# Moderna's Personalized mRNA Cancer Vaccine Breakthrough

**Podcast:** a16z Podcast
**Published:** 2026-09-02

## Transcript

It's the first time there is a cancer vaccine working.
The field has been doing that for 20 plus years, more than a thousand clinical trials that have all failed.
What was different this time?
What is it about mRNA technology that enables the immune system to learn in a way that other approaches were unable?
We all have cancer cells all the time in our body.
Our immune system is very well trained to basically notice those cancer cells very early and get rid of them.
But if your cancer grows, Then the question is how can you reteach immune system?
We're going to basically take a biopsy of your tumor.
We're going to read all the letters of its DNA.
And then we're going to do the same things with a healthy cell of your body.
And we're going to literally compare.
letter by letter, nucleotide by nucleotide.
And then we're going to use an algorithm to identify which one of those mutations are the most relevant.
And so when this is injected in your body, it teaches the immune system the signature of your cancer cell that it missed.
What gets regulated here?
Because every dose is different, so obviously every dose doesn't get approved.
Basically, what the FDA wants to know.
Before Moderna's mRNA platform helped produce a COVID vaccine, the company was already working toward another goal, using mRNA to fight cancer.
A decade later, that bet has reached a major milestone.
A16Z general partner Jorge Conde sits down with Moderna CEO Stefan Bancel following positive phase three results from Moderna and Merck's individualized mRNA treatment for melanoma.
It's the culmination of a project Moderna has been working on for roughly a decade.
Stefan explains how Moderna takes a patient's tumor, sequences it against their healthy cells, identifies the mutations unique to that cancer.
and uses that information to manufacture a personalized mRNA designed to teach the immune system what to attack.
And because roughly 90% of the selected antigens differ from one patient to another, personalization isn't an edge case.
It's fundamental to how the treatment works.
They also get into the extraordinary operational challenge behind this.
How do you manufacture thousands of different medicines, one patient at a time, quickly and reliably enough to make personalized medicine work at scale?
And finally, they look at where the platform could go next, from other cancers to rare genetic and autoimmune diseases.
Hi, welcome to the A6NZ podcast.
I'm Jorge Conde, a general partner on the A6NZ Bio and Health team.
I am thrilled today to welcome back Moderna CEO, Stéphane Bancel.
For folks that have been long-time listeners may recall, Stéphane joined us on the A6NZ podcast back in December of 2020.
when we were talking about all of the work Moderna did to bring us the mRNA COVID vaccine.
And at the time, if you can go back and listen to that episode, you'll hear how quickly Moderna was able to react to the existence of the virus, to analyze it, to essentially print a vaccine that would protect people against the COVID-19 vaccine and hence put us in a much better position.
with respect to the COVID pandemic.
We titled that episode, The Machine That Made the Vaccine.
And the reason why we're talking again today in August, 2026, is because Moderna has put out news on a really big advancement on what they can do with mRNA technology when it comes to cancer.
And so I want to hand it over to you, Stefan.
Again, a very big welcome.
Thank you for coming back.
But maybe the place to start is let's lead with the news.
Moderna and Merck announced earlier this month in August that you had conducted a phase three clinical trial in melanoma and have gotten very encouraging results.
So why don't I hand it over to you and tell us what have you announced?
What have you seen in this phase three trial?
And then I do want to dig in into what Moderna is doing in cancer.
Wonderful, Soror.
Thank you so much for having us back.
We're very happy to be with you.
So indeed, last week now, we shared the news with our colleagues at Merck that after working 10 years on an individualized treatment against cancer using mRNA technology, that the phase three was positive.
It's the first of many.
It's the first time that there is an agent in melanoma that is better than ketidra alone.
So it's a big deal for patients, of course, with melanoma.
It's the first time there is a cancer vaccine working.
The field, as you know, has been doing that for 20 plus years.
I think more than a thousand clinical trials that have all failed.
And if you look at the phase two data, because what we announced last week is that we met the primary endpoint of a study, which was recurrence-free survival, i.e.
people having the cancer back or dying, that we met.
And then to our own surprise, because this was the first interim analysis.
This is not the end of a study.
It's the first interim analysis of a study.
And what was a surprise event to us is we met the secondary endpoint, which was distant metastasis-free survival, which is, of course, takes more time to mature because it means that you have distant metastasis from your primary tumor.
And we also met that endpoint, which was great.
