New test uses cell-wide networks to find differences in cancer types

For the first time scientists have harnessed the power of a cell-wide network to identify tissue specific differences between cancer types and in parts of the brain.

The study led by the University of Sheffields Institute for Cancer Research Institute (IGRI) uncovered evidence that challenged longstanding theories of what make brain tumours unique and outlined a new way to identify the cells most affected by various types of cancer.

Led by Dr. Mona Singh and senior author Professor Robert Taylor from the University of Sheffields Institute for Cancer Research the milestone study published today in Nature Communications follows on from publication in 2016 that neuroblastoma – the most common brain cancer in children and adolescent – is more womb- and brain-specific.

Initially identified as a benign cyst neuroblastoma tumors form tumours that over time can grow to include blood vessels and sometimes grow into solid tumors stressful the organ causing limited blood supply and threatening survival. The cancer then became resistant to treatment and as a result is the most difficult to treat.

Showing why the development of neuroblastoma is unique the researchers say that instead of examining the size and location of a tumour in different organs they offer to measure how cancerous changes within the cerebrospinal fluid of cancer patients. This fluid is a common and naturally occurring liquid taken from the back of the throat and around the brain – the blood brain barrier – which normally protects it from damage by bad toxins and microbes.

In the study itself nanoscale network cell-wide cytometry was used to develop new findings but also in a mouse model confirming how they arrived at their findings.

The scientists then evaluated at the dataset using novel data analysis capabilities and computer algorithms.

Professor Robert Taylor from the University of Sheffields Institute for Cancer Research said: The existing data isnt good enough in the current way. Now we can use the next generation techniques this time using a network with micro-architectural features to look at the alignment of different cell types within a given area in reference to their surrounding area as well as whether there are systematic differences between brain cancer types.

This could offer exciting insights into quantifying cancer heterogeneity and might help doctors decide the best approach to target this aggressive disease. In the meantime those affected by neuroblastoma might benefit as the different cell types are more distinguishable in the brain.