Biomedical Engineering Reference
In-Depth Information
underlying causes. This complexity, which often does not manifest at the phe-
notypic level, makes it particularly diffi cult to classify disease variants and
select the most effective treatment for a specifi c patient.
The only obvious mechanism to resolve this problem is to assess the par-
ticular genetic basis of an individual patient's disease and then select a treat-
ment known to be effective against that particular abnormality. This
“personalized medicine” paradigm has been a dream of many in the biomedi-
cal community but interestingly has been successfully used since the 1970s in
the treatment of childhood acute lymphoblastic leukemia (ALL). Five-year
survival for ALL patients aged 10-14 at diagnosis have increased from 58.8%
for children diagnosed between 1975 and 1977 to 79.7% for those diagnosed
between 1996 and 2002. This increase can be attributed to several factors,
notably the fact that children are treated in an environment that blends care
and research and the use of (for the time) modern molecular biology technol-
ogy, in this case karyotypes (a view of the number and gross physical structure
of the chromosomes in a cell) [2]. By combining genetic technology with out-
comes research, it was possible to determine a patient's risk profi le and provide
a treatment that was most likely to achieve a desirable outcome. This model
(the blending of care and research coupled with the use of genomics technol-
ogy to provide a molecular characterization of the patient's specifi c disease)
is the goal of those that wish to bring personalized medicine to all. However,
its success is dependent on removing the barriers between care and research
and ensuring that data from a wide range of sources (clinical outcomes, genom-
ics, pathology, images) can be successfully integrated.
This need to provide data liquidity (that is, the free fl ow of information
among those authorized to use it) required by the molecular medicine para-
digm was the genesis of the cancer Biomedical Informatics Grid (caBIG
®
)
program (http://cabig.cancer.gov). The program was initiated in 2004 (at the
request of the NCI's National Cancer Advisory Board) to be overseen by
the Center for Biomedical Information and Information Technology (CBIIT)
of the U.S. National Cancer Institute (NCI), a part of the National Institutes
of Health (NIH), U.S. Department of Health and Human Services. It was
tasked with creating a virtual network of interconnected data (including clini-
cal, pathology, genomics, and imaging data), individuals, and organizations
whose goal is to redefi ne how research is conducted, care is provided, and
patients/participants interact with the biomedical research enterprise—in
effect, to create a “world wide web” for cancer among a highly diverse col-
lection of stakeholders (cancer centers, cooperative groups, individual
researchers, etc.) that were widely distributed across the United States.
Further, these researchers tended to exist within professional silos (pathology,
clinical research, genomics, information technology, etc.) To accomplish this
goal, therefore, caBIG needed to resolve two fundamental issues: First, it had
to create a technology platform that would allow for interoperability between
and among various biomedical information systems, and it had to address the
social issues associated with the large-scale data sharing required by the per-
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