Again, unexpected from our side, but again, means it is really good.
We will share the data very soon at a big medical oncology conference, which is what the field does.
But just to give you a sense, maybe to orient kind of directionally, we showed at ASCO, the big oncology conference in the spring of 2026, a few months ago, that our phase two study, which was also randomized against Ketudra alone, so the same type of study, showed that around 50% of people had recurrence-free survival versus people only getting Ketudra.
And this is five years after treatment.
And as you know, in oncology, five years is considered by a doctor like a cure.
And so it's a big, big deal.
And if you look at the data from, again, that phase two, you had around 80% of people that were disease free five years after their treatment and their surgery of melanoma.
And so we're very excited about what it means for the field.
We are working very hard already with regulators to file so the drug can be available to patients as soon as possible.
And we hope in 2027, then we're going to try to make it as fast as we can.
The factory is ready in Massachusetts, but we have figured out how to make and scale a product for every human being at the time.
I mean, there's so much to unpack here.
So this is an extraordinary advance for the treatment of, in this case, melanoma and hopefully over time, cancer support broadly.
So let's unpack some of this if we could, Stefan.
The first one, for folks that are less familiar with cancer treatment, what is Keytruda and why is Keytruda alone not sufficient?
Why was this particular mRNA cancer vaccine necessary in conjunction with Keytruda?
Sure.
So if you look at Keytruda, which is kind of one of the leading immunotherapy that people might have heard of, it's basically, if I oversimplify for non-biologists, it's basically a molecule that basically opens the gates for letting the dogs out that are going to go and attack your cancer from your immune system, if you want.
The thing about checkpoints is when they work, they are fantastic because those people...
five years after treatment are cured in the sense of no remaining disease.
But only 60% of people are disease-free after five years, if you look at the phase three published data of Ketudra.
So again, for those 60% of people, it's amazing.
But it means that there's 40% of people where you go through the treatment, you're fighting for cancer, and the treatment doesn't really work for you.
And also what is difficult is those treatments are wonderful, but they...
a lot of times come with very serious side effects, very different from chemotherapy or radiotherapy.
The side effects of immunotherapy are mostly immune disease.
So you see people, if you look at the label or the clinical studies, people that get checkpoints, whether it's Ketudra or any other type of checkpoints from other companies, they end up having type 1 diabetes, lupus, Crohn disease, these type of things.
Of course, it's better to have those diseases than, of course, being dead from your cancer, which is why those have become standard of care.
But think about those 40% of people who don't respond to checkpoint.
They get an autoimmune disease most of the time, and they don't get the benefit of a medicine.
And so what we try to do with Merck, and it's really a technology that emerged from Moderna Labs back in 2015, 2016, when we did a partnership with Merck, as we were looking for one of the best companies in immunotherapy to partner and to have a complement approach.
The idea that we have...
At the time, using our infectious disease learning from our infectious disease vaccine, we learned a lot about the immune system and how mRNA interacts with the immune system.
We thought we could develop a mechanism of action that was totally orthogonal, so very different from immunotherapy, because you will be able to start from a sequence of your tumor so that we could design a product to basically teach, if I go back to the dog analogy, to teach those dogs what to look for.
very specifically.
And that's really the beauty of our technology is if you think about K2Droid, basically unleash the dogs, but they go a bit randomly sometimes.
It's your immune system.
Whereas, when our product is available at the molecular level inside your immune system to very specifically teach your T cell, this is what you need to look for.
And that thing is actually on your cancer cell.
So that those T cells go and basically attack your cancer cells.
Okay, so now that's where the personalized cancer vaccine technology will make a big impact on the treatment of melanoma.
We'll come back to the personalized piece because I think that's just fascinating, not only from a technological standpoint, but just from an operational standpoint.
So I do want to come back to that.
But let's focus on the word vaccine.
So typically when you think of the word vaccine, it is to prevent a disease.
Here in this context, these patients already have cancer.
So in a sense, you are preventing something.
You're not preventing the cancer.
What you're preventing is the return of the cancer, which is in and of itself an extraordinary thing to think about from a therapeutic intervention perspective.
So number one, is that a fair characterization?
That's a fair characterization.
And the reason the field has used the word vaccine, it was not us using it, we just followed the field.
As I mentioned, there's been a thousand plus clinical trial.
It's because it's about teaching your immune system.
