Cancer’s evolving DNA can be detected using a blood test
Sometimes it feels like cancer research is progressing at a dizzying speed.
Just last year, we reported how Cancer Research UK scientists had reconstructed the evolution of a patient’s kidney tumour during treatment – one of many studies over the past few years illustrating cancer’s fearsome genetic complexity and adaptability.
This phenomenon, known as ‘intratumour heterogeneity’, led many to predict a long, hard slog to fully understand it – let alone get a handle on its implications for treatment.
One key concern was that patients would need to undergo a series of small operations (biopsies) to take repeated tissue samples to track how their cancer develops – and that this could be painful, costly and risky – especially for cancers deep in the body. And even then, because of the genetic variation within each patient’s cancer, there would be no guarantee that the biopsy results would represent an accurate picture.
Others also pointed out that such heterogeneity was a blow to the optimism around new-generation ‘targeted’ therapies, designed to treat cancer cells driven by individual mutations.
But recent discoveries have renewed this optimism. It turns out that tumours release DNA into the bloodstream, and that this seems to contain signals about what’s going on inside it. Consequently, there’s been a growing hope that analysing these DNA fingerprints could provide a quick, simple ‘liquid biopsy’ to track tumours’ progress.
And last month, researchers at our Cambridge Institute published compelling evidence that circulating DNA could indeed be used to take a snapshot of the DNA errors (mutations) in a patient’s breast cancer.
Today they’ve gone one step further proving, in a beautifully detailed paper in the journal Nature, that blood samples can be used to monitor genetic changes in a patient’s disease over time.
This has the potential to be a game-changer, and rapidly accelerate research into what makes cancers tick, in real patients, in timeframes that can impact on clinical decision making.
Let’s look at what they found.
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