A common recurring theme of this blog, reflecting our work generally at the ICR, is cancer's seemingly limitless diversity — within tumours, between patients, and across the spectrum of types of cancers — and the challenges that this creates for research and treatment. It is difficult to efficiently develop new therapies or effectively plan a patient's personal regimen when cancer's variation and varieties remain such enigmas.
A
paper published this month by a consortium of European research groups running trials in the children's cancer neuroblastoma — led by Prof Sue Burchill at Leeds, and including our own Professor Andy Pearson — suggests how we might help clinicians to dig through that diversity and give kids with the disease the best treatment for their particular tumour. Their work, looking for the signatures of prognostically-relevant genes in blood and bone marrow, illustrates how cancer tests are becoming increasingly detailed and less invasive than traditional biopsies.
Affecting just under 100 children in the UK each year, neuroblastoma — a cancer that begins in developing embryonic cells — is the most common cancer of infancy. The disease is a particularly devastating one, as it's often difficult to spot and diagnose until it has reached an advanced stage, spread to the bone marrow, and all too often become incurable even when bombarded with the suite of currently available treatments. But it’s also one of the most diverse and unpredictable of childhood cancers, with
some cases of metastatic neuroblastoma going into spontaneous regression for reasons we still don't fully understand.
In their paper, the consortium looked at 290 children with advanced neuroblastoma who had been enrolled on a clinical trial. Armed with blood samples and bone marrow from their patients, they looked for easily identified molecules called messenger RNAs — telltale signs of gene expression — for a panel of genes that had previously been implicated in neuroblastoma. By monitoring the children for five years, they could get build a picture of which genes are associated with aggressive and incurable forms of the disease, and which are predictive of regression and survival. This "risk stratification" application could be used in the short term for recruitment of patients who might benefit from future clinical trials, and might ultimately inform the development of standard tests for the disease.
The work is one small part of a larger revolution in how we test for and understand cancer's diversity, all brought about by new methods of finding fragments of genes and gene products that leak from tumours into the blood. We've previously reported, for example, on
the use of these so-called "liquid biopsies" in the diagnosis of breast cancer: by testing for signs of the
HER2 cancer gene in a patient's blood, we can determine whether the targeted therapy Herceptin — which blocks HER2 activity — would be a beneficial treatment. Because of the diversity of cells within a tumour, with only a subset of the cells expressing
HER2, traditional tumour biopsies could test negative for the gene and so might misguide doctors in their treatment recommendations. Meanwhile, as part of our work in the London Lung Cancer Alliance,
we are using liquid biopsies to monitor how well patients respond to new treatments — the blood tests can be taken far more frequently than a tumour biopsy could, and from patients who would otherwise be too ill to undergo such tests. Elsewhere, Cancer Research UK have
blogged about their fascinating research using liquid biopsies to understand exactly how cancers evolve as they progress through stages and react to treatments.
Although our increased understanding is already changing the way we diagnose and treat cancer patients, this is just the beginning. Cancer’s diversity means that each type of the disease has a different panel of genes driving its development. Liquid biopsies could provide a pivotal tool to help uncover this complexity, but it will take many more years of research until we fully understand the factors driving individual cancers and how we can harness this information to better personalise treatments.
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