MAPPING CANCER: NIH launches large-scale genomic analysis project
With an eye toward creating the first comprehensive genetic map of cancer, the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI) have launched a pilot effort to study the feasibility of a large-scale sequencing project. If successful, The Cancer Genome Atlas (TCGA) will serve as a basis for a better understanding of the genomic changes and molecular mechanisms of cancer.
WASHINGTON—With an eye toward creating the first comprehensive genetic map of cancer, the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI) have launched a pilot effort to study the feasibility of a large-scale sequencing project. If successful, The Cancer Genome Atlas (TCGA) will serve as a basis for a better understanding of the genomic changes and molecular mechanisms of cancer.
The NCI and NHGRI have each committed $50 million over three years to develop and test the technological framework necessary to identify and characterize the genetic changes associated with a range of cancer types. The institutes are currently determining what cancers will be targeted in the pilot project.
"Detailed genomic analysis of cancer specimens can provide valuable information about cancer's molecular pathways," says Dr. Daniela Gerhard, director of NCI's Office of Cancer Genomics. "This understanding can, in turn, lead to the identification of new molecular targets for cancer therapies and new molecular markers to aid in cancer diagnosis and prevention. While current genetically-based treatments have demonstrated impressive performance, the list of successful drugs is still much shorter than it likely would be if the whole atlas of genetic changes that occur in cancer was available."
According to Dr. Mark Guyer, director of NHGRI's division of extramural research, there are strong similarities between the new initiative and the Human Genome Project and HapMap Project that came before. "All have been, or are being, implemented in a staged approach that features ambitious goals, explicit milestones, rigorous quality assessment and rapid pre-publication data release," he says. "In each effort, multiple technologies have been required, and there has been an explicit focus on the development of new technology that will significantly reduce costs and increase throughput."
By the same token, TCGA scientists will be examining much more complex, heterogeneous DNA from tumors as opposed to the blood samples used in the other projects. Tumors, Guyer explains, have a very high mutation rate, raising important issues of signal-to-noise ratio in detecting biologically significant mutations. TCGA will also connect DNA sequence data and other molecular analyses with phenotypic and clinical data, raising a number of new issues with respect to data release and participant confidentiality.
The TCGA effort will revolve around three main components. A Human Cancer Biospecimen Core Resource will support the collection, processing and distribution of cancer and healthy tissue samples. Cancer genome sequencing centers will use high-throughput methods to elucidate the genetic changes correlated with the test cancers. And cancer genome characterization centers will work to identify other types of gross genetic changes that trigger cancer formation and progression.
Data generated by the TCGA centers will be deposited in public databases supported by the National Center for Biotechnology Information (NCBI) and the NCI's cancer Biomedical Informatics Grid (caBIG). As such, the information will be freely available to researchers worldwide and thereby enable scientists to incorporate the data into their own efforts to develop new cancer diagnostics and therapeutics.
According to Gerhard, any decision to "green light" a larger effort will require success in a variety of areas, including the ability to find genomic alternations and correlate them with in-depth gene sequencing; the discovery of new cancer genes; the ability to differentiate tumor subtypes based on specific genomic alterations; and the development of technology approaches that provide the ability to differentiate meaningful biologic data from the "noise".