Quantitative AML patient risk assessment using RNA signatures associated with sensitivity and resistance to immune cell killing

Stanford researchers have discovered RNA signatures that can be used to predict patient outcomes and identify optimal treatments in acute myeloid leukemia.

Acute myeloid leukemia (AML) is the most common leukemia in adults, with over 20,000 new cases a year in the US and a five-year overall survival rate of only 27%. While reliable methods for predicting patient outcomes in AML could aid clinical decision making by e.g. predicting which patients would benefit from more aggressive treatment, AML risk is currently evaluated by a complex combination of factors including age, patient performance status, cytogenetics, and medical history. This method is unreliable particularly for the 30% of patients that lack genetic abnormalities associated with clinical outcomes.

To address this, Stanford researchers discovered transcriptional programs in AML that are associated with either improved or worse patient outcomes. Researchers treated AML patient cells with human immune cells, identifying subsets of AML cells that are either sensitive or resistant to immune cell killing. Immune-resistant AML cells were found to express RNA transcripts associated with poor survival rates in a large database of AML patient transcriptomes. Better and poorer patient outcomes could be predicted from the expression of RNAs associated with sensitivity and resistance to immune cell killing.

Stage of Development
Proof of concept: AML patient cells that are resistant to immune cell killing in vitro express transcripts associated with poorer patient outcomes, while patient cells that are sensitive to immune cell killing express transcripts associated with improved outcomes.