Morning, and thank you for joining us today for the second session of our Explorer Series, focused on our stromal myeloid checkpoint inhibitor programs, NGM831 and NGM438. My name is Brian Schoelkopf, Head of Investor Relations at NGM Bio. We will be making forward-looking statements during today's presentations, including statements about anticipated timing of events and the potential benefits of our product candidates. We refer you to our most recent Form 10-K, which identifies factors that could cause actual results to differ materially from today's forward-looking statements. I encourage you to read the full language on this slide and all of the slides that will be presented today. They will be posted to the Investors and Media section of our website. With that, I will hand it over to David Woodhouse, PhD, Chief Executive Officer here at NGM Bio. David. Thank you, Brian. I'm pleased to welcome you today to the second installment in our multi-episode R&D day we call our Explorer Series. As I hope you now appreciate from tuning into the first installment in this series, we have many exciting programs in development at NGM, spanning from oncology to ophthalmology to liver and metabolic disease. Within each of these areas, we have phase II programs poised to report out key proof of concept clinical data. This later stage clinical portion of the pipeline is followed by next generation assets that have just entered the clinic or are preparing to enter the clinic. With a pipeline this broad and deep, we often get the question, where did these programs come from? The first installment in the Explorer Series addressed that question. All of our product candidates were created in-house from our highly productive discovery engine. It's truly the heart and soul of NGM, and a key driver of our long-term growth strategy to become a self-sustaining biologics discovery powerhouse. Let's take a look again at the roadmap for the Explorer Series. As I just mentioned in session one, we looked under the hood at NGM Bio's discovery engine and introduced you to a new program stemming from its productivity. In the next three segments in the series, we'll be taking a deeper dive into certain of our individual pipeline programs at NGM. In today's session two, we'll focus on the foundation of two programs in our myeloid reprogramming portfolio, NGM831 and NGM438, that share a similar mechanistic and therapeutic rationale. In session three, we'll focus on our lead myeloid reprogramming and checkpoint inhibition program called NGM707 Finally, in session four, we'll cover NGM621, our anti-complement C3 antibody that we think can be a category leading treatment for geographic atrophy. Before I walk you through the specific items in the agenda for today's session, I wanna just take a moment to tell you how excited I am that we will have the opportunity today to cover in detail the scientific evidence and therapeutic rationale behind NGM831 and NGM438. Seven years ago, our research teams began to investigate the biological underpinnings of tumor resistance to T cell checkpoint inhibitors. We quickly honed in on innate immune cells from the myeloid lineage that are commonly present in the tumor microenvironment as a key driver of this resistance, and explored many potential target receptors that influence the immunosuppressive or stimulatory state of these cells as potential drug targets. We will share with you today our research and insights into a particularly powerful axis in that resistance, driven by the communication of a tumorous extracellular matrix or stroma and macrophages and dendritic cells in the immune system that signal to the rest of the immune system to stand down rather than attack the tumor. You'll hear from both our own researchers and Dr. Don Gibbons from MD Anderson Cancer Center, who we are fortunate to have with us today, to share his clinical and research experience in this area. Here's how we've organized the time. Dan Kaplan, our head of Translational Immune Oncology, will start with an introduction to the myeloid checkpoint inhibition thesis and to our myeloid reprogramming portfolio. Julie Roda, the lead scientist for our NGM831 program targeting ILT3, will then give an overview of our research for that program. Next, Don Gibbons, MD PhD, will join us from MD Anderson Cancer Center, where he is the director of the Thoracic/Head and Neck Medical Oncology Translational Genetic Models Laboratory and co-leader of the Lung Cancer Moon Shot Program. He will discuss the work he and his team have done looking at the role that LAIR1 plays in tumor resistance. Jonathan Sitrin, the lead scientist for our NGM-438 program that targets LAIR1, will then give an overview of our research for the program. Finally, Hsiao D. Lieu, our Chief Medical Officer at NGM, and Sherry Wang, our project team lead for both NGM-831 and NGM-438, will review our clinical development strategy for these two programs. With that, I'll turn it over to Dr. Daniel Kaplan to kick us off. Thank you, David. My name is Dan Kaplan. I lead Translational Immune Oncology at NGM. Today, it's my great pleasure to introduce you to our myeloid and stromal checkpoint inhibitor programs. I'd like to start by taking a step back and considering the history of cancer therapies, which will provide important context as we discuss our programs and how they came about. Cancer treatment progressed immensely during the twentieth century. Whereas in the preceding millennia, surgery was really the only option for cancer patients, the twentieth century brought radiation therapy and then chemotherapies and finally targeted therapies. What all of these treatments had in common is they focused on the tumor cells in isolation without considering the other components of the tumor microenvironment that impact cancer development and progression. The idea of using the body's own immune system to fight cancer dates back to the late 1890s when Dr. William B. Coley began studying bacterial toxins as treatments for cancer. In retrospect, the occasional responses that were observed with treatment of Coley's toxins are believed to have resulted from stimulation of the patient's own immune system in ways that occasionally created tumor-fighting effects. Throughout the twentieth century, many pioneering researchers and physicians advanced the field of cancer immunotherapy, but these efforts remained largely away from the oncology limelight. It was only with the approval of the anti-CTLA-4 antibody Yervoy for the treatment of melanoma in 2011 that the oncology mainstream began to recognize the true promise of targeting T cell checkpoints for cancer immunotherapy. The subsequent approval of anti-PD-1 and PD-L1 antibodies served to reinforce the importance of T cell checkpoint inhibitors in cancer therapy. The discoverers of CTLA-4 and PD-1, James P. Allison and Tasuku Honjo, they were awarded the 2018 Nobel Prize in Physiology or Medicine for their discoveries. However, while there can be no denying that T cell checkpoint inhibitors have been a breakthrough that's revolutionized cancer therapy, they're still unable to benefit the majority of cancer patients. Part of the problem may be that even by broadening our gaze from the tumor cells to the T cells, we still fail to see many of the components of the tumor microenvironment that impact treatment responses. As David mentioned, seven years ago, while most of the immuno-oncology field was focused on T cell biology, NGM instead focused on other components within the tumor microenvironment that limit responses to existing therapies. This exploration led us to direct our attention to the relatively poorly understood field of myeloid cells. We began investigating the role that suppressive myeloid cells play in the tumor microenvironment and how these cells can enable cancers to evade immune detection. The cancer immunology field has recently begun to appreciate that myeloid checkpoints may serve as a second set of breaks that limit antitumor immune responses and that may prevent T cell checkpoint inhibitors from working for the majority of patients. Thus, myeloid checkpoint inhibitors may be able to act in concert with T cell checkpoint inhibitors to deepen and broaden antitumor immune responses. We've seen the first clinical evidence of this with recent data from Merck's ILT-4 antibody showing antitumor immune responses in combination with anti-PD-1 in patients who are unable to benefit from or who'd relapsed on anti-PD-1 or PD-L1 therapies. We can also see the promise of myeloid checkpoint inhibitors in the increased competition in this space, with numerous companies racing side by side to bring these new therapies to patients. Now, to illustrate just how important myeloid cells are in cancer and the potential benefit that myeloid checkpoint inhibitors may bring, the data on the right side of this slide show the relationship between T cells, myeloid cells, and survival in bladder cancer patients. I find these data to be truly striking. You can see in the red line at the top of the graph that tumors with lots of T cells and few myeloid cells are associated with the highest level of patient survival. In contrast, the blue line that's diving down towards the bottom of the chart shows that a high myeloid-to-T cell ratio is associated with dramatically poorer survival. Now, even with the recent appreciation of the role that myeloid cells play in regulating antitumor immune responses, key players were missing from our picture of the tumor microenvironment. Recent work from many labs, including our labs right