“The tool allows us to ‘zoom in’ on the whole tumour image to identify regions of interest based on the image and the statistics,” says Dr Schulz, whose collaborators are a mix of biologists, bioinformaticians and software builders. This then helps focus on the best regions of interest. To overcome this problem, the team built a webtool that allows them to take the mIF image and derive a set of pre-calculated statistics like tumour content and immune cell content, for all possible regions of the tumour. Researchers, essentially, have to start out blind. The problem, however, is that it is difficult to decide which bits of the sectioned tumour should be singled out for IMC imaging.
And then on that section, we identify regions which have interesting immune cells and a mix of tumour cells to zoom into those with our multiplex, high-dimensional method - the IMC.” "So we cut it into very thin slices for microscopic analysis assuming that the slice is pretty much representative of the big structure. But we don’t have good ways to analyse large three-dimensional structures at sufficient throughput - meaning that with thousands of samples that need to be processed, some are not fast enough and would represent a significant bottleneck in the pipeline." That’s why another type of imaging, called imaging mass cytometry (IMC) is used to create highly descriptive images of smaller sections of tumour. Daniel Schulz, a researcher at Zurich University and member of the Broad Profiling team that carried out the research, explains: “A tumour section is a three-dimensional piece of tissue.
However, this type of imaging is limited by a low number of important markers that it can detect, with only seven markers detectable out of a possible 25,000+. Essentially, highly-personalised tumour profiling.Ī type of imaging called multiplex immunofluorescence (mIF), generates images of whole tumour sections. A team of researchers from the University Hospital of Lausanne, the Swiss Institute of Bioinformatics and the University of Zurich, all part of the IMI-funded IMMUCAN project, has produced a web-based tool that allows huge volumes of data, taken from imaging of tumours, to be speedily analysed to help decide the most representative part of the tumour to further study and understand its unique microenvironment.Īs part of the project, which will collect and analyse tumour samples of up to 3,000 patients, the researchers wanted to find innovative ways to use imaging data to help select parts of tumours that contain both cancer cells and immune cells, their interactions and adjacent activity, to better understand what's going on at a microscopic level, which is different for every patient.