Atreca, Inc. (NASDAQ:BCEL) Q4 2022 Earnings Call Transcript

Atreca, Inc. (NASDAQ:BCEL) Q4 2022 Earnings Call Transcript March 29, 2023

Operator: Thank you for standing by. Welcome to the Atreca’s Fourth Quarter and Year End 2022 Earnings Call. At this time, all participants are in a listen-only mode. After the speaker presentation, there will be a question-and-answer session. I’d now like to hand the call over to Head of Investor Relations, Alex Gray. Please, go ahead.

Alex Gray: Thank you, operator, and thank you to those joining us today. We pleased to host our year-end 2022 conference call and webcast, including updated data from our ongoing Phase 1b trial of ATRC-101. Joining me for prepared remarks are John Orwin, CEO; Dr. Tito Serafini, Chief Strategy Officer and Atreca Founder; Dr. Philippe Bishop, Chief Medical Officer; and Dr. Stephen Gould, Chief Scientific Officer. Also on the line is Herb Cross, Chief Financial Officer who will be available during the Q&A session. For those joining by phone, I’d note that we are presenting slides as part of today’s program, which can be viewed via the webcast link included in our earnings release and posted to the Events and Presentation section of our Investor Relations website at ir.atreca.com.

An archived replay of today’s webcast and the accompanying slides will be available on our IR site, following the live presentation. During today’s call, we will be making forward-looking statements based on current expectations. These statements are subject to a number of significant risks and uncertainties, and our actual results may differ materially. For a description of risks and factors that could affect our future financial results and business, please refer the disclosure in the accompanying slides, our most recent forms 10-K and 10-Q and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All our statements are made as of today, March 29, 2023, based on information currently available to us. We can give no assurance that these statements will prove to be correct.

We undertake no duty to update these statements, except as required by law. I’ll now turn it over to John Orwin. John?

John Orwin : Thank you, Alex. On today’s call, Tito will provide an update on our discovery platform and its evolution. Philippe will present data from the ATRC-101 monotherapy and pembrolizumab combination cohorts in our ongoing Phase 1b trial, and Stephen will then provide an overview of our preclinical pipeline. I will then discuss our financials and upcoming milestones before opening the line for Q&A. Shown on Slide 4 is a broad overview of our platform and pipeline. As a reminder, Atreca has a proprietary platform that enables us to access novel antibodies binding to novel tumor targets that would unlikely be found via traditional discovery approaches. As Tito will discuss in more detail, we’ve continued to invest in the development of the platform, which has enabled us to find promising antibodies and identify their targets more efficiently.

From the platform, we’ve generated a robust pipeline led by ATRC-101, which as we’ll discuss later, has demonstrated durable anti-tumor activity in the ongoing Phase 1b trial, and which we believe validates the ability of our platform to identify active tumor targeting antibodies with therapeutic potential. We plan to provide an update on our clinical strategy for ATRC-101, including a go/no-go decision for Phase 2 development by the end of this year. We’re also advancing multiple promising preclinical programs, including APN-497444, which targets a novel tumor specific glycan and is advancing as an antibody drug conjugate, as well as APN-346958, which recognizes a cell surface RNA-binding protein target and is advancing as a T-cell engager in partnership with Xencor.

We expect to nominate clinical candidates from both programs later this year and begin clinical studies for additional oncology programs in 2025. I will now turn it over to Tito to briefly discuss our discovery platform and its evolution before we review the updated ATRC-101 data and preclinical pipeline.

Tito Serafini: Thanks, John. On Slide 5, our pipeline is generated by an approach in which the active immune response guides us to novel antibodies binding novel targets. On the top half of the slide, starting on the left, our approach begins with blood samples from patients whose adaptive immune system is attacking tumor tissue. Moving to the right, via those samples, we access that immune response, and importantly, the active immune response by analyzing single B cells of a defined type to generate a high fidelity, the natively paired heavy and light chains of the antibodies that are being generated in that patient at that point in time. Moving again to the right, we then analyze and select those antibody sequences in silico for synthesis and further evaluation in the laboratory.