here at NGM, have demonstrated that the tumor stroma, which consists of the non-cancer, non-immune components of the tumor microenvironment, including fibroblasts and the extracellular matrix, has a critical role in regulating immune cells in the tumor and ultimately determining whether a robust antitumor immune response can be generated. Indeed, while we've spoken about the critical role that T cells play in mediating antitumor immunity, it's actually important to understand that it's the tumor stroma that can determine whether those T cells are actually effective in fighting tumors. The data shown on the right-hand side of this slide illustrate how high levels of tumor stroma can block the beneficial effects of T cells in bladder cancer patients treated with the PD-1 antibody Opdivo. You can see in the solid red line at the top that patients whose tumors have high T cells and low stroma content, they have the highest survival. If you compare these tumors to those with high T cells but also high stromal content, as shown by the dotted red line, you see much lower survival. Essentially, the benefit of the high T cells is lost in tumors with lots of stroma, and survival is similar to tumors with very few T cells, as shown in the blue lines. We now have a more complete picture of the tumor microenvironment, and that provides us with a platform from which to develop more effective cancer therapies. We focus our efforts in particular on a family of inhibitory receptors that are enriched in suppressive myeloid cells. These receptors are encoded in a single chromosomal locus on chromosome 19, the LIR locus, which is shown here. Due to these receptors' strong myeloid expression, their upregulation in many tumors, association with poor survival in cancer patients, and powerful immune regulatory biology, we believe that these receptors represent particularly promising therapeutic targets. We've developed humanized antibodies targeting these LIR family receptors that we're pursuing for the treatment of solid tumors. NGM-707, which is shown here in gold, is an antibody targeting ILT-2 and ILT-4. This is our most advanced clinical myeloid checkpoint inhibitor program, and we'll discuss it in detail at our next Explorer Series event, which is coming up in May. The other two programs, NGM831 and NGM438, we'll discuss today. Through our own in-house research, we've discovered that ligands from the tumor stroma bind to ILT3 and LAIR1 receptors on myeloid cells, and in doing so, drive those myeloid cells towards a suppressive state. Today, members of the NGM research team are going to share with you how we've developed inhibitors of the suppressive interface between the tumor stroma and the myeloid cells that we refer to as stromal checkpoints. NGM831, which is shown in red, is a humanized antibody designed to bind to ILT3 and block its interaction with fibronectin, which is a central component of the tumor stroma that we discovered right here at NGM to be a key ligand for ILT3. NGM438, which is shown in blue, is a humanized antibody designed to bind to LAIR1 and block its interaction with collagens, which are the most abundant extracellular matrix proteins within the tumor. In the coming presentations, our team will share data with you that demonstrate that by blocking these stromal checkpoints, NGM831 and NGM438 can repolarize suppressive myeloid cells to a stimulatory phenotype that promotes immune activation and antitumor immunity. That is, we're gonna tell you about our strategy, which focuses not on elimination of the suppressive cells, but on reprogramming them into beneficial cells that will help to promote antitumor immune responses. Now that we have a bit more context on the role that myeloid and stromal checkpoints can play in cancer, it's my great pleasure to hand the floor to my colleague, Julie Roda, who will introduce you to our NGM831 program. Hello, my name is Julie Roda, and I am excited to have the opportunity to share with you the incredible preclinical work that the NGM team has done to bring NGM831, our ILT3 antagonist antibody into the clinic for the treatment of solid tumors. Dan introduced us to the concept of suppressive myeloid cells, which can inhibit T cell activity and contribute to resistance to immune checkpoint inhibitors. We wanted to identify ways to target suppressive myeloid cells, and we focused on ILT3 as the key receptor contributing to myeloid cell suppression. ILT3 is a myeloid cell-specific inhibitory receptor that is particularly highly expressed on suppressive myeloid cells, such as M2 macrophages and tolerogenic dendritic cells. As you can see from the survival plots on the top, a high level of ILT3 expression is associated with poor survival in a number of solid tumor types. What is especially interesting is that ILT3 expression by tumor-associated myeloid cells is increased in tumors that do not respond to T cell checkpoint blockade compared with responding tumors. This can be seen in the violin plots on the bottom, which show a higher level of ILT3 expression in non-responding tumors, represented in red on the left, as compared to responding tumors, represented in blue on the right. This data suggests that ILT3 may be a resistance mechanism that inhibits T cell responses by contributing to the suppressive phenotype of tumor-associated myeloid cells. We therefore hypothesized that blocking ILT3 would reprogram myeloid cells to a more stimulatory phenotype, which would increase T cell activation and responsiveness to immune checkpoint blockade. Many cancers also express high levels of extracellular matrix proteins like collagen and fibronectin. When we set out to study ILT3, it was an orphan receptor, but the NGM team recently identified the extracellular matrix protein fibronectin as a ligand for ILT3. I will tell you more about that exciting discovery in the next couple of slides. The immunohistochemistry image on the right shows the colocalization of ILT3 positive myeloid cells, shown in green, and fibronectin, shown in orange, in an ovarian tumor. This image highlights that fibronectin can contribute to immunosuppression in the tumor in two ways. First, it can form thick bands that physically trap immune cells and prevent them from entering the tumor and coming into contact with tumor cells. Second, NGM discovered that fibronectin can transmit a suppressive signal to myeloid cells through ILT3 that downregulates their activity. This means that the myeloid cells that do make it into the tumor will be less able to activate T cells. We therefore think of the interaction between fibronectin and ILT3 as a stromal checkpoint through which the extracellular matrix contributes to myeloid cell suppression in the tumor microenvironment, leading to reduced T cell responses. Blocking this interaction, therefore, represents a potential therapeutic strategy to increase antitumor immune responses. When we started working on ILT3, the ligand that engaged ILT3 to promote myeloid cell suppression in the tumor microenvironment was unknown, and we set out to identify it. We conducted a cell line screen in which we co-cultured over a hundred different cell lines with ILT3 reporter cells. As you can see on the top graph, the ILT3 reporter cells were activated by a fibroblast-like cell line called LX2 cells, suggesting that these cells express the ILT3 binding partner. Next, we used mass spectrometry, an analytical approach for protein identification, to pinpoint the binding partner expressed by LX2 cells that bound to ILT3 and activated the ILT3 reporter cells. This analysis revealed that the ILT3 binding partner expressed by LX2 cells was the extracellular matrix protein fibronectin. Finally, we confirmed the interaction between fibronectin and ILT3 by genetic deletion of fibronectin. In the top panel of images, LX2 cells were stained with an anti-fibronectin antibody or with ILT3 protein. You can see from the merged images on the top right that the staining pattern overlapped, implying that both the fibronectin antibody and the ILT3 protein were binding to fibronectin. In the bottom set of images, we performed the same analysis on LX2 cells in which fibronectin was genetically deleted. You can see from the blank squares that when fibronectin was deleted, the ability of both the fibronectin antibody and the ILT3 protein to bind to the LX2 cell surface was lost. These data confirm that fibronectin is the ILT3 binding partner expressed by LX2 cells. ILT3 was first identified as the suppressive myeloid cell receptor all the way back in 1997. For almost 25 years, the ligand for ILT3 remained unknown. On the left, I am showing you the pivotal piece of data from the study published by Marco Colonna's lab that first demonstrated that ILT3 is a suppressive myeloid cell receptor. In this experiment, the scientist stimulated monocytes through an activating receptor called an Fc receptor, which caused them to secrete the cytokine TNF-alpha. When ILT3 on the monocytes was also engaged, cytokine production was reduced. Because the ILT3 ligand was unknown at that time, the authors activated ILT3 using an ILT3 antibody. Our team hypothesized that if fibronectin were truly a ligand for ILT3, we could activate ILT3 with fibronectin instead of an ILT3 antibody and see the same results. Sure enough, this turned out to be the case. As shown in the gray bars in the graph on the right, when monocytes were activated through their Fc receptors, cytokine secretion was induced. When the monocytes were activated on fibronectin-coated wells, cytokine secretion was significantly