A comprehensive screen for oncogenic microRNA mutations in an acute myeloid leukemia cohort and across the Cancer Genome Atlas

Acute myeloid leukemia (AML) is a cancer in which blood cells grow out of control. Blood cells have to suffer at least two mutations to become cancerous: one to make them grow faster, and another to stop them developing normally. However, even with whole genome sequencing, in some patients we have been unable to find both mutations using existing methods, and we need to look deeper.   

MicroRNAs are one place we can look. These are small pieces of RNA which reduce the production of proteins by targeting specific messenger RNAs. We know that cancers tend to have more or less of some microRNAs, and that many of these play important roles in cancer biology. However, whole-genome studies have mainly looked at the amounts of well-known microRNAs, without looking deeply at mutations of the microRNAs themselves, which can completely change their targets. Smaller studies have shown that microRNA mutations (as well as normal variations between people) can be important drivers of cancers, but nobody has investigated these at the genome-wide scale.    

I will examine mutations of microRNAs in the genomes of around 200 AML and myelodysplastic syndrome patients. I will measure the effects of each mutation on messenger RNA levels. I will then look especially in patients in which two driver mutations could not be found to see whether any microRNA mutations could be oncogenic. The results will increase our understanding of the biology of AML, thereby leading to new research into improved therapies. They will also improve our ability to diagnose AML, which will give more information to doctors and patients making difficult decisions on treatments.    After analysing our local dataset, I intend to similarly analyse all cancers in the Cancer Genome Atlas (TCGA) data set. Since the microRNA sequencing for the TCGA was performed at the Michael Smith Genome Science Centre in Vancouver, this is an excellent opportunity to extract further value from a locally-produced resource.   

For knowledge translation activities, I intend to present this work at the annual meetings of the American Society for Hematology and the International Society for Computational Biology. Further, I will write up the AML analysis for submission to Genome Research or Leukaemia, and the later work applying the method to the TCGA data to a similar (or higher-impact) venue. Lastly, I will release the source code to perform the analysis as an open source software package.