Hello, and welcome to the Transgene conference call. Please note this conference is being recorded, and for the duration of the call, your lines will be on listen only. However, you will have the opportunity to ask questions at the end of the presentation. This can be done by pressing star one on your telephone keypad to register your question. I will now hand over to your host, Lucie Larguier, Head of Investor Relations, to begin today's conference. Thank you.
Thank you. Hello, everyone. I'm Lucie Larguier, Director of Investor Relations at Transgene. Today we're going to present the first positive results of the clinical trial evaluating TG 4050, our individualized cancer immunotherapy. With me today are Alessandro Riva, CEO of Transgene; Maurizio Ceppi, Chief Scientific Officer; Maud Brandely , Chief Medical Officer; and Katell Bidet-Huang , who is our Director for Next Generation Vaccines. We are honored to be joined by Professor Christian Ottensmeier, MD, PhD, who's professor of immuno-oncology at the University of Liverpool and the global coordinator of the head and neck trial currently evaluating TG 4050. I remind you that today's discussion contains forward-looking statements which are subject to numerous risks and uncertainties. The presentation material is available on the investor page of our website, transgene.fr.
If you wish to ask questions, you will need to connect to the conference call numbers available in the press release issued last night. I now hand over the call to Alessandro Riva.
Hello, everybody. I'm very happy to be here today to share with you, with my colleagues from Transgene and Professor Ottensmeier, our latest update regarding the results of myvac®. You know it was a commitment we took at the beginning of the year, and we are very happy to be on time and to share this news. On top of that, these news are extremely interesting, very promising data that we're gonna detail to you today. You know we've developed an individualized treatment that is designed for each patient based on the genetic mutation that can be found in the tumor. We start from the patient's tumor to identify the most relevant mutations to stimulate the immune system, and then we design the treatment and manufacture it for injection to the patients.
Beyond the scientific, technical, and manufacturing process, we demonstrate with today's data that we are delivering on our commitments. Regarding the results, first, the safety of the product is good, and we have not observed any serious side effects. More importantly, our first results show on the first six patients who received the treatment that our approach is extremely powerful from an immunological point of view. We are also very proud to see the first sign of effectiveness, which makes us very optimistic for the further development of the product. I'd like to hand over to Maurizio Ceppi to introduce you to myvac®.
Thank you, Alessandro Riva. Before we enter into the details of the current trials, I take some minutes to recall the challenges we have addressed in the development of TG4050 and the way we have overcome them. There are three major scientific and technological challenges when designing a potent vaccine strategy against certain tumors. The first one is the tumor heterogeneity, and we had to deal with the genetic and phenotyping diversity of the tumor and find the proper ways to design the vaccine for each single patient. The second challenge is the potency challenge, and we believe that by choosing new antigens and the selection of the MVA vector has been the solution for that.
The third challenges were the pharmaceutical challenges and the ability to be organized and provide the product in due time for the good pursuit of the clinical trials. I also should start this introduction by recalling the overall collaboration we have had from the very beginning with academic people, and we have really felt their request for innovation in the field of personalized anti-cancer vaccines. Maybe a few words on neoantigens that were selected as the way to match the challenges of a potent vaccine. They result from the accumulation of mutations in the genome of the tumor during the onset of the tumor burden. They will translate into abnormal protein sequences that will be expressed specifically by each tumor cells.
They are largely unique for each patient and potentially very immunogenic considering their difference to self. Of course, the challenge of the vaccine design is to select the best set of neoantigens. Our TG4050 vaccines include 30 selected neoantigens from the deep analysis of the tumor genome. The next slide will give you some more details. By sequencing the entire genome of the tumor, we can build an exhaustive and comprehensive vision of the tumor heterogeneity, including all mutations that might be responsible for escape to the immune system or escape to the current anti-cancer treatment. In our current calculation, a set of 30 neoantigens is sufficient for optimal coverage of the diversity of mutations, provided we can select the best 30 sequences among the large number of mutations.