If you think about a COVID or a full shot, You teach your immune system before you get the virus infecting your body.
Here, you teach the immune system not about the virus, but about basically the cancer signal that your immune system missed.
Because what we know today in the field is that we all have cancer cells all the time in our body.
Whether it's outside factors or just as you have cell replication and you have mistakes that happen that create mutations of DNA that are cancer cells.
Our immune system is very well trained.
to basically notice those cancer cells very early and get rid of them.
But if your cancer grows, then the question is, how can you reteach your immune system?
So I think that's why the field use the word vaccine for that approach as well.
Even as you said, it's a therapeutic treatment approach post-cancer.
It's about the teaching of the immune system.
Okay, and so as you point out, the field has tried this many times before, on the order of a thousand clinical trials.
have tested this theory.
They've all failed.
You and Moderna and Merck have succeeded here.
What was different this time?
What is it about mRNA technology that enables the immune system to learn in a way that other approaches were unable to be successful?
Yeah, I think there are two components.
One is the mRNA technology, and I think the other one is the individualization by...
designing a product for one human at a time.
So let me go through those two.
On the technology of mRNA side, what we have known and published actually with Karolinska, as you know, the institute in Sweden that gave the Nobel Prize for Medicine back in 2015, is that with all technology, I cannot speak about all the mRNA companies that have different mRNAs, different lipids, and so on, but with all technology, when we inject our mRNA in a muscle, whether it's a COVID shot, a flu shot, or this cancer treatment.
Basically, the mRNA goes down to your lymph node and enters the APCs, the antigen-presidentic cell, which, as you know, are key components of your immune cells.
And the mRNA gets inside the APCs.
This we've demonstrated and proven at the time of Karolinska.
And then it basically translates the message contained in the mRNA inside the APCs, inside your immune cells.
and present it from within.
So I think it's a very important differentiation from most of the previous vaccine in cancer in the field that were made by protein or peptide that basically are made in reactors, are injected in the patient, but that basically turn into the blood because, as you know, recombinant or protein, when you inject them, they just go into your blood and they turn around.
So your immune system sees them, but not in the same manner from within as it's done with the mRNA.
So we think that's one very important component of immune presentation, if that makes sense.
The other component is really the individualization.
In the past, a lot of times people have tried with non-mRNA, i.e.
protein technology or peptide, but also we tried a shared antigen.
Whereas here what we said is because cancer is a disease of DNA, what we're going to do here, because the cost of sequencing has dropped so much in the last...
20 years, is we're going to basically take a biopsy of your tumor.
We're going to read all the letters of its DNA, the three gigabytes of its genes.
And then we're going to do the same things with a healthy cell of your body.
And we're going to literally compare letter by letter, nucleotide by nucleotide.
And then we're going to use an algorithm to identify of your hundreds or thousands of mutations, which one, based on the current knowledge of a field of immunology in cancer.
which one of those mutations are the most relevant.
We select the 34 that we believe are the most relevant and we stitch them together into one big mRNA molecule, which we make in 30-ish days for you, that is injected then in a hospital intramuscularly.
And so when this is injected in your body, basically it teaches the immune system the signature of your cancer cell that it missed.
Not the signature of every other patient with a shared antigen, but a very specific signature.
of your cancer cell.
And what we showed at ASCO and we published from a phase two study, but we believe it's the same thing in the phase three because it's mechanistic, is that around 90, 90% of the antigen are different patient to patient.
Because when we started, we had no idea because again, the field came from shared antigen.
So when we started, we were like, we have no idea we're going to get 2%, 5%, 90% of the same antigen across all the patients.
Actually, 90% of the antigen are different from a human to another one.
Wow.
So the only way this can work is through personalization.
We can individualize, exactly.
You know, assuming that carries over.
And so in that regard, you know, if you're doing, let's say, this normal to tumor comparison of the genome, you find the differences.
I'm curious how you arrive at 34, up to 34 as the right number.
I'm sure there's a very good reason for that.
But what is the algorithm that enables you to do that?
Is this something that's proprietary to Moderna?
Is this something that is known within the field?
Help us understand, like help us look into that black box.
Sure.
So I want to share a little bit about the black box, not too much because there's a lot of know-how and things that are very confidential to us.
But basically we started with, of course, what is known in the field.
And so we basically use a lot of database and publication and a lot of scientists and doctors.
kind of best in class in immunology and in oncology.