By screening these synthesized antibodies in vitro, we ask a simple question, does this antibody which comes from the immune response of one patient blinded tumor tissue of other patients that is non-autologous tumor tissue and does it do so preferentially over normal tissue. If the answer is yes, and the antibody also binds to the surface of a tumor cell line as measured by flow cytometry, we have a hit antibody. On the bottom half of the slide, we then further evaluate and develop our hit antibodies in order to turn them into clinical candidates as I’ll describe in a moment in more detail. Moving to Slide 6. This slide illustrates the biology behind why our approach works. The key point of this slide is that we generate antibodies from plasmablast, those four B cells in the middle of the figure.

Plasmablasts uniquely provide information on the antigens being processed and the antibodies being generated during immune response. In the blowup, we’re showing as dots, antibodies generated at two time points from one patient before treatment in purple and after treatment in green, and we’ve circled dots. These groups of plasmablast antibodies are clonal families derived from a single progenitor B-cell and these particular families, colored green, we generated in this case as the patient was successfully attacking its tumor tissue to yield a partial response. By analyzing clonal families such as these, their parents, persistence, disappearance, size, all of that enabled by our approach as well as by analyzing antibody sequences more directly, we focus on and select particular antibodies for further evaluation in the laboratory.

Our in silico analysis delivers synthesized antibodies binding non-autologous tumor tissue approximately 50% of the time in our primary screens. And as noted at the bottom of the slide, our proprietary approach is protected by composition of matter claims in multiple jurisdictions worldwide, which goes beyond the more typical method of use claims often granted around processes. Moving to Slide 7. This is the discovery platform that we have built in over a decade execute on our approach. I’ll focus on a few key points. Starting on the left, we run our own non-interventional studies in order to acquire samples from cancer patients, and we have generated a valuable sample repository now with over 1,700 clinically annotated samples from over 500 donors, representing nearly 40 tumor types.

Moving right, I briefly described that we use these samples to generate, analyze and select plasmablast antibodies for analysis by histology and flow cytometry to generate antibodies. And then moving further right, turning these antibodies into leads requires their evaluation in vitro for tumoricidal activity, but in a manner that provides information about which weaponization would be most suitable. And then with the active antibody in hand that also has positive histology data, we work to identify the target bound by the antibody, and eventually the epitope on that target. Moving further right again. If an antibody meets our criteria, we then work to generate a clinical candidate from that antibody by engineering a final weaponized format and optimizing its sequence by assuring a suitable safety profile and by identifying biomarkers suitable for use during clinical development.

Moving to Slide 8. As we promised to do, we’ve evolved the platform over time to achieve greater efficiency and deliver pipeline assets. On this slide, I’m highlighting a few of the more salient advances that we’ve made. Starting on the top left, we’ve expanded our sample collection to new sites and indications. Moving down, we’re operating our flow cytometry now at an industrial scale and speed. And below that, our histological analysis has now incorporated more automation to enable earlier evaluation of tumor and normal tissue expression of our antibody targets. Moving to the top right, we’ve implemented proprietary high-throughput assays to measure in vitro the activity relevant to weaponization. Next down, genome functional genomic screens were added to existing biochemical methods to remove a bottleneck that target identification had represented in our platform.

And finally, but again, not exclusively, we implemented structure-based and unbiased lead optimization to generate candidates in multiple weaponization formats. Moving to Slide 9, we’ll quickly review our current pipeline, all the assets of which have been generated by our platform. On Slide 10, you see a listing of our clinical and later stage preclinical assets. There’s our clinical stage asset, ATRC-101, which Philippe will discuss in detail. You’ll then hear from Stephen about the other currently preclinical pipeline assets. Turning it over to Philippe.

Philippe Bishop: Thanks, Tito, and thank you, everyone, for joining the call today. On Slide 12 is the top line summary of ATRC-101 Phase 1b study update I am presenting today, which includes information from 71 patients treated in the ongoing trial. 21 of them were enrolled since our March ’22 update. The primary objective of the study is to evaluate safety, and we’re pleased to share that ATRC-101 continues to be well tolerated in the monotherapy and combination arms including at the highest dose level of 30-milligram per kilogram. We continue to see antitumor activity in patients that express target. Furthermore, we’re excited to report that in this heterogeneous and heavily pretreated participant group, we are seeing durable responses and stable disease across several cancer types in participants treated with monotherapy and combination therapy.