In this respect, the collaboration with NEC has been instrumental, and we have used their machine learning environment and the possibility to classify and predict the most potent neoantigens to be included in the vaccine vector. In terms of the vaccine design, we have selected the MVA viral vector as the most appropriate platform to design this product for several reasons. The first one is its very good safety and tolerability track record. I just remind you that the vaccine was used for decades in the prophylactic campaign against smallpox, and also showed very remarkable properties in the design of anti-cancer vaccines in our experience.
It is also a very potent viral vector in its capability to induce a strong and long-lasting immune response, generating both an efficient priming and antigen presentation, and also its ability to boost durable specific responses. We have a very good knowledge on its structure and function relationship. We know how to engineer a very efficient antigen display system by optimizing the cloning strategy, by optimizing the way the poly neoepitope sequences are built, and the way we select the best early promoter for more efficient display to APCs. Last but not least, of course, we have a lot of experience on the way to manufacture this product under GMP conditions. I recall you that the manufacturing challenges was most prominent one in the design of this program.
The top challenges is really to deliver on time the product. We have built a dedicated GMP facility to match the delay and cost requirements. We have worked on a target of 12 weeks between the sequencing of the tumor material and the release of the product on site ready for administration to the patient. There were many issues that we could solve from the biology itself, the quality control and the supply chain. The shipment was also optimized, and we were in a position six months ago to have everything in place for the start of the clinical trials. By meeting all these challenges, we have made myvac® and TG4050 a reality.
It has assembled the best of our experience in vaccine design, in tumor genomics and manufacturing excellence to support the two clinical trials. I will give the floor to our colleague, Christian Ottensmeier, to give a perspective from the point of view of a hospital practitioner.
Thank you so much. I look really quite desperate on that photograph, don't I? Thank you so much for giving me the opportunity to present. I have a long-standing relationship with Transgene, and I was a part of previous studies, specifically with the MUC1 vaccine, but also more recently on the scientific advisory board for one of the trials that Transgene is collaborating with. Together with Kydray, we've had a long-standing immunological collaboration also, in which we've examined the immunobiology of delivering vaccines in an MVA context.
I think the particular paper that is referenced here is under revision in the Journal for ImmunoTherapy of Cancer, really demonstrates that even for tumors with a relatively low tumor mutational burden, we can pick immune cells from the blood, determine their reactivity and show that they're reactive with the neoepitopes, much in the same way that we are trying to achieve here. Please move on to the next slide. The key driver for these two trials actually, although I'm going to focus only on the head and neck cancer trial, is really to assess whether in a clinically optimal setting where there is very little tumor burden to suppress the patient's immune response, we can achieve the elimination of minimal or microscopic residual disease that is present after resection of the tumor.
For head and neck cancer, we've picked a group of patients at very high risk of recurrent disease, so human papillomavirus-independent tumors. Within those patients, which account for the majority, we've picked again a high-risk subgroup, where we would predict that, on average, about 50%-60% of patients will eventually relapse from their malignancy and die. The design takes advantage of our intention to understand whether we can reduce the risk of recurrence and whether we can generate early qualitative and quantitative data for the risk reduction and use the data to power a definitive study, one. Then secondly, whether we can identify immune response to the neoepitopes in the blood of the patients after vaccination, two.
Thirdly, if patients were to relapse, either independent of vaccination or having been given vaccination, whether we can understand if the vaccine has altered the immune attack. Our argument has been as follows. If the immune system shapes the tumor microenvironment, then vaccination and relapse would lead to a different complexity or a different sequence of neoepitopes in the tumor at relapse. If, however, the immune system is completely irrelevant, then we would find or we would predict that there will be no meaningful changes in the mutational landscape of the patient at recurrence.
