16 March 2018

A newly-developed technique could help clinicians predict whether children with B-cell precursor acute lymphoblastic leukaemia (ALL) will relapse after treatment.

The method can predict relapse with 85% accuracy, significantly better than the rate achieved by the currently-used risk stratification method, say the team, led by Dr Garry Nolan at the Stanford University School of Medicine, California, USA.

The 'Developmentally Dependent Predictor of Relapse' examines single cancer cells using mass cytometry, and gathers information on the cells' stage of development and signalling behaviour. In this way, a tiny subset of malignant cells can be identified, which are believed to raise the risk of relapse.

The method, published in Nature Medicine, was tested on 60 patients at diagnosis, and they were followed for three to 15 years. Their leukaemic cells were compared with their most similar normal cell population along the trajectory of healthy B-cell development. This identified two adjacent stages in malignant B-cell maturation, the pro-B2 and pre-B1 stages. These were linked to relapse when there was certain signalling behaviour at diagnosis.

First author Zinaida Good said: "We really need to personalise treatment to leukaemia patients better than we do now. There is a lot of room for improvement here."

Co-senior author Dr Kara Davis added: "With existing prediction tools, when we identify someone as high-risk for relapse, we don't know what it is about their leukaemia that raises their risk. We wondered, can we identify those cells at the time the patient first presents to clinic, and can we treat patients with a specific therapy to target them?"

"We do not understand the mechanisms by which malignant cells from the pro-B2 and pre-B1 stages of development resist treatment," she added.

Source: Good, Z. et al. Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nature Medicine 5 March 2018 doi: 10.1038/nm.4505

Link: https://www.nature.com/articles/nm.4505


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