Before we discuss the data, I will briefly review the trial design, the baseline characteristics of participants treated and the analysis set in the next three slides. As a reminder, and as shown on Slide 13, this is a basket trial enrolling patients across multiple tumor types. The primary objective of the trial is to determine the safety and tolerability of ATRC-101 when administered as monotherapy or in combination, with key additional objectives, including measuring clinical activity, analyzing target expression and determining indications for expansion. The trial began with ATRC-101 administered as monotherapy every three or two weeks, followed by studying the combination with pembrolizumab every three weeks in patients who had progressed or in the opinion of the treating physician, achieved a non-satisfactory response following an anti-PD-1 or PD-L1 therapy.

Initial dose escalation cohort included patients regardless of target expression. Last year, when early data suggested a target expression appear to discriminate patients likely to respond, we amended the protocol and started to enrich based on target expression. To date, we have treated participants at 5 dose levels ranging from 0.3 to 30 milligrams per kilogram. Having completed the dose escalation phases of the trial, we’re now expanding enrollment at the 30-milligram per kilogram every three-week dose level for both monotherapy and combination cohorts. Slide 14 is an overview of the baseline characteristics of participants enrolled on to study. As the data cutoff of February 17, 2023, 71 participants had enrolled overall, with 48 on the three-week monotherapy schedule, 13 on a two-week monotherapy schedule and 9 in the combination arm.

The median age of participants was 62 years with most having an ECOG performance status of 1. You can see how patients enrolled in the study are distributed across cancer types. It is very important to note that the majority of participants enrolled in the study have metastatic disease and were heavily pretreated with a median of 5 prior lines of therapy. Nearly half of the patients had prior exposure to one or more checkpoint inhibitor. And for those enrolled on to the combination arm, as per protocol, all patients had to have received prior therapy with a PD-1 or PD-L1 agent and experienced an unsatisfactory response or disease progression following treatment. On Slide 15, I would like to describe the pertinent data sets used in today’s presentation.

Beginning with the safety, today’s results will include all 71 participants enrolled on to study who received at least one dose of ATRC-101. Although we studied 5 dose levels, we consider pharmacologically doses to be at the 3, 10 and 30 milligrams per kilogram, either as a monotherapy or in combination with pembrolizumab. Participants treated at these doses will be the focus of the interim analysis for activity presented today. But these analyses, there are 52 patients with a large number enrolled at the 30-milligram per kilogram dose. Of the 62 subjects, 50 were evaluable for H-Score that is defined as a composite score comprised of target expression intensity by immunohistochemistry and the proportion of cells positive for target expression.

When examining target expression correlations to clinical activity, we will focus on the 45 who were evaluable for RECIST and H-Score and the 49 who were evaluable for best overall response and target expression. With these results, we will present the monotherapy results separately from the overall population that includes 9 individuals who received combination therapy. Presented on Slide 16 is a distribution of participants assessable for overall response and H-Score by cancer type. As you can see, the number of subjects enrolled with specific tumor types range from 18 to zero. As such, the small numbers make it difficult for conclusive inferences about ATRC-101 activity for a specific cancer type or indication. And although we are getting close to sufficient numbers in some of the tumor types, we believe it is important to continue enrollment of the study to provide additional insights and inform future development decisions.

Moving to Slide 17. ATRC continues to be well tolerated. Well, the 71 participants enrolled and who received at least 1 dose of ATRC-101, most of the reported adverse events were grade 1 or 2, as shown by the dark and blue bars for the treatment adverse events on the left and the treatment-related adverse events on the right. Of these adverse events observed, there remains no pattern to suggest a particular toxicity profile nor were there a relationship between incidents and severity of adverse events with dose or interest of adverse events with target expression. The most common treatment-related adverse events with pain, fatigue and nausea with no serious adverse events determined to be related to treatment with ATRC-101. Only two grade 3 AEs were reported as potentially treatment related, including one instance, each of the headache and a small intestinal obstruction.

6 patients experienced an AE leading to dose interruptions. These included patients who had a grade 2 infusion reaction on cycle one, day one. The infusion was interrupted temporarily, and the AE managed as per protocol, the dosing was restarted without complication. Others included liver function laboratory abnormalities, a grade 1 tachycardia, a grade 2 nausea, a grade 2 fatigue and the grade 3 small intestinal obstruction that was noted earlier in my comments. No participant has had to come off study due to drug-related toxicity. Moving to Slide 18. The interim assessment for activity once again supports the use of a H-Score cutoff to predict the probability of observing disease control or tumor response to ATRC-101. As noted previously, the H-Score is obtained using a CAP-CLIA immunohistochemistry based assay, an H-Score can range from 0 at the negative end to a maximum target expression score of 300.

