons? Yana: We haven't looked yet for that. Okay, no problem. Thank you. And what marker is typically used to identify pancreatic tumor cells as most pancreatic epithelial lights up with Pan-CK? Yana So in our panel we did use Pan-CK, but we're combining it together with the CD44v9. So the v9, that particular splice variant of CD44 is a known cancer stem cell marker. It had been not clearly understood in the context of pancreatic cancer. So we use that marker in combination with Pan-CK to differentiate between tumor cells and also our resistant cancer stem cell population. Speaker 1 (00:55:24): Great, thank you. This question's quite specific, so we may focus on the second half. But this two part question reads, do you have the same sequencing as the Medical College of Wisconsin? And do you know if there's a standard sequencing for identification and therapies? Yana: I am not sure how to answer that question. I dunno, tad you, are they referring to the panel? I'm not. Tad: I'm not sure on that one. I know in the chat, there's a YouTube link, but I don't know what the sequencing at Medical College of Wisconsin is referring to. So that's hard to answer for me. If you're familiar with it then, Yana I mean in terms of the SMI data, the CosMX data, we used 1000 clicks for this particular analysis. I don't know if that helps answer that question. I think it does. And if any members of our audience wanted a few more details or to chat, we'll share some contact information at the end of the webinar as well for further discussion on that point. Yep. We've had someone else ask as well and more of a comment, but I see the advantage of high volume for 20 channels in a single round, but is there any way to get even higher plex than this? Tad: Oh yeah, that's a very common question. Yes, you're correct that the Orion technology sort of focused on enabling that translational throughput with single round high plex. But certainly people have used, actually recently demonstrated doing a 51 plex on basically doing three rounds of 17. So you can certainly apply the technology in a cyclic fashion. Your throughput goes down of course, but we typically, customers that do that usually are doing more of a discovery panel to informatically determine what is that single round plex panel that they can apply to hundreds of samples in a larger study. And that's been quite nice. You can also use it because the stain slides are pretty stable. You can also take archive samples that have already been processed and then maybe add an extra few biomarkers as a second round. So that would be the way to go above that sort of 20 channel space. Fantastic, thank you for clarifying. We've also had someone ask, what was the polarization like on your Schwann cells? Were there any difference to regular cells? Yana: I cannot answer that question at this time because we have made the initial observation that there is a presence of Schwann cells within the tumor microenvironment. But in terms of identifying them and fully characterizing them, we haven't reached that stage yet. Alright, thank you for clarifying that as well. This question from someone in our audience reads, did you notice any differences in tumor associated macrophages in your studies with the treatment or with the resistant population of tumor cells? Yana: There's also a high infiltration of tumor associated macrophages within the tumor microenvironment that are in close proximity to the other immunosuppressive cells, including the myeloid-derived suppressive cells. But I'm not sure if that answers the question in detail enough, but we know that they are there in terms of their function. I think it's documented in the literature that they do play an important role in disease progression and immunosuppression. But for our studies, our primary focus was on the myeloid derived suppressive cells. Great, thank you. Another question has come in that says, how scalable is the PDO and spatial biology approach for routine clinical profiling or drug testing? Yana Yeah, that's another excellent question. So the way we would approach this, at least from our studies is what we are trying or what we are striving to do is really use the patient derived organoids to define them as predictive models of two things, predictive models of whether a patient will respond to a certain targeted therapy or combination therapy. And then secondly, using the PDO model to not necessarily directly implement it in the clinic, but to use it to stratify, to help stratify patients and identify patients that are candidates for targeted therapies. And for the Orion approach or the spatial transcriptomic approach, I do see that the Orion approach, that we would like to see a refined panel being implemented to the clinic. Given that overlap, given that technology where you can benchmark the expression of key markers, biomarkers that will tell us whether a patient is going to respond to a certain treatment or is a candidate for a targeted therapy. Benchmarking that back to the pathology is a really powerful tool for the pathologist to identify those patients that could be candidates for clinical trials or candidates for a certain effective targeted treatment. So that approach I do see being implemented in the clinic, the spatial transcriptomics, that I find a little more challenging to implement that into the clinic. And I see the spatial transcriptomics approach is more of an initiating, initial approach to really identify those key biomarkers that can be implemented into the clinic and applied to patient care. Great, thank you. I think we'll take another clinical question. This one says, why isn't Cabozantinib targeting cells in the liver, lung and abdominal wall parietal peritoneum? These are locations I understand pancreatic cancer spread to even at early stages as it is spread via blood shedding cells and lymph nodes. Can you comment? Yana Yeah, I can't really answer the question of whether carbo targets cells in the liver, lung or abdominal and peritoneum because we haven't tested that. First of all, we haven't tested the effects of carbo directly on those cell populations. But what we're anticipating is perhaps we will see or be able to answer some of those questions from our clinical trial data, which I can't discuss at this time. But hopefully implementing these approaches, especially now that we have a refined panel from the multiplex, the Orion, understanding how the tumor microenvironment is going to change longitudinally pre and on treatment will answer hopefully some of those questions. Great. It sounds like our audience can keep their eyes out for that upcoming data. Thank you. Have you observed