Our prediction then is that the majority of patients that we vaccinate in the adjuvant setting will not relapse because we will have removed minimal residual disease. If we are unfortunate enough that that is the case, we will re-biopsy the patient and examine whether there is any evidence of immunological selection. In the patients who have been randomized to the follow-up arm only without vaccination, at recurrence, we will again sample the tumor and resequence with the expectation that in the absence of immune therapy, the neoepitopes will be the same. We would then be able to address multiple levels of outcome for this trial in a way that is really extraordinarily unusual.
The first, we would potentially have early evidence of clinical benefit, and that is clearly the important thing for our patients, as well as linking immunological consequences to genomic consequences in the tumor microenvironment. I think this enormously rich translational data set will put Transgene in an astounding place for looking at the further development of this platform. The reason why these particular neoepitopes, and if you want to move to the next slide, please, are so important is for really two reasons. One is that the neoepitopes that are delivered in the tumor are not subject to all the normal control mechanisms that would limit T-cell attack.
In the graphic on the right-hand side, we've tried to illustrate this by ordering the likelihood of specificity versus the likelihood of immunogenicity of various categories of tumor antigens. The highest bar are antigens that are true self, so molecules that are found in our healthy cells. The second highest hurdle are antigens that are not normally present in healthy cells, but that are deregulated or altered by the cancer process, and MUC1 is one example of these. The next most likely target that is going to be able to activate immune responses are viral antigens that transform the tumor. Finally, at the lowest bar and highest chance of success are those epitopes that are being encoded in the TG4050 vaccine with the intention of targeting neoepitopes.
We have not only a very safe and extremely immunogenic vaccine delivery strategy, but encoded therein a group of antigens that are patient specific and therefore very low risk in terms of toxicity, and that maps perfectly on what we've seen from the data generated so far, as well as really immunogenic, and therefore likely to confirm clinical benefit. Our data so far confirm the immunogenicity, and at least within the data available so far, the odds of recurrence seem to sit on the group of patients who have not been vaccinated rather than patients that have been vaccinated. Of course, that will come out when the trial is fully completed.
From my expertise in immuno-oncology, and I've taken several dozen early phase concepts into the clinic, this is probably the single most exciting approach, mainly because the idea of encoding the antigens in a virus takes advantage of all the tools that our immune system has developed in the development of us as a species, which would make it very immunogenic. I'm super excited about this. With this, I would like to hand back to the Transgene team.
Thank you very much, Christian, for this convincing evidence on the medical need in head and neck cancer. We have the same need in ovarian cancer. Just to remind you that we are targeting patients with minimal residual disease, meaning that patients who are operated for the purpose of curing their cancer and receive complementary treatment, whether it is radiotherapy or chemotherapy. Unfortunately for patients who are diagnosed at advanced stages of the cancer, the risk is very high of recurrence because we know that even though we are not able to detect any residual disease by conventional imaging or clinical procedures, actually those patients have some residual undetectable tumor cells which may lead to a recurrence. This is the population we are targeting in our trial.
Let me justify the selection of ovarian cancer and head and neck. As I said before, those patients are in clinical remission, but with minimal residual disease and high risk of relapse, which can be detected by biomarkers. CA-125 is one example in the field of ovarian cancer. But ctDNA and new tools we had using liquid biopsy could also be a useful tool to detect the presence of tumor cells. Those indications are limited or no sensitivity to checkpoint blockers. There is obviously a need for additional tools in the field of immunotherapy. Importantly, the immune system is functional because those patients have received a limited number of lines of radiotherapy or chemotherapy.
The two tumors which have been selected have low to medium tumor mutational burden, meaning that we are in a good position with the tools we use thanks to artificial intelligence to select and include all the mutation of interest in our vaccine and to test the vaccine as a monotherapy. Let me move to the next slide, which is a description of the trial in ovarian cancer. Those patients undergo surgery followed by platinum-based chemotherapy, and the majority of them have achieved clinical complete remission, approximately 80% let's say. Half of them, about half of them, when they are diagnosed at advanced stage are at high risk of having a relapse of their disease within 12 months.