inhibited. Importantly, when we performed the same experiment using ILT3 knockout cells, as shown in the dark green bars, the inhibitory effect of fibronectin was lost. These results demonstrate that fibronectin is a functional ligand for ILT3. Since we began working on ILT3 seven years ago, our goal has been to develop an antibody to block the function of this key suppressive receptor. Before we could achieve that, we had to first understand how ILT3 functioned and which ligands it interacted with. Our identification of fibronectin as a ligand for ILT3 give us important insight into how ILT3 works to inhibit myeloid cells and paved the way for us to develop NGM-831. NGM-831 binds to ILT3 with high affinity and specificity and blocks the interaction of ILT3 with key ligands, including fibronectin. We believe that NGM-831 has the potential to reprogram suppressive myeloid cells by blocking the inhibitory signal delivered to myeloid cells by fibronectin within the tumor microenvironment. The data shown on this slide demonstrate that we have indeed achieved our goal of developing an ILT3 antibody capable of blocking the interaction of ILT3 with both of its known ligands. As shown on the left, NGM-831 potently inhibits the interaction of ILT3 with fibronectin. As shown on the right, NGM-831 also blocks the interaction of ILT3 with ApoE, another ILT3 ligand identified by a team at the University of Texas Southwestern Medical Center. For the remainder of this presentation, we will focus on the key biological activities that are mediated through disruption of the fibronectin ILT3 interaction. We have focused on this interaction because we believe that fibronectin is the more important ILT3 ligand within the tumor microenvironment. NGM-831 will block all of the activity of ILT3 that is mediated through interactions with both fibronectin and ApoE. First, the NGM team wanted to evaluate the ability of NGM-831 to reprogram suppressive myeloid cells exposed to fibronectin. In this experiment, we isolated monocytes, shown in orange in the cartoon to the left, and differentiated them into tolerogenic dendritic cells, shown in red, which are a particularly suppressive type of myeloid cell. We then cultured them on uncoated or fibronectin-coated plates in the presence of a control antibody or NGM-831, and then harvested their RNA and evaluated their gene expression by RNA sequencing. The heat map in the middle is a big-picture way of looking at the gene expression changes in tolerogenic dendritic cells that were treated with NGM-831 in the presence of fibronectin. The red lines indicate upregulated genes, while the blue lines indicate downregulated genes. Just from this high-level visualization, you can see that NGM-831 caused dramatic changes in gene expression in tolerogenic DCs exposed to fibronectin. The numbers in the Venn diagram represent the numbers of differentially expressed genes in each comparison. The light green circle represents the genes that are regulated by fibronectin, and the dark green circle represents the genes that are regulated by NGM831. In the middle, you can see that there is a large number of genes that are found within both groups. This means that most of the gene expression changes that are induced by fibronectin are mediated by ILT3 and reversed by NGM831. Now let's look in a bit more detail at the specific genes regulated by NGM831. As you can see from the cartoon on the left, suppressive myeloid cells like M2 macrophages and tolerogenic DCs express high levels of inhibitory receptors, molecules called scavenger receptors, and markers of myeloid cell immaturity. While stimulatory myeloid cells express higher levels of molecules involved in antigen presentation, T cell co-stimulation, and differentiation of helper T cells. They also express high levels of chemokines that can recruit other immune cells to sites of inflammation. On the right, you can see a heat map representing some of the specific genes that were regulated by NGM831 in tolerogenic dendritic cells cultured on fibronectin. The blue boxes represent down-regulated genes, and the red boxes represent up-regulated genes. As you can see, NGM831 decreased the expression of many molecules expressed by suppressive myeloid cells and up-regulated many genes expressed by stimulatory myeloid cells. This data is particularly meaningful as it suggests that these suppressive myeloid cells became reprogrammed towards a more stimulatory phenotype after treatment with NGM831. The gene expression data I just showed you suggests that NGM831 reprograms suppressive myeloid cells by blocking the interaction between ILT3 and fibronectin. We also wanted to test this using assays that measure myeloid cell activity. Here, we are measuring myeloid cell activation in response to engagement of one of the key myeloid cell receptors, the Fc receptor. This experiment is similar to the assay I previously showed you, which we used to validate fibronectin as a functional ligand for ILT3. When dendritic cells are activated through their Fc receptors, they secrete a cytokine called TNF-alpha. This is shown in the light green data point. When the cells are activated through their Fc receptors in the presence of fibronectin, they are inhibited and no longer secrete TNF-alpha. This is shown in the gray on the graph. However, when NGM831 is added to block the interaction between fibronectin and ILT3, the inhibitory effect of fibronectin is reversed and dendritic cell cytokine secretion is restored. This is shown in dark green. In fact, in the presence of NGM831, TNF-alpha secretion is increased to the same level as it is when no fibronectin is present at all. Thus, myeloid cells treated with NGM831 become reprogrammed and are now able to respond to stimulation through activating receptors such as the Fc receptor. As I showed you previously, one of the characteristics of stimulatory myeloid cells is that they secrete chemokines that can recruit other types of immune cells to tumors or sites of inflammation. In this assay, we plated tolerogenic dendritic cells on fibronectin-coated wells in the presence of increasing concentrations of NGM831 and measured the secretion of two chemokines, MIP-1 alpha and MIP-1 beta. Tolerogenic dendritic cells cultured on fibronectin secrete very low levels of these chemokines, as shown in gray in the graphs on the right. In the presence of NGM831 however, chemokine secretion increases, as shown in dark green. This is another indication that NGM831 can convert suppressive myeloid cells into stimulatory cells and suggests that NGM831 could promote the migration of T cells or other activated immune cells into the tumor. Now I would like to share with you what I think is the most important piece of data I will tell you about today. The data I have shown you so far demonstrates that blockade of the ILT3 fibronectin interaction with NGM831 can reprogram suppressive myeloid cells. Because T cells are the main effectors of tumor cell killing, the NGM team wanted to test whether the myeloid cell reprogramming induced by NGM831 would translate into an increased ability to activate T cells. We tested this using mixed lymphocyte reactions. In this assay, tolerogenic dendritic cells from one donor co-cultured with T cells from another donor, and the HLA mismatch induces T cell activation, which is indicated by interferon gamma secretion. As shown in the white bars, there is very little T cell activation when the cells are combined in the presence of a control antibody, because tolerogenic dendritic cells are very suppressive and have very little ability to activate T cells. In the top two graphs, I am showing you examples of pembrolizumab-sensitive donor pairs. In these donors, addition of NGM831 or the anti-PD-1 antibody pembrolizumab, each had a modest effect on T cell activation, as shown in the solid green bars. However, T cell activation was the strongest when NGM831 and pembrolizumab were combined, as shown in the striped bars. In the bottom graphs, I am showing you examples of pembrolizumab-insensitive donors. In these donor pairs, pembrolizumab by itself had little effect on T cell activation. Nonetheless, we saw a significant effect on T cell activation when pembrolizumab was combined with NGM831. This data suggests that NGM831 can reprogram suppressive myeloid cells and increase their ability to stimulate T cell responses in combination with pembrolizumab, even in donors that don't respond well to pembrolizumab alone. The preclinical data I have shown you today highlights several ways through which NGM831 reprograms myeloid cells. Our data suggest that the fibronectin ILT3 interaction represents a stromal checkpoint through which the extracellular matrix promotes myeloid cell suppression within the tumor microenvironment. We believe that by blocking the suppressive signal with NGM831, suppressive myeloid cells within the tumor can be reprogrammed, resulting in increased antitumor T cell responses. Here are the key takeaways for ILT3 and NGM831. ILT3 is expressed from suppressive myeloid cells in the tumor microenvironment and is associated with poor survival and failure to respond to immune checkpoint inhibitors. NGM has identified fibronectin as a novel ligand for ILT3 and characterized this interaction as a stromal checkpoint through which the extracellular matrix suppresses myeloid cells within tumors. We believe that by blocking the suppressive interaction with NGM-831, we will be able to reprogram tumor-associated myeloid cells and thereby increase responses to T cell checkpoint inhibitors. Based