A new technique can detect cancer stem cells in samples from patients with acute myeloid leukaemia (AML), among other cell types such as mature cancer cells and healthy stem cells, it has been reported.
Most of a tumour consists of rapidly dividing cells with limited capacity for self-renewal. Cancer stem cells, by contrast, are slow-growing cells that can replace themselves indefinitely, fuelling long-term cancer growth and driving relapse.
However, the low division rate of cancer stem cells makes them difficult to target therapeutically, and their rarity makes them difficult to isolate to study. So, a team of researchers have tested whether cancer stem cells can be distinguished from mature cancer cells based on gene expression, and from healthy stem cells based on mutational status.
The method is based on the studying of genetics and gene expression of single cells, analysed with a tool called MutaSeq. It was developed by Professor Lars Steinmetz of the European Molecular Biology Laboratory in Heidelberg, Germany, and colleagues. They outline the method in the journal Nature Communications.
They used samples from AML patients to show that the three cell types can be identified using the MutaSeq tool. Mutational status discriminates between healthy and cancerous cells, and gene expression distinguishes stem cells and progenitor cell populations. The technique allowed them to identify stem-cell specific gene expression programmes, which could potentially be targeted with drugs in the future.
Professor Steinmetz added: “Our method can identify drug targets that might not have been tested in the right context. These tests will need to be carried out in controlled clinical studies, but knowing what to try is an important first step.”
Velten L, Story BA, Hernández-Malmierca P, Raffel S, Leonce DR, Milbank J, Paulsen M, Demir A, Szu-Tu C, Frömel R, Lutz C, Nowak D, Jann JC, Pabst C, Boch T, Hofmann WK, Müller-Tidow C, Trumpp A, Haas S, Steinmetz LM. (2021) “Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics.” Nature Communications, doi: 10.1038/s41467-021-21650-1
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