And then from that starting point, we use a lot of internal data that we generated over time.
We also partnered with some companies that because they are in the diagnostic space or they are, let's say, cell therapy and other space in oncology, had access to a lot of data, a lot of T-cell mapping and so on that were very useful for the learning.
The thing that is interesting about the data we shared last week is this is what I consider.
in Tismarine version 1.0.
Because the algorithm from a phase three was the same from a phase two, was the same from a phase one.
But because we've been doing this for 10 years, it's a 10-year-old algorithm.
So as you look at the data, it works pretty well, right?
As we said about the phase two data, 80% of people are disease-free after five years.
It's amazing for those patients.
But there's still 20% of patients that don't respond.
And so one other thing we're going to be doing now that we have access to a phase three patient data and samples, is to go back and mine that data to figure out why some patient responded and why some other patient did not respond because we have access to all their blood samples, the sequence, everything.
And we're going to try to see can we improve the algorithm and we will go to see FDA if we find scientific reason why we should change the algorithm to go from, let's say, 1.0 to a 2.0 algorithm and then change it.
Of course, we have to do that in a very controlled way.
to ensure we don't lose efficacy, very obviously.
But the way I think about it is a bit like when we talk about AI, we always joke that the current version of AI is the worst we're going to see in our lifetime.
Well, it's exactly the same for Intismeran, which is the current version of Intismeran at Moderna is the worst version of Intismeran you're going to see for the rest of medical history.
And so that gave me a lot of hope, not only in melanoma for those 20% of patients that don't respond, but also for potentially over tumors that have been really hard in the field, like, you know, pancreas cancer and others where immunotherapy doesn't work.
We want to be able to learn a lot about the technology using also what the field has learned in the last 10 years because the field has learned a lot, as you know.
This is even not in Intisperine 1.0.
So that's why I'm so excited about what's coming next.
That's fantastic.
So looking back, you and I have had, we've known each other for a very long time.
I won't depress.
you or me by saying how long.
But you were in kindergarten.
So I've had the benefit of seeing the Moderna from the earliest days.
And one thing that is true, that was true then, is true today, is you are, well, first of all, you are an engineer at heart.
And you have, from the very, very beginning, been obsessed with process, with operations, with being efficient.
And those things need to be absolutely true if you're going to attempt to do what you're trying to do here with personalized cancer vaccines and make a medicine for each individual patient precisely because in 90% of the cases, there's no overlap in terms of the antigens.
Can you walk us through a bit the operational lift that is required here that you've already had to do to even run the trial?
but that you would have to do if you eventually commercialize this product.
Sure.
And let me, maybe one way to frame it is, I think a lot of people think about the other big personalized therapy that exists in cancer is CAR T cell therapy, right?
And in that case, the thing that, in CAR T cell therapy, for folks that may not be familiar, is this idea that you take a patient's tumor and then you take the patient's immune cells, you take them out of the body.
And you essentially reprogram the immune cells and re-engineer them to be reactive to the tumor and put them back into the patient.
I'm oversimplifying, of course.
But that's CAR T cell therapy in a very sort of simple nutshell.
In this case, you're doing, in some ways, things that are very similar, right?
You're taking a piece of the tumor that you have in the form of a biopsy, presumably, and you're trying to sequence the tumor.
to generate a vaccine that is very specific to that patient's tumor.
How do you think about essentially the vein-to-vein time?
Like what needs to be true from the moment you, you know, sort of see a patient and get access to the tumor to the moment that patient receives their personalized vaccine?
Sure.
So the time is around 42 days now, needle to needle.
So from taking the biopsy to getting the vaccine in the hospital ready for you.
I think we're going to be able to improve that as we still have a lot of efficiencies to work on and automations and robotics.
The place where this is very different from CAR-T is that we don't have to take your immune cells.
We program them ex vivo in a reactor in our factory and send them back to your hospital.
The only thing we need is the information.
As you and I talked about, the beautiful thing of mRNA, it's an information molecule.
And so basically what we get from the lab is the sequence of your healthy cells and the sequence of your cancer cells.
So we just get a file.
And then we use that information to basically make the DNA.
But now we don't make it with plasmid growing E.
coli or whatever.
We make it all synthetic.
So it's all enzymatic in liquid, in water.
Then we make the RNA from the template.
Then we put the lipid around it.
And because of that, and that it's a synthetic, process, it's much more like small molecule than a large molecule.
