Science is trying new ways to determine a cancer vaccine.
Could predictive algorithms be the key to creating a successful cancer vaccine? Two US nonprofit organizations plan to find out by pitting a range of computer programs against each other to see which can best predict a candidate for a personalized vaccine from a patient’s tumour DNA.
The Parker Institute for Cancer Immunotherapy in San Francisco, California, and the Cancer Research Institute of New York City announced the algorithmic battle on 1 December. It is part of a multimillion-dollar joint project to solve a major puzzle in the nascent field of cancer immunotherapy: which of a patient’s sometimes hundreds of cancer mutations could serve as a call-to-arms for their immune system to attack their tumours.
If the effort succeeds, it could spur the development of personalized cancer vaccines that use fragments of these mutated proteins to fire up the body’s natural immune responses to them. Because these mutations are found in cancer cells and not healthy ones, the hope is that this would provide a non-toxic way to battle tumours.
The idea is gaining traction. In 2014, news that vaccines containing such mutated proteins had vanquished tumours in mice set off a mad dash to find out whether the approach would work in people. A generation of biotechnology companies has been founded around the concept, and clinical trials run by academic labs are under way.
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