In this video, Jonathan Allen from LLNL describes how Lawrence Livermore’s supercomputers are playing a crucial role in advancing cancer research and treatment.
“A historic partnership between the Department of Energy (DOE) and the National Cancer Institute (NCI) is applying the formidable computing resources at Livermore and other DOE national laboratories to advance cancer research and treatment. Announced in late 2015, the effort will help researchers and physicians better understand the complexity of cancer, choose the best treatment options for every patient, and reveal possible patterns hidden in vast patient and experimental data sets.”
The DOE–NCI agreement features three pilot programs that bring together nearly 100 cancer and biomedical researchers, computer scientists, and engineers. Livermore researchers are playing important roles in all three programs. Participants also include Argonne, Los Alamos, and Oak Ridge national laboratories; NCI’s Frederick National Laboratory for Cancer Research; and the U.S. Department of Veterans Affairs.
“One of the goals of this partnership is to bring about a huge shift in how biological and medical research will be performed in the future,” says Fred Streitz, director of Livermore’s High Performance Computing Innovation Center. “We are investing in the computational tools needed to move the medical community toward a predictive approach to cancer,” he says. “Such tools may help explain why one cancer treatment is successful with one patient but fails with the next.” In that respect, the DOE–NCI partnership supports President Barack Obama’s Precision Medicine Initiative, which promotes developing treatments for various medical conditions that take into account patients’ individual variability in genes, microbiomes (the collection of microbes in or on the body), environment, health history, lifestyle, and diet.
Jonathan Allen leads an LDRD project, in collaboration with Argonne National Laboratory, the University of Chicago, and other research groups. Allen’s team is working to predict the potential for hospital patients in intensive care units (ICUs) to develop antibiotic-resistant infections, a serious problem that has resulted from overuse of antibiotics. Some drug-resistant bacteria survive these treatments or mutate to become resistant, transforming simple diseases into killers. Allen’s group has been studying collections of microbial genomes identified as resistant or susceptible to antibiotics to develop a predictive model of which ICU patients will become susceptible. The group’s methods search massive amounts of genomic data to recognize important biological features, leading to better predictions of pathogen emergence. The team is developing an analytic framework for storing and searching terabytes (1 trillion or 1012 bytes) of genomic data and metadata.
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