Cancer immunotherapy aims to recruit the body's own immune system to attack a tumor, which requires that the tumor be recognized as "foreign" by the immune system. An exciting line of research indicates that genetic mutations acquired by cancer cells during tumor development can be the basis for this recognition when they result in mutated protein fragments displayed to immune cells (also known as "neoantigens"). Because neoantigens distinguish tumor cells from normal cells, they create actionable targets for the body's immune system.
Sometimes, the immune system fails to initiate a response against a tumor. In these cases, it’s reasonable to imagine that a vaccine might help, and indeed personalized cancer vaccination is an emerging branch of cancer immunotherapy with many ongoing clinical trials. The idea behind neoantigen vaccines is to train the patient’s immune system to mount a response against the specific mutated cancer proteins present in his or her tumor. As a personalized therapy, it is different from other immunotherapy approaches like checkpoint blockade, where the immune system as a whole is stimulated but not directed toward a specific antigen.
Earlier this year, we published a paper describing the Mount Sinai PGV-001 personalized vaccine clinical trial and the computational pipeline we built to support it, along with Hammerlab members Isaac Hodes, Arman Aksoy, and Seb Mondet. We have built an updated version of the pipeline that is a Dockerized end-to-end workflow that starts with raw tumor/normal sequencing data and does all the necessary processing to generate neoantigen predictions. It is easy to set up and run, contains all needed dependencies, and does not require a cluster. We’ve made it available as a public Docker image. For detailed instructions on how to run the pipeline, please see the README. Like other tools we’ve developed at the OpenVax research lab, this pipeline is open-source and available on GitHub.
In a nutshell, our neoantigen pipeline calls somatic variants and uses a suite of tools we’ve developed (vaxrank, isovar, mhctools and others) to prioritize those mutations for vaccination based on tumor RNA expression and the likelihood that tumor cells will present these mutated protein fragments to the immune system. Note that while the pipeline supports FASTQ-to-vaccine-peptide processing, which includes neoantigen prediction and ranking, it can also be used just for germline and somatic variant calling in the absence of tumor RNA sequencing data.
We hope that this pipeline can enable other research groups in their own neoantigen or variant calling work. Please don’t hesitate to reach out to us with any questions or suggestions.
Post by Julia Kodysh
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