We know that before overt clinical relapse, the patients can develop some first sign of asymptomatic relapse, whether it is increase in the specific biomarker I mentioned, CA-125, or appearance of small lesion on CT scan. At that time, the vaccine is started when the patient develops his first sign. The goal is to enroll 13 patients in total in the U.S. and in France. We have data now which has been generated from the first four patients included in this trial. Let us move now to the head and neck trial. This trial was elegantly described by Christian.
Very briefly, I just mentioned that they were randomized as said by Christian between immediate vaccination at the time of complete remission after surgery and radiotherapy, or delayed vaccination in combination with standard care, including checkpoint blockers at the time of relapse. Our ambition is to enroll a total of 30 patients. Of course, Christian is the principal investigator of this trial, which also involves France and the U.S. Actually we have generated data on six patients who were randomized, but two were randomized in the immediate vaccination versus four in the delayed vaccination. We have generated data on the first two patients who all immediate vaccination are.
Now a few words about immune monitoring, and clinical monitoring those patients are undergoing in both trials. They are screened and, at the time of screening, tumor samples are removed so that, tumor sequencing and manufacturing of the vaccine can be initiated. When the patients become eligible, before starting vaccination, the patients undergo leukapheresis and later on, during treatment on day 64. The reason for leukapheresis is to collect, enough, lymphocytes so that, we have enough material to test, the specific immune response towards the, let's say 40, up to 30 epitopes which, are included in the, in the vaccine. In parallel, regular blood samplings are taken, to follow, specific biomarker. We mentioned CA-125, but also ctDNA.
On the top of that, a regular CT scan to monitor the clinical outcome of the patient. I forgot to mention the vaccine scheme, which is a six weekly injection during the induction period of the vaccination, followed by an every three weeks regimen up to a total of 20 injections. To just mention, as said before by Elie and Eric, that the safety profile is not an issue with our material. The vaccine is very well tolerated. With that, I will hand over to Katell for the description of the immune response.
Thank you, Maud. Indeed, since the inception of this program and those studies, we've been working pretty intensively with Christian and other investigators to design a monitoring plan and a study that include a monitoring plan that is exhaustive and allow us to have a pretty accurate description of what's going on in those patients that do receive the vaccine.
As you all know, it's one key question is whether we are able to identify those targets that are of interest, in the sense those targets and those mutations that are going to be indeed solid targets for the immune system and those targets for which we would be able to induce an immune response once we've vaccinated the patients. This is basically the starting point of our immune monitoring plan. What we start with is an assessment of the response in the patients after vaccinations using an ex vivo ELISpot against every single target that is in the vaccine. In other words, it's a technology that allow us to measure, or it's a method that allow us to measure whether there is a given...
There is a response, a cellular response against each given target in those patients. We call it ex vivo because that's also done after obtaining the sample without exposing the cells to long periods of culture. It's an ex vivo ELISpot because that's the picture at the time we have taken the sample and not some potential methodology artifact. This is a key starting point for us, and this is a technology that is giving us the major answer associated with the studies from an immunological point of view, whether the vaccine does induce a response. We'll go through the results over the next slide.
The other analysis we're looking at today is the phenotyping of those cells because, of course, we want cells that are trained, that do recognize the target peptide, but we also want those cells to exhibit some particular activation feature and maturation features that associate with the clinical activity. This is the other type of results we'll be looking at today. On the next slide 29, on the left upper part of the slide, there's a description of the mutational load or in other terms, the number of mutations we do see in the six patients we've treated so far. We see that those patients have anywhere between 300 and the low thousands mutations identified in the tumor.
Those are genomic alterations that lead to the expression of abnormal proteins in the tumor, but not all of them do lead to a potential immune response. We estimate that the actual number of immunogenic mutations would be somewhere around 1% of the mutations we observe in the cells. The first challenge is to identify which of those mutations are going to be relevant targets. The lower part on the same side of the slide, the graph with the blue and the red bars, is showing the class one and the class two epitopes that the artificial intelligence have identified among all those mutations that are seen in the patient.
