Broad-spectrum vaccines might be one useful place to look. Just last year, there was this paper on a universal flu vaccine. The numbers aren’t super-impressive, but they’re significant, and the study itself used a peptide vaccine. (See also this paper, or this page on a broad-spectrum vaccine against bacteria, which I didn’t even know was a thing.)
A more speculative category might be therapeutic cancer vaccines. Some of these are tailored to one person’s existing cancer, so they only make sense for someone with the disease. But my understanding is that some of them target common cancer antigens, so in-principle they could be used preventatively. (Seems like the magic word to search for here is “neoantigens”.)
On the ultra-speculative and potentially-dangerous end of the spectrum, I have wondered before if a vaccine could make the immune system attack cells with transposon activity. Note that both this and cancer vaccines involve getting the immune system to attack one’s own cells, which makes them both difficult and dangerous. But the rewards could potentially be quite large: transposons are top-of-list of likely root causes of the major age-related diseases (including cancer itself). For instance, this page mentions a current project to develop “transposon-derived neoantigens” for cancer vaccines, and that’s exactly the sort of thing which would potentially be effective against other diseases of aging as well.
Also, I’ve heard there are reasons to ingest peptides other than just vaccines. I don’t know much about other applications, though.
I went down the neoantigen rabbithole, and it was quite interesting.
I liked this talk on “Developing Personalized Neoantigen-Based Cancer Vaccines”.
It seems a core part of their methodology is using machine learning to predict which peptides will elicit a T-cell response, based on sequencing the patient’s tumour. (Discussed starting from around 11 minutes in.)
They use this algorithm, which seems to be a neural network with a single hidden layer just ~60 neurons wide, and some amount of handcrafting of input features (based on papers from 2003 and 2009). I wonder what one could accomplish with more modern tools (though I haven’t yet read the papers deeply enough to have a model of how big of a bottleneck this is to creating an effective treatment, and how much room for improvement there is).
They use this algorithm, which seems to be a neural network with a single hidden layer just ~60 neurons wide, and some amount of handcrafting of input features (based on papers from 2003 and 2009).
To me the website looks like the published in 2020 a paper with the newest version. I would expect that you can solve any problems with that algorithm underperforming by simply taking more plausible neoantigens. If you want 20 one’s that actually work and you have a successrate of 50% you can just take 40 different peptides. That makes it a bit more expensive but still doable.
I would expect that in the span of this decade AlphaFold gets the capability to model those interactions nearly perfectly but without large resources I don’t think you will easily improve on the existing bioinformatics models.
But my understanding is that some of them target common cancer antigens, so in-principle they could be used preventatively. (Seems like the magic word to search for here is “neoantigens”.)
Common cancer antigens aren’t neoantigens. The common cancer antigens are proteins that normally only get produced in fetal development or in other specific circumstances. As far as I understand the vaccines based on them also didn’t produce good results.
Neoantigens is when you use the feature of the tumor to produce a lot of random mutations.
For instance, this page mentions a current project to develop “transposon-derived neoantigens” for cancer vaccines, and that’s exactly the sort of thing which would potentially be effective against other diseases of aging as well.
It’s basically a tool to get the immune system to fight any cell that has a specific mutation provided you can create a peptide with the mutation that does bind to an MHC molecule.
Broad-spectrum vaccines might be one useful place to look. Just last year, there was this paper on a universal flu vaccine. The numbers aren’t super-impressive, but they’re significant, and the study itself used a peptide vaccine. (See also this paper, or this page on a broad-spectrum vaccine against bacteria, which I didn’t even know was a thing.)
A more speculative category might be therapeutic cancer vaccines. Some of these are tailored to one person’s existing cancer, so they only make sense for someone with the disease. But my understanding is that some of them target common cancer antigens, so in-principle they could be used preventatively. (Seems like the magic word to search for here is “neoantigens”.)
On the ultra-speculative and potentially-dangerous end of the spectrum, I have wondered before if a vaccine could make the immune system attack cells with transposon activity. Note that both this and cancer vaccines involve getting the immune system to attack one’s own cells, which makes them both difficult and dangerous. But the rewards could potentially be quite large: transposons are top-of-list of likely root causes of the major age-related diseases (including cancer itself). For instance, this page mentions a current project to develop “transposon-derived neoantigens” for cancer vaccines, and that’s exactly the sort of thing which would potentially be effective against other diseases of aging as well.
Also, I’ve heard there are reasons to ingest peptides other than just vaccines. I don’t know much about other applications, though.
I went down the neoantigen rabbithole, and it was quite interesting.
I liked this talk on “Developing Personalized Neoantigen-Based Cancer Vaccines”.
It seems a core part of their methodology is using machine learning to predict which peptides will elicit a T-cell response, based on sequencing the patient’s tumour. (Discussed starting from around 11 minutes in.)
They use this algorithm, which seems to be a neural network with a single hidden layer just ~60 neurons wide, and some amount of handcrafting of input features (based on papers from 2003 and 2009). I wonder what one could accomplish with more modern tools (though I haven’t yet read the papers deeply enough to have a model of how big of a bottleneck this is to creating an effective treatment, and how much room for improvement there is).
To me the website looks like the published in 2020 a paper with the newest version. I would expect that you can solve any problems with that algorithm underperforming by simply taking more plausible neoantigens. If you want 20 one’s that actually work and you have a successrate of 50% you can just take 40 different peptides. That makes it a bit more expensive but still doable.
I would expect that in the span of this decade AlphaFold gets the capability to model those interactions nearly perfectly but without large resources I don’t think you will easily improve on the existing bioinformatics models.
Common cancer antigens aren’t neoantigens. The common cancer antigens are proteins that normally only get produced in fetal development or in other specific circumstances. As far as I understand the vaccines based on them also didn’t produce good results.
Neoantigens is when you use the feature of the tumor to produce a lot of random mutations.
It’s basically a tool to get the immune system to fight any cell that has a specific mutation provided you can create a peptide with the mutation that does bind to an MHC molecule.