on the preclinical data I have shared with you today, we recently moved NGM-831 into the clinic, and we look forward to sharing clinical data with you soon. Now, it is my pleasure to introduce you to our next speaker, Dr. Don Gibbons, MD, PhD, who joins us today from the MD Anderson Cancer Center, where he is the Director of the Thoracic/Head and Neck Medical Oncology Translational Genetic Models Laboratory and co-leader of the Lung Cancer Moon Shot Program. Don will be describing his laboratory studies into the mechanism of immune evasion and immunotherapy resistance in lung cancer. His group is particularly focused on immunosuppressive mechanisms caused by the extracellular matrix. Don? Thank you, Julie. I appreciate the opportunity to present to you all today, specifically about our work on the role of the extracellular matrix and the response of immunotherapy to tumors. By way of background, since I'm a thoracic oncologist, this is my particular view of the world, and although it's biased, I think it's correct to say that lung cancer is still, unfortunately, the leading cause of cancer-related death for both men and women in the United States, as you can readily see in the statistics in this chart. The primary reasons for that are as shown across the bottom of the slide, where the vast majority of patients are still diagnosed at the time at which they have metastatic or stage four disease. That's a whole two-thirds of patients. Even for patients with early-stage disease, there are still, unfortunately, poor overall treatment options. In the last five-10 years, we have made some progress in the therapeutic landscape, specifically with the incorporation of anti-PD-1 or anti-PD-L1 immune checkpoint therapies. As shown in this slide, in one of the original pivotal trials, in patients who had widespread metastatic disease and were multiply refractory to therapy, there was an overall long-term durable response to anti-PD-1 or nivolumab of approximately 20%. Many of these patients, in fact, are still alive today, suggesting that with single agent immunotherapy, even in this highly refractory patient populations, that there can be enormous benefit from effective engagement of the immune system. Unfortunately, as also shown here in the first portion of this curve, approximately 40% of patients have no response to either of the two therapies, and another 40% of patients have response, but it's a transient response that doesn't translate into durable survival. It was really this middle portion of the curve that was quite intriguing to us in terms of our overall studies going forward. The other thing that I should note is, as stated across the bottom of this slide, is that as immune checkpoint therapies have moved into earlier stages of disease and as they've been used in combinations with chemotherapy or in other IO combinations, very similar patterns have emerged in terms of acquired resistance. To be able to understand these sorts of patterns, we have for many years used genetically engineered and syngeneic mouse models to be able to study both the progression, metastasis, and treatment response or resistance in these sorts of models. You can see when we combined a mutation of K-RAS along with mutation of TP53, that we actually recapitulate the disease in the animal models that is very similar to what we find in patients in terms of rapid tumor growth, tumor dedifferentiated, and widespread metastases to organs all over the body. From these original GEM models, we've derived a large panel of K-RAS P53 mutant syngeneic models that can be now manipulated individually and tested back in animals. This just summarizes about a decade of work where using these various models, we have defined that there is an overall tumor-directed set of microenvironmental changes that really is critical to deforming the epithelial landscape, changing the interactions between tumor cells and cancer-associated fibroblasts or immune cells, as well as interactions between the tumor cells and the extracellular matrix that really drives the overall process of malignancy with enhanced invasion, immune suppression, and metastasis. As outlined here, much of this work has really focused directly around the roles that cancer cells have in laying down a collagenous extracellular matrix, deriving enhanced cross-linking of that matrix, and how that really facilitates the malignant transformation and invasion of the tumor cells. At the same time, it's a double-edged sword because it also then hampers the overall immune response and changes the immune landscape. For the remainder of the talk, I'm really gonna be focusing around one of the very particular vignettes, where we have demonstrated that the collagenous extracellular matrix is important because of specific interactions between LAIR1 on T cells and how that modifies and drives resistance to immune checkpoint therapy in cancer. This work really grew out of observations such as this, where using the models that I've just described to you, we know that there is a period of clinical benefit in these animals for single or even combination immune checkpoint therapies such as anti-PD-1 and anti-PD-L1. Then over time, this overall benefit is lost. We know that the benefit is dependent upon an effective CD8 T cell response because if we deplete the CD8 T cells, there's no response whatsoever. This work really grew out of observations such as this, where using the models that I've just described to you, we know that there is a period of clinical benefit in these animals for single or even combination immune checkpoint therapies such as anti-PD-1 and anti-PD-L1. Then over time, this overall benefit is lost. We know that the benefit is dependent upon an effective CD8 T cell response because if we deplete the CD8 T cells, there's no response whatsoever. In trying to understand this particular set of models, which is very similar to the patients that I showed you up front that have a transient benefit to therapy that is lost over time and does not become durable, what we found is two things. We found, first of all, that there is really a limited window in which the animals have to be treated to have a benefit, suggesting that the tumor microenvironment is evolving over time. That, additionally, it evolves in the face of the checkpoint therapy. In both of those cases, when we looked both at the RNA changes and the proteomic changes, what we found is that there were substantial changes in all the collagen isoforms that were being expressed in the tumors. In fact, as shown on the right, and is stained in blue, the amount of fibrous collagen is dramatically enhanced over time in the face of either anti-PD-L1 or anti-PD-1 checkpoint therapies, suggesting that this could be a potential driver of the lack of immune response or activity over the long- term. This observation got us to thinking about what might be the potential mechanisms by which collagen could be driving an immune suppressive microenvironment. As shown in the cartoon here on the left, we focused in on LAIR1, which is a receptor that is broadly expressed on many immune cell types. This is specifically a collagen receptor that binds to a defined motif on collagen. Interestingly, mice only have LAIR1, whereas humans have LAIR1 and LAIR2, and LAIR2 is actually a soluble competitor or decoy for the same binding motif on collagen. Now, many years ago, LAIR1 knockout mice were made, and it was actually observed in these mice that they had the same number or no change in the CD8 T cells, but that there was a higher percentage of the CD8 T cells that are activated and in a memory effector state. Overall, it appears that LAIR1 recruits SHP-1 as a downstream molecule needed in the signaling pathway that helps to drive the inhibition of T cell activity. It seemed highly possible, you know, based upon what we were observing that this may be a mechanistic pathway at play in driving the immune suppression that we were seeing in relation to the extracellular matrix. In the work that I'm referring to and throughout this talk, we actually tested basically all of the aspects, starting with the collagen extracellular matrix side and working down through the SHP-1 signaling to understand if this particular pathway is at play. For this particular shorter talk, for brevity, I've only focused on the data that I'll show you next around where we've used LAIR2 as a potential way to block this pathway. In this particular data, we've taken the same syngeneic models that I outlined for you, and now since mice don't express a LAIR2, we've expressed the human LAIR2 constitutively in these tumor cells so that now there should be soluble levels of LAIR2 in the tumor microenvironment. As shown in the blue curve, there is a small degree of transient efficacy of this by itself in terms of blunting primary tumor growth. What's really important, obviously, in this slide is the red curve where that particular modulation has been added along with pharmacologic treatment with an anti-PD-1 antibody. This shows essentially that the tumor cell growth is stagnant, and in just as pronounced a fashion, on the far right, you can see that it also essentially inhibits the ability of these tumor cells to metastasize. In the prior studies that we've performed over the last many years, we've noted that whenever we see this degree of tumor response, that it really only happens in the face of an effective change in the immune landscape. That's actually shown here. From the same experiments, if we take the tumors and grind them up and use them for flow