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2013 ; 31
(15
): 1825-33
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Clinical analysis and interpretation of cancer genome data
#MMPMID23589549
Van Allen EM
; Wagle N
; Levy MA
J Clin Oncol
2013[May]; 31
(15
): 1825-33
PMID23589549
show ga
The scale of tumor genomic profiling is rapidly outpacing human cognitive
capacity to make clinical decisions without the aid of tools. New frameworks are
needed to help researchers and clinicians process the information emerging from
the explosive growth in both the number of tumor genetic variants routinely
tested and the respective knowledge to interpret their clinical significance. We
review the current state, limitations, and future trends in methods to support
the clinical analysis and interpretation of cancer genomes. This includes the
processes of genome-scale variant identification, including tools for sequence
alignment, tumor-germline comparison, and molecular annotation of variants. The
process of clinical interpretation of tumor variants includes classification of
the effect of the variant, reporting the results to clinicians, and enabling the
clinician to make a clinical decision based on the genomic information integrated
with other clinical features. We describe existing knowledge bases, databases,
algorithms, and tools for identification and visualization of tumor variants and
their actionable subsets. With the decreasing cost of tumor gene mutation testing
and the increasing number of actionable therapeutics, we expect the methods for
analysis and interpretation of cancer genomes to continue to evolve to meet the
needs of patient-centered clinical decision making. The science of computational
cancer medicine is still in its infancy; however, there is a clear need to
continue the development of knowledge bases, best practices, tools, and
validation experiments for successful clinical implementation in oncology.
|Algorithms
[MESH]
|Computational Biology/methods
[MESH]
|Genetic Predisposition to Disease/genetics
[MESH]
free
free
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