You think about CAR T, for me, the analogy is the recombinant world, where you have, you know, cells and big reactors and you have big volumes.
Because as you know, the reason you have big volumes in biotech industry is if you compress the cells too much, they die.
You have the same issue with CAR T.
So it's all, everything's big.
Whereas here, because it's all in water and it's all enzymatic, meaning it just, it's very catalytic, the reactors are very, very tiny.
And so what we been doing since before the clinic, because as you said, we had to start developing the technology to individualize it one human at a time to even do a phase one study, right?
We shrunk everything down.
So the first version of a machine that looks like a big American fridge was a bit big and clunky because we told the team, make it good enough so you have good quality, but we're not serving for efficiencies yet because if it doesn't work in a clinic, what's the point of wasting five years?
making a beautiful, amazing, optimized robot if science doesn't work, right?
So we told the team, make it good so that we have no quality issue and we don't have a false experiment, a false negative in the clinic because it would be terrible for patients if you build a robot kind of working but not working.
You run the study and it tells you the science doesn't work and you don't.
If it should have worked, right?
That would be a disaster for humanity, obviously.
And so the team did exactly that.
They developed a robot.
That was good in terms of quality, but it was not very efficient.
And when we got the phase two data that this was working, the first interim of phase two data at only two years, now we have five years of data maturing beautifully for duration of efficacy.
We told the team, okay, now we are behind.
I knew this was going to happen in case of success, which is a happy program.
And so we dedicated a lot of very smart engineers to think about, okay, now how do you make a very efficient machine?
that you can compress even the volume of a machine because one of the important vectors, of course, is time, as you mentioned, cycle time from needle to needle, but also cost.
And so one way to reduce the cost is reduce the footprint on the floor.
Because if you have a fixed envelope of a clean room facility and you can put 2x or 10x more machines in that surface area, of course, you're going to get a bigger throughput and a much lower price on your fixed cost.
And so we are obsessed about cycle time because the more you can reduce cycle time, the more you can get a turnaround, let's say, in a year on your assets.
And the second vector I'm obsessed about is square inches.
Literally, I'm always a pain about, sorry, when I go to the factory to look at all the space we can save, how we can be creative, how even we can move some compute out of the clean rooms just to shrink things as much as you can.
got a huge impact on cost of the product at the end of the day.
And at scale, how many, roughly, how many, let's just focus on melanoma, how many doses would you need to produce in a given year?
So we've already got thousands of doses because of nine clinical studies that are ongoing.
The facility will be able to make the one in Marlboro, Mass, tens of thousands of doses.
And then, As we keep improving the technology, that number is going to go up in the same facility.
And then we might need to build several other facilities.
But if you look at the incidence of melanoma, if you're in a tenth of a thousand of doses, you're going to cover easily the melanoma market.
Yeah, I can believe that.
Have you disclosed how you think about COGS and price, or is that something that...
We have not disclosed yet.
We need first to disclose the data.
We want to make colleagues.
We need to engage with the payers.
Once we can share the data with them in terms of what is the value being driven there and so on.
But this has not been discussed yet.
So maybe one place to focus is on this concept of personalization.
I'm sure you've seen the story of the GitLab founder who went founder mode on his own osteosarcoma.
Number one, do the future SIDS of the world come to Moderna?
Or is this end of one phenomenon something that you think will just happen and exist in parallel?
So I think they will come most of them to Moderna because it will just going to be easier and safer.
Because as you know, making an injectable product always carry risk of contamination of a product.
If you inject to somebody a product that has even one copier bacteria, you might give the patient sepsis.
Then you always have a question of quality because when you have...
a multi-step process, a mistake can happen.
And of course, if it's industrialized and has been validated in terms of good manufacturing practice, kind of FDA standard, you have much less chance of this happening.
So it's a bit like every tool in life, which is, you know, do you make your first knife because there's no knife store and you are in a cave and you need to feed your family?
Yes, of course, you make your first knife because you have to feed your family, right?
But when you have a store making high quality knives down the street, you're going to use your time to do something else.
So I think it's a bit of the same phenomenon, which is like in any technology, which is when you have industrial scale of high quality product, you use your time as a human to do something else with your time, right?
And in that world, how does the regulatory environment, the regulatory apparatus function here?
So in other words, you mentioned earlier, you along with Merck will prepare a regulatory filing soon.
What gets regulated here?