any differences in the tumor microenvironment across different metastatic sites and how might those influence therapy response? Yana Yes. So there is a difference in the cellular neighborhoods between, for example, lung and liver metastatic lesions, which I think is what the question is asking. So those cellular neighborhoods are quite different whereby what we observe in the lung metastatic lesions, there's more a KAFS infiltration and there's more of a immune infiltration there as well. But to add a level of complexity, even though the general niches are different between liver and lung, then when we look at liver metastatic lesions, for example, those neighborhoods may also then change depending on whether a patient is responding to the combinatorial therapy versus patients that are not responding. So again, you have that intra, inter tumor heterogeneity and intra tumor heterogeneity. So it's not a simple broadly lung and liver metastatic lesions or the tumor microenvironment differs. However, there's differences even within those microenvironments when you're looking across patients depending on responsiveness. Fantastic, thank you. This question is also quite specific, but we'll see here. So this one reads, do you check the CA 19-9, CEA, and CA125 to see if your treatment has any changes to those tumor markers specifically? Yana: Yes. So those are the markers that are measured in the clinic as part of the clinical information that we obtain from the patient. So moving forward, when the clinical trial data is reported, there will be matched clinical information from the patient inform, including those markers, but also imaging from the patients as well. But yes, those are Speaker 1 (01:06:44): Alright. Great, thank you. Someone has also asked, what is the technical method that you used for your immunohistochemistry? Yana For the immunohistochemistry Tad May be IHC plus IF, Yana, maybe Yana I'm sorry, Tad: It might be immunohistochemistry plus immunofluorescence, maybe because you showed H and E, you showed some IHC, but mostly it was immunofluorescence with cosmics and Orion, I think. Yana I don't think I showed any IHC. I mean I showed a hematoxalin stain, and then the Orion was what we used for the multiplex immunofluorescence. And then there was one image where we looked at the expression of Amylase variant 9 and CK19. That was by immunofluorescence using confocal microscopy to image those three markers. But I don't think we used IHC for this. Okay, thank you. If that individual wants to reach out, we will show your contact information at the end of the webinar as well, if any more details are needed there. Someone else has also asked, was the Schwann cell for the tumor or for the person / mouse, and what state was the sheath in? Yana I cannot answer the second question of that. I'm assuming that's referring to the expression of GFAP in the orthotopic transplants. If there's reference to mouse, so GFAP, the antibody was custom made and that was, I believe there was no information related to its cross reactivity to mouse, which meant that it was more human specific. Certainly the antibody was chosen based on human specificity. So I'm not sure if that answers the question there, but I think that's a good answer. Yes. I think you've answered the primary topics there. Another individual has asked, was there an increase or decrease in the Acinar cells? Were they at all changed? I'm wondering if there was an increase or decrease in the internal forces at the pancreatic islet level? Yana I can't comment regarding the internal forces at the pancreatic islets. I'm assuming that's going to require probably its own study. And it might be referring to perhaps the change in the extra cellular matrix surrounding the tumor microenvironment, controlling maybe tumor stiffness. But I would need maybe to speak to the person that asked that question for clarification. But in terms of the increase or decrease in the Acinar cells, it was quite interesting. There were almost, there were I think three different populations of Acinar cells. So we did see the Acinar cells that are expected in the pancreas, but then also we identified two other Acinar cell populations with high level, high expression of CD44 in particular, and also then validating that to be variant 9. And those cells were more, they expressed a lot of the cancer stem cell markers. But in addition to that, we're defined at the transcriptomic level as in ourselves as well. Great. Thank you so much for all those details. It's fantastic. I think we have time this morning for just one more question, which is of course, the future directions and what's next style of questions. So can you discuss some of the next steps in your research and what is on the horizon? Yana Me or Ted? Do you want to take that one first? Tad It's probably more for you, but for us, certainly as we of course provide the Orion system. So for us, it's all about enabling people to get good quantitative results from large cohorts, high plex in fluorescence. So that's what we are always driving for. But probably Yana, they're more interested in. Yana I think, I mean, our next steps is, so watch for the manuscript. We're ready to hopefully at least make it available through Bioarchive. So that study is almost ready to be submitted. And then the next steps really are to develop therapeutic strategies to target that resistant cell population that we've identified to persist through progression of disease. And then of course, really trying to understand what the role is of the major cell populations within the tumor microenvironment, including the CD44v9+ cells and the Schwann cells and the cancer associated fibroblasts. But more importantly, understanding functionally how that tumor microenvironment changes over time. And I think given one of the strengths of our research team is that we can access longitudinal samples from patients that participate, and without their participation, there's no way we could have access to those precious samples. So really understanding changes in the tumor microenvironment over time is sort of our primary focus moving forward. Fantastic. Thank you both. I think that is all we have time for question from any to the speakers directly. Their email addresses are shown on the screen in front of you. As a reminder, the webinar will be archived on our website and you'll receive an email notifying you when the webinar is available on demand. On behalf of DDN, I'd like to thank our speakers, Dr. Zavros and Dr. George, as well as our sponsor, RareCyte of course. Thank you to our audience as well for attending today's webinar. Enjoy the rest of your day.