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Immunotherapy has unleashed a revolution in care for some cancer patients. But most immunotherapies help only a small subset of patients, meaning doctors often have to resort to a trial-and-error process to determine who might actually benefit from the novel treatments.

Now, scientists have developed a new metric they believe can help predict whether patients will respond to a class of immunotherapies known as checkpoint inhibitors, drugs that train the body’s natural defenses on cancer cells.

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  • The elephant in the room is a scientific question never asked: Why are neo-antigens important and predictive in the first place?
    A tumor of a size that can be imaged an attracts immune cells consists of around 10^9 to 10^10 cells. Neo-antigens are by definition not necessary driver proteins, but any protein in the proteome that is randomly mutated. Hence each given neo-antigen need not be enriched in these gazillions of cancer cells (this is the strength/weakness of immunotherapy: it is not restricted to targeting the causative mechanism). So, a given neo-antigen is always expressed in just a tiny subfraction of these gazillions of cells in the tumor – as recent sub-regional and single-cell analysis has shown.

    Why then does immunotherapy directed at specific neo-antigen work in the first place?

    There is this leap of faith at the core of immunotherapy. One way to reconcile this is to assume “antigen-spreading” – so far more hand-waving than well-documented. Another is to assume innocent bystander killing.

    Perhaps the ability of transcriptomes/proteome information for prediction reflects the potency of promoting anti-gen-spreading and innocent bystander effect???

  • Dear Stat News,

    I highly encourage you to contact Genocea regarding their ATLAS Neoantigen ID Platform for cancer immunotherapy and to discuss GEN-009 & GEN-011.

    Thanks,

    Allen Brzozowski

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