Because every dose is different, so obviously every dose doesn't get approved.
Is it obviously the process for synthesizing mRNA?
Is it the algorithm?
Is it a combination of the entire system?
Help us understand that and build intuition around how we think that these kinds of personalized medicines will be regulated in the future.
Yes.
So the good news is there are precedents.
As you mentioned, CAR-T was also...
approved in the same way as we believe Intismeran will be, which is as a process BLA, not a product BLA.
So as you know, Moderna has five products approved.
So that's really product approval.
On this one, the whole process since we started in the clinic, the IND was a process IND.
Because we had to ask the FDA, can we go to the clinic?
Do you think it's safe?
And do you think we have a good control of the process?
So we can do safely a phase one study.
So we already had that discussion just to go into the clinic years ago.
And then before we started every phase three, we need to have the end of phase two meeting and agree with design of a study, the manufacturing protocol with the FDA.
So those discussions have happened for years.
And so it's not like we have not talked to FDA for the last 10 years and we're going to show up at their front door in a week or two and tell them this is a new product.
And they're going to like, why is this?
There's been a lot of discussion, a lot of engagement.
Several times we've had technical questions on the manufacturing front where we basically requested additional meetings to ask the guidance, to also educate them on the technology, what we learned and so on.
So there's already been a lot of discussions and there's a very clear regulatory pathway into approving the entire process.
Basically, Jorge, what the FDA wants to know, which is very legitimate, which I would want for my own family's sake, obviously, which is...
If you get the same sample at the beginning, the tumor and the blood, do you get the same product made at the end of a big black box?
And that's what we have to demonstrate first to ourselves and then with the data to the FDA so that we have really robustness of the whole process.
So if we have the same input, we're going to get the same output going to a patient as an individualized medicine.
How do we think about moving beyond, or how do you all think about moving beyond melanoma?
Is this an approach that's going to be applicable to a broad range of cancers?
Are there cancers that are much more likely where this is going to be a viable option versus others?
And sort of what's your, I'll use the word, what's the ambition here for where cancer vaccines can have an impact?
So the ambition is pretty big because we believe we have demonstrated, at least to ourselves and we hope to the world, as well as we'll be skeptics, but that's always true, that we are able to create a de novo education of T-cells.
And this, we showed it even at ASCO this year, where we took the blood before treatment, after treatment of cancer patients, melanoma, we were technology, and we showed that I don't think we have an expansion of the T-cells, but we have de novo, some new T-cells being created that recognize what we code in the mRNA that was not in the patient's body before the treatment.
So we really...
in my book, have proven to ourselves and to the clinical community that modernized mRNA technology can teach the immune system to develop new T-cells to go attack your cancer.
So based on that, there are basically, I would say, three different vectors we're going after in terms of expansion from melanoma.
So this study, to remind people, was a cancer patient in stage 2, stage 3, and stage 4 that were enrolled in that phase 3 study.
So what we are doing is we are going first everywhere where K2DRA works.
Because as I told you, we believe the mechanism of action of a PD-1 and modernitis MIRAN are totally orthogonal.
So we think these allow to have synergistic elements and performance of efficacy for the patients.
And so we are in phase three for lung.
We are in phase two for kidney cancer, bladder cancer.
So we have a whole slew of studies ongoing.
where the world knows that Ketudria works because Ketudria has been approved there.
And we believe you're going to see a material improvement versus Ketudria alone.
Before you run the clinical experiment, it's impossible to know are you going to get 50% like we saw in the phase two of melanoma, or are you going to get 30% or 40% or another number.
We have to run the study.
So these are a lot ongoing.
The second vector is to go early in disease where checkpoint work.
And the best example is we announced in the spring.
of 2026, starting a phase three study for patients with stage one lung cancer.
But as Intismarine, so Moderna's product, as a monotherapy, without checkpoint.
And we are doing that because we believe when you go early in disease, checkpoints are not used because of a side effect that they bring.
Because if you have stage one cancer, the medical field thinks it's worth monitoring your cancer versus giving you a checkpoint because Not everybody is going to respond, but everybody is going to get pretty serious lifelong side effects like autoimmune disease.
But what if you could have an mRNA made for a cancer patient that has lung disease, stage one, which you can find easily with x-ray, let's say, in former smokers.
It's the easiest target population.
Just screen regularly with x-ray your former smokers.