sorting, a multi-parameter flow, to understand the immune microenvironment, you can see across the top in the dot plots that the total number of CD8 T-cells found in these tumors is substantially higher with the combination treatment, and that the CD8 T-cells that are there are no longer in the exhausted state, but now roughly 60%-70% of them are effective CD8 T-cells. Similarly, as shown by immunohistochemical staining of the tumor sections, as shown across the bottom, you can see that there's a really robust increase throughout the entire tumors. Actually it isn't geographically localized, but spread throughout these tumors. There's really a robust increase in the CD8 T-cells that we observe. Obviously, these experimental data are provocative and important for us to move forward and to be able to perform additional experiments. At the end of the day, I'm an oncologist, and these things don't really matter if we don't find the same thing in patients. We turned to some of our patient tumors to do similar staining studies. What you can see here is that we have taken either tissue microarrays in a microarray format where you have multiple punches from an individual tumor, or we have taken full sections and stained them for four different markers, two different isoforms of collagen, one and three, CD8 to mark the CD8 T cells, and then TIM-3 as a marker of T cell exhaustion. As you can readily note here in the images of the example shown, where there are very high levels of collagen, there are low levels of total CD8 TILs, and the TILs that are there are in an exhausted state based upon the TIM-3 staining. In either case, whether we score the tissue microarrays, as shown in the bottom left dot plots, we find a negative correlation between the total CD8 T cells and the amount of collagen and a positive correlation between the exhaustion marker TIM-3 and the amount of collagen. This was even more pronounced when we used the full sections and scored across the entire section rather than using the smaller tissue microarray format. This really suggested to us that in non-small cell lung cancer cases, that we were observing something very similar. To test this, we moved to analyzing an intervention cohort. Here we wanted patients who were receiving anti-PD-1 checkpoint therapy but who had both pre-treatment biopsies and on-treatment biopsies so that we can understand differences over time. In this case, we turned to a publicly available data set for melanoma patients who were receiving such therapy, and who had RNA profiling of the tumors at baseline and then on treatment. As shown here in panels A and B, the baseline levels of collagen were actually important in terms of understanding progression versus likely response. As you can see in the heat map on the left, the patients with higher level of multiple different collagen isoforms were more likely to have progressive disease, in the face of treatment versus the patients who had lower levels were more likely to attain either a partial or complete response to treatment. Further, as shown here in panel C and D across the bottom of the slide, not only were the baseline levels important, but the dynamic change of pre-treatment versus on treatment was also quite robust in terms of outcome. Here you can see that for patients who had an increase in the LAIR1 levels over time, that they actually had a much worse overall survival than patients who had a decrease in their LAIR1 levels over time. This was actually mirrored in the levels of TIM-3, the marker for the CD8 T cells. T cell exhaustion. This represents our working model, where we think that there are actually multiple tumor-mediated changes with regard to the extracellular matrix that really drive different facets of the overall tumor biology. Specifically here today, it's the ability of the extracellular matrix changes to drive immunosuppression, that we think are significantly important, especially in today's therapeutic landscape. As shown in the cartoon on the right, we think much of this biology comes down to the role of the LAIR1 receptor and the ability of the immune cells to be inhibited in the face of a collagenous extracellular matrix. Now, the data that I've presented to you has been very T cell-centric, but it is well known that LAIR1 is expressed on myeloid cells, and that's one of the open questions that we're trying to understand as to whether or not in the animal models and in patients the efficacy of the inhibition is primarily dependent upon T cells or a potential combination of T cells and myeloid cells. An additional question that I think becomes therapeutically important is whether or not PD-1 or PD-L1 blockade is really the best sort of treatment partner for LAIR1, or if there may be other treatment combinations that we really need to be investigating in moving forward into the clinic, either wholesale or for particular patient subgroups. I'll stop right here and move to the acknowledgments slide. Obviously thanking the individuals in my lab who performed this work, our important collaborators here at MD Anderson, as well as at UNC, and then our multiple funding sources, including NGM. Thank you very much for attending today, and I'll be happy to try and answer your questions during the live Q&A session. Thank you, Don, for sharing your team's important work in elucidating the role of LAIR1 in immune suppression within the tumor microenvironment. My name is Jonathan Sitrin, research lead at NGM Bio for NGM438, our LAIR1 antagonist antibody. I'm excited to speak with you today about this program, the third program in our myeloid reprogramming and checkpoint inhibitor portfolio. As my colleagues have described before me, T-cell checkpoint inhibitor therapies such as anti-PD-1 are a revolutionary treatment paradigm to positively impact cancer patients' lives. Checkpoint inhibitors fail in many cancers, and even in cancers where they are powerful for some patients, many tumors do not respond or the responses are not durable. Suffice to say, there's still great room for improvement. The fundamental role of an immune oncology scientist is to understand how tumors evade productive immune responses and resist existing therapies. Equipped with these insights, we can develop the next generation of immunotherapies. This brings us to the story of LAIR1 and NGM438. As you can see on the cartoon on the right side of this slide, tumors include a mixture of tumor cells, immune cells, as well as tumor stroma. The stroma is made up of connective tissues and extracellular matrix, the most abundant component of which is collagen. Let's focus in on the tumor stroma, which was once thought to be a structural component of the tumor microenvironment for tumor cells to grow. However, we now appreciate it to be a meaningful source of immune suppression, and a growing body of evidence suggests that the extracellular matrix and collagens in particular, may inhibit antitumor immunity and blunt the immune response to T-cell checkpoint inhibitor therapies. The thesis underpinning NGM438 is that if we could antagonize the immune suppression caused by abundant tumor collagens, we may be able to drive antitumor immunity and unlock greater responses to T-cell checkpoint inhibitors. We focused our efforts on the only documented immune cell expressed inhibitory receptor for collagens, LAIR1, which we believe to be a key stromal checkpoint pathway in tumors. As my colleague Don explained earlier, and as we summarize on the left side of this slide, LAIR1 is an inhibitory receptor expressed by immune cells, particularly expressed by myeloid cells such as macrophages. We will now review some of the data describing the expression of LAIR1 and collagens in cancer, and explore how this receptor and ligand pair prevents effective antitumor immunity and limits responses to immunotherapy. There's a growing body of evidence that associates LAIR1 with immunosuppression in tumors. For example, the four charts on the left hand of this slide show real patient data from the Cancer Genome Atlas, which demonstrate a correlation between LAIR1 and poor survival outcomes for several cancers. The red lines represent tumors with high LAIR1 expression, and blue represents low LAIR1 expression. Both sets of data are graphed against survival probability. As you can see, higher expression in red was associated with lower chance of survival. On the right side of this slide is an analysis of a published single-cell RNA sequencing data set that my colleagues alluded to earlier, measuring LAIR1 expression in macrophages of melanoma. These tumors were classified as responders or non-responders to checkpoint immunotherapy treatment, and the authors made note that myeloid cells in general were associated with non-response. We also found that LAIR1 expression was clearly elevated in tumor macrophages from non-responders. These data sets build confidence that our targets are associated with poor clinical outcomes and immunosuppression in tumors. We next explored the expression patterns of LAIR1 in tumors from additional published single-cell RNA sequencing data sets. These data, as well as the following data, can be found on our recent 2022 AACR late-breaking poster presentation. On the left side of this slide, you can see expression of LAIR1 in cells found in the tumor of lung cancer patients. Gray dots represent all cells from these patients, and blue dots represent single cells that express LAIR1. The degree of LAIR1 expression is noted by the brighter blue-colored dots. You can see clear enrichment of LAIR1 expression