Based on prior analysis showing a correlation of H-Score to response or disease control, the target positive H-Score cutoff implies an H-Score greater to or equal to 50. On the left bar graph, for patients who received monotherapy, 59% achieved disease control if their tumor expressed the target. This is in contrast to 25% of patients whose tumor has an H-Score below 50. The bar graph on the right includes patients treated with combination therapy and yielded a similar result. On Slide 19, there’s another way of assessing antitumor activity associated with target expression. Here, we are looking at individual patient data over time for patients whose tumor has a low H-Score. Those treated with monotherapy are shown on the left; and on the right, the graph also includes those treated with combination therapy.

As is shown here, most patients’ disease progression occurred early, soon after the first month following initiation of protocol therapy. Only 4 patients had stable disease going past 100 days and all experienced disease progression at 180 days or soon thereafter. On Slide 20, we compare those with low target scores in the light blue lines to those with H-Scores of 50 or greater in the dark blue lines. And we can see that there is a greater number of patients experiencing a durable objective response for durable disease control. Several of them are well past 180 days after having initiated protocol therapy. In the monotherapy cohort, the bottom curve shows a patient with 40% tumor size reduction that lasted for more than 300 days. This is the patient with non-small-cell lung cancer we had previously reported to have achieved the PR.

That patient’s tumor had an H-Score of 80 and PFS was documented at study day 350. Including those who received combination therapy on the right, we can see the melanoma patient at the bottom that we had previously reported to have achieved the CR and that patient had their most recent response assessment at day 418 following initiation of protocol therapy. The patient’s tumor had an H-Score of 75 and to this day, continues to remain on study. Slide 21 focuses on the durability of tumor response observed by cancer type for all patients treated with ATRC-101, a small therapy or in combination with pembro and whose tumor was target positive. As shown by the different line colors depicting different cancer type, durable activity is seen across multiple tumor types, including melanoma, non-small cell lung cancer, head and neck cancer, colon cancer as well as breast cancer patients who had progression-free survival documented at study day 210.

Another way of looking at the antitumor activity by tumor type and H-Score is shown in the waterfall plot on Slide 22. On the left bar graph, patients with low-target expression experience increase in the size of target lesions, except for one patient with ovarian cancer who had a small reduction in the size of her cancer. Patients who received ATRC-101 alone are shown in the solid bars. Those who receive combination therapy are shown in the hash bars. This is in contrast to patients whose tumor had a higher target expansion on the right bar graph. Here, we see that several patients had tumor reductions in both the monotherapy cohort, the solid bars; and the combination therapy cohort, the hash bars. Slide 23 is a Kaplan-Meier analysis, measuring progression-free survival for patients treated as a function of H-Score.

On the left are the patients who received monotherapy; on the right, the Kaplan-Meier analysis includes a patient treated with monotherapy or combination therapy. Patients with an H-Score of 50 or greater in the dark line have a longer progression-free survival than patients with H-Score less than 50 in the blue light lines. The curve separates early at 30 days and remains separated over time. For the monotherapy cohort, the hazard ratio is 0.47 for the difference of target positive and target negative patients. And is similar to the hazard ratio observed for all patients treated with ATRC-101, that also includes those who receive combination therapy. Here, the hazard ratio is 0.4 for the difference of target positive and target negative patients.

Of interest is the small vertical line on the curve representing patients who have not experienced an event, these patients remain on study and are still receiving protocol therapy. As you can see, some patients have passed 180 days at the tail end of the dark progression-free survival curve. Now moving to the data summary and next steps on Slide 24. This is a Phase 1b trial, and it has and continues to enroll patients that have, for the most part, exhausted standard of care. This data update, ATRC-101 continues to be well tolerated. There were no DLTs up to the 30-milligram per kilogram dose level tested. What we are learning with this data update is that longer progression-free survival appears to be associated with ATRC-101 target expression in patients with cancer.