And if you do it regularly, you're going to go from not seeing the cancer to seeing the cancer.
And then the idea is, do you do a surgery, which is to the side of care, and you give intesmirate monotherapy, which the side effect is similar to a vaccine.
You might feel tired for a day, but that's it.
So in cancer, it's a pretty cool type of side effect, right?
And that's another approach we have in terms of clinical studies where checkpoints are not available today.
The third vector is where checkpoints don't work.
So of course, it's where you have a highest risk.
But because the mechanism of action is different from a checkpoint, we and Merck believe that there is a very good scientific rationale to go try.
So two places we are trying right now is pancreas cancer and also gastric cancer.
Those two cancer type checkpoints and K2 do not work.
The clinical studies have been run in the past and they were negative.
But we think because, again, the mechanism of action is different from checkpoint.
And we know now that we have a proof that we can create de novo T cell.
We think it's an experiment worth running.
If we have good signal, we'll think about combination.
As you know, literally yesterday, you know, Revolution Medicine had a wonderful new medicine approved for pancreas cancer using the CARAS mutation.
What if you could combine that medicine and intismaran?
Those are very orthogonal mechanism of action.
For me, it makes no scientific sense that if intismaran works, And we should know soon in pancreas cancer by itself.
And of course, the revolution medicine does great improvement of survival in pancreas cancer.
If you combine those two things, we believe it should have benefit.
Again, you need to run the clinical experiment to see how much.
But that's the type of things we're going to want to do.
So if you think about intismaran, the modern medicine is going to be used with a cathedra.
It's going to be used early in disease with a cathedra.
And it might be used.
in places where K2G doesn't work, but with over-agents.
Well, that gives a lot of reason for, I think, cancer patients and their families to have a lot of hope for the future of novel therapies in this field.
Yes, and on top of what we just said, remember, this is Intism Run 1.0.
So what I saw is very powerful, and I'm really pushing our team to think really outside the box and to do a lot of analysis and to use AI to look at that.
gigantic set of data that we have, which is what are the things we can learn from the clinical studies to understand about the people that did not respond?
Because I think you always learn more from things that don't work and things that work.
So I want to obsess about the 20% of patients that do not respond five years out so we can understand why they did not respond and can we tweak anything in the algorithm or in the technology to be able to help them?
Well, that's remarkable.
And just to wrap, I think It's remarkable to see how you were able to take a technology platform that originally wasn't built for a pandemic, pointed at a pandemic, create a vaccine for millions and millions and millions of people, and essentially point it back towards treating some of the diseases that you had originally intended 10 years later, as you described.
Yeah, it's really remarkable.
And the piece that's going to be exciting, or maybe to close, is before the end of the year.
We should have our pivotal study, so late-stage study, for rare genetic disease for kids that have rare genetic disease of the liver.
So it's another vertical at which we're pointing the technology.
The phase one, two, I've shown kids three years on drugs doing fantastic.
So we'll see when we get that data.
And in June, we had our annual science day, and we announced that the next mountain where we're pointing on mRNA platform is autoimmune disease.
Because if you think about it, We've learned a lot from infectious diseases, which are mediated by the immune system.
Cancer, we've just been talking a lot about the immune system.
So we've learned so much about the immune system that we think we have some very novel approach on how to treat the root cause of autoimmune disease, not the symptoms, which is what the pharma industry has been doing.
It's, of course, very helpful to patients to treat the symptoms so they can have higher quality of life, but it doesn't treat the root cause.
And we think we might have found ways to use the immune system.
to treat the root cause of autoimmune disease.
So there's still a lot of ways to point the platform.
So we're quite excited about what's to come.
Would the theory there be that you'd have personalized autoimmune modulators?
Or would this be more product or more process?
So we're doing both.
So what we presented in the spring was a product that would be the same for everybody.
But what I'm most excited about, which is in the lab still, is the ability to do individualized autoimmune treatment where you target directly to the immune cells that are attacking your body as self when you have an autoimmune disease and to have basically part of your immune system going attacking those immune cells that are out of order so that you are able to take the symptom of the immune disease out.
Again, it's still early days, but that's what I'm excited about today.
Well, going from infectious disease to cancer to eventually autoimmune disease, we would love to have you back.
on the podcast to film episode three and complete the trilogy when you're ready.
Stefan, thank you so much for joining us on the A16Z podcast.
It's always, as always, it's great to see you.
And congratulations.
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