in macrophages and modest expression in other lymphocytes such as T cells and NK cells. To confirm these findings, we measured LAIR1 expression as described on the right side of this slide from patient-derived tumor samples using flow cytometry of circulating monocytes or tumor-derived macrophage cells. As you can see, we identified in these patient samples that LAIR1 was elevated in the tumor. Having built confidence in the expression of LAIR1 on immune cells that infiltrate the tumor, we wanted to explore where LAIR1 positive cells were inside the tumor and their location relative to collagen. We developed a histological staining method for both. You might think of this concept similar to PD-L1 expression in tumors with PD-1 expression on T cells. However, we're now focused on collagen-rich extracellular matrix and LAIR1 expressing immune cells. Strikingly, we found that LAIR1 expressing immune cells in tumors often resided in close proximity or even surrounded by robust stromal collagen deposits. On this slide, LAIR1 staining is in brown, collagen staining is in red, and tumor cells are in blue. As you can see from these four different tumors, while collagen-rich regions are not the only place brown LAIR1 positive cells reside, we often found significant overlap of LAIR1 expressing immune cells surrounded by robust red collagen deposits nearby to tumor cells. Close proximity between LAIR1 expressing cells and collagen would be necessary for this pathway to suppress local immune responses in the tumor. Data such as these support our hypothesis about LAIR1 and tumor immune suppression. To that end, we developed NGM438, a LAIR1 antagonist antibody. As we outline on the left, NGM438 is an antibody that potently antagonizes the LAIR1 and collagen interaction. We will share data with you today demonstrating the suppressive nature of collagen on immune cells and NGM438's ability to stimulate immune responses. Our data suggests NGM438 could reprogram myeloid cells, activate T cells, and work in synergy with T-cell checkpoint inhibitor antibodies such as anti-PD-1. We plan to initiate first in human trials in the second quarter of 2022. NGM438 is a highly specific and potent antagonist antibody. As you heard in the last Explorer Series, we have an expertise in coupling antibody development, protein engineering, and powerful biology. Our team set off on a course to develop the best antagonist antibody against LAIR1. We generated and screened thousands of antibodies and tested them for their ability to potently bind to LAIR1, to antagonize the LAIR1 collagen interaction, and to disrupt LAIR1 signaling. NGM438 was selected as our final drug candidate. One of the assays that supported this selection is described in this slide. A reporter cell assay that was responsive to LAIR1 signaling due to various collagen proteins. In all four collagen assays, NGM438 in green potently antagonized the fluorescent LAIR1 signaling in reporter cells. In black, the control antibody had no such effect. We were really pleased with the potency and consistency that NGM438 antagonized collagen signaling, particularly for the three-dimensional polymerized collagen matrix on the right. A true test for NGM438 was whether or not we could reprogram myeloid cells. We used healthy donor macrophages and measured their ability to secrete inflammatory cytokines. These cytokines are how our immune system signals to the other immune cells and orchestrates anti-tumor immunity. On the left side of the slide, you can see that TNF-alpha, one such cytokine, was induced by stimulation through a class of activating immune receptors known as Fc receptors. The addition of collagen into the assay suppressed Fc receptor activation. NGM438 potently reversed that immune suppression and drove inflammatory cytokine production to heat up the immune response and bring in additional immune cells to the site of inflammation. On the right, we also show how NGM438 activated dendritic cells in the presence of collagen, even without an activating signal such as the Fc receptor assay we just shared. In this case, we see NGM438 inducing inflammatory chemokines MIP-1 alpha and MIP-1 beta, which should help promote immune cell migration into the tumor. The take-home message I want to relay here is that collagen is not just a structural protein. These data demonstrate that collagen signaling via LAIR1 actively suppresses immune-driven inflammation in real immune cells, and that NGM438 potently disrupts collagen's suppressive effects in these pre-clinical models. Clinical success of T-cell checkpoint inhibitors support that effective immunotherapy needs to ultimately support T cell responses. We tested NGM438 for its ability to do just that, using an assay that my colleague Julie alluded to earlier, the mixed lymphocyte reaction. In this case, the assays were done in the presence of collagen to mimic the collagen-rich stromal tumor microenvironment. We found that NGM438 not only induced T-cell proliferation, as can be seen in the left panel on this slide, it also synergized with anti-PD-1 antibodies. Some donors had a pattern like the bar graph on the left, and some had a pattern like the bar graph on the right. Notably, anti-PD-1 antibodies did not have an obvious proliferation stimulation capability in this collagen-rich assay, where NGM438 could do so. Additionally, NGM438 unlocked new anti-PD-1 antibody responses when the two antibodies were combined. On the right panel, we measured inflammatory cytokines in responding donors and found even more synergy between anti-PD-1 and NGM438. Anti-PD-1 alone induced T-cell inflammatory cytokines such as interferon gamma and interleukin 2, both critical for productive tumor inflammation as well as a T-cell function. NGM438 had modest effects by itself, but strongly synergized with anti-PD-1 and drove robust cytokine responses when they were combined. We were particularly excited by this data because it suggests that NGM438 may broaden or deepen anti-PD-1 antibody treatment responses in patients. All of these in vitro analyses supported the rationale for targeting LAIR1 to reverse immune suppression. In order to further validate this mechanism, we turn to in vivo mouse tumor models. Fortunately, Don Gibbons and his team at MD Anderson Cancer Center had been studying resistance mechanisms to T-cell checkpoint inhibitors and independently came to study collagen and LAIR-1. NGM Bio teamed up with Don to test our hypothesis in vivo. To do so, we developed a surrogate mouse antagonist antibody for NGM438 with similar pharmacological properties and demonstrated in this checkpoint inhibitor-resistant lung tumor model that when LAIR-1 and PD-1 antagonism were combined, they yielded powerful and synergistic effects on antitumor immunity, leading to tumor growth inhibition. As you can see on the right, when we profiled the immune cells from these treated tumors, we identified an increase in the percentage of CD8 T-cells, and that those T-cells displayed decreased levels of T-cell exhaustion markers due to the combination of anti-LAIR1 and anti-PD-1 antibodies. We found this work in collaboration with Don to be incredibly exciting because it further validates our hypothesis regarding LAIR1 in an in vivo setting and allows us to explore immune-related outcomes and the mechanism of action of LAIR1 antagonism in a live tumor setting. Let me wrap up by reminding you a few key details from my presentation today. We've shared with you today that LAIR1 is enriched in tumors and particularly expressed on macrophages. LAIR1 is an inhibitory receptor that signals through collagen binding, and LAIR1 expressing immune cells are often found in collagen-rich stromal regions of solid tumors. NGM438 antagonizes the interaction between LAIR1 and collagen and has the potential to address a stromal resistance mechanism by reprogramming myeloid cells and synergizing with checkpoint inhibitor antibodies such as anti-PD-1 to induce T-cell responses and antitumor immunity. This program represents great promise for our myeloid reprogramming strategy, and we expect to dose patients with NGM438 in the second quarter of 2022. Now I would like to pass it off to my colleague, Hsiao D. Lieu, MD, Senior Vice President and Chief Medical Officer. Thank you, Jon. With that foundation from our preclinical research supporting the clinical investigation of NGM831 and NGM438 as treatments for cancer, I will now discuss our clinical development strategy for these two programs. I'll start by reviewing our rationale for tumor type selection for our initial clinical trials. Consistent with our approach at NGM, our selection of tumor types is heavily based in science, combining our understanding of tumor biology, how these mechanisms play a role in the tumor microenvironment, and the clinical feasibility to enroll the patient with these selected tumors. As Dan discussed earlier, we have come to understand that multiple cell types within the tumor microenvironment contribute to immune suppression, including T-cells, myeloid cell, and also the non-cancer, non-immune cell components of the tumor, also known as the stroma. We believe myeloid checkpoint inhibitors such as NGM831, NGM438, and NGM707, our ILT2, ILT4 dual inhibitor antibody that is currently in a phase Ia trial, may represent the next frontier in immune cancer therapy that is distinct from the T-cell checkpoint inhibition. More interestingly, NGM831 and NGM438 are even more unique in their mechanism of actions as they represent stromal-induced myeloid checkpoint inhibitors. As my