Gene signature reveals the severity of disease in patients with blood cancer

Professor Sergio Rutella

A team led by Professor Sergio Rutella, Professor of Cancer Immunotherapy at Nottingham Trent University’s John van Geest Cancer Research Centre, has used artificial intelligence to identify a gene signature which predicts outcomes for patients with acute myeloid leukaemia (AML).

A gene signature is the genetic make-up of an individual. Everyone has the same genes but some genes may be switched on or off in different people.

Chemotherapy remains the standard-of-care for most patients with AML, in spite of the recent approval of novel drugs. Only about 20 percent of patients survive for beyond five years following its initial diagnosis.

However, the research team – which includes experts from University of Pennsylvania, USA, and Technische Universität Dresden, Germany – have now found that a patient may have a better chance of survival, and may not require intensive treatment, if genes coding for CALCRLCD109 and LSP1 are switched off.

Professor Rutella, who is based in NTU’s School of Science and Technology, said: “This is the first time ever that the genetic make-up of bone marrow cells involved in leukaemia has been analysed in this way.

“We believe that it will significantly improve the accuracy of how badly a patient is affected by AML.

“The work might also accelerate the design of effective therapeutic approaches for each tumour subtype.

“This could include new drugs, such as monoclonal antibodies, which are able to unleash the power of the immune system’s response to cancer.”

The research has led to the establishment of new sub-categories of risk – very low-risk and very high-risk – for which different treatment options should be offered to maximise patient benefit while keeping unwanted toxicity to a minimum.

Currently, patients are only put into one of three risk categories: favourable, intermediate and adverse risk.

The findings are now being considered to help establish the implementation of a diagnostic device that supports treatment decision-making in the haematology clinic.

The research is published in the journal Blood Advances.