A new method for analysing tumour samples has been revealed by scientists at Imperial College London. Current methods are laborious, involving the manual testing and interpretation of tumour characteristics by a histologist. In a new study, published in the Proceedings of the National Academy of Sciences, researchers describe how they are trying to take cancer diagnosis into the digital age.
The technology is based on a widely used technique called mass spectrometry, which is used to work out what biological molecules are present in a sample, such as blood or urine. Using a modified version called mass spectrometry imaging; the researchers are able to generate an image of a patient’s tissue sample, showing the location and density of different biological molecules.
The technique works by passing a laser beam over a tissue sample, such as a tumour, which reacts with the biological molecules to produce a signal that can be converted into a pixelated image. Each pixel of the image reveals how much of a specific molecule is present in that region of the tissue – producing a map that can reveal specific characteristics of the diseased tissue.
A cancer patient’s tumour is currently characterised based on structural features that are detected by staining with expensive reagents that require expertise to interpret. Mass spectrometry imaging characterises tumours based on their molecular features and could provide a more accurate assessment of an individual’s cancer. This could help guide clinicians to make the best treatment decisions for each patient and support a move towards more personalised medicine. The technique can also be automated, allowing a computer to rapidly analyse hundreds of different molecules in one go, reducing testing time from over a week to only a few hours.
Dr Kirill Veselkov, corresponding author of the study from the Department of Surgery and Cancer at Imperial College London, said: “MSI is an extremely promising technology, but the analysis required to provide information that doctors or scientists can interpret easily is very complex. This work overcomes some of the obstacles to translating MSI’s potential into the clinic. It’s the first step towards creating the next generation of fully automated histological analysis.”