colleagues have discussed earlier, while T-cell checkpoint inhibitors are highly effective for a subset of cancer patients, for most patients, they do not have a meaningful clinical efficacy. As such, we need to find additional immune pathways, such as the myeloid checkpoint inhibitors, to work in conjunction or in synergy with these T-cell checkpoint inhibitors. From a tumor biology perspective, this is not a surprise, as there are multiple checkpoints in our immune system that a tumor can manipulate to avoid immune response. We believe that collagen and fibronectin are examples of such stromal-induced myeloid checkpoints that can effectively break against our innate immune response. Tumors high in fibronectin or collagen expression or collagen fragment turnover have been associated with worse prognosis. Similarly, tumors with high expression of ILT3 or LAIR1 have been independently associated with worse prognosis across multiple different cancers. We are excited to be moving this program into the clinic to determine what clinical impact that releasing these myeloid checkpoints will have. An important step in moving this program to the clinic was to pick tumors that have scientific reasons to believe they will be responsive to myeloid checkpoint inhibition. Our tumor selection process started with a bioinformatic approach. We utilized the Cancer Genome Atlas, or TCGA, which is the largest clinical database examining cancer gene expression and which include data from over 20,000 tumors representing 33 different cancer types. We examined these 33 cancer types for expression of ILT3 or LAIR1. We categorized tumors that have a high expression level of ILT3 or LAIR1. We further combined these observations by looking for concordance or high expression level in two other independent clinical databases. From these, we identify a number of tumors that either highly expressed ILT3, LAIR1, or both. We also categorized a number of tumors by an NGM proprietary desmoplastic signature, high myeloid content, or sensitivity to PD-1 inhibition. From these independent exercises, we identify a subset of tumors where all three mechanisms play a role, as shown in the overlapping tumor area in the Venn diagram on the left. We're focusing our efforts on both PD-1 insensitive and sensitive tumors. We are interested in T-cell checkpoint inhibitor-sensitive tumors, especially those that have acquired resistance to a T-cell checkpoint inhibitor therapy, because in those tumors, we believe we may be able to deepen and broaden the response by adding a NGM831 or NGM438, thereby releasing the secondary break from the stromal-induced immune response. We are interested in T-cell checkpoint insensitive tumors for a similar reason. We believe that these are tumors that may not be responding to increased T-cell activity because the myeloid checkpoint is gating and suppressing the effect. If we remove the myeloid checkpoint, we may start to see an impact of the combined immuno-immunostimulatory therapies. This rationale plays a significant role in how we think about combining each molecule with anti-PD-1 inhibitors. After confirming our initial tumor set with our rigorous scientific selection, we further screen tumor indications by applying a clinical development feasibility filter, specifically ensuring that we are looking at indications where there are sufficient patients to enroll in our trials. Our clinical development strategy is to pick a number of tumors with a high probability of technical success, the ability to differentiate in the marketplace, and a unique ability to tailor the tumor to a high expression level of either NGM831 or NGM438 biology. Our initial strategy is to predominantly enrich for the tumors that we believe have the highest chance of success of reaching a positive proof of concept. We also identify a small subset of tumors that we characterize as high-risk, high-reward tumors. These tumor types have not been responsive to initial T-cell checkpoint inhibitor therapies. Clearly, this represent a greater unmet need for patients. From this exhaustive mapping of tumor expression and clinical specifications, we whittled down a large tumor list to a handful of tumors to test in our initial clinical trials. Our strategy is to identify a number of prevalent tumors with inadequate treatments available to initially test or showcase the clinical efficacy of these two molecules and mechanisms. Depending on the clinical data, we will later decide on how to branch out to more tumor types and potentially go after high-risk, high-reward tumors. Of course, part of our strategy in early clinical development is also to monitor a variety of biomarkers to attempt to identify tumors that may respond to NGM831 and/or NGM438. If successful, this could provide an additional powerful predictive tool to speed the molecules through late-stage clinical development, so we can get these potential therapies to the patients as quickly as possible. Overall, we are optimistic that if there are signs of efficacy in our combination approach with an anti-PD-1, we may be able to help many cancer patients who have either primary or acquired resistance to anti-PD-1 therapy. I will now turn it over to Sherry Wang, our program team lead for both NGM831 and NGM438 product candidates. Sherry? Thanks, Hsiao. At NGM, we are laser focused on the execution of our clinical development plan for NGM831 and NGM438. For both programs, we have successfully submitted INDs and received clearance from the FDA to proceed into the clinic. We have initiated first-in-human clinical trials for the NGM831 program and are currently enrolling patients in selected advanced and metastatic stage solid tumor malignancies into our Part 1A monotherapy dose escalation portion of the study. For our NGM438 clinical study, we plan to initiate the trial in the second quarter of this year. In the Part 1A portion of our trial, we will be dosing patients at escalating doses of our compound to identify a safe monotherapy dose. Once we have determined a dose for the monotherapy dose escalation portion, we will proceed to the Part 1B combination dose finding portion of the trial with an anti-PD-1 inhibitor. The primary study objectives for Part 1A and 1B of the trial are to evaluate safety and tolerability of our compounds with secondary and exploratory objectives focusing on pharmacokinetics, receptor occupancy, and any preliminary efficacy as measured by the overall response rate per RECIST v1.1. In addition, we have a comprehensive biomarker strategy in place that involves assessing candidate predictive biomarkers with potential to be used for future patient stratification, as well as pharmacodynamic biomarkers that will allow us to interrogate markers of drug action in both the peripheral blood as well as in tumor biopsies. Finally, we will select a recommended phase II dose to take into expansion cohorts of select tumor indications to explore the efficacy of our compounds, NGM831 and NGM438, in combination with a PD-1 inhibitor. We are committed to streamlining the development of NGM831 and NGM438 and moving these programs through the clinic efficiently so that we can further cancer research for the benefit of cancer patients. To that end, we will leverage not only adaptive clinical trial designs, but also our experienced and highly motivated internal teams and engagement with principal investigators from leading phase I clinical oncology centers. With that, I would like to turn it over to David with some closing remarks. Thank you, Sherry. Before we transition to the Q&A portion of today's event, I want to remind you of the list of exciting milestones that we plan to achieve in 2022. Already this year, we have completed enrollment in our ALPINE 4 study of aldafermin for the treatment of patients with cirrhotic NASH and initiated the phase I clinical trial for NGM-831. Our most meaningful milestones for the year are still to come, and you can expect we'll have a regular presence at oncology and ophthalmology medical meetings through this year and next. In the second half of the year, we will share updated clinical data from our phase I trial of NGM-120, provide an initial clinical update from the phase Ia portion of our NGM-707 trial, and provide a top-line data readout from our phase II proof-of-concept CATALINA trial for NGM-621 in patients with geographic atrophy. We look forward to sharing these updates with you through the rest of the year and continuing to drive our pipeline of therapeutic candidates forward for the benefit of patients. With that, we'll now begin our Q&A session. Good afternoon, and thank you again for joining us today for the second session of our Explorer series. This is Brian Schoelkopf, Head of Investor Relations at NGM Bio. I'd like to acknowledge that earlier in the presentation, there was an error with the slides projected during a portion of Dr. Gibbons' presentation. In the replay posted to our website and the deck posted, that will be corrected. In the room with me today are David Woodhouse, our CEO; Siobhan Nolan Mangini, our CFO; Hsiao Lieu, our Chief Medical Officer; and Daniel Kaplan, Head of Translational Immune Oncology. I invite you to submit your questions through the Q&A inbox in the webcast platform at this time, and we'll start with a question from Anvita Gupta at Cowen on for Ritu Baral. A two-part question here. Starting with NGM831, are there other competitive anti-ILT3 antibodies in development landscape? If so, what are the lessons learned from their preclinical and clinical data? Are there plans to enroll