Hello, and thank you for standing by. My name is Lacey, and I will be your conference operator today. At this time, I would like to welcome everyone to the Measuring Protein Expression in Cardiac Gene Therapy (KOL) webcast event. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you would like to ask a question during this time, simply press star followed by the number one on your telephone keypad. If you would like to withdraw your question, press star one again. Thank you. I would now like to turn the conference over to Michelle Corrall, Vice President of Investor Relations and Corporate Communications. You may begin.
Thank you so much, Lacey, and good morning to everyone. I appreciate you joining us once again for our KOL webinar and hope that everybody can see and hear our presentation this time. Welcome to our KOL webinar event to discuss the measurement of protein expression in cardiac gene therapy. Joining me today on today's call from Team Tenaya are Faraz Ali, Tenaya's Chief Executive Officer, Dr. Whit Tingley, our Chief Medical Officer, and Kathy Ivey, our Senior Vice President of Research, as well as our special guest. We have with us Dr. Michael Previs of the University of Vermont. As a reminder, the information discussed during this call will include forward-looking statements which represent the company's view as of today, August 26th, 2025. These statements involve certain assumptions, and we caution investors not to place undue reliance on this information.
Please refer to our filings with the SEC for information concerning risk factors that could cause actual results to differ materially from those expressed or implied by these statements. I would also like to note that any opinions expressed by our guest speaker, Dr. Michael Previs, are his own and do not necessarily reflect the opinions of the company. With that out of the way, I will pass the mic to Faraz Ali for some introductory remarks. Faraz?
Thank you, Michelle, and good morning, everyone. I'm Faraz Ali, Chief Executive Officer of Tenaya Therapeutics. At Tenaya, we're driven by a bold and urgent mission: to discover, develop, and deliver curative therapies that address the underlying drivers of heart disease. This mission is not just aspirational; it's foundational to everything we do. On behalf of the Tenaya team, I thank you for joining us today for our session focused on measuring protein expression in cardiac gene therapy, during which we will discuss the groundbreaking work being done at Tenaya to transform the standard of care for patients living with genetic cardiomyopathies. Today, I'm proud to share how we are translating that mission into meaningful progress for patients and families affected by devastating genetic cardiomyopathies. We were founded in 2016 with a singular focus, and the company was built very intentionally with deep expertise and integrative capabilities.
In addition to our core value of patients first, Tenaya's culture is rooted in scientific excellence and tenacity, which together have resulted in a pipeline of three clinical stage programs, including two novel gene therapies that each have near-term data readouts. We're at a pivotal moment in our journey. As an emerging leader in gene therapy for inherited heart conditions, we're advancing a pipeline that targets the root causes of disease, not just the symptoms. These are not incremental steps; they represent a paradigm shift in how we think about treating and ultimately, hopefully, curing cardiomyopathies. They are made possible by the deep scientific expertise, relentless innovation, and unwavering commitment of our team and collaborators. Here's our pipeline.
Our lead program is focused on hypertrophic cardiomyopathy, or HCM, where we're developing TN-201, an adeno-associated virus or AAV-based gene therapy designed to treat adults and children with HCM due to MYBPC3 gene mutations, the most prevalent form of genetic HCM. This program is advancing through the clinic and progressing steadily towards pivotal studies. A data readout encompassing the first six patients to receive TN-201 is planned for the fourth quarter of this year. Our second gene therapy program is TN-401, another AAV9-based gene therapy being developed for the treatment of arrhythmogenic right ventricular cardiomyopathy, or ARVC, caused by mutations of the plakophilin-2, or PKP2 gene. Our RIDGE- 1 clinical study for TN-401 is ongoing, with an initial data readout for the first three patients coming in the fourth quarter of this year.
Both MYBPC3-associated HCM and PKP2-associated ARVC are the result of protein haploinsufficiency, which is the lack of certain proteins that are crucial for the heart to beat properly and is the cause of the condition. Unlike other gene therapies you may be more familiar with, in MYBPC3-associated HCM and PKP2-associated ARVC, the vast majority of patients are heterozygous and producing some protein with their one working gene. That also means that the protein products produced by TN-201 and TN-401 are indistinguishable from the background protein. As a result, the task of understanding how much background protein already exists and discerning whether gene therapy is increasing the patient's protein levels and ameliorating the cause of disease is more complicated versus other gene therapies.
This is also quite important in the context of seeking potential accelerated approvals based on using protein as at least one surrogate marker for efficacy, for which there is recent and relevant FDA precedence. We have learned a lot from the TN-201 program that we're now applying to our second TN-401 program. Today, we hope to illuminate our learnings as we set out to conquer the challenge of protein measurement in cardiac gene therapy. In today's session, Dr. Whit Tingley, Tenaya's Chief Medical Officer, will review the clinical status of TN-201 and TN-401 and outline expectations for the near-term data readouts. In order to offer additional context for those readouts, Dr. Kathy Ivey, Tenaya's Senior Vice President of Research, will be joined by Dr. Michael Previs to discuss the rigorous methodology Tenaya has undertaken to measure proteins.
Evidence of protein expression is a critical early sign of our gene therapy's success in addressing the underlying cause of each of these conditions, as well as providing a valuable clue to the therapy's durability over time. Dr. Michael Previs is a world-leading expert in heart muscle disease and specifically the characterizations of key proteins critical to healthy heart function. Professor of Molecular Physiology and Biophysics at the University of Vermont's Larner College of Medicine, Dr. Previs's lab uses a combination of mass spectrometry-based proteomic strategies and state-of-the-art single-molecule imaging techniques to characterize the structure and function of muscle protein complexes in health and disease. Specifically, he is focused on understanding the molecular mechanisms by which myosin-binding protein C regulates the heart's ability to contract. Finally, we will open up the call to questions from our covering analysts for Dr. Tingley, Dr. Ivey, and Dr. Previs.
We also invite questions from investors, which may be submitted into the chat box on your screen. With that brief overview, let me turn our event over to Dr. Tingley.
Thanks, Faraz. As Faraz mentioned, this is an exciting time for Tenaya Therapeutics and for our lead gene therapy programs, both TN-201 for MYBPC3-associated hypertrophic cardiomyopathy and TN-401 for PKP2-associated ARVC. As of now, midway through the year, we've achieved significant progress against our goals for both programs. For TN-201, we presented initial data at the American College of Cardiology in March, covering the first dose cohort, cohort one, and we've announced we have fully enrolled the second dose cohort and that the DSMB has reviewed all available data from these cohorts and recommended expanding the study per protocol. For TN-401, we presented initial data on the natural history at the Heart Rhythm Society this year. Cohort one is fully enrolled. The DSMB reviewed the cohort one data and recommended we proceed per protocol to expand that dose level and escalate to the next dose level, which we have initiated.
We will have data readouts from each program in the fourth quarter of this year. Before providing details on what to expect, let's take a moment to cover important characteristics of each condition and the aims of the current studies. Let's start with MYBPC3-associated hypertrophic cardiomyopathy. MYBPC3 is the most common genetic cause of HCM, estimated to affect 120,000 patients in the United States and many more around the world. As Faraz mentioned, the vast majority of patients have heterozygous mutations, though on rare occasions, infants are born with two MYBPC3 mutations. Their disease is so severe they require heart transplantation to survive even the first year of life. Heterozygous mutations can affect people at any age. Affected children, such as Gabe, pictured here, typically have earlier progression and a higher overall burden of disease than adults.
We look forward to sharing natural history data from our MyCLIMB study of pediatric MYBPC3 HCM patients this weekend at the European Society of Cardiology Congress. In this disease, the muscle of the left ventricle thickens and is unable to relax and fill properly, limiting cardiac output and the ability to meet the body's demands for blood supply. Initial symptoms are shortness of breath, chest pain, fatigue, syncope, and palpitations from arrhythmias. Patients are at high risk of sudden cardiac arrest and death. Fortunately for patients, the treatment of HCM has gotten more attention in recent years. However, there's still no treatment that addresses the underlying genetic cause of disease. First-line therapy involves generic heart failure medications. Recently, cardiac myosin inhibitors have emerged, but these are only available for the subpopulation of patients with the obstructive subtype of HCM.