You see many mutations, several hundreds, but it drives to a few predicted algorithm that we use to design the vaccine. We clone 30 of them into the vaccines, and we give the vaccine to the patient. On the right side of the graph of the slide is the results we observe and the number for each patient, the number of mutations for which we see a response. You can see that for all patients, we get a response against a certain number of targets in the vaccine. This number goes from six to 11 with on average 10 positive response per patient. That's the number of targets for each patient that do becomes positive after vaccination.
When I'm saying becoming positive, it may be an amplification of a T-cell response that was preexisting in the patients or an induction and the priming of a de novo response into the patients. Meaning that some of those patients did not have a response and had a response after the vaccination, and that was directly driven by the vaccinations. This number of positive responses of 10 is something we're really happy with when we compare ourselves. Of course, we benchmark ourselves to other neoantigen vaccines, and up to now, all the neoantigen vaccines that have been published, mainly using messenger RNA platforms, have been around five positive targets per patient. We're doing pretty well on that side.
Besides the specific, the number of response, we are also happy with the type of response we obtain. Of course this is very early data, but we can see that using the viral vector, and this is a known advantage of the MVA, do induce response that are class I and class II, response. I guess for most of you, this is a concept that is clear, but basically the class II response are epitopes for the CD4 cells that are going to support the CD8 cells that do recognize class I epitopes. It's important to have the two cell population.
The CD8 will do the job, but the CD4 will provide support to the CD8 and make sure that the CD8 response will be maintained over time. The other aspect of this early part of the data is the phenotype and the activation markers that are expressed by the different relevant immune cells population circulating in the patients. Another known advantage of viral vaccines are the capacity to improve the innate immunity, and this is something we can see here because in all patients, basically through the treatment trial from baseline to day 63, we do see an increase in one NK cells population that is associated with actual antitumor activity. Those patients do have activated NK cells and NK cells that are engaged into cytotoxic activity.
We see that increasing between baseline and day 63, showing that something is happening on the innate immunity side. On the other part, in terms of phenotyping, you can see that CD4 and CD8 cells mature over the course of vaccination. By saying that, what I mean is that when you look at naive CD4 and CD8, they tend to decrease over the vaccinations while the effector cells tend to increase over vaccination. This is true for both subpopulations of cells. In summary, on slide 32, we are having a priming of innate immunity, and this is a known feature of viral vectors.
We also see a maturation of cells with the shift from naive population to effector population, and this is also a particular feature of viral vectors. We are also very happy with the number of response we obtain with basically a number of response that is twice the target we had to be at least as efficient as other vaccination platform. This is particularly relevant as it leads to some early signs of efficacy that Maud is going to describe now.
Thank you, Katell. Let's move to some data regarding the clinical outcome of the patients. 104 patients with ovarian cancer. One patient was treated at the time of CA-125 elevation. Went back to normal in terms of the biomarker without clinical progression during nine months. I will give you more details about this specific patient history. Another patient is stable and under treatment after nine months. Regarding the two patients treated with the vaccine, immediate vaccine in the head and neck trial, one patient is under treatment 10 months after initiation, and the other one 9.5 months after vaccine initiation. Let me give you more details about the very first patient we treated, by the way, the patient with ovarian cancer.
She was a 73 year-old woman with a high-grade 3C ovarian cancer with some mutation of DNA repair mechanisms, meaning that she had poor prognostic features. She had in medical history a severe cardiac comorbidity with atrial fibrillation, aortic stenosis, and on top of that a severe cardiac insufficiency. On the next slide you have the clinical outcome of the patient. She was screened on January 2020. She experienced asymptomatic relapse on August 2020. You can see that with the red curve the continuous elevation of CA-125 with, on CT scan, parallel increase in some lymph node lesions, meaning lymph node nodal metastasis.
