LW - introduction to cancer vaccines by bhauth

The Nonlinear Library: LessWrong

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Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: introduction to cancer vaccines, published by bhauth on May 5, 2024 on LessWrong. cancer neoantigens For cells to become cancerous, they must have mutations that cause uncontrolled replication and mutations that prevent that uncontrolled replication from causing apoptosis. Because cancer requires several mutations, it often begins with damage to mutation-preventing mechanisms. As such, cancers often have many mutations not required for their growth, which often cause changes to structure of some surface proteins. The modified surface proteins of cancer cells are called "neoantigens". An approach to cancer treatment that's currently being researched is to identify some specific neoantigens of a patient's cancer, and create a personalized vaccine to cause their immune system to recognize them. Such vaccines would use either mRNA or synthetic long peptides. The steps required are as follows: 1. The cancer must develop neoantigens that are sufficiently distinct from human surface proteins and consistent across the cancer. 2. Cancer cells must be isolated and have their surface proteins characterized. 3. A surface protein must be found that the immune system can recognize well without (much) cross-reactivity to normal human proteins. 4. A vaccine that contains that neoantigen or its RNA sequence must be produced. Most drugs are mass-produced, but with cancer vaccines that target neoantigens, all those steps must be done for every patient, which is expensive. protein characterization The current methods for (2) are DNA sequencing and mass spectrometry. sequencing DNA sequencing is now good enough to sequence the full genome of cancer cells. That sequence can be compared to the DNA of normal cells, and some algorithms can be used to find differences that correspond to mutant proteins. However, guessing how DNA will be transcribed, how proteins will be modified, and which proteins will be displayed on the surface is difficult. Practical nanopore sequencing has been a long time coming, but it's recently become a good option for sequencing cancer cell DNA. MHC mass spec Proteins are often bound to a MHC for presentation on the surface, and those complexes can be isolated by mass spectrometry. You then know that the attached proteins can be on the cell surface. However... It's currently hard to guess which of those MHC-bound proteins could have a good immune response. This requires more cells than sequencing. This doesn't find all the mutant surface proteins. Peptide sequencing is necessary, and it's not easy. comments on AlphaFold I've seen a lot of comments on AlphaFold by people who don't really understand how it works or what it can do, so I thought I'd explain that. AlphaFold (and similar systems) input the amino acid sequence of a protein to a neural network, using a typical Transformer design. That NN predicts relative positions of atoms, which is possible because: Some sequences form common types of local structures, and relative positions within those structures can be predicted. Some distant pairs of sequences tend to bind to each other. AlphaFold training included evolutionary history, and multiple mutations that happen at the same time tend to be near each other. The positions predicted by the neural network are not used directly; they're an initial guess for a protein force field model. What neural networks provide is a better initialization than previous approaches. The above points indicate some limitations that AlphaFold-type approaches have, such as: They're not as good for prions or otherwise "unnatural" proteins. They don't predict protein functions from structure, or vice-versa. They're not as good when evolutionary history isn't available. While this approach is more limited than some people seem to think, it's still effective enough that, if a surface prot...