Only about 30% of patients with MYBPC3-associated HCM have obstructive HCM, so the majority are not eligible for current cardiac myosin inhibitors. MYBPC3 HCM is caused by protein insufficiency. Mutations to the MYBPC3 gene result in a lack of myosin-binding protein C, which is critical for regulating the contraction and relaxation of the heart. Protein C deficiency results in hypercontractility, stiffness, thickening of the ventricle, and disorganization of the muscle cells themselves, which places patients at greater risk for lethal arrhythmias and progressive heart failure. TN-201 gene therapy is designed to address this protein deficit by inserting a full-length working MYBPC3 gene into the heart, where it can produce more C protein and restore contraction and relaxation. This is expected to halt disease progression and potentially reverse disease, ultimately improving patient outcomes, symptoms, and quality of life.
In November 2023, we dosed the first patient with TN-201 gene therapy as part of our MyPEAK-1 phase 1b/2 clinical trial. The primary objective of the study is to establish the safety profile of TN-201 and evaluate tolerability and performance at two different dose levels. In addition to safety, we are taking heart biopsies to assess the transduction and expression of TN-201, using cardiac imaging to examine changes in heart structure and function, following levels of plasma biomarkers associated with disease and with heart strain, monitoring heart failure functional class and exercise capacity, and measuring patient-reported outcomes for symptom improvement and quality of life. The ultimate goal is to see many of these parameters of disease improving together consistently. We are often asked what will be included in our planned Q4 data readout and what success would look like.
For cohort one, we plan to share longer-term follow-up data building on our prior presentation at ACC in March. The three patients who receive TN-201 at the 3E13 dose should have assessments out to one year and more, including heart biopsies. Success here is a continuation of the positive observations we've shared thus far. We shared at ACC that TN-201 has been well tolerated, with robust evidence of DNA transduction and increasing RNA expression, as well as improvements in protein levels for the first two patients. Two of the cohort one patients saw improvements in one or more measures of cardiac hypertrophy, and all three achieved New York Heart Association Class 1 status, meaning they were no longer experiencing heart failure symptoms that interfered in daily activities. For cohort two, our focus is going to be on safety and biopsy results.
This will be the first data for the 6E13 VG/kg dose. We look forward to reporting baseline and post-dose biopsy data at this dose. Success here is continued good tolerability, and we'd hope to see dose-dependent increases in DNA, RNA, and protein levels. We'll be looking at clinical endpoints, but for these patients, it may be too soon to see post-dose changes. The goals of TN-401 treatment in our RIDGE-1 clinical trial are similar to what we've been sharing with TN-201. PKP2-associated ARVC is a devastating disease characterized by life-threatening arrhythmias in adolescents and young adults. PKP2 mutations are responsible for approximately 40% of ARVC cases. Current estimates are that the disease affects about 70,000 patients in the U.S. and many more globally. We believe that this disease is underdiagnosed. These numbers may be higher. Nearly a fourth of those affected present with their first symptom being sudden cardiac death.
Other early symptoms of disease include palpitations, lightheadedness, and fainting. Patients often experience constant premature ventricular beats and other ventricular arrhythmias. Physical exertion and exercise, such as sports, aggravate arrhythmias and accelerate disease progression, so patients are placed on tight exercise restrictions. Due to the risk of sudden cardiac death, most patients have implanted cardioverter defibrillator devices. These devices restore normal rhythms during life-threatening events but leave significant emotional burden for patients, sometimes described as post-traumatic stress disorder. PKP2 encodes the plakophilin-2 protein. This is an essential protein within the desmosome complex. The desmosome is like a fastener between heart cells, helping to hold heart muscle cells together. They also support proteins responsible for the electrical signaling in the heart, coordinating each heartbeat. A deficiency in the PKP2 protein leads to the collapse of the desmosomal cell adhesion structure.
Heart muscle cells are damaged, electrical signaling is disturbed, and heart muscle is gradually replaced by nonfunctional fibrofatty tissue. Over time, the ventricle can distend and weaken, leading to heart failure symptoms and an even greater risk of life-threatening arrhythmias. Much like we discussed for TN-201, TN-401 delivers a full-length copy of the PKP2 gene to heart muscle cells, where it produces the plakophilin-2 protein with the goal of restoring the structural integrity of the desmosomes and improving the heart function, improving electrical stability, reducing arrhythmias, and slowing or even halting the progression of the fibrofatty replacement that leads to heart failure. In November 2024, we dosed the first patient in our RIDGE-1 Phase 1b clinical trial of TN-401 for the treatment of PKP2-associated ARVC. The primary objective of the study is to establish the safety profile and evaluate the tolerability and performance of two different doses.
We've completed enrollment of the first cohort at the 3E13 vector genome-per-kilogram dose, and dosing is underway in cohort two at 6E13. In addition to safety, we are taking biopsies at baseline, eight weeks, and one year post-dose to assess transduction and expression of TN-401. We're monitoring ICD activity and gathering arrhythmia data. We are imaging the heart for structural and functional changes. We are assessing changes in plasma biomarkers, and we're surveying for patient-reported outcomes and quality of life measures. The treatment goal for TN-401 gene therapy is to reduce arrhythmic events and halt progression of heart failure by restoring the structural integrity of the desmosome. Our initial data from the first cohort of three patients dosed at 3E13 will focus on safety and tolerability and whether our immunological regimen is working appropriately.
We're also going to look closely at heart biopsy data at this stage, from which we'll be able to see the levels of TN-401 DNA transduction, mRNA transcription, and whether these increase plakophilin-2 protein expression. Success at this stage is seeing all three of these measures increasing from baseline. We are also monitoring for any early changes in arrhythmic activity. With that, I'd like to hand the call over to Dr. Kathy Ivey, who leads our research group, including the Translational Medicine team responsible for overseeing the analyses of our biopsy data. Kathy.
Thank you, Whit, and good morning to everyone joining us. As Whit introduced, cardiac biopsies are collected as part of Tenaya Therapeutics' gene therapy clinical trials. Next, I'll describe what those biopsies are being used for. In the TN-201 and TN-401 clinical trials, biopsy sample analyses and the resulting measurements are our first opportunity to affirm that our gene therapy is reaching the heart, that it is first entering the heart cells through the process of transduction, and that the healthy working gene being delivered is transcribed by the cell's machinery to produce messenger RNA. This mRNA provides the instructions needed by the cell to produce protein. Biopsies are taken at various time points in each of our clinical trials. Using a catheter, a small snip of tissue, just 1 mm- 2 mm in diameter, is collected from the septum.
A number of these tissue samples are collected in the cardiac cath lab, and that's where they first receive an initial visual inspection for quality. Each of these precious tissue samples is preserved and earmarked for specific quantitative analysis of either DNA, RNA, or protein. For each of the components of the biopsy that we are trying to measure, specific assays have been qualified. Our DNA assay utilizes digital droplet PCR, or ddPCR, and is able to distinguish TN-201 or TN-401 DNA from endogenous DNA encoding MYBPC3 or PKP2 and quantify the number of vector copies per cell. This measure provides the first evidence that our gene therapy is reaching the heart and is reported as the VCN or vector copy number. Shortly after infusion, DNA levels in the heart should be at their highest.
Over time, a decrease is anticipated as DNA is cleared from non-cardiomyocytes, such as fibroblasts, and a durable steady state is reached that represents long-term retention of the TN-201 DNA inside cardiac myocytes. Using reverse transcriptase quantitative PCR, or RT-qPCR, we can quantify the expression of TN-201 or TN-401 messenger RNA. The gene therapy-derived mRNA can also be distinguished from the patient's endogenous mRNA encoding MYBPC3 or PKP2. RNA expression provides an important proxy for potential protein expression via gene therapy, since without one, you certainly won't see the other. Over time, we expect to see the levels of gene therapy mRNA increase and then stabilize, which brings us to the focus of today's discussion, protein measurement and cardiomyocytes. For this, we utilize liquid chromatography–mass spectrometry, or LCMS, which can quantify the amount of particular proteins in a sample.