The vaccine was started at that time. On October 2020, CA-125 went back to normal. CT scan was, I mentioned semi-normal because, indeed, lymph node was still there because they are almost undetectable in imaging, but, apparently, they are the reduced size, so it's a good sign. After nine months, unfortunately, the patient died from her severe cardiac insufficiency. I just want to underline the fact that when the time of diagnosis of asymptomatic relapse, usually within weeks, overt clinical recurrence is apparent, meaning the need for additional chemotherapy. This nine months is something quite remarkable.
Let's move to the next slide, which illustrates the immune response of the specific patient and in line with what Katell showed. You can see the simulation, the innate immunity and the adaptive immunity as well, with a decrease in naive T-cells and increase in effector T-cells. On the right-hand side of the slide, you have the specific response towards epitope introduced in the vaccine, with the gray bar, which represents the response at baseline were almost undetectable, and the orange bar, which represents the immune response on day 64 after vaccination. Overall, this patient is the type of history we want to see in all our patients.
Thank you, Maud. Indeed, this is really very exciting data, so we are very proud at Transgene and also with our partner NEC and all our other partners, whether it's hospital, whether it's other companies that have supported us, to share that with you. We're very happy to answer your question right now.
The first question comes from the line of Sebastiaan van der Schoot from Kempen. Please go ahead.
Hey, good afternoon, guys, and thank you for taking my questions and congrats on these results. I have a couple of questions on the neoantigens and then on the quality of T-cell responses and then on what to expect next. My first question is regarding the neoantigens that you selected. Can you maybe give some color on the type of neoantigens that ended up in the final vaccine? Did these include some well-known ones like shared neoantigens or maybe driving mutations that are known in literature and are shared between patients? Or are they mostly patient specific and are not shared?
Do you want to reply or? Should we reply or do you want to put your question?
Oh, yeah. If you could answer that first question, that would be great. Thank you.
Throughout those first six patients, we have been targeting a total of 136 mutations. These 136 mutations, none of them were common to two patients. They were all unique mutations. Of course, many patients do have a common gene that is mutated, meaning that many patients have point mutation on the TP53 gene. But that does not mean that they do have the same mutations and the same mutated sequence. We do see some like usual suspects, I would say, in the genomic profile without necessarily finding shared mutations across the patients, which is very expected. You know, these questions very often resurface, but when you look at the TCGA, which is data from thousands of patients, and you look through an indication, you'll find very few mutations that are shared across patients.
Okay. Okay.
Maybe I should add that the selection algorithm is based on the databases for several criteria, among which the distance to the self and the ability to be presented by the TCR/MHC system. It is quite well known now that the oncogene drivers are not very immunogenic in their properties. It's not a surprise for us that most immunogenic subset of mutations were actually privately represented.
Okay. Got it. Thank you. Then, on the machine learning approach of NEC, you show that you get up to 1,000 mutations back from the AI algorithm. I assume that you then perform a certain ranking of those epitopes and then select the best 30. Can you also expand on whether the T-cell responses that you observed were against those epitopes that were a lot higher in the ranking of these 30 epitopes or were more spread over those 30 epitopes equally?
We do use the word ranking, but I think it's somehow a little bit of a misconception to think of this list as a ranking because of course, there's their capacity, I would say their pure immunogenicity potential that is taken into account. There is also the diversity and the need to cover the entire clone's population. When we do assemble the 30 mutations, you cannot consider that the 30th epitope is necessarily something we assume to be of lower value than the first epitopes.
We have just selected an array of epitopes that do represent the maximum diversity of mutation and genes and physicochemical features together with a relatively high allelic frequency to make sure those are frequent mutations and not only present in subclone. To answer the question, no, but it's expected not to see like the one, two, top three epitopes to be better than the other one.
Okay. Great.
To add on , it's really the job of the NEC artificial intelligence tool to propose the ranking and the selection of the neoepitopes. The 1,000 mutations are really done by a standard analysis of the genome of the patient, of the tumor and the normal genome. It's the algorithm from NEC that we select and propose the selection of the neoepitopes, and then it comes to us, and then we manufacture and so on.