While durable disease control was seen across several tumor types, it is too early to quantify and properly qualify the nature of these responses. To do so, we will need to enroll additional patients across cancer types and our current recruitment efforts will need to focus on underrepresented patients. We continue to focus enrollment based on target expression at the 30-milligram per kilogram dose in both the monotherapy, in combination with pembrolizumab cohorts. In the past several months, we opened the study at 5 new sites and closed 2, and we have contracted with 9 additional sites in start-up activities. In addition, we have 7 sites undergoing feasibility. Going forward, a total of 28 centers are expected to participate and contribute to the study.

In addition, we adapted the protocol eligibility criteria based on investigative feedback, and in some instances, work with specific clinical centers to overcome staffing issues post pandemic. All these activities have already and are expected to continue to help increase enrollment on the study. Overall, we are targeting 30 to 40 new additional patients for recruitment distributed across indications to make adequate go/no-go decisions by year-end 2023. That said, because we are enriching for target expression, we have seen not unexpectedly, screen failures due to restricting enrollment to patients whose tumor are target positive. The screen failures are in line with prior expectations to target Finally, our recruiting efforts have already seen improvements.

The additional data we expect to collect should position us well to enable a go/no-go decision for further development of ATRC-101 by year-end, paving the way for determining a registration path we define commercial opportunity for ATRC-101. I will now turn it over to Stephen to discuss our preclinical pipeline program.

Stephen Gould: Thank you, Philippe. I’ll now review three of our late lead oncology programs moving towards candidate nomination. The first highlighted on Slide 26 is a novel tumor-specific anti-glycan antibody that we’re advancing as an antibody drug conjugate or ADC referred subsequently to as antibody 444. 444 is one of a growing number of anti-glycan antibodies that we are uncovering with our platform. This is exciting as aberrant glycosylation has long been recognized as a hallmark of cancer, but this class of targets has been difficult to address through standard methods. As I’ll show you, 444 displays uniform and selective binding on tumors with high prevalence on colorectal cancer and is active as an ADC, both in vitro and in vivo.

We’re currently optimizing both the antibody portion of the molecule and the linker drug portion to yield a candidate — a clinical candidate that we expect to nominate by year-end with an IND target for late 2024. The bar plot in the top left of Slide 27 shows why we’re so excited about this antibody. 444 exhibits strong immunoreactivity, i.e., 2+ and 3+ staining in greater than 70% of colorectal cancer. And I hope you can appreciate that this staining is very uniform in the images to the right where essentially all tumor cells are positive for the epitope. We also see high prevalence in gastric cancer with a lower prevalence in several indications, including uterine, pancreatic, esophageal and lung cancer. Importantly, we do not detect membranous immunoreactivity on 27 normal fresh frozen tissues.

The highest signal in normal tissue is moderate cytoplasmic staining in the stomach, which we suspect from data that I’ll share in the coming slides is not accessible to the antibody. On Slide 28, you can see that consistent with the abundant immunoreactivity in colorectal and gastric cancer, 444 binds to cell lines derived from these tissues, highlighted in blue and not to those derived from other tissues. We screened for ADC activity shown on the right. 444 leads to cytotoxicity in cells that express high and low-to-moderate levels of target in the case of LoVo and NUGC4, respectively. On Slide 29, you can see that 444 before any affinity maturation when formatted as an ADC using ZymeLink Auristatin based linker payload leads to dose-responsive antitumor activity after a single dose in a LoVo Xenograft model.

This model expresses a relatively high level of target. But as shown to the right, the level of staining is comparable to what can be found in human cancer and therefore is a relevant model system. Importantly, 444 has been tolerated in both single and multiple dose studies in mice without body weight loss or histopathologic finding. 444 as an ADC has also been explored in initial PK tolerability in rats and has been well tolerated up to 30 mgs per kg with the ZymeLink Auristatin payload. I should note that the moderate cytoplasmic staining I pointed out earlier in the human stomach is also observed in both mice and rats. The fact that the ADC is well tolerated in both species suggests again that this intracellular pool of target is not accessible to the ADC.

Moving now to Slide 30. At this point in the project, we had a very interesting antibody with tumor-specific expression and intriguing antitumor activity, but we didn’t know the target. Attempts to deorphanize the antibody using standard approaches such as commercial protein or glycan arrays had failed to this point. And as Tito mentioned, we have now enabled whole genome CRISPR screens in-house, and 444 was one of the first antibodies we assayed with this approach. Using a CRISPR knockout approach, we knocked out genes individually and assess which genes were essential for 444 binding to a flow positive cell line. Three genes were identified as being required for 444 binding, two glycosyltransferases, B4GALNT3 and FUT4 as well as a fucose transporter.

