The findings could help explain why attempts at using single biopsies to identify biomarkers to which personalised cancer treatments can be targeted have not been more successful – but also points to a way forward.
Cancer Research UK scientists carried out the first ever genome-wide analysis of the genetic variation between different regions of the same tumour using kidney cancer samples. They found that the majority, around two thirds of gene faults, were not shared across other biopsies from the same tumour.
Lead author Professor Charles Swanton, based at Cancer Research UK’s London Research Institute and the UCL Cancer Institute, said: “We’ve known for some time that tumours are a ‘patchwork’ of faults, but this is the first time we’ve been able to use cutting-edge genome sequencing technology to map out the genetic landscape of a tumour in such exquisite detail.
“This has revealed an extraordinary amount of diversity, with more differences between biopsies from the same tumour at the genetic level than there are similarities. The next step will be to understand what’s driving this diversity in different cancers and identify key driver mutations that are common throughout all parts of a tumour.”
The tumour samples analysed in this study were donated by patients with advanced kidney cancer treated at the Royal Marsden Hospital under the supervision of Dr James Larkin.
Dr Larkin said: “The idea of personalised medicine is to tailor treatments to suit individual patients. This study in kidney cancer has shown significant molecular changes between different parts of the same tumour. We have also seen differences between primary kidney tumours and cancer cells that have spread to other organs. This may be relevant to how we treat kidney cancer with drugs because the molecular changes that drive the growth of the cancer once it has spread may be different from those that drive the growth of the primary tumour.”
The researchers – funded by Cancer Research UK, the Medical Research Council and the Wellcome Trust – compared the genetic faults in samples taken from different parts of four separate kidney tumours, and also from sites where the cancer had spread to other organs.
This allowed them to identify 118 different mutations – 40 of which were ‘ubiquitous mutations’ found in all biopsies, 53 ‘shared mutations’ that were present in most but not all biopsies and 25 ‘private mutations’ that were only detected in a single biopsy.
By analysing the location of shared mutations in relation to the whole tumour, the researchers were able to trace the origins of particular subtypes of cancer cells back to key driver mutations. This allowed the scientists to create a ‘map’ of how the pattern of faults within the tumour might have evolved over time.
Professor Swanton added: “For the first time we’ve been able to use the pattern of genetic faults in a tumour to trace the origins of certain populations of cancers cells, much in the same way as Darwin used his ‘tree of life’ theory to show how different species are related.
“This underscores the importance of targeting common mutations found in the ‘trunk’ of the tree as opposed to those found in the ‘branches’, which may only be present in a relatively small number of cells. It may also explain why surgery to remove the primary kidney tumour can improve survival, by decreasing the likelihood that resistant cells will be present that could go on to re-grow the tumour after treatment.”
Professor Peter Johnson, Cancer Research UK’s chief clinician, said: “These findings highlight important differences that exist within tumours and suggest a way to improve the success rate of personalised cancer medicines.
“We are now planning to see if these results can be replicated in larger groups of patients as part of Cancer Research UK’s Genomics Initiative – a set of groundbreaking projects using the latest high-tech gene sequencing machines to track down the genetic faults driving different types of cancer.”
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Gerlinger M. et al, Intratumor heterogeneity and branched evolution revealed by multiregion sequencing (2012), New England Journal of Medicine, DOI: 10.1056/NEJMoa1113205.