For researchers exploring the complexity of cancer genomes linked to mutations that carry increased risks for the disease, it can be like searching for a proverbial needle in a haystack.
However, supercomputers at the Texas Advanced Computing Center in Austin, given their ability to manage and analyze massive datasets, are helping to find correlations between chromosomal rearrangements—one of the hallmarks of cancer genomes.
“One of the things we’d like to understand in a predictive sense is where mutations are likely to occur, because mutations can lead to cancerous outcomes,” says Matt Vaughn, director of life sciences computing at the University of Texas at Austin’s TACC.
Chromosomal rearrangements in cancer genomes are critical for diagnosis and treatment, adds Vaughn, noting that “pieces of chromosome literally break and reattach, and the pieces don’t necessary fit well together.”
Matt VaughnLeveraging TACC’s Lonestar and Stampede supercomputers and tailored algorithms, researchers from UT-Austin, the University of Texas MD Anderson Cancer Center, and Cardiff University tested the hypothesis that potential non-B DNA structures (PONDS) might be found at, or near, rearrangement breakpoints or locations on a chromosome where DNA might get deleted, inverted or swapped around.
“The significance of PONDS is being able to predict where that might happen,” says Vaughn. “It kind of gives you an approximate roadmap of places that you might be able to look in the genome when you’re trying to find causative mutations that might have led to a cancerous or pre-cancerous condition.”
Researchers utilizing the supercomputers at TACC found a significant association between PONDS-forming sequences and cancer, concluding that they represent an intrinsic risk factor for genomic rearrangements in cancer genomes.
“There was a strong correlation between the location of PONDS sequences and rearrangements,” he observes, noting that understanding the processes by which PONDS lead to chromosomal rearrangements impacting cancer will be important for future diagnosis and treatment. “That was a good outcome.”