Similar to RNA, we expect to see an initial rise in protein levels but anticipate that over time the increase will level off and endure. In many cases, the aim of gene therapy is to introduce a protein that is entirely absent, with the most established example of this being the approved gene therapy Zolgensma for treatment of spinal muscular atrophy, or SMA, which occurs only when both copies of the SMN1 gene are dysfunctional. In homozygous conditions like SMA, there's no protein being produced, so the measurement is from zero to something. In diseases of haploinsufficiency, such as MYBPC3 HCM and PKP2 ARVC, patients are producing some of the needed protein from their one healthy copy of the gene, just not enough of the protein.
If we're successful, the protein produced from the gene therapy will be identical to the protein produced from the patient's own single healthy copy of the gene. While the gene therapy RNA can be distinguished from the endogenous RNA, the resulting gene therapy proteins are indistinguishable from their corresponding endogenous protein. With this in mind, we knew at the outset of these programs that we needed a highly sensitive tool for measuring these protein levels from cardiac biopsies. That's where the work of Dr. Previs and his lab comes in. Dr. Previs, thank you so much for joining us today. Before we dive into the methodology, can you tell us a little bit about how you came to be an expert in this field?
Sure. Thanks for having me, Kathy. It's great to be here with you today. I did my PhD in cellular molecular biology at the University of Vermont, but I subspecialized in analytical chemistry. During grad school, I developed a mass spectrometry-based platform to measure levels of protein phosphorylation, which really sparked my interest in muscle protein structure and function. To expand this expertise, I did a postdoc in biophysics, where I developed single-molecule techniques to study your favorite protein, myosin-binding protein C. At the time, much less was known about myosin-binding protein C, and my work contributed to a fundamental understanding of its function in healthy hearts. When I started my own lab, I wanted to move things in a more translational direction.
The big question at the time was whether the most common variants in the MYBPC3 gene resulted in hypertrophic cardiomyopathy by creating toxic protein fragments called poison peptides, or if the variants impacted myosin-binding protein C translation, resulting in a lack of functional protein or haploinsufficiency. To address this question, I teamed up with Dr. Charlene Day at the University of Michigan and examined the expression of myosin-binding protein C in what we considered to be a large cohort of human samples that she collected in her clinic. The results from that study were Faraz and Whit referenced earlier and brought us here today.
Thanks, Mike. Can you talk us through the role and function of myosin-binding protein C?
Yeah, absolutely. The cardiac version of myosin-binding protein C that you're interested in is solely found in specialized heart muscle cells called cardiomyocytes. These cells are what contract for the heart to pump blood. Each heartbeat is produced by sarcomeres within these cells, which are tiny contractile units. You can think about these as microscopic machines capable of performing mechanical work. If we return here to that slide that Whit showed earlier, we can see that myosin and actin form filaments, and these filaments slide past one another to shorten the length of the sarcomere to produce each contraction. The myosin heads, shown in green here, extend away from the filament backbone, kind of like hands, and these are clearly the business end of the molecule. Actin is more like a rope that is pulled by these hands in a game of tug of war to contract the sarcomere.
The myosin-binding protein C, though, is a long tether-like protein that sits in a specific part of the myosin filament called the C zone. Its positioning and interactions allow myosin-binding protein C to modulate both the force and speed of contraction. While myosin-binding protein C doesn't generate force by itself, it is critically involved in modulating how the sarcomere behaves and therefore how the heart beats.
How does myosin-binding protein C interact with other components of the sarcomere to regulate contraction of the heart?
Yeah, that's a great question, and I'd love to give you a straightforward answer. The truth is, I'm one of the world's experts in this area, and there's still a very active debate with lots and lots of open questions. What we do know is that one end of myosin-binding protein C is anchored to the myosin filament, as shown in this cartoon. The other end could actually be untethered to anything, it could interact with the myosin head, as shown here, or it could interact with the actin filament. There are these three options for interaction. The balance between these interactions influences how quickly myosin heads bind to the actin filament. We could think about this as how quickly those hands can grab the rope and start pulling, and they also modulate how long the hands stay attached to the rope and continue to pull.
Myosin-binding protein C essentially serves as a master regulator of both the timing and magnitude of each contraction. That being said, there are many models in the current literature that try to explain exactly how this regulation works. As an expert, I really want to use this platform to say, don't believe in all the hype of these models. These models often ignore data that don't fit and oversimplify things that are clearly very dynamic, and there's a complex set of interactions. The reality is, my field is still working towards consensus, and there's really a need for more detailed studies to tease these mechanisms apart.
What is happening in the heart with MYBPC3 mutation? How does that insufficiency of the protein result in HCM?
Yeah, that's a really important question that I think everybody wants to answer, and it's one that really continues to drive my lab's research in new directions. What we do know from our previous work is that most variants in the MYBPC3 gene lead to these premature stop codons in the mRNA, as Faraz and Whit said, which is responsible for making protein. If we go to the next slide here, yeah, that one. Okay, if we go to this slide, our work from tissue, this is from our work from tissue with patients with obstructive hypertrophic cardiomyopathy. This work shows that there's a 40% decrease in the levels of myosin-binding protein C compared to donor controls.
This reduction in our hands appears to be independent of the location of where that premature stop codon happened in the mRNA, and we don't see any evidence of a poison peptide resulting from the translation of the variant mRNA that's associated with the disease. We know from many animal models, human iPSCs, and human tissue that a lack of myosin-binding protein C disrupts the function of the sarcomere. This contractile defect then at the level of the sarcomere appears to set off this cascade of changes in the heart over time, and the heart begins to remodel itself. Unfortunately, this remodeling is what is impacting people's lives and driving them into the clinic.
I think the short, simple answer here is insufficient levels of myosin-binding protein C clearly lead to poorly regulated contractions, which the heart actually may be trying to fix by remodeling, but the remodeling is what causes symptoms and complications of HCM.
What are some approaches available for measuring protein levels in cardiomyocytes? Can you talk about that and then their benefits and limitations?
Yeah, sure. I can talk about this for a day if you want. Right now, the main approaches for measuring protein levels in any system are generally antibody-based or mass spectrometry-based. Each of these methods has its own nuances and pros and cons. Antibody-based methods rely on the generation of an antibody that you can bind specifically to a protein of interest. You then detect that binding as an indirect readout of how much protein is present. These methods can be pretty straightforward and accessible at very low cost, but they depend heavily on the quality and specificity of the antibody that you're producing. If the antibody isn't great or the binding affinity in the samples differs from what you've tested this in from something like post-translational modification of your protein of interest, you could run into issues with accuracy.
In contrast, mass spectrometry is a much, much, much more direct, robust, and unbiased approach, but it's really expensive and requires specialized expertise. In most cases, proteins for mass spectrometry assays are going to be digested into peptides, and those peptides will then go on to be identified and measured by the mass spectrometer as proxies for protein abundance. The technique is really powerful, especially for the use in complex tissues like the heart, where you have many, many different proteins in the same tissue. With both antibody and mass spectrometry-based approaches, the readouts need to be normalized. This is just the reality of both of these assays. If I think we take a look at the next slide, right? Oh, yeah, there you go. We can see the biggest challenge with the normalization.
When working with small samples from patients with heart disease, such as the samples from your trial, the samples are the size of a tiny crystal of sea salt. These are tiny, tiny biopsies. The size is actually indicated by the circles in this image. You have a yellow, a blue, and a red circle. As you can see, the number of cardiomyocytes within these circles, being those cells that are in pink, can really vary in the biopsies relative to the other material in that image. Therefore, if we use the weight of the biopsy or the total protein content of the biopsy for normalization, this isn't very desirable. With Western blotting, what you typically do is you're going to normalize your readout of your protein of interest against a readout of a secondary antibody to a housekeeping protein.
Here again, you're at the mercy of your primary antibody to your protein of interest and to the secondary antibody to your housekeeping protein. There's also going to be the potential that the expression of the housekeeping protein varies in your disease state, which will further skew your data. One big advantage to the MS-based approach is that in a single run, you can get quantitative information on hundreds of proteins, giving you a wide yet detailed view of the entire sample. This allows for an unbiased look at the overall composition of the sample and provides many, many opportunities for normalization. If the assay is designed properly, it only increases the confidence in the quality of the results.