Okay, great. That was very clear. Thank you. Maybe on the repeated injection that you provide to boost the immune response, I was wondering whether you will do more measurements in which you can actually see or do you actually expect to see them, maybe to get additional responses against additional epitopes? Or is that out of the question and you would expect only to see maybe a boost of the response?
Yeah, I think it's we have other sampling points throughout the times and we probably in a few patients take them all because we may expect that throughout the time we do see response against different epitopes and not have only boost of some of them, and they may have a different profile through time. We may test throughout time all epitopes in two patients, and if we only see boost of those positive patients, then we restrict the analysis on characterizing the positive epitopes throughout times. I don't know if I'm fully answering your question, but.
May I jump in, Katell?
Yeah, sure.
The purpose of the leukapheresis was to enable a broad screening assessment and would enable us to characterize T-cell responses at these two critical time points. The expectation would be that following those reactivities over time or the dominant reactivities over time can be done on follow-up blood samples. It would be perfectly reasonable, and I think Katell and the team are planning to look at antigens that are not encoded in the vaccine, but that form part of the transcriptomic features of the tumors that have been vaccinated against. That would enable us to look at antigens epitope spreading, and indeed, the Transgene team already has past data with a shared antigen that observed that such epitope spreading was common.
From this background data set, I think it will be very interesting to see whether epitopes that are mutation derived but that are not encoded in the vaccine also elicit a response, and how the kinetics of these responses develop over time, and then specifically, whether any of these responses link with clinical outcomes. I think it's too early to judge that. For the ovarian cohort, there's also, of course, a possibility that by using the blood T-cells, it becomes possible to sequence both the alpha and the beta sequence of the T-cell receptor and then track these T-cells back into the tumor, even if tissue is only available in paraffin-embedded format because the T-cell receptor enables a molecular fingerprint that would allow us to look in different tissues of different origins.
I think your question is highly pertinent and expect that there will be data to demonstrate antigen epitope spreading over time also.
Great. Thank you. That was a very clear answer. My final question is regarding the update in H1 2022. How large a data set can we expect by then? Can you maybe talk about how many patients, what additional metrics, we can expect? At what length of a median follow-up period would you be confident that you can actually show a difference in relapse rates that would be meaningful?
Well, practically, what is planned is to enroll a total of about 13 patients in this order of magnitude for the ovarian cancer trial, and a total of 30 patients, 15 in the immediate vaccine arm and 15 in the delayed vaccine arm, in the head and neck trial. The enrollment is going very well. What we expect is to have the next year additional data to share with you and to present at, in the context of a scientific congress.
Maud, may I offer a clinical comment?
Sure.
The median time to relapse in these patients with this very high risk disease is under two years. I expect that if the relapse rate is different two years into the follow-up of the cohort, then we'll have a very solid data set. I expect that the curves will begin to diverge much earlier than that. I would expect that we would want to wait until a year after the first patient, the last patient has been recruited and vaccinated to be able to begin assessing whether there are true differences. With the data that, Maud and Kate represented on the ovarian cancer patients, if this is reproducible and is observed in a larger group of patients in the ovarian cohort, then I suspect that the divergence will happen much more quickly.
I think an ongoing analysis of the comparison will reveal whether my assertion, my expectation is true, or whether the cynic's expectation of what might happen in the phase I study is true.
Okay. Thank you very much. That was very clear. Congrats on the results again.
Thank you.
The next question comes from the line of Dominic Rose from Intron Health. Please go ahead.
Hi, this is Dominic from Intron Health. Thanks for taking my question. I've just got the one. Of the six patients who were treated, only four had valuable data. Can you please help explain a bit more about why the other two patients were excluded? Will those two still be included in the final analysis?
Yes, I forgot to mention that those patients are in the study, and they will be part of the study. There's absolutely nothing to hide with those patients. The reason there was no immune data shown for those patients was that they did not have enough cells to allow us to do the characterization, so we could not generate the data for those patients. As you might know, those technologies require you to provide cells that are alive, properly stored. You know, there's quite a few mishaps in the way of having those samples available for analysis. It's purely a technical issue.
