Recent advances in single-cell sequencing technology have revolutionised the field of cancer immunology. Dr Santiago Carmona explains the challenges of interpreting big data and why the key to making progress lies in collaboration.

Our bodies are made out of tissue, and this tissue is composed of hundreds of different cell types. Tumours can emerge from the deregulation of any cell type in any tissue. Until recently, technologies allowed us to analyse tumours as a whole, but the results were ‘averaged out’ across cells without distinguishing between the different types. It’s only in the last decade, thanks to huge strides in single-cell technologies, that we’ve been able to look at each cell individually.

This is revolutionary for cancer immunology. In a tumour, for example, we can identify each cell population – not just cancer cells, but also immune cells. And we can dive deep into each cell population, so if immune cells aren’t killing tumour cells, we can try to figure out why by looking at the activity of their genes.

In our lab at the Lausanne Branch of the Ludwig Institute for Cancer Research, we’re trying to understand how the immune system responds to cancer by developing computational methods and creating databases to interpret single-cell sequencing data. We call these ‘reference atlases’ as they’re like detailed maps of immune cell profiles. The aim of these ‘atlases’ is to help researchers interpret their data by comparing cells they’ve found in their own research with pooled data from other experiments. By getting a clear picture of a tumour’s immune composition, we can better understand how tumours evade the immune response in each patient and find ways to prevent this from happening.

Our work is mainly computational, but we rely on other researchers to help us prove our findings in experiments on mice or human tissues. We’re very lucky that Lausanne is a real hub for experts working in this area, and we have collaborated with many colleagues within the Ludwig Lausanne Branch and the Department of oncology UNIL CHUV, who are also based at Biopôle

As everything we do is open-source and collaborative, the tools we develop are used by researchers all over the world. We’re witnessing a shift in the field of oncology towards data sharing, which will be of huge benefit when it comes to treating cancer. It’s not enough to look at hundreds of patients. If we’re looking at the properties that make a tumour resistant to therapies, we’ll need tens of thousands of samples. Only through collective efforts and shared data can this be achieved.

There is still a long way to go before these technologies can guide the treatment of cancer patients at scale. One challenge is cost, as single-cell technologies are expensive, and their application requires complex logistics. We’ll also need sophisticated algorithms and the right experts to accurately analyse and interpret the large amount of data produced. However, the field is rapidly advancing and there is no doubt that these challenges will eventually be overcome. Single-cell technologies and data science will play a key role when it comes to developing the next generation of cancer therapies.

Complementary content on this topic