Okay, Mike. Based on your background, you're clearly a fan of mass spec-based approaches. Can you walk us through your approach using data from human heart samples as a sort of case study?
Yeah, yeah, absolutely. This has really become our approach now because we've collectively worked to refine techniques. Mass spectrometry, you know, many forms of mass spectrometry are the cornerstone of my lab's work for many different reasons, just aside from what we're talking about here. We've really refined the techniques specific to this application, working in collaboration with your team over the past few years. With your samples, what we do is we start the process by inspecting each biopsy under a dissecting microscope to get a sense of its quality, and then we loosen the tissue with forceps in the presence of surfactant. We kind of loosen up those samples. We then relax the proteins chemically and then digest them into peptides using enzymes. We do this because peptide abundances are a much more manageable measure with mass spectrometry.
Next, we inject those peptides onto a chromatography column, which separates them based on their chemical properties over a two-hour period. During this entire period, those peptides are fed directly into a high-resolution mass spectrometer, which allows us to identify them and record their abundance with high accuracy and precision. One of the unique aspects of our approach is really how we normalize the data. We normalize the abundance of myosin-binding protein C to peptides shared between cardiac myosin isoforms because cardiac myosin is found exclusively in cardiomyocytes. While we validated this approach over the years in our own lab, when we started working with your team, we were asked to systematically reevaluate our methods and fully understand our reproducibility. I think if we go to the next slide, we have some data. Yeah, there we go.
If we take a look at this slide, we see an example of where we performed the assay on several biological replicants. These are individual samples from both donor control hearts, where we had bigger pieces of tissue, and from septal myectomy biopsies, again, where we have bigger pieces of tissue that are unlike what we get from your clinical trial. Of course, if we look at these individual data points, there's always going to be some variability in the numbers, but the measurements for multiple pieces of the same heart are very consistent. Across the x-axis here is individual samples, and we see this consistency in these measurements.
If we compare these data from the donor controls, which are shown in green, to that from three separate septal myectomy samples, which are shown in red, we can see that the variability between repeat measurements from the same heart is similar. Again, if we make repeat measurements from the same heart, we have very little variability. There is some variability between septal myectomy patients, those data points shown in red. This variability is really similar to what we originally reported in 2019 in our initial study. Internal studies like these with your team have really increased my confidence in the robustness of our methods and that we are detecting real biological differences rather than just measuring technical noise. I think if we go to the next slide, here we have an example of what happens if we try to normalize the data differently.
These are examples of a similar data set that I just showed you, but the readout for myosin-binding protein C is normalized to either peptides for myosin or GAPDH independently. The data for myosin are on the left of this graph, and the data for GAPDH normalization are on the right of this graph. We selected GAPDH here because this is a common housekeeping protein that's used for normalization of Western blotting data in other biological systems. What we see is that when the readout is normalized to GAPDH, there's high variability in the measurements of myosin-binding protein C within pieces of the same heart, and this variability between hearts is even higher. There's more disparity in these data points.
Based on this data set in red, we would no longer be able to detect a difference in myosin-binding protein C levels between the HCM samples and donor controls using this GAPDH normalization strategy. To me, these data really highlight the importance of rigorously testing these methods, like your team has forced me to do, to make sure that these preparations are really consistent and reproducible. They also really introduce the concept that you need to get the normalization strategy right. Even when you're working with a piece of a very expensive piece of equipment like a mass spectrometer that's capable of making these high accuracy and precision measurements, if you're normalizing the data incorrectly, you're still going to get the wrong answers.
Mike, what gives you confidence or reassurance that when there are seemingly modest increases in protein levels, those changes that we observe are not just noise?
Yeah. It's important here, you know, to say, first and foremost, I'm a scientist, so I am trained in skepticism. This runs, you know, deep inside of me. I question our data constantly, which my trainees absolutely love and appreciate. They're excited to show me data, and then I just start picking it apart, right? The unfortunate reality with the collaboration with you is that when we're working with human samples, there's no real gold standard, and we've really learned this, you know, through this internal testing. It's not like we could just take a gold clock and place it on a scale and get repeat measurements and really, really tell you what our precision and accuracy are because each piece of tissue is unique. There's no perfect benchmark for these measurements, right?
That being said, the one thing that I've really appreciated working with your team is that you guys, you bring the same level of skepticism to these projects, which has pushed me to be even more rigorous and really creative learning opportunities for my own lab. The in-house testing has really allowed us to understand the limits of our detection methods, which provides me comfort and reassurance that we're providing, you know, the top quality data here. This gives me confidence and pride that every measurement I make in Vermont is as good as you'll get anywhere in the world. Other big advantages, you know, in our interactions really do come by chance and by both chance and design. In some cases, a surgeon gets a large enough biopsy that we can analyze biological replicates from the same time point, right?
Only a few of your biopsies are coming to me. You're using them for other things, but I really like when we have these analytical replicates from that same time point. Even in the absence of these replicates, we always get the longitudinal biopsies from those same patients. When I see consistent numbers from replicate measurements and directional increases in protein levels changing over time, I believe these measurements are reflective of the underlying biology. While, you know, for me, as a basic scientist, the early data from cohort one have been really great and super exciting, and they make me believe that the adeno-associated virus is really, we are getting protein expression from that adeno-associated virus, but really, they just increase my excitement for cohort two and the higher doses of the adeno-associated virus.
Tell us then how imaging or other measures can serve to supplement or complement the mass spec analysis.
Yeah, I mean, that's another really great question. One of my early mentors during my PhD is a cardiologist, and he said to me, "If you need statistics, it's not real." I know that people don't want to hear that, but the statement has really stuck with me through the years, and I think it's real. It's something that we carry into all of our own work in our lab to kind of build confidence, right? When we're working on projects, we really aim for conclusions that aren't just supported by statistical measurements. I laugh, and the statistics are the last thing that we usually do. Our data are usually supported by complementary assays that are going to provide multiple viewpoints or multiple angles to address that same question. That's where I get confidence.
For instance, in my lab, we often pair our mass spectrometry data with fluorescence imaging or functional assays to validate the findings from the mass spec. The really exciting thing for me about our collaboration is that our data that we're collecting, proteomic data, are continually being integrated with other key measurements like RNA expression levels and functional outcomes. Unfortunately here for me, I'm blind to these data, and I learn about them in things like this presentation. That's the limitation I have with our relationship. From what I've seen so far from the changes that were reported, even though the myosin-binding protein C levels have been modest, we've seen a modest increase in these levels, they're reproducible, and they seem to align really well with the rest of what your team is seeing at the RNA level and at the functional level. To me, it's super exciting.
I think that it's this kind of orthogonal validation that's really this convergence of data when multiple methods are telling the same story that always gives me the most confidence that we really understand the underlying biology. The biology is really complicated.
Given what you know about myosin's function in the sarcomere, what do you make of the range of protein levels in healthy samples? How does that impact your thinking about myosin-binding protein C levels in HCM patients?
Yeah, you know, this question keeps coming up. It's come up offline a bunch of times, and it's a really complex question, I think. It's much harder to talk about than the details of this protein assay, but I'll take a shot. If I think back to my days as a postdoc, one of our key findings was that even a modest change in the phosphorylation level, the phosphorylation status of myosin-binding protein C, could have a significant effect on actomyosin interaction, so how that sarcomere shortens. To me, it's not just about how much myosin-binding protein C is present. It's about how many of those molecules are functionally active and interacting with those binding partners that I talked about at any given time. This leads me to think that normal protein levels might not be telling the whole story.
While the full replacement of myosin-binding protein C in your clinical trial would obviously be a home run, I'm not entirely sure that it's not possible that we don't even need these full levels of myosin-binding protein C to restore its function in the sarcomere to get back to what appears to be a normal heart. I think for me, the reality here is that your team, not my work, it's your team, that's doing the incredibly important work of doing the study of myosin-binding protein C replacement in these humans that have disease that I could have really only dreamed of as a postdoc. I really, really appreciate the collaboration here. In the end, I'm very confident that my data, our mass spectrometry data, will provide a very, very accurate picture of myosin-binding protein C levels during all stages of your trial.