The fucose transporter makes sense in the context of FUT4, which requires fucose as a substrate. Interestingly, when one looks at the expression of the two glycosyltransferases at the RNA level in normal tissue, they are largely not co-expressed, a feature that helps explain the near absence of immunoreactivity in tissues. We validated that these two enzymes were required by expressing them in a saline that did not express the target for 444. Upon co-expression, a dramatic right shift in the flow cytometry plot shown to the right suggests that these enzymes are required to present the glycan on the cell surface, at least in this context. Based on the substrate specificity of these two enzymes, we understand what glycan structure is likely required for binding and are confirming this through biochemical means.

Advancing to Slide 31. I alluded to the fact that 444 is one of a growing number of anti-glycan antibodies being discovered by our platform. In fact, half of the antibodies for which we have identified the target are anti-glycan antibodies. Our platform is uniquely positioned to help uncover the potential of this underexploited class of antibodies. The platform, of course, also identifies the antibodies to protein targets, and one thing that has emerged is the mislocalization of normal intercellular targets to the cell surface. A good example of this is our next late lead asset, 958, highlighted on Slide 32. 958 recognizes an RNA binding protein that is normally sequestered in the nucleus, but is mislocalized to the cell surface in tumors potentially via stress granule.

958 is advancing as the CD3 bispecific T cell engager in collaboration with Xencor, where Atreca is taking the lead on the preclinical and clinical development. We expect to nominate a clinical candidate by year-end and are targeting an IND submission by early 2025. On Slide 33, similar to the anti-glycan antibody 444, the native antibody from — of 958 straight from the patient shows preclinical activity. In this case, however, we form anything anybody as a T cell engager using Xencor’s XmAb CD3 bispecific platform. As shown on the left, the 958 specific leads to tumor stasis in a target positive prostate cancer xenograft model in the blue line. We’re currently optimizing the antibody to further enhance the potency to match or beat the positive control T cell bispecific use in the study, which is direct against a well-known prostate cancer target PSMA, shown in orange.

On the right, you can see that 958 leads to the expected pharmacodynamic effects for molecules with this mechanism of action, including robust immune activation as evidenced by an increase in interferon-gamma levels in plasma and expansion of CD8 positive T-cells in blood. 958 is the first molecule to advance as part of our collaboration with Xencor, the details of which are described in more detail on Slide 34. In this collaboration, Atreca will provide antibodies against novel targets and Xencor will engineer bispecific antibodies using their XmAb CD3 platform. The two companies will mutually select up to two joint programs for further development and commercialization with each partner sharing 50% of the costs and profits. While each company will lead one of the joint programs, the agreement allows each partner to pursue up to two programs independently.

The last oncology lead I will review is highlighted on Slide 35. 597 is our anti-EphA2 antibody, which targets a novel well-conserved membrane proximal epitope on EphA2, a validated oncology target that is overexpressed in a range of tumor types. We have generated a series of antibody variants with a range of affinities to EphA2 and have tested these in various weaponization schemes where they’re broadly active. Previously, we advanced a high-affinity variant as an ADC and are now evaluating antibody variants in CD3 T cell engager format based on the in vivo activity we’ve observed. Moving to Slide 36. I hope you share our excitement about the molecules I highlighted for you today. We’re equally excited about molecules coming from the platform, which are at an earlier stage.

As Tito mentioned at the beginning of the presentation, we believe investments we’ve made across our platform, especially around target identification with the implementation of CRISPR screening, have eliminated common bottlenecks and made the platform even more productive. We believe Atreca is well positioned to capitalize on the discovery and development of novel oncology targets. Finally, while not an internal pipeline molecule, we wish to highlight another exciting asset Atreca’s anti-malaria antibody, ATRC-501 on Slide 37. ATRC-501 is an engineered version of an Atreca discovered antibody designed to prevent malaria infection. The antibody is licensed to the Gates Medical Research Institute, which is preparing to file an IND this year.

Atreca retains rights in the U.S., Europe and parts of Asia with potential product development opportunities, including prophylaxis for those malaria endemic regions. And with that, I’ll hand it back over to John to discuss our upcoming milestones and provide an overview of our financials and IP.