pembro-insensitive donors in your phase I study? We can start there. Okay, great. It's David Woodhouse here. Thanks everybody for joining. I'll take the first part A, and then I'll hand it over to Daniel Kaplan for B. The ILT3 landscape does have some competitors in it. Let me give you a sense of kind of how our strategy maps to that. We are developing NGM831 specifically for solid tumor treatments. Our antibody has been designed, as you heard during the presentation, to block what we think is a particularly important interaction, which is the interaction with fibronectin that as you've heard is a component of the tumor stroma, and therefore we think a key signaling aspect of the dynamic we're trying to block. That's distinct from some of the other ILT3 antibodies in development, which do not block that interaction. Separately, from an effector-enabled aspect of the antibody, we made this an effectorless antibody. Our strategy is not to ablate these myeloid cells in the tumor microenvironment, but actually reprogram them to invite immune response rather than suppress it. Some of the other ILT3 antibodies in development have been effector enabled, and we think that could limit the utility in the context of the solid tumor treatments. We're really thrilled to have this in the clinic and push forward. We think the clinical data will be important to read through these hypotheses, and we'll look forward to sharing that in the future. Dan, do you wanna address the question about pembrolizumab-insensitive donors in the clinical trial? Yes, absolutely. Indeed, we do plan to enroll pembrolizumab-sensitive and pembrolizumab-insensitive donors. Maybe I could just speak a bit more to the strategy there. The goal is to enroll patients with, if you will, kind of tumors that we think have the highest probability of success, and then other sets of patients with what you might call kind of high risk, high reward tumors. The tumor types with the highest probability of success would be those represented by the overlap of the three circles that were shown in the Venn diagram that Hsiao spoke to. That is tumors with high myeloid content, high desmoplastic signature, and PD-1, PD-L1 sensitivity. Within that category, those patients who are PD-1/PD-L1 experienced but who have relapsed or been refractory to these therapies, they may be those patients that could show you the clearest early signal of activity as the responses could be attributed to NGM831 or NGM438 rather than the anti-PD-1. An example here would be something like second line non-small cell lung cancers. In contrast, the high risk, high reward tumors would be those tumor types that have high myeloid content, high desmoplastic signature, but that are not typically sensitive to anti-PD-1 or PD-L1 therapies. Examples here could include tumors like pancreatic cancers or ovarian cancers. Great. Thank you, Dan. The second portion of the question here moves on to NGM438. From the preclinical data thus far, do you know what are the certain tumor types that would be most suitable for the program, or is a basket tumor study the strategy that you're planning for? And then do you have thoughts on the competitive landscape for this program? Sure. Dan, why don't you cover the first part, and I could do on the competitive landscape. Sure. In terms of tumor types, we will initially be investigating NGM438 in a basket of tumor types. These will again be tumor types collectively that have high levels of target expression, high myeloid content, relatively desmoplastic tumors. Then the goal will be to focus in on tumors in these different categories as we've spoken to, some of which again could fall into that sort of higher probability of success category and others, the high- risk, high- reward category. Yeah. In terms of the competitive landscape, we are in the lead here with a very highly potent LAIR1 anti-LAIR1 antibody that Jon described the development of earlier in the webinar. But there is another program in development that's actually a LAIR2 mimic with Fc fragment that is actually less potent. The strategy here with ours is to come at the mechanism with a highly potent antibody that we think will be more effective at driving the effect. Great. Thanks, David. The next question here is from Swapnil Malekar at Piper Sandler. What% of patients have high myeloid cells, and would you stratify for such patients in your clinical trials? And then given that there's a physical barrier in these tumors, how does the therapeutic antibody reach the site of action? These are great translational sciences questions. Dan, why don't you take the lead on these? Okay. Yeah. Great. Thank you for the question, Swapnil. In terms of the percentage of patients with high myeloid cells, this really varies by tumor type, but in the majority of the most prevalent tumor types, myeloid cells are really quite abundant. Now, it's not yet clear whether it'll be necessary to stratify patients receiving these myeloid checkpoint inhibitors based on the levels of myeloid cell content or target abundance, but we will be assessing myeloid cell abundance, target abundance, and the association of those with clinical responses. These data are gonna help us understand whether myeloid cells, target abundance are predictive of patient responses. If they do turn out to be predictive, we'll have assays in place that we could potentially use to enroll patients who would be most likely to respond to our therapies. To your question about the stromal barrier. I think it's useful to think about the stromal barrier. It's more like a barbed wire fence than it is like a wall. While it can limit the transit of cells, it's relatively permeable to antibodies. I think you could even point to some of the preclinical data as well showing, you know, responses with the anti-LAIR1 antibodies in combination with anti-PD-1 to suggest that it is possible to transit this barrier with antibodies. Great. Thank you, Dan. The next question here is for Dr. Gibbons, who we have on the line with us today. The question is, in Don's presentation, he described an observation where the amount of fibrous collagen dramatically enhanced over time in the face of anti-PD-1 checkpoint therapies, and this could be driving a lack of immune response or effectiveness to therapy over time. What are some theories on why PD-1 or PD-L1 therapies would be having this effect, and can you say more about this dynamic? Sure. Can you hear me okay? Yes, we can. Thank you, Don. Thanks for the question. That's a really good question. You know, there's a lot of heterogeneity in tumors, especially in something like non-small cell lung cancer, which is not one disease, but a big basket of many different subtypes. In some of the data that I showed on the types of response that we see to single agent checkpoint therapy, there are patients who are all over the map, either fantastic responses, fairly modest responses, and then absolutely no response. We think that that's because at baseline, many of these tumors are cold or have very high- levels of things like collagen and the extracellular matrix, and that prevents an outright upfront response. For patients who are in the middle of the curve who have some degree of transient response that doesn't become a durable response, we think that there are, whatever the baseline is of those tumors is probably irrelevant, and it's really the dynamic changes over time that occur in the tumors in the face of an immune response that's engaged because of the checkpoint therapy, because of some degree of tumor cell death that starts to occur. Tumors are not static, and they're very dynamic over time. There are multiple cell types in tumors that make collagen. The tumor cells themselves very frequently make collagen, and that can be modulated over time, and there are also what are called fibroblasts or cancer-associated fibroblasts, a different cell type in tumors that also make very high levels of collagen. Both of those cell types have been shown to be reactive to the changing environment of a tumor. We think that in many cases, in the face of therapy, whether it's a cytotoxic chemotherapy or an immunotherapy, that these reactive cell types can lay down additional matrix, be it collagen, fibronectin, other extracellular matrix proteins. Great. Thank you, Don. That was great. The next question here, you mentioned that you were looking at biomarkers to help inform patient stratification in these trials. What specific biomarkers are you going to be looking at? Yeah, that's a great question. This is something we're really focused on at NGM and making an investment in around these early studies in the clinic. Again, I think it's a good translational sciences type of question, so I'll hand it over to Dan for specifics. Yes. For both NGM831 and NGM438, we're evaluating a panel of biomarkers both in the peripheral blood and in tumor biopsies. These could be both pharmacodynamic biomarkers that may provide evidence of target engagement and target-mediated biology, as well as we spoke to a bit earlier, possible predictive biomarkers that could potentially be used to select patients who are more likely to respond to NGM831 or NGM438. Great. Thank you, Dan. With that, we will conclude the Q&A session and the second module of our Explorer Series. I wanna make sure to thank Dr. Don Gibbons for joining today for the Q&A session and for his presentation in this session. We look forward to speaking with you again soon for the next module that will detail NGM707, our lead myeloid checkpoint inhibition program. Thank you.