These data really are going to be transformed when they're paired with your clinical outcomes. I think it's this critical combination of data that is what will ultimately help us define that question of how much myosin-binding protein C is involved to restore function.
All right, Mike, last question here. How does the methodology apply to measuring other cardiomyocyte proteins, such as PKP2? Can you talk about what's normal for PKP2, and is there also a range?
Yeah, absolutely. Just as mentioned multiple times earlier, mass spectrometry is an incredibly powerful tool, right? If it's really used properly, it can quantify the abundance of hundreds of proteins across a wide dynamic range with high accuracy and precision. It's super powerful. While my interactions with your team initially were centered around myosin-binding protein C due to my combined expertise in this protein and mass spectrometry, it was super easy for me to extract PKP2 data from the control data set. I'm an expert in cardiac muscle protein expression, so this was easy for me to do. However, in true form, your team wanted more validation on my end because I'm not an expert in PKP2. I think if we go to the next slide, we could see why your team is correct here. What we did is we ran many additional samples.
We looked back at our C protein samples, and then we ran many additional samples and tested various normalization strategies that we talked about in many meetings. Although PKP2 is localized to the intercalated disc, as Whit said, rather than the sarcomere, we still settled in on normalizing its readout peptide shared between cardiac myosin isoforms because, again, myosin is the most reliable marker for these cardiomyocytes in the samples, right? Seeing that you're trying to express this protein through cardiomyocytes, this normalization again gives us confidence that we're comparing apples to apples across samples. We're normalizing things correctly. In our new internal studies, like the ones I'm showing on the slide, a few really interesting things popped out. First, the variability in PKP2 levels when measured normalizing these things, those peptides shared between myosin isoforms, was similar when measured in multiple pieces from the same sample.
If we look, I believe we have four donors here, and you see these are four independent multiple measurements from those four donors, and the variability is very tight. This was good and again gave me confidence in the reproducibility of the assay. However, the variability between donor control patients was surprisingly large. You could see donor one levels were much, much lower. To my colleague's dismay, this was statistically significant, which did give me pause here. We don't really fully understand what's driving this variability, but all signs point to this variability being biological. Another challenge for measuring PKP2 is that we don't have a large cohort of samples from patients with the disease. We can't yet say anything about the variability in the disease cohort.
While I'm fully confident in our ability to measure PKP2 accurately, we're still really learning what normal looks like and how normal is going to differ with disease. I think by the end of the TN-401 study, we're really going to be able to answer these questions. In the short term, just like in the TN-201 trial, we're analyzing longitudinal samples. Each patient essentially acts as their own internal control. We really don't need this information to determine if things are working. In summary, I'm again confident that we'll be able to tell if the adeno-associated virus (AAV) is increasing the PKP2 levels in this clinical trial. Again here, your team will also benefit from pairing our proteomic data. The protein levels might not be telling that story. You'll pair our proteomic data with the same orthogonal approaches that you're using in the TN-201 trial.
It's again this integrated view that will really help us move from just measuring the change in protein expression to really understanding the therapeutic results. That's really what I'm excited about.
Okay, Mike, I'd like to thank you again for walking us through this work. It's been a truly productive collaboration with you and your lab, and we've learned a lot along the way about the strengths of mass spec to discern potentially really subtle changes in protein levels, which is critical evidence of gene therapy's potential for success. We're convinced that mass spectrometry is the best method to obtain direct measurements of the proteins of interest and also to quantify their levels in the sample. It's apparent from today's explanation that the steps that have been taken to navigate the challenges of tissue quality and sample variability. We know, as you pointed out, the literature has examples of different conclusions being reached based on the selection of proteins for normalization.
By selecting myosin-heavy chain, which is expressed only in cardiomyocytes but not in other cells of the heart, we can be confident that the measurements are directly applicable to the goals of treatment. The work in normal control hearts shared here also brings home the fact that there isn't just one threshold that we need to get to. The biology of the individual matters. It will be the changes in each patient over time that will be the most important way of looking at and interpreting biopsy data. Thanks so much again for this discussion, Dr. Previs. Operator, I think we're ready for Q&A.
At this time, I would like to remind everyone, if you would like to ask a question, please press star one on your telephone keypad. We will pause for just a moment to compile the Q&A roster. Your first question comes from the line of Cory Jubinville with LifeSci Capital. You may go ahead.
Hey, good afternoon, and thanks for taking our questions. This is one for Dr. Previs. You emphasize normalizing MYBPC3 levels to myosin-heavy chain 6 and 7 to help kind of mitigate that biopsy heterogeneity. Does HCM status impact the stoichiometry of the sarcomere or, in the case of PKP2, the desmosome? If so, how do you validate that myosin-heavy chain 6 and 7 content is stable or comparable, whether it be across time points or disease states or sampling sites?
Great, thanks for the question. As always, we know that you're going to go deep, and I'm so glad that we have Dr. Previs here to talk about the methods that we use for normalization. Dr. Previs, why don't you get started? After that, I'll invite either Kathy or Whit to add any more on top of your comments.
Yeah, that's a great question, right? For myosin-binding protein C, the answer is very simple that myosin is in complex with myosin-binding protein C. We know under normal conditions exactly how many molecules of myosin are in a thick filament. We know how many molecules of myosin-binding protein C are in a thick filament. This is rock solid from everything from zebrafish to mice to humans. We've measured this in all of these things. HCM in that case is clearly due to a lack of stoichiometry of myosin-binding protein C to myosin. We also get quantitative data on actin, the regulatory proteins on thin filament. All of those things are highly regulated and precise, and we're only seeing loss of myosin-binding protein C in the case of disease triggered by that, by mutations in the MYBPC3 gene.
PKP2 is a little bit more complicated, right, because it's in the intercalated disc. As we showed from those donor controls, there are definitely differences from one control to the other, and we were very surprised. If we try to, say, normalize to some other protein in the intercalated disc, which is what we've also tried to do, we're concerned that we don't know enough about biology, the normal biology of PKP2. Our most consistent data are when we normalize to myosin-heavy chain because it seems like the myosin-heavy chain is the most representative of what is in a cardiomyocyte. For my lab, if we image, we see very, you know, with fluorescence imaging, we see very consistent reproducibility in the expression of myosin within the cardiomyocyte. There's not big gaps in expression. I hope that answers your question, but it's a great question.
Yeah, it is a great question. For in HCM, due to loss of MYBPC3, the goal is to restore the stoichiometry, and so the appropriate number of hands are physiologically regulated. Of course, as Kathy mentioned, the protein we express is indistinguishable from wild-type protein, so it should have all the regulatory capabilities. That stoichiometry that we're measuring, the ratio we're measuring, is what we're trying to improve. For ARVC and PKP2, it's a little more complicated. You could think, we want to improve the stoichiometry of the desmosome. In that case, PKP2 is a critical anchoring protein for the structure, so the whole structure falls apart. We hope and expect that all the desmosomal proteins will increase in expression. There'll be more on the cell surface as we express the PKP2 protein, so not appropriate to normalize to those.
The same is actually true of the gap junctions, the critical electrical signaling molecule in the heart. Those also drop off without enough PKP2 to anchor the desmosomes. We do actually, we wouldn't want to normalize to any of those, and that's why normalizing to myosin-heavy chain makes the most sense.
Got it. That's helpful. As a follow-up on PKP2 expression, on the final slide, it notes the variability of PKP2 expression within a patient is low, but across patients is high. When you outline some of that historical data across donors on MYBPC3 protein expression, it seemed pretty remarkably consistent at about 60% of normal. I guess, what's the biological rationale behind this discrepancy between MYBPC3 and PKP2, assuming these are, you know, relatively healthy donors on PKP2 expression? How should we make sense of this as it relates to evaluating the efficacy of a PKP2 gene therapy? Is it less about hitting the specific threshold relative to normal, or is it more about an individual's specific improvement from baseline? Thanks again, Cory, for the question.