John Orwin: Thank you, Stephen. On Slide 39 is an overview of our upcoming milestones. As Philippe noted, we are planning to provide updated data and a go/no-go decision for Phase 2 development of ATRC-101 later this year and potentially begin Phase 2 studies next year. On the preclinical side, we expect to nominate clinical candidates in the 444 and 958 programs later this year and are targeting INDs in late 2024 and early 2025, respectively. And behind those, our early-stage discovery and lead generation efforts are ongoing. Finally, Gates MRI continues to advance ATRC-501 and is expecting to file an IND this year. Turning to Slide 40. This addresses our financials and intellectual property. As of December 31 of last year, we had $70.5 million in cash equivalents and investments, which is expected to fund operations through the end of this year.

We have a strong IP position with patents issued covering critical aspects of Atreca’s discovery platform as well as ATRC-101 and related antibodies with pending applications covering other pipeline assets. That concludes today’s prepared remarks. We’d like to thank our trial participants and their families as well as investigators and research staff at our clinical sites. Thanks again to everyone who joined the conference call and webcast today. With that, operator, could you please open the line for the Q&A?

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Q&A Session

Operator: Our first question comes from the line of Divya Rao of Cowen.

Divya Rao: Just based on your experience, when you’re trying to enrich for high H-Score patients, what would you say is the percent of patients that fit that eligibility criteria? And then I guess as a follow-up, how does that change in terms of indications that maybe you’re not seeing as many patients enrolled in?

John Orwin: Philippe?

Philippe Bishop: Yes. Well, thank you. So as you know, in the preclinical assessment of the program, we had done some prevalent assessments in microarrays. So these are tumors in microarrays looking at the presence of target on these tumors. And we had assessed that somewhere in the neighborhood of between 30% to 50% of the tumors that we were looking across all the indications, we’re expressing the target. What we’re observing in the clinical trial is similar to that. It’s consistent with that prevalence data that we had generated previously. And depending on which indication we’re looking for the most part, we’re seeing about 50% of the tumors expressing the target.

Operator: Our next question comes from the line of Roger Song of Jefferies.

Roger Song : A couple from us. So the first one is for this patient, you have median 5 prior lines. Can you just help us to conceptualize what is the expected or PFS in this population from standard of care or whatever the therapy available to those patients?

Philippe Bishop: Yes. So Roger, it depends on which indication you’re looking at for some patients, as you may be aware, such as lung cancer, breast cancer would be another example. As you begin to exhaust standard of care, the expected progression-free survival gets shorter and shorter with subsequent lines of therapy. There are some other indications where you can observe from time to time disease. I would think some colorectal cancers, like that. And as you know, with melanoma as well, we have seen with the advent of immunotherapy, individuals now having longer period where you see progression-free survivals occurring. I think with — for the most part in our study with the patients that we are looking at, they come in already heavily pretreated.

They tend to have an performance status of which also predicts were for these individuals. And so for them, I think looking at the data, I think it’s important to note anyone that would go past the six-month mark. I think the PFS at 6 months is an important landmark. The PFS at four months for some of those indications may also be equally interesting to look at.

John Orwin: Yes, I probably just add to that, Roger, that I think given the heterogeneity of these patients, different tumor types and the sort of open-label nature of the trial, I think we certainly need to have more patients before we could really draw conclusions about the efficacy or the activity that we’re seeing and how it compares with standard of care. I think we are encouraged by the fact that where we do see more disease control and where we have seen responses is primarily in the context of patients who are expressing targets. So that does give us some encouragement that what we’re seeing is real and a function of ATRC-101. But clearly, we’ll need more patients, and that’s why we’re continuing to enroll adding additional centers, and hope to have sufficient data by the end of this year to really be in a position to make a decision about whether and where to go to Phase 2 or ATRC-101.

Roger Song: Okay. Great. Maybe just that leads to my next question. Regarding the additional patient about 40 to 50 patients, would you expect those patients will be slightly different from what you have been enrolled in terms of the tumor type and the prior line of therapy. And just remind us, what is the breakdown between the monotherapy versus the combination therapy? And when you make the go/no-go decision, what will be the key efficacy endpoint you were looking at, like the disease control or PFS? Sorry for the long questions here.