Okay. I see. We should expect issues like this to be ironed out in the future. I only wondered because it was quite 1/3 of the total who were done. What you've said makes sense. Thanks.
We have some immunological information, though.
Yeah, we have some, but not the immune.
W e have some CD4.
Yes.
We have in the graph you see for the CD4 and the CD8 evolution, all patients going in the right way. Depends on the graph, but when we could, when we had them, we shared them, and we have some and they are in line with the others. We don't have the detailed analysis for the epitopes. So, when we say on average we have 10 out of 30 on the two patients that were not available, we have no idea about this figure. Otherwise, very interesting.
Okay, thanks very much.
We currently have no questions on the line. If you would like to ask a question, please press star one. The next question comes from the line of Jean-Jacques Le Fur of Bryan, Garnier & Co. Please go ahead.
Hello. Thank you for taking my question. Just one quick question regarding the future of TG4050, if I may say in the real life. Let's assume it will be approved. For the phase I trial, I understand that it's a proof of concept. You have a careful selection of patient with clinical remission, minimal residual disease, low to medium TMB. Could we think that in the future, with the help of AI tools and so on, you may have a larger population or extended population with not a minimal residual disease or even whatever the TMB status is, so larger population than the one you carefully choose for evident reasons for this trial?
Thank you for the question, Jean-Jacques. Yes, indeed, those trials are there to be sure that the concept is right, that we are going in the good direction by selecting patients with minimal residual disease. Unfortunately we know that in many clinical settings and other indications in the field of cancer, there are strong medical needs to avoid relapses and the list is pretty long. Maybe in monotherapy but also in combination, and I'm thinking about a potential combination with immune checkpoint blockers. We may consider, if the concept is right, to broaden the application of this vaccine approach to many tumor types having a high risk of relapse. Maybe Christian, you would like to elaborate a little bit about that?
We've lost him.
We have lost Christian.
Christian?
Yes, I missed the last two sentences of what Maud had said.
Oh, sorry. I was saying that if the concept is right, we can consider other indication where the risk of relapse is quite high as a potentially curative treatment. Do you agree with that, Christian?
Well, absolutely. I think that the current studies will bracket the assessment of measurable disease, but small volume disease in ovarian cancer and clinically disease-free patients at high risk of disease. If we can demonstrate efficacy in the very high-risk adjuvant setting, particularly in a group where other strategies have a very poor track record, that's in HPV-negative head and neck cancer. I think the obvious steps would be to roll this out to other tumor types where high-risk adjuvant patients are very well characterized. That could include multiple solid tumors where the methodology would be equally available.
I think it would also then make a strong case for developing this concept into patients with measurable disease, where objective assessment of responses in the individual patient could also be added, so much along the lines of a slightly worse group of patients in the ovarian cancer setting. I think if the concept in head and neck cancer pans out, then of course, the next step will also be to do a larger and pivotal randomized study that would be formally powered to detect clinical benefit. I think if the data, all the data that we currently see are supportive of that, this has the potential to become blockbuster approach to the management of multiple solid tumors.
I think the challenge will not be where to go, but how to limit the effort and to, you know, prioritize the clinical settings that will give the most clinical benefit for the patients, but also the best development opportunities. Certainly, if we look at other immunotherapy settings in head and neck, and ovarian cancer, I think we would be very well placed to make a major impact in these two solid tumors, but of course extend that to other tumors also. Okay. Very clear. Thank you.
Any other question for today?
We currently have no questions on the line. As a final reminder, you can press star one.
just finished the hour that was planned. Thanks again for your interest and your time and your great questions. Just to summarize for, you know, Transgene and also our partner, NEC, we are extremely happy about the data that we've been able to share yesterday and today. We thank all our partners in academia and other companies that have supported us. It gives us a lot of motivation to continue the existing trials and already to prepare the future of the product. Once again, we talk to you soon. Bye-bye.