I think for this one, maybe Whit, I'm going to ask you to go first to just talk about the different thresholds and how we think about thresholds versus absolute protein and how we think about expression relative to the normal range.
I think both of our studies will really help inform how much protein makes a difference in the phenotype. Going in, we don't think that there is a threshold effect. We do think that the variability across both healthy people and in these patient populations suggests that everybody has their own sensitivity to protein loss and protein reduction. Another way of saying it, the same amount of protein loss or reduction could cause disease in one person and not in the next person. Our goal is to increase the protein level in patients towards normal, but not achieve any specific threshold.
Maybe just to add one more comment to that, if it's possible to go back to that slide, the last slide. Cory, it's an important question, right? This is the first time we're showing this data. I believe the first time data like this has been presented, showing sort of a range of normal using this very, very precise method and showing PKP2 protein in this way. It is interesting to see this wide range of variability. I think a few things are true here, right? One is that we don't think that we need to get to the top end of this range. Probably don't even need to get to the middle of this range if there was like a median or an average to that, right? People at the very low end of this range have perfectly normal hearts. They have no disease, right?
This is from, what's it, NF12, right? If we were to run this, you know, NF24, we'd probably see some clustering towards a mean, and we might also pick up even more variability at the top and bottom end. For the purposes, we hope to get patients closer to the lower end of that range because we know that at that lower end of that range, patients have completely normal hearts. We don't think it's a magical threshold effect even at that level. Every little bit that we give to these patients can go a very long way, and we've seen this from other genetic cardiomyopathies, including from some of our peers, that a little bit can go a long way.
The general goal is to get to in the direction of the lower end of this range, but the absolute goal is to give each patient more than what they have because it appears that the heart is very sensitive to the loss of this protein for each patient in their own way. If we give them a little bit more back, that should go a long way. Would you agree with that statement?
Yeah.
Thank you, Cory. I think we're ready for the next question.
Our next question comes from the line of Ritu Baral with TD. You may go ahead.
Hi, team. This is Joshua Fleishman on the line for Ritu. Thanks for taking our question. Curious, from prior data, are there any preliminary covariates that you guys have identified which can help predict the quantity of transgene expression needed to provide a meaningful functional benefit?
Joshua, it's a good question. Are you referring to the TN-201 MYBPC3 program?
Yes, sorry.
Are there any covariants, other proteins that are changing that might be predictive? You're the experts. Dr. Previs, I'm going to give you a first shot at answering that question, whether we see other proteins changing at the same time with MYBPC3. Then, Whit, I'll ask you to follow up with that about how we think about predictors of success.
Yeah, that's a complicated question. Again, we've published papers on this. At the protein level, there are many proteins that are also changing. You know, myosin-binding protein C is lost, but there's metabolic proteins that are changing because there's complex remodeling that is happening, right? In previous studies, we've tried to team up with geneticists.
Take our protein data and, you know, correlate it with their genetic data to answer that exact question, right? Is any protein that is decreased, also in combination with myosin-binding protein C, does that have another mutation in that other protein? My understanding is that there is no other protein that has been shown to be, or other gene to be, co-affected to trigger the disease. I definitely, five years ago, went down the same path. Maybe we just don't have the technology yet or the informatic ability to kind of go through the genetic data. Whit might be much better at answering this than myself.
Yeah, just additional thoughts. We don't have good clinical predictors of the protein level. We have looked at the heterozygous patients and compared the protein level to the age of onset and other things. Didn't find a clear pattern, but we're very much underpowered for that analysis. More to be done as we get more data. As Mike was saying, the best predictor is the genetics and particularly the type of the mutation, number of mutations occurring in the MYBPC3 gene. If you have two truncating mutations, you probably have zero protein. We look forward to more data coming out about that. That's the presumption of the field. There are leaky truncation mutations. If you have a little bit of leak, you may have a little bit of protein. Those with zero protein have very severe disease.
The infants I talked about need heart transplant in the first year of life. If you have one truncation mutation and the other is normal, that's the patients we've been talking about here with roughly 60%, but some variability there. If you have two wild type copies, you average at 100%, but there's a range there as well. That is the biggest predictor of protein level, the number of truncating or loss of function mutations and their leakiness. My understanding is that we have not found polymorphisms that regulate the level of C protein. Quantitative trait loci like that. We can't build a polygenic model of C protein expression at this point.
Okay, thank you. I just have one follow-up question, please. Just to confirm, using the LCMS approach over other traditional approaches, it appears that the major benefit is just a higher sensitivity and higher specificity of the assay. It looks like both approaches are still limited by only measuring total protein, not protein expressed solely from TN-201 or TN-401, correct?
Yes, correct.
Yeah, go ahead. Yeah.
Go ahead, Whit.
Because the protein products of our gene therapies are full-length wild type sequences, they're identical to the endogenous. These techniques cannot distinguish where that protein came from, transgene or the endogenous gene. There are many more advantages to mass spec that Mike listed. We're measuring many peptides across the length of the protein, and we have much better control than when we're measuring a single epitope with a single antibody.
Okay, yeah, thank you all very much.
The only thing I'd add to that, Joshua, is that on slide 27 that was presented, it really is so telling how methods matter. We really, really have sweat the small stuff here with Dr. Previs over now many years. This is why we've decided to select mass spec. Even within mass spec, selecting what are we going to do? Myosin, normalized to myosin, not only myosin, but specific peptides. We actually had a whole family of peptides to select from within myosin, and looking for stability, looking for things that are not going to vary as much to make sure that we're picking the right anchor against which we were measuring the changes in the myosin-binding protein C. It was a similar method that went into plakophilin-2.
If you compare the methods here, normalizing to GAPDH versus myosin, we just wouldn't even come to the right conclusions about who is normal and who has disease. That is the central insight for us here and why we decided to go the direction of mass spec. Within that, be nearly obsessive and beyond the scope of today's presentation about which specific peptides we're measuring for each cardiac biopsy sample within the myosin. A lot of detail here is beyond the scope of today's presentation, but it gives us a lot of confidence about the mass spec method and the normalization to myosin from both programs. Operator, let's take the next question.
Your next question comes from the line of Yasmin Rahimi with Piper Sandler. You may go ahead.
Hi, this is Dominic calling for Yasmin Rahimi. Thank you for the great presentation and the helpful insights. We just had a quick question. Considering the MyPEAK data will be helpful to inform dosing selection, how informative do you expect the additional cohort one and new cohort two data to be? Specifically, how do you plan to use the totality of data across the protein improvements and the safety measures to inform continued development? Thank you.
Hey, Dominic, thanks for the question. Whit, I'll turn it over to you. How do we think about the incremental data that we'll be getting and presenting in Q4 this year relative to what we've presented to date, and how that informs our future direction?
Yeah, the best way to choose a dose is to compare doses, and we really look forward to sharing the first data from the higher dose, 6.013 billion vector genomes per kg. That comparison will be the starting work to help select the dose for future pivotal studies. Safety, of course, is paramount. We want to choose the dose that is fully safe and very well tolerated. We are encouraged that our DSMB has reviewed the data and endorsed proceeding, indicating that both doses are well tolerated. We will be sharing biopsy results from the high dose cohort. The biopsies, for all the reasons we've said today, provide very quantitative and early in the post-treatment time course data on the expression of protein, which we predict will lead to efficacy. The dose-protein expression relationship will be very informative for dose selection.
We also want to confirm that with clinical endpoints, which take longer to mature. We're certainly following those. The end-of-the-year readout might be early to really compare the clinical efficacy across the dose cohorts. Later next year might be the time for that analysis. All these factors will go into the ultimate dose selection to take forward to subsequent trials.
Dominic, the only thing I'll add to that is it's a good question, right? We're at an exciting moment in the program. We'll be presenting early, you know, additional data from the dose cohort one. We're already pleased with what we're seeing from dose cohort one, right? We were quite pleased with what we were able to share at the ACC earlier this year, and we summarized that at the top of this call. We're looking forward to sharing even more from longer follow-up from these patients. What we've seen in some of our peers and what early evidence seems to bear out here is that the longer we wait, the more we see both the durability of the effect and even some additional effects over time. We're excited with cohort one. We're excited for dose cohort two with a biopsy.
From here, there are many directions we can go, right? From a program perspective, and we've said this for a while, we can continue exploring adults. Right now, the patients dosed to date have been mostly non-obstructive patients. There's also obstructive adults, but then there's also the children. We're quite excited to be presenting data, the first ever data presentation from our MyCLIMB Natural History Study of more than 200 children, both retrospective and prospective data that will be presented at the upcoming European Society of Cardiology. Whit will be there with other members of the team to share that data and to really bring a spotlight on the very severe children, both the homozygous infants as well as compound heterozygotes and other severe heterozygotes. That's another exciting direction that this program can go.
The data we're generating now helps us decide what's the right dose, irrespective of the population we're going after, whether non-obstructive or obstructive adults or children, what's the right dose that strikes the right balance between safety, protein expression, and signals of efficacy. In Q4, we'll look forward to presenting the data, but we won't be sharing the direction that we're going with the program at that time. That would be more of a 2026 discussion. Hopefully, that answers your question, Dominic. Operator, ready for the next question.
Your next question comes from the line of Mike Wilson with Morgan Stanley. You may go ahead.
Hey guys, thanks for taking the question and thanks for all the details on your methods here. I guess maybe just with your more precise methods in terms of protein expression, what's the lowest level of threshold, you know, over time in the same patient where you're confident you're seeing a real change in the expression versus just noise? It sounds like maybe very low percentage difference would be meaningful with these methods.
Yeah, great question, Mike. Thanks for asking it. For here, I'll ask Michelle to wind back one slide, and Dr. Previs, if you don't mind speaking about how do you think about the noise, right? There is a certain level of difference in the samples on slide 29. Dr. Previs, maybe you can speak about that for each protein in each program.
Yeah, I could definitely do that. What slide are we going to go back to?
It's this one just because the variability. Yeah.
Yeah, yeah, yeah. I don't want to give you an actual number for what the level is because I don't want to mislead you. I've been shocked. We've done many mixtures assays where we're mixing known levels of proteins together, and our methods are even more robust than I would have ever thought at the beginning of the process here. You could see for donor one, for PKP2, which again, PKP2, I want just to keep in context here, is expressed at a much lower level in the cardiomyocyte than something like myosin-binding protein C or myosin, because it's only in the intercalated disc rather than distributed everywhere. Our absolute signal is always a little bit lower for PKP2, so there's probably a little bit more variability in that measurement.
The variability between repeat measurements, as shown here on the left these are individual pieces of heart from the same patient is quite low. It seems like a shotgun blast that we could see, and we could tell the difference between donor one, donor two, donor three, and donor four, really, when the data are lined up like this. To really address your question, I think we showed for TN-201 maybe a 3%- 5% change for multiple samples. To me, that seemed real because those data came from repeat measurements of biopsies, as shown here, when they were available, and we saw that increase over time. I think that in cohort two, we have both the access to the tissue a little bit better than when we started, and we're repurposing them for mass spec a little bit better. I do think that the higher dose is going to be telling.
I don't know if that quite addresses your question.
Yeah, no, that was very helpful. Thank you.
Yeah, you're welcome.
Your next question comes from the line of Sami Corwin with William Blair.
Hey, thanks for the presentation and for taking my questions. I have one for Dr. Previs, and a couple for the company. Dr. Previs, is curious how low cardiomyocyte levels in a biopsy could affect the mass spectrometry's sensitivity and accuracy. For the company, can you validate or clarify that mass spec was used for protein quantification in that initial TN-201 data? This presentation highlighted how powerful of a tool mass spec is. Given some of your competitors who are also developing PKP2-based gene therapies have presented protein data using other methodologies, do you plan on supplementing the use of mass spec with other methods for measuring protein expression? Thank you.
Those are three questions there. We'll try to do them just as one by one. I will maybe, Dr. Previs, you can confirm the answer to one of the questions, which is the methods. Have the methods changed at all between TN-201 and TN-401, or have we generally used the same methods?
Yeah, the method is the sample prep, the LCMS, how the sample goes through the LCMS is all identical. Obviously, we're selecting different peptides for PKP2 versus myosin. In the background, we're also looking at peptides from collagen, from albumin to really assess the quality of that biopsy and get a sense of how many cardiomyocytes are there. Faraz, would you like me to answer the question about how low we can go?
Yeah, so the other one is, you know, the question was, now we have seen that other companies, this is not in the MYBPC3 space, Dr. Previs, but this has been on the PKP2 side. There are two other companies, and they have different methods. We're not commenting on the quality of their data or anything like that. Methodologically, there's some differences using Western blot versus mass spec, normalizing to GAPDH versus normalizing to myosin. Two very obvious differences between our methods were there. The question was, would we be changing our methods now that we know that others have presented this in a different way? I think the answer is no on our side. No, we're not changing our methods. Do you want to add to that in any way about reaffirming the methods that we've used compared to others?
Yeah, I mean, as an expert here, without seeing the actual how the sample was handled to the output that, you know, came about, it's really hard for me to comment on any other method. I make this look easy often, even with your team, right? It's from 20+ years of experience of handling cardiac muscle biopsies, right? There's a lot that goes into the front end of the sample prep to make sure that these things are right. With your team, we've looked at, we've taken data that was run in 2017 that was published in 2019 that I didn't even touch. It was a technician in my lab. We've compared those data to the sample sitting in the freezer for God knows how many years, and me doing the preparation, me running the sample, and the data are at the end, you know, virtually identical.
We can't tell the difference between the biological variants versus the technical variants on those data, which is fantastic. I think it would be premature to comment on anybody else's method because Western blotting is not Western blotting. The same thing, I try to make the point that mass spectrometry is not mass spectrometry. You could buy a $700,000 piece of equipment. You could try to do this yourself, but you need the expertise to do that in that area. It kills me when faculty members tell the student, "Oh, just do mass spec on this." It's its own specialty in and of itself, what we're doing.
The way I first started the question on doing mass spec, I'm a grad student. I did a lot of Western blots back in the day. I'm really impressed with mass spec and its level of, not the sensitivity, but accuracy overall for so many different reasons. They are apples and oranges, very difficult to compare. Different labs, different samples, that would be a very hard crosswalk to do. Please do, Mike, go ahead and answer the other question about, you know, how low is too low.
I think there was a, if you don't mind going to slide 24, because there was also this question about the sample and if, you know, depending on the content of that sample, how does the method handle that? I think this was sort of glanced at at the very beginning, three hypothetical pinches with varying levels of cardiomyocytes in there. Dr. Previs, do you mind just sharing how the method adjusts to this and mass spec better than other methods at quantifying the protein in the backdrop of fibrofatty replacement or fibrosis?
Yeah, I mean, these are very critical. They're very special samples. There's a lot of anxiety whenever one of these samples arrives, right? I think from your end, we had a sample that produced no data, right? When we look at that sample, there is very little myosin heavy chain in that sample, which is the most abundant protein, right? That raised an alarm in my mind that the quality of the sample is really critical. The surgeon is blind when they go in and take these biopsies, right? They're sampling wall. They can get some fatty material. They can get fibrotic material, right? They're tiny. That's exactly why we use myosin, because again, it's a marker of how many cardiomyocytes are in there.
We are now in the process of just revising exactly so we know exactly what level of myosin we need to be able to see on the mass spectrometer to be able to know that we're detecting, you know, we're able to detect PKP2 or myosin-binding protein C. To your question, it's very low. It's very sensitive. We do need cardiomyocytes. If the biopsy is a vessel or collagen or fatty material, we are not going to get data from that biopsy. That's the reality.
I believe what you're referring to is in our TN-201 program, the third patient, a C1P3, had a baseline biopsy. The initial post-dose biopsy, we weren't able to really quantify protein from that because there were simply just not enough cardiomyocytes in there. Of course, we have a second shot at a biopsy of that patient, which is a second biopsy, which would be part of the data readout in Q4 of this year, where we do have meaningful data for that patient, as well as subsequent patients in the high dose cohort. Was there something else that you were trying to add before I jump to this slide?
No, this was exactly it. Thanks.
Perfect. Thank you. I think we may have time for one last question before we get to the end of the allotted time. Operator?
Your final question comes from the line of Joe Pantginis with HC Wainwright. You may go ahead.
Yeah, everybody, thanks for taking the questions. My first question out of two is a bit layered, and I'll qualify it by saying your views as of today, because it could certainly change tomorrow. As the program moves forward, I wanted to get your sense of the role of biopsy, say the level and frequency in later stage studies versus just assessing clinical parameters, and what your views might be regarding the commercial and regulatory scales for mass spec.
Oh, that's an interesting question, Joe. Thanks for asking that. Let me first turn to the second part of your question. I'll address the first part of your question with Whit's help. For the second part, you know, the scalability of mass spec, you know, imagine a future world, Dr. Previs, where we're analyzing a lot more samples in parallel. Are these methods scalable?
Yeah, the short answer is absolutely. If the method is scalable, it's transferable to a different mass spectrometer or a different column chromatography setup where the data could be produced even faster, right? It's very scalable.
Thank you. Go ahead, Whit.
Yeah, the vision is not to need a mass spec or tissue samples for commercial use. These protein levels are very, very valuable for showing the efficacy of the gene therapies. We're very encouraged by increasing enthusiasm at the FDA for mechanism-based approvals for these types of therapies, replacing protein in a disease that's caused by whole protein. Very valuable for dose selection, for demonstrating activity, and for approvals of gene therapies at this point. At commercial launch, we'll use the genetics. As I said, the genotype really predicts whether there is insufficient protein causing disease. It would be the genotype, pathogenic, likely pathogenic mutations in MYBPC3 or PKP2, that would determine eligibility for clinical use without requiring heart biopsy.
No, I appreciate that. Thank you. My second question is...
Yeah, Joe. I do want to be fair. There's, I think, somebody else waiting in queue. The other thing I just want to be clear, in a commercial setting, we think that protein biopsies are going to be really important for the potential for accelerated approval based on protein as a surrogate marker. That's been done with many gene therapy programs, including some of our peers, as well as, obviously, there have been some high-profile cases in the Duchenne muscular dystrophy space. Protein is really important when you're seeking that initial regulatory approval based on a surrogate marker. However, in a commercial setting, we don't see that biopsies continue to be necessary. It's really important at this stage when we're trying to understand the relationship between, you know, protein and benefit, but not something that we would be doing at commercial scale later.
I just wanted to sort of put that to rest because I thought that might be linked to your question about scalability of the method. We would not be suggesting to do baseline biopsies and post-dose biopsies on thousands and thousands of patients in a commercial setting. We are over, but there was one more question from one of our analysts. Operator, if you don't mind opening it one last time.
We have a question from the line of Geulah Livshits with Chardan. You may go ahead.
Thanks, guys, for squeezing me in and also the presentation and the added context here. Maybe just another question on normalization. You showed the nice intrapatient analytical consistency across the different samples. As I think about the trial interpretation, I'm wondering if there are longitudinal data showing consistency over time for MYBPC3 and perhaps less so for PKP2. Also, for the trial, could there be an impact of the immune regimen on the protein expression dynamics for, for example, the myosin heavy chain or the MYBPC3 and the PKP2 that could affect the interpretation of the data?
Good question. Maybe, Whit, can I first ask you just immune regimen and whether you had any reason to believe from our work or the work of others that the immune regimen might in any way complicate these measurements? I'll ask you, Dr. Previs, to also add to that.
Yeah, so great question. Thank you. Obviously, in the gene therapy field, there has been question about the immune system, you know, removing cells that are expressing the therapeutic protein. We don't believe that will be a concern. We have not seen any evidence of reaction against the protein, probably because these are heterozygous patients that are, you know, CRIM positive, meaning they're already tolerant to all the peptides in the MYBPC3 and PKP2 proteins. Also, these are intracellular proteins, so that makes that issue less likely. Now, to your question of inflammatory cells potentially being there, of course, we do heart biopsies. We don't really see that. That's not going to be a problem. Theoretically, that would be compensated by the methodology that Mike has developed to normalize to the myosin-heavy chain. I guess a no across the board.
Dr. Previs?
I don't think I could add anything to that. Again, that's the reason why we're normalizing to myosin, because it is in the cardiomyocyte. I think it does take care of those questions.
To address the other part of your question, Gula, the good one, and it's come up before, it may be worth putting to rest. Michelle, do you mind going to slide 22? This is your original work, Dr. Previs. One thing that struck us when we first saw this and why we approached you about using this method prospectively in our studies is how remarkably consistent it is. Isn't one insight from here that these are different patients, all had MYBPC3 mutations, all had myectomies? They were at different stages of their disease, right? Some might have been 30- years- old, somebody might be 40- years- old, somebody might have been 50- years- old. This idea that these data may fluctuate a bit over time longitudinally, it feels like the tightness of this measurement suggests that if there is any such thing, it would be within a very tight range.
Is that a fair assessment of this data set?
Absolutely. I am always shocked in healthy, you know, healthy animals, healthy individuals. I am shocked by how consistent that number is between, you know, mice, again, rats, humans, and the one thing that did come out of my, you know, my work post-publishing this paper with you is I do think that, you know, the data points that are high here above this blue line were consistently high from biopsies. I think that's just what's in the person, you know, what's in that person. We don't have multiple biopsies from that person over time, so we don't know how much it'll fluctuate. I think the coolest thing about the collaboration with you is we get those multiple biopsies, right? We'll see if the level increases, if it continues to be increased, how it varies between biopsies and that sort of thing.
Although you're doing a clinical trial, me as a basic scientist, I'm super excited about basic science, and the basic science aspect of this also, which is kind of what some of these questions are touching upon.
Yeah, and the only last thing I'll add to that, it is important. It's a relevant question. I would say that obviously we'll have two shots on goal here for each patient for which we have the baseline biopsy and one post-dose biopsy, as well as more immediate, for example, in the PKP2 program, eight-week biopsy. We'll have another one at the 52-week. We'll be able to see if there are any changes with time. There's always going to be a certain amount of pinch-to-pinch variability that has nothing to do with the time course of the disease or the changes in the disease. We just have to have the humility there that there will be changes from pinch to pinch and over time. We'll have a chance to see that.
I'll also remind you that it's not just the protein and this normalization of myosin, but there's other things like RNA that we're also looking at with the totality of the data that we're looking at. There may be small differences in RNA from time to time or protein from time to time. We'll be able to see some of that. Overall, we don't think that there's meaningful changes over time. That's what this data set seems to tell us. Final thing I would say is in our peers who published in the New England Journal last year, some excellent work done in Danon disease, they were able to show longitudinally over many years, they showed RNA and protein. There were changes over time. Some of that may have been biological variability. Some of that may have been methods.
The importance of getting your methods right up front cannot be overstated. That has been the whole point of today's presentation. I think we're over time. We're so glad that there was some good Q&A here and opportunity for a back and forth with our speakers. I want to thank you, Dr. Previs, for lending your time and your expertise to us today and for committing your career to these methods. That enables us and our mission to move forward towards patients and really enables us to make sense of the data that we're getting from these studies. Thank you for your work. Thank you for your partnership over many years. Thanks to Whit and Kathy for adding their voice to this. Thanks to all of you for attending today. Our analysts had a chance to ask questions, and we were able to respond.
Not everybody on the webcast, if you submitted a question, had a chance to do so. You have an opportunity to do so later. You know how to reach us. You know, Michelle Corrall, our VP of Investor Relations, our email is in the public domain. Please, if you have burning questions that came from today's presentation, please don't hesitate to reach out to her. We'll find a way to answer that. This is just setting some stage for data releases in Q4. More to come. It's an exciting time for Tenaya Therapeutics. We thank you for your attention and looking forward to the second half of the year and these data releases and the discussion that will follow. With that, Operator, I think we can close. Thank you, everybody.
Thank you. You may disconnect.