Adelaide-based researchers are moving closer to being able to predict which chronic myeloid leukaemia (CML) patients can safely stop taking their life-saving medication after discovering a more accurate measure of residual disease.
Dr Ilaria Pagani, from SAHMRI’s CML laboratory led by Professor Tim Hughes, says targeting a specific type of white blood cell will give clinicians the best measure of a patient’s residual disease.
“Our study involved 20 CML patients who’ve been in treatment free remission (TFR) for more than a year and some of them up to 11 years,” Dr Pagani said.
“Chronic myeloid leukaemia is characterised by increased granulocytes, but we found that there we no leukaemic granulocytes remaining in these patients. Instead, there were rare lymphocytes that came from the CML cells.
“This is important because those lymphocytes can’t cause relapse of the whole CML disease. This pattern of disease, which has never been observed before, might explain why those patients stay in remission.”
Researchers now hope these findings, published in Leukemia, can be used to predict the chance of success for patients who are yet to attempt TFR.
“The more precise information we have about the state of a patient’s disease, the better we will be able to predict their chances of staying in remission after stopping their medication,” Dr Pagani said.
“A failed attempt at (TFR) can cause pain and anxiety for patients. It also means they have to resume taking their drugs for several years before making another attempt at TFR.”
The discovery of tyrosine kinase inhibitors as a treatment at the turn of the century dramatically extended the life expectancy of most CML patients but the daily dose can have side effects including increased rates of kidney, lung and heart disease.
Over the past decade researchers and clinicians, including Associate Professor David Ross, have shown that roughly half of patients who achieve a deep molecular response to treatment can stop taking their drug and remain in remission.
“What we don’t know yet however is exactly who can successfully stop treatment and why,” Associate Professor Ross said.
“If we can understand the clinical and biological factors driving TRF we’ll be able to design better interventions to give patients the best chance of success.”
The next stage of this research team’s work involves combining those clinical and biological determinants in an algorithm.
“To improve the accuracy and power of the prediction we need a large number of samples,” Associate Professor Ross says.
“For that, we have organised a large-scale collaboration with Australian and international researchers to pool patients’ samples from their biobanks.”