Researchers at Washington University in St. Louis have used "powerful algorithms" developed by computer scientists at Brown University to assemble the most complete genetic profile yet of acute myeloid leukemia, Brown announced this week.
The work was part of The Cancer Genome Atlas project, which aims to catalog the genetic mutations that cause cells to become cancerous. Doing that requires sequencing the entire genome of cancer cells and comparing it to the genome of healthy cells.
"For us as computational people it's fun to push these algorithms and apply them to new datasets," said Ben Raphael, part of the team from Brown's department of computer science and the Center for Computational Molecular Biology that helped develop the algorithm. "At the same time, in analyzing cancer data we hope that the algorithms produce actionable information that is clinically important."
Technology has played a key role in making sense of giant datasets required for medical research.
In another recent example, Harvard researchers began using a big data analytics platform to build computational models of the mechanisms involved in cell differentiation in hopes of building better treatments.
"If we can figure out how to regulate the mechanisms by which cells send signals and coordinate growth throughout the body, we can create better treatments for cancer," said Marc Kirschner, professor and chair of the Department of Systems Biology at Harvard Medical School.
And Advancements in gene sequencing and computational capabilities are among the factors behind M.D. Anderson Cancer Center's Moon Shots program, which focuses the latest research and technology toward fighting eight common cancers. Ronald A. DePinho, M.D. Anderson's president, has referred to these as a "confluence of enabling technologies" making this a prime time to systematically tackle cancer.
